From 59a2f70d1577f9ec763bd53bd000899cda2bbdda Mon Sep 17 00:00:00 2001 From: klemen1999 Date: Sun, 28 Sep 2025 13:48:03 +0800 Subject: [PATCH 01/39] added poc of data_collector and adjusted snaps example --- custom-frontend/data-collection/.gitignore | 3 + custom-frontend/data-collection/.oakappignore | 38 + custom-frontend/data-collection/README.md | 73 + .../data-collection/backend-run.sh | 3 + .../src/depthai_models/yolo_world_l.RVC4.yaml | 2 + .../yolo_world_l_fp16.RVC4.yaml | 2 + .../depthai_models/yoloe_v8_l_fp16.RVC4.yaml | 2 + .../data-collection/backend/src/main.py | 519 + .../backend/src/requirements.txt | 13 + .../backend/src/utils/__init__.py | 0 .../backend/src/utils/annotation_node.py | 45 + .../backend/src/utils/arguments.py | 72 + .../backend/src/utils/frame_cache_node.py | 20 + .../backend/src/utils/helper_functions.py | 247 + .../backend/src/utils/snap_utils.py | 60 + .../data-collection/frontend/.gitignore | 28 + .../data-collection/frontend/biome.json | 30 + .../data-collection/frontend/index.html | 13 + .../frontend/package-lock.json | 22382 ++++++++++++++++ .../data-collection/frontend/package.json | 31 + .../data-collection/frontend/panda.config.ts | 12 + .../frontend/postcss.config.mjs | 5 + .../data-collection/frontend/src/App.tsx | 322 + .../frontend/src/ClassSelector.tsx | 66 + .../frontend/src/ConfidenceSlider.tsx | 70 + .../frontend/src/ImageUploader.tsx | 108 + .../frontend/src/Notifications.tsx | 107 + .../frontend/src/SnapCollectionButton.tsx | 174 + .../data-collection/frontend/src/index.css | 1 + .../data-collection/frontend/src/main.tsx | 33 + .../frontend/src/vite-env.d.ts | 1 + .../data-collection/frontend/src/window.d.ts | 7 + .../frontend/tsconfig.app.json | 30 + .../data-collection/frontend/tsconfig.json | 7 + .../frontend/tsconfig.node.json | 13 + .../data-collection/frontend/vite.config.ts | 22 + .../media/dynamic_yolo_world.gif | Bin 0 -> 2656194 bytes custom-frontend/data-collection/oakapp.toml | 33 + integrations/hub-snaps-events/main.py | 11 +- .../hub-snaps-events/requirements.txt | 8 +- 40 files changed, 24607 insertions(+), 6 deletions(-) create mode 100644 custom-frontend/data-collection/.gitignore create mode 100644 custom-frontend/data-collection/.oakappignore create mode 100644 custom-frontend/data-collection/README.md create mode 100644 custom-frontend/data-collection/backend-run.sh create mode 100644 custom-frontend/data-collection/backend/src/depthai_models/yolo_world_l.RVC4.yaml create mode 100644 custom-frontend/data-collection/backend/src/depthai_models/yolo_world_l_fp16.RVC4.yaml create mode 100644 custom-frontend/data-collection/backend/src/depthai_models/yoloe_v8_l_fp16.RVC4.yaml create mode 100644 custom-frontend/data-collection/backend/src/main.py create mode 100644 custom-frontend/data-collection/backend/src/requirements.txt create mode 100644 custom-frontend/data-collection/backend/src/utils/__init__.py create mode 100644 custom-frontend/data-collection/backend/src/utils/annotation_node.py create mode 100644 custom-frontend/data-collection/backend/src/utils/arguments.py create mode 100644 custom-frontend/data-collection/backend/src/utils/frame_cache_node.py create mode 100644 custom-frontend/data-collection/backend/src/utils/helper_functions.py create mode 100644 custom-frontend/data-collection/backend/src/utils/snap_utils.py create mode 100644 custom-frontend/data-collection/frontend/.gitignore create mode 100644 custom-frontend/data-collection/frontend/biome.json create mode 100644 custom-frontend/data-collection/frontend/index.html create mode 100644 custom-frontend/data-collection/frontend/package-lock.json create mode 100644 custom-frontend/data-collection/frontend/package.json create mode 100644 custom-frontend/data-collection/frontend/panda.config.ts create mode 100644 custom-frontend/data-collection/frontend/postcss.config.mjs create mode 100644 custom-frontend/data-collection/frontend/src/App.tsx create mode 100644 custom-frontend/data-collection/frontend/src/ClassSelector.tsx create mode 100644 custom-frontend/data-collection/frontend/src/ConfidenceSlider.tsx create mode 100644 custom-frontend/data-collection/frontend/src/ImageUploader.tsx create mode 100644 custom-frontend/data-collection/frontend/src/Notifications.tsx create mode 100644 custom-frontend/data-collection/frontend/src/SnapCollectionButton.tsx create mode 100644 custom-frontend/data-collection/frontend/src/index.css create mode 100644 custom-frontend/data-collection/frontend/src/main.tsx create mode 100644 custom-frontend/data-collection/frontend/src/vite-env.d.ts create mode 100644 custom-frontend/data-collection/frontend/src/window.d.ts create mode 100644 custom-frontend/data-collection/frontend/tsconfig.app.json create mode 100644 custom-frontend/data-collection/frontend/tsconfig.json create mode 100644 custom-frontend/data-collection/frontend/tsconfig.node.json create mode 100644 custom-frontend/data-collection/frontend/vite.config.ts create mode 100644 custom-frontend/data-collection/media/dynamic_yolo_world.gif create mode 100644 custom-frontend/data-collection/oakapp.toml diff --git a/custom-frontend/data-collection/.gitignore b/custom-frontend/data-collection/.gitignore new file mode 100644 index 000000000..6018a0cbe --- /dev/null +++ b/custom-frontend/data-collection/.gitignore @@ -0,0 +1,3 @@ +clip_textual_hf.onnx +clip_visual_with_projector.onnx +tokenizer.json \ No newline at end of file diff --git a/custom-frontend/data-collection/.oakappignore b/custom-frontend/data-collection/.oakappignore new file mode 100644 index 000000000..00f160e89 --- /dev/null +++ b/custom-frontend/data-collection/.oakappignore @@ -0,0 +1,38 @@ +# Python virtual environments +venv/ +.venv/ +env + +# Node.js +# ignore node_modules, it will be reinstalled in the container +node_modules/ + +# Multimedia files +media/ + +# Local models +*.onnx + +# Documentation +README.md + +# VCS +.gitignore +.git/ +.github/ +.gitlab/ + +# The following files are ignored by default +# uncomment a line if you explicitly need it + +# !*.oakapp + +# Python +# !**/.mypy_cache/ +# !**/.ruff_cache/ + +# IDE files +# !**/.idea +# !**/.vscode +# !**/.zed + diff --git a/custom-frontend/data-collection/README.md b/custom-frontend/data-collection/README.md new file mode 100644 index 000000000..5d95544e3 --- /dev/null +++ b/custom-frontend/data-collection/README.md @@ -0,0 +1,73 @@ +TODO + + diff --git a/custom-frontend/data-collection/backend-run.sh b/custom-frontend/data-collection/backend-run.sh new file mode 100644 index 000000000..28ace22ac --- /dev/null +++ b/custom-frontend/data-collection/backend-run.sh @@ -0,0 +1,3 @@ +#!/bin/sh +echo "Starting Backend" +exec python3.12 /app/backend/src/main.py --api_key \ No newline at end of file diff --git a/custom-frontend/data-collection/backend/src/depthai_models/yolo_world_l.RVC4.yaml b/custom-frontend/data-collection/backend/src/depthai_models/yolo_world_l.RVC4.yaml new file mode 100644 index 000000000..6ddc030fc --- /dev/null +++ b/custom-frontend/data-collection/backend/src/depthai_models/yolo_world_l.RVC4.yaml @@ -0,0 +1,2 @@ +model: yolo-world-l:640x640-host-decoding +platform: RVC4 \ No newline at end of file diff --git a/custom-frontend/data-collection/backend/src/depthai_models/yolo_world_l_fp16.RVC4.yaml b/custom-frontend/data-collection/backend/src/depthai_models/yolo_world_l_fp16.RVC4.yaml new file mode 100644 index 000000000..f37bae36c --- /dev/null +++ b/custom-frontend/data-collection/backend/src/depthai_models/yolo_world_l_fp16.RVC4.yaml @@ -0,0 +1,2 @@ +model: yolo-world-l:640x640-host-decoding-fp16 +platform: RVC4 \ No newline at end of file diff --git a/custom-frontend/data-collection/backend/src/depthai_models/yoloe_v8_l_fp16.RVC4.yaml b/custom-frontend/data-collection/backend/src/depthai_models/yoloe_v8_l_fp16.RVC4.yaml new file mode 100644 index 000000000..a887528e3 --- /dev/null +++ b/custom-frontend/data-collection/backend/src/depthai_models/yoloe_v8_l_fp16.RVC4.yaml @@ -0,0 +1,2 @@ +model: yoloe-v8-l:640x640-text-visual-prompt:e23da0f +platform: RVC4 \ No newline at end of file diff --git a/custom-frontend/data-collection/backend/src/main.py b/custom-frontend/data-collection/backend/src/main.py new file mode 100644 index 000000000..6f319f318 --- /dev/null +++ b/custom-frontend/data-collection/backend/src/main.py @@ -0,0 +1,519 @@ +from pathlib import Path +import numpy as np +from dotenv import load_dotenv +import os +from functools import partial + +import depthai as dai +from depthai_nodes.node import ( + ParsingNeuralNetwork, + ImgDetectionsFilter, + ImgFrameOverlay, + ApplyColormap, + SnapsProducer, +) + +from utils.helper_functions import ( + extract_text_embeddings, + extract_image_prompt_embeddings, + base64_to_cv2_image, + QUANT_VALUES, +) +from utils.arguments import initialize_argparser +from utils.annotation_node import AnnotationNode +from utils.frame_cache_node import FrameCacheNode +from utils.snap_utils import custom_snap_process + +load_dotenv(override=True) + +_, args = initialize_argparser() + +if args.api_key: + os.environ["DEPTHAI_HUB_API_KEY"] = args.api_key + +IP = args.ip or "localhost" +PORT = args.port or 8080 + +CLASS_NAMES = ["person", "chair", "TV"] +# For unified YOLOE, 0-79 are text classes, 80-159 are image-prompt classes +CLASS_OFFSET = 0 +MAX_NUM_CLASSES = 80 +CONFIDENCE_THRESHOLD = 0.1 +VISUALIZATION_RESOLUTION = (1080, 1080) + +visualizer = dai.RemoteConnection(serveFrontend=False) +device = dai.Device() +platform = device.getPlatformAsString() + +if platform != "RVC4": + raise ValueError("This example is supported only on RVC4 platform") + +frame_type = dai.ImgFrame.Type.BGR888i + + +def make_dummy_features(max_num_classes: int, model_name: str, precision: str): + if precision == "fp16": + return np.zeros((1, 512, max_num_classes), dtype=np.float16) + qzp = int(round(QUANT_VALUES.get(model_name, {}).get("quant_zero_point", 0))) + return np.full((1, 512, max_num_classes), qzp, dtype=np.uint8) + + +# choose initial features: text for yolo-world/yoloe +text_features = extract_text_embeddings( + class_names=CLASS_NAMES, + max_num_classes=MAX_NUM_CLASSES, + model_name=args.model if args.model != "yolo-world" else "yolo-world", + precision=args.precision, +) +image_prompt_features = None +if args.model == "yoloe": + # send dummy image-prompts initially + image_prompt_features = make_dummy_features( + MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision + ) + +if args.fps_limit is None: + args.fps_limit = 5 + print( + f"\nFPS limit set to {args.fps_limit} for {platform} platform. If you want to set a custom FPS limit, use the --fps_limit flag.\n" + ) + +with dai.Pipeline(device) as pipeline: + print("Creating pipeline...") + + # Model selection with precision-aware YAMLs for YOLOE variants + models_dir = Path(__file__).parent / "depthai_models" + if args.model == "yolo-world": + yaml_base = "yolo_world_l_fp16" if args.precision == "fp16" else "yolo_world_l" + yaml_filename = f"{yaml_base}.{platform}.yaml" + yaml_path = models_dir / yaml_filename + if not yaml_path.exists(): + raise SystemExit( + f"Model YAML not found: {yaml_path}. Ensure the model config exists." + ) + model_description = dai.NNModelDescription.fromYamlFile(str(yaml_path)) + elif args.model == "yoloe": + yaml_base = "yoloe_v8_l_fp16" if args.precision == "fp16" else "yoloe_v8_l" + yaml_filename = f"{yaml_base}.{platform}.yaml" + yaml_path = models_dir / yaml_filename + print(f"YOLOE YAML path: {yaml_path}") + if not yaml_path.exists(): + raise SystemExit( + f"Model YAML not found for YOLOE with precision {args.precision}: {yaml_path}. " + f"YOLOE int8 YAML is not available; run with --precision fp16." + ) + model_description = dai.NNModelDescription.fromYamlFile(str(yaml_path)) + model_description.platform = platform + model_nn_archive = dai.NNArchive(dai.getModelFromZoo(model_description)) + model_w, model_h = model_nn_archive.getInputSize() + + # media/camera input at high resolution for visualization + if args.media_path: + replay = pipeline.create(dai.node.ReplayVideo) + replay.setReplayVideoFile(Path(args.media_path)) + replay.setOutFrameType(dai.ImgFrame.Type.NV12) + replay.setLoop(True) + if args.fps_limit: + replay.setFps(args.fps_limit) + replay.setSize(VISUALIZATION_RESOLUTION[0], VISUALIZATION_RESOLUTION[1]) + video_src_out = replay.out + else: + cam = pipeline.create(dai.node.Camera).build( + boardSocket=dai.CameraBoardSocket.CAM_A + ) + # Request high-res NV12 frames for visualization/encoding + video_src_out = cam.requestOutput( + size=VISUALIZATION_RESOLUTION, + type=dai.ImgFrame.Type.NV12, + fps=args.fps_limit, + ) + + image_manip = pipeline.create(dai.node.ImageManip) + image_manip.setMaxOutputFrameSize(model_w * model_h * 3) + image_manip.initialConfig.setOutputSize(model_w, model_h) + image_manip.initialConfig.setFrameType(frame_type) + video_src_out.link(image_manip.inputImage) + + video_enc = pipeline.create(dai.node.VideoEncoder) + video_enc.setDefaultProfilePreset( + fps=args.fps_limit, profile=dai.VideoEncoderProperties.Profile.H264_MAIN + ) + video_src_out.link(video_enc.input) + + input_node = image_manip.out + + nn_with_parser = pipeline.create(ParsingNeuralNetwork) + nn_with_parser.setNNArchive(model_nn_archive) + nn_with_parser.setBackend("snpe") + nn_with_parser.setBackendProperties( + {"runtime": "dsp", "performance_profile": "default"} + ) + nn_with_parser.setNumInferenceThreads(1) + nn_with_parser.getParser(0).setConfidenceThreshold(CONFIDENCE_THRESHOLD) + + input_node.link(nn_with_parser.inputs["images"]) + + textInputQueue = nn_with_parser.inputs["texts"].createInputQueue() + nn_with_parser.inputs["texts"].setReusePreviousMessage(True) + if args.model == "yoloe": + imagePromptInputQueue = nn_with_parser.inputs[ + "image_prompts" + ].createInputQueue() + nn_with_parser.inputs["image_prompts"].setReusePreviousMessage(True) + + # filter and rename detection labels + det_process_filter = pipeline.create(ImgDetectionsFilter).build(nn_with_parser.out) + annotation_node = pipeline.create(AnnotationNode).build( + det_process_filter.out, + video_src_out, + ) + + # produce snaps + snaps_producer = pipeline.create(SnapsProducer).build( + frame=video_src_out, + msg=det_process_filter.out, + running=False, + process_fn=partial( + custom_snap_process, class_names=CLASS_NAMES, model=args.model + ), + ) + snaps_producer.setUrl("https://events.cloud-stg.luxonis.com") # TODO: Remove + print("Snaps producer API KEY:", os.getenv("DEPTHAI_HUB_API_KEY")) + + def update_labels(label_names: list[str], offset: int = 0): + det_process_filter.setLabels( + labels=[i for i in range(offset, offset + len(label_names))], keep=True + ) + annotation_node.setLabelEncoding( + {offset + k: v for k, v in enumerate(label_names)} + ) + + # Cache last frame for services that need full frame content + frame_cache = pipeline.create(FrameCacheNode).build(video_src_out) + + if args.model == "yolo-world": + visualizer.addTopic("Video", video_enc.out, "images") + elif args.model == "yoloe": + apply_colormap_node = pipeline.create(ApplyColormap).build(nn_with_parser.out) + overlay_frames_node = pipeline.create(ImgFrameOverlay).build( + video_src_out, + apply_colormap_node.out, + preserve_background=True, + ) + overlay_to_nv12 = pipeline.create(dai.node.ImageManip) + overlay_to_nv12.setMaxOutputFrameSize( + VISUALIZATION_RESOLUTION[0] * VISUALIZATION_RESOLUTION[1] * 3 + ) + overlay_to_nv12.initialConfig.setFrameType(dai.ImgFrame.Type.NV12) + overlay_frames_node.out.link(overlay_to_nv12.inputImage) + + overlay_enc = pipeline.create(dai.node.VideoEncoder) + overlay_enc.setDefaultProfilePreset( + fps=args.fps_limit, profile=dai.VideoEncoderProperties.Profile.H264_MAIN + ) + overlay_to_nv12.out.link(overlay_enc.input) + + visualizer.addTopic("Video", overlay_enc.out, "images") + + visualizer.addTopic("Detections", annotation_node.out) + + def class_update_service(new_classes: list[str]): + """Changes classes to detect based on the user input""" + if len(new_classes) == 0: + print("List of new classes empty, skipping.") + return + if len(new_classes) > MAX_NUM_CLASSES: + print( + f"Number of new classes ({len(new_classes)}) exceeds maximum number of classes ({MAX_NUM_CLASSES}), skipping." + ) + return + CLASS_NAMES = new_classes + text_features = extract_text_embeddings( + class_names=CLASS_NAMES, + max_num_classes=MAX_NUM_CLASSES, + model_name=args.model, + precision=args.precision, + ) + inputNNData = dai.NNData() + inputNNData.addTensor( + "texts", + text_features, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + textInputQueue.send(inputNNData) + # In unified YOLOE, ensure image_prompts are dummy when text prompts are active + if args.model == "yoloe": + dummy = make_dummy_features( + MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision + ) + inputNNDataImg = dai.NNData() + inputNNDataImg.addTensor( + "image_prompts", + dummy, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + imagePromptInputQueue.send(inputNNDataImg) + + update_labels(CLASS_NAMES, offset=0) + print(f"Classes set to: {CLASS_NAMES}") + + def conf_threshold_update_service(new_conf_threshold: float): + """Changes confidence threshold based on the user input""" + CONFIDENCE_THRESHOLD = max(0, min(1, new_conf_threshold)) + nn_with_parser.getParser(0).setConfidenceThreshold(CONFIDENCE_THRESHOLD) + print(f"Confidence threshold set to: {CONFIDENCE_THRESHOLD}:") + + def image_upload_service(image_data): + image = base64_to_cv2_image(image_data["data"]) + if args.model == "yolo-world": + image_features = extract_image_prompt_embeddings( + image, model_name=args.model, precision=args.precision + ) + print("Image features extracted, sending to model as texts...") + inputNNData = dai.NNData() + inputNNData.addTensor( + "texts", + image_features, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + textInputQueue.send(inputNNData) + filename = image_data["filename"] + CLASS_NAMES = [filename.split(".")[0]] + update_labels(CLASS_NAMES, offset=0) + print(f"Classes set to: {CLASS_NAMES}") + else: # yoloe unified with image_prompts input + image_features = extract_image_prompt_embeddings( + image, model_name="yoloe", precision=args.precision + ) + print("Image features extracted, sending to model as image_prompts...") + inputNNDataImg = dai.NNData() + inputNNDataImg.addTensor( + "image_prompts", + image_features, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + imagePromptInputQueue.send(inputNNDataImg) + # Send dummy texts so only image prompts are considered + dummy = make_dummy_features( + MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision + ) + inputNNDataTxt = dai.NNData() + inputNNDataTxt.addTensor( + "texts", + dummy, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + textInputQueue.send(inputNNDataTxt) + + filename = image_data["filename"] + CLASS_NAMES = [filename.split(".")[0]] + update_labels(CLASS_NAMES, offset=80) + print(f"Classes set to (image prompts, offset 80): {CLASS_NAMES}") + + def bbox_prompt_service(payload): + """ + Accepts a full-frame PNG (base64) plus a normalized bbox {x,y,width,height} in viewport space. + - For yolo-world: crops the bbox region and extracts image prompt features from the crop. + - For yoloe: builds a binary mask from the bbox over the full frame and extracts features with mask_prompt. + """ + print("[BBox] Service payload keys:", list(payload.keys())) + # Try FE-provided image first, else fall back to cached live frame + image = base64_to_cv2_image(payload["data"]) if payload.get("data") else None + if image is None: + image = frame_cache.get_last_frame() + if image is None: + print("[BBox] No image data and no cached frame available") + return {"ok": False, "reason": "no_image"} + if image is None: + print("[BBox] Decoded image is None") + return {"ok": False, "reason": "decode_failed"} + if (image == 0).all(): + print("[BBox] Warning: decoded image is all zeros") + # print image stats + print(f"[BBox] Image shape: {image.shape}") + print(f"[BBox] Image dtype: {image.dtype}") + print(f"[BBox] Image min: {image.min()}") + print(f"[BBox] Image max: {image.max()}") + print(f"[BBox] Image mean: {image.mean()}") + print(f"[BBox] Image std: {image.std()}") + + bbox = payload.get("bbox", {}) + bx = float(bbox.get("x", 0.0)) + by = float(bbox.get("y", 0.0)) + bw = float(bbox.get("width", 0.0)) + bh = float(bbox.get("height", 0.0)) + + H, W = image.shape[:2] + is_pixel = payload.get("bboxType", "normalized") == "pixel" + if is_pixel: + x0 = int(round(bx)) + y0 = int(round(by)) + x1 = int(round(bx + bw)) + y1 = int(round(by + bh)) + else: + # bbox is normalized in source frame space + x0 = int(round(bx * W)) + y0 = int(round(by * H)) + x1 = int(round((bx + bw) * W)) + y1 = int(round((by + bh) * H)) + + x0, x1 = sorted((x0, x1)) + y0, y1 = sorted((y0, y1)) + print( + f"[BBox] Image size: {W}x{H}, bbox(px): x0={x0}, y0={y0}, x1={x1}, y1={y1}" + ) + + if x1 <= x0 or y1 <= y0: + print("Invalid bbox, ignoring bbox prompt request.") + return {"ok": False, "reason": "invalid_bbox"} + + if args.model == "yolo-world": + crop = image[y0:y1, x0:x1] + print( + f"[BBox] YOLO-World crop shape: {crop.shape if crop is not None else None}" + ) + image_features = extract_image_prompt_embeddings( + crop, model_name=args.model, precision=args.precision + ) + elif args.model == "yoloe": + mask = np.zeros((H, W), dtype=np.float32) + mask[y0:y1, x0:x1] = 1.0 + print(f"[BBox] YOLOE mask sum: {float(mask.sum())}") + image_features = extract_image_prompt_embeddings( + image, + model_name="yoloe", + mask_prompt=mask, + precision=args.precision, + ) + else: + print(f"Unsupported model for bbox prompt: {args.model}") + return {"ok": False, "reason": "unsupported_model"} + + if args.model == "yolo-world": + inputNNData = dai.NNData() + inputNNData.addTensor( + "texts", + image_features, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + textInputQueue.send(inputNNData) + label = payload.get("label", "object") + CLASS_NAMES = [label] + update_labels(CLASS_NAMES, offset=0) + print(f"BBox prompt applied (yolo-world). Classes set to: {CLASS_NAMES}") + else: # yoloe unified + inputNNDataImg = dai.NNData() + inputNNDataImg.addTensor( + "image_prompts", + image_features, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + imagePromptInputQueue.send(inputNNDataImg) + # Send dummy texts so only image prompts are considered + dummy = make_dummy_features( + MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision + ) + inputNNDataTxt = dai.NNData() + inputNNDataTxt.addTensor( + "texts", + dummy, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + textInputQueue.send(inputNNDataTxt) + label = payload.get("label", "object") + CLASS_NAMES = [label] + update_labels(CLASS_NAMES, offset=80) + print( + f"BBox prompt applied (yoloe). Classes set to: {CLASS_NAMES} at offset 80" + ) + return {"ok": True, "bbox": {"x0": x0, "y0": y0, "x1": x1, "y1": y1}} + + def snap_collection_service(payload): + start_collection, time_interval = payload + if start_collection: + snaps_producer.setTimeInterval(time_interval) + snaps_producer.setRunning(True) + else: + snaps_producer.setRunning(False) + print("Snap collection state:", snaps_producer._running) + + visualizer.registerService("Class Update Service", class_update_service) + visualizer.registerService( + "Threshold Update Service", conf_threshold_update_service + ) + if args.model in ("yolo-world", "yoloe"): + visualizer.registerService("Image Upload Service", image_upload_service) + visualizer.registerService("BBox Prompt Service", bbox_prompt_service) + visualizer.registerService("Snap Collection Service", snap_collection_service) + + print("Pipeline created.") + + pipeline.start() + visualizer.registerPipeline(pipeline) + + update_labels(CLASS_NAMES, offset=CLASS_OFFSET) + + inputNNData = dai.NNData() + inputNNData.addTensor( + "texts", + text_features, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + textInputQueue.send(inputNNData) + if args.model == "yoloe": + inputNNDataImg = dai.NNData() + inputNNDataImg.addTensor( + "image_prompts", + image_prompt_features, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + imagePromptInputQueue.send(inputNNDataImg) + + print("Press 'q' to stop") + + while pipeline.isRunning(): + pipeline.processTasks() + key = visualizer.waitKey(1) + if key == ord("q"): + print("Got q key. Exiting...") + break diff --git a/custom-frontend/data-collection/backend/src/requirements.txt b/custom-frontend/data-collection/backend/src/requirements.txt new file mode 100644 index 000000000..8604ae5b6 --- /dev/null +++ b/custom-frontend/data-collection/backend/src/requirements.txt @@ -0,0 +1,13 @@ +# depthai==3.0.0 +--extra-index-url https://artifacts.luxonis.com/artifactory/luxonis-python-snapshot-local +--pre +depthai==3.0.0.dev0+c74b28c8e4ccdc9869fbfa99d73b95ac0893249e +#depthai-nodes==0.3.4 +depthai-nodes @ git+https://github.com/luxonis/depthai-nodes.git@feat/dai_dev_req +opencv-python-headless~=4.10.0 +numpy>=1.22 +tokenizers~=0.21.0 +onnxruntime +# onnxruntime-gpu # if you want to use CUDAExecutionProvider +requests +dotenv \ No newline at end of file diff --git a/custom-frontend/data-collection/backend/src/utils/__init__.py b/custom-frontend/data-collection/backend/src/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/custom-frontend/data-collection/backend/src/utils/annotation_node.py b/custom-frontend/data-collection/backend/src/utils/annotation_node.py new file mode 100644 index 000000000..119aae34c --- /dev/null +++ b/custom-frontend/data-collection/backend/src/utils/annotation_node.py @@ -0,0 +1,45 @@ +import depthai as dai +from depthai_nodes import ImgDetectionsExtended +from typing import Dict + + +class AnnotationNode(dai.node.HostNode): + def __init__(self, label_encoding: Dict[int, str] = {}) -> None: + super().__init__() + self._label_encoding = label_encoding + self.out_detections = self.createOutput() + + def setLabelEncoding(self, label_encoding: Dict[int, str]) -> None: + """Sets the label encoding. + + @param label_encoding: The label encoding with labels as keys and label names as + values. + @type label_encoding: Dict[int, str] + """ + if not isinstance(label_encoding, Dict): + raise ValueError("label_encoding must be a dictionary.") + self._label_encoding = label_encoding + + def build( + self, + detections: dai.Node.Output, + frame: dai.Node.Output, + label_encoding: Dict[int, str] = None, + ) -> "AnnotationNode": + if label_encoding is not None: + self.setLabelEncoding(label_encoding) + # Link detections and reference frame inputs + self.link_args(detections, frame) + return self + + def process( + self, + detections_message: dai.Buffer, + frame_message: dai.ImgFrame, + ) -> None: + assert isinstance(detections_message, ImgDetectionsExtended) + # Ensure detections align with the provided frame (e.g., high-res stream) + detections_message.setTransformation(frame_message.getTransformation()) + for detection in detections_message.detections: + detection.label_name = self._label_encoding.get(detection.label, "unknown") + return detections_message diff --git a/custom-frontend/data-collection/backend/src/utils/arguments.py b/custom-frontend/data-collection/backend/src/utils/arguments.py new file mode 100644 index 000000000..6eeb89d98 --- /dev/null +++ b/custom-frontend/data-collection/backend/src/utils/arguments.py @@ -0,0 +1,72 @@ +import argparse + + +def initialize_argparser(): + """Initialize the argument parser for the script.""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "-fps", + "--fps_limit", + help="FPS limit for the model runtime.", + required=False, + default=None, + type=int, + ) + + parser.add_argument( + "-api", + "--api_key", + help="HubAI API key to access private model. Can also use 'DEPTHAI_HUB_API_KEY' environment variable instead.", + required=False, + default="", + type=str, + ) + + parser.add_argument( + "-media", + "--media_path", + help="Path to the media file you aim to run the model on. If not set, the model will run on the camera input.", + required=False, + default=None, + type=str, + ) + + parser.add_argument( + "-ip", + "--ip", + help="IP address to serve the frontend on.", + required=False, + type=str, + ) + parser.add_argument( + "-p", + "--port", + help="Port to serve the frontend on.", + required=False, + type=int, + ) + parser.add_argument( + "-m", + "--model", + help="Name of the model to use: yolo-world or yoloe", + required=False, + # default="yoloe", + default="yolo-world", + type=str, + choices=["yolo-world", "yoloe"], + ) + parser.add_argument( + "--precision", + help="Model precision for YOLOE models: int8 (faster) or fp16 (more accurate).", + required=False, + default="fp16", + type=str, + choices=["int8", "fp16"], + ) + + args = parser.parse_args() + + return parser, args diff --git a/custom-frontend/data-collection/backend/src/utils/frame_cache_node.py b/custom-frontend/data-collection/backend/src/utils/frame_cache_node.py new file mode 100644 index 000000000..621a7c7e1 --- /dev/null +++ b/custom-frontend/data-collection/backend/src/utils/frame_cache_node.py @@ -0,0 +1,20 @@ +import depthai as dai +import numpy as np + + +class FrameCacheNode(dai.node.HostNode): + def __init__(self) -> None: + super().__init__() + self._last_frame: np.ndarray | None = None + + def build(self, frame: dai.Node.Output) -> "FrameCacheNode": + self.link_args(frame) + return self + + def process(self, frame: dai.ImgFrame) -> dai.ImgFrame: + # Cache last BGR frame + self._last_frame = frame.getCvFrame() + return frame + + def get_last_frame(self) -> np.ndarray | None: + return self._last_frame diff --git a/custom-frontend/data-collection/backend/src/utils/helper_functions.py b/custom-frontend/data-collection/backend/src/utils/helper_functions.py new file mode 100644 index 000000000..e4e919d3f --- /dev/null +++ b/custom-frontend/data-collection/backend/src/utils/helper_functions.py @@ -0,0 +1,247 @@ +from tokenizers import Tokenizer +import os +import requests +import onnxruntime +import numpy as np +import cv2 +import base64 + +QUANT_ZERO_POINT = 90.0 +QUANT_SCALE = 0.003925696481 + +QUANT_VALUES = { + "yolo-world": { + "quant_zero_point": 90.0, + "quant_scale": 0.003925696481, + }, + "yoloe": { + "quant_zero_point": 174.0, + "quant_scale": 0.003328413470, + }, + "yoloe-image": { + "quant_zero_point": 137.0, + "quant_scale": 0.002327915514, + }, +} + + +def pad_and_quantize_features( + features, max_num_classes=80, model_name="yolo-world", precision="int8" +): + """ + Pad features to (1, 512, max_num_classes) and quantize if precision is int8. + For FP16, return padded float16 features (no quantization). + """ + num_padding = max_num_classes - features.shape[0] + padded_features = np.pad( + features, ((0, num_padding), (0, 0)), mode="constant" + ).T.reshape(1, 512, max_num_classes) + + if precision == "fp16": + return padded_features.astype(np.float16) + + quant_scale = QUANT_VALUES[model_name]["quant_scale"] + quant_zero_point = QUANT_VALUES[model_name]["quant_zero_point"] + quantized_features = (padded_features / quant_scale) + quant_zero_point + quantized_features = quantized_features.astype("uint8") + return quantized_features + + +def extract_text_embeddings( + class_names, max_num_classes=80, model_name="yolo-world", precision="int8" +): + tokenizer_json_path = download_tokenizer( + url="https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json", + save_path="tokenizer.json", + ) + tokenizer = Tokenizer.from_file(tokenizer_json_path) + tokenizer.enable_padding( + pad_id=tokenizer.token_to_id("<|endoftext|>"), pad_token="<|endoftext|>" + ) + encodings = tokenizer.encode_batch(class_names) + + text_onnx = np.array([e.ids for e in encodings], dtype=np.int64) + + if model_name == "yolo-world": + attention_mask = np.array([e.attention_mask for e in encodings], dtype=np.int64) + + textual_onnx_model_path = download_model( + "https://huggingface.co/jmzzomg/clip-vit-base-patch32-text-onnx/resolve/main/model.onnx", + "clip_textual_hf.onnx", + ) + + session_textual = onnxruntime.InferenceSession( + textual_onnx_model_path, + providers=[ + "TensorrtExecutionProvider", + "CUDAExecutionProvider", + "CPUExecutionProvider", + ], + ) + textual_output = session_textual.run( + None, + { + session_textual.get_inputs()[0].name: text_onnx, + "attention_mask": attention_mask, + }, + )[0] + elif model_name == "yoloe": + if text_onnx.shape[1] < 77: + text_onnx = np.pad( + text_onnx, ((0, 0), (0, 77 - text_onnx.shape[1])), mode="constant" + ) + + textual_onnx_model_path = download_model( + "https://huggingface.co/Xenova/mobileclip_blt/resolve/main/onnx/text_model.onnx", + "mobileclip_textual_hf.onnx", + ) + + session_textual = onnxruntime.InferenceSession( + textual_onnx_model_path, + providers=[ + "TensorrtExecutionProvider", + "CUDAExecutionProvider", + "CPUExecutionProvider", + ], + ) + textual_output = session_textual.run( + None, + { + session_textual.get_inputs()[0].name: text_onnx, + }, + )[0] + + textual_output /= np.linalg.norm( + textual_output, ord=2, axis=-1, keepdims=True + ) # Normalize the output + + text_features = pad_and_quantize_features( + textual_output, max_num_classes, model_name, precision + ) + + del session_textual + + return text_features + + +def extract_image_prompt_embeddings( + image, + max_num_classes=80, + model_name="yolo-world", + mask_prompt=None, + precision="int8", +): + # Select model and preprocess accordingly + if model_name == "yoloe": + image_resized = cv2.resize(image, (640, 640)) + image_array = image_resized.astype(np.float32) / 255.0 + image_array = np.transpose(image_array, (2, 0, 1)) + input_tensor = np.expand_dims(image_array, axis=0).astype(np.float32) + model_url = ( + "https://huggingface.co/sokovninn/yoloe-v8l-seg-visual-encoder/resolve/main/" + "yoloe-v8l-seg_visual_encoder.onnx" + ) + model_path = "yoloe-v8l-seg_visual_encoder.onnx" + else: + input_tensor = preprocess_image(image) + model_url = ( + "https://huggingface.co/sokovninn/clip-visual-with-projector/resolve/main/" + "clip_visual_with_projector.onnx" + ) + model_path = "clip_visual_with_projector.onnx" + + onnx_model_path = download_model(model_url, model_path) + + session = onnxruntime.InferenceSession( + onnx_model_path, + providers=[ + "TensorrtExecutionProvider", + "CUDAExecutionProvider", + "CPUExecutionProvider", + ], + ) + + if model_name == "yoloe": + if mask_prompt is None: + prompts = np.zeros((1, 1, 80, 80), dtype=np.float32) + prompts[0, 0, 5:75, 5:75] = 1.0 + else: + prompts = np.asarray(mask_prompt, dtype=np.float32) + if prompts.ndim == 2: + if prompts.shape != (80, 80): + prompts = cv2.resize( + prompts, (80, 80), interpolation=cv2.INTER_NEAREST + ) + prompts = prompts[None, None, :, :] + elif prompts.shape == (1, 1, 80, 80): + pass + else: + raise ValueError("mask_prompt must have shape (80,80) or (1,1,80,80)") + outputs = session.run(None, {"images": input_tensor, "prompts": prompts}) + else: + input_name = session.get_inputs()[0].name + outputs = session.run(None, {input_name: input_tensor}) + + image_embeddings = outputs[0].squeeze(0).reshape(1, -1) + image_features = pad_and_quantize_features( + image_embeddings, max_num_classes, model_name, precision + ) + + del session + + return image_features + + +def download_tokenizer(url, save_path): + if not os.path.exists(save_path): + print(f"Downloading tokenizer config from {url}...") + with open(save_path, "wb") as f: + f.write(requests.get(url).content) + return save_path + + +def download_model(url, save_path): + if not os.path.exists(save_path): + print(f"Downloading model from {url}...") + response = requests.get(url) + if response.status_code == 200: + with open(save_path, "wb") as f: + f.write(response.content) + print(f"Model saved to {save_path}.") + else: + raise Exception( + f"Failed to download model. Status code: {response.status_code}" + ) + else: + print(f"Model already exists at {save_path}.") + + return save_path + + +def preprocess_image(image): + """Preprocess image for CLIP vision model input""" + image = cv2.resize(image, (224, 224)) + + image_array = np.array(image).astype(np.float32) / 255.0 + + mean = np.array([0.48145466, 0.4578275, 0.40821073]) + std = np.array([0.26862954, 0.26130258, 0.27577711]) + + image_array = (image_array - mean) / std + + image_array = np.transpose(image_array, (2, 0, 1)) + image_array = np.expand_dims(image_array, axis=0) + + return image_array.astype(np.float32) + + +def base64_to_cv2_image(base64_data_uri: str): + if "," in base64_data_uri: + header, base64_data = base64_data_uri.split(",", 1) + else: + base64_data = base64_data_uri + + binary_data = base64.b64decode(base64_data) + np_arr = np.frombuffer(binary_data, np.uint8) + img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) + return img diff --git a/custom-frontend/data-collection/backend/src/utils/snap_utils.py b/custom-frontend/data-collection/backend/src/utils/snap_utils.py new file mode 100644 index 000000000..78e7204be --- /dev/null +++ b/custom-frontend/data-collection/backend/src/utils/snap_utils.py @@ -0,0 +1,60 @@ +from typing import List +import depthai as dai + +from depthai_nodes.node import SnapsProducer +from depthai_nodes import ImgDetectionsExtended + + +def custom_snap_process( + producer: SnapsProducer, + frame: dai.ImgFrame, + det_data: dai.ImgDetections | ImgDetectionsExtended, + class_names: List[str], + model: str, +): + detections = det_data.detections + if len(detections) == 0: + return + + file_group = [dai.FileData(frame, "rgb")] + + dets_labels = [det.label for det in detections] + dets_confs = [det.confidence for det in detections] + + extras = { + "model": model, + "detection_label": str(dets_labels), + "detection_confidence": str(dets_confs), + } + + if isinstance(det_data, dai.ImgDetections): + dets_labels_str = [class_names[det.label] for det in detections] + dets_xyxy = [(det.xmin, det.ymin, det.xmax, det.ymax) for det in detections] + extras["detection_xyxy"] = str(dets_xyxy) + + file_group.append(dai.FileData(det_data, "detections")) + + elif isinstance(det_data, ImgDetectionsExtended): + dets_labels_str = [det.label_name for det in detections] + dets_cxcywh = [ + ( + det.rotated_rect.center.x, + det.rotated_rect.center.y, + det.rotated_rect.size.width, + det.rotated_rect.size.height, + ) + for det in detections + ] + extras["detection_cxcywh"] = str(dets_cxcywh) + else: + raise NotImplementedError + + extras["detection_label_str"] = str(dets_labels_str) + + # if producer.sendSnap( + # name="rgb", file_group=file_group, tags=["demo"], extras=extras + # ): + # print("Snap sent!") + + if producer.sendSnap(name="rgb", file_group=file_group, tags=["demo"]): + print("Snap sent!") diff --git a/custom-frontend/data-collection/frontend/.gitignore b/custom-frontend/data-collection/frontend/.gitignore new file mode 100644 index 000000000..dafadc4b9 --- /dev/null +++ b/custom-frontend/data-collection/frontend/.gitignore @@ -0,0 +1,28 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? + +## Panda +styled-system +styled-system-studio diff --git a/custom-frontend/data-collection/frontend/biome.json b/custom-frontend/data-collection/frontend/biome.json new file mode 100644 index 000000000..5d96cd100 --- /dev/null +++ b/custom-frontend/data-collection/frontend/biome.json @@ -0,0 +1,30 @@ +{ + "$schema": "https://biomejs.dev/schemas/1.9.4/schema.json", + "vcs": { + "enabled": false, + "clientKind": "git", + "useIgnoreFile": false + }, + "files": { + "ignoreUnknown": false, + "ignore": ["node_modules/", "dist/"] + }, + "formatter": { + "enabled": true, + "indentStyle": "tab" + }, + "organizeImports": { + "enabled": true + }, + "linter": { + "enabled": true, + "rules": { + "recommended": true + } + }, + "javascript": { + "formatter": { + "quoteStyle": "single" + } + } +} diff --git a/custom-frontend/data-collection/frontend/index.html b/custom-frontend/data-collection/frontend/index.html new file mode 100644 index 000000000..3f5e422fa --- /dev/null +++ b/custom-frontend/data-collection/frontend/index.html @@ -0,0 +1,13 @@ + + + + + + + Data Collection Application + + +
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"vite", + "build": "tsc -b && vite build", + "lint": "biome check .", + "lint:fix": "biome check --write .", + "preview": "vite preview" + }, + "dependencies": { + "@luxonis/depthai-viewer-common": "1.2.8", + "react": "^18.3.1", + "react-dom": "^18.3.1", + "react-router": "^7.5.0", + "react-router-dom": "^7.5.0" + }, + "devDependencies": { + "@biomejs/biome": "1.9.4", + "@pandacss/dev": "0.53.0", + "@types/react": "^18.3.20", + "@types/react-dom": "^18.3.6", + "@vitejs/plugin-react": "^4.3.4", + "globals": "^16.0.0", + "typescript": "~5.7.2", + "vite": "^6.3.1" + } +} diff --git a/custom-frontend/data-collection/frontend/panda.config.ts b/custom-frontend/data-collection/frontend/panda.config.ts new file mode 100644 index 000000000..f1c3e8249 --- /dev/null +++ b/custom-frontend/data-collection/frontend/panda.config.ts @@ -0,0 +1,12 @@ +import { defineConfig, defineGlobalStyles } from "@pandacss/dev"; +import { pandaPreset } from "@luxonis/common-fe-components"; + +export default defineConfig({ + presets: [pandaPreset], + preflight: true, + include: ["./src/**/*.{ts,tsx}"], + exclude: [], + jsxFramework: "react", + outdir: "styled-system", + forceConsistentTypeExtension: true, +}); diff --git a/custom-frontend/data-collection/frontend/postcss.config.mjs b/custom-frontend/data-collection/frontend/postcss.config.mjs new file mode 100644 index 000000000..cdaaad9ae --- /dev/null +++ b/custom-frontend/data-collection/frontend/postcss.config.mjs @@ -0,0 +1,5 @@ +export default { + plugins: { + '@pandacss/dev/postcss': {}, + }, +} \ No newline at end of file diff --git a/custom-frontend/data-collection/frontend/src/App.tsx b/custom-frontend/data-collection/frontend/src/App.tsx new file mode 100644 index 000000000..8b8196909 --- /dev/null +++ b/custom-frontend/data-collection/frontend/src/App.tsx @@ -0,0 +1,322 @@ +import { css } from "../styled-system/css/css.mjs"; +import { Streams, useConnection } from "@luxonis/depthai-viewer-common"; +import { ClassSelector } from "./ClassSelector.tsx"; +import { ConfidenceSlider } from "./ConfidenceSlider.tsx"; +import { ImageUploader } from "./ImageUploader.tsx"; +import { SnapCollectionButton } from "./SnapCollectionButton.tsx"; +import { useCallback, useEffect, useRef, useState } from "react"; +import { useNotifications } from "./Notifications.tsx"; + +function App() { + const connection = useConnection(); + const streamContainerRef = useRef(null); + const overlayCanvasRef = useRef(null); + const [isDrawing, setIsDrawing] = useState(false); + const [dragStart, setDragStart] = useState<{ x: number; y: number } | null>(null); + const [currentRect, setCurrentRect] = useState<{ x: number; y: number; w: number; h: number } | null>(null); + const { notify } = useNotifications(); + + const getUnderlyingMediaAndSize = () => { + const container = streamContainerRef.current; + if (!container) return null; + const videoEl = container.querySelector("video") as HTMLVideoElement | null; + const canvases = Array.from(container.querySelectorAll("canvas")) as HTMLCanvasElement[]; + const canvasEl = canvases.find((c) => c.getAttribute("data-role") !== "overlay") || null; + const containerRect = container.getBoundingClientRect(); + if (videoEl && videoEl.videoWidth && videoEl.videoHeight) { + const r = videoEl.getBoundingClientRect(); + const displayWidth = r.width; + const displayHeight = r.height; + const offsetX = r.left - containerRect.left; + const offsetY = r.top - containerRect.top; + console.log("[BBox] Capturing from video element", { width: videoEl.videoWidth, height: videoEl.videoHeight, displayWidth, displayHeight, offsetX, offsetY }); + return { + type: "video" as const, + el: videoEl, + width: videoEl.videoWidth, + height: videoEl.videoHeight, + displayWidth, + displayHeight, + offsetX, + offsetY, + }; + } + if (canvasEl && canvasEl.width && canvasEl.height) { + const r = canvasEl.getBoundingClientRect(); + const displayWidth = r.width; + const displayHeight = r.height; + const offsetX = r.left - containerRect.left; + const offsetY = r.top - containerRect.top; + console.log("[BBox] Capturing from canvas element", { width: canvasEl.width, height: canvasEl.height, displayWidth, displayHeight, offsetX, offsetY }); + return { + type: "canvas" as const, + el: canvasEl, + width: canvasEl.width, + height: canvasEl.height, + displayWidth, + displayHeight, + offsetX, + offsetY, + }; + } + return null; + }; + + + const finalizeBBox = useCallback(() => { + if (!currentRect) return; + const overlay = overlayCanvasRef.current; + if (!overlay) return; + const { x, y, w, h } = currentRect; + if (w <= 0 || h <= 0) { + setIsDrawing(false); + setCurrentRect(null); + setDragStart(null); + const ctx = overlay.getContext("2d"); + if (ctx) ctx.clearRect(0, 0, overlay.width, overlay.height); + notify('Selection too small. Please draw a larger box.', { type: 'warning' }); + return; + } + + // Map overlay-space bbox to source frame using displayed media rect + const media = getUnderlyingMediaAndSize(); + if (!media) { + console.warn("[BBox] No media found under overlay; aborting bbox post"); + notify('No video/canvas found. Reset the view and try again.', { type: 'error', durationMs: 6000 }); + return; + } + + const overlayW = overlay.width; + const overlayH = overlay.height; + const srcW = media.width; + const srcH = media.height; + const mediaOffsetX = (media as any).offsetX ?? 0; + const mediaOffsetY = (media as any).offsetY ?? 0; + const mediaDispW = (media as any).displayWidth ?? overlayW; + const mediaDispH = (media as any).displayHeight ?? overlayH; + + let contentX = mediaOffsetX; + let contentY = mediaOffsetY; + let contentW = mediaDispW; + let contentH = mediaDispH; + if (media.type === "canvas") { + const side = Math.min(mediaDispW, mediaDispH); + contentX = mediaOffsetX + (mediaDispW - side) / 2; + contentY = mediaOffsetY + (mediaDispH - side) / 2; + contentW = side; + contentH = side; + } + + const rx0 = Math.max(x, contentX); + const ry0 = Math.max(y, contentY); + const rx1 = Math.min(x + w, contentX + contentW); + const ry1 = Math.min(y + h, contentY + contentH); + const rw = Math.max(0, rx1 - rx0); + const rh = Math.max(0, ry1 - ry0); + if (rw <= 1 || rh <= 1) { + console.warn("[BBox] BBox outside content area; aborting"); + notify('Box outside of content area. Try again within the stream.', { type: 'warning', durationMs: 6000 }); + return; + } + + const scaleX = srcW / contentW; + const scaleY = srcH / contentH; + const sx0 = Math.max(0, Math.min(srcW - 1, Math.round((rx0 - contentX) * scaleX))); + const sy0 = Math.max(0, Math.min(srcH - 1, Math.round((ry0 - contentY) * scaleY))); + const sx1 = Math.max(0, Math.min(srcW, Math.round((rx1 - contentX) * scaleX))); + const sy1 = Math.max(0, Math.min(srcH, Math.round((ry1 - contentY) * scaleY))); + const sw = Math.max(1, sx1 - sx0); + const sh = Math.max(1, sy1 - sy0); + + const xNorm = sx0 / srcW; + const yNorm = sy0 / srcH; + const wNorm = sw / srcW; + const hNorm = sh / srcH; + + console.log("[BBox] Posting BBox Prompt Service (normalized source)", { + bbox: { x: xNorm, y: yNorm, width: wNorm, height: hNorm }, + src: { width: srcW, height: srcH }, + overlay: { width: overlayW, height: overlayH }, + display: { width: mediaDispW, height: mediaDispH, offsetX: mediaOffsetX, offsetY: mediaOffsetY }, + content: { x: contentX, y: contentY, width: contentW, height: contentH }, + scales: { scaleX, scaleY } + }); + notify( + `Sending box [${xNorm.toFixed(2)}, ${yNorm.toFixed(2)}, ${wNorm.toFixed(2)}, ${hNorm.toFixed(2)}]`, + { type: 'info' } + ); + // @ts-ignore - Custom service + (connection as any).daiConnection?.postToService( + "BBox Prompt Service", + { + filename: "object.png", + type: "application/json", + data: null, + bbox: { x: xNorm, y: yNorm, width: wNorm, height: hNorm }, + bboxType: "normalized", + label: "object" + }, + (resp: any) => { + console.log("[BBox] Service ack:", resp); + notify('Bounding box sent', { type: 'success' }); + } + ); + + setIsDrawing(false); + setCurrentRect(null); + setDragStart(null); + const ctx = overlay.getContext("2d"); + if (ctx) ctx.clearRect(0, 0, overlay.width, overlay.height); + }, [connection, currentRect]); + + const handleBeginBBoxDraw = useCallback(() => { + console.log("[BBox] Begin drawing requested"); + setIsDrawing(true); + setCurrentRect(null); + setDragStart(null); + }, []); + + useEffect(() => { + if (!isDrawing) return; + const container = streamContainerRef.current; + const overlay = overlayCanvasRef.current; + if (!container || !overlay) return; + const sizeOverlay = () => { + const rect = container.getBoundingClientRect(); + overlay.width = Math.max(1, Math.round(rect.width)); + overlay.height = Math.max(1, Math.round(rect.height)); + const ctx = overlay.getContext("2d"); + if (ctx) ctx.clearRect(0, 0, overlay.width, overlay.height); + console.log("[BBox] Overlay sized", { width: overlay.width, height: overlay.height }); + }; + sizeOverlay(); + window.addEventListener("resize", sizeOverlay); + return () => window.removeEventListener("resize", sizeOverlay); + }, [isDrawing]); + + useEffect(() => { + notify(connection.connected ? 'Connected to device' : 'Disconnected from device', { type: connection.connected ? 'success' : 'warning', durationMs: 1800 }); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [connection.connected]); + + const onOverlayMouseDown = (e: any) => { + if (!isDrawing) return; + const canvas = overlayCanvasRef.current; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + const x = e.clientX - rect.left; + const y = e.clientY - rect.top; + setDragStart({ x, y }); + setCurrentRect({ x, y, w: 0, h: 0 }); + console.log("[BBox] Mouse down", { x, y }); + }; + + const onOverlayMouseMove = (e: any) => { + if (!isDrawing || !dragStart) return; + const canvas = overlayCanvasRef.current; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + const x = e.clientX - rect.left; + const y = e.clientY - rect.top; + const x0 = Math.min(dragStart.x, x); + const y0 = Math.min(dragStart.y, y); + const w = Math.abs(x - dragStart.x); + const h = Math.abs(y - dragStart.y); + setCurrentRect({ x: x0, y: y0, w, h }); + + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.clearRect(0, 0, canvas.width, canvas.height); + ctx.strokeStyle = "#22c55e"; // green + ctx.lineWidth = 2; + ctx.strokeRect(x0, y0, w, h); + if ((w * h) % 20 === 0) { + console.log("[BBox] Mouse move bbox", { x: x0, y: y0, w, h }); + } + }; + + const onOverlayMouseUp = () => { + if (!isDrawing) return; + console.log("[BBox] Mouse up, finalizing bbox", currentRect); + finalizeBBox(); + }; + + return ( +
+ {/* Left: Stream Viewer */} +
+ + {isDrawing && ( + + )} +
+ + {/* Vertical Divider */} +
+ + {/* Right: Sidebar (Info and Controls) */} +
+

+ Open Vocabulary Object Detection +

+

+ Run open‑vocabulary detection on‑device (YOLOE or YOLO‑World) with a custom UI. + Define classes via text prompts or image crops, adjust confidence, and visualize results live. +

+ + {/* Class Input */} + + + {/* Image Uploader */} + + + {/* Confidence Slider */} + + + {/* Snap Collection */} + + + {/* Connection Status */} +
+
+ {connection.connected ? 'Connected to device' : 'Disconnected'} +
+
+
+ ); +} + +export default App; diff --git a/custom-frontend/data-collection/frontend/src/ClassSelector.tsx b/custom-frontend/data-collection/frontend/src/ClassSelector.tsx new file mode 100644 index 000000000..956d3f502 --- /dev/null +++ b/custom-frontend/data-collection/frontend/src/ClassSelector.tsx @@ -0,0 +1,66 @@ +import { Flex, Button, Input } from "@luxonis/common-fe-components"; +import { useRef, useState } from "react"; +import { css } from "../styled-system/css/css.mjs"; +import { useConnection } from "@luxonis/depthai-viewer-common"; +import { useNotifications } from "./Notifications.tsx"; + +export function ClassSelector() { + const inputRef = useRef(null); + const connection = useConnection(); + const [selectedClasses, setSelectedClasses] = useState(["person", "chair", "TV"]); + const { notify } = useNotifications(); + + const handleSendMessage = () => { + if (inputRef.current) { + const value = inputRef.current.value; + const updatedClasses = value + .split(',') + .map((c: string) => c.trim()) + .filter(Boolean); + + if (updatedClasses.length === 0) { + notify('Please enter at least one class (comma separated).', { type: 'warning', durationMs: 5000 }); + return; + } + if (!connection.connected) { + notify('Not connected to device. Unable to update classes.', { type: 'error' }); + return; + } + + console.log('Sending new class list to backend:', updatedClasses); + notify(`Updating ${updatedClasses.length} class${updatedClasses.length > 1 ? 'es' : ''}…`, { type: 'info' }); + + connection.daiConnection?.postToService( + // @ts-ignore - Custom service + "Class Update Service", + updatedClasses, + () => { + console.log('Backend acknowledged class update'); + setSelectedClasses(updatedClasses); + notify(`Classes updated (${updatedClasses.join(', ')})`, { type: 'success', durationMs: 6000 }); + } + ); + + inputRef.current.value = ''; + } + }; + + return ( +
+ {/* Class List Display */} +

Update Classes with Text Input:

+
    + {selectedClasses.map((cls: string, idx: number) => ( +
  • {cls}
    • + ))} +
+ + + {/* Input + Button */} + + + + +
+ ); +} diff --git a/custom-frontend/data-collection/frontend/src/ConfidenceSlider.tsx b/custom-frontend/data-collection/frontend/src/ConfidenceSlider.tsx new file mode 100644 index 000000000..636b52a87 --- /dev/null +++ b/custom-frontend/data-collection/frontend/src/ConfidenceSlider.tsx @@ -0,0 +1,70 @@ +import { css } from "../styled-system/css/css.mjs"; +import { useState } from "react"; +import { useConnection } from "@luxonis/depthai-viewer-common"; + +interface ConfidenceSliderProps { + initialValue?: number; +} + +export function ConfidenceSlider({ initialValue = 0.5 }: ConfidenceSliderProps) { + const connection = useConnection(); + const [value, setValue] = useState(initialValue); + + const handleCommit = () => { + if (typeof value === "number" && !isNaN(value)) { + console.log('Sending threshold to backend:', value); + + connection.daiConnection?.postToService( + // @ts-ignore - Custom service + "Threshold Update Service", + value, + (response: any) => { + console.log('Backend acknowledged threshold update:', response); + } + ); + } else { + console.warn("Invalid value, skipping update:", value); + } + }; + + return ( +
+ + setValue(parseFloat(e.target.value))} + onMouseUp={handleCommit} + onTouchEnd={handleCommit} + className={css({ + width: '100%', + appearance: 'none', + height: '4px', + borderRadius: 'full', + backgroundColor: 'gray.300', + '&::-webkit-slider-thumb': { + appearance: 'none', + width: '12px', + height: '12px', + borderRadius: 'full', + backgroundColor: 'blue.500', + cursor: 'pointer', + }, + '&::-moz-range-thumb': { + appearance: 'none', + width: '12px', + height: '12px', + borderRadius: 'full', + backgroundColor: 'blue.500', + cursor: 'pointer', + } + })} + /> +
+ ); +} diff --git a/custom-frontend/data-collection/frontend/src/ImageUploader.tsx b/custom-frontend/data-collection/frontend/src/ImageUploader.tsx new file mode 100644 index 000000000..949695f1f --- /dev/null +++ b/custom-frontend/data-collection/frontend/src/ImageUploader.tsx @@ -0,0 +1,108 @@ +import { Button, Flex } from "@luxonis/common-fe-components"; +import { css } from "../styled-system/css/css.mjs"; +import { useState } from "react"; +import { useConnection } from "@luxonis/depthai-viewer-common"; +import { useNotifications } from "./Notifications.tsx"; + +type Props = { + onDrawBBox?: () => void; +} + +export function ImageUploader({ onDrawBBox }: Props) { + const connection = useConnection(); + const [selectedFile, setSelectedFile] = useState(null); + const { notify } = useNotifications(); + + const handleFileSelect = (event: React.ChangeEvent) => { + const file: File | null = event.target.files?.[0] || null; + setSelectedFile(file); + if (file) { + notify(`Selected: ${file.name}`, { type: 'info', durationMs: 2000 }); + } + }; + + const handleUpload = () => { + if (!selectedFile) { + notify('Please choose an image first', { type: 'warning' }); + return; + } + if (!connection.connected) { + notify('Not connected to device. Unable to upload image.', { type: 'error' }); + return; + } + + const reader = new FileReader(); + reader.onload = () => { + const fileData = reader.result; + + console.log("Uploading image to backend:", selectedFile.name); + const sizeKb = Math.max(1, Math.round((selectedFile.size || 0) / 1024)); + notify(`Uploading ${selectedFile.name} (${sizeKb} KB)…`, { type: 'info' }); + + // @ts-ignore - Custom service + (connection as any).daiConnection?.postToService( + "Image Upload Service", + { + filename: selectedFile.name, + type: selectedFile.type, + data: fileData + }, + (resp: any) => { + console.log("[ImageUpload] Service ack:", resp); + notify(`Image uploaded: ${selectedFile.name}`, { type: 'success', durationMs: 6000 }); + } + ); + }; + + reader.readAsDataURL(selectedFile); + }; + + return ( +
+

Update Classes with Image Input:

+ Important: reset view before drawing a bounding box + + {/* Clickable file selection area */} + + + {/* Hidden file input */} + + + {/* Upload / Draw buttons */} + + + OR + + +
+ ); +} diff --git a/custom-frontend/data-collection/frontend/src/Notifications.tsx b/custom-frontend/data-collection/frontend/src/Notifications.tsx new file mode 100644 index 000000000..7301845f2 --- /dev/null +++ b/custom-frontend/data-collection/frontend/src/Notifications.tsx @@ -0,0 +1,107 @@ +import { createContext, useCallback, useContext, useMemo, useRef, useState } from "react"; +import { css } from "../styled-system/css/css.mjs"; + +type Notification = { + id: number; + message: string; + type?: "info" | "success" | "warning" | "error"; + durationMs?: number; +}; + +type NotificationContextValue = { + notify: (message: string, options?: { type?: Notification["type"]; durationMs?: number }) => void; +}; + +const NotificationContext = createContext(null); + +export function useNotifications() { + const ctx = useContext(NotificationContext); + if (!ctx) throw new Error("useNotifications must be used within NotificationProvider"); + return ctx; +} + +export function NotificationProvider({ children }: { children: React.ReactNode }) { + const [items, setItems] = useState([]); + const idRef = useRef(1); + + const remove = useCallback((id: number) => { + setItems((prev) => prev.filter((n) => n.id !== id)); + }, []); + + const notify = useCallback((message, options) => { + const id = idRef.current++; + const durationMs = options?.durationMs ?? 4500; + const type = options?.type ?? "info"; + setItems((prev) => [...prev, { id, message, type, durationMs }].slice(-5)); + if (durationMs > 0) { + window.setTimeout(() => remove(id), durationMs); + } + }, [remove]); + + const value = useMemo(() => ({ notify }), [notify]); + + return ( + + {children} +
+ {items.map((n, idx) => ( + remove(n.id)} index={idx} /> + ))} +
+ + ); +} + +function Toast({ notification, onClose, index }: { notification: Notification; onClose: () => void; index: number }) { + const { message, type } = notification; + const colorMap: Record, { bg: string; border: string; text: string }> = { + info: { bg: "blue.50", border: "blue.300", text: "blue.900" }, + success: { bg: "green.50", border: "green.300", text: "green.900" }, + warning: { bg: "yellow.50", border: "yellow.300", text: "yellow.900" }, + error: { bg: "red.50", border: "red.300", text: "red.900" }, + }; + const colors = colorMap[type ?? "info"]; + + return ( +
+
+ {message} + +
+
+ ); +} + +// Keyframes for smooth appear (no opacity change to keep background fully opaque) +const styleEl = (typeof document !== 'undefined') ? document.createElement('style') : null; +if (styleEl && !document.getElementById('notif-keyframes')) { + styleEl.id = 'notif-keyframes'; + styleEl.innerHTML = ` +@keyframes slideInUp { from { transform: translateY(8px); } to { transform: translateY(0); } } +@keyframes slideOutDown { from { transform: translateY(0); } to { transform: translateY(8px); } } +`; + document.head.appendChild(styleEl); +} + diff --git a/custom-frontend/data-collection/frontend/src/SnapCollectionButton.tsx b/custom-frontend/data-collection/frontend/src/SnapCollectionButton.tsx new file mode 100644 index 000000000..27de24258 --- /dev/null +++ b/custom-frontend/data-collection/frontend/src/SnapCollectionButton.tsx @@ -0,0 +1,174 @@ +import { useState, useMemo } from "react"; +import { Button } from "@luxonis/common-fe-components"; +import { css } from "../styled-system/css/css.mjs"; +import { useConnection } from "@luxonis/depthai-viewer-common"; +import { useNotifications } from "./Notifications.tsx"; + +export function SnapCollectionButton() { + const connection = useConnection(); + const { notify } = useNotifications(); + + const [isCollecting, setIsCollecting] = useState(false); + const [busy, setBusy] = useState(false); + + // Keep as string for forgiving typing; validate on toggle. + const [timeIntervalStr, setTimeIntervalStr] = useState("60"); // default 60s + const timeInterval = useMemo( + () => Number.parseInt(timeIntervalStr, 10), + [timeIntervalStr] + ); + const timeIntervalIsValid = + Number.isInteger(timeInterval) && timeInterval > 0; + + const handleToggle = () => { + if (!connection.connected) { + notify("Not connected to device. Unable to toggle collection.", { + type: "error", + }); + return; + } + if (busy) return; + + const nextState = !isCollecting; // true = start, false = end + + // When starting, require a valid positive integer interval. + if (nextState && !timeIntervalIsValid) { + notify("Please enter a positive integer time interval (seconds).", { + type: "error", + }); + return; + } + + setBusy(true); + notify( + nextState + ? `Starting collection (every ${timeInterval}s)…` + : "Ending collection…", + { type: "info" } + ); + + // Tuple payload: [startFlag:boolean, interval:number] + // Start -> [true, ] + // End -> [false, -1] + const payload: [boolean, number] = nextState + ? [true, timeInterval] + : [false, -1]; + + (connection as any).daiConnection?.postToService( + "Snap Collection Service", + payload, + (resp: any) => { + console.log("[SnapCollect] Ack:", resp); + setBusy(false); + setIsCollecting(nextState); + notify( + nextState + ? `Collection started (every ${timeInterval}s).` + : "Collection ended.", + { type: "success" } + ); + } + ); + }; + + const label = busy + ? isCollecting + ? "Ending…" + : "Starting…" + : isCollecting + ? "End Collection" + : "Start Collection"; + + const inputDisabled = busy || isCollecting; + + return ( +
+ {/* Horizontal divider */} +
+ +

+ Click below to start Snap Collection +

+ + {/* Time interval input (seconds) */} + + + {/* Full-width toggle button */} + +
+ ); +} diff --git a/custom-frontend/data-collection/frontend/src/index.css b/custom-frontend/data-collection/frontend/src/index.css new file mode 100644 index 000000000..009ed44b9 --- /dev/null +++ b/custom-frontend/data-collection/frontend/src/index.css @@ -0,0 +1 @@ +@layer reset, base, tokens, recipes, utilities; \ No newline at end of file diff --git a/custom-frontend/data-collection/frontend/src/main.tsx b/custom-frontend/data-collection/frontend/src/main.tsx new file mode 100644 index 000000000..116fda8d2 --- /dev/null +++ b/custom-frontend/data-collection/frontend/src/main.tsx @@ -0,0 +1,33 @@ +import {StrictMode} from 'react'; +import {createRoot} from 'react-dom/client'; +import './index.css'; +import '@luxonis/depthai-viewer-common/styles'; +import '@luxonis/common-fe-components/styles'; +import '@luxonis/depthai-pipeline-lib/styles'; +import App from './App.tsx'; +import {BrowserRouter, Route, Routes} from "react-router"; +import {DepthAIContext} from "@luxonis/depthai-viewer-common"; +import { NotificationProvider } from './Notifications.tsx'; + +// This function extracts the base path with app version from the current URL. +// This is essential for access via domain luxonis.app +function getBasePath(): string { + return window.location.pathname.match(/^\/\d+\.\d+\.\d+\/$/)?.[0] ?? '' +} + +createRoot(document.getElementById('root')!).render( + + + + + + }/> + + + + + , +); diff --git a/custom-frontend/data-collection/frontend/src/vite-env.d.ts b/custom-frontend/data-collection/frontend/src/vite-env.d.ts new file mode 100644 index 000000000..11f02fe2a --- /dev/null +++ b/custom-frontend/data-collection/frontend/src/vite-env.d.ts @@ -0,0 +1 @@ +/// diff --git a/custom-frontend/data-collection/frontend/src/window.d.ts b/custom-frontend/data-collection/frontend/src/window.d.ts new file mode 100644 index 000000000..f02d957b7 --- /dev/null +++ b/custom-frontend/data-collection/frontend/src/window.d.ts @@ -0,0 +1,7 @@ +declare global { + interface Window { + __basePath: string; + } +} + +export {}; diff --git a/custom-frontend/data-collection/frontend/tsconfig.app.json b/custom-frontend/data-collection/frontend/tsconfig.app.json new file mode 100644 index 000000000..8f9007000 --- /dev/null +++ b/custom-frontend/data-collection/frontend/tsconfig.app.json @@ -0,0 +1,30 @@ +{ + "compilerOptions": { + "composite": true, + "tsBuildInfoFile": "./node_modules/.tmp/example.app.tsbuildinfo", + "target": "ESNext", + "useDefineForClassFields": true, + "lib": [ + "ESNext", + "WebWorker", + "DOM", + "DOM.Iterable" + ], + "module": "ESNext", + "skipLibCheck": true, + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "resolveJsonModule": true, + "isolatedModules": true, + "moduleDetection": "force", + "noEmit": true, + "jsx": "react-jsx", + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "noFallthroughCasesInSwitch": true + }, + "include": [ + "src" + ] +} diff --git a/custom-frontend/data-collection/frontend/tsconfig.json b/custom-frontend/data-collection/frontend/tsconfig.json new file mode 100644 index 000000000..fb1241881 --- /dev/null +++ b/custom-frontend/data-collection/frontend/tsconfig.json @@ -0,0 +1,7 @@ +{ + "files": [], + "references": [ + { "path": "./tsconfig.app.json" }, + { "path": "./tsconfig.node.json" } + ] +} diff --git a/custom-frontend/data-collection/frontend/tsconfig.node.json b/custom-frontend/data-collection/frontend/tsconfig.node.json new file mode 100644 index 000000000..29ead3807 --- /dev/null +++ b/custom-frontend/data-collection/frontend/tsconfig.node.json @@ -0,0 +1,13 @@ +{ + "compilerOptions": { + "composite": true, + "tsBuildInfoFile": "./node_modules/.tmp/tsconfig-example.node.tsbuildinfo", + "skipLibCheck": true, + "module": "ESNext", + "moduleResolution": "bundler", + "allowSyntheticDefaultImports": true, + "strict": true, + "noEmit": true + }, + "include": ["vite.config.ts"] +} diff --git a/custom-frontend/data-collection/frontend/vite.config.ts b/custom-frontend/data-collection/frontend/vite.config.ts new file mode 100644 index 000000000..5c678f860 --- /dev/null +++ b/custom-frontend/data-collection/frontend/vite.config.ts @@ -0,0 +1,22 @@ +import react from '@vitejs/plugin-react'; +import { defineConfig } from 'vite'; + +// https://vite.dev/config/ +export default defineConfig({ + base: "", + plugins: [react(),], + // This is needed by FoxGlove + define: { + global: {}, + }, + worker: { + format: "es", + }, + build: { + rollupOptions: { + output: { + format: "esm", + }, + }, + }, +}); diff --git a/custom-frontend/data-collection/media/dynamic_yolo_world.gif b/custom-frontend/data-collection/media/dynamic_yolo_world.gif new file mode 100644 index 0000000000000000000000000000000000000000..fb72f7cc204d216507bad1ba479d7713393c0e63 GIT binary patch literal 2656194 zcmW(+WmFX0(_fJgBm|KXDVJ^}RX}>_T)I1!?v`#?x()T)PVmE5C8xH01yBG3IMA z5Fh{w1i*m+1Q3Ay-&Y_A00IFZAOI8ufP(-C5CHivP0uWFD@_(9uZ~zDnfWQGzH~(l#02~28 zAOOh!VFDrnAS3{S1VE7hI1+$B0+9a?6buA_K>#oW0EPmB4BU?41s_l5ikG}21LR@NEieOgCb#YBn*LsA(1cu00jh~ zKmZg3fPw;0Z~zJcKp_Dr01yQPqCh|t1c-tHQE(s%0Yo8zC;$ir1ff766a<8Vf>3Y} z3IRePK_~zO1%#kL5EKN0fa3~4^MIoUm02~E` zqd;&J1df8jQE)g40Y@R>C;$QlM4&(j6a;~SB2aJy3V}c&5hwr>1w^7iNE8H#f+A6H zBnp8$42z+`Esw?OX zdG(e>f28h5f5e-Qa^}_vxXdwRvNjXe&bxyNR1(3I%ZbI($<-<_ct+Cxa9XkPn4j!i z$z%qf^M=AGCL?Q(u=?}f@mp)vLh-m}Q@#`pl~QgU;SVf)EDM#IjU4MMEc=?823^4z zt9&e4^;V;$c$LlUt94G>usC_o!u1ZXtCRIFGmE21j4xj+RU~pv^)YtsXiRw3?+!t! 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TODO: Remove + snaps_producer._em.setSourceAppId(os.getenv("OAKAGENT_CONTAINER_ID")) print("Snaps producer API KEY:", os.getenv("DEPTHAI_HUB_API_KEY")) def update_labels(label_names: list[str], offset: int = 0): diff --git a/custom-frontend/data-collection/backend/src/requirements.txt b/custom-frontend/data-collection/backend/src/requirements.txt index 8604ae5b6..b9912470b 100644 --- a/custom-frontend/data-collection/backend/src/requirements.txt +++ b/custom-frontend/data-collection/backend/src/requirements.txt @@ -1,7 +1,7 @@ # depthai==3.0.0 --extra-index-url https://artifacts.luxonis.com/artifactory/luxonis-python-snapshot-local --pre -depthai==3.0.0.dev0+c74b28c8e4ccdc9869fbfa99d73b95ac0893249e +depthai==3.0.0.dev0+3aa210a2d54c32e0f75f8ac6b4a81f80b6230f2f #depthai-nodes==0.3.4 depthai-nodes @ git+https://github.com/luxonis/depthai-nodes.git@feat/dai_dev_req opencv-python-headless~=4.10.0 diff --git a/custom-frontend/data-collection/backend/src/utils/snap_utils.py b/custom-frontend/data-collection/backend/src/utils/snap_utils.py index 78e7204be..694460d6a 100644 --- a/custom-frontend/data-collection/backend/src/utils/snap_utils.py +++ b/custom-frontend/data-collection/backend/src/utils/snap_utils.py @@ -32,7 +32,7 @@ def custom_snap_process( dets_xyxy = [(det.xmin, det.ymin, det.xmax, det.ymax) for det in detections] extras["detection_xyxy"] = str(dets_xyxy) - file_group.append(dai.FileData(det_data, "detections")) + # file_group.append(dai.FileData(det_data, "detections")) elif isinstance(det_data, ImgDetectionsExtended): dets_labels_str = [det.label_name for det in detections] @@ -50,11 +50,9 @@ def custom_snap_process( raise NotImplementedError extras["detection_label_str"] = str(dets_labels_str) + extras = {"model": model} - # if producer.sendSnap( - # name="rgb", file_group=file_group, tags=["demo"], extras=extras - # ): - # print("Snap sent!") - - if producer.sendSnap(name="rgb", file_group=file_group, tags=["demo"]): + if producer.sendSnap( + name="rgb", file_group=file_group, tags=["demo"], extras=extras + ): print("Snap sent!") From cd76d980b093c4e25c0559d549538718c6ecc8e6 Mon Sep 17 00:00:00 2001 From: klemen1999 Date: Tue, 30 Sep 2025 15:19:56 +0800 Subject: [PATCH 03/39] build prepare --- custom-frontend/data-collection/backend-run.sh | 2 +- custom-frontend/data-collection/oakapp.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/custom-frontend/data-collection/backend-run.sh b/custom-frontend/data-collection/backend-run.sh index 28ace22ac..dbbada4d4 100644 --- a/custom-frontend/data-collection/backend-run.sh +++ b/custom-frontend/data-collection/backend-run.sh @@ -1,3 +1,3 @@ #!/bin/sh echo "Starting Backend" -exec python3.12 /app/backend/src/main.py --api_key \ No newline at end of file +exec python3.12 /app/backend/src/main.py \ No newline at end of file diff --git a/custom-frontend/data-collection/oakapp.toml b/custom-frontend/data-collection/oakapp.toml index 8c3563101..396e5c745 100644 --- a/custom-frontend/data-collection/oakapp.toml +++ b/custom-frontend/data-collection/oakapp.toml @@ -1,4 +1,4 @@ -identifier = "com.example.custom-frontend.open-vocabulary-object-detection" +identifier = "com.luxonis.custom-frontend.open-vocabulary-object-detection" entrypoint = ["bash", "-c", "/usr/bin/runsvdir -P /etc/service"] app_version = "1.0.0" From f1505e773d4d308074e9911e9d2419c361324934 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Thu, 16 Oct 2025 17:06:42 +0200 Subject: [PATCH 04/39] Added snapping conditions app --- performant_PoCs/hub_snaps/.gitignore | 3 + performant_PoCs/hub_snaps/.oakappignore | 38 + performant_PoCs/hub_snaps/README.md | 73 + performant_PoCs/hub_snaps/backend-run.sh | 3 + .../src/depthai_models/yolo_world_l.RVC4.yaml | 2 + .../yolo_world_l_fp16.RVC4.yaml | 2 + .../depthai_models/yoloe_v8_l_fp16.RVC4.yaml | 2 + performant_PoCs/hub_snaps/backend/src/main.py | 523 + .../hub_snaps/backend/src/requirements.txt | 13 + .../hub_snaps/backend/src/utils/__init__.py | 0 .../backend/src/utils/annotation_node.py | 45 + .../hub_snaps/backend/src/utils/arguments.py | 72 + .../backend/src/utils/frame_cache_node.py | 20 + .../backend/src/utils/helper_functions.py | 247 + .../hub_snaps/backend/src/utils/snap_utils.py | 211 + performant_PoCs/hub_snaps/frontend/.gitignore | 28 + performant_PoCs/hub_snaps/frontend/biome.json | 30 + performant_PoCs/hub_snaps/frontend/index.html | 13 + .../hub_snaps/frontend/package-lock.json | 22382 ++++++++++++++++ .../hub_snaps/frontend/package.json | 31 + .../hub_snaps/frontend/panda.config.ts | 12 + .../hub_snaps/frontend/postcss.config.mjs | 5 + .../hub_snaps/frontend/src/App.tsx | 355 + .../hub_snaps/frontend/src/ClassSelector.tsx | 66 + .../frontend/src/ConfidenceSlider.tsx | 70 + .../hub_snaps/frontend/src/ImageUploader.tsx | 108 + .../hub_snaps/frontend/src/Notifications.tsx | 107 + .../frontend/src/SnapCollectionButton.tsx | 189 + .../hub_snaps/frontend/src/index.css | 1 + .../hub_snaps/frontend/src/main.tsx | 33 + .../hub_snaps/frontend/src/vite-env.d.ts | 1 + .../hub_snaps/frontend/src/window.d.ts | 7 + .../hub_snaps/frontend/tsconfig.app.json | 30 + .../hub_snaps/frontend/tsconfig.json | 7 + .../hub_snaps/frontend/tsconfig.node.json | 13 + .../hub_snaps/frontend/vite.config.ts | 22 + performant_PoCs/hub_snaps/oakapp.toml | 33 + 37 files changed, 24797 insertions(+) create mode 100644 performant_PoCs/hub_snaps/.gitignore create mode 100644 performant_PoCs/hub_snaps/.oakappignore create mode 100644 performant_PoCs/hub_snaps/README.md create mode 100644 performant_PoCs/hub_snaps/backend-run.sh create mode 100644 performant_PoCs/hub_snaps/backend/src/depthai_models/yolo_world_l.RVC4.yaml create mode 100644 performant_PoCs/hub_snaps/backend/src/depthai_models/yolo_world_l_fp16.RVC4.yaml create mode 100644 performant_PoCs/hub_snaps/backend/src/depthai_models/yoloe_v8_l_fp16.RVC4.yaml create mode 100644 performant_PoCs/hub_snaps/backend/src/main.py create mode 100644 performant_PoCs/hub_snaps/backend/src/requirements.txt create mode 100644 performant_PoCs/hub_snaps/backend/src/utils/__init__.py create mode 100644 performant_PoCs/hub_snaps/backend/src/utils/annotation_node.py create mode 100644 performant_PoCs/hub_snaps/backend/src/utils/arguments.py create mode 100644 performant_PoCs/hub_snaps/backend/src/utils/frame_cache_node.py create mode 100644 performant_PoCs/hub_snaps/backend/src/utils/helper_functions.py create mode 100644 performant_PoCs/hub_snaps/backend/src/utils/snap_utils.py create mode 100644 performant_PoCs/hub_snaps/frontend/.gitignore create mode 100644 performant_PoCs/hub_snaps/frontend/biome.json create mode 100644 performant_PoCs/hub_snaps/frontend/index.html create mode 100644 performant_PoCs/hub_snaps/frontend/package-lock.json create mode 100644 performant_PoCs/hub_snaps/frontend/package.json create mode 100644 performant_PoCs/hub_snaps/frontend/panda.config.ts create mode 100644 performant_PoCs/hub_snaps/frontend/postcss.config.mjs create mode 100644 performant_PoCs/hub_snaps/frontend/src/App.tsx create mode 100644 performant_PoCs/hub_snaps/frontend/src/ClassSelector.tsx create mode 100644 performant_PoCs/hub_snaps/frontend/src/ConfidenceSlider.tsx create mode 100644 performant_PoCs/hub_snaps/frontend/src/ImageUploader.tsx create mode 100644 performant_PoCs/hub_snaps/frontend/src/Notifications.tsx create mode 100644 performant_PoCs/hub_snaps/frontend/src/SnapCollectionButton.tsx create mode 100644 performant_PoCs/hub_snaps/frontend/src/index.css create mode 100644 performant_PoCs/hub_snaps/frontend/src/main.tsx create mode 100644 performant_PoCs/hub_snaps/frontend/src/vite-env.d.ts create mode 100644 performant_PoCs/hub_snaps/frontend/src/window.d.ts create mode 100644 performant_PoCs/hub_snaps/frontend/tsconfig.app.json create mode 100644 performant_PoCs/hub_snaps/frontend/tsconfig.json create mode 100644 performant_PoCs/hub_snaps/frontend/tsconfig.node.json create mode 100644 performant_PoCs/hub_snaps/frontend/vite.config.ts create mode 100644 performant_PoCs/hub_snaps/oakapp.toml diff --git a/performant_PoCs/hub_snaps/.gitignore b/performant_PoCs/hub_snaps/.gitignore new file mode 100644 index 000000000..6018a0cbe --- /dev/null +++ b/performant_PoCs/hub_snaps/.gitignore @@ -0,0 +1,3 @@ +clip_textual_hf.onnx +clip_visual_with_projector.onnx +tokenizer.json \ No newline at end of file diff --git a/performant_PoCs/hub_snaps/.oakappignore b/performant_PoCs/hub_snaps/.oakappignore new file mode 100644 index 000000000..00f160e89 --- /dev/null +++ b/performant_PoCs/hub_snaps/.oakappignore @@ -0,0 +1,38 @@ +# Python virtual environments +venv/ +.venv/ +env + +# Node.js +# ignore node_modules, it will be reinstalled in the container +node_modules/ + +# Multimedia files +media/ + +# Local models +*.onnx + +# Documentation +README.md + +# VCS +.gitignore +.git/ +.github/ +.gitlab/ + +# The following files are ignored by default +# uncomment a line if you explicitly need it + +# !*.oakapp + +# Python +# !**/.mypy_cache/ +# !**/.ruff_cache/ + +# IDE files +# !**/.idea +# !**/.vscode +# !**/.zed + diff --git a/performant_PoCs/hub_snaps/README.md b/performant_PoCs/hub_snaps/README.md new file mode 100644 index 000000000..5d95544e3 --- /dev/null +++ b/performant_PoCs/hub_snaps/README.md @@ -0,0 +1,73 @@ +TODO + + diff --git a/performant_PoCs/hub_snaps/backend-run.sh b/performant_PoCs/hub_snaps/backend-run.sh new file mode 100644 index 000000000..448b7059b --- /dev/null +++ b/performant_PoCs/hub_snaps/backend-run.sh @@ -0,0 +1,3 @@ +#!/bin/sh +echo "Starting Backend" +exec python3.12 /app/backend/src/main.py -m yoloe \ No newline at end of file diff --git a/performant_PoCs/hub_snaps/backend/src/depthai_models/yolo_world_l.RVC4.yaml b/performant_PoCs/hub_snaps/backend/src/depthai_models/yolo_world_l.RVC4.yaml new file mode 100644 index 000000000..6ddc030fc --- /dev/null +++ b/performant_PoCs/hub_snaps/backend/src/depthai_models/yolo_world_l.RVC4.yaml @@ -0,0 +1,2 @@ +model: yolo-world-l:640x640-host-decoding +platform: RVC4 \ No newline at end of file diff --git a/performant_PoCs/hub_snaps/backend/src/depthai_models/yolo_world_l_fp16.RVC4.yaml b/performant_PoCs/hub_snaps/backend/src/depthai_models/yolo_world_l_fp16.RVC4.yaml new file mode 100644 index 000000000..f37bae36c --- /dev/null +++ b/performant_PoCs/hub_snaps/backend/src/depthai_models/yolo_world_l_fp16.RVC4.yaml @@ -0,0 +1,2 @@ +model: yolo-world-l:640x640-host-decoding-fp16 +platform: RVC4 \ No newline at end of file diff --git a/performant_PoCs/hub_snaps/backend/src/depthai_models/yoloe_v8_l_fp16.RVC4.yaml b/performant_PoCs/hub_snaps/backend/src/depthai_models/yoloe_v8_l_fp16.RVC4.yaml new file mode 100644 index 000000000..a887528e3 --- /dev/null +++ b/performant_PoCs/hub_snaps/backend/src/depthai_models/yoloe_v8_l_fp16.RVC4.yaml @@ -0,0 +1,2 @@ +model: yoloe-v8-l:640x640-text-visual-prompt:e23da0f +platform: RVC4 \ No newline at end of file diff --git a/performant_PoCs/hub_snaps/backend/src/main.py b/performant_PoCs/hub_snaps/backend/src/main.py new file mode 100644 index 000000000..47306c04b --- /dev/null +++ b/performant_PoCs/hub_snaps/backend/src/main.py @@ -0,0 +1,523 @@ +from pathlib import Path +import numpy as np +from dotenv import load_dotenv +import os +from functools import partial + +import depthai as dai +from depthai_nodes.node import ( + ParsingNeuralNetwork, + ImgDetectionsFilter, + ImgFrameOverlay, + ApplyColormap, + SnapsProducer, + ImgDetectionsBridge, +) + +from utils.helper_functions import ( + extract_text_embeddings, + extract_image_prompt_embeddings, + base64_to_cv2_image, + QUANT_VALUES, +) +from utils.arguments import initialize_argparser +from utils.annotation_node import AnnotationNode +from utils.frame_cache_node import FrameCacheNode +from utils.snap_utils import ( + custom_snap_process, + NoDetectionsGate, + tracklet_new_detection_process, +) + +load_dotenv(override=True) + +_, args = initialize_argparser() + +if args.api_key: + os.environ["DEPTHAI_HUB_API_KEY"] = args.api_key + +IP = args.ip or "localhost" +PORT = args.port or 8080 + +CLASS_NAMES = ["person", "chair", "TV"] +# For unified YOLOE, 0-79 are text classes, 80-159 are image-prompt classes +CLASS_OFFSET = 0 +MAX_NUM_CLASSES = 80 +CONFIDENCE_THRESHOLD = 0.1 +VISUALIZATION_RESOLUTION = (1080, 1080) + +visualizer = dai.RemoteConnection(serveFrontend=False) +device = dai.Device() +platform = device.getPlatformAsString() + +if platform != "RVC4": + raise ValueError("This example is supported only on RVC4 platform") + +frame_type = dai.ImgFrame.Type.BGR888i + + +def make_dummy_features(max_num_classes: int, model_name: str, precision: str): + if precision == "fp16": + return np.zeros((1, 512, max_num_classes), dtype=np.float16) + qzp = int(round(QUANT_VALUES.get(model_name, {}).get("quant_zero_point", 0))) + return np.full((1, 512, max_num_classes), qzp, dtype=np.uint8) + + +# choose initial features: text for yolo-world/yoloe +text_features = extract_text_embeddings( + class_names=CLASS_NAMES, + max_num_classes=MAX_NUM_CLASSES, + model_name=args.model if args.model != "yolo-world" else "yolo-world", + precision=args.precision, +) +image_prompt_features = None +if args.model == "yoloe": + # send dummy image-prompts initially + image_prompt_features = make_dummy_features( + MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision + ) + +if args.fps_limit is None: + args.fps_limit = 5 + print( + f"\nFPS limit set to {args.fps_limit} for {platform} platform. If you want to set a custom FPS limit, use the --fps_limit flag.\n" + ) + +with dai.Pipeline(device) as pipeline: + print("Creating pipeline...") + + # Model selection with precision-aware YAMLs for YOLOE variants + models_dir = Path(__file__).parent / "depthai_models" + if args.model == "yolo-world": + yaml_base = "yolo_world_l_fp16" if args.precision == "fp16" else "yolo_world_l" + yaml_filename = f"{yaml_base}.{platform}.yaml" + yaml_path = models_dir / yaml_filename + if not yaml_path.exists(): + raise SystemExit( + f"Model YAML not found: {yaml_path}. Ensure the model config exists." + ) + model_description = dai.NNModelDescription.fromYamlFile(str(yaml_path)) + elif args.model == "yoloe": + yaml_base = "yoloe_v8_l_fp16" if args.precision == "fp16" else "yoloe_v8_l" + yaml_filename = f"{yaml_base}.{platform}.yaml" + yaml_path = models_dir / yaml_filename + print(f"YOLOE YAML path: {yaml_path}") + if not yaml_path.exists(): + raise SystemExit( + f"Model YAML not found for YOLOE with precision {args.precision}: {yaml_path}. " + f"YOLOE int8 YAML is not available; run with --precision fp16." + ) + model_description = dai.NNModelDescription.fromYamlFile(str(yaml_path)) + model_description.platform = platform + model_nn_archive = dai.NNArchive(dai.getModelFromZoo(model_description)) + model_w, model_h = model_nn_archive.getInputSize() + + # media/camera input at high resolution for visualization + if args.media_path: + replay = pipeline.create(dai.node.ReplayVideo) + replay.setReplayVideoFile(Path(args.media_path)) + replay.setOutFrameType(dai.ImgFrame.Type.NV12) + replay.setLoop(True) + if args.fps_limit: + replay.setFps(args.fps_limit) + replay.setSize(VISUALIZATION_RESOLUTION[0], VISUALIZATION_RESOLUTION[1]) + video_src_out = replay.out + else: + cam = pipeline.create(dai.node.Camera).build( + boardSocket=dai.CameraBoardSocket.CAM_A + ) + # Request high-res NV12 frames for visualization/encoding + video_src_out = cam.requestOutput( + size=VISUALIZATION_RESOLUTION, + type=dai.ImgFrame.Type.NV12, + fps=args.fps_limit, + ) + + image_manip = pipeline.create(dai.node.ImageManip) + image_manip.setMaxOutputFrameSize(model_w * model_h * 3) + image_manip.initialConfig.setOutputSize(model_w, model_h) + image_manip.initialConfig.setFrameType(frame_type) + video_src_out.link(image_manip.inputImage) + + video_enc = pipeline.create(dai.node.VideoEncoder) + video_enc.setDefaultProfilePreset( + fps=args.fps_limit, profile=dai.VideoEncoderProperties.Profile.H264_MAIN + ) + video_src_out.link(video_enc.input) + + input_node = image_manip.out + + nn_with_parser = pipeline.create(ParsingNeuralNetwork) + nn_with_parser.setNNArchive(model_nn_archive) + nn_with_parser.setBackend("snpe") + nn_with_parser.setBackendProperties( + {"runtime": "dsp", "performance_profile": "default"} + ) + nn_with_parser.setNumInferenceThreads(1) + nn_with_parser.getParser(0).setConfidenceThreshold(CONFIDENCE_THRESHOLD) + + input_node.link(nn_with_parser.inputs["images"]) + + textInputQueue = nn_with_parser.inputs["texts"].createInputQueue() + nn_with_parser.inputs["texts"].setReusePreviousMessage(True) + if args.model == "yoloe": + imagePromptInputQueue = nn_with_parser.inputs["image_prompts"].createInputQueue() + nn_with_parser.inputs["image_prompts"].setReusePreviousMessage(True) + + # filter and rename detection labels + det_process_filter = pipeline.create(ImgDetectionsFilter).build(nn_with_parser.out) + annotation_node = pipeline.create(AnnotationNode).build( + det_process_filter.out, + video_src_out, + ) + + filtered_bridge = pipeline.create(ImgDetectionsBridge).build(det_process_filter.out) + + # Cache last frame for services that need full frame content + frame_cache = pipeline.create(FrameCacheNode).build(video_src_out) + + # --- state for triggers (timed + no-detections) + no_det_gate = NoDetectionsGate() + _snap_state = {"timed_enabled": False, "interval": 60, "last_sent_s": -1.0} + + # TIMED + NO-DET producer (works via the process_fn, ticking internally) + snaps_producer = pipeline.create(SnapsProducer).build( + frame=video_src_out, + msg=det_process_filter.out, + running=False, # controlled by service + process_fn=partial( + custom_snap_process, + class_names=CLASS_NAMES, + model=args.model, + no_det_gate=no_det_gate, + timed_state=_snap_state, + ), + ) + snaps_producer._em.setSourceAppId(os.getenv("OAKAGENT_CONTAINER_ID")) + print("Snaps producer API KEY:", os.getenv("DEPTHAI_HUB_API_KEY")) + + # --------- ObjectTracker + producer for "new detections" (tuned) ---------- + object_tracker = pipeline.create(dai.node.ObjectTracker) + + # RVC4-supported tracker; log will say it anyway if a different type is forced + object_tracker.setTrackerType(dai.TrackerType.SHORT_TERM_IMAGELESS) + + # Keep IDs stable even if detections overlap/vanish briefly + object_tracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.UNIQUE_ID) + object_tracker.setTrackingPerClass(True) + + # TUNABLES (adjust as needed) + object_tracker.setTrackletBirthThreshold(3) # frames before TRACKED (was 3) + object_tracker.setTrackletMaxLifespan(180) # frames until LOST track is removed + object_tracker.setOcclusionRatioThreshold(0.5) # filter heavy overlaps a bit more + object_tracker.setTrackerThreshold(0.25) # ignore very low-conf dets in tracking + + # Inputs + # Use the same resized/manip frames for both tracker inputs so they align with detections + input_node.link(object_tracker.inputTrackerFrame) + input_node.link(object_tracker.inputDetectionFrame) + filtered_bridge.out.link(object_tracker.inputDetections) + + # Producer that listens to tracklets and sends a snap when any TRACKED appears first time + snaps_newdet_producer = pipeline.create(SnapsProducer).build( + frame=video_src_out, + msg=object_tracker.out, + running=False, # toggled by service + process_fn=partial( + tracklet_new_detection_process, + class_names=CLASS_NAMES, + model=args.model, + ), + ) + snaps_newdet_producer._em.setSourceAppId(os.getenv("OAKAGENT_CONTAINER_ID")) + + def update_labels(label_names: list[str], offset: int = 0): + det_process_filter.setLabels( + labels=[i for i in range(offset, offset + len(label_names))], keep=True + ) + annotation_node.setLabelEncoding( + {offset + k: v for k, v in enumerate(label_names)} + ) + # If you want to restrict tracker by labels, enable below: + # object_tracker.setDetectionLabelsToTrack(list(range(offset, offset + len(label_names)))) + + # visualization topics + if args.model == "yolo-world": + visualizer.addTopic("Video", video_enc.out, "images") + elif args.model == "yoloe": + apply_colormap_node = pipeline.create(ApplyColormap).build(nn_with_parser.out) + overlay_frames_node = pipeline.create(ImgFrameOverlay).build( + video_src_out, + apply_colormap_node.out, + preserve_background=True, + ) + overlay_to_nv12 = pipeline.create(dai.node.ImageManip) + overlay_to_nv12.setMaxOutputFrameSize( + VISUALIZATION_RESOLUTION[0] * VISUALIZATION_RESOLUTION[1] * 3 + ) + overlay_to_nv12.initialConfig.setFrameType(dai.ImgFrame.Type.NV12) + overlay_frames_node.out.link(overlay_to_nv12.inputImage) + + overlay_enc = pipeline.create(dai.node.VideoEncoder) + overlay_enc.setDefaultProfilePreset( + fps=args.fps_limit, profile=dai.VideoEncoderProperties.Profile.H264_MAIN + ) + overlay_to_nv12.out.link(overlay_enc.input) + + visualizer.addTopic("Video", overlay_enc.out, "images") + + visualizer.addTopic("Detections", annotation_node.out) + + def class_update_service(new_classes: list[str]): + """Changes classes to detect based on the user input""" + if len(new_classes) == 0: + print("List of new classes empty, skipping.") + return + if len(new_classes) > MAX_NUM_CLASSES: + print( + f"Number of new classes ({len(new_classes)}) exceeds maximum number of classes ({MAX_NUM_CLASSES}), skipping." + ) + return + CLASS_NAMES = new_classes + text_features = extract_text_embeddings( + class_names=CLASS_NAMES, + max_num_classes=MAX_NUM_CLASSES, + model_name=args.model, + precision=args.precision, + ) + inputNNData = dai.NNData() + inputNNData.addTensor( + "texts", + text_features, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + textInputQueue.send(inputNNData) + # In unified YOLOE, ensure image_prompts are dummy when text prompts are active + if args.model == "yoloe": + dummy = make_dummy_features( + MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision + ) + inputNNDataImg = dai.NNData() + inputNNDataImg.addTensor( + "image_prompts", + dummy, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + imagePromptInputQueue.send(inputNNDataImg) + + update_labels(CLASS_NAMES, offset=0) + print(f"Classes set to: {CLASS_NAMES}") + + def conf_threshold_update_service(new_conf_threshold: float): + """Changes confidence threshold based on the user input""" + CONFIDENCE_THRESHOLD = max(0, min(1, new_conf_threshold)) + nn_with_parser.getParser(0).setConfidenceThreshold(CONFIDENCE_THRESHOLD) + print(f"Confidence threshold set to: {CONFIDENCE_THRESHOLD}:") + + def image_upload_service(image_data): + image = base64_to_cv2_image(image_data["data"]) + if args.model == "yolo-world": + image_features = extract_image_prompt_embeddings( + image, model_name=args.model, precision=args.precision + ) + print("Image features extracted, sending to model as texts...") + inputNNData = dai.NNData() + inputNNData.addTensor( + "texts", + image_features, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + textInputQueue.send(inputNNData) + filename = image_data["filename"] + CLASS_NAMES = [filename.split(".")[0]] + update_labels(CLASS_NAMES, offset=0) + print(f"Classes set to: {CLASS_NAMES}") + else: # yoloe unified with image_prompts input + image_features = extract_image_prompt_embeddings( + image, model_name="yoloe", precision=args.precision + ) + print("Image features extracted, sending to model as image_prompts...") + inputNNDataImg = dai.NNData() + inputNNDataImg.addTensor( + "image_prompts", + image_features, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + imagePromptInputQueue.send(inputNNDataImg) + # Send dummy texts so only image prompts are considered + dummy = make_dummy_features( + MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision + ) + inputNNDataTxt = dai.NNData() + inputNNDataTxt.addTensor( + "texts", + dummy, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + textInputQueue.send(inputNNDataTxt) + + filename = image_data["filename"] + CLASS_NAMES = [filename.split(".")[0]] + update_labels(CLASS_NAMES, offset=80) + print(f"Classes set to (image prompts, offset 80): {CLASS_NAMES}") + return + + def bbox_prompt_service(payload): + print("[BBox] Service payload keys:", list(payload.keys())) + image = base64_to_cv2_image(payload["data"]) if payload.get("data") else None + if image is None: + image = frame_cache.get_last_frame() + if image is None: + print("[BBox] No image data and no cached frame available") + return {"ok": False, "reason": "no_image"} + if image is None: + print("[BBox] Decoded image is None") + return {"ok": False, "reason": "decode_failed"} + + bbox = payload.get("bbox", {}) + bx = float(bbox.get("x", 0.0)) + by = float(bbox.get("y", 0.0)) + bw = float(bbox.get("width", 0.0)) + bh = float(bbox.get("height", 0.0)) + + H, W = image.shape[:2] + is_pixel = payload.get("bboxType", "normalized") == "pixel" + if is_pixel: + x0 = int(round(bx)); y0 = int(round(by)) + x1 = int(round(bx + bw)); y1 = int(round(by + bh)) + else: + x0 = int(round(bx * W)); y0 = int(round(by * H)) + x1 = int(round((bx + bw) * W)); y1 = int(round((by + bh) * H)) + + x0, x1 = sorted((x0, x1)); y0, y1 = sorted((y0, y1)) + print(f"[BBox] Image size: {W}x{H}, bbox(px): x0={x0}, y0={y0}, x1={x1}, y1={y1}") + return {"ok": True} + + # ---------- EXTENDED: Snap Collection Service ---------- + # Supports: + # legacy tuple [start:bool, interval:int] + # new dict { timed: {enabled, interval}, noDetections: {enabled}, newDetections: {enabled} } + def snap_collection_service(payload): + base_dt_seconds = 1 # internal polling tick for timed/no-det producer + + # Legacy: tuple/list -> timed only + if isinstance(payload, (list, tuple)) and len(payload) == 2: + start_collection, time_interval = payload + _snap_state["timed_enabled"] = bool(start_collection) + _snap_state["interval"] = max(1, int(time_interval)) + _snap_state["last_sent_s"] = -1.0 + any_active = _snap_state["timed_enabled"] or no_det_gate.enabled + snaps_producer.setRunning(any_active) + if any_active: + snaps_producer.setTimeInterval(base_dt_seconds) + # Do not touch new-detection producer in legacy mode + print(f"[SnapService] (legacy) timed={_snap_state['timed_enabled']}, interval={_snap_state['interval']}, noDet={no_det_gate.enabled}") + return {"ok": True} + + # New structured payload + if isinstance(payload, dict): + # timed + if "timed" in payload: + tcfg = payload["timed"] or {} + _snap_state["timed_enabled"] = bool(tcfg.get("enabled", _snap_state["timed_enabled"])) + if "interval" in tcfg: + try: + _snap_state["interval"] = max(1, int(tcfg.get("interval", _snap_state["interval"]))) + except Exception: + pass + _snap_state["last_sent_s"] = -1.0 + + # no detections + if "noDetections" in payload: + ncfg = payload["noDetections"] or {} + no_det_gate.set_enabled(bool(ncfg.get("enabled", no_det_gate.enabled))) + + # new detections (tracker) + if "newDetections" in payload: + n2cfg = payload["newDetections"] or {} + new_on = bool(n2cfg.get("enabled", False)) + snaps_newdet_producer.setRunning(new_on) + # React on every message if allowed + try: + snaps_newdet_producer.setTimeInterval(0) + except Exception: + snaps_newdet_producer.setTimeInterval(base_dt_seconds) + + # Effective running for timed/no-det producer + any_active = _snap_state["timed_enabled"] or no_det_gate.enabled + snaps_producer.setRunning(any_active) + if any_active: + snaps_producer.setTimeInterval(base_dt_seconds) + + print(f"[SnapService] timed={_snap_state['timed_enabled']}, interval={_snap_state['interval']}, noDet={no_det_gate.enabled}") + return {"ok": True} + + print("[SnapService] Unsupported payload format") + return {"ok": False, "reason": "bad_payload"} + + visualizer.registerService("Class Update Service", class_update_service) + visualizer.registerService("Threshold Update Service", conf_threshold_update_service) + if args.model in ("yolo-world", "yoloe"): + visualizer.registerService("Image Upload Service", image_upload_service) + visualizer.registerService("BBox Prompt Service", bbox_prompt_service) + visualizer.registerService("Snap Collection Service", snap_collection_service) + + print("Pipeline created.") + + pipeline.start() + visualizer.registerPipeline(pipeline) + + update_labels(CLASS_NAMES, offset=CLASS_OFFSET) + + inputNNData = dai.NNData() + inputNNData.addTensor( + "texts", + text_features, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + textInputQueue.send(inputNNData) + if args.model == "yoloe": + inputNNDataImg = dai.NNData() + inputNNDataImg.addTensor( + "image_prompts", + image_prompt_features, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + imagePromptInputQueue.send(inputNNDataImg) + + print("Press 'q' to stop") + + while pipeline.isRunning(): + pipeline.processTasks() + key = visualizer.waitKey(1) + if key == ord("q"): + print("Got q key. Exiting...") + break diff --git a/performant_PoCs/hub_snaps/backend/src/requirements.txt b/performant_PoCs/hub_snaps/backend/src/requirements.txt new file mode 100644 index 000000000..b9912470b --- /dev/null +++ b/performant_PoCs/hub_snaps/backend/src/requirements.txt @@ -0,0 +1,13 @@ +# depthai==3.0.0 +--extra-index-url https://artifacts.luxonis.com/artifactory/luxonis-python-snapshot-local +--pre +depthai==3.0.0.dev0+3aa210a2d54c32e0f75f8ac6b4a81f80b6230f2f +#depthai-nodes==0.3.4 +depthai-nodes @ git+https://github.com/luxonis/depthai-nodes.git@feat/dai_dev_req +opencv-python-headless~=4.10.0 +numpy>=1.22 +tokenizers~=0.21.0 +onnxruntime +# onnxruntime-gpu # if you want to use CUDAExecutionProvider +requests +dotenv \ No newline at end of file diff --git a/performant_PoCs/hub_snaps/backend/src/utils/__init__.py b/performant_PoCs/hub_snaps/backend/src/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/performant_PoCs/hub_snaps/backend/src/utils/annotation_node.py b/performant_PoCs/hub_snaps/backend/src/utils/annotation_node.py new file mode 100644 index 000000000..119aae34c --- /dev/null +++ b/performant_PoCs/hub_snaps/backend/src/utils/annotation_node.py @@ -0,0 +1,45 @@ +import depthai as dai +from depthai_nodes import ImgDetectionsExtended +from typing import Dict + + +class AnnotationNode(dai.node.HostNode): + def __init__(self, label_encoding: Dict[int, str] = {}) -> None: + super().__init__() + self._label_encoding = label_encoding + self.out_detections = self.createOutput() + + def setLabelEncoding(self, label_encoding: Dict[int, str]) -> None: + """Sets the label encoding. + + @param label_encoding: The label encoding with labels as keys and label names as + values. + @type label_encoding: Dict[int, str] + """ + if not isinstance(label_encoding, Dict): + raise ValueError("label_encoding must be a dictionary.") + self._label_encoding = label_encoding + + def build( + self, + detections: dai.Node.Output, + frame: dai.Node.Output, + label_encoding: Dict[int, str] = None, + ) -> "AnnotationNode": + if label_encoding is not None: + self.setLabelEncoding(label_encoding) + # Link detections and reference frame inputs + self.link_args(detections, frame) + return self + + def process( + self, + detections_message: dai.Buffer, + frame_message: dai.ImgFrame, + ) -> None: + assert isinstance(detections_message, ImgDetectionsExtended) + # Ensure detections align with the provided frame (e.g., high-res stream) + detections_message.setTransformation(frame_message.getTransformation()) + for detection in detections_message.detections: + detection.label_name = self._label_encoding.get(detection.label, "unknown") + return detections_message diff --git a/performant_PoCs/hub_snaps/backend/src/utils/arguments.py b/performant_PoCs/hub_snaps/backend/src/utils/arguments.py new file mode 100644 index 000000000..6eeb89d98 --- /dev/null +++ b/performant_PoCs/hub_snaps/backend/src/utils/arguments.py @@ -0,0 +1,72 @@ +import argparse + + +def initialize_argparser(): + """Initialize the argument parser for the script.""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "-fps", + "--fps_limit", + help="FPS limit for the model runtime.", + required=False, + default=None, + type=int, + ) + + parser.add_argument( + "-api", + "--api_key", + help="HubAI API key to access private model. Can also use 'DEPTHAI_HUB_API_KEY' environment variable instead.", + required=False, + default="", + type=str, + ) + + parser.add_argument( + "-media", + "--media_path", + help="Path to the media file you aim to run the model on. If not set, the model will run on the camera input.", + required=False, + default=None, + type=str, + ) + + parser.add_argument( + "-ip", + "--ip", + help="IP address to serve the frontend on.", + required=False, + type=str, + ) + parser.add_argument( + "-p", + "--port", + help="Port to serve the frontend on.", + required=False, + type=int, + ) + parser.add_argument( + "-m", + "--model", + help="Name of the model to use: yolo-world or yoloe", + required=False, + # default="yoloe", + default="yolo-world", + type=str, + choices=["yolo-world", "yoloe"], + ) + parser.add_argument( + "--precision", + help="Model precision for YOLOE models: int8 (faster) or fp16 (more accurate).", + required=False, + default="fp16", + type=str, + choices=["int8", "fp16"], + ) + + args = parser.parse_args() + + return parser, args diff --git a/performant_PoCs/hub_snaps/backend/src/utils/frame_cache_node.py b/performant_PoCs/hub_snaps/backend/src/utils/frame_cache_node.py new file mode 100644 index 000000000..621a7c7e1 --- /dev/null +++ b/performant_PoCs/hub_snaps/backend/src/utils/frame_cache_node.py @@ -0,0 +1,20 @@ +import depthai as dai +import numpy as np + + +class FrameCacheNode(dai.node.HostNode): + def __init__(self) -> None: + super().__init__() + self._last_frame: np.ndarray | None = None + + def build(self, frame: dai.Node.Output) -> "FrameCacheNode": + self.link_args(frame) + return self + + def process(self, frame: dai.ImgFrame) -> dai.ImgFrame: + # Cache last BGR frame + self._last_frame = frame.getCvFrame() + return frame + + def get_last_frame(self) -> np.ndarray | None: + return self._last_frame diff --git a/performant_PoCs/hub_snaps/backend/src/utils/helper_functions.py b/performant_PoCs/hub_snaps/backend/src/utils/helper_functions.py new file mode 100644 index 000000000..e4e919d3f --- /dev/null +++ b/performant_PoCs/hub_snaps/backend/src/utils/helper_functions.py @@ -0,0 +1,247 @@ +from tokenizers import Tokenizer +import os +import requests +import onnxruntime +import numpy as np +import cv2 +import base64 + +QUANT_ZERO_POINT = 90.0 +QUANT_SCALE = 0.003925696481 + +QUANT_VALUES = { + "yolo-world": { + "quant_zero_point": 90.0, + "quant_scale": 0.003925696481, + }, + "yoloe": { + "quant_zero_point": 174.0, + "quant_scale": 0.003328413470, + }, + "yoloe-image": { + "quant_zero_point": 137.0, + "quant_scale": 0.002327915514, + }, +} + + +def pad_and_quantize_features( + features, max_num_classes=80, model_name="yolo-world", precision="int8" +): + """ + Pad features to (1, 512, max_num_classes) and quantize if precision is int8. + For FP16, return padded float16 features (no quantization). + """ + num_padding = max_num_classes - features.shape[0] + padded_features = np.pad( + features, ((0, num_padding), (0, 0)), mode="constant" + ).T.reshape(1, 512, max_num_classes) + + if precision == "fp16": + return padded_features.astype(np.float16) + + quant_scale = QUANT_VALUES[model_name]["quant_scale"] + quant_zero_point = QUANT_VALUES[model_name]["quant_zero_point"] + quantized_features = (padded_features / quant_scale) + quant_zero_point + quantized_features = quantized_features.astype("uint8") + return quantized_features + + +def extract_text_embeddings( + class_names, max_num_classes=80, model_name="yolo-world", precision="int8" +): + tokenizer_json_path = download_tokenizer( + url="https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json", + save_path="tokenizer.json", + ) + tokenizer = Tokenizer.from_file(tokenizer_json_path) + tokenizer.enable_padding( + pad_id=tokenizer.token_to_id("<|endoftext|>"), pad_token="<|endoftext|>" + ) + encodings = tokenizer.encode_batch(class_names) + + text_onnx = np.array([e.ids for e in encodings], dtype=np.int64) + + if model_name == "yolo-world": + attention_mask = np.array([e.attention_mask for e in encodings], dtype=np.int64) + + textual_onnx_model_path = download_model( + "https://huggingface.co/jmzzomg/clip-vit-base-patch32-text-onnx/resolve/main/model.onnx", + "clip_textual_hf.onnx", + ) + + session_textual = onnxruntime.InferenceSession( + textual_onnx_model_path, + providers=[ + "TensorrtExecutionProvider", + "CUDAExecutionProvider", + "CPUExecutionProvider", + ], + ) + textual_output = session_textual.run( + None, + { + session_textual.get_inputs()[0].name: text_onnx, + "attention_mask": attention_mask, + }, + )[0] + elif model_name == "yoloe": + if text_onnx.shape[1] < 77: + text_onnx = np.pad( + text_onnx, ((0, 0), (0, 77 - text_onnx.shape[1])), mode="constant" + ) + + textual_onnx_model_path = download_model( + "https://huggingface.co/Xenova/mobileclip_blt/resolve/main/onnx/text_model.onnx", + "mobileclip_textual_hf.onnx", + ) + + session_textual = onnxruntime.InferenceSession( + textual_onnx_model_path, + providers=[ + "TensorrtExecutionProvider", + "CUDAExecutionProvider", + "CPUExecutionProvider", + ], + ) + textual_output = session_textual.run( + None, + { + session_textual.get_inputs()[0].name: text_onnx, + }, + )[0] + + textual_output /= np.linalg.norm( + textual_output, ord=2, axis=-1, keepdims=True + ) # Normalize the output + + text_features = pad_and_quantize_features( + textual_output, max_num_classes, model_name, precision + ) + + del session_textual + + return text_features + + +def extract_image_prompt_embeddings( + image, + max_num_classes=80, + model_name="yolo-world", + mask_prompt=None, + precision="int8", +): + # Select model and preprocess accordingly + if model_name == "yoloe": + image_resized = cv2.resize(image, (640, 640)) + image_array = image_resized.astype(np.float32) / 255.0 + image_array = np.transpose(image_array, (2, 0, 1)) + input_tensor = np.expand_dims(image_array, axis=0).astype(np.float32) + model_url = ( + "https://huggingface.co/sokovninn/yoloe-v8l-seg-visual-encoder/resolve/main/" + "yoloe-v8l-seg_visual_encoder.onnx" + ) + model_path = "yoloe-v8l-seg_visual_encoder.onnx" + else: + input_tensor = preprocess_image(image) + model_url = ( + "https://huggingface.co/sokovninn/clip-visual-with-projector/resolve/main/" + "clip_visual_with_projector.onnx" + ) + model_path = "clip_visual_with_projector.onnx" + + onnx_model_path = download_model(model_url, model_path) + + session = onnxruntime.InferenceSession( + onnx_model_path, + providers=[ + "TensorrtExecutionProvider", + "CUDAExecutionProvider", + "CPUExecutionProvider", + ], + ) + + if model_name == "yoloe": + if mask_prompt is None: + prompts = np.zeros((1, 1, 80, 80), dtype=np.float32) + prompts[0, 0, 5:75, 5:75] = 1.0 + else: + prompts = np.asarray(mask_prompt, dtype=np.float32) + if prompts.ndim == 2: + if prompts.shape != (80, 80): + prompts = cv2.resize( + prompts, (80, 80), interpolation=cv2.INTER_NEAREST + ) + prompts = prompts[None, None, :, :] + elif prompts.shape == (1, 1, 80, 80): + pass + else: + raise ValueError("mask_prompt must have shape (80,80) or (1,1,80,80)") + outputs = session.run(None, {"images": input_tensor, "prompts": prompts}) + else: + input_name = session.get_inputs()[0].name + outputs = session.run(None, {input_name: input_tensor}) + + image_embeddings = outputs[0].squeeze(0).reshape(1, -1) + image_features = pad_and_quantize_features( + image_embeddings, max_num_classes, model_name, precision + ) + + del session + + return image_features + + +def download_tokenizer(url, save_path): + if not os.path.exists(save_path): + print(f"Downloading tokenizer config from {url}...") + with open(save_path, "wb") as f: + f.write(requests.get(url).content) + return save_path + + +def download_model(url, save_path): + if not os.path.exists(save_path): + print(f"Downloading model from {url}...") + response = requests.get(url) + if response.status_code == 200: + with open(save_path, "wb") as f: + f.write(response.content) + print(f"Model saved to {save_path}.") + else: + raise Exception( + f"Failed to download model. Status code: {response.status_code}" + ) + else: + print(f"Model already exists at {save_path}.") + + return save_path + + +def preprocess_image(image): + """Preprocess image for CLIP vision model input""" + image = cv2.resize(image, (224, 224)) + + image_array = np.array(image).astype(np.float32) / 255.0 + + mean = np.array([0.48145466, 0.4578275, 0.40821073]) + std = np.array([0.26862954, 0.26130258, 0.27577711]) + + image_array = (image_array - mean) / std + + image_array = np.transpose(image_array, (2, 0, 1)) + image_array = np.expand_dims(image_array, axis=0) + + return image_array.astype(np.float32) + + +def base64_to_cv2_image(base64_data_uri: str): + if "," in base64_data_uri: + header, base64_data = base64_data_uri.split(",", 1) + else: + base64_data = base64_data_uri + + binary_data = base64.b64decode(base64_data) + np_arr = np.frombuffer(binary_data, np.uint8) + img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) + return img diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils.py new file mode 100644 index 000000000..87108b518 --- /dev/null +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils.py @@ -0,0 +1,211 @@ +from typing import List, Optional +import time +import depthai as dai + +from depthai_nodes.node import SnapsProducer +from depthai_nodes import ImgDetectionsExtended + + +class NoDetectionsGate: + """ + Minimal state machine: + - enabled: externally toggled + - in_run: True while we are in a zero-detection streak + """ + def __init__(self) -> None: + self.enabled: bool = False + self.in_run: bool = False + + def set_enabled(self, enabled: bool) -> None: + self.enabled = bool(enabled) + self.in_run = False # reset streak whenever toggled + + def on_frame(self, num_detections: int) -> bool: + """ + Returns True exactly on the first zero-detection frame of a streak (when enabled). + Resets when detections resume (>0). + """ + if not self.enabled: + self.in_run = False + return False + if num_detections == 0: + if not self.in_run: + self.in_run = True + return True + return False + # detections present -> exit streak + if self.in_run: + self.in_run = False + return False + + +def _build_extras( + model: str, + det_data: dai.ImgDetections | ImgDetectionsExtended, + class_names: List[str], +): + detections = det_data.detections or [] + dets_labels = [det.label for det in detections] + dets_confs = [det.confidence for det in detections] + + extras = { + "model": model, + "detection_label": str(dets_labels), + "detection_confidence": str(dets_confs), + } + + if isinstance(det_data, dai.ImgDetections): + dets_labels_str = [class_names[det.label] for det in detections] if detections else [] + dets_xyxy = [(det.xmin, det.ymin, det.xmax, det.ymax) for det in detections] + extras["detection_xyxy"] = str(dets_xyxy) + extras["detection_label_str"] = str(dets_labels_str) + elif isinstance(det_data, ImgDetectionsExtended): + dets_labels_str = [det.label_name for det in detections] + dets_cxcywh = [ + ( + det.rotated_rect.center.x, + det.rotated_rect.center.y, + det.rotated_rect.size.width, + det.rotated_rect.size.height, + ) + for det in detections + ] + extras["detection_cxcywh"] = str(dets_cxcywh) + extras["detection_label_str"] = str(dets_labels_str) + else: + raise NotImplementedError + + return extras + + +def custom_snap_process( + producer: SnapsProducer, + frame: dai.ImgFrame, + det_data: dai.ImgDetections | ImgDetectionsExtended, + class_names: List[str], + model: str, + no_det_gate: Optional[NoDetectionsGate] = None, + timed_state: Optional[dict] = None, +): + """ + Called frequently by SnapsProducer; we decide what to send and with which tags. + - TIMED snaps: respect user interval using timed_state["interval"] and ["last_sent_s"]. + Always tagged "Timing_snap", regardless of detections. + - NO-DETECTIONS snaps: fire once at the start of a zero-detection streak, independent + of timing interval. Tagged "no_detections". + """ + detections = det_data.detections or [] + num = len(detections) + + # Current time from frame (seconds) + now_s = None + if timed_state is not None: + try: + ts = frame.getTimestamp() + now_s = float(ts.total_seconds()) + except Exception: + now_s = None + + # 1) TIMED (priority path) + if timed_state and timed_state.get("timed_enabled", False): + interval = float(timed_state.get("interval", 0)) or 0.0 + last_sent = float(timed_state.get("last_sent_s", -1.0)) + due = (now_s is not None) and (interval > 0.0) and ((last_sent < 0) or (now_s - last_sent >= interval)) + if due: + file_group = [dai.FileData(frame, "rgb")] + extras = _build_extras(model, det_data, class_names) + if producer.sendSnap(name="rgb", file_group=file_group, tags=["Timing_snap"], extras=extras): + timed_state["last_sent_s"] = now_s + if no_det_gate: + no_det_gate.on_frame(num) # keep gate in sync + print("[Timed] Snap sent!") + return # don't also send no-det on the same tick + + # 2) NO-DETECTIONS one-shot at streak start (independent cadence) + if num == 0: + if no_det_gate and no_det_gate.on_frame(0): + file_group = [dai.FileData(frame, "rgb")] + extras = {"model": model, "reason": "no_detections_start"} + if producer.sendSnap(name="rgb", file_group=file_group, tags=["no_detections"], extras=extras): + print("[NoDet] Snap sent (start of empty streak)") + return + + # 3) detections present -> keep gate in sync + if no_det_gate: + no_det_gate.on_frame(num) + return + + +# ------------------ New-detection snap (TRACKED gating + cooldown) ------------------ + +class _NewDetectionsState: + """Plain dicts (no defaultdict) + a set for seen IDs.""" + def __init__(self) -> None: + self.seen_ids: set[int] = set() + self.last_label_ts: dict[str, float] = {} # label -> last snap epoch seconds + +_NEWDET_STATE = _NewDetectionsState() + + +def tracklet_new_detection_process( + producer: SnapsProducer, + frame: dai.ImgFrame, + tracklets_msg: dai.Tracklets, + class_names: List[str], + model: str, + min_label_cooldown_s: float = 2.0, +): + """ + Send a snap when a tracklet becomes TRACKED for the first time (ID not seen). + - Waits for TRACKED (stable after birth threshold) to reduce flicker re-IDs. + - One snap per unique track ID. + - Per-label cooldown to avoid bursts (uses classic dict .get()). + """ + tks = getattr(tracklets_msg, "tracklets", []) or [] + now = time.time() + fired_any = False + + for t in tks: + # Robustly read status (enum or int) + status_val = getattr(t, "status", None) + try: + is_tracked = (status_val == dai.Tracklet.TrackingStatus.TRACKED) + except Exception: + # Fallback: enum may not be available; typical mapping NEW=0, TRACKED=1, LOST=2 + is_tracked = int(status_val) == 1 + + if not is_tracked: + continue + + # Only once per ID + try: + tid = int(getattr(t, "id", -1)) + except Exception: + continue + if tid < 0 or tid in _NEWDET_STATE.seen_ids: + continue + + # Optional per-label cooldown + label_idx = int(getattr(t, "label", -1)) + label_str = class_names[label_idx] if 0 <= label_idx < len(class_names) else "" + last_ts = _NEWDET_STATE.last_label_ts.get(label_str, 0.0) + if label_str and (now - last_ts) < float(min_label_cooldown_s): + # still in cooldown window for this label + continue + + # Build extras + send + file_group = [dai.FileData(frame, "rgb")] + extras = { + "model": model, + "tracker_id": str(tid), + "label_idx": str(label_idx), + "label_str": label_str, + "status": "TRACKED", + } + if producer.sendSnap(name="rgb", file_group=file_group, tags=["new_detection"], extras=extras): + _NEWDET_STATE.seen_ids.add(tid) + if label_str: + _NEWDET_STATE.last_label_ts[label_str] = now + fired_any = True + + return diff --git a/performant_PoCs/hub_snaps/frontend/.gitignore b/performant_PoCs/hub_snaps/frontend/.gitignore new file mode 100644 index 000000000..dafadc4b9 --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/.gitignore @@ -0,0 +1,28 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? + +## Panda +styled-system +styled-system-studio diff --git a/performant_PoCs/hub_snaps/frontend/biome.json b/performant_PoCs/hub_snaps/frontend/biome.json new file mode 100644 index 000000000..5d96cd100 --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/biome.json @@ -0,0 +1,30 @@ +{ + "$schema": "https://biomejs.dev/schemas/1.9.4/schema.json", + "vcs": { + "enabled": false, + "clientKind": "git", + "useIgnoreFile": false + }, + "files": { + "ignoreUnknown": false, + "ignore": ["node_modules/", "dist/"] + }, + "formatter": { + "enabled": true, + "indentStyle": "tab" + }, + "organizeImports": { + "enabled": true + }, + "linter": { + "enabled": true, + "rules": { + "recommended": true + } + }, + "javascript": { + "formatter": { + "quoteStyle": "single" + } + } +} diff --git a/performant_PoCs/hub_snaps/frontend/index.html b/performant_PoCs/hub_snaps/frontend/index.html new file mode 100644 index 000000000..3f5e422fa --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/index.html @@ -0,0 +1,13 @@ + + + + + + + Data Collection Application + + +

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"tsc -b && vite build", + "lint": "biome check .", + "lint:fix": "biome check --write .", + "preview": "vite preview" + }, + "dependencies": { + "@luxonis/depthai-viewer-common": "1.2.8", + "react": "^18.3.1", + "react-dom": "^18.3.1", + "react-router": "^7.5.0", + "react-router-dom": "^7.5.0" + }, + "devDependencies": { + "@biomejs/biome": "1.9.4", + "@pandacss/dev": "0.53.0", + "@types/react": "^18.3.20", + "@types/react-dom": "^18.3.6", + "@vitejs/plugin-react": "^4.3.4", + "globals": "^16.0.0", + "typescript": "~5.7.2", + "vite": "^6.3.1" + } +} diff --git a/performant_PoCs/hub_snaps/frontend/panda.config.ts b/performant_PoCs/hub_snaps/frontend/panda.config.ts new file mode 100644 index 000000000..f1c3e8249 --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/panda.config.ts @@ -0,0 +1,12 @@ +import { defineConfig, defineGlobalStyles } from "@pandacss/dev"; +import { pandaPreset } from "@luxonis/common-fe-components"; + +export default defineConfig({ + presets: [pandaPreset], + preflight: true, + include: ["./src/**/*.{ts,tsx}"], + exclude: [], + jsxFramework: "react", + outdir: "styled-system", + forceConsistentTypeExtension: true, +}); diff --git a/performant_PoCs/hub_snaps/frontend/postcss.config.mjs b/performant_PoCs/hub_snaps/frontend/postcss.config.mjs new file mode 100644 index 000000000..cdaaad9ae --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/postcss.config.mjs @@ -0,0 +1,5 @@ +export default { + plugins: { + '@pandacss/dev/postcss': {}, + }, +} \ No newline at end of file diff --git a/performant_PoCs/hub_snaps/frontend/src/App.tsx b/performant_PoCs/hub_snaps/frontend/src/App.tsx new file mode 100644 index 000000000..84f33533a --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/src/App.tsx @@ -0,0 +1,355 @@ +import { css } from "../styled-system/css/css.mjs"; +import { Streams, useConnection } from "@luxonis/depthai-viewer-common"; +import { ClassSelector } from "./ClassSelector.tsx"; +import { ConfidenceSlider } from "./ConfidenceSlider.tsx"; +import { ImageUploader } from "./ImageUploader.tsx"; +import { SnapCollectionButton } from "./SnapCollectionButton.tsx"; +import { useCallback, useEffect, useRef, useState } from "react"; +import { useNotifications } from "./Notifications.tsx"; + +function App() { + const connection = useConnection(); + const streamContainerRef = useRef(null); + const overlayCanvasRef = useRef(null); + const [isDrawing, setIsDrawing] = useState(false); + const [dragStart, setDragStart] = useState<{ x: number; y: number } | null>(null); + const [currentRect, setCurrentRect] = useState<{ x: number; y: number; w: number; h: number } | null>(null); + const { notify } = useNotifications(); + + const getUnderlyingMediaAndSize = () => { + const container = streamContainerRef.current; + if (!container) return null; + const videoEl = container.querySelector("video") as HTMLVideoElement | null; + const canvases = Array.from(container.querySelectorAll("canvas")) as HTMLCanvasElement[]; + const canvasEl = canvases.find((c) => c.getAttribute("data-role") !== "overlay") || null; + const containerRect = container.getBoundingClientRect(); + if (videoEl && videoEl.videoWidth && videoEl.videoHeight) { + const r = videoEl.getBoundingClientRect(); + const displayWidth = r.width; + const displayHeight = r.height; + const offsetX = r.left - containerRect.left; + const offsetY = r.top - containerRect.top; + console.log("[BBox] Capturing from video element", { width: videoEl.videoWidth, height: videoEl.videoHeight, displayWidth, displayHeight, offsetX, offsetY }); + return { + type: "video" as const, + el: videoEl, + width: videoEl.videoWidth, + height: videoEl.videoHeight, + displayWidth, + displayHeight, + offsetX, + offsetY, + }; + } + if (canvasEl && canvasEl.width && canvasEl.height) { + const r = canvasEl.getBoundingClientRect(); + const displayWidth = r.width; + const displayHeight = r.height; + const offsetX = r.left - containerRect.left; + const offsetY = r.top - containerRect.top; + console.log("[BBox] Capturing from canvas element", { width: canvasEl.width, height: canvasEl.height, displayWidth, displayHeight, offsetX, offsetY }); + return { + type: "canvas" as const, + el: canvasEl, + width: canvasEl.width, + height: canvasEl.height, + displayWidth, + displayHeight, + offsetX, + offsetY, + }; + } + return null; + }; + + + const finalizeBBox = useCallback(() => { + if (!currentRect) return; + const overlay = overlayCanvasRef.current; + if (!overlay) return; + const { x, y, w, h } = currentRect; + if (w <= 0 || h <= 0) { + setIsDrawing(false); + setCurrentRect(null); + setDragStart(null); + const ctx = overlay.getContext("2d"); + if (ctx) ctx.clearRect(0, 0, overlay.width, overlay.height); + notify('Selection too small. Please draw a larger box.', { type: 'warning' }); + return; + } + + // Map overlay-space bbox to source frame using displayed media rect + const media = getUnderlyingMediaAndSize(); + if (!media) { + console.warn("[BBox] No media found under overlay; aborting bbox post"); + notify('No video/canvas found. Reset the view and try again.', { type: 'error', durationMs: 6000 }); + return; + } + + const overlayW = overlay.width; + const overlayH = overlay.height; + const srcW = media.width; + const srcH = media.height; + const mediaOffsetX = (media as any).offsetX ?? 0; + const mediaOffsetY = (media as any).offsetY ?? 0; + const mediaDispW = (media as any).displayWidth ?? overlayW; + const mediaDispH = (media as any).displayHeight ?? overlayH; + + let contentX = mediaOffsetX; + let contentY = mediaOffsetY; + let contentW = mediaDispW; + let contentH = mediaDispH; + if (media.type === "canvas") { + const side = Math.min(mediaDispW, mediaDispH); + contentX = mediaOffsetX + (mediaDispW - side) / 2; + contentY = mediaOffsetY + (mediaDispH - side) / 2; + contentW = side; + contentH = side; + } + + const rx0 = Math.max(x, contentX); + const ry0 = Math.max(y, contentY); + const rx1 = Math.min(x + w, contentX + contentW); + const ry1 = Math.min(y + h, contentY + contentH); + const rw = Math.max(0, rx1 - rx0); + const rh = Math.max(0, ry1 - ry0); + if (rw <= 1 || rh <= 1) { + console.warn("[BBox] BBox outside content area; aborting"); + notify('Box outside of content area. Try again within the stream.', { type: 'warning', durationMs: 6000 }); + return; + } + + const scaleX = srcW / contentW; + const scaleY = srcH / contentH; + const sx0 = Math.max(0, Math.min(srcW - 1, Math.round((rx0 - contentX) * scaleX))); + const sy0 = Math.max(0, Math.min(srcH - 1, Math.round((ry0 - contentY) * scaleY))); + const sx1 = Math.max(0, Math.min(srcW, Math.round((rx1 - contentX) * scaleX))); + const sy1 = Math.max(0, Math.min(srcH, Math.round((ry1 - contentY) * scaleY))); + const sw = Math.max(1, sx1 - sx0); + const sh = Math.max(1, sy1 - sy0); + + const xNorm = sx0 / srcW; + const yNorm = sy0 / srcH; + const wNorm = sw / srcW; + const hNorm = sh / srcH; + + console.log("[BBox] Posting BBox Prompt Service (normalized source)", { + bbox: { x: xNorm, y: yNorm, width: wNorm, height: hNorm }, + src: { width: srcW, height: srcH }, + overlay: { width: overlayW, height: overlayH }, + display: { width: mediaDispW, height: mediaDispH, offsetX: mediaOffsetX, offsetY: mediaOffsetY }, + content: { x: contentX, y: contentY, width: contentW, height: contentH }, + scales: { scaleX, scaleY } + }); + notify( + `Sending box [${xNorm.toFixed(2)}, ${yNorm.toFixed(2)}, ${wNorm.toFixed(2)}, ${hNorm.toFixed(2)}]`, + { type: 'info' } + ); + // @ts-ignore - Custom service + (connection as any).daiConnection?.postToService( + "BBox Prompt Service", + { + filename: "object.png", + type: "application/json", + data: null, + bbox: { x: xNorm, y: yNorm, width: wNorm, height: hNorm }, + bboxType: "normalized", + label: "object" + }, + (resp: any) => { + console.log("[BBox] Service ack:", resp); + notify('Bounding box sent', { type: 'success' }); + } + ); + + setIsDrawing(false); + setCurrentRect(null); + setDragStart(null); + const ctx = overlay.getContext("2d"); + if (ctx) ctx.clearRect(0, 0, overlay.width, overlay.height); + }, [connection, currentRect]); + + const handleBeginBBoxDraw = useCallback(() => { + console.log("[BBox] Begin drawing requested"); + setIsDrawing(true); + setCurrentRect(null); + setDragStart(null); + }, []); + + useEffect(() => { + if (!isDrawing) return; + const container = streamContainerRef.current; + const overlay = overlayCanvasRef.current; + if (!container || !overlay) return; + const sizeOverlay = () => { + const rect = container.getBoundingClientRect(); + overlay.width = Math.max(1, Math.round(rect.width)); + overlay.height = Math.max(1, Math.round(rect.height)); + const ctx = overlay.getContext("2d"); + if (ctx) ctx.clearRect(0, 0, overlay.width, overlay.height); + console.log("[BBox] Overlay sized", { width: overlay.width, height: overlay.height }); + }; + sizeOverlay(); + window.addEventListener("resize", sizeOverlay); + return () => window.removeEventListener("resize", sizeOverlay); + }, [isDrawing]); + + useEffect(() => { + notify(connection.connected ? 'Connected to device' : 'Disconnected from device', { type: connection.connected ? 'success' : 'warning', durationMs: 1800 }); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [connection.connected]); + + const onOverlayMouseDown = (e: any) => { + if (!isDrawing) return; + const canvas = overlayCanvasRef.current; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + const x = e.clientX - rect.left; + const y = e.clientY - rect.top; + setDragStart({ x, y }); + setCurrentRect({ x, y, w: 0, h: 0 }); + console.log("[BBox] Mouse down", { x, y }); + }; + + const onOverlayMouseMove = (e: any) => { + if (!isDrawing || !dragStart) return; + const canvas = overlayCanvasRef.current; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + const x = e.clientX - rect.left; + const y = e.clientY - rect.top; + const x0 = Math.min(dragStart.x, x); + const y0 = Math.min(dragStart.y, y); + const w = Math.abs(x - dragStart.x); + const h = Math.abs(y - dragStart.y); + setCurrentRect({ x: x0, y: y0, w, h }); + + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.clearRect(0, 0, canvas.width, canvas.height); + ctx.strokeStyle = "#22c55e"; // green + ctx.lineWidth = 2; + ctx.strokeRect(x0, y0, w, h); + if ((w * h) % 20 === 0) { + console.log("[BBox] Mouse move bbox", { x: x0, y: y0, w, h }); + } + }; + + const onOverlayMouseUp = () => { + if (!isDrawing) return; + console.log("[BBox] Mouse up, finalizing bbox", currentRect); + finalizeBBox(); + }; + + return ( +
+ {/* Left: Stream Viewer */} +
+ + {isDrawing && ( + + )} +
+ + {/* Vertical Divider */} +
+ + {/* Right: Sidebar (Info and Controls) */} +
+

+ Open Vocabulary Object Detection +

+

+ Run open‑vocabulary detection on‑device (YOLOE or YOLO‑World) with a custom UI. + Define classes via text prompts or image crops, adjust confidence, and visualize results live. +

+ + {/* Class Input */} + + + {/* Image Uploader */} + + + {/* Confidence Slider */} + + + {/* Snap Collection */} + + + {/* Connection Status */} +
+
+ + {connection.connected ? 'Connected to device' : 'Disconnected'} + +
+
+
+ ); +} + + +export default App; diff --git a/performant_PoCs/hub_snaps/frontend/src/ClassSelector.tsx b/performant_PoCs/hub_snaps/frontend/src/ClassSelector.tsx new file mode 100644 index 000000000..956d3f502 --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/src/ClassSelector.tsx @@ -0,0 +1,66 @@ +import { Flex, Button, Input } from "@luxonis/common-fe-components"; +import { useRef, useState } from "react"; +import { css } from "../styled-system/css/css.mjs"; +import { useConnection } from "@luxonis/depthai-viewer-common"; +import { useNotifications } from "./Notifications.tsx"; + +export function ClassSelector() { + const inputRef = useRef(null); + const connection = useConnection(); + const [selectedClasses, setSelectedClasses] = useState(["person", "chair", "TV"]); + const { notify } = useNotifications(); + + const handleSendMessage = () => { + if (inputRef.current) { + const value = inputRef.current.value; + const updatedClasses = value + .split(',') + .map((c: string) => c.trim()) + .filter(Boolean); + + if (updatedClasses.length === 0) { + notify('Please enter at least one class (comma separated).', { type: 'warning', durationMs: 5000 }); + return; + } + if (!connection.connected) { + notify('Not connected to device. Unable to update classes.', { type: 'error' }); + return; + } + + console.log('Sending new class list to backend:', updatedClasses); + notify(`Updating ${updatedClasses.length} class${updatedClasses.length > 1 ? 'es' : ''}…`, { type: 'info' }); + + connection.daiConnection?.postToService( + // @ts-ignore - Custom service + "Class Update Service", + updatedClasses, + () => { + console.log('Backend acknowledged class update'); + setSelectedClasses(updatedClasses); + notify(`Classes updated (${updatedClasses.join(', ')})`, { type: 'success', durationMs: 6000 }); + } + ); + + inputRef.current.value = ''; + } + }; + + return ( +
+ {/* Class List Display */} +

Update Classes with Text Input:

+
    + {selectedClasses.map((cls: string, idx: number) => ( +
  • {cls}
    • + ))} +
+ + + {/* Input + Button */} + + + + +
+ ); +} diff --git a/performant_PoCs/hub_snaps/frontend/src/ConfidenceSlider.tsx b/performant_PoCs/hub_snaps/frontend/src/ConfidenceSlider.tsx new file mode 100644 index 000000000..636b52a87 --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/src/ConfidenceSlider.tsx @@ -0,0 +1,70 @@ +import { css } from "../styled-system/css/css.mjs"; +import { useState } from "react"; +import { useConnection } from "@luxonis/depthai-viewer-common"; + +interface ConfidenceSliderProps { + initialValue?: number; +} + +export function ConfidenceSlider({ initialValue = 0.5 }: ConfidenceSliderProps) { + const connection = useConnection(); + const [value, setValue] = useState(initialValue); + + const handleCommit = () => { + if (typeof value === "number" && !isNaN(value)) { + console.log('Sending threshold to backend:', value); + + connection.daiConnection?.postToService( + // @ts-ignore - Custom service + "Threshold Update Service", + value, + (response: any) => { + console.log('Backend acknowledged threshold update:', response); + } + ); + } else { + console.warn("Invalid value, skipping update:", value); + } + }; + + return ( +
+ + setValue(parseFloat(e.target.value))} + onMouseUp={handleCommit} + onTouchEnd={handleCommit} + className={css({ + width: '100%', + appearance: 'none', + height: '4px', + borderRadius: 'full', + backgroundColor: 'gray.300', + '&::-webkit-slider-thumb': { + appearance: 'none', + width: '12px', + height: '12px', + borderRadius: 'full', + backgroundColor: 'blue.500', + cursor: 'pointer', + }, + '&::-moz-range-thumb': { + appearance: 'none', + width: '12px', + height: '12px', + borderRadius: 'full', + backgroundColor: 'blue.500', + cursor: 'pointer', + } + })} + /> +
+ ); +} diff --git a/performant_PoCs/hub_snaps/frontend/src/ImageUploader.tsx b/performant_PoCs/hub_snaps/frontend/src/ImageUploader.tsx new file mode 100644 index 000000000..949695f1f --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/src/ImageUploader.tsx @@ -0,0 +1,108 @@ +import { Button, Flex } from "@luxonis/common-fe-components"; +import { css } from "../styled-system/css/css.mjs"; +import { useState } from "react"; +import { useConnection } from "@luxonis/depthai-viewer-common"; +import { useNotifications } from "./Notifications.tsx"; + +type Props = { + onDrawBBox?: () => void; +} + +export function ImageUploader({ onDrawBBox }: Props) { + const connection = useConnection(); + const [selectedFile, setSelectedFile] = useState(null); + const { notify } = useNotifications(); + + const handleFileSelect = (event: React.ChangeEvent) => { + const file: File | null = event.target.files?.[0] || null; + setSelectedFile(file); + if (file) { + notify(`Selected: ${file.name}`, { type: 'info', durationMs: 2000 }); + } + }; + + const handleUpload = () => { + if (!selectedFile) { + notify('Please choose an image first', { type: 'warning' }); + return; + } + if (!connection.connected) { + notify('Not connected to device. Unable to upload image.', { type: 'error' }); + return; + } + + const reader = new FileReader(); + reader.onload = () => { + const fileData = reader.result; + + console.log("Uploading image to backend:", selectedFile.name); + const sizeKb = Math.max(1, Math.round((selectedFile.size || 0) / 1024)); + notify(`Uploading ${selectedFile.name} (${sizeKb} KB)…`, { type: 'info' }); + + // @ts-ignore - Custom service + (connection as any).daiConnection?.postToService( + "Image Upload Service", + { + filename: selectedFile.name, + type: selectedFile.type, + data: fileData + }, + (resp: any) => { + console.log("[ImageUpload] Service ack:", resp); + notify(`Image uploaded: ${selectedFile.name}`, { type: 'success', durationMs: 6000 }); + } + ); + }; + + reader.readAsDataURL(selectedFile); + }; + + return ( +
+

Update Classes with Image Input:

+ Important: reset view before drawing a bounding box + + {/* Clickable file selection area */} + + + {/* Hidden file input */} + + + {/* Upload / Draw buttons */} + + + OR + + +
+ ); +} diff --git a/performant_PoCs/hub_snaps/frontend/src/Notifications.tsx b/performant_PoCs/hub_snaps/frontend/src/Notifications.tsx new file mode 100644 index 000000000..7301845f2 --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/src/Notifications.tsx @@ -0,0 +1,107 @@ +import { createContext, useCallback, useContext, useMemo, useRef, useState } from "react"; +import { css } from "../styled-system/css/css.mjs"; + +type Notification = { + id: number; + message: string; + type?: "info" | "success" | "warning" | "error"; + durationMs?: number; +}; + +type NotificationContextValue = { + notify: (message: string, options?: { type?: Notification["type"]; durationMs?: number }) => void; +}; + +const NotificationContext = createContext(null); + +export function useNotifications() { + const ctx = useContext(NotificationContext); + if (!ctx) throw new Error("useNotifications must be used within NotificationProvider"); + return ctx; +} + +export function NotificationProvider({ children }: { children: React.ReactNode }) { + const [items, setItems] = useState([]); + const idRef = useRef(1); + + const remove = useCallback((id: number) => { + setItems((prev) => prev.filter((n) => n.id !== id)); + }, []); + + const notify = useCallback((message, options) => { + const id = idRef.current++; + const durationMs = options?.durationMs ?? 4500; + const type = options?.type ?? "info"; + setItems((prev) => [...prev, { id, message, type, durationMs }].slice(-5)); + if (durationMs > 0) { + window.setTimeout(() => remove(id), durationMs); + } + }, [remove]); + + const value = useMemo(() => ({ notify }), [notify]); + + return ( + + {children} +
+ {items.map((n, idx) => ( + remove(n.id)} index={idx} /> + ))} +
+ + ); +} + +function Toast({ notification, onClose, index }: { notification: Notification; onClose: () => void; index: number }) { + const { message, type } = notification; + const colorMap: Record, { bg: string; border: string; text: string }> = { + info: { bg: "blue.50", border: "blue.300", text: "blue.900" }, + success: { bg: "green.50", border: "green.300", text: "green.900" }, + warning: { bg: "yellow.50", border: "yellow.300", text: "yellow.900" }, + error: { bg: "red.50", border: "red.300", text: "red.900" }, + }; + const colors = colorMap[type ?? "info"]; + + return ( +
+
+ {message} + +
+
+ ); +} + +// Keyframes for smooth appear (no opacity change to keep background fully opaque) +const styleEl = (typeof document !== 'undefined') ? document.createElement('style') : null; +if (styleEl && !document.getElementById('notif-keyframes')) { + styleEl.id = 'notif-keyframes'; + styleEl.innerHTML = ` +@keyframes slideInUp { from { transform: translateY(8px); } to { transform: translateY(0); } } +@keyframes slideOutDown { from { transform: translateY(0); } to { transform: translateY(8px); } } +`; + document.head.appendChild(styleEl); +} + diff --git a/performant_PoCs/hub_snaps/frontend/src/SnapCollectionButton.tsx b/performant_PoCs/hub_snaps/frontend/src/SnapCollectionButton.tsx new file mode 100644 index 000000000..c4b516e5b --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/src/SnapCollectionButton.tsx @@ -0,0 +1,189 @@ +import { useState, useMemo } from "react"; +import { Button } from "@luxonis/common-fe-components"; +import { css } from "../styled-system/css/css.mjs"; +import { useConnection } from "@luxonis/depthai-viewer-common"; +import { useNotifications } from "./Notifications.tsx"; + +export function SnapCollectionButton() { + const connection = useConnection(); + const { notify } = useNotifications(); + + // master run/stop + const [running, setRunning] = useState(false); + const [busy, setBusy] = useState(false); + + // timing toggle + interval (only visible when toggle is ON) + const [timingEnabled, setTimingEnabled] = useState(false); + const [timeIntervalStr, setTimeIntervalStr] = useState("60"); + const timeInterval = useMemo(() => Number.parseInt(timeIntervalStr, 10), [timeIntervalStr]); + const timeIntervalIsValid = Number.isInteger(timeInterval) && timeInterval > 0; + + // no-detections toggle + const [noDetEnabled, setNoDetEnabled] = useState(false); + // new-detections (tracker) toggle + const [newDetEnabled, setNewDetEnabled] = useState(false); + + const postConfig = (runFlag: boolean) => { + const payload = runFlag + ? { + timed: { enabled: timingEnabled, interval: timingEnabled ? timeInterval : 0 }, + noDetections: { enabled: noDetEnabled }, + newDetections: { enabled: newDetEnabled }, + } + : { + timed: { enabled: false, interval: 0 }, + noDetections: { enabled: false }, + newDetections: { enabled: false }, + }; + + (connection as any).daiConnection?.postToService( + "Snap Collection Service", + payload, + (_resp: any) => { + setBusy(false); + setRunning(runFlag); + notify(runFlag ? "Snapping started." : "Snapping stopped.", { type: "success" }); + } + ); + }; + + const handleStartStop = () => { + if (!connection.connected) { + notify("Not connected to device.", { type: "error" }); + return; + } + if (busy) return; + + if (!running && timingEnabled && !timeIntervalIsValid) { + notify("Please enter a positive integer time interval (seconds).", { type: "error" }); + return; + } + + setBusy(true); + if (!running) { + notify("Starting snapping…", { type: "info" }); + postConfig(true); + } else { + notify("Stopping snapping…", { type: "info" }); + postConfig(false); + } + }; + + const disabledControls = busy || running; + + return ( +
+
+ + {/* Timing switch (checkbox) */} +
+ + setTimingEnabled(e.target.checked)} + disabled={disabledControls} + className={css({ + width: "5", + height: "5", + cursor: disabledControls ? "not-allowed" : "pointer", + })} + /> +
+ + {/* Interval input — only when timing is ON */} + {timingEnabled && ( + + )} + + {/* No detections switch */} +
+ + setNoDetEnabled(e.target.checked)} + disabled={disabledControls} + className={css({ + width: "5", + height: "5", + cursor: disabledControls ? "not-allowed" : "pointer", + })} + /> +
+ + {/* New detections switch */} +
+ + setNewDetEnabled(e.target.checked)} + disabled={disabledControls} + className={css({ + width: "5", + height: "5", + cursor: disabledControls ? "not-allowed" : "pointer", + })} + /> +
+ + {/* Start / Stop */} + +
+ ); +} diff --git a/performant_PoCs/hub_snaps/frontend/src/index.css b/performant_PoCs/hub_snaps/frontend/src/index.css new file mode 100644 index 000000000..009ed44b9 --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/src/index.css @@ -0,0 +1 @@ +@layer reset, base, tokens, recipes, utilities; \ No newline at end of file diff --git a/performant_PoCs/hub_snaps/frontend/src/main.tsx b/performant_PoCs/hub_snaps/frontend/src/main.tsx new file mode 100644 index 000000000..116fda8d2 --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/src/main.tsx @@ -0,0 +1,33 @@ +import {StrictMode} from 'react'; +import {createRoot} from 'react-dom/client'; +import './index.css'; +import '@luxonis/depthai-viewer-common/styles'; +import '@luxonis/common-fe-components/styles'; +import '@luxonis/depthai-pipeline-lib/styles'; +import App from './App.tsx'; +import {BrowserRouter, Route, Routes} from "react-router"; +import {DepthAIContext} from "@luxonis/depthai-viewer-common"; +import { NotificationProvider } from './Notifications.tsx'; + +// This function extracts the base path with app version from the current URL. +// This is essential for access via domain luxonis.app +function getBasePath(): string { + return window.location.pathname.match(/^\/\d+\.\d+\.\d+\/$/)?.[0] ?? '' +} + +createRoot(document.getElementById('root')!).render( + + + + + + }/> + + + + + , +); diff --git a/performant_PoCs/hub_snaps/frontend/src/vite-env.d.ts b/performant_PoCs/hub_snaps/frontend/src/vite-env.d.ts new file mode 100644 index 000000000..11f02fe2a --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/src/vite-env.d.ts @@ -0,0 +1 @@ +/// diff --git a/performant_PoCs/hub_snaps/frontend/src/window.d.ts b/performant_PoCs/hub_snaps/frontend/src/window.d.ts new file mode 100644 index 000000000..f02d957b7 --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/src/window.d.ts @@ -0,0 +1,7 @@ +declare global { + interface Window { + __basePath: string; + } +} + +export {}; diff --git a/performant_PoCs/hub_snaps/frontend/tsconfig.app.json b/performant_PoCs/hub_snaps/frontend/tsconfig.app.json new file mode 100644 index 000000000..8f9007000 --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/tsconfig.app.json @@ -0,0 +1,30 @@ +{ + "compilerOptions": { + "composite": true, + "tsBuildInfoFile": "./node_modules/.tmp/example.app.tsbuildinfo", + "target": "ESNext", + "useDefineForClassFields": true, + "lib": [ + "ESNext", + "WebWorker", + "DOM", + "DOM.Iterable" + ], + "module": "ESNext", + "skipLibCheck": true, + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "resolveJsonModule": true, + "isolatedModules": true, + "moduleDetection": "force", + "noEmit": true, + "jsx": "react-jsx", + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "noFallthroughCasesInSwitch": true + }, + "include": [ + "src" + ] +} diff --git a/performant_PoCs/hub_snaps/frontend/tsconfig.json b/performant_PoCs/hub_snaps/frontend/tsconfig.json new file mode 100644 index 000000000..fb1241881 --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/tsconfig.json @@ -0,0 +1,7 @@ +{ + "files": [], + "references": [ + { "path": "./tsconfig.app.json" }, + { "path": "./tsconfig.node.json" } + ] +} diff --git a/performant_PoCs/hub_snaps/frontend/tsconfig.node.json b/performant_PoCs/hub_snaps/frontend/tsconfig.node.json new file mode 100644 index 000000000..29ead3807 --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/tsconfig.node.json @@ -0,0 +1,13 @@ +{ + "compilerOptions": { + "composite": true, + "tsBuildInfoFile": "./node_modules/.tmp/tsconfig-example.node.tsbuildinfo", + "skipLibCheck": true, + "module": "ESNext", + "moduleResolution": "bundler", + "allowSyntheticDefaultImports": true, + "strict": true, + "noEmit": true + }, + "include": ["vite.config.ts"] +} diff --git a/performant_PoCs/hub_snaps/frontend/vite.config.ts b/performant_PoCs/hub_snaps/frontend/vite.config.ts new file mode 100644 index 000000000..5c678f860 --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/vite.config.ts @@ -0,0 +1,22 @@ +import react from '@vitejs/plugin-react'; +import { defineConfig } from 'vite'; + +// https://vite.dev/config/ +export default defineConfig({ + base: "", + plugins: [react(),], + // This is needed by FoxGlove + define: { + global: {}, + }, + worker: { + format: "es", + }, + build: { + rollupOptions: { + output: { + format: "esm", + }, + }, + }, +}); diff --git a/performant_PoCs/hub_snaps/oakapp.toml b/performant_PoCs/hub_snaps/oakapp.toml new file mode 100644 index 000000000..1fe86af0d --- /dev/null +++ b/performant_PoCs/hub_snaps/oakapp.toml @@ -0,0 +1,33 @@ +identifier = "com.example.performant-pocs.focused-vision.hub-snaps" +entrypoint = ["bash", "-c", "/usr/bin/runsvdir -P /etc/service"] +app_version = "1.0.0" + +prepare_container = [ + { type = "COPY", source = "./backend/src/requirements.txt", target = "./backend/src/requirements.txt" }, + { type = "RUN", command = "python3.12 -m pip install -r /app/backend/src/requirements.txt --break-system-packages"}, +] + +prepare_build_container = [{type = "RUN", command = "bash -c 'curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.3/install.sh | bash && . \"$HOME/.nvm/nvm.sh\" && nvm install 24'"}] +build_steps = [ + "mkdir -p /etc/service/backend", + "cp /app/backend-run.sh /etc/service/backend/run", + "chmod +x /etc/service/backend/run", +] + +depthai_models = { yaml_path = "./backend/src/depthai_models" } + +[static_frontend] +dist_path = "./frontend/dist" + +[static_frontend.build] +source_path = "./frontend" +steps = ["bash -c 'cd /app/frontend/src && export NODE_OPTIONS=--max-old-space-size=4096 && . $HOME/.nvm/nvm.sh && npm install && npm run build'"] + +[base_image] +api_url = "https://registry-1.docker.io" +service = "registry.docker.io" +oauth_url = "https://auth.docker.io/token" +auth_type = "repository" +auth_name = "luxonis/oakapp-base" +image_name = "luxonis/oakapp-base" +image_tag = "1.2.4" \ No newline at end of file From 7921e0700f107a7de954424f9275bc8a7410e06d Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Mon, 20 Oct 2025 18:08:10 +0200 Subject: [PATCH 05/39] Added conditioning snapping --- performant_PoCs/hub_snaps/backend/src/main.py | 52 +++++++++++-------- .../hub_snaps/backend/src/utils/snap_utils.py | 44 ++++++++-------- 2 files changed, 52 insertions(+), 44 deletions(-) diff --git a/performant_PoCs/hub_snaps/backend/src/main.py b/performant_PoCs/hub_snaps/backend/src/main.py index 47306c04b..5fd46e34f 100644 --- a/performant_PoCs/hub_snaps/backend/src/main.py +++ b/performant_PoCs/hub_snaps/backend/src/main.py @@ -27,6 +27,7 @@ custom_snap_process, NoDetectionsGate, tracklet_new_detection_process, + reset_new_detections_state, # NEW ) load_dotenv(override=True) @@ -72,7 +73,6 @@ def make_dummy_features(max_num_classes: int, model_name: str, precision: str): ) image_prompt_features = None if args.model == "yoloe": - # send dummy image-prompts initially image_prompt_features = make_dummy_features( MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision ) @@ -126,7 +126,6 @@ def make_dummy_features(max_num_classes: int, model_name: str, precision: str): cam = pipeline.create(dai.node.Camera).build( boardSocket=dai.CameraBoardSocket.CAM_A ) - # Request high-res NV12 frames for visualization/encoding video_src_out = cam.requestOutput( size=VISUALIZATION_RESOLUTION, type=dai.ImgFrame.Type.NV12, @@ -176,9 +175,13 @@ def make_dummy_features(max_num_classes: int, model_name: str, precision: str): # Cache last frame for services that need full frame content frame_cache = pipeline.create(FrameCacheNode).build(video_src_out) - # --- state for triggers (timed + no-detections) + # --- state for triggers (timed + no-detections) and runtime flags (no nonlocal) no_det_gate = NoDetectionsGate() _snap_state = {"timed_enabled": False, "interval": 60, "last_sent_s": -1.0} + _runtime = { + "newdet_running": False, + "conf_threshold": CONFIDENCE_THRESHOLD, # optional live copy + } # TIMED + NO-DET producer (works via the process_fn, ticking internally) snaps_producer = pipeline.create(SnapsProducer).build( @@ -196,29 +199,24 @@ def make_dummy_features(max_num_classes: int, model_name: str, precision: str): snaps_producer._em.setSourceAppId(os.getenv("OAKAGENT_CONTAINER_ID")) print("Snaps producer API KEY:", os.getenv("DEPTHAI_HUB_API_KEY")) - # --------- ObjectTracker + producer for "new detections" (tuned) ---------- + # --------- ObjectTracker + producer for "new detections" ---------- object_tracker = pipeline.create(dai.node.ObjectTracker) - # RVC4-supported tracker; log will say it anyway if a different type is forced + # Use RVC4-supported type; SDK will warn if it forces something else object_tracker.setTrackerType(dai.TrackerType.SHORT_TERM_IMAGELESS) - - # Keep IDs stable even if detections overlap/vanish briefly object_tracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.UNIQUE_ID) object_tracker.setTrackingPerClass(True) + object_tracker.setTrackletBirthThreshold(1) # frames before TRACKED + object_tracker.setTrackletMaxLifespan(180) # frames until LOST removed + object_tracker.setOcclusionRatioThreshold(0.5) # filter heavy overlaps + object_tracker.setTrackerThreshold(0.25) # ignore low-conf dets - # TUNABLES (adjust as needed) - object_tracker.setTrackletBirthThreshold(3) # frames before TRACKED (was 3) - object_tracker.setTrackletMaxLifespan(180) # frames until LOST track is removed - object_tracker.setOcclusionRatioThreshold(0.5) # filter heavy overlaps a bit more - object_tracker.setTrackerThreshold(0.25) # ignore very low-conf dets in tracking - - # Inputs - # Use the same resized/manip frames for both tracker inputs so they align with detections + # Inputs: use same resized/manip frames to align with detections input_node.link(object_tracker.inputTrackerFrame) input_node.link(object_tracker.inputDetectionFrame) filtered_bridge.out.link(object_tracker.inputDetections) - # Producer that listens to tracklets and sends a snap when any TRACKED appears first time + # Producer that sends a snap when any TRACKED appears first time after (re)start snaps_newdet_producer = pipeline.create(SnapsProducer).build( frame=video_src_out, msg=object_tracker.out, @@ -238,7 +236,7 @@ def update_labels(label_names: list[str], offset: int = 0): annotation_node.setLabelEncoding( {offset + k: v for k, v in enumerate(label_names)} ) - # If you want to restrict tracker by labels, enable below: + # To restrict tracker labels, you could enable: # object_tracker.setDetectionLabelsToTrack(list(range(offset, offset + len(label_names)))) # visualization topics @@ -296,7 +294,6 @@ def class_update_service(new_classes: list[str]): ), ) textInputQueue.send(inputNNData) - # In unified YOLOE, ensure image_prompts are dummy when text prompts are active if args.model == "yoloe": dummy = make_dummy_features( MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision @@ -318,9 +315,9 @@ def class_update_service(new_classes: list[str]): def conf_threshold_update_service(new_conf_threshold: float): """Changes confidence threshold based on the user input""" - CONFIDENCE_THRESHOLD = max(0, min(1, new_conf_threshold)) - nn_with_parser.getParser(0).setConfidenceThreshold(CONFIDENCE_THRESHOLD) - print(f"Confidence threshold set to: {CONFIDENCE_THRESHOLD}:") + _runtime["conf_threshold"] = max(0.0, min(1.0, float(new_conf_threshold))) + nn_with_parser.getParser(0).setConfidenceThreshold(_runtime["conf_threshold"]) + print(f"Confidence threshold set to: {_runtime['conf_threshold']}") def image_upload_service(image_data): image = base64_to_cv2_image(image_data["data"]) @@ -430,7 +427,6 @@ def snap_collection_service(payload): snaps_producer.setRunning(any_active) if any_active: snaps_producer.setTimeInterval(base_dt_seconds) - # Do not touch new-detection producer in legacy mode print(f"[SnapService] (legacy) timed={_snap_state['timed_enabled']}, interval={_snap_state['interval']}, noDet={no_det_gate.enabled}") return {"ok": True} @@ -456,7 +452,13 @@ def snap_collection_service(payload): if "newDetections" in payload: n2cfg = payload["newDetections"] or {} new_on = bool(n2cfg.get("enabled", False)) + + # OFF -> ON => reset memory so current TRACKED items count as "new" + if new_on and not _runtime["newdet_running"]: + reset_new_detections_state() + snaps_newdet_producer.setRunning(new_on) + _runtime["newdet_running"] = new_on # React on every message if allowed try: snaps_newdet_producer.setTimeInterval(0) @@ -469,7 +471,11 @@ def snap_collection_service(payload): if any_active: snaps_producer.setTimeInterval(base_dt_seconds) - print(f"[SnapService] timed={_snap_state['timed_enabled']}, interval={_snap_state['interval']}, noDet={no_det_gate.enabled}") + print( + f"[SnapService] timed={_snap_state['timed_enabled']}, " + f"interval={_snap_state['interval']}, noDet={no_det_gate.enabled}, " + f"newDet={_runtime['newdet_running']}" + ) return {"ok": True} print("[SnapService] Unsupported payload format") diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils.py index 87108b518..85d3bddf0 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils.py @@ -12,6 +12,7 @@ class NoDetectionsGate: - enabled: externally toggled - in_run: True while we are in a zero-detection streak """ + def __init__(self) -> None: self.enabled: bool = False self.in_run: bool = False @@ -40,9 +41,9 @@ def on_frame(self, num_detections: int) -> bool: def _build_extras( - model: str, - det_data: dai.ImgDetections | ImgDetectionsExtended, - class_names: List[str], + model: str, + det_data: dai.ImgDetections | ImgDetectionsExtended, + class_names: List[str], ): detections = det_data.detections or [] dets_labels = [det.label for det in detections] @@ -79,13 +80,13 @@ def _build_extras( def custom_snap_process( - producer: SnapsProducer, - frame: dai.ImgFrame, - det_data: dai.ImgDetections | ImgDetectionsExtended, - class_names: List[str], - model: str, - no_det_gate: Optional[NoDetectionsGate] = None, - timed_state: Optional[dict] = None, + producer: SnapsProducer, + frame: dai.ImgFrame, + det_data: dai.ImgDetections | ImgDetectionsExtended, + class_names: List[str], + model: str, + no_det_gate: Optional[NoDetectionsGate] = None, + timed_state: Optional[dict] = None, ): """ Called frequently by SnapsProducer; we decide what to send and with which tags. @@ -139,14 +140,23 @@ def custom_snap_process( # ------------------ New-detection snap (TRACKED gating + cooldown) ------------------ class _NewDetectionsState: - """Plain dicts (no defaultdict) + a set for seen IDs.""" + """Plain dicts + a set (no defaultdict, no nonlocal).""" + def __init__(self) -> None: self.seen_ids: set[int] = set() - self.last_label_ts: dict[str, float] = {} # label -> last snap epoch seconds + self.last_label_ts: dict[str, float] = {} + _NEWDET_STATE = _NewDetectionsState() +def reset_new_detections_state() -> None: + """Clear memory so currently present TRACKED objects will be treated as 'new'.""" + _NEWDET_STATE.seen_ids.clear() + _NEWDET_STATE.last_label_ts.clear() + print("[NewDet] State reset") + + def tracklet_new_detection_process( producer: SnapsProducer, frame: dai.ImgFrame, @@ -163,21 +173,17 @@ def tracklet_new_detection_process( """ tks = getattr(tracklets_msg, "tracklets", []) or [] now = time.time() - fired_any = False for t in tks: - # Robustly read status (enum or int) + # Robustly read status (enum or int) -> TRACKED? status_val = getattr(t, "status", None) try: is_tracked = (status_val == dai.Tracklet.TrackingStatus.TRACKED) except Exception: - # Fallback: enum may not be available; typical mapping NEW=0, TRACKED=1, LOST=2 is_tracked = int(status_val) == 1 - if not is_tracked: continue - # Only once per ID try: tid = int(getattr(t, "id", -1)) except Exception: @@ -185,15 +191,12 @@ def tracklet_new_detection_process( if tid < 0 or tid in _NEWDET_STATE.seen_ids: continue - # Optional per-label cooldown label_idx = int(getattr(t, "label", -1)) label_str = class_names[label_idx] if 0 <= label_idx < len(class_names) else "" last_ts = _NEWDET_STATE.last_label_ts.get(label_str, 0.0) if label_str and (now - last_ts) < float(min_label_cooldown_s): - # still in cooldown window for this label continue - # Build extras + send file_group = [dai.FileData(frame, "rgb")] extras = { "model": model, @@ -206,6 +209,5 @@ def tracklet_new_detection_process( _NEWDET_STATE.seen_ids.add(tid) if label_str: _NEWDET_STATE.last_label_ts[label_str] = now - fired_any = True return From d518809040306ca994dfb6fc6e3f7c7769e3c706 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Mon, 20 Oct 2025 20:35:56 +0200 Subject: [PATCH 06/39] refactored leagcy code and snap utils --- performant_PoCs/hub_snaps/backend/src/main.py | 395 +++++++----------- .../hub_snaps/backend/src/utils/snap_utils.py | 213 ---------- .../backend/src/utils/snap_utils/__init__.py | 3 + .../backend/src/utils/snap_utils/extras.py | 57 +++ .../backend/src/utils/snap_utils/helpers.py | 59 +++ .../src/utils/snap_utils/new_detection.py | 77 ++++ .../utils/snap_utils/no_detections_gate.py | 24 ++ .../src/utils/snap_utils/timed_no_det.py | 45 ++ 8 files changed, 421 insertions(+), 452 deletions(-) delete mode 100644 performant_PoCs/hub_snaps/backend/src/utils/snap_utils.py create mode 100644 performant_PoCs/hub_snaps/backend/src/utils/snap_utils/__init__.py create mode 100644 performant_PoCs/hub_snaps/backend/src/utils/snap_utils/extras.py create mode 100644 performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py create mode 100644 performant_PoCs/hub_snaps/backend/src/utils/snap_utils/new_detection.py create mode 100644 performant_PoCs/hub_snaps/backend/src/utils/snap_utils/no_detections_gate.py create mode 100644 performant_PoCs/hub_snaps/backend/src/utils/snap_utils/timed_no_det.py diff --git a/performant_PoCs/hub_snaps/backend/src/main.py b/performant_PoCs/hub_snaps/backend/src/main.py index 5fd46e34f..dd4cca394 100644 --- a/performant_PoCs/hub_snaps/backend/src/main.py +++ b/performant_PoCs/hub_snaps/backend/src/main.py @@ -27,7 +27,7 @@ custom_snap_process, NoDetectionsGate, tracklet_new_detection_process, - reset_new_detections_state, # NEW + reset_new_detections_state, ) load_dotenv(override=True) @@ -41,7 +41,6 @@ PORT = args.port or 8080 CLASS_NAMES = ["person", "chair", "TV"] -# For unified YOLOE, 0-79 are text classes, 80-159 are image-prompt classes CLASS_OFFSET = 0 MAX_NUM_CLASSES = 80 CONFIDENCE_THRESHOLD = 0.1 @@ -64,18 +63,15 @@ def make_dummy_features(max_num_classes: int, model_name: str, precision: str): return np.full((1, 512, max_num_classes), qzp, dtype=np.uint8) -# choose initial features: text for yolo-world/yoloe text_features = extract_text_embeddings( class_names=CLASS_NAMES, max_num_classes=MAX_NUM_CLASSES, - model_name=args.model if args.model != "yolo-world" else "yolo-world", + model_name="yoloe", precision=args.precision, ) -image_prompt_features = None -if args.model == "yoloe": - image_prompt_features = make_dummy_features( - MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision - ) +image_prompt_features = make_dummy_features( + MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision +) if args.fps_limit is None: args.fps_limit = 5 @@ -86,28 +82,18 @@ def make_dummy_features(max_num_classes: int, model_name: str, precision: str): with dai.Pipeline(device) as pipeline: print("Creating pipeline...") - # Model selection with precision-aware YAMLs for YOLOE variants models_dir = Path(__file__).parent / "depthai_models" - if args.model == "yolo-world": - yaml_base = "yolo_world_l_fp16" if args.precision == "fp16" else "yolo_world_l" - yaml_filename = f"{yaml_base}.{platform}.yaml" - yaml_path = models_dir / yaml_filename - if not yaml_path.exists(): - raise SystemExit( - f"Model YAML not found: {yaml_path}. Ensure the model config exists." - ) - model_description = dai.NNModelDescription.fromYamlFile(str(yaml_path)) - elif args.model == "yoloe": - yaml_base = "yoloe_v8_l_fp16" if args.precision == "fp16" else "yoloe_v8_l" - yaml_filename = f"{yaml_base}.{platform}.yaml" - yaml_path = models_dir / yaml_filename - print(f"YOLOE YAML path: {yaml_path}") - if not yaml_path.exists(): - raise SystemExit( - f"Model YAML not found for YOLOE with precision {args.precision}: {yaml_path}. " - f"YOLOE int8 YAML is not available; run with --precision fp16." - ) - model_description = dai.NNModelDescription.fromYamlFile(str(yaml_path)) + if args.precision != "fp16": + raise SystemExit( + f"Model YAML not found for YOLOE with precision {args.precision}. " + f"YOLOE int8 YAML is not available; run with --precision fp16." + ) + yaml_base = "yoloe_v8_l_fp16" + yaml_filename = f"{yaml_base}.{platform}.yaml" + yaml_path = models_dir / yaml_filename + if not yaml_path.exists(): + raise SystemExit(f"Model YAML not found for YOLOE: {yaml_path}.") + model_description = dai.NNModelDescription.fromYamlFile(str(yaml_path)) model_description.platform = platform model_nn_archive = dai.NNArchive(dai.getModelFromZoo(model_description)) model_w, model_h = model_nn_archive.getInputSize() @@ -159,9 +145,10 @@ def make_dummy_features(max_num_classes: int, model_name: str, precision: str): textInputQueue = nn_with_parser.inputs["texts"].createInputQueue() nn_with_parser.inputs["texts"].setReusePreviousMessage(True) - if args.model == "yoloe": - imagePromptInputQueue = nn_with_parser.inputs["image_prompts"].createInputQueue() - nn_with_parser.inputs["image_prompts"].setReusePreviousMessage(True) + + # YOLOE always uses image_prompts input (we feed dummy by default) + imagePromptInputQueue = nn_with_parser.inputs["image_prompts"].createInputQueue() + nn_with_parser.inputs["image_prompts"].setReusePreviousMessage(True) # filter and rename detection labels det_process_filter = pipeline.create(ImgDetectionsFilter).build(nn_with_parser.out) @@ -183,50 +170,45 @@ def make_dummy_features(max_num_classes: int, model_name: str, precision: str): "conf_threshold": CONFIDENCE_THRESHOLD, # optional live copy } - # TIMED + NO-DET producer (works via the process_fn, ticking internally) snaps_producer = pipeline.create(SnapsProducer).build( frame=video_src_out, msg=det_process_filter.out, - running=False, # controlled by service + running=False, process_fn=partial( custom_snap_process, class_names=CLASS_NAMES, - model=args.model, + model="yoloe", no_det_gate=no_det_gate, timed_state=_snap_state, ), ) snaps_producer._em.setSourceAppId(os.getenv("OAKAGENT_CONTAINER_ID")) - print("Snaps producer API KEY:", os.getenv("DEPTHAI_HUB_API_KEY")) - # --------- ObjectTracker + producer for "new detections" ---------- object_tracker = pipeline.create(dai.node.ObjectTracker) - # Use RVC4-supported type; SDK will warn if it forces something else object_tracker.setTrackerType(dai.TrackerType.SHORT_TERM_IMAGELESS) object_tracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.UNIQUE_ID) object_tracker.setTrackingPerClass(True) - object_tracker.setTrackletBirthThreshold(1) # frames before TRACKED - object_tracker.setTrackletMaxLifespan(180) # frames until LOST removed - object_tracker.setOcclusionRatioThreshold(0.5) # filter heavy overlaps - object_tracker.setTrackerThreshold(0.25) # ignore low-conf dets + object_tracker.setTrackletBirthThreshold(1) + object_tracker.setTrackletMaxLifespan(180) + object_tracker.setOcclusionRatioThreshold(0.5) + object_tracker.setTrackerThreshold(0.25) - # Inputs: use same resized/manip frames to align with detections input_node.link(object_tracker.inputTrackerFrame) input_node.link(object_tracker.inputDetectionFrame) filtered_bridge.out.link(object_tracker.inputDetections) - # Producer that sends a snap when any TRACKED appears first time after (re)start snaps_newdet_producer = pipeline.create(SnapsProducer).build( frame=video_src_out, msg=object_tracker.out, - running=False, # toggled by service + running=False, process_fn=partial( tracklet_new_detection_process, class_names=CLASS_NAMES, - model=args.model, + model="yoloe", ), ) + snaps_newdet_producer._em.setSourceAppId(os.getenv("OAKAGENT_CONTAINER_ID")) def update_labels(label_names: list[str], offset: int = 0): @@ -236,38 +218,31 @@ def update_labels(label_names: list[str], offset: int = 0): annotation_node.setLabelEncoding( {offset + k: v for k, v in enumerate(label_names)} ) - # To restrict tracker labels, you could enable: - # object_tracker.setDetectionLabelsToTrack(list(range(offset, offset + len(label_names)))) - - # visualization topics - if args.model == "yolo-world": - visualizer.addTopic("Video", video_enc.out, "images") - elif args.model == "yoloe": - apply_colormap_node = pipeline.create(ApplyColormap).build(nn_with_parser.out) - overlay_frames_node = pipeline.create(ImgFrameOverlay).build( - video_src_out, - apply_colormap_node.out, - preserve_background=True, - ) - overlay_to_nv12 = pipeline.create(dai.node.ImageManip) - overlay_to_nv12.setMaxOutputFrameSize( - VISUALIZATION_RESOLUTION[0] * VISUALIZATION_RESOLUTION[1] * 3 - ) - overlay_to_nv12.initialConfig.setFrameType(dai.ImgFrame.Type.NV12) - overlay_frames_node.out.link(overlay_to_nv12.inputImage) - overlay_enc = pipeline.create(dai.node.VideoEncoder) - overlay_enc.setDefaultProfilePreset( - fps=args.fps_limit, profile=dai.VideoEncoderProperties.Profile.H264_MAIN - ) - overlay_to_nv12.out.link(overlay_enc.input) + apply_colormap_node = pipeline.create(ApplyColormap).build(nn_with_parser.out) + overlay_frames_node = pipeline.create(ImgFrameOverlay).build( + video_src_out, + apply_colormap_node.out, + preserve_background=True, + ) + overlay_to_nv12 = pipeline.create(dai.node.ImageManip) + overlay_to_nv12.setMaxOutputFrameSize( + VISUALIZATION_RESOLUTION[0] * VISUALIZATION_RESOLUTION[1] * 3 + ) + overlay_to_nv12.initialConfig.setFrameType(dai.ImgFrame.Type.NV12) + overlay_frames_node.out.link(overlay_to_nv12.inputImage) - visualizer.addTopic("Video", overlay_enc.out, "images") + overlay_enc = pipeline.create(dai.node.VideoEncoder) + overlay_enc.setDefaultProfilePreset( + fps=args.fps_limit, profile=dai.VideoEncoderProperties.Profile.H264_MAIN + ) + overlay_to_nv12.out.link(overlay_enc.input) + visualizer.addTopic("Video", overlay_enc.out, "images") visualizer.addTopic("Detections", annotation_node.out) def class_update_service(new_classes: list[str]): - """Changes classes to detect based on the user input""" + """Changes classes to detect based on the user input (YOLOE-only)""" if len(new_classes) == 0: print("List of new classes empty, skipping.") return @@ -276,15 +251,10 @@ def class_update_service(new_classes: list[str]): f"Number of new classes ({len(new_classes)}) exceeds maximum number of classes ({MAX_NUM_CLASSES}), skipping." ) return - CLASS_NAMES = new_classes - text_features = extract_text_embeddings( - class_names=CLASS_NAMES, - max_num_classes=MAX_NUM_CLASSES, - model_name=args.model, - precision=args.precision, - ) - inputNNData = dai.NNData() - inputNNData.addTensor( + + class_names = new_classes + input_NN_data_img = dai.NNData() + input_NN_data_img.addTensor( "texts", text_features, dataType=( @@ -293,102 +263,76 @@ def class_update_service(new_classes: list[str]): else dai.TensorInfo.DataType.U8F ), ) - textInputQueue.send(inputNNData) - if args.model == "yoloe": - dummy = make_dummy_features( - MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision - ) - inputNNDataImg = dai.NNData() - inputNNDataImg.addTensor( - "image_prompts", - dummy, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - imagePromptInputQueue.send(inputNNDataImg) + textInputQueue.send(input_NN_data_img) + + dummy = make_dummy_features( + MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision + ) + input_NN_data_img = dai.NNData() + input_NN_data_img.addTensor( + "image_prompts", + dummy, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + imagePromptInputQueue.send(input_NN_data_img) - update_labels(CLASS_NAMES, offset=0) - print(f"Classes set to: {CLASS_NAMES}") + update_labels(class_names, offset=0) def conf_threshold_update_service(new_conf_threshold: float): """Changes confidence threshold based on the user input""" _runtime["conf_threshold"] = max(0.0, min(1.0, float(new_conf_threshold))) nn_with_parser.getParser(0).setConfidenceThreshold(_runtime["conf_threshold"]) - print(f"Confidence threshold set to: {_runtime['conf_threshold']}") def image_upload_service(image_data): image = base64_to_cv2_image(image_data["data"]) - if args.model == "yolo-world": - image_features = extract_image_prompt_embeddings( - image, model_name=args.model, precision=args.precision - ) - print("Image features extracted, sending to model as texts...") - inputNNData = dai.NNData() - inputNNData.addTensor( - "texts", - image_features, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - textInputQueue.send(inputNNData) - filename = image_data["filename"] - CLASS_NAMES = [filename.split(".")[0]] - update_labels(CLASS_NAMES, offset=0) - print(f"Classes set to: {CLASS_NAMES}") - else: # yoloe unified with image_prompts input - image_features = extract_image_prompt_embeddings( - image, model_name="yoloe", precision=args.precision - ) - print("Image features extracted, sending to model as image_prompts...") - inputNNDataImg = dai.NNData() - inputNNDataImg.addTensor( - "image_prompts", - image_features, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - imagePromptInputQueue.send(inputNNDataImg) - # Send dummy texts so only image prompts are considered - dummy = make_dummy_features( - MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision - ) - inputNNDataTxt = dai.NNData() - inputNNDataTxt.addTensor( - "texts", - dummy, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - textInputQueue.send(inputNNDataTxt) - filename = image_data["filename"] - CLASS_NAMES = [filename.split(".")[0]] - update_labels(CLASS_NAMES, offset=80) - print(f"Classes set to (image prompts, offset 80): {CLASS_NAMES}") + image_features = extract_image_prompt_embeddings( + image, model_name="yoloe", precision=args.precision + ) + + input_NN_data_img = dai.NNData() + input_NN_data_img.addTensor( + "image_prompts", + image_features, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + imagePromptInputQueue.send(input_NN_data_img) + + dummy = make_dummy_features( + MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision + ) + inputNNDataTxt = dai.NNData() + inputNNDataTxt.addTensor( + "texts", + dummy, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + textInputQueue.send(inputNNDataTxt) + + filename = image_data["filename"] + class_names = [filename.split(".")[0]] + update_labels(class_names, offset=80) return def bbox_prompt_service(payload): - print("[BBox] Service payload keys:", list(payload.keys())) image = base64_to_cv2_image(payload["data"]) if payload.get("data") else None if image is None: image = frame_cache.get_last_frame() if image is None: - print("[BBox] No image data and no cached frame available") return {"ok": False, "reason": "no_image"} if image is None: - print("[BBox] Decoded image is None") return {"ok": False, "reason": "decode_failed"} bbox = payload.get("bbox", {}) @@ -407,84 +351,57 @@ def bbox_prompt_service(payload): x1 = int(round((bx + bw) * W)); y1 = int(round((by + bh) * H)) x0, x1 = sorted((x0, x1)); y0, y1 = sorted((y0, y1)) - print(f"[BBox] Image size: {W}x{H}, bbox(px): x0={x0}, y0={y0}, x1={x1}, y1={y1}") return {"ok": True} - # ---------- EXTENDED: Snap Collection Service ---------- - # Supports: - # legacy tuple [start:bool, interval:int] - # new dict { timed: {enabled, interval}, noDetections: {enabled}, newDetections: {enabled} } + def snap_collection_service(payload): - base_dt_seconds = 1 # internal polling tick for timed/no-det producer - - # Legacy: tuple/list -> timed only - if isinstance(payload, (list, tuple)) and len(payload) == 2: - start_collection, time_interval = payload - _snap_state["timed_enabled"] = bool(start_collection) - _snap_state["interval"] = max(1, int(time_interval)) - _snap_state["last_sent_s"] = -1.0 - any_active = _snap_state["timed_enabled"] or no_det_gate.enabled - snaps_producer.setRunning(any_active) - if any_active: - snaps_producer.setTimeInterval(base_dt_seconds) - print(f"[SnapService] (legacy) timed={_snap_state['timed_enabled']}, interval={_snap_state['interval']}, noDet={no_det_gate.enabled}") - return {"ok": True} - - # New structured payload - if isinstance(payload, dict): - # timed - if "timed" in payload: - tcfg = payload["timed"] or {} - _snap_state["timed_enabled"] = bool(tcfg.get("enabled", _snap_state["timed_enabled"])) - if "interval" in tcfg: - try: - _snap_state["interval"] = max(1, int(tcfg.get("interval", _snap_state["interval"]))) - except Exception: - pass - _snap_state["last_sent_s"] = -1.0 - - # no detections - if "noDetections" in payload: - ncfg = payload["noDetections"] or {} - no_det_gate.set_enabled(bool(ncfg.get("enabled", no_det_gate.enabled))) - - # new detections (tracker) - if "newDetections" in payload: - n2cfg = payload["newDetections"] or {} - new_on = bool(n2cfg.get("enabled", False)) - - # OFF -> ON => reset memory so current TRACKED items count as "new" - if new_on and not _runtime["newdet_running"]: - reset_new_detections_state() - - snaps_newdet_producer.setRunning(new_on) - _runtime["newdet_running"] = new_on - # React on every message if allowed - try: - snaps_newdet_producer.setTimeInterval(0) - except Exception: - snaps_newdet_producer.setTimeInterval(base_dt_seconds) + base_dt_seconds = 1 - # Effective running for timed/no-det producer - any_active = _snap_state["timed_enabled"] or no_det_gate.enabled - snaps_producer.setRunning(any_active) - if any_active: - snaps_producer.setTimeInterval(base_dt_seconds) + if not isinstance(payload, dict): + return {"ok": False, "reason": "payload_must_be_dict"} - print( - f"[SnapService] timed={_snap_state['timed_enabled']}, " - f"interval={_snap_state['interval']}, noDet={no_det_gate.enabled}, " - f"newDet={_runtime['newdet_running']}" + # timed + tcfg = payload.get("timed") + if isinstance(tcfg, dict): + _snap_state["timed_enabled"] = bool( + tcfg.get("enabled", _snap_state["timed_enabled"]) ) - return {"ok": True} + if "interval" in tcfg: + try: + _snap_state["interval"] = max(1, int(tcfg["interval"])) + except Exception: + pass + _snap_state["last_sent_s"] = -1.0 # restart schedule + + # no detections + ncfg = payload.get("noDetections") + if isinstance(ncfg, dict): + no_det_gate.set_enabled(bool(ncfg.get("enabled", no_det_gate.enabled))) + + # new detections (tracker) + n2cfg = payload.get("newDetections") + if isinstance(n2cfg, dict): + new_on = bool(n2cfg.get("enabled", _runtime["newdet_running"])) + if new_on and not _runtime["newdet_running"]: + reset_new_detections_state() + snaps_newdet_producer.setRunning(new_on) + _runtime["newdet_running"] = new_on + try: + snaps_newdet_producer.setTimeInterval(0) + except Exception: + snaps_newdet_producer.setTimeInterval(base_dt_seconds) + + any_active = _snap_state["timed_enabled"] or no_det_gate.enabled + snaps_producer.setRunning(any_active) + if any_active: + snaps_producer.setTimeInterval(base_dt_seconds) + return {"ok": True} - print("[SnapService] Unsupported payload format") - return {"ok": False, "reason": "bad_payload"} + # Always register these (YOLOE-only app) visualizer.registerService("Class Update Service", class_update_service) visualizer.registerService("Threshold Update Service", conf_threshold_update_service) - if args.model in ("yolo-world", "yoloe"): - visualizer.registerService("Image Upload Service", image_upload_service) + visualizer.registerService("Image Upload Service", image_upload_service) visualizer.registerService("BBox Prompt Service", bbox_prompt_service) visualizer.registerService("Snap Collection Service", snap_collection_service) @@ -506,18 +423,18 @@ def snap_collection_service(payload): ), ) textInputQueue.send(inputNNData) - if args.model == "yoloe": - inputNNDataImg = dai.NNData() - inputNNDataImg.addTensor( - "image_prompts", - image_prompt_features, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - imagePromptInputQueue.send(inputNNDataImg) + + inputNNDataImg = dai.NNData() + inputNNDataImg.addTensor( + "image_prompts", + image_prompt_features, + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + imagePromptInputQueue.send(inputNNDataImg) print("Press 'q' to stop") diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils.py deleted file mode 100644 index 85d3bddf0..000000000 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils.py +++ /dev/null @@ -1,213 +0,0 @@ -from typing import List, Optional -import time -import depthai as dai - -from depthai_nodes.node import SnapsProducer -from depthai_nodes import ImgDetectionsExtended - - -class NoDetectionsGate: - """ - Minimal state machine: - - enabled: externally toggled - - in_run: True while we are in a zero-detection streak - """ - - def __init__(self) -> None: - self.enabled: bool = False - self.in_run: bool = False - - def set_enabled(self, enabled: bool) -> None: - self.enabled = bool(enabled) - self.in_run = False # reset streak whenever toggled - - def on_frame(self, num_detections: int) -> bool: - """ - Returns True exactly on the first zero-detection frame of a streak (when enabled). - Resets when detections resume (>0). - """ - if not self.enabled: - self.in_run = False - return False - if num_detections == 0: - if not self.in_run: - self.in_run = True - return True - return False - # detections present -> exit streak - if self.in_run: - self.in_run = False - return False - - -def _build_extras( - model: str, - det_data: dai.ImgDetections | ImgDetectionsExtended, - class_names: List[str], -): - detections = det_data.detections or [] - dets_labels = [det.label for det in detections] - dets_confs = [det.confidence for det in detections] - - extras = { - "model": model, - "detection_label": str(dets_labels), - "detection_confidence": str(dets_confs), - } - - if isinstance(det_data, dai.ImgDetections): - dets_labels_str = [class_names[det.label] for det in detections] if detections else [] - dets_xyxy = [(det.xmin, det.ymin, det.xmax, det.ymax) for det in detections] - extras["detection_xyxy"] = str(dets_xyxy) - extras["detection_label_str"] = str(dets_labels_str) - elif isinstance(det_data, ImgDetectionsExtended): - dets_labels_str = [det.label_name for det in detections] - dets_cxcywh = [ - ( - det.rotated_rect.center.x, - det.rotated_rect.center.y, - det.rotated_rect.size.width, - det.rotated_rect.size.height, - ) - for det in detections - ] - extras["detection_cxcywh"] = str(dets_cxcywh) - extras["detection_label_str"] = str(dets_labels_str) - else: - raise NotImplementedError - - return extras - - -def custom_snap_process( - producer: SnapsProducer, - frame: dai.ImgFrame, - det_data: dai.ImgDetections | ImgDetectionsExtended, - class_names: List[str], - model: str, - no_det_gate: Optional[NoDetectionsGate] = None, - timed_state: Optional[dict] = None, -): - """ - Called frequently by SnapsProducer; we decide what to send and with which tags. - - TIMED snaps: respect user interval using timed_state["interval"] and ["last_sent_s"]. - Always tagged "Timing_snap", regardless of detections. - - NO-DETECTIONS snaps: fire once at the start of a zero-detection streak, independent - of timing interval. Tagged "no_detections". - """ - detections = det_data.detections or [] - num = len(detections) - - # Current time from frame (seconds) - now_s = None - if timed_state is not None: - try: - ts = frame.getTimestamp() - now_s = float(ts.total_seconds()) - except Exception: - now_s = None - - # 1) TIMED (priority path) - if timed_state and timed_state.get("timed_enabled", False): - interval = float(timed_state.get("interval", 0)) or 0.0 - last_sent = float(timed_state.get("last_sent_s", -1.0)) - due = (now_s is not None) and (interval > 0.0) and ((last_sent < 0) or (now_s - last_sent >= interval)) - if due: - file_group = [dai.FileData(frame, "rgb")] - extras = _build_extras(model, det_data, class_names) - if producer.sendSnap(name="rgb", file_group=file_group, tags=["Timing_snap"], extras=extras): - timed_state["last_sent_s"] = now_s - if no_det_gate: - no_det_gate.on_frame(num) # keep gate in sync - print("[Timed] Snap sent!") - return # don't also send no-det on the same tick - - # 2) NO-DETECTIONS one-shot at streak start (independent cadence) - if num == 0: - if no_det_gate and no_det_gate.on_frame(0): - file_group = [dai.FileData(frame, "rgb")] - extras = {"model": model, "reason": "no_detections_start"} - if producer.sendSnap(name="rgb", file_group=file_group, tags=["no_detections"], extras=extras): - print("[NoDet] Snap sent (start of empty streak)") - return - - # 3) detections present -> keep gate in sync - if no_det_gate: - no_det_gate.on_frame(num) - return - - -# ------------------ New-detection snap (TRACKED gating + cooldown) ------------------ - -class _NewDetectionsState: - """Plain dicts + a set (no defaultdict, no nonlocal).""" - - def __init__(self) -> None: - self.seen_ids: set[int] = set() - self.last_label_ts: dict[str, float] = {} - - -_NEWDET_STATE = _NewDetectionsState() - - -def reset_new_detections_state() -> None: - """Clear memory so currently present TRACKED objects will be treated as 'new'.""" - _NEWDET_STATE.seen_ids.clear() - _NEWDET_STATE.last_label_ts.clear() - print("[NewDet] State reset") - - -def tracklet_new_detection_process( - producer: SnapsProducer, - frame: dai.ImgFrame, - tracklets_msg: dai.Tracklets, - class_names: List[str], - model: str, - min_label_cooldown_s: float = 2.0, -): - """ - Send a snap when a tracklet becomes TRACKED for the first time (ID not seen). - - Waits for TRACKED (stable after birth threshold) to reduce flicker re-IDs. - - One snap per unique track ID. - - Per-label cooldown to avoid bursts (uses classic dict .get()). - """ - tks = getattr(tracklets_msg, "tracklets", []) or [] - now = time.time() - - for t in tks: - # Robustly read status (enum or int) -> TRACKED? - status_val = getattr(t, "status", None) - try: - is_tracked = (status_val == dai.Tracklet.TrackingStatus.TRACKED) - except Exception: - is_tracked = int(status_val) == 1 - if not is_tracked: - continue - - try: - tid = int(getattr(t, "id", -1)) - except Exception: - continue - if tid < 0 or tid in _NEWDET_STATE.seen_ids: - continue - - label_idx = int(getattr(t, "label", -1)) - label_str = class_names[label_idx] if 0 <= label_idx < len(class_names) else "" - last_ts = _NEWDET_STATE.last_label_ts.get(label_str, 0.0) - if label_str and (now - last_ts) < float(min_label_cooldown_s): - continue - - file_group = [dai.FileData(frame, "rgb")] - extras = { - "model": model, - "tracker_id": str(tid), - "label_idx": str(label_idx), - "label_str": label_str, - "status": "TRACKED", - } - if producer.sendSnap(name="rgb", file_group=file_group, tags=["new_detection"], extras=extras): - _NEWDET_STATE.seen_ids.add(tid) - if label_str: - _NEWDET_STATE.last_label_ts[label_str] = now - - return diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/__init__.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/__init__.py new file mode 100644 index 000000000..b0c9a44f6 --- /dev/null +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/__init__.py @@ -0,0 +1,3 @@ +from .no_detections_gate import NoDetectionsGate +from .timed_no_det import custom_snap_process +from .new_detection import tracklet_new_detection_process, reset_new_detections_state diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/extras.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/extras.py new file mode 100644 index 000000000..dde02ddd1 --- /dev/null +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/extras.py @@ -0,0 +1,57 @@ +from __future__ import annotations +from typing import List +import depthai as dai +from depthai_nodes import ImgDetectionsExtended +from .helpers import _sanitize_extras + + +def build_extras( + model: str, + det_data: dai.ImgDetections | ImgDetectionsExtended, + class_names: List[str], +) -> dict[str, str]: + dets = getattr(det_data, "detections", None) or [] + labels = [getattr(d, "label", -1) for d in dets[:20]] + confs = [getattr(d, "confidence", 0.0) for d in dets[:20]] + + extras: dict = { + "model": model, + "detection_count": str(len(dets)), + "detection_label": ",".join(map(str, labels)), + "detection_conf": ",".join(f"{c:.2f}" for c in confs), + } + + if isinstance(det_data, dai.ImgDetections): + xyxy = [ + (getattr(d, "xmin", 0.0), getattr(d, "ymin", 0.0), + getattr(d, "xmax", 0.0), getattr(d, "ymax", 0.0)) + for d in dets[:10] + ] + extras["detection_xyxy"] = ";".join(f"{x1:.3f},{y1:.3f},{x2:.3f},{y2:.3f}" for x1, y1, x2, y2 in xyxy) + names = [] + if dets: + for d in dets[:20]: + li = getattr(d, "label", -1) + names.append(class_names[li] if 0 <= li < len(class_names) else "") + extras["detection_label_str"] = ",".join(names) if names else "" + elif isinstance(det_data, ImgDetectionsExtended): + cxcywh = [] + for d in dets[:10]: + try: + c = d.rotated_rect.center + s = d.rotated_rect.size + cxcywh.append((c.x, c.y, s.width, s.height)) + except Exception: + pass + extras["detection_cxcywh"] = ";".join(f"{cx:.3f},{cy:.3f},{w:.3f},{h:.3f}" for cx, cy, w, h in cxcywh) + names = [] + for d in dets[:20]: + try: + names.append(str(d.label_name)) + except Exception: + names.append("") + extras["detection_label_str"] = ",".join(names) + else: + extras["dtype"] = type(det_data).__name__ + + return _sanitize_extras(extras) diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py new file mode 100644 index 000000000..98b3fcda7 --- /dev/null +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py @@ -0,0 +1,59 @@ +from __future__ import annotations +from typing import List, Optional, Tuple +import depthai as dai + + +def _frame_ts_seconds(frame: dai.ImgFrame) -> Optional[float]: + try: + return float(frame.getTimestamp().total_seconds()) + except Exception: + return None + + +def _status_is_tracked(tracklet) -> bool: + val = getattr(tracklet, "status", None) + try: + return val == dai.Tracklet.TrackingStatus.TRACKED + except Exception: + try: + return int(val) == 1 + except Exception: + return False + + +def _track_id(tracklet) -> Optional[int]: + try: + tid = int(getattr(tracklet, "id", -1)) + return tid if tid >= 0 else None + except Exception: + return None + + +def _label_idx_name(tracklet, class_names: List[str]) -> Tuple[int, str]: + try: + idx = int(getattr(tracklet, "label", -1)) + except Exception: + idx = -1 + name = class_names[idx] if 0 <= idx < len(class_names) else "" + return idx, name + + +def _sanitize_extras(extras: dict, max_key: int = 40, max_val: int = 100) -> dict[str, str]: + out: dict[str, str] = {} + for k, v in (extras or {}).items(): + ks = str(k)[:max_key] or "k" + vs = str(v) + if len(vs) > max_val: + vs = vs[: max_val - 3] + "..." + out[ks] = vs + return out + + +def _send_snap(producer, frame: dai.ImgFrame, tags: List[str], extras: dict) -> bool: + """ + Use positional-only call. If the wrapper signature differs on your build, + fall back to EventsManager. + """ + fg = [dai.FileData(frame, "rgb")] + ex = _sanitize_extras(extras) + return producer.sendSnap("rgb", fg, tags, ex) diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/new_detection.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/new_detection.py new file mode 100644 index 000000000..236148a9c --- /dev/null +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/new_detection.py @@ -0,0 +1,77 @@ +from __future__ import annotations +from typing import List +import time +import depthai as dai +from depthai_nodes.node import SnapsProducer + +from .helpers import ( + _frame_ts_seconds, + _status_is_tracked, + _track_id, + _label_idx_name, + _send_snap, +) + + +class _NewDetectionsState: + def __init__(self) -> None: + self.seen_ids: set[int] = set() + self.last_label_ts: dict[str, float] = {} + + +_NEWDET_STATE = _NewDetectionsState() + + +def reset_new_detections_state() -> None: + _NEWDET_STATE.seen_ids.clear() + _NEWDET_STATE.last_label_ts.clear() + print("[NewDet] State reset") + + +def tracklet_new_detection_process( + producer: SnapsProducer, + frame: dai.ImgFrame, + tracklets_msg: dai.Tracklets, + class_names: List[str], + model: str, + min_label_cooldown_s: float = 2.0, +): + """ + Send a snap when a tracklet becomes TRACKED for the first time (ID not seen). + One snap per unique track ID; per-label cooldown to avoid bursts. + """ + tks = getattr(tracklets_msg, "tracklets", []) or [] + now_s = _frame_ts_seconds(frame) or time.time() + + for t in tks: + if not _status_is_tracked(t): + continue + + tid = _track_id(t) + if tid is None or tid in _NEWDET_STATE.seen_ids: + continue + + label_idx, label_str = _label_idx_name(t, class_names) + last_ts = _NEWDET_STATE.last_label_ts.get(label_str, -1.0) + if label_str and last_ts >= 0 and (now_s - last_ts) < float(min_label_cooldown_s): + continue + + extras = { + "model": model, + "reason": "new_detection", + "track_id": str(int(tid)), + "label_idx": str(int(label_idx)), + "label": label_str, + "ts_s": f"{now_s:.3f}", + } + try: + tl = t.roi.topLeft(); br = t.roi.bottomRight() + extras["roi_xyxy_norm"] = f"{tl.x:.3f},{tl.y:.3f},{br.x:.3f},{br.y:.3f}" + except Exception: + pass + + if _send_snap(producer, frame, ["new_detection"], extras): + _NEWDET_STATE.seen_ids.add(tid) + if label_str: + _NEWDET_STATE.last_label_ts[label_str] = now_s + print(f"[NewDet] Snap sent: id={tid}, label='{label_str}'") diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/no_detections_gate.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/no_detections_gate.py new file mode 100644 index 000000000..620fc05db --- /dev/null +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/no_detections_gate.py @@ -0,0 +1,24 @@ +from __future__ import annotations + + +class NoDetectionsGate: + def __init__(self) -> None: + self.enabled: bool = False + self.in_run: bool = False + + def set_enabled(self, enabled: bool) -> None: + self.enabled = bool(enabled) + self.in_run = False + + def on_frame(self, num_detections: int) -> bool: + if not self.enabled: + self.in_run = False + return False + if num_detections == 0: + if not self.in_run: + self.in_run = True + return True + return False + if self.in_run: + self.in_run = False + return False diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/timed_no_det.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/timed_no_det.py new file mode 100644 index 000000000..548772f52 --- /dev/null +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/timed_no_det.py @@ -0,0 +1,45 @@ +from __future__ import annotations +from typing import Optional, List +import depthai as dai +from depthai_nodes.node import SnapsProducer + +from .helpers import _frame_ts_seconds, _send_snap +from .extras import build_extras +from .no_detections_gate import NoDetectionsGate + + +def custom_snap_process( + producer: SnapsProducer, + frame: dai.ImgFrame, + det_data: dai.ImgDetections | dai.ImgDetections, + class_names: List[str], + model: str, + no_det_gate: Optional[NoDetectionsGate] = None, + timed_state: Optional[dict] = None, +): + dets = getattr(det_data, "detections", None) or [] + num = len(dets) + + now_s = _frame_ts_seconds(frame) if timed_state is not None else None + if timed_state and timed_state.get("timed_enabled", False): + interval = float(timed_state.get("interval", 0.0)) or 0.0 + last = float(timed_state.get("last_sent_s", -1.0)) + due = (now_s is not None) and (interval > 0.0) and ((last < 0.0) or (now_s - last >= interval)) + if due: + extras = build_extras(model, det_data, class_names) + if _send_snap(producer, frame, ["Timing_snap"], extras): + timed_state["last_sent_s"] = now_s + if no_det_gate: + no_det_gate.on_frame(num) + print("[Timed] Snap sent!") + return + + if num == 0: + if no_det_gate and no_det_gate.on_frame(0): + extras = {"model": model, "reason": "no_detections_start"} + if _send_snap(producer, frame, ["no_detections"], extras): + print("[NoDet] Snap sent (start of empty streak)") + return + + if no_det_gate: + no_det_gate.on_frame(num) From 2fd8b82affa85138c0c5df30acff0d798a19a808 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Tue, 21 Oct 2025 16:37:21 +0200 Subject: [PATCH 07/39] Added detections sending with the snaps --- performant_PoCs/hub_snaps/backend/src/main.py | 13 ++++++++----- .../hub_snaps/backend/src/requirements.txt | 4 ++-- .../backend/src/utils/snap_utils/helpers.py | 7 ++++--- .../backend/src/utils/snap_utils/new_detection.py | 3 ++- .../backend/src/utils/snap_utils/timed_no_det.py | 7 +++---- 5 files changed, 19 insertions(+), 15 deletions(-) diff --git a/performant_PoCs/hub_snaps/backend/src/main.py b/performant_PoCs/hub_snaps/backend/src/main.py index dd4cca394..6b7266851 100644 --- a/performant_PoCs/hub_snaps/backend/src/main.py +++ b/performant_PoCs/hub_snaps/backend/src/main.py @@ -4,6 +4,7 @@ import os from functools import partial +os.environ["DEPTHAI_LEVEL"] = "info" import depthai as dai from depthai_nodes.node import ( ParsingNeuralNetwork, @@ -11,6 +12,7 @@ ImgFrameOverlay, ApplyColormap, SnapsProducer, + SnapsProducer2Buffered, ImgDetectionsBridge, ) @@ -172,7 +174,7 @@ def make_dummy_features(max_num_classes: int, model_name: str, precision: str): snaps_producer = pipeline.create(SnapsProducer).build( frame=video_src_out, - msg=det_process_filter.out, + msg=filtered_bridge.out, running=False, process_fn=partial( custom_snap_process, @@ -182,7 +184,7 @@ def make_dummy_features(max_num_classes: int, model_name: str, precision: str): timed_state=_snap_state, ), ) - snaps_producer._em.setSourceAppId(os.getenv("OAKAGENT_CONTAINER_ID")) + #snaps_producer._em.setSourceAppId(os.getenv("OAKAGENT_CONTAINER_ID")) object_tracker = pipeline.create(dai.node.ObjectTracker) @@ -198,9 +200,10 @@ def make_dummy_features(max_num_classes: int, model_name: str, precision: str): input_node.link(object_tracker.inputDetectionFrame) filtered_bridge.out.link(object_tracker.inputDetections) - snaps_newdet_producer = pipeline.create(SnapsProducer).build( + snaps_newdet_producer = pipeline.create(SnapsProducer2Buffered).build( frame=video_src_out, - msg=object_tracker.out, + msg=filtered_bridge.out, + msg2=object_tracker.out, running=False, process_fn=partial( tracklet_new_detection_process, @@ -209,7 +212,7 @@ def make_dummy_features(max_num_classes: int, model_name: str, precision: str): ), ) - snaps_newdet_producer._em.setSourceAppId(os.getenv("OAKAGENT_CONTAINER_ID")) + #snaps_newdet_producer._em.setSourceAppId(os.getenv("OAKAGENT_CONTAINER_ID")) def update_labels(label_names: list[str], offset: int = 0): det_process_filter.setLabels( diff --git a/performant_PoCs/hub_snaps/backend/src/requirements.txt b/performant_PoCs/hub_snaps/backend/src/requirements.txt index b9912470b..33d2db652 100644 --- a/performant_PoCs/hub_snaps/backend/src/requirements.txt +++ b/performant_PoCs/hub_snaps/backend/src/requirements.txt @@ -1,9 +1,9 @@ # depthai==3.0.0 --extra-index-url https://artifacts.luxonis.com/artifactory/luxonis-python-snapshot-local --pre -depthai==3.0.0.dev0+3aa210a2d54c32e0f75f8ac6b4a81f80b6230f2f +depthai==3.0.0.dev0+d66489c671da0aee5713135222d0c628eec01d4b #depthai-nodes==0.3.4 -depthai-nodes @ git+https://github.com/luxonis/depthai-nodes.git@feat/dai_dev_req +depthai-nodes @ git+https://github.com/luxonis/depthai-nodes.git@feat/dai_dev_req_TEST_GROUP opencv-python-headless~=4.10.0 numpy>=1.22 tokenizers~=0.21.0 diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py index 98b3fcda7..f1be97b99 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py @@ -1,6 +1,7 @@ from __future__ import annotations from typing import List, Optional, Tuple import depthai as dai +from depthai_nodes.node import SnapsProducer def _frame_ts_seconds(frame: dai.ImgFrame) -> Optional[float]: @@ -49,11 +50,11 @@ def _sanitize_extras(extras: dict, max_key: int = 40, max_val: int = 100) -> dic return out -def _send_snap(producer, frame: dai.ImgFrame, tags: List[str], extras: dict) -> bool: +def _send_snap(name: str, producer: SnapsProducer, frame: dai.ImgFrame, tags: List[str], extras: dict, + detections: dai.ImgDetections = None) -> bool: """ Use positional-only call. If the wrapper signature differs on your build, fall back to EventsManager. """ - fg = [dai.FileData(frame, "rgb")] ex = _sanitize_extras(extras) - return producer.sendSnap("rgb", fg, tags, ex) + return producer.sendSnap(name, frame, detections, tags, ex) diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/new_detection.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/new_detection.py index 236148a9c..dd1cad621 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/new_detection.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/new_detection.py @@ -32,6 +32,7 @@ def tracklet_new_detection_process( producer: SnapsProducer, frame: dai.ImgFrame, tracklets_msg: dai.Tracklets, + det_data: dai.ImgDetections, class_names: List[str], model: str, min_label_cooldown_s: float = 2.0, @@ -70,7 +71,7 @@ def tracklet_new_detection_process( except Exception: pass - if _send_snap(producer, frame, ["new_detection"], extras): + if _send_snap("New Detection", producer, frame, ["new_detection"], extras, det_data): _NEWDET_STATE.seen_ids.add(tid) if label_str: _NEWDET_STATE.last_label_ts[label_str] = now_s diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/timed_no_det.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/timed_no_det.py index 548772f52..fd67bdbc0 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/timed_no_det.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/timed_no_det.py @@ -11,7 +11,7 @@ def custom_snap_process( producer: SnapsProducer, frame: dai.ImgFrame, - det_data: dai.ImgDetections | dai.ImgDetections, + det_data: dai.ImgDetections, class_names: List[str], model: str, no_det_gate: Optional[NoDetectionsGate] = None, @@ -19,7 +19,6 @@ def custom_snap_process( ): dets = getattr(det_data, "detections", None) or [] num = len(dets) - now_s = _frame_ts_seconds(frame) if timed_state is not None else None if timed_state and timed_state.get("timed_enabled", False): interval = float(timed_state.get("interval", 0.0)) or 0.0 @@ -27,7 +26,7 @@ def custom_snap_process( due = (now_s is not None) and (interval > 0.0) and ((last < 0.0) or (now_s - last >= interval)) if due: extras = build_extras(model, det_data, class_names) - if _send_snap(producer, frame, ["Timing_snap"], extras): + if _send_snap("Timing Snap", producer, frame, ["timing_snap"], extras, det_data): timed_state["last_sent_s"] = now_s if no_det_gate: no_det_gate.on_frame(num) @@ -37,7 +36,7 @@ def custom_snap_process( if num == 0: if no_det_gate and no_det_gate.on_frame(0): extras = {"model": model, "reason": "no_detections_start"} - if _send_snap(producer, frame, ["no_detections"], extras): + if _send_snap("No Detections", producer, frame, ["no_detections"], extras): print("[NoDet] Snap sent (start of empty streak)") return From 239547b0860e68a43d61abd56607970a657b692e Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Tue, 21 Oct 2025 18:05:43 +0200 Subject: [PATCH 08/39] Moved to up to date version of snaps V2 and added detections to snaps --- performant_PoCs/hub_snaps/backend/src/main.py | 4 ++-- .../hub_snaps/backend/src/utils/snap_utils/helpers.py | 4 ---- 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/performant_PoCs/hub_snaps/backend/src/main.py b/performant_PoCs/hub_snaps/backend/src/main.py index 6b7266851..afdda7f21 100644 --- a/performant_PoCs/hub_snaps/backend/src/main.py +++ b/performant_PoCs/hub_snaps/backend/src/main.py @@ -202,8 +202,8 @@ def make_dummy_features(max_num_classes: int, model_name: str, precision: str): snaps_newdet_producer = pipeline.create(SnapsProducer2Buffered).build( frame=video_src_out, - msg=filtered_bridge.out, - msg2=object_tracker.out, + msg=object_tracker.out, + msg2=filtered_bridge.out, running=False, process_fn=partial( tracklet_new_detection_process, diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py index f1be97b99..ea344416d 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py @@ -52,9 +52,5 @@ def _sanitize_extras(extras: dict, max_key: int = 40, max_val: int = 100) -> dic def _send_snap(name: str, producer: SnapsProducer, frame: dai.ImgFrame, tags: List[str], extras: dict, detections: dai.ImgDetections = None) -> bool: - """ - Use positional-only call. If the wrapper signature differs on your build, - fall back to EventsManager. - """ ex = _sanitize_extras(extras) return producer.sendSnap(name, frame, detections, tags, ex) From 08af653e0bb78f737819d3a5b3c9763e6dafa684 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Tue, 21 Oct 2025 18:16:34 +0200 Subject: [PATCH 09/39] Cleanup --- .../backend/src/utils/snap_utils/extras.py | 3 +-- .../backend/src/utils/snap_utils/helpers.py | 14 +------------- 2 files changed, 2 insertions(+), 15 deletions(-) diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/extras.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/extras.py index dde02ddd1..c701d89ea 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/extras.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/extras.py @@ -2,7 +2,6 @@ from typing import List import depthai as dai from depthai_nodes import ImgDetectionsExtended -from .helpers import _sanitize_extras def build_extras( @@ -54,4 +53,4 @@ def build_extras( else: extras["dtype"] = type(det_data).__name__ - return _sanitize_extras(extras) + return extras diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py index ea344416d..299416b8b 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py @@ -39,18 +39,6 @@ def _label_idx_name(tracklet, class_names: List[str]) -> Tuple[int, str]: return idx, name -def _sanitize_extras(extras: dict, max_key: int = 40, max_val: int = 100) -> dict[str, str]: - out: dict[str, str] = {} - for k, v in (extras or {}).items(): - ks = str(k)[:max_key] or "k" - vs = str(v) - if len(vs) > max_val: - vs = vs[: max_val - 3] + "..." - out[ks] = vs - return out - - def _send_snap(name: str, producer: SnapsProducer, frame: dai.ImgFrame, tags: List[str], extras: dict, detections: dai.ImgDetections = None) -> bool: - ex = _sanitize_extras(extras) - return producer.sendSnap(name, frame, detections, tags, ex) + return producer.sendSnap(name, frame, detections, tags, extras) From 5aa202e1ca382eae2716c441ef8665a5ec2c96dc Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Tue, 21 Oct 2025 18:56:21 +0200 Subject: [PATCH 10/39] Fixed labels issue in FE view --- performant_PoCs/hub_snaps/backend/src/main.py | 50 +++++++++---------- 1 file changed, 23 insertions(+), 27 deletions(-) diff --git a/performant_PoCs/hub_snaps/backend/src/main.py b/performant_PoCs/hub_snaps/backend/src/main.py index afdda7f21..89b4da56a 100644 --- a/performant_PoCs/hub_snaps/backend/src/main.py +++ b/performant_PoCs/hub_snaps/backend/src/main.py @@ -245,45 +245,41 @@ def update_labels(label_names: list[str], offset: int = 0): visualizer.addTopic("Detections", annotation_node.out) def class_update_service(new_classes: list[str]): - """Changes classes to detect based on the user input (YOLOE-only)""" - if len(new_classes) == 0: + if not new_classes: print("List of new classes empty, skipping.") return if len(new_classes) > MAX_NUM_CLASSES: - print( - f"Number of new classes ({len(new_classes)}) exceeds maximum number of classes ({MAX_NUM_CLASSES}), skipping." - ) + print(f"Too many classes ({len(new_classes)}) > {MAX_NUM_CLASSES}, skipping.") return - class_names = new_classes - input_NN_data_img = dai.NNData() - input_NN_data_img.addTensor( + feats = extract_text_embeddings( + class_names=new_classes, + max_num_classes=MAX_NUM_CLASSES, + model_name="yoloe", + precision=args.precision, + ) + nn_txt = dai.NNData() + nn_txt.addTensor( "texts", - text_features, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), + feats, + dataType=(dai.TensorInfo.DataType.FP16 if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F), ) - textInputQueue.send(input_NN_data_img) + textInputQueue.send(nn_txt) - dummy = make_dummy_features( - MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision - ) - input_NN_data_img = dai.NNData() - input_NN_data_img.addTensor( + dummy = make_dummy_features(MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision) + nn_img = dai.NNData() + nn_img.addTensor( "image_prompts", dummy, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), + dataType=(dai.TensorInfo.DataType.FP16 if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F), ) - imagePromptInputQueue.send(input_NN_data_img) + imagePromptInputQueue.send(nn_img) + + update_labels(new_classes, offset=0) - update_labels(class_names, offset=0) + print(f"Classes updated (YOLOE text): {new_classes}") def conf_threshold_update_service(new_conf_threshold: float): """Changes confidence threshold based on the user input""" From 21c57f8bf1a70ac2fd840f55cc704cafaf884b50 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Sun, 26 Oct 2025 12:07:31 +0100 Subject: [PATCH 11/39] Enhanced FE and changed seconds to minutes --- performant_PoCs/hub_snaps/backend/src/main.py | 165 +++-- .../backend/src/utils/snap_utils/__init__.py | 3 +- .../src/utils/snap_utils/conditions_gate.py | 58 ++ .../backend/src/utils/snap_utils/helpers.py | 47 ++ .../src/utils/snap_utils/new_detection.py | 78 --- .../utils/snap_utils/no_detections_gate.py | 24 - .../src/utils/snap_utils/timed_no_det.py | 109 ++- .../hub_snaps/frontend/src/App.tsx | 653 +++++++++--------- .../frontend/src/ConfidenceSlider.tsx | 2 +- .../frontend/src/SnapCollectionButton.tsx | 528 +++++++++++--- 10 files changed, 1045 insertions(+), 622 deletions(-) create mode 100644 performant_PoCs/hub_snaps/backend/src/utils/snap_utils/conditions_gate.py delete mode 100644 performant_PoCs/hub_snaps/backend/src/utils/snap_utils/new_detection.py delete mode 100644 performant_PoCs/hub_snaps/backend/src/utils/snap_utils/no_detections_gate.py diff --git a/performant_PoCs/hub_snaps/backend/src/main.py b/performant_PoCs/hub_snaps/backend/src/main.py index 89b4da56a..ac4ad9300 100644 --- a/performant_PoCs/hub_snaps/backend/src/main.py +++ b/performant_PoCs/hub_snaps/backend/src/main.py @@ -11,7 +11,6 @@ ImgDetectionsFilter, ImgFrameOverlay, ApplyColormap, - SnapsProducer, SnapsProducer2Buffered, ImgDetectionsBridge, ) @@ -27,9 +26,7 @@ from utils.frame_cache_node import FrameCacheNode from utils.snap_utils import ( custom_snap_process, - NoDetectionsGate, - tracklet_new_detection_process, - reset_new_detections_state, + ConditionsGate, ) load_dotenv(override=True) @@ -164,35 +161,31 @@ def make_dummy_features(max_num_classes: int, model_name: str, precision: str): # Cache last frame for services that need full frame content frame_cache = pipeline.create(FrameCacheNode).build(video_src_out) - # --- state for triggers (timed + no-detections) and runtime flags (no nonlocal) - no_det_gate = NoDetectionsGate() - _snap_state = {"timed_enabled": False, "interval": 60, "last_sent_s": -1.0} + cond_gate = ConditionsGate(default_cooldown_s=0.0, enabled=True) + cond_gate.set_key_enabled("no_detections", False) + cond_gate.set_cooldown("no_detections", 15.0) + + cond_gate.set_key_enabled("timed", False) + cond_gate.set_cooldown("timed", 15.0) + + cond_gate.set_key_enabled("low_conf", False) + cond_gate.set_cooldown("low_conf", 15.0) + + cond_gate.set_key_enabled("lost_mid", False) + cond_gate.set_cooldown("lost_mid", 15.0) + _runtime = { "newdet_running": False, - "conf_threshold": CONFIDENCE_THRESHOLD, # optional live copy + "conf_threshold": CONFIDENCE_THRESHOLD, + "lost_mid_margin": 0.20, } - - snaps_producer = pipeline.create(SnapsProducer).build( - frame=video_src_out, - msg=filtered_bridge.out, - running=False, - process_fn=partial( - custom_snap_process, - class_names=CLASS_NAMES, - model="yoloe", - no_det_gate=no_det_gate, - timed_state=_snap_state, - ), - ) - #snaps_producer._em.setSourceAppId(os.getenv("OAKAGENT_CONTAINER_ID")) - object_tracker = pipeline.create(dai.node.ObjectTracker) object_tracker.setTrackerType(dai.TrackerType.SHORT_TERM_IMAGELESS) object_tracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.UNIQUE_ID) object_tracker.setTrackingPerClass(True) - object_tracker.setTrackletBirthThreshold(1) - object_tracker.setTrackletMaxLifespan(180) + object_tracker.setTrackletBirthThreshold(3) + object_tracker.setTrackletMaxLifespan(90) object_tracker.setOcclusionRatioThreshold(0.5) object_tracker.setTrackerThreshold(0.25) @@ -200,20 +193,20 @@ def make_dummy_features(max_num_classes: int, model_name: str, precision: str): input_node.link(object_tracker.inputDetectionFrame) filtered_bridge.out.link(object_tracker.inputDetections) - snaps_newdet_producer = pipeline.create(SnapsProducer2Buffered).build( + snaps_producer = pipeline.create(SnapsProducer2Buffered).build( frame=video_src_out, msg=object_tracker.out, msg2=filtered_bridge.out, running=False, process_fn=partial( - tracklet_new_detection_process, + custom_snap_process, class_names=CLASS_NAMES, model="yoloe", + cond_gate=cond_gate, + runtime=_runtime, ), ) - #snaps_newdet_producer._em.setSourceAppId(os.getenv("OAKAGENT_CONTAINER_ID")) - def update_labels(label_names: list[str], offset: int = 0): det_process_filter.setLabels( labels=[i for i in range(offset, offset + len(label_names))], keep=True @@ -343,13 +336,18 @@ def bbox_prompt_service(payload): H, W = image.shape[:2] is_pixel = payload.get("bboxType", "normalized") == "pixel" if is_pixel: - x0 = int(round(bx)); y0 = int(round(by)) - x1 = int(round(bx + bw)); y1 = int(round(by + bh)) + x0 = int(round(bx)) + y0 = int(round(by)) + x1 = int(round(bx + bw)) + y1 = int(round(by + bh)) else: - x0 = int(round(bx * W)); y0 = int(round(by * H)) - x1 = int(round((bx + bw) * W)); y1 = int(round((by + bh) * H)) + x0 = int(round(bx * W)) + y0 = int(round(by * H)) + x1 = int(round((bx + bw) * W)) + y1 = int(round((by + bh) * H)) - x0, x1 = sorted((x0, x1)); y0, y1 = sorted((y0, y1)) + x0, x1 = sorted((x0, x1)) + y0, y1 = sorted((y0, y1)) return {"ok": True} @@ -362,42 +360,95 @@ def snap_collection_service(payload): # timed tcfg = payload.get("timed") if isinstance(tcfg, dict): - _snap_state["timed_enabled"] = bool( - tcfg.get("enabled", _snap_state["timed_enabled"]) - ) + if "enabled" in tcfg: + cond_gate.set_key_enabled("timed", bool(tcfg["enabled"])) if "interval" in tcfg: try: - _snap_state["interval"] = max(1, int(tcfg["interval"])) + cond_gate.set_cooldown("timed", float(tcfg["interval"])) except Exception: pass - _snap_state["last_sent_s"] = -1.0 # restart schedule + if tcfg.get("reset"): + cond_gate.reset(["timed"]) # no detections ncfg = payload.get("noDetections") if isinstance(ncfg, dict): - no_det_gate.set_enabled(bool(ncfg.get("enabled", no_det_gate.enabled))) - - # new detections (tracker) - n2cfg = payload.get("newDetections") - if isinstance(n2cfg, dict): - new_on = bool(n2cfg.get("enabled", _runtime["newdet_running"])) - if new_on and not _runtime["newdet_running"]: - reset_new_detections_state() - snaps_newdet_producer.setRunning(new_on) - _runtime["newdet_running"] = new_on - try: - snaps_newdet_producer.setTimeInterval(0) - except Exception: - snaps_newdet_producer.setTimeInterval(base_dt_seconds) - - any_active = _snap_state["timed_enabled"] or no_det_gate.enabled + if "enabled" in ncfg: + cond_gate.set_key_enabled("no_detections", bool(ncfg["enabled"])) + if "cooldown" in ncfg: + try: + cond_gate.set_cooldown("no_detections", float(ncfg["cooldown"])) + except Exception: + pass + if ncfg.get("reset"): + cond_gate.reset(["no_detections"]) + + # low confidence + lcfg = payload.get("lowConfidence") + if isinstance(lcfg, dict): + enable = bool(lcfg.get("enabled", False)) + thr_raw = lcfg.get("threshold", None) + + if enable: + + if thr_raw is None: + return {"ok": False, "reason": "low_conf_threshold_required"} + + try: + thr = float(thr_raw) + if thr > 1.0: + thr = thr / 100.0 + if not (0.0 <= thr <= 1.0): + raise ValueError + except Exception: + return {"ok": False, "reason": "invalid_low_conf_threshold"} + + _runtime["low_conf_thresh"] = thr + cond_gate.set_key_enabled("low_conf", True) + else: + cond_gate.set_key_enabled("low_conf", False) + _runtime.pop("low_conf_thresh", None) + + if "cooldown" in lcfg: + try: + cond_gate.set_cooldown("low_conf", float(lcfg["cooldown"])) + except Exception: + pass + + if lcfg.get("reset"): + cond_gate.reset(["low_conf"]) + + # lost in middle + lmcfg = payload.get("lostMid") + if isinstance(lmcfg, dict): + if "enabled" in lmcfg: + cond_gate.set_key_enabled("lost_mid", bool(lmcfg["enabled"])) + if "cooldown" in lmcfg: + try: + cond_gate.set_cooldown("lost_mid", float(lmcfg["cooldown"])) + except Exception: + pass + if "margin" in lmcfg: + try: + m = float(lmcfg["margin"]) + _runtime["lost_mid_margin"] = max(0.0, min(0.49, m)) + except Exception: + pass + if lmcfg.get("reset"): + cond_gate.reset(["lost_mid"]) + + any_active = cond_gate.enabled and ( + cond_gate.is_key_enabled("timed") + or cond_gate.is_key_enabled("no_detections") + or cond_gate.is_key_enabled("low_conf") + or cond_gate.is_key_enabled("lost_mid") + ) snaps_producer.setRunning(any_active) if any_active: snaps_producer.setTimeInterval(base_dt_seconds) - return {"ok": True} + return {"ok": True} - # Always register these (YOLOE-only app) visualizer.registerService("Class Update Service", class_update_service) visualizer.registerService("Threshold Update Service", conf_threshold_update_service) visualizer.registerService("Image Upload Service", image_upload_service) diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/__init__.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/__init__.py index b0c9a44f6..c40b9a41c 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/__init__.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/__init__.py @@ -1,3 +1,2 @@ -from .no_detections_gate import NoDetectionsGate +from .conditions_gate import ConditionsGate from .timed_no_det import custom_snap_process -from .new_detection import tracklet_new_detection_process, reset_new_detections_state diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/conditions_gate.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/conditions_gate.py new file mode 100644 index 000000000..10f3e2073 --- /dev/null +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/conditions_gate.py @@ -0,0 +1,58 @@ +import time +from typing import Dict, Iterable, Optional + + +class ConditionsGate: + """ + Generic per-condition cooldown gate. + - First hit for a key fires immediately. + - Subsequent hits for the same key are allowed only after its cooldown. + - Supports global enable + per-key enable + per-key cooldown overrides. + """ + + def __init__(self, default_cooldown_s: float = 0.0, enabled: bool = True): + self.enabled = bool(enabled) + self.default_cooldown_s = max(0.0, float(default_cooldown_s)) + self.cooldowns: Dict[str, float] = {} + self.key_enabled: Dict[str, bool] = {} + self.last_sent: Dict[str, float] = {} + + def set_enabled(self, on: bool) -> None: + self.enabled = bool(on) + + def set_default(self, seconds: float) -> None: + self.default_cooldown_s = max(0.0, float(seconds)) + + def set_key_enabled(self, key: str, on: bool) -> None: + self.key_enabled[str(key)] = bool(on) + + def set_cooldown(self, key: str, seconds: float) -> None: + self.cooldowns[str(key)] = max(0.0, float(seconds)) + + def reset(self, keys: Optional[Iterable[str]] = None) -> None: + if keys is None: + self.last_sent.clear() + else: + for k in keys: + self.last_sent.pop(str(k), None) + + def is_key_enabled(self, key: str) -> bool: + return self.key_enabled.get(str(key), True) + + def get_cooldown(self, key: str) -> float: + return self.cooldowns.get(str(key), self.default_cooldown_s) + + def hit(self, key: str) -> bool: + """ + Returns True if this condition/key should fire now (and records the hit), + False if it's still cooling down or globally/key disabled. + """ + if not self.enabled or not self.is_key_enabled(key): + return False + now = time.time() + last = self.last_sent.get(key, -1.0) + cd = self.get_cooldown(key) + if last < 0.0 or (now - last) >= cd: + self.last_sent[key] = now + return True + return False diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py index 299416b8b..5c82a4e3a 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py @@ -42,3 +42,50 @@ def _label_idx_name(tracklet, class_names: List[str]) -> Tuple[int, str]: def _send_snap(name: str, producer: SnapsProducer, frame: dai.ImgFrame, tags: List[str], extras: dict, detections: dai.ImgDetections = None) -> bool: return producer.sendSnap(name, frame, detections, tags, extras) + + +def _status_is_tracked(t) -> bool: + try: + return t.status == dai.Tracklet.TrackingStatus.TRACKED + except Exception: + try: + return int(getattr(t, "status", -1)) == 1 + except Exception: + return False + + +class _LostMidState: + def __init__(self) -> None: + self.prev_tracked: dict[int, bool] = {} + + +_LOSTMID_STATE = _LostMidState() + + +def _status_is_lost(t) -> bool: + try: + return t.status == dai.Tracklet.TrackingStatus.LOST + except Exception: + try: + return int(getattr(t, "status", -1)) == 2 + except Exception: + return False + + +def _roi_center_area_norm(t) -> Optional[Tuple[float, float, float]]: + roi = getattr(t, "roi", None) + if roi is not None: + try: + tl = roi.topLeft(); br = roi.bottomRight() + x0, y0, x1, y1 = float(tl.x), float(tl.y), float(br.x), float(br.y) + cx, cy = 0.5 * (x0 + x1), 0.5 * (y0 + y1) + return cx, cy, max(0.0, (x1 - x0) * (y1 - y0)) + except Exception: + pass + d = getattr(t, "srcImgDetection", None) + if d is not None: + x = float(getattr(d, "x", getattr(d, "xmin", 0.0))) + y = float(getattr(d, "y", getattr(d, "ymin", 0.0))) + w = float(getattr(d, "width", 0.0)); h = float(getattr(d, "height", 0.0)) + return x + 0.5*w, y + 0.5*h, max(0.0, w*h) + return None diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/new_detection.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/new_detection.py deleted file mode 100644 index dd1cad621..000000000 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/new_detection.py +++ /dev/null @@ -1,78 +0,0 @@ -from __future__ import annotations -from typing import List -import time -import depthai as dai -from depthai_nodes.node import SnapsProducer - -from .helpers import ( - _frame_ts_seconds, - _status_is_tracked, - _track_id, - _label_idx_name, - _send_snap, -) - - -class _NewDetectionsState: - def __init__(self) -> None: - self.seen_ids: set[int] = set() - self.last_label_ts: dict[str, float] = {} - - -_NEWDET_STATE = _NewDetectionsState() - - -def reset_new_detections_state() -> None: - _NEWDET_STATE.seen_ids.clear() - _NEWDET_STATE.last_label_ts.clear() - print("[NewDet] State reset") - - -def tracklet_new_detection_process( - producer: SnapsProducer, - frame: dai.ImgFrame, - tracklets_msg: dai.Tracklets, - det_data: dai.ImgDetections, - class_names: List[str], - model: str, - min_label_cooldown_s: float = 2.0, -): - """ - Send a snap when a tracklet becomes TRACKED for the first time (ID not seen). - One snap per unique track ID; per-label cooldown to avoid bursts. - """ - tks = getattr(tracklets_msg, "tracklets", []) or [] - now_s = _frame_ts_seconds(frame) or time.time() - - for t in tks: - if not _status_is_tracked(t): - continue - - tid = _track_id(t) - if tid is None or tid in _NEWDET_STATE.seen_ids: - continue - - label_idx, label_str = _label_idx_name(t, class_names) - last_ts = _NEWDET_STATE.last_label_ts.get(label_str, -1.0) - if label_str and last_ts >= 0 and (now_s - last_ts) < float(min_label_cooldown_s): - continue - - extras = { - "model": model, - "reason": "new_detection", - "track_id": str(int(tid)), - "label_idx": str(int(label_idx)), - "label": label_str, - "ts_s": f"{now_s:.3f}", - } - try: - tl = t.roi.topLeft(); br = t.roi.bottomRight() - extras["roi_xyxy_norm"] = f"{tl.x:.3f},{tl.y:.3f},{br.x:.3f},{br.y:.3f}" - except Exception: - pass - - if _send_snap("New Detection", producer, frame, ["new_detection"], extras, det_data): - _NEWDET_STATE.seen_ids.add(tid) - if label_str: - _NEWDET_STATE.last_label_ts[label_str] = now_s - print(f"[NewDet] Snap sent: id={tid}, label='{label_str}'") diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/no_detections_gate.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/no_detections_gate.py deleted file mode 100644 index 620fc05db..000000000 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/no_detections_gate.py +++ /dev/null @@ -1,24 +0,0 @@ -from __future__ import annotations - - -class NoDetectionsGate: - def __init__(self) -> None: - self.enabled: bool = False - self.in_run: bool = False - - def set_enabled(self, enabled: bool) -> None: - self.enabled = bool(enabled) - self.in_run = False - - def on_frame(self, num_detections: int) -> bool: - if not self.enabled: - self.in_run = False - return False - if num_detections == 0: - if not self.in_run: - self.in_run = True - return True - return False - if self.in_run: - self.in_run = False - return False diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/timed_no_det.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/timed_no_det.py index fd67bdbc0..11f98ed04 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/timed_no_det.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/timed_no_det.py @@ -1,44 +1,91 @@ -from __future__ import annotations -from typing import Optional, List +from typing import List, Optional import depthai as dai from depthai_nodes.node import SnapsProducer - -from .helpers import _frame_ts_seconds, _send_snap +from .conditions_gate import ConditionsGate +from .helpers import (_send_snap, _track_id, _label_idx_name, _status_is_lost, + _roi_center_area_norm, _LOSTMID_STATE, _status_is_tracked) from .extras import build_extras -from .no_detections_gate import NoDetectionsGate def custom_snap_process( producer: SnapsProducer, frame: dai.ImgFrame, - det_data: dai.ImgDetections, + tracklets_msg: Optional[dai.Tracklets], + det_data: Optional[dai.ImgDetections], class_names: List[str], model: str, - no_det_gate: Optional[NoDetectionsGate] = None, - timed_state: Optional[dict] = None, + cond_gate: ConditionsGate, + runtime: dict | None = None, ): + # timed + if cond_gate.is_key_enabled("timed") and cond_gate.hit("timed"): + extras = build_extras(model, det_data, class_names) if det_data is not None else {"model": model} + if _send_snap("Timing Snap", producer, frame, ["timing_snap"], extras, det_data): + print("[Timed] Snap sent!") + dets = getattr(det_data, "detections", None) or [] num = len(dets) - now_s = _frame_ts_seconds(frame) if timed_state is not None else None - if timed_state and timed_state.get("timed_enabled", False): - interval = float(timed_state.get("interval", 0.0)) or 0.0 - last = float(timed_state.get("last_sent_s", -1.0)) - due = (now_s is not None) and (interval > 0.0) and ((last < 0.0) or (now_s - last >= interval)) - if due: - extras = build_extras(model, det_data, class_names) - if _send_snap("Timing Snap", producer, frame, ["timing_snap"], extras, det_data): - timed_state["last_sent_s"] = now_s - if no_det_gate: - no_det_gate.on_frame(num) - print("[Timed] Snap sent!") - return - - if num == 0: - if no_det_gate and no_det_gate.on_frame(0): - extras = {"model": model, "reason": "no_detections_start"} - if _send_snap("No Detections", producer, frame, ["no_detections"], extras): - print("[NoDet] Snap sent (start of empty streak)") - return - - if no_det_gate: - no_det_gate.on_frame(num) + + # no detections + if det_data is not None and num == 0: + if cond_gate.is_key_enabled("no_detections") and cond_gate.hit("no_detections"): + if _send_snap("No Detections", producer, frame, ["no_detections"], {"model": model, "reason": "no_detections"}): + print("[NoDet] Snap sent") + + # low confidence + if det_data is not None and cond_gate.is_key_enabled("low_conf"): + thr = None if runtime is None else runtime.get("low_conf_thresh") + if thr is not None: + has_low = any(float(getattr(d, "confidence", 1.0)) < float(thr) for d in dets) + if has_low and cond_gate.hit("low_conf"): + min_conf = min(float(getattr(d, "confidence", 1.0)) for d in dets) if dets else 1.0 + extras = { + "model": model, + "reason": "low_confidence", + "min_conf": f"{min_conf:.3f}", + "threshold": f"{float(thr):.3f}", + } + if _send_snap("Low Confidence", producer, frame, ["low_confidence"], extras, det_data): + print(f"[LowConf] Snap sent (min={min_conf:.3f} < thr={float(thr):.3f})") + + # lost detection + if tracklets_msg is not None and cond_gate.is_key_enabled("lost_mid"): + margin = float((runtime or {}).get("lost_mid_margin", 0.20)) + margin = max(0.0, min(0.49, margin)) + + fired = False + tks = getattr(tracklets_msg, "tracklets", None) or [] + for t in tks: + tid = _track_id(t) + if tid is None: + continue + + was_tracked_prev = _LOSTMID_STATE.prev_tracked.get(tid, False) + is_lost_now = _status_is_lost(t) + is_tracked_now = _status_is_tracked(t) + + # Fire only on TRACKED -> LOST transition + if is_lost_now and was_tracked_prev: + rc = _roi_center_area_norm(t) + if rc is not None: + cx, cy, area = rc + if (margin <= cx <= 1.0 - margin) and (margin <= cy <= 1.0 - margin): + if cond_gate.hit("lost_mid"): + lbl_idx, lbl_name = _label_idx_name(t, class_names) + extras = { + "model": str(model), + "reason": "lost_in_middle", + "track_id": str(int(tid)), + "label_idx": str(int(lbl_idx)), + "label": str(lbl_name), + "area_frac": f"{area:.6f}", + "margin": f"{margin:.3f}", + } + if _send_snap("Lost in middle", producer, frame, ["lost_in_middle"], extras, det_data): + fired = True + + _LOSTMID_STATE.prev_tracked[tid] = bool(is_tracked_now) + + if fired: + print("[LostMid] Snap(s) sent") + diff --git a/performant_PoCs/hub_snaps/frontend/src/App.tsx b/performant_PoCs/hub_snaps/frontend/src/App.tsx index 84f33533a..90ae1d1c1 100644 --- a/performant_PoCs/hub_snaps/frontend/src/App.tsx +++ b/performant_PoCs/hub_snaps/frontend/src/App.tsx @@ -8,348 +8,321 @@ import { useCallback, useEffect, useRef, useState } from "react"; import { useNotifications } from "./Notifications.tsx"; function App() { - const connection = useConnection(); - const streamContainerRef = useRef(null); - const overlayCanvasRef = useRef(null); - const [isDrawing, setIsDrawing] = useState(false); - const [dragStart, setDragStart] = useState<{ x: number; y: number } | null>(null); - const [currentRect, setCurrentRect] = useState<{ x: number; y: number; w: number; h: number } | null>(null); - const { notify } = useNotifications(); - - const getUnderlyingMediaAndSize = () => { - const container = streamContainerRef.current; - if (!container) return null; - const videoEl = container.querySelector("video") as HTMLVideoElement | null; - const canvases = Array.from(container.querySelectorAll("canvas")) as HTMLCanvasElement[]; - const canvasEl = canvases.find((c) => c.getAttribute("data-role") !== "overlay") || null; - const containerRect = container.getBoundingClientRect(); - if (videoEl && videoEl.videoWidth && videoEl.videoHeight) { - const r = videoEl.getBoundingClientRect(); - const displayWidth = r.width; - const displayHeight = r.height; - const offsetX = r.left - containerRect.left; - const offsetY = r.top - containerRect.top; - console.log("[BBox] Capturing from video element", { width: videoEl.videoWidth, height: videoEl.videoHeight, displayWidth, displayHeight, offsetX, offsetY }); - return { - type: "video" as const, - el: videoEl, - width: videoEl.videoWidth, - height: videoEl.videoHeight, - displayWidth, - displayHeight, - offsetX, - offsetY, - }; - } - if (canvasEl && canvasEl.width && canvasEl.height) { - const r = canvasEl.getBoundingClientRect(); - const displayWidth = r.width; - const displayHeight = r.height; - const offsetX = r.left - containerRect.left; - const offsetY = r.top - containerRect.top; - console.log("[BBox] Capturing from canvas element", { width: canvasEl.width, height: canvasEl.height, displayWidth, displayHeight, offsetX, offsetY }); - return { - type: "canvas" as const, - el: canvasEl, - width: canvasEl.width, - height: canvasEl.height, - displayWidth, - displayHeight, - offsetX, - offsetY, - }; - } - return null; - }; - - - const finalizeBBox = useCallback(() => { - if (!currentRect) return; - const overlay = overlayCanvasRef.current; - if (!overlay) return; - const { x, y, w, h } = currentRect; - if (w <= 0 || h <= 0) { - setIsDrawing(false); - setCurrentRect(null); - setDragStart(null); - const ctx = overlay.getContext("2d"); - if (ctx) ctx.clearRect(0, 0, overlay.width, overlay.height); - notify('Selection too small. Please draw a larger box.', { type: 'warning' }); - return; - } - - // Map overlay-space bbox to source frame using displayed media rect - const media = getUnderlyingMediaAndSize(); - if (!media) { - console.warn("[BBox] No media found under overlay; aborting bbox post"); - notify('No video/canvas found. Reset the view and try again.', { type: 'error', durationMs: 6000 }); - return; - } - - const overlayW = overlay.width; - const overlayH = overlay.height; - const srcW = media.width; - const srcH = media.height; - const mediaOffsetX = (media as any).offsetX ?? 0; - const mediaOffsetY = (media as any).offsetY ?? 0; - const mediaDispW = (media as any).displayWidth ?? overlayW; - const mediaDispH = (media as any).displayHeight ?? overlayH; - - let contentX = mediaOffsetX; - let contentY = mediaOffsetY; - let contentW = mediaDispW; - let contentH = mediaDispH; - if (media.type === "canvas") { - const side = Math.min(mediaDispW, mediaDispH); - contentX = mediaOffsetX + (mediaDispW - side) / 2; - contentY = mediaOffsetY + (mediaDispH - side) / 2; - contentW = side; - contentH = side; - } - - const rx0 = Math.max(x, contentX); - const ry0 = Math.max(y, contentY); - const rx1 = Math.min(x + w, contentX + contentW); - const ry1 = Math.min(y + h, contentY + contentH); - const rw = Math.max(0, rx1 - rx0); - const rh = Math.max(0, ry1 - ry0); - if (rw <= 1 || rh <= 1) { - console.warn("[BBox] BBox outside content area; aborting"); - notify('Box outside of content area. Try again within the stream.', { type: 'warning', durationMs: 6000 }); - return; - } - - const scaleX = srcW / contentW; - const scaleY = srcH / contentH; - const sx0 = Math.max(0, Math.min(srcW - 1, Math.round((rx0 - contentX) * scaleX))); - const sy0 = Math.max(0, Math.min(srcH - 1, Math.round((ry0 - contentY) * scaleY))); - const sx1 = Math.max(0, Math.min(srcW, Math.round((rx1 - contentX) * scaleX))); - const sy1 = Math.max(0, Math.min(srcH, Math.round((ry1 - contentY) * scaleY))); - const sw = Math.max(1, sx1 - sx0); - const sh = Math.max(1, sy1 - sy0); - - const xNorm = sx0 / srcW; - const yNorm = sy0 / srcH; - const wNorm = sw / srcW; - const hNorm = sh / srcH; - - console.log("[BBox] Posting BBox Prompt Service (normalized source)", { - bbox: { x: xNorm, y: yNorm, width: wNorm, height: hNorm }, - src: { width: srcW, height: srcH }, - overlay: { width: overlayW, height: overlayH }, - display: { width: mediaDispW, height: mediaDispH, offsetX: mediaOffsetX, offsetY: mediaOffsetY }, - content: { x: contentX, y: contentY, width: contentW, height: contentH }, - scales: { scaleX, scaleY } - }); - notify( - `Sending box [${xNorm.toFixed(2)}, ${yNorm.toFixed(2)}, ${wNorm.toFixed(2)}, ${hNorm.toFixed(2)}]`, - { type: 'info' } - ); - // @ts-ignore - Custom service - (connection as any).daiConnection?.postToService( - "BBox Prompt Service", - { - filename: "object.png", - type: "application/json", - data: null, - bbox: { x: xNorm, y: yNorm, width: wNorm, height: hNorm }, - bboxType: "normalized", - label: "object" - }, - (resp: any) => { - console.log("[BBox] Service ack:", resp); - notify('Bounding box sent', { type: 'success' }); - } - ); - - setIsDrawing(false); - setCurrentRect(null); - setDragStart(null); - const ctx = overlay.getContext("2d"); - if (ctx) ctx.clearRect(0, 0, overlay.width, overlay.height); - }, [connection, currentRect]); - - const handleBeginBBoxDraw = useCallback(() => { - console.log("[BBox] Begin drawing requested"); - setIsDrawing(true); - setCurrentRect(null); - setDragStart(null); - }, []); - - useEffect(() => { - if (!isDrawing) return; - const container = streamContainerRef.current; - const overlay = overlayCanvasRef.current; - if (!container || !overlay) return; - const sizeOverlay = () => { - const rect = container.getBoundingClientRect(); - overlay.width = Math.max(1, Math.round(rect.width)); - overlay.height = Math.max(1, Math.round(rect.height)); - const ctx = overlay.getContext("2d"); - if (ctx) ctx.clearRect(0, 0, overlay.width, overlay.height); - console.log("[BBox] Overlay sized", { width: overlay.width, height: overlay.height }); - }; - sizeOverlay(); - window.addEventListener("resize", sizeOverlay); - return () => window.removeEventListener("resize", sizeOverlay); - }, [isDrawing]); - - useEffect(() => { - notify(connection.connected ? 'Connected to device' : 'Disconnected from device', { type: connection.connected ? 'success' : 'warning', durationMs: 1800 }); - // eslint-disable-next-line react-hooks/exhaustive-deps - }, [connection.connected]); - - const onOverlayMouseDown = (e: any) => { - if (!isDrawing) return; - const canvas = overlayCanvasRef.current; - if (!canvas) return; - const rect = canvas.getBoundingClientRect(); - const x = e.clientX - rect.left; - const y = e.clientY - rect.top; - setDragStart({ x, y }); - setCurrentRect({ x, y, w: 0, h: 0 }); - console.log("[BBox] Mouse down", { x, y }); - }; - - const onOverlayMouseMove = (e: any) => { - if (!isDrawing || !dragStart) return; - const canvas = overlayCanvasRef.current; - if (!canvas) return; - const rect = canvas.getBoundingClientRect(); - const x = e.clientX - rect.left; - const y = e.clientY - rect.top; - const x0 = Math.min(dragStart.x, x); - const y0 = Math.min(dragStart.y, y); - const w = Math.abs(x - dragStart.x); - const h = Math.abs(y - dragStart.y); - setCurrentRect({ x: x0, y: y0, w, h }); - - const ctx = canvas.getContext("2d"); - if (!ctx) return; - ctx.clearRect(0, 0, canvas.width, canvas.height); - ctx.strokeStyle = "#22c55e"; // green - ctx.lineWidth = 2; - ctx.strokeRect(x0, y0, w, h); - if ((w * h) % 20 === 0) { - console.log("[BBox] Mouse move bbox", { x: x0, y: y0, w, h }); - } - }; + const connection = useConnection(); + const streamContainerRef = useRef(null); + const overlayCanvasRef = useRef(null); + const [isDrawing, setIsDrawing] = useState(false); + const [dragStart, setDragStart] = useState<{ x: number; y: number } | null>(null); + const [currentRect, setCurrentRect] = useState<{ x: number; y: number; w: number; h: number } | null>(null); + const { notify } = useNotifications(); + + const getUnderlyingMediaAndSize = () => { + const container = streamContainerRef.current; + if (!container) return null; + const videoEl = container.querySelector("video") as HTMLVideoElement | null; + const canvases = Array.from(container.querySelectorAll("canvas")) as HTMLCanvasElement[]; + const canvasEl = canvases.find((c) => c.getAttribute("data-role") !== "overlay") || null; + const containerRect = container.getBoundingClientRect(); + if (videoEl && videoEl.videoWidth && videoEl.videoHeight) { + const r = videoEl.getBoundingClientRect(); + const displayWidth = r.width; + const displayHeight = r.height; + const offsetX = r.left - containerRect.left; + const offsetY = r.top - containerRect.top; + return { + type: "video" as const, + el: videoEl, + width: videoEl.videoWidth, + height: videoEl.videoHeight, + displayWidth, + displayHeight, + offsetX, + offsetY, + }; + } + if (canvasEl && canvasEl.width && canvasEl.height) { + const r = canvasEl.getBoundingClientRect(); + const displayWidth = r.width; + const displayHeight = r.height; + const offsetX = r.left - containerRect.left; + const offsetY = r.top - containerRect.top; + return { + type: "canvas" as const, + el: canvasEl, + width: canvasEl.width, + height: canvasEl.height, + displayWidth, + displayHeight, + offsetX, + offsetY, + }; + } + return null; + }; + + const finalizeBBox = useCallback(() => { + if (!currentRect) return; + const overlay = overlayCanvasRef.current; + if (!overlay) return; + const { x, y, w, h } = currentRect; + if (w <= 0 || h <= 0) { + setIsDrawing(false); + setCurrentRect(null); + setDragStart(null); + const ctx = overlay.getContext("2d"); + if (ctx) ctx.clearRect(0, 0, overlay.width, overlay.height); + notify("Selection too small. Please draw a larger box.", { type: "warning" }); + return; + } + + const media = getUnderlyingMediaAndSize(); + if (!media) { + notify("No video/canvas found. Reset the view and try again.", { type: "error", durationMs: 6000 }); + return; + } + + const overlayW = overlay.width; + const overlayH = overlay.height; + const srcW = media.width; + const srcH = media.height; + const mediaOffsetX = (media as any).offsetX ?? 0; + const mediaOffsetY = (media as any).offsetY ?? 0; + const mediaDispW = (media as any).displayWidth ?? overlayW; + const mediaDispH = (media as any).displayHeight ?? overlayH; + + let contentX = mediaOffsetX; + let contentY = mediaOffsetY; + let contentW = mediaDispW; + let contentH = mediaDispH; + if (media.type === "canvas") { + const side = Math.min(mediaDispW, mediaDispH); + contentX = mediaOffsetX + (mediaDispW - side) / 2; + contentY = mediaOffsetY + (mediaDispH - side) / 2; + contentW = side; + contentH = side; + } + + const rx0 = Math.max(x, contentX); + const ry0 = Math.max(y, contentY); + const rx1 = Math.min(x + w, contentX + contentW); + const ry1 = Math.min(y + h, contentY + contentH); + const rw = Math.max(0, rx1 - rx0); + const rh = Math.max(0, ry1 - ry0); + if (rw <= 1 || rh <= 1) { + notify("Box outside of content area. Try again within the stream.", { type: "warning", durationMs: 6000 }); + return; + } + + const scaleX = srcW / contentW; + const scaleY = srcH / contentH; + const sx0 = Math.max(0, Math.min(srcW - 1, Math.round((rx0 - contentX) * scaleX))); + const sy0 = Math.max(0, Math.min(srcH - 1, Math.round((ry0 - contentY) * scaleY))); + const sx1 = Math.max(0, Math.min(srcW, Math.round((rx1 - contentX) * scaleX))); + const sy1 = Math.max(0, Math.min(srcH, Math.round((ry1 - contentY) * scaleY))); + const sw = Math.max(1, sx1 - sx0); + const sh = Math.max(1, sy1 - sy0); + + const xNorm = sx0 / srcW; + const yNorm = sy0 / srcH; + const wNorm = sw / srcW; + const hNorm = sh / srcH; + + notify(`Sending box [${xNorm.toFixed(2)}, ${yNorm.toFixed(2)}, ${wNorm.toFixed(2)}, ${hNorm.toFixed(2)}]`, { + type: "info", + }); + + (connection as any).daiConnection?.postToService( + "BBox Prompt Service", + { + filename: "object.png", + type: "application/json", + data: null, + bbox: { x: xNorm, y: yNorm, width: wNorm, height: hNorm }, + bboxType: "normalized", + label: "object", + }, + (resp: any) => { + console.log("[BBox] Service ack:", resp); + notify("Bounding box sent", { type: "success" }); + } + ); - const onOverlayMouseUp = () => { - if (!isDrawing) return; - console.log("[BBox] Mouse up, finalizing bbox", currentRect); - finalizeBBox(); + setIsDrawing(false); + setCurrentRect(null); + setDragStart(null); + const ctx = overlay.getContext("2d"); + if (ctx) ctx.clearRect(0, 0, overlay.width, overlay.height); + }, [connection, currentRect, notify]); + + const handleBeginBBoxDraw = useCallback(() => { + setIsDrawing(true); + setCurrentRect(null); + setDragStart(null); + }, []); + + useEffect(() => { + if (!isDrawing) return; + const container = streamContainerRef.current; + const overlay = overlayCanvasRef.current; + if (!container || !overlay) return; + const sizeOverlay = () => { + const rect = container.getBoundingClientRect(); + overlay.width = Math.max(1, Math.round(rect.width)); + overlay.height = Math.max(1, Math.round(rect.height)); + const ctx = overlay.getContext("2d"); + if (ctx) ctx.clearRect(0, 0, overlay.width, overlay.height); }; - - return ( -
- {/* Left: Stream Viewer */} -
- - {isDrawing && ( - - )} -
- - {/* Vertical Divider */} -
- - {/* Right: Sidebar (Info and Controls) */} -
-

- Open Vocabulary Object Detection -

-

- Run open‑vocabulary detection on‑device (YOLOE or YOLO‑World) with a custom UI. - Define classes via text prompts or image crops, adjust confidence, and visualize results live. -

- - {/* Class Input */} - - - {/* Image Uploader */} - - - {/* Confidence Slider */} - - - {/* Snap Collection */} - - - {/* Connection Status */} -
-
- - {connection.connected ? 'Connected to device' : 'Disconnected'} - -
-
-
- ); + sizeOverlay(); + window.addEventListener("resize", sizeOverlay); + return () => window.removeEventListener("resize", sizeOverlay); + }, [isDrawing]); + + useEffect(() => { + notify(connection.connected ? "Connected to device" : "Disconnected from device", { + type: connection.connected ? "success" : "warning", + durationMs: 1200, + }); + }, [connection.connected, notify]); + + const onOverlayMouseDown = (e: any) => { + if (!isDrawing) return; + const canvas = overlayCanvasRef.current; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + const x = e.clientX - rect.left; + const y = e.clientY - rect.top; + setDragStart({ x, y }); + setCurrentRect({ x, y, w: 0, h: 0 }); + }; + + const onOverlayMouseMove = (e: any) => { + if (!isDrawing || !dragStart) return; + const canvas = overlayCanvasRef.current; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + const x = e.clientX - rect.left; + const y = e.clientY - rect.top; + const x0 = Math.min(dragStart.x, x); + const y0 = Math.min(dragStart.y, y); + const w = Math.abs(x - dragStart.x); + const h = Math.abs(y - dragStart.y); + setCurrentRect({ x: x0, y: y0, w, h }); + + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.clearRect(0, 0, canvas.width, canvas.height); + ctx.strokeStyle = "#22c55e"; + ctx.lineWidth = 2; + ctx.strokeRect(x0, y0, w, h); + }; + + const onOverlayMouseUp = () => { + if (!isDrawing) return; + finalizeBBox(); + }; + + const SectionTitle = (props: { children: any }) => ( +

{props.children}

+ ); + + return ( +
+
+ + {isDrawing && ( + + )} +
+ + {/* Vertical Divider */} +
+ + {/* Right: Sidebar — restored width */} +
+

Data Collection

+

+ Detect by name or example and auto-capture snaps based on conditions. +

+ + Labels by Text +

Enter labels to find (e.g., person, chair, TV).

+ + + Labels by Image +

Upload a photo or draw a box on the stream.

+ + + Confidence Filter +

Detections below this confidence are dropped.

+ + +
+

Snap conditions

+

Choose when to auto-capture a snap.

+ +
+ +
+
+ + {connection.connected ? "Connected to device" : "Disconnected"} + +
+
+
+ ); } - export default App; diff --git a/performant_PoCs/hub_snaps/frontend/src/ConfidenceSlider.tsx b/performant_PoCs/hub_snaps/frontend/src/ConfidenceSlider.tsx index 636b52a87..75459c73a 100644 --- a/performant_PoCs/hub_snaps/frontend/src/ConfidenceSlider.tsx +++ b/performant_PoCs/hub_snaps/frontend/src/ConfidenceSlider.tsx @@ -30,7 +30,7 @@ export function ConfidenceSlider({ initialValue = 0.5 }: ConfidenceSliderProps) return (
/\.\d{2,}$/.test(s.trim()); + const oneDecimalOrInt = (s: string) => /^\d+(\.\d{0,1})?$/.test(s.trim()); + const asFloat = (s: string) => Number.parseFloat(s.trim()); + + // timing (minutes, allow one decimal; default 5.0) const [timingEnabled, setTimingEnabled] = useState(false); - const [timeIntervalStr, setTimeIntervalStr] = useState("60"); - const timeInterval = useMemo(() => Number.parseInt(timeIntervalStr, 10), [timeIntervalStr]); - const timeIntervalIsValid = Number.isInteger(timeInterval) && timeInterval > 0; + const [timeIntervalStr, setTimeIntervalStr] = useState("5.0"); + const timeIntervalMin = useMemo(() => asFloat(timeIntervalStr), [timeIntervalStr]); + const timeIntervalValid = + (!timingEnabled) || + (timeIntervalStr !== "" && oneDecimalOrInt(timeIntervalStr) && Number.isFinite(timeIntervalMin) && timeIntervalMin > 0); - // no-detections toggle + // no detections (minutes, allow one decimal) const [noDetEnabled, setNoDetEnabled] = useState(false); - // new-detections (tracker) toggle - const [newDetEnabled, setNewDetEnabled] = useState(false); + const [noDetCooldownStr, setNoDetCooldownStr] = useState("15.0"); + const noDetCooldownMin = useMemo(() => asFloat(noDetCooldownStr), [noDetCooldownStr]); + const noDetCooldownValid = + (!noDetEnabled) || + (noDetCooldownStr !== "" && oneDecimalOrInt(noDetCooldownStr) && Number.isFinite(noDetCooldownMin) && noDetCooldownMin >= 0); + + // low confidence (cooldown minutes, allow one decimal; threshold unchanged) + const [lowConfEnabled, setLowConfEnabled] = useState(false); + const [lowConfCooldownStr, setLowConfCooldownStr] = useState("15.0"); + const lowConfCooldownMin = useMemo(() => asFloat(lowConfCooldownStr), [lowConfCooldownStr]); + const lowConfCooldownValid = + (!lowConfEnabled) || + (lowConfCooldownStr !== "" && oneDecimalOrInt(lowConfCooldownStr) && Number.isFinite(lowConfCooldownMin) && lowConfCooldownMin >= 0); + const [lowConfThreshold, setLowConfThreshold] = useState(0.30); // slider 0..1 + + // lost in middle (cooldown minutes, allow one decimal; margin unchanged) + const [lostMidEnabled, setLostMidEnabled] = useState(false); + const [lostMidCooldownStr, setLostMidCooldownStr] = useState("15.0"); + const lostMidCooldownMin = useMemo(() => asFloat(lostMidCooldownStr), [lostMidCooldownStr]); + const lostMidCooldownValid = + (!lostMidEnabled) || + (lostMidCooldownStr !== "" && oneDecimalOrInt(lostMidCooldownStr) && Number.isFinite(lostMidCooldownMin) && lostMidCooldownMin >= 0); + const [lostMidMarginStr, setLostMidMarginStr] = useState("0.20"); // fraction 0..0.49 + const lostMidMargin = useMemo(() => Number.parseFloat(lostMidMarginStr), [lostMidMarginStr]); + const lostMidMarginValid = !Number.isNaN(lostMidMargin) && lostMidMargin >= 0 && lostMidMargin <= 0.49; + + const anyInvalid = + !timeIntervalValid || !noDetCooldownValid || !lowConfCooldownValid || !lostMidCooldownValid || !lostMidMarginValid; + + const Divider = () => ( +
+ ); + + // helper: post to BE + const postToService = (payload: any, onDone?: () => void) => { + (connection as any).daiConnection?.postToService("Snap Collection Service", payload, (_resp: any) => { + onDone?.(); + }); + }; + // compose full payload when starting/stopping (convert minutes -> seconds) const postConfig = (runFlag: boolean) => { const payload = runFlag ? { - timed: { enabled: timingEnabled, interval: timingEnabled ? timeInterval : 0 }, - noDetections: { enabled: noDetEnabled }, - newDetections: { enabled: newDetEnabled }, + timed: { + enabled: timingEnabled, + interval: timingEnabled ? timeIntervalMin * 60 : 0, + }, + noDetections: { + enabled: noDetEnabled, + cooldown: noDetEnabled ? noDetCooldownMin * 60 : undefined, + }, + lowConfidence: lowConfEnabled + ? { + enabled: true, + threshold: lowConfThreshold, + cooldown: lowConfCooldownMin * 60, + } + : { enabled: false }, + lostMid: lostMidEnabled + ? { + enabled: true, + cooldown: lostMidCooldownMin * 60, + margin: lostMidMargin, + } + : { enabled: false }, } : { timed: { enabled: false, interval: 0 }, noDetections: { enabled: false }, - newDetections: { enabled: false }, + lowConfidence: { enabled: false }, + lostMid: { enabled: false }, }; - (connection as any).daiConnection?.postToService( - "Snap Collection Service", - payload, - (_resp: any) => { - setBusy(false); - setRunning(runFlag); - notify(runFlag ? "Snapping started." : "Snapping stopped.", { type: "success" }); - } - ); + postToService(payload, () => { + setBusy(false); + setRunning(runFlag); + notify(runFlag ? "Snapping started." : "Snapping stopped.", { type: "success" }); + }); + }; + + // live-update lowConfidence while running (minutes -> seconds) + const pushLowConfUpdate = () => { + if (!connection.connected || !running || !lowConfEnabled) return; + if (!lowConfCooldownValid) return; + postToService({ + lowConfidence: { + enabled: true, + threshold: lowConfThreshold, + cooldown: lowConfCooldownMin * 60, + }, + }); + }; + + // live-update lostMid while running (minutes -> seconds) + const pushLostMidUpdate = () => { + if (!connection.connected || !running || !lostMidEnabled) return; + if (!lostMidMarginValid || !lostMidCooldownValid) return; + postToService({ + lostMid: { + enabled: true, + cooldown: lostMidCooldownMin * 60, + margin: lostMidMargin, + }, + }); + }; + + // warn on blur if too many decimals + const warnIfTooManyDecimals = (label: string, value: string) => { + if (value.trim() !== "" && hasTooManyDecimals(value)) { + notify(`${label} allows at most one decimal place.`, { type: "warning" }); + } }; const handleStartStop = () => { @@ -54,28 +148,49 @@ export function SnapCollectionButton() { } if (busy) return; - if (!running && timingEnabled && !timeIntervalIsValid) { - notify("Please enter a positive integer time interval (seconds).", { type: "error" }); - return; + if (!running) { + if (!timeIntervalValid && timingEnabled) { + notify("Please enter a positive timing cooldown (minutes, max 1 decimal).", { type: "error" }); + return; + } + if (!noDetCooldownValid && noDetEnabled) { + notify("Please enter a non-negative no-detections cooldown (minutes, max 1 decimal).", { type: "error" }); + return; + } + if (lowConfEnabled) { + if (!lowConfCooldownValid) { + notify("Please enter a non-negative low-confidence cooldown (minutes, max 1 decimal).", { type: "error" }); + return; + } + if (!(lowConfThreshold >= 0 && lowConfThreshold <= 1)) { + notify("Confidence threshold must be between 0.00 and 1.00.", { type: "error" }); + return; + } + } + if (lostMidEnabled) { + if (!lostMidCooldownValid) { + notify("Please enter a non-negative lost-in-middle cooldown (minutes, max 1 decimal).", { type: "error" }); + return; + } + if (!lostMidMarginValid) { + notify("Margin must be a number between 0.00 and 0.49.", { type: "error" }); + return; + } + } } setBusy(true); - if (!running) { - notify("Starting snapping…", { type: "info" }); - postConfig(true); - } else { - notify("Stopping snapping…", { type: "info" }); - postConfig(false); - } + notify(!running ? "Starting snapping…" : "Stopping snapping…", { type: "info" }); + postConfig(!running); }; const disabledControls = busy || running; return (
-
+ - {/* Timing switch (checkbox) */} + {/* Timing */}
- {/* Interval input — only when timing is ON */} {timingEnabled && ( - + <> +

+ Sends a snap periodically; throttled by the cooldown. +

+ + )} - {/* No detections switch */} + + + {/* No detections */}
- {/* New detections switch */} + {noDetEnabled && ( + <> +

+ Sends a snap when a frame has zero detections; throttled by the cooldown. +

+ + + )} + + + + {/* Low confidence */}
-
- {/* Start / Stop */} + {lowConfEnabled && ( +
+

+ Sends a snap if any detection confidence falls below the threshold; throttled by the cooldown. +

+ + + setLowConfThreshold(parseFloat(e.target.value))} + onMouseUp={pushLowConfUpdate} + onTouchEnd={pushLowConfUpdate} + disabled={disabledControls} + className={css({ + width: "100%", + appearance: "none", + height: "4px", + borderRadius: "full", + backgroundColor: "gray.300", + "&::-webkit-slider-thumb": { + appearance: "none", + width: "12px", + height: "12px", + borderRadius: "full", + backgroundColor: "blue.500", + cursor: "pointer", + }, + "&::-moz-range-thumb": { + appearance: "none", + width: "12px", + height: "12px", + borderRadius: "full", + backgroundColor: "blue.500", + cursor: "pointer", + }, + })} + /> + + +
+ )} + + + + {/* Lost in middle */} +
+ + setLostMidEnabled(e.target.checked)} + disabled={disabledControls} + className={css({ width: "5", height: "5", cursor: disabledControls ? "not-allowed" : "pointer" })} + /> +
+ + {lostMidEnabled && ( +
+

+ Sends a snap the moment a tracked object disappears inside the center area; throttled by the cooldown. +

+ + + + +
+ )} + + +
); From 44b733719803b66c96bcbf97532392b008186156 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Sun, 26 Oct 2025 12:13:55 +0100 Subject: [PATCH 12/39] clean up --- .../backend/src/utils/snap_utils/__init__.py | 2 +- .../{timed_no_det.py => custon_snap_process.py} | 0 .../backend/src/utils/snap_utils/helpers.py | 17 ----------------- 3 files changed, 1 insertion(+), 18 deletions(-) rename performant_PoCs/hub_snaps/backend/src/utils/snap_utils/{timed_no_det.py => custon_snap_process.py} (100%) diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/__init__.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/__init__.py index c40b9a41c..46db19bba 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/__init__.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/__init__.py @@ -1,2 +1,2 @@ from .conditions_gate import ConditionsGate -from .timed_no_det import custom_snap_process +from .custon_snap_process import custom_snap_process diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/timed_no_det.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/custon_snap_process.py similarity index 100% rename from performant_PoCs/hub_snaps/backend/src/utils/snap_utils/timed_no_det.py rename to performant_PoCs/hub_snaps/backend/src/utils/snap_utils/custon_snap_process.py diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py index 5c82a4e3a..7914c5233 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py @@ -4,13 +4,6 @@ from depthai_nodes.node import SnapsProducer -def _frame_ts_seconds(frame: dai.ImgFrame) -> Optional[float]: - try: - return float(frame.getTimestamp().total_seconds()) - except Exception: - return None - - def _status_is_tracked(tracklet) -> bool: val = getattr(tracklet, "status", None) try: @@ -44,16 +37,6 @@ def _send_snap(name: str, producer: SnapsProducer, frame: dai.ImgFrame, tags: Li return producer.sendSnap(name, frame, detections, tags, extras) -def _status_is_tracked(t) -> bool: - try: - return t.status == dai.Tracklet.TrackingStatus.TRACKED - except Exception: - try: - return int(getattr(t, "status", -1)) == 1 - except Exception: - return False - - class _LostMidState: def __init__(self) -> None: self.prev_tracked: dict[int, bool] = {} From c5e90b7c2c002b4815ca253767f249332f0c46c9 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Mon, 27 Oct 2025 12:24:45 +0100 Subject: [PATCH 13/39] FE update + BE refactoring --- performant_PoCs/hub_snaps/backend/src/main.py | 15 +- .../backend/src/utils/helper_functions.py | 152 +++++++----------- .../src/utils/snap_utils/conditions_gate.py | 2 +- .../utils/snap_utils/custon_snap_process.py | 1 - .../frontend/src/SnapCollectionButton.tsx | 105 +++++------- 5 files changed, 102 insertions(+), 173 deletions(-) diff --git a/performant_PoCs/hub_snaps/backend/src/main.py b/performant_PoCs/hub_snaps/backend/src/main.py index ac4ad9300..22a7f7313 100644 --- a/performant_PoCs/hub_snaps/backend/src/main.py +++ b/performant_PoCs/hub_snaps/backend/src/main.py @@ -19,7 +19,7 @@ extract_text_embeddings, extract_image_prompt_embeddings, base64_to_cv2_image, - QUANT_VALUES, + make_dummy_features ) from utils.arguments import initialize_argparser from utils.annotation_node import AnnotationNode @@ -54,14 +54,6 @@ frame_type = dai.ImgFrame.Type.BGR888i - -def make_dummy_features(max_num_classes: int, model_name: str, precision: str): - if precision == "fp16": - return np.zeros((1, 512, max_num_classes), dtype=np.float16) - qzp = int(round(QUANT_VALUES.get(model_name, {}).get("quant_zero_point", 0))) - return np.full((1, 512, max_num_classes), qzp, dtype=np.uint8) - - text_features = extract_text_embeddings( class_names=CLASS_NAMES, max_num_classes=MAX_NUM_CLASSES, @@ -97,7 +89,6 @@ def make_dummy_features(max_num_classes: int, model_name: str, precision: str): model_nn_archive = dai.NNArchive(dai.getModelFromZoo(model_description)) model_w, model_h = model_nn_archive.getInputSize() - # media/camera input at high resolution for visualization if args.media_path: replay = pipeline.create(dai.node.ReplayVideo) replay.setReplayVideoFile(Path(args.media_path)) @@ -145,11 +136,9 @@ def make_dummy_features(max_num_classes: int, model_name: str, precision: str): textInputQueue = nn_with_parser.inputs["texts"].createInputQueue() nn_with_parser.inputs["texts"].setReusePreviousMessage(True) - # YOLOE always uses image_prompts input (we feed dummy by default) imagePromptInputQueue = nn_with_parser.inputs["image_prompts"].createInputQueue() nn_with_parser.inputs["image_prompts"].setReusePreviousMessage(True) - # filter and rename detection labels det_process_filter = pipeline.create(ImgDetectionsFilter).build(nn_with_parser.out) annotation_node = pipeline.create(AnnotationNode).build( det_process_filter.out, @@ -158,7 +147,6 @@ def make_dummy_features(max_num_classes: int, model_name: str, precision: str): filtered_bridge = pipeline.create(ImgDetectionsBridge).build(det_process_filter.out) - # Cache last frame for services that need full frame content frame_cache = pipeline.create(FrameCacheNode).build(video_src_out) cond_gate = ConditionsGate(default_cooldown_s=0.0, enabled=True) @@ -350,7 +338,6 @@ def bbox_prompt_service(payload): y0, y1 = sorted((y0, y1)) return {"ok": True} - def snap_collection_service(payload): base_dt_seconds = 1 diff --git a/performant_PoCs/hub_snaps/backend/src/utils/helper_functions.py b/performant_PoCs/hub_snaps/backend/src/utils/helper_functions.py index e4e919d3f..42a533292 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/helper_functions.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/helper_functions.py @@ -10,10 +10,6 @@ QUANT_SCALE = 0.003925696481 QUANT_VALUES = { - "yolo-world": { - "quant_zero_point": 90.0, - "quant_scale": 0.003925696481, - }, "yoloe": { "quant_zero_point": 174.0, "quant_scale": 0.003328413470, @@ -26,7 +22,7 @@ def pad_and_quantize_features( - features, max_num_classes=80, model_name="yolo-world", precision="int8" + features, max_num_classes=80, model_name="yoloe", precision="int8" ): """ Pad features to (1, 512, max_num_classes) and quantize if precision is int8. @@ -48,7 +44,7 @@ def pad_and_quantize_features( def extract_text_embeddings( - class_names, max_num_classes=80, model_name="yolo-world", precision="int8" + class_names, max_num_classes=80, model_name="yoloe", precision="int8" ): tokenizer_json_path = download_tokenizer( url="https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json", @@ -62,58 +58,34 @@ def extract_text_embeddings( text_onnx = np.array([e.ids for e in encodings], dtype=np.int64) - if model_name == "yolo-world": - attention_mask = np.array([e.attention_mask for e in encodings], dtype=np.int64) - - textual_onnx_model_path = download_model( - "https://huggingface.co/jmzzomg/clip-vit-base-patch32-text-onnx/resolve/main/model.onnx", - "clip_textual_hf.onnx", - ) - - session_textual = onnxruntime.InferenceSession( - textual_onnx_model_path, - providers=[ - "TensorrtExecutionProvider", - "CUDAExecutionProvider", - "CPUExecutionProvider", - ], - ) - textual_output = session_textual.run( - None, - { - session_textual.get_inputs()[0].name: text_onnx, - "attention_mask": attention_mask, - }, - )[0] - elif model_name == "yoloe": - if text_onnx.shape[1] < 77: - text_onnx = np.pad( - text_onnx, ((0, 0), (0, 77 - text_onnx.shape[1])), mode="constant" - ) - - textual_onnx_model_path = download_model( - "https://huggingface.co/Xenova/mobileclip_blt/resolve/main/onnx/text_model.onnx", - "mobileclip_textual_hf.onnx", + if text_onnx.shape[1] < 77: + text_onnx = np.pad( + text_onnx, ((0, 0), (0, 77 - text_onnx.shape[1])), mode="constant" ) - session_textual = onnxruntime.InferenceSession( - textual_onnx_model_path, - providers=[ - "TensorrtExecutionProvider", - "CUDAExecutionProvider", - "CPUExecutionProvider", - ], - ) - textual_output = session_textual.run( - None, - { - session_textual.get_inputs()[0].name: text_onnx, - }, - )[0] + textual_onnx_model_path = download_model( + "https://huggingface.co/Xenova/mobileclip_blt/resolve/main/onnx/text_model.onnx", + "mobileclip_textual_hf.onnx", + ) - textual_output /= np.linalg.norm( - textual_output, ord=2, axis=-1, keepdims=True - ) # Normalize the output + session_textual = onnxruntime.InferenceSession( + textual_onnx_model_path, + providers=[ + "TensorrtExecutionProvider", + "CUDAExecutionProvider", + "CPUExecutionProvider", + ], + ) + textual_output = session_textual.run( + None, + { + session_textual.get_inputs()[0].name: text_onnx, + }, + )[0] + + textual_output /= np.linalg.norm( + textual_output, ord=2, axis=-1, keepdims=True + ) text_features = pad_and_quantize_features( textual_output, max_num_classes, model_name, precision @@ -127,28 +99,19 @@ def extract_text_embeddings( def extract_image_prompt_embeddings( image, max_num_classes=80, - model_name="yolo-world", + model_name="yoloe", mask_prompt=None, precision="int8", ): - # Select model and preprocess accordingly - if model_name == "yoloe": - image_resized = cv2.resize(image, (640, 640)) - image_array = image_resized.astype(np.float32) / 255.0 - image_array = np.transpose(image_array, (2, 0, 1)) - input_tensor = np.expand_dims(image_array, axis=0).astype(np.float32) - model_url = ( - "https://huggingface.co/sokovninn/yoloe-v8l-seg-visual-encoder/resolve/main/" - "yoloe-v8l-seg_visual_encoder.onnx" - ) - model_path = "yoloe-v8l-seg_visual_encoder.onnx" - else: - input_tensor = preprocess_image(image) - model_url = ( - "https://huggingface.co/sokovninn/clip-visual-with-projector/resolve/main/" - "clip_visual_with_projector.onnx" - ) - model_path = "clip_visual_with_projector.onnx" + image_resized = cv2.resize(image, (640, 640)) + image_array = image_resized.astype(np.float32) / 255.0 + image_array = np.transpose(image_array, (2, 0, 1)) + input_tensor = np.expand_dims(image_array, axis=0).astype(np.float32) + model_url = ( + "https://huggingface.co/sokovninn/yoloe-v8l-seg-visual-encoder/resolve/main/" + "yoloe-v8l-seg_visual_encoder.onnx" + ) + model_path = "yoloe-v8l-seg_visual_encoder.onnx" onnx_model_path = download_model(model_url, model_path) @@ -161,26 +124,22 @@ def extract_image_prompt_embeddings( ], ) - if model_name == "yoloe": - if mask_prompt is None: - prompts = np.zeros((1, 1, 80, 80), dtype=np.float32) - prompts[0, 0, 5:75, 5:75] = 1.0 - else: - prompts = np.asarray(mask_prompt, dtype=np.float32) - if prompts.ndim == 2: - if prompts.shape != (80, 80): - prompts = cv2.resize( - prompts, (80, 80), interpolation=cv2.INTER_NEAREST - ) - prompts = prompts[None, None, :, :] - elif prompts.shape == (1, 1, 80, 80): - pass - else: - raise ValueError("mask_prompt must have shape (80,80) or (1,1,80,80)") - outputs = session.run(None, {"images": input_tensor, "prompts": prompts}) + if mask_prompt is None: + prompts = np.zeros((1, 1, 80, 80), dtype=np.float32) + prompts[0, 0, 5:75, 5:75] = 1.0 else: - input_name = session.get_inputs()[0].name - outputs = session.run(None, {input_name: input_tensor}) + prompts = np.asarray(mask_prompt, dtype=np.float32) + if prompts.ndim == 2: + if prompts.shape != (80, 80): + prompts = cv2.resize( + prompts, (80, 80), interpolation=cv2.INTER_NEAREST + ) + prompts = prompts[None, None, :, :] + elif prompts.shape == (1, 1, 80, 80): + pass + else: + raise ValueError("mask_prompt must have shape (80,80) or (1,1,80,80)") + outputs = session.run(None, {"images": input_tensor, "prompts": prompts}) image_embeddings = outputs[0].squeeze(0).reshape(1, -1) image_features = pad_and_quantize_features( @@ -245,3 +204,10 @@ def base64_to_cv2_image(base64_data_uri: str): np_arr = np.frombuffer(binary_data, np.uint8) img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) return img + + +def make_dummy_features(max_num_classes: int, model_name: str, precision: str): + if precision == "fp16": + return np.zeros((1, 512, max_num_classes), dtype=np.float16) + qzp = int(round(QUANT_VALUES.get(model_name, {}).get("quant_zero_point", 0))) + return np.full((1, 512, max_num_classes), qzp, dtype=np.uint8) \ No newline at end of file diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/conditions_gate.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/conditions_gate.py index 10f3e2073..b68b07ad3 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/conditions_gate.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/conditions_gate.py @@ -37,7 +37,7 @@ def reset(self, keys: Optional[Iterable[str]] = None) -> None: self.last_sent.pop(str(k), None) def is_key_enabled(self, key: str) -> bool: - return self.key_enabled.get(str(key), True) + return self.key_enabled.get(str(key), False) def get_cooldown(self, key: str) -> float: return self.cooldowns.get(str(key), self.default_cooldown_s) diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/custon_snap_process.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/custon_snap_process.py index 11f98ed04..f416799e4 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/custon_snap_process.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/custon_snap_process.py @@ -64,7 +64,6 @@ def custom_snap_process( is_lost_now = _status_is_lost(t) is_tracked_now = _status_is_tracked(t) - # Fire only on TRACKED -> LOST transition if is_lost_now and was_tracked_prev: rc = _roi_center_area_norm(t) if rc is not None: diff --git a/performant_PoCs/hub_snaps/frontend/src/SnapCollectionButton.tsx b/performant_PoCs/hub_snaps/frontend/src/SnapCollectionButton.tsx index e100e1b49..9adeed060 100644 --- a/performant_PoCs/hub_snaps/frontend/src/SnapCollectionButton.tsx +++ b/performant_PoCs/hub_snaps/frontend/src/SnapCollectionButton.tsx @@ -12,46 +12,47 @@ export function SnapCollectionButton() { const [running, setRunning] = useState(false); const [busy, setBusy] = useState(false); - // --- helpers for validation/state --- + // helpers for validation const hasTooManyDecimals = (s: string) => /\.\d{2,}$/.test(s.trim()); const oneDecimalOrInt = (s: string) => /^\d+(\.\d{0,1})?$/.test(s.trim()); const asFloat = (s: string) => Number.parseFloat(s.trim()); - // timing (minutes, allow one decimal; default 5.0) + // timing cooldown (minutes, one decimal; default 5.0) const [timingEnabled, setTimingEnabled] = useState(false); const [timeIntervalStr, setTimeIntervalStr] = useState("5.0"); const timeIntervalMin = useMemo(() => asFloat(timeIntervalStr), [timeIntervalStr]); const timeIntervalValid = - (!timingEnabled) || + !timingEnabled || (timeIntervalStr !== "" && oneDecimalOrInt(timeIntervalStr) && Number.isFinite(timeIntervalMin) && timeIntervalMin > 0); - // no detections (minutes, allow one decimal) + // no detections (minutes, one decimal; default 5.0) const [noDetEnabled, setNoDetEnabled] = useState(false); - const [noDetCooldownStr, setNoDetCooldownStr] = useState("15.0"); + const [noDetCooldownStr, setNoDetCooldownStr] = useState("5.0"); const noDetCooldownMin = useMemo(() => asFloat(noDetCooldownStr), [noDetCooldownStr]); const noDetCooldownValid = - (!noDetEnabled) || + !noDetEnabled || (noDetCooldownStr !== "" && oneDecimalOrInt(noDetCooldownStr) && Number.isFinite(noDetCooldownMin) && noDetCooldownMin >= 0); - // low confidence (cooldown minutes, allow one decimal; threshold unchanged) + // low confidence (cooldown minutes, one decimal; threshold unchanged; default 5.0) const [lowConfEnabled, setLowConfEnabled] = useState(false); - const [lowConfCooldownStr, setLowConfCooldownStr] = useState("15.0"); + const [lowConfCooldownStr, setLowConfCooldownStr] = useState("5.0"); const lowConfCooldownMin = useMemo(() => asFloat(lowConfCooldownStr), [lowConfCooldownStr]); const lowConfCooldownValid = - (!lowConfEnabled) || + !lowConfEnabled || (lowConfCooldownStr !== "" && oneDecimalOrInt(lowConfCooldownStr) && Number.isFinite(lowConfCooldownMin) && lowConfCooldownMin >= 0); const [lowConfThreshold, setLowConfThreshold] = useState(0.30); // slider 0..1 - // lost in middle (cooldown minutes, allow one decimal; margin unchanged) + // lost in middle (cooldown minutes, one decimal; edge buffer in PERCENT 0..49, default 20%) const [lostMidEnabled, setLostMidEnabled] = useState(false); - const [lostMidCooldownStr, setLostMidCooldownStr] = useState("15.0"); + const [lostMidCooldownStr, setLostMidCooldownStr] = useState("5.0"); const lostMidCooldownMin = useMemo(() => asFloat(lostMidCooldownStr), [lostMidCooldownStr]); const lostMidCooldownValid = - (!lostMidEnabled) || + !lostMidEnabled || (lostMidCooldownStr !== "" && oneDecimalOrInt(lostMidCooldownStr) && Number.isFinite(lostMidCooldownMin) && lostMidCooldownMin >= 0); - const [lostMidMarginStr, setLostMidMarginStr] = useState("0.20"); // fraction 0..0.49 - const lostMidMargin = useMemo(() => Number.parseFloat(lostMidMarginStr), [lostMidMarginStr]); - const lostMidMarginValid = !Number.isNaN(lostMidMargin) && lostMidMargin >= 0 && lostMidMargin <= 0.49; + + const [lostMidMarginPctStr, setLostMidMarginPctStr] = useState("20"); // percent 0..49 + const lostMidMarginPct = useMemo(() => Number.parseFloat(lostMidMarginPctStr), [lostMidMarginPctStr]); + const lostMidMarginValid = Number.isFinite(lostMidMarginPct) && lostMidMarginPct >= 0 && lostMidMarginPct <= 49; const anyInvalid = !timeIntervalValid || !noDetCooldownValid || !lowConfCooldownValid || !lostMidCooldownValid || !lostMidMarginValid; @@ -67,8 +68,9 @@ export function SnapCollectionButton() { }); }; - // compose full payload when starting/stopping (convert minutes -> seconds) + // compose payload (convert minutes -> seconds; margin percent -> fraction) const postConfig = (runFlag: boolean) => { + const marginFrac = Math.max(0, Math.min(49, lostMidMarginPct || 0)) / 100; const payload = runFlag ? { timed: { @@ -90,7 +92,7 @@ export function SnapCollectionButton() { ? { enabled: true, cooldown: lostMidCooldownMin * 60, - margin: lostMidMargin, + margin: marginFrac, } : { enabled: false }, } @@ -108,10 +110,9 @@ export function SnapCollectionButton() { }); }; - // live-update lowConfidence while running (minutes -> seconds) + // live updates const pushLowConfUpdate = () => { - if (!connection.connected || !running || !lowConfEnabled) return; - if (!lowConfCooldownValid) return; + if (!connection.connected || !running || !lowConfEnabled || !lowConfCooldownValid) return; postToService({ lowConfidence: { enabled: true, @@ -121,20 +122,18 @@ export function SnapCollectionButton() { }); }; - // live-update lostMid while running (minutes -> seconds) const pushLostMidUpdate = () => { - if (!connection.connected || !running || !lostMidEnabled) return; - if (!lostMidMarginValid || !lostMidCooldownValid) return; + if (!connection.connected || !running || !lostMidEnabled || !lostMidCooldownValid || !lostMidMarginValid) return; + const marginFrac = Math.max(0, Math.min(49, lostMidMarginPct || 0)) / 100; postToService({ lostMid: { enabled: true, cooldown: lostMidCooldownMin * 60, - margin: lostMidMargin, + margin: marginFrac, }, }); }; - // warn on blur if too many decimals const warnIfTooManyDecimals = (label: string, value: string) => { if (value.trim() !== "" && hasTooManyDecimals(value)) { notify(`${label} allows at most one decimal place.`, { type: "warning" }); @@ -173,7 +172,7 @@ export function SnapCollectionButton() { return; } if (!lostMidMarginValid) { - notify("Margin must be a number between 0.00 and 0.49.", { type: "error" }); + notify("Edge buffer must be between 0% and 49%.", { type: "error" }); return; } } @@ -227,12 +226,7 @@ export function SnapCollectionButton() { px: "sm", py: "xs", borderWidth: "1px", - borderColor: - disabledControls - ? "gray.300" - : timeIntervalValid - ? "gray.300" - : "red.500", + borderColor: disabledControls ? "gray.300" : timeIntervalValid ? "gray.300" : "red.500", rounded: "md", _disabled: { bg: "gray.100", color: "gray.500", cursor: "not-allowed" }, })} @@ -289,12 +283,7 @@ export function SnapCollectionButton() { px: "sm", py: "xs", borderWidth: "1px", - borderColor: - disabledControls - ? "gray.300" - : noDetCooldownValid - ? "gray.300" - : "red.500", + borderColor: disabledControls ? "gray.300" : noDetCooldownValid ? "gray.300" : "red.500", rounded: "md", _disabled: { bg: "gray.100", color: "gray.500", cursor: "not-allowed" }, })} @@ -390,12 +379,7 @@ export function SnapCollectionButton() { px: "sm", py: "xs", borderWidth: "1px", - borderColor: - disabledControls - ? "gray.300" - : lowConfCooldownValid - ? "gray.300" - : "red.500", + borderColor: disabledControls ? "gray.300" : lowConfCooldownValid ? "gray.300" : "red.500", rounded: "md", _disabled: { bg: "gray.100", color: "gray.500", cursor: "not-allowed" }, })} @@ -437,32 +421,28 @@ export function SnapCollectionButton() {

void; + disabled?: boolean; + description?: string; + children?: ReactNode; +} + +export function ConditionCard({ + title, + enabled, + onToggle, + disabled, + description, + children, +}: ConditionCardProps) { + const Divider = () => ( +
+ ); + + return ( +
+ +
+ + onToggle(e.target.checked)} + disabled={!!disabled} + className={css({ width: "5", height: "5", cursor: disabled ? "not-allowed" : "pointer" })} + /> +
+ {enabled && description && ( +

{description}

+ )} + {enabled && children} +
+ ); +} diff --git a/performant_PoCs/hub_snaps/frontend/src/ConfidenceSlider.tsx b/performant_PoCs/hub_snaps/frontend/src/ConfidenceSlider.tsx index 75459c73a..7a504a304 100644 --- a/performant_PoCs/hub_snaps/frontend/src/ConfidenceSlider.tsx +++ b/performant_PoCs/hub_snaps/frontend/src/ConfidenceSlider.tsx @@ -1,70 +1,38 @@ -import { css } from "../styled-system/css/css.mjs"; import { useState } from "react"; import { useConnection } from "@luxonis/depthai-viewer-common"; +import { SliderControl } from "./inputs/SliderControl"; interface ConfidenceSliderProps { - initialValue?: number; + initialValue?: number; // defaults to 0.5 + disabled?: boolean; } -export function ConfidenceSlider({ initialValue = 0.5 }: ConfidenceSliderProps) { - const connection = useConnection(); - const [value, setValue] = useState(initialValue); +export function ConfidenceSlider({ initialValue = 0.5, disabled }: ConfidenceSliderProps) { + const connection = useConnection(); + const [value, setValue] = useState(initialValue); - const handleCommit = () => { - if (typeof value === "number" && !isNaN(value)) { - console.log('Sending threshold to backend:', value); + const handleCommit = (v: number) => { + if (Number.isFinite(v)) { + connection.daiConnection?.postToService( + // @ts-ignore - Custom service + "Threshold Update Service", + v, + (resp: any) => console.log("[ConfidenceSlider] BE ack:", resp) + ); + } + }; - connection.daiConnection?.postToService( - // @ts-ignore - Custom service - "Threshold Update Service", - value, - (response: any) => { - console.log('Backend acknowledged threshold update:', response); - } - ); - } else { - console.warn("Invalid value, skipping update:", value); - } - }; - - return ( -
- - setValue(parseFloat(e.target.value))} - onMouseUp={handleCommit} - onTouchEnd={handleCommit} - className={css({ - width: '100%', - appearance: 'none', - height: '4px', - borderRadius: 'full', - backgroundColor: 'gray.300', - '&::-webkit-slider-thumb': { - appearance: 'none', - width: '12px', - height: '12px', - borderRadius: 'full', - backgroundColor: 'blue.500', - cursor: 'pointer', - }, - '&::-moz-range-thumb': { - appearance: 'none', - width: '12px', - height: '12px', - borderRadius: 'full', - backgroundColor: 'blue.500', - cursor: 'pointer', - } - })} - /> -
- ); + return ( + + ); } diff --git a/performant_PoCs/hub_snaps/frontend/src/SnapCollectionButton.tsx b/performant_PoCs/hub_snaps/frontend/src/SnapCollectionButton.tsx index 9adeed060..dae2b89dd 100644 --- a/performant_PoCs/hub_snaps/frontend/src/SnapCollectionButton.tsx +++ b/performant_PoCs/hub_snaps/frontend/src/SnapCollectionButton.tsx @@ -1,516 +1,30 @@ -import { useState, useMemo } from "react"; import { Button } from "@luxonis/common-fe-components"; import { css } from "../styled-system/css/css.mjs"; -import { useConnection } from "@luxonis/depthai-viewer-common"; -import { useNotifications } from "./Notifications.tsx"; -export function SnapCollectionButton() { - const connection = useConnection(); - const { notify } = useNotifications(); - - // master run/stop - const [running, setRunning] = useState(false); - const [busy, setBusy] = useState(false); - - // helpers for validation - const hasTooManyDecimals = (s: string) => /\.\d{2,}$/.test(s.trim()); - const oneDecimalOrInt = (s: string) => /^\d+(\.\d{0,1})?$/.test(s.trim()); - const asFloat = (s: string) => Number.parseFloat(s.trim()); - - // timing cooldown (minutes, one decimal; default 5.0) - const [timingEnabled, setTimingEnabled] = useState(false); - const [timeIntervalStr, setTimeIntervalStr] = useState("5.0"); - const timeIntervalMin = useMemo(() => asFloat(timeIntervalStr), [timeIntervalStr]); - const timeIntervalValid = - !timingEnabled || - (timeIntervalStr !== "" && oneDecimalOrInt(timeIntervalStr) && Number.isFinite(timeIntervalMin) && timeIntervalMin > 0); - - // no detections (minutes, one decimal; default 5.0) - const [noDetEnabled, setNoDetEnabled] = useState(false); - const [noDetCooldownStr, setNoDetCooldownStr] = useState("5.0"); - const noDetCooldownMin = useMemo(() => asFloat(noDetCooldownStr), [noDetCooldownStr]); - const noDetCooldownValid = - !noDetEnabled || - (noDetCooldownStr !== "" && oneDecimalOrInt(noDetCooldownStr) && Number.isFinite(noDetCooldownMin) && noDetCooldownMin >= 0); - - // low confidence (cooldown minutes, one decimal; threshold unchanged; default 5.0) - const [lowConfEnabled, setLowConfEnabled] = useState(false); - const [lowConfCooldownStr, setLowConfCooldownStr] = useState("5.0"); - const lowConfCooldownMin = useMemo(() => asFloat(lowConfCooldownStr), [lowConfCooldownStr]); - const lowConfCooldownValid = - !lowConfEnabled || - (lowConfCooldownStr !== "" && oneDecimalOrInt(lowConfCooldownStr) && Number.isFinite(lowConfCooldownMin) && lowConfCooldownMin >= 0); - const [lowConfThreshold, setLowConfThreshold] = useState(0.30); // slider 0..1 - - // lost in middle (cooldown minutes, one decimal; edge buffer in PERCENT 0..49, default 20%) - const [lostMidEnabled, setLostMidEnabled] = useState(false); - const [lostMidCooldownStr, setLostMidCooldownStr] = useState("5.0"); - const lostMidCooldownMin = useMemo(() => asFloat(lostMidCooldownStr), [lostMidCooldownStr]); - const lostMidCooldownValid = - !lostMidEnabled || - (lostMidCooldownStr !== "" && oneDecimalOrInt(lostMidCooldownStr) && Number.isFinite(lostMidCooldownMin) && lostMidCooldownMin >= 0); - - const [lostMidMarginPctStr, setLostMidMarginPctStr] = useState("20"); // percent 0..49 - const lostMidMarginPct = useMemo(() => Number.parseFloat(lostMidMarginPctStr), [lostMidMarginPctStr]); - const lostMidMarginValid = Number.isFinite(lostMidMarginPct) && lostMidMarginPct >= 0 && lostMidMarginPct <= 49; - - const anyInvalid = - !timeIntervalValid || !noDetCooldownValid || !lowConfCooldownValid || !lostMidCooldownValid || !lostMidMarginValid; - - const Divider = () => ( -
- ); - - // helper: post to BE - const postToService = (payload: any, onDone?: () => void) => { - (connection as any).daiConnection?.postToService("Snap Collection Service", payload, (_resp: any) => { - onDone?.(); - }); - }; - - // compose payload (convert minutes -> seconds; margin percent -> fraction) - const postConfig = (runFlag: boolean) => { - const marginFrac = Math.max(0, Math.min(49, lostMidMarginPct || 0)) / 100; - const payload = runFlag - ? { - timed: { - enabled: timingEnabled, - interval: timingEnabled ? timeIntervalMin * 60 : 0, - }, - noDetections: { - enabled: noDetEnabled, - cooldown: noDetEnabled ? noDetCooldownMin * 60 : undefined, - }, - lowConfidence: lowConfEnabled - ? { - enabled: true, - threshold: lowConfThreshold, - cooldown: lowConfCooldownMin * 60, - } - : { enabled: false }, - lostMid: lostMidEnabled - ? { - enabled: true, - cooldown: lostMidCooldownMin * 60, - margin: marginFrac, - } - : { enabled: false }, - } - : { - timed: { enabled: false, interval: 0 }, - noDetections: { enabled: false }, - lowConfidence: { enabled: false }, - lostMid: { enabled: false }, - }; - - postToService(payload, () => { - setBusy(false); - setRunning(runFlag); - notify(runFlag ? "Snapping started." : "Snapping stopped.", { type: "success" }); - }); - }; - - // live updates - const pushLowConfUpdate = () => { - if (!connection.connected || !running || !lowConfEnabled || !lowConfCooldownValid) return; - postToService({ - lowConfidence: { - enabled: true, - threshold: lowConfThreshold, - cooldown: lowConfCooldownMin * 60, - }, - }); - }; - - const pushLostMidUpdate = () => { - if (!connection.connected || !running || !lostMidEnabled || !lostMidCooldownValid || !lostMidMarginValid) return; - const marginFrac = Math.max(0, Math.min(49, lostMidMarginPct || 0)) / 100; - postToService({ - lostMid: { - enabled: true, - cooldown: lostMidCooldownMin * 60, - margin: marginFrac, - }, - }); - }; - - const warnIfTooManyDecimals = (label: string, value: string) => { - if (value.trim() !== "" && hasTooManyDecimals(value)) { - notify(`${label} allows at most one decimal place.`, { type: "warning" }); - } - }; - - const handleStartStop = () => { - if (!connection.connected) { - notify("Not connected to device.", { type: "error" }); - return; - } - if (busy) return; - - if (!running) { - if (!timeIntervalValid && timingEnabled) { - notify("Please enter a positive timing cooldown (minutes, max 1 decimal).", { type: "error" }); - return; - } - if (!noDetCooldownValid && noDetEnabled) { - notify("Please enter a non-negative no-detections cooldown (minutes, max 1 decimal).", { type: "error" }); - return; - } - if (lowConfEnabled) { - if (!lowConfCooldownValid) { - notify("Please enter a non-negative low-confidence cooldown (minutes, max 1 decimal).", { type: "error" }); - return; - } - if (!(lowConfThreshold >= 0 && lowConfThreshold <= 1)) { - notify("Confidence threshold must be between 0.00 and 1.00.", { type: "error" }); - return; - } - } - if (lostMidEnabled) { - if (!lostMidCooldownValid) { - notify("Please enter a non-negative lost-in-middle cooldown (minutes, max 1 decimal).", { type: "error" }); - return; - } - if (!lostMidMarginValid) { - notify("Edge buffer must be between 0% and 49%.", { type: "error" }); - return; - } - } - } - - setBusy(true); - notify(!running ? "Starting snapping…" : "Stopping snapping…", { type: "info" }); - postConfig(!running); - }; - - const disabledControls = busy || running; +interface SnapActionButtonProps { + running: boolean; + busy: boolean; + disabled?: boolean; + onClick: () => void; +} +export function SnapCollectionButton({ running, busy, disabled, onClick }: SnapActionButtonProps) { return ( -
- - - {/* Timing */} -
- - setTimingEnabled(e.target.checked)} - disabled={disabledControls} - className={css({ width: "5", height: "5", cursor: disabledControls ? "not-allowed" : "pointer" })} - /> -
- - {timingEnabled && ( - <> -

- Sends a snap periodically; throttled by the cooldown. -

- - - )} - - - - {/* No detections */} -
- - setNoDetEnabled(e.target.checked)} - disabled={disabledControls} - className={css({ width: "5", height: "5", cursor: disabledControls ? "not-allowed" : "pointer" })} - /> -
- - {noDetEnabled && ( - <> -

- Sends a snap when a frame has zero detections; throttled by the cooldown. -

- - - )} - - - - {/* Low confidence */} -
- - setLowConfEnabled(e.target.checked)} - disabled={disabledControls} - className={css({ width: "5", height: "5", cursor: disabledControls ? "not-allowed" : "pointer" })} - /> -
- - {lowConfEnabled && ( -
-

- Sends a snap if any detection confidence falls below the threshold; throttled by the cooldown. -

- - - setLowConfThreshold(parseFloat(e.target.value))} - onMouseUp={pushLowConfUpdate} - onTouchEnd={pushLowConfUpdate} - disabled={disabledControls} - className={css({ - width: "100%", - appearance: "none", - height: "4px", - borderRadius: "full", - backgroundColor: "gray.300", - "&::-webkit-slider-thumb": { - appearance: "none", - width: "12px", - height: "12px", - borderRadius: "full", - backgroundColor: "blue.500", - cursor: "pointer", - }, - "&::-moz-range-thumb": { - appearance: "none", - width: "12px", - height: "12px", - borderRadius: "full", - backgroundColor: "blue.500", - cursor: "pointer", - }, - })} - /> - - -
- )} - - - - {/* Lost in middle */} -
- - setLostMidEnabled(e.target.checked)} - disabled={disabledControls} - className={css({ width: "5", height: "5", cursor: disabledControls ? "not-allowed" : "pointer" })} - /> -
- - {lostMidEnabled && ( -
-

- Sends a snap the moment a tracked object disappears inside the center area; throttled by the cooldown. -

- - - - -
- )} - - - - -
+ ); } diff --git a/performant_PoCs/hub_snaps/frontend/src/SnapConditionsPanel.tsx b/performant_PoCs/hub_snaps/frontend/src/SnapConditionsPanel.tsx new file mode 100644 index 000000000..bd658456a --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/src/SnapConditionsPanel.tsx @@ -0,0 +1,268 @@ +import { useMemo, useState } from "react"; +import { css } from "../styled-system/css/css.mjs"; +import { useConnection } from "@luxonis/depthai-viewer-common"; +import { useNotifications } from "./Notifications.tsx"; +import { ConditionCard } from "./ConditionCard"; +import { CooldownMinutesInput } from "./inputs/CooldownMinutesInput"; +import { EdgeBufferPercentInput } from "./inputs/EdgeBufferPercentInput"; +import { SnapCollectionButton } from "./SnapCollectionButton"; +import { SliderControl } from "./inputs/SliderControl"; + +export function SnapConditionsPanel() { + const connection = useConnection(); + const { notify } = useNotifications(); + + const [running, setRunning] = useState(false); + const [busy, setBusy] = useState(false); + + // helpers + const hasTooManyDecimals = (s: string) => /\.\d{2,}$/.test(s.trim()); + const oneDecimalOrInt = (s: string) => /^\d+(\.\d{0,1})?$/.test(s.trim()); + const asFloat = (s: string) => Number.parseFloat(s.trim()); + + // Timing (default 5.0m) + const [timingEnabled, setTimingEnabled] = useState(false); + const [timingStr, setTimingStr] = useState("5.0"); + const timingMin = useMemo(() => asFloat(timingStr), [timingStr]); + const timingValid = !timingEnabled || + (timingStr !== "" && oneDecimalOrInt(timingStr) && Number.isFinite(timingMin) && timingMin > 0); + + // No detections (default 5.0m) + const [noDetEnabled, setNoDetEnabled] = useState(false); + const [noDetStr, setNoDetStr] = useState("5.0"); + const noDetMin = useMemo(() => asFloat(noDetStr), [noDetStr]); + const noDetValid = !noDetEnabled || + (noDetStr !== "" && oneDecimalOrInt(noDetStr) && Number.isFinite(noDetMin) && noDetMin >= 0); + + // Low confidence (default 5.0m) + const [lowConfEnabled, setLowConfEnabled] = useState(false); + const [lowConfStr, setLowConfStr] = useState("5.0"); + const lowConfMin = useMemo(() => asFloat(lowConfStr), [lowConfStr]); + const lowConfValid = !lowConfEnabled || + (lowConfStr !== "" && oneDecimalOrInt(lowConfStr) && Number.isFinite(lowConfMin) && lowConfMin >= 0); + const [lowConfThreshold, setLowConfThreshold] = useState(0.30); // slider 0..1 + + // Lost in middle (default 5.0m cooldown; 20% edge) + const [lostMidEnabled, setLostMidEnabled] = useState(false); + const [lostMidStr, setLostMidStr] = useState("5.0"); + const lostMidMin = useMemo(() => asFloat(lostMidStr), [lostMidStr]); + const lostMidValid = !lostMidEnabled || + (lostMidStr !== "" && oneDecimalOrInt(lostMidStr) && Number.isFinite(lostMidMin) && lostMidMin >= 0); + + const [lostMidPctStr, setLostMidPctStr] = useState("20"); + const lostMidPct = useMemo(() => Number.parseFloat(lostMidPctStr), [lostMidPctStr]); + const lostMidPctValid = Number.isFinite(lostMidPct) && lostMidPct >= 0 && lostMidPct <= 49; + + const anyInvalid = !timingValid || !noDetValid || !lowConfValid || !lostMidValid || !lostMidPctValid; + const disabledControls = busy || running; + + const warnIfTooManyDecimals = (label: string, value: string) => { + if (value.trim() !== "" && hasTooManyDecimals(value)) { + notify(`${label} allows at most one decimal place.`, { type: "warning" }); + } + }; + + const postToService = (payload: any, onDone?: () => void) => { + (connection as any).daiConnection?.postToService("Snap Collection Service", payload, (_resp: any) => { + onDone?.(); + }); + }; + + const postConfig = (runFlag: boolean) => { + const marginFrac = Math.max(0, Math.min(49, lostMidPct || 0)) / 100; + + const payload = runFlag + ? { + timed: { enabled: timingEnabled, interval: timingEnabled ? timingMin * 60 : 0 }, + noDetections: { enabled: noDetEnabled, cooldown: noDetEnabled ? noDetMin * 60 : undefined }, + lowConfidence: lowConfEnabled + ? { enabled: true, threshold: lowConfThreshold, cooldown: lowConfMin * 60 } + : { enabled: false }, + lostMid: lostMidEnabled + ? { enabled: true, cooldown: lostMidMin * 60, margin: marginFrac } + : { enabled: false }, + } + : { + timed: { enabled: false, interval: 0 }, + noDetections: { enabled: false }, + lowConfidence: { enabled: false }, + lostMid: { enabled: false }, + }; + + postToService(payload, () => { + setBusy(false); + setRunning(runFlag); + notify(runFlag ? "Snapping started." : "Snapping stopped.", { type: "success" }); + }); + }; + + // live updates while running + const pushLowConfUpdate = () => { + if (!connection.connected || !running || !lowConfEnabled || !lowConfValid) return; + postToService({ + lowConfidence: { enabled: true, threshold: lowConfThreshold, cooldown: lowConfMin * 60 }, + }); + }; + + const pushLostMidUpdate = () => { + if (!connection.connected || !running || !lostMidEnabled || !lostMidValid || !lostMidPctValid) return; + const marginFrac = Math.max(0, Math.min(49, lostMidPct || 0)) / 100; + postToService({ + lostMid: { enabled: true, cooldown: lostMidMin * 60, margin: marginFrac }, + }); + }; + + const handleStartStop = () => { + if (!connection.connected) { + notify("Not connected to device.", { type: "error" }); + return; + } + if (busy) return; + + // validations before starting + if (!running) { + if (timingEnabled && !timingValid) { + notify("Please enter a positive timing cooldown (minutes, max 1 decimal).", { type: "error" }); + return; + } + if (noDetEnabled && !noDetValid) { + notify("Please enter a non-negative no-detections cooldown (minutes, max 1 decimal).", { type: "error" }); + return; + } + if (lowConfEnabled) { + if (!lowConfValid) { + notify("Please enter a non-negative low-confidence cooldown (minutes, max 1 decimal).", { type: "error" }); + return; + } + if (!(lowConfThreshold >= 0 && lowConfThreshold <= 1)) { + notify("Confidence threshold must be between 0.00 and 1.00.", { type: "error" }); + return; + } + } + if (lostMidEnabled) { + if (!lostMidValid) { + notify("Please enter a non-negative lost-in-middle cooldown (minutes, max 1 decimal).", { type: "error" }); + return; + } + if (!lostMidPctValid) { + notify("Edge buffer must be between 0% and 49%.", { type: "error" }); + return; + } + } + } + + setBusy(true); + notify(!running ? "Starting snapping…" : "Stopping snapping…", { type: "info" }); + postConfig(!running); + }; + + return ( +
+ {/* Timing */} + + warnIfTooManyDecimals("Timing cooldown", timingStr)} + valid={timingValid} + disabled={disabledControls} + /> + + + {/* No detections */} + + warnIfTooManyDecimals("No-detections cooldown", noDetStr)} + valid={noDetValid} + disabled={disabledControls} + /> + + + {/* Low confidence */} + + pushLowConfUpdate()} + min={0} + max={1} + step={0.01} + disabled={disabledControls} + aria-label="Low confidence threshold" + /> + + warnIfTooManyDecimals("Low-confidence cooldown", lowConfStr)} + valid={lowConfValid} + disabled={disabledControls} + /> + + + {/* Lost in middle */} + + + { + if (lostMidStr.trim() !== "" && /\.\d{2,}$/.test(lostMidStr)) { + notify("Lost-in-middle cooldown allows at most one decimal place.", { type: "warning" }); + } + pushLostMidUpdate(); + }} + valid={lostMidValid} + disabled={disabledControls} + /> + + + {/* Start / Stop */} +
+ = 0 && lowConfThreshold <= 1))} + onClick={handleStartStop} + /> +
+
+ ); +} diff --git a/performant_PoCs/hub_snaps/frontend/src/inputs/CooldownMinutesInput.tsx b/performant_PoCs/hub_snaps/frontend/src/inputs/CooldownMinutesInput.tsx new file mode 100644 index 000000000..aecb394f5 --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/src/inputs/CooldownMinutesInput.tsx @@ -0,0 +1,56 @@ +import { css } from "../../styled-system/css/css.mjs"; + +interface CooldownMinutesInputProps { + label?: string; + value: string; + onChange: (v: string) => void; + onBlur?: () => void; + valid: boolean; + disabled?: boolean; + ariaLabel?: string; +} + +export function CooldownMinutesInput({ + label = "Cooldown (minutes)", + value, + onChange, + onBlur, + valid, + disabled, + ariaLabel, +}: CooldownMinutesInputProps) { + return ( + + ); +} diff --git a/performant_PoCs/hub_snaps/frontend/src/inputs/EdgeBufferPercentInput.tsx b/performant_PoCs/hub_snaps/frontend/src/inputs/EdgeBufferPercentInput.tsx new file mode 100644 index 000000000..6dab4661c --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/src/inputs/EdgeBufferPercentInput.tsx @@ -0,0 +1,48 @@ +import { css } from "../../styled-system/css/css.mjs"; + +interface EdgeBufferPercentInputProps { + value: string; + onChange: (v: string) => void; + onBlur?: () => void; + valid: boolean; + disabled?: boolean; +} + +export function EdgeBufferPercentInput({ + value, + onChange, + onBlur, + valid, + disabled, +}: EdgeBufferPercentInputProps) { + return ( + + ); +} diff --git a/performant_PoCs/hub_snaps/frontend/src/inputs/SliderControl.tsx b/performant_PoCs/hub_snaps/frontend/src/inputs/SliderControl.tsx new file mode 100644 index 000000000..1e7694c3c --- /dev/null +++ b/performant_PoCs/hub_snaps/frontend/src/inputs/SliderControl.tsx @@ -0,0 +1,74 @@ +import { css } from "../../styled-system/css/css.mjs"; +import { ReactNode, useCallback } from "react"; + +export interface SliderControlProps { + label?: ReactNode; + value: number; + onChange: (v: number) => void; + onCommit?: (v: number) => void; + min?: number; + max?: number; + step?: number; + disabled?: boolean; + id?: string; + "aria-label"?: string; +} + +export function SliderControl({ + label, + value, + onChange, + onCommit, + min = 0, + max = 1, + step = 0.01, + disabled, + id, + ...aria +}: SliderControlProps) { + const commit = useCallback(() => onCommit?.(value), [onCommit, value]); + + return ( +
+ {label && } + onChange(parseFloat(e.target.value))} + onMouseUp={commit} + onTouchEnd={commit} + onBlur={commit} + onKeyUp={(e) => (e.key === "Enter" || e.key === " ") && commit()} + disabled={disabled} + className={css({ + width: "100%", + appearance: "none", + height: "4px", + borderRadius: "full", + backgroundColor: "gray.300", + "&::-webkit-slider-thumb": { + appearance: "none", + width: "12px", + height: "12px", + borderRadius: "full", + backgroundColor: "blue.500", + cursor: "pointer", + }, + "&::-moz-range-thumb": { + appearance: "none", + width: "12px", + height: "12px", + borderRadius: "full", + backgroundColor: "blue.500", + cursor: "pointer", + }, + })} + {...aria} + /> +
+ ); +} From 503a57a2bf50b03dd854d7b38fe0ae4e624f0a96 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Mon, 27 Oct 2025 14:16:09 +0100 Subject: [PATCH 15/39] Pre commit --- performant_PoCs/hub_snaps/backend/src/main.py | 38 +++++++---- .../backend/src/utils/helper_functions.py | 10 +-- .../utils/snap_utils/custon_snap_process.py | 66 +++++++++++++++---- .../backend/src/utils/snap_utils/extras.py | 18 +++-- .../backend/src/utils/snap_utils/helpers.py | 18 +++-- 5 files changed, 107 insertions(+), 43 deletions(-) diff --git a/performant_PoCs/hub_snaps/backend/src/main.py b/performant_PoCs/hub_snaps/backend/src/main.py index 22a7f7313..2be36c29b 100644 --- a/performant_PoCs/hub_snaps/backend/src/main.py +++ b/performant_PoCs/hub_snaps/backend/src/main.py @@ -1,5 +1,4 @@ from pathlib import Path -import numpy as np from dotenv import load_dotenv import os from functools import partial @@ -19,7 +18,7 @@ extract_text_embeddings, extract_image_prompt_embeddings, base64_to_cv2_image, - make_dummy_features + make_dummy_features, ) from utils.arguments import initialize_argparser from utils.annotation_node import AnnotationNode @@ -230,7 +229,9 @@ def class_update_service(new_classes: list[str]): print("List of new classes empty, skipping.") return if len(new_classes) > MAX_NUM_CLASSES: - print(f"Too many classes ({len(new_classes)}) > {MAX_NUM_CLASSES}, skipping.") + print( + f"Too many classes ({len(new_classes)}) > {MAX_NUM_CLASSES}, skipping." + ) return feats = extract_text_embeddings( @@ -243,18 +244,26 @@ def class_update_service(new_classes: list[str]): nn_txt.addTensor( "texts", feats, - dataType=(dai.TensorInfo.DataType.FP16 if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F), + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), ) textInputQueue.send(nn_txt) - dummy = make_dummy_features(MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision) + dummy = make_dummy_features( + MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision + ) nn_img = dai.NNData() nn_img.addTensor( "image_prompts", dummy, - dataType=(dai.TensorInfo.DataType.FP16 if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F), + dataType=( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), ) imagePromptInputQueue.send(nn_img) @@ -377,7 +386,6 @@ def snap_collection_service(payload): thr_raw = lcfg.get("threshold", None) if enable: - if thr_raw is None: return {"ok": False, "reason": "low_conf_threshold_required"} @@ -425,10 +433,10 @@ def snap_collection_service(payload): cond_gate.reset(["lost_mid"]) any_active = cond_gate.enabled and ( - cond_gate.is_key_enabled("timed") - or cond_gate.is_key_enabled("no_detections") - or cond_gate.is_key_enabled("low_conf") - or cond_gate.is_key_enabled("lost_mid") + cond_gate.is_key_enabled("timed") + or cond_gate.is_key_enabled("no_detections") + or cond_gate.is_key_enabled("low_conf") + or cond_gate.is_key_enabled("lost_mid") ) snaps_producer.setRunning(any_active) if any_active: @@ -437,7 +445,9 @@ def snap_collection_service(payload): return {"ok": True} visualizer.registerService("Class Update Service", class_update_service) - visualizer.registerService("Threshold Update Service", conf_threshold_update_service) + visualizer.registerService( + "Threshold Update Service", conf_threshold_update_service + ) visualizer.registerService("Image Upload Service", image_upload_service) visualizer.registerService("BBox Prompt Service", bbox_prompt_service) visualizer.registerService("Snap Collection Service", snap_collection_service) diff --git a/performant_PoCs/hub_snaps/backend/src/utils/helper_functions.py b/performant_PoCs/hub_snaps/backend/src/utils/helper_functions.py index 42a533292..4057cae31 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/helper_functions.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/helper_functions.py @@ -83,9 +83,7 @@ def extract_text_embeddings( }, )[0] - textual_output /= np.linalg.norm( - textual_output, ord=2, axis=-1, keepdims=True - ) + textual_output /= np.linalg.norm(textual_output, ord=2, axis=-1, keepdims=True) text_features = pad_and_quantize_features( textual_output, max_num_classes, model_name, precision @@ -131,9 +129,7 @@ def extract_image_prompt_embeddings( prompts = np.asarray(mask_prompt, dtype=np.float32) if prompts.ndim == 2: if prompts.shape != (80, 80): - prompts = cv2.resize( - prompts, (80, 80), interpolation=cv2.INTER_NEAREST - ) + prompts = cv2.resize(prompts, (80, 80), interpolation=cv2.INTER_NEAREST) prompts = prompts[None, None, :, :] elif prompts.shape == (1, 1, 80, 80): pass @@ -210,4 +206,4 @@ def make_dummy_features(max_num_classes: int, model_name: str, precision: str): if precision == "fp16": return np.zeros((1, 512, max_num_classes), dtype=np.float16) qzp = int(round(QUANT_VALUES.get(model_name, {}).get("quant_zero_point", 0))) - return np.full((1, 512, max_num_classes), qzp, dtype=np.uint8) \ No newline at end of file + return np.full((1, 512, max_num_classes), qzp, dtype=np.uint8) diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/custon_snap_process.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/custon_snap_process.py index f416799e4..32a10be1e 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/custon_snap_process.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/custon_snap_process.py @@ -2,8 +2,15 @@ import depthai as dai from depthai_nodes.node import SnapsProducer from .conditions_gate import ConditionsGate -from .helpers import (_send_snap, _track_id, _label_idx_name, _status_is_lost, - _roi_center_area_norm, _LOSTMID_STATE, _status_is_tracked) +from .helpers import ( + _send_snap, + _track_id, + _label_idx_name, + _status_is_lost, + _roi_center_area_norm, + _LOSTMID_STATE, + _status_is_tracked, +) from .extras import build_extras @@ -19,8 +26,14 @@ def custom_snap_process( ): # timed if cond_gate.is_key_enabled("timed") and cond_gate.hit("timed"): - extras = build_extras(model, det_data, class_names) if det_data is not None else {"model": model} - if _send_snap("Timing Snap", producer, frame, ["timing_snap"], extras, det_data): + extras = ( + build_extras(model, det_data, class_names) + if det_data is not None + else {"model": model} + ) + if _send_snap( + "Timing Snap", producer, frame, ["timing_snap"], extras, det_data + ): print("[Timed] Snap sent!") dets = getattr(det_data, "detections", None) or [] @@ -29,24 +42,45 @@ def custom_snap_process( # no detections if det_data is not None and num == 0: if cond_gate.is_key_enabled("no_detections") and cond_gate.hit("no_detections"): - if _send_snap("No Detections", producer, frame, ["no_detections"], {"model": model, "reason": "no_detections"}): + if _send_snap( + "No Detections", + producer, + frame, + ["no_detections"], + {"model": model, "reason": "no_detections"}, + ): print("[NoDet] Snap sent") # low confidence if det_data is not None and cond_gate.is_key_enabled("low_conf"): thr = None if runtime is None else runtime.get("low_conf_thresh") if thr is not None: - has_low = any(float(getattr(d, "confidence", 1.0)) < float(thr) for d in dets) + has_low = any( + float(getattr(d, "confidence", 1.0)) < float(thr) for d in dets + ) if has_low and cond_gate.hit("low_conf"): - min_conf = min(float(getattr(d, "confidence", 1.0)) for d in dets) if dets else 1.0 + min_conf = ( + min(float(getattr(d, "confidence", 1.0)) for d in dets) + if dets + else 1.0 + ) extras = { "model": model, "reason": "low_confidence", "min_conf": f"{min_conf:.3f}", "threshold": f"{float(thr):.3f}", } - if _send_snap("Low Confidence", producer, frame, ["low_confidence"], extras, det_data): - print(f"[LowConf] Snap sent (min={min_conf:.3f} < thr={float(thr):.3f})") + if _send_snap( + "Low Confidence", + producer, + frame, + ["low_confidence"], + extras, + det_data, + ): + print( + f"[LowConf] Snap sent (min={min_conf:.3f} < thr={float(thr):.3f})" + ) # lost detection if tracklets_msg is not None and cond_gate.is_key_enabled("lost_mid"): @@ -68,7 +102,9 @@ def custom_snap_process( rc = _roi_center_area_norm(t) if rc is not None: cx, cy, area = rc - if (margin <= cx <= 1.0 - margin) and (margin <= cy <= 1.0 - margin): + if (margin <= cx <= 1.0 - margin) and ( + margin <= cy <= 1.0 - margin + ): if cond_gate.hit("lost_mid"): lbl_idx, lbl_name = _label_idx_name(t, class_names) extras = { @@ -80,11 +116,17 @@ def custom_snap_process( "area_frac": f"{area:.6f}", "margin": f"{margin:.3f}", } - if _send_snap("Lost in middle", producer, frame, ["lost_in_middle"], extras, det_data): + if _send_snap( + "Lost in middle", + producer, + frame, + ["lost_in_middle"], + extras, + det_data, + ): fired = True _LOSTMID_STATE.prev_tracked[tid] = bool(is_tracked_now) if fired: print("[LostMid] Snap(s) sent") - diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/extras.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/extras.py index c701d89ea..0a7b1ae25 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/extras.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/extras.py @@ -11,7 +11,7 @@ def build_extras( ) -> dict[str, str]: dets = getattr(det_data, "detections", None) or [] labels = [getattr(d, "label", -1) for d in dets[:20]] - confs = [getattr(d, "confidence", 0.0) for d in dets[:20]] + confs = [getattr(d, "confidence", 0.0) for d in dets[:20]] extras: dict = { "model": model, @@ -22,11 +22,17 @@ def build_extras( if isinstance(det_data, dai.ImgDetections): xyxy = [ - (getattr(d, "xmin", 0.0), getattr(d, "ymin", 0.0), - getattr(d, "xmax", 0.0), getattr(d, "ymax", 0.0)) + ( + getattr(d, "xmin", 0.0), + getattr(d, "ymin", 0.0), + getattr(d, "xmax", 0.0), + getattr(d, "ymax", 0.0), + ) for d in dets[:10] ] - extras["detection_xyxy"] = ";".join(f"{x1:.3f},{y1:.3f},{x2:.3f},{y2:.3f}" for x1, y1, x2, y2 in xyxy) + extras["detection_xyxy"] = ";".join( + f"{x1:.3f},{y1:.3f},{x2:.3f},{y2:.3f}" for x1, y1, x2, y2 in xyxy + ) names = [] if dets: for d in dets[:20]: @@ -42,7 +48,9 @@ def build_extras( cxcywh.append((c.x, c.y, s.width, s.height)) except Exception: pass - extras["detection_cxcywh"] = ";".join(f"{cx:.3f},{cy:.3f},{w:.3f},{h:.3f}" for cx, cy, w, h in cxcywh) + extras["detection_cxcywh"] = ";".join( + f"{cx:.3f},{cy:.3f},{w:.3f},{h:.3f}" for cx, cy, w, h in cxcywh + ) names = [] for d in dets[:20]: try: diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py index 7914c5233..aba08cf05 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py @@ -32,8 +32,14 @@ def _label_idx_name(tracklet, class_names: List[str]) -> Tuple[int, str]: return idx, name -def _send_snap(name: str, producer: SnapsProducer, frame: dai.ImgFrame, tags: List[str], extras: dict, - detections: dai.ImgDetections = None) -> bool: +def _send_snap( + name: str, + producer: SnapsProducer, + frame: dai.ImgFrame, + tags: List[str], + extras: dict, + detections: dai.ImgDetections = None, +) -> bool: return producer.sendSnap(name, frame, detections, tags, extras) @@ -59,7 +65,8 @@ def _roi_center_area_norm(t) -> Optional[Tuple[float, float, float]]: roi = getattr(t, "roi", None) if roi is not None: try: - tl = roi.topLeft(); br = roi.bottomRight() + tl = roi.topLeft() + br = roi.bottomRight() x0, y0, x1, y1 = float(tl.x), float(tl.y), float(br.x), float(br.y) cx, cy = 0.5 * (x0 + x1), 0.5 * (y0 + y1) return cx, cy, max(0.0, (x1 - x0) * (y1 - y0)) @@ -69,6 +76,7 @@ def _roi_center_area_norm(t) -> Optional[Tuple[float, float, float]]: if d is not None: x = float(getattr(d, "x", getattr(d, "xmin", 0.0))) y = float(getattr(d, "y", getattr(d, "ymin", 0.0))) - w = float(getattr(d, "width", 0.0)); h = float(getattr(d, "height", 0.0)) - return x + 0.5*w, y + 0.5*h, max(0.0, w*h) + w = float(getattr(d, "width", 0.0)) + h = float(getattr(d, "height", 0.0)) + return x + 0.5 * w, y + 0.5 * h, max(0.0, w * h) return None From 7408fbcdb6c34b699aded116ee7fd813f87b1586 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Mon, 27 Oct 2025 14:18:27 +0100 Subject: [PATCH 16/39] fixed init --- performant_PoCs/hub_snaps/backend/src/main.py | 6 ++---- .../hub_snaps/backend/src/utils/snap_utils/__init__.py | 2 -- 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/performant_PoCs/hub_snaps/backend/src/main.py b/performant_PoCs/hub_snaps/backend/src/main.py index 2be36c29b..5dc2a5d42 100644 --- a/performant_PoCs/hub_snaps/backend/src/main.py +++ b/performant_PoCs/hub_snaps/backend/src/main.py @@ -23,10 +23,8 @@ from utils.arguments import initialize_argparser from utils.annotation_node import AnnotationNode from utils.frame_cache_node import FrameCacheNode -from utils.snap_utils import ( - custom_snap_process, - ConditionsGate, -) +from utils.snap_utils.conditions_gate import ConditionsGate +from utils.snap_utils.custon_snap_process import custom_snap_process load_dotenv(override=True) diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/__init__.py b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/__init__.py index 46db19bba..e69de29bb 100644 --- a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/__init__.py +++ b/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/__init__.py @@ -1,2 +0,0 @@ -from .conditions_gate import ConditionsGate -from .custon_snap_process import custom_snap_process From 6db11ea9f37d042c02cb6c95f0949ad580a29820 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Wed, 29 Oct 2025 19:33:21 +0100 Subject: [PATCH 17/39] Added FE fixes to keep data on reload + background of notifications --- performant_PoCs/hub_snaps/backend/src/main.py | 61 ++++++++++++++- .../hub_snaps/frontend/src/App.tsx | 76 ++++++++++++++++++- .../hub_snaps/frontend/src/ClassSelector.tsx | 16 +++- .../frontend/src/ConfidenceSlider.tsx | 12 ++- .../hub_snaps/frontend/src/Notifications.tsx | 2 +- .../frontend/src/SnapConditionsPanel.tsx | 66 +++++++++++++++- 6 files changed, 219 insertions(+), 14 deletions(-) diff --git a/performant_PoCs/hub_snaps/backend/src/main.py b/performant_PoCs/hub_snaps/backend/src/main.py index 5dc2a5d42..ae2aac221 100644 --- a/performant_PoCs/hub_snaps/backend/src/main.py +++ b/performant_PoCs/hub_snaps/backend/src/main.py @@ -148,22 +148,25 @@ cond_gate = ConditionsGate(default_cooldown_s=0.0, enabled=True) cond_gate.set_key_enabled("no_detections", False) - cond_gate.set_cooldown("no_detections", 15.0) + cond_gate.set_cooldown("no_detections", 300.0) cond_gate.set_key_enabled("timed", False) - cond_gate.set_cooldown("timed", 15.0) + cond_gate.set_cooldown("timed", 300.0) cond_gate.set_key_enabled("low_conf", False) - cond_gate.set_cooldown("low_conf", 15.0) + cond_gate.set_cooldown("low_conf", 300.0) cond_gate.set_key_enabled("lost_mid", False) - cond_gate.set_cooldown("lost_mid", 15.0) + cond_gate.set_cooldown("lost_mid", 300.0) _runtime = { "newdet_running": False, "conf_threshold": CONFIDENCE_THRESHOLD, "lost_mid_margin": 0.20, + "snapping_running": False, } + + current_classes = CLASS_NAMES.copy() object_tracker = pipeline.create(dai.node.ObjectTracker) object_tracker.setTrackerType(dai.TrackerType.SHORT_TERM_IMAGELESS) @@ -267,6 +270,9 @@ def class_update_service(new_classes: list[str]): update_labels(new_classes, offset=0) + current_classes.clear() + current_classes.extend(new_classes) + print(f"Classes updated (YOLOE text): {new_classes}") def conf_threshold_update_service(new_conf_threshold: float): @@ -351,6 +357,8 @@ def snap_collection_service(payload): if not isinstance(payload, dict): return {"ok": False, "reason": "payload_must_be_dict"} + prev_running = bool(_runtime.get("snapping_running", False)) + # timed tcfg = payload.get("timed") if isinstance(tcfg, dict): @@ -436,12 +444,56 @@ def snap_collection_service(payload): or cond_gate.is_key_enabled("low_conf") or cond_gate.is_key_enabled("lost_mid") ) + + if any_active and not prev_running: + cond_gate.reset(["timed", "no_detections", "low_conf", "lost_mid"]) + snaps_producer.setRunning(any_active) + _runtime["snapping_running"] = any_active if any_active: snaps_producer.setTimeInterval(base_dt_seconds) return {"ok": True} + def get_config_service(_payload=None): + """ + Returns the current backend configuration state so FE can restore UI. + Cooldowns returned in minutes for FE display. + """ + + def s_to_min(seconds: float) -> float: + try: + return float(seconds) / 60.0 if float(seconds) > 0 else 0.0 + except Exception: + return 0.0 + + config = { + "classes": current_classes.copy(), + "confidence_threshold": _runtime["conf_threshold"], + "snapping": { + "running": _runtime.get("snapping_running", False), + "timed": { + "enabled": cond_gate.is_key_enabled("timed"), + "interval": s_to_min(cond_gate.get_cooldown("timed")), + }, + "noDetections": { + "enabled": cond_gate.is_key_enabled("no_detections"), + "cooldown": s_to_min(cond_gate.get_cooldown("no_detections")), + }, + "lowConfidence": { + "enabled": cond_gate.is_key_enabled("low_conf"), + "threshold": _runtime.get("low_conf_thresh", 0.30), + "cooldown": s_to_min(cond_gate.get_cooldown("low_conf")), + }, + "lostMid": { + "enabled": cond_gate.is_key_enabled("lost_mid"), + "cooldown": s_to_min(cond_gate.get_cooldown("lost_mid")), + "margin": _runtime.get("lost_mid_margin", 0.20), + }, + }, + } + return config + visualizer.registerService("Class Update Service", class_update_service) visualizer.registerService( "Threshold Update Service", conf_threshold_update_service @@ -449,6 +501,7 @@ def snap_collection_service(payload): visualizer.registerService("Image Upload Service", image_upload_service) visualizer.registerService("BBox Prompt Service", bbox_prompt_service) visualizer.registerService("Snap Collection Service", snap_collection_service) + visualizer.registerService("Get Config Service", get_config_service) print("Pipeline created.") diff --git a/performant_PoCs/hub_snaps/frontend/src/App.tsx b/performant_PoCs/hub_snaps/frontend/src/App.tsx index 167805e06..3f21b63c1 100644 --- a/performant_PoCs/hub_snaps/frontend/src/App.tsx +++ b/performant_PoCs/hub_snaps/frontend/src/App.tsx @@ -7,6 +7,18 @@ import { SnapConditionsPanel } from "./SnapConditionsPanel.tsx"; import { useCallback, useEffect, useRef, useState } from "react"; import { useNotifications } from "./Notifications.tsx"; +interface BackendConfig { + classes: string[]; + confidence_threshold: number; + snapping: { + running: boolean; + timed: { enabled: boolean; interval: number }; + noDetections: { enabled: boolean; cooldown: number }; + lowConfidence: { enabled: boolean; threshold: number; cooldown: number }; + lostMid: { enabled: boolean; cooldown: number; margin: number }; + }; +} + function App() { const connection = useConnection(); const streamContainerRef = useRef(null); @@ -14,6 +26,8 @@ function App() { const [isDrawing, setIsDrawing] = useState(false); const [dragStart, setDragStart] = useState<{ x: number; y: number } | null>(null); const [currentRect, setCurrentRect] = useState<{ x: number; y: number; w: number; h: number } | null>(null); + const [backendConfig, setBackendConfig] = useState(null); + const [configLoaded, setConfigLoaded] = useState(false); const { notify } = useNotifications(); const getUnderlyingMediaAndSize = () => { @@ -184,6 +198,62 @@ function App() { }); }, [connection.connected, notify]); + // Fetch backend config on connection + useEffect(() => { + if (!connection.connected || configLoaded) return; + + const timeoutId = setTimeout(() => { + console.log("[App] Fetching backend configuration..."); + (connection as any).daiConnection?.postToService( + "Get Config Service", + null, + (response: any) => { + if (response === null || response === undefined) { + console.log("[App] Config service not available - using defaults"); + return; + } + + let config: BackendConfig | null = null; + try { + let obj: any = response; + const td = new TextDecoder('utf-8'); + const view = new Uint8Array(obj.buffer, obj.byteOffset, obj.byteLength); + const jsonStr = td.decode(view); + obj = JSON.parse(jsonStr); + + if (obj && obj.data && typeof obj.data === 'object') { + obj = obj.data; + } + if (obj && typeof obj === 'object' && 'classes' in obj) { + config = obj as BackendConfig; + } + } catch (e) { + console.error('[App] Failed to parse service response:', e); + } + + if (config && Array.isArray(config.classes) && typeof config.confidence_threshold === 'number') { + setBackendConfig(config); + setConfigLoaded(true); + console.log("[App] Config restored from backend"); + notify("Configuration restored from backend", { type: "success", durationMs: 2000 }); + } else { + console.log("[App] Invalid config format - using defaults"); + } + } + ); + }, 1500); + + return () => clearTimeout(timeoutId); + }, [connection.connected, configLoaded, notify]); + + // Reset config loaded flag when disconnected + useEffect(() => { + if (!connection.connected) { + setConfigLoaded(false); + setBackendConfig(null); + } + }, [connection.connected]); + const onOverlayMouseDown = (e: any) => { if (!isDrawing) return; const canvas = overlayCanvasRef.current; @@ -275,7 +345,7 @@ function App() { Labels by Text

Enter labels to find (e.g., person, chair, TV).

- + Labels by Image

Upload a photo or draw a box on the stream.

@@ -283,7 +353,7 @@ function App() { Confidence Filter

Detections below this confidence are dropped.

- +

Snap conditions

Choose when to auto-capture a snap.

- +
(null); const connection = useConnection(); const [selectedClasses, setSelectedClasses] = useState(["person", "chair", "TV"]); const { notify } = useNotifications(); + // Update classes from backend config + useEffect(() => { + if (initialClasses && Array.isArray(initialClasses) && initialClasses.length > 0) { + console.log("[ClassSelector] Restoring classes from backend:", initialClasses); + setSelectedClasses([...initialClasses]); // Create new array to ensure update + } + }, [initialClasses]); + const handleSendMessage = () => { if (inputRef.current) { const value = inputRef.current.value; diff --git a/performant_PoCs/hub_snaps/frontend/src/ConfidenceSlider.tsx b/performant_PoCs/hub_snaps/frontend/src/ConfidenceSlider.tsx index 7a504a304..43d9c768c 100644 --- a/performant_PoCs/hub_snaps/frontend/src/ConfidenceSlider.tsx +++ b/performant_PoCs/hub_snaps/frontend/src/ConfidenceSlider.tsx @@ -1,9 +1,9 @@ -import { useState } from "react"; +import { useState, useEffect } from "react"; import { useConnection } from "@luxonis/depthai-viewer-common"; import { SliderControl } from "./inputs/SliderControl"; interface ConfidenceSliderProps { - initialValue?: number; // defaults to 0.5 + initialValue?: number; disabled?: boolean; } @@ -11,6 +11,14 @@ export function ConfidenceSlider({ initialValue = 0.5, disabled }: ConfidenceSli const connection = useConnection(); const [value, setValue] = useState(initialValue); + // Update value from backend config + useEffect(() => { + if (initialValue !== undefined && Number.isFinite(initialValue)) { + console.log("[ConfidenceSlider] Restoring value from backend:", initialValue); + setValue(initialValue); + } + }, [initialValue]); + const handleCommit = (v: number) => { if (Number.isFinite(v)) { connection.daiConnection?.postToService( diff --git a/performant_PoCs/hub_snaps/frontend/src/Notifications.tsx b/performant_PoCs/hub_snaps/frontend/src/Notifications.tsx index 7301845f2..d77312608 100644 --- a/performant_PoCs/hub_snaps/frontend/src/Notifications.tsx +++ b/performant_PoCs/hub_snaps/frontend/src/Notifications.tsx @@ -64,7 +64,7 @@ function Toast({ notification, onClose, index }: { notification: Notification; o return (
{ + if (!initialConfig) { + console.log("[SnapConditionsPanel] No initialConfig provided"); + return; + } + + console.log("[SnapConditionsPanel] Restoring config from backend:", initialConfig); + setRunning(initialConfig.running); + + // Timing + if (initialConfig.timed) { + setTimingEnabled(initialConfig.timed.enabled); + if (initialConfig.timed.interval > 0) { + setTimingStr(initialConfig.timed.interval.toFixed(1)); + } + } + + // No detections + if (initialConfig.noDetections) { + setNoDetEnabled(initialConfig.noDetections.enabled); + if (initialConfig.noDetections.cooldown > 0) { + setNoDetStr(initialConfig.noDetections.cooldown.toFixed(1)); + } + } + + // Low confidence + if (initialConfig.lowConfidence) { + setLowConfEnabled(initialConfig.lowConfidence.enabled); + if (initialConfig.lowConfidence.threshold !== undefined) { + setLowConfThreshold(initialConfig.lowConfidence.threshold); + } + if (initialConfig.lowConfidence.cooldown > 0) { + setLowConfStr(initialConfig.lowConfidence.cooldown.toFixed(1)); + } + } + + // Lost in middle + if (initialConfig.lostMid) { + setLostMidEnabled(initialConfig.lostMid.enabled); + if (initialConfig.lostMid.cooldown > 0) { + setLostMidStr(initialConfig.lostMid.cooldown.toFixed(1)); + } + if (initialConfig.lostMid.margin !== undefined) { + const marginPercent = Math.round(initialConfig.lostMid.margin * 100); + setLostMidPctStr(marginPercent.toString()); + } + } + }, [initialConfig]); + const warnIfTooManyDecimals = (label: string, value: string) => { if (value.trim() !== "" && hasTooManyDecimals(value)) { notify(`${label} allows at most one decimal place.`, { type: "warning" }); From f2d9d23a4d29cf5c7131834ce33b180fc6cd4dae Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Wed, 29 Oct 2025 20:06:00 +0100 Subject: [PATCH 18/39] moved to dir + added README --- .../data-collection/.gitignore | 0 .../data-collection/.oakappignore | 0 apps/data-collection/README.md | 68 + .../data-collection/backend-run.sh | 0 .../depthai_models/yoloe_v8_l_fp16.RVC4.yaml | 0 .../data-collection}/backend/src/main.py | 0 .../backend/src/requirements.txt | 0 .../backend/src/utils/__init__.py | 0 .../backend/src/utils/annotation_node.py | 0 .../backend/src/utils/arguments.py | 0 .../backend/src/utils/frame_cache_node.py | 0 .../backend/src/utils/helper_functions.py | 0 .../backend/src/utils/snap_utils/__init__.py | 0 .../src/utils/snap_utils/conditions_gate.py | 0 .../utils/snap_utils/custon_snap_process.py | 0 .../backend/src/utils/snap_utils/extras.py | 0 .../backend/src/utils/snap_utils/helpers.py | 0 .../data-collection}/frontend/.gitignore | 0 .../data-collection}/frontend/biome.json | 0 .../data-collection}/frontend/index.html | 0 .../frontend/package-lock.json | 0 .../data-collection/frontend/package.json | 0 .../data-collection/frontend/panda.config.ts | 0 .../frontend/postcss.config.mjs | 0 .../data-collection}/frontend/src/App.tsx | 0 .../frontend/src/ClassSelector.tsx | 0 .../frontend/src/ConditionCard.tsx | 0 .../frontend/src/ConfidenceSlider.tsx | 0 .../frontend/src/ImageUploader.tsx | 0 .../frontend/src/Notifications.tsx | 0 .../frontend/src/SnapCollectionButton.tsx | 0 .../frontend/src/SnapConditionsPanel.tsx | 0 .../data-collection}/frontend/src/index.css | 0 .../src/inputs/CooldownMinutesInput.tsx | 0 .../src/inputs/EdgeBufferPercentInput.tsx | 0 .../frontend/src/inputs/SliderControl.tsx | 0 .../data-collection}/frontend/src/main.tsx | 0 .../frontend/src/vite-env.d.ts | 0 .../data-collection}/frontend/src/window.d.ts | 0 .../frontend/tsconfig.app.json | 0 .../data-collection/frontend/tsconfig.json | 0 .../frontend/tsconfig.node.json | 0 .../data-collection/frontend/vite.config.ts | 0 .../data-collection}/oakapp.toml | 2 +- performant_PoCs/hub_snaps/.gitignore | 3 - performant_PoCs/hub_snaps/.oakappignore | 38 - performant_PoCs/hub_snaps/README.md | 73 - performant_PoCs/hub_snaps/backend-run.sh | 3 - .../src/depthai_models/yolo_world_l.RVC4.yaml | 2 - .../yolo_world_l_fp16.RVC4.yaml | 2 - .../hub_snaps/frontend/package-lock.json | 22382 ---------------- .../hub_snaps/frontend/package.json | 31 - .../hub_snaps/frontend/panda.config.ts | 12 - .../hub_snaps/frontend/postcss.config.mjs | 5 - .../hub_snaps/frontend/tsconfig.app.json | 30 - .../hub_snaps/frontend/tsconfig.json | 7 - .../hub_snaps/frontend/tsconfig.node.json | 13 - .../hub_snaps/frontend/vite.config.ts | 22 - 58 files changed, 69 insertions(+), 22624 deletions(-) rename {custom-frontend => apps}/data-collection/.gitignore (100%) rename {custom-frontend => apps}/data-collection/.oakappignore (100%) create mode 100644 apps/data-collection/README.md rename {custom-frontend => apps}/data-collection/backend-run.sh (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/backend/src/depthai_models/yoloe_v8_l_fp16.RVC4.yaml (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/backend/src/main.py (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/backend/src/requirements.txt (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/backend/src/utils/__init__.py (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/backend/src/utils/annotation_node.py (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/backend/src/utils/arguments.py (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/backend/src/utils/frame_cache_node.py (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/backend/src/utils/helper_functions.py (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/backend/src/utils/snap_utils/__init__.py (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/backend/src/utils/snap_utils/conditions_gate.py (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/backend/src/utils/snap_utils/custon_snap_process.py (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/backend/src/utils/snap_utils/extras.py (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/backend/src/utils/snap_utils/helpers.py (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/frontend/.gitignore (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/frontend/biome.json (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/frontend/index.html (100%) rename {custom-frontend => apps}/data-collection/frontend/package-lock.json (100%) rename {custom-frontend => apps}/data-collection/frontend/package.json (100%) rename {custom-frontend => apps}/data-collection/frontend/panda.config.ts (100%) rename {custom-frontend => apps}/data-collection/frontend/postcss.config.mjs (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/frontend/src/App.tsx (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/frontend/src/ClassSelector.tsx (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/frontend/src/ConditionCard.tsx (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/frontend/src/ConfidenceSlider.tsx (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/frontend/src/ImageUploader.tsx (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/frontend/src/Notifications.tsx (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/frontend/src/SnapCollectionButton.tsx (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/frontend/src/SnapConditionsPanel.tsx (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/frontend/src/index.css (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/frontend/src/inputs/CooldownMinutesInput.tsx (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/frontend/src/inputs/EdgeBufferPercentInput.tsx (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/frontend/src/inputs/SliderControl.tsx (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/frontend/src/main.tsx (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/frontend/src/vite-env.d.ts (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/frontend/src/window.d.ts (100%) rename {custom-frontend => apps}/data-collection/frontend/tsconfig.app.json (100%) rename {custom-frontend => apps}/data-collection/frontend/tsconfig.json (100%) rename {custom-frontend => apps}/data-collection/frontend/tsconfig.node.json (100%) rename {custom-frontend => apps}/data-collection/frontend/vite.config.ts (100%) rename {performant_PoCs/hub_snaps => apps/data-collection}/oakapp.toml (94%) delete mode 100644 performant_PoCs/hub_snaps/.gitignore delete mode 100644 performant_PoCs/hub_snaps/.oakappignore delete mode 100644 performant_PoCs/hub_snaps/README.md delete mode 100644 performant_PoCs/hub_snaps/backend-run.sh delete mode 100644 performant_PoCs/hub_snaps/backend/src/depthai_models/yolo_world_l.RVC4.yaml delete mode 100644 performant_PoCs/hub_snaps/backend/src/depthai_models/yolo_world_l_fp16.RVC4.yaml delete mode 100644 performant_PoCs/hub_snaps/frontend/package-lock.json delete mode 100644 performant_PoCs/hub_snaps/frontend/package.json delete mode 100644 performant_PoCs/hub_snaps/frontend/panda.config.ts delete mode 100644 performant_PoCs/hub_snaps/frontend/postcss.config.mjs delete mode 100644 performant_PoCs/hub_snaps/frontend/tsconfig.app.json delete mode 100644 performant_PoCs/hub_snaps/frontend/tsconfig.json delete mode 100644 performant_PoCs/hub_snaps/frontend/tsconfig.node.json delete mode 100644 performant_PoCs/hub_snaps/frontend/vite.config.ts diff --git a/custom-frontend/data-collection/.gitignore b/apps/data-collection/.gitignore similarity index 100% rename from custom-frontend/data-collection/.gitignore rename to apps/data-collection/.gitignore diff --git a/custom-frontend/data-collection/.oakappignore b/apps/data-collection/.oakappignore similarity index 100% rename from custom-frontend/data-collection/.oakappignore rename to apps/data-collection/.oakappignore diff --git a/apps/data-collection/README.md b/apps/data-collection/README.md new file mode 100644 index 000000000..c19e36525 --- /dev/null +++ b/apps/data-collection/README.md @@ -0,0 +1,68 @@ +# Data Collection + +This application combines on-device open-vocabulary detection with an interactive frontend to **auto-collect “snaps” (images + metadata) under configurable conditions**.\ +It runs **YOLOE** on the DepthAI backend, and exposes controls in the UI for: + +- Selecting labels (by **text** or **image prompt**) +- Adjusting **confidence threshold** +- Enabling **snap conditions** (timed, no detections, low confidence, lost-in-middle) + +> **Note:** RVC4 standalone mode only. + +## Features + +- **Class control** + - Update classes by text or upload an image to create a visual prompt +- **Confidence filter** + - Drop detections below a chosen threshold +- **Snapping (auto-capture)** + - **Timed** (periodic) + - **No detections** (when a frame has zero detections) + - **Low confidence** (if any detection falls below threshold) + - **Lost-in-middle** (object disappears inside central area; edge buffer configurable) + - Cooldowns **reset** when snapping is (re)started + +______________________________________________________________________ + +## Usage + +A **Luxonis device** (RVC4) is required + +### Arguments + +```text +-fps FPS_LIMIT, --fps_limit FPS_LIMIT + FPS limit. (default: None) +-ip IP, --ip IP IP address to serve the frontend on. (default: None) +-p PORT, --port PORT + Port to serve the frontend on. (default: None) +--precision PRECISION + Model precision for YOLOE models: int8 (faster) or fp16 (more accurate) (default: fp16) +``` + +## Prerequisites (Frontend) + +Build the FE once before running: + +```bash +cd frontend/ +npm i +npm run build +cd .. +``` + +______________________________________________________________________ + +## Standalone Mode (RVC4) + +Install `oakctl` (see [docs](https://docs.luxonis.com/software-v3/oak-apps/oakctl)), then: + +```bash +oakctl connect +oakctl app run . +``` + +### Remote access + +1. You can upload oakapp to Luxonis Hub via oakctl +2. And then you can just remotely open App UI via App detail --> diff --git a/custom-frontend/data-collection/backend-run.sh b/apps/data-collection/backend-run.sh similarity index 100% rename from custom-frontend/data-collection/backend-run.sh rename to apps/data-collection/backend-run.sh diff --git a/performant_PoCs/hub_snaps/backend/src/depthai_models/yoloe_v8_l_fp16.RVC4.yaml b/apps/data-collection/backend/src/depthai_models/yoloe_v8_l_fp16.RVC4.yaml similarity index 100% rename from performant_PoCs/hub_snaps/backend/src/depthai_models/yoloe_v8_l_fp16.RVC4.yaml rename to apps/data-collection/backend/src/depthai_models/yoloe_v8_l_fp16.RVC4.yaml diff --git a/performant_PoCs/hub_snaps/backend/src/main.py b/apps/data-collection/backend/src/main.py similarity index 100% rename from performant_PoCs/hub_snaps/backend/src/main.py rename to apps/data-collection/backend/src/main.py diff --git a/performant_PoCs/hub_snaps/backend/src/requirements.txt b/apps/data-collection/backend/src/requirements.txt similarity index 100% rename from performant_PoCs/hub_snaps/backend/src/requirements.txt rename to apps/data-collection/backend/src/requirements.txt diff --git a/performant_PoCs/hub_snaps/backend/src/utils/__init__.py b/apps/data-collection/backend/src/utils/__init__.py similarity index 100% rename from performant_PoCs/hub_snaps/backend/src/utils/__init__.py rename to apps/data-collection/backend/src/utils/__init__.py diff --git a/performant_PoCs/hub_snaps/backend/src/utils/annotation_node.py b/apps/data-collection/backend/src/utils/annotation_node.py similarity index 100% rename from performant_PoCs/hub_snaps/backend/src/utils/annotation_node.py rename to apps/data-collection/backend/src/utils/annotation_node.py diff --git a/performant_PoCs/hub_snaps/backend/src/utils/arguments.py b/apps/data-collection/backend/src/utils/arguments.py similarity index 100% rename from performant_PoCs/hub_snaps/backend/src/utils/arguments.py rename to apps/data-collection/backend/src/utils/arguments.py diff --git a/performant_PoCs/hub_snaps/backend/src/utils/frame_cache_node.py b/apps/data-collection/backend/src/utils/frame_cache_node.py similarity index 100% rename from performant_PoCs/hub_snaps/backend/src/utils/frame_cache_node.py rename to apps/data-collection/backend/src/utils/frame_cache_node.py diff --git a/performant_PoCs/hub_snaps/backend/src/utils/helper_functions.py b/apps/data-collection/backend/src/utils/helper_functions.py similarity index 100% rename from performant_PoCs/hub_snaps/backend/src/utils/helper_functions.py rename to apps/data-collection/backend/src/utils/helper_functions.py diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/__init__.py b/apps/data-collection/backend/src/utils/snap_utils/__init__.py similarity index 100% rename from performant_PoCs/hub_snaps/backend/src/utils/snap_utils/__init__.py rename to apps/data-collection/backend/src/utils/snap_utils/__init__.py diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/conditions_gate.py b/apps/data-collection/backend/src/utils/snap_utils/conditions_gate.py similarity index 100% rename from performant_PoCs/hub_snaps/backend/src/utils/snap_utils/conditions_gate.py rename to apps/data-collection/backend/src/utils/snap_utils/conditions_gate.py diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/custon_snap_process.py b/apps/data-collection/backend/src/utils/snap_utils/custon_snap_process.py similarity index 100% rename from performant_PoCs/hub_snaps/backend/src/utils/snap_utils/custon_snap_process.py rename to apps/data-collection/backend/src/utils/snap_utils/custon_snap_process.py diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/extras.py b/apps/data-collection/backend/src/utils/snap_utils/extras.py similarity index 100% rename from performant_PoCs/hub_snaps/backend/src/utils/snap_utils/extras.py rename to apps/data-collection/backend/src/utils/snap_utils/extras.py diff --git a/performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py b/apps/data-collection/backend/src/utils/snap_utils/helpers.py similarity index 100% rename from performant_PoCs/hub_snaps/backend/src/utils/snap_utils/helpers.py rename to apps/data-collection/backend/src/utils/snap_utils/helpers.py diff --git a/performant_PoCs/hub_snaps/frontend/.gitignore b/apps/data-collection/frontend/.gitignore similarity index 100% rename from performant_PoCs/hub_snaps/frontend/.gitignore rename to apps/data-collection/frontend/.gitignore diff --git a/performant_PoCs/hub_snaps/frontend/biome.json b/apps/data-collection/frontend/biome.json similarity index 100% rename from performant_PoCs/hub_snaps/frontend/biome.json rename to apps/data-collection/frontend/biome.json diff --git a/performant_PoCs/hub_snaps/frontend/index.html b/apps/data-collection/frontend/index.html similarity index 100% rename from performant_PoCs/hub_snaps/frontend/index.html rename to apps/data-collection/frontend/index.html diff --git a/custom-frontend/data-collection/frontend/package-lock.json b/apps/data-collection/frontend/package-lock.json similarity index 100% rename from custom-frontend/data-collection/frontend/package-lock.json rename to apps/data-collection/frontend/package-lock.json diff --git a/custom-frontend/data-collection/frontend/package.json b/apps/data-collection/frontend/package.json similarity index 100% rename from custom-frontend/data-collection/frontend/package.json rename to apps/data-collection/frontend/package.json diff --git a/custom-frontend/data-collection/frontend/panda.config.ts b/apps/data-collection/frontend/panda.config.ts similarity index 100% rename from custom-frontend/data-collection/frontend/panda.config.ts rename to apps/data-collection/frontend/panda.config.ts diff --git a/custom-frontend/data-collection/frontend/postcss.config.mjs b/apps/data-collection/frontend/postcss.config.mjs similarity index 100% rename from custom-frontend/data-collection/frontend/postcss.config.mjs rename to apps/data-collection/frontend/postcss.config.mjs diff --git a/performant_PoCs/hub_snaps/frontend/src/App.tsx b/apps/data-collection/frontend/src/App.tsx similarity index 100% rename from performant_PoCs/hub_snaps/frontend/src/App.tsx rename to apps/data-collection/frontend/src/App.tsx diff --git a/performant_PoCs/hub_snaps/frontend/src/ClassSelector.tsx b/apps/data-collection/frontend/src/ClassSelector.tsx similarity index 100% rename from performant_PoCs/hub_snaps/frontend/src/ClassSelector.tsx rename to apps/data-collection/frontend/src/ClassSelector.tsx diff --git a/performant_PoCs/hub_snaps/frontend/src/ConditionCard.tsx b/apps/data-collection/frontend/src/ConditionCard.tsx similarity index 100% rename from performant_PoCs/hub_snaps/frontend/src/ConditionCard.tsx rename to apps/data-collection/frontend/src/ConditionCard.tsx diff --git a/performant_PoCs/hub_snaps/frontend/src/ConfidenceSlider.tsx b/apps/data-collection/frontend/src/ConfidenceSlider.tsx similarity index 100% rename from performant_PoCs/hub_snaps/frontend/src/ConfidenceSlider.tsx rename to apps/data-collection/frontend/src/ConfidenceSlider.tsx diff --git a/performant_PoCs/hub_snaps/frontend/src/ImageUploader.tsx b/apps/data-collection/frontend/src/ImageUploader.tsx similarity index 100% rename from performant_PoCs/hub_snaps/frontend/src/ImageUploader.tsx rename to apps/data-collection/frontend/src/ImageUploader.tsx diff --git a/performant_PoCs/hub_snaps/frontend/src/Notifications.tsx b/apps/data-collection/frontend/src/Notifications.tsx similarity index 100% rename from performant_PoCs/hub_snaps/frontend/src/Notifications.tsx rename to apps/data-collection/frontend/src/Notifications.tsx diff --git a/performant_PoCs/hub_snaps/frontend/src/SnapCollectionButton.tsx b/apps/data-collection/frontend/src/SnapCollectionButton.tsx similarity index 100% rename from performant_PoCs/hub_snaps/frontend/src/SnapCollectionButton.tsx rename to apps/data-collection/frontend/src/SnapCollectionButton.tsx diff --git a/performant_PoCs/hub_snaps/frontend/src/SnapConditionsPanel.tsx b/apps/data-collection/frontend/src/SnapConditionsPanel.tsx similarity index 100% rename from performant_PoCs/hub_snaps/frontend/src/SnapConditionsPanel.tsx rename to apps/data-collection/frontend/src/SnapConditionsPanel.tsx diff --git a/performant_PoCs/hub_snaps/frontend/src/index.css b/apps/data-collection/frontend/src/index.css similarity index 100% rename from performant_PoCs/hub_snaps/frontend/src/index.css rename to apps/data-collection/frontend/src/index.css diff --git a/performant_PoCs/hub_snaps/frontend/src/inputs/CooldownMinutesInput.tsx b/apps/data-collection/frontend/src/inputs/CooldownMinutesInput.tsx similarity index 100% rename from performant_PoCs/hub_snaps/frontend/src/inputs/CooldownMinutesInput.tsx rename to apps/data-collection/frontend/src/inputs/CooldownMinutesInput.tsx diff --git a/performant_PoCs/hub_snaps/frontend/src/inputs/EdgeBufferPercentInput.tsx b/apps/data-collection/frontend/src/inputs/EdgeBufferPercentInput.tsx similarity index 100% rename from performant_PoCs/hub_snaps/frontend/src/inputs/EdgeBufferPercentInput.tsx rename to apps/data-collection/frontend/src/inputs/EdgeBufferPercentInput.tsx diff --git a/performant_PoCs/hub_snaps/frontend/src/inputs/SliderControl.tsx b/apps/data-collection/frontend/src/inputs/SliderControl.tsx similarity index 100% rename from performant_PoCs/hub_snaps/frontend/src/inputs/SliderControl.tsx rename to apps/data-collection/frontend/src/inputs/SliderControl.tsx diff --git a/performant_PoCs/hub_snaps/frontend/src/main.tsx b/apps/data-collection/frontend/src/main.tsx similarity index 100% rename from performant_PoCs/hub_snaps/frontend/src/main.tsx rename to apps/data-collection/frontend/src/main.tsx diff --git a/performant_PoCs/hub_snaps/frontend/src/vite-env.d.ts b/apps/data-collection/frontend/src/vite-env.d.ts similarity index 100% rename from performant_PoCs/hub_snaps/frontend/src/vite-env.d.ts rename to apps/data-collection/frontend/src/vite-env.d.ts diff --git a/performant_PoCs/hub_snaps/frontend/src/window.d.ts b/apps/data-collection/frontend/src/window.d.ts similarity index 100% rename from performant_PoCs/hub_snaps/frontend/src/window.d.ts rename to apps/data-collection/frontend/src/window.d.ts diff --git a/custom-frontend/data-collection/frontend/tsconfig.app.json b/apps/data-collection/frontend/tsconfig.app.json similarity index 100% rename from custom-frontend/data-collection/frontend/tsconfig.app.json rename to apps/data-collection/frontend/tsconfig.app.json diff --git a/custom-frontend/data-collection/frontend/tsconfig.json b/apps/data-collection/frontend/tsconfig.json similarity index 100% rename from custom-frontend/data-collection/frontend/tsconfig.json rename to apps/data-collection/frontend/tsconfig.json diff --git a/custom-frontend/data-collection/frontend/tsconfig.node.json b/apps/data-collection/frontend/tsconfig.node.json similarity index 100% rename from custom-frontend/data-collection/frontend/tsconfig.node.json rename to apps/data-collection/frontend/tsconfig.node.json diff --git a/custom-frontend/data-collection/frontend/vite.config.ts b/apps/data-collection/frontend/vite.config.ts similarity index 100% rename from custom-frontend/data-collection/frontend/vite.config.ts rename to apps/data-collection/frontend/vite.config.ts diff --git a/performant_PoCs/hub_snaps/oakapp.toml b/apps/data-collection/oakapp.toml similarity index 94% rename from performant_PoCs/hub_snaps/oakapp.toml rename to apps/data-collection/oakapp.toml index 1fe86af0d..6bb45cf34 100644 --- a/performant_PoCs/hub_snaps/oakapp.toml +++ b/apps/data-collection/oakapp.toml @@ -1,4 +1,4 @@ -identifier = "com.example.performant-pocs.focused-vision.hub-snaps" +identifier = "com.example.apps.data-collection" entrypoint = ["bash", "-c", "/usr/bin/runsvdir -P /etc/service"] app_version = "1.0.0" diff --git a/performant_PoCs/hub_snaps/.gitignore b/performant_PoCs/hub_snaps/.gitignore deleted file mode 100644 index 6018a0cbe..000000000 --- a/performant_PoCs/hub_snaps/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -clip_textual_hf.onnx -clip_visual_with_projector.onnx -tokenizer.json \ No newline at end of file diff --git a/performant_PoCs/hub_snaps/.oakappignore b/performant_PoCs/hub_snaps/.oakappignore deleted file mode 100644 index 00f160e89..000000000 --- a/performant_PoCs/hub_snaps/.oakappignore +++ /dev/null @@ -1,38 +0,0 @@ -# Python virtual environments -venv/ -.venv/ -env - -# Node.js -# ignore node_modules, it will be reinstalled in the container -node_modules/ - -# Multimedia files -media/ - -# Local models -*.onnx - -# Documentation -README.md - -# VCS -.gitignore -.git/ -.github/ -.gitlab/ - -# The following files are ignored by default -# uncomment a line if you explicitly need it - -# !*.oakapp - -# Python -# !**/.mypy_cache/ -# !**/.ruff_cache/ - -# IDE files -# !**/.idea -# !**/.vscode -# !**/.zed - diff --git a/performant_PoCs/hub_snaps/README.md b/performant_PoCs/hub_snaps/README.md deleted file mode 100644 index 5d95544e3..000000000 --- a/performant_PoCs/hub_snaps/README.md +++ /dev/null @@ -1,73 +0,0 @@ -TODO - - diff --git a/performant_PoCs/hub_snaps/backend-run.sh b/performant_PoCs/hub_snaps/backend-run.sh deleted file mode 100644 index 448b7059b..000000000 --- a/performant_PoCs/hub_snaps/backend-run.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/bin/sh -echo "Starting Backend" -exec python3.12 /app/backend/src/main.py -m yoloe \ No newline at end of file diff --git a/performant_PoCs/hub_snaps/backend/src/depthai_models/yolo_world_l.RVC4.yaml b/performant_PoCs/hub_snaps/backend/src/depthai_models/yolo_world_l.RVC4.yaml deleted file mode 100644 index 6ddc030fc..000000000 --- a/performant_PoCs/hub_snaps/backend/src/depthai_models/yolo_world_l.RVC4.yaml +++ /dev/null @@ -1,2 +0,0 @@ -model: yolo-world-l:640x640-host-decoding -platform: RVC4 \ No newline at end of file diff --git a/performant_PoCs/hub_snaps/backend/src/depthai_models/yolo_world_l_fp16.RVC4.yaml b/performant_PoCs/hub_snaps/backend/src/depthai_models/yolo_world_l_fp16.RVC4.yaml deleted file mode 100644 index f37bae36c..000000000 --- a/performant_PoCs/hub_snaps/backend/src/depthai_models/yolo_world_l_fp16.RVC4.yaml +++ /dev/null @@ -1,2 +0,0 @@ -model: yolo-world-l:640x640-host-decoding-fp16 -platform: RVC4 \ No newline at end of file diff --git a/performant_PoCs/hub_snaps/frontend/package-lock.json b/performant_PoCs/hub_snaps/frontend/package-lock.json deleted file mode 100644 index 5ca548ede..000000000 --- a/performant_PoCs/hub_snaps/frontend/package-lock.json +++ /dev/null @@ -1,22382 +0,0 @@ -{ - "name": "frontend", - "version": "0.0.0", - "lockfileVersion": 3, - "requires": true, - 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"build": "tsc -b && vite build", - "lint": "biome check .", - "lint:fix": "biome check --write .", - "preview": "vite preview" - }, - "dependencies": { - "@luxonis/depthai-viewer-common": "1.2.8", - "react": "^18.3.1", - "react-dom": "^18.3.1", - "react-router": "^7.5.0", - "react-router-dom": "^7.5.0" - }, - "devDependencies": { - "@biomejs/biome": "1.9.4", - "@pandacss/dev": "0.53.0", - "@types/react": "^18.3.20", - "@types/react-dom": "^18.3.6", - "@vitejs/plugin-react": "^4.3.4", - "globals": "^16.0.0", - "typescript": "~5.7.2", - "vite": "^6.3.1" - } -} diff --git a/performant_PoCs/hub_snaps/frontend/panda.config.ts b/performant_PoCs/hub_snaps/frontend/panda.config.ts deleted file mode 100644 index f1c3e8249..000000000 --- a/performant_PoCs/hub_snaps/frontend/panda.config.ts +++ /dev/null @@ -1,12 +0,0 @@ -import { defineConfig, defineGlobalStyles } from "@pandacss/dev"; -import { pandaPreset } from "@luxonis/common-fe-components"; - -export default defineConfig({ - presets: [pandaPreset], - preflight: true, - include: ["./src/**/*.{ts,tsx}"], - exclude: [], - jsxFramework: "react", - outdir: "styled-system", - forceConsistentTypeExtension: true, -}); diff --git a/performant_PoCs/hub_snaps/frontend/postcss.config.mjs b/performant_PoCs/hub_snaps/frontend/postcss.config.mjs deleted file mode 100644 index cdaaad9ae..000000000 --- a/performant_PoCs/hub_snaps/frontend/postcss.config.mjs +++ /dev/null @@ -1,5 +0,0 @@ -export default { - plugins: { - '@pandacss/dev/postcss': {}, - }, -} \ No newline at end of file diff --git a/performant_PoCs/hub_snaps/frontend/tsconfig.app.json b/performant_PoCs/hub_snaps/frontend/tsconfig.app.json deleted file mode 100644 index 8f9007000..000000000 --- a/performant_PoCs/hub_snaps/frontend/tsconfig.app.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "compilerOptions": { - "composite": true, - "tsBuildInfoFile": "./node_modules/.tmp/example.app.tsbuildinfo", - "target": "ESNext", - "useDefineForClassFields": true, - "lib": [ - "ESNext", - "WebWorker", - "DOM", - "DOM.Iterable" - ], - "module": "ESNext", - "skipLibCheck": true, - "moduleResolution": "bundler", - "allowImportingTsExtensions": true, - "resolveJsonModule": true, - "isolatedModules": true, - "moduleDetection": "force", - "noEmit": true, - "jsx": "react-jsx", - "strict": true, - "noUnusedLocals": true, - "noUnusedParameters": true, - "noFallthroughCasesInSwitch": true - }, - "include": [ - "src" - ] -} diff --git a/performant_PoCs/hub_snaps/frontend/tsconfig.json b/performant_PoCs/hub_snaps/frontend/tsconfig.json deleted file mode 100644 index fb1241881..000000000 --- a/performant_PoCs/hub_snaps/frontend/tsconfig.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "files": [], - "references": [ - { "path": "./tsconfig.app.json" }, - { "path": "./tsconfig.node.json" } - ] -} diff --git a/performant_PoCs/hub_snaps/frontend/tsconfig.node.json b/performant_PoCs/hub_snaps/frontend/tsconfig.node.json deleted file mode 100644 index 29ead3807..000000000 --- a/performant_PoCs/hub_snaps/frontend/tsconfig.node.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "compilerOptions": { - "composite": true, - "tsBuildInfoFile": "./node_modules/.tmp/tsconfig-example.node.tsbuildinfo", - "skipLibCheck": true, - "module": "ESNext", - "moduleResolution": "bundler", - "allowSyntheticDefaultImports": true, - "strict": true, - "noEmit": true - }, - "include": ["vite.config.ts"] -} diff --git a/performant_PoCs/hub_snaps/frontend/vite.config.ts b/performant_PoCs/hub_snaps/frontend/vite.config.ts deleted file mode 100644 index 5c678f860..000000000 --- a/performant_PoCs/hub_snaps/frontend/vite.config.ts +++ /dev/null @@ -1,22 +0,0 @@ -import react from '@vitejs/plugin-react'; -import { defineConfig } from 'vite'; - -// https://vite.dev/config/ -export default defineConfig({ - base: "", - plugins: [react(),], - // This is needed by FoxGlove - define: { - global: {}, - }, - worker: { - format: "es", - }, - build: { - rollupOptions: { - output: { - format: "esm", - }, - }, - }, -}); From 45b3f46062ab4172e6fc8382cd26d006bffef6e1 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Wed, 29 Oct 2025 20:08:09 +0100 Subject: [PATCH 19/39] removed old data collection app from custom FE --- custom-frontend/data-collection/README.md | 73 --- .../src/depthai_models/yolo_world_l.RVC4.yaml | 2 - .../yolo_world_l_fp16.RVC4.yaml | 2 - .../depthai_models/yoloe_v8_l_fp16.RVC4.yaml | 2 - .../data-collection/backend/src/main.py | 520 ------------------ .../backend/src/requirements.txt | 13 - .../backend/src/utils/__init__.py | 0 .../backend/src/utils/annotation_node.py | 45 -- .../backend/src/utils/arguments.py | 72 --- .../backend/src/utils/frame_cache_node.py | 20 - .../backend/src/utils/helper_functions.py | 247 --------- .../backend/src/utils/snap_utils.py | 58 -- .../data-collection/frontend/.gitignore | 28 - .../data-collection/frontend/biome.json | 30 - .../data-collection/frontend/index.html | 13 - .../data-collection/frontend/src/App.tsx | 322 ----------- .../frontend/src/ClassSelector.tsx | 66 --- .../frontend/src/ConfidenceSlider.tsx | 70 --- .../frontend/src/ImageUploader.tsx | 108 ---- .../frontend/src/Notifications.tsx | 107 ---- .../frontend/src/SnapCollectionButton.tsx | 174 ------ .../data-collection/frontend/src/index.css | 1 - .../data-collection/frontend/src/main.tsx | 33 -- .../frontend/src/vite-env.d.ts | 1 - .../data-collection/frontend/src/window.d.ts | 7 - .../media/dynamic_yolo_world.gif | Bin 2656194 -> 0 bytes custom-frontend/data-collection/oakapp.toml | 33 -- 27 files changed, 2047 deletions(-) delete mode 100644 custom-frontend/data-collection/README.md delete mode 100644 custom-frontend/data-collection/backend/src/depthai_models/yolo_world_l.RVC4.yaml delete mode 100644 custom-frontend/data-collection/backend/src/depthai_models/yolo_world_l_fp16.RVC4.yaml delete mode 100644 custom-frontend/data-collection/backend/src/depthai_models/yoloe_v8_l_fp16.RVC4.yaml delete mode 100644 custom-frontend/data-collection/backend/src/main.py delete mode 100644 custom-frontend/data-collection/backend/src/requirements.txt delete mode 100644 custom-frontend/data-collection/backend/src/utils/__init__.py delete mode 100644 custom-frontend/data-collection/backend/src/utils/annotation_node.py delete mode 100644 custom-frontend/data-collection/backend/src/utils/arguments.py delete mode 100644 custom-frontend/data-collection/backend/src/utils/frame_cache_node.py delete mode 100644 custom-frontend/data-collection/backend/src/utils/helper_functions.py delete mode 100644 custom-frontend/data-collection/backend/src/utils/snap_utils.py delete mode 100644 custom-frontend/data-collection/frontend/.gitignore delete mode 100644 custom-frontend/data-collection/frontend/biome.json delete mode 100644 custom-frontend/data-collection/frontend/index.html delete mode 100644 custom-frontend/data-collection/frontend/src/App.tsx delete mode 100644 custom-frontend/data-collection/frontend/src/ClassSelector.tsx delete mode 100644 custom-frontend/data-collection/frontend/src/ConfidenceSlider.tsx delete mode 100644 custom-frontend/data-collection/frontend/src/ImageUploader.tsx delete mode 100644 custom-frontend/data-collection/frontend/src/Notifications.tsx delete mode 100644 custom-frontend/data-collection/frontend/src/SnapCollectionButton.tsx delete mode 100644 custom-frontend/data-collection/frontend/src/index.css delete mode 100644 custom-frontend/data-collection/frontend/src/main.tsx delete mode 100644 custom-frontend/data-collection/frontend/src/vite-env.d.ts delete mode 100644 custom-frontend/data-collection/frontend/src/window.d.ts delete mode 100644 custom-frontend/data-collection/media/dynamic_yolo_world.gif delete mode 100644 custom-frontend/data-collection/oakapp.toml diff --git a/custom-frontend/data-collection/README.md b/custom-frontend/data-collection/README.md deleted file mode 100644 index 5d95544e3..000000000 --- a/custom-frontend/data-collection/README.md +++ /dev/null @@ -1,73 +0,0 @@ -TODO - - diff --git a/custom-frontend/data-collection/backend/src/depthai_models/yolo_world_l.RVC4.yaml b/custom-frontend/data-collection/backend/src/depthai_models/yolo_world_l.RVC4.yaml deleted file mode 100644 index 6ddc030fc..000000000 --- a/custom-frontend/data-collection/backend/src/depthai_models/yolo_world_l.RVC4.yaml +++ /dev/null @@ -1,2 +0,0 @@ -model: yolo-world-l:640x640-host-decoding -platform: RVC4 \ No newline at end of file diff --git a/custom-frontend/data-collection/backend/src/depthai_models/yolo_world_l_fp16.RVC4.yaml b/custom-frontend/data-collection/backend/src/depthai_models/yolo_world_l_fp16.RVC4.yaml deleted file mode 100644 index f37bae36c..000000000 --- a/custom-frontend/data-collection/backend/src/depthai_models/yolo_world_l_fp16.RVC4.yaml +++ /dev/null @@ -1,2 +0,0 @@ -model: yolo-world-l:640x640-host-decoding-fp16 -platform: RVC4 \ No newline at end of file diff --git a/custom-frontend/data-collection/backend/src/depthai_models/yoloe_v8_l_fp16.RVC4.yaml b/custom-frontend/data-collection/backend/src/depthai_models/yoloe_v8_l_fp16.RVC4.yaml deleted file mode 100644 index a887528e3..000000000 --- a/custom-frontend/data-collection/backend/src/depthai_models/yoloe_v8_l_fp16.RVC4.yaml +++ /dev/null @@ -1,2 +0,0 @@ -model: yoloe-v8-l:640x640-text-visual-prompt:e23da0f -platform: RVC4 \ No newline at end of file diff --git a/custom-frontend/data-collection/backend/src/main.py b/custom-frontend/data-collection/backend/src/main.py deleted file mode 100644 index 1bd936b49..000000000 --- a/custom-frontend/data-collection/backend/src/main.py +++ /dev/null @@ -1,520 +0,0 @@ -from pathlib import Path -import numpy as np -from dotenv import load_dotenv -import os -from functools import partial - -import depthai as dai -from depthai_nodes.node import ( - ParsingNeuralNetwork, - ImgDetectionsFilter, - ImgFrameOverlay, - ApplyColormap, - SnapsProducer, -) - -from utils.helper_functions import ( - extract_text_embeddings, - extract_image_prompt_embeddings, - base64_to_cv2_image, - QUANT_VALUES, -) -from utils.arguments import initialize_argparser -from utils.annotation_node import AnnotationNode -from utils.frame_cache_node import FrameCacheNode -from utils.snap_utils import custom_snap_process - -load_dotenv(override=True) - -_, args = initialize_argparser() - -if args.api_key: - os.environ["DEPTHAI_HUB_API_KEY"] = args.api_key - -IP = args.ip or "localhost" -PORT = args.port or 8080 - -CLASS_NAMES = ["person", "chair", "TV"] -# For unified YOLOE, 0-79 are text classes, 80-159 are image-prompt classes -CLASS_OFFSET = 0 -MAX_NUM_CLASSES = 80 -CONFIDENCE_THRESHOLD = 0.1 -VISUALIZATION_RESOLUTION = (1080, 1080) - -visualizer = dai.RemoteConnection(serveFrontend=False) -device = dai.Device() -platform = device.getPlatformAsString() - -if platform != "RVC4": - raise ValueError("This example is supported only on RVC4 platform") - -frame_type = dai.ImgFrame.Type.BGR888i - - -def make_dummy_features(max_num_classes: int, model_name: str, precision: str): - if precision == "fp16": - return np.zeros((1, 512, max_num_classes), dtype=np.float16) - qzp = int(round(QUANT_VALUES.get(model_name, {}).get("quant_zero_point", 0))) - return np.full((1, 512, max_num_classes), qzp, dtype=np.uint8) - - -# choose initial features: text for yolo-world/yoloe -text_features = extract_text_embeddings( - class_names=CLASS_NAMES, - max_num_classes=MAX_NUM_CLASSES, - model_name=args.model if args.model != "yolo-world" else "yolo-world", - precision=args.precision, -) -image_prompt_features = None -if args.model == "yoloe": - # send dummy image-prompts initially - image_prompt_features = make_dummy_features( - MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision - ) - -if args.fps_limit is None: - args.fps_limit = 5 - print( - f"\nFPS limit set to {args.fps_limit} for {platform} platform. If you want to set a custom FPS limit, use the --fps_limit flag.\n" - ) - -with dai.Pipeline(device) as pipeline: - print("Creating pipeline...") - - # Model selection with precision-aware YAMLs for YOLOE variants - models_dir = Path(__file__).parent / "depthai_models" - if args.model == "yolo-world": - yaml_base = "yolo_world_l_fp16" if args.precision == "fp16" else "yolo_world_l" - yaml_filename = f"{yaml_base}.{platform}.yaml" - yaml_path = models_dir / yaml_filename - if not yaml_path.exists(): - raise SystemExit( - f"Model YAML not found: {yaml_path}. Ensure the model config exists." - ) - model_description = dai.NNModelDescription.fromYamlFile(str(yaml_path)) - elif args.model == "yoloe": - yaml_base = "yoloe_v8_l_fp16" if args.precision == "fp16" else "yoloe_v8_l" - yaml_filename = f"{yaml_base}.{platform}.yaml" - yaml_path = models_dir / yaml_filename - print(f"YOLOE YAML path: {yaml_path}") - if not yaml_path.exists(): - raise SystemExit( - f"Model YAML not found for YOLOE with precision {args.precision}: {yaml_path}. " - f"YOLOE int8 YAML is not available; run with --precision fp16." - ) - model_description = dai.NNModelDescription.fromYamlFile(str(yaml_path)) - model_description.platform = platform - model_nn_archive = dai.NNArchive(dai.getModelFromZoo(model_description)) - model_w, model_h = model_nn_archive.getInputSize() - - # media/camera input at high resolution for visualization - if args.media_path: - replay = pipeline.create(dai.node.ReplayVideo) - replay.setReplayVideoFile(Path(args.media_path)) - replay.setOutFrameType(dai.ImgFrame.Type.NV12) - replay.setLoop(True) - if args.fps_limit: - replay.setFps(args.fps_limit) - replay.setSize(VISUALIZATION_RESOLUTION[0], VISUALIZATION_RESOLUTION[1]) - video_src_out = replay.out - else: - cam = pipeline.create(dai.node.Camera).build( - boardSocket=dai.CameraBoardSocket.CAM_A - ) - # Request high-res NV12 frames for visualization/encoding - video_src_out = cam.requestOutput( - size=VISUALIZATION_RESOLUTION, - type=dai.ImgFrame.Type.NV12, - fps=args.fps_limit, - ) - - image_manip = pipeline.create(dai.node.ImageManip) - image_manip.setMaxOutputFrameSize(model_w * model_h * 3) - image_manip.initialConfig.setOutputSize(model_w, model_h) - image_manip.initialConfig.setFrameType(frame_type) - video_src_out.link(image_manip.inputImage) - - video_enc = pipeline.create(dai.node.VideoEncoder) - video_enc.setDefaultProfilePreset( - fps=args.fps_limit, profile=dai.VideoEncoderProperties.Profile.H264_MAIN - ) - video_src_out.link(video_enc.input) - - input_node = image_manip.out - - nn_with_parser = pipeline.create(ParsingNeuralNetwork) - nn_with_parser.setNNArchive(model_nn_archive) - nn_with_parser.setBackend("snpe") - nn_with_parser.setBackendProperties( - {"runtime": "dsp", "performance_profile": "default"} - ) - nn_with_parser.setNumInferenceThreads(1) - nn_with_parser.getParser(0).setConfidenceThreshold(CONFIDENCE_THRESHOLD) - - input_node.link(nn_with_parser.inputs["images"]) - - textInputQueue = nn_with_parser.inputs["texts"].createInputQueue() - nn_with_parser.inputs["texts"].setReusePreviousMessage(True) - if args.model == "yoloe": - imagePromptInputQueue = nn_with_parser.inputs[ - "image_prompts" - ].createInputQueue() - nn_with_parser.inputs["image_prompts"].setReusePreviousMessage(True) - - # filter and rename detection labels - det_process_filter = pipeline.create(ImgDetectionsFilter).build(nn_with_parser.out) - annotation_node = pipeline.create(AnnotationNode).build( - det_process_filter.out, - video_src_out, - ) - - # produce snaps - snaps_producer = pipeline.create(SnapsProducer).build( - frame=video_src_out, - msg=det_process_filter.out, - running=False, - process_fn=partial( - custom_snap_process, class_names=CLASS_NAMES, model=args.model - ), - ) - snaps_producer.setUrl("https://events.cloud-stg.luxonis.com") # TODO: Remove - snaps_producer._em.setSourceAppId(os.getenv("OAKAGENT_CONTAINER_ID")) - print("Snaps producer API KEY:", os.getenv("DEPTHAI_HUB_API_KEY")) - - def update_labels(label_names: list[str], offset: int = 0): - det_process_filter.setLabels( - labels=[i for i in range(offset, offset + len(label_names))], keep=True - ) - annotation_node.setLabelEncoding( - {offset + k: v for k, v in enumerate(label_names)} - ) - - # Cache last frame for services that need full frame content - frame_cache = pipeline.create(FrameCacheNode).build(video_src_out) - - if args.model == "yolo-world": - visualizer.addTopic("Video", video_enc.out, "images") - elif args.model == "yoloe": - apply_colormap_node = pipeline.create(ApplyColormap).build(nn_with_parser.out) - overlay_frames_node = pipeline.create(ImgFrameOverlay).build( - video_src_out, - apply_colormap_node.out, - preserve_background=True, - ) - overlay_to_nv12 = pipeline.create(dai.node.ImageManip) - overlay_to_nv12.setMaxOutputFrameSize( - VISUALIZATION_RESOLUTION[0] * VISUALIZATION_RESOLUTION[1] * 3 - ) - overlay_to_nv12.initialConfig.setFrameType(dai.ImgFrame.Type.NV12) - overlay_frames_node.out.link(overlay_to_nv12.inputImage) - - overlay_enc = pipeline.create(dai.node.VideoEncoder) - overlay_enc.setDefaultProfilePreset( - fps=args.fps_limit, profile=dai.VideoEncoderProperties.Profile.H264_MAIN - ) - overlay_to_nv12.out.link(overlay_enc.input) - - visualizer.addTopic("Video", overlay_enc.out, "images") - - visualizer.addTopic("Detections", annotation_node.out) - - def class_update_service(new_classes: list[str]): - """Changes classes to detect based on the user input""" - if len(new_classes) == 0: - print("List of new classes empty, skipping.") - return - if len(new_classes) > MAX_NUM_CLASSES: - print( - f"Number of new classes ({len(new_classes)}) exceeds maximum number of classes ({MAX_NUM_CLASSES}), skipping." - ) - return - CLASS_NAMES = new_classes - text_features = extract_text_embeddings( - class_names=CLASS_NAMES, - max_num_classes=MAX_NUM_CLASSES, - model_name=args.model, - precision=args.precision, - ) - inputNNData = dai.NNData() - inputNNData.addTensor( - "texts", - text_features, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - textInputQueue.send(inputNNData) - # In unified YOLOE, ensure image_prompts are dummy when text prompts are active - if args.model == "yoloe": - dummy = make_dummy_features( - MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision - ) - inputNNDataImg = dai.NNData() - inputNNDataImg.addTensor( - "image_prompts", - dummy, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - imagePromptInputQueue.send(inputNNDataImg) - - update_labels(CLASS_NAMES, offset=0) - print(f"Classes set to: {CLASS_NAMES}") - - def conf_threshold_update_service(new_conf_threshold: float): - """Changes confidence threshold based on the user input""" - CONFIDENCE_THRESHOLD = max(0, min(1, new_conf_threshold)) - nn_with_parser.getParser(0).setConfidenceThreshold(CONFIDENCE_THRESHOLD) - print(f"Confidence threshold set to: {CONFIDENCE_THRESHOLD}:") - - def image_upload_service(image_data): - image = base64_to_cv2_image(image_data["data"]) - if args.model == "yolo-world": - image_features = extract_image_prompt_embeddings( - image, model_name=args.model, precision=args.precision - ) - print("Image features extracted, sending to model as texts...") - inputNNData = dai.NNData() - inputNNData.addTensor( - "texts", - image_features, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - textInputQueue.send(inputNNData) - filename = image_data["filename"] - CLASS_NAMES = [filename.split(".")[0]] - update_labels(CLASS_NAMES, offset=0) - print(f"Classes set to: {CLASS_NAMES}") - else: # yoloe unified with image_prompts input - image_features = extract_image_prompt_embeddings( - image, model_name="yoloe", precision=args.precision - ) - print("Image features extracted, sending to model as image_prompts...") - inputNNDataImg = dai.NNData() - inputNNDataImg.addTensor( - "image_prompts", - image_features, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - imagePromptInputQueue.send(inputNNDataImg) - # Send dummy texts so only image prompts are considered - dummy = make_dummy_features( - MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision - ) - inputNNDataTxt = dai.NNData() - inputNNDataTxt.addTensor( - "texts", - dummy, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - textInputQueue.send(inputNNDataTxt) - - filename = image_data["filename"] - CLASS_NAMES = [filename.split(".")[0]] - update_labels(CLASS_NAMES, offset=80) - print(f"Classes set to (image prompts, offset 80): {CLASS_NAMES}") - - def bbox_prompt_service(payload): - """ - Accepts a full-frame PNG (base64) plus a normalized bbox {x,y,width,height} in viewport space. - - For yolo-world: crops the bbox region and extracts image prompt features from the crop. - - For yoloe: builds a binary mask from the bbox over the full frame and extracts features with mask_prompt. - """ - print("[BBox] Service payload keys:", list(payload.keys())) - # Try FE-provided image first, else fall back to cached live frame - image = base64_to_cv2_image(payload["data"]) if payload.get("data") else None - if image is None: - image = frame_cache.get_last_frame() - if image is None: - print("[BBox] No image data and no cached frame available") - return {"ok": False, "reason": "no_image"} - if image is None: - print("[BBox] Decoded image is None") - return {"ok": False, "reason": "decode_failed"} - if (image == 0).all(): - print("[BBox] Warning: decoded image is all zeros") - # print image stats - print(f"[BBox] Image shape: {image.shape}") - print(f"[BBox] Image dtype: {image.dtype}") - print(f"[BBox] Image min: {image.min()}") - print(f"[BBox] Image max: {image.max()}") - print(f"[BBox] Image mean: {image.mean()}") - print(f"[BBox] Image std: {image.std()}") - - bbox = payload.get("bbox", {}) - bx = float(bbox.get("x", 0.0)) - by = float(bbox.get("y", 0.0)) - bw = float(bbox.get("width", 0.0)) - bh = float(bbox.get("height", 0.0)) - - H, W = image.shape[:2] - is_pixel = payload.get("bboxType", "normalized") == "pixel" - if is_pixel: - x0 = int(round(bx)) - y0 = int(round(by)) - x1 = int(round(bx + bw)) - y1 = int(round(by + bh)) - else: - # bbox is normalized in source frame space - x0 = int(round(bx * W)) - y0 = int(round(by * H)) - x1 = int(round((bx + bw) * W)) - y1 = int(round((by + bh) * H)) - - x0, x1 = sorted((x0, x1)) - y0, y1 = sorted((y0, y1)) - print( - f"[BBox] Image size: {W}x{H}, bbox(px): x0={x0}, y0={y0}, x1={x1}, y1={y1}" - ) - - if x1 <= x0 or y1 <= y0: - print("Invalid bbox, ignoring bbox prompt request.") - return {"ok": False, "reason": "invalid_bbox"} - - if args.model == "yolo-world": - crop = image[y0:y1, x0:x1] - print( - f"[BBox] YOLO-World crop shape: {crop.shape if crop is not None else None}" - ) - image_features = extract_image_prompt_embeddings( - crop, model_name=args.model, precision=args.precision - ) - elif args.model == "yoloe": - mask = np.zeros((H, W), dtype=np.float32) - mask[y0:y1, x0:x1] = 1.0 - print(f"[BBox] YOLOE mask sum: {float(mask.sum())}") - image_features = extract_image_prompt_embeddings( - image, - model_name="yoloe", - mask_prompt=mask, - precision=args.precision, - ) - else: - print(f"Unsupported model for bbox prompt: {args.model}") - return {"ok": False, "reason": "unsupported_model"} - - if args.model == "yolo-world": - inputNNData = dai.NNData() - inputNNData.addTensor( - "texts", - image_features, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - textInputQueue.send(inputNNData) - label = payload.get("label", "object") - CLASS_NAMES = [label] - update_labels(CLASS_NAMES, offset=0) - print(f"BBox prompt applied (yolo-world). Classes set to: {CLASS_NAMES}") - else: # yoloe unified - inputNNDataImg = dai.NNData() - inputNNDataImg.addTensor( - "image_prompts", - image_features, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - imagePromptInputQueue.send(inputNNDataImg) - # Send dummy texts so only image prompts are considered - dummy = make_dummy_features( - MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision - ) - inputNNDataTxt = dai.NNData() - inputNNDataTxt.addTensor( - "texts", - dummy, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - textInputQueue.send(inputNNDataTxt) - label = payload.get("label", "object") - CLASS_NAMES = [label] - update_labels(CLASS_NAMES, offset=80) - print( - f"BBox prompt applied (yoloe). Classes set to: {CLASS_NAMES} at offset 80" - ) - return {"ok": True, "bbox": {"x0": x0, "y0": y0, "x1": x1, "y1": y1}} - - def snap_collection_service(payload): - start_collection, time_interval = payload - if start_collection: - snaps_producer.setTimeInterval(time_interval) - snaps_producer.setRunning(True) - else: - snaps_producer.setRunning(False) - print("Snap collection state:", snaps_producer._running) - - visualizer.registerService("Class Update Service", class_update_service) - visualizer.registerService( - "Threshold Update Service", conf_threshold_update_service - ) - if args.model in ("yolo-world", "yoloe"): - visualizer.registerService("Image Upload Service", image_upload_service) - visualizer.registerService("BBox Prompt Service", bbox_prompt_service) - visualizer.registerService("Snap Collection Service", snap_collection_service) - - print("Pipeline created.") - - pipeline.start() - visualizer.registerPipeline(pipeline) - - update_labels(CLASS_NAMES, offset=CLASS_OFFSET) - - inputNNData = dai.NNData() - inputNNData.addTensor( - "texts", - text_features, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - textInputQueue.send(inputNNData) - if args.model == "yoloe": - inputNNDataImg = dai.NNData() - inputNNDataImg.addTensor( - "image_prompts", - image_prompt_features, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - imagePromptInputQueue.send(inputNNDataImg) - - print("Press 'q' to stop") - - while pipeline.isRunning(): - pipeline.processTasks() - key = visualizer.waitKey(1) - if key == ord("q"): - print("Got q key. Exiting...") - break diff --git a/custom-frontend/data-collection/backend/src/requirements.txt b/custom-frontend/data-collection/backend/src/requirements.txt deleted file mode 100644 index b9912470b..000000000 --- a/custom-frontend/data-collection/backend/src/requirements.txt +++ /dev/null @@ -1,13 +0,0 @@ -# depthai==3.0.0 ---extra-index-url https://artifacts.luxonis.com/artifactory/luxonis-python-snapshot-local ---pre -depthai==3.0.0.dev0+3aa210a2d54c32e0f75f8ac6b4a81f80b6230f2f -#depthai-nodes==0.3.4 -depthai-nodes @ git+https://github.com/luxonis/depthai-nodes.git@feat/dai_dev_req -opencv-python-headless~=4.10.0 -numpy>=1.22 -tokenizers~=0.21.0 -onnxruntime -# onnxruntime-gpu # if you want to use CUDAExecutionProvider -requests -dotenv \ No newline at end of file diff --git a/custom-frontend/data-collection/backend/src/utils/__init__.py b/custom-frontend/data-collection/backend/src/utils/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/custom-frontend/data-collection/backend/src/utils/annotation_node.py b/custom-frontend/data-collection/backend/src/utils/annotation_node.py deleted file mode 100644 index 119aae34c..000000000 --- a/custom-frontend/data-collection/backend/src/utils/annotation_node.py +++ /dev/null @@ -1,45 +0,0 @@ -import depthai as dai -from depthai_nodes import ImgDetectionsExtended -from typing import Dict - - -class AnnotationNode(dai.node.HostNode): - def __init__(self, label_encoding: Dict[int, str] = {}) -> None: - super().__init__() - self._label_encoding = label_encoding - self.out_detections = self.createOutput() - - def setLabelEncoding(self, label_encoding: Dict[int, str]) -> None: - """Sets the label encoding. - - @param label_encoding: The label encoding with labels as keys and label names as - values. - @type label_encoding: Dict[int, str] - """ - if not isinstance(label_encoding, Dict): - raise ValueError("label_encoding must be a dictionary.") - self._label_encoding = label_encoding - - def build( - self, - detections: dai.Node.Output, - frame: dai.Node.Output, - label_encoding: Dict[int, str] = None, - ) -> "AnnotationNode": - if label_encoding is not None: - self.setLabelEncoding(label_encoding) - # Link detections and reference frame inputs - self.link_args(detections, frame) - return self - - def process( - self, - detections_message: dai.Buffer, - frame_message: dai.ImgFrame, - ) -> None: - assert isinstance(detections_message, ImgDetectionsExtended) - # Ensure detections align with the provided frame (e.g., high-res stream) - detections_message.setTransformation(frame_message.getTransformation()) - for detection in detections_message.detections: - detection.label_name = self._label_encoding.get(detection.label, "unknown") - return detections_message diff --git a/custom-frontend/data-collection/backend/src/utils/arguments.py b/custom-frontend/data-collection/backend/src/utils/arguments.py deleted file mode 100644 index 6eeb89d98..000000000 --- a/custom-frontend/data-collection/backend/src/utils/arguments.py +++ /dev/null @@ -1,72 +0,0 @@ -import argparse - - -def initialize_argparser(): - """Initialize the argument parser for the script.""" - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter - ) - - parser.add_argument( - "-fps", - "--fps_limit", - help="FPS limit for the model runtime.", - required=False, - default=None, - type=int, - ) - - parser.add_argument( - "-api", - "--api_key", - help="HubAI API key to access private model. Can also use 'DEPTHAI_HUB_API_KEY' environment variable instead.", - required=False, - default="", - type=str, - ) - - parser.add_argument( - "-media", - "--media_path", - help="Path to the media file you aim to run the model on. If not set, the model will run on the camera input.", - required=False, - default=None, - type=str, - ) - - parser.add_argument( - "-ip", - "--ip", - help="IP address to serve the frontend on.", - required=False, - type=str, - ) - parser.add_argument( - "-p", - "--port", - help="Port to serve the frontend on.", - required=False, - type=int, - ) - parser.add_argument( - "-m", - "--model", - help="Name of the model to use: yolo-world or yoloe", - required=False, - # default="yoloe", - default="yolo-world", - type=str, - choices=["yolo-world", "yoloe"], - ) - parser.add_argument( - "--precision", - help="Model precision for YOLOE models: int8 (faster) or fp16 (more accurate).", - required=False, - default="fp16", - type=str, - choices=["int8", "fp16"], - ) - - args = parser.parse_args() - - return parser, args diff --git a/custom-frontend/data-collection/backend/src/utils/frame_cache_node.py b/custom-frontend/data-collection/backend/src/utils/frame_cache_node.py deleted file mode 100644 index 621a7c7e1..000000000 --- a/custom-frontend/data-collection/backend/src/utils/frame_cache_node.py +++ /dev/null @@ -1,20 +0,0 @@ -import depthai as dai -import numpy as np - - -class FrameCacheNode(dai.node.HostNode): - def __init__(self) -> None: - super().__init__() - self._last_frame: np.ndarray | None = None - - def build(self, frame: dai.Node.Output) -> "FrameCacheNode": - self.link_args(frame) - return self - - def process(self, frame: dai.ImgFrame) -> dai.ImgFrame: - # Cache last BGR frame - self._last_frame = frame.getCvFrame() - return frame - - def get_last_frame(self) -> np.ndarray | None: - return self._last_frame diff --git a/custom-frontend/data-collection/backend/src/utils/helper_functions.py b/custom-frontend/data-collection/backend/src/utils/helper_functions.py deleted file mode 100644 index e4e919d3f..000000000 --- a/custom-frontend/data-collection/backend/src/utils/helper_functions.py +++ /dev/null @@ -1,247 +0,0 @@ -from tokenizers import Tokenizer -import os -import requests -import onnxruntime -import numpy as np -import cv2 -import base64 - -QUANT_ZERO_POINT = 90.0 -QUANT_SCALE = 0.003925696481 - -QUANT_VALUES = { - "yolo-world": { - "quant_zero_point": 90.0, - "quant_scale": 0.003925696481, - }, - "yoloe": { - "quant_zero_point": 174.0, - "quant_scale": 0.003328413470, - }, - "yoloe-image": { - "quant_zero_point": 137.0, - "quant_scale": 0.002327915514, - }, -} - - -def pad_and_quantize_features( - features, max_num_classes=80, model_name="yolo-world", precision="int8" -): - """ - Pad features to (1, 512, max_num_classes) and quantize if precision is int8. - For FP16, return padded float16 features (no quantization). - """ - num_padding = max_num_classes - features.shape[0] - padded_features = np.pad( - features, ((0, num_padding), (0, 0)), mode="constant" - ).T.reshape(1, 512, max_num_classes) - - if precision == "fp16": - return padded_features.astype(np.float16) - - quant_scale = QUANT_VALUES[model_name]["quant_scale"] - quant_zero_point = QUANT_VALUES[model_name]["quant_zero_point"] - quantized_features = (padded_features / quant_scale) + quant_zero_point - quantized_features = quantized_features.astype("uint8") - return quantized_features - - -def extract_text_embeddings( - class_names, max_num_classes=80, model_name="yolo-world", precision="int8" -): - tokenizer_json_path = download_tokenizer( - url="https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json", - save_path="tokenizer.json", - ) - tokenizer = Tokenizer.from_file(tokenizer_json_path) - tokenizer.enable_padding( - pad_id=tokenizer.token_to_id("<|endoftext|>"), pad_token="<|endoftext|>" - ) - encodings = tokenizer.encode_batch(class_names) - - text_onnx = np.array([e.ids for e in encodings], dtype=np.int64) - - if model_name == "yolo-world": - attention_mask = np.array([e.attention_mask for e in encodings], dtype=np.int64) - - textual_onnx_model_path = download_model( - "https://huggingface.co/jmzzomg/clip-vit-base-patch32-text-onnx/resolve/main/model.onnx", - "clip_textual_hf.onnx", - ) - - session_textual = onnxruntime.InferenceSession( - textual_onnx_model_path, - providers=[ - "TensorrtExecutionProvider", - "CUDAExecutionProvider", - "CPUExecutionProvider", - ], - ) - textual_output = session_textual.run( - None, - { - session_textual.get_inputs()[0].name: text_onnx, - "attention_mask": attention_mask, - }, - )[0] - elif model_name == "yoloe": - if text_onnx.shape[1] < 77: - text_onnx = np.pad( - text_onnx, ((0, 0), (0, 77 - text_onnx.shape[1])), mode="constant" - ) - - textual_onnx_model_path = download_model( - "https://huggingface.co/Xenova/mobileclip_blt/resolve/main/onnx/text_model.onnx", - "mobileclip_textual_hf.onnx", - ) - - session_textual = onnxruntime.InferenceSession( - textual_onnx_model_path, - providers=[ - "TensorrtExecutionProvider", - "CUDAExecutionProvider", - "CPUExecutionProvider", - ], - ) - textual_output = session_textual.run( - None, - { - session_textual.get_inputs()[0].name: text_onnx, - }, - )[0] - - textual_output /= np.linalg.norm( - textual_output, ord=2, axis=-1, keepdims=True - ) # Normalize the output - - text_features = pad_and_quantize_features( - textual_output, max_num_classes, model_name, precision - ) - - del session_textual - - return text_features - - -def extract_image_prompt_embeddings( - image, - max_num_classes=80, - model_name="yolo-world", - mask_prompt=None, - precision="int8", -): - # Select model and preprocess accordingly - if model_name == "yoloe": - image_resized = cv2.resize(image, (640, 640)) - image_array = image_resized.astype(np.float32) / 255.0 - image_array = np.transpose(image_array, (2, 0, 1)) - input_tensor = np.expand_dims(image_array, axis=0).astype(np.float32) - model_url = ( - "https://huggingface.co/sokovninn/yoloe-v8l-seg-visual-encoder/resolve/main/" - "yoloe-v8l-seg_visual_encoder.onnx" - ) - model_path = "yoloe-v8l-seg_visual_encoder.onnx" - else: - input_tensor = preprocess_image(image) - model_url = ( - "https://huggingface.co/sokovninn/clip-visual-with-projector/resolve/main/" - "clip_visual_with_projector.onnx" - ) - model_path = "clip_visual_with_projector.onnx" - - onnx_model_path = download_model(model_url, model_path) - - session = onnxruntime.InferenceSession( - onnx_model_path, - providers=[ - "TensorrtExecutionProvider", - "CUDAExecutionProvider", - "CPUExecutionProvider", - ], - ) - - if model_name == "yoloe": - if mask_prompt is None: - prompts = np.zeros((1, 1, 80, 80), dtype=np.float32) - prompts[0, 0, 5:75, 5:75] = 1.0 - else: - prompts = np.asarray(mask_prompt, dtype=np.float32) - if prompts.ndim == 2: - if prompts.shape != (80, 80): - prompts = cv2.resize( - prompts, (80, 80), interpolation=cv2.INTER_NEAREST - ) - prompts = prompts[None, None, :, :] - elif prompts.shape == (1, 1, 80, 80): - pass - else: - raise ValueError("mask_prompt must have shape (80,80) or (1,1,80,80)") - outputs = session.run(None, {"images": input_tensor, "prompts": prompts}) - else: - input_name = session.get_inputs()[0].name - outputs = session.run(None, {input_name: input_tensor}) - - image_embeddings = outputs[0].squeeze(0).reshape(1, -1) - image_features = pad_and_quantize_features( - image_embeddings, max_num_classes, model_name, precision - ) - - del session - - return image_features - - -def download_tokenizer(url, save_path): - if not os.path.exists(save_path): - print(f"Downloading tokenizer config from {url}...") - with open(save_path, "wb") as f: - f.write(requests.get(url).content) - return save_path - - -def download_model(url, save_path): - if not os.path.exists(save_path): - print(f"Downloading model from {url}...") - response = requests.get(url) - if response.status_code == 200: - with open(save_path, "wb") as f: - f.write(response.content) - print(f"Model saved to {save_path}.") - else: - raise Exception( - f"Failed to download model. Status code: {response.status_code}" - ) - else: - print(f"Model already exists at {save_path}.") - - return save_path - - -def preprocess_image(image): - """Preprocess image for CLIP vision model input""" - image = cv2.resize(image, (224, 224)) - - image_array = np.array(image).astype(np.float32) / 255.0 - - mean = np.array([0.48145466, 0.4578275, 0.40821073]) - std = np.array([0.26862954, 0.26130258, 0.27577711]) - - image_array = (image_array - mean) / std - - image_array = np.transpose(image_array, (2, 0, 1)) - image_array = np.expand_dims(image_array, axis=0) - - return image_array.astype(np.float32) - - -def base64_to_cv2_image(base64_data_uri: str): - if "," in base64_data_uri: - header, base64_data = base64_data_uri.split(",", 1) - else: - base64_data = base64_data_uri - - binary_data = base64.b64decode(base64_data) - np_arr = np.frombuffer(binary_data, np.uint8) - img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) - return img diff --git a/custom-frontend/data-collection/backend/src/utils/snap_utils.py b/custom-frontend/data-collection/backend/src/utils/snap_utils.py deleted file mode 100644 index 694460d6a..000000000 --- a/custom-frontend/data-collection/backend/src/utils/snap_utils.py +++ /dev/null @@ -1,58 +0,0 @@ -from typing import List -import depthai as dai - -from depthai_nodes.node import SnapsProducer -from depthai_nodes import ImgDetectionsExtended - - -def custom_snap_process( - producer: SnapsProducer, - frame: dai.ImgFrame, - det_data: dai.ImgDetections | ImgDetectionsExtended, - class_names: List[str], - model: str, -): - detections = det_data.detections - if len(detections) == 0: - return - - file_group = [dai.FileData(frame, "rgb")] - - dets_labels = [det.label for det in detections] - dets_confs = [det.confidence for det in detections] - - extras = { - "model": model, - "detection_label": str(dets_labels), - "detection_confidence": str(dets_confs), - } - - if isinstance(det_data, dai.ImgDetections): - dets_labels_str = [class_names[det.label] for det in detections] - dets_xyxy = [(det.xmin, det.ymin, det.xmax, det.ymax) for det in detections] - extras["detection_xyxy"] = str(dets_xyxy) - - # file_group.append(dai.FileData(det_data, "detections")) - - elif isinstance(det_data, ImgDetectionsExtended): - dets_labels_str = [det.label_name for det in detections] - dets_cxcywh = [ - ( - det.rotated_rect.center.x, - det.rotated_rect.center.y, - det.rotated_rect.size.width, - det.rotated_rect.size.height, - ) - for det in detections - ] - extras["detection_cxcywh"] = str(dets_cxcywh) - else: - raise NotImplementedError - - extras["detection_label_str"] = str(dets_labels_str) - extras = {"model": model} - - if producer.sendSnap( - name="rgb", file_group=file_group, tags=["demo"], extras=extras - ): - print("Snap sent!") diff --git a/custom-frontend/data-collection/frontend/.gitignore b/custom-frontend/data-collection/frontend/.gitignore deleted file mode 100644 index dafadc4b9..000000000 --- a/custom-frontend/data-collection/frontend/.gitignore +++ /dev/null @@ -1,28 +0,0 @@ -# Logs -logs -*.log -npm-debug.log* -yarn-debug.log* -yarn-error.log* -pnpm-debug.log* -lerna-debug.log* - -node_modules -dist -dist-ssr -*.local - -# Editor directories and files -.vscode/* -!.vscode/extensions.json -.idea -.DS_Store -*.suo -*.ntvs* -*.njsproj -*.sln -*.sw? - -## Panda -styled-system -styled-system-studio diff --git a/custom-frontend/data-collection/frontend/biome.json b/custom-frontend/data-collection/frontend/biome.json deleted file mode 100644 index 5d96cd100..000000000 --- a/custom-frontend/data-collection/frontend/biome.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "$schema": "https://biomejs.dev/schemas/1.9.4/schema.json", - "vcs": { - "enabled": false, - "clientKind": "git", - "useIgnoreFile": false - }, - "files": { - "ignoreUnknown": false, - "ignore": ["node_modules/", "dist/"] - }, - "formatter": { - "enabled": true, - "indentStyle": "tab" - }, - "organizeImports": { - "enabled": true - }, - "linter": { - "enabled": true, - "rules": { - "recommended": true - } - }, - "javascript": { - "formatter": { - "quoteStyle": "single" - } - } -} diff --git a/custom-frontend/data-collection/frontend/index.html b/custom-frontend/data-collection/frontend/index.html deleted file mode 100644 index 3f5e422fa..000000000 --- a/custom-frontend/data-collection/frontend/index.html +++ /dev/null @@ -1,13 +0,0 @@ - - - - - - - Data Collection Application - - -
- - - diff --git a/custom-frontend/data-collection/frontend/src/App.tsx b/custom-frontend/data-collection/frontend/src/App.tsx deleted file mode 100644 index 8b8196909..000000000 --- a/custom-frontend/data-collection/frontend/src/App.tsx +++ /dev/null @@ -1,322 +0,0 @@ -import { css } from "../styled-system/css/css.mjs"; -import { Streams, useConnection } from "@luxonis/depthai-viewer-common"; -import { ClassSelector } from "./ClassSelector.tsx"; -import { ConfidenceSlider } from "./ConfidenceSlider.tsx"; -import { ImageUploader } from "./ImageUploader.tsx"; -import { SnapCollectionButton } from "./SnapCollectionButton.tsx"; -import { useCallback, useEffect, useRef, useState } from "react"; -import { useNotifications } from "./Notifications.tsx"; - -function App() { - const connection = useConnection(); - const streamContainerRef = useRef(null); - const overlayCanvasRef = useRef(null); - const [isDrawing, setIsDrawing] = useState(false); - const [dragStart, setDragStart] = useState<{ x: number; y: number } | null>(null); - const [currentRect, setCurrentRect] = useState<{ x: number; y: number; w: number; h: number } | null>(null); - const { notify } = useNotifications(); - - const getUnderlyingMediaAndSize = () => { - const container = streamContainerRef.current; - if (!container) return null; - const videoEl = container.querySelector("video") as HTMLVideoElement | null; - const canvases = Array.from(container.querySelectorAll("canvas")) as HTMLCanvasElement[]; - const canvasEl = canvases.find((c) => c.getAttribute("data-role") !== "overlay") || null; - const containerRect = container.getBoundingClientRect(); - if (videoEl && videoEl.videoWidth && videoEl.videoHeight) { - const r = videoEl.getBoundingClientRect(); - const displayWidth = r.width; - const displayHeight = r.height; - const offsetX = r.left - containerRect.left; - const offsetY = r.top - containerRect.top; - console.log("[BBox] Capturing from video element", { width: videoEl.videoWidth, height: videoEl.videoHeight, displayWidth, displayHeight, offsetX, offsetY }); - return { - type: "video" as const, - el: videoEl, - width: videoEl.videoWidth, - height: videoEl.videoHeight, - displayWidth, - displayHeight, - offsetX, - offsetY, - }; - } - if (canvasEl && canvasEl.width && canvasEl.height) { - const r = canvasEl.getBoundingClientRect(); - const displayWidth = r.width; - const displayHeight = r.height; - const offsetX = r.left - containerRect.left; - const offsetY = r.top - containerRect.top; - console.log("[BBox] Capturing from canvas element", { width: canvasEl.width, height: canvasEl.height, displayWidth, displayHeight, offsetX, offsetY }); - return { - type: "canvas" as const, - el: canvasEl, - width: canvasEl.width, - height: canvasEl.height, - displayWidth, - displayHeight, - offsetX, - offsetY, - }; - } - return null; - }; - - - const finalizeBBox = useCallback(() => { - if (!currentRect) return; - const overlay = overlayCanvasRef.current; - if (!overlay) return; - const { x, y, w, h } = currentRect; - if (w <= 0 || h <= 0) { - setIsDrawing(false); - setCurrentRect(null); - setDragStart(null); - const ctx = overlay.getContext("2d"); - if (ctx) ctx.clearRect(0, 0, overlay.width, overlay.height); - notify('Selection too small. Please draw a larger box.', { type: 'warning' }); - return; - } - - // Map overlay-space bbox to source frame using displayed media rect - const media = getUnderlyingMediaAndSize(); - if (!media) { - console.warn("[BBox] No media found under overlay; aborting bbox post"); - notify('No video/canvas found. Reset the view and try again.', { type: 'error', durationMs: 6000 }); - return; - } - - const overlayW = overlay.width; - const overlayH = overlay.height; - const srcW = media.width; - const srcH = media.height; - const mediaOffsetX = (media as any).offsetX ?? 0; - const mediaOffsetY = (media as any).offsetY ?? 0; - const mediaDispW = (media as any).displayWidth ?? overlayW; - const mediaDispH = (media as any).displayHeight ?? overlayH; - - let contentX = mediaOffsetX; - let contentY = mediaOffsetY; - let contentW = mediaDispW; - let contentH = mediaDispH; - if (media.type === "canvas") { - const side = Math.min(mediaDispW, mediaDispH); - contentX = mediaOffsetX + (mediaDispW - side) / 2; - contentY = mediaOffsetY + (mediaDispH - side) / 2; - contentW = side; - contentH = side; - } - - const rx0 = Math.max(x, contentX); - const ry0 = Math.max(y, contentY); - const rx1 = Math.min(x + w, contentX + contentW); - const ry1 = Math.min(y + h, contentY + contentH); - const rw = Math.max(0, rx1 - rx0); - const rh = Math.max(0, ry1 - ry0); - if (rw <= 1 || rh <= 1) { - console.warn("[BBox] BBox outside content area; aborting"); - notify('Box outside of content area. Try again within the stream.', { type: 'warning', durationMs: 6000 }); - return; - } - - const scaleX = srcW / contentW; - const scaleY = srcH / contentH; - const sx0 = Math.max(0, Math.min(srcW - 1, Math.round((rx0 - contentX) * scaleX))); - const sy0 = Math.max(0, Math.min(srcH - 1, Math.round((ry0 - contentY) * scaleY))); - const sx1 = Math.max(0, Math.min(srcW, Math.round((rx1 - contentX) * scaleX))); - const sy1 = Math.max(0, Math.min(srcH, Math.round((ry1 - contentY) * scaleY))); - const sw = Math.max(1, sx1 - sx0); - const sh = Math.max(1, sy1 - sy0); - - const xNorm = sx0 / srcW; - const yNorm = sy0 / srcH; - const wNorm = sw / srcW; - const hNorm = sh / srcH; - - console.log("[BBox] Posting BBox Prompt Service (normalized source)", { - bbox: { x: xNorm, y: yNorm, width: wNorm, height: hNorm }, - src: { width: srcW, height: srcH }, - overlay: { width: overlayW, height: overlayH }, - display: { width: mediaDispW, height: mediaDispH, offsetX: mediaOffsetX, offsetY: mediaOffsetY }, - content: { x: contentX, y: contentY, width: contentW, height: contentH }, - scales: { scaleX, scaleY } - }); - notify( - `Sending box [${xNorm.toFixed(2)}, ${yNorm.toFixed(2)}, ${wNorm.toFixed(2)}, ${hNorm.toFixed(2)}]`, - { type: 'info' } - ); - // @ts-ignore - Custom service - (connection as any).daiConnection?.postToService( - "BBox Prompt Service", - { - filename: "object.png", - type: "application/json", - data: null, - bbox: { x: xNorm, y: yNorm, width: wNorm, height: hNorm }, - bboxType: "normalized", - label: "object" - }, - (resp: any) => { - console.log("[BBox] Service ack:", resp); - notify('Bounding box sent', { type: 'success' }); - } - ); - - setIsDrawing(false); - setCurrentRect(null); - setDragStart(null); - const ctx = overlay.getContext("2d"); - if (ctx) ctx.clearRect(0, 0, overlay.width, overlay.height); - }, [connection, currentRect]); - - const handleBeginBBoxDraw = useCallback(() => { - console.log("[BBox] Begin drawing requested"); - setIsDrawing(true); - setCurrentRect(null); - setDragStart(null); - }, []); - - useEffect(() => { - if (!isDrawing) return; - const container = streamContainerRef.current; - const overlay = overlayCanvasRef.current; - if (!container || !overlay) return; - const sizeOverlay = () => { - const rect = container.getBoundingClientRect(); - overlay.width = Math.max(1, Math.round(rect.width)); - overlay.height = Math.max(1, Math.round(rect.height)); - const ctx = overlay.getContext("2d"); - if (ctx) ctx.clearRect(0, 0, overlay.width, overlay.height); - console.log("[BBox] Overlay sized", { width: overlay.width, height: overlay.height }); - }; - sizeOverlay(); - window.addEventListener("resize", sizeOverlay); - return () => window.removeEventListener("resize", sizeOverlay); - }, [isDrawing]); - - useEffect(() => { - notify(connection.connected ? 'Connected to device' : 'Disconnected from device', { type: connection.connected ? 'success' : 'warning', durationMs: 1800 }); - // eslint-disable-next-line react-hooks/exhaustive-deps - }, [connection.connected]); - - const onOverlayMouseDown = (e: any) => { - if (!isDrawing) return; - const canvas = overlayCanvasRef.current; - if (!canvas) return; - const rect = canvas.getBoundingClientRect(); - const x = e.clientX - rect.left; - const y = e.clientY - rect.top; - setDragStart({ x, y }); - setCurrentRect({ x, y, w: 0, h: 0 }); - console.log("[BBox] Mouse down", { x, y }); - }; - - const onOverlayMouseMove = (e: any) => { - if (!isDrawing || !dragStart) return; - const canvas = overlayCanvasRef.current; - if (!canvas) return; - const rect = canvas.getBoundingClientRect(); - const x = e.clientX - rect.left; - const y = e.clientY - rect.top; - const x0 = Math.min(dragStart.x, x); - const y0 = Math.min(dragStart.y, y); - const w = Math.abs(x - dragStart.x); - const h = Math.abs(y - dragStart.y); - setCurrentRect({ x: x0, y: y0, w, h }); - - const ctx = canvas.getContext("2d"); - if (!ctx) return; - ctx.clearRect(0, 0, canvas.width, canvas.height); - ctx.strokeStyle = "#22c55e"; // green - ctx.lineWidth = 2; - ctx.strokeRect(x0, y0, w, h); - if ((w * h) % 20 === 0) { - console.log("[BBox] Mouse move bbox", { x: x0, y: y0, w, h }); - } - }; - - const onOverlayMouseUp = () => { - if (!isDrawing) return; - console.log("[BBox] Mouse up, finalizing bbox", currentRect); - finalizeBBox(); - }; - - return ( -
- {/* Left: Stream Viewer */} -
- - {isDrawing && ( - - )} -
- - {/* Vertical Divider */} -
- - {/* Right: Sidebar (Info and Controls) */} -
-

- Open Vocabulary Object Detection -

-

- Run open‑vocabulary detection on‑device (YOLOE or YOLO‑World) with a custom UI. - Define classes via text prompts or image crops, adjust confidence, and visualize results live. -

- - {/* Class Input */} - - - {/* Image Uploader */} - - - {/* Confidence Slider */} - - - {/* Snap Collection */} - - - {/* Connection Status */} -
-
- {connection.connected ? 'Connected to device' : 'Disconnected'} -
-
-
- ); -} - -export default App; diff --git a/custom-frontend/data-collection/frontend/src/ClassSelector.tsx b/custom-frontend/data-collection/frontend/src/ClassSelector.tsx deleted file mode 100644 index 956d3f502..000000000 --- a/custom-frontend/data-collection/frontend/src/ClassSelector.tsx +++ /dev/null @@ -1,66 +0,0 @@ -import { Flex, Button, Input } from "@luxonis/common-fe-components"; -import { useRef, useState } from "react"; -import { css } from "../styled-system/css/css.mjs"; -import { useConnection } from "@luxonis/depthai-viewer-common"; -import { useNotifications } from "./Notifications.tsx"; - -export function ClassSelector() { - const inputRef = useRef(null); - const connection = useConnection(); - const [selectedClasses, setSelectedClasses] = useState(["person", "chair", "TV"]); - const { notify } = useNotifications(); - - const handleSendMessage = () => { - if (inputRef.current) { - const value = inputRef.current.value; - const updatedClasses = value - .split(',') - .map((c: string) => c.trim()) - .filter(Boolean); - - if (updatedClasses.length === 0) { - notify('Please enter at least one class (comma separated).', { type: 'warning', durationMs: 5000 }); - return; - } - if (!connection.connected) { - notify('Not connected to device. Unable to update classes.', { type: 'error' }); - return; - } - - console.log('Sending new class list to backend:', updatedClasses); - notify(`Updating ${updatedClasses.length} class${updatedClasses.length > 1 ? 'es' : ''}…`, { type: 'info' }); - - connection.daiConnection?.postToService( - // @ts-ignore - Custom service - "Class Update Service", - updatedClasses, - () => { - console.log('Backend acknowledged class update'); - setSelectedClasses(updatedClasses); - notify(`Classes updated (${updatedClasses.join(', ')})`, { type: 'success', durationMs: 6000 }); - } - ); - - inputRef.current.value = ''; - } - }; - - return ( -
- {/* Class List Display */} -

Update Classes with Text Input:

-
    - {selectedClasses.map((cls: string, idx: number) => ( -
  • {cls}
    • - ))} -
- - - {/* Input + Button */} - - - - -
- ); -} diff --git a/custom-frontend/data-collection/frontend/src/ConfidenceSlider.tsx b/custom-frontend/data-collection/frontend/src/ConfidenceSlider.tsx deleted file mode 100644 index 636b52a87..000000000 --- a/custom-frontend/data-collection/frontend/src/ConfidenceSlider.tsx +++ /dev/null @@ -1,70 +0,0 @@ -import { css } from "../styled-system/css/css.mjs"; -import { useState } from "react"; -import { useConnection } from "@luxonis/depthai-viewer-common"; - -interface ConfidenceSliderProps { - initialValue?: number; -} - -export function ConfidenceSlider({ initialValue = 0.5 }: ConfidenceSliderProps) { - const connection = useConnection(); - const [value, setValue] = useState(initialValue); - - const handleCommit = () => { - if (typeof value === "number" && !isNaN(value)) { - console.log('Sending threshold to backend:', value); - - connection.daiConnection?.postToService( - // @ts-ignore - Custom service - "Threshold Update Service", - value, - (response: any) => { - console.log('Backend acknowledged threshold update:', response); - } - ); - } else { - console.warn("Invalid value, skipping update:", value); - } - }; - - return ( -
- - setValue(parseFloat(e.target.value))} - onMouseUp={handleCommit} - onTouchEnd={handleCommit} - className={css({ - width: '100%', - appearance: 'none', - height: '4px', - borderRadius: 'full', - backgroundColor: 'gray.300', - '&::-webkit-slider-thumb': { - appearance: 'none', - width: '12px', - height: '12px', - borderRadius: 'full', - backgroundColor: 'blue.500', - cursor: 'pointer', - }, - '&::-moz-range-thumb': { - appearance: 'none', - width: '12px', - height: '12px', - borderRadius: 'full', - backgroundColor: 'blue.500', - cursor: 'pointer', - } - })} - /> -
- ); -} diff --git a/custom-frontend/data-collection/frontend/src/ImageUploader.tsx b/custom-frontend/data-collection/frontend/src/ImageUploader.tsx deleted file mode 100644 index 949695f1f..000000000 --- a/custom-frontend/data-collection/frontend/src/ImageUploader.tsx +++ /dev/null @@ -1,108 +0,0 @@ -import { Button, Flex } from "@luxonis/common-fe-components"; -import { css } from "../styled-system/css/css.mjs"; -import { useState } from "react"; -import { useConnection } from "@luxonis/depthai-viewer-common"; -import { useNotifications } from "./Notifications.tsx"; - -type Props = { - onDrawBBox?: () => void; -} - -export function ImageUploader({ onDrawBBox }: Props) { - const connection = useConnection(); - const [selectedFile, setSelectedFile] = useState(null); - const { notify } = useNotifications(); - - const handleFileSelect = (event: React.ChangeEvent) => { - const file: File | null = event.target.files?.[0] || null; - setSelectedFile(file); - if (file) { - notify(`Selected: ${file.name}`, { type: 'info', durationMs: 2000 }); - } - }; - - const handleUpload = () => { - if (!selectedFile) { - notify('Please choose an image first', { type: 'warning' }); - return; - } - if (!connection.connected) { - notify('Not connected to device. Unable to upload image.', { type: 'error' }); - return; - } - - const reader = new FileReader(); - reader.onload = () => { - const fileData = reader.result; - - console.log("Uploading image to backend:", selectedFile.name); - const sizeKb = Math.max(1, Math.round((selectedFile.size || 0) / 1024)); - notify(`Uploading ${selectedFile.name} (${sizeKb} KB)…`, { type: 'info' }); - - // @ts-ignore - Custom service - (connection as any).daiConnection?.postToService( - "Image Upload Service", - { - filename: selectedFile.name, - type: selectedFile.type, - data: fileData - }, - (resp: any) => { - console.log("[ImageUpload] Service ack:", resp); - notify(`Image uploaded: ${selectedFile.name}`, { type: 'success', durationMs: 6000 }); - } - ); - }; - - reader.readAsDataURL(selectedFile); - }; - - return ( -
-

Update Classes with Image Input:

- Important: reset view before drawing a bounding box - - {/* Clickable file selection area */} - - - {/* Hidden file input */} - - - {/* Upload / Draw buttons */} - - - OR - - -
- ); -} diff --git a/custom-frontend/data-collection/frontend/src/Notifications.tsx b/custom-frontend/data-collection/frontend/src/Notifications.tsx deleted file mode 100644 index 7301845f2..000000000 --- a/custom-frontend/data-collection/frontend/src/Notifications.tsx +++ /dev/null @@ -1,107 +0,0 @@ -import { createContext, useCallback, useContext, useMemo, useRef, useState } from "react"; -import { css } from "../styled-system/css/css.mjs"; - -type Notification = { - id: number; - message: string; - type?: "info" | "success" | "warning" | "error"; - durationMs?: number; -}; - -type NotificationContextValue = { - notify: (message: string, options?: { type?: Notification["type"]; durationMs?: number }) => void; -}; - -const NotificationContext = createContext(null); - -export function useNotifications() { - const ctx = useContext(NotificationContext); - if (!ctx) throw new Error("useNotifications must be used within NotificationProvider"); - return ctx; -} - -export function NotificationProvider({ children }: { children: React.ReactNode }) { - const [items, setItems] = useState([]); - const idRef = useRef(1); - - const remove = useCallback((id: number) => { - setItems((prev) => prev.filter((n) => n.id !== id)); - }, []); - - const notify = useCallback((message, options) => { - const id = idRef.current++; - const durationMs = options?.durationMs ?? 4500; - const type = options?.type ?? "info"; - setItems((prev) => [...prev, { id, message, type, durationMs }].slice(-5)); - if (durationMs > 0) { - window.setTimeout(() => remove(id), durationMs); - } - }, [remove]); - - const value = useMemo(() => ({ notify }), [notify]); - - return ( - - {children} -
- {items.map((n, idx) => ( - remove(n.id)} index={idx} /> - ))} -
- - ); -} - -function Toast({ notification, onClose, index }: { notification: Notification; onClose: () => void; index: number }) { - const { message, type } = notification; - const colorMap: Record, { bg: string; border: string; text: string }> = { - info: { bg: "blue.50", border: "blue.300", text: "blue.900" }, - success: { bg: "green.50", border: "green.300", text: "green.900" }, - warning: { bg: "yellow.50", border: "yellow.300", text: "yellow.900" }, - error: { bg: "red.50", border: "red.300", text: "red.900" }, - }; - const colors = colorMap[type ?? "info"]; - - return ( -
-
- {message} - -
-
- ); -} - -// Keyframes for smooth appear (no opacity change to keep background fully opaque) -const styleEl = (typeof document !== 'undefined') ? document.createElement('style') : null; -if (styleEl && !document.getElementById('notif-keyframes')) { - styleEl.id = 'notif-keyframes'; - styleEl.innerHTML = ` -@keyframes slideInUp { from { transform: translateY(8px); } to { transform: translateY(0); } } -@keyframes slideOutDown { from { transform: translateY(0); } to { transform: translateY(8px); } } -`; - document.head.appendChild(styleEl); -} - diff --git a/custom-frontend/data-collection/frontend/src/SnapCollectionButton.tsx b/custom-frontend/data-collection/frontend/src/SnapCollectionButton.tsx deleted file mode 100644 index 27de24258..000000000 --- a/custom-frontend/data-collection/frontend/src/SnapCollectionButton.tsx +++ /dev/null @@ -1,174 +0,0 @@ -import { useState, useMemo } from "react"; -import { Button } from "@luxonis/common-fe-components"; -import { css } from "../styled-system/css/css.mjs"; -import { useConnection } from "@luxonis/depthai-viewer-common"; -import { useNotifications } from "./Notifications.tsx"; - -export function SnapCollectionButton() { - const connection = useConnection(); - const { notify } = useNotifications(); - - const [isCollecting, setIsCollecting] = useState(false); - const [busy, setBusy] = useState(false); - - // Keep as string for forgiving typing; validate on toggle. - const [timeIntervalStr, setTimeIntervalStr] = useState("60"); // default 60s - const timeInterval = useMemo( - () => Number.parseInt(timeIntervalStr, 10), - [timeIntervalStr] - ); - const timeIntervalIsValid = - Number.isInteger(timeInterval) && timeInterval > 0; - - const handleToggle = () => { - if (!connection.connected) { - notify("Not connected to device. Unable to toggle collection.", { - type: "error", - }); - return; - } - if (busy) return; - - const nextState = !isCollecting; // true = start, false = end - - // When starting, require a valid positive integer interval. - if (nextState && !timeIntervalIsValid) { - notify("Please enter a positive integer time interval (seconds).", { - type: "error", - }); - return; - } - - setBusy(true); - notify( - nextState - ? `Starting collection (every ${timeInterval}s)…` - : "Ending collection…", - { type: "info" } - ); - - // Tuple payload: [startFlag:boolean, interval:number] - // Start -> [true, ] - // End -> [false, -1] - const payload: [boolean, number] = nextState - ? [true, timeInterval] - : [false, -1]; - - (connection as any).daiConnection?.postToService( - "Snap Collection Service", - payload, - (resp: any) => { - console.log("[SnapCollect] Ack:", resp); - setBusy(false); - setIsCollecting(nextState); - notify( - nextState - ? `Collection started (every ${timeInterval}s).` - : "Collection ended.", - { type: "success" } - ); - } - ); - }; - - const label = busy - ? isCollecting - ? "Ending…" - : "Starting…" - : isCollecting - ? "End Collection" - : "Start Collection"; - - const inputDisabled = busy || isCollecting; - - return ( -
- {/* Horizontal divider */} -
- -

- Click below to start Snap Collection -

- - {/* Time interval input (seconds) */} - - - {/* Full-width toggle button */} - -
- ); -} diff --git a/custom-frontend/data-collection/frontend/src/index.css b/custom-frontend/data-collection/frontend/src/index.css deleted file mode 100644 index 009ed44b9..000000000 --- a/custom-frontend/data-collection/frontend/src/index.css +++ /dev/null @@ -1 +0,0 @@ -@layer reset, base, tokens, recipes, utilities; \ No newline at end of file diff --git a/custom-frontend/data-collection/frontend/src/main.tsx b/custom-frontend/data-collection/frontend/src/main.tsx deleted file mode 100644 index 116fda8d2..000000000 --- a/custom-frontend/data-collection/frontend/src/main.tsx +++ /dev/null @@ -1,33 +0,0 @@ -import {StrictMode} from 'react'; -import {createRoot} from 'react-dom/client'; -import './index.css'; -import '@luxonis/depthai-viewer-common/styles'; -import '@luxonis/common-fe-components/styles'; -import '@luxonis/depthai-pipeline-lib/styles'; -import App from './App.tsx'; -import {BrowserRouter, Route, Routes} from "react-router"; -import {DepthAIContext} from "@luxonis/depthai-viewer-common"; -import { NotificationProvider } from './Notifications.tsx'; - -// This function extracts the base path with app version from the current URL. -// This is essential for access via domain luxonis.app -function getBasePath(): string { - return window.location.pathname.match(/^\/\d+\.\d+\.\d+\/$/)?.[0] ?? '' -} - -createRoot(document.getElementById('root')!).render( - - - - - - }/> - - - - - , -); diff --git a/custom-frontend/data-collection/frontend/src/vite-env.d.ts b/custom-frontend/data-collection/frontend/src/vite-env.d.ts deleted file mode 100644 index 11f02fe2a..000000000 --- a/custom-frontend/data-collection/frontend/src/vite-env.d.ts +++ /dev/null @@ -1 +0,0 @@ -/// diff --git a/custom-frontend/data-collection/frontend/src/window.d.ts b/custom-frontend/data-collection/frontend/src/window.d.ts deleted file mode 100644 index f02d957b7..000000000 --- a/custom-frontend/data-collection/frontend/src/window.d.ts +++ /dev/null @@ -1,7 +0,0 @@ -declare global { - interface Window { - __basePath: string; - } -} - -export {}; diff --git a/custom-frontend/data-collection/media/dynamic_yolo_world.gif b/custom-frontend/data-collection/media/dynamic_yolo_world.gif deleted file mode 100644 index fb72f7cc204d216507bad1ba479d7713393c0e63..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2656194 zcmW(+WmFX0(_fJgBm|KXDVJ^}RX}>_T)I1!?v`#?x()T)PVmE5C8xH01yBG3IMA z5Fh{w1i*m+1Q3Ay-&Y_A00IFZAOI8ufP(-C5CHivP0uWFD@_(9uZ~zDnfWQGzH~(l#02~28 zAOOh!VFDrnAS3{S1VE7hI1+$B0+9a?6buA_K>#oW0EPmB4BU?41s_l5ikG}21LR@NEieOgCb#YBn*LsA(1cu00jh~ zKmZg3fPw;0Z~zJcKp_Dr01yQPqCh|t1c-tHQE(s%0Yo8zC;$ir1ff766a<8Vf>3Y} z3IRePK_~zO1%#kL5EKN0fa3~4^MIoUm02~E` zqd;&J1df8jQE)g40Y@R>C;$QlM4&(j6a;~SB2aJy3V}c&5hwr>1w^7iNE8H#f+A6H zBnp8$42z+`Esw?OX zdG(e>f28h5f5e-Qa^}_vxXdwRvNjXe&bxyNR1(3I%ZbI($<-<_ct+Cxa9XkPn4j!i z$z%qf^M=AGCL?Q(u=?}f@mp)vLh-m}Q@#`pl~QgU;SVf)EDM#IjU4MMEc=?823^4z zt9&e4^;V;$c$LlUt94G>usC_o!u1ZXtCRIFGmE21j4xj+RU~pv^)YtsXiRw3?+!t! 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insertions(+) diff --git a/tests/constants.py b/tests/constants.py index bc7c4e0c8..38f52b982 100644 --- a/tests/constants.py +++ b/tests/constants.py @@ -28,6 +28,10 @@ "reason": "Not supported for peripheral", "mode": ["peripheral"], }, + "apps/data-collection": { + "reason": "Not supported for peripheral", + "mode": ["peripheral"], + }, "integrations/roboflow-integration": { "reason": "Can't run without arguments (e.g. roboflow api-key)", "mode": "all", From 01a4b107661c8be83305677628faa8ca41b36dd3 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Fri, 31 Oct 2025 00:06:48 +0100 Subject: [PATCH 21/39] Refactored the code base --- apps/data-collection/backend/src/main.py | 451 +++--------------- .../backend/src/utils/constants.py | 30 ++ .../utils/{snap_utils => core}/__init__.py | 0 .../backend/src/utils/core/io.py | 26 + .../backend/src/utils/core/label_manager.py | 20 + .../backend/src/utils/core/quantization.py | 33 ++ .../src/utils/core/tokenizer_manager.py | 75 +++ .../src/utils/core/visual_encoder_manager.py | 57 +++ .../backend/src/utils/helper_functions.py | 209 -------- .../backend/src/utils/services/__init__.py | 0 .../src/utils/services/base_service.py | 21 + .../src/utils/services/bbox_prompt_service.py | 48 ++ .../utils/services/class_update_service.py | 84 ++++ .../src/utils/services/get_config_service.py | 53 ++ .../utils/services/image_upload_service.py | 66 +++ .../utils/services/snap_collection_service.py | 80 ++++ .../services/threshold_update_service.py | 21 + .../utils/snap_utils/custon_snap_process.py | 132 ----- .../backend/src/utils/snap_utils/extras.py | 64 --- .../backend/src/utils/snaps/__init__.py | 0 .../src/utils/snaps/conditions/__init__.py | 0 .../utils/snaps/conditions/base_condition.py | 17 + .../snaps/conditions/lost_mid_condition.py | 76 +++ .../snaps/conditions/low_conf_condition.py | 36 ++ .../conditions/no_detections_condition.py | 18 + .../utils/snaps/conditions/timed_condition.py | 16 + .../conditions_manager.py} | 43 +- .../src/utils/snaps/custon_snap_process.py | 33 ++ .../helpers.py => snaps/tracklets.py} | 38 +- apps/data-collection/frontend/src/App.tsx | 8 +- .../src/{inputs => utils}/SliderControl.tsx | 0 .../src/{ => utils/classes}/ClassSelector.tsx | 4 +- .../{ => utils/classes}/ConfidenceSlider.tsx | 2 +- .../src/{ => utils/classes}/ImageUploader.tsx | 4 +- .../{ => utils/conditions}/ConditionCard.tsx | 2 +- .../conditions}/CooldownMinutesInput.tsx | 2 +- .../conditions}/EdgeBufferPercentInput.tsx | 2 +- .../conditions}/SnapCollectionButton.tsx | 2 +- .../conditions}/SnapConditionsPanel.tsx | 14 +- 39 files changed, 959 insertions(+), 828 deletions(-) create mode 100644 apps/data-collection/backend/src/utils/constants.py rename apps/data-collection/backend/src/utils/{snap_utils => core}/__init__.py (100%) create mode 100644 apps/data-collection/backend/src/utils/core/io.py create mode 100644 apps/data-collection/backend/src/utils/core/label_manager.py create mode 100644 apps/data-collection/backend/src/utils/core/quantization.py create mode 100644 apps/data-collection/backend/src/utils/core/tokenizer_manager.py create mode 100644 apps/data-collection/backend/src/utils/core/visual_encoder_manager.py delete mode 100644 apps/data-collection/backend/src/utils/helper_functions.py create mode 100644 apps/data-collection/backend/src/utils/services/__init__.py create mode 100644 apps/data-collection/backend/src/utils/services/base_service.py create mode 100644 apps/data-collection/backend/src/utils/services/bbox_prompt_service.py create mode 100644 apps/data-collection/backend/src/utils/services/class_update_service.py create mode 100644 apps/data-collection/backend/src/utils/services/get_config_service.py create mode 100644 apps/data-collection/backend/src/utils/services/image_upload_service.py create mode 100644 apps/data-collection/backend/src/utils/services/snap_collection_service.py create mode 100644 apps/data-collection/backend/src/utils/services/threshold_update_service.py delete mode 100644 apps/data-collection/backend/src/utils/snap_utils/custon_snap_process.py delete mode 100644 apps/data-collection/backend/src/utils/snap_utils/extras.py create mode 100644 apps/data-collection/backend/src/utils/snaps/__init__.py create mode 100644 apps/data-collection/backend/src/utils/snaps/conditions/__init__.py create mode 100644 apps/data-collection/backend/src/utils/snaps/conditions/base_condition.py create mode 100644 apps/data-collection/backend/src/utils/snaps/conditions/lost_mid_condition.py create mode 100644 apps/data-collection/backend/src/utils/snaps/conditions/low_conf_condition.py create mode 100644 apps/data-collection/backend/src/utils/snaps/conditions/no_detections_condition.py create mode 100644 apps/data-collection/backend/src/utils/snaps/conditions/timed_condition.py rename apps/data-collection/backend/src/utils/{snap_utils/conditions_gate.py => snaps/conditions_manager.py} (55%) create mode 100644 apps/data-collection/backend/src/utils/snaps/custon_snap_process.py rename apps/data-collection/backend/src/utils/{snap_utils/helpers.py => snaps/tracklets.py} (81%) rename apps/data-collection/frontend/src/{inputs => utils}/SliderControl.tsx (100%) rename apps/data-collection/frontend/src/{ => utils/classes}/ClassSelector.tsx (96%) rename apps/data-collection/frontend/src/{ => utils/classes}/ConfidenceSlider.tsx (95%) rename apps/data-collection/frontend/src/{ => utils/classes}/ImageUploader.tsx (97%) rename apps/data-collection/frontend/src/{ => utils/conditions}/ConditionCard.tsx (95%) rename apps/data-collection/frontend/src/{inputs => utils/conditions}/CooldownMinutesInput.tsx (96%) rename apps/data-collection/frontend/src/{inputs => utils/conditions}/EdgeBufferPercentInput.tsx (95%) rename apps/data-collection/frontend/src/{ => utils/conditions}/SnapCollectionButton.tsx (93%) rename apps/data-collection/frontend/src/{ => utils/conditions}/SnapConditionsPanel.tsx (96%) diff --git a/apps/data-collection/backend/src/main.py b/apps/data-collection/backend/src/main.py index ae2aac221..57acf6e94 100644 --- a/apps/data-collection/backend/src/main.py +++ b/apps/data-collection/backend/src/main.py @@ -1,30 +1,33 @@ +import os + from pathlib import Path from dotenv import load_dotenv -import os from functools import partial -os.environ["DEPTHAI_LEVEL"] = "info" import depthai as dai from depthai_nodes.node import ( ParsingNeuralNetwork, ImgDetectionsFilter, - ImgFrameOverlay, - ApplyColormap, SnapsProducer2Buffered, ImgDetectionsBridge, ) -from utils.helper_functions import ( - extract_text_embeddings, - extract_image_prompt_embeddings, - base64_to_cv2_image, - make_dummy_features, -) from utils.arguments import initialize_argparser from utils.annotation_node import AnnotationNode from utils.frame_cache_node import FrameCacheNode -from utils.snap_utils.conditions_gate import ConditionsGate -from utils.snap_utils.custon_snap_process import custom_snap_process +from utils.snaps.conditions_manager import ConditionsManager +from utils.snaps.custon_snap_process import custom_snap_process +from utils.snaps.tracklets import setup_tracker +from utils.services.class_update_service import ClassUpdateService +from utils.services.threshold_update_service import ThresholdUpdateService +from utils.services.image_upload_service import ImageUploadService +from utils.services.bbox_prompt_service import BBoxPromptService +from utils.services.snap_collection_service import SnapCollectionService +from utils.services.get_config_service import GetConfigService +from utils.core.tokenizer_manager import TokenizerManager +from utils.core.quantization import make_dummy_features +from utils.core.label_manager import LabelManager +import utils.constants as const load_dotenv(override=True) @@ -36,12 +39,6 @@ IP = args.ip or "localhost" PORT = args.port or 8080 -CLASS_NAMES = ["person", "chair", "TV"] -CLASS_OFFSET = 0 -MAX_NUM_CLASSES = 80 -CONFIDENCE_THRESHOLD = 0.1 -VISUALIZATION_RESOLUTION = (1080, 1080) - visualizer = dai.RemoteConnection(serveFrontend=False) device = dai.Device() platform = device.getPlatformAsString() @@ -51,18 +48,18 @@ frame_type = dai.ImgFrame.Type.BGR888i -text_features = extract_text_embeddings( - class_names=CLASS_NAMES, - max_num_classes=MAX_NUM_CLASSES, - model_name="yoloe", - precision=args.precision, +tokenizer = TokenizerManager( + model_name=const.MODEL, precision=args.precision, max_classes=const.MAX_NUM_CLASSES ) + +text_features = tokenizer.extract_text_embeddings(const.CLASS_NAMES) + image_prompt_features = make_dummy_features( - MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision + const.MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision ) if args.fps_limit is None: - args.fps_limit = 5 + args.fps_limit = 30 print( f"\nFPS limit set to {args.fps_limit} for {platform} platform. If you want to set a custom FPS limit, use the --fps_limit flag.\n" ) @@ -76,11 +73,14 @@ f"Model YAML not found for YOLOE with precision {args.precision}. " f"YOLOE int8 YAML is not available; run with --precision fp16." ) + yaml_base = "yoloe_v8_l_fp16" yaml_filename = f"{yaml_base}.{platform}.yaml" yaml_path = models_dir / yaml_filename + if not yaml_path.exists(): raise SystemExit(f"Model YAML not found for YOLOE: {yaml_path}.") + model_description = dai.NNModelDescription.fromYamlFile(str(yaml_path)) model_description.platform = platform model_nn_archive = dai.NNArchive(dai.getModelFromZoo(model_description)) @@ -93,23 +93,31 @@ replay.setLoop(True) if args.fps_limit: replay.setFps(args.fps_limit) - replay.setSize(VISUALIZATION_RESOLUTION[0], VISUALIZATION_RESOLUTION[1]) + replay.setSize( + const.VISUALIZATION_RESOLUTION[0], const.VISUALIZATION_RESOLUTION[1] + ) video_src_out = replay.out + + image_manip = pipeline.create(dai.node.ImageManip) + image_manip.setMaxOutputFrameSize(model_w * model_h * 3) + image_manip.initialConfig.setOutputSize(model_w, model_h) + image_manip.initialConfig.setFrameType(frame_type) + video_src_out.link(image_manip.inputImage) + + input_node = image_manip.out else: cam = pipeline.create(dai.node.Camera).build( boardSocket=dai.CameraBoardSocket.CAM_A ) video_src_out = cam.requestOutput( - size=VISUALIZATION_RESOLUTION, + size=const.VISUALIZATION_RESOLUTION, type=dai.ImgFrame.Type.NV12, fps=args.fps_limit, ) - image_manip = pipeline.create(dai.node.ImageManip) - image_manip.setMaxOutputFrameSize(model_w * model_h * 3) - image_manip.initialConfig.setOutputSize(model_w, model_h) - image_manip.initialConfig.setFrameType(frame_type) - video_src_out.link(image_manip.inputImage) + input_node = cam.requestOutput( + size=(model_w, model_h), type=frame_type, fps=args.fps_limit + ) video_enc = pipeline.create(dai.node.VideoEncoder) video_enc.setDefaultProfilePreset( @@ -117,8 +125,6 @@ ) video_src_out.link(video_enc.input) - input_node = image_manip.out - nn_with_parser = pipeline.create(ParsingNeuralNetwork) nn_with_parser.setNNArchive(model_nn_archive) nn_with_parser.setBackend("snpe") @@ -126,7 +132,7 @@ {"runtime": "dsp", "performance_profile": "default"} ) nn_with_parser.setNumInferenceThreads(1) - nn_with_parser.getParser(0).setConfidenceThreshold(CONFIDENCE_THRESHOLD) + nn_with_parser.getParser(0).setConfidenceThreshold(const.CONFIDENCE_THRESHOLD) input_node.link(nn_with_parser.inputs["images"]) @@ -146,36 +152,18 @@ frame_cache = pipeline.create(FrameCacheNode).build(video_src_out) - cond_gate = ConditionsGate(default_cooldown_s=0.0, enabled=True) - cond_gate.set_key_enabled("no_detections", False) - cond_gate.set_cooldown("no_detections", 300.0) - - cond_gate.set_key_enabled("timed", False) - cond_gate.set_cooldown("timed", 300.0) - - cond_gate.set_key_enabled("low_conf", False) - cond_gate.set_cooldown("low_conf", 300.0) - - cond_gate.set_key_enabled("lost_mid", False) - cond_gate.set_cooldown("lost_mid", 300.0) + cond_manager = ConditionsManager(default_cooldown_s=300.0, enabled=False) + cond_manager.register_conditions(const.CONDITIONS) _runtime = { - "newdet_running": False, - "conf_threshold": CONFIDENCE_THRESHOLD, + "conf_threshold": const.CONFIDENCE_THRESHOLD, "lost_mid_margin": 0.20, "snapping_running": False, } - current_classes = CLASS_NAMES.copy() + current_classes = const.CLASS_NAMES.copy() object_tracker = pipeline.create(dai.node.ObjectTracker) - - object_tracker.setTrackerType(dai.TrackerType.SHORT_TERM_IMAGELESS) - object_tracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.UNIQUE_ID) - object_tracker.setTrackingPerClass(True) - object_tracker.setTrackletBirthThreshold(3) - object_tracker.setTrackletMaxLifespan(90) - object_tracker.setOcclusionRatioThreshold(0.5) - object_tracker.setTrackerThreshold(0.25) + setup_tracker(object_tracker) input_node.link(object_tracker.inputTrackerFrame) input_node.link(object_tracker.inputDetectionFrame) @@ -188,337 +176,60 @@ running=False, process_fn=partial( custom_snap_process, - class_names=CLASS_NAMES, - model="yoloe", - cond_gate=cond_gate, + class_names=const.CLASS_NAMES, + cond_manager=cond_manager, runtime=_runtime, ), ) - def update_labels(label_names: list[str], offset: int = 0): - det_process_filter.setLabels( - labels=[i for i in range(offset, offset + len(label_names))], keep=True - ) - annotation_node.setLabelEncoding( - {offset + k: v for k, v in enumerate(label_names)} - ) - - apply_colormap_node = pipeline.create(ApplyColormap).build(nn_with_parser.out) - overlay_frames_node = pipeline.create(ImgFrameOverlay).build( - video_src_out, - apply_colormap_node.out, - preserve_background=True, - ) - overlay_to_nv12 = pipeline.create(dai.node.ImageManip) - overlay_to_nv12.setMaxOutputFrameSize( - VISUALIZATION_RESOLUTION[0] * VISUALIZATION_RESOLUTION[1] * 3 - ) - overlay_to_nv12.initialConfig.setFrameType(dai.ImgFrame.Type.NV12) - overlay_frames_node.out.link(overlay_to_nv12.inputImage) - - overlay_enc = pipeline.create(dai.node.VideoEncoder) - overlay_enc.setDefaultProfilePreset( - fps=args.fps_limit, profile=dai.VideoEncoderProperties.Profile.H264_MAIN - ) - overlay_to_nv12.out.link(overlay_enc.input) - - visualizer.addTopic("Video", overlay_enc.out, "images") + visualizer.addTopic("Video", video_enc.out, "images") visualizer.addTopic("Detections", annotation_node.out) - def class_update_service(new_classes: list[str]): - if not new_classes: - print("List of new classes empty, skipping.") - return - if len(new_classes) > MAX_NUM_CLASSES: - print( - f"Too many classes ({len(new_classes)}) > {MAX_NUM_CLASSES}, skipping." - ) - return - - feats = extract_text_embeddings( - class_names=new_classes, - max_num_classes=MAX_NUM_CLASSES, - model_name="yoloe", - precision=args.precision, - ) - nn_txt = dai.NNData() - nn_txt.addTensor( - "texts", - feats, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - textInputQueue.send(nn_txt) - - dummy = make_dummy_features( - MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision - ) - nn_img = dai.NNData() - nn_img.addTensor( - "image_prompts", - dummy, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - imagePromptInputQueue.send(nn_img) - - update_labels(new_classes, offset=0) + ClassUpdateService( + visualizer, + textInputQueue, + imagePromptInputQueue, + args.precision, + current_classes, + det_process_filter, + annotation_node, + ).register() - current_classes.clear() - current_classes.extend(new_classes) + ThresholdUpdateService(visualizer, nn_with_parser, _runtime).register() - print(f"Classes updated (YOLOE text): {new_classes}") + ImageUploadService( + visualizer, + args.precision, + imagePromptInputQueue, + textInputQueue, + det_process_filter, + annotation_node, + ).register() - def conf_threshold_update_service(new_conf_threshold: float): - """Changes confidence threshold based on the user input""" - _runtime["conf_threshold"] = max(0.0, min(1.0, float(new_conf_threshold))) - nn_with_parser.getParser(0).setConfidenceThreshold(_runtime["conf_threshold"]) + BBoxPromptService(visualizer, frame_cache).register() - def image_upload_service(image_data): - image = base64_to_cv2_image(image_data["data"]) + SnapCollectionService(visualizer, _runtime, cond_manager, snaps_producer).register() - image_features = extract_image_prompt_embeddings( - image, model_name="yoloe", precision=args.precision - ) - - input_NN_data_img = dai.NNData() - input_NN_data_img.addTensor( - "image_prompts", - image_features, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - imagePromptInputQueue.send(input_NN_data_img) - - dummy = make_dummy_features( - MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision - ) - inputNNDataTxt = dai.NNData() - inputNNDataTxt.addTensor( - "texts", - dummy, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - textInputQueue.send(inputNNDataTxt) - - filename = image_data["filename"] - class_names = [filename.split(".")[0]] - update_labels(class_names, offset=80) - return - - def bbox_prompt_service(payload): - image = base64_to_cv2_image(payload["data"]) if payload.get("data") else None - if image is None: - image = frame_cache.get_last_frame() - if image is None: - return {"ok": False, "reason": "no_image"} - if image is None: - return {"ok": False, "reason": "decode_failed"} - - bbox = payload.get("bbox", {}) - bx = float(bbox.get("x", 0.0)) - by = float(bbox.get("y", 0.0)) - bw = float(bbox.get("width", 0.0)) - bh = float(bbox.get("height", 0.0)) - - H, W = image.shape[:2] - is_pixel = payload.get("bboxType", "normalized") == "pixel" - if is_pixel: - x0 = int(round(bx)) - y0 = int(round(by)) - x1 = int(round(bx + bw)) - y1 = int(round(by + bh)) - else: - x0 = int(round(bx * W)) - y0 = int(round(by * H)) - x1 = int(round((bx + bw) * W)) - y1 = int(round((by + bh) * H)) - - x0, x1 = sorted((x0, x1)) - y0, y1 = sorted((y0, y1)) - return {"ok": True} - - def snap_collection_service(payload): - base_dt_seconds = 1 - - if not isinstance(payload, dict): - return {"ok": False, "reason": "payload_must_be_dict"} - - prev_running = bool(_runtime.get("snapping_running", False)) - - # timed - tcfg = payload.get("timed") - if isinstance(tcfg, dict): - if "enabled" in tcfg: - cond_gate.set_key_enabled("timed", bool(tcfg["enabled"])) - if "interval" in tcfg: - try: - cond_gate.set_cooldown("timed", float(tcfg["interval"])) - except Exception: - pass - if tcfg.get("reset"): - cond_gate.reset(["timed"]) - - # no detections - ncfg = payload.get("noDetections") - if isinstance(ncfg, dict): - if "enabled" in ncfg: - cond_gate.set_key_enabled("no_detections", bool(ncfg["enabled"])) - if "cooldown" in ncfg: - try: - cond_gate.set_cooldown("no_detections", float(ncfg["cooldown"])) - except Exception: - pass - if ncfg.get("reset"): - cond_gate.reset(["no_detections"]) - - # low confidence - lcfg = payload.get("lowConfidence") - if isinstance(lcfg, dict): - enable = bool(lcfg.get("enabled", False)) - thr_raw = lcfg.get("threshold", None) - - if enable: - if thr_raw is None: - return {"ok": False, "reason": "low_conf_threshold_required"} - - try: - thr = float(thr_raw) - if thr > 1.0: - thr = thr / 100.0 - if not (0.0 <= thr <= 1.0): - raise ValueError - except Exception: - return {"ok": False, "reason": "invalid_low_conf_threshold"} - - _runtime["low_conf_thresh"] = thr - cond_gate.set_key_enabled("low_conf", True) - else: - cond_gate.set_key_enabled("low_conf", False) - _runtime.pop("low_conf_thresh", None) - - if "cooldown" in lcfg: - try: - cond_gate.set_cooldown("low_conf", float(lcfg["cooldown"])) - except Exception: - pass - - if lcfg.get("reset"): - cond_gate.reset(["low_conf"]) - - # lost in middle - lmcfg = payload.get("lostMid") - if isinstance(lmcfg, dict): - if "enabled" in lmcfg: - cond_gate.set_key_enabled("lost_mid", bool(lmcfg["enabled"])) - if "cooldown" in lmcfg: - try: - cond_gate.set_cooldown("lost_mid", float(lmcfg["cooldown"])) - except Exception: - pass - if "margin" in lmcfg: - try: - m = float(lmcfg["margin"]) - _runtime["lost_mid_margin"] = max(0.0, min(0.49, m)) - except Exception: - pass - if lmcfg.get("reset"): - cond_gate.reset(["lost_mid"]) - - any_active = cond_gate.enabled and ( - cond_gate.is_key_enabled("timed") - or cond_gate.is_key_enabled("no_detections") - or cond_gate.is_key_enabled("low_conf") - or cond_gate.is_key_enabled("lost_mid") - ) - - if any_active and not prev_running: - cond_gate.reset(["timed", "no_detections", "low_conf", "lost_mid"]) - - snaps_producer.setRunning(any_active) - _runtime["snapping_running"] = any_active - if any_active: - snaps_producer.setTimeInterval(base_dt_seconds) - - return {"ok": True} - - def get_config_service(_payload=None): - """ - Returns the current backend configuration state so FE can restore UI. - Cooldowns returned in minutes for FE display. - """ - - def s_to_min(seconds: float) -> float: - try: - return float(seconds) / 60.0 if float(seconds) > 0 else 0.0 - except Exception: - return 0.0 - - config = { - "classes": current_classes.copy(), - "confidence_threshold": _runtime["conf_threshold"], - "snapping": { - "running": _runtime.get("snapping_running", False), - "timed": { - "enabled": cond_gate.is_key_enabled("timed"), - "interval": s_to_min(cond_gate.get_cooldown("timed")), - }, - "noDetections": { - "enabled": cond_gate.is_key_enabled("no_detections"), - "cooldown": s_to_min(cond_gate.get_cooldown("no_detections")), - }, - "lowConfidence": { - "enabled": cond_gate.is_key_enabled("low_conf"), - "threshold": _runtime.get("low_conf_thresh", 0.30), - "cooldown": s_to_min(cond_gate.get_cooldown("low_conf")), - }, - "lostMid": { - "enabled": cond_gate.is_key_enabled("lost_mid"), - "cooldown": s_to_min(cond_gate.get_cooldown("lost_mid")), - "margin": _runtime.get("lost_mid_margin", 0.20), - }, - }, - } - return config - - visualizer.registerService("Class Update Service", class_update_service) - visualizer.registerService( - "Threshold Update Service", conf_threshold_update_service - ) - visualizer.registerService("Image Upload Service", image_upload_service) - visualizer.registerService("BBox Prompt Service", bbox_prompt_service) - visualizer.registerService("Snap Collection Service", snap_collection_service) - visualizer.registerService("Get Config Service", get_config_service) + GetConfigService(visualizer, current_classes, _runtime, cond_manager).register() print("Pipeline created.") pipeline.start() visualizer.registerPipeline(pipeline) - update_labels(CLASS_NAMES, offset=CLASS_OFFSET) + label_manager = LabelManager(det_process_filter, annotation_node) + label_manager.update_labels(const.CLASS_NAMES, offset=const.CLASS_OFFSET) + dataType = ( + dai.TensorInfo.DataType.FP16 + if args.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ) inputNNData = dai.NNData() inputNNData.addTensor( "texts", text_features, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), + dataType=dataType, ) textInputQueue.send(inputNNData) @@ -526,11 +237,7 @@ def s_to_min(seconds: float) -> float: inputNNDataImg.addTensor( "image_prompts", image_prompt_features, - dataType=( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), + dataType=dataType, ) imagePromptInputQueue.send(inputNNDataImg) diff --git a/apps/data-collection/backend/src/utils/constants.py b/apps/data-collection/backend/src/utils/constants.py new file mode 100644 index 000000000..9c60d0dc3 --- /dev/null +++ b/apps/data-collection/backend/src/utils/constants.py @@ -0,0 +1,30 @@ +from .snaps.conditions.timed_condition import TimedCondition +from .snaps.conditions.no_detections_condition import NoDetectionsCondition +from .snaps.conditions.low_conf_condition import LowConfidenceCondition +from .snaps.conditions.lost_mid_condition import LostMidCondition + + +CLASS_NAMES = ["person", "chair", "TV"] +CLASS_OFFSET = 0 +MAX_NUM_CLASSES = 80 +CONFIDENCE_THRESHOLD = 0.1 +VISUALIZATION_RESOLUTION = (1080, 1080) +MODEL = "yoloe" + +QUANT_VALUES = { + "yoloe": { + "quant_zero_point": 174.0, + "quant_scale": 0.003328413470, + }, + "yoloe-image": { + "quant_zero_point": 137.0, + "quant_scale": 0.002327915514, + }, +} + +CONDITIONS = [ + TimedCondition(), + NoDetectionsCondition(), + LowConfidenceCondition(), + LostMidCondition(), +] diff --git a/apps/data-collection/backend/src/utils/snap_utils/__init__.py b/apps/data-collection/backend/src/utils/core/__init__.py similarity index 100% rename from apps/data-collection/backend/src/utils/snap_utils/__init__.py rename to apps/data-collection/backend/src/utils/core/__init__.py diff --git a/apps/data-collection/backend/src/utils/core/io.py b/apps/data-collection/backend/src/utils/core/io.py new file mode 100644 index 000000000..d02d8fafb --- /dev/null +++ b/apps/data-collection/backend/src/utils/core/io.py @@ -0,0 +1,26 @@ +import os +import base64 +import numpy as np +import cv2 +import requests +from pathlib import Path + + +def download_file(url: str, path: str) -> Path: + if not os.path.exists(path): + print(f"Downloading tokenizer config from {url}...") + with open(path, "wb") as f: + f.write(requests.get(url).content) + return path + + +def base64_to_cv2_image(base64_data_uri: str): + if "," in base64_data_uri: + header, base64_data = base64_data_uri.split(",", 1) + else: + base64_data = base64_data_uri + + binary_data = base64.b64decode(base64_data) + np_arr = np.frombuffer(binary_data, np.uint8) + img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) + return img diff --git a/apps/data-collection/backend/src/utils/core/label_manager.py b/apps/data-collection/backend/src/utils/core/label_manager.py new file mode 100644 index 000000000..7e7c51c5b --- /dev/null +++ b/apps/data-collection/backend/src/utils/core/label_manager.py @@ -0,0 +1,20 @@ +class LabelManager: + """ + Manages synchronization of label data between the detection filter + and the annotation node. + + Handles assigning numeric label IDs used by the neural network and + mapping them to human-readable class names for visualization. + """ + + def __init__(self, det_filter, annotation_node): + self.det_filter = det_filter + self.annotation_node = annotation_node + + def update_labels(self, label_names, offset=0): + self.det_filter.setLabels( + labels=[i for i in range(offset, offset + len(label_names))], keep=True + ) + self.annotation_node.setLabelEncoding( + {offset + k: v for k, v in enumerate(label_names)} + ) diff --git a/apps/data-collection/backend/src/utils/core/quantization.py b/apps/data-collection/backend/src/utils/core/quantization.py new file mode 100644 index 000000000..e80e33eb2 --- /dev/null +++ b/apps/data-collection/backend/src/utils/core/quantization.py @@ -0,0 +1,33 @@ +import numpy as np +from ..constants import QUANT_VALUES + + +def pad_and_quantize_features( + features, max_num_classes: int, model_name: str, precision="int8" +): + """ + Pad features to (1, 512, max_num_classes) and quantize if precision is int8. + For FP16, return padded float16 features (no quantization). + """ + num_padding = max_num_classes - features.shape[0] + padded = np.pad(features, ((0, num_padding), (0, 0)), "constant").T.reshape( + 1, 512, max_num_classes + ) + + if precision == "fp16": + return padded.astype(np.float16) + + quant = QUANT_VALUES[model_name] + out = (padded / quant["quant_scale"]) + quant["quant_zero_point"] + return out.astype(np.uint8) + + +def make_dummy_features(max_num_classes: int, model_name: str, precision: str): + """ + Create a dummy tensor of shape (1, 512, max_num_classes) for model input. + For FP16, return zeros; for INT8, fill with the model's quantization zero point. + """ + if precision == "fp16": + return np.zeros((1, 512, max_num_classes), dtype=np.float16) + qzp = int(round(QUANT_VALUES.get(model_name, {}).get("quant_zero_point", 0))) + return np.full((1, 512, max_num_classes), qzp, dtype=np.uint8) diff --git a/apps/data-collection/backend/src/utils/core/tokenizer_manager.py b/apps/data-collection/backend/src/utils/core/tokenizer_manager.py new file mode 100644 index 000000000..325bc3f60 --- /dev/null +++ b/apps/data-collection/backend/src/utils/core/tokenizer_manager.py @@ -0,0 +1,75 @@ +from tokenizers import Tokenizer +import numpy as np +import onnxruntime +from .io import download_file +from .quantization import pad_and_quantize_features +from ..constants import MODEL, MAX_NUM_CLASSES + + +class TokenizerManager: + """ + Handles text tokenization and embedding extraction for class names. + + Loads a CLIP-compatible tokenizer and ONNX text encoder, processes + input class labels, and produces normalized and quantized text features + suitable for model input. + """ + + def __init__(self, model_name="yoloe", precision="fp16", max_classes=80): + self.model_name = model_name + self.precision = precision + self.max_classes = max_classes + + def _load(self): + path = download_file( + "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json", + "tokenizer.json", + ) + + self.tokenizer = Tokenizer.from_file(str(path)) + + model_path = download_file( + "https://huggingface.co/Xenova/mobileclip_blt/resolve/main/onnx/text_model.onnx", + "mobileclip_textual_hf.onnx", + ) + + self.session = onnxruntime.InferenceSession( + model_path, + providers=[ + "TensorrtExecutionProvider", + "CUDAExecutionProvider", + "CPUExecutionProvider", + ], + ) + + def extract_text_embeddings(self, class_names: list[str]) -> np.ndarray: + self._load() + self.tokenizer.enable_padding( + pad_id=self.tokenizer.token_to_id("<|endoftext|>"), + pad_token="<|endoftext|>", + ) + + encodings = self.tokenizer.encode_batch(class_names) + text_onnx = np.array([e.ids for e in encodings], dtype=np.int64) + + if text_onnx.shape[1] < 77: + text_onnx = np.pad( + text_onnx, ((0, 0), (0, 77 - text_onnx.shape[1])), mode="constant" + ) + + textual_output = self.session.run( + None, + { + self.session.get_inputs()[0].name: text_onnx, + }, + )[0] + + textual_output /= np.linalg.norm(textual_output, ord=2, axis=-1, keepdims=True) + + text_features = pad_and_quantize_features( + textual_output, MAX_NUM_CLASSES, MODEL, self.precision + ) + + del self.session + + return text_features diff --git a/apps/data-collection/backend/src/utils/core/visual_encoder_manager.py b/apps/data-collection/backend/src/utils/core/visual_encoder_manager.py new file mode 100644 index 000000000..6b8432cd1 --- /dev/null +++ b/apps/data-collection/backend/src/utils/core/visual_encoder_manager.py @@ -0,0 +1,57 @@ +import cv2 +import numpy as np +import onnxruntime +from .io import download_file +from .quantization import pad_and_quantize_features +from ..constants import MODEL, MAX_NUM_CLASSES + + +class VisualEncoderManager: + """ + Handles visual embedding extraction using a YOLOE visual encoder. + + Loads an ONNX visual encoder, preprocesses an image input, performs + forward inference, and returns quantized visual feature tensors + compatible with downstream models. + """ + + def __init__(self, model_name="yoloe", precision="fp16", max_classes=80): + self.model_name = model_name + self.precision = precision + self.max_classes = max_classes + + def _load(self): + path = download_file( + "https://huggingface.co/sokovninn/yoloe-v8l-seg-visual-encoder/resolve/main/" + "yoloe-v8l-seg_visual_encoder.onnx", + "yoloe-v8l-seg_visual_encoder.onnx", + ) + + self.session = onnxruntime.InferenceSession( + path, + providers=[ + "TensorrtExecutionProvider", + "CUDAExecutionProvider", + "CPUExecutionProvider", + ], + ) + + def extract_embeddings(self, image: np.ndarray) -> np.ndarray: + self._load() + image_resized = cv2.resize(image, (640, 640)) + image_array = image_resized.astype(np.float32) / 255.0 + image_array = np.transpose(image_array, (2, 0, 1)) + input_tensor = np.expand_dims(image_array, axis=0).astype(np.float32) + + prompts = np.zeros((1, 1, 80, 80), dtype=np.float32) + prompts[0, 0, 5:75, 5:75] = 1.0 + outputs = self.session.run(None, {"images": input_tensor, "prompts": prompts}) + + image_embeddings = outputs[0].squeeze(0).reshape(1, -1) + image_features = pad_and_quantize_features( + image_embeddings, MAX_NUM_CLASSES, MODEL, self.precision + ) + + del self.session + + return image_features diff --git a/apps/data-collection/backend/src/utils/helper_functions.py b/apps/data-collection/backend/src/utils/helper_functions.py deleted file mode 100644 index 4057cae31..000000000 --- a/apps/data-collection/backend/src/utils/helper_functions.py +++ /dev/null @@ -1,209 +0,0 @@ -from tokenizers import Tokenizer -import os -import requests -import onnxruntime -import numpy as np -import cv2 -import base64 - -QUANT_ZERO_POINT = 90.0 -QUANT_SCALE = 0.003925696481 - -QUANT_VALUES = { - "yoloe": { - "quant_zero_point": 174.0, - "quant_scale": 0.003328413470, - }, - "yoloe-image": { - "quant_zero_point": 137.0, - "quant_scale": 0.002327915514, - }, -} - - -def pad_and_quantize_features( - features, max_num_classes=80, model_name="yoloe", precision="int8" -): - """ - Pad features to (1, 512, max_num_classes) and quantize if precision is int8. - For FP16, return padded float16 features (no quantization). - """ - num_padding = max_num_classes - features.shape[0] - padded_features = np.pad( - features, ((0, num_padding), (0, 0)), mode="constant" - ).T.reshape(1, 512, max_num_classes) - - if precision == "fp16": - return padded_features.astype(np.float16) - - quant_scale = QUANT_VALUES[model_name]["quant_scale"] - quant_zero_point = QUANT_VALUES[model_name]["quant_zero_point"] - quantized_features = (padded_features / quant_scale) + quant_zero_point - quantized_features = quantized_features.astype("uint8") - return quantized_features - - -def extract_text_embeddings( - class_names, max_num_classes=80, model_name="yoloe", precision="int8" -): - tokenizer_json_path = download_tokenizer( - url="https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json", - save_path="tokenizer.json", - ) - tokenizer = Tokenizer.from_file(tokenizer_json_path) - tokenizer.enable_padding( - pad_id=tokenizer.token_to_id("<|endoftext|>"), pad_token="<|endoftext|>" - ) - encodings = tokenizer.encode_batch(class_names) - - text_onnx = np.array([e.ids for e in encodings], dtype=np.int64) - - if text_onnx.shape[1] < 77: - text_onnx = np.pad( - text_onnx, ((0, 0), (0, 77 - text_onnx.shape[1])), mode="constant" - ) - - textual_onnx_model_path = download_model( - "https://huggingface.co/Xenova/mobileclip_blt/resolve/main/onnx/text_model.onnx", - "mobileclip_textual_hf.onnx", - ) - - session_textual = onnxruntime.InferenceSession( - textual_onnx_model_path, - providers=[ - "TensorrtExecutionProvider", - "CUDAExecutionProvider", - "CPUExecutionProvider", - ], - ) - textual_output = session_textual.run( - None, - { - session_textual.get_inputs()[0].name: text_onnx, - }, - )[0] - - textual_output /= np.linalg.norm(textual_output, ord=2, axis=-1, keepdims=True) - - text_features = pad_and_quantize_features( - textual_output, max_num_classes, model_name, precision - ) - - del session_textual - - return text_features - - -def extract_image_prompt_embeddings( - image, - max_num_classes=80, - model_name="yoloe", - mask_prompt=None, - precision="int8", -): - image_resized = cv2.resize(image, (640, 640)) - image_array = image_resized.astype(np.float32) / 255.0 - image_array = np.transpose(image_array, (2, 0, 1)) - input_tensor = np.expand_dims(image_array, axis=0).astype(np.float32) - model_url = ( - "https://huggingface.co/sokovninn/yoloe-v8l-seg-visual-encoder/resolve/main/" - "yoloe-v8l-seg_visual_encoder.onnx" - ) - model_path = "yoloe-v8l-seg_visual_encoder.onnx" - - onnx_model_path = download_model(model_url, model_path) - - session = onnxruntime.InferenceSession( - onnx_model_path, - providers=[ - "TensorrtExecutionProvider", - "CUDAExecutionProvider", - "CPUExecutionProvider", - ], - ) - - if mask_prompt is None: - prompts = np.zeros((1, 1, 80, 80), dtype=np.float32) - prompts[0, 0, 5:75, 5:75] = 1.0 - else: - prompts = np.asarray(mask_prompt, dtype=np.float32) - if prompts.ndim == 2: - if prompts.shape != (80, 80): - prompts = cv2.resize(prompts, (80, 80), interpolation=cv2.INTER_NEAREST) - prompts = prompts[None, None, :, :] - elif prompts.shape == (1, 1, 80, 80): - pass - else: - raise ValueError("mask_prompt must have shape (80,80) or (1,1,80,80)") - outputs = session.run(None, {"images": input_tensor, "prompts": prompts}) - - image_embeddings = outputs[0].squeeze(0).reshape(1, -1) - image_features = pad_and_quantize_features( - image_embeddings, max_num_classes, model_name, precision - ) - - del session - - return image_features - - -def download_tokenizer(url, save_path): - if not os.path.exists(save_path): - print(f"Downloading tokenizer config from {url}...") - with open(save_path, "wb") as f: - f.write(requests.get(url).content) - return save_path - - -def download_model(url, save_path): - if not os.path.exists(save_path): - print(f"Downloading model from {url}...") - response = requests.get(url) - if response.status_code == 200: - with open(save_path, "wb") as f: - f.write(response.content) - print(f"Model saved to {save_path}.") - else: - raise Exception( - f"Failed to download model. Status code: {response.status_code}" - ) - else: - print(f"Model already exists at {save_path}.") - - return save_path - - -def preprocess_image(image): - """Preprocess image for CLIP vision model input""" - image = cv2.resize(image, (224, 224)) - - image_array = np.array(image).astype(np.float32) / 255.0 - - mean = np.array([0.48145466, 0.4578275, 0.40821073]) - std = np.array([0.26862954, 0.26130258, 0.27577711]) - - image_array = (image_array - mean) / std - - image_array = np.transpose(image_array, (2, 0, 1)) - image_array = np.expand_dims(image_array, axis=0) - - return image_array.astype(np.float32) - - -def base64_to_cv2_image(base64_data_uri: str): - if "," in base64_data_uri: - header, base64_data = base64_data_uri.split(",", 1) - else: - base64_data = base64_data_uri - - binary_data = base64.b64decode(base64_data) - np_arr = np.frombuffer(binary_data, np.uint8) - img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) - return img - - -def make_dummy_features(max_num_classes: int, model_name: str, precision: str): - if precision == "fp16": - return np.zeros((1, 512, max_num_classes), dtype=np.float16) - qzp = int(round(QUANT_VALUES.get(model_name, {}).get("quant_zero_point", 0))) - return np.full((1, 512, max_num_classes), qzp, dtype=np.uint8) diff --git a/apps/data-collection/backend/src/utils/services/__init__.py b/apps/data-collection/backend/src/utils/services/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/data-collection/backend/src/utils/services/base_service.py b/apps/data-collection/backend/src/utils/services/base_service.py new file mode 100644 index 000000000..036f92ceb --- /dev/null +++ b/apps/data-collection/backend/src/utils/services/base_service.py @@ -0,0 +1,21 @@ +import abc + + +class BaseService(abc.ABC): + """ + Minimal abstract base for DepthAI backend services. + Only defines common interface + registration logic. + """ + + def __init__(self, visualizer, name: str): + self.visualizer = visualizer + self.name = name + + @abc.abstractmethod + def handle(self, payload: dict | None = None): + """Process request from frontend.""" + raise NotImplementedError + + def register(self): + """Register service with the DepthAI visualizer.""" + self.visualizer.registerService(self.name, self.handle) diff --git a/apps/data-collection/backend/src/utils/services/bbox_prompt_service.py b/apps/data-collection/backend/src/utils/services/bbox_prompt_service.py new file mode 100644 index 000000000..edd131dc2 --- /dev/null +++ b/apps/data-collection/backend/src/utils/services/bbox_prompt_service.py @@ -0,0 +1,48 @@ +from .base_service import BaseService +from ..core.io import base64_to_cv2_image + + +class BBoxPromptService(BaseService): + """ + Handles bounding box prompts from the frontend. + + Decodes an image (or retrieves the last cached frame) and + converts a normalized or pixel-based bounding box to absolute + pixel coordinates for further processing. + """ + + def __init__(self, visualizer, frame_cache): + super().__init__(visualizer, "BBox Prompt Service") + self.frame_cache = frame_cache + + def handle(self, payload: dict | None = None): + image = base64_to_cv2_image(payload["data"]) if payload.get("data") else None + if image is None: + image = self.frame_cache.get_last_frame() + if image is None: + return {"ok": False, "reason": "no_image"} + if image is None: + return {"ok": False, "reason": "decode_failed"} + + bbox = payload.get("bbox", {}) + bx = float(bbox.get("x", 0.0)) + by = float(bbox.get("y", 0.0)) + bw = float(bbox.get("width", 0.0)) + bh = float(bbox.get("height", 0.0)) + + H, W = image.shape[:2] + is_pixel = payload.get("bboxType", "normalized") == "pixel" + if is_pixel: + x0 = int(round(bx)) + y0 = int(round(by)) + x1 = int(round(bx + bw)) + y1 = int(round(by + bh)) + else: + x0 = int(round(bx * W)) + y0 = int(round(by * H)) + x1 = int(round((bx + bw) * W)) + y1 = int(round((by + bh) * H)) + + x0, x1 = sorted((x0, x1)) + y0, y1 = sorted((y0, y1)) + return {"ok": True} diff --git a/apps/data-collection/backend/src/utils/services/class_update_service.py b/apps/data-collection/backend/src/utils/services/class_update_service.py new file mode 100644 index 000000000..6e4fd2e2d --- /dev/null +++ b/apps/data-collection/backend/src/utils/services/class_update_service.py @@ -0,0 +1,84 @@ +from .base_service import BaseService +import depthai as dai +from ..constants import MAX_NUM_CLASSES, MODEL +from ..core.tokenizer_manager import TokenizerManager +from ..core.quantization import make_dummy_features +from ..core.label_manager import LabelManager + + +class ClassUpdateService(BaseService): + """ + Handles class updates from the frontend. + + Regenerates text embeddings for new class labels, updates the + corresponding neural network inputs, and synchronizes label mappings + in the detection and annotation pipeline. + """ + + def __init__( + self, + visualizer, + text_queue, + img_queue, + precision, + current_classes, + det_filter, + annotation, + ): + super().__init__(visualizer, "Class Update Service") + self.text_queue = text_queue + self.img_queue = img_queue + self.precision = precision + self.current_classes = current_classes + self.det_filter = det_filter + self.annotation = annotation + self.embedder = TokenizerManager( + model_name=MODEL, precision=precision, max_classes=MAX_NUM_CLASSES + ) + self.label_manager = LabelManager(det_filter, annotation) + + def handle(self, payload: list | None = None): + new_classes = payload + if not new_classes: + print("[ClassUpdateService] Empty class list.") + return + + if len(new_classes) > MAX_NUM_CLASSES: + print( + f"Too many classes ({len(new_classes)}) > {MAX_NUM_CLASSES}, skipping." + ) + return + + feats = self.embedder.extract_text_embeddings(new_classes) + + nn_txt = dai.NNData() + nn_txt.addTensor( + "texts", + feats, + dataType=( + dai.TensorInfo.DataType.FP16 + if self.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + self.text_queue.send(nn_txt) + + dummy = make_dummy_features(MAX_NUM_CLASSES, MODEL, self.precision) + nn_img = dai.NNData() + nn_img.addTensor( + "image_prompts", + dummy, + dataType=( + dai.TensorInfo.DataType.FP16 + if self.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + self.img_queue.send(nn_img) + + self.label_manager.update_labels(new_classes) + self.current_classes.clear() + self.current_classes.extend(new_classes) + + print(f"[ClassUpdateService] Classes updated: {new_classes}") + return {"ok": True, "classes": new_classes} diff --git a/apps/data-collection/backend/src/utils/services/get_config_service.py b/apps/data-collection/backend/src/utils/services/get_config_service.py new file mode 100644 index 000000000..acc9a557a --- /dev/null +++ b/apps/data-collection/backend/src/utils/services/get_config_service.py @@ -0,0 +1,53 @@ +from .base_service import BaseService + + +class GetConfigService(BaseService): + """ + Returns the current configuration state to the frontend. + + Reports runtime parameters such as confidence threshold, + active snap conditions, cooldown timers, and margin/threshold + values for each configured condition. + """ + + def __init__(self, visualizer, current_classes, runtime, cond_manager): + super().__init__(visualizer, "Get Config Service") + self.current_classes = current_classes + self.runtime = runtime + self.cond_manager = cond_manager + + def handle(self, payload=None): + def s_to_min(seconds: float) -> float: + try: + return float(seconds) / 60.0 if float(seconds) > 0 else 0.0 + except Exception: + return 0.0 + + config = { + "classes": self.current_classes.copy(), + "confidence_threshold": self.runtime["conf_threshold"], + "snapping": { + "running": self.runtime.get("snapping_running", False), + "timed": { + "enabled": self.cond_manager.is_key_enabled("timed"), + "interval": s_to_min(self.cond_manager.get_cooldown("timed")), + }, + "noDetections": { + "enabled": self.cond_manager.is_key_enabled("no_detections"), + "cooldown": s_to_min( + self.cond_manager.get_cooldown("no_detections") + ), + }, + "lowConfidence": { + "enabled": self.cond_manager.is_key_enabled("low_conf"), + "threshold": self.runtime.get("low_conf_thresh", 0.30), + "cooldown": s_to_min(self.cond_manager.get_cooldown("low_conf")), + }, + "lostMid": { + "enabled": self.cond_manager.is_key_enabled("lost_mid"), + "cooldown": s_to_min(self.cond_manager.get_cooldown("lost_mid")), + "margin": self.runtime.get("lost_mid_margin", 0.20), + }, + }, + } + return config diff --git a/apps/data-collection/backend/src/utils/services/image_upload_service.py b/apps/data-collection/backend/src/utils/services/image_upload_service.py new file mode 100644 index 000000000..6992cc76e --- /dev/null +++ b/apps/data-collection/backend/src/utils/services/image_upload_service.py @@ -0,0 +1,66 @@ +import depthai as dai +from .base_service import BaseService +from ..constants import MAX_NUM_CLASSES, MODEL +from ..core.visual_encoder_manager import VisualEncoderManager +from ..core.io import base64_to_cv2_image +from ..core.quantization import make_dummy_features +from ..core.label_manager import LabelManager + + +class ImageUploadService(BaseService): + """ + Handles image uploads from the frontend. + + Decodes base64-encoded images, extracts visual embeddings via + the visual encoder, updates neural network tensors, and synchronizes + class labels for the uploaded image. + """ + + def __init__( + self, visualizer, precision, img_queue, text_queue, det_filter, annotation + ): + super().__init__(visualizer, "Image Upload Service") + self.precision = precision + self.img_queue = img_queue + self.text_queue = text_queue + self.det_filter = det_filter + self.annotation = annotation + self.image = None + self.visual_encoder = VisualEncoderManager(MODEL, precision, MAX_NUM_CLASSES) + self.label_manager = LabelManager(det_filter, annotation) + + def handle(self, payload: dict | None = None): + self.image = base64_to_cv2_image(payload["data"]) + image_features = self.visual_encoder.extract_embeddings(self.image) + + input_NN_data_img = dai.NNData() + input_NN_data_img.addTensor( + "image_prompts", + image_features, + dataType=( + dai.TensorInfo.DataType.FP16 + if self.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + self.img_queue.send(input_NN_data_img) + + dummy = make_dummy_features( + MAX_NUM_CLASSES, model_name=MODEL, precision=self.precision + ) + inputNNDataTxt = dai.NNData() + inputNNDataTxt.addTensor( + "texts", + dummy, + dataType=( + dai.TensorInfo.DataType.FP16 + if self.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ), + ) + self.text_queue.send(inputNNDataTxt) + + filename = payload["filename"] + class_names = [filename.split(".")[0]] + self.label_manager.update_labels(class_names, offset=MAX_NUM_CLASSES) + return {"ok": True, "class": class_names} diff --git a/apps/data-collection/backend/src/utils/services/snap_collection_service.py b/apps/data-collection/backend/src/utils/services/snap_collection_service.py new file mode 100644 index 000000000..a387ab0e9 --- /dev/null +++ b/apps/data-collection/backend/src/utils/services/snap_collection_service.py @@ -0,0 +1,80 @@ +from .base_service import BaseService +import depthai as dai +from ..snaps.conditions_manager import ConditionsManager +from depthai_nodes.node import SnapsProducer + + +class SnapCollectionService(BaseService): + """ + Handles frontend updates to snap collection configuration: + enabling/disabling conditions, adjusting cooldowns, and + controlling the SnapsProducer runtime state. + """ + + def __init__( + self, + visualizer: dai.RemoteConnection, + runtime: dict, + cond_manager: ConditionsManager, + snaps_producer: SnapsProducer, + ): + super().__init__(visualizer, "Snap Collection Service") + self.runtime = runtime + self.cond_manager = cond_manager + self.snaps_producer = snaps_producer + + def handle(self, payload: dict | None = None): + if not isinstance(payload, dict): + return {"ok": False, "reason": "payload_must_be_dict"} + + base_dt_seconds = 1 + prev_running = bool(self.runtime.get("snapping_running", False)) + + for key, conf in payload.items(): + if key not in self.cond_manager.conditions: + continue + if not isinstance(conf, dict): + continue + + if "enabled" in conf: + self.cond_manager.set_key_enabled(key, bool(conf["enabled"])) + + if "cooldown" in conf or "interval" in conf: + try: + cooldown = float(conf.get("cooldown", conf.get("interval", 0))) + self.cond_manager.set_cooldown(key, cooldown) + except Exception: + pass + + if key == "lowConfidence" and "threshold" in conf: + try: + thr = float(conf["threshold"]) + if thr > 1.0: + thr /= 100.0 + self.runtime["low_conf_thresh"] = max(0.0, min(1.0, thr)) + except Exception: + return {"ok": False, "reason": "invalid_low_conf_threshold"} + + if key == "lostMid" and "margin" in conf: + try: + m = float(conf["margin"]) + self.runtime["lost_mid_margin"] = max(0.0, min(0.49, m)) + except Exception: + pass + + any_active = any( + self.cond_manager.is_key_enabled(k) + for k in self.cond_manager.conditions.keys() + ) + + self.cond_manager.set_enabled(any_active) + + if any_active and not prev_running: + self.cond_manager.reset_cooldowns() + + self.snaps_producer.setRunning(any_active) + self.runtime["snapping_running"] = any_active + if any_active: + self.snaps_producer.setTimeInterval(base_dt_seconds) + + return {"ok": True, "active": any_active} diff --git a/apps/data-collection/backend/src/utils/services/threshold_update_service.py b/apps/data-collection/backend/src/utils/services/threshold_update_service.py new file mode 100644 index 000000000..e01629243 --- /dev/null +++ b/apps/data-collection/backend/src/utils/services/threshold_update_service.py @@ -0,0 +1,21 @@ +from .base_service import BaseService + + +class ThresholdUpdateService(BaseService): + """ + Updates and synchronizes the neural network confidence threshold. + + Receives a new threshold value from the frontend, clamps it to + a valid range [0.0, 1.0], and updates both the runtime state and + the active neural network parser configuration. + """ + + def __init__(self, visualizer, nn, runtime): + super().__init__(visualizer, "Threshold Update Service") + self.nn = nn + self.runtime = runtime + + def handle(self, payload: float | None = None): + self.runtime["conf_threshold"] = max(0.0, min(1.0, payload)) + self.nn.getParser(0).setConfidenceThreshold(self.runtime["conf_threshold"]) + return {"ok": True, "threshold": self.runtime["conf_threshold"]} diff --git a/apps/data-collection/backend/src/utils/snap_utils/custon_snap_process.py b/apps/data-collection/backend/src/utils/snap_utils/custon_snap_process.py deleted file mode 100644 index 32a10be1e..000000000 --- a/apps/data-collection/backend/src/utils/snap_utils/custon_snap_process.py +++ /dev/null @@ -1,132 +0,0 @@ -from typing import List, Optional -import depthai as dai -from depthai_nodes.node import SnapsProducer -from .conditions_gate import ConditionsGate -from .helpers import ( - _send_snap, - _track_id, - _label_idx_name, - _status_is_lost, - _roi_center_area_norm, - _LOSTMID_STATE, - _status_is_tracked, -) -from .extras import build_extras - - -def custom_snap_process( - producer: SnapsProducer, - frame: dai.ImgFrame, - tracklets_msg: Optional[dai.Tracklets], - det_data: Optional[dai.ImgDetections], - class_names: List[str], - model: str, - cond_gate: ConditionsGate, - runtime: dict | None = None, -): - # timed - if cond_gate.is_key_enabled("timed") and cond_gate.hit("timed"): - extras = ( - build_extras(model, det_data, class_names) - if det_data is not None - else {"model": model} - ) - if _send_snap( - "Timing Snap", producer, frame, ["timing_snap"], extras, det_data - ): - print("[Timed] Snap sent!") - - dets = getattr(det_data, "detections", None) or [] - num = len(dets) - - # no detections - if det_data is not None and num == 0: - if cond_gate.is_key_enabled("no_detections") and cond_gate.hit("no_detections"): - if _send_snap( - "No Detections", - producer, - frame, - ["no_detections"], - {"model": model, "reason": "no_detections"}, - ): - print("[NoDet] Snap sent") - - # low confidence - if det_data is not None and cond_gate.is_key_enabled("low_conf"): - thr = None if runtime is None else runtime.get("low_conf_thresh") - if thr is not None: - has_low = any( - float(getattr(d, "confidence", 1.0)) < float(thr) for d in dets - ) - if has_low and cond_gate.hit("low_conf"): - min_conf = ( - min(float(getattr(d, "confidence", 1.0)) for d in dets) - if dets - else 1.0 - ) - extras = { - "model": model, - "reason": "low_confidence", - "min_conf": f"{min_conf:.3f}", - "threshold": f"{float(thr):.3f}", - } - if _send_snap( - "Low Confidence", - producer, - frame, - ["low_confidence"], - extras, - det_data, - ): - print( - f"[LowConf] Snap sent (min={min_conf:.3f} < thr={float(thr):.3f})" - ) - - # lost detection - if tracklets_msg is not None and cond_gate.is_key_enabled("lost_mid"): - margin = float((runtime or {}).get("lost_mid_margin", 0.20)) - margin = max(0.0, min(0.49, margin)) - - fired = False - tks = getattr(tracklets_msg, "tracklets", None) or [] - for t in tks: - tid = _track_id(t) - if tid is None: - continue - - was_tracked_prev = _LOSTMID_STATE.prev_tracked.get(tid, False) - is_lost_now = _status_is_lost(t) - is_tracked_now = _status_is_tracked(t) - - if is_lost_now and was_tracked_prev: - rc = _roi_center_area_norm(t) - if rc is not None: - cx, cy, area = rc - if (margin <= cx <= 1.0 - margin) and ( - margin <= cy <= 1.0 - margin - ): - if cond_gate.hit("lost_mid"): - lbl_idx, lbl_name = _label_idx_name(t, class_names) - extras = { - "model": str(model), - "reason": "lost_in_middle", - "track_id": str(int(tid)), - "label_idx": str(int(lbl_idx)), - "label": str(lbl_name), - "area_frac": f"{area:.6f}", - "margin": f"{margin:.3f}", - } - if _send_snap( - "Lost in middle", - producer, - frame, - ["lost_in_middle"], - extras, - det_data, - ): - fired = True - - _LOSTMID_STATE.prev_tracked[tid] = bool(is_tracked_now) - - if fired: - print("[LostMid] Snap(s) sent") diff --git a/apps/data-collection/backend/src/utils/snap_utils/extras.py b/apps/data-collection/backend/src/utils/snap_utils/extras.py deleted file mode 100644 index 0a7b1ae25..000000000 --- a/apps/data-collection/backend/src/utils/snap_utils/extras.py +++ /dev/null @@ -1,64 +0,0 @@ -from __future__ import annotations -from typing import List -import depthai as dai -from depthai_nodes import ImgDetectionsExtended - - -def build_extras( - model: str, - det_data: dai.ImgDetections | ImgDetectionsExtended, - class_names: List[str], -) -> dict[str, str]: - dets = getattr(det_data, "detections", None) or [] - labels = [getattr(d, "label", -1) for d in dets[:20]] - confs = [getattr(d, "confidence", 0.0) for d in dets[:20]] - - extras: dict = { - "model": model, - "detection_count": str(len(dets)), - "detection_label": ",".join(map(str, labels)), - "detection_conf": ",".join(f"{c:.2f}" for c in confs), - } - - if isinstance(det_data, dai.ImgDetections): - xyxy = [ - ( - getattr(d, "xmin", 0.0), - getattr(d, "ymin", 0.0), - getattr(d, "xmax", 0.0), - getattr(d, "ymax", 0.0), - ) - for d in dets[:10] - ] - extras["detection_xyxy"] = ";".join( - f"{x1:.3f},{y1:.3f},{x2:.3f},{y2:.3f}" for x1, y1, x2, y2 in xyxy - ) - names = [] - if dets: - for d in dets[:20]: - li = getattr(d, "label", -1) - names.append(class_names[li] if 0 <= li < len(class_names) else "") - extras["detection_label_str"] = ",".join(names) if names else "" - elif isinstance(det_data, ImgDetectionsExtended): - cxcywh = [] - for d in dets[:10]: - try: - c = d.rotated_rect.center - s = d.rotated_rect.size - cxcywh.append((c.x, c.y, s.width, s.height)) - except Exception: - pass - extras["detection_cxcywh"] = ";".join( - f"{cx:.3f},{cy:.3f},{w:.3f},{h:.3f}" for cx, cy, w, h in cxcywh - ) - names = [] - for d in dets[:20]: - try: - names.append(str(d.label_name)) - except Exception: - names.append("") - extras["detection_label_str"] = ",".join(names) - else: - extras["dtype"] = type(det_data).__name__ - - return extras diff --git a/apps/data-collection/backend/src/utils/snaps/__init__.py b/apps/data-collection/backend/src/utils/snaps/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/data-collection/backend/src/utils/snaps/conditions/__init__.py b/apps/data-collection/backend/src/utils/snaps/conditions/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/data-collection/backend/src/utils/snaps/conditions/base_condition.py b/apps/data-collection/backend/src/utils/snaps/conditions/base_condition.py new file mode 100644 index 000000000..4fc54c4f6 --- /dev/null +++ b/apps/data-collection/backend/src/utils/snaps/conditions/base_condition.py @@ -0,0 +1,17 @@ +from abc import ABC, abstractmethod + + +class ConditionBase(ABC): + """Interface for all snap trigger conditions.""" + + def __init__(self, key: str, name: str): + self.key = key + self.name = name + + @abstractmethod + def should_trigger(self, cond_man, **kwargs) -> bool: + """Return True if this condition should fire.""" + pass + + def handle(self, **kwargs) -> dict: + return {"reason": f"{self.key}_snap"} diff --git a/apps/data-collection/backend/src/utils/snaps/conditions/lost_mid_condition.py b/apps/data-collection/backend/src/utils/snaps/conditions/lost_mid_condition.py new file mode 100644 index 000000000..bea682687 --- /dev/null +++ b/apps/data-collection/backend/src/utils/snaps/conditions/lost_mid_condition.py @@ -0,0 +1,76 @@ +from .base_condition import ConditionBase +from ..tracklets import ( + _track_id, + _status_is_lost, + _status_is_tracked, + _roi_center_area_norm, + _label_idx_name, +) +from collections import defaultdict + + +class LostMidCondition(ConditionBase): + """ + Triggers when a tracked object becomes lost while still + positioned near the center of the frame. + + Detects transition from 'tracked' to 'lost' state within a + user-defined margin, and records affected tracklets for reporting. + """ + + def __init__(self, key="lostMid", name="Lost in Middle", default_margin=0.20): + super().__init__(key, name) + self._last_fired = None + self.prev_tracked = defaultdict(bool) + self.default_margin = default_margin + + def should_trigger(self, cond_manager, tracklets=None, runtime=None, **_): + if not cond_manager.is_key_enabled(self.key) or tracklets is None: + return False + + margin = float(runtime.get("lost_mid_margin", self.default_margin)) + margin = max(0.0, min(0.49, margin)) + fired = False + fired_tracklets = [] + + tks = getattr(tracklets, "tracklets", None) or [] + for t in tks: + tid = _track_id(t) + if tid is None: + continue + + was_tracked = self.prev_tracked[tid] + is_lost = _status_is_lost(t) + is_tracked = _status_is_tracked(t) + + if is_lost and was_tracked: + rc = _roi_center_area_norm(t) + if rc is not None: + cx, cy, area = rc + if margin <= cx <= 1.0 - margin and margin <= cy <= 1.0 - margin: + if cond_manager.hit(self.key): + fired = True + fired_tracklets.append((tid, t, area, margin)) + + self.prev_tracked[tid] = is_tracked + + self._last_fired = fired_tracklets + return fired + + def handle(self, tracklets=None, class_names=None, model=None, **_): + results = [] + for tid, t, area, margin in getattr(self, "_last_fired", []): + lbl_idx, lbl_name = _label_idx_name(t, class_names) + results.append( + { + "reason": "lost_in_middle", + "model": str(model), + "track_id": str(int(tid)), + "label_idx": str(int(lbl_idx)), + "label": str(lbl_name), + "area_frac": f"{area:.6f}", + "margin": f"{margin:.3f}", + } + ) + + return results[0] if len(results) == 1 else results diff --git a/apps/data-collection/backend/src/utils/snaps/conditions/low_conf_condition.py b/apps/data-collection/backend/src/utils/snaps/conditions/low_conf_condition.py new file mode 100644 index 000000000..90b5de444 --- /dev/null +++ b/apps/data-collection/backend/src/utils/snaps/conditions/low_conf_condition.py @@ -0,0 +1,36 @@ +from .base_condition import ConditionBase + + +class LowConfidenceCondition(ConditionBase): + """ + Triggers a snap when detections fall below a defined confidence threshold. + + Compares detection confidences against the current runtime threshold + and activates when any detection is lower than that value. + """ + + def __init__(self, key="lowConfidence", name="Low Confidence"): + super().__init__(key, name) + + def should_trigger(self, cond_manager, det_data=None, runtime=None, **_) -> bool: + if det_data is None or not cond_manager.is_key_enabled(self.key): + return False + + thr = runtime.get("low_conf_thresh") + if thr is None: + return False + dets = getattr(det_data, "detections", []) + has_low = any(float(getattr(d, "confidence", 1.0)) < float(thr) for d in dets) + return has_low and cond_manager.hit(self.key) + + def handle(self, det_data=None, runtime=None, **_): + dets = getattr(det_data, "detections", []) + min_conf = min( + (float(getattr(d, "confidence", 1.0)) for d in dets), default=1.0 + ) + thr = (runtime or {}).get("low_conf_thresh", 0.3) + return { + "reason": "low_confidence", + "min_conf": f"{min_conf:.3f}", + "threshold": f"{float(thr):.3f}", + } diff --git a/apps/data-collection/backend/src/utils/snaps/conditions/no_detections_condition.py b/apps/data-collection/backend/src/utils/snaps/conditions/no_detections_condition.py new file mode 100644 index 000000000..683ee6bcb --- /dev/null +++ b/apps/data-collection/backend/src/utils/snaps/conditions/no_detections_condition.py @@ -0,0 +1,18 @@ +from .base_condition import ConditionBase + + +class NoDetectionsCondition(ConditionBase): + """ + Triggers when no objects are detected in the current frame. + + Activates only when the detections list is empty and the + cooldown period for this condition has expired. + """ + + def __init__(self, key="noDetections", name="No Detections"): + super().__init__(key, name) + + def should_trigger(self, cond_manager, det_data=None, **_): + if det_data is None or len(getattr(det_data, "detections", [])) > 0: + return False + return cond_manager.hit(self.key) diff --git a/apps/data-collection/backend/src/utils/snaps/conditions/timed_condition.py b/apps/data-collection/backend/src/utils/snaps/conditions/timed_condition.py new file mode 100644 index 000000000..9b614dfc1 --- /dev/null +++ b/apps/data-collection/backend/src/utils/snaps/conditions/timed_condition.py @@ -0,0 +1,16 @@ +from .base_condition import ConditionBase + + +class TimedCondition(ConditionBase): + """ + Triggers snaps at fixed time intervals. + + Fires independently of detection data, based solely on the + configured cooldown period in the condition manager. + """ + + def __init__(self, key="timed", name="Timed Snap"): + super().__init__(key, name) + + def should_trigger(self, cond_manager, **_): + return cond_manager.hit(self.key) diff --git a/apps/data-collection/backend/src/utils/snap_utils/conditions_gate.py b/apps/data-collection/backend/src/utils/snaps/conditions_manager.py similarity index 55% rename from apps/data-collection/backend/src/utils/snap_utils/conditions_gate.py rename to apps/data-collection/backend/src/utils/snaps/conditions_manager.py index b68b07ad3..d50a7b958 100644 --- a/apps/data-collection/backend/src/utils/snap_utils/conditions_gate.py +++ b/apps/data-collection/backend/src/utils/snaps/conditions_manager.py @@ -1,27 +1,39 @@ import time from typing import Dict, Iterable, Optional +from .conditions.base_condition import ConditionBase -class ConditionsGate: +class ConditionsManager: """ - Generic per-condition cooldown gate. - - First hit for a key fires immediately. - - Subsequent hits for the same key are allowed only after its cooldown. - - Supports global enable + per-key enable + per-key cooldown overrides. + Manages registration and evaluation of snap trigger conditions. + + Tracks cooldowns, enabled states, and timing for each condition, + ensuring that individual triggers fire only when their criteria + and cooldown constraints are met. """ - def __init__(self, default_cooldown_s: float = 0.0, enabled: bool = True): + def __init__(self, default_cooldown_s: float = 300, enabled: bool = True): self.enabled = bool(enabled) self.default_cooldown_s = max(0.0, float(default_cooldown_s)) self.cooldowns: Dict[str, float] = {} self.key_enabled: Dict[str, bool] = {} self.last_sent: Dict[str, float] = {} + self.conditions: Dict[str, "ConditionBase"] = {} - def set_enabled(self, on: bool) -> None: - self.enabled = bool(on) + def register_condition( + self, condition: ConditionBase, enabled=False, cooldown_s=-1.0 + ): + key = condition.key + self.conditions[key] = condition + self.key_enabled[key] = enabled + self.cooldowns[key] = cooldown_s if cooldown_s >= 0 else self.default_cooldown_s - def set_default(self, seconds: float) -> None: - self.default_cooldown_s = max(0.0, float(seconds)) + def register_conditions(self, conditions: Iterable[ConditionBase]): + for cond in conditions: + self.register_condition(cond) + + def set_enabled(self, on: bool) -> None: + self.enabled = on def set_key_enabled(self, key: str, on: bool) -> None: self.key_enabled[str(key)] = bool(on) @@ -29,7 +41,7 @@ def set_key_enabled(self, key: str, on: bool) -> None: def set_cooldown(self, key: str, seconds: float) -> None: self.cooldowns[str(key)] = max(0.0, float(seconds)) - def reset(self, keys: Optional[Iterable[str]] = None) -> None: + def reset_cooldowns(self, keys: Optional[Iterable[str]] = None) -> None: if keys is None: self.last_sent.clear() else: @@ -56,3 +68,12 @@ def hit(self, key: str) -> bool: self.last_sent[key] = now return True return False + + def evaluate(self, **kwargs): + """ + Iterate through all registered conditions, + yield (condition, extras) for those that fire. + """ + for key, cond in self.conditions.items(): + if self.is_key_enabled(key) and cond.should_trigger(self, **kwargs): + yield cond, cond.handle(**kwargs) diff --git a/apps/data-collection/backend/src/utils/snaps/custon_snap_process.py b/apps/data-collection/backend/src/utils/snaps/custon_snap_process.py new file mode 100644 index 000000000..a3b1bb311 --- /dev/null +++ b/apps/data-collection/backend/src/utils/snaps/custon_snap_process.py @@ -0,0 +1,33 @@ +from depthai_nodes.node import SnapsProducer +from ..constants import MODEL +import depthai as dai +from .conditions_manager import ConditionsManager + + +def custom_snap_process( + producer: SnapsProducer, + frame: dai.ImgFrame, + tracklets_msg: dai.Tracklets | None, + det_data: dai.ImgDetections | None, + class_names: list[str], + cond_manager: ConditionsManager, + runtime: dict | None = None, +): + """ + Evaluates all registered snap conditions and sends triggered snaps. + + Iterates through active conditions managed by `cond_manager`, checks + whether each should fire based on the current detections and + tracklets, and sends the corresponding frame via the SnapsProducer. + """ + + for condition, extras in cond_manager.evaluate( + det_data=det_data, + tracklets=tracklets_msg, + frame=frame, + runtime=runtime, + class_names=class_names, + model=MODEL, + ): + if producer.sendSnap(condition.name, frame, det_data, [condition.key], extras): + print(f"[{condition.key}] Snap sent!") diff --git a/apps/data-collection/backend/src/utils/snap_utils/helpers.py b/apps/data-collection/backend/src/utils/snaps/tracklets.py similarity index 81% rename from apps/data-collection/backend/src/utils/snap_utils/helpers.py rename to apps/data-collection/backend/src/utils/snaps/tracklets.py index aba08cf05..a23ba6992 100644 --- a/apps/data-collection/backend/src/utils/snap_utils/helpers.py +++ b/apps/data-collection/backend/src/utils/snaps/tracklets.py @@ -1,7 +1,14 @@ from __future__ import annotations from typing import List, Optional, Tuple import depthai as dai -from depthai_nodes.node import SnapsProducer + + +class _LostMidState: + def __init__(self) -> None: + self.prev_tracked: dict[int, bool] = {} + + +_LOSTMID_STATE = _LostMidState() def _status_is_tracked(tracklet) -> bool: @@ -32,25 +39,6 @@ def _label_idx_name(tracklet, class_names: List[str]) -> Tuple[int, str]: return idx, name -def _send_snap( - name: str, - producer: SnapsProducer, - frame: dai.ImgFrame, - tags: List[str], - extras: dict, - detections: dai.ImgDetections = None, -) -> bool: - return producer.sendSnap(name, frame, detections, tags, extras) - - -class _LostMidState: - def __init__(self) -> None: - self.prev_tracked: dict[int, bool] = {} - - -_LOSTMID_STATE = _LostMidState() - - def _status_is_lost(t) -> bool: try: return t.status == dai.Tracklet.TrackingStatus.LOST @@ -80,3 +68,13 @@ def _roi_center_area_norm(t) -> Optional[Tuple[float, float, float]]: h = float(getattr(d, "height", 0.0)) return x + 0.5 * w, y + 0.5 * h, max(0.0, w * h) return None + + +def setup_tracker(object_tracker: dai.node.ObjectTracker): + object_tracker.setTrackerType(dai.TrackerType.SHORT_TERM_IMAGELESS) + object_tracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.UNIQUE_ID) + object_tracker.setTrackingPerClass(True) + object_tracker.setTrackletBirthThreshold(3) + object_tracker.setTrackletMaxLifespan(90) + object_tracker.setOcclusionRatioThreshold(0.5) + object_tracker.setTrackerThreshold(0.25) diff --git a/apps/data-collection/frontend/src/App.tsx b/apps/data-collection/frontend/src/App.tsx index 3f21b63c1..19cf8c3f6 100644 --- a/apps/data-collection/frontend/src/App.tsx +++ b/apps/data-collection/frontend/src/App.tsx @@ -1,9 +1,9 @@ import { css } from "../styled-system/css/css.mjs"; import { Streams, useConnection } from "@luxonis/depthai-viewer-common"; -import { ClassSelector } from "./ClassSelector.tsx"; -import { ConfidenceSlider } from "./ConfidenceSlider.tsx"; -import { ImageUploader } from "./ImageUploader.tsx"; -import { SnapConditionsPanel } from "./SnapConditionsPanel.tsx"; +import { ClassSelector } from "./utils/classes/ClassSelector.tsx"; +import { ConfidenceSlider } from "./utils/classes/ConfidenceSlider.tsx"; +import { ImageUploader } from "./utils/classes/ImageUploader.tsx"; +import { SnapConditionsPanel } from "./utils/conditions/SnapConditionsPanel.tsx"; import { useCallback, useEffect, useRef, useState } from "react"; import { useNotifications } from "./Notifications.tsx"; diff --git a/apps/data-collection/frontend/src/inputs/SliderControl.tsx b/apps/data-collection/frontend/src/utils/SliderControl.tsx similarity index 100% rename from apps/data-collection/frontend/src/inputs/SliderControl.tsx rename to apps/data-collection/frontend/src/utils/SliderControl.tsx diff --git a/apps/data-collection/frontend/src/ClassSelector.tsx b/apps/data-collection/frontend/src/utils/classes/ClassSelector.tsx similarity index 96% rename from apps/data-collection/frontend/src/ClassSelector.tsx rename to apps/data-collection/frontend/src/utils/classes/ClassSelector.tsx index eb298e87b..cd66051e1 100644 --- a/apps/data-collection/frontend/src/ClassSelector.tsx +++ b/apps/data-collection/frontend/src/utils/classes/ClassSelector.tsx @@ -1,8 +1,8 @@ import { Flex, Button, Input } from "@luxonis/common-fe-components"; import { useRef, useState, useEffect } from "react"; -import { css } from "../styled-system/css/css.mjs"; +import { css } from "../../../styled-system/css/css.mjs"; import { useConnection } from "@luxonis/depthai-viewer-common"; -import { useNotifications } from "./Notifications.tsx"; +import { useNotifications } from "../../Notifications.tsx"; interface ClassSelectorProps { initialClasses?: string[]; diff --git a/apps/data-collection/frontend/src/ConfidenceSlider.tsx b/apps/data-collection/frontend/src/utils/classes/ConfidenceSlider.tsx similarity index 95% rename from apps/data-collection/frontend/src/ConfidenceSlider.tsx rename to apps/data-collection/frontend/src/utils/classes/ConfidenceSlider.tsx index 43d9c768c..7f5b6a320 100644 --- a/apps/data-collection/frontend/src/ConfidenceSlider.tsx +++ b/apps/data-collection/frontend/src/utils/classes/ConfidenceSlider.tsx @@ -1,6 +1,6 @@ import { useState, useEffect } from "react"; import { useConnection } from "@luxonis/depthai-viewer-common"; -import { SliderControl } from "./inputs/SliderControl"; +import { SliderControl } from "../SliderControl.tsx"; interface ConfidenceSliderProps { initialValue?: number; diff --git a/apps/data-collection/frontend/src/ImageUploader.tsx b/apps/data-collection/frontend/src/utils/classes/ImageUploader.tsx similarity index 97% rename from apps/data-collection/frontend/src/ImageUploader.tsx rename to apps/data-collection/frontend/src/utils/classes/ImageUploader.tsx index 949695f1f..5df0bd676 100644 --- a/apps/data-collection/frontend/src/ImageUploader.tsx +++ b/apps/data-collection/frontend/src/utils/classes/ImageUploader.tsx @@ -1,8 +1,8 @@ import { Button, Flex } from "@luxonis/common-fe-components"; -import { css } from "../styled-system/css/css.mjs"; +import { css } from "../../../styled-system/css/css.mjs"; import { useState } from "react"; import { useConnection } from "@luxonis/depthai-viewer-common"; -import { useNotifications } from "./Notifications.tsx"; +import { useNotifications } from "../../Notifications.tsx"; type Props = { onDrawBBox?: () => void; diff --git a/apps/data-collection/frontend/src/ConditionCard.tsx b/apps/data-collection/frontend/src/utils/conditions/ConditionCard.tsx similarity index 95% rename from apps/data-collection/frontend/src/ConditionCard.tsx rename to apps/data-collection/frontend/src/utils/conditions/ConditionCard.tsx index d2148eb30..6c252e120 100644 --- a/apps/data-collection/frontend/src/ConditionCard.tsx +++ b/apps/data-collection/frontend/src/utils/conditions/ConditionCard.tsx @@ -1,5 +1,5 @@ import { ReactNode } from "react"; -import { css } from "../styled-system/css/css.mjs"; +import { css } from "../../../styled-system/css/css.mjs"; interface ConditionCardProps { title: string; diff --git a/apps/data-collection/frontend/src/inputs/CooldownMinutesInput.tsx b/apps/data-collection/frontend/src/utils/conditions/CooldownMinutesInput.tsx similarity index 96% rename from apps/data-collection/frontend/src/inputs/CooldownMinutesInput.tsx rename to apps/data-collection/frontend/src/utils/conditions/CooldownMinutesInput.tsx index aecb394f5..ad8e8f742 100644 --- a/apps/data-collection/frontend/src/inputs/CooldownMinutesInput.tsx +++ b/apps/data-collection/frontend/src/utils/conditions/CooldownMinutesInput.tsx @@ -1,4 +1,4 @@ -import { css } from "../../styled-system/css/css.mjs"; +import { css } from "../../../styled-system/css/css.mjs"; interface CooldownMinutesInputProps { label?: string; diff --git a/apps/data-collection/frontend/src/inputs/EdgeBufferPercentInput.tsx b/apps/data-collection/frontend/src/utils/conditions/EdgeBufferPercentInput.tsx similarity index 95% rename from apps/data-collection/frontend/src/inputs/EdgeBufferPercentInput.tsx rename to apps/data-collection/frontend/src/utils/conditions/EdgeBufferPercentInput.tsx index 6dab4661c..38fe594c4 100644 --- a/apps/data-collection/frontend/src/inputs/EdgeBufferPercentInput.tsx +++ b/apps/data-collection/frontend/src/utils/conditions/EdgeBufferPercentInput.tsx @@ -1,4 +1,4 @@ -import { css } from "../../styled-system/css/css.mjs"; +import { css } from "../../../styled-system/css/css.mjs"; interface EdgeBufferPercentInputProps { value: string; diff --git a/apps/data-collection/frontend/src/SnapCollectionButton.tsx b/apps/data-collection/frontend/src/utils/conditions/SnapCollectionButton.tsx similarity index 93% rename from apps/data-collection/frontend/src/SnapCollectionButton.tsx rename to apps/data-collection/frontend/src/utils/conditions/SnapCollectionButton.tsx index dae2b89dd..2d7058b3d 100644 --- a/apps/data-collection/frontend/src/SnapCollectionButton.tsx +++ b/apps/data-collection/frontend/src/utils/conditions/SnapCollectionButton.tsx @@ -1,5 +1,5 @@ import { Button } from "@luxonis/common-fe-components"; -import { css } from "../styled-system/css/css.mjs"; +import { css } from "../../../styled-system/css/css.mjs"; interface SnapActionButtonProps { running: boolean; diff --git a/apps/data-collection/frontend/src/SnapConditionsPanel.tsx b/apps/data-collection/frontend/src/utils/conditions/SnapConditionsPanel.tsx similarity index 96% rename from apps/data-collection/frontend/src/SnapConditionsPanel.tsx rename to apps/data-collection/frontend/src/utils/conditions/SnapConditionsPanel.tsx index adf94cb8f..2ff1c4ddf 100644 --- a/apps/data-collection/frontend/src/SnapConditionsPanel.tsx +++ b/apps/data-collection/frontend/src/utils/conditions/SnapConditionsPanel.tsx @@ -1,12 +1,12 @@ import { useMemo, useState, useEffect } from "react"; -import { css } from "../styled-system/css/css.mjs"; +import { css } from "../../../styled-system/css/css.mjs"; import { useConnection } from "@luxonis/depthai-viewer-common"; -import { useNotifications } from "./Notifications.tsx"; -import { ConditionCard } from "./ConditionCard"; -import { CooldownMinutesInput } from "./inputs/CooldownMinutesInput"; -import { EdgeBufferPercentInput } from "./inputs/EdgeBufferPercentInput"; -import { SnapCollectionButton } from "./SnapCollectionButton"; -import { SliderControl } from "./inputs/SliderControl"; +import { useNotifications } from "../../Notifications.tsx"; +import { ConditionCard } from "./ConditionCard.tsx"; +import { CooldownMinutesInput } from "./CooldownMinutesInput.tsx"; +import { EdgeBufferPercentInput } from "./EdgeBufferPercentInput.tsx"; +import { SnapCollectionButton } from "./SnapCollectionButton.tsx"; +import { SliderControl } from "../SliderControl.tsx"; interface SnappingConfig { running: boolean; From efca2d73d44e93ae67cc0110c2878673e9590b83 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Tue, 4 Nov 2025 00:39:27 +0100 Subject: [PATCH 22/39] Finished Application Refactoring --- .../backend/src/{utils => }/__init__.py | 0 .../src/{utils/core => config}/__init__.py | 0 .../src/{utils => config}/arguments.py | 0 .../backend/src/config/config_data_classes.py | 26 ++ .../src/config/system_configuration.py | 67 +++++ .../src/config/system_state_exporter.py | 30 ++ .../src/config/yaml_configs/conditions.yaml | 21 ++ .../src/config/yaml_configs/constants.yaml | 6 + .../src/config/yaml_configs/model.yaml | 26 ++ .../src/config/yaml_configs/nn_config.yaml | 12 + .../backend/src/config/yaml_loader.py | 26 ++ .../src/{utils/services => core}/__init__.py | 0 .../snaps => core/controllers}/__init__.py | 0 .../src/core/controllers/nn_controller.py | 59 ++++ .../conditions => core/encoders}/__init__.py | 0 .../backend/src/core/encoders/base_encoder.py | 90 ++++++ .../src/core/encoders/textual_encoder.py | 58 ++++ .../src/core/encoders/visual_encoder.py | 64 +++++ .../backend/src/core/handlers/__init__.py | 0 .../core/handlers/base_embedding_handler.py | 24 ++ .../src/core/handlers/bbox_prompt_handler.py | 30 ++ .../src/core/handlers/class_update_handler.py | 14 + .../src/core/handlers/image_upload_handler.py | 28 ++ .../src/{utils => }/core/label_manager.py | 12 +- .../backend/src/core/model_state.py | 18 ++ .../backend/src/core/snapping/__init__.py | 0 .../src/core/snapping/conditions/__init__.py | 0 .../snapping/conditions/base_condition.py | 76 +++++ .../core/snapping/conditions/condition_key.py | 8 + .../snapping/conditions/low_conf_condition.py | 45 +++ .../conditions/no_detections_condition.py | 22 ++ .../snapping/conditions/timed_condition.py | 20 ++ .../conditions/tracker_conditions/__init__.py | 0 .../tracker_conditions/lost_mid_condition.py | 58 ++++ .../tracker_conditions/tracklet_analyzer.py | 90 ++++++ .../src/core/snapping/conditions_engine.py | 60 ++++ .../src/core/snapping/custom_snap_process.py | 30 ++ .../backend/src/infrastructure/__init__.py | 0 .../src/infrastructure/conditions_factory.py | 35 +++ .../src/infrastructure/export/__init__.py | 0 .../infrastructure/export/export_manager.py | 40 +++ .../frame_cache_node.py | 0 .../infrastructure/neural_network/__init__.py | 0 .../neural_network}/annotation_node.py | 8 +- .../neural_network/encoders_manager.py | 44 +++ .../neural_network/handlers_manager.py | 23 ++ .../neural_network/neural_network_manager.py | 88 ++++++ .../neural_network/nn_service_manager.py | 32 +++ .../neural_network/pipeline_builder.py | 105 +++++++ .../src/infrastructure/snaps/__init__.py | 0 .../src/infrastructure/snaps/snaps_manager.py | 56 ++++ .../snaps/snaps_producer_factory.py | 23 ++ .../infrastructure/video_source_manager.py | 97 +++++++ apps/data-collection/backend/src/main.py | 264 ++---------------- .../backend/src/services/__init__.py | 0 .../backend/src/services/base_service.py | 26 ++ .../src/services/bbox_prompt/__init__.py | 0 .../bbox_prompt/bbox_prompt_payload.py | 14 + .../bbox_prompt/bbox_prompt_service.py | 37 +++ .../src/services/class_update/__init__.py | 0 .../class_update/class_update_payload.py | 7 + .../class_update/class_update_service.py | 31 ++ .../src/services/export_service/__init__.py | 0 .../export_service/get_config_service.py | 22 ++ .../src/services/image_upload/__init__.py | 0 .../image_upload/image_upload_payload.py | 9 + .../image_upload/image_upload_service.py | 23 ++ .../backend/src/services/service_name.py | 12 + .../src/services/snap_collection/__init__.py | 0 .../snap_collection_service.py | 31 ++ .../services/snap_collection/snap_payload.py | 15 + .../src/services/threshold_update/__init__.py | 0 .../threshold_update_payload.py | 7 + .../threshold_update_service.py | 25 ++ .../backend/src/utils/constants.py | 30 -- .../backend/src/utils/core/io.py | 26 -- .../backend/src/utils/core/quantization.py | 33 --- .../src/utils/core/tokenizer_manager.py | 75 ----- .../src/utils/core/visual_encoder_manager.py | 57 ---- .../src/utils/services/base_service.py | 21 -- .../src/utils/services/bbox_prompt_service.py | 48 ---- .../utils/services/class_update_service.py | 84 ------ .../src/utils/services/get_config_service.py | 53 ---- .../utils/services/image_upload_service.py | 66 ----- .../utils/services/snap_collection_service.py | 80 ------ .../services/threshold_update_service.py | 21 -- .../utils/snaps/conditions/base_condition.py | 17 -- .../snaps/conditions/lost_mid_condition.py | 76 ----- .../snaps/conditions/low_conf_condition.py | 36 --- .../conditions/no_detections_condition.py | 18 -- .../utils/snaps/conditions/timed_condition.py | 16 -- .../src/utils/snaps/conditions_manager.py | 79 ------ .../src/utils/snaps/custon_snap_process.py | 33 --- .../backend/src/utils/snaps/tracklets.py | 80 ------ apps/data-collection/frontend/src/App.tsx | 9 +- .../src/utils/classes/ClassSelector.tsx | 2 +- .../src/utils/classes/ConfidenceSlider.tsx | 6 +- .../utils/conditions/SnapConditionsPanel.tsx | 10 +- 98 files changed, 1863 insertions(+), 1207 deletions(-) rename apps/data-collection/backend/src/{utils => }/__init__.py (100%) rename apps/data-collection/backend/src/{utils/core => config}/__init__.py (100%) rename apps/data-collection/backend/src/{utils => config}/arguments.py (100%) create mode 100644 apps/data-collection/backend/src/config/config_data_classes.py create mode 100644 apps/data-collection/backend/src/config/system_configuration.py create mode 100644 apps/data-collection/backend/src/config/system_state_exporter.py create mode 100644 apps/data-collection/backend/src/config/yaml_configs/conditions.yaml create mode 100644 apps/data-collection/backend/src/config/yaml_configs/constants.yaml create mode 100644 apps/data-collection/backend/src/config/yaml_configs/model.yaml create mode 100644 apps/data-collection/backend/src/config/yaml_configs/nn_config.yaml create mode 100644 apps/data-collection/backend/src/config/yaml_loader.py rename apps/data-collection/backend/src/{utils/services => core}/__init__.py (100%) rename apps/data-collection/backend/src/{utils/snaps => core/controllers}/__init__.py (100%) create mode 100644 apps/data-collection/backend/src/core/controllers/nn_controller.py rename apps/data-collection/backend/src/{utils/snaps/conditions => core/encoders}/__init__.py (100%) create mode 100644 apps/data-collection/backend/src/core/encoders/base_encoder.py create mode 100644 apps/data-collection/backend/src/core/encoders/textual_encoder.py create mode 100644 apps/data-collection/backend/src/core/encoders/visual_encoder.py create mode 100644 apps/data-collection/backend/src/core/handlers/__init__.py create mode 100644 apps/data-collection/backend/src/core/handlers/base_embedding_handler.py create mode 100644 apps/data-collection/backend/src/core/handlers/bbox_prompt_handler.py create mode 100644 apps/data-collection/backend/src/core/handlers/class_update_handler.py create mode 100644 apps/data-collection/backend/src/core/handlers/image_upload_handler.py rename apps/data-collection/backend/src/{utils => }/core/label_manager.py (61%) create mode 100644 apps/data-collection/backend/src/core/model_state.py create mode 100644 apps/data-collection/backend/src/core/snapping/__init__.py create mode 100644 apps/data-collection/backend/src/core/snapping/conditions/__init__.py create mode 100644 apps/data-collection/backend/src/core/snapping/conditions/base_condition.py create mode 100644 apps/data-collection/backend/src/core/snapping/conditions/condition_key.py create mode 100644 apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py create mode 100644 apps/data-collection/backend/src/core/snapping/conditions/no_detections_condition.py create mode 100644 apps/data-collection/backend/src/core/snapping/conditions/timed_condition.py create mode 100644 apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/__init__.py create mode 100644 apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py create mode 100644 apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/tracklet_analyzer.py create mode 100644 apps/data-collection/backend/src/core/snapping/conditions_engine.py create mode 100644 apps/data-collection/backend/src/core/snapping/custom_snap_process.py create mode 100644 apps/data-collection/backend/src/infrastructure/__init__.py create mode 100644 apps/data-collection/backend/src/infrastructure/conditions_factory.py create mode 100644 apps/data-collection/backend/src/infrastructure/export/__init__.py create mode 100644 apps/data-collection/backend/src/infrastructure/export/export_manager.py rename apps/data-collection/backend/src/{utils => infrastructure}/frame_cache_node.py (100%) create mode 100644 apps/data-collection/backend/src/infrastructure/neural_network/__init__.py rename apps/data-collection/backend/src/{utils => infrastructure/neural_network}/annotation_node.py (83%) create mode 100644 apps/data-collection/backend/src/infrastructure/neural_network/encoders_manager.py create mode 100644 apps/data-collection/backend/src/infrastructure/neural_network/handlers_manager.py create mode 100644 apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py create mode 100644 apps/data-collection/backend/src/infrastructure/neural_network/nn_service_manager.py create mode 100644 apps/data-collection/backend/src/infrastructure/neural_network/pipeline_builder.py create mode 100644 apps/data-collection/backend/src/infrastructure/snaps/__init__.py create mode 100644 apps/data-collection/backend/src/infrastructure/snaps/snaps_manager.py create mode 100644 apps/data-collection/backend/src/infrastructure/snaps/snaps_producer_factory.py create mode 100644 apps/data-collection/backend/src/infrastructure/video_source_manager.py create mode 100644 apps/data-collection/backend/src/services/__init__.py create mode 100644 apps/data-collection/backend/src/services/base_service.py create mode 100644 apps/data-collection/backend/src/services/bbox_prompt/__init__.py create mode 100644 apps/data-collection/backend/src/services/bbox_prompt/bbox_prompt_payload.py create mode 100644 apps/data-collection/backend/src/services/bbox_prompt/bbox_prompt_service.py create mode 100644 apps/data-collection/backend/src/services/class_update/__init__.py create mode 100644 apps/data-collection/backend/src/services/class_update/class_update_payload.py create mode 100644 apps/data-collection/backend/src/services/class_update/class_update_service.py create mode 100644 apps/data-collection/backend/src/services/export_service/__init__.py create mode 100644 apps/data-collection/backend/src/services/export_service/get_config_service.py create mode 100644 apps/data-collection/backend/src/services/image_upload/__init__.py create mode 100644 apps/data-collection/backend/src/services/image_upload/image_upload_payload.py create mode 100644 apps/data-collection/backend/src/services/image_upload/image_upload_service.py create mode 100644 apps/data-collection/backend/src/services/service_name.py create mode 100644 apps/data-collection/backend/src/services/snap_collection/__init__.py create mode 100644 apps/data-collection/backend/src/services/snap_collection/snap_collection_service.py create mode 100644 apps/data-collection/backend/src/services/snap_collection/snap_payload.py create mode 100644 apps/data-collection/backend/src/services/threshold_update/__init__.py create mode 100644 apps/data-collection/backend/src/services/threshold_update/threshold_update_payload.py create mode 100644 apps/data-collection/backend/src/services/threshold_update/threshold_update_service.py delete mode 100644 apps/data-collection/backend/src/utils/constants.py delete mode 100644 apps/data-collection/backend/src/utils/core/io.py delete mode 100644 apps/data-collection/backend/src/utils/core/quantization.py delete mode 100644 apps/data-collection/backend/src/utils/core/tokenizer_manager.py delete mode 100644 apps/data-collection/backend/src/utils/core/visual_encoder_manager.py delete mode 100644 apps/data-collection/backend/src/utils/services/base_service.py delete mode 100644 apps/data-collection/backend/src/utils/services/bbox_prompt_service.py delete mode 100644 apps/data-collection/backend/src/utils/services/class_update_service.py delete mode 100644 apps/data-collection/backend/src/utils/services/get_config_service.py delete mode 100644 apps/data-collection/backend/src/utils/services/image_upload_service.py delete mode 100644 apps/data-collection/backend/src/utils/services/snap_collection_service.py delete mode 100644 apps/data-collection/backend/src/utils/services/threshold_update_service.py delete mode 100644 apps/data-collection/backend/src/utils/snaps/conditions/base_condition.py delete mode 100644 apps/data-collection/backend/src/utils/snaps/conditions/lost_mid_condition.py delete mode 100644 apps/data-collection/backend/src/utils/snaps/conditions/low_conf_condition.py delete mode 100644 apps/data-collection/backend/src/utils/snaps/conditions/no_detections_condition.py delete mode 100644 apps/data-collection/backend/src/utils/snaps/conditions/timed_condition.py delete mode 100644 apps/data-collection/backend/src/utils/snaps/conditions_manager.py delete mode 100644 apps/data-collection/backend/src/utils/snaps/custon_snap_process.py delete mode 100644 apps/data-collection/backend/src/utils/snaps/tracklets.py diff --git a/apps/data-collection/backend/src/utils/__init__.py b/apps/data-collection/backend/src/__init__.py similarity index 100% rename from apps/data-collection/backend/src/utils/__init__.py rename to apps/data-collection/backend/src/__init__.py diff --git a/apps/data-collection/backend/src/utils/core/__init__.py b/apps/data-collection/backend/src/config/__init__.py similarity index 100% rename from apps/data-collection/backend/src/utils/core/__init__.py rename to apps/data-collection/backend/src/config/__init__.py diff --git a/apps/data-collection/backend/src/utils/arguments.py b/apps/data-collection/backend/src/config/arguments.py similarity index 100% rename from apps/data-collection/backend/src/utils/arguments.py rename to apps/data-collection/backend/src/config/arguments.py diff --git a/apps/data-collection/backend/src/config/config_data_classes.py b/apps/data-collection/backend/src/config/config_data_classes.py new file mode 100644 index 000000000..3cb2d1f5c --- /dev/null +++ b/apps/data-collection/backend/src/config/config_data_classes.py @@ -0,0 +1,26 @@ +from pathlib import Path +from dataclasses import dataclass +import depthai as dai + + +@dataclass +class ModelInfo: + """Stores paths and dimensions of the detection model.""" + + yaml_path: Path + width: int + height: int + description: dai.NNModelDescription + archive: dai.NNArchive + + +@dataclass +class RuntimeConfig: + """Stores runtime configuration (precision, FPS, platform, etc.).""" + + precision: str + fps_limit: int + platform: str + model_info: ModelInfo + ip: str + port: int diff --git a/apps/data-collection/backend/src/config/system_configuration.py b/apps/data-collection/backend/src/config/system_configuration.py new file mode 100644 index 000000000..c784b47e5 --- /dev/null +++ b/apps/data-collection/backend/src/config/system_configuration.py @@ -0,0 +1,67 @@ +from config.yaml_loader import YamlLoader +from config.config_data_classes import ModelInfo, RuntimeConfig +from config.arguments import initialize_argparser +from dotenv import load_dotenv +from pathlib import Path +import os +import depthai as dai + + +class SystemConfiguration: + """Central system config. Loads each YAML file once and exposes it as an attribute.""" + + def __init__(self): + load_dotenv(override=True) + _, args = initialize_argparser() + self.args = args + + self.visualizer = dai.RemoteConnection(serveFrontend=False) + self.device = dai.Device() + self.platform = self.device.getPlatformAsString() + + base = Path(__file__).parent / "yaml_configs" + self.constants = YamlLoader(base / "constants.yaml") + self.model = YamlLoader(base / "model.yaml") + self.nn_config = YamlLoader(base / "nn_config.yaml") + self.conditions = YamlLoader(base / "conditions.yaml") + + self._initialize() + + def _initialize(self): + if self.args.api_key: + os.environ["DEPTHAI_HUB_API_KEY"] = self.args.api_key + + if self.platform != "RVC4": + raise ValueError("This example is supported only on RVC4 platform") + + if self.args.fps_limit is None: + self.args.fps_limit = self.constants.default_fps + print(f"\nFPS limit set to {self.args.fps_limit} for {self.platform}\n") + + if self.args.precision != self.model.default_precision: + raise SystemExit(f"{self.model.name} int8 YAML not available; use fp16.") + + self.model_info = self._load_model_info() + + def _load_model_info(self): + models_dir = Path(__file__).parent.parent / "depthai_models" + yaml_file = f"{self.model.name}_v8_l_fp16.{self.platform}.yaml" + yaml_path = models_dir / yaml_file + if not yaml_path.exists(): + raise SystemExit(f"Model YAML not found for {self.model.name}: {yaml_path}") + + desc = dai.NNModelDescription.fromYamlFile(str(yaml_path)) + desc.platform = self.platform + archive = dai.NNArchive(dai.getModelFromZoo(desc)) + w, h = archive.getInputSize() + return ModelInfo(yaml_path, w, h, desc, archive) + + def build_runtime_config(self): + return RuntimeConfig( + precision=self.args.precision, + fps_limit=self.args.fps_limit, + platform=self.platform, + model_info=self.model_info, + ip=self.args.ip or "localhost", + port=self.args.port or 8080, + ) diff --git a/apps/data-collection/backend/src/config/system_state_exporter.py b/apps/data-collection/backend/src/config/system_state_exporter.py new file mode 100644 index 000000000..08956f95a --- /dev/null +++ b/apps/data-collection/backend/src/config/system_state_exporter.py @@ -0,0 +1,30 @@ +from typing import Dict, Any +from core.snapping.conditions_engine import ConditionsEngine +from core.model_state import ModelState + + +class SystemStateExporter: + """ + Builds a full frontend-friendly snapshot of the current system configuration. + """ + + def __init__( + self, + model_state: ModelState, + condition_engine: ConditionsEngine, + ): + self._model_state = model_state + self._condition_engine = condition_engine + + def export_config(self) -> Dict[str, Any]: + """ + Construct the unified configuration dictionary expected by the frontend. + """ + return { + "classes": self._model_state.get_classes(), + "confidence_threshold": self._model_state.get_threshold(), + "snapping": { + "running": self._condition_engine.any_active(), + **self._condition_engine.export_conditions_config(), + }, + } diff --git a/apps/data-collection/backend/src/config/yaml_configs/conditions.yaml b/apps/data-collection/backend/src/config/yaml_configs/conditions.yaml new file mode 100644 index 000000000..75580e4bf --- /dev/null +++ b/apps/data-collection/backend/src/config/yaml_configs/conditions.yaml @@ -0,0 +1,21 @@ +cooldown: 300 +conditions: + - path: core.snapping.conditions.timed_condition.TimedCondition + enabled: false + name: Timed Snap + tags: ["timed"] + + - path: core.snapping.conditions.low_conf_condition.LowConfidenceCondition + enabled: false + name: Low Confidence + tags: ["confidence"] + + - path: core.snapping.conditions.no_detections_condition.NoDetectionsCondition + enabled: false + name: No Detections + tags: ["no detections"] + + - path: core.snapping.conditions.tracker_conditions.lost_mid_condition.LostMidCondition + enabled: false + name: Lost in Middle + tags: ["lost"] diff --git a/apps/data-collection/backend/src/config/yaml_configs/constants.yaml b/apps/data-collection/backend/src/config/yaml_configs/constants.yaml new file mode 100644 index 000000000..d428e70be --- /dev/null +++ b/apps/data-collection/backend/src/config/yaml_configs/constants.yaml @@ -0,0 +1,6 @@ +class_names: ["person", "chair", "TV"] +class_offset: 0 +max_num_classes: 80 +confidence_threshold: 0.1 +visualization_resolution: [1080, 1080] +default_fps: 30 \ No newline at end of file diff --git a/apps/data-collection/backend/src/config/yaml_configs/model.yaml b/apps/data-collection/backend/src/config/yaml_configs/model.yaml new file mode 100644 index 000000000..3cbfe1749 --- /dev/null +++ b/apps/data-collection/backend/src/config/yaml_configs/model.yaml @@ -0,0 +1,26 @@ +name: "yoloe" +default_precision: "fp16" +max_num_classes: 80 +confidence_threshold: 0.25 + +quant_values: + yoloe: + quant_zero_point: 174.0 + quant_scale: 0.003328413470 + yoloe-image: + quant_zero_point: 137.0 + quant_scale: 0.002327915514 + + +paths: + tokenizer: + url: "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json" + path: "tokenizer.json" + + text_encoder: + url: "https://huggingface.co/Xenova/mobileclip_blt/resolve/main/onnx/text_model.onnx" + path: "mobileclip_textual_hf.onnx" + + visual_encoder: + url: "https://huggingface.co/sokovninn/yoloe-v8l-seg-visual-encoder/resolve/main/yoloe-v8l-seg_visual_encoder.onnx" + path: "yoloe-v8l-seg_visual_encoder.onnx" diff --git a/apps/data-collection/backend/src/config/yaml_configs/nn_config.yaml b/apps/data-collection/backend/src/config/yaml_configs/nn_config.yaml new file mode 100644 index 000000000..45685e76a --- /dev/null +++ b/apps/data-collection/backend/src/config/yaml_configs/nn_config.yaml @@ -0,0 +1,12 @@ +nn: + type: "snpe" + runtime: "dsp" + performance_profile: "default" + inference_threads: 1 + +tracker: + track_per_class: true + birth_threshold: 3 + max_lifespan: 90 + occlusion_ratio_threshold: 0.5 + tracker_threshold: 0.25 diff --git a/apps/data-collection/backend/src/config/yaml_loader.py b/apps/data-collection/backend/src/config/yaml_loader.py new file mode 100644 index 000000000..990bd31ed --- /dev/null +++ b/apps/data-collection/backend/src/config/yaml_loader.py @@ -0,0 +1,26 @@ +import yaml +from pathlib import Path + + +class YamlLoader: + """YAML file loader that converts dicts to objects with dot access.""" + + def __init__(self, file_path: str | Path): + path = Path(file_path) + if not path.exists(): + raise FileNotFoundError(f"Config file not found: {path}") + + with open(path, "r", encoding="utf-8") as f: + data = yaml.safe_load(f) or {} + + self._data = self._wrap(data) + + def _wrap(self, obj): + if isinstance(obj, dict): + return type("YamlNamespace", (), {k: self._wrap(v) for k, v in obj.items()}) + elif isinstance(obj, list): + return [self._wrap(x) for x in obj] + return obj + + def __getattr__(self, name): + return getattr(self._data, name) diff --git a/apps/data-collection/backend/src/utils/services/__init__.py b/apps/data-collection/backend/src/core/__init__.py similarity index 100% rename from apps/data-collection/backend/src/utils/services/__init__.py rename to apps/data-collection/backend/src/core/__init__.py diff --git a/apps/data-collection/backend/src/utils/snaps/__init__.py b/apps/data-collection/backend/src/core/controllers/__init__.py similarity index 100% rename from apps/data-collection/backend/src/utils/snaps/__init__.py rename to apps/data-collection/backend/src/core/controllers/__init__.py diff --git a/apps/data-collection/backend/src/core/controllers/nn_controller.py b/apps/data-collection/backend/src/core/controllers/nn_controller.py new file mode 100644 index 000000000..cbc0e94ed --- /dev/null +++ b/apps/data-collection/backend/src/core/controllers/nn_controller.py @@ -0,0 +1,59 @@ +import depthai as dai +import numpy as np +from depthai_nodes.node import YOLOExtendedParser + +from core.model_state import ModelState + + +class YOLONNController: + """Handles sending conditioning inputs to the DepthAI YOlO NN.""" + + def __init__( + self, + image_prompt_queue: dai.InputQueue, + text_prompt_queue: dai.InputQueue, + precision: str, + parser: YOLOExtendedParser, + model_state: ModelState, + ): + self.image_prompt_queue = image_prompt_queue + self.text_prompt_queue = text_prompt_queue + self.precision = precision + self.parser: YOLOExtendedParser = parser + self.model_state = model_state + + def _tensor_type(self): + return ( + dai.TensorInfo.DataType.FP16 + if self.precision == "fp16" + else dai.TensorInfo.DataType.U8F + ) + + def _send(self, queue: dai.InputQueue, name: str, data: np.ndarray): + nn_data = dai.NNData() + nn_data.addTensor(name, data, dataType=self._tensor_type()) + queue.send(nn_data) + + def _send_text_inputs(self, embeddings: np.ndarray): + """Send class text embeddings (semantic prompts) to the NN.""" + self._send(self.text_prompt_queue, "texts", embeddings) + + def _send_visual_inputs(self, embeddings: np.ndarray): + """Send visual prompts (mask- or bbox-based) to the NN.""" + self._send(self.image_prompt_queue, "image_prompts", embeddings) + + def send_embeddings_pair( + self, + visual_embeddings: np.ndarray, + text_embeddings: np.ndarray, + class_names: list[str], + ): + """Send both text and visual conditioning inputs if available.""" + self._send_visual_inputs(visual_embeddings) + self._send_text_inputs(text_embeddings) + self.model_state.update_classes(class_names) + + def set_confidence_threshold(self, threshold: float): + """Apply threshold update directly to the NN parser.""" + self.parser.setConfidenceThreshold(threshold) + self.model_state.update_threshold(threshold) diff --git a/apps/data-collection/backend/src/utils/snaps/conditions/__init__.py b/apps/data-collection/backend/src/core/encoders/__init__.py similarity index 100% rename from apps/data-collection/backend/src/utils/snaps/conditions/__init__.py rename to apps/data-collection/backend/src/core/encoders/__init__.py diff --git a/apps/data-collection/backend/src/core/encoders/base_encoder.py b/apps/data-collection/backend/src/core/encoders/base_encoder.py new file mode 100644 index 000000000..abc14cfaa --- /dev/null +++ b/apps/data-collection/backend/src/core/encoders/base_encoder.py @@ -0,0 +1,90 @@ +import os +from abc import ABC, abstractmethod +import onnxruntime +import numpy as np +import requests +from pathlib import Path + +from config.yaml_loader import YamlLoader + + +class BaseEncoder(ABC): + """ + Abstract base class for all embedding encoders (visual, text, etc.). + + Provides: + - Model download & caching via `download_file` + - ONNX session initialization + - Common quantization pipeline + """ + + def __init__( + self, + precision: str, + max_classes: int, + config: YamlLoader, + encoder_model_url: str, + encoder_model_path: str, + ): + self.model_name = config.name + self.precision = precision + self.max_classes = max_classes + self.quant_values = config.quant_values + self.encoder_model_url = encoder_model_url + self.encoder_model_path = encoder_model_path + self.providers = [ + "TensorrtExecutionProvider", + "CUDAExecutionProvider", + "CPUExecutionProvider", + ] + self.session = None + + def _load_model(self) -> None: + """Download and initialize the ONNX model.""" + path = self._download_file() + self.session = onnxruntime.InferenceSession(path, providers=self.providers) + + @abstractmethod + def extract_embeddings(self, *args, **kwargs) -> np.ndarray: + """Subclasses must implement modality-specific preprocessing and inference.""" + pass + + def _pad_and_quantize_features(self, features): + """ + Pad features to (1, 512, max_num_classes) and quantize if precision is int8. + For FP16, return padded float16 features (no quantization). + """ + num_padding = self.max_classes - features.shape[0] + padded = np.pad(features, ((0, num_padding), (0, 0)), "constant").T.reshape( + 1, 512, self.max_classes + ) + + if self.precision == "fp16": + return padded.astype(np.float16) + + quant = self.quant_values[self.model_name] + out = (padded / quant["quant_scale"]) + quant["quant_zero_point"] + return out.astype(np.uint8) + + def make_dummy_features(self) -> np.ndarray: + """ + Create a dummy tensor of shape (1, 512, max_num_classes) for model input. + For FP16, return zeros; for INT8, fill with the model's quantization zero point. + """ + if self.precision == "fp16": + return np.zeros((1, 512, self.max_classes), dtype=np.float16) + qzp = int( + round(self.quant_values.get(self.model_name, {}).get("quant_zero_point", 0)) + ) + return np.full((1, 512, self.max_classes), qzp, dtype=np.uint8) + + def _download_file(self, url: str = "", path: str = "") -> Path: + if url == "": + url = self.encoder_model_url + if path == "": + path = self.encoder_model_path + if not os.path.exists(path): + print(f"Downloading tokenizer config from {url}...") + with open(path, "wb") as f: + f.write(requests.get(url).content) + return path diff --git a/apps/data-collection/backend/src/core/encoders/textual_encoder.py b/apps/data-collection/backend/src/core/encoders/textual_encoder.py new file mode 100644 index 000000000..e58a347c4 --- /dev/null +++ b/apps/data-collection/backend/src/core/encoders/textual_encoder.py @@ -0,0 +1,58 @@ +from tokenizers import Tokenizer +import numpy as np + +from config.yaml_loader import YamlLoader +from core.encoders.base_encoder import BaseEncoder + + +class TextualEncoder(BaseEncoder): + """ + Handles text tokenization and embedding extraction (CLIP-compatible). + """ + + def __init__( + self, + precision: str, + max_classes: int, + config: YamlLoader, + ): + super().__init__( + precision, + max_classes, + config, + config.paths.text_encoder.url, + config.paths.text_encoder.path, + ) + self.tokenizer_url = config.paths.tokenizer.url + self.tokenizer_path = config.paths.tokenizer.path + self.tokenizer = None + + def _load_tokenizer(self): + path = self._download_file(self.tokenizer_url, self.tokenizer_path) + self.tokenizer = Tokenizer.from_file(str(path)) + + def extract_embeddings(self, class_names: list[str]) -> np.ndarray: + self._load_tokenizer() + self._load_model() + + self.tokenizer.enable_padding( + pad_id=self.tokenizer.token_to_id("<|endoftext|>"), + pad_token="<|endoftext|>", + ) + + encodings = self.tokenizer.encode_batch(class_names) + text_ids = np.array([e.ids for e in encodings], dtype=np.int64) + if text_ids.shape[1] < 77: + text_ids = np.pad( + text_ids, ((0, 0), (0, 77 - text_ids.shape[1])), mode="constant" + ) + + outputs = self.session.run(None, {self.session.get_inputs()[0].name: text_ids}) + embeddings = outputs[0] + embeddings /= np.linalg.norm(embeddings, ord=2, axis=-1, keepdims=True) + + quantized = self._pad_and_quantize_features(embeddings) + + del self.session + + return quantized diff --git a/apps/data-collection/backend/src/core/encoders/visual_encoder.py b/apps/data-collection/backend/src/core/encoders/visual_encoder.py new file mode 100644 index 000000000..8e6b67160 --- /dev/null +++ b/apps/data-collection/backend/src/core/encoders/visual_encoder.py @@ -0,0 +1,64 @@ +import cv2 +import numpy as np + +from config.yaml_loader import YamlLoader +from core.encoders.base_encoder import BaseEncoder + + +class VisualEncoder(BaseEncoder): + """ + Handles visual embedding extraction using a visual encoder. + + Loads an ONNX visual encoder, preprocesses an image input, performs + forward inference, and returns quantized visual feature tensors + compatible with downstream models. + """ + + def __init__( + self, + precision: str, + max_classes: int, + config: YamlLoader, + ): + super().__init__( + precision, + max_classes, + config, + config.paths.visual_encoder.url, + config.paths.visual_encoder.path, + ) + self.tokenizer_url = config.paths.tokenizer.url + self.tokenizer_path = config.paths.tokenizer.path + self.tokenizer = None + + def extract_embeddings(self, image: np.ndarray, mask_prompt=None) -> np.ndarray: + self._load_model() + if mask_prompt is None: + prompts = np.zeros((1, 1, 80, 80), dtype=np.float32) + prompts[0, 0, 5:75, 5:75] = 1.0 + else: + prompts = np.asarray(mask_prompt, dtype=np.float32) + if prompts.ndim == 2: + if prompts.shape != (80, 80): + prompts = cv2.resize( + prompts, (80, 80), interpolation=cv2.INTER_NEAREST + ) + prompts = prompts[None, None, :, :] + elif prompts.shape == (1, 1, 80, 80): + pass + else: + raise ValueError("mask_prompt must have shape (80,80) or (1,1,80,80)") + + image_resized = cv2.resize(image, (640, 640)) + image_array = image_resized.astype(np.float32) / 255.0 + image_array = np.transpose(image_array, (2, 0, 1)) + input_tensor = np.expand_dims(image_array, axis=0).astype(np.float32) + + outputs = self.session.run(None, {"images": input_tensor, "prompts": prompts}) + + image_embeddings = outputs[0].squeeze(0).reshape(1, -1) + image_features = self._pad_and_quantize_features(image_embeddings) + + del self.session + + return image_features diff --git a/apps/data-collection/backend/src/core/handlers/__init__.py b/apps/data-collection/backend/src/core/handlers/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/data-collection/backend/src/core/handlers/base_embedding_handler.py b/apps/data-collection/backend/src/core/handlers/base_embedding_handler.py new file mode 100644 index 000000000..f3d431984 --- /dev/null +++ b/apps/data-collection/backend/src/core/handlers/base_embedding_handler.py @@ -0,0 +1,24 @@ +# app/modules/handlers/base_embedding_handler.py +from __future__ import annotations +from typing import Any, List +import numpy as np +from core.label_manager import LabelManager + + +class BasePromptHandler: + """ + Abstract base handler for extracting embeddings from various input modalities. + Provides shared utilities for feature extraction and label management. + """ + + def __init__(self, encoder: Any, label_manager: LabelManager): + self.encoder = encoder + self.label_manager = label_manager + + def _make_dummy(self) -> np.ndarray: + """Create dummy tensor for balancing model inputs.""" + return self.encoder.make_dummy_features() + + def _update_labels(self, class_names: List[str], offset: int = 0): + """Synchronize the label manager with new or updated class names.""" + self.label_manager.update_labels(class_names, offset=offset) diff --git a/apps/data-collection/backend/src/core/handlers/bbox_prompt_handler.py b/apps/data-collection/backend/src/core/handlers/bbox_prompt_handler.py new file mode 100644 index 000000000..1d05dfc2f --- /dev/null +++ b/apps/data-collection/backend/src/core/handlers/bbox_prompt_handler.py @@ -0,0 +1,30 @@ +import numpy as np +from typing import Tuple, Dict, List +from core.handlers.base_embedding_handler import BasePromptHandler + + +class BBoxPromptHandler(BasePromptHandler): + """Handles extraction of embeddings for a specific bounding box region.""" + + def process( + self, image: np.ndarray, bbox: Dict[str, float] + ) -> Tuple[np.ndarray, List[str], np.ndarray]: + H, W = image.shape[:2] + bx, by, bw, bh = bbox["x"], bbox["y"], bbox["width"], bbox["height"] + + x0, y0 = int(bx * W), int(by * H) + x1, y1 = int((bx + bw) * W), int((by + bh) * H) + x0, x1 = sorted((x0, x1)) + y0, y1 = sorted((y0, y1)) + if x1 <= x0 or y1 <= y0: + raise ValueError(f"Invalid bbox coordinates: {(x0, y0, x1, y1)}") + + mask = np.zeros((H, W), dtype=np.float32) + mask[y0:y1, x0:x1] = 1.0 + + embeddings = self.encoder.extract_embeddings(image, mask) + dummy = self._make_dummy() + class_names = ["Bbox Object"] + + self._update_labels(class_names, offset=self.encoder.max_classes) + return embeddings, class_names, dummy diff --git a/apps/data-collection/backend/src/core/handlers/class_update_handler.py b/apps/data-collection/backend/src/core/handlers/class_update_handler.py new file mode 100644 index 000000000..b3be493e8 --- /dev/null +++ b/apps/data-collection/backend/src/core/handlers/class_update_handler.py @@ -0,0 +1,14 @@ +from __future__ import annotations +from typing import Tuple, List +import numpy as np +from core.handlers.base_embedding_handler import BasePromptHandler + + +class TextPromptHandler(BasePromptHandler): + """Handles embedding extraction and label synchronization for class name updates.""" + + def process(self, new_classes: List[str]) -> Tuple[np.ndarray, np.ndarray]: + embeddings = self.encoder.extract_embeddings(new_classes) + dummy = self._make_dummy() + self._update_labels(new_classes) + return embeddings, dummy diff --git a/apps/data-collection/backend/src/core/handlers/image_upload_handler.py b/apps/data-collection/backend/src/core/handlers/image_upload_handler.py new file mode 100644 index 000000000..39b1fd327 --- /dev/null +++ b/apps/data-collection/backend/src/core/handlers/image_upload_handler.py @@ -0,0 +1,28 @@ +import base64 +import cv2 +import numpy as np +from typing import Tuple, List, Dict +from core.handlers.base_embedding_handler import BasePromptHandler + + +def decode_base64_image(base64_data_uri: str) -> np.ndarray: + """Convert base64-encoded image to OpenCV array.""" + if "," in base64_data_uri: + _, base64_data = base64_data_uri.split(",", 1) + else: + base64_data = base64_data_uri + np_arr = np.frombuffer(base64.b64decode(base64_data), np.uint8) + return cv2.imdecode(np_arr, cv2.IMREAD_COLOR) + + +class ImagePromptHandler(BasePromptHandler): + """Handles decoding and embedding extraction for uploaded images.""" + + def process(self, payload: Dict) -> Tuple[np.ndarray, List[str], np.ndarray]: + image = decode_base64_image(payload["data"]) + image_features = self.encoder.extract_embeddings(image) + dummy = self._make_dummy() + class_names = [payload["filename"].split(".")[0]] + + self._update_labels(class_names, offset=self.encoder.max_classes) + return image_features, class_names, dummy diff --git a/apps/data-collection/backend/src/utils/core/label_manager.py b/apps/data-collection/backend/src/core/label_manager.py similarity index 61% rename from apps/data-collection/backend/src/utils/core/label_manager.py rename to apps/data-collection/backend/src/core/label_manager.py index 7e7c51c5b..2376502c5 100644 --- a/apps/data-collection/backend/src/utils/core/label_manager.py +++ b/apps/data-collection/backend/src/core/label_manager.py @@ -1,3 +1,7 @@ +from depthai_nodes.node import ImgDetectionsFilter +from infrastructure.neural_network.annotation_node import AnnotationNode + + class LabelManager: """ Manages synchronization of label data between the detection filter @@ -7,14 +11,16 @@ class LabelManager: mapping them to human-readable class names for visualization. """ - def __init__(self, det_filter, annotation_node): + def __init__( + self, det_filter: ImgDetectionsFilter, annotation_node: AnnotationNode + ): self.det_filter = det_filter self.annotation_node = annotation_node - def update_labels(self, label_names, offset=0): + def update_labels(self, label_names: list[str], offset: int = 0): self.det_filter.setLabels( labels=[i for i in range(offset, offset + len(label_names))], keep=True ) - self.annotation_node.setLabelEncoding( + self.annotation_node.set_label_encoding( {offset + k: v for k, v in enumerate(label_names)} ) diff --git a/apps/data-collection/backend/src/core/model_state.py b/apps/data-collection/backend/src/core/model_state.py new file mode 100644 index 000000000..2a10aca27 --- /dev/null +++ b/apps/data-collection/backend/src/core/model_state.py @@ -0,0 +1,18 @@ +class ModelState: + """Holds model-related metadata and class definitions.""" + + def __init__(self): + self.__current_classes: list[str] = [] + self.__conf_threshold = 0.1 + + def update_classes(self, new_classes: list[str]): + self.__current_classes = list(new_classes) + + def update_threshold(self, value: float): + self.__conf_threshold = max(0.0, min(1.0, value)) + + def get_classes(self) -> list[str]: + return list(self.__current_classes) + + def get_threshold(self) -> float: + return self.__conf_threshold diff --git a/apps/data-collection/backend/src/core/snapping/__init__.py b/apps/data-collection/backend/src/core/snapping/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/data-collection/backend/src/core/snapping/conditions/__init__.py b/apps/data-collection/backend/src/core/snapping/conditions/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/data-collection/backend/src/core/snapping/conditions/base_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/base_condition.py new file mode 100644 index 000000000..b94687e25 --- /dev/null +++ b/apps/data-collection/backend/src/core/snapping/conditions/base_condition.py @@ -0,0 +1,76 @@ +from abc import ABC, abstractmethod +from typing import Dict, Any, List, Optional +from core.snapping.conditions.condition_key import ConditionKey +from time import time + + +class Condition(ABC): + """ + Abstract base class for all snap trigger conditions. + + Each condition encapsulates: + - its unique KEY (class-level) + - human-readable name and optional tags + - configuration: enabled state, cooldown interval, etc. + """ + + KEY: ConditionKey # must be defined in subclasses + + def __init__( + self, + name: str, + default_cooldown: float, + tags: Optional[List[str]] = None, + ): + if not getattr(self, "KEY", None): + raise ValueError(f"{self.__class__.__name__} must define a KEY constant") + + self.__key = self.KEY + self.name = name + self.tags = tags or [] + self.enabled: bool = False + self.cooldown: float = max(0.0, float(default_cooldown)) + self._last_trigger_time: Optional[float] = None + + @abstractmethod + def should_trigger(self, *args, **kwargs) -> bool: + """Return True if this condition should trigger.""" + pass + + @abstractmethod + def make_extras(self, *args, **kwargs) -> Dict[str, str]: + """Return optional metadata attached to the snap.""" + pass + + def apply_config(self, conf: Dict[str, Any]) -> None: + if "enabled" in conf: + self.enabled = bool(conf["enabled"]) + if not self.enabled: + self.reset_cooldown() + if "cooldown" in conf: + self.cooldown = max(0.0, float(conf["cooldown"])) + + def export_config(self) -> Dict[str, Any]: + return { + "enabled": self.enabled, + "cooldown": self.cooldown, + } + + def get_key(self) -> ConditionKey: + return self.__key + + def reset_cooldown(self) -> None: + """Reset internal cooldown tracking.""" + self._last_trigger_time = None + + def _cooldown_passed(self) -> bool: + """Return True if enough time passed since the last trigger.""" + now = time() + if self._last_trigger_time is None: + return True + return (now - self._last_trigger_time) >= self.cooldown + + def mark_triggered(self) -> None: + """Record that this condition has just fired.""" + now = time() + self._last_trigger_time = now diff --git a/apps/data-collection/backend/src/core/snapping/conditions/condition_key.py b/apps/data-collection/backend/src/core/snapping/conditions/condition_key.py new file mode 100644 index 000000000..a794b7af8 --- /dev/null +++ b/apps/data-collection/backend/src/core/snapping/conditions/condition_key.py @@ -0,0 +1,8 @@ +from enum import Enum + + +class ConditionKey(str, Enum): + TIMED = "timed" + NO_DETECTIONS = "noDetections" + LOW_CONFIDENCE = "lowConfidence" + LOST_MID = "lostMid" diff --git a/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py new file mode 100644 index 000000000..4365537b4 --- /dev/null +++ b/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py @@ -0,0 +1,45 @@ +from core.snapping.conditions.base_condition import Condition +from core.snapping.conditions.condition_key import ConditionKey +from typing import Dict, Any +import depthai as dai + + +class LowConfidenceCondition(Condition): + """Triggers when any detection has confidence below a threshold.""" + + KEY = ConditionKey.LOW_CONFIDENCE + + def __init__(self, name: str, default_cooldown: float, tags: list[str]): + super().__init__(name, default_cooldown, tags or []) + self.threshold: float = 0.3 + self.last_lowest: float = 0.0 + + def apply_config(self, conf: Dict[str, Any]) -> None: + super().apply_config(conf) + if "threshold" in conf: + val = float(conf["threshold"]) + self.threshold = max(0.0, min(1.0, val)) + + def export_config(self) -> Dict[str, Any]: + base = super().export_config() + base["threshold"] = self.threshold + return base + + def should_trigger(self, detections: list[dai.ImgDetection], **kwargs) -> bool: + if self.enabled and self._cooldown_passed(): + if self._check_detections(detections): + return True + return False + + def _check_detections(self, detections: list[dai.ImgDetection]) -> bool: + if not detections: + return False + self.last_lowest = min((float(d.confidence) for d in detections), default=1.0) + return self.last_lowest < self.threshold + + def make_extras(self) -> Dict[str, str]: + return { + "reason": "low_confidence", + "threshold": f"{round(self.threshold, 3)}", + "min_conf": f"{round(self.last_lowest, 3)}", + } diff --git a/apps/data-collection/backend/src/core/snapping/conditions/no_detections_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/no_detections_condition.py new file mode 100644 index 000000000..e2492b335 --- /dev/null +++ b/apps/data-collection/backend/src/core/snapping/conditions/no_detections_condition.py @@ -0,0 +1,22 @@ +from core.snapping.conditions.base_condition import Condition +from core.snapping.conditions.condition_key import ConditionKey +from typing import Dict +import depthai as dai + + +class NoDetectionsCondition(Condition): + """Triggers when no objects are detected in the current frame.""" + + KEY = ConditionKey.NO_DETECTIONS + + def __init__(self, name: str, default_cooldown: float, tags: list[str]): + super().__init__(name, default_cooldown, tags or []) + + def should_trigger(self, detections: list[dai.ImgDetection], **kwargs) -> bool: + if self.enabled and self._cooldown_passed(): + if not detections or len(detections) == 0: + return True + return False + + def make_extras(self) -> Dict[str, str]: + return {"reason": "no_detections"} diff --git a/apps/data-collection/backend/src/core/snapping/conditions/timed_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/timed_condition.py new file mode 100644 index 000000000..163a29a5d --- /dev/null +++ b/apps/data-collection/backend/src/core/snapping/conditions/timed_condition.py @@ -0,0 +1,20 @@ +from core.snapping.conditions.base_condition import Condition +from core.snapping.conditions.condition_key import ConditionKey +from typing import Dict + + +class TimedCondition(Condition): + """Triggers snaps at regular time intervals.""" + + KEY = ConditionKey.TIMED + + def __init__(self, name: str, default_cooldown: float, tags: list[str]): + super().__init__(name, default_cooldown, tags or []) + + def should_trigger(self, **kwargs) -> bool: + if self.enabled and self._cooldown_passed(): + return True + return False + + def make_extras(self) -> Dict[str, str]: + return {"reason": "timed_snap"} diff --git a/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/__init__.py b/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py new file mode 100644 index 000000000..55be89713 --- /dev/null +++ b/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py @@ -0,0 +1,58 @@ +from core.snapping.conditions.base_condition import Condition +from core.snapping.conditions.tracker_conditions.tracklet_analyzer import ( + TrackletAnalyzer, +) +from core.snapping.conditions.condition_key import ConditionKey +from typing import Dict, Any +import depthai as dai + + +class LostMidCondition(Condition): + """Triggers when an object is lost inside the frame center region.""" + + KEY = ConditionKey.LOST_MID + + def __init__(self, name: str, default_cooldown: float, tags: list[str]): + super().__init__(name, default_cooldown, tags or []) + self.margin: float = 0.2 + self.prev_tracked: dict[int, bool] = {} + + def apply_config(self, conf: Dict[str, Any]) -> None: + super().apply_config(conf) + if "margin" in conf: + val = float(conf["margin"]) + self.margin = max(0.0, min(0.49, val)) + + def export_config(self) -> Dict[str, Any]: + base = super().export_config() + base["margin"] = self.margin + return base + + def should_trigger(self, tracklets: dai.Tracklet, **kwargs) -> bool: + if self.enabled and self._cooldown_passed(): + if self._check_tracklets(tracklets): + return True + return False + + def _check_tracklets(self, tracklets) -> bool: + if tracklets is None: + return False + + triggered = False + for t in getattr(tracklets, "tracklets", []): + tr = TrackletAnalyzer(t) + if tr.is_lost and tr.was_tracked(self.prev_tracked): + rc = tr.center_area() + if rc is not None: + cx, cy, _ = rc + if ( + self.margin <= cx <= 1 - self.margin + and self.margin <= cy <= 1 - self.margin + ): + triggered = True + tr.update_state(self.prev_tracked) + + return triggered + + def make_extras(self) -> Dict[str, str]: + return {"reason": "lost_in_middle", "margin": f"{round(self.margin, 3)}"} diff --git a/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/tracklet_analyzer.py b/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/tracklet_analyzer.py new file mode 100644 index 000000000..b72dddb86 --- /dev/null +++ b/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/tracklet_analyzer.py @@ -0,0 +1,90 @@ +from __future__ import annotations +from typing import Optional, Tuple, Dict +import depthai as dai + + +class TrackletAnalyzer: + """ + Provides safe, normalized access to dai.Tracklet properties + """ + + def __init__(self, tracklet: dai.Tracklet): + self._t = tracklet + + @property + def id(self) -> Optional[int]: + try: + tid = int(getattr(self._t, "id", -1)) + return tid if tid >= 0 else None + except Exception: + return None + + @property + def label_index(self) -> int: + try: + return int(getattr(self._t, "label", -1)) + except Exception: + return -1 + + def label_name(self, class_names: list[str]) -> str: + idx = self.label_index + return class_names[idx] if 0 <= idx < len(class_names) else "" + + @property + def is_tracked(self) -> bool: + val = getattr(self._t, "status", None) + try: + return val == dai.Tracklet.TrackingStatus.TRACKED + except Exception: + try: + return int(val) == 1 + except Exception: + return False + + @property + def is_lost(self) -> bool: + val = getattr(self._t, "status", None) + try: + return val == dai.Tracklet.TrackingStatus.LOST + except Exception: + try: + return int(val) == 2 + except Exception: + return False + + def center_area(self) -> Optional[Tuple[float, float, float]]: + """ + Return (cx, cy, area_norm) in normalized coordinates. + """ + roi = getattr(self._t, "roi", None) + if roi is not None: + try: + tl = roi.topLeft() + br = roi.bottomRight() + x0, y0 = float(tl.x), float(tl.y) + x1, y1 = float(br.x), float(br.y) + cx, cy = 0.5 * (x0 + x1), 0.5 * (y0 + y1) + area = max(0.0, (x1 - x0) * (y1 - y0)) + return cx, cy, area + except Exception: + pass + + det = getattr(self._t, "srcImgDetection", None) + if det is not None: + x = float(getattr(det, "x", getattr(det, "xmin", 0.0))) + y = float(getattr(det, "y", getattr(det, "ymin", 0.0))) + w = float(getattr(det, "width", 0.0)) + h = float(getattr(det, "height", 0.0)) + cx, cy = x + 0.5 * w, y + 0.5 * h + return cx, cy, max(0.0, w * h) + + return None + + def was_tracked(self, prev_state: Dict[int, bool]) -> bool: + tid = self.id + return tid is not None and prev_state.get(tid, False) + + def update_state(self, state: Dict[int, bool]) -> None: + tid = self.id + if tid is not None: + state[tid] = self.is_tracked diff --git a/apps/data-collection/backend/src/core/snapping/conditions_engine.py b/apps/data-collection/backend/src/core/snapping/conditions_engine.py new file mode 100644 index 000000000..756cb63d7 --- /dev/null +++ b/apps/data-collection/backend/src/core/snapping/conditions_engine.py @@ -0,0 +1,60 @@ +from __future__ import annotations +from typing import Dict, Generator, Any + +from core.snapping.conditions.base_condition import Condition +from core.snapping.conditions.condition_key import ConditionKey + + +class ConditionsEngine: + """ + Engine for managing and evaluating snap trigger conditions. + """ + + def __init__(self): + self.conditions: Dict[ConditionKey, Condition] = {} + + def register(self, condition: Condition) -> None: + """Register a condition instance (e.g., LowConfidenceCondition()).""" + key = condition.get_key() + self.conditions[key] = condition + + def evaluate(self, **context: Any) -> Generator[Condition, None, None]: + """ + Evaluate all enabled conditions. + Returns an iterator of those that should trigger. + Context may include: frame, det_data, tracklets, runtime, etc. + """ + for cond in self.conditions.values(): + if not cond.enabled: + continue + if cond.should_trigger(**context): + yield cond + + def import_conditions_config(self, config: Dict[str, dict]) -> None: + """ + Apply configuration dict to registered conditions. + """ + for key, params in config.items(): + cond = self.conditions.get(key) + if not cond: + continue + cond.apply_config(params) + + def export_conditions_config(self) -> Dict[str, dict]: + """ + Export current configuration of all registered conditions. + """ + configs: Dict[str, dict] = {} + + for key, condition in self.conditions.items(): + cfg = condition.export_config() + if "cooldown" in cfg: + cfg["cooldown"] = round(cfg["cooldown"] / 60.0, 1) + + configs[key.value] = cfg + + return configs + + def any_active(self) -> bool: + """Return True if any condition is currently enabled.""" + return any(cond.enabled for cond in self.conditions.values()) diff --git a/apps/data-collection/backend/src/core/snapping/custom_snap_process.py b/apps/data-collection/backend/src/core/snapping/custom_snap_process.py new file mode 100644 index 000000000..88f490bf0 --- /dev/null +++ b/apps/data-collection/backend/src/core/snapping/custom_snap_process.py @@ -0,0 +1,30 @@ +import depthai as dai +from depthai_nodes.node import SnapsProducer +from core.snapping.conditions_engine import ConditionsEngine + + +def process_snaps( + producer: SnapsProducer, + frame: dai.ImgFrame, + tracklets: dai.Tracklets | None, + det_data: dai.ImgDetections | None, + engine: ConditionsEngine, +): + """ + Evaluates all registered snap conditions and sends triggered snaps. + """ + + for condition in engine.evaluate( + detections=det_data.detections, + tracklets=tracklets, + ): + sent = producer.sendSnap( + condition.name, + frame, + det_data, + condition.tags, + condition.make_extras(), + ) + if sent: + condition.mark_triggered() + print(f"[{condition.get_key().value}] Snap sent!") diff --git a/apps/data-collection/backend/src/infrastructure/__init__.py b/apps/data-collection/backend/src/infrastructure/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/data-collection/backend/src/infrastructure/conditions_factory.py b/apps/data-collection/backend/src/infrastructure/conditions_factory.py new file mode 100644 index 000000000..10e7a09e4 --- /dev/null +++ b/apps/data-collection/backend/src/infrastructure/conditions_factory.py @@ -0,0 +1,35 @@ +import importlib +from config.yaml_loader import YamlLoader +from core.snapping.conditions_engine import ConditionsEngine +from core.snapping.conditions.base_condition import Condition + + +class ConditionsFactory: + def __init__(self, conditions_yaml: YamlLoader): + self.conditions_yaml = conditions_yaml + self._engine: ConditionsEngine = self._build_engine() + + def _build_engine(self) -> ConditionsEngine: + engine = ConditionsEngine() + + for entry in self.conditions_yaml.conditions: + if not entry.path: + continue + + try: + module_name, class_name = entry.path.rsplit(".", 1) + module = importlib.import_module(module_name) + cls = getattr(module, class_name) + cond: Condition = cls( + name=entry.name, + default_cooldown=self.conditions_yaml.cooldown, + tags=entry.tags, + ) + engine.register(cond) + except Exception as e: + print(f"[WARN] Failed to import condition {entry.path}: {e}") + + return engine + + def get_engine(self) -> ConditionsEngine: + return self._engine diff --git a/apps/data-collection/backend/src/infrastructure/export/__init__.py b/apps/data-collection/backend/src/infrastructure/export/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/data-collection/backend/src/infrastructure/export/export_manager.py b/apps/data-collection/backend/src/infrastructure/export/export_manager.py new file mode 100644 index 000000000..86f81eb78 --- /dev/null +++ b/apps/data-collection/backend/src/infrastructure/export/export_manager.py @@ -0,0 +1,40 @@ +from config.system_configuration import SystemConfiguration +from core.model_state import ModelState +from core.snapping.conditions_engine import ConditionsEngine +from config.system_state_exporter import SystemStateExporter +from services.export_service.get_config_service import ExportService + + +class ExportManager: + """ + Facade for the configuration export subsystem. + + Responsibilities: + • Build SystemStateExporter + • Create GetConfigService exposing export API + """ + + def __init__( + self, + model_state: ModelState, + condition_engine: ConditionsEngine, + config: SystemConfiguration, + ): + self._model_state = model_state + self._condition_engine = condition_engine + self._config = config + + self._exporter: SystemStateExporter = None + self._service: ExportService = None + self._build() + + def _build(self): + self._exporter = SystemStateExporter(self._model_state, self._condition_engine) + export_service = ExportService(self._exporter) + self._built = True + self._register_service(export_service) + + def _register_service(self, export_service: ExportService): + self._config.visualizer.registerService( + export_service.get_name(), export_service.handle + ) diff --git a/apps/data-collection/backend/src/utils/frame_cache_node.py b/apps/data-collection/backend/src/infrastructure/frame_cache_node.py similarity index 100% rename from apps/data-collection/backend/src/utils/frame_cache_node.py rename to apps/data-collection/backend/src/infrastructure/frame_cache_node.py diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/__init__.py b/apps/data-collection/backend/src/infrastructure/neural_network/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/data-collection/backend/src/utils/annotation_node.py b/apps/data-collection/backend/src/infrastructure/neural_network/annotation_node.py similarity index 83% rename from apps/data-collection/backend/src/utils/annotation_node.py rename to apps/data-collection/backend/src/infrastructure/neural_network/annotation_node.py index 119aae34c..124ab7af5 100644 --- a/apps/data-collection/backend/src/utils/annotation_node.py +++ b/apps/data-collection/backend/src/infrastructure/neural_network/annotation_node.py @@ -4,12 +4,12 @@ class AnnotationNode(dai.node.HostNode): - def __init__(self, label_encoding: Dict[int, str] = {}) -> None: + def __init__(self, label_encoding: Dict[int, str] = None) -> None: super().__init__() - self._label_encoding = label_encoding + self._label_encoding = label_encoding if label_encoding is not None else {} self.out_detections = self.createOutput() - def setLabelEncoding(self, label_encoding: Dict[int, str]) -> None: + def set_label_encoding(self, label_encoding: Dict[int, str]) -> None: """Sets the label encoding. @param label_encoding: The label encoding with labels as keys and label names as @@ -27,7 +27,7 @@ def build( label_encoding: Dict[int, str] = None, ) -> "AnnotationNode": if label_encoding is not None: - self.setLabelEncoding(label_encoding) + self.set_label_encoding(label_encoding) # Link detections and reference frame inputs self.link_args(detections, frame) return self diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/encoders_manager.py b/apps/data-collection/backend/src/infrastructure/neural_network/encoders_manager.py new file mode 100644 index 000000000..4b8df25c4 --- /dev/null +++ b/apps/data-collection/backend/src/infrastructure/neural_network/encoders_manager.py @@ -0,0 +1,44 @@ +from core.encoders.textual_encoder import TextualEncoder +from core.encoders.visual_encoder import VisualEncoder + + +class EncodersManager: + """ + Central manager for initializing and caching encoder components. + """ + + def __init__(self, config, runtime): + self._config = config + self._runtime = runtime + + self.textual_encoder = self._init_textual_encoder() + self.visual_encoder = self._init_visual_encoder() + + self.text_prompt, self.image_prompt = self._prepare_initial_prompts() + + def _init_textual_encoder(self): + model_cfg = self._config.model + constants = self._config.constants + + return TextualEncoder( + config=model_cfg, + precision=self._runtime.precision, + max_classes=constants.max_num_classes, + ) + + def _init_visual_encoder(self): + model_cfg = self._config.model + constants = self._config.constants + + return VisualEncoder( + config=model_cfg, + precision=self._runtime.precision, + max_classes=constants.max_num_classes, + ) + + def _prepare_initial_prompts(self): + text_prompt = self.textual_encoder.extract_embeddings( + self._config.constants.class_names + ) + image_prompt = self.textual_encoder.make_dummy_features() + return text_prompt, image_prompt diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/handlers_manager.py b/apps/data-collection/backend/src/infrastructure/neural_network/handlers_manager.py new file mode 100644 index 000000000..6a1602240 --- /dev/null +++ b/apps/data-collection/backend/src/infrastructure/neural_network/handlers_manager.py @@ -0,0 +1,23 @@ +from core.label_manager import LabelManager +from core.handlers.class_update_handler import TextPromptHandler +from core.handlers.image_upload_handler import ImagePromptHandler +from core.handlers.bbox_prompt_handler import BBoxPromptHandler +from infrastructure.neural_network.encoders_manager import EncodersManager + + +class HandlersManager: + def __init__(self, encoders: EncodersManager, det_filter, annotation_node): + self.label_manager = LabelManager(det_filter, annotation_node) + self.encoders = encoders + self.class_update_handler = self._class_update_handler() + self.image_update_handler = self._image_update_handler() + self.bbox_prompt_handler = self._bbox_prompt_handler() + + def _class_update_handler(self) -> TextPromptHandler: + return TextPromptHandler(self.encoders.textual_encoder, self.label_manager) + + def _image_update_handler(self) -> ImagePromptHandler: + return ImagePromptHandler(self.encoders.visual_encoder, self.label_manager) + + def _bbox_prompt_handler(self) -> BBoxPromptHandler: + return BBoxPromptHandler(self.encoders.visual_encoder, self.label_manager) diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py b/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py new file mode 100644 index 000000000..ef7daa9ea --- /dev/null +++ b/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py @@ -0,0 +1,88 @@ +from typing import List +import depthai as dai + +from depthai_nodes.node import ImgDetectionsBridge + +from config.system_configuration import SystemConfiguration +from core.controllers.nn_controller import YOLONNController +from core.model_state import ModelState +from infrastructure.neural_network.annotation_node import AnnotationNode +from infrastructure.neural_network.nn_service_manager import NNServiceManager +from infrastructure.video_source_manager import VideoSourceManager +from infrastructure.neural_network.pipeline_builder import NNPipelineSetup +from infrastructure.neural_network.encoders_manager import EncodersManager +from infrastructure.neural_network.handlers_manager import HandlersManager + + +class NeuralNetworkManager: + """ + Facade for the neural-network subsystem. + """ + + def __init__( + self, + pipeline: dai.Pipeline, + video_source: VideoSourceManager, + runtime, + config: SystemConfiguration, + model_state: ModelState, + ): + self._pipeline = pipeline + self._video_source = video_source + self._runtime = runtime + self._config = config + self._model_state = model_state + self._tracker: dai.node.ObjectTracker = None + self._filtered_detections: ImgDetectionsBridge = None + self._controller: YOLONNController = None + self.build() + + def build(self) -> "NeuralNetworkManager": + pipeline_builder = NNPipelineSetup( + self._pipeline, + self._video_source, + self._runtime, + self._config.nn_config, + self._model_state, + ) + self._controller = pipeline_builder.get_controller() + + encoders = EncodersManager(self._config, self._runtime) + + handlers = HandlersManager( + encoders, + pipeline_builder.get_filter(), + pipeline_builder.get_annotation_node(), + ) + handlers.label_manager.update_labels( + self._config.constants.class_names, self._config.constants.class_offset + ) + + service_manager = NNServiceManager( + self._controller, handlers, self._video_source + ) + + self._register_services(service_manager.services) + self._controller.send_embeddings_pair( + encoders.image_prompt, + encoders.text_prompt, + self._config.constants.class_names, + ) + self._register_annotations(pipeline_builder.get_annotation_node()) + self._tracker = pipeline_builder.get_tracker() + self._filtered_detections = pipeline_builder.get_detections() + + return self + + def _register_services(self, services: List): + for service in services: + self._config.visualizer.registerService(service.get_name(), service.handle) + + def _register_annotations(self, annotation_node: AnnotationNode): + self._config.visualizer.addTopic("Detections", annotation_node.out) + + def get_tracker(self): + return self._tracker + + def get_detections(self): + return self._filtered_detections diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/nn_service_manager.py b/apps/data-collection/backend/src/infrastructure/neural_network/nn_service_manager.py new file mode 100644 index 000000000..df21b4ce7 --- /dev/null +++ b/apps/data-collection/backend/src/infrastructure/neural_network/nn_service_manager.py @@ -0,0 +1,32 @@ +from core.controllers.nn_controller import YOLONNController +from infrastructure.neural_network.handlers_manager import HandlersManager +from infrastructure.video_source_manager import VideoSourceManager +from services.class_update.class_update_service import ClassUpdateService +from services.threshold_update.threshold_update_service import ThresholdUpdateService +from services.image_upload.image_upload_service import ImageUploadService +from services.bbox_prompt.bbox_prompt_service import BBoxPromptService + + +class NNServiceManager: + def __init__( + self, + controller: YOLONNController, + handlers: HandlersManager, + video_source: VideoSourceManager, + ): + self.controller = controller + self.handlers = handlers + self.video_source = video_source + self.services = self._build_services() + + def _build_services(self): + return [ + ClassUpdateService(self.controller, self.handlers.class_update_handler), + ThresholdUpdateService(self.controller), + ImageUploadService(self.controller, self.handlers.image_update_handler), + BBoxPromptService( + self.handlers.bbox_prompt_handler, + self.video_source.frame_cache, + self.controller, + ), + ] diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/pipeline_builder.py b/apps/data-collection/backend/src/infrastructure/neural_network/pipeline_builder.py new file mode 100644 index 000000000..1fc4e65a3 --- /dev/null +++ b/apps/data-collection/backend/src/infrastructure/neural_network/pipeline_builder.py @@ -0,0 +1,105 @@ +import depthai as dai +from depthai_nodes.node import ( + ParsingNeuralNetwork, + ImgDetectionsFilter, + ImgDetectionsBridge, +) +from infrastructure.neural_network.annotation_node import AnnotationNode +from core.controllers.nn_controller import YOLONNController +from core.model_state import ModelState +from infrastructure.video_source_manager import VideoSourceManager + + +class NNPipelineSetup: + def __init__( + self, + pipeline: dai.Pipeline, + video_source: VideoSourceManager, + runtime, + config, + model_state: ModelState, + ): + self._pipeline = pipeline + self._video_source = video_source + self._runtime = runtime + self._config = config + self._model_state = model_state + + self._det_filter = None + self._annotation_node = None + self._filtered_bridge = None + self._object_tracker = None + self._controller = None + self._nn = None + + self.build() + + def build(self): + nn = self._build_nn() + self._controller = self._build_controller(nn) + self._filtered_bridge = self._build_filters(nn) + self._object_tracker = self._build_tracker() + + def _build_nn(self): + nn = self._pipeline.create(ParsingNeuralNetwork) + nn.setNNArchive(self._runtime.model_info.archive) + nn.setBackend(self._config.nn.type) + nn.setBackendProperties( + { + "runtime": self._config.nn.runtime, + "performance_profile": self._config.nn.performance_profile, + } + ) + nn.setNumInferenceThreads(self._config.nn.inference_threads) + nn.getParser(0).setConfidenceThreshold(0.1) + self._video_source.input_node.link(nn.inputs["images"]) + self._nn = nn + return nn + + def _build_filters(self, nn) -> ImgDetectionsBridge: + self._det_filter = self._pipeline.create(ImgDetectionsFilter).build(nn.out) + self._annotation_node = self._pipeline.create(AnnotationNode).build( + self._det_filter.out, self._video_source.video_src_out + ) + return self._pipeline.create(ImgDetectionsBridge).build(self._det_filter.out) + + def _build_controller(self, nn) -> YOLONNController: + text_q = nn.inputs["texts"].createInputQueue() + img_q = nn.inputs["image_prompts"].createInputQueue() + nn.inputs["texts"].setReusePreviousMessage(True) + nn.inputs["image_prompts"].setReusePreviousMessage(True) + parser = nn.getParser(0) + return YOLONNController( + img_q, text_q, self._runtime.precision, parser, self._model_state + ) + + def _build_tracker(self) -> dai.node.ObjectTracker: + tracker = self._pipeline.create(dai.node.ObjectTracker) + tracker.setTrackerType(dai.TrackerType.SHORT_TERM_IMAGELESS) + tracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.UNIQUE_ID) + tracker.setTrackingPerClass(self._config.tracker.track_per_class) + tracker.setTrackletBirthThreshold(self._config.tracker.birth_threshold) + tracker.setTrackletMaxLifespan(self._config.tracker.max_lifespan) + tracker.setOcclusionRatioThreshold( + self._config.tracker.occlusion_ratio_threshold + ) + tracker.setTrackerThreshold(self._config.tracker.tracker_threshold) + self._video_source.input_node.link(tracker.inputTrackerFrame) + self._video_source.input_node.link(tracker.inputDetectionFrame) + self._filtered_bridge.out.link(tracker.inputDetections) + return tracker + + def get_tracker(self) -> dai.node.ObjectTracker: + return self._object_tracker + + def get_detections(self) -> ImgDetectionsBridge: + return self._filtered_bridge + + def get_filter(self) -> ImgDetectionsFilter: + return self._det_filter + + def get_annotation_node(self) -> AnnotationNode: + return self._annotation_node + + def get_controller(self) -> YOLONNController: + return self._controller diff --git a/apps/data-collection/backend/src/infrastructure/snaps/__init__.py b/apps/data-collection/backend/src/infrastructure/snaps/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/data-collection/backend/src/infrastructure/snaps/snaps_manager.py b/apps/data-collection/backend/src/infrastructure/snaps/snaps_manager.py new file mode 100644 index 000000000..218410ce9 --- /dev/null +++ b/apps/data-collection/backend/src/infrastructure/snaps/snaps_manager.py @@ -0,0 +1,56 @@ +import depthai as dai +from config.system_configuration import SystemConfiguration +from core.snapping.conditions_engine import ConditionsEngine +from infrastructure.neural_network.neural_network_manager import NeuralNetworkManager +from services.snap_collection.snap_collection_service import SnapCollectionService +from infrastructure.conditions_factory import ConditionsFactory +from infrastructure.snaps.snaps_producer_factory import SnapsProducerFactory +from infrastructure.video_source_manager import VideoSourceManager +from depthai_nodes.node import SnapsProducer + + +class SnapsManager: + """ + Facade for the snapping subsystem. + """ + + def __init__( + self, + pipeline: dai.Pipeline, + video_source: VideoSourceManager, + nn_manager: NeuralNetworkManager, + config: SystemConfiguration, + ): + self._pipeline = pipeline + self._video_source = video_source + self._nn_manager = nn_manager + self._config = config + self._producer: SnapsProducer = None + + self._engine: ConditionsEngine = None + + self._build() + + def _build(self) -> "SnapsManager": + cond_manager = ConditionsFactory(self._config.conditions) + self._engine = cond_manager.get_engine() + + snaps_producer = SnapsProducerFactory.create( + self._pipeline, + self._video_source, + self._nn_manager.get_tracker(), + self._nn_manager.get_detections(), + self._engine, + ) + self._producer = snaps_producer + snap_service = SnapCollectionService(self._engine, self._producer) + self._register_service(snap_service) + return self + + def _register_service(self, snap_service: SnapCollectionService) -> None: + self._config.visualizer.registerService( + snap_service.get_name(), snap_service.handle + ) + + def get_engine(self): + return self._engine diff --git a/apps/data-collection/backend/src/infrastructure/snaps/snaps_producer_factory.py b/apps/data-collection/backend/src/infrastructure/snaps/snaps_producer_factory.py new file mode 100644 index 000000000..b576631ab --- /dev/null +++ b/apps/data-collection/backend/src/infrastructure/snaps/snaps_producer_factory.py @@ -0,0 +1,23 @@ +from functools import partial +from depthai_nodes.node import SnapsProducer2Buffered, SnapsProducer +from core.snapping.custom_snap_process import process_snaps +import depthai as dai +from depthai_nodes.node import ImgDetectionsBridge + + +class SnapsProducerFactory: + @staticmethod + def create( + pipeline, + video_source, + tracker: dai.node.ObjectTracker, + detections: ImgDetectionsBridge, + engine, + ) -> SnapsProducer: + return pipeline.create(SnapsProducer2Buffered).build( + frame=video_source.video_src_out, + msg=tracker.out, + msg2=detections.out, + running=False, + process_fn=partial(process_snaps, engine=engine), + ) diff --git a/apps/data-collection/backend/src/infrastructure/video_source_manager.py b/apps/data-collection/backend/src/infrastructure/video_source_manager.py new file mode 100644 index 000000000..628e7d45b --- /dev/null +++ b/apps/data-collection/backend/src/infrastructure/video_source_manager.py @@ -0,0 +1,97 @@ +from pathlib import Path +import depthai as dai +from infrastructure.frame_cache_node import FrameCacheNode +from config.config_data_classes import RuntimeConfig +from config.system_configuration import SystemConfiguration + + +class VideoSourceManager: + """ + Creates and manages video input nodes (camera or replay) and encodes video streams. + """ + + def __init__( + self, + pipeline: dai.Pipeline, + config: SystemConfiguration, + runtime: RuntimeConfig, + ): + self._pipeline = pipeline + self._config = config + self._runtime = runtime + + self.video_src_out: dai.Node.Output = None + self.input_node: dai.Node.Output = None + self.video_enc: dai.node.VideoEncoder = None + self.frame_cache: FrameCacheNode = None + + self._build() + + def _build(self): + """Decide source type and build full video setup.""" + if self._config.args.media_path: + self._create_replay_source() + else: + self._create_camera_source() + self._create_encoder() + self.frame_cache = self._pipeline.create(FrameCacheNode).build( + self.video_src_out + ) + self._add_video_topic() + + def _create_replay_source(self): + """Replay from a video file.""" + model_w, model_h = ( + self._runtime.model_info.width, + self._runtime.model_info.height, + ) + + replay = self._pipeline.create(dai.node.ReplayVideo) + replay.setReplayVideoFile(Path(self._config.args.media_path)) + replay.setOutFrameType(dai.ImgFrame.Type.NV12) + replay.setLoop(True) + if self._runtime.fps_limit: + replay.setFps(self._runtime.fps_limit) + replay.setSize(self._config.constants.visualization_resolution) + self.video_src_out = replay.out + + manip = self._pipeline.create(dai.node.ImageManip) + manip.setMaxOutputFrameSize(model_w * model_h * 3) + manip.initialConfig.setOutputSize(model_w, model_h) + manip.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888i) + self.video_src_out.link(manip.inputImage) + self.input_node = manip.out + + def _create_camera_source(self): + """Live camera source.""" + model_w, model_h = ( + self._runtime.model_info.width, + self._runtime.model_info.height, + ) + + cam = self._pipeline.create(dai.node.Camera).build( + boardSocket=dai.CameraBoardSocket.CAM_A + ) + self.video_src_out = cam.requestOutput( + size=self._config.constants.visualization_resolution, + type=dai.ImgFrame.Type.NV12, + fps=self._runtime.fps_limit, + ) + self.input_node = cam.requestOutput( + size=(model_w, model_h), + type=dai.ImgFrame.Type.BGR888i, + fps=self._runtime.fps_limit, + ) + + def _create_encoder(self): + """Attach encoder to video source.""" + self.video_enc = self._pipeline.create(dai.node.VideoEncoder) + self.video_enc.setDefaultProfilePreset( + fps=self._runtime.fps_limit, + profile=dai.VideoEncoderProperties.Profile.H264_MAIN, + ) + self.video_src_out.link(self.video_enc.input) + + def _add_video_topic(self): + """Register video stream with visualizer.""" + self._config.visualizer.addTopic("Video", self.video_enc.out) diff --git a/apps/data-collection/backend/src/main.py b/apps/data-collection/backend/src/main.py index 57acf6e94..b4442845f 100644 --- a/apps/data-collection/backend/src/main.py +++ b/apps/data-collection/backend/src/main.py @@ -1,251 +1,45 @@ -import os - -from pathlib import Path -from dotenv import load_dotenv -from functools import partial - import depthai as dai -from depthai_nodes.node import ( - ParsingNeuralNetwork, - ImgDetectionsFilter, - SnapsProducer2Buffered, - ImgDetectionsBridge, -) - -from utils.arguments import initialize_argparser -from utils.annotation_node import AnnotationNode -from utils.frame_cache_node import FrameCacheNode -from utils.snaps.conditions_manager import ConditionsManager -from utils.snaps.custon_snap_process import custom_snap_process -from utils.snaps.tracklets import setup_tracker -from utils.services.class_update_service import ClassUpdateService -from utils.services.threshold_update_service import ThresholdUpdateService -from utils.services.image_upload_service import ImageUploadService -from utils.services.bbox_prompt_service import BBoxPromptService -from utils.services.snap_collection_service import SnapCollectionService -from utils.services.get_config_service import GetConfigService -from utils.core.tokenizer_manager import TokenizerManager -from utils.core.quantization import make_dummy_features -from utils.core.label_manager import LabelManager -import utils.constants as const - -load_dotenv(override=True) - -_, args = initialize_argparser() - -if args.api_key: - os.environ["DEPTHAI_HUB_API_KEY"] = args.api_key - -IP = args.ip or "localhost" -PORT = args.port or 8080 - -visualizer = dai.RemoteConnection(serveFrontend=False) -device = dai.Device() -platform = device.getPlatformAsString() - -if platform != "RVC4": - raise ValueError("This example is supported only on RVC4 platform") - -frame_type = dai.ImgFrame.Type.BGR888i -tokenizer = TokenizerManager( - model_name=const.MODEL, precision=args.precision, max_classes=const.MAX_NUM_CLASSES -) +from config.system_configuration import SystemConfiguration +from core.model_state import ModelState +from infrastructure.neural_network.neural_network_manager import NeuralNetworkManager +from infrastructure.snaps.snaps_manager import SnapsManager +from infrastructure.video_source_manager import VideoSourceManager +from infrastructure.export.export_manager import ExportManager -text_features = tokenizer.extract_text_embeddings(const.CLASS_NAMES) -image_prompt_features = make_dummy_features( - const.MAX_NUM_CLASSES, model_name="yoloe", precision=args.precision -) +def main(): + config = SystemConfiguration() + runtime = config.build_runtime_config() -if args.fps_limit is None: - args.fps_limit = 30 - print( - f"\nFPS limit set to {args.fps_limit} for {platform} platform. If you want to set a custom FPS limit, use the --fps_limit flag.\n" - ) + with dai.Pipeline(config.device) as pipeline: + print("Creating pipeline...") -with dai.Pipeline(device) as pipeline: - print("Creating pipeline...") + video_source = VideoSourceManager(pipeline, config, runtime) - models_dir = Path(__file__).parent / "depthai_models" - if args.precision != "fp16": - raise SystemExit( - f"Model YAML not found for YOLOE with precision {args.precision}. " - f"YOLOE int8 YAML is not available; run with --precision fp16." - ) - - yaml_base = "yoloe_v8_l_fp16" - yaml_filename = f"{yaml_base}.{platform}.yaml" - yaml_path = models_dir / yaml_filename - - if not yaml_path.exists(): - raise SystemExit(f"Model YAML not found for YOLOE: {yaml_path}.") - - model_description = dai.NNModelDescription.fromYamlFile(str(yaml_path)) - model_description.platform = platform - model_nn_archive = dai.NNArchive(dai.getModelFromZoo(model_description)) - model_w, model_h = model_nn_archive.getInputSize() - - if args.media_path: - replay = pipeline.create(dai.node.ReplayVideo) - replay.setReplayVideoFile(Path(args.media_path)) - replay.setOutFrameType(dai.ImgFrame.Type.NV12) - replay.setLoop(True) - if args.fps_limit: - replay.setFps(args.fps_limit) - replay.setSize( - const.VISUALIZATION_RESOLUTION[0], const.VISUALIZATION_RESOLUTION[1] - ) - video_src_out = replay.out - - image_manip = pipeline.create(dai.node.ImageManip) - image_manip.setMaxOutputFrameSize(model_w * model_h * 3) - image_manip.initialConfig.setOutputSize(model_w, model_h) - image_manip.initialConfig.setFrameType(frame_type) - video_src_out.link(image_manip.inputImage) - - input_node = image_manip.out - else: - cam = pipeline.create(dai.node.Camera).build( - boardSocket=dai.CameraBoardSocket.CAM_A - ) - video_src_out = cam.requestOutput( - size=const.VISUALIZATION_RESOLUTION, - type=dai.ImgFrame.Type.NV12, - fps=args.fps_limit, - ) + model_state = ModelState() - input_node = cam.requestOutput( - size=(model_w, model_h), type=frame_type, fps=args.fps_limit + nn_manager = NeuralNetworkManager( + pipeline, video_source, runtime, config, model_state ) - video_enc = pipeline.create(dai.node.VideoEncoder) - video_enc.setDefaultProfilePreset( - fps=args.fps_limit, profile=dai.VideoEncoderProperties.Profile.H264_MAIN - ) - video_src_out.link(video_enc.input) - - nn_with_parser = pipeline.create(ParsingNeuralNetwork) - nn_with_parser.setNNArchive(model_nn_archive) - nn_with_parser.setBackend("snpe") - nn_with_parser.setBackendProperties( - {"runtime": "dsp", "performance_profile": "default"} - ) - nn_with_parser.setNumInferenceThreads(1) - nn_with_parser.getParser(0).setConfidenceThreshold(const.CONFIDENCE_THRESHOLD) - - input_node.link(nn_with_parser.inputs["images"]) - - textInputQueue = nn_with_parser.inputs["texts"].createInputQueue() - nn_with_parser.inputs["texts"].setReusePreviousMessage(True) - - imagePromptInputQueue = nn_with_parser.inputs["image_prompts"].createInputQueue() - nn_with_parser.inputs["image_prompts"].setReusePreviousMessage(True) - - det_process_filter = pipeline.create(ImgDetectionsFilter).build(nn_with_parser.out) - annotation_node = pipeline.create(AnnotationNode).build( - det_process_filter.out, - video_src_out, - ) - - filtered_bridge = pipeline.create(ImgDetectionsBridge).build(det_process_filter.out) - - frame_cache = pipeline.create(FrameCacheNode).build(video_src_out) - - cond_manager = ConditionsManager(default_cooldown_s=300.0, enabled=False) - cond_manager.register_conditions(const.CONDITIONS) - - _runtime = { - "conf_threshold": const.CONFIDENCE_THRESHOLD, - "lost_mid_margin": 0.20, - "snapping_running": False, - } - - current_classes = const.CLASS_NAMES.copy() - object_tracker = pipeline.create(dai.node.ObjectTracker) - setup_tracker(object_tracker) - - input_node.link(object_tracker.inputTrackerFrame) - input_node.link(object_tracker.inputDetectionFrame) - filtered_bridge.out.link(object_tracker.inputDetections) - - snaps_producer = pipeline.create(SnapsProducer2Buffered).build( - frame=video_src_out, - msg=object_tracker.out, - msg2=filtered_bridge.out, - running=False, - process_fn=partial( - custom_snap_process, - class_names=const.CLASS_NAMES, - cond_manager=cond_manager, - runtime=_runtime, - ), - ) - - visualizer.addTopic("Video", video_enc.out, "images") - visualizer.addTopic("Detections", annotation_node.out) - - ClassUpdateService( - visualizer, - textInputQueue, - imagePromptInputQueue, - args.precision, - current_classes, - det_process_filter, - annotation_node, - ).register() - - ThresholdUpdateService(visualizer, nn_with_parser, _runtime).register() - - ImageUploadService( - visualizer, - args.precision, - imagePromptInputQueue, - textInputQueue, - det_process_filter, - annotation_node, - ).register() - - BBoxPromptService(visualizer, frame_cache).register() - - SnapCollectionService(visualizer, _runtime, cond_manager, snaps_producer).register() - - GetConfigService(visualizer, current_classes, _runtime, cond_manager).register() - - print("Pipeline created.") + snaps_manager = SnapsManager(pipeline, video_source, nn_manager, config) - pipeline.start() - visualizer.registerPipeline(pipeline) + ExportManager(model_state, snaps_manager.get_engine(), config) - label_manager = LabelManager(det_process_filter, annotation_node) - label_manager.update_labels(const.CLASS_NAMES, offset=const.CLASS_OFFSET) + print("Pipeline created.") + pipeline.start() + config.visualizer.registerPipeline(pipeline) - dataType = ( - dai.TensorInfo.DataType.FP16 - if args.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ) - inputNNData = dai.NNData() - inputNNData.addTensor( - "texts", - text_features, - dataType=dataType, - ) - textInputQueue.send(inputNNData) + print("Press 'q' to stop") - inputNNDataImg = dai.NNData() - inputNNDataImg.addTensor( - "image_prompts", - image_prompt_features, - dataType=dataType, - ) - imagePromptInputQueue.send(inputNNDataImg) + while pipeline.isRunning(): + pipeline.processTasks() + key = config.visualizer.waitKey(1) + if key == ord("q"): + print("Got q key. Exiting...") + break - print("Press 'q' to stop") - while pipeline.isRunning(): - pipeline.processTasks() - key = visualizer.waitKey(1) - if key == ord("q"): - print("Got q key. Exiting...") - break +if __name__ == "__main__": + main() diff --git a/apps/data-collection/backend/src/services/__init__.py b/apps/data-collection/backend/src/services/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/data-collection/backend/src/services/base_service.py b/apps/data-collection/backend/src/services/base_service.py new file mode 100644 index 000000000..897995160 --- /dev/null +++ b/apps/data-collection/backend/src/services/base_service.py @@ -0,0 +1,26 @@ +from abc import ABC, abstractmethod +from typing import Generic, TypeVar, Dict +from services.service_name import ServiceName + + +PayloadT = TypeVar("PayloadT", bound=dict) + + +class BaseService(ABC, Generic[PayloadT]): + """ + Abstract base class for all backend services. + Defines a single consistent interface for handling typed payloads. + """ + + NAME: ServiceName # must be defined in subclasses + + def __init__(self): + self.__name = self.NAME + + @abstractmethod + def handle(self, payload: PayloadT) -> Dict: + """Execute service logic and return a JSON-serializable response.""" + pass + + def get_name(self) -> ServiceName: + return self.__name diff --git a/apps/data-collection/backend/src/services/bbox_prompt/__init__.py b/apps/data-collection/backend/src/services/bbox_prompt/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/data-collection/backend/src/services/bbox_prompt/bbox_prompt_payload.py b/apps/data-collection/backend/src/services/bbox_prompt/bbox_prompt_payload.py new file mode 100644 index 000000000..a216c3a2b --- /dev/null +++ b/apps/data-collection/backend/src/services/bbox_prompt/bbox_prompt_payload.py @@ -0,0 +1,14 @@ +from typing import TypedDict + + +class BBox(TypedDict): + x: float + y: float + width: float + height: float + + +class BBoxPromptPayload(TypedDict): + """Payload for bounding box prompts""" + + bbox: BBox diff --git a/apps/data-collection/backend/src/services/bbox_prompt/bbox_prompt_service.py b/apps/data-collection/backend/src/services/bbox_prompt/bbox_prompt_service.py new file mode 100644 index 000000000..96c88d1cd --- /dev/null +++ b/apps/data-collection/backend/src/services/bbox_prompt/bbox_prompt_service.py @@ -0,0 +1,37 @@ +from services.base_service import BaseService +from services.bbox_prompt.bbox_prompt_payload import BBoxPromptPayload +from core.handlers.bbox_prompt_handler import BBoxPromptHandler +from core.controllers.nn_controller import YOLONNController +from infrastructure.frame_cache_node import FrameCacheNode +from services.service_name import ServiceName + + +class BBoxPromptService(BaseService[BBoxPromptPayload]): + NAME = ServiceName.BBOX_PROMPT + + def __init__( + self, + handler: BBoxPromptHandler, + frame_cache: FrameCacheNode, + repository: YOLONNController, + ): + super().__init__() + self.handler = handler + self.frame_cache = frame_cache + self.repository = repository + + def handle(self, payload: BBoxPromptPayload): + image = self.frame_cache.get_last_frame() + if image is None: + return {"ok": False, "reason": "no_frame_available"} + + try: + embeddings, class_names, dummy = self.handler.process( + image, payload["bbox"] + ) + except ValueError as e: + return {"ok": False, "reason": "invalid_bbox"} + + self.repository.send_embeddings_pair(embeddings, dummy, class_names) + + return {"ok": True, "classes": class_names} diff --git a/apps/data-collection/backend/src/services/class_update/__init__.py b/apps/data-collection/backend/src/services/class_update/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/data-collection/backend/src/services/class_update/class_update_payload.py b/apps/data-collection/backend/src/services/class_update/class_update_payload.py new file mode 100644 index 000000000..1ffd51f7a --- /dev/null +++ b/apps/data-collection/backend/src/services/class_update/class_update_payload.py @@ -0,0 +1,7 @@ +from typing import TypedDict, List + + +class ClassUpdatePayload(TypedDict): + """Payload for updating detection classes.""" + + classes: List[str] diff --git a/apps/data-collection/backend/src/services/class_update/class_update_service.py b/apps/data-collection/backend/src/services/class_update/class_update_service.py new file mode 100644 index 000000000..a30b3f570 --- /dev/null +++ b/apps/data-collection/backend/src/services/class_update/class_update_service.py @@ -0,0 +1,31 @@ +from services.base_service import BaseService +from services.class_update.class_update_payload import ClassUpdatePayload +from core.handlers.class_update_handler import TextPromptHandler +from core.controllers.nn_controller import YOLONNController +from services.service_name import ServiceName + + +class ClassUpdateService(BaseService[ClassUpdatePayload]): + """Coordinates text-based class updates across model, repository, and state.""" + + NAME = ServiceName.CLASS_UPDATE + + def __init__( + self, + repository: YOLONNController, + handler: TextPromptHandler, + ): + super().__init__() + self.repository = repository + self.handler = handler + + def handle(self, payload: ClassUpdatePayload): + new_classes = payload["classes"] + if not new_classes: + return {"ok": False, "reason": "empty_class_list"} + + text_inputs, dummy = self.handler.process(new_classes) + + self.repository.send_embeddings_pair(dummy, text_inputs, new_classes) + + return {"ok": True, "classes": new_classes} diff --git a/apps/data-collection/backend/src/services/export_service/__init__.py b/apps/data-collection/backend/src/services/export_service/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/data-collection/backend/src/services/export_service/get_config_service.py b/apps/data-collection/backend/src/services/export_service/get_config_service.py new file mode 100644 index 000000000..43cff3203 --- /dev/null +++ b/apps/data-collection/backend/src/services/export_service/get_config_service.py @@ -0,0 +1,22 @@ +from services.base_service import BaseService +from config.system_state_exporter import SystemStateExporter +from services.service_name import ServiceName + + +class ExportService(BaseService[None]): + """Returns the current configuration state to the frontend.""" + + NAME = ServiceName.EXPORT + + def __init__(self, config_exporter: SystemStateExporter): + super().__init__() + self.config_exporter = config_exporter + + def handle(self, payload: None = None) -> dict: + try: + config = self.config_exporter.export_config() + print("[ExportService] returning:", config) + return config + except Exception as e: + print("[ExportService] ERROR:", e) + raise diff --git a/apps/data-collection/backend/src/services/image_upload/__init__.py b/apps/data-collection/backend/src/services/image_upload/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/data-collection/backend/src/services/image_upload/image_upload_payload.py b/apps/data-collection/backend/src/services/image_upload/image_upload_payload.py new file mode 100644 index 000000000..8fd4c8b66 --- /dev/null +++ b/apps/data-collection/backend/src/services/image_upload/image_upload_payload.py @@ -0,0 +1,9 @@ +from typing import TypedDict + + +class ImageUploadPayload(TypedDict): + """Payload for uploading an image from the frontend.""" + + filename: str + type: str + data: str diff --git a/apps/data-collection/backend/src/services/image_upload/image_upload_service.py b/apps/data-collection/backend/src/services/image_upload/image_upload_service.py new file mode 100644 index 000000000..7ac0c3351 --- /dev/null +++ b/apps/data-collection/backend/src/services/image_upload/image_upload_service.py @@ -0,0 +1,23 @@ +from services.base_service import BaseService +from services.image_upload.image_upload_payload import ImageUploadPayload +from core.handlers.image_upload_handler import ImagePromptHandler +from core.controllers.nn_controller import YOLONNController +from services.service_name import ServiceName + + +class ImageUploadService(BaseService[ImageUploadPayload]): + """Coordinates image upload flow: decode → extract → send → update labels.""" + + NAME = ServiceName.IMAGE_UPLOAD + + def __init__(self, repository: YOLONNController, handler: ImagePromptHandler): + super().__init__() + self.repository = repository + self.handler = handler + + def handle(self, payload: ImageUploadPayload): + image_inputs, class_names, dummy = self.handler.process(payload) + + self.repository.send_embeddings_pair(image_inputs, dummy, class_names) + + return {"ok": True, "class": class_names} diff --git a/apps/data-collection/backend/src/services/service_name.py b/apps/data-collection/backend/src/services/service_name.py new file mode 100644 index 000000000..689d6893a --- /dev/null +++ b/apps/data-collection/backend/src/services/service_name.py @@ -0,0 +1,12 @@ +from enum import Enum + + +class ServiceName(str, Enum): + """Unique identifiers for all backend services.""" + + CLASS_UPDATE = "Class Update Service" + THRESHOLD_UPDATE = "Threshold Update Service" + IMAGE_UPLOAD = "Image Upload Service" + SNAP_COLLECTION = "Snap Collection Service" + BBOX_PROMPT = "BBox Prompt Service" + EXPORT = "Export Service" diff --git a/apps/data-collection/backend/src/services/snap_collection/__init__.py b/apps/data-collection/backend/src/services/snap_collection/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/data-collection/backend/src/services/snap_collection/snap_collection_service.py b/apps/data-collection/backend/src/services/snap_collection/snap_collection_service.py new file mode 100644 index 000000000..fa4cd748a --- /dev/null +++ b/apps/data-collection/backend/src/services/snap_collection/snap_collection_service.py @@ -0,0 +1,31 @@ +from services.base_service import BaseService +from core.snapping.conditions_engine import ConditionsEngine +from depthai_nodes.node import SnapsProducer +from services.snap_collection.snap_payload import SnapPayload +from services.service_name import ServiceName + + +class SnapCollectionService(BaseService[SnapPayload]): + """ + Handles updates to snapping conditions and manages SnapsProducer state. + """ + + NAME = ServiceName.SNAP_COLLECTION + + def __init__( + self, + engine: ConditionsEngine, + snaps_producer: SnapsProducer, + ): + super().__init__() + self.engine = engine + self.snaps_producer = snaps_producer + + def handle(self, payload: SnapPayload) -> dict: + self.engine.import_conditions_config(payload) + + any_active = self.engine.any_active() + + self.snaps_producer.setRunning(any_active) + + return {"ok": True, "active": any_active} diff --git a/apps/data-collection/backend/src/services/snap_collection/snap_payload.py b/apps/data-collection/backend/src/services/snap_collection/snap_payload.py new file mode 100644 index 000000000..ec5f3596b --- /dev/null +++ b/apps/data-collection/backend/src/services/snap_collection/snap_payload.py @@ -0,0 +1,15 @@ +from typing import TypedDict, Optional +from core.snapping.conditions.condition_key import ConditionKey + + +class ConditionConfig(TypedDict, total=False): + """Configuration of a single snapping condition.""" + + enabled: bool + cooldown: Optional[float] + interval: Optional[float] + threshold: Optional[float] + margin: Optional[float] + + +SnapPayload = dict[ConditionKey, ConditionConfig] diff --git a/apps/data-collection/backend/src/services/threshold_update/__init__.py b/apps/data-collection/backend/src/services/threshold_update/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/data-collection/backend/src/services/threshold_update/threshold_update_payload.py b/apps/data-collection/backend/src/services/threshold_update/threshold_update_payload.py new file mode 100644 index 000000000..e73ab3ae0 --- /dev/null +++ b/apps/data-collection/backend/src/services/threshold_update/threshold_update_payload.py @@ -0,0 +1,7 @@ +from typing import TypedDict + + +class ThresholdUpdatePayload(TypedDict): + """Payload for updating NN confidence threshold.""" + + threshold: float diff --git a/apps/data-collection/backend/src/services/threshold_update/threshold_update_service.py b/apps/data-collection/backend/src/services/threshold_update/threshold_update_service.py new file mode 100644 index 000000000..47c1da820 --- /dev/null +++ b/apps/data-collection/backend/src/services/threshold_update/threshold_update_service.py @@ -0,0 +1,25 @@ +from services.base_service import BaseService +from services.threshold_update.threshold_update_payload import ThresholdUpdatePayload +from core.controllers.nn_controller import YOLONNController +from services.service_name import ServiceName + + +class ThresholdUpdateService(BaseService[ThresholdUpdatePayload]): + """Coordinates NN confidence threshold updates between handler, repository, and state.""" + + NAME = ServiceName.THRESHOLD_UPDATE + + def __init__( + self, + repository: YOLONNController, + ): + super().__init__() + self.repository = repository + + def handle(self, payload: ThresholdUpdatePayload): + new_threshold = payload["threshold"] + + clamped = max(0.0, min(1.0, new_threshold)) + self.repository.set_confidence_threshold(clamped) + + return {"ok": True, "threshold": clamped} diff --git a/apps/data-collection/backend/src/utils/constants.py b/apps/data-collection/backend/src/utils/constants.py deleted file mode 100644 index 9c60d0dc3..000000000 --- a/apps/data-collection/backend/src/utils/constants.py +++ /dev/null @@ -1,30 +0,0 @@ -from .snaps.conditions.timed_condition import TimedCondition -from .snaps.conditions.no_detections_condition import NoDetectionsCondition -from .snaps.conditions.low_conf_condition import LowConfidenceCondition -from .snaps.conditions.lost_mid_condition import LostMidCondition - - -CLASS_NAMES = ["person", "chair", "TV"] -CLASS_OFFSET = 0 -MAX_NUM_CLASSES = 80 -CONFIDENCE_THRESHOLD = 0.1 -VISUALIZATION_RESOLUTION = (1080, 1080) -MODEL = "yoloe" - -QUANT_VALUES = { - "yoloe": { - "quant_zero_point": 174.0, - "quant_scale": 0.003328413470, - }, - "yoloe-image": { - "quant_zero_point": 137.0, - "quant_scale": 0.002327915514, - }, -} - -CONDITIONS = [ - TimedCondition(), - NoDetectionsCondition(), - LowConfidenceCondition(), - LostMidCondition(), -] diff --git a/apps/data-collection/backend/src/utils/core/io.py b/apps/data-collection/backend/src/utils/core/io.py deleted file mode 100644 index d02d8fafb..000000000 --- a/apps/data-collection/backend/src/utils/core/io.py +++ /dev/null @@ -1,26 +0,0 @@ -import os -import base64 -import numpy as np -import cv2 -import requests -from pathlib import Path - - -def download_file(url: str, path: str) -> Path: - if not os.path.exists(path): - print(f"Downloading tokenizer config from {url}...") - with open(path, "wb") as f: - f.write(requests.get(url).content) - return path - - -def base64_to_cv2_image(base64_data_uri: str): - if "," in base64_data_uri: - header, base64_data = base64_data_uri.split(",", 1) - else: - base64_data = base64_data_uri - - binary_data = base64.b64decode(base64_data) - np_arr = np.frombuffer(binary_data, np.uint8) - img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) - return img diff --git a/apps/data-collection/backend/src/utils/core/quantization.py b/apps/data-collection/backend/src/utils/core/quantization.py deleted file mode 100644 index e80e33eb2..000000000 --- a/apps/data-collection/backend/src/utils/core/quantization.py +++ /dev/null @@ -1,33 +0,0 @@ -import numpy as np -from ..constants import QUANT_VALUES - - -def pad_and_quantize_features( - features, max_num_classes: int, model_name: str, precision="int8" -): - """ - Pad features to (1, 512, max_num_classes) and quantize if precision is int8. - For FP16, return padded float16 features (no quantization). - """ - num_padding = max_num_classes - features.shape[0] - padded = np.pad(features, ((0, num_padding), (0, 0)), "constant").T.reshape( - 1, 512, max_num_classes - ) - - if precision == "fp16": - return padded.astype(np.float16) - - quant = QUANT_VALUES[model_name] - out = (padded / quant["quant_scale"]) + quant["quant_zero_point"] - return out.astype(np.uint8) - - -def make_dummy_features(max_num_classes: int, model_name: str, precision: str): - """ - Create a dummy tensor of shape (1, 512, max_num_classes) for model input. - For FP16, return zeros; for INT8, fill with the model's quantization zero point. - """ - if precision == "fp16": - return np.zeros((1, 512, max_num_classes), dtype=np.float16) - qzp = int(round(QUANT_VALUES.get(model_name, {}).get("quant_zero_point", 0))) - return np.full((1, 512, max_num_classes), qzp, dtype=np.uint8) diff --git a/apps/data-collection/backend/src/utils/core/tokenizer_manager.py b/apps/data-collection/backend/src/utils/core/tokenizer_manager.py deleted file mode 100644 index 325bc3f60..000000000 --- a/apps/data-collection/backend/src/utils/core/tokenizer_manager.py +++ /dev/null @@ -1,75 +0,0 @@ -from tokenizers import Tokenizer -import numpy as np -import onnxruntime -from .io import download_file -from .quantization import pad_and_quantize_features -from ..constants import MODEL, MAX_NUM_CLASSES - - -class TokenizerManager: - """ - Handles text tokenization and embedding extraction for class names. - - Loads a CLIP-compatible tokenizer and ONNX text encoder, processes - input class labels, and produces normalized and quantized text features - suitable for model input. - """ - - def __init__(self, model_name="yoloe", precision="fp16", max_classes=80): - self.model_name = model_name - self.precision = precision - self.max_classes = max_classes - - def _load(self): - path = download_file( - "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json", - "tokenizer.json", - ) - - self.tokenizer = Tokenizer.from_file(str(path)) - - model_path = download_file( - "https://huggingface.co/Xenova/mobileclip_blt/resolve/main/onnx/text_model.onnx", - "mobileclip_textual_hf.onnx", - ) - - self.session = onnxruntime.InferenceSession( - model_path, - providers=[ - "TensorrtExecutionProvider", - "CUDAExecutionProvider", - "CPUExecutionProvider", - ], - ) - - def extract_text_embeddings(self, class_names: list[str]) -> np.ndarray: - self._load() - self.tokenizer.enable_padding( - pad_id=self.tokenizer.token_to_id("<|endoftext|>"), - pad_token="<|endoftext|>", - ) - - encodings = self.tokenizer.encode_batch(class_names) - text_onnx = np.array([e.ids for e in encodings], dtype=np.int64) - - if text_onnx.shape[1] < 77: - text_onnx = np.pad( - text_onnx, ((0, 0), (0, 77 - text_onnx.shape[1])), mode="constant" - ) - - textual_output = self.session.run( - None, - { - self.session.get_inputs()[0].name: text_onnx, - }, - )[0] - - textual_output /= np.linalg.norm(textual_output, ord=2, axis=-1, keepdims=True) - - text_features = pad_and_quantize_features( - textual_output, MAX_NUM_CLASSES, MODEL, self.precision - ) - - del self.session - - return text_features diff --git a/apps/data-collection/backend/src/utils/core/visual_encoder_manager.py b/apps/data-collection/backend/src/utils/core/visual_encoder_manager.py deleted file mode 100644 index 6b8432cd1..000000000 --- a/apps/data-collection/backend/src/utils/core/visual_encoder_manager.py +++ /dev/null @@ -1,57 +0,0 @@ -import cv2 -import numpy as np -import onnxruntime -from .io import download_file -from .quantization import pad_and_quantize_features -from ..constants import MODEL, MAX_NUM_CLASSES - - -class VisualEncoderManager: - """ - Handles visual embedding extraction using a YOLOE visual encoder. - - Loads an ONNX visual encoder, preprocesses an image input, performs - forward inference, and returns quantized visual feature tensors - compatible with downstream models. - """ - - def __init__(self, model_name="yoloe", precision="fp16", max_classes=80): - self.model_name = model_name - self.precision = precision - self.max_classes = max_classes - - def _load(self): - path = download_file( - "https://huggingface.co/sokovninn/yoloe-v8l-seg-visual-encoder/resolve/main/" - "yoloe-v8l-seg_visual_encoder.onnx", - "yoloe-v8l-seg_visual_encoder.onnx", - ) - - self.session = onnxruntime.InferenceSession( - path, - providers=[ - "TensorrtExecutionProvider", - "CUDAExecutionProvider", - "CPUExecutionProvider", - ], - ) - - def extract_embeddings(self, image: np.ndarray) -> np.ndarray: - self._load() - image_resized = cv2.resize(image, (640, 640)) - image_array = image_resized.astype(np.float32) / 255.0 - image_array = np.transpose(image_array, (2, 0, 1)) - input_tensor = np.expand_dims(image_array, axis=0).astype(np.float32) - - prompts = np.zeros((1, 1, 80, 80), dtype=np.float32) - prompts[0, 0, 5:75, 5:75] = 1.0 - outputs = self.session.run(None, {"images": input_tensor, "prompts": prompts}) - - image_embeddings = outputs[0].squeeze(0).reshape(1, -1) - image_features = pad_and_quantize_features( - image_embeddings, MAX_NUM_CLASSES, MODEL, self.precision - ) - - del self.session - - return image_features diff --git a/apps/data-collection/backend/src/utils/services/base_service.py b/apps/data-collection/backend/src/utils/services/base_service.py deleted file mode 100644 index 036f92ceb..000000000 --- a/apps/data-collection/backend/src/utils/services/base_service.py +++ /dev/null @@ -1,21 +0,0 @@ -import abc - - -class BaseService(abc.ABC): - """ - Minimal abstract base for DepthAI backend services. - Only defines common interface + registration logic. - """ - - def __init__(self, visualizer, name: str): - self.visualizer = visualizer - self.name = name - - @abc.abstractmethod - def handle(self, payload: dict | None = None): - """Process request from frontend.""" - raise NotImplementedError - - def register(self): - """Register service with the DepthAI visualizer.""" - self.visualizer.registerService(self.name, self.handle) diff --git a/apps/data-collection/backend/src/utils/services/bbox_prompt_service.py b/apps/data-collection/backend/src/utils/services/bbox_prompt_service.py deleted file mode 100644 index edd131dc2..000000000 --- a/apps/data-collection/backend/src/utils/services/bbox_prompt_service.py +++ /dev/null @@ -1,48 +0,0 @@ -from .base_service import BaseService -from ..core.io import base64_to_cv2_image - - -class BBoxPromptService(BaseService): - """ - Handles bounding box prompts from the frontend. - - Decodes an image (or retrieves the last cached frame) and - converts a normalized or pixel-based bounding box to absolute - pixel coordinates for further processing. - """ - - def __init__(self, visualizer, frame_cache): - super().__init__(visualizer, "BBox Prompt Service") - self.frame_cache = frame_cache - - def handle(self, payload: dict | None = None): - image = base64_to_cv2_image(payload["data"]) if payload.get("data") else None - if image is None: - image = self.frame_cache.get_last_frame() - if image is None: - return {"ok": False, "reason": "no_image"} - if image is None: - return {"ok": False, "reason": "decode_failed"} - - bbox = payload.get("bbox", {}) - bx = float(bbox.get("x", 0.0)) - by = float(bbox.get("y", 0.0)) - bw = float(bbox.get("width", 0.0)) - bh = float(bbox.get("height", 0.0)) - - H, W = image.shape[:2] - is_pixel = payload.get("bboxType", "normalized") == "pixel" - if is_pixel: - x0 = int(round(bx)) - y0 = int(round(by)) - x1 = int(round(bx + bw)) - y1 = int(round(by + bh)) - else: - x0 = int(round(bx * W)) - y0 = int(round(by * H)) - x1 = int(round((bx + bw) * W)) - y1 = int(round((by + bh) * H)) - - x0, x1 = sorted((x0, x1)) - y0, y1 = sorted((y0, y1)) - return {"ok": True} diff --git a/apps/data-collection/backend/src/utils/services/class_update_service.py b/apps/data-collection/backend/src/utils/services/class_update_service.py deleted file mode 100644 index 6e4fd2e2d..000000000 --- a/apps/data-collection/backend/src/utils/services/class_update_service.py +++ /dev/null @@ -1,84 +0,0 @@ -from .base_service import BaseService -import depthai as dai -from ..constants import MAX_NUM_CLASSES, MODEL -from ..core.tokenizer_manager import TokenizerManager -from ..core.quantization import make_dummy_features -from ..core.label_manager import LabelManager - - -class ClassUpdateService(BaseService): - """ - Handles class updates from the frontend. - - Regenerates text embeddings for new class labels, updates the - corresponding neural network inputs, and synchronizes label mappings - in the detection and annotation pipeline. - """ - - def __init__( - self, - visualizer, - text_queue, - img_queue, - precision, - current_classes, - det_filter, - annotation, - ): - super().__init__(visualizer, "Class Update Service") - self.text_queue = text_queue - self.img_queue = img_queue - self.precision = precision - self.current_classes = current_classes - self.det_filter = det_filter - self.annotation = annotation - self.embedder = TokenizerManager( - model_name=MODEL, precision=precision, max_classes=MAX_NUM_CLASSES - ) - self.label_manager = LabelManager(det_filter, annotation) - - def handle(self, payload: list | None = None): - new_classes = payload - if not new_classes: - print("[ClassUpdateService] Empty class list.") - return - - if len(new_classes) > MAX_NUM_CLASSES: - print( - f"Too many classes ({len(new_classes)}) > {MAX_NUM_CLASSES}, skipping." - ) - return - - feats = self.embedder.extract_text_embeddings(new_classes) - - nn_txt = dai.NNData() - nn_txt.addTensor( - "texts", - feats, - dataType=( - dai.TensorInfo.DataType.FP16 - if self.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - self.text_queue.send(nn_txt) - - dummy = make_dummy_features(MAX_NUM_CLASSES, MODEL, self.precision) - nn_img = dai.NNData() - nn_img.addTensor( - "image_prompts", - dummy, - dataType=( - dai.TensorInfo.DataType.FP16 - if self.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - self.img_queue.send(nn_img) - - self.label_manager.update_labels(new_classes) - self.current_classes.clear() - self.current_classes.extend(new_classes) - - print(f"[ClassUpdateService] Classes updated: {new_classes}") - return {"ok": True, "classes": new_classes} diff --git a/apps/data-collection/backend/src/utils/services/get_config_service.py b/apps/data-collection/backend/src/utils/services/get_config_service.py deleted file mode 100644 index acc9a557a..000000000 --- a/apps/data-collection/backend/src/utils/services/get_config_service.py +++ /dev/null @@ -1,53 +0,0 @@ -from .base_service import BaseService - - -class GetConfigService(BaseService): - """ - Returns the current configuration state to the frontend. - - Reports runtime parameters such as confidence threshold, - active snap conditions, cooldown timers, and margin/threshold - values for each configured condition. - """ - - def __init__(self, visualizer, current_classes, runtime, cond_manager): - super().__init__(visualizer, "Get Config Service") - self.current_classes = current_classes - self.runtime = runtime - self.cond_manager = cond_manager - - def handle(self, payload=None): - def s_to_min(seconds: float) -> float: - try: - return float(seconds) / 60.0 if float(seconds) > 0 else 0.0 - except Exception: - return 0.0 - - config = { - "classes": self.current_classes.copy(), - "confidence_threshold": self.runtime["conf_threshold"], - "snapping": { - "running": self.runtime.get("snapping_running", False), - "timed": { - "enabled": self.cond_manager.is_key_enabled("timed"), - "interval": s_to_min(self.cond_manager.get_cooldown("timed")), - }, - "noDetections": { - "enabled": self.cond_manager.is_key_enabled("no_detections"), - "cooldown": s_to_min( - self.cond_manager.get_cooldown("no_detections") - ), - }, - "lowConfidence": { - "enabled": self.cond_manager.is_key_enabled("low_conf"), - "threshold": self.runtime.get("low_conf_thresh", 0.30), - "cooldown": s_to_min(self.cond_manager.get_cooldown("low_conf")), - }, - "lostMid": { - "enabled": self.cond_manager.is_key_enabled("lost_mid"), - "cooldown": s_to_min(self.cond_manager.get_cooldown("lost_mid")), - "margin": self.runtime.get("lost_mid_margin", 0.20), - }, - }, - } - return config diff --git a/apps/data-collection/backend/src/utils/services/image_upload_service.py b/apps/data-collection/backend/src/utils/services/image_upload_service.py deleted file mode 100644 index 6992cc76e..000000000 --- a/apps/data-collection/backend/src/utils/services/image_upload_service.py +++ /dev/null @@ -1,66 +0,0 @@ -import depthai as dai -from .base_service import BaseService -from ..constants import MAX_NUM_CLASSES, MODEL -from ..core.visual_encoder_manager import VisualEncoderManager -from ..core.io import base64_to_cv2_image -from ..core.quantization import make_dummy_features -from ..core.label_manager import LabelManager - - -class ImageUploadService(BaseService): - """ - Handles image uploads from the frontend. - - Decodes base64-encoded images, extracts visual embeddings via - the visual encoder, updates neural network tensors, and synchronizes - class labels for the uploaded image. - """ - - def __init__( - self, visualizer, precision, img_queue, text_queue, det_filter, annotation - ): - super().__init__(visualizer, "Image Upload Service") - self.precision = precision - self.img_queue = img_queue - self.text_queue = text_queue - self.det_filter = det_filter - self.annotation = annotation - self.image = None - self.visual_encoder = VisualEncoderManager(MODEL, precision, MAX_NUM_CLASSES) - self.label_manager = LabelManager(det_filter, annotation) - - def handle(self, payload: dict | None = None): - self.image = base64_to_cv2_image(payload["data"]) - image_features = self.visual_encoder.extract_embeddings(self.image) - - input_NN_data_img = dai.NNData() - input_NN_data_img.addTensor( - "image_prompts", - image_features, - dataType=( - dai.TensorInfo.DataType.FP16 - if self.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - self.img_queue.send(input_NN_data_img) - - dummy = make_dummy_features( - MAX_NUM_CLASSES, model_name=MODEL, precision=self.precision - ) - inputNNDataTxt = dai.NNData() - inputNNDataTxt.addTensor( - "texts", - dummy, - dataType=( - dai.TensorInfo.DataType.FP16 - if self.precision == "fp16" - else dai.TensorInfo.DataType.U8F - ), - ) - self.text_queue.send(inputNNDataTxt) - - filename = payload["filename"] - class_names = [filename.split(".")[0]] - self.label_manager.update_labels(class_names, offset=MAX_NUM_CLASSES) - return {"ok": True, "class": class_names} diff --git a/apps/data-collection/backend/src/utils/services/snap_collection_service.py b/apps/data-collection/backend/src/utils/services/snap_collection_service.py deleted file mode 100644 index a387ab0e9..000000000 --- a/apps/data-collection/backend/src/utils/services/snap_collection_service.py +++ /dev/null @@ -1,80 +0,0 @@ -from .base_service import BaseService -import depthai as dai -from ..snaps.conditions_manager import ConditionsManager -from depthai_nodes.node import SnapsProducer - - -class SnapCollectionService(BaseService): - """ - Handles frontend updates to snap collection configuration: - enabling/disabling conditions, adjusting cooldowns, and - controlling the SnapsProducer runtime state. - """ - - def __init__( - self, - visualizer: dai.RemoteConnection, - runtime: dict, - cond_manager: ConditionsManager, - snaps_producer: SnapsProducer, - ): - super().__init__(visualizer, "Snap Collection Service") - self.runtime = runtime - self.cond_manager = cond_manager - self.snaps_producer = snaps_producer - - def handle(self, payload: dict | None = None): - if not isinstance(payload, dict): - return {"ok": False, "reason": "payload_must_be_dict"} - - base_dt_seconds = 1 - prev_running = bool(self.runtime.get("snapping_running", False)) - - for key, conf in payload.items(): - if key not in self.cond_manager.conditions: - continue - if not isinstance(conf, dict): - continue - - if "enabled" in conf: - self.cond_manager.set_key_enabled(key, bool(conf["enabled"])) - - if "cooldown" in conf or "interval" in conf: - try: - cooldown = float(conf.get("cooldown", conf.get("interval", 0))) - self.cond_manager.set_cooldown(key, cooldown) - except Exception: - pass - - if key == "lowConfidence" and "threshold" in conf: - try: - thr = float(conf["threshold"]) - if thr > 1.0: - thr /= 100.0 - self.runtime["low_conf_thresh"] = max(0.0, min(1.0, thr)) - except Exception: - return {"ok": False, "reason": "invalid_low_conf_threshold"} - - if key == "lostMid" and "margin" in conf: - try: - m = float(conf["margin"]) - self.runtime["lost_mid_margin"] = max(0.0, min(0.49, m)) - except Exception: - pass - - any_active = any( - self.cond_manager.is_key_enabled(k) - for k in self.cond_manager.conditions.keys() - ) - - self.cond_manager.set_enabled(any_active) - - if any_active and not prev_running: - self.cond_manager.reset_cooldowns() - - self.snaps_producer.setRunning(any_active) - self.runtime["snapping_running"] = any_active - if any_active: - self.snaps_producer.setTimeInterval(base_dt_seconds) - - return {"ok": True, "active": any_active} diff --git a/apps/data-collection/backend/src/utils/services/threshold_update_service.py b/apps/data-collection/backend/src/utils/services/threshold_update_service.py deleted file mode 100644 index e01629243..000000000 --- a/apps/data-collection/backend/src/utils/services/threshold_update_service.py +++ /dev/null @@ -1,21 +0,0 @@ -from .base_service import BaseService - - -class ThresholdUpdateService(BaseService): - """ - Updates and synchronizes the neural network confidence threshold. - - Receives a new threshold value from the frontend, clamps it to - a valid range [0.0, 1.0], and updates both the runtime state and - the active neural network parser configuration. - """ - - def __init__(self, visualizer, nn, runtime): - super().__init__(visualizer, "Threshold Update Service") - self.nn = nn - self.runtime = runtime - - def handle(self, payload: float | None = None): - self.runtime["conf_threshold"] = max(0.0, min(1.0, payload)) - self.nn.getParser(0).setConfidenceThreshold(self.runtime["conf_threshold"]) - return {"ok": True, "threshold": self.runtime["conf_threshold"]} diff --git a/apps/data-collection/backend/src/utils/snaps/conditions/base_condition.py b/apps/data-collection/backend/src/utils/snaps/conditions/base_condition.py deleted file mode 100644 index 4fc54c4f6..000000000 --- a/apps/data-collection/backend/src/utils/snaps/conditions/base_condition.py +++ /dev/null @@ -1,17 +0,0 @@ -from abc import ABC, abstractmethod - - -class ConditionBase(ABC): - """Interface for all snap trigger conditions.""" - - def __init__(self, key: str, name: str): - self.key = key - self.name = name - - @abstractmethod - def should_trigger(self, cond_man, **kwargs) -> bool: - """Return True if this condition should fire.""" - pass - - def handle(self, **kwargs) -> dict: - return {"reason": f"{self.key}_snap"} diff --git a/apps/data-collection/backend/src/utils/snaps/conditions/lost_mid_condition.py b/apps/data-collection/backend/src/utils/snaps/conditions/lost_mid_condition.py deleted file mode 100644 index bea682687..000000000 --- a/apps/data-collection/backend/src/utils/snaps/conditions/lost_mid_condition.py +++ /dev/null @@ -1,76 +0,0 @@ -from .base_condition import ConditionBase -from ..tracklets import ( - _track_id, - _status_is_lost, - _status_is_tracked, - _roi_center_area_norm, - _label_idx_name, -) -from collections import defaultdict - - -class LostMidCondition(ConditionBase): - """ - Triggers when a tracked object becomes lost while still - positioned near the center of the frame. - - Detects transition from 'tracked' to 'lost' state within a - user-defined margin, and records affected tracklets for reporting. - """ - - def __init__(self, key="lostMid", name="Lost in Middle", default_margin=0.20): - super().__init__(key, name) - self._last_fired = None - self.prev_tracked = defaultdict(bool) - self.default_margin = default_margin - - def should_trigger(self, cond_manager, tracklets=None, runtime=None, **_): - if not cond_manager.is_key_enabled(self.key) or tracklets is None: - return False - - margin = float(runtime.get("lost_mid_margin", self.default_margin)) - margin = max(0.0, min(0.49, margin)) - fired = False - fired_tracklets = [] - - tks = getattr(tracklets, "tracklets", None) or [] - for t in tks: - tid = _track_id(t) - if tid is None: - continue - - was_tracked = self.prev_tracked[tid] - is_lost = _status_is_lost(t) - is_tracked = _status_is_tracked(t) - - if is_lost and was_tracked: - rc = _roi_center_area_norm(t) - if rc is not None: - cx, cy, area = rc - if margin <= cx <= 1.0 - margin and margin <= cy <= 1.0 - margin: - if cond_manager.hit(self.key): - fired = True - fired_tracklets.append((tid, t, area, margin)) - - self.prev_tracked[tid] = is_tracked - - self._last_fired = fired_tracklets - return fired - - def handle(self, tracklets=None, class_names=None, model=None, **_): - results = [] - for tid, t, area, margin in getattr(self, "_last_fired", []): - lbl_idx, lbl_name = _label_idx_name(t, class_names) - results.append( - { - "reason": "lost_in_middle", - "model": str(model), - "track_id": str(int(tid)), - "label_idx": str(int(lbl_idx)), - "label": str(lbl_name), - "area_frac": f"{area:.6f}", - "margin": f"{margin:.3f}", - } - ) - - return results[0] if len(results) == 1 else results diff --git a/apps/data-collection/backend/src/utils/snaps/conditions/low_conf_condition.py b/apps/data-collection/backend/src/utils/snaps/conditions/low_conf_condition.py deleted file mode 100644 index 90b5de444..000000000 --- a/apps/data-collection/backend/src/utils/snaps/conditions/low_conf_condition.py +++ /dev/null @@ -1,36 +0,0 @@ -from .base_condition import ConditionBase - - -class LowConfidenceCondition(ConditionBase): - """ - Triggers a snap when detections fall below a defined confidence threshold. - - Compares detection confidences against the current runtime threshold - and activates when any detection is lower than that value. - """ - - def __init__(self, key="lowConfidence", name="Low Confidence"): - super().__init__(key, name) - - def should_trigger(self, cond_manager, det_data=None, runtime=None, **_) -> bool: - if det_data is None or not cond_manager.is_key_enabled(self.key): - return False - - thr = runtime.get("low_conf_thresh") - if thr is None: - return False - dets = getattr(det_data, "detections", []) - has_low = any(float(getattr(d, "confidence", 1.0)) < float(thr) for d in dets) - return has_low and cond_manager.hit(self.key) - - def handle(self, det_data=None, runtime=None, **_): - dets = getattr(det_data, "detections", []) - min_conf = min( - (float(getattr(d, "confidence", 1.0)) for d in dets), default=1.0 - ) - thr = (runtime or {}).get("low_conf_thresh", 0.3) - return { - "reason": "low_confidence", - "min_conf": f"{min_conf:.3f}", - "threshold": f"{float(thr):.3f}", - } diff --git a/apps/data-collection/backend/src/utils/snaps/conditions/no_detections_condition.py b/apps/data-collection/backend/src/utils/snaps/conditions/no_detections_condition.py deleted file mode 100644 index 683ee6bcb..000000000 --- a/apps/data-collection/backend/src/utils/snaps/conditions/no_detections_condition.py +++ /dev/null @@ -1,18 +0,0 @@ -from .base_condition import ConditionBase - - -class NoDetectionsCondition(ConditionBase): - """ - Triggers when no objects are detected in the current frame. - - Activates only when the detections list is empty and the - cooldown period for this condition has expired. - """ - - def __init__(self, key="noDetections", name="No Detections"): - super().__init__(key, name) - - def should_trigger(self, cond_manager, det_data=None, **_): - if det_data is None or len(getattr(det_data, "detections", [])) > 0: - return False - return cond_manager.hit(self.key) diff --git a/apps/data-collection/backend/src/utils/snaps/conditions/timed_condition.py b/apps/data-collection/backend/src/utils/snaps/conditions/timed_condition.py deleted file mode 100644 index 9b614dfc1..000000000 --- a/apps/data-collection/backend/src/utils/snaps/conditions/timed_condition.py +++ /dev/null @@ -1,16 +0,0 @@ -from .base_condition import ConditionBase - - -class TimedCondition(ConditionBase): - """ - Triggers snaps at fixed time intervals. - - Fires independently of detection data, based solely on the - configured cooldown period in the condition manager. - """ - - def __init__(self, key="timed", name="Timed Snap"): - super().__init__(key, name) - - def should_trigger(self, cond_manager, **_): - return cond_manager.hit(self.key) diff --git a/apps/data-collection/backend/src/utils/snaps/conditions_manager.py b/apps/data-collection/backend/src/utils/snaps/conditions_manager.py deleted file mode 100644 index d50a7b958..000000000 --- a/apps/data-collection/backend/src/utils/snaps/conditions_manager.py +++ /dev/null @@ -1,79 +0,0 @@ -import time -from typing import Dict, Iterable, Optional -from .conditions.base_condition import ConditionBase - - -class ConditionsManager: - """ - Manages registration and evaluation of snap trigger conditions. - - Tracks cooldowns, enabled states, and timing for each condition, - ensuring that individual triggers fire only when their criteria - and cooldown constraints are met. - """ - - def __init__(self, default_cooldown_s: float = 300, enabled: bool = True): - self.enabled = bool(enabled) - self.default_cooldown_s = max(0.0, float(default_cooldown_s)) - self.cooldowns: Dict[str, float] = {} - self.key_enabled: Dict[str, bool] = {} - self.last_sent: Dict[str, float] = {} - self.conditions: Dict[str, "ConditionBase"] = {} - - def register_condition( - self, condition: ConditionBase, enabled=False, cooldown_s=-1.0 - ): - key = condition.key - self.conditions[key] = condition - self.key_enabled[key] = enabled - self.cooldowns[key] = cooldown_s if cooldown_s >= 0 else self.default_cooldown_s - - def register_conditions(self, conditions: Iterable[ConditionBase]): - for cond in conditions: - self.register_condition(cond) - - def set_enabled(self, on: bool) -> None: - self.enabled = on - - def set_key_enabled(self, key: str, on: bool) -> None: - self.key_enabled[str(key)] = bool(on) - - def set_cooldown(self, key: str, seconds: float) -> None: - self.cooldowns[str(key)] = max(0.0, float(seconds)) - - def reset_cooldowns(self, keys: Optional[Iterable[str]] = None) -> None: - if keys is None: - self.last_sent.clear() - else: - for k in keys: - self.last_sent.pop(str(k), None) - - def is_key_enabled(self, key: str) -> bool: - return self.key_enabled.get(str(key), False) - - def get_cooldown(self, key: str) -> float: - return self.cooldowns.get(str(key), self.default_cooldown_s) - - def hit(self, key: str) -> bool: - """ - Returns True if this condition/key should fire now (and records the hit), - False if it's still cooling down or globally/key disabled. - """ - if not self.enabled or not self.is_key_enabled(key): - return False - now = time.time() - last = self.last_sent.get(key, -1.0) - cd = self.get_cooldown(key) - if last < 0.0 or (now - last) >= cd: - self.last_sent[key] = now - return True - return False - - def evaluate(self, **kwargs): - """ - Iterate through all registered conditions, - yield (condition, extras) for those that fire. - """ - for key, cond in self.conditions.items(): - if self.is_key_enabled(key) and cond.should_trigger(self, **kwargs): - yield cond, cond.handle(**kwargs) diff --git a/apps/data-collection/backend/src/utils/snaps/custon_snap_process.py b/apps/data-collection/backend/src/utils/snaps/custon_snap_process.py deleted file mode 100644 index a3b1bb311..000000000 --- a/apps/data-collection/backend/src/utils/snaps/custon_snap_process.py +++ /dev/null @@ -1,33 +0,0 @@ -from depthai_nodes.node import SnapsProducer -from ..constants import MODEL -import depthai as dai -from .conditions_manager import ConditionsManager - - -def custom_snap_process( - producer: SnapsProducer, - frame: dai.ImgFrame, - tracklets_msg: dai.Tracklets | None, - det_data: dai.ImgDetections | None, - class_names: list[str], - cond_manager: ConditionsManager, - runtime: dict | None = None, -): - """ - Evaluates all registered snap conditions and sends triggered snaps. - - Iterates through active conditions managed by `cond_manager`, checks - whether each should fire based on the current detections and - tracklets, and sends the corresponding frame via the SnapsProducer. - """ - - for condition, extras in cond_manager.evaluate( - det_data=det_data, - tracklets=tracklets_msg, - frame=frame, - runtime=runtime, - class_names=class_names, - model=MODEL, - ): - if producer.sendSnap(condition.name, frame, det_data, [condition.key], extras): - print(f"[{condition.key}] Snap sent!") diff --git a/apps/data-collection/backend/src/utils/snaps/tracklets.py b/apps/data-collection/backend/src/utils/snaps/tracklets.py deleted file mode 100644 index a23ba6992..000000000 --- a/apps/data-collection/backend/src/utils/snaps/tracklets.py +++ /dev/null @@ -1,80 +0,0 @@ -from __future__ import annotations -from typing import List, Optional, Tuple -import depthai as dai - - -class _LostMidState: - def __init__(self) -> None: - self.prev_tracked: dict[int, bool] = {} - - -_LOSTMID_STATE = _LostMidState() - - -def _status_is_tracked(tracklet) -> bool: - val = getattr(tracklet, "status", None) - try: - return val == dai.Tracklet.TrackingStatus.TRACKED - except Exception: - try: - return int(val) == 1 - except Exception: - return False - - -def _track_id(tracklet) -> Optional[int]: - try: - tid = int(getattr(tracklet, "id", -1)) - return tid if tid >= 0 else None - except Exception: - return None - - -def _label_idx_name(tracklet, class_names: List[str]) -> Tuple[int, str]: - try: - idx = int(getattr(tracklet, "label", -1)) - except Exception: - idx = -1 - name = class_names[idx] if 0 <= idx < len(class_names) else "" - return idx, name - - -def _status_is_lost(t) -> bool: - try: - return t.status == dai.Tracklet.TrackingStatus.LOST - except Exception: - try: - return int(getattr(t, "status", -1)) == 2 - except Exception: - return False - - -def _roi_center_area_norm(t) -> Optional[Tuple[float, float, float]]: - roi = getattr(t, "roi", None) - if roi is not None: - try: - tl = roi.topLeft() - br = roi.bottomRight() - x0, y0, x1, y1 = float(tl.x), float(tl.y), float(br.x), float(br.y) - cx, cy = 0.5 * (x0 + x1), 0.5 * (y0 + y1) - return cx, cy, max(0.0, (x1 - x0) * (y1 - y0)) - except Exception: - pass - d = getattr(t, "srcImgDetection", None) - if d is not None: - x = float(getattr(d, "x", getattr(d, "xmin", 0.0))) - y = float(getattr(d, "y", getattr(d, "ymin", 0.0))) - w = float(getattr(d, "width", 0.0)) - h = float(getattr(d, "height", 0.0)) - return x + 0.5 * w, y + 0.5 * h, max(0.0, w * h) - return None - - -def setup_tracker(object_tracker: dai.node.ObjectTracker): - object_tracker.setTrackerType(dai.TrackerType.SHORT_TERM_IMAGELESS) - object_tracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.UNIQUE_ID) - object_tracker.setTrackingPerClass(True) - object_tracker.setTrackletBirthThreshold(3) - object_tracker.setTrackletMaxLifespan(90) - object_tracker.setOcclusionRatioThreshold(0.5) - object_tracker.setTrackerThreshold(0.25) diff --git a/apps/data-collection/frontend/src/App.tsx b/apps/data-collection/frontend/src/App.tsx index 19cf8c3f6..401d3feee 100644 --- a/apps/data-collection/frontend/src/App.tsx +++ b/apps/data-collection/frontend/src/App.tsx @@ -12,7 +12,7 @@ interface BackendConfig { confidence_threshold: number; snapping: { running: boolean; - timed: { enabled: boolean; interval: number }; + timed: { enabled: boolean; cooldown: number }; noDetections: { enabled: boolean; cooldown: number }; lowConfidence: { enabled: boolean; threshold: number; cooldown: number }; lostMid: { enabled: boolean; cooldown: number; margin: number }; @@ -148,12 +148,7 @@ function App() { (connection as any).daiConnection?.postToService( "BBox Prompt Service", { - filename: "object.png", - type: "application/json", - data: null, bbox: { x: xNorm, y: yNorm, width: wNorm, height: hNorm }, - bboxType: "normalized", - label: "object", }, (resp: any) => { console.log("[BBox] Service ack:", resp); @@ -205,7 +200,7 @@ function App() { const timeoutId = setTimeout(() => { console.log("[App] Fetching backend configuration..."); (connection as any).daiConnection?.postToService( - "Get Config Service", + "Export Service", null, (response: any) => { if (response === null || response === undefined) { diff --git a/apps/data-collection/frontend/src/utils/classes/ClassSelector.tsx b/apps/data-collection/frontend/src/utils/classes/ClassSelector.tsx index cd66051e1..575e26a14 100644 --- a/apps/data-collection/frontend/src/utils/classes/ClassSelector.tsx +++ b/apps/data-collection/frontend/src/utils/classes/ClassSelector.tsx @@ -45,7 +45,7 @@ export function ClassSelector({ initialClasses }: ClassSelectorProps) { connection.daiConnection?.postToService( // @ts-ignore - Custom service "Class Update Service", - updatedClasses, + { classes : updatedClasses }, () => { console.log('Backend acknowledged class update'); setSelectedClasses(updatedClasses); diff --git a/apps/data-collection/frontend/src/utils/classes/ConfidenceSlider.tsx b/apps/data-collection/frontend/src/utils/classes/ConfidenceSlider.tsx index 7f5b6a320..59a3379db 100644 --- a/apps/data-collection/frontend/src/utils/classes/ConfidenceSlider.tsx +++ b/apps/data-collection/frontend/src/utils/classes/ConfidenceSlider.tsx @@ -19,12 +19,12 @@ export function ConfidenceSlider({ initialValue = 0.5, disabled }: ConfidenceSli } }, [initialValue]); - const handleCommit = (v: number) => { - if (Number.isFinite(v)) { + const handleCommit = (new_threshold: number) => { + if (Number.isFinite(new_threshold)) { connection.daiConnection?.postToService( // @ts-ignore - Custom service "Threshold Update Service", - v, + { threshold: new_threshold }, (resp: any) => console.log("[ConfidenceSlider] BE ack:", resp) ); } diff --git a/apps/data-collection/frontend/src/utils/conditions/SnapConditionsPanel.tsx b/apps/data-collection/frontend/src/utils/conditions/SnapConditionsPanel.tsx index 2ff1c4ddf..54400e851 100644 --- a/apps/data-collection/frontend/src/utils/conditions/SnapConditionsPanel.tsx +++ b/apps/data-collection/frontend/src/utils/conditions/SnapConditionsPanel.tsx @@ -10,7 +10,7 @@ import { SliderControl } from "../SliderControl.tsx"; interface SnappingConfig { running: boolean; - timed: { enabled: boolean; interval: number }; + timed: { enabled: boolean; cooldown: number }; noDetections: { enabled: boolean; cooldown: number }; lowConfidence: { enabled: boolean; threshold: number; cooldown: number }; lostMid: { enabled: boolean; cooldown: number; margin: number }; @@ -81,8 +81,8 @@ export function SnapConditionsPanel({ initialConfig }: SnapConditionsPanelProps) // Timing if (initialConfig.timed) { setTimingEnabled(initialConfig.timed.enabled); - if (initialConfig.timed.interval > 0) { - setTimingStr(initialConfig.timed.interval.toFixed(1)); + if (initialConfig.timed.cooldown > 0) { + setTimingStr(initialConfig.timed.cooldown.toFixed(1)); } } @@ -135,7 +135,7 @@ export function SnapConditionsPanel({ initialConfig }: SnapConditionsPanelProps) const payload = runFlag ? { - timed: { enabled: timingEnabled, interval: timingEnabled ? timingMin * 60 : 0 }, + timed: { enabled: timingEnabled, cooldown: timingEnabled ? timingMin * 60 : 0 }, noDetections: { enabled: noDetEnabled, cooldown: noDetEnabled ? noDetMin * 60 : undefined }, lowConfidence: lowConfEnabled ? { enabled: true, threshold: lowConfThreshold, cooldown: lowConfMin * 60 } @@ -145,7 +145,7 @@ export function SnapConditionsPanel({ initialConfig }: SnapConditionsPanelProps) : { enabled: false }, } : { - timed: { enabled: false, interval: 0 }, + timed: { enabled: false}, noDetections: { enabled: false }, lowConfidence: { enabled: false }, lostMid: { enabled: false }, From 5b4431185c39a4a4b9708b672a6a0affbed0331b Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Tue, 4 Nov 2025 01:48:13 +0100 Subject: [PATCH 23/39] Fixed several typing issues --- .../backend/src/config/yaml_loader.py | 4 +- .../src/core/controllers/nn_controller.py | 22 +++++----- .../core/handlers/base_embedding_handler.py | 6 ++- ...oad_handler.py => image_prompt_handler.py} | 0 ...date_handler.py => text_prompt_handler.py} | 0 .../snapping/conditions/base_condition.py | 6 +-- .../snapping/conditions/low_conf_condition.py | 2 +- .../tracker_conditions/lost_mid_condition.py | 2 +- .../tracker_conditions/tracklet_analyzer.py | 2 +- .../src/infrastructure/conditions_factory.py | 7 ++-- .../neural_network/handlers_manager.py | 4 +- .../neural_network/neural_network_manager.py | 2 +- .../neural_network/nn_service_manager.py | 8 ++-- .../neural_network/pipeline_builder.py | 8 ++-- .../snaps/snaps_producer_factory.py | 12 ++++-- .../infrastructure/video_source_manager.py | 42 ++++++++++++------- .../backend/src/services/base_service.py | 4 +- .../bbox_prompt/bbox_prompt_service.py | 4 +- .../class_update/class_update_service.py | 4 +- .../export_service/get_config_service.py | 2 +- .../image_upload/image_upload_service.py | 4 +- .../snap_collection_service.py | 2 +- .../threshold_update_service.py | 2 +- 23 files changed, 84 insertions(+), 65 deletions(-) rename apps/data-collection/backend/src/core/handlers/{image_upload_handler.py => image_prompt_handler.py} (100%) rename apps/data-collection/backend/src/core/handlers/{class_update_handler.py => text_prompt_handler.py} (100%) diff --git a/apps/data-collection/backend/src/config/yaml_loader.py b/apps/data-collection/backend/src/config/yaml_loader.py index 990bd31ed..ef894345b 100644 --- a/apps/data-collection/backend/src/config/yaml_loader.py +++ b/apps/data-collection/backend/src/config/yaml_loader.py @@ -15,12 +15,12 @@ def __init__(self, file_path: str | Path): self._data = self._wrap(data) - def _wrap(self, obj): + def _wrap(self, obj) -> any: if isinstance(obj, dict): return type("YamlNamespace", (), {k: self._wrap(v) for k, v in obj.items()}) elif isinstance(obj, list): return [self._wrap(x) for x in obj] return obj - def __getattr__(self, name): + def __getattr__(self, name: str) -> any: return getattr(self._data, name) diff --git a/apps/data-collection/backend/src/core/controllers/nn_controller.py b/apps/data-collection/backend/src/core/controllers/nn_controller.py index cbc0e94ed..08869e289 100644 --- a/apps/data-collection/backend/src/core/controllers/nn_controller.py +++ b/apps/data-collection/backend/src/core/controllers/nn_controller.py @@ -16,16 +16,16 @@ def __init__( parser: YOLOExtendedParser, model_state: ModelState, ): - self.image_prompt_queue = image_prompt_queue - self.text_prompt_queue = text_prompt_queue - self.precision = precision - self.parser: YOLOExtendedParser = parser - self.model_state = model_state + self._image_prompt_queue = image_prompt_queue + self._text_prompt_queue = text_prompt_queue + self._precision = precision + self._parser: YOLOExtendedParser = parser + self._model_state = model_state def _tensor_type(self): return ( dai.TensorInfo.DataType.FP16 - if self.precision == "fp16" + if self._precision == "fp16" else dai.TensorInfo.DataType.U8F ) @@ -36,11 +36,11 @@ def _send(self, queue: dai.InputQueue, name: str, data: np.ndarray): def _send_text_inputs(self, embeddings: np.ndarray): """Send class text embeddings (semantic prompts) to the NN.""" - self._send(self.text_prompt_queue, "texts", embeddings) + self._send(self._text_prompt_queue, "texts", embeddings) def _send_visual_inputs(self, embeddings: np.ndarray): """Send visual prompts (mask- or bbox-based) to the NN.""" - self._send(self.image_prompt_queue, "image_prompts", embeddings) + self._send(self._image_prompt_queue, "image_prompts", embeddings) def send_embeddings_pair( self, @@ -51,9 +51,9 @@ def send_embeddings_pair( """Send both text and visual conditioning inputs if available.""" self._send_visual_inputs(visual_embeddings) self._send_text_inputs(text_embeddings) - self.model_state.update_classes(class_names) + self._model_state.update_classes(class_names) def set_confidence_threshold(self, threshold: float): """Apply threshold update directly to the NN parser.""" - self.parser.setConfidenceThreshold(threshold) - self.model_state.update_threshold(threshold) + self._parser.setConfidenceThreshold(threshold) + self._model_state.update_threshold(threshold) diff --git a/apps/data-collection/backend/src/core/handlers/base_embedding_handler.py b/apps/data-collection/backend/src/core/handlers/base_embedding_handler.py index f3d431984..e7820e31e 100644 --- a/apps/data-collection/backend/src/core/handlers/base_embedding_handler.py +++ b/apps/data-collection/backend/src/core/handlers/base_embedding_handler.py @@ -1,7 +1,9 @@ # app/modules/handlers/base_embedding_handler.py from __future__ import annotations -from typing import Any, List +from typing import List import numpy as np + +from core.encoders.base_encoder import BaseEncoder from core.label_manager import LabelManager @@ -11,7 +13,7 @@ class BasePromptHandler: Provides shared utilities for feature extraction and label management. """ - def __init__(self, encoder: Any, label_manager: LabelManager): + def __init__(self, encoder: BaseEncoder, label_manager: LabelManager): self.encoder = encoder self.label_manager = label_manager diff --git a/apps/data-collection/backend/src/core/handlers/image_upload_handler.py b/apps/data-collection/backend/src/core/handlers/image_prompt_handler.py similarity index 100% rename from apps/data-collection/backend/src/core/handlers/image_upload_handler.py rename to apps/data-collection/backend/src/core/handlers/image_prompt_handler.py diff --git a/apps/data-collection/backend/src/core/handlers/class_update_handler.py b/apps/data-collection/backend/src/core/handlers/text_prompt_handler.py similarity index 100% rename from apps/data-collection/backend/src/core/handlers/class_update_handler.py rename to apps/data-collection/backend/src/core/handlers/text_prompt_handler.py diff --git a/apps/data-collection/backend/src/core/snapping/conditions/base_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/base_condition.py index b94687e25..19ebea064 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions/base_condition.py +++ b/apps/data-collection/backend/src/core/snapping/conditions/base_condition.py @@ -42,7 +42,7 @@ def make_extras(self, *args, **kwargs) -> Dict[str, str]: """Return optional metadata attached to the snap.""" pass - def apply_config(self, conf: Dict[str, Any]) -> None: + def apply_config(self, conf: Dict[str, Any]): if "enabled" in conf: self.enabled = bool(conf["enabled"]) if not self.enabled: @@ -59,7 +59,7 @@ def export_config(self) -> Dict[str, Any]: def get_key(self) -> ConditionKey: return self.__key - def reset_cooldown(self) -> None: + def reset_cooldown(self): """Reset internal cooldown tracking.""" self._last_trigger_time = None @@ -70,7 +70,7 @@ def _cooldown_passed(self) -> bool: return True return (now - self._last_trigger_time) >= self.cooldown - def mark_triggered(self) -> None: + def mark_triggered(self): """Record that this condition has just fired.""" now = time() self._last_trigger_time = now diff --git a/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py index 4365537b4..7d95a8503 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py +++ b/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py @@ -14,7 +14,7 @@ def __init__(self, name: str, default_cooldown: float, tags: list[str]): self.threshold: float = 0.3 self.last_lowest: float = 0.0 - def apply_config(self, conf: Dict[str, Any]) -> None: + def apply_config(self, conf: Dict[str, Any]): super().apply_config(conf) if "threshold" in conf: val = float(conf["threshold"]) diff --git a/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py index 55be89713..e97f72da8 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py +++ b/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py @@ -17,7 +17,7 @@ def __init__(self, name: str, default_cooldown: float, tags: list[str]): self.margin: float = 0.2 self.prev_tracked: dict[int, bool] = {} - def apply_config(self, conf: Dict[str, Any]) -> None: + def apply_config(self, conf: Dict[str, Any]): super().apply_config(conf) if "margin" in conf: val = float(conf["margin"]) diff --git a/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/tracklet_analyzer.py b/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/tracklet_analyzer.py index b72dddb86..86052e451 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/tracklet_analyzer.py +++ b/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/tracklet_analyzer.py @@ -84,7 +84,7 @@ def was_tracked(self, prev_state: Dict[int, bool]) -> bool: tid = self.id return tid is not None and prev_state.get(tid, False) - def update_state(self, state: Dict[int, bool]) -> None: + def update_state(self, state: Dict[int, bool]): tid = self.id if tid is not None: state[tid] = self.is_tracked diff --git a/apps/data-collection/backend/src/infrastructure/conditions_factory.py b/apps/data-collection/backend/src/infrastructure/conditions_factory.py index 10e7a09e4..b3f96fe4b 100644 --- a/apps/data-collection/backend/src/infrastructure/conditions_factory.py +++ b/apps/data-collection/backend/src/infrastructure/conditions_factory.py @@ -6,13 +6,14 @@ class ConditionsFactory: def __init__(self, conditions_yaml: YamlLoader): - self.conditions_yaml = conditions_yaml + self._conditions_yaml = conditions_yaml + self._engine: ConditionsEngine = self._build_engine() def _build_engine(self) -> ConditionsEngine: engine = ConditionsEngine() - for entry in self.conditions_yaml.conditions: + for entry in self._conditions_yaml.conditions: if not entry.path: continue @@ -22,7 +23,7 @@ def _build_engine(self) -> ConditionsEngine: cls = getattr(module, class_name) cond: Condition = cls( name=entry.name, - default_cooldown=self.conditions_yaml.cooldown, + default_cooldown=self._conditions_yaml.cooldown, tags=entry.tags, ) engine.register(cond) diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/handlers_manager.py b/apps/data-collection/backend/src/infrastructure/neural_network/handlers_manager.py index 6a1602240..e057c11f6 100644 --- a/apps/data-collection/backend/src/infrastructure/neural_network/handlers_manager.py +++ b/apps/data-collection/backend/src/infrastructure/neural_network/handlers_manager.py @@ -1,6 +1,6 @@ from core.label_manager import LabelManager -from core.handlers.class_update_handler import TextPromptHandler -from core.handlers.image_upload_handler import ImagePromptHandler +from core.handlers.text_prompt_handler import TextPromptHandler +from core.handlers.image_prompt_handler import ImagePromptHandler from core.handlers.bbox_prompt_handler import BBoxPromptHandler from infrastructure.neural_network.encoders_manager import EncodersManager diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py b/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py index ef7daa9ea..eb8b5a038 100644 --- a/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py +++ b/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py @@ -59,7 +59,7 @@ def build(self) -> "NeuralNetworkManager": ) service_manager = NNServiceManager( - self._controller, handlers, self._video_source + self._controller, handlers, self._video_source.get_frame_cache() ) self._register_services(service_manager.services) diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/nn_service_manager.py b/apps/data-collection/backend/src/infrastructure/neural_network/nn_service_manager.py index df21b4ce7..ccfc7e9c8 100644 --- a/apps/data-collection/backend/src/infrastructure/neural_network/nn_service_manager.py +++ b/apps/data-collection/backend/src/infrastructure/neural_network/nn_service_manager.py @@ -1,6 +1,6 @@ from core.controllers.nn_controller import YOLONNController +from infrastructure.frame_cache_node import FrameCacheNode from infrastructure.neural_network.handlers_manager import HandlersManager -from infrastructure.video_source_manager import VideoSourceManager from services.class_update.class_update_service import ClassUpdateService from services.threshold_update.threshold_update_service import ThresholdUpdateService from services.image_upload.image_upload_service import ImageUploadService @@ -12,11 +12,11 @@ def __init__( self, controller: YOLONNController, handlers: HandlersManager, - video_source: VideoSourceManager, + frame_cache: FrameCacheNode, ): self.controller = controller self.handlers = handlers - self.video_source = video_source + self.frame_cache = frame_cache self.services = self._build_services() def _build_services(self): @@ -26,7 +26,7 @@ def _build_services(self): ImageUploadService(self.controller, self.handlers.image_update_handler), BBoxPromptService( self.handlers.bbox_prompt_handler, - self.video_source.frame_cache, + self.frame_cache, self.controller, ), ] diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/pipeline_builder.py b/apps/data-collection/backend/src/infrastructure/neural_network/pipeline_builder.py index 1fc4e65a3..6c86e653c 100644 --- a/apps/data-collection/backend/src/infrastructure/neural_network/pipeline_builder.py +++ b/apps/data-collection/backend/src/infrastructure/neural_network/pipeline_builder.py @@ -52,14 +52,14 @@ def _build_nn(self): ) nn.setNumInferenceThreads(self._config.nn.inference_threads) nn.getParser(0).setConfidenceThreshold(0.1) - self._video_source.input_node.link(nn.inputs["images"]) + self._video_source.get_input_node().link(nn.inputs["images"]) self._nn = nn return nn def _build_filters(self, nn) -> ImgDetectionsBridge: self._det_filter = self._pipeline.create(ImgDetectionsFilter).build(nn.out) self._annotation_node = self._pipeline.create(AnnotationNode).build( - self._det_filter.out, self._video_source.video_src_out + self._det_filter.out, self._video_source.get_video_source_output() ) return self._pipeline.create(ImgDetectionsBridge).build(self._det_filter.out) @@ -84,8 +84,8 @@ def _build_tracker(self) -> dai.node.ObjectTracker: self._config.tracker.occlusion_ratio_threshold ) tracker.setTrackerThreshold(self._config.tracker.tracker_threshold) - self._video_source.input_node.link(tracker.inputTrackerFrame) - self._video_source.input_node.link(tracker.inputDetectionFrame) + self._video_source.get_input_node().link(tracker.inputTrackerFrame) + self._video_source.get_input_node().link(tracker.inputDetectionFrame) self._filtered_bridge.out.link(tracker.inputDetections) return tracker diff --git a/apps/data-collection/backend/src/infrastructure/snaps/snaps_producer_factory.py b/apps/data-collection/backend/src/infrastructure/snaps/snaps_producer_factory.py index b576631ab..114f4bfb1 100644 --- a/apps/data-collection/backend/src/infrastructure/snaps/snaps_producer_factory.py +++ b/apps/data-collection/backend/src/infrastructure/snaps/snaps_producer_factory.py @@ -1,21 +1,25 @@ from functools import partial from depthai_nodes.node import SnapsProducer2Buffered, SnapsProducer + +from core.snapping.conditions_engine import ConditionsEngine from core.snapping.custom_snap_process import process_snaps import depthai as dai from depthai_nodes.node import ImgDetectionsBridge +from infrastructure.video_source_manager import VideoSourceManager + class SnapsProducerFactory: @staticmethod def create( - pipeline, - video_source, + pipeline: dai.Pipeline, + video_source: VideoSourceManager, tracker: dai.node.ObjectTracker, detections: ImgDetectionsBridge, - engine, + engine: ConditionsEngine, ) -> SnapsProducer: return pipeline.create(SnapsProducer2Buffered).build( - frame=video_source.video_src_out, + frame=video_source.get_video_source_output(), msg=tracker.out, msg2=detections.out, running=False, diff --git a/apps/data-collection/backend/src/infrastructure/video_source_manager.py b/apps/data-collection/backend/src/infrastructure/video_source_manager.py index 628e7d45b..c85596b0f 100644 --- a/apps/data-collection/backend/src/infrastructure/video_source_manager.py +++ b/apps/data-collection/backend/src/infrastructure/video_source_manager.py @@ -20,10 +20,10 @@ def __init__( self._config = config self._runtime = runtime - self.video_src_out: dai.Node.Output = None - self.input_node: dai.Node.Output = None - self.video_enc: dai.node.VideoEncoder = None - self.frame_cache: FrameCacheNode = None + self._video_src_out: dai.Node.Output = None + self._input_node: dai.Node.Output = None + self._video_enc: dai.node.VideoEncoder = None + self._frame_cache: FrameCacheNode = None self._build() @@ -34,8 +34,8 @@ def _build(self): else: self._create_camera_source() self._create_encoder() - self.frame_cache = self._pipeline.create(FrameCacheNode).build( - self.video_src_out + self._frame_cache = self._pipeline.create(FrameCacheNode).build( + self._video_src_out ) self._add_video_topic() @@ -53,14 +53,14 @@ def _create_replay_source(self): if self._runtime.fps_limit: replay.setFps(self._runtime.fps_limit) replay.setSize(self._config.constants.visualization_resolution) - self.video_src_out = replay.out + self._video_src_out = replay.out manip = self._pipeline.create(dai.node.ImageManip) manip.setMaxOutputFrameSize(model_w * model_h * 3) manip.initialConfig.setOutputSize(model_w, model_h) manip.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888i) - self.video_src_out.link(manip.inputImage) - self.input_node = manip.out + self._video_src_out.link(manip.inputImage) + self._input_node = manip.out def _create_camera_source(self): """Live camera source.""" @@ -72,12 +72,12 @@ def _create_camera_source(self): cam = self._pipeline.create(dai.node.Camera).build( boardSocket=dai.CameraBoardSocket.CAM_A ) - self.video_src_out = cam.requestOutput( + self._video_src_out = cam.requestOutput( size=self._config.constants.visualization_resolution, type=dai.ImgFrame.Type.NV12, fps=self._runtime.fps_limit, ) - self.input_node = cam.requestOutput( + self._input_node = cam.requestOutput( size=(model_w, model_h), type=dai.ImgFrame.Type.BGR888i, fps=self._runtime.fps_limit, @@ -85,13 +85,25 @@ def _create_camera_source(self): def _create_encoder(self): """Attach encoder to video source.""" - self.video_enc = self._pipeline.create(dai.node.VideoEncoder) - self.video_enc.setDefaultProfilePreset( + self._video_enc = self._pipeline.create(dai.node.VideoEncoder) + self._video_enc.setDefaultProfilePreset( fps=self._runtime.fps_limit, profile=dai.VideoEncoderProperties.Profile.H264_MAIN, ) - self.video_src_out.link(self.video_enc.input) + self._video_src_out.link(self._video_enc.input) def _add_video_topic(self): """Register video stream with visualizer.""" - self._config.visualizer.addTopic("Video", self.video_enc.out) + self._config.visualizer.addTopic("Video", self._video_enc.out) + + def get_input_node(self) -> dai.Node.Output: + """Get the input node for model processing.""" + return self._input_node + + def get_frame_cache(self) -> FrameCacheNode: + """Get the frame cache node.""" + return self._frame_cache + + def get_video_source_output(self) -> dai.Node.Output: + """Get the video source output node.""" + return self._video_src_out diff --git a/apps/data-collection/backend/src/services/base_service.py b/apps/data-collection/backend/src/services/base_service.py index 897995160..7f65820ca 100644 --- a/apps/data-collection/backend/src/services/base_service.py +++ b/apps/data-collection/backend/src/services/base_service.py @@ -1,5 +1,5 @@ from abc import ABC, abstractmethod -from typing import Generic, TypeVar, Dict +from typing import Generic, TypeVar from services.service_name import ServiceName @@ -18,7 +18,7 @@ def __init__(self): self.__name = self.NAME @abstractmethod - def handle(self, payload: PayloadT) -> Dict: + def handle(self, payload: PayloadT) -> dict[str, any]: """Execute service logic and return a JSON-serializable response.""" pass diff --git a/apps/data-collection/backend/src/services/bbox_prompt/bbox_prompt_service.py b/apps/data-collection/backend/src/services/bbox_prompt/bbox_prompt_service.py index 96c88d1cd..e50708ea4 100644 --- a/apps/data-collection/backend/src/services/bbox_prompt/bbox_prompt_service.py +++ b/apps/data-collection/backend/src/services/bbox_prompt/bbox_prompt_service.py @@ -20,7 +20,7 @@ def __init__( self.frame_cache = frame_cache self.repository = repository - def handle(self, payload: BBoxPromptPayload): + def handle(self, payload: BBoxPromptPayload) -> dict[str, any]: image = self.frame_cache.get_last_frame() if image is None: return {"ok": False, "reason": "no_frame_available"} @@ -30,7 +30,7 @@ def handle(self, payload: BBoxPromptPayload): image, payload["bbox"] ) except ValueError as e: - return {"ok": False, "reason": "invalid_bbox"} + return {"ok": False, "reason": f"{e}"} self.repository.send_embeddings_pair(embeddings, dummy, class_names) diff --git a/apps/data-collection/backend/src/services/class_update/class_update_service.py b/apps/data-collection/backend/src/services/class_update/class_update_service.py index a30b3f570..06c171eaf 100644 --- a/apps/data-collection/backend/src/services/class_update/class_update_service.py +++ b/apps/data-collection/backend/src/services/class_update/class_update_service.py @@ -1,6 +1,6 @@ from services.base_service import BaseService from services.class_update.class_update_payload import ClassUpdatePayload -from core.handlers.class_update_handler import TextPromptHandler +from core.handlers.text_prompt_handler import TextPromptHandler from core.controllers.nn_controller import YOLONNController from services.service_name import ServiceName @@ -19,7 +19,7 @@ def __init__( self.repository = repository self.handler = handler - def handle(self, payload: ClassUpdatePayload): + def handle(self, payload: ClassUpdatePayload) -> dict[str, any]: new_classes = payload["classes"] if not new_classes: return {"ok": False, "reason": "empty_class_list"} diff --git a/apps/data-collection/backend/src/services/export_service/get_config_service.py b/apps/data-collection/backend/src/services/export_service/get_config_service.py index 43cff3203..8dd0d5e33 100644 --- a/apps/data-collection/backend/src/services/export_service/get_config_service.py +++ b/apps/data-collection/backend/src/services/export_service/get_config_service.py @@ -12,7 +12,7 @@ def __init__(self, config_exporter: SystemStateExporter): super().__init__() self.config_exporter = config_exporter - def handle(self, payload: None = None) -> dict: + def handle(self, payload: None = None) -> dict[str, any]: try: config = self.config_exporter.export_config() print("[ExportService] returning:", config) diff --git a/apps/data-collection/backend/src/services/image_upload/image_upload_service.py b/apps/data-collection/backend/src/services/image_upload/image_upload_service.py index 7ac0c3351..5f3d73094 100644 --- a/apps/data-collection/backend/src/services/image_upload/image_upload_service.py +++ b/apps/data-collection/backend/src/services/image_upload/image_upload_service.py @@ -1,6 +1,6 @@ from services.base_service import BaseService from services.image_upload.image_upload_payload import ImageUploadPayload -from core.handlers.image_upload_handler import ImagePromptHandler +from core.handlers.image_prompt_handler import ImagePromptHandler from core.controllers.nn_controller import YOLONNController from services.service_name import ServiceName @@ -15,7 +15,7 @@ def __init__(self, repository: YOLONNController, handler: ImagePromptHandler): self.repository = repository self.handler = handler - def handle(self, payload: ImageUploadPayload): + def handle(self, payload: ImageUploadPayload) -> dict[str, any]: image_inputs, class_names, dummy = self.handler.process(payload) self.repository.send_embeddings_pair(image_inputs, dummy, class_names) diff --git a/apps/data-collection/backend/src/services/snap_collection/snap_collection_service.py b/apps/data-collection/backend/src/services/snap_collection/snap_collection_service.py index fa4cd748a..683e1cf21 100644 --- a/apps/data-collection/backend/src/services/snap_collection/snap_collection_service.py +++ b/apps/data-collection/backend/src/services/snap_collection/snap_collection_service.py @@ -21,7 +21,7 @@ def __init__( self.engine = engine self.snaps_producer = snaps_producer - def handle(self, payload: SnapPayload) -> dict: + def handle(self, payload: SnapPayload) -> dict[str, any]: self.engine.import_conditions_config(payload) any_active = self.engine.any_active() diff --git a/apps/data-collection/backend/src/services/threshold_update/threshold_update_service.py b/apps/data-collection/backend/src/services/threshold_update/threshold_update_service.py index 47c1da820..37335a5e5 100644 --- a/apps/data-collection/backend/src/services/threshold_update/threshold_update_service.py +++ b/apps/data-collection/backend/src/services/threshold_update/threshold_update_service.py @@ -16,7 +16,7 @@ def __init__( super().__init__() self.repository = repository - def handle(self, payload: ThresholdUpdatePayload): + def handle(self, payload: ThresholdUpdatePayload) -> dict[str, any]: new_threshold = payload["threshold"] clamped = max(0.0, min(1.0, new_threshold)) From 09ae1613801f0a4f126418b3c50724b6ea746374 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Tue, 4 Nov 2025 02:26:29 +0100 Subject: [PATCH 24/39] Fixed non typed argument in claasses --- .../neural_network/encoders_manager.py | 12 ++++++++---- .../neural_network/handlers_manager.py | 9 ++++++++- .../neural_network/neural_network_manager.py | 3 ++- .../neural_network/pipeline_builder.py | 7 +++++-- 4 files changed, 23 insertions(+), 8 deletions(-) diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/encoders_manager.py b/apps/data-collection/backend/src/infrastructure/neural_network/encoders_manager.py index 4b8df25c4..9d5e8a6d6 100644 --- a/apps/data-collection/backend/src/infrastructure/neural_network/encoders_manager.py +++ b/apps/data-collection/backend/src/infrastructure/neural_network/encoders_manager.py @@ -1,3 +1,7 @@ +import numpy as np + +from config.config_data_classes import RuntimeConfig +from config.system_configuration import SystemConfiguration from core.encoders.textual_encoder import TextualEncoder from core.encoders.visual_encoder import VisualEncoder @@ -7,7 +11,7 @@ class EncodersManager: Central manager for initializing and caching encoder components. """ - def __init__(self, config, runtime): + def __init__(self, config: SystemConfiguration, runtime: RuntimeConfig): self._config = config self._runtime = runtime @@ -16,7 +20,7 @@ def __init__(self, config, runtime): self.text_prompt, self.image_prompt = self._prepare_initial_prompts() - def _init_textual_encoder(self): + def _init_textual_encoder(self) -> TextualEncoder: model_cfg = self._config.model constants = self._config.constants @@ -26,7 +30,7 @@ def _init_textual_encoder(self): max_classes=constants.max_num_classes, ) - def _init_visual_encoder(self): + def _init_visual_encoder(self) -> VisualEncoder: model_cfg = self._config.model constants = self._config.constants @@ -36,7 +40,7 @@ def _init_visual_encoder(self): max_classes=constants.max_num_classes, ) - def _prepare_initial_prompts(self): + def _prepare_initial_prompts(self) -> tuple[np.ndarray, np.ndarray]: text_prompt = self.textual_encoder.extract_embeddings( self._config.constants.class_names ) diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/handlers_manager.py b/apps/data-collection/backend/src/infrastructure/neural_network/handlers_manager.py index e057c11f6..c850aeafe 100644 --- a/apps/data-collection/backend/src/infrastructure/neural_network/handlers_manager.py +++ b/apps/data-collection/backend/src/infrastructure/neural_network/handlers_manager.py @@ -2,11 +2,18 @@ from core.handlers.text_prompt_handler import TextPromptHandler from core.handlers.image_prompt_handler import ImagePromptHandler from core.handlers.bbox_prompt_handler import BBoxPromptHandler +from infrastructure.neural_network.annotation_node import AnnotationNode from infrastructure.neural_network.encoders_manager import EncodersManager +from depthai_nodes.node import ImgDetectionsFilter class HandlersManager: - def __init__(self, encoders: EncodersManager, det_filter, annotation_node): + def __init__( + self, + encoders: EncodersManager, + det_filter: ImgDetectionsFilter, + annotation_node: AnnotationNode, + ): self.label_manager = LabelManager(det_filter, annotation_node) self.encoders = encoders self.class_update_handler = self._class_update_handler() diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py b/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py index eb8b5a038..916259641 100644 --- a/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py +++ b/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py @@ -3,6 +3,7 @@ from depthai_nodes.node import ImgDetectionsBridge +from config.config_data_classes import RuntimeConfig from config.system_configuration import SystemConfiguration from core.controllers.nn_controller import YOLONNController from core.model_state import ModelState @@ -23,7 +24,7 @@ def __init__( self, pipeline: dai.Pipeline, video_source: VideoSourceManager, - runtime, + runtime: RuntimeConfig, config: SystemConfiguration, model_state: ModelState, ): diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/pipeline_builder.py b/apps/data-collection/backend/src/infrastructure/neural_network/pipeline_builder.py index 6c86e653c..61b1f2846 100644 --- a/apps/data-collection/backend/src/infrastructure/neural_network/pipeline_builder.py +++ b/apps/data-collection/backend/src/infrastructure/neural_network/pipeline_builder.py @@ -4,6 +4,9 @@ ImgDetectionsFilter, ImgDetectionsBridge, ) + +from config.config_data_classes import RuntimeConfig +from config.system_configuration import SystemConfiguration from infrastructure.neural_network.annotation_node import AnnotationNode from core.controllers.nn_controller import YOLONNController from core.model_state import ModelState @@ -15,8 +18,8 @@ def __init__( self, pipeline: dai.Pipeline, video_source: VideoSourceManager, - runtime, - config, + runtime: RuntimeConfig, + config: SystemConfiguration, model_state: ModelState, ): self._pipeline = pipeline From 54ea10022e187e58bac6c7917c9395650d856c57 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Tue, 4 Nov 2025 10:40:13 +0100 Subject: [PATCH 25/39] fixed several naming issues --- .../{base_embedding_handler.py => base_prompt_handler.py} | 0 .../backend/src/core/handlers/bbox_prompt_handler.py | 2 +- .../backend/src/core/handlers/image_prompt_handler.py | 2 +- .../backend/src/core/handlers/text_prompt_handler.py | 2 +- .../backend/src/infrastructure/export/export_manager.py | 2 +- .../infrastructure/neural_network/neural_network_manager.py | 6 +++--- .../{pipeline_builder.py => nn_pipeline_setup.py} | 0 .../{nn_service_manager.py => nn_service_factory.py} | 2 +- .../src/infrastructure/{ => snaps}/conditions_factory.py | 0 .../backend/src/infrastructure/snaps/snaps_manager.py | 2 +- .../{get_config_service.py => export_service.py} | 0 11 files changed, 9 insertions(+), 9 deletions(-) rename apps/data-collection/backend/src/core/handlers/{base_embedding_handler.py => base_prompt_handler.py} (100%) rename apps/data-collection/backend/src/infrastructure/neural_network/{pipeline_builder.py => nn_pipeline_setup.py} (100%) rename apps/data-collection/backend/src/infrastructure/neural_network/{nn_service_manager.py => nn_service_factory.py} (98%) rename apps/data-collection/backend/src/infrastructure/{ => snaps}/conditions_factory.py (100%) rename apps/data-collection/backend/src/services/export_service/{get_config_service.py => export_service.py} (100%) diff --git a/apps/data-collection/backend/src/core/handlers/base_embedding_handler.py b/apps/data-collection/backend/src/core/handlers/base_prompt_handler.py similarity index 100% rename from apps/data-collection/backend/src/core/handlers/base_embedding_handler.py rename to apps/data-collection/backend/src/core/handlers/base_prompt_handler.py diff --git a/apps/data-collection/backend/src/core/handlers/bbox_prompt_handler.py b/apps/data-collection/backend/src/core/handlers/bbox_prompt_handler.py index 1d05dfc2f..36decbc2c 100644 --- a/apps/data-collection/backend/src/core/handlers/bbox_prompt_handler.py +++ b/apps/data-collection/backend/src/core/handlers/bbox_prompt_handler.py @@ -1,6 +1,6 @@ import numpy as np from typing import Tuple, Dict, List -from core.handlers.base_embedding_handler import BasePromptHandler +from core.handlers.base_prompt_handler import BasePromptHandler class BBoxPromptHandler(BasePromptHandler): diff --git a/apps/data-collection/backend/src/core/handlers/image_prompt_handler.py b/apps/data-collection/backend/src/core/handlers/image_prompt_handler.py index 39b1fd327..0c2a9b41c 100644 --- a/apps/data-collection/backend/src/core/handlers/image_prompt_handler.py +++ b/apps/data-collection/backend/src/core/handlers/image_prompt_handler.py @@ -2,7 +2,7 @@ import cv2 import numpy as np from typing import Tuple, List, Dict -from core.handlers.base_embedding_handler import BasePromptHandler +from core.handlers.base_prompt_handler import BasePromptHandler def decode_base64_image(base64_data_uri: str) -> np.ndarray: diff --git a/apps/data-collection/backend/src/core/handlers/text_prompt_handler.py b/apps/data-collection/backend/src/core/handlers/text_prompt_handler.py index b3be493e8..f1d093743 100644 --- a/apps/data-collection/backend/src/core/handlers/text_prompt_handler.py +++ b/apps/data-collection/backend/src/core/handlers/text_prompt_handler.py @@ -1,7 +1,7 @@ from __future__ import annotations from typing import Tuple, List import numpy as np -from core.handlers.base_embedding_handler import BasePromptHandler +from core.handlers.base_prompt_handler import BasePromptHandler class TextPromptHandler(BasePromptHandler): diff --git a/apps/data-collection/backend/src/infrastructure/export/export_manager.py b/apps/data-collection/backend/src/infrastructure/export/export_manager.py index 86f81eb78..76974ab08 100644 --- a/apps/data-collection/backend/src/infrastructure/export/export_manager.py +++ b/apps/data-collection/backend/src/infrastructure/export/export_manager.py @@ -2,7 +2,7 @@ from core.model_state import ModelState from core.snapping.conditions_engine import ConditionsEngine from config.system_state_exporter import SystemStateExporter -from services.export_service.get_config_service import ExportService +from services.export_service.export_service import ExportService class ExportManager: diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py b/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py index 916259641..00824036a 100644 --- a/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py +++ b/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py @@ -8,9 +8,9 @@ from core.controllers.nn_controller import YOLONNController from core.model_state import ModelState from infrastructure.neural_network.annotation_node import AnnotationNode -from infrastructure.neural_network.nn_service_manager import NNServiceManager +from infrastructure.neural_network.nn_service_factory import NNServiceFactory from infrastructure.video_source_manager import VideoSourceManager -from infrastructure.neural_network.pipeline_builder import NNPipelineSetup +from infrastructure.neural_network.nn_pipeline_setup import NNPipelineSetup from infrastructure.neural_network.encoders_manager import EncodersManager from infrastructure.neural_network.handlers_manager import HandlersManager @@ -59,7 +59,7 @@ def build(self) -> "NeuralNetworkManager": self._config.constants.class_names, self._config.constants.class_offset ) - service_manager = NNServiceManager( + service_manager = NNServiceFactory( self._controller, handlers, self._video_source.get_frame_cache() ) diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/pipeline_builder.py b/apps/data-collection/backend/src/infrastructure/neural_network/nn_pipeline_setup.py similarity index 100% rename from apps/data-collection/backend/src/infrastructure/neural_network/pipeline_builder.py rename to apps/data-collection/backend/src/infrastructure/neural_network/nn_pipeline_setup.py diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/nn_service_manager.py b/apps/data-collection/backend/src/infrastructure/neural_network/nn_service_factory.py similarity index 98% rename from apps/data-collection/backend/src/infrastructure/neural_network/nn_service_manager.py rename to apps/data-collection/backend/src/infrastructure/neural_network/nn_service_factory.py index ccfc7e9c8..c30222545 100644 --- a/apps/data-collection/backend/src/infrastructure/neural_network/nn_service_manager.py +++ b/apps/data-collection/backend/src/infrastructure/neural_network/nn_service_factory.py @@ -7,7 +7,7 @@ from services.bbox_prompt.bbox_prompt_service import BBoxPromptService -class NNServiceManager: +class NNServiceFactory: def __init__( self, controller: YOLONNController, diff --git a/apps/data-collection/backend/src/infrastructure/conditions_factory.py b/apps/data-collection/backend/src/infrastructure/snaps/conditions_factory.py similarity index 100% rename from apps/data-collection/backend/src/infrastructure/conditions_factory.py rename to apps/data-collection/backend/src/infrastructure/snaps/conditions_factory.py diff --git a/apps/data-collection/backend/src/infrastructure/snaps/snaps_manager.py b/apps/data-collection/backend/src/infrastructure/snaps/snaps_manager.py index 218410ce9..e5bc9d3a4 100644 --- a/apps/data-collection/backend/src/infrastructure/snaps/snaps_manager.py +++ b/apps/data-collection/backend/src/infrastructure/snaps/snaps_manager.py @@ -3,7 +3,7 @@ from core.snapping.conditions_engine import ConditionsEngine from infrastructure.neural_network.neural_network_manager import NeuralNetworkManager from services.snap_collection.snap_collection_service import SnapCollectionService -from infrastructure.conditions_factory import ConditionsFactory +from infrastructure.snaps.conditions_factory import ConditionsFactory from infrastructure.snaps.snaps_producer_factory import SnapsProducerFactory from infrastructure.video_source_manager import VideoSourceManager from depthai_nodes.node import SnapsProducer diff --git a/apps/data-collection/backend/src/services/export_service/get_config_service.py b/apps/data-collection/backend/src/services/export_service/export_service.py similarity index 100% rename from apps/data-collection/backend/src/services/export_service/get_config_service.py rename to apps/data-collection/backend/src/services/export_service/export_service.py From 91c6d5db22d60352ab9860dfc1cd44cf1380f602 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Tue, 4 Nov 2025 10:48:41 +0100 Subject: [PATCH 26/39] changed export manager tratment in main --- .../backend/src/infrastructure/export/export_manager.py | 3 +-- apps/data-collection/backend/src/main.py | 3 ++- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/apps/data-collection/backend/src/infrastructure/export/export_manager.py b/apps/data-collection/backend/src/infrastructure/export/export_manager.py index 76974ab08..d7fb4c800 100644 --- a/apps/data-collection/backend/src/infrastructure/export/export_manager.py +++ b/apps/data-collection/backend/src/infrastructure/export/export_manager.py @@ -26,9 +26,8 @@ def __init__( self._exporter: SystemStateExporter = None self._service: ExportService = None - self._build() - def _build(self): + def build(self): self._exporter = SystemStateExporter(self._model_state, self._condition_engine) export_service = ExportService(self._exporter) self._built = True diff --git a/apps/data-collection/backend/src/main.py b/apps/data-collection/backend/src/main.py index b4442845f..e2d0d3e1d 100644 --- a/apps/data-collection/backend/src/main.py +++ b/apps/data-collection/backend/src/main.py @@ -25,7 +25,8 @@ def main(): snaps_manager = SnapsManager(pipeline, video_source, nn_manager, config) - ExportManager(model_state, snaps_manager.get_engine(), config) + export_manager = ExportManager(model_state, snaps_manager.get_engine(), config) + export_manager.build() print("Pipeline created.") pipeline.start() From b203ab28d65e92f312af792d71bc495aa403fa9a Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Tue, 4 Nov 2025 11:59:20 +0100 Subject: [PATCH 27/39] cleaned up tracklet analyzer --- .../tracker_conditions/tracklet_analyzer.py | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/tracklet_analyzer.py b/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/tracklet_analyzer.py index 86052e451..eb4ac6317 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/tracklet_analyzer.py +++ b/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/tracklet_analyzer.py @@ -19,17 +19,6 @@ def id(self) -> Optional[int]: except Exception: return None - @property - def label_index(self) -> int: - try: - return int(getattr(self._t, "label", -1)) - except Exception: - return -1 - - def label_name(self, class_names: list[str]) -> str: - idx = self.label_index - return class_names[idx] if 0 <= idx < len(class_names) else "" - @property def is_tracked(self) -> bool: val = getattr(self._t, "status", None) From 6b9fa34a51cf8eb8a672f190355fd5787f0b59fd Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Tue, 4 Nov 2025 16:00:54 +0100 Subject: [PATCH 28/39] refactored configurator class --- .../backend/src/config/config_data_classes.py | 29 ++-- .../src/config/system_configuration.py | 126 ++++++++++++------ .../src/config/yaml_configs/constants.yaml | 6 - .../yaml_configs/detection_constants.yaml | 4 + .../src/config/yaml_configs/model.yaml | 26 ---- .../src/config/yaml_configs/nn_config.yaml | 30 ++++- .../config/yaml_configs/visual_constants.yaml | 2 + .../infrastructure/export/export_manager.py | 22 +-- .../neural_network/encoders_manager.py | 29 ++-- .../neural_network/neural_network_manager.py | 35 ++--- .../neural_network/nn_pipeline_setup.py | 33 +++-- .../neural_network/nn_service_factory.py | 3 +- .../src/infrastructure/snaps/snaps_manager.py | 22 ++- .../src/infrastructure/system_context.py | 29 ++++ .../infrastructure/video_source_manager.py | 55 ++++---- apps/data-collection/backend/src/main.py | 36 ++--- 16 files changed, 278 insertions(+), 209 deletions(-) delete mode 100644 apps/data-collection/backend/src/config/yaml_configs/constants.yaml create mode 100644 apps/data-collection/backend/src/config/yaml_configs/detection_constants.yaml delete mode 100644 apps/data-collection/backend/src/config/yaml_configs/model.yaml create mode 100644 apps/data-collection/backend/src/config/yaml_configs/visual_constants.yaml create mode 100644 apps/data-collection/backend/src/infrastructure/system_context.py diff --git a/apps/data-collection/backend/src/config/config_data_classes.py b/apps/data-collection/backend/src/config/config_data_classes.py index 3cb2d1f5c..338e0cca0 100644 --- a/apps/data-collection/backend/src/config/config_data_classes.py +++ b/apps/data-collection/backend/src/config/config_data_classes.py @@ -2,6 +2,8 @@ from dataclasses import dataclass import depthai as dai +from config.yaml_loader import YamlLoader + @dataclass class ModelInfo: @@ -12,15 +14,24 @@ class ModelInfo: height: int description: dai.NNModelDescription archive: dai.NNArchive + precision: str = "FP16" + + +@dataclass +class VideoConfig: + """Stores video configuration (resolution, FPS).""" + + resolution: list[int] + fps: int + media_path: str + width: int + height: int @dataclass -class RuntimeConfig: - """Stores runtime configuration (precision, FPS, platform, etc.).""" - - precision: str - fps_limit: int - platform: str - model_info: ModelInfo - ip: str - port: int +class NeuralNetworkConfig: + """Stores neural network configuration (confidence thresholds, etc.).""" + + nn_yaml: YamlLoader + constants: YamlLoader + model: ModelInfo diff --git a/apps/data-collection/backend/src/config/system_configuration.py b/apps/data-collection/backend/src/config/system_configuration.py index c784b47e5..265562b9b 100644 --- a/apps/data-collection/backend/src/config/system_configuration.py +++ b/apps/data-collection/backend/src/config/system_configuration.py @@ -1,67 +1,113 @@ -from config.yaml_loader import YamlLoader -from config.config_data_classes import ModelInfo, RuntimeConfig -from config.arguments import initialize_argparser -from dotenv import load_dotenv -from pathlib import Path +from __future__ import annotations import os +from pathlib import Path +from dotenv import load_dotenv import depthai as dai +from config.yaml_loader import YamlLoader +from config.config_data_classes import ( + ModelInfo, + VideoConfig, + NeuralNetworkConfig, +) +from config.arguments import initialize_argparser + class SystemConfiguration: - """Central system config. Loads each YAML file once and exposes it as an attribute.""" + """ + Loads and aggregates static configuration for all subsystems. + Handles: + • YAML loading + • CLI + dotenv parsing + • Model info resolution + • Provides ready-to-use config slices + """ - def __init__(self): + def __init__(self, platform: str): load_dotenv(override=True) - _, args = initialize_argparser() - self.args = args - - self.visualizer = dai.RemoteConnection(serveFrontend=False) - self.device = dai.Device() - self.platform = self.device.getPlatformAsString() + self._platform: str = platform + # Allow explicit args (for tests) or parse CLI when None + _, self.args = initialize_argparser() base = Path(__file__).parent / "yaml_configs" - self.constants = YamlLoader(base / "constants.yaml") - self.model = YamlLoader(base / "model.yaml") - self.nn_config = YamlLoader(base / "nn_config.yaml") - self.conditions = YamlLoader(base / "conditions.yaml") + self._nn_yaml = YamlLoader(base / "nn_config.yaml") + self._conditions_yaml = YamlLoader(base / "conditions.yaml") + self._detection_yaml = YamlLoader(base / "detection_constants.yaml") + self._video_yaml = YamlLoader(base / "visual_constants.yaml") + + self._model_info: ModelInfo | None = None + self._initialize_for_platform() - self._initialize() + def _initialize_for_platform(self) -> None: + """ + Perform initialization that depends on runtime platform. + """ - def _initialize(self): if self.args.api_key: os.environ["DEPTHAI_HUB_API_KEY"] = self.args.api_key - if self.platform != "RVC4": - raise ValueError("This example is supported only on RVC4 platform") + if self._platform != "RVC4": + raise ValueError( + f"This application currently supports only RVC4, got {self._platform}" + ) + # Use default FPS if not specified if self.args.fps_limit is None: - self.args.fps_limit = self.constants.default_fps - print(f"\nFPS limit set to {self.args.fps_limit} for {self.platform}\n") + self.args.fps_limit = self._video_yaml.default_fps + print(f"\nFPS limit set to {self.args.fps_limit} for {self._platform}\n") - if self.args.precision != self.model.default_precision: - raise SystemExit(f"{self.model.name} int8 YAML not available; use fp16.") + self._model_info = self._load_model_info() - self.model_info = self._load_model_info() - - def _load_model_info(self): + def _load_model_info(self) -> ModelInfo: models_dir = Path(__file__).parent.parent / "depthai_models" - yaml_file = f"{self.model.name}_v8_l_fp16.{self.platform}.yaml" + model_name = self._nn_yaml.model.name + yaml_file = f"{model_name}_v8_l_fp16.{self._platform}.yaml" yaml_path = models_dir / yaml_file + if not yaml_path.exists(): - raise SystemExit(f"Model YAML not found for {self.model.name}: {yaml_path}") + raise SystemExit(f"Model YAML not found for {model_name}: {yaml_path}") + + if self.args.precision != self._nn_yaml.model.default_precision: + raise SystemExit(f"{model_name} int8 YAML not available; use fp16.") desc = dai.NNModelDescription.fromYamlFile(str(yaml_path)) - desc.platform = self.platform + desc.platform = self._platform archive = dai.NNArchive(dai.getModelFromZoo(desc)) w, h = archive.getInputSize() - return ModelInfo(yaml_path, w, h, desc, archive) - - def build_runtime_config(self): - return RuntimeConfig( + return ModelInfo( + yaml_path=yaml_path, + width=w, + height=h, + description=desc, + archive=archive, precision=self.args.precision, - fps_limit=self.args.fps_limit, - platform=self.platform, - model_info=self.model_info, - ip=self.args.ip or "localhost", - port=self.args.port or 8080, ) + + def get_video_config(self) -> VideoConfig: + if not self._model_info: + raise RuntimeError( + "Model info not loaded yet — call initialize_for_platform() first." + ) + + return VideoConfig( + resolution=self._video_yaml.video_resolution, + fps=self.args.fps_limit, + media_path=self.args.media_path, + width=self._model_info.width, + height=self._model_info.height, + ) + + def get_neural_network_config(self) -> NeuralNetworkConfig: + if not self._model_info: + raise RuntimeError( + "Model info not loaded yet — call initialize_for_platform() first." + ) + + return NeuralNetworkConfig( + nn_yaml=self._nn_yaml, + constants=self._detection_yaml, + model=self._model_info, + ) + + def get_snaps_config(self) -> YamlLoader: + return self._conditions_yaml diff --git a/apps/data-collection/backend/src/config/yaml_configs/constants.yaml b/apps/data-collection/backend/src/config/yaml_configs/constants.yaml deleted file mode 100644 index d428e70be..000000000 --- a/apps/data-collection/backend/src/config/yaml_configs/constants.yaml +++ /dev/null @@ -1,6 +0,0 @@ -class_names: ["person", "chair", "TV"] -class_offset: 0 -max_num_classes: 80 -confidence_threshold: 0.1 -visualization_resolution: [1080, 1080] -default_fps: 30 \ No newline at end of file diff --git a/apps/data-collection/backend/src/config/yaml_configs/detection_constants.yaml b/apps/data-collection/backend/src/config/yaml_configs/detection_constants.yaml new file mode 100644 index 000000000..b74dfcf4e --- /dev/null +++ b/apps/data-collection/backend/src/config/yaml_configs/detection_constants.yaml @@ -0,0 +1,4 @@ +class_names: ["person", "chair", "TV"] +class_offset: 0 +max_num_classes: 80 +confidence_threshold: 0.1 \ No newline at end of file diff --git a/apps/data-collection/backend/src/config/yaml_configs/model.yaml b/apps/data-collection/backend/src/config/yaml_configs/model.yaml deleted file mode 100644 index 3cbfe1749..000000000 --- a/apps/data-collection/backend/src/config/yaml_configs/model.yaml +++ /dev/null @@ -1,26 +0,0 @@ -name: "yoloe" -default_precision: "fp16" -max_num_classes: 80 -confidence_threshold: 0.25 - -quant_values: - yoloe: - quant_zero_point: 174.0 - quant_scale: 0.003328413470 - yoloe-image: - quant_zero_point: 137.0 - quant_scale: 0.002327915514 - - -paths: - tokenizer: - url: "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json" - path: "tokenizer.json" - - text_encoder: - url: "https://huggingface.co/Xenova/mobileclip_blt/resolve/main/onnx/text_model.onnx" - path: "mobileclip_textual_hf.onnx" - - visual_encoder: - url: "https://huggingface.co/sokovninn/yoloe-v8l-seg-visual-encoder/resolve/main/yoloe-v8l-seg_visual_encoder.onnx" - path: "yoloe-v8l-seg_visual_encoder.onnx" diff --git a/apps/data-collection/backend/src/config/yaml_configs/nn_config.yaml b/apps/data-collection/backend/src/config/yaml_configs/nn_config.yaml index 45685e76a..f2d9ae76a 100644 --- a/apps/data-collection/backend/src/config/yaml_configs/nn_config.yaml +++ b/apps/data-collection/backend/src/config/yaml_configs/nn_config.yaml @@ -1,4 +1,4 @@ -nn: +nn_backend: type: "snpe" runtime: "dsp" performance_profile: "default" @@ -10,3 +10,31 @@ tracker: max_lifespan: 90 occlusion_ratio_threshold: 0.5 tracker_threshold: 0.25 + +model: + name: "yoloe" + default_precision: "fp16" + max_num_classes: 80 + confidence_threshold: 0.25 + + quant_values: + yoloe: + quant_zero_point: 174.0 + quant_scale: 0.003328413470 + yoloe-image: + quant_zero_point: 137.0 + quant_scale: 0.002327915514 + + + paths: + tokenizer: + url: "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json" + path: "tokenizer.json" + + text_encoder: + url: "https://huggingface.co/Xenova/mobileclip_blt/resolve/main/onnx/text_model.onnx" + path: "mobileclip_textual_hf.onnx" + + visual_encoder: + url: "https://huggingface.co/sokovninn/yoloe-v8l-seg-visual-encoder/resolve/main/yoloe-v8l-seg_visual_encoder.onnx" + path: "yoloe-v8l-seg_visual_encoder.onnx" diff --git a/apps/data-collection/backend/src/config/yaml_configs/visual_constants.yaml b/apps/data-collection/backend/src/config/yaml_configs/visual_constants.yaml new file mode 100644 index 000000000..7e07ad692 --- /dev/null +++ b/apps/data-collection/backend/src/config/yaml_configs/visual_constants.yaml @@ -0,0 +1,2 @@ +video_resolution: [1080, 1080] +default_fps: 30 \ No newline at end of file diff --git a/apps/data-collection/backend/src/infrastructure/export/export_manager.py b/apps/data-collection/backend/src/infrastructure/export/export_manager.py index d7fb4c800..5bc32f5ca 100644 --- a/apps/data-collection/backend/src/infrastructure/export/export_manager.py +++ b/apps/data-collection/backend/src/infrastructure/export/export_manager.py @@ -1,4 +1,3 @@ -from config.system_configuration import SystemConfiguration from core.model_state import ModelState from core.snapping.conditions_engine import ConditionsEngine from config.system_state_exporter import SystemStateExporter @@ -14,26 +13,17 @@ class ExportManager: • Create GetConfigService exposing export API """ - def __init__( - self, - model_state: ModelState, - condition_engine: ConditionsEngine, - config: SystemConfiguration, - ): + def __init__(self, model_state: ModelState, condition_engine: ConditionsEngine): self._model_state = model_state self._condition_engine = condition_engine - self._config = config self._exporter: SystemStateExporter = None self._service: ExportService = None + self._build() - def build(self): + def _build(self): self._exporter = SystemStateExporter(self._model_state, self._condition_engine) - export_service = ExportService(self._exporter) - self._built = True - self._register_service(export_service) + self._service = ExportService(self._exporter) - def _register_service(self, export_service: ExportService): - self._config.visualizer.registerService( - export_service.get_name(), export_service.handle - ) + def get_service(self) -> ExportService: + return self._service diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/encoders_manager.py b/apps/data-collection/backend/src/infrastructure/neural_network/encoders_manager.py index 9d5e8a6d6..745d4cc36 100644 --- a/apps/data-collection/backend/src/infrastructure/neural_network/encoders_manager.py +++ b/apps/data-collection/backend/src/infrastructure/neural_network/encoders_manager.py @@ -1,7 +1,6 @@ import numpy as np -from config.config_data_classes import RuntimeConfig -from config.system_configuration import SystemConfiguration +from config.yaml_loader import YamlLoader from core.encoders.textual_encoder import TextualEncoder from core.encoders.visual_encoder import VisualEncoder @@ -11,9 +10,9 @@ class EncodersManager: Central manager for initializing and caching encoder components. """ - def __init__(self, config: SystemConfiguration, runtime: RuntimeConfig): - self._config = config - self._runtime = runtime + def __init__(self, config: YamlLoader, constants: YamlLoader): + self._model_config = config + self._constants = constants self.textual_encoder = self._init_textual_encoder() self.visual_encoder = self._init_visual_encoder() @@ -21,28 +20,22 @@ def __init__(self, config: SystemConfiguration, runtime: RuntimeConfig): self.text_prompt, self.image_prompt = self._prepare_initial_prompts() def _init_textual_encoder(self) -> TextualEncoder: - model_cfg = self._config.model - constants = self._config.constants - return TextualEncoder( - config=model_cfg, - precision=self._runtime.precision, - max_classes=constants.max_num_classes, + config=self._model_config, + precision=self._model_config.default_precision, + max_classes=self._constants.max_num_classes, ) def _init_visual_encoder(self) -> VisualEncoder: - model_cfg = self._config.model - constants = self._config.constants - return VisualEncoder( - config=model_cfg, - precision=self._runtime.precision, - max_classes=constants.max_num_classes, + config=self._model_config, + precision=self._model_config.default_precision, + max_classes=self._constants.max_num_classes, ) def _prepare_initial_prompts(self) -> tuple[np.ndarray, np.ndarray]: text_prompt = self.textual_encoder.extract_embeddings( - self._config.constants.class_names + self._constants.class_names ) image_prompt = self.textual_encoder.make_dummy_features() return text_prompt, image_prompt diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py b/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py index 00824036a..98a0d4a36 100644 --- a/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py +++ b/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py @@ -3,8 +3,7 @@ from depthai_nodes.node import ImgDetectionsBridge -from config.config_data_classes import RuntimeConfig -from config.system_configuration import SystemConfiguration +from config.config_data_classes import NeuralNetworkConfig from core.controllers.nn_controller import YOLONNController from core.model_state import ModelState from infrastructure.neural_network.annotation_node import AnnotationNode @@ -13,6 +12,7 @@ from infrastructure.neural_network.nn_pipeline_setup import NNPipelineSetup from infrastructure.neural_network.encoders_manager import EncodersManager from infrastructure.neural_network.handlers_manager import HandlersManager +from services.base_service import BaseService class NeuralNetworkManager: @@ -24,31 +24,30 @@ def __init__( self, pipeline: dai.Pipeline, video_source: VideoSourceManager, - runtime: RuntimeConfig, - config: SystemConfiguration, + config: NeuralNetworkConfig, model_state: ModelState, ): self._pipeline = pipeline self._video_source = video_source - self._runtime = runtime self._config = config self._model_state = model_state self._tracker: dai.node.ObjectTracker = None self._filtered_detections: ImgDetectionsBridge = None self._controller: YOLONNController = None + self._annotations: AnnotationNode = None + self._services: List[BaseService] = [] self.build() def build(self) -> "NeuralNetworkManager": pipeline_builder = NNPipelineSetup( self._pipeline, self._video_source, - self._runtime, - self._config.nn_config, + self._config, self._model_state, ) self._controller = pipeline_builder.get_controller() - encoders = EncodersManager(self._config, self._runtime) + encoders = EncodersManager(self._config.nn_yaml.model, self._config.constants) handlers = HandlersManager( encoders, @@ -63,27 +62,29 @@ def build(self) -> "NeuralNetworkManager": self._controller, handlers, self._video_source.get_frame_cache() ) - self._register_services(service_manager.services) + self._services = service_manager.services + self._controller.send_embeddings_pair( encoders.image_prompt, encoders.text_prompt, self._config.constants.class_names, ) - self._register_annotations(pipeline_builder.get_annotation_node()) + + self._annotations = pipeline_builder.get_annotation_node() + self._tracker = pipeline_builder.get_tracker() self._filtered_detections = pipeline_builder.get_detections() return self - def _register_services(self, services: List): - for service in services: - self._config.visualizer.registerService(service.get_name(), service.handle) - - def _register_annotations(self, annotation_node: AnnotationNode): - self._config.visualizer.addTopic("Detections", annotation_node.out) - def get_tracker(self): return self._tracker def get_detections(self): return self._filtered_detections + + def get_services(self) -> list[BaseService]: + return self._services + + def get_annotations(self) -> dai.Node.Output: + return self._annotations.out diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/nn_pipeline_setup.py b/apps/data-collection/backend/src/infrastructure/neural_network/nn_pipeline_setup.py index 61b1f2846..6e4ef5684 100644 --- a/apps/data-collection/backend/src/infrastructure/neural_network/nn_pipeline_setup.py +++ b/apps/data-collection/backend/src/infrastructure/neural_network/nn_pipeline_setup.py @@ -5,8 +5,7 @@ ImgDetectionsBridge, ) -from config.config_data_classes import RuntimeConfig -from config.system_configuration import SystemConfiguration +from config.config_data_classes import NeuralNetworkConfig from infrastructure.neural_network.annotation_node import AnnotationNode from core.controllers.nn_controller import YOLONNController from core.model_state import ModelState @@ -18,14 +17,12 @@ def __init__( self, pipeline: dai.Pipeline, video_source: VideoSourceManager, - runtime: RuntimeConfig, - config: SystemConfiguration, + nn_config: NeuralNetworkConfig, model_state: ModelState, ): self._pipeline = pipeline self._video_source = video_source - self._runtime = runtime - self._config = config + self._nn_config = nn_config self._model_state = model_state self._det_filter = None @@ -45,15 +42,15 @@ def build(self): def _build_nn(self): nn = self._pipeline.create(ParsingNeuralNetwork) - nn.setNNArchive(self._runtime.model_info.archive) - nn.setBackend(self._config.nn.type) + nn.setNNArchive(self._nn_config.model.archive) + nn.setBackend(self._nn_config.nn_yaml.nn_backend.type) nn.setBackendProperties( { - "runtime": self._config.nn.runtime, - "performance_profile": self._config.nn.performance_profile, + "runtime": self._nn_config.nn_yaml.nn_backend.runtime, + "performance_profile": self._nn_config.nn_yaml.nn_backend.performance_profile, } ) - nn.setNumInferenceThreads(self._config.nn.inference_threads) + nn.setNumInferenceThreads(self._nn_config.nn_yaml.nn_backend.inference_threads) nn.getParser(0).setConfidenceThreshold(0.1) self._video_source.get_input_node().link(nn.inputs["images"]) self._nn = nn @@ -73,20 +70,22 @@ def _build_controller(self, nn) -> YOLONNController: nn.inputs["image_prompts"].setReusePreviousMessage(True) parser = nn.getParser(0) return YOLONNController( - img_q, text_q, self._runtime.precision, parser, self._model_state + img_q, text_q, self._nn_config.model.precision, parser, self._model_state ) def _build_tracker(self) -> dai.node.ObjectTracker: tracker = self._pipeline.create(dai.node.ObjectTracker) tracker.setTrackerType(dai.TrackerType.SHORT_TERM_IMAGELESS) tracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.UNIQUE_ID) - tracker.setTrackingPerClass(self._config.tracker.track_per_class) - tracker.setTrackletBirthThreshold(self._config.tracker.birth_threshold) - tracker.setTrackletMaxLifespan(self._config.tracker.max_lifespan) + tracker.setTrackingPerClass(self._nn_config.nn_yaml.tracker.track_per_class) + tracker.setTrackletBirthThreshold( + self._nn_config.nn_yaml.tracker.birth_threshold + ) + tracker.setTrackletMaxLifespan(self._nn_config.nn_yaml.tracker.max_lifespan) tracker.setOcclusionRatioThreshold( - self._config.tracker.occlusion_ratio_threshold + self._nn_config.nn_yaml.tracker.occlusion_ratio_threshold ) - tracker.setTrackerThreshold(self._config.tracker.tracker_threshold) + tracker.setTrackerThreshold(self._nn_config.nn_yaml.tracker.tracker_threshold) self._video_source.get_input_node().link(tracker.inputTrackerFrame) self._video_source.get_input_node().link(tracker.inputDetectionFrame) self._filtered_bridge.out.link(tracker.inputDetections) diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/nn_service_factory.py b/apps/data-collection/backend/src/infrastructure/neural_network/nn_service_factory.py index c30222545..0303d8d10 100644 --- a/apps/data-collection/backend/src/infrastructure/neural_network/nn_service_factory.py +++ b/apps/data-collection/backend/src/infrastructure/neural_network/nn_service_factory.py @@ -1,6 +1,7 @@ from core.controllers.nn_controller import YOLONNController from infrastructure.frame_cache_node import FrameCacheNode from infrastructure.neural_network.handlers_manager import HandlersManager +from services.base_service import BaseService from services.class_update.class_update_service import ClassUpdateService from services.threshold_update.threshold_update_service import ThresholdUpdateService from services.image_upload.image_upload_service import ImageUploadService @@ -19,7 +20,7 @@ def __init__( self.frame_cache = frame_cache self.services = self._build_services() - def _build_services(self): + def _build_services(self) -> list[BaseService]: return [ ClassUpdateService(self.controller, self.handlers.class_update_handler), ThresholdUpdateService(self.controller), diff --git a/apps/data-collection/backend/src/infrastructure/snaps/snaps_manager.py b/apps/data-collection/backend/src/infrastructure/snaps/snaps_manager.py index e5bc9d3a4..3effb7ab9 100644 --- a/apps/data-collection/backend/src/infrastructure/snaps/snaps_manager.py +++ b/apps/data-collection/backend/src/infrastructure/snaps/snaps_manager.py @@ -1,12 +1,13 @@ import depthai as dai -from config.system_configuration import SystemConfiguration +from depthai_nodes.node import SnapsProducer + +from config.yaml_loader import YamlLoader from core.snapping.conditions_engine import ConditionsEngine from infrastructure.neural_network.neural_network_manager import NeuralNetworkManager -from services.snap_collection.snap_collection_service import SnapCollectionService from infrastructure.snaps.conditions_factory import ConditionsFactory from infrastructure.snaps.snaps_producer_factory import SnapsProducerFactory from infrastructure.video_source_manager import VideoSourceManager -from depthai_nodes.node import SnapsProducer +from services.snap_collection.snap_collection_service import SnapCollectionService class SnapsManager: @@ -19,12 +20,12 @@ def __init__( pipeline: dai.Pipeline, video_source: VideoSourceManager, nn_manager: NeuralNetworkManager, - config: SystemConfiguration, + conditions_config: YamlLoader, ): self._pipeline = pipeline self._video_source = video_source self._nn_manager = nn_manager - self._config = config + self._conditions_config = conditions_config self._producer: SnapsProducer = None self._engine: ConditionsEngine = None @@ -32,7 +33,7 @@ def __init__( self._build() def _build(self) -> "SnapsManager": - cond_manager = ConditionsFactory(self._config.conditions) + cond_manager = ConditionsFactory(self._conditions_config) self._engine = cond_manager.get_engine() snaps_producer = SnapsProducerFactory.create( @@ -43,14 +44,11 @@ def _build(self) -> "SnapsManager": self._engine, ) self._producer = snaps_producer - snap_service = SnapCollectionService(self._engine, self._producer) - self._register_service(snap_service) + self._snap_service = SnapCollectionService(self._engine, self._producer) return self - def _register_service(self, snap_service: SnapCollectionService) -> None: - self._config.visualizer.registerService( - snap_service.get_name(), snap_service.handle - ) + def get_service(self) -> SnapCollectionService: + return self._snap_service def get_engine(self): return self._engine diff --git a/apps/data-collection/backend/src/infrastructure/system_context.py b/apps/data-collection/backend/src/infrastructure/system_context.py new file mode 100644 index 000000000..ce41b0b7e --- /dev/null +++ b/apps/data-collection/backend/src/infrastructure/system_context.py @@ -0,0 +1,29 @@ +import depthai as dai + +from services.base_service import BaseService + + +class SystemContext: + """Holds runtime-specific resources (hardware and visualization).""" + + def __init__(self, platform: str | None = None): + self.device = dai.Device() + self.visualizer = dai.RemoteConnection(serveFrontend=False) + self.platform = platform or self.device.getPlatformAsString() + + def register_service(self, service: BaseService) -> None: + """Registers a service in the system context.""" + self.visualizer.registerService(service.get_name(), service.handle) + + def register_services(self, services: list[BaseService]) -> None: + """Registers multiple services in the system context.""" + for service in services: + self.register_service(service) + + def add_visualizer_topic(self, topic: dai.Node.Output, name: str = "Video") -> None: + """Adds an annotations topic to the visualizer.""" + self.visualizer.addTopic(name, topic) + + def register_pipeline(self, pipeline: dai.Pipeline) -> None: + """Registers a pipeline with the visualizer.""" + self.visualizer.registerPipeline(pipeline) diff --git a/apps/data-collection/backend/src/infrastructure/video_source_manager.py b/apps/data-collection/backend/src/infrastructure/video_source_manager.py index c85596b0f..9200c16f4 100644 --- a/apps/data-collection/backend/src/infrastructure/video_source_manager.py +++ b/apps/data-collection/backend/src/infrastructure/video_source_manager.py @@ -1,8 +1,7 @@ from pathlib import Path import depthai as dai from infrastructure.frame_cache_node import FrameCacheNode -from config.config_data_classes import RuntimeConfig -from config.system_configuration import SystemConfiguration +from config.config_data_classes import VideoConfig class VideoSourceManager: @@ -13,12 +12,10 @@ class VideoSourceManager: def __init__( self, pipeline: dai.Pipeline, - config: SystemConfiguration, - runtime: RuntimeConfig, + video_config: VideoConfig, ): self._pipeline = pipeline - self._config = config - self._runtime = runtime + self._video_config = video_config self._video_src_out: dai.Node.Output = None self._input_node: dai.Node.Output = None @@ -29,7 +26,7 @@ def __init__( def _build(self): """Decide source type and build full video setup.""" - if self._config.args.media_path: + if self._video_config.media_path: self._create_replay_source() else: self._create_camera_source() @@ -37,65 +34,57 @@ def _build(self): self._frame_cache = self._pipeline.create(FrameCacheNode).build( self._video_src_out ) - self._add_video_topic() def _create_replay_source(self): """Replay from a video file.""" - model_w, model_h = ( - self._runtime.model_info.width, - self._runtime.model_info.height, - ) replay = self._pipeline.create(dai.node.ReplayVideo) - replay.setReplayVideoFile(Path(self._config.args.media_path)) + replay.setReplayVideoFile(Path(self._video_config.media_path)) replay.setOutFrameType(dai.ImgFrame.Type.NV12) replay.setLoop(True) - if self._runtime.fps_limit: - replay.setFps(self._runtime.fps_limit) - replay.setSize(self._config.constants.visualization_resolution) + if self._video_config.fps: + replay.setFps(self._video_config.fps) + replay.setSize(self._video_config.resolution) self._video_src_out = replay.out manip = self._pipeline.create(dai.node.ImageManip) - manip.setMaxOutputFrameSize(model_w * model_h * 3) - manip.initialConfig.setOutputSize(model_w, model_h) + manip.setMaxOutputFrameSize( + self._video_config.width * self._video_config.height * 3 + ) + manip.initialConfig.setOutputSize( + self._video_config.width, self._video_config.height + ) manip.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888i) + self._video_src_out.link(manip.inputImage) self._input_node = manip.out def _create_camera_source(self): """Live camera source.""" - model_w, model_h = ( - self._runtime.model_info.width, - self._runtime.model_info.height, - ) cam = self._pipeline.create(dai.node.Camera).build( boardSocket=dai.CameraBoardSocket.CAM_A ) self._video_src_out = cam.requestOutput( - size=self._config.constants.visualization_resolution, + size=self._video_config.resolution, type=dai.ImgFrame.Type.NV12, - fps=self._runtime.fps_limit, + fps=self._video_config.fps, ) self._input_node = cam.requestOutput( - size=(model_w, model_h), + size=(self._video_config.width, self._video_config.height), type=dai.ImgFrame.Type.BGR888i, - fps=self._runtime.fps_limit, + fps=self._video_config.fps, ) def _create_encoder(self): """Attach encoder to video source.""" self._video_enc = self._pipeline.create(dai.node.VideoEncoder) self._video_enc.setDefaultProfilePreset( - fps=self._runtime.fps_limit, + fps=self._video_config.fps, profile=dai.VideoEncoderProperties.Profile.H264_MAIN, ) self._video_src_out.link(self._video_enc.input) - def _add_video_topic(self): - """Register video stream with visualizer.""" - self._config.visualizer.addTopic("Video", self._video_enc.out) - def get_input_node(self) -> dai.Node.Output: """Get the input node for model processing.""" return self._input_node @@ -107,3 +96,7 @@ def get_frame_cache(self) -> FrameCacheNode: def get_video_source_output(self) -> dai.Node.Output: """Get the video source output node.""" return self._video_src_out + + def get_video_topic(self) -> dai.Node.Output: + """Get the video encoder output node.""" + return self._video_enc.out diff --git a/apps/data-collection/backend/src/main.py b/apps/data-collection/backend/src/main.py index e2d0d3e1d..2c237960d 100644 --- a/apps/data-collection/backend/src/main.py +++ b/apps/data-collection/backend/src/main.py @@ -4,42 +4,48 @@ from core.model_state import ModelState from infrastructure.neural_network.neural_network_manager import NeuralNetworkManager from infrastructure.snaps.snaps_manager import SnapsManager +from infrastructure.system_context import SystemContext from infrastructure.video_source_manager import VideoSourceManager from infrastructure.export.export_manager import ExportManager def main(): - config = SystemConfiguration() - runtime = config.build_runtime_config() + system_context = SystemContext() + config = SystemConfiguration(system_context.platform) - with dai.Pipeline(config.device) as pipeline: + with dai.Pipeline(system_context.device) as pipeline: print("Creating pipeline...") - video_source = VideoSourceManager(pipeline, config, runtime) + video_source = VideoSourceManager(pipeline, config.get_video_config()) + + system_context.add_visualizer_topic(video_source.get_video_topic(), "Video") model_state = ModelState() nn_manager = NeuralNetworkManager( - pipeline, video_source, runtime, config, model_state + pipeline, video_source, config.get_neural_network_config(), model_state ) - snaps_manager = SnapsManager(pipeline, video_source, nn_manager, config) + system_context.register_services(nn_manager.get_services()) + + system_context.add_visualizer_topic(nn_manager.get_annotations(), "Annotations") + + snaps_manager = SnapsManager( + pipeline, video_source, nn_manager, config.get_snaps_config() + ) - export_manager = ExportManager(model_state, snaps_manager.get_engine(), config) - export_manager.build() + system_context.register_service(snaps_manager.get_service()) + + export_manager = ExportManager(model_state, snaps_manager.get_engine()) + + system_context.register_service(export_manager.get_service()) print("Pipeline created.") pipeline.start() - config.visualizer.registerPipeline(pipeline) - - print("Press 'q' to stop") + system_context.register_pipeline(pipeline) while pipeline.isRunning(): pipeline.processTasks() - key = config.visualizer.waitKey(1) - if key == ord("q"): - print("Got q key. Exiting...") - break if __name__ == "__main__": From 2f209463c7e601de674660f278e3dcd96d816e65 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Thu, 6 Nov 2025 03:28:02 +0100 Subject: [PATCH 29/39] application refactoring --- .../backend/src/config/arguments.py | 18 --- .../backend/src/config/cli_env_loader.py | 20 +++ .../backend/src/config/config_data_classes.py | 8 +- .../backend/src/config/model_loader.py | 33 +++++ .../src/config/system_configuration.py | 119 +++++---------- .../backend/src/config/yaml_config_manager.py | 29 ++++ .../yaml_configs/detection_constants.yaml | 4 - .../src/config/yaml_configs/nn_config.yaml | 32 +--- .../config/yaml_configs/prompts_config.yaml | 28 ++++ .../backend/src/config/yaml_loader.py | 26 ---- ...controller.py => nn_prompts_controller.py} | 30 ++-- ...base_encoder.py => base_prompt_encoder.py} | 62 ++++---- ...l_encoder.py => textual_prompt_encoder.py} | 22 ++- ...al_encoder.py => visual_prompt_encoder.py} | 17 +-- .../src/core/handlers/base_prompt_handler.py | 39 +++-- .../src/core/handlers/bbox_prompt_handler.py | 52 +++++-- .../src/core/handlers/image_prompt_handler.py | 47 +++--- .../src/core/handlers/text_prompt_handler.py | 18 ++- .../src/{ => core}/infrastructure/__init__.py | 0 .../infrastructure/export/__init__.py | 0 .../infrastructure/export/export_manager.py | 2 +- .../infrastructure/frame_cache_node.py | 1 - .../infrastructure/neural_network/__init__.py | 0 .../neural_network/annotation_node.py | 0 .../neural_network/handlers_manager.py | 25 ++++ .../neural_network/neural_network_manager.py | 49 ++++++ .../neural_network/nn_pipeline_setup.py | 139 ++++++++++++++++++ .../neural_network/prompt_encoders_manager.py | 32 ++++ .../neural_network/prompt_service_factory.py | 26 ++++ .../infrastructure/snaps/__init__.py | 0 .../infrastructure/snaps/snaps_manager.py | 28 ++-- .../snaps/snaps_producer_factory.py | 6 +- .../infrastructure/system_context.py | 4 +- .../src/core/infrastructure/video_factory.py | 39 +++++ .../video_providers}/__init__.py | 0 .../video_providers/base_video_provider.py | 21 +++ .../video_providers/camera_video_provvider.py | 31 ++++ .../video_providers/replay_video_provider.py | 33 +++++ .../backend/src/core/label_manager.py | 2 +- .../bbox_prompt => core/services}/__init__.py | 0 .../backend/src/core/services/base_service.py | 30 ++++ .../src/core/services/bbox_prompt_service.py | 16 ++ .../src/core/services/class_update_service.py | 18 +++ .../services}/export_service.py | 4 +- .../src/core/services/image_upload_service.py | 19 +++ .../services/payloads}/__init__.py | 0 .../services/payloads}/bbox_prompt_payload.py | 0 .../payloads}/class_update_payload.py | 0 .../payloads}/image_upload_payload.py | 0 .../services/payloads}/snap_payload.py | 0 .../payloads}/threshold_update_payload.py | 0 .../src/{ => core}/services/service_name.py | 0 .../services}/snap_collection_service.py | 6 +- .../core/services/threshold_update_service.py | 17 +++ .../snapping}/conditions_factory.py | 6 +- .../neural_network/encoders_manager.py | 41 ------ .../neural_network/handlers_manager.py | 30 ---- .../neural_network/neural_network_manager.py | 90 ------------ .../neural_network/nn_pipeline_setup.py | 107 -------------- .../neural_network/nn_service_factory.py | 33 ----- .../infrastructure/video_source_manager.py | 102 ------------- apps/data-collection/backend/src/main.py | 63 +++++--- .../backend/src/requirements.txt | 3 +- .../backend/src/services/base_service.py | 26 ---- .../bbox_prompt/bbox_prompt_service.py | 37 ----- .../class_update/class_update_service.py | 31 ---- .../src/services/export_service/__init__.py | 0 .../src/services/image_upload/__init__.py | 0 .../image_upload/image_upload_service.py | 23 --- .../src/services/snap_collection/__init__.py | 0 .../src/services/threshold_update/__init__.py | 0 .../threshold_update_service.py | 25 ---- 72 files changed, 878 insertions(+), 891 deletions(-) create mode 100644 apps/data-collection/backend/src/config/cli_env_loader.py create mode 100644 apps/data-collection/backend/src/config/model_loader.py create mode 100644 apps/data-collection/backend/src/config/yaml_config_manager.py delete mode 100644 apps/data-collection/backend/src/config/yaml_configs/detection_constants.yaml create mode 100644 apps/data-collection/backend/src/config/yaml_configs/prompts_config.yaml delete mode 100644 apps/data-collection/backend/src/config/yaml_loader.py rename apps/data-collection/backend/src/core/controllers/{nn_controller.py => nn_prompts_controller.py} (61%) rename apps/data-collection/backend/src/core/encoders/{base_encoder.py => base_prompt_encoder.py} (57%) rename apps/data-collection/backend/src/core/encoders/{textual_encoder.py => textual_prompt_encoder.py} (77%) rename apps/data-collection/backend/src/core/encoders/{visual_encoder.py => visual_prompt_encoder.py} (83%) rename apps/data-collection/backend/src/{ => core}/infrastructure/__init__.py (100%) rename apps/data-collection/backend/src/{ => core}/infrastructure/export/__init__.py (100%) rename apps/data-collection/backend/src/{ => core}/infrastructure/export/export_manager.py (93%) rename apps/data-collection/backend/src/{ => core}/infrastructure/frame_cache_node.py (94%) rename apps/data-collection/backend/src/{ => core}/infrastructure/neural_network/__init__.py (100%) rename apps/data-collection/backend/src/{ => core}/infrastructure/neural_network/annotation_node.py (100%) create mode 100644 apps/data-collection/backend/src/core/infrastructure/neural_network/handlers_manager.py create mode 100644 apps/data-collection/backend/src/core/infrastructure/neural_network/neural_network_manager.py create mode 100644 apps/data-collection/backend/src/core/infrastructure/neural_network/nn_pipeline_setup.py create mode 100644 apps/data-collection/backend/src/core/infrastructure/neural_network/prompt_encoders_manager.py create mode 100644 apps/data-collection/backend/src/core/infrastructure/neural_network/prompt_service_factory.py rename apps/data-collection/backend/src/{ => core}/infrastructure/snaps/__init__.py (100%) rename apps/data-collection/backend/src/{ => core}/infrastructure/snaps/snaps_manager.py (57%) rename apps/data-collection/backend/src/{ => core}/infrastructure/snaps/snaps_producer_factory.py (81%) rename apps/data-collection/backend/src/{ => core}/infrastructure/system_context.py (89%) create mode 100644 apps/data-collection/backend/src/core/infrastructure/video_factory.py rename apps/data-collection/backend/src/{services => core/infrastructure/video_providers}/__init__.py (100%) create mode 100644 apps/data-collection/backend/src/core/infrastructure/video_providers/base_video_provider.py create mode 100644 apps/data-collection/backend/src/core/infrastructure/video_providers/camera_video_provvider.py create mode 100644 apps/data-collection/backend/src/core/infrastructure/video_providers/replay_video_provider.py rename apps/data-collection/backend/src/{services/bbox_prompt => core/services}/__init__.py (100%) create mode 100644 apps/data-collection/backend/src/core/services/base_service.py create mode 100644 apps/data-collection/backend/src/core/services/bbox_prompt_service.py create mode 100644 apps/data-collection/backend/src/core/services/class_update_service.py rename apps/data-collection/backend/src/{services/export_service => core/services}/export_service.py (86%) create mode 100644 apps/data-collection/backend/src/core/services/image_upload_service.py rename apps/data-collection/backend/src/{services/class_update => core/services/payloads}/__init__.py (100%) rename apps/data-collection/backend/src/{services/bbox_prompt => core/services/payloads}/bbox_prompt_payload.py (100%) rename apps/data-collection/backend/src/{services/class_update => core/services/payloads}/class_update_payload.py (100%) rename apps/data-collection/backend/src/{services/image_upload => core/services/payloads}/image_upload_payload.py (100%) rename apps/data-collection/backend/src/{services/snap_collection => core/services/payloads}/snap_payload.py (100%) rename apps/data-collection/backend/src/{services/threshold_update => core/services/payloads}/threshold_update_payload.py (100%) rename apps/data-collection/backend/src/{ => core}/services/service_name.py (100%) rename apps/data-collection/backend/src/{services/snap_collection => core/services}/snap_collection_service.py (82%) create mode 100644 apps/data-collection/backend/src/core/services/threshold_update_service.py rename apps/data-collection/backend/src/{infrastructure/snaps => core/snapping}/conditions_factory.py (92%) delete mode 100644 apps/data-collection/backend/src/infrastructure/neural_network/encoders_manager.py delete mode 100644 apps/data-collection/backend/src/infrastructure/neural_network/handlers_manager.py delete mode 100644 apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py delete mode 100644 apps/data-collection/backend/src/infrastructure/neural_network/nn_pipeline_setup.py delete mode 100644 apps/data-collection/backend/src/infrastructure/neural_network/nn_service_factory.py delete mode 100644 apps/data-collection/backend/src/infrastructure/video_source_manager.py delete mode 100644 apps/data-collection/backend/src/services/base_service.py delete mode 100644 apps/data-collection/backend/src/services/bbox_prompt/bbox_prompt_service.py delete mode 100644 apps/data-collection/backend/src/services/class_update/class_update_service.py delete mode 100644 apps/data-collection/backend/src/services/export_service/__init__.py delete mode 100644 apps/data-collection/backend/src/services/image_upload/__init__.py delete mode 100644 apps/data-collection/backend/src/services/image_upload/image_upload_service.py delete mode 100644 apps/data-collection/backend/src/services/snap_collection/__init__.py delete mode 100644 apps/data-collection/backend/src/services/threshold_update/__init__.py delete mode 100644 apps/data-collection/backend/src/services/threshold_update/threshold_update_service.py diff --git a/apps/data-collection/backend/src/config/arguments.py b/apps/data-collection/backend/src/config/arguments.py index 6eeb89d98..5ecd971c0 100644 --- a/apps/data-collection/backend/src/config/arguments.py +++ b/apps/data-collection/backend/src/config/arguments.py @@ -48,24 +48,6 @@ def initialize_argparser(): required=False, type=int, ) - parser.add_argument( - "-m", - "--model", - help="Name of the model to use: yolo-world or yoloe", - required=False, - # default="yoloe", - default="yolo-world", - type=str, - choices=["yolo-world", "yoloe"], - ) - parser.add_argument( - "--precision", - help="Model precision for YOLOE models: int8 (faster) or fp16 (more accurate).", - required=False, - default="fp16", - type=str, - choices=["int8", "fp16"], - ) args = parser.parse_args() diff --git a/apps/data-collection/backend/src/config/cli_env_loader.py b/apps/data-collection/backend/src/config/cli_env_loader.py new file mode 100644 index 000000000..34e534169 --- /dev/null +++ b/apps/data-collection/backend/src/config/cli_env_loader.py @@ -0,0 +1,20 @@ +import os +from dotenv import load_dotenv +from config.arguments import initialize_argparser + + +class CLIEnvLoader: + """Handles .env loading and CLI argument parsing.""" + + def __init__(self): + self.args = None + + def load(self): + """Load environment and parse CLI args.""" + load_dotenv(override=True) + _, self.args = initialize_argparser() + + if self.args.api_key: + os.environ["DEPTHAI_HUB_API_KEY"] = self.args.api_key + + return self.args diff --git a/apps/data-collection/backend/src/config/config_data_classes.py b/apps/data-collection/backend/src/config/config_data_classes.py index 338e0cca0..8e30c5da6 100644 --- a/apps/data-collection/backend/src/config/config_data_classes.py +++ b/apps/data-collection/backend/src/config/config_data_classes.py @@ -1,8 +1,7 @@ from pathlib import Path from dataclasses import dataclass import depthai as dai - -from config.yaml_loader import YamlLoader +from box import Box @dataclass @@ -14,7 +13,7 @@ class ModelInfo: height: int description: dai.NNModelDescription archive: dai.NNArchive - precision: str = "FP16" + precision: str @dataclass @@ -32,6 +31,5 @@ class VideoConfig: class NeuralNetworkConfig: """Stores neural network configuration (confidence thresholds, etc.).""" - nn_yaml: YamlLoader - constants: YamlLoader + nn_yaml: Box model: ModelInfo diff --git a/apps/data-collection/backend/src/config/model_loader.py b/apps/data-collection/backend/src/config/model_loader.py new file mode 100644 index 000000000..b440aec2a --- /dev/null +++ b/apps/data-collection/backend/src/config/model_loader.py @@ -0,0 +1,33 @@ +import depthai as dai +from pathlib import Path +from config.config_data_classes import ModelInfo + + +class ModelLoader: + """Resolves DepthAI model archive and metadata.""" + + def __init__(self, platform: str, args): + self.platform = platform + self.args = args + + def load_model_info(self) -> ModelInfo: + models_dir = Path(__file__).parent.parent / "depthai_models" + yaml_file = f"yoloe_v8_l_fp16.{self.platform}.yaml" + yaml_path = models_dir / yaml_file + + if not yaml_path.exists(): + raise SystemExit(f"Model YAML not found for yoloe: {yaml_path}") + + desc = dai.NNModelDescription.fromYamlFile(str(yaml_path)) + desc.platform = self.platform + archive = dai.NNArchive(dai.getModelFromZoo(desc)) + width, height = archive.getInputSize() + + return ModelInfo( + yaml_path=yaml_path, + width=width, + height=height, + description=desc, + archive=archive, + precision="", + ) diff --git a/apps/data-collection/backend/src/config/system_configuration.py b/apps/data-collection/backend/src/config/system_configuration.py index 265562b9b..16ff2f193 100644 --- a/apps/data-collection/backend/src/config/system_configuration.py +++ b/apps/data-collection/backend/src/config/system_configuration.py @@ -1,113 +1,60 @@ from __future__ import annotations -import os from pathlib import Path -from dotenv import load_dotenv -import depthai as dai +from typing import Optional +from box import Box -from config.yaml_loader import YamlLoader -from config.config_data_classes import ( - ModelInfo, - VideoConfig, - NeuralNetworkConfig, -) -from config.arguments import initialize_argparser +from config.cli_env_loader import CLIEnvLoader +from config.yaml_config_manager import YamlConfigManager +from config.model_loader import ModelLoader +from config.config_data_classes import ModelInfo, VideoConfig, NeuralNetworkConfig class SystemConfiguration: """ - Loads and aggregates static configuration for all subsystems. - Handles: - • YAML loading - • CLI + dotenv parsing - • Model info resolution - • Provides ready-to-use config slices + Class that manages configuration initialization. """ def __init__(self, platform: str): - load_dotenv(override=True) - self._platform: str = platform - # Allow explicit args (for tests) or parse CLI when None - _, self.args = initialize_argparser() + self._platform = platform + self._args = None + self._yaml: Optional[YamlConfigManager] = None + self._model_info: Optional[ModelInfo] = None - base = Path(__file__).parent / "yaml_configs" - self._nn_yaml = YamlLoader(base / "nn_config.yaml") - self._conditions_yaml = YamlLoader(base / "conditions.yaml") - self._detection_yaml = YamlLoader(base / "detection_constants.yaml") - self._video_yaml = YamlLoader(base / "visual_constants.yaml") - - self._model_info: ModelInfo | None = None - self._initialize_for_platform() - - def _initialize_for_platform(self) -> None: - """ - Perform initialization that depends on runtime platform. - """ - - if self.args.api_key: - os.environ["DEPTHAI_HUB_API_KEY"] = self.args.api_key - - if self._platform != "RVC4": - raise ValueError( - f"This application currently supports only RVC4, got {self._platform}" - ) - - # Use default FPS if not specified - if self.args.fps_limit is None: - self.args.fps_limit = self._video_yaml.default_fps - print(f"\nFPS limit set to {self.args.fps_limit} for {self._platform}\n") + def build(self): + """Initialize all configuration subsystems.""" + cli_env = CLIEnvLoader() + self._args = cli_env.load() - self._model_info = self._load_model_info() - - def _load_model_info(self) -> ModelInfo: - models_dir = Path(__file__).parent.parent / "depthai_models" - model_name = self._nn_yaml.model.name - yaml_file = f"{model_name}_v8_l_fp16.{self._platform}.yaml" - yaml_path = models_dir / yaml_file - - if not yaml_path.exists(): - raise SystemExit(f"Model YAML not found for {model_name}: {yaml_path}") + base = Path(__file__).parent / "yaml_configs" - if self.args.precision != self._nn_yaml.model.default_precision: - raise SystemExit(f"{model_name} int8 YAML not available; use fp16.") + self._yaml = YamlConfigManager(base) + self._yaml.load_all() - desc = dai.NNModelDescription.fromYamlFile(str(yaml_path)) - desc.platform = self._platform - archive = dai.NNArchive(dai.getModelFromZoo(desc)) - w, h = archive.getInputSize() - return ModelInfo( - yaml_path=yaml_path, - width=w, - height=h, - description=desc, - archive=archive, - precision=self.args.precision, - ) + model_loader = ModelLoader(self._platform, self._args) + self._model_info = model_loader.load_model_info() + self._model_info.precision = self._yaml.prompts.precision def get_video_config(self) -> VideoConfig: - if not self._model_info: - raise RuntimeError( - "Model info not loaded yet — call initialize_for_platform() first." - ) + if self._args.fps_limit is None: + self._args.fps_limit = self._yaml.video.default_fps + print(f"\nFPS limit set to {self._args.fps_limit} for {self._platform}\n") return VideoConfig( - resolution=self._video_yaml.video_resolution, - fps=self.args.fps_limit, - media_path=self.args.media_path, + resolution=self._yaml.video.video_resolution, + fps=self._args.fps_limit, + media_path=self._args.media_path, width=self._model_info.width, height=self._model_info.height, ) def get_neural_network_config(self) -> NeuralNetworkConfig: - if not self._model_info: - raise RuntimeError( - "Model info not loaded yet — call initialize_for_platform() first." - ) - return NeuralNetworkConfig( - nn_yaml=self._nn_yaml, - constants=self._detection_yaml, + nn_yaml=self._yaml.nn, model=self._model_info, ) - def get_snaps_config(self) -> YamlLoader: - return self._conditions_yaml + def get_snaps_config(self) -> Box: + return self._yaml.conditions + + def get_prompts_config(self) -> Box: + return self._yaml.prompts diff --git a/apps/data-collection/backend/src/config/yaml_config_manager.py b/apps/data-collection/backend/src/config/yaml_config_manager.py new file mode 100644 index 000000000..6881e4fc7 --- /dev/null +++ b/apps/data-collection/backend/src/config/yaml_config_manager.py @@ -0,0 +1,29 @@ +from pathlib import Path +from box import Box + + +class YamlConfigManager: + """Loads all YAML configuration files and exposes them as Box objects.""" + + def __init__(self, base_dir: Path): + self._base = base_dir + self.nn: Box | None = None + self.video: Box | None = None + self.conditions: Box | None = None + self.prompts: Box | None = None + + def load_all(self) -> None: + """Load all YAML configs from the given base directory.""" + print(f"[YamlConfigManager] Loading from: {self._base.resolve()}") + + def safe_load(name: str, file: str) -> Box: + path = self._base / file + if not path.exists(): + raise FileNotFoundError(f"Missing YAML: {path}") + print(f"[YamlConfigManager] ✓ Loaded {name}: {path.name}") + return Box.from_yaml(filename=path) + + self.nn = safe_load("nn", "nn_config.yaml") + self.video = safe_load("video", "visual_constants.yaml") + self.conditions = safe_load("conditions", "conditions.yaml") + self.prompts = safe_load("prompts", "prompts_config.yaml") diff --git a/apps/data-collection/backend/src/config/yaml_configs/detection_constants.yaml b/apps/data-collection/backend/src/config/yaml_configs/detection_constants.yaml deleted file mode 100644 index b74dfcf4e..000000000 --- a/apps/data-collection/backend/src/config/yaml_configs/detection_constants.yaml +++ /dev/null @@ -1,4 +0,0 @@ -class_names: ["person", "chair", "TV"] -class_offset: 0 -max_num_classes: 80 -confidence_threshold: 0.1 \ No newline at end of file diff --git a/apps/data-collection/backend/src/config/yaml_configs/nn_config.yaml b/apps/data-collection/backend/src/config/yaml_configs/nn_config.yaml index f2d9ae76a..1193a8f5b 100644 --- a/apps/data-collection/backend/src/config/yaml_configs/nn_config.yaml +++ b/apps/data-collection/backend/src/config/yaml_configs/nn_config.yaml @@ -3,38 +3,12 @@ nn_backend: runtime: "dsp" performance_profile: "default" inference_threads: 1 + confidence_threshold: 0.1 + precision: "fp16" tracker: track_per_class: true birth_threshold: 3 max_lifespan: 90 occlusion_ratio_threshold: 0.5 - tracker_threshold: 0.25 - -model: - name: "yoloe" - default_precision: "fp16" - max_num_classes: 80 - confidence_threshold: 0.25 - - quant_values: - yoloe: - quant_zero_point: 174.0 - quant_scale: 0.003328413470 - yoloe-image: - quant_zero_point: 137.0 - quant_scale: 0.002327915514 - - - paths: - tokenizer: - url: "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json" - path: "tokenizer.json" - - text_encoder: - url: "https://huggingface.co/Xenova/mobileclip_blt/resolve/main/onnx/text_model.onnx" - path: "mobileclip_textual_hf.onnx" - - visual_encoder: - url: "https://huggingface.co/sokovninn/yoloe-v8l-seg-visual-encoder/resolve/main/yoloe-v8l-seg_visual_encoder.onnx" - path: "yoloe-v8l-seg_visual_encoder.onnx" + tracker_threshold: 0.25 \ No newline at end of file diff --git a/apps/data-collection/backend/src/config/yaml_configs/prompts_config.yaml b/apps/data-collection/backend/src/config/yaml_configs/prompts_config.yaml new file mode 100644 index 000000000..f7f3ddb1a --- /dev/null +++ b/apps/data-collection/backend/src/config/yaml_configs/prompts_config.yaml @@ -0,0 +1,28 @@ +name: "yoloe" +precision: "fp16" +class_names: ["person", "chair", "TV"] +text_offset: 0 +visual_offset: 80 +max_num_classes: 80 + + +quant_values: + yoloe: + quant_zero_point: 174.0 + quant_scale: 0.003328413470 + yoloe-image: + quant_zero_point: 137.0 + quant_scale: 0.002327915514 + +paths: + tokenizer: + url: "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json" + path: "tokenizer.json" + + text_encoder: + url: "https://huggingface.co/Xenova/mobileclip_blt/resolve/main/onnx/text_model.onnx" + path: "mobileclip_textual_hf.onnx" + + visual_encoder: + url: "https://huggingface.co/sokovninn/yoloe-v8l-seg-visual-encoder/resolve/main/yoloe-v8l-seg_visual_encoder.onnx" + path: "yoloe-v8l-seg_visual_encoder.onnx" \ No newline at end of file diff --git a/apps/data-collection/backend/src/config/yaml_loader.py b/apps/data-collection/backend/src/config/yaml_loader.py deleted file mode 100644 index ef894345b..000000000 --- a/apps/data-collection/backend/src/config/yaml_loader.py +++ /dev/null @@ -1,26 +0,0 @@ -import yaml -from pathlib import Path - - -class YamlLoader: - """YAML file loader that converts dicts to objects with dot access.""" - - def __init__(self, file_path: str | Path): - path = Path(file_path) - if not path.exists(): - raise FileNotFoundError(f"Config file not found: {path}") - - with open(path, "r", encoding="utf-8") as f: - data = yaml.safe_load(f) or {} - - self._data = self._wrap(data) - - def _wrap(self, obj) -> any: - if isinstance(obj, dict): - return type("YamlNamespace", (), {k: self._wrap(v) for k, v in obj.items()}) - elif isinstance(obj, list): - return [self._wrap(x) for x in obj] - return obj - - def __getattr__(self, name: str) -> any: - return getattr(self._data, name) diff --git a/apps/data-collection/backend/src/core/controllers/nn_controller.py b/apps/data-collection/backend/src/core/controllers/nn_prompts_controller.py similarity index 61% rename from apps/data-collection/backend/src/core/controllers/nn_controller.py rename to apps/data-collection/backend/src/core/controllers/nn_prompts_controller.py index 08869e289..66c5350fb 100644 --- a/apps/data-collection/backend/src/core/controllers/nn_controller.py +++ b/apps/data-collection/backend/src/core/controllers/nn_prompts_controller.py @@ -2,11 +2,12 @@ import numpy as np from depthai_nodes.node import YOLOExtendedParser +from core.label_manager import LabelManager from core.model_state import ModelState -class YOLONNController: - """Handles sending conditioning inputs to the DepthAI YOlO NN.""" +class NnPromptsController: + """Handles sending conditioning inputs to the NN.""" def __init__( self, @@ -15,14 +16,16 @@ def __init__( precision: str, parser: YOLOExtendedParser, model_state: ModelState, + label_manager: LabelManager, ): self._image_prompt_queue = image_prompt_queue self._text_prompt_queue = text_prompt_queue self._precision = precision self._parser: YOLOExtendedParser = parser self._model_state = model_state + self._label_manager = label_manager - def _tensor_type(self): + def _tensor_type(self) -> dai.TensorInfo.DataType: return ( dai.TensorInfo.DataType.FP16 if self._precision == "fp16" @@ -34,24 +37,17 @@ def _send(self, queue: dai.InputQueue, name: str, data: np.ndarray): nn_data.addTensor(name, data, dataType=self._tensor_type()) queue.send(nn_data) - def _send_text_inputs(self, embeddings: np.ndarray): - """Send class text embeddings (semantic prompts) to the NN.""" - self._send(self._text_prompt_queue, "texts", embeddings) - - def _send_visual_inputs(self, embeddings: np.ndarray): - """Send visual prompts (mask- or bbox-based) to the NN.""" - self._send(self._image_prompt_queue, "image_prompts", embeddings) - - def send_embeddings_pair( + def send_prompts_pair( self, - visual_embeddings: np.ndarray, - text_embeddings: np.ndarray, + visual_prompt: np.ndarray, + text_prompt: np.ndarray, class_names: list[str], + offset: int, ): - """Send both text and visual conditioning inputs if available.""" - self._send_visual_inputs(visual_embeddings) - self._send_text_inputs(text_embeddings) + self._send(self._text_prompt_queue, "text_prompts", text_prompt) + self._send(self._image_prompt_queue, "image_prompts", visual_prompt) self._model_state.update_classes(class_names) + self._label_manager.update_labels(class_names, offset) def set_confidence_threshold(self, threshold: float): """Apply threshold update directly to the NN parser.""" diff --git a/apps/data-collection/backend/src/core/encoders/base_encoder.py b/apps/data-collection/backend/src/core/encoders/base_prompt_encoder.py similarity index 57% rename from apps/data-collection/backend/src/core/encoders/base_encoder.py rename to apps/data-collection/backend/src/core/encoders/base_prompt_encoder.py index abc14cfaa..7abf90bba 100644 --- a/apps/data-collection/backend/src/core/encoders/base_encoder.py +++ b/apps/data-collection/backend/src/core/encoders/base_prompt_encoder.py @@ -1,48 +1,39 @@ import os from abc import ABC, abstractmethod -import onnxruntime +from onnxruntime import InferenceSession import numpy as np import requests from pathlib import Path -from config.yaml_loader import YamlLoader +from box import Box -class BaseEncoder(ABC): +class BasePromptEncoder(ABC): """ Abstract base class for all embedding encoders (visual, text, etc.). - - Provides: - - Model download & caching via `download_file` - - ONNX session initialization - - Common quantization pipeline """ def __init__( self, - precision: str, - max_classes: int, - config: YamlLoader, + config: Box, encoder_model_url: str, encoder_model_path: str, ): - self.model_name = config.name - self.precision = precision - self.max_classes = max_classes - self.quant_values = config.quant_values - self.encoder_model_url = encoder_model_url - self.encoder_model_path = encoder_model_path - self.providers = [ + self._config: Box = config + self._encoder_model_url: str = encoder_model_url + self._encoder_model_path: str = encoder_model_path + self._providers: list[str] = [ "TensorrtExecutionProvider", "CUDAExecutionProvider", "CPUExecutionProvider", ] - self.session = None + self._session: InferenceSession = None + self._offset: int = None def _load_model(self) -> None: """Download and initialize the ONNX model.""" path = self._download_file() - self.session = onnxruntime.InferenceSession(path, providers=self.providers) + self._session = InferenceSession(path, providers=self._providers) @abstractmethod def extract_embeddings(self, *args, **kwargs) -> np.ndarray: @@ -54,37 +45,46 @@ def _pad_and_quantize_features(self, features): Pad features to (1, 512, max_num_classes) and quantize if precision is int8. For FP16, return padded float16 features (no quantization). """ - num_padding = self.max_classes - features.shape[0] + num_padding = self._config.max_num_classes - features.shape[0] padded = np.pad(features, ((0, num_padding), (0, 0)), "constant").T.reshape( - 1, 512, self.max_classes + 1, 512, self._config.max_num_classes ) - if self.precision == "fp16": + if self._config.precision == "fp16": return padded.astype(np.float16) - quant = self.quant_values[self.model_name] + quant = self._config.quant_values[self._config.name] out = (padded / quant["quant_scale"]) + quant["quant_zero_point"] return out.astype(np.uint8) - def make_dummy_features(self) -> np.ndarray: + def make_dummy(self) -> np.ndarray: """ Create a dummy tensor of shape (1, 512, max_num_classes) for model input. For FP16, return zeros; for INT8, fill with the model's quantization zero point. """ - if self.precision == "fp16": - return np.zeros((1, 512, self.max_classes), dtype=np.float16) + if self._config.precision == "fp16": + return np.zeros((1, 512, self._config.max_num_classes), dtype=np.float16) qzp = int( - round(self.quant_values.get(self.model_name, {}).get("quant_zero_point", 0)) + round( + self._config.quant_values.get(self._config.model_name, {}).get( + "quant_zero_point", 0 + ) + ) ) - return np.full((1, 512, self.max_classes), qzp, dtype=np.uint8) + return np.full((1, 512, self._config.max_num_classes), qzp, dtype=np.uint8) def _download_file(self, url: str = "", path: str = "") -> Path: if url == "": - url = self.encoder_model_url + url = self._encoder_model_url if path == "": - path = self.encoder_model_path + path = self._encoder_model_path if not os.path.exists(path): print(f"Downloading tokenizer config from {url}...") with open(path, "wb") as f: f.write(requests.get(url).content) return path + + @property + def offset(self) -> int: + """Return class offset or encoder index limit.""" + return self._offset diff --git a/apps/data-collection/backend/src/core/encoders/textual_encoder.py b/apps/data-collection/backend/src/core/encoders/textual_prompt_encoder.py similarity index 77% rename from apps/data-collection/backend/src/core/encoders/textual_encoder.py rename to apps/data-collection/backend/src/core/encoders/textual_prompt_encoder.py index e58a347c4..5082bd1b8 100644 --- a/apps/data-collection/backend/src/core/encoders/textual_encoder.py +++ b/apps/data-collection/backend/src/core/encoders/textual_prompt_encoder.py @@ -1,28 +1,22 @@ +from box import Box from tokenizers import Tokenizer import numpy as np -from config.yaml_loader import YamlLoader -from core.encoders.base_encoder import BaseEncoder +from core.encoders.base_prompt_encoder import BasePromptEncoder -class TextualEncoder(BaseEncoder): +class TextualPromptEncoder(BasePromptEncoder): """ Handles text tokenization and embedding extraction (CLIP-compatible). """ - def __init__( - self, - precision: str, - max_classes: int, - config: YamlLoader, - ): + def __init__(self, config: Box): super().__init__( - precision, - max_classes, config, config.paths.text_encoder.url, config.paths.text_encoder.path, ) + self._offset = config.text_offset self.tokenizer_url = config.paths.tokenizer.url self.tokenizer_path = config.paths.tokenizer.path self.tokenizer = None @@ -47,12 +41,14 @@ def extract_embeddings(self, class_names: list[str]) -> np.ndarray: text_ids, ((0, 0), (0, 77 - text_ids.shape[1])), mode="constant" ) - outputs = self.session.run(None, {self.session.get_inputs()[0].name: text_ids}) + outputs = self._session.run( + None, {self._session.get_inputs()[0].name: text_ids} + ) embeddings = outputs[0] embeddings /= np.linalg.norm(embeddings, ord=2, axis=-1, keepdims=True) quantized = self._pad_and_quantize_features(embeddings) - del self.session + del self._session return quantized diff --git a/apps/data-collection/backend/src/core/encoders/visual_encoder.py b/apps/data-collection/backend/src/core/encoders/visual_prompt_encoder.py similarity index 83% rename from apps/data-collection/backend/src/core/encoders/visual_encoder.py rename to apps/data-collection/backend/src/core/encoders/visual_prompt_encoder.py index 8e6b67160..37be96bf4 100644 --- a/apps/data-collection/backend/src/core/encoders/visual_encoder.py +++ b/apps/data-collection/backend/src/core/encoders/visual_prompt_encoder.py @@ -1,11 +1,11 @@ import cv2 import numpy as np +from box import Box -from config.yaml_loader import YamlLoader -from core.encoders.base_encoder import BaseEncoder +from core.encoders.base_prompt_encoder import BasePromptEncoder -class VisualEncoder(BaseEncoder): +class VisualPromptEncoder(BasePromptEncoder): """ Handles visual embedding extraction using a visual encoder. @@ -16,17 +16,14 @@ class VisualEncoder(BaseEncoder): def __init__( self, - precision: str, - max_classes: int, - config: YamlLoader, + config: Box, ): super().__init__( - precision, - max_classes, config, config.paths.visual_encoder.url, config.paths.visual_encoder.path, ) + self._offset = config.visual_offset self.tokenizer_url = config.paths.tokenizer.url self.tokenizer_path = config.paths.tokenizer.path self.tokenizer = None @@ -54,11 +51,11 @@ def extract_embeddings(self, image: np.ndarray, mask_prompt=None) -> np.ndarray: image_array = np.transpose(image_array, (2, 0, 1)) input_tensor = np.expand_dims(image_array, axis=0).astype(np.float32) - outputs = self.session.run(None, {"images": input_tensor, "prompts": prompts}) + outputs = self._session.run(None, {"images": input_tensor, "prompts": prompts}) image_embeddings = outputs[0].squeeze(0).reshape(1, -1) image_features = self._pad_and_quantize_features(image_embeddings) - del self.session + del self._session return image_features diff --git a/apps/data-collection/backend/src/core/handlers/base_prompt_handler.py b/apps/data-collection/backend/src/core/handlers/base_prompt_handler.py index e7820e31e..c65db755d 100644 --- a/apps/data-collection/backend/src/core/handlers/base_prompt_handler.py +++ b/apps/data-collection/backend/src/core/handlers/base_prompt_handler.py @@ -1,26 +1,33 @@ -# app/modules/handlers/base_embedding_handler.py from __future__ import annotations -from typing import List +from abc import ABC, abstractmethod +from typing import List, Tuple, TypedDict import numpy as np -from core.encoders.base_encoder import BaseEncoder -from core.label_manager import LabelManager +from core.encoders.base_prompt_encoder import BasePromptEncoder -class BasePromptHandler: +class BasePromptHandler(ABC): """ - Abstract base handler for extracting embeddings from various input modalities. - Provides shared utilities for feature extraction and label management. + Abstract base handler for converting various input modalities + (image, text, bbox, etc.) into model-ready prompts. """ - def __init__(self, encoder: BaseEncoder, label_manager: LabelManager): - self.encoder = encoder - self.label_manager = label_manager + def __init__(self, encoder: BasePromptEncoder): + self._encoder = encoder + self._class_names: List[str] = [] - def _make_dummy(self) -> np.ndarray: - """Create dummy tensor for balancing model inputs.""" - return self.encoder.make_dummy_features() + @abstractmethod + def process(self, payload: TypedDict) -> Tuple[np.ndarray, np.ndarray]: + """ + Transform input (payload, image, bbox, etc.) into model-ready tensors. + Must return (embeddings, dummy) tuple. + """ + raise NotImplementedError - def _update_labels(self, class_names: List[str], offset: int = 0): - """Synchronize the label manager with new or updated class names.""" - self.label_manager.update_labels(class_names, offset=offset) + def get_class_names(self) -> List[str]: + """Return list of class names associated with the processed data.""" + return self._class_names + + def get_offset(self) -> int: + """Return class offset or encoder index limit.""" + return self._encoder.offset diff --git a/apps/data-collection/backend/src/core/handlers/bbox_prompt_handler.py b/apps/data-collection/backend/src/core/handlers/bbox_prompt_handler.py index 36decbc2c..feecc6470 100644 --- a/apps/data-collection/backend/src/core/handlers/bbox_prompt_handler.py +++ b/apps/data-collection/backend/src/core/handlers/bbox_prompt_handler.py @@ -1,30 +1,54 @@ import numpy as np -from typing import Tuple, Dict, List + +from core.encoders.visual_prompt_encoder import VisualPromptEncoder from core.handlers.base_prompt_handler import BasePromptHandler +from core.infrastructure.frame_cache_node import FrameCacheNode +from core.services.payloads.bbox_prompt_payload import BBoxPromptPayload class BBoxPromptHandler(BasePromptHandler): - """Handles extraction of embeddings for a specific bounding box region.""" + """ + Handles extraction of embeddings for a specific bounding-box region. + Takes an image and a bounding box (normalized coordinates) and produces + model-ready visual embeddings with a dummy tensor. + """ + + def __init__(self, encoder: VisualPromptEncoder, frame_cache: FrameCacheNode): + super().__init__(encoder) + self._frame_cache: FrameCacheNode = frame_cache + self._image: np.ndarray | None = None + self._bbox: dict[str, float] | None = None + self._class_names: list[str] = ["Bounding Box Object"] + + def process(self, payload: BBoxPromptPayload) -> tuple[np.ndarray, np.ndarray]: + """Crop region mask based on bbox and extract embeddings.""" + self._image = self._frame_cache.get_last_frame() + self._bbox = payload["bbox"] + + mask = self._make_mask() + embeddings = self._encoder.extract_embeddings(self._image, mask) + dummy = self._encoder.make_dummy() - def process( - self, image: np.ndarray, bbox: Dict[str, float] - ) -> Tuple[np.ndarray, List[str], np.ndarray]: - H, W = image.shape[:2] - bx, by, bw, bh = bbox["x"], bbox["y"], bbox["width"], bbox["height"] + return embeddings, dummy + + def _make_mask(self) -> np.ndarray: + """Build a binary mask corresponding to the provided bounding box.""" + H, W = self._image.shape[:2] + bx, by, bw, bh = ( + self._bbox["x"], + self._bbox["y"], + self._bbox["width"], + self._bbox["height"], + ) x0, y0 = int(bx * W), int(by * H) x1, y1 = int((bx + bw) * W), int((by + bh) * H) x0, x1 = sorted((x0, x1)) y0, y1 = sorted((y0, y1)) + if x1 <= x0 or y1 <= y0: raise ValueError(f"Invalid bbox coordinates: {(x0, y0, x1, y1)}") mask = np.zeros((H, W), dtype=np.float32) mask[y0:y1, x0:x1] = 1.0 - - embeddings = self.encoder.extract_embeddings(image, mask) - dummy = self._make_dummy() - class_names = ["Bbox Object"] - - self._update_labels(class_names, offset=self.encoder.max_classes) - return embeddings, class_names, dummy + return mask diff --git a/apps/data-collection/backend/src/core/handlers/image_prompt_handler.py b/apps/data-collection/backend/src/core/handlers/image_prompt_handler.py index 0c2a9b41c..303f99eca 100644 --- a/apps/data-collection/backend/src/core/handlers/image_prompt_handler.py +++ b/apps/data-collection/backend/src/core/handlers/image_prompt_handler.py @@ -1,28 +1,37 @@ import base64 import cv2 import numpy as np -from typing import Tuple, List, Dict -from core.handlers.base_prompt_handler import BasePromptHandler - -def decode_base64_image(base64_data_uri: str) -> np.ndarray: - """Convert base64-encoded image to OpenCV array.""" - if "," in base64_data_uri: - _, base64_data = base64_data_uri.split(",", 1) - else: - base64_data = base64_data_uri - np_arr = np.frombuffer(base64.b64decode(base64_data), np.uint8) - return cv2.imdecode(np_arr, cv2.IMREAD_COLOR) +from core.encoders.visual_prompt_encoder import VisualPromptEncoder +from core.handlers.base_prompt_handler import BasePromptHandler +from core.services.payloads.image_upload_payload import ImageUploadPayload class ImagePromptHandler(BasePromptHandler): - """Handles decoding and embedding extraction for uploaded images.""" + """ + Handles full image prompt preparation flow: + decode base64 → preprocess → extract visual embeddings → prepare dummy features. + """ + + def __init__(self, encoder: VisualPromptEncoder): + super().__init__(encoder) + self._payload: ImageUploadPayload = None - def process(self, payload: Dict) -> Tuple[np.ndarray, List[str], np.ndarray]: - image = decode_base64_image(payload["data"]) - image_features = self.encoder.extract_embeddings(image) - dummy = self._make_dummy() - class_names = [payload["filename"].split(".")[0]] + def process(self, payload: ImageUploadPayload) -> tuple[np.ndarray, np.ndarray]: + """Decode and process uploaded image payload into model-ready features.""" + self._payload = payload + self._class_names = [self._payload["filename"].split(".")[0]] + image = self._decode_image() + embeddings = self._encoder.extract_embeddings(image) + dummy = self._encoder.make_dummy() + return embeddings, dummy - self._update_labels(class_names, offset=self.encoder.max_classes) - return image_features, class_names, dummy + def _decode_image(self) -> np.ndarray: + """Convert base64-encoded image to OpenCV array.""" + data_uri = self._payload["data"] + if "," in data_uri: + _, base64_data = data_uri.split(",", 1) + else: + base64_data = data_uri + np_arr = np.frombuffer(base64.b64decode(base64_data), np.uint8) + return cv2.imdecode(np_arr, cv2.IMREAD_COLOR) diff --git a/apps/data-collection/backend/src/core/handlers/text_prompt_handler.py b/apps/data-collection/backend/src/core/handlers/text_prompt_handler.py index f1d093743..d57916960 100644 --- a/apps/data-collection/backend/src/core/handlers/text_prompt_handler.py +++ b/apps/data-collection/backend/src/core/handlers/text_prompt_handler.py @@ -1,14 +1,22 @@ from __future__ import annotations -from typing import Tuple, List +from typing import Tuple import numpy as np + +from core.encoders.textual_prompt_encoder import TextualPromptEncoder from core.handlers.base_prompt_handler import BasePromptHandler +from core.services.payloads.class_update_payload import ClassUpdatePayload class TextPromptHandler(BasePromptHandler): """Handles embedding extraction and label synchronization for class name updates.""" - def process(self, new_classes: List[str]) -> Tuple[np.ndarray, np.ndarray]: - embeddings = self.encoder.extract_embeddings(new_classes) - dummy = self._make_dummy() - self._update_labels(new_classes) + def __init__(self, encoder: TextualPromptEncoder): + super().__init__(encoder) + self._payload: ClassUpdatePayload = None + + def process(self, payload: ClassUpdatePayload) -> Tuple[np.ndarray, np.ndarray]: + self._class_names = payload["classes"] + embeddings = self._encoder.extract_embeddings(self.get_class_names()) + dummy = self._encoder.make_dummy() + return embeddings, dummy diff --git a/apps/data-collection/backend/src/infrastructure/__init__.py b/apps/data-collection/backend/src/core/infrastructure/__init__.py similarity index 100% rename from apps/data-collection/backend/src/infrastructure/__init__.py rename to apps/data-collection/backend/src/core/infrastructure/__init__.py diff --git a/apps/data-collection/backend/src/infrastructure/export/__init__.py b/apps/data-collection/backend/src/core/infrastructure/export/__init__.py similarity index 100% rename from apps/data-collection/backend/src/infrastructure/export/__init__.py rename to apps/data-collection/backend/src/core/infrastructure/export/__init__.py diff --git a/apps/data-collection/backend/src/infrastructure/export/export_manager.py b/apps/data-collection/backend/src/core/infrastructure/export/export_manager.py similarity index 93% rename from apps/data-collection/backend/src/infrastructure/export/export_manager.py rename to apps/data-collection/backend/src/core/infrastructure/export/export_manager.py index 5bc32f5ca..383231d0b 100644 --- a/apps/data-collection/backend/src/infrastructure/export/export_manager.py +++ b/apps/data-collection/backend/src/core/infrastructure/export/export_manager.py @@ -1,7 +1,7 @@ from core.model_state import ModelState from core.snapping.conditions_engine import ConditionsEngine from config.system_state_exporter import SystemStateExporter -from services.export_service.export_service import ExportService +from core.services.export_service import ExportService class ExportManager: diff --git a/apps/data-collection/backend/src/infrastructure/frame_cache_node.py b/apps/data-collection/backend/src/core/infrastructure/frame_cache_node.py similarity index 94% rename from apps/data-collection/backend/src/infrastructure/frame_cache_node.py rename to apps/data-collection/backend/src/core/infrastructure/frame_cache_node.py index 621a7c7e1..4156a87a6 100644 --- a/apps/data-collection/backend/src/infrastructure/frame_cache_node.py +++ b/apps/data-collection/backend/src/core/infrastructure/frame_cache_node.py @@ -12,7 +12,6 @@ def build(self, frame: dai.Node.Output) -> "FrameCacheNode": return self def process(self, frame: dai.ImgFrame) -> dai.ImgFrame: - # Cache last BGR frame self._last_frame = frame.getCvFrame() return frame diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/__init__.py b/apps/data-collection/backend/src/core/infrastructure/neural_network/__init__.py similarity index 100% rename from apps/data-collection/backend/src/infrastructure/neural_network/__init__.py rename to apps/data-collection/backend/src/core/infrastructure/neural_network/__init__.py diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/annotation_node.py b/apps/data-collection/backend/src/core/infrastructure/neural_network/annotation_node.py similarity index 100% rename from apps/data-collection/backend/src/infrastructure/neural_network/annotation_node.py rename to apps/data-collection/backend/src/core/infrastructure/neural_network/annotation_node.py diff --git a/apps/data-collection/backend/src/core/infrastructure/neural_network/handlers_manager.py b/apps/data-collection/backend/src/core/infrastructure/neural_network/handlers_manager.py new file mode 100644 index 000000000..e667c4abf --- /dev/null +++ b/apps/data-collection/backend/src/core/infrastructure/neural_network/handlers_manager.py @@ -0,0 +1,25 @@ +from core.handlers.text_prompt_handler import TextPromptHandler +from core.handlers.image_prompt_handler import ImagePromptHandler +from core.handlers.bbox_prompt_handler import BBoxPromptHandler +from core.infrastructure.frame_cache_node import FrameCacheNode +from core.infrastructure.neural_network.prompt_encoders_manager import ( + PromptEncodersManager, +) + + +class HandlersManager: + def __init__(self, encoders: PromptEncodersManager, frame_cache: FrameCacheNode): + self._encoders = encoders + self._frame_cache = frame_cache + self.class_update_handler = self._class_update_handler() + self.image_update_handler = self._image_update_handler() + self.bbox_prompt_handler = self._bbox_prompt_handler() + + def _class_update_handler(self) -> TextPromptHandler: + return TextPromptHandler(self._encoders.textual_encoder) + + def _image_update_handler(self) -> ImagePromptHandler: + return ImagePromptHandler(self._encoders.visual_encoder) + + def _bbox_prompt_handler(self) -> BBoxPromptHandler: + return BBoxPromptHandler(self._encoders.visual_encoder, self._frame_cache) diff --git a/apps/data-collection/backend/src/core/infrastructure/neural_network/neural_network_manager.py b/apps/data-collection/backend/src/core/infrastructure/neural_network/neural_network_manager.py new file mode 100644 index 000000000..f562f86bc --- /dev/null +++ b/apps/data-collection/backend/src/core/infrastructure/neural_network/neural_network_manager.py @@ -0,0 +1,49 @@ +from typing import List +import depthai as dai +from box import Box + +from core.controllers.nn_prompts_controller import NnPromptsController +from core.infrastructure.frame_cache_node import FrameCacheNode +from core.infrastructure.neural_network.prompt_service_factory import ( + PromptServiceFactory, +) +from core.infrastructure.neural_network.prompt_encoders_manager import ( + PromptEncodersManager, +) +from core.infrastructure.neural_network.handlers_manager import HandlersManager +from core.services.base_service import BaseService + + +class NeuralNetworkManager: + """ + Facade for the neural-network subsystem. + """ + + def __init__( + self, + pipeline: dai.Pipeline, + input_node: dai.Node.Output, + config: Box, + controller: NnPromptsController, + ): + self._pipeline = pipeline + self._input_node = input_node + self._config = config + self._controller = controller + self._services: List[BaseService] = [] + + def build(self): + encoders = PromptEncodersManager(self._config) + frame_cache = self._pipeline.create(FrameCacheNode).build(self._input_node) + handlers = HandlersManager(encoders, frame_cache) + service_manager = PromptServiceFactory(self._controller, handlers) + self._services = service_manager.services + self._controller.send_prompts_pair( + encoders.image_prompt, + encoders.text_prompt, + self._config.class_names, + self._config.text_offset, + ) + + def get_services(self) -> list[BaseService]: + return self._services diff --git a/apps/data-collection/backend/src/core/infrastructure/neural_network/nn_pipeline_setup.py b/apps/data-collection/backend/src/core/infrastructure/neural_network/nn_pipeline_setup.py new file mode 100644 index 000000000..78a178130 --- /dev/null +++ b/apps/data-collection/backend/src/core/infrastructure/neural_network/nn_pipeline_setup.py @@ -0,0 +1,139 @@ +import depthai as dai +from depthai_nodes.node import ( + ParsingNeuralNetwork, + ImgDetectionsFilter, + ImgDetectionsBridge, +) +from config.config_data_classes import NeuralNetworkConfig +from core.label_manager import LabelManager +from core.infrastructure.neural_network.annotation_node import AnnotationNode +from core.controllers.nn_prompts_controller import NnPromptsController +from core.model_state import ModelState + + +class NNPipelineSetup: + """ + Handles creation of all neural network–related nodes in a DepthAI pipeline. + """ + + def __init__( + self, + pipeline: dai.Pipeline, + input_node: dai.Node.Output, + nn_config: NeuralNetworkConfig, + model_state: ModelState, + ): + self._pipeline = pipeline + self._input_node = input_node + self._nn_config = nn_config + self._model_state = model_state + + self._nn = None + self._prompt_controller = None + self._det_filter = None + self._annotation_node = None + self._filtered_bridge = None + self._tracker = None + + def build(self) -> "NNPipelineSetup": + self._build_nn() + self._build_filters() + self._build_tracker() + self._build_controller() + return self + + def _build_nn(self): + backend = self._nn_config.nn_yaml.nn_backend + + nn = self._pipeline.create(ParsingNeuralNetwork) + nn.setNNArchive(self._nn_config.model.archive) + nn.setBackend(backend.type) + nn.setBackendProperties( + { + "runtime": backend.runtime, + "performance_profile": backend.performance_profile, + } + ) + nn.setNumInferenceThreads(backend.inference_threads) + nn.getParser(0).setConfidenceThreshold(backend.confidence_threshold) + + self._input_node.link(nn.inputs["images"]) + self._nn = nn + + def _build_filters(self): + if not self._nn: + raise RuntimeError("Neural Network must be built before filters.") + + det_filter = self._pipeline.create(ImgDetectionsFilter).build(self._nn.out) + annotation_node = self._pipeline.create(AnnotationNode).build( + det_filter.out, self._input_node + ) + filtered_bridge = self._pipeline.create(ImgDetectionsBridge).build( + det_filter.out + ) + + self._det_filter = det_filter + self._annotation_node = annotation_node + self._filtered_bridge = filtered_bridge + + def _build_tracker(self): + if not self._filtered_bridge: + raise RuntimeError("Detections must be built before tracker.") + + tcfg = self._nn_config.nn_yaml.tracker + tracker = self._pipeline.create(dai.node.ObjectTracker) + + tracker.setTrackerType(dai.TrackerType.SHORT_TERM_IMAGELESS) + tracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.UNIQUE_ID) + tracker.setTrackingPerClass(tcfg.track_per_class) + tracker.setTrackletBirthThreshold(tcfg.birth_threshold) + tracker.setTrackletMaxLifespan(tcfg.max_lifespan) + tracker.setOcclusionRatioThreshold(tcfg.occlusion_ratio_threshold) + tracker.setTrackerThreshold(tcfg.tracker_threshold) + + source = self._input_node + source.link(tracker.inputTrackerFrame) + source.link(tracker.inputDetectionFrame) + self._filtered_bridge.out.link(tracker.inputDetections) + + self._tracker = tracker + + def _build_controller(self): + if not self._nn: + raise RuntimeError("Neural Network must be built before controller.") + + text_q = self._nn.inputs["texts"].createInputQueue() + img_q = self._nn.inputs["image_prompts"].createInputQueue() + self._nn.inputs["texts"].setReusePreviousMessage(True) + self._nn.inputs["image_prompts"].setReusePreviousMessage(True) + + parser = self._nn.getParser(0) + label_manager = LabelManager(self._det_filter, self._annotation_node) + self._prompt_controller = NnPromptsController( + img_q, + text_q, + self._nn_config.model.precision, + parser, + self._model_state, + label_manager, + ) + + @property + def nn(self) -> ParsingNeuralNetwork: + return self._nn + + @property + def controller(self) -> NnPromptsController: + return self._prompt_controller + + @property + def detections(self) -> ImgDetectionsBridge: + return self._filtered_bridge + + @property + def tracker(self) -> dai.node.ObjectTracker: + return self._tracker + + @property + def annotation_node(self) -> AnnotationNode: + return self._annotation_node diff --git a/apps/data-collection/backend/src/core/infrastructure/neural_network/prompt_encoders_manager.py b/apps/data-collection/backend/src/core/infrastructure/neural_network/prompt_encoders_manager.py new file mode 100644 index 000000000..0fa7f1fa0 --- /dev/null +++ b/apps/data-collection/backend/src/core/infrastructure/neural_network/prompt_encoders_manager.py @@ -0,0 +1,32 @@ +import numpy as np +from box import Box + +from core.encoders.textual_prompt_encoder import TextualPromptEncoder +from core.encoders.visual_prompt_encoder import VisualPromptEncoder + + +class PromptEncodersManager: + """ + Central manager for initializing and caching encoder components. + """ + + def __init__(self, config: Box): + self._model_config = config + + self.textual_encoder = self._init_textual_encoder() + self.visual_encoder = self._init_visual_encoder() + + self.text_prompt, self.image_prompt = self._prepare_initial_prompts() + + def _init_textual_encoder(self) -> TextualPromptEncoder: + return TextualPromptEncoder(config=self._model_config) + + def _init_visual_encoder(self) -> VisualPromptEncoder: + return VisualPromptEncoder(config=self._model_config) + + def _prepare_initial_prompts(self) -> tuple[np.ndarray, np.ndarray]: + text_prompt = self.textual_encoder.extract_embeddings( + self._model_config.class_names + ) + image_prompt = self.textual_encoder.make_dummy() + return text_prompt, image_prompt diff --git a/apps/data-collection/backend/src/core/infrastructure/neural_network/prompt_service_factory.py b/apps/data-collection/backend/src/core/infrastructure/neural_network/prompt_service_factory.py new file mode 100644 index 000000000..ab378dd0e --- /dev/null +++ b/apps/data-collection/backend/src/core/infrastructure/neural_network/prompt_service_factory.py @@ -0,0 +1,26 @@ +from core.controllers.nn_prompts_controller import NnPromptsController +from core.infrastructure.neural_network.handlers_manager import HandlersManager +from core.services.base_service import BaseService +from core.services.class_update_service import ClassUpdateService +from core.services.threshold_update_service import ThresholdUpdateService +from core.services.image_upload_service import ImageUploadService +from core.services.bbox_prompt_service import BBoxPromptService + + +class PromptServiceFactory: + def __init__( + self, + controller: NnPromptsController, + handlers: HandlersManager, + ): + self.controller = controller + self.handlers = handlers + self.services = self._build_services() + + def _build_services(self) -> list[BaseService]: + return [ + ClassUpdateService(self.controller, self.handlers.class_update_handler), + ThresholdUpdateService(self.controller), + ImageUploadService(self.controller, self.handlers.image_update_handler), + BBoxPromptService(self.controller, self.handlers.bbox_prompt_handler), + ] diff --git a/apps/data-collection/backend/src/infrastructure/snaps/__init__.py b/apps/data-collection/backend/src/core/infrastructure/snaps/__init__.py similarity index 100% rename from apps/data-collection/backend/src/infrastructure/snaps/__init__.py rename to apps/data-collection/backend/src/core/infrastructure/snaps/__init__.py diff --git a/apps/data-collection/backend/src/infrastructure/snaps/snaps_manager.py b/apps/data-collection/backend/src/core/infrastructure/snaps/snaps_manager.py similarity index 57% rename from apps/data-collection/backend/src/infrastructure/snaps/snaps_manager.py rename to apps/data-collection/backend/src/core/infrastructure/snaps/snaps_manager.py index 3effb7ab9..f6c601d95 100644 --- a/apps/data-collection/backend/src/infrastructure/snaps/snaps_manager.py +++ b/apps/data-collection/backend/src/core/infrastructure/snaps/snaps_manager.py @@ -1,13 +1,11 @@ import depthai as dai +from box import Box from depthai_nodes.node import SnapsProducer -from config.yaml_loader import YamlLoader from core.snapping.conditions_engine import ConditionsEngine -from infrastructure.neural_network.neural_network_manager import NeuralNetworkManager -from infrastructure.snaps.conditions_factory import ConditionsFactory -from infrastructure.snaps.snaps_producer_factory import SnapsProducerFactory -from infrastructure.video_source_manager import VideoSourceManager -from services.snap_collection.snap_collection_service import SnapCollectionService +from core.snapping.conditions_factory import ConditionsFactory +from core.infrastructure.snaps.snaps_producer_factory import SnapsProducerFactory +from core.services.snap_collection_service import SnapCollectionService class SnapsManager: @@ -18,13 +16,15 @@ class SnapsManager: def __init__( self, pipeline: dai.Pipeline, - video_source: VideoSourceManager, - nn_manager: NeuralNetworkManager, - conditions_config: YamlLoader, + input_node: dai.Node.Output, + tracker: dai.node.ObjectTracker, + detections: dai.ImgDetections, + conditions_config: Box, ): self._pipeline = pipeline - self._video_source = video_source - self._nn_manager = nn_manager + self._input_node = input_node + self._tracker = tracker + self._detections = detections self._conditions_config = conditions_config self._producer: SnapsProducer = None @@ -38,9 +38,9 @@ def _build(self) -> "SnapsManager": snaps_producer = SnapsProducerFactory.create( self._pipeline, - self._video_source, - self._nn_manager.get_tracker(), - self._nn_manager.get_detections(), + self._input_node, + self._tracker, + self._detections, self._engine, ) self._producer = snaps_producer diff --git a/apps/data-collection/backend/src/infrastructure/snaps/snaps_producer_factory.py b/apps/data-collection/backend/src/core/infrastructure/snaps/snaps_producer_factory.py similarity index 81% rename from apps/data-collection/backend/src/infrastructure/snaps/snaps_producer_factory.py rename to apps/data-collection/backend/src/core/infrastructure/snaps/snaps_producer_factory.py index 114f4bfb1..7b04eb77d 100644 --- a/apps/data-collection/backend/src/infrastructure/snaps/snaps_producer_factory.py +++ b/apps/data-collection/backend/src/core/infrastructure/snaps/snaps_producer_factory.py @@ -6,20 +6,18 @@ import depthai as dai from depthai_nodes.node import ImgDetectionsBridge -from infrastructure.video_source_manager import VideoSourceManager - class SnapsProducerFactory: @staticmethod def create( pipeline: dai.Pipeline, - video_source: VideoSourceManager, + input_node: dai.Node.Output, tracker: dai.node.ObjectTracker, detections: ImgDetectionsBridge, engine: ConditionsEngine, ) -> SnapsProducer: return pipeline.create(SnapsProducer2Buffered).build( - frame=video_source.get_video_source_output(), + frame=input_node, msg=tracker.out, msg2=detections.out, running=False, diff --git a/apps/data-collection/backend/src/infrastructure/system_context.py b/apps/data-collection/backend/src/core/infrastructure/system_context.py similarity index 89% rename from apps/data-collection/backend/src/infrastructure/system_context.py rename to apps/data-collection/backend/src/core/infrastructure/system_context.py index ce41b0b7e..e437d10f9 100644 --- a/apps/data-collection/backend/src/infrastructure/system_context.py +++ b/apps/data-collection/backend/src/core/infrastructure/system_context.py @@ -1,6 +1,6 @@ import depthai as dai -from services.base_service import BaseService +from core.services.base_service import BaseService class SystemContext: @@ -13,7 +13,7 @@ def __init__(self, platform: str | None = None): def register_service(self, service: BaseService) -> None: """Registers a service in the system context.""" - self.visualizer.registerService(service.get_name(), service.handle) + self.visualizer.registerService(service.name(), service.handle) def register_services(self, services: list[BaseService]) -> None: """Registers multiple services in the system context.""" diff --git a/apps/data-collection/backend/src/core/infrastructure/video_factory.py b/apps/data-collection/backend/src/core/infrastructure/video_factory.py new file mode 100644 index 000000000..ce6d4dc69 --- /dev/null +++ b/apps/data-collection/backend/src/core/infrastructure/video_factory.py @@ -0,0 +1,39 @@ +import depthai as dai +from config.config_data_classes import VideoConfig +from core.infrastructure.video_providers.base_video_provider import BaseVideoProvider +from core.infrastructure.video_providers.camera_video_provvider import ( + CameraVideoProvider, +) +from core.infrastructure.video_providers.replay_video_provider import ( + ReplayVideoProvider, +) + + +class VideoFactory: + """Facade that chooses the right source type and provides encoder.""" + + def __init__(self, pipeline: dai.Pipeline, cfg: VideoConfig): + self._pipeline = pipeline + self._config = cfg + self._source = self._select_source() + + def _select_source(self) -> BaseVideoProvider: + if self._config.media_path: + return ReplayVideoProvider(self._pipeline, self._config) + return CameraVideoProvider(self._pipeline, self._config) + + def get_input_node(self) -> dai.Node.Output: + return self._source.get_input_node() + + def get_nv12_output(self) -> dai.Node.Output: + return self._source.get_nv12_output() + + def get_encoded_output(self) -> dai.Node.Output: + """Attach H.264 encoder to NV12 output.""" + encoder = self._pipeline.create(dai.node.VideoEncoder) + encoder.setDefaultProfilePreset( + fps=self._config.fps, + profile=dai.VideoEncoderProperties.Profile.H264_MAIN, + ) + self.get_nv12_output().link(encoder.input) + return encoder.out diff --git a/apps/data-collection/backend/src/services/__init__.py b/apps/data-collection/backend/src/core/infrastructure/video_providers/__init__.py similarity index 100% rename from apps/data-collection/backend/src/services/__init__.py rename to apps/data-collection/backend/src/core/infrastructure/video_providers/__init__.py diff --git a/apps/data-collection/backend/src/core/infrastructure/video_providers/base_video_provider.py b/apps/data-collection/backend/src/core/infrastructure/video_providers/base_video_provider.py new file mode 100644 index 000000000..3b901072d --- /dev/null +++ b/apps/data-collection/backend/src/core/infrastructure/video_providers/base_video_provider.py @@ -0,0 +1,21 @@ +from abc import ABC, abstractmethod +import depthai as dai +from config.config_data_classes import VideoConfig + + +class BaseVideoProvider(ABC): + """Abstract interface for any DepthAI video input source.""" + + def __init__(self, pipeline: dai.Pipeline, config: VideoConfig): + self._pipeline = pipeline + self._config = config + + @abstractmethod + def get_input_node(self) -> dai.Node.Output: + """Return BGR888i output for NN input.""" + pass + + @abstractmethod + def get_nv12_output(self) -> dai.Node.Output: + """Return NV12 output for visualization.""" + pass diff --git a/apps/data-collection/backend/src/core/infrastructure/video_providers/camera_video_provvider.py b/apps/data-collection/backend/src/core/infrastructure/video_providers/camera_video_provvider.py new file mode 100644 index 000000000..b51f394fd --- /dev/null +++ b/apps/data-collection/backend/src/core/infrastructure/video_providers/camera_video_provvider.py @@ -0,0 +1,31 @@ +import depthai as dai + +from config.config_data_classes import VideoConfig +from core.infrastructure.video_providers.base_video_provider import BaseVideoProvider + + +class CameraVideoProvider(BaseVideoProvider): + """Video source from live DepthAI camera.""" + + def __init__(self, pipeline: dai.Pipeline, config: VideoConfig): + super().__init__(pipeline, config) + self._camera: dai.node.Camera = self._build_camera() + + def get_input_node(self) -> dai.Node.Output: + return self._camera.requestOutput( + size=(self._config.width, self._config.height), + type=dai.ImgFrame.Type.BGR888i, + fps=self._config.fps, + ) + + def get_nv12_output(self) -> dai.Node.Output: + return self._camera.requestOutput( + size=self._config.resolution, + type=dai.ImgFrame.Type.NV12, + fps=self._config.fps, + ) + + def _build_camera(self) -> dai.node.Camera: + return self._pipeline.create(dai.node.Camera).build( + boardSocket=dai.CameraBoardSocket.CAM_A + ) diff --git a/apps/data-collection/backend/src/core/infrastructure/video_providers/replay_video_provider.py b/apps/data-collection/backend/src/core/infrastructure/video_providers/replay_video_provider.py new file mode 100644 index 000000000..2f83deef6 --- /dev/null +++ b/apps/data-collection/backend/src/core/infrastructure/video_providers/replay_video_provider.py @@ -0,0 +1,33 @@ +from pathlib import Path + +from config.config_data_classes import VideoConfig +from core.infrastructure.video_providers.base_video_provider import BaseVideoProvider +import depthai as dai + + +class ReplayVideoProvider(BaseVideoProvider): + """Video source from file replay.""" + + def __init__(self, pipeline: dai.Pipeline, config: VideoConfig): + super().__init__(pipeline, config) + self._replay = self._build_replay() + + def get_input_node(self) -> dai.Node.Output: + manip = self._pipeline.create(dai.node.ImageManip) + manip.setMaxOutputFrameSize(self._config.width * self._config.height * 3) + manip.initialConfig.setOutputSize(self._config.width, self._config.height) + manip.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888i) + self._replay.out.link(manip.inputImage) + return manip.out + + def get_nv12_output(self) -> dai.Node.Output: + return self._replay.out + + def _build_replay(self) -> dai.node.ReplayVideo: + replay = self._pipeline.create(dai.node.ReplayVideo) + replay.setReplayVideoFile(Path(self._config.media_path)) + replay.setOutFrameType(dai.ImgFrame.Type.NV12) + replay.setLoop(True) + replay.setFps(self._config.fps) + replay.setSize(self._config.resolution) + return replay diff --git a/apps/data-collection/backend/src/core/label_manager.py b/apps/data-collection/backend/src/core/label_manager.py index 2376502c5..1a5c0fb3b 100644 --- a/apps/data-collection/backend/src/core/label_manager.py +++ b/apps/data-collection/backend/src/core/label_manager.py @@ -1,5 +1,5 @@ from depthai_nodes.node import ImgDetectionsFilter -from infrastructure.neural_network.annotation_node import AnnotationNode +from core.infrastructure.neural_network.annotation_node import AnnotationNode class LabelManager: diff --git a/apps/data-collection/backend/src/services/bbox_prompt/__init__.py b/apps/data-collection/backend/src/core/services/__init__.py similarity index 100% rename from apps/data-collection/backend/src/services/bbox_prompt/__init__.py rename to apps/data-collection/backend/src/core/services/__init__.py diff --git a/apps/data-collection/backend/src/core/services/base_service.py b/apps/data-collection/backend/src/core/services/base_service.py new file mode 100644 index 000000000..07399574e --- /dev/null +++ b/apps/data-collection/backend/src/core/services/base_service.py @@ -0,0 +1,30 @@ +from abc import ABC, abstractmethod +from typing import Generic, TypeVar + +from core.controllers.nn_prompts_controller import NnPromptsController +from core.handlers.base_prompt_handler import BasePromptHandler +from core.services.service_name import ServiceName + +PayloadT = TypeVar("PayloadT", bound=dict) + + +class BaseService(ABC, Generic[PayloadT]): + NAME: ServiceName + + def __init__( + self, + controller: NnPromptsController | None = None, + handler: BasePromptHandler | None = None, + ): + self._controller = controller + self._handler = handler + self.__name = self.NAME + + @abstractmethod + def handle(self, payload: PayloadT) -> dict[str, any]: + """Execute service logic and return a JSON-serializable response.""" + pass + + @property + def name(self) -> ServiceName: + return self.__name diff --git a/apps/data-collection/backend/src/core/services/bbox_prompt_service.py b/apps/data-collection/backend/src/core/services/bbox_prompt_service.py new file mode 100644 index 000000000..d9a0bd955 --- /dev/null +++ b/apps/data-collection/backend/src/core/services/bbox_prompt_service.py @@ -0,0 +1,16 @@ +from core.services.base_service import BaseService +from core.services.payloads.bbox_prompt_payload import BBoxPromptPayload +from core.services.service_name import ServiceName + + +class BBoxPromptService(BaseService[BBoxPromptPayload]): + NAME = ServiceName.BBOX_PROMPT + + def handle(self, payload: BBoxPromptPayload) -> dict[str, any]: + image_inputs, dummy = self._handler.process(payload) + class_names = self._handler.get_class_names() + self._controller.send_prompts_pair( + image_inputs, dummy, class_names, self._handler.get_offset() + ) + + return {"ok": True, "classes": class_names} diff --git a/apps/data-collection/backend/src/core/services/class_update_service.py b/apps/data-collection/backend/src/core/services/class_update_service.py new file mode 100644 index 000000000..c53c47ccb --- /dev/null +++ b/apps/data-collection/backend/src/core/services/class_update_service.py @@ -0,0 +1,18 @@ +from core.services.base_service import BaseService +from core.services.payloads.class_update_payload import ClassUpdatePayload +from core.services.service_name import ServiceName + + +class ClassUpdateService(BaseService[ClassUpdatePayload]): + """Coordinates text-based class updates across model, repository, and state.""" + + NAME = ServiceName.CLASS_UPDATE + + def handle(self, payload: ClassUpdatePayload) -> dict[str, any]: + text_inputs, dummy = self._handler.process(payload) + new_classes = self._handler.get_class_names() + self._controller.send_prompts_pair( + dummy, text_inputs, new_classes, self._handler.get_offset() + ) + + return {"ok": True, "classes": new_classes} diff --git a/apps/data-collection/backend/src/services/export_service/export_service.py b/apps/data-collection/backend/src/core/services/export_service.py similarity index 86% rename from apps/data-collection/backend/src/services/export_service/export_service.py rename to apps/data-collection/backend/src/core/services/export_service.py index 8dd0d5e33..8c02d41a2 100644 --- a/apps/data-collection/backend/src/services/export_service/export_service.py +++ b/apps/data-collection/backend/src/core/services/export_service.py @@ -1,6 +1,6 @@ -from services.base_service import BaseService +from core.services.base_service import BaseService from config.system_state_exporter import SystemStateExporter -from services.service_name import ServiceName +from core.services.service_name import ServiceName class ExportService(BaseService[None]): diff --git a/apps/data-collection/backend/src/core/services/image_upload_service.py b/apps/data-collection/backend/src/core/services/image_upload_service.py new file mode 100644 index 000000000..eb77b4f2f --- /dev/null +++ b/apps/data-collection/backend/src/core/services/image_upload_service.py @@ -0,0 +1,19 @@ +from core.services.base_service import BaseService +from core.services.payloads.image_upload_payload import ImageUploadPayload +from core.services.service_name import ServiceName + + +class ImageUploadService(BaseService[ImageUploadPayload]): + """Coordinates image upload flow: decode → extract → send → update labels.""" + + NAME = ServiceName.IMAGE_UPLOAD + + def handle(self, payload: ImageUploadPayload) -> dict[str, any]: + image_inputs, dummy = self._handler.process(payload) + class_names = self._handler.get_class_names() + + self._controller.send_prompts_pair( + image_inputs, dummy, class_names, self._handler.get_offset() + ) + + return {"ok": True, "class": class_names} diff --git a/apps/data-collection/backend/src/services/class_update/__init__.py b/apps/data-collection/backend/src/core/services/payloads/__init__.py similarity index 100% rename from apps/data-collection/backend/src/services/class_update/__init__.py rename to apps/data-collection/backend/src/core/services/payloads/__init__.py diff --git a/apps/data-collection/backend/src/services/bbox_prompt/bbox_prompt_payload.py b/apps/data-collection/backend/src/core/services/payloads/bbox_prompt_payload.py similarity index 100% rename from apps/data-collection/backend/src/services/bbox_prompt/bbox_prompt_payload.py rename to apps/data-collection/backend/src/core/services/payloads/bbox_prompt_payload.py diff --git a/apps/data-collection/backend/src/services/class_update/class_update_payload.py b/apps/data-collection/backend/src/core/services/payloads/class_update_payload.py similarity index 100% rename from apps/data-collection/backend/src/services/class_update/class_update_payload.py rename to apps/data-collection/backend/src/core/services/payloads/class_update_payload.py diff --git a/apps/data-collection/backend/src/services/image_upload/image_upload_payload.py b/apps/data-collection/backend/src/core/services/payloads/image_upload_payload.py similarity index 100% rename from apps/data-collection/backend/src/services/image_upload/image_upload_payload.py rename to apps/data-collection/backend/src/core/services/payloads/image_upload_payload.py diff --git a/apps/data-collection/backend/src/services/snap_collection/snap_payload.py b/apps/data-collection/backend/src/core/services/payloads/snap_payload.py similarity index 100% rename from apps/data-collection/backend/src/services/snap_collection/snap_payload.py rename to apps/data-collection/backend/src/core/services/payloads/snap_payload.py diff --git a/apps/data-collection/backend/src/services/threshold_update/threshold_update_payload.py b/apps/data-collection/backend/src/core/services/payloads/threshold_update_payload.py similarity index 100% rename from apps/data-collection/backend/src/services/threshold_update/threshold_update_payload.py rename to apps/data-collection/backend/src/core/services/payloads/threshold_update_payload.py diff --git a/apps/data-collection/backend/src/services/service_name.py b/apps/data-collection/backend/src/core/services/service_name.py similarity index 100% rename from apps/data-collection/backend/src/services/service_name.py rename to apps/data-collection/backend/src/core/services/service_name.py diff --git a/apps/data-collection/backend/src/services/snap_collection/snap_collection_service.py b/apps/data-collection/backend/src/core/services/snap_collection_service.py similarity index 82% rename from apps/data-collection/backend/src/services/snap_collection/snap_collection_service.py rename to apps/data-collection/backend/src/core/services/snap_collection_service.py index 683e1cf21..cea1dfe67 100644 --- a/apps/data-collection/backend/src/services/snap_collection/snap_collection_service.py +++ b/apps/data-collection/backend/src/core/services/snap_collection_service.py @@ -1,8 +1,8 @@ -from services.base_service import BaseService +from core.services.base_service import BaseService from core.snapping.conditions_engine import ConditionsEngine from depthai_nodes.node import SnapsProducer -from services.snap_collection.snap_payload import SnapPayload -from services.service_name import ServiceName +from core.services.payloads.snap_payload import SnapPayload +from core.services.service_name import ServiceName class SnapCollectionService(BaseService[SnapPayload]): diff --git a/apps/data-collection/backend/src/core/services/threshold_update_service.py b/apps/data-collection/backend/src/core/services/threshold_update_service.py new file mode 100644 index 000000000..4bd4bc208 --- /dev/null +++ b/apps/data-collection/backend/src/core/services/threshold_update_service.py @@ -0,0 +1,17 @@ +from core.services.base_service import BaseService +from core.services.payloads.threshold_update_payload import ThresholdUpdatePayload +from core.services.service_name import ServiceName + + +class ThresholdUpdateService(BaseService[ThresholdUpdatePayload]): + """Coordinates NN confidence threshold updates between handler, repository, and state.""" + + NAME = ServiceName.THRESHOLD_UPDATE + + def handle(self, payload: ThresholdUpdatePayload) -> dict[str, any]: + new_threshold = payload["threshold"] + + clamped = max(0.0, min(1.0, new_threshold)) + self._controller.set_confidence_threshold(clamped) + + return {"ok": True, "threshold": clamped} diff --git a/apps/data-collection/backend/src/infrastructure/snaps/conditions_factory.py b/apps/data-collection/backend/src/core/snapping/conditions_factory.py similarity index 92% rename from apps/data-collection/backend/src/infrastructure/snaps/conditions_factory.py rename to apps/data-collection/backend/src/core/snapping/conditions_factory.py index b3f96fe4b..30d4cb0a3 100644 --- a/apps/data-collection/backend/src/infrastructure/snaps/conditions_factory.py +++ b/apps/data-collection/backend/src/core/snapping/conditions_factory.py @@ -1,11 +1,13 @@ import importlib -from config.yaml_loader import YamlLoader + +from box import Box + from core.snapping.conditions_engine import ConditionsEngine from core.snapping.conditions.base_condition import Condition class ConditionsFactory: - def __init__(self, conditions_yaml: YamlLoader): + def __init__(self, conditions_yaml: Box): self._conditions_yaml = conditions_yaml self._engine: ConditionsEngine = self._build_engine() diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/encoders_manager.py b/apps/data-collection/backend/src/infrastructure/neural_network/encoders_manager.py deleted file mode 100644 index 745d4cc36..000000000 --- a/apps/data-collection/backend/src/infrastructure/neural_network/encoders_manager.py +++ /dev/null @@ -1,41 +0,0 @@ -import numpy as np - -from config.yaml_loader import YamlLoader -from core.encoders.textual_encoder import TextualEncoder -from core.encoders.visual_encoder import VisualEncoder - - -class EncodersManager: - """ - Central manager for initializing and caching encoder components. - """ - - def __init__(self, config: YamlLoader, constants: YamlLoader): - self._model_config = config - self._constants = constants - - self.textual_encoder = self._init_textual_encoder() - self.visual_encoder = self._init_visual_encoder() - - self.text_prompt, self.image_prompt = self._prepare_initial_prompts() - - def _init_textual_encoder(self) -> TextualEncoder: - return TextualEncoder( - config=self._model_config, - precision=self._model_config.default_precision, - max_classes=self._constants.max_num_classes, - ) - - def _init_visual_encoder(self) -> VisualEncoder: - return VisualEncoder( - config=self._model_config, - precision=self._model_config.default_precision, - max_classes=self._constants.max_num_classes, - ) - - def _prepare_initial_prompts(self) -> tuple[np.ndarray, np.ndarray]: - text_prompt = self.textual_encoder.extract_embeddings( - self._constants.class_names - ) - image_prompt = self.textual_encoder.make_dummy_features() - return text_prompt, image_prompt diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/handlers_manager.py b/apps/data-collection/backend/src/infrastructure/neural_network/handlers_manager.py deleted file mode 100644 index c850aeafe..000000000 --- a/apps/data-collection/backend/src/infrastructure/neural_network/handlers_manager.py +++ /dev/null @@ -1,30 +0,0 @@ -from core.label_manager import LabelManager -from core.handlers.text_prompt_handler import TextPromptHandler -from core.handlers.image_prompt_handler import ImagePromptHandler -from core.handlers.bbox_prompt_handler import BBoxPromptHandler -from infrastructure.neural_network.annotation_node import AnnotationNode -from infrastructure.neural_network.encoders_manager import EncodersManager -from depthai_nodes.node import ImgDetectionsFilter - - -class HandlersManager: - def __init__( - self, - encoders: EncodersManager, - det_filter: ImgDetectionsFilter, - annotation_node: AnnotationNode, - ): - self.label_manager = LabelManager(det_filter, annotation_node) - self.encoders = encoders - self.class_update_handler = self._class_update_handler() - self.image_update_handler = self._image_update_handler() - self.bbox_prompt_handler = self._bbox_prompt_handler() - - def _class_update_handler(self) -> TextPromptHandler: - return TextPromptHandler(self.encoders.textual_encoder, self.label_manager) - - def _image_update_handler(self) -> ImagePromptHandler: - return ImagePromptHandler(self.encoders.visual_encoder, self.label_manager) - - def _bbox_prompt_handler(self) -> BBoxPromptHandler: - return BBoxPromptHandler(self.encoders.visual_encoder, self.label_manager) diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py b/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py deleted file mode 100644 index 98a0d4a36..000000000 --- a/apps/data-collection/backend/src/infrastructure/neural_network/neural_network_manager.py +++ /dev/null @@ -1,90 +0,0 @@ -from typing import List -import depthai as dai - -from depthai_nodes.node import ImgDetectionsBridge - -from config.config_data_classes import NeuralNetworkConfig -from core.controllers.nn_controller import YOLONNController -from core.model_state import ModelState -from infrastructure.neural_network.annotation_node import AnnotationNode -from infrastructure.neural_network.nn_service_factory import NNServiceFactory -from infrastructure.video_source_manager import VideoSourceManager -from infrastructure.neural_network.nn_pipeline_setup import NNPipelineSetup -from infrastructure.neural_network.encoders_manager import EncodersManager -from infrastructure.neural_network.handlers_manager import HandlersManager -from services.base_service import BaseService - - -class NeuralNetworkManager: - """ - Facade for the neural-network subsystem. - """ - - def __init__( - self, - pipeline: dai.Pipeline, - video_source: VideoSourceManager, - config: NeuralNetworkConfig, - model_state: ModelState, - ): - self._pipeline = pipeline - self._video_source = video_source - self._config = config - self._model_state = model_state - self._tracker: dai.node.ObjectTracker = None - self._filtered_detections: ImgDetectionsBridge = None - self._controller: YOLONNController = None - self._annotations: AnnotationNode = None - self._services: List[BaseService] = [] - self.build() - - def build(self) -> "NeuralNetworkManager": - pipeline_builder = NNPipelineSetup( - self._pipeline, - self._video_source, - self._config, - self._model_state, - ) - self._controller = pipeline_builder.get_controller() - - encoders = EncodersManager(self._config.nn_yaml.model, self._config.constants) - - handlers = HandlersManager( - encoders, - pipeline_builder.get_filter(), - pipeline_builder.get_annotation_node(), - ) - handlers.label_manager.update_labels( - self._config.constants.class_names, self._config.constants.class_offset - ) - - service_manager = NNServiceFactory( - self._controller, handlers, self._video_source.get_frame_cache() - ) - - self._services = service_manager.services - - self._controller.send_embeddings_pair( - encoders.image_prompt, - encoders.text_prompt, - self._config.constants.class_names, - ) - - self._annotations = pipeline_builder.get_annotation_node() - - self._tracker = pipeline_builder.get_tracker() - self._filtered_detections = pipeline_builder.get_detections() - - return self - - def get_tracker(self): - return self._tracker - - def get_detections(self): - return self._filtered_detections - - def get_services(self) -> list[BaseService]: - return self._services - - def get_annotations(self) -> dai.Node.Output: - return self._annotations.out diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/nn_pipeline_setup.py b/apps/data-collection/backend/src/infrastructure/neural_network/nn_pipeline_setup.py deleted file mode 100644 index 6e4ef5684..000000000 --- a/apps/data-collection/backend/src/infrastructure/neural_network/nn_pipeline_setup.py +++ /dev/null @@ -1,107 +0,0 @@ -import depthai as dai -from depthai_nodes.node import ( - ParsingNeuralNetwork, - ImgDetectionsFilter, - ImgDetectionsBridge, -) - -from config.config_data_classes import NeuralNetworkConfig -from infrastructure.neural_network.annotation_node import AnnotationNode -from core.controllers.nn_controller import YOLONNController -from core.model_state import ModelState -from infrastructure.video_source_manager import VideoSourceManager - - -class NNPipelineSetup: - def __init__( - self, - pipeline: dai.Pipeline, - video_source: VideoSourceManager, - nn_config: NeuralNetworkConfig, - model_state: ModelState, - ): - self._pipeline = pipeline - self._video_source = video_source - self._nn_config = nn_config - self._model_state = model_state - - self._det_filter = None - self._annotation_node = None - self._filtered_bridge = None - self._object_tracker = None - self._controller = None - self._nn = None - - self.build() - - def build(self): - nn = self._build_nn() - self._controller = self._build_controller(nn) - self._filtered_bridge = self._build_filters(nn) - self._object_tracker = self._build_tracker() - - def _build_nn(self): - nn = self._pipeline.create(ParsingNeuralNetwork) - nn.setNNArchive(self._nn_config.model.archive) - nn.setBackend(self._nn_config.nn_yaml.nn_backend.type) - nn.setBackendProperties( - { - "runtime": self._nn_config.nn_yaml.nn_backend.runtime, - "performance_profile": self._nn_config.nn_yaml.nn_backend.performance_profile, - } - ) - nn.setNumInferenceThreads(self._nn_config.nn_yaml.nn_backend.inference_threads) - nn.getParser(0).setConfidenceThreshold(0.1) - self._video_source.get_input_node().link(nn.inputs["images"]) - self._nn = nn - return nn - - def _build_filters(self, nn) -> ImgDetectionsBridge: - self._det_filter = self._pipeline.create(ImgDetectionsFilter).build(nn.out) - self._annotation_node = self._pipeline.create(AnnotationNode).build( - self._det_filter.out, self._video_source.get_video_source_output() - ) - return self._pipeline.create(ImgDetectionsBridge).build(self._det_filter.out) - - def _build_controller(self, nn) -> YOLONNController: - text_q = nn.inputs["texts"].createInputQueue() - img_q = nn.inputs["image_prompts"].createInputQueue() - nn.inputs["texts"].setReusePreviousMessage(True) - nn.inputs["image_prompts"].setReusePreviousMessage(True) - parser = nn.getParser(0) - return YOLONNController( - img_q, text_q, self._nn_config.model.precision, parser, self._model_state - ) - - def _build_tracker(self) -> dai.node.ObjectTracker: - tracker = self._pipeline.create(dai.node.ObjectTracker) - tracker.setTrackerType(dai.TrackerType.SHORT_TERM_IMAGELESS) - tracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.UNIQUE_ID) - tracker.setTrackingPerClass(self._nn_config.nn_yaml.tracker.track_per_class) - tracker.setTrackletBirthThreshold( - self._nn_config.nn_yaml.tracker.birth_threshold - ) - tracker.setTrackletMaxLifespan(self._nn_config.nn_yaml.tracker.max_lifespan) - tracker.setOcclusionRatioThreshold( - self._nn_config.nn_yaml.tracker.occlusion_ratio_threshold - ) - tracker.setTrackerThreshold(self._nn_config.nn_yaml.tracker.tracker_threshold) - self._video_source.get_input_node().link(tracker.inputTrackerFrame) - self._video_source.get_input_node().link(tracker.inputDetectionFrame) - self._filtered_bridge.out.link(tracker.inputDetections) - return tracker - - def get_tracker(self) -> dai.node.ObjectTracker: - return self._object_tracker - - def get_detections(self) -> ImgDetectionsBridge: - return self._filtered_bridge - - def get_filter(self) -> ImgDetectionsFilter: - return self._det_filter - - def get_annotation_node(self) -> AnnotationNode: - return self._annotation_node - - def get_controller(self) -> YOLONNController: - return self._controller diff --git a/apps/data-collection/backend/src/infrastructure/neural_network/nn_service_factory.py b/apps/data-collection/backend/src/infrastructure/neural_network/nn_service_factory.py deleted file mode 100644 index 0303d8d10..000000000 --- a/apps/data-collection/backend/src/infrastructure/neural_network/nn_service_factory.py +++ /dev/null @@ -1,33 +0,0 @@ -from core.controllers.nn_controller import YOLONNController -from infrastructure.frame_cache_node import FrameCacheNode -from infrastructure.neural_network.handlers_manager import HandlersManager -from services.base_service import BaseService -from services.class_update.class_update_service import ClassUpdateService -from services.threshold_update.threshold_update_service import ThresholdUpdateService -from services.image_upload.image_upload_service import ImageUploadService -from services.bbox_prompt.bbox_prompt_service import BBoxPromptService - - -class NNServiceFactory: - def __init__( - self, - controller: YOLONNController, - handlers: HandlersManager, - frame_cache: FrameCacheNode, - ): - self.controller = controller - self.handlers = handlers - self.frame_cache = frame_cache - self.services = self._build_services() - - def _build_services(self) -> list[BaseService]: - return [ - ClassUpdateService(self.controller, self.handlers.class_update_handler), - ThresholdUpdateService(self.controller), - ImageUploadService(self.controller, self.handlers.image_update_handler), - BBoxPromptService( - self.handlers.bbox_prompt_handler, - self.frame_cache, - self.controller, - ), - ] diff --git a/apps/data-collection/backend/src/infrastructure/video_source_manager.py b/apps/data-collection/backend/src/infrastructure/video_source_manager.py deleted file mode 100644 index 9200c16f4..000000000 --- a/apps/data-collection/backend/src/infrastructure/video_source_manager.py +++ /dev/null @@ -1,102 +0,0 @@ -from pathlib import Path -import depthai as dai -from infrastructure.frame_cache_node import FrameCacheNode -from config.config_data_classes import VideoConfig - - -class VideoSourceManager: - """ - Creates and manages video input nodes (camera or replay) and encodes video streams. - """ - - def __init__( - self, - pipeline: dai.Pipeline, - video_config: VideoConfig, - ): - self._pipeline = pipeline - self._video_config = video_config - - self._video_src_out: dai.Node.Output = None - self._input_node: dai.Node.Output = None - self._video_enc: dai.node.VideoEncoder = None - self._frame_cache: FrameCacheNode = None - - self._build() - - def _build(self): - """Decide source type and build full video setup.""" - if self._video_config.media_path: - self._create_replay_source() - else: - self._create_camera_source() - self._create_encoder() - self._frame_cache = self._pipeline.create(FrameCacheNode).build( - self._video_src_out - ) - - def _create_replay_source(self): - """Replay from a video file.""" - - replay = self._pipeline.create(dai.node.ReplayVideo) - replay.setReplayVideoFile(Path(self._video_config.media_path)) - replay.setOutFrameType(dai.ImgFrame.Type.NV12) - replay.setLoop(True) - if self._video_config.fps: - replay.setFps(self._video_config.fps) - replay.setSize(self._video_config.resolution) - self._video_src_out = replay.out - - manip = self._pipeline.create(dai.node.ImageManip) - manip.setMaxOutputFrameSize( - self._video_config.width * self._video_config.height * 3 - ) - manip.initialConfig.setOutputSize( - self._video_config.width, self._video_config.height - ) - manip.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888i) - - self._video_src_out.link(manip.inputImage) - self._input_node = manip.out - - def _create_camera_source(self): - """Live camera source.""" - - cam = self._pipeline.create(dai.node.Camera).build( - boardSocket=dai.CameraBoardSocket.CAM_A - ) - self._video_src_out = cam.requestOutput( - size=self._video_config.resolution, - type=dai.ImgFrame.Type.NV12, - fps=self._video_config.fps, - ) - self._input_node = cam.requestOutput( - size=(self._video_config.width, self._video_config.height), - type=dai.ImgFrame.Type.BGR888i, - fps=self._video_config.fps, - ) - - def _create_encoder(self): - """Attach encoder to video source.""" - self._video_enc = self._pipeline.create(dai.node.VideoEncoder) - self._video_enc.setDefaultProfilePreset( - fps=self._video_config.fps, - profile=dai.VideoEncoderProperties.Profile.H264_MAIN, - ) - self._video_src_out.link(self._video_enc.input) - - def get_input_node(self) -> dai.Node.Output: - """Get the input node for model processing.""" - return self._input_node - - def get_frame_cache(self) -> FrameCacheNode: - """Get the frame cache node.""" - return self._frame_cache - - def get_video_source_output(self) -> dai.Node.Output: - """Get the video source output node.""" - return self._video_src_out - - def get_video_topic(self) -> dai.Node.Output: - """Get the video encoder output node.""" - return self._video_enc.out diff --git a/apps/data-collection/backend/src/main.py b/apps/data-collection/backend/src/main.py index 2c237960d..44245e06e 100644 --- a/apps/data-collection/backend/src/main.py +++ b/apps/data-collection/backend/src/main.py @@ -2,50 +2,69 @@ from config.system_configuration import SystemConfiguration from core.model_state import ModelState -from infrastructure.neural_network.neural_network_manager import NeuralNetworkManager -from infrastructure.snaps.snaps_manager import SnapsManager -from infrastructure.system_context import SystemContext -from infrastructure.video_source_manager import VideoSourceManager -from infrastructure.export.export_manager import ExportManager +from core.infrastructure.neural_network.neural_network_manager import ( + NeuralNetworkManager, +) +from core.infrastructure.neural_network.nn_pipeline_setup import NNPipelineSetup +from core.infrastructure.snaps.snaps_manager import SnapsManager +from core.infrastructure.video_factory import VideoFactory +from core.infrastructure.export.export_manager import ExportManager def main(): - system_context = SystemContext() - config = SystemConfiguration(system_context.platform) + device = dai.Device() + visualizer = dai.RemoteConnection(serveFrontend=False) - with dai.Pipeline(system_context.device) as pipeline: - print("Creating pipeline...") + platform = device.getPlatformAsString() - video_source = VideoSourceManager(pipeline, config.get_video_config()) + if platform != "RVC4": + raise ValueError("This example is supported only on RVC4 platform") - system_context.add_visualizer_topic(video_source.get_video_topic(), "Video") + config = SystemConfiguration(platform) + config.build() + with dai.Pipeline(device) as pipeline: + print("Creating pipeline...") model_state = ModelState() - nn_manager = NeuralNetworkManager( - pipeline, video_source, config.get_neural_network_config(), model_state - ) + video_factory = VideoFactory(pipeline, config.get_video_config()) + visualizer.addTopic("Video", video_factory.get_encoded_output()) + input_node = video_factory.get_input_node() - system_context.register_services(nn_manager.get_services()) + nn_pipeline = NNPipelineSetup( + pipeline, input_node, config.get_neural_network_config(), model_state + ) + nn_pipeline.build() + visualizer.addTopic("Annotations", nn_pipeline.annotation_node.out) - system_context.add_visualizer_topic(nn_manager.get_annotations(), "Annotations") + nn_manager = NeuralNetworkManager( + pipeline, input_node, config.get_prompts_config(), nn_pipeline.controller + ) + nn_manager.build() + for service in nn_manager.get_services(): + visualizer.registerService(service.name, service.handle) snaps_manager = SnapsManager( - pipeline, video_source, nn_manager, config.get_snaps_config() + pipeline, + input_node, + nn_pipeline.tracker, + nn_pipeline.detections, + config.get_snaps_config(), ) - - system_context.register_service(snaps_manager.get_service()) + snaps_service = snaps_manager.get_service() + visualizer.registerService(snaps_service.name, snaps_service.handle) export_manager = ExportManager(model_state, snaps_manager.get_engine()) - - system_context.register_service(export_manager.get_service()) + export_service = export_manager.get_service() + visualizer.registerService(export_service.name, export_service.handle) print("Pipeline created.") pipeline.start() - system_context.register_pipeline(pipeline) + visualizer.registerPipeline(pipeline) while pipeline.isRunning(): pipeline.processTasks() + visualizer.waitKey(1) if __name__ == "__main__": diff --git a/apps/data-collection/backend/src/requirements.txt b/apps/data-collection/backend/src/requirements.txt index 33d2db652..b1978d1c9 100644 --- a/apps/data-collection/backend/src/requirements.txt +++ b/apps/data-collection/backend/src/requirements.txt @@ -10,4 +10,5 @@ tokenizers~=0.21.0 onnxruntime # onnxruntime-gpu # if you want to use CUDAExecutionProvider requests -dotenv \ No newline at end of file +dotenv +python-box \ No newline at end of file diff --git a/apps/data-collection/backend/src/services/base_service.py b/apps/data-collection/backend/src/services/base_service.py deleted file mode 100644 index 7f65820ca..000000000 --- a/apps/data-collection/backend/src/services/base_service.py +++ /dev/null @@ -1,26 +0,0 @@ -from abc import ABC, abstractmethod -from typing import Generic, TypeVar -from services.service_name import ServiceName - - -PayloadT = TypeVar("PayloadT", bound=dict) - - -class BaseService(ABC, Generic[PayloadT]): - """ - Abstract base class for all backend services. - Defines a single consistent interface for handling typed payloads. - """ - - NAME: ServiceName # must be defined in subclasses - - def __init__(self): - self.__name = self.NAME - - @abstractmethod - def handle(self, payload: PayloadT) -> dict[str, any]: - """Execute service logic and return a JSON-serializable response.""" - pass - - def get_name(self) -> ServiceName: - return self.__name diff --git a/apps/data-collection/backend/src/services/bbox_prompt/bbox_prompt_service.py b/apps/data-collection/backend/src/services/bbox_prompt/bbox_prompt_service.py deleted file mode 100644 index e50708ea4..000000000 --- a/apps/data-collection/backend/src/services/bbox_prompt/bbox_prompt_service.py +++ /dev/null @@ -1,37 +0,0 @@ -from services.base_service import BaseService -from services.bbox_prompt.bbox_prompt_payload import BBoxPromptPayload -from core.handlers.bbox_prompt_handler import BBoxPromptHandler -from core.controllers.nn_controller import YOLONNController -from infrastructure.frame_cache_node import FrameCacheNode -from services.service_name import ServiceName - - -class BBoxPromptService(BaseService[BBoxPromptPayload]): - NAME = ServiceName.BBOX_PROMPT - - def __init__( - self, - handler: BBoxPromptHandler, - frame_cache: FrameCacheNode, - repository: YOLONNController, - ): - super().__init__() - self.handler = handler - self.frame_cache = frame_cache - self.repository = repository - - def handle(self, payload: BBoxPromptPayload) -> dict[str, any]: - image = self.frame_cache.get_last_frame() - if image is None: - return {"ok": False, "reason": "no_frame_available"} - - try: - embeddings, class_names, dummy = self.handler.process( - image, payload["bbox"] - ) - except ValueError as e: - return {"ok": False, "reason": f"{e}"} - - self.repository.send_embeddings_pair(embeddings, dummy, class_names) - - return {"ok": True, "classes": class_names} diff --git a/apps/data-collection/backend/src/services/class_update/class_update_service.py b/apps/data-collection/backend/src/services/class_update/class_update_service.py deleted file mode 100644 index 06c171eaf..000000000 --- a/apps/data-collection/backend/src/services/class_update/class_update_service.py +++ /dev/null @@ -1,31 +0,0 @@ -from services.base_service import BaseService -from services.class_update.class_update_payload import ClassUpdatePayload -from core.handlers.text_prompt_handler import TextPromptHandler -from core.controllers.nn_controller import YOLONNController -from services.service_name import ServiceName - - -class ClassUpdateService(BaseService[ClassUpdatePayload]): - """Coordinates text-based class updates across model, repository, and state.""" - - NAME = ServiceName.CLASS_UPDATE - - def __init__( - self, - repository: YOLONNController, - handler: TextPromptHandler, - ): - super().__init__() - self.repository = repository - self.handler = handler - - def handle(self, payload: ClassUpdatePayload) -> dict[str, any]: - new_classes = payload["classes"] - if not new_classes: - return {"ok": False, "reason": "empty_class_list"} - - text_inputs, dummy = self.handler.process(new_classes) - - self.repository.send_embeddings_pair(dummy, text_inputs, new_classes) - - return {"ok": True, "classes": new_classes} diff --git a/apps/data-collection/backend/src/services/export_service/__init__.py b/apps/data-collection/backend/src/services/export_service/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/apps/data-collection/backend/src/services/image_upload/__init__.py b/apps/data-collection/backend/src/services/image_upload/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/apps/data-collection/backend/src/services/image_upload/image_upload_service.py b/apps/data-collection/backend/src/services/image_upload/image_upload_service.py deleted file mode 100644 index 5f3d73094..000000000 --- a/apps/data-collection/backend/src/services/image_upload/image_upload_service.py +++ /dev/null @@ -1,23 +0,0 @@ -from services.base_service import BaseService -from services.image_upload.image_upload_payload import ImageUploadPayload -from core.handlers.image_prompt_handler import ImagePromptHandler -from core.controllers.nn_controller import YOLONNController -from services.service_name import ServiceName - - -class ImageUploadService(BaseService[ImageUploadPayload]): - """Coordinates image upload flow: decode → extract → send → update labels.""" - - NAME = ServiceName.IMAGE_UPLOAD - - def __init__(self, repository: YOLONNController, handler: ImagePromptHandler): - super().__init__() - self.repository = repository - self.handler = handler - - def handle(self, payload: ImageUploadPayload) -> dict[str, any]: - image_inputs, class_names, dummy = self.handler.process(payload) - - self.repository.send_embeddings_pair(image_inputs, dummy, class_names) - - return {"ok": True, "class": class_names} diff --git a/apps/data-collection/backend/src/services/snap_collection/__init__.py b/apps/data-collection/backend/src/services/snap_collection/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/apps/data-collection/backend/src/services/threshold_update/__init__.py b/apps/data-collection/backend/src/services/threshold_update/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/apps/data-collection/backend/src/services/threshold_update/threshold_update_service.py b/apps/data-collection/backend/src/services/threshold_update/threshold_update_service.py deleted file mode 100644 index 37335a5e5..000000000 --- a/apps/data-collection/backend/src/services/threshold_update/threshold_update_service.py +++ /dev/null @@ -1,25 +0,0 @@ -from services.base_service import BaseService -from services.threshold_update.threshold_update_payload import ThresholdUpdatePayload -from core.controllers.nn_controller import YOLONNController -from services.service_name import ServiceName - - -class ThresholdUpdateService(BaseService[ThresholdUpdatePayload]): - """Coordinates NN confidence threshold updates between handler, repository, and state.""" - - NAME = ServiceName.THRESHOLD_UPDATE - - def __init__( - self, - repository: YOLONNController, - ): - super().__init__() - self.repository = repository - - def handle(self, payload: ThresholdUpdatePayload) -> dict[str, any]: - new_threshold = payload["threshold"] - - clamped = max(0.0, min(1.0, new_threshold)) - self.repository.set_confidence_threshold(clamped) - - return {"ok": True, "threshold": clamped} From 209b2a8dc2853d24b6e999a78e4c78ce0555dd9d Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Thu, 6 Nov 2025 15:48:31 +0100 Subject: [PATCH 30/39] Continued refactor + added payload validation --- .../backend/src/config/arguments.py | 8 +- .../backend/src/config/cli_env_loader.py | 73 +++++++-- .../backend/src/config/model_loader.py | 6 +- .../src/config/system_configuration.py | 9 +- .../backend/src/config/yaml_config_manager.py | 12 +- .../core/{controllers => export}/__init__.py | 0 .../export/export_manager.py | 19 +-- .../export}/system_state_exporter.py | 0 .../neural_network/neural_network_manager.py | 49 ------ .../neural_network/nn_pipeline_setup.py | 139 ------------------ .../neural_network/prompt_encoders_manager.py | 32 ---- .../src/core/infrastructure/system_context.py | 29 ---- .../backend/src/core/label_manager.py | 2 +- .../{encoders => neural_network}/__init__.py | 0 .../pipeline}/__init__.py | 0 .../pipeline}/annotation_node.py | 0 .../pipeline/detection_graph_factory.py | 32 ++++ .../pipeline/nn_node_factory.py | 35 +++++ .../pipeline/nn_pipeline_setup.py | 90 ++++++++++++ .../pipeline/prompt_controller_factory.py | 43 ++++++ .../pipeline/tracker_factory.py | 32 ++++ .../prompts}/__init__.py | 0 .../prompts/encoders}/__init__.py | 0 .../prompts}/encoders/base_prompt_encoder.py | 2 +- .../encoders/textual_prompt_encoder.py | 10 +- .../encoders/visual_prompt_encoder.py | 7 +- .../prompts}/frame_cache_node.py | 0 .../prompts/handlers}/__init__.py | 0 .../prompts}/handlers/base_prompt_handler.py | 2 +- .../prompts}/handlers/bbox_prompt_handler.py | 22 +-- .../prompts}/handlers/image_prompt_handler.py | 10 +- .../prompts}/handlers/text_prompt_handler.py | 8 +- .../prompts/handlers_factory.py} | 18 ++- .../prompts}/nn_prompts_controller.py | 0 .../prompts/nn_prompts_manager.py | 56 +++++++ .../prompts/prompt_encoders_manager.py | 42 ++++++ .../prompts}/prompt_service_factory.py | 13 +- .../backend/src/core/services/base_service.py | 7 +- .../src/core/services/bbox_prompt_service.py | 6 + .../src/core/services/class_update_service.py | 5 + .../src/core/services/export_service.py | 2 +- .../src/core/services/image_upload_service.py | 5 + .../services/payloads/bbox_prompt_payload.py | 18 +-- .../services/payloads/class_update_payload.py | 6 +- .../services/payloads/image_upload_payload.py | 10 +- .../core/services/payloads/snap_payload.py | 21 ++- .../payloads/threshold_update_payload.py | 6 +- .../core/services/snap_collection_service.py | 12 +- .../core/services/threshold_update_service.py | 7 +- .../snapping/conditions/base_condition.py | 14 +- .../snapping/conditions/low_conf_condition.py | 7 +- .../tracker_conditions/lost_mid_condition.py | 7 +- .../src/core/snapping/conditions_engine.py | 3 +- .../src/core/snapping/conditions_factory.py | 11 +- .../snaps => snapping}/snaps_manager.py | 19 ++- .../snaps_producer_factory.py | 0 .../snaps => video}/__init__.py | 0 .../video_factory.py | 12 +- .../video_providers/__init__.py | 0 .../video_providers/base_video_provider.py | 0 .../video_providers/camera_video_provvider.py | 2 +- .../video_providers/replay_video_provider.py | 4 +- apps/data-collection/backend/src/main.py | 33 ++--- .../backend/src/requirements.txt | 3 +- apps/data-collection/frontend/src/App.tsx | 2 +- 65 files changed, 587 insertions(+), 435 deletions(-) rename apps/data-collection/backend/src/core/{controllers => export}/__init__.py (100%) rename apps/data-collection/backend/src/core/{infrastructure => }/export/export_manager.py (57%) rename apps/data-collection/backend/src/{config => core/export}/system_state_exporter.py (100%) delete mode 100644 apps/data-collection/backend/src/core/infrastructure/neural_network/neural_network_manager.py delete mode 100644 apps/data-collection/backend/src/core/infrastructure/neural_network/nn_pipeline_setup.py delete mode 100644 apps/data-collection/backend/src/core/infrastructure/neural_network/prompt_encoders_manager.py delete mode 100644 apps/data-collection/backend/src/core/infrastructure/system_context.py rename apps/data-collection/backend/src/core/{encoders => neural_network}/__init__.py (100%) rename apps/data-collection/backend/src/core/{handlers => neural_network/pipeline}/__init__.py (100%) rename apps/data-collection/backend/src/core/{infrastructure/neural_network => neural_network/pipeline}/annotation_node.py (100%) create mode 100644 apps/data-collection/backend/src/core/neural_network/pipeline/detection_graph_factory.py create mode 100644 apps/data-collection/backend/src/core/neural_network/pipeline/nn_node_factory.py create mode 100644 apps/data-collection/backend/src/core/neural_network/pipeline/nn_pipeline_setup.py create mode 100644 apps/data-collection/backend/src/core/neural_network/pipeline/prompt_controller_factory.py create mode 100644 apps/data-collection/backend/src/core/neural_network/pipeline/tracker_factory.py rename apps/data-collection/backend/src/core/{infrastructure => neural_network/prompts}/__init__.py (100%) rename apps/data-collection/backend/src/core/{infrastructure/export => neural_network/prompts/encoders}/__init__.py (100%) rename apps/data-collection/backend/src/core/{ => neural_network/prompts}/encoders/base_prompt_encoder.py (97%) rename apps/data-collection/backend/src/core/{ => neural_network/prompts}/encoders/textual_prompt_encoder.py (83%) rename apps/data-collection/backend/src/core/{ => neural_network/prompts}/encoders/visual_prompt_encoder.py (88%) rename apps/data-collection/backend/src/core/{infrastructure => neural_network/prompts}/frame_cache_node.py (100%) rename apps/data-collection/backend/src/core/{infrastructure/neural_network => neural_network/prompts/handlers}/__init__.py (100%) rename apps/data-collection/backend/src/core/{ => neural_network/prompts}/handlers/base_prompt_handler.py (91%) rename apps/data-collection/backend/src/core/{ => neural_network/prompts}/handlers/bbox_prompt_handler.py (76%) rename apps/data-collection/backend/src/core/{ => neural_network/prompts}/handlers/image_prompt_handler.py (80%) rename apps/data-collection/backend/src/core/{ => neural_network/prompts}/handlers/text_prompt_handler.py (74%) rename apps/data-collection/backend/src/core/{infrastructure/neural_network/handlers_manager.py => neural_network/prompts/handlers_factory.py} (52%) rename apps/data-collection/backend/src/core/{controllers => neural_network/prompts}/nn_prompts_controller.py (100%) create mode 100644 apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_manager.py create mode 100644 apps/data-collection/backend/src/core/neural_network/prompts/prompt_encoders_manager.py rename apps/data-collection/backend/src/core/{infrastructure/neural_network => neural_network/prompts}/prompt_service_factory.py (68%) rename apps/data-collection/backend/src/core/{infrastructure/snaps => snapping}/snaps_manager.py (73%) rename apps/data-collection/backend/src/core/{infrastructure/snaps => snapping}/snaps_producer_factory.py (100%) rename apps/data-collection/backend/src/core/{infrastructure/snaps => video}/__init__.py (100%) rename apps/data-collection/backend/src/core/{infrastructure => video}/video_factory.py (75%) rename apps/data-collection/backend/src/core/{infrastructure => video}/video_providers/__init__.py (100%) rename apps/data-collection/backend/src/core/{infrastructure => video}/video_providers/base_video_provider.py (100%) rename apps/data-collection/backend/src/core/{infrastructure => video}/video_providers/camera_video_provvider.py (91%) rename apps/data-collection/backend/src/core/{infrastructure => video}/video_providers/replay_video_provider.py (89%) diff --git a/apps/data-collection/backend/src/config/arguments.py b/apps/data-collection/backend/src/config/arguments.py index 5ecd971c0..6b47b127c 100644 --- a/apps/data-collection/backend/src/config/arguments.py +++ b/apps/data-collection/backend/src/config/arguments.py @@ -1,11 +1,9 @@ -import argparse +from argparse import Namespace, ArgumentParser, ArgumentDefaultsHelpFormatter -def initialize_argparser(): +def initialize_argparser() -> Namespace: """Initialize the argument parser for the script.""" - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter - ) + parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter) parser.add_argument( "-fps", diff --git a/apps/data-collection/backend/src/config/cli_env_loader.py b/apps/data-collection/backend/src/config/cli_env_loader.py index 34e534169..07b69e522 100644 --- a/apps/data-collection/backend/src/config/cli_env_loader.py +++ b/apps/data-collection/backend/src/config/cli_env_loader.py @@ -1,20 +1,67 @@ -import os -from dotenv import load_dotenv -from config.arguments import initialize_argparser +import argparse class CLIEnvLoader: - """Handles .env loading and CLI argument parsing.""" + """ + Handles environment (.env) loading and command-line argument parsing. + """ - def __init__(self): - self.args = None + @staticmethod + def parse_arguments() -> argparse.Namespace: + """ + Private helper to initialize and parse command-line arguments. + """ + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) - def load(self): - """Load environment and parse CLI args.""" - load_dotenv(override=True) - _, self.args = initialize_argparser() + parser.add_argument( + "-fps", + "--fps_limit", + help="FPS limit for the model runtime.", + required=False, + default=None, + type=int, + ) - if self.args.api_key: - os.environ["DEPTHAI_HUB_API_KEY"] = self.args.api_key + parser.add_argument( + "-api", + "--api_key", + help=( + "HubAI API key to access private model. " + "Alternatively, set 'DEPTHAI_HUB_API_KEY' environment variable." + ), + required=False, + default="", + type=str, + ) - return self.args + parser.add_argument( + "-media", + "--media_path", + help=( + "Path to the media file to run the model on. " + "If not set, the model runs on the live camera input." + ), + required=False, + default=None, + type=str, + ) + + parser.add_argument( + "-ip", + "--ip", + help="IP address to serve the frontend on.", + required=False, + type=str, + ) + + parser.add_argument( + "-p", + "--port", + help="Port to serve the frontend on.", + required=False, + type=int, + ) + + return parser.parse_args() diff --git a/apps/data-collection/backend/src/config/model_loader.py b/apps/data-collection/backend/src/config/model_loader.py index b440aec2a..786e27f44 100644 --- a/apps/data-collection/backend/src/config/model_loader.py +++ b/apps/data-collection/backend/src/config/model_loader.py @@ -1,3 +1,5 @@ +from argparse import Namespace + import depthai as dai from pathlib import Path from config.config_data_classes import ModelInfo @@ -7,8 +9,8 @@ class ModelLoader: """Resolves DepthAI model archive and metadata.""" def __init__(self, platform: str, args): - self.platform = platform - self.args = args + self.platform: str = platform + self.args: Namespace = args def load_model_info(self) -> ModelInfo: models_dir = Path(__file__).parent.parent / "depthai_models" diff --git a/apps/data-collection/backend/src/config/system_configuration.py b/apps/data-collection/backend/src/config/system_configuration.py index 16ff2f193..8ed2e204e 100644 --- a/apps/data-collection/backend/src/config/system_configuration.py +++ b/apps/data-collection/backend/src/config/system_configuration.py @@ -1,4 +1,6 @@ from __future__ import annotations + +from argparse import Namespace from pathlib import Path from typing import Optional from box import Box @@ -15,15 +17,14 @@ class SystemConfiguration: """ def __init__(self, platform: str): - self._platform = platform - self._args = None + self._platform: str = platform + self._args: Namespace = None self._yaml: Optional[YamlConfigManager] = None self._model_info: Optional[ModelInfo] = None def build(self): """Initialize all configuration subsystems.""" - cli_env = CLIEnvLoader() - self._args = cli_env.load() + self._args = CLIEnvLoader.parse_arguments() base = Path(__file__).parent / "yaml_configs" diff --git a/apps/data-collection/backend/src/config/yaml_config_manager.py b/apps/data-collection/backend/src/config/yaml_config_manager.py index 6881e4fc7..fb3c80c9e 100644 --- a/apps/data-collection/backend/src/config/yaml_config_manager.py +++ b/apps/data-collection/backend/src/config/yaml_config_manager.py @@ -6,13 +6,13 @@ class YamlConfigManager: """Loads all YAML configuration files and exposes them as Box objects.""" def __init__(self, base_dir: Path): - self._base = base_dir - self.nn: Box | None = None - self.video: Box | None = None - self.conditions: Box | None = None - self.prompts: Box | None = None + self._base: Path = base_dir + self.nn: Box = None + self.video: Box = None + self.conditions: Box = None + self.prompts: Box = None - def load_all(self) -> None: + def load_all(self): """Load all YAML configs from the given base directory.""" print(f"[YamlConfigManager] Loading from: {self._base.resolve()}") diff --git a/apps/data-collection/backend/src/core/controllers/__init__.py b/apps/data-collection/backend/src/core/export/__init__.py similarity index 100% rename from apps/data-collection/backend/src/core/controllers/__init__.py rename to apps/data-collection/backend/src/core/export/__init__.py diff --git a/apps/data-collection/backend/src/core/infrastructure/export/export_manager.py b/apps/data-collection/backend/src/core/export/export_manager.py similarity index 57% rename from apps/data-collection/backend/src/core/infrastructure/export/export_manager.py rename to apps/data-collection/backend/src/core/export/export_manager.py index 383231d0b..01bf78544 100644 --- a/apps/data-collection/backend/src/core/infrastructure/export/export_manager.py +++ b/apps/data-collection/backend/src/core/export/export_manager.py @@ -1,29 +1,24 @@ from core.model_state import ModelState from core.snapping.conditions_engine import ConditionsEngine -from config.system_state_exporter import SystemStateExporter +from core.export.system_state_exporter import SystemStateExporter from core.services.export_service import ExportService -class ExportManager: +class ExportServiceManager: """ Facade for the configuration export subsystem. - - Responsibilities: - • Build SystemStateExporter - • Create GetConfigService exposing export API """ def __init__(self, model_state: ModelState, condition_engine: ConditionsEngine): - self._model_state = model_state - self._condition_engine = condition_engine + self._model_state: ModelState = model_state + self._condition_engine: ConditionsEngine = condition_engine self._exporter: SystemStateExporter = None self._service: ExportService = None - self._build() - def _build(self): + def build(self): self._exporter = SystemStateExporter(self._model_state, self._condition_engine) self._service = ExportService(self._exporter) - def get_service(self) -> ExportService: - return self._service + def register_service(self, visualizer): + visualizer.registerService(self._service.name, self._service.handle) diff --git a/apps/data-collection/backend/src/config/system_state_exporter.py b/apps/data-collection/backend/src/core/export/system_state_exporter.py similarity index 100% rename from apps/data-collection/backend/src/config/system_state_exporter.py rename to apps/data-collection/backend/src/core/export/system_state_exporter.py diff --git a/apps/data-collection/backend/src/core/infrastructure/neural_network/neural_network_manager.py b/apps/data-collection/backend/src/core/infrastructure/neural_network/neural_network_manager.py deleted file mode 100644 index f562f86bc..000000000 --- a/apps/data-collection/backend/src/core/infrastructure/neural_network/neural_network_manager.py +++ /dev/null @@ -1,49 +0,0 @@ -from typing import List -import depthai as dai -from box import Box - -from core.controllers.nn_prompts_controller import NnPromptsController -from core.infrastructure.frame_cache_node import FrameCacheNode -from core.infrastructure.neural_network.prompt_service_factory import ( - PromptServiceFactory, -) -from core.infrastructure.neural_network.prompt_encoders_manager import ( - PromptEncodersManager, -) -from core.infrastructure.neural_network.handlers_manager import HandlersManager -from core.services.base_service import BaseService - - -class NeuralNetworkManager: - """ - Facade for the neural-network subsystem. - """ - - def __init__( - self, - pipeline: dai.Pipeline, - input_node: dai.Node.Output, - config: Box, - controller: NnPromptsController, - ): - self._pipeline = pipeline - self._input_node = input_node - self._config = config - self._controller = controller - self._services: List[BaseService] = [] - - def build(self): - encoders = PromptEncodersManager(self._config) - frame_cache = self._pipeline.create(FrameCacheNode).build(self._input_node) - handlers = HandlersManager(encoders, frame_cache) - service_manager = PromptServiceFactory(self._controller, handlers) - self._services = service_manager.services - self._controller.send_prompts_pair( - encoders.image_prompt, - encoders.text_prompt, - self._config.class_names, - self._config.text_offset, - ) - - def get_services(self) -> list[BaseService]: - return self._services diff --git a/apps/data-collection/backend/src/core/infrastructure/neural_network/nn_pipeline_setup.py b/apps/data-collection/backend/src/core/infrastructure/neural_network/nn_pipeline_setup.py deleted file mode 100644 index 78a178130..000000000 --- a/apps/data-collection/backend/src/core/infrastructure/neural_network/nn_pipeline_setup.py +++ /dev/null @@ -1,139 +0,0 @@ -import depthai as dai -from depthai_nodes.node import ( - ParsingNeuralNetwork, - ImgDetectionsFilter, - ImgDetectionsBridge, -) -from config.config_data_classes import NeuralNetworkConfig -from core.label_manager import LabelManager -from core.infrastructure.neural_network.annotation_node import AnnotationNode -from core.controllers.nn_prompts_controller import NnPromptsController -from core.model_state import ModelState - - -class NNPipelineSetup: - """ - Handles creation of all neural network–related nodes in a DepthAI pipeline. - """ - - def __init__( - self, - pipeline: dai.Pipeline, - input_node: dai.Node.Output, - nn_config: NeuralNetworkConfig, - model_state: ModelState, - ): - self._pipeline = pipeline - self._input_node = input_node - self._nn_config = nn_config - self._model_state = model_state - - self._nn = None - self._prompt_controller = None - self._det_filter = None - self._annotation_node = None - self._filtered_bridge = None - self._tracker = None - - def build(self) -> "NNPipelineSetup": - self._build_nn() - self._build_filters() - self._build_tracker() - self._build_controller() - return self - - def _build_nn(self): - backend = self._nn_config.nn_yaml.nn_backend - - nn = self._pipeline.create(ParsingNeuralNetwork) - nn.setNNArchive(self._nn_config.model.archive) - nn.setBackend(backend.type) - nn.setBackendProperties( - { - "runtime": backend.runtime, - "performance_profile": backend.performance_profile, - } - ) - nn.setNumInferenceThreads(backend.inference_threads) - nn.getParser(0).setConfidenceThreshold(backend.confidence_threshold) - - self._input_node.link(nn.inputs["images"]) - self._nn = nn - - def _build_filters(self): - if not self._nn: - raise RuntimeError("Neural Network must be built before filters.") - - det_filter = self._pipeline.create(ImgDetectionsFilter).build(self._nn.out) - annotation_node = self._pipeline.create(AnnotationNode).build( - det_filter.out, self._input_node - ) - filtered_bridge = self._pipeline.create(ImgDetectionsBridge).build( - det_filter.out - ) - - self._det_filter = det_filter - self._annotation_node = annotation_node - self._filtered_bridge = filtered_bridge - - def _build_tracker(self): - if not self._filtered_bridge: - raise RuntimeError("Detections must be built before tracker.") - - tcfg = self._nn_config.nn_yaml.tracker - tracker = self._pipeline.create(dai.node.ObjectTracker) - - tracker.setTrackerType(dai.TrackerType.SHORT_TERM_IMAGELESS) - tracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.UNIQUE_ID) - tracker.setTrackingPerClass(tcfg.track_per_class) - tracker.setTrackletBirthThreshold(tcfg.birth_threshold) - tracker.setTrackletMaxLifespan(tcfg.max_lifespan) - tracker.setOcclusionRatioThreshold(tcfg.occlusion_ratio_threshold) - tracker.setTrackerThreshold(tcfg.tracker_threshold) - - source = self._input_node - source.link(tracker.inputTrackerFrame) - source.link(tracker.inputDetectionFrame) - self._filtered_bridge.out.link(tracker.inputDetections) - - self._tracker = tracker - - def _build_controller(self): - if not self._nn: - raise RuntimeError("Neural Network must be built before controller.") - - text_q = self._nn.inputs["texts"].createInputQueue() - img_q = self._nn.inputs["image_prompts"].createInputQueue() - self._nn.inputs["texts"].setReusePreviousMessage(True) - self._nn.inputs["image_prompts"].setReusePreviousMessage(True) - - parser = self._nn.getParser(0) - label_manager = LabelManager(self._det_filter, self._annotation_node) - self._prompt_controller = NnPromptsController( - img_q, - text_q, - self._nn_config.model.precision, - parser, - self._model_state, - label_manager, - ) - - @property - def nn(self) -> ParsingNeuralNetwork: - return self._nn - - @property - def controller(self) -> NnPromptsController: - return self._prompt_controller - - @property - def detections(self) -> ImgDetectionsBridge: - return self._filtered_bridge - - @property - def tracker(self) -> dai.node.ObjectTracker: - return self._tracker - - @property - def annotation_node(self) -> AnnotationNode: - return self._annotation_node diff --git a/apps/data-collection/backend/src/core/infrastructure/neural_network/prompt_encoders_manager.py b/apps/data-collection/backend/src/core/infrastructure/neural_network/prompt_encoders_manager.py deleted file mode 100644 index 0fa7f1fa0..000000000 --- a/apps/data-collection/backend/src/core/infrastructure/neural_network/prompt_encoders_manager.py +++ /dev/null @@ -1,32 +0,0 @@ -import numpy as np -from box import Box - -from core.encoders.textual_prompt_encoder import TextualPromptEncoder -from core.encoders.visual_prompt_encoder import VisualPromptEncoder - - -class PromptEncodersManager: - """ - Central manager for initializing and caching encoder components. - """ - - def __init__(self, config: Box): - self._model_config = config - - self.textual_encoder = self._init_textual_encoder() - self.visual_encoder = self._init_visual_encoder() - - self.text_prompt, self.image_prompt = self._prepare_initial_prompts() - - def _init_textual_encoder(self) -> TextualPromptEncoder: - return TextualPromptEncoder(config=self._model_config) - - def _init_visual_encoder(self) -> VisualPromptEncoder: - return VisualPromptEncoder(config=self._model_config) - - def _prepare_initial_prompts(self) -> tuple[np.ndarray, np.ndarray]: - text_prompt = self.textual_encoder.extract_embeddings( - self._model_config.class_names - ) - image_prompt = self.textual_encoder.make_dummy() - return text_prompt, image_prompt diff --git a/apps/data-collection/backend/src/core/infrastructure/system_context.py b/apps/data-collection/backend/src/core/infrastructure/system_context.py deleted file mode 100644 index e437d10f9..000000000 --- a/apps/data-collection/backend/src/core/infrastructure/system_context.py +++ /dev/null @@ -1,29 +0,0 @@ -import depthai as dai - -from core.services.base_service import BaseService - - -class SystemContext: - """Holds runtime-specific resources (hardware and visualization).""" - - def __init__(self, platform: str | None = None): - self.device = dai.Device() - self.visualizer = dai.RemoteConnection(serveFrontend=False) - self.platform = platform or self.device.getPlatformAsString() - - def register_service(self, service: BaseService) -> None: - """Registers a service in the system context.""" - self.visualizer.registerService(service.name(), service.handle) - - def register_services(self, services: list[BaseService]) -> None: - """Registers multiple services in the system context.""" - for service in services: - self.register_service(service) - - def add_visualizer_topic(self, topic: dai.Node.Output, name: str = "Video") -> None: - """Adds an annotations topic to the visualizer.""" - self.visualizer.addTopic(name, topic) - - def register_pipeline(self, pipeline: dai.Pipeline) -> None: - """Registers a pipeline with the visualizer.""" - self.visualizer.registerPipeline(pipeline) diff --git a/apps/data-collection/backend/src/core/label_manager.py b/apps/data-collection/backend/src/core/label_manager.py index 1a5c0fb3b..a827a1577 100644 --- a/apps/data-collection/backend/src/core/label_manager.py +++ b/apps/data-collection/backend/src/core/label_manager.py @@ -1,5 +1,5 @@ from depthai_nodes.node import ImgDetectionsFilter -from core.infrastructure.neural_network.annotation_node import AnnotationNode +from core.neural_network.pipeline.annotation_node import AnnotationNode class LabelManager: diff --git a/apps/data-collection/backend/src/core/encoders/__init__.py b/apps/data-collection/backend/src/core/neural_network/__init__.py similarity index 100% rename from apps/data-collection/backend/src/core/encoders/__init__.py rename to apps/data-collection/backend/src/core/neural_network/__init__.py diff --git a/apps/data-collection/backend/src/core/handlers/__init__.py b/apps/data-collection/backend/src/core/neural_network/pipeline/__init__.py similarity index 100% rename from apps/data-collection/backend/src/core/handlers/__init__.py rename to apps/data-collection/backend/src/core/neural_network/pipeline/__init__.py diff --git a/apps/data-collection/backend/src/core/infrastructure/neural_network/annotation_node.py b/apps/data-collection/backend/src/core/neural_network/pipeline/annotation_node.py similarity index 100% rename from apps/data-collection/backend/src/core/infrastructure/neural_network/annotation_node.py rename to apps/data-collection/backend/src/core/neural_network/pipeline/annotation_node.py diff --git a/apps/data-collection/backend/src/core/neural_network/pipeline/detection_graph_factory.py b/apps/data-collection/backend/src/core/neural_network/pipeline/detection_graph_factory.py new file mode 100644 index 000000000..af471a8e1 --- /dev/null +++ b/apps/data-collection/backend/src/core/neural_network/pipeline/detection_graph_factory.py @@ -0,0 +1,32 @@ +import depthai as dai +from depthai_nodes.node import ImgDetectionsFilter, ImgDetectionsBridge +from core.neural_network.pipeline.annotation_node import AnnotationNode +from depthai_nodes.node import ParsingNeuralNetwork + + +class DetectionGraphFactory: + """ + Builds the detection-processing subgraph: + ParsingNeuralNetwork → ImgDetectionsFilter ─┬─→ AnnotationNode + └─→ ImgDetectionsBridge + """ + + def __init__( + self, + pipeline: dai.Pipeline, + input_node: dai.Node.Output, + nn: ParsingNeuralNetwork, + ): + self._pipeline: dai.Pipeline = pipeline + self._input_node: dai.Node.Output = input_node + self._nn: ParsingNeuralNetwork = nn + + def build(self): + det_filter = self._pipeline.create(ImgDetectionsFilter).build(self._nn.out) + annotation_node = self._pipeline.create(AnnotationNode).build( + det_filter.out, self._input_node + ) + filtered_bridge = self._pipeline.create(ImgDetectionsBridge).build( + det_filter.out + ) + return det_filter, annotation_node, filtered_bridge diff --git a/apps/data-collection/backend/src/core/neural_network/pipeline/nn_node_factory.py b/apps/data-collection/backend/src/core/neural_network/pipeline/nn_node_factory.py new file mode 100644 index 000000000..87c8f1ee6 --- /dev/null +++ b/apps/data-collection/backend/src/core/neural_network/pipeline/nn_node_factory.py @@ -0,0 +1,35 @@ +import depthai as dai +from config.config_data_classes import NeuralNetworkConfig +from depthai_nodes.node import ParsingNeuralNetwork + + +class NnNodeFactory: + """Builds and configures the core ParsingNeuralNetwork node.""" + + def __init__( + self, + pipeline: dai.Pipeline, + input_node: dai.Node.Output, + config: NeuralNetworkConfig, + ): + self._pipeline: dai.Pipeline = pipeline + self._input_node: dai.Node.Output = input_node + self._config: NeuralNetworkConfig = config + + def build(self) -> ParsingNeuralNetwork: + backend = self._config.nn_yaml.nn_backend + + nn = self._pipeline.create(ParsingNeuralNetwork) + nn.setNNArchive(self._config.model.archive) + nn.setBackend(backend.type) + nn.setBackendProperties( + { + "runtime": backend.runtime, + "performance_profile": backend.performance_profile, + } + ) + nn.setNumInferenceThreads(backend.inference_threads) + nn.getParser(0).setConfidenceThreshold(backend.confidence_threshold) + + self._input_node.link(nn.inputs["images"]) + return nn diff --git a/apps/data-collection/backend/src/core/neural_network/pipeline/nn_pipeline_setup.py b/apps/data-collection/backend/src/core/neural_network/pipeline/nn_pipeline_setup.py new file mode 100644 index 000000000..d70e1a5f7 --- /dev/null +++ b/apps/data-collection/backend/src/core/neural_network/pipeline/nn_pipeline_setup.py @@ -0,0 +1,90 @@ +import depthai as dai +from config.config_data_classes import NeuralNetworkConfig +from core.neural_network.prompts.nn_prompts_controller import NnPromptsController +from core.neural_network.pipeline.annotation_node import AnnotationNode +from core.neural_network.pipeline.detection_graph_factory import DetectionGraphFactory +from core.neural_network.pipeline.nn_node_factory import NnNodeFactory +from core.neural_network.pipeline.prompt_controller_factory import ( + PromptControllerFactory, +) +from core.neural_network.pipeline.tracker_factory import TrackerFactory +from core.model_state import ModelState + +from depthai_nodes.node import ( + ParsingNeuralNetwork, + ImgDetectionsFilter, + ImgDetectionsBridge, +) + + +class NNPipelineBuilder: + """ + Facade that orchestrates the creation of all neural-network related + DepthAI nodes and supporting controller components. + """ + + def __init__( + self, + pipeline: dai.Pipeline, + input_node: dai.Node.Output, + nn_config: NeuralNetworkConfig, + model_state: ModelState, + ): + self._pipeline: dai.Pipeline = pipeline + self._input_node: dai.Node.Output = input_node + self._config: NeuralNetworkConfig = nn_config + self._model_state: ModelState = model_state + + self._nn: ParsingNeuralNetwork = None + self._det_filter: ImgDetectionsFilter = None + self._annotation_node: AnnotationNode = None + self._filtered_bridge: ImgDetectionsBridge = None + self._tracker: dai.node.ObjectTracker = None + self._controller: NnPromptsController = None + + def build(self) -> "NNPipelineBuilder": + """Build full neural-network subgraph.""" + nn_builder = NnNodeFactory(self._pipeline, self._input_node, self._config) + self._nn = nn_builder.build() + + det_graph = DetectionGraphFactory(self._pipeline, self._input_node, self._nn) + self._det_filter, self._annotation_node, self._filtered_bridge = ( + det_graph.build() + ) + + tracker_factory = TrackerFactory( + self._pipeline, + self._filtered_bridge.out, + self._input_node, + self._config.nn_yaml.tracker, + ) + self._tracker = tracker_factory.build() + + controller_factory = PromptControllerFactory( + self._nn, + self._det_filter, + self._annotation_node, + self._config.model.precision, + self._model_state, + ) + self._controller = controller_factory.build() + + @property + def nn(self): + return self._nn + + @property + def detections(self): + return self._filtered_bridge + + @property + def tracker(self): + return self._tracker + + @property + def annotation_node(self): + return self._annotation_node + + @property + def controller(self): + return self._controller diff --git a/apps/data-collection/backend/src/core/neural_network/pipeline/prompt_controller_factory.py b/apps/data-collection/backend/src/core/neural_network/pipeline/prompt_controller_factory.py new file mode 100644 index 000000000..a8b8cd079 --- /dev/null +++ b/apps/data-collection/backend/src/core/neural_network/pipeline/prompt_controller_factory.py @@ -0,0 +1,43 @@ +from core.neural_network.pipeline.annotation_node import AnnotationNode +from core.model_state import ModelState +from core.neural_network.prompts.nn_prompts_controller import NnPromptsController +from core.label_manager import LabelManager +from depthai_nodes.node import ParsingNeuralNetwork, ImgDetectionsFilter + + +class PromptControllerFactory: + """Creates and configures the NnPromptsController and LabelManager.""" + + def __init__( + self, + nn_node: ParsingNeuralNetwork, + det_filter: ImgDetectionsFilter, + annotation_node: AnnotationNode, + precision: str, + model_state: ModelState, + ): + self._nn_node: ParsingNeuralNetwork = nn_node + self._det_filter: ImgDetectionsFilter = det_filter + self._annotation_node: AnnotationNode = annotation_node + self._precision: str = precision + self._model_state: ModelState = model_state + + def build(self) -> NnPromptsController: + text_q = self._nn_node.inputs["texts"].createInputQueue() + img_q = self._nn_node.inputs["image_prompts"].createInputQueue() + self._nn_node.inputs["texts"].setReusePreviousMessage(True) + self._nn_node.inputs["image_prompts"].setReusePreviousMessage(True) + + parser = self._nn_node.getParser(0) + label_manager = LabelManager(self._det_filter, self._annotation_node) + + controller = NnPromptsController( + img_q, + text_q, + self._precision, + parser, + self._model_state, + label_manager, + ) + + return controller diff --git a/apps/data-collection/backend/src/core/neural_network/pipeline/tracker_factory.py b/apps/data-collection/backend/src/core/neural_network/pipeline/tracker_factory.py new file mode 100644 index 000000000..938227c36 --- /dev/null +++ b/apps/data-collection/backend/src/core/neural_network/pipeline/tracker_factory.py @@ -0,0 +1,32 @@ +import depthai as dai +from box import Box + + +class TrackerFactory: + def __init__( + self, + pipeline: dai.Pipeline, + detections_bridge: dai.Node.Output, + input_node: dai.Node.Output, + config: Box, + ): + self._pipeline: dai.Pipeline = pipeline + self._detections_bridge: dai.Node.Output = detections_bridge + self._input_node: dai.Node.Output = input_node + self._config: Box = config + + def build(self) -> dai.node.ObjectTracker: + tracker = self._pipeline.create(dai.node.ObjectTracker) + tracker.setTrackerType(dai.TrackerType.SHORT_TERM_IMAGELESS) + tracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.UNIQUE_ID) + tracker.setTrackingPerClass(self._config.track_per_class) + tracker.setTrackletBirthThreshold(self._config.birth_threshold) + tracker.setTrackletMaxLifespan(self._config.max_lifespan) + tracker.setOcclusionRatioThreshold(self._config.occlusion_ratio_threshold) + tracker.setTrackerThreshold(self._config.tracker_threshold) + + self._input_node.link(tracker.inputTrackerFrame) + self._input_node.link(tracker.inputDetectionFrame) + self._detections_bridge.link(tracker.inputDetections) + + return tracker diff --git a/apps/data-collection/backend/src/core/infrastructure/__init__.py b/apps/data-collection/backend/src/core/neural_network/prompts/__init__.py similarity index 100% rename from apps/data-collection/backend/src/core/infrastructure/__init__.py rename to apps/data-collection/backend/src/core/neural_network/prompts/__init__.py diff --git a/apps/data-collection/backend/src/core/infrastructure/export/__init__.py b/apps/data-collection/backend/src/core/neural_network/prompts/encoders/__init__.py similarity index 100% rename from apps/data-collection/backend/src/core/infrastructure/export/__init__.py rename to apps/data-collection/backend/src/core/neural_network/prompts/encoders/__init__.py diff --git a/apps/data-collection/backend/src/core/encoders/base_prompt_encoder.py b/apps/data-collection/backend/src/core/neural_network/prompts/encoders/base_prompt_encoder.py similarity index 97% rename from apps/data-collection/backend/src/core/encoders/base_prompt_encoder.py rename to apps/data-collection/backend/src/core/neural_network/prompts/encoders/base_prompt_encoder.py index 7abf90bba..0efb8115d 100644 --- a/apps/data-collection/backend/src/core/encoders/base_prompt_encoder.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/encoders/base_prompt_encoder.py @@ -40,7 +40,7 @@ def extract_embeddings(self, *args, **kwargs) -> np.ndarray: """Subclasses must implement modality-specific preprocessing and inference.""" pass - def _pad_and_quantize_features(self, features): + def _pad_and_quantize_features(self, features) -> np.ndarray: """ Pad features to (1, 512, max_num_classes) and quantize if precision is int8. For FP16, return padded float16 features (no quantization). diff --git a/apps/data-collection/backend/src/core/encoders/textual_prompt_encoder.py b/apps/data-collection/backend/src/core/neural_network/prompts/encoders/textual_prompt_encoder.py similarity index 83% rename from apps/data-collection/backend/src/core/encoders/textual_prompt_encoder.py rename to apps/data-collection/backend/src/core/neural_network/prompts/encoders/textual_prompt_encoder.py index 5082bd1b8..ee8fab393 100644 --- a/apps/data-collection/backend/src/core/encoders/textual_prompt_encoder.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/encoders/textual_prompt_encoder.py @@ -2,7 +2,7 @@ from tokenizers import Tokenizer import numpy as np -from core.encoders.base_prompt_encoder import BasePromptEncoder +from core.neural_network.prompts.encoders.base_prompt_encoder import BasePromptEncoder class TextualPromptEncoder(BasePromptEncoder): @@ -16,10 +16,10 @@ def __init__(self, config: Box): config.paths.text_encoder.url, config.paths.text_encoder.path, ) - self._offset = config.text_offset - self.tokenizer_url = config.paths.tokenizer.url - self.tokenizer_path = config.paths.tokenizer.path - self.tokenizer = None + self._offset: str = config.text_offset + self.tokenizer_url: str = config.paths.tokenizer.url + self.tokenizer_path: str = config.paths.tokenizer.path + self.tokenizer: Tokenizer = None def _load_tokenizer(self): path = self._download_file(self.tokenizer_url, self.tokenizer_path) diff --git a/apps/data-collection/backend/src/core/encoders/visual_prompt_encoder.py b/apps/data-collection/backend/src/core/neural_network/prompts/encoders/visual_prompt_encoder.py similarity index 88% rename from apps/data-collection/backend/src/core/encoders/visual_prompt_encoder.py rename to apps/data-collection/backend/src/core/neural_network/prompts/encoders/visual_prompt_encoder.py index 37be96bf4..2b0f968b2 100644 --- a/apps/data-collection/backend/src/core/encoders/visual_prompt_encoder.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/encoders/visual_prompt_encoder.py @@ -2,7 +2,7 @@ import numpy as np from box import Box -from core.encoders.base_prompt_encoder import BasePromptEncoder +from core.neural_network.prompts.encoders.base_prompt_encoder import BasePromptEncoder class VisualPromptEncoder(BasePromptEncoder): @@ -23,10 +23,7 @@ def __init__( config.paths.visual_encoder.url, config.paths.visual_encoder.path, ) - self._offset = config.visual_offset - self.tokenizer_url = config.paths.tokenizer.url - self.tokenizer_path = config.paths.tokenizer.path - self.tokenizer = None + self._offset: str = config.visual_offset def extract_embeddings(self, image: np.ndarray, mask_prompt=None) -> np.ndarray: self._load_model() diff --git a/apps/data-collection/backend/src/core/infrastructure/frame_cache_node.py b/apps/data-collection/backend/src/core/neural_network/prompts/frame_cache_node.py similarity index 100% rename from apps/data-collection/backend/src/core/infrastructure/frame_cache_node.py rename to apps/data-collection/backend/src/core/neural_network/prompts/frame_cache_node.py diff --git a/apps/data-collection/backend/src/core/infrastructure/neural_network/__init__.py b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/__init__.py similarity index 100% rename from apps/data-collection/backend/src/core/infrastructure/neural_network/__init__.py rename to apps/data-collection/backend/src/core/neural_network/prompts/handlers/__init__.py diff --git a/apps/data-collection/backend/src/core/handlers/base_prompt_handler.py b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/base_prompt_handler.py similarity index 91% rename from apps/data-collection/backend/src/core/handlers/base_prompt_handler.py rename to apps/data-collection/backend/src/core/neural_network/prompts/handlers/base_prompt_handler.py index c65db755d..ba4dc15ee 100644 --- a/apps/data-collection/backend/src/core/handlers/base_prompt_handler.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/base_prompt_handler.py @@ -3,7 +3,7 @@ from typing import List, Tuple, TypedDict import numpy as np -from core.encoders.base_prompt_encoder import BasePromptEncoder +from core.neural_network.prompts.encoders.base_prompt_encoder import BasePromptEncoder class BasePromptHandler(ABC): diff --git a/apps/data-collection/backend/src/core/handlers/bbox_prompt_handler.py b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/bbox_prompt_handler.py similarity index 76% rename from apps/data-collection/backend/src/core/handlers/bbox_prompt_handler.py rename to apps/data-collection/backend/src/core/neural_network/prompts/handlers/bbox_prompt_handler.py index feecc6470..d3cd7dcf1 100644 --- a/apps/data-collection/backend/src/core/handlers/bbox_prompt_handler.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/bbox_prompt_handler.py @@ -1,8 +1,10 @@ import numpy as np -from core.encoders.visual_prompt_encoder import VisualPromptEncoder -from core.handlers.base_prompt_handler import BasePromptHandler -from core.infrastructure.frame_cache_node import FrameCacheNode +from core.neural_network.prompts.encoders.visual_prompt_encoder import ( + VisualPromptEncoder, +) +from core.neural_network.prompts.handlers.base_prompt_handler import BasePromptHandler +from core.neural_network.prompts.frame_cache_node import FrameCacheNode from core.services.payloads.bbox_prompt_payload import BBoxPromptPayload @@ -16,14 +18,14 @@ class BBoxPromptHandler(BasePromptHandler): def __init__(self, encoder: VisualPromptEncoder, frame_cache: FrameCacheNode): super().__init__(encoder) self._frame_cache: FrameCacheNode = frame_cache - self._image: np.ndarray | None = None - self._bbox: dict[str, float] | None = None + self._image: np.ndarray = None + self._bbox: BBoxPromptPayload = None self._class_names: list[str] = ["Bounding Box Object"] def process(self, payload: BBoxPromptPayload) -> tuple[np.ndarray, np.ndarray]: """Crop region mask based on bbox and extract embeddings.""" self._image = self._frame_cache.get_last_frame() - self._bbox = payload["bbox"] + self._bbox = payload mask = self._make_mask() embeddings = self._encoder.extract_embeddings(self._image, mask) @@ -35,10 +37,10 @@ def _make_mask(self) -> np.ndarray: """Build a binary mask corresponding to the provided bounding box.""" H, W = self._image.shape[:2] bx, by, bw, bh = ( - self._bbox["x"], - self._bbox["y"], - self._bbox["width"], - self._bbox["height"], + self._bbox.x, + self._bbox.y, + self._bbox.width, + self._bbox.height, ) x0, y0 = int(bx * W), int(by * H) diff --git a/apps/data-collection/backend/src/core/handlers/image_prompt_handler.py b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/image_prompt_handler.py similarity index 80% rename from apps/data-collection/backend/src/core/handlers/image_prompt_handler.py rename to apps/data-collection/backend/src/core/neural_network/prompts/handlers/image_prompt_handler.py index 303f99eca..a8fdc9e8d 100644 --- a/apps/data-collection/backend/src/core/handlers/image_prompt_handler.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/image_prompt_handler.py @@ -2,8 +2,10 @@ import cv2 import numpy as np -from core.encoders.visual_prompt_encoder import VisualPromptEncoder -from core.handlers.base_prompt_handler import BasePromptHandler +from core.neural_network.prompts.encoders.visual_prompt_encoder import ( + VisualPromptEncoder, +) +from core.neural_network.prompts.handlers.base_prompt_handler import BasePromptHandler from core.services.payloads.image_upload_payload import ImageUploadPayload @@ -20,7 +22,7 @@ def __init__(self, encoder: VisualPromptEncoder): def process(self, payload: ImageUploadPayload) -> tuple[np.ndarray, np.ndarray]: """Decode and process uploaded image payload into model-ready features.""" self._payload = payload - self._class_names = [self._payload["filename"].split(".")[0]] + self._class_names = [self._payload.filename.split(".")[0]] image = self._decode_image() embeddings = self._encoder.extract_embeddings(image) dummy = self._encoder.make_dummy() @@ -28,7 +30,7 @@ def process(self, payload: ImageUploadPayload) -> tuple[np.ndarray, np.ndarray]: def _decode_image(self) -> np.ndarray: """Convert base64-encoded image to OpenCV array.""" - data_uri = self._payload["data"] + data_uri = self._payload.data if "," in data_uri: _, base64_data = data_uri.split(",", 1) else: diff --git a/apps/data-collection/backend/src/core/handlers/text_prompt_handler.py b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/text_prompt_handler.py similarity index 74% rename from apps/data-collection/backend/src/core/handlers/text_prompt_handler.py rename to apps/data-collection/backend/src/core/neural_network/prompts/handlers/text_prompt_handler.py index d57916960..ae943d8f1 100644 --- a/apps/data-collection/backend/src/core/handlers/text_prompt_handler.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/text_prompt_handler.py @@ -2,8 +2,10 @@ from typing import Tuple import numpy as np -from core.encoders.textual_prompt_encoder import TextualPromptEncoder -from core.handlers.base_prompt_handler import BasePromptHandler +from core.neural_network.prompts.encoders.textual_prompt_encoder import ( + TextualPromptEncoder, +) +from core.neural_network.prompts.handlers.base_prompt_handler import BasePromptHandler from core.services.payloads.class_update_payload import ClassUpdatePayload @@ -15,7 +17,7 @@ def __init__(self, encoder: TextualPromptEncoder): self._payload: ClassUpdatePayload = None def process(self, payload: ClassUpdatePayload) -> Tuple[np.ndarray, np.ndarray]: - self._class_names = payload["classes"] + self._class_names = payload.classes embeddings = self._encoder.extract_embeddings(self.get_class_names()) dummy = self._encoder.make_dummy() diff --git a/apps/data-collection/backend/src/core/infrastructure/neural_network/handlers_manager.py b/apps/data-collection/backend/src/core/neural_network/prompts/handlers_factory.py similarity index 52% rename from apps/data-collection/backend/src/core/infrastructure/neural_network/handlers_manager.py rename to apps/data-collection/backend/src/core/neural_network/prompts/handlers_factory.py index e667c4abf..fe72969dc 100644 --- a/apps/data-collection/backend/src/core/infrastructure/neural_network/handlers_manager.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/handlers_factory.py @@ -1,16 +1,22 @@ -from core.handlers.text_prompt_handler import TextPromptHandler -from core.handlers.image_prompt_handler import ImagePromptHandler -from core.handlers.bbox_prompt_handler import BBoxPromptHandler -from core.infrastructure.frame_cache_node import FrameCacheNode -from core.infrastructure.neural_network.prompt_encoders_manager import ( +from core.neural_network.prompts.handlers.base_prompt_handler import BasePromptHandler +from core.neural_network.prompts.handlers.text_prompt_handler import TextPromptHandler +from core.neural_network.prompts.handlers.image_prompt_handler import ImagePromptHandler +from core.neural_network.prompts.handlers.bbox_prompt_handler import BBoxPromptHandler +from core.neural_network.prompts.frame_cache_node import FrameCacheNode +from core.neural_network.prompts.prompt_encoders_manager import ( PromptEncodersManager, ) -class HandlersManager: +class HandlersFactory: def __init__(self, encoders: PromptEncodersManager, frame_cache: FrameCacheNode): self._encoders = encoders self._frame_cache = frame_cache + self.class_update_handler: BasePromptHandler = None + self.image_update_handler: BasePromptHandler = None + self.bbox_prompt_handler: BasePromptHandler = None + + def build(self): self.class_update_handler = self._class_update_handler() self.image_update_handler = self._image_update_handler() self.bbox_prompt_handler = self._bbox_prompt_handler() diff --git a/apps/data-collection/backend/src/core/controllers/nn_prompts_controller.py b/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_controller.py similarity index 100% rename from apps/data-collection/backend/src/core/controllers/nn_prompts_controller.py rename to apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_controller.py diff --git a/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_manager.py b/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_manager.py new file mode 100644 index 000000000..c576ba23d --- /dev/null +++ b/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_manager.py @@ -0,0 +1,56 @@ +from typing import List +import depthai as dai +from box import Box + +from core.neural_network.prompts.nn_prompts_controller import NnPromptsController +from core.neural_network.prompts.frame_cache_node import FrameCacheNode +from core.neural_network.prompts.prompt_service_factory import ( + PromptServiceFactory, +) +from core.neural_network.prompts.prompt_encoders_manager import ( + PromptEncodersManager, +) +from core.neural_network.prompts.handlers_factory import HandlersFactory +from core.services.base_service import BaseService + + +class NNPromptsManager: + """ + Facade for the neural-network subsystem. + """ + + def __init__( + self, + pipeline: dai.Pipeline, + input_node: dai.Node.Output, + config: Box, + controller: NnPromptsController, + ): + self._pipeline: dai.Pipeline = pipeline + self._input_node: dai.Node.Output = input_node + self._config: Box = config + self._controller: NnPromptsController = controller + self._services: List[BaseService] = [] + + def build(self): + encoders = PromptEncodersManager(self._config) + encoders.build() + + frame_cache = self._pipeline.create(FrameCacheNode).build(self._input_node) + handlers = HandlersFactory(encoders, frame_cache) + handlers.build() + + service_factory = PromptServiceFactory(self._controller, handlers) + self._services = service_factory.build_services() + + text_prompt, image_prompt = encoders.prepare_initial_prompts() + self._controller.send_prompts_pair( + image_prompt, + text_prompt, + self._config.class_names, + self._config.text_offset, + ) + + def register_services(self, visualizer: dai.RemoteConnection): + for service in self._services: + visualizer.registerService(service.name, service.handle) diff --git a/apps/data-collection/backend/src/core/neural_network/prompts/prompt_encoders_manager.py b/apps/data-collection/backend/src/core/neural_network/prompts/prompt_encoders_manager.py new file mode 100644 index 000000000..9b0c95a21 --- /dev/null +++ b/apps/data-collection/backend/src/core/neural_network/prompts/prompt_encoders_manager.py @@ -0,0 +1,42 @@ +import numpy as np +from box import Box + +from core.neural_network.prompts.encoders.base_prompt_encoder import BasePromptEncoder +from core.neural_network.prompts.encoders.textual_prompt_encoder import ( + TextualPromptEncoder, +) +from core.neural_network.prompts.encoders.visual_prompt_encoder import ( + VisualPromptEncoder, +) + + +class PromptEncodersManager: + """ + Central manager for initializing and caching encoder components. + """ + + def __init__(self, config: Box): + self._model_config = config + + self.textual_encoder: BasePromptEncoder = None + self.visual_encoder: BasePromptEncoder = None + + self.text_prompt: np.ndarray = None + self.image_prompt: np.ndarray = None + + def build(self): + self.textual_encoder = self._init_textual_encoder() + self.visual_encoder = self._init_visual_encoder() + + def _init_textual_encoder(self) -> TextualPromptEncoder: + return TextualPromptEncoder(self._model_config) + + def _init_visual_encoder(self) -> VisualPromptEncoder: + return VisualPromptEncoder(self._model_config) + + def prepare_initial_prompts(self) -> tuple[np.ndarray, np.ndarray]: + text_prompt = self.textual_encoder.extract_embeddings( + self._model_config.class_names + ) + image_prompt = self.textual_encoder.make_dummy() + return text_prompt, image_prompt diff --git a/apps/data-collection/backend/src/core/infrastructure/neural_network/prompt_service_factory.py b/apps/data-collection/backend/src/core/neural_network/prompts/prompt_service_factory.py similarity index 68% rename from apps/data-collection/backend/src/core/infrastructure/neural_network/prompt_service_factory.py rename to apps/data-collection/backend/src/core/neural_network/prompts/prompt_service_factory.py index ab378dd0e..376bfd338 100644 --- a/apps/data-collection/backend/src/core/infrastructure/neural_network/prompt_service_factory.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/prompt_service_factory.py @@ -1,5 +1,5 @@ -from core.controllers.nn_prompts_controller import NnPromptsController -from core.infrastructure.neural_network.handlers_manager import HandlersManager +from core.neural_network.prompts.nn_prompts_controller import NnPromptsController +from core.neural_network.prompts.handlers_factory import HandlersFactory from core.services.base_service import BaseService from core.services.class_update_service import ClassUpdateService from core.services.threshold_update_service import ThresholdUpdateService @@ -11,13 +11,12 @@ class PromptServiceFactory: def __init__( self, controller: NnPromptsController, - handlers: HandlersManager, + handlers: HandlersFactory, ): - self.controller = controller - self.handlers = handlers - self.services = self._build_services() + self.controller: NnPromptsController = controller + self.handlers: HandlersFactory = handlers - def _build_services(self) -> list[BaseService]: + def build_services(self) -> list[BaseService]: return [ ClassUpdateService(self.controller, self.handlers.class_update_handler), ThresholdUpdateService(self.controller), diff --git a/apps/data-collection/backend/src/core/services/base_service.py b/apps/data-collection/backend/src/core/services/base_service.py index 07399574e..1dfb7193e 100644 --- a/apps/data-collection/backend/src/core/services/base_service.py +++ b/apps/data-collection/backend/src/core/services/base_service.py @@ -1,11 +1,12 @@ from abc import ABC, abstractmethod from typing import Generic, TypeVar +from pydantic import BaseModel -from core.controllers.nn_prompts_controller import NnPromptsController -from core.handlers.base_prompt_handler import BasePromptHandler +from core.neural_network.prompts.nn_prompts_controller import NnPromptsController +from core.neural_network.prompts.handlers.base_prompt_handler import BasePromptHandler from core.services.service_name import ServiceName -PayloadT = TypeVar("PayloadT", bound=dict) +PayloadT = TypeVar("PayloadT", bound=BaseModel) class BaseService(ABC, Generic[PayloadT]): diff --git a/apps/data-collection/backend/src/core/services/bbox_prompt_service.py b/apps/data-collection/backend/src/core/services/bbox_prompt_service.py index d9a0bd955..a0f0b5aba 100644 --- a/apps/data-collection/backend/src/core/services/bbox_prompt_service.py +++ b/apps/data-collection/backend/src/core/services/bbox_prompt_service.py @@ -1,3 +1,4 @@ +from pydantic import ValidationError from core.services.base_service import BaseService from core.services.payloads.bbox_prompt_payload import BBoxPromptPayload from core.services.service_name import ServiceName @@ -7,6 +8,11 @@ class BBoxPromptService(BaseService[BBoxPromptPayload]): NAME = ServiceName.BBOX_PROMPT def handle(self, payload: BBoxPromptPayload) -> dict[str, any]: + try: + payload = BBoxPromptPayload.model_validate(payload) + except ValidationError as e: + return {"ok": False, "error": e.errors()} + image_inputs, dummy = self._handler.process(payload) class_names = self._handler.get_class_names() self._controller.send_prompts_pair( diff --git a/apps/data-collection/backend/src/core/services/class_update_service.py b/apps/data-collection/backend/src/core/services/class_update_service.py index c53c47ccb..2c97a7931 100644 --- a/apps/data-collection/backend/src/core/services/class_update_service.py +++ b/apps/data-collection/backend/src/core/services/class_update_service.py @@ -1,3 +1,4 @@ +from pydantic import ValidationError from core.services.base_service import BaseService from core.services.payloads.class_update_payload import ClassUpdatePayload from core.services.service_name import ServiceName @@ -9,6 +10,10 @@ class ClassUpdateService(BaseService[ClassUpdatePayload]): NAME = ServiceName.CLASS_UPDATE def handle(self, payload: ClassUpdatePayload) -> dict[str, any]: + try: + payload = ClassUpdatePayload.model_validate(payload) + except ValidationError as e: + return {"ok": False, "error": e.errors()} text_inputs, dummy = self._handler.process(payload) new_classes = self._handler.get_class_names() self._controller.send_prompts_pair( diff --git a/apps/data-collection/backend/src/core/services/export_service.py b/apps/data-collection/backend/src/core/services/export_service.py index 8c02d41a2..3ee384e7d 100644 --- a/apps/data-collection/backend/src/core/services/export_service.py +++ b/apps/data-collection/backend/src/core/services/export_service.py @@ -1,5 +1,5 @@ from core.services.base_service import BaseService -from config.system_state_exporter import SystemStateExporter +from core.export.system_state_exporter import SystemStateExporter from core.services.service_name import ServiceName diff --git a/apps/data-collection/backend/src/core/services/image_upload_service.py b/apps/data-collection/backend/src/core/services/image_upload_service.py index eb77b4f2f..23b3fb322 100644 --- a/apps/data-collection/backend/src/core/services/image_upload_service.py +++ b/apps/data-collection/backend/src/core/services/image_upload_service.py @@ -1,3 +1,4 @@ +from pydantic import ValidationError from core.services.base_service import BaseService from core.services.payloads.image_upload_payload import ImageUploadPayload from core.services.service_name import ServiceName @@ -9,6 +10,10 @@ class ImageUploadService(BaseService[ImageUploadPayload]): NAME = ServiceName.IMAGE_UPLOAD def handle(self, payload: ImageUploadPayload) -> dict[str, any]: + try: + payload = ImageUploadPayload.model_validate(payload) + except ValidationError as e: + return {"ok": False, "error": e.errors()} image_inputs, dummy = self._handler.process(payload) class_names = self._handler.get_class_names() diff --git a/apps/data-collection/backend/src/core/services/payloads/bbox_prompt_payload.py b/apps/data-collection/backend/src/core/services/payloads/bbox_prompt_payload.py index a216c3a2b..6580b6168 100644 --- a/apps/data-collection/backend/src/core/services/payloads/bbox_prompt_payload.py +++ b/apps/data-collection/backend/src/core/services/payloads/bbox_prompt_payload.py @@ -1,14 +1,8 @@ -from typing import TypedDict +from pydantic import BaseModel, Field -class BBox(TypedDict): - x: float - y: float - width: float - height: float - - -class BBoxPromptPayload(TypedDict): - """Payload for bounding box prompts""" - - bbox: BBox +class BBoxPromptPayload(BaseModel): + x: float = Field(..., ge=0.0, le=1.0, description="Normalized x coordinate [0–1]") + y: float = Field(..., ge=0.0, le=1.0, description="Normalized y coordinate [0–1]") + width: float = Field(..., gt=0.0, le=1.0, description="Normalized width [0–1]") + height: float = Field(..., gt=0.0, le=1.0, description="Normalized height [0–1]") diff --git a/apps/data-collection/backend/src/core/services/payloads/class_update_payload.py b/apps/data-collection/backend/src/core/services/payloads/class_update_payload.py index 1ffd51f7a..1106d0ed6 100644 --- a/apps/data-collection/backend/src/core/services/payloads/class_update_payload.py +++ b/apps/data-collection/backend/src/core/services/payloads/class_update_payload.py @@ -1,7 +1,7 @@ -from typing import TypedDict, List +from pydantic import BaseModel, Field -class ClassUpdatePayload(TypedDict): +class ClassUpdatePayload(BaseModel): """Payload for updating detection classes.""" - classes: List[str] + classes: list[str] = Field(..., min_length=1, description="List of class names") diff --git a/apps/data-collection/backend/src/core/services/payloads/image_upload_payload.py b/apps/data-collection/backend/src/core/services/payloads/image_upload_payload.py index 8fd4c8b66..75ccf3890 100644 --- a/apps/data-collection/backend/src/core/services/payloads/image_upload_payload.py +++ b/apps/data-collection/backend/src/core/services/payloads/image_upload_payload.py @@ -1,9 +1,9 @@ -from typing import TypedDict +from pydantic import BaseModel, Field -class ImageUploadPayload(TypedDict): +class ImageUploadPayload(BaseModel): """Payload for uploading an image from the frontend.""" - filename: str - type: str - data: str + filename: str = Field(..., min_length=1) + type: str = Field(..., min_length=1) + data: str = Field(..., description="Base64-encoded image data") diff --git a/apps/data-collection/backend/src/core/services/payloads/snap_payload.py b/apps/data-collection/backend/src/core/services/payloads/snap_payload.py index ec5f3596b..875fe7f2e 100644 --- a/apps/data-collection/backend/src/core/services/payloads/snap_payload.py +++ b/apps/data-collection/backend/src/core/services/payloads/snap_payload.py @@ -1,15 +1,20 @@ -from typing import TypedDict, Optional +from typing import Optional +from pydantic import BaseModel, Field, RootModel from core.snapping.conditions.condition_key import ConditionKey -class ConditionConfig(TypedDict, total=False): +class ConditionConfig(BaseModel): """Configuration of a single snapping condition.""" - enabled: bool - cooldown: Optional[float] - interval: Optional[float] - threshold: Optional[float] - margin: Optional[float] + enabled: bool = Field(default=True) + cooldown: Optional[float] = Field(default=None, ge=0.0) + interval: Optional[float] = Field(default=None, ge=0.0) + threshold: Optional[float] = Field(default=None, ge=0.0, le=1.0) + margin: Optional[float] = Field(default=None, ge=0.0) -SnapPayload = dict[ConditionKey, ConditionConfig] +class SnapPayload(RootModel[dict[ConditionKey, ConditionConfig]]): + """Map of snapping conditions keyed by ConditionKey enum.""" + + def to_dict(self) -> dict[ConditionKey, ConditionConfig]: + return self.root diff --git a/apps/data-collection/backend/src/core/services/payloads/threshold_update_payload.py b/apps/data-collection/backend/src/core/services/payloads/threshold_update_payload.py index e73ab3ae0..a58b97a51 100644 --- a/apps/data-collection/backend/src/core/services/payloads/threshold_update_payload.py +++ b/apps/data-collection/backend/src/core/services/payloads/threshold_update_payload.py @@ -1,7 +1,7 @@ -from typing import TypedDict +from pydantic import BaseModel, Field -class ThresholdUpdatePayload(TypedDict): +class ThresholdUpdatePayload(BaseModel): """Payload for updating NN confidence threshold.""" - threshold: float + threshold: float = Field(..., ge=0.0, le=1.0) diff --git a/apps/data-collection/backend/src/core/services/snap_collection_service.py b/apps/data-collection/backend/src/core/services/snap_collection_service.py index cea1dfe67..644341ad0 100644 --- a/apps/data-collection/backend/src/core/services/snap_collection_service.py +++ b/apps/data-collection/backend/src/core/services/snap_collection_service.py @@ -1,3 +1,4 @@ +from pydantic import ValidationError from core.services.base_service import BaseService from core.snapping.conditions_engine import ConditionsEngine from depthai_nodes.node import SnapsProducer @@ -18,14 +19,17 @@ def __init__( snaps_producer: SnapsProducer, ): super().__init__() - self.engine = engine - self.snaps_producer = snaps_producer + self.engine: ConditionsEngine = engine + self.snaps_producer: SnapsProducer = snaps_producer def handle(self, payload: SnapPayload) -> dict[str, any]: - self.engine.import_conditions_config(payload) + try: + payload = SnapPayload.model_validate(payload) + except ValidationError as e: + return {"ok": False, "error": e.errors()} + self.engine.import_conditions_config(payload.root) any_active = self.engine.any_active() - self.snaps_producer.setRunning(any_active) return {"ok": True, "active": any_active} diff --git a/apps/data-collection/backend/src/core/services/threshold_update_service.py b/apps/data-collection/backend/src/core/services/threshold_update_service.py index 4bd4bc208..aeb690da5 100644 --- a/apps/data-collection/backend/src/core/services/threshold_update_service.py +++ b/apps/data-collection/backend/src/core/services/threshold_update_service.py @@ -1,3 +1,4 @@ +from pydantic import ValidationError from core.services.base_service import BaseService from core.services.payloads.threshold_update_payload import ThresholdUpdatePayload from core.services.service_name import ServiceName @@ -9,7 +10,11 @@ class ThresholdUpdateService(BaseService[ThresholdUpdatePayload]): NAME = ServiceName.THRESHOLD_UPDATE def handle(self, payload: ThresholdUpdatePayload) -> dict[str, any]: - new_threshold = payload["threshold"] + try: + payload = ThresholdUpdatePayload.model_validate(payload) + except ValidationError as e: + return {"ok": False, "error": e.errors()} + new_threshold = payload.threshold clamped = max(0.0, min(1.0, new_threshold)) self._controller.set_confidence_threshold(clamped) diff --git a/apps/data-collection/backend/src/core/snapping/conditions/base_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/base_condition.py index 19ebea064..f057b4c90 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions/base_condition.py +++ b/apps/data-collection/backend/src/core/snapping/conditions/base_condition.py @@ -1,5 +1,7 @@ from abc import ABC, abstractmethod from typing import Dict, Any, List, Optional + +from core.services.payloads.snap_payload import ConditionConfig from core.snapping.conditions.condition_key import ConditionKey from time import time @@ -42,13 +44,11 @@ def make_extras(self, *args, **kwargs) -> Dict[str, str]: """Return optional metadata attached to the snap.""" pass - def apply_config(self, conf: Dict[str, Any]): - if "enabled" in conf: - self.enabled = bool(conf["enabled"]) - if not self.enabled: - self.reset_cooldown() - if "cooldown" in conf: - self.cooldown = max(0.0, float(conf["cooldown"])) + def apply_config(self, conf: ConditionConfig): + self.enabled = conf.enabled + if not self.enabled: + self.reset_cooldown() + self.cooldown = conf.cooldown def export_config(self) -> Dict[str, Any]: return { diff --git a/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py index 7d95a8503..846905810 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py +++ b/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py @@ -1,3 +1,4 @@ +from core.services.payloads.snap_payload import ConditionConfig from core.snapping.conditions.base_condition import Condition from core.snapping.conditions.condition_key import ConditionKey from typing import Dict, Any @@ -14,10 +15,10 @@ def __init__(self, name: str, default_cooldown: float, tags: list[str]): self.threshold: float = 0.3 self.last_lowest: float = 0.0 - def apply_config(self, conf: Dict[str, Any]): + def apply_config(self, conf: ConditionConfig): super().apply_config(conf) - if "threshold" in conf: - val = float(conf["threshold"]) + if conf.threshold: + val = float(conf.threshold) self.threshold = max(0.0, min(1.0, val)) def export_config(self) -> Dict[str, Any]: diff --git a/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py index e97f72da8..1f08ebbc4 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py +++ b/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py @@ -1,3 +1,4 @@ +from core.services.payloads.snap_payload import ConditionConfig from core.snapping.conditions.base_condition import Condition from core.snapping.conditions.tracker_conditions.tracklet_analyzer import ( TrackletAnalyzer, @@ -17,10 +18,10 @@ def __init__(self, name: str, default_cooldown: float, tags: list[str]): self.margin: float = 0.2 self.prev_tracked: dict[int, bool] = {} - def apply_config(self, conf: Dict[str, Any]): + def apply_config(self, conf: ConditionConfig): super().apply_config(conf) - if "margin" in conf: - val = float(conf["margin"]) + if conf.margin: + val = float(conf.margin) self.margin = max(0.0, min(0.49, val)) def export_config(self) -> Dict[str, Any]: diff --git a/apps/data-collection/backend/src/core/snapping/conditions_engine.py b/apps/data-collection/backend/src/core/snapping/conditions_engine.py index 756cb63d7..c820b89a5 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions_engine.py +++ b/apps/data-collection/backend/src/core/snapping/conditions_engine.py @@ -1,6 +1,7 @@ from __future__ import annotations from typing import Dict, Generator, Any +from core.services.payloads.snap_payload import ConditionConfig from core.snapping.conditions.base_condition import Condition from core.snapping.conditions.condition_key import ConditionKey @@ -30,7 +31,7 @@ def evaluate(self, **context: Any) -> Generator[Condition, None, None]: if cond.should_trigger(**context): yield cond - def import_conditions_config(self, config: Dict[str, dict]) -> None: + def import_conditions_config(self, config: Dict[str, ConditionConfig]) -> None: """ Apply configuration dict to registered conditions. """ diff --git a/apps/data-collection/backend/src/core/snapping/conditions_factory.py b/apps/data-collection/backend/src/core/snapping/conditions_factory.py index 30d4cb0a3..b4a38c341 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions_factory.py +++ b/apps/data-collection/backend/src/core/snapping/conditions_factory.py @@ -8,11 +8,9 @@ class ConditionsFactory: def __init__(self, conditions_yaml: Box): - self._conditions_yaml = conditions_yaml + self._conditions_yaml: Box = conditions_yaml - self._engine: ConditionsEngine = self._build_engine() - - def _build_engine(self) -> ConditionsEngine: + def build_engine(self) -> ConditionsEngine: engine = ConditionsEngine() for entry in self._conditions_yaml.conditions: @@ -30,9 +28,6 @@ def _build_engine(self) -> ConditionsEngine: ) engine.register(cond) except Exception as e: - print(f"[WARN] Failed to import condition {entry.path}: {e}") + print(f"Failed to import condition {entry.path}: {e}") return engine - - def get_engine(self) -> ConditionsEngine: - return self._engine diff --git a/apps/data-collection/backend/src/core/infrastructure/snaps/snaps_manager.py b/apps/data-collection/backend/src/core/snapping/snaps_manager.py similarity index 73% rename from apps/data-collection/backend/src/core/infrastructure/snaps/snaps_manager.py rename to apps/data-collection/backend/src/core/snapping/snaps_manager.py index f6c601d95..c81dd4c45 100644 --- a/apps/data-collection/backend/src/core/infrastructure/snaps/snaps_manager.py +++ b/apps/data-collection/backend/src/core/snapping/snaps_manager.py @@ -4,11 +4,11 @@ from core.snapping.conditions_engine import ConditionsEngine from core.snapping.conditions_factory import ConditionsFactory -from core.infrastructure.snaps.snaps_producer_factory import SnapsProducerFactory +from core.snapping.snaps_producer_factory import SnapsProducerFactory from core.services.snap_collection_service import SnapCollectionService -class SnapsManager: +class SnappingServiceManager: """ Facade for the snapping subsystem. """ @@ -29,12 +29,11 @@ def __init__( self._producer: SnapsProducer = None self._engine: ConditionsEngine = None + self._snap_service: SnapCollectionService = None - self._build() - - def _build(self) -> "SnapsManager": - cond_manager = ConditionsFactory(self._conditions_config) - self._engine = cond_manager.get_engine() + def build(self): + cond_factory = ConditionsFactory(self._conditions_config) + self._engine = cond_factory.build_engine() snaps_producer = SnapsProducerFactory.create( self._pipeline, @@ -44,11 +43,11 @@ def _build(self) -> "SnapsManager": self._engine, ) self._producer = snaps_producer + self._snap_service = SnapCollectionService(self._engine, self._producer) - return self - def get_service(self) -> SnapCollectionService: - return self._snap_service + def register_service(self, visualizer: dai.RemoteConnection): + visualizer.registerService(self._snap_service.name, self._snap_service.handle) def get_engine(self): return self._engine diff --git a/apps/data-collection/backend/src/core/infrastructure/snaps/snaps_producer_factory.py b/apps/data-collection/backend/src/core/snapping/snaps_producer_factory.py similarity index 100% rename from apps/data-collection/backend/src/core/infrastructure/snaps/snaps_producer_factory.py rename to apps/data-collection/backend/src/core/snapping/snaps_producer_factory.py diff --git a/apps/data-collection/backend/src/core/infrastructure/snaps/__init__.py b/apps/data-collection/backend/src/core/video/__init__.py similarity index 100% rename from apps/data-collection/backend/src/core/infrastructure/snaps/__init__.py rename to apps/data-collection/backend/src/core/video/__init__.py diff --git a/apps/data-collection/backend/src/core/infrastructure/video_factory.py b/apps/data-collection/backend/src/core/video/video_factory.py similarity index 75% rename from apps/data-collection/backend/src/core/infrastructure/video_factory.py rename to apps/data-collection/backend/src/core/video/video_factory.py index ce6d4dc69..b90aa4962 100644 --- a/apps/data-collection/backend/src/core/infrastructure/video_factory.py +++ b/apps/data-collection/backend/src/core/video/video_factory.py @@ -1,10 +1,10 @@ import depthai as dai from config.config_data_classes import VideoConfig -from core.infrastructure.video_providers.base_video_provider import BaseVideoProvider -from core.infrastructure.video_providers.camera_video_provvider import ( +from core.video.video_providers.base_video_provider import BaseVideoProvider +from core.video.video_providers.camera_video_provvider import ( CameraVideoProvider, ) -from core.infrastructure.video_providers.replay_video_provider import ( +from core.video.video_providers.replay_video_provider import ( ReplayVideoProvider, ) @@ -13,9 +13,9 @@ class VideoFactory: """Facade that chooses the right source type and provides encoder.""" def __init__(self, pipeline: dai.Pipeline, cfg: VideoConfig): - self._pipeline = pipeline - self._config = cfg - self._source = self._select_source() + self._pipeline: dai.Pipeline = pipeline + self._config: VideoConfig = cfg + self._source: BaseVideoProvider = self._select_source() def _select_source(self) -> BaseVideoProvider: if self._config.media_path: diff --git a/apps/data-collection/backend/src/core/infrastructure/video_providers/__init__.py b/apps/data-collection/backend/src/core/video/video_providers/__init__.py similarity index 100% rename from apps/data-collection/backend/src/core/infrastructure/video_providers/__init__.py rename to apps/data-collection/backend/src/core/video/video_providers/__init__.py diff --git a/apps/data-collection/backend/src/core/infrastructure/video_providers/base_video_provider.py b/apps/data-collection/backend/src/core/video/video_providers/base_video_provider.py similarity index 100% rename from apps/data-collection/backend/src/core/infrastructure/video_providers/base_video_provider.py rename to apps/data-collection/backend/src/core/video/video_providers/base_video_provider.py diff --git a/apps/data-collection/backend/src/core/infrastructure/video_providers/camera_video_provvider.py b/apps/data-collection/backend/src/core/video/video_providers/camera_video_provvider.py similarity index 91% rename from apps/data-collection/backend/src/core/infrastructure/video_providers/camera_video_provvider.py rename to apps/data-collection/backend/src/core/video/video_providers/camera_video_provvider.py index b51f394fd..dea1285df 100644 --- a/apps/data-collection/backend/src/core/infrastructure/video_providers/camera_video_provvider.py +++ b/apps/data-collection/backend/src/core/video/video_providers/camera_video_provvider.py @@ -1,7 +1,7 @@ import depthai as dai from config.config_data_classes import VideoConfig -from core.infrastructure.video_providers.base_video_provider import BaseVideoProvider +from core.video.video_providers.base_video_provider import BaseVideoProvider class CameraVideoProvider(BaseVideoProvider): diff --git a/apps/data-collection/backend/src/core/infrastructure/video_providers/replay_video_provider.py b/apps/data-collection/backend/src/core/video/video_providers/replay_video_provider.py similarity index 89% rename from apps/data-collection/backend/src/core/infrastructure/video_providers/replay_video_provider.py rename to apps/data-collection/backend/src/core/video/video_providers/replay_video_provider.py index 2f83deef6..5a45affb1 100644 --- a/apps/data-collection/backend/src/core/infrastructure/video_providers/replay_video_provider.py +++ b/apps/data-collection/backend/src/core/video/video_providers/replay_video_provider.py @@ -1,7 +1,7 @@ from pathlib import Path from config.config_data_classes import VideoConfig -from core.infrastructure.video_providers.base_video_provider import BaseVideoProvider +from core.video.video_providers.base_video_provider import BaseVideoProvider import depthai as dai @@ -10,7 +10,7 @@ class ReplayVideoProvider(BaseVideoProvider): def __init__(self, pipeline: dai.Pipeline, config: VideoConfig): super().__init__(pipeline, config) - self._replay = self._build_replay() + self._replay: dai.node.ReplayVideo = self._build_replay() def get_input_node(self) -> dai.Node.Output: manip = self._pipeline.create(dai.node.ImageManip) diff --git a/apps/data-collection/backend/src/main.py b/apps/data-collection/backend/src/main.py index 44245e06e..e92586765 100644 --- a/apps/data-collection/backend/src/main.py +++ b/apps/data-collection/backend/src/main.py @@ -2,13 +2,11 @@ from config.system_configuration import SystemConfiguration from core.model_state import ModelState -from core.infrastructure.neural_network.neural_network_manager import ( - NeuralNetworkManager, -) -from core.infrastructure.neural_network.nn_pipeline_setup import NNPipelineSetup -from core.infrastructure.snaps.snaps_manager import SnapsManager -from core.infrastructure.video_factory import VideoFactory -from core.infrastructure.export.export_manager import ExportManager +from core.neural_network.prompts.nn_prompts_manager import NNPromptsManager +from core.neural_network.pipeline.nn_pipeline_setup import NNPipelineBuilder +from core.snapping.snaps_manager import SnappingServiceManager +from core.video.video_factory import VideoFactory +from core.export.export_manager import ExportServiceManager def main(): @@ -31,32 +29,31 @@ def main(): visualizer.addTopic("Video", video_factory.get_encoded_output()) input_node = video_factory.get_input_node() - nn_pipeline = NNPipelineSetup( + nn_pipeline = NNPipelineBuilder( pipeline, input_node, config.get_neural_network_config(), model_state ) nn_pipeline.build() visualizer.addTopic("Annotations", nn_pipeline.annotation_node.out) - nn_manager = NeuralNetworkManager( + prompts_manager = NNPromptsManager( pipeline, input_node, config.get_prompts_config(), nn_pipeline.controller ) - nn_manager.build() - for service in nn_manager.get_services(): - visualizer.registerService(service.name, service.handle) + prompts_manager.build() + prompts_manager.register_services(visualizer) - snaps_manager = SnapsManager( + snaps_manager = SnappingServiceManager( pipeline, input_node, nn_pipeline.tracker, nn_pipeline.detections, config.get_snaps_config(), ) - snaps_service = snaps_manager.get_service() - visualizer.registerService(snaps_service.name, snaps_service.handle) + snaps_manager.build() + snaps_manager.register_service(visualizer) - export_manager = ExportManager(model_state, snaps_manager.get_engine()) - export_service = export_manager.get_service() - visualizer.registerService(export_service.name, export_service.handle) + export_manager = ExportServiceManager(model_state, snaps_manager.get_engine()) + export_manager.build() + export_manager.register_service(visualizer) print("Pipeline created.") pipeline.start() diff --git a/apps/data-collection/backend/src/requirements.txt b/apps/data-collection/backend/src/requirements.txt index b1978d1c9..5f3975e79 100644 --- a/apps/data-collection/backend/src/requirements.txt +++ b/apps/data-collection/backend/src/requirements.txt @@ -11,4 +11,5 @@ onnxruntime # onnxruntime-gpu # if you want to use CUDAExecutionProvider requests dotenv -python-box \ No newline at end of file +python-box +pydantic \ No newline at end of file diff --git a/apps/data-collection/frontend/src/App.tsx b/apps/data-collection/frontend/src/App.tsx index 401d3feee..eebd86214 100644 --- a/apps/data-collection/frontend/src/App.tsx +++ b/apps/data-collection/frontend/src/App.tsx @@ -148,7 +148,7 @@ function App() { (connection as any).daiConnection?.postToService( "BBox Prompt Service", { - bbox: { x: xNorm, y: yNorm, width: wNorm, height: hNorm }, + x: xNorm, y: yNorm, width: wNorm, height: hNorm }, (resp: any) => { console.log("[BBox] Service ack:", resp); From e78bb9d638a46a84f0b4cd25290e17f5a21a8197 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Fri, 7 Nov 2025 14:23:26 +0100 Subject: [PATCH 31/39] Changed model state to be a dataclass --- .../config/yaml_configs/prompts_config.yaml | 1 + .../backend/src/core/export/export_manager.py | 2 +- .../src/core/export/system_state_exporter.py | 6 +++--- .../backend/src/core/model_state.py | 18 ------------------ .../neural_network/pipeline/model_state.py | 9 +++++++++ .../pipeline/nn_pipeline_setup.py | 4 ---- .../pipeline/prompt_controller_factory.py | 10 +--------- .../prompts/nn_prompts_controller.py | 13 ++++++++----- .../prompts/nn_prompts_manager.py | 1 + .../src/core/services/export_service.py | 9 ++------- apps/data-collection/backend/src/main.py | 14 ++++++++++---- apps/data-collection/frontend/src/App.tsx | 2 ++ 12 files changed, 38 insertions(+), 51 deletions(-) delete mode 100644 apps/data-collection/backend/src/core/model_state.py create mode 100644 apps/data-collection/backend/src/core/neural_network/pipeline/model_state.py diff --git a/apps/data-collection/backend/src/config/yaml_configs/prompts_config.yaml b/apps/data-collection/backend/src/config/yaml_configs/prompts_config.yaml index f7f3ddb1a..95abf707e 100644 --- a/apps/data-collection/backend/src/config/yaml_configs/prompts_config.yaml +++ b/apps/data-collection/backend/src/config/yaml_configs/prompts_config.yaml @@ -1,6 +1,7 @@ name: "yoloe" precision: "fp16" class_names: ["person", "chair", "TV"] +detection_threshold: 0.1 text_offset: 0 visual_offset: 80 max_num_classes: 80 diff --git a/apps/data-collection/backend/src/core/export/export_manager.py b/apps/data-collection/backend/src/core/export/export_manager.py index 01bf78544..4bc41a386 100644 --- a/apps/data-collection/backend/src/core/export/export_manager.py +++ b/apps/data-collection/backend/src/core/export/export_manager.py @@ -1,4 +1,4 @@ -from core.model_state import ModelState +from core.neural_network.pipeline.model_state import ModelState from core.snapping.conditions_engine import ConditionsEngine from core.export.system_state_exporter import SystemStateExporter from core.services.export_service import ExportService diff --git a/apps/data-collection/backend/src/core/export/system_state_exporter.py b/apps/data-collection/backend/src/core/export/system_state_exporter.py index 08956f95a..0878f5940 100644 --- a/apps/data-collection/backend/src/core/export/system_state_exporter.py +++ b/apps/data-collection/backend/src/core/export/system_state_exporter.py @@ -1,6 +1,6 @@ from typing import Dict, Any from core.snapping.conditions_engine import ConditionsEngine -from core.model_state import ModelState +from core.neural_network.pipeline.model_state import ModelState class SystemStateExporter: @@ -21,8 +21,8 @@ def export_config(self) -> Dict[str, Any]: Construct the unified configuration dictionary expected by the frontend. """ return { - "classes": self._model_state.get_classes(), - "confidence_threshold": self._model_state.get_threshold(), + "classes": self._model_state.current_classes, + "confidence_threshold": self._model_state.confidence_threshold, "snapping": { "running": self._condition_engine.any_active(), **self._condition_engine.export_conditions_config(), diff --git a/apps/data-collection/backend/src/core/model_state.py b/apps/data-collection/backend/src/core/model_state.py deleted file mode 100644 index 2a10aca27..000000000 --- a/apps/data-collection/backend/src/core/model_state.py +++ /dev/null @@ -1,18 +0,0 @@ -class ModelState: - """Holds model-related metadata and class definitions.""" - - def __init__(self): - self.__current_classes: list[str] = [] - self.__conf_threshold = 0.1 - - def update_classes(self, new_classes: list[str]): - self.__current_classes = list(new_classes) - - def update_threshold(self, value: float): - self.__conf_threshold = max(0.0, min(1.0, value)) - - def get_classes(self) -> list[str]: - return list(self.__current_classes) - - def get_threshold(self) -> float: - return self.__conf_threshold diff --git a/apps/data-collection/backend/src/core/neural_network/pipeline/model_state.py b/apps/data-collection/backend/src/core/neural_network/pipeline/model_state.py new file mode 100644 index 000000000..34d0e6925 --- /dev/null +++ b/apps/data-collection/backend/src/core/neural_network/pipeline/model_state.py @@ -0,0 +1,9 @@ +from dataclasses import dataclass, field + + +@dataclass +class ModelState: + """Holds current NN model prompt state""" + + confidence_threshold: float = 0.1 + current_classes: list[str] = field(default_factory=list) diff --git a/apps/data-collection/backend/src/core/neural_network/pipeline/nn_pipeline_setup.py b/apps/data-collection/backend/src/core/neural_network/pipeline/nn_pipeline_setup.py index d70e1a5f7..f6353d078 100644 --- a/apps/data-collection/backend/src/core/neural_network/pipeline/nn_pipeline_setup.py +++ b/apps/data-collection/backend/src/core/neural_network/pipeline/nn_pipeline_setup.py @@ -8,7 +8,6 @@ PromptControllerFactory, ) from core.neural_network.pipeline.tracker_factory import TrackerFactory -from core.model_state import ModelState from depthai_nodes.node import ( ParsingNeuralNetwork, @@ -28,12 +27,10 @@ def __init__( pipeline: dai.Pipeline, input_node: dai.Node.Output, nn_config: NeuralNetworkConfig, - model_state: ModelState, ): self._pipeline: dai.Pipeline = pipeline self._input_node: dai.Node.Output = input_node self._config: NeuralNetworkConfig = nn_config - self._model_state: ModelState = model_state self._nn: ParsingNeuralNetwork = None self._det_filter: ImgDetectionsFilter = None @@ -65,7 +62,6 @@ def build(self) -> "NNPipelineBuilder": self._det_filter, self._annotation_node, self._config.model.precision, - self._model_state, ) self._controller = controller_factory.build() diff --git a/apps/data-collection/backend/src/core/neural_network/pipeline/prompt_controller_factory.py b/apps/data-collection/backend/src/core/neural_network/pipeline/prompt_controller_factory.py index a8b8cd079..51f36fde2 100644 --- a/apps/data-collection/backend/src/core/neural_network/pipeline/prompt_controller_factory.py +++ b/apps/data-collection/backend/src/core/neural_network/pipeline/prompt_controller_factory.py @@ -1,5 +1,4 @@ from core.neural_network.pipeline.annotation_node import AnnotationNode -from core.model_state import ModelState from core.neural_network.prompts.nn_prompts_controller import NnPromptsController from core.label_manager import LabelManager from depthai_nodes.node import ParsingNeuralNetwork, ImgDetectionsFilter @@ -14,13 +13,11 @@ def __init__( det_filter: ImgDetectionsFilter, annotation_node: AnnotationNode, precision: str, - model_state: ModelState, ): self._nn_node: ParsingNeuralNetwork = nn_node self._det_filter: ImgDetectionsFilter = det_filter self._annotation_node: AnnotationNode = annotation_node self._precision: str = precision - self._model_state: ModelState = model_state def build(self) -> NnPromptsController: text_q = self._nn_node.inputs["texts"].createInputQueue() @@ -32,12 +29,7 @@ def build(self) -> NnPromptsController: label_manager = LabelManager(self._det_filter, self._annotation_node) controller = NnPromptsController( - img_q, - text_q, - self._precision, - parser, - self._model_state, - label_manager, + img_q, text_q, self._precision, parser, label_manager ) return controller diff --git a/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_controller.py b/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_controller.py index 66c5350fb..32af2927c 100644 --- a/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_controller.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_controller.py @@ -3,7 +3,7 @@ from depthai_nodes.node import YOLOExtendedParser from core.label_manager import LabelManager -from core.model_state import ModelState +from core.neural_network.pipeline.model_state import ModelState class NnPromptsController: @@ -15,16 +15,16 @@ def __init__( text_prompt_queue: dai.InputQueue, precision: str, parser: YOLOExtendedParser, - model_state: ModelState, label_manager: LabelManager, ): self._image_prompt_queue = image_prompt_queue self._text_prompt_queue = text_prompt_queue self._precision = precision self._parser: YOLOExtendedParser = parser - self._model_state = model_state self._label_manager = label_manager + self._model_state = ModelState() + def _tensor_type(self) -> dai.TensorInfo.DataType: return ( dai.TensorInfo.DataType.FP16 @@ -46,10 +46,13 @@ def send_prompts_pair( ): self._send(self._text_prompt_queue, "text_prompts", text_prompt) self._send(self._image_prompt_queue, "image_prompts", visual_prompt) - self._model_state.update_classes(class_names) self._label_manager.update_labels(class_names, offset) + self._model_state.current_classes = class_names def set_confidence_threshold(self, threshold: float): """Apply threshold update directly to the NN parser.""" self._parser.setConfidenceThreshold(threshold) - self._model_state.update_threshold(threshold) + self._model_state.confidence_threshold = threshold + + def get_model_state(self) -> ModelState: + return self._model_state diff --git a/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_manager.py b/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_manager.py index c576ba23d..07cf4571a 100644 --- a/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_manager.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_manager.py @@ -50,6 +50,7 @@ def build(self): self._config.class_names, self._config.text_offset, ) + self._controller.set_confidence_threshold(self._config.detection_threshold) def register_services(self, visualizer: dai.RemoteConnection): for service in self._services: diff --git a/apps/data-collection/backend/src/core/services/export_service.py b/apps/data-collection/backend/src/core/services/export_service.py index 3ee384e7d..e887b17a8 100644 --- a/apps/data-collection/backend/src/core/services/export_service.py +++ b/apps/data-collection/backend/src/core/services/export_service.py @@ -13,10 +13,5 @@ def __init__(self, config_exporter: SystemStateExporter): self.config_exporter = config_exporter def handle(self, payload: None = None) -> dict[str, any]: - try: - config = self.config_exporter.export_config() - print("[ExportService] returning:", config) - return config - except Exception as e: - print("[ExportService] ERROR:", e) - raise + config = self.config_exporter.export_config() + return config diff --git a/apps/data-collection/backend/src/main.py b/apps/data-collection/backend/src/main.py index e92586765..3ccefae41 100644 --- a/apps/data-collection/backend/src/main.py +++ b/apps/data-collection/backend/src/main.py @@ -1,7 +1,10 @@ +import os + +os.environ.setdefault("DEPTHAI_LEVEL", "info") + import depthai as dai from config.system_configuration import SystemConfiguration -from core.model_state import ModelState from core.neural_network.prompts.nn_prompts_manager import NNPromptsManager from core.neural_network.pipeline.nn_pipeline_setup import NNPipelineBuilder from core.snapping.snaps_manager import SnappingServiceManager @@ -23,14 +26,15 @@ def main(): with dai.Pipeline(device) as pipeline: print("Creating pipeline...") - model_state = ModelState() video_factory = VideoFactory(pipeline, config.get_video_config()) visualizer.addTopic("Video", video_factory.get_encoded_output()) input_node = video_factory.get_input_node() nn_pipeline = NNPipelineBuilder( - pipeline, input_node, config.get_neural_network_config(), model_state + pipeline, + input_node, + config.get_neural_network_config(), ) nn_pipeline.build() visualizer.addTopic("Annotations", nn_pipeline.annotation_node.out) @@ -51,7 +55,9 @@ def main(): snaps_manager.build() snaps_manager.register_service(visualizer) - export_manager = ExportServiceManager(model_state, snaps_manager.get_engine()) + export_manager = ExportServiceManager( + nn_pipeline.controller.get_model_state(), snaps_manager.get_engine() + ) export_manager.build() export_manager.register_service(visualizer) diff --git a/apps/data-collection/frontend/src/App.tsx b/apps/data-collection/frontend/src/App.tsx index eebd86214..fa314019b 100644 --- a/apps/data-collection/frontend/src/App.tsx +++ b/apps/data-collection/frontend/src/App.tsx @@ -216,6 +216,8 @@ function App() { const jsonStr = td.decode(view); obj = JSON.parse(jsonStr); + console.log("[App] Received payload:", obj); + if (obj && obj.data && typeof obj.data === 'object') { obj = obj.data; } From 7692b651ca8f60cdabcc5cd41676d0115fd8013b Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Fri, 7 Nov 2025 15:41:21 +0100 Subject: [PATCH 32/39] simplified export service --- .../backend/src/core/export/export_manager.py | 24 --------------- .../backend/src/core/export/export_service.py | 25 ++++++++++++++++ .../src/core/export/system_state_exporter.py | 30 ------------------- .../src/core/services/export_service.py | 17 ----------- apps/data-collection/backend/src/main.py | 10 ++++--- 5 files changed, 31 insertions(+), 75 deletions(-) delete mode 100644 apps/data-collection/backend/src/core/export/export_manager.py create mode 100644 apps/data-collection/backend/src/core/export/export_service.py delete mode 100644 apps/data-collection/backend/src/core/export/system_state_exporter.py delete mode 100644 apps/data-collection/backend/src/core/services/export_service.py diff --git a/apps/data-collection/backend/src/core/export/export_manager.py b/apps/data-collection/backend/src/core/export/export_manager.py deleted file mode 100644 index 4bc41a386..000000000 --- a/apps/data-collection/backend/src/core/export/export_manager.py +++ /dev/null @@ -1,24 +0,0 @@ -from core.neural_network.pipeline.model_state import ModelState -from core.snapping.conditions_engine import ConditionsEngine -from core.export.system_state_exporter import SystemStateExporter -from core.services.export_service import ExportService - - -class ExportServiceManager: - """ - Facade for the configuration export subsystem. - """ - - def __init__(self, model_state: ModelState, condition_engine: ConditionsEngine): - self._model_state: ModelState = model_state - self._condition_engine: ConditionsEngine = condition_engine - - self._exporter: SystemStateExporter = None - self._service: ExportService = None - - def build(self): - self._exporter = SystemStateExporter(self._model_state, self._condition_engine) - self._service = ExportService(self._exporter) - - def register_service(self, visualizer): - visualizer.registerService(self._service.name, self._service.handle) diff --git a/apps/data-collection/backend/src/core/export/export_service.py b/apps/data-collection/backend/src/core/export/export_service.py new file mode 100644 index 000000000..cf1901ad0 --- /dev/null +++ b/apps/data-collection/backend/src/core/export/export_service.py @@ -0,0 +1,25 @@ +from core.neural_network.pipeline.model_state import ModelState +from core.services.base_service import BaseService +from core.services.service_name import ServiceName +from core.snapping.conditions_engine import ConditionsEngine + + +class ExportService(BaseService[None]): + """Returns the current configuration state to the frontend.""" + + NAME = ServiceName.EXPORT + + def __init__(self, model_state: ModelState, condition_engine: ConditionsEngine): + super().__init__() + self._model_state: ModelState = model_state + self._condition_engine: ConditionsEngine = condition_engine + + def handle(self, payload: None = None) -> dict[str, any]: + return { + "classes": self._model_state.current_classes, + "confidence_threshold": self._model_state.confidence_threshold, + "snapping": { + "running": self._condition_engine.any_active(), + **self._condition_engine.export_conditions_config(), + }, + } diff --git a/apps/data-collection/backend/src/core/export/system_state_exporter.py b/apps/data-collection/backend/src/core/export/system_state_exporter.py deleted file mode 100644 index 0878f5940..000000000 --- a/apps/data-collection/backend/src/core/export/system_state_exporter.py +++ /dev/null @@ -1,30 +0,0 @@ -from typing import Dict, Any -from core.snapping.conditions_engine import ConditionsEngine -from core.neural_network.pipeline.model_state import ModelState - - -class SystemStateExporter: - """ - Builds a full frontend-friendly snapshot of the current system configuration. - """ - - def __init__( - self, - model_state: ModelState, - condition_engine: ConditionsEngine, - ): - self._model_state = model_state - self._condition_engine = condition_engine - - def export_config(self) -> Dict[str, Any]: - """ - Construct the unified configuration dictionary expected by the frontend. - """ - return { - "classes": self._model_state.current_classes, - "confidence_threshold": self._model_state.confidence_threshold, - "snapping": { - "running": self._condition_engine.any_active(), - **self._condition_engine.export_conditions_config(), - }, - } diff --git a/apps/data-collection/backend/src/core/services/export_service.py b/apps/data-collection/backend/src/core/services/export_service.py deleted file mode 100644 index e887b17a8..000000000 --- a/apps/data-collection/backend/src/core/services/export_service.py +++ /dev/null @@ -1,17 +0,0 @@ -from core.services.base_service import BaseService -from core.export.system_state_exporter import SystemStateExporter -from core.services.service_name import ServiceName - - -class ExportService(BaseService[None]): - """Returns the current configuration state to the frontend.""" - - NAME = ServiceName.EXPORT - - def __init__(self, config_exporter: SystemStateExporter): - super().__init__() - self.config_exporter = config_exporter - - def handle(self, payload: None = None) -> dict[str, any]: - config = self.config_exporter.export_config() - return config diff --git a/apps/data-collection/backend/src/main.py b/apps/data-collection/backend/src/main.py index 3ccefae41..0fc201b0e 100644 --- a/apps/data-collection/backend/src/main.py +++ b/apps/data-collection/backend/src/main.py @@ -2,6 +2,10 @@ os.environ.setdefault("DEPTHAI_LEVEL", "info") +from core.export.export_service import ExportService + +os.environ.setdefault("DEPTHAI_LEVEL", "info") + import depthai as dai from config.system_configuration import SystemConfiguration @@ -9,7 +13,6 @@ from core.neural_network.pipeline.nn_pipeline_setup import NNPipelineBuilder from core.snapping.snaps_manager import SnappingServiceManager from core.video.video_factory import VideoFactory -from core.export.export_manager import ExportServiceManager def main(): @@ -55,11 +58,10 @@ def main(): snaps_manager.build() snaps_manager.register_service(visualizer) - export_manager = ExportServiceManager( + export_service = ExportService( nn_pipeline.controller.get_model_state(), snaps_manager.get_engine() ) - export_manager.build() - export_manager.register_service(visualizer) + visualizer.registerService(export_service.name, export_service.handle) print("Pipeline created.") pipeline.start() From aecf2ed0ae471c92dd89cccf960c996cd4067239 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Sat, 8 Nov 2025 15:44:35 +0100 Subject: [PATCH 33/39] tiny refactoring + renaming --- .../src/config/yaml_configs/nn_config.yaml | 1 - .../{export => front_end_services}/__init__.py | 0 .../base_service.py | 2 +- .../bbox_prompt_service.py | 6 +++--- .../class_update_service.py | 6 +++--- .../export_service.py | 4 ++-- .../image_upload_service.py | 6 +++--- .../payloads}/__init__.py | 0 .../payloads/bbox_prompt_payload.py | 0 .../payloads/class_update_payload.py | 0 .../payloads/image_upload_payload.py | 0 .../payloads/snap_payload.py | 0 .../payloads/threshold_update_payload.py | 0 .../service_name.py | 0 .../snap_collection_service.py | 6 +++--- .../threshold_update_service.py | 8 +++++--- .../pipeline/detection_graph_factory.py | 6 +++--- .../neural_network/pipeline/nn_node_factory.py | 8 ++++---- .../pipeline/nn_pipeline_setup.py | 10 +++++----- .../neural_network/pipeline/tracker_factory.py | 8 ++++---- .../prompts/handlers/bbox_prompt_handler.py | 2 +- .../prompts/handlers/image_prompt_handler.py | 2 +- .../prompts/handlers/text_prompt_handler.py | 2 +- .../prompts/nn_prompts_manager.py | 8 ++++---- .../prompts/prompt_service_factory.py | 10 +++++----- .../src/core/services/payloads/__init__.py | 0 .../core/snapping/conditions/base_condition.py | 2 +- .../snapping/conditions/low_conf_condition.py | 2 +- .../tracker_conditions/lost_mid_condition.py | 2 +- .../src/core/snapping/conditions_engine.py | 2 +- .../backend/src/core/snapping/snaps_manager.py | 8 ++++---- .../backend/src/core/video/video_factory.py | 4 ++-- .../video_providers/base_video_provider.py | 2 +- .../video_providers/camera_video_provvider.py | 2 +- .../video_providers/replay_video_provider.py | 2 +- apps/data-collection/backend/src/main.py | 18 +++++------------- 36 files changed, 66 insertions(+), 73 deletions(-) rename apps/data-collection/backend/src/core/{export => front_end_services}/__init__.py (100%) rename apps/data-collection/backend/src/core/{services => front_end_services}/base_service.py (93%) rename apps/data-collection/backend/src/core/{services => front_end_services}/bbox_prompt_service.py (76%) rename apps/data-collection/backend/src/core/{services => front_end_services}/class_update_service.py (78%) rename apps/data-collection/backend/src/core/{export => front_end_services}/export_service.py (88%) rename apps/data-collection/backend/src/core/{services => front_end_services}/image_upload_service.py (78%) rename apps/data-collection/backend/src/core/{services => front_end_services/payloads}/__init__.py (100%) rename apps/data-collection/backend/src/core/{services => front_end_services}/payloads/bbox_prompt_payload.py (100%) rename apps/data-collection/backend/src/core/{services => front_end_services}/payloads/class_update_payload.py (100%) rename apps/data-collection/backend/src/core/{services => front_end_services}/payloads/image_upload_payload.py (100%) rename apps/data-collection/backend/src/core/{services => front_end_services}/payloads/snap_payload.py (100%) rename apps/data-collection/backend/src/core/{services => front_end_services}/payloads/threshold_update_payload.py (100%) rename apps/data-collection/backend/src/core/{services => front_end_services}/service_name.py (100%) rename apps/data-collection/backend/src/core/{services => front_end_services}/snap_collection_service.py (84%) rename apps/data-collection/backend/src/core/{services => front_end_services}/threshold_update_service.py (75%) delete mode 100644 apps/data-collection/backend/src/core/services/payloads/__init__.py diff --git a/apps/data-collection/backend/src/config/yaml_configs/nn_config.yaml b/apps/data-collection/backend/src/config/yaml_configs/nn_config.yaml index 1193a8f5b..cd003a1a0 100644 --- a/apps/data-collection/backend/src/config/yaml_configs/nn_config.yaml +++ b/apps/data-collection/backend/src/config/yaml_configs/nn_config.yaml @@ -3,7 +3,6 @@ nn_backend: runtime: "dsp" performance_profile: "default" inference_threads: 1 - confidence_threshold: 0.1 precision: "fp16" tracker: diff --git a/apps/data-collection/backend/src/core/export/__init__.py b/apps/data-collection/backend/src/core/front_end_services/__init__.py similarity index 100% rename from apps/data-collection/backend/src/core/export/__init__.py rename to apps/data-collection/backend/src/core/front_end_services/__init__.py diff --git a/apps/data-collection/backend/src/core/services/base_service.py b/apps/data-collection/backend/src/core/front_end_services/base_service.py similarity index 93% rename from apps/data-collection/backend/src/core/services/base_service.py rename to apps/data-collection/backend/src/core/front_end_services/base_service.py index 1dfb7193e..ebc3dd385 100644 --- a/apps/data-collection/backend/src/core/services/base_service.py +++ b/apps/data-collection/backend/src/core/front_end_services/base_service.py @@ -4,7 +4,7 @@ from core.neural_network.prompts.nn_prompts_controller import NnPromptsController from core.neural_network.prompts.handlers.base_prompt_handler import BasePromptHandler -from core.services.service_name import ServiceName +from core.front_end_services.service_name import ServiceName PayloadT = TypeVar("PayloadT", bound=BaseModel) diff --git a/apps/data-collection/backend/src/core/services/bbox_prompt_service.py b/apps/data-collection/backend/src/core/front_end_services/bbox_prompt_service.py similarity index 76% rename from apps/data-collection/backend/src/core/services/bbox_prompt_service.py rename to apps/data-collection/backend/src/core/front_end_services/bbox_prompt_service.py index a0f0b5aba..de53a4439 100644 --- a/apps/data-collection/backend/src/core/services/bbox_prompt_service.py +++ b/apps/data-collection/backend/src/core/front_end_services/bbox_prompt_service.py @@ -1,7 +1,7 @@ from pydantic import ValidationError -from core.services.base_service import BaseService -from core.services.payloads.bbox_prompt_payload import BBoxPromptPayload -from core.services.service_name import ServiceName +from core.front_end_services.base_service import BaseService +from core.front_end_services.payloads.bbox_prompt_payload import BBoxPromptPayload +from core.front_end_services.service_name import ServiceName class BBoxPromptService(BaseService[BBoxPromptPayload]): diff --git a/apps/data-collection/backend/src/core/services/class_update_service.py b/apps/data-collection/backend/src/core/front_end_services/class_update_service.py similarity index 78% rename from apps/data-collection/backend/src/core/services/class_update_service.py rename to apps/data-collection/backend/src/core/front_end_services/class_update_service.py index 2c97a7931..71cf24bca 100644 --- a/apps/data-collection/backend/src/core/services/class_update_service.py +++ b/apps/data-collection/backend/src/core/front_end_services/class_update_service.py @@ -1,7 +1,7 @@ from pydantic import ValidationError -from core.services.base_service import BaseService -from core.services.payloads.class_update_payload import ClassUpdatePayload -from core.services.service_name import ServiceName +from core.front_end_services.base_service import BaseService +from core.front_end_services.payloads.class_update_payload import ClassUpdatePayload +from core.front_end_services.service_name import ServiceName class ClassUpdateService(BaseService[ClassUpdatePayload]): diff --git a/apps/data-collection/backend/src/core/export/export_service.py b/apps/data-collection/backend/src/core/front_end_services/export_service.py similarity index 88% rename from apps/data-collection/backend/src/core/export/export_service.py rename to apps/data-collection/backend/src/core/front_end_services/export_service.py index cf1901ad0..a09d4de9c 100644 --- a/apps/data-collection/backend/src/core/export/export_service.py +++ b/apps/data-collection/backend/src/core/front_end_services/export_service.py @@ -1,6 +1,6 @@ from core.neural_network.pipeline.model_state import ModelState -from core.services.base_service import BaseService -from core.services.service_name import ServiceName +from core.front_end_services.base_service import BaseService +from core.front_end_services.service_name import ServiceName from core.snapping.conditions_engine import ConditionsEngine diff --git a/apps/data-collection/backend/src/core/services/image_upload_service.py b/apps/data-collection/backend/src/core/front_end_services/image_upload_service.py similarity index 78% rename from apps/data-collection/backend/src/core/services/image_upload_service.py rename to apps/data-collection/backend/src/core/front_end_services/image_upload_service.py index 23b3fb322..21b9cd0ea 100644 --- a/apps/data-collection/backend/src/core/services/image_upload_service.py +++ b/apps/data-collection/backend/src/core/front_end_services/image_upload_service.py @@ -1,7 +1,7 @@ from pydantic import ValidationError -from core.services.base_service import BaseService -from core.services.payloads.image_upload_payload import ImageUploadPayload -from core.services.service_name import ServiceName +from core.front_end_services.base_service import BaseService +from core.front_end_services.payloads.image_upload_payload import ImageUploadPayload +from core.front_end_services.service_name import ServiceName class ImageUploadService(BaseService[ImageUploadPayload]): diff --git a/apps/data-collection/backend/src/core/services/__init__.py b/apps/data-collection/backend/src/core/front_end_services/payloads/__init__.py similarity index 100% rename from apps/data-collection/backend/src/core/services/__init__.py rename to apps/data-collection/backend/src/core/front_end_services/payloads/__init__.py diff --git a/apps/data-collection/backend/src/core/services/payloads/bbox_prompt_payload.py b/apps/data-collection/backend/src/core/front_end_services/payloads/bbox_prompt_payload.py similarity index 100% rename from apps/data-collection/backend/src/core/services/payloads/bbox_prompt_payload.py rename to apps/data-collection/backend/src/core/front_end_services/payloads/bbox_prompt_payload.py diff --git a/apps/data-collection/backend/src/core/services/payloads/class_update_payload.py b/apps/data-collection/backend/src/core/front_end_services/payloads/class_update_payload.py similarity index 100% rename from apps/data-collection/backend/src/core/services/payloads/class_update_payload.py rename to apps/data-collection/backend/src/core/front_end_services/payloads/class_update_payload.py diff --git a/apps/data-collection/backend/src/core/services/payloads/image_upload_payload.py b/apps/data-collection/backend/src/core/front_end_services/payloads/image_upload_payload.py similarity index 100% rename from apps/data-collection/backend/src/core/services/payloads/image_upload_payload.py rename to apps/data-collection/backend/src/core/front_end_services/payloads/image_upload_payload.py diff --git a/apps/data-collection/backend/src/core/services/payloads/snap_payload.py b/apps/data-collection/backend/src/core/front_end_services/payloads/snap_payload.py similarity index 100% rename from apps/data-collection/backend/src/core/services/payloads/snap_payload.py rename to apps/data-collection/backend/src/core/front_end_services/payloads/snap_payload.py diff --git a/apps/data-collection/backend/src/core/services/payloads/threshold_update_payload.py b/apps/data-collection/backend/src/core/front_end_services/payloads/threshold_update_payload.py similarity index 100% rename from apps/data-collection/backend/src/core/services/payloads/threshold_update_payload.py rename to apps/data-collection/backend/src/core/front_end_services/payloads/threshold_update_payload.py diff --git a/apps/data-collection/backend/src/core/services/service_name.py b/apps/data-collection/backend/src/core/front_end_services/service_name.py similarity index 100% rename from apps/data-collection/backend/src/core/services/service_name.py rename to apps/data-collection/backend/src/core/front_end_services/service_name.py diff --git a/apps/data-collection/backend/src/core/services/snap_collection_service.py b/apps/data-collection/backend/src/core/front_end_services/snap_collection_service.py similarity index 84% rename from apps/data-collection/backend/src/core/services/snap_collection_service.py rename to apps/data-collection/backend/src/core/front_end_services/snap_collection_service.py index 644341ad0..ee2d8ebfb 100644 --- a/apps/data-collection/backend/src/core/services/snap_collection_service.py +++ b/apps/data-collection/backend/src/core/front_end_services/snap_collection_service.py @@ -1,9 +1,9 @@ from pydantic import ValidationError -from core.services.base_service import BaseService +from core.front_end_services.base_service import BaseService from core.snapping.conditions_engine import ConditionsEngine from depthai_nodes.node import SnapsProducer -from core.services.payloads.snap_payload import SnapPayload -from core.services.service_name import ServiceName +from core.front_end_services.payloads.snap_payload import SnapPayload +from core.front_end_services.service_name import ServiceName class SnapCollectionService(BaseService[SnapPayload]): diff --git a/apps/data-collection/backend/src/core/services/threshold_update_service.py b/apps/data-collection/backend/src/core/front_end_services/threshold_update_service.py similarity index 75% rename from apps/data-collection/backend/src/core/services/threshold_update_service.py rename to apps/data-collection/backend/src/core/front_end_services/threshold_update_service.py index aeb690da5..d77591981 100644 --- a/apps/data-collection/backend/src/core/services/threshold_update_service.py +++ b/apps/data-collection/backend/src/core/front_end_services/threshold_update_service.py @@ -1,7 +1,9 @@ from pydantic import ValidationError -from core.services.base_service import BaseService -from core.services.payloads.threshold_update_payload import ThresholdUpdatePayload -from core.services.service_name import ServiceName +from core.front_end_services.base_service import BaseService +from core.front_end_services.payloads.threshold_update_payload import ( + ThresholdUpdatePayload, +) +from core.front_end_services.service_name import ServiceName class ThresholdUpdateService(BaseService[ThresholdUpdatePayload]): diff --git a/apps/data-collection/backend/src/core/neural_network/pipeline/detection_graph_factory.py b/apps/data-collection/backend/src/core/neural_network/pipeline/detection_graph_factory.py index af471a8e1..b34c82e49 100644 --- a/apps/data-collection/backend/src/core/neural_network/pipeline/detection_graph_factory.py +++ b/apps/data-collection/backend/src/core/neural_network/pipeline/detection_graph_factory.py @@ -14,17 +14,17 @@ class DetectionGraphFactory: def __init__( self, pipeline: dai.Pipeline, - input_node: dai.Node.Output, + video_node: dai.Node.Output, nn: ParsingNeuralNetwork, ): self._pipeline: dai.Pipeline = pipeline - self._input_node: dai.Node.Output = input_node + self._video_node: dai.Node.Output = video_node self._nn: ParsingNeuralNetwork = nn def build(self): det_filter = self._pipeline.create(ImgDetectionsFilter).build(self._nn.out) annotation_node = self._pipeline.create(AnnotationNode).build( - det_filter.out, self._input_node + det_filter.out, self._video_node ) filtered_bridge = self._pipeline.create(ImgDetectionsBridge).build( det_filter.out diff --git a/apps/data-collection/backend/src/core/neural_network/pipeline/nn_node_factory.py b/apps/data-collection/backend/src/core/neural_network/pipeline/nn_node_factory.py index 87c8f1ee6..746518a51 100644 --- a/apps/data-collection/backend/src/core/neural_network/pipeline/nn_node_factory.py +++ b/apps/data-collection/backend/src/core/neural_network/pipeline/nn_node_factory.py @@ -9,11 +9,11 @@ class NnNodeFactory: def __init__( self, pipeline: dai.Pipeline, - input_node: dai.Node.Output, + video_node: dai.Node.Output, config: NeuralNetworkConfig, ): self._pipeline: dai.Pipeline = pipeline - self._input_node: dai.Node.Output = input_node + self._video_node: dai.Node.Output = video_node self._config: NeuralNetworkConfig = config def build(self) -> ParsingNeuralNetwork: @@ -29,7 +29,7 @@ def build(self) -> ParsingNeuralNetwork: } ) nn.setNumInferenceThreads(backend.inference_threads) - nn.getParser(0).setConfidenceThreshold(backend.confidence_threshold) + nn.getParser(0).setConfidenceThreshold(0.0) - self._input_node.link(nn.inputs["images"]) + self._video_node.link(nn.inputs["images"]) return nn diff --git a/apps/data-collection/backend/src/core/neural_network/pipeline/nn_pipeline_setup.py b/apps/data-collection/backend/src/core/neural_network/pipeline/nn_pipeline_setup.py index f6353d078..43768a848 100644 --- a/apps/data-collection/backend/src/core/neural_network/pipeline/nn_pipeline_setup.py +++ b/apps/data-collection/backend/src/core/neural_network/pipeline/nn_pipeline_setup.py @@ -25,11 +25,11 @@ class NNPipelineBuilder: def __init__( self, pipeline: dai.Pipeline, - input_node: dai.Node.Output, + video_node: dai.Node.Output, nn_config: NeuralNetworkConfig, ): self._pipeline: dai.Pipeline = pipeline - self._input_node: dai.Node.Output = input_node + self._video_node: dai.Node.Output = video_node self._config: NeuralNetworkConfig = nn_config self._nn: ParsingNeuralNetwork = None @@ -41,10 +41,10 @@ def __init__( def build(self) -> "NNPipelineBuilder": """Build full neural-network subgraph.""" - nn_builder = NnNodeFactory(self._pipeline, self._input_node, self._config) + nn_builder = NnNodeFactory(self._pipeline, self._video_node, self._config) self._nn = nn_builder.build() - det_graph = DetectionGraphFactory(self._pipeline, self._input_node, self._nn) + det_graph = DetectionGraphFactory(self._pipeline, self._video_node, self._nn) self._det_filter, self._annotation_node, self._filtered_bridge = ( det_graph.build() ) @@ -52,7 +52,7 @@ def build(self) -> "NNPipelineBuilder": tracker_factory = TrackerFactory( self._pipeline, self._filtered_bridge.out, - self._input_node, + self._video_node, self._config.nn_yaml.tracker, ) self._tracker = tracker_factory.build() diff --git a/apps/data-collection/backend/src/core/neural_network/pipeline/tracker_factory.py b/apps/data-collection/backend/src/core/neural_network/pipeline/tracker_factory.py index 938227c36..5f2956632 100644 --- a/apps/data-collection/backend/src/core/neural_network/pipeline/tracker_factory.py +++ b/apps/data-collection/backend/src/core/neural_network/pipeline/tracker_factory.py @@ -7,12 +7,12 @@ def __init__( self, pipeline: dai.Pipeline, detections_bridge: dai.Node.Output, - input_node: dai.Node.Output, + video_node: dai.Node.Output, config: Box, ): self._pipeline: dai.Pipeline = pipeline self._detections_bridge: dai.Node.Output = detections_bridge - self._input_node: dai.Node.Output = input_node + self._video_node: dai.Node.Output = video_node self._config: Box = config def build(self) -> dai.node.ObjectTracker: @@ -25,8 +25,8 @@ def build(self) -> dai.node.ObjectTracker: tracker.setOcclusionRatioThreshold(self._config.occlusion_ratio_threshold) tracker.setTrackerThreshold(self._config.tracker_threshold) - self._input_node.link(tracker.inputTrackerFrame) - self._input_node.link(tracker.inputDetectionFrame) + self._video_node.link(tracker.inputTrackerFrame) + self._video_node.link(tracker.inputDetectionFrame) self._detections_bridge.link(tracker.inputDetections) return tracker diff --git a/apps/data-collection/backend/src/core/neural_network/prompts/handlers/bbox_prompt_handler.py b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/bbox_prompt_handler.py index d3cd7dcf1..c109a5c62 100644 --- a/apps/data-collection/backend/src/core/neural_network/prompts/handlers/bbox_prompt_handler.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/bbox_prompt_handler.py @@ -5,7 +5,7 @@ ) from core.neural_network.prompts.handlers.base_prompt_handler import BasePromptHandler from core.neural_network.prompts.frame_cache_node import FrameCacheNode -from core.services.payloads.bbox_prompt_payload import BBoxPromptPayload +from core.front_end_services.payloads.bbox_prompt_payload import BBoxPromptPayload class BBoxPromptHandler(BasePromptHandler): diff --git a/apps/data-collection/backend/src/core/neural_network/prompts/handlers/image_prompt_handler.py b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/image_prompt_handler.py index a8fdc9e8d..7f4719ff8 100644 --- a/apps/data-collection/backend/src/core/neural_network/prompts/handlers/image_prompt_handler.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/image_prompt_handler.py @@ -6,7 +6,7 @@ VisualPromptEncoder, ) from core.neural_network.prompts.handlers.base_prompt_handler import BasePromptHandler -from core.services.payloads.image_upload_payload import ImageUploadPayload +from core.front_end_services.payloads.image_upload_payload import ImageUploadPayload class ImagePromptHandler(BasePromptHandler): diff --git a/apps/data-collection/backend/src/core/neural_network/prompts/handlers/text_prompt_handler.py b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/text_prompt_handler.py index ae943d8f1..a50f34181 100644 --- a/apps/data-collection/backend/src/core/neural_network/prompts/handlers/text_prompt_handler.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/text_prompt_handler.py @@ -6,7 +6,7 @@ TextualPromptEncoder, ) from core.neural_network.prompts.handlers.base_prompt_handler import BasePromptHandler -from core.services.payloads.class_update_payload import ClassUpdatePayload +from core.front_end_services.payloads.class_update_payload import ClassUpdatePayload class TextPromptHandler(BasePromptHandler): diff --git a/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_manager.py b/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_manager.py index 07cf4571a..3260f0ccb 100644 --- a/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_manager.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_manager.py @@ -11,7 +11,7 @@ PromptEncodersManager, ) from core.neural_network.prompts.handlers_factory import HandlersFactory -from core.services.base_service import BaseService +from core.front_end_services.base_service import BaseService class NNPromptsManager: @@ -22,12 +22,12 @@ class NNPromptsManager: def __init__( self, pipeline: dai.Pipeline, - input_node: dai.Node.Output, + video_node: dai.Node.Output, config: Box, controller: NnPromptsController, ): self._pipeline: dai.Pipeline = pipeline - self._input_node: dai.Node.Output = input_node + self._video_node: dai.Node.Output = video_node self._config: Box = config self._controller: NnPromptsController = controller self._services: List[BaseService] = [] @@ -36,7 +36,7 @@ def build(self): encoders = PromptEncodersManager(self._config) encoders.build() - frame_cache = self._pipeline.create(FrameCacheNode).build(self._input_node) + frame_cache = self._pipeline.create(FrameCacheNode).build(self._video_node) handlers = HandlersFactory(encoders, frame_cache) handlers.build() diff --git a/apps/data-collection/backend/src/core/neural_network/prompts/prompt_service_factory.py b/apps/data-collection/backend/src/core/neural_network/prompts/prompt_service_factory.py index 376bfd338..78726f405 100644 --- a/apps/data-collection/backend/src/core/neural_network/prompts/prompt_service_factory.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/prompt_service_factory.py @@ -1,10 +1,10 @@ from core.neural_network.prompts.nn_prompts_controller import NnPromptsController from core.neural_network.prompts.handlers_factory import HandlersFactory -from core.services.base_service import BaseService -from core.services.class_update_service import ClassUpdateService -from core.services.threshold_update_service import ThresholdUpdateService -from core.services.image_upload_service import ImageUploadService -from core.services.bbox_prompt_service import BBoxPromptService +from core.front_end_services.base_service import BaseService +from core.front_end_services.class_update_service import ClassUpdateService +from core.front_end_services.threshold_update_service import ThresholdUpdateService +from core.front_end_services.image_upload_service import ImageUploadService +from core.front_end_services.bbox_prompt_service import BBoxPromptService class PromptServiceFactory: diff --git a/apps/data-collection/backend/src/core/services/payloads/__init__.py b/apps/data-collection/backend/src/core/services/payloads/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/apps/data-collection/backend/src/core/snapping/conditions/base_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/base_condition.py index f057b4c90..082fcd777 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions/base_condition.py +++ b/apps/data-collection/backend/src/core/snapping/conditions/base_condition.py @@ -1,7 +1,7 @@ from abc import ABC, abstractmethod from typing import Dict, Any, List, Optional -from core.services.payloads.snap_payload import ConditionConfig +from core.front_end_services.payloads.snap_payload import ConditionConfig from core.snapping.conditions.condition_key import ConditionKey from time import time diff --git a/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py index 846905810..389ac9c97 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py +++ b/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py @@ -1,4 +1,4 @@ -from core.services.payloads.snap_payload import ConditionConfig +from core.front_end_services.payloads.snap_payload import ConditionConfig from core.snapping.conditions.base_condition import Condition from core.snapping.conditions.condition_key import ConditionKey from typing import Dict, Any diff --git a/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py index 1f08ebbc4..9fafc951b 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py +++ b/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py @@ -1,4 +1,4 @@ -from core.services.payloads.snap_payload import ConditionConfig +from core.front_end_services.payloads.snap_payload import ConditionConfig from core.snapping.conditions.base_condition import Condition from core.snapping.conditions.tracker_conditions.tracklet_analyzer import ( TrackletAnalyzer, diff --git a/apps/data-collection/backend/src/core/snapping/conditions_engine.py b/apps/data-collection/backend/src/core/snapping/conditions_engine.py index c820b89a5..bf32a054b 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions_engine.py +++ b/apps/data-collection/backend/src/core/snapping/conditions_engine.py @@ -1,7 +1,7 @@ from __future__ import annotations from typing import Dict, Generator, Any -from core.services.payloads.snap_payload import ConditionConfig +from core.front_end_services.payloads.snap_payload import ConditionConfig from core.snapping.conditions.base_condition import Condition from core.snapping.conditions.condition_key import ConditionKey diff --git a/apps/data-collection/backend/src/core/snapping/snaps_manager.py b/apps/data-collection/backend/src/core/snapping/snaps_manager.py index c81dd4c45..9307d71d7 100644 --- a/apps/data-collection/backend/src/core/snapping/snaps_manager.py +++ b/apps/data-collection/backend/src/core/snapping/snaps_manager.py @@ -5,7 +5,7 @@ from core.snapping.conditions_engine import ConditionsEngine from core.snapping.conditions_factory import ConditionsFactory from core.snapping.snaps_producer_factory import SnapsProducerFactory -from core.services.snap_collection_service import SnapCollectionService +from core.front_end_services.snap_collection_service import SnapCollectionService class SnappingServiceManager: @@ -16,13 +16,13 @@ class SnappingServiceManager: def __init__( self, pipeline: dai.Pipeline, - input_node: dai.Node.Output, + video_node: dai.Node.Output, tracker: dai.node.ObjectTracker, detections: dai.ImgDetections, conditions_config: Box, ): self._pipeline = pipeline - self._input_node = input_node + self._video_node = video_node self._tracker = tracker self._detections = detections self._conditions_config = conditions_config @@ -37,7 +37,7 @@ def build(self): snaps_producer = SnapsProducerFactory.create( self._pipeline, - self._input_node, + self._video_node, self._tracker, self._detections, self._engine, diff --git a/apps/data-collection/backend/src/core/video/video_factory.py b/apps/data-collection/backend/src/core/video/video_factory.py index b90aa4962..b945c36c8 100644 --- a/apps/data-collection/backend/src/core/video/video_factory.py +++ b/apps/data-collection/backend/src/core/video/video_factory.py @@ -22,8 +22,8 @@ def _select_source(self) -> BaseVideoProvider: return ReplayVideoProvider(self._pipeline, self._config) return CameraVideoProvider(self._pipeline, self._config) - def get_input_node(self) -> dai.Node.Output: - return self._source.get_input_node() + def get_video_node(self) -> dai.Node.Output: + return self._source.get_video_node() def get_nv12_output(self) -> dai.Node.Output: return self._source.get_nv12_output() diff --git a/apps/data-collection/backend/src/core/video/video_providers/base_video_provider.py b/apps/data-collection/backend/src/core/video/video_providers/base_video_provider.py index 3b901072d..d6a0f7616 100644 --- a/apps/data-collection/backend/src/core/video/video_providers/base_video_provider.py +++ b/apps/data-collection/backend/src/core/video/video_providers/base_video_provider.py @@ -11,7 +11,7 @@ def __init__(self, pipeline: dai.Pipeline, config: VideoConfig): self._config = config @abstractmethod - def get_input_node(self) -> dai.Node.Output: + def get_video_node(self) -> dai.Node.Output: """Return BGR888i output for NN input.""" pass diff --git a/apps/data-collection/backend/src/core/video/video_providers/camera_video_provvider.py b/apps/data-collection/backend/src/core/video/video_providers/camera_video_provvider.py index dea1285df..df323c6a4 100644 --- a/apps/data-collection/backend/src/core/video/video_providers/camera_video_provvider.py +++ b/apps/data-collection/backend/src/core/video/video_providers/camera_video_provvider.py @@ -11,7 +11,7 @@ def __init__(self, pipeline: dai.Pipeline, config: VideoConfig): super().__init__(pipeline, config) self._camera: dai.node.Camera = self._build_camera() - def get_input_node(self) -> dai.Node.Output: + def get_video_node(self) -> dai.Node.Output: return self._camera.requestOutput( size=(self._config.width, self._config.height), type=dai.ImgFrame.Type.BGR888i, diff --git a/apps/data-collection/backend/src/core/video/video_providers/replay_video_provider.py b/apps/data-collection/backend/src/core/video/video_providers/replay_video_provider.py index 5a45affb1..1ec918fd3 100644 --- a/apps/data-collection/backend/src/core/video/video_providers/replay_video_provider.py +++ b/apps/data-collection/backend/src/core/video/video_providers/replay_video_provider.py @@ -12,7 +12,7 @@ def __init__(self, pipeline: dai.Pipeline, config: VideoConfig): super().__init__(pipeline, config) self._replay: dai.node.ReplayVideo = self._build_replay() - def get_input_node(self) -> dai.Node.Output: + def get_video_node(self) -> dai.Node.Output: manip = self._pipeline.create(dai.node.ImageManip) manip.setMaxOutputFrameSize(self._config.width * self._config.height * 3) manip.initialConfig.setOutputSize(self._config.width, self._config.height) diff --git a/apps/data-collection/backend/src/main.py b/apps/data-collection/backend/src/main.py index 0fc201b0e..20660faa5 100644 --- a/apps/data-collection/backend/src/main.py +++ b/apps/data-collection/backend/src/main.py @@ -1,14 +1,6 @@ -import os - -os.environ.setdefault("DEPTHAI_LEVEL", "info") - -from core.export.export_service import ExportService - -os.environ.setdefault("DEPTHAI_LEVEL", "info") - import depthai as dai - from config.system_configuration import SystemConfiguration +from core.front_end_services.export_service import ExportService from core.neural_network.prompts.nn_prompts_manager import NNPromptsManager from core.neural_network.pipeline.nn_pipeline_setup import NNPipelineBuilder from core.snapping.snaps_manager import SnappingServiceManager @@ -32,25 +24,25 @@ def main(): video_factory = VideoFactory(pipeline, config.get_video_config()) visualizer.addTopic("Video", video_factory.get_encoded_output()) - input_node = video_factory.get_input_node() + video_node = video_factory.get_video_node() nn_pipeline = NNPipelineBuilder( pipeline, - input_node, + video_node, config.get_neural_network_config(), ) nn_pipeline.build() visualizer.addTopic("Annotations", nn_pipeline.annotation_node.out) prompts_manager = NNPromptsManager( - pipeline, input_node, config.get_prompts_config(), nn_pipeline.controller + pipeline, video_node, config.get_prompts_config(), nn_pipeline.controller ) prompts_manager.build() prompts_manager.register_services(visualizer) snaps_manager = SnappingServiceManager( pipeline, - input_node, + video_node, nn_pipeline.tracker, nn_pipeline.detections, config.get_snaps_config(), From 86276c6fa8cffbc9f9c6f5690a3b0366d3e27224 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Mon, 10 Nov 2025 15:20:17 +0100 Subject: [PATCH 34/39] cahnged snap sending logic according to new snaps uploader node --- ...lection_service.py => snapping_service.py} | 8 +-- .../pipeline/nn_pipeline_setup.py | 2 +- .../snapping/conditions/low_conf_condition.py | 1 + .../conditions/no_detections_condition.py | 1 + .../snapping/conditions/timed_condition.py | 1 + .../tracker_conditions/lost_mid_condition.py | 1 + .../src/core/snapping/custom_snap_process.py | 30 ---------- .../src/core/snapping/snaps_manager.py | 27 +++++---- .../src/core/snapping/snaps_producer.py | 55 +++++++++++++++++++ .../core/snapping/snaps_producer_factory.py | 25 --------- apps/data-collection/backend/src/main.py | 2 +- .../backend/src/requirements.txt | 2 +- 12 files changed, 79 insertions(+), 76 deletions(-) rename apps/data-collection/backend/src/core/front_end_services/{snap_collection_service.py => snapping_service.py} (76%) delete mode 100644 apps/data-collection/backend/src/core/snapping/custom_snap_process.py create mode 100644 apps/data-collection/backend/src/core/snapping/snaps_producer.py delete mode 100644 apps/data-collection/backend/src/core/snapping/snaps_producer_factory.py diff --git a/apps/data-collection/backend/src/core/front_end_services/snap_collection_service.py b/apps/data-collection/backend/src/core/front_end_services/snapping_service.py similarity index 76% rename from apps/data-collection/backend/src/core/front_end_services/snap_collection_service.py rename to apps/data-collection/backend/src/core/front_end_services/snapping_service.py index ee2d8ebfb..c1b177f95 100644 --- a/apps/data-collection/backend/src/core/front_end_services/snap_collection_service.py +++ b/apps/data-collection/backend/src/core/front_end_services/snapping_service.py @@ -1,12 +1,11 @@ from pydantic import ValidationError from core.front_end_services.base_service import BaseService from core.snapping.conditions_engine import ConditionsEngine -from depthai_nodes.node import SnapsProducer from core.front_end_services.payloads.snap_payload import SnapPayload from core.front_end_services.service_name import ServiceName -class SnapCollectionService(BaseService[SnapPayload]): +class SnappingService(BaseService[SnapPayload]): """ Handles updates to snapping conditions and manages SnapsProducer state. """ @@ -15,12 +14,10 @@ class SnapCollectionService(BaseService[SnapPayload]): def __init__( self, - engine: ConditionsEngine, - snaps_producer: SnapsProducer, + engine: ConditionsEngine ): super().__init__() self.engine: ConditionsEngine = engine - self.snaps_producer: SnapsProducer = snaps_producer def handle(self, payload: SnapPayload) -> dict[str, any]: try: @@ -30,6 +27,5 @@ def handle(self, payload: SnapPayload) -> dict[str, any]: self.engine.import_conditions_config(payload.root) any_active = self.engine.any_active() - self.snaps_producer.setRunning(any_active) return {"ok": True, "active": any_active} diff --git a/apps/data-collection/backend/src/core/neural_network/pipeline/nn_pipeline_setup.py b/apps/data-collection/backend/src/core/neural_network/pipeline/nn_pipeline_setup.py index 43768a848..e6046515d 100644 --- a/apps/data-collection/backend/src/core/neural_network/pipeline/nn_pipeline_setup.py +++ b/apps/data-collection/backend/src/core/neural_network/pipeline/nn_pipeline_setup.py @@ -70,7 +70,7 @@ def nn(self): return self._nn @property - def detections(self): + def detections_bridge(self): return self._filtered_bridge @property diff --git a/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py index 389ac9c97..55cc354a6 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py +++ b/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py @@ -29,6 +29,7 @@ def export_config(self) -> Dict[str, Any]: def should_trigger(self, detections: list[dai.ImgDetection], **kwargs) -> bool: if self.enabled and self._cooldown_passed(): if self._check_detections(detections): + self.mark_triggered() return True return False diff --git a/apps/data-collection/backend/src/core/snapping/conditions/no_detections_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/no_detections_condition.py index e2492b335..efd6d1099 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions/no_detections_condition.py +++ b/apps/data-collection/backend/src/core/snapping/conditions/no_detections_condition.py @@ -15,6 +15,7 @@ def __init__(self, name: str, default_cooldown: float, tags: list[str]): def should_trigger(self, detections: list[dai.ImgDetection], **kwargs) -> bool: if self.enabled and self._cooldown_passed(): if not detections or len(detections) == 0: + self.mark_triggered() return True return False diff --git a/apps/data-collection/backend/src/core/snapping/conditions/timed_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/timed_condition.py index 163a29a5d..f2f0b3204 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions/timed_condition.py +++ b/apps/data-collection/backend/src/core/snapping/conditions/timed_condition.py @@ -13,6 +13,7 @@ def __init__(self, name: str, default_cooldown: float, tags: list[str]): def should_trigger(self, **kwargs) -> bool: if self.enabled and self._cooldown_passed(): + self.mark_triggered() return True return False diff --git a/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py index 9fafc951b..8552e525e 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py +++ b/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py @@ -32,6 +32,7 @@ def export_config(self) -> Dict[str, Any]: def should_trigger(self, tracklets: dai.Tracklet, **kwargs) -> bool: if self.enabled and self._cooldown_passed(): if self._check_tracklets(tracklets): + self.mark_triggered() return True return False diff --git a/apps/data-collection/backend/src/core/snapping/custom_snap_process.py b/apps/data-collection/backend/src/core/snapping/custom_snap_process.py deleted file mode 100644 index 88f490bf0..000000000 --- a/apps/data-collection/backend/src/core/snapping/custom_snap_process.py +++ /dev/null @@ -1,30 +0,0 @@ -import depthai as dai -from depthai_nodes.node import SnapsProducer -from core.snapping.conditions_engine import ConditionsEngine - - -def process_snaps( - producer: SnapsProducer, - frame: dai.ImgFrame, - tracklets: dai.Tracklets | None, - det_data: dai.ImgDetections | None, - engine: ConditionsEngine, -): - """ - Evaluates all registered snap conditions and sends triggered snaps. - """ - - for condition in engine.evaluate( - detections=det_data.detections, - tracklets=tracklets, - ): - sent = producer.sendSnap( - condition.name, - frame, - det_data, - condition.tags, - condition.make_extras(), - ) - if sent: - condition.mark_triggered() - print(f"[{condition.get_key().value}] Snap sent!") diff --git a/apps/data-collection/backend/src/core/snapping/snaps_manager.py b/apps/data-collection/backend/src/core/snapping/snaps_manager.py index 9307d71d7..a71abfe17 100644 --- a/apps/data-collection/backend/src/core/snapping/snaps_manager.py +++ b/apps/data-collection/backend/src/core/snapping/snaps_manager.py @@ -1,11 +1,14 @@ import depthai as dai from box import Box -from depthai_nodes.node import SnapsProducer from core.snapping.conditions_engine import ConditionsEngine from core.snapping.conditions_factory import ConditionsFactory -from core.snapping.snaps_producer_factory import SnapsProducerFactory -from core.front_end_services.snap_collection_service import SnapCollectionService +from core.snapping.snaps_producer import SnapsProducer +from core.front_end_services.snapping_service import SnappingService + +from depthai_nodes.node import ImgDetectionsBridge + +from depthai_nodes.node import SnapsUploader class SnappingServiceManager: @@ -18,7 +21,7 @@ def __init__( pipeline: dai.Pipeline, video_node: dai.Node.Output, tracker: dai.node.ObjectTracker, - detections: dai.ImgDetections, + detections: ImgDetectionsBridge, conditions_config: Box, ): self._pipeline = pipeline @@ -26,25 +29,25 @@ def __init__( self._tracker = tracker self._detections = detections self._conditions_config = conditions_config - self._producer: SnapsProducer = None + self._uploader: SnapsUploader = None self._engine: ConditionsEngine = None - self._snap_service: SnapCollectionService = None + self._snap_service: SnappingService = None def build(self): cond_factory = ConditionsFactory(self._conditions_config) self._engine = cond_factory.build_engine() - snaps_producer = SnapsProducerFactory.create( - self._pipeline, + collector = self._pipeline.create(SnapsProducer).build( self._video_node, - self._tracker, - self._detections, self._engine, + self._detections.out, + self._tracker.out, ) - self._producer = snaps_producer - self._snap_service = SnapCollectionService(self._engine, self._producer) + self._uploader = self._pipeline.create(SnapsUploader).build(collector.out) + + self._snap_service = SnappingService(self._engine) def register_service(self, visualizer: dai.RemoteConnection): visualizer.registerService(self._snap_service.name, self._snap_service.handle) diff --git a/apps/data-collection/backend/src/core/snapping/snaps_producer.py b/apps/data-collection/backend/src/core/snapping/snaps_producer.py new file mode 100644 index 000000000..21165b2e0 --- /dev/null +++ b/apps/data-collection/backend/src/core/snapping/snaps_producer.py @@ -0,0 +1,55 @@ +import depthai as dai +import time +from typing import Optional +from core.snapping.conditions_engine import ConditionsEngine +from depthai_nodes.message import SnapData + + +class SnapsProducer(dai.node.HostNode): + """ + Host node that evaluates snapping conditions each pipeline tick and emits + SnapData messages for downstream SnapsProducer. + + Attributes + ---------- + _engine : ConditionsEngine + Engine responsible for evaluating all registered snap conditions. + """ + + def __init__(self): + super().__init__() + self._engine: Optional[ConditionsEngine] = None + + def build( + self, + frame: dai.Node.Output, + engine: ConditionsEngine, + detections: dai.Node.Output, + tracklets: dai.Node.Output, + ): + self._engine = engine + self.link_args(frame, detections, tracklets) + return self + + def process( + self, + frame: dai.ImgFrame, + detections: dai.Buffer, + tracklets: dai.Tracklets, + ) -> None: + assert isinstance(detections, dai.ImgDetections) + conditions = self._engine.evaluate(detections=detections.detections, tracklets=tracklets) + if not conditions: + return + + for cond in conditions: + snap = SnapData( + snap_name=cond.name, + file_name=f"{cond.name}_{int(time.time())}", + frame=frame, + detections=detections, + tags=cond.tags, + extras=cond.make_extras(), + ) + + self.out.send(snap) diff --git a/apps/data-collection/backend/src/core/snapping/snaps_producer_factory.py b/apps/data-collection/backend/src/core/snapping/snaps_producer_factory.py deleted file mode 100644 index 7b04eb77d..000000000 --- a/apps/data-collection/backend/src/core/snapping/snaps_producer_factory.py +++ /dev/null @@ -1,25 +0,0 @@ -from functools import partial -from depthai_nodes.node import SnapsProducer2Buffered, SnapsProducer - -from core.snapping.conditions_engine import ConditionsEngine -from core.snapping.custom_snap_process import process_snaps -import depthai as dai -from depthai_nodes.node import ImgDetectionsBridge - - -class SnapsProducerFactory: - @staticmethod - def create( - pipeline: dai.Pipeline, - input_node: dai.Node.Output, - tracker: dai.node.ObjectTracker, - detections: ImgDetectionsBridge, - engine: ConditionsEngine, - ) -> SnapsProducer: - return pipeline.create(SnapsProducer2Buffered).build( - frame=input_node, - msg=tracker.out, - msg2=detections.out, - running=False, - process_fn=partial(process_snaps, engine=engine), - ) diff --git a/apps/data-collection/backend/src/main.py b/apps/data-collection/backend/src/main.py index 20660faa5..74f148126 100644 --- a/apps/data-collection/backend/src/main.py +++ b/apps/data-collection/backend/src/main.py @@ -44,7 +44,7 @@ def main(): pipeline, video_node, nn_pipeline.tracker, - nn_pipeline.detections, + nn_pipeline.detections_bridge, config.get_snaps_config(), ) snaps_manager.build() diff --git a/apps/data-collection/backend/src/requirements.txt b/apps/data-collection/backend/src/requirements.txt index 5f3975e79..66babd870 100644 --- a/apps/data-collection/backend/src/requirements.txt +++ b/apps/data-collection/backend/src/requirements.txt @@ -3,7 +3,7 @@ --pre depthai==3.0.0.dev0+d66489c671da0aee5713135222d0c628eec01d4b #depthai-nodes==0.3.4 -depthai-nodes @ git+https://github.com/luxonis/depthai-nodes.git@feat/dai_dev_req_TEST_GROUP +depthai-nodes @ git+https://github.com/luxonis/depthai-nodes.git@feat/new_snaps_producer opencv-python-headless~=4.10.0 numpy>=1.22 tokenizers~=0.21.0 From b7451f66e0219a53ccda617e838e04e42f5b81c7 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Mon, 10 Nov 2025 15:20:42 +0100 Subject: [PATCH 35/39] cahnged snap sending logic according to new snaps uploader node --- .../backend/src/core/front_end_services/snapping_service.py | 5 +---- .../backend/src/core/snapping/snaps_producer.py | 4 +++- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/apps/data-collection/backend/src/core/front_end_services/snapping_service.py b/apps/data-collection/backend/src/core/front_end_services/snapping_service.py index c1b177f95..09ac2583f 100644 --- a/apps/data-collection/backend/src/core/front_end_services/snapping_service.py +++ b/apps/data-collection/backend/src/core/front_end_services/snapping_service.py @@ -12,10 +12,7 @@ class SnappingService(BaseService[SnapPayload]): NAME = ServiceName.SNAP_COLLECTION - def __init__( - self, - engine: ConditionsEngine - ): + def __init__(self, engine: ConditionsEngine): super().__init__() self.engine: ConditionsEngine = engine diff --git a/apps/data-collection/backend/src/core/snapping/snaps_producer.py b/apps/data-collection/backend/src/core/snapping/snaps_producer.py index 21165b2e0..59977f063 100644 --- a/apps/data-collection/backend/src/core/snapping/snaps_producer.py +++ b/apps/data-collection/backend/src/core/snapping/snaps_producer.py @@ -38,7 +38,9 @@ def process( tracklets: dai.Tracklets, ) -> None: assert isinstance(detections, dai.ImgDetections) - conditions = self._engine.evaluate(detections=detections.detections, tracklets=tracklets) + conditions = self._engine.evaluate( + detections=detections.detections, tracklets=tracklets + ) if not conditions: return From c4654565d1bd486c7ac586e15ad4023459f74766 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Tue, 11 Nov 2025 17:11:57 +0100 Subject: [PATCH 36/39] changed snapping logic + refactored services --- .../{front_end_services => }/base_service.py | 2 +- .../backend/src/core/export_service.py | 43 +++++++++++++ .../core/front_end_services/export_service.py | 25 -------- .../front_end_services/snapping_service.py | 28 --------- .../pipeline/prompt_controller_factory.py | 2 +- .../front_end_prompt_services}/__init__.py | 0 .../bbox_prompt_service.py | 8 ++- .../class_update_service.py | 8 ++- .../image_upload_service.py | 8 ++- .../payloads/__init__.py | 0 .../payloads/bbox_prompt_payload.py | 0 .../payloads/class_update_payload.py | 0 .../payloads/image_upload_payload.py | 0 .../payloads/threshold_update_payload.py | 0 .../threshold_update_service.py | 6 +- .../prompts/handlers/bbox_prompt_handler.py | 4 +- .../prompts/handlers/image_prompt_handler.py | 4 +- .../prompts/handlers/text_prompt_handler.py | 4 +- .../prompts}/label_manager.py | 0 .../prompts/nn_prompts_controller.py | 2 +- .../prompts/nn_prompts_manager.py | 2 +- .../prompts/prompt_service_factory.py | 18 ++++-- .../{front_end_services => }/service_name.py | 0 .../snapping/conditions/base_condition.py | 2 +- .../snapping/conditions/low_conf_condition.py | 2 +- .../tracker_conditions/lost_mid_condition.py | 2 +- .../src/core/snapping/conditions_engine.py | 61 ------------------- .../src/core/snapping/conditions_factory.py | 20 +++--- .../front_end_config_service/__init__.py | 0 .../front_end_config_service}/snap_payload.py | 0 .../snapping_service.py | 34 +++++++++++ .../src/core/snapping/snaps_manager.py | 20 +++--- .../src/core/snapping/snaps_producer.py | 26 ++++---- apps/data-collection/backend/src/main.py | 4 +- 34 files changed, 160 insertions(+), 175 deletions(-) rename apps/data-collection/backend/src/core/{front_end_services => }/base_service.py (93%) create mode 100644 apps/data-collection/backend/src/core/export_service.py delete mode 100644 apps/data-collection/backend/src/core/front_end_services/export_service.py delete mode 100644 apps/data-collection/backend/src/core/front_end_services/snapping_service.py rename apps/data-collection/backend/src/core/{front_end_services => neural_network/prompts/front_end_prompt_services}/__init__.py (100%) rename apps/data-collection/backend/src/core/{front_end_services => neural_network/prompts/front_end_prompt_services}/bbox_prompt_service.py (76%) rename apps/data-collection/backend/src/core/{front_end_services => neural_network/prompts/front_end_prompt_services}/class_update_service.py (78%) rename apps/data-collection/backend/src/core/{front_end_services => neural_network/prompts/front_end_prompt_services}/image_upload_service.py (78%) rename apps/data-collection/backend/src/core/{front_end_services => neural_network/prompts/front_end_prompt_services}/payloads/__init__.py (100%) rename apps/data-collection/backend/src/core/{front_end_services => neural_network/prompts/front_end_prompt_services}/payloads/bbox_prompt_payload.py (100%) rename apps/data-collection/backend/src/core/{front_end_services => neural_network/prompts/front_end_prompt_services}/payloads/class_update_payload.py (100%) rename apps/data-collection/backend/src/core/{front_end_services => neural_network/prompts/front_end_prompt_services}/payloads/image_upload_payload.py (100%) rename apps/data-collection/backend/src/core/{front_end_services => neural_network/prompts/front_end_prompt_services}/payloads/threshold_update_payload.py (100%) rename apps/data-collection/backend/src/core/{front_end_services => neural_network/prompts/front_end_prompt_services}/threshold_update_service.py (78%) rename apps/data-collection/backend/src/core/{ => neural_network/prompts}/label_manager.py (100%) rename apps/data-collection/backend/src/core/{front_end_services => }/service_name.py (100%) delete mode 100644 apps/data-collection/backend/src/core/snapping/conditions_engine.py create mode 100644 apps/data-collection/backend/src/core/snapping/front_end_config_service/__init__.py rename apps/data-collection/backend/src/core/{front_end_services/payloads => snapping/front_end_config_service}/snap_payload.py (100%) create mode 100644 apps/data-collection/backend/src/core/snapping/front_end_config_service/snapping_service.py diff --git a/apps/data-collection/backend/src/core/front_end_services/base_service.py b/apps/data-collection/backend/src/core/base_service.py similarity index 93% rename from apps/data-collection/backend/src/core/front_end_services/base_service.py rename to apps/data-collection/backend/src/core/base_service.py index ebc3dd385..decf7c46a 100644 --- a/apps/data-collection/backend/src/core/front_end_services/base_service.py +++ b/apps/data-collection/backend/src/core/base_service.py @@ -4,7 +4,7 @@ from core.neural_network.prompts.nn_prompts_controller import NnPromptsController from core.neural_network.prompts.handlers.base_prompt_handler import BasePromptHandler -from core.front_end_services.service_name import ServiceName +from core.service_name import ServiceName PayloadT = TypeVar("PayloadT", bound=BaseModel) diff --git a/apps/data-collection/backend/src/core/export_service.py b/apps/data-collection/backend/src/core/export_service.py new file mode 100644 index 000000000..80ab3d3b3 --- /dev/null +++ b/apps/data-collection/backend/src/core/export_service.py @@ -0,0 +1,43 @@ +from core.neural_network.pipeline.model_state import ModelState +from core.base_service import BaseService +from core.service_name import ServiceName +from core.snapping.conditions.base_condition import Condition +from core.snapping.conditions.condition_key import ConditionKey + + +class ExportService(BaseService[None]): + """Returns the current configuration state to the frontend.""" + + NAME = ServiceName.EXPORT + + def __init__( + self, model_state: ModelState, conditions: dict[ConditionKey, Condition] + ): + super().__init__() + self._model_state: ModelState = model_state + self._conditions: dict[ConditionKey, Condition] = conditions + + def export_conditions_config(self) -> dict[str, dict]: + """ + Export current configuration of all conditions. + """ + configs: dict[str, dict] = {} + + for key, condition in self._conditions.items(): + cfg = condition.export_config() + if "cooldown" in cfg: + cfg["cooldown"] = round(cfg["cooldown"] / 60.0, 1) + + configs[key.value] = cfg + + return configs + + def handle(self, payload: None = None) -> dict[str, any]: + return { + "classes": self._model_state.current_classes, + "confidence_threshold": self._model_state.confidence_threshold, + "snapping": { + "running": any(cond.enabled for cond in self._conditions.values()), + **self.export_conditions_config(), + }, + } diff --git a/apps/data-collection/backend/src/core/front_end_services/export_service.py b/apps/data-collection/backend/src/core/front_end_services/export_service.py deleted file mode 100644 index a09d4de9c..000000000 --- a/apps/data-collection/backend/src/core/front_end_services/export_service.py +++ /dev/null @@ -1,25 +0,0 @@ -from core.neural_network.pipeline.model_state import ModelState -from core.front_end_services.base_service import BaseService -from core.front_end_services.service_name import ServiceName -from core.snapping.conditions_engine import ConditionsEngine - - -class ExportService(BaseService[None]): - """Returns the current configuration state to the frontend.""" - - NAME = ServiceName.EXPORT - - def __init__(self, model_state: ModelState, condition_engine: ConditionsEngine): - super().__init__() - self._model_state: ModelState = model_state - self._condition_engine: ConditionsEngine = condition_engine - - def handle(self, payload: None = None) -> dict[str, any]: - return { - "classes": self._model_state.current_classes, - "confidence_threshold": self._model_state.confidence_threshold, - "snapping": { - "running": self._condition_engine.any_active(), - **self._condition_engine.export_conditions_config(), - }, - } diff --git a/apps/data-collection/backend/src/core/front_end_services/snapping_service.py b/apps/data-collection/backend/src/core/front_end_services/snapping_service.py deleted file mode 100644 index 09ac2583f..000000000 --- a/apps/data-collection/backend/src/core/front_end_services/snapping_service.py +++ /dev/null @@ -1,28 +0,0 @@ -from pydantic import ValidationError -from core.front_end_services.base_service import BaseService -from core.snapping.conditions_engine import ConditionsEngine -from core.front_end_services.payloads.snap_payload import SnapPayload -from core.front_end_services.service_name import ServiceName - - -class SnappingService(BaseService[SnapPayload]): - """ - Handles updates to snapping conditions and manages SnapsProducer state. - """ - - NAME = ServiceName.SNAP_COLLECTION - - def __init__(self, engine: ConditionsEngine): - super().__init__() - self.engine: ConditionsEngine = engine - - def handle(self, payload: SnapPayload) -> dict[str, any]: - try: - payload = SnapPayload.model_validate(payload) - except ValidationError as e: - return {"ok": False, "error": e.errors()} - - self.engine.import_conditions_config(payload.root) - any_active = self.engine.any_active() - - return {"ok": True, "active": any_active} diff --git a/apps/data-collection/backend/src/core/neural_network/pipeline/prompt_controller_factory.py b/apps/data-collection/backend/src/core/neural_network/pipeline/prompt_controller_factory.py index 51f36fde2..fa90b4eeb 100644 --- a/apps/data-collection/backend/src/core/neural_network/pipeline/prompt_controller_factory.py +++ b/apps/data-collection/backend/src/core/neural_network/pipeline/prompt_controller_factory.py @@ -1,6 +1,6 @@ from core.neural_network.pipeline.annotation_node import AnnotationNode from core.neural_network.prompts.nn_prompts_controller import NnPromptsController -from core.label_manager import LabelManager +from core.neural_network.prompts.label_manager import LabelManager from depthai_nodes.node import ParsingNeuralNetwork, ImgDetectionsFilter diff --git a/apps/data-collection/backend/src/core/front_end_services/__init__.py b/apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/__init__.py similarity index 100% rename from apps/data-collection/backend/src/core/front_end_services/__init__.py rename to apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/__init__.py diff --git a/apps/data-collection/backend/src/core/front_end_services/bbox_prompt_service.py b/apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/bbox_prompt_service.py similarity index 76% rename from apps/data-collection/backend/src/core/front_end_services/bbox_prompt_service.py rename to apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/bbox_prompt_service.py index de53a4439..b3da9b69f 100644 --- a/apps/data-collection/backend/src/core/front_end_services/bbox_prompt_service.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/bbox_prompt_service.py @@ -1,7 +1,9 @@ from pydantic import ValidationError -from core.front_end_services.base_service import BaseService -from core.front_end_services.payloads.bbox_prompt_payload import BBoxPromptPayload -from core.front_end_services.service_name import ServiceName +from core.base_service import BaseService +from core.neural_network.prompts.front_end_prompt_services.payloads.bbox_prompt_payload import ( + BBoxPromptPayload, +) +from core.service_name import ServiceName class BBoxPromptService(BaseService[BBoxPromptPayload]): diff --git a/apps/data-collection/backend/src/core/front_end_services/class_update_service.py b/apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/class_update_service.py similarity index 78% rename from apps/data-collection/backend/src/core/front_end_services/class_update_service.py rename to apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/class_update_service.py index 71cf24bca..da881786a 100644 --- a/apps/data-collection/backend/src/core/front_end_services/class_update_service.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/class_update_service.py @@ -1,7 +1,9 @@ from pydantic import ValidationError -from core.front_end_services.base_service import BaseService -from core.front_end_services.payloads.class_update_payload import ClassUpdatePayload -from core.front_end_services.service_name import ServiceName +from core.base_service import BaseService +from core.neural_network.prompts.front_end_prompt_services.payloads.class_update_payload import ( + ClassUpdatePayload, +) +from core.service_name import ServiceName class ClassUpdateService(BaseService[ClassUpdatePayload]): diff --git a/apps/data-collection/backend/src/core/front_end_services/image_upload_service.py b/apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/image_upload_service.py similarity index 78% rename from apps/data-collection/backend/src/core/front_end_services/image_upload_service.py rename to apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/image_upload_service.py index 21b9cd0ea..6acd7c680 100644 --- a/apps/data-collection/backend/src/core/front_end_services/image_upload_service.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/image_upload_service.py @@ -1,7 +1,9 @@ from pydantic import ValidationError -from core.front_end_services.base_service import BaseService -from core.front_end_services.payloads.image_upload_payload import ImageUploadPayload -from core.front_end_services.service_name import ServiceName +from core.base_service import BaseService +from core.neural_network.prompts.front_end_prompt_services.payloads.image_upload_payload import ( + ImageUploadPayload, +) +from core.service_name import ServiceName class ImageUploadService(BaseService[ImageUploadPayload]): diff --git a/apps/data-collection/backend/src/core/front_end_services/payloads/__init__.py b/apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/payloads/__init__.py similarity index 100% rename from apps/data-collection/backend/src/core/front_end_services/payloads/__init__.py rename to apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/payloads/__init__.py diff --git a/apps/data-collection/backend/src/core/front_end_services/payloads/bbox_prompt_payload.py b/apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/payloads/bbox_prompt_payload.py similarity index 100% rename from apps/data-collection/backend/src/core/front_end_services/payloads/bbox_prompt_payload.py rename to apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/payloads/bbox_prompt_payload.py diff --git a/apps/data-collection/backend/src/core/front_end_services/payloads/class_update_payload.py b/apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/payloads/class_update_payload.py similarity index 100% rename from apps/data-collection/backend/src/core/front_end_services/payloads/class_update_payload.py rename to apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/payloads/class_update_payload.py diff --git a/apps/data-collection/backend/src/core/front_end_services/payloads/image_upload_payload.py b/apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/payloads/image_upload_payload.py similarity index 100% rename from apps/data-collection/backend/src/core/front_end_services/payloads/image_upload_payload.py rename to apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/payloads/image_upload_payload.py diff --git a/apps/data-collection/backend/src/core/front_end_services/payloads/threshold_update_payload.py b/apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/payloads/threshold_update_payload.py similarity index 100% rename from apps/data-collection/backend/src/core/front_end_services/payloads/threshold_update_payload.py rename to apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/payloads/threshold_update_payload.py diff --git a/apps/data-collection/backend/src/core/front_end_services/threshold_update_service.py b/apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/threshold_update_service.py similarity index 78% rename from apps/data-collection/backend/src/core/front_end_services/threshold_update_service.py rename to apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/threshold_update_service.py index d77591981..b1e38a7d0 100644 --- a/apps/data-collection/backend/src/core/front_end_services/threshold_update_service.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/front_end_prompt_services/threshold_update_service.py @@ -1,9 +1,9 @@ from pydantic import ValidationError -from core.front_end_services.base_service import BaseService -from core.front_end_services.payloads.threshold_update_payload import ( +from core.base_service import BaseService +from core.neural_network.prompts.front_end_prompt_services.payloads.threshold_update_payload import ( ThresholdUpdatePayload, ) -from core.front_end_services.service_name import ServiceName +from core.service_name import ServiceName class ThresholdUpdateService(BaseService[ThresholdUpdatePayload]): diff --git a/apps/data-collection/backend/src/core/neural_network/prompts/handlers/bbox_prompt_handler.py b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/bbox_prompt_handler.py index c109a5c62..97455d485 100644 --- a/apps/data-collection/backend/src/core/neural_network/prompts/handlers/bbox_prompt_handler.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/bbox_prompt_handler.py @@ -5,7 +5,9 @@ ) from core.neural_network.prompts.handlers.base_prompt_handler import BasePromptHandler from core.neural_network.prompts.frame_cache_node import FrameCacheNode -from core.front_end_services.payloads.bbox_prompt_payload import BBoxPromptPayload +from core.neural_network.prompts.front_end_prompt_services.payloads.bbox_prompt_payload import ( + BBoxPromptPayload, +) class BBoxPromptHandler(BasePromptHandler): diff --git a/apps/data-collection/backend/src/core/neural_network/prompts/handlers/image_prompt_handler.py b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/image_prompt_handler.py index 7f4719ff8..035839682 100644 --- a/apps/data-collection/backend/src/core/neural_network/prompts/handlers/image_prompt_handler.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/image_prompt_handler.py @@ -6,7 +6,9 @@ VisualPromptEncoder, ) from core.neural_network.prompts.handlers.base_prompt_handler import BasePromptHandler -from core.front_end_services.payloads.image_upload_payload import ImageUploadPayload +from core.neural_network.prompts.front_end_prompt_services.payloads.image_upload_payload import ( + ImageUploadPayload, +) class ImagePromptHandler(BasePromptHandler): diff --git a/apps/data-collection/backend/src/core/neural_network/prompts/handlers/text_prompt_handler.py b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/text_prompt_handler.py index a50f34181..28b4f364f 100644 --- a/apps/data-collection/backend/src/core/neural_network/prompts/handlers/text_prompt_handler.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/handlers/text_prompt_handler.py @@ -6,7 +6,9 @@ TextualPromptEncoder, ) from core.neural_network.prompts.handlers.base_prompt_handler import BasePromptHandler -from core.front_end_services.payloads.class_update_payload import ClassUpdatePayload +from core.neural_network.prompts.front_end_prompt_services.payloads.class_update_payload import ( + ClassUpdatePayload, +) class TextPromptHandler(BasePromptHandler): diff --git a/apps/data-collection/backend/src/core/label_manager.py b/apps/data-collection/backend/src/core/neural_network/prompts/label_manager.py similarity index 100% rename from apps/data-collection/backend/src/core/label_manager.py rename to apps/data-collection/backend/src/core/neural_network/prompts/label_manager.py diff --git a/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_controller.py b/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_controller.py index 32af2927c..57b4463fe 100644 --- a/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_controller.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_controller.py @@ -2,7 +2,7 @@ import numpy as np from depthai_nodes.node import YOLOExtendedParser -from core.label_manager import LabelManager +from core.neural_network.prompts.label_manager import LabelManager from core.neural_network.pipeline.model_state import ModelState diff --git a/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_manager.py b/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_manager.py index 3260f0ccb..7fa706d9e 100644 --- a/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_manager.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_manager.py @@ -11,7 +11,7 @@ PromptEncodersManager, ) from core.neural_network.prompts.handlers_factory import HandlersFactory -from core.front_end_services.base_service import BaseService +from core.base_service import BaseService class NNPromptsManager: diff --git a/apps/data-collection/backend/src/core/neural_network/prompts/prompt_service_factory.py b/apps/data-collection/backend/src/core/neural_network/prompts/prompt_service_factory.py index 78726f405..4076dd6e5 100644 --- a/apps/data-collection/backend/src/core/neural_network/prompts/prompt_service_factory.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/prompt_service_factory.py @@ -1,10 +1,18 @@ from core.neural_network.prompts.nn_prompts_controller import NnPromptsController from core.neural_network.prompts.handlers_factory import HandlersFactory -from core.front_end_services.base_service import BaseService -from core.front_end_services.class_update_service import ClassUpdateService -from core.front_end_services.threshold_update_service import ThresholdUpdateService -from core.front_end_services.image_upload_service import ImageUploadService -from core.front_end_services.bbox_prompt_service import BBoxPromptService +from core.base_service import BaseService +from core.neural_network.prompts.front_end_prompt_services.class_update_service import ( + ClassUpdateService, +) +from core.neural_network.prompts.front_end_prompt_services.threshold_update_service import ( + ThresholdUpdateService, +) +from core.neural_network.prompts.front_end_prompt_services.image_upload_service import ( + ImageUploadService, +) +from core.neural_network.prompts.front_end_prompt_services.bbox_prompt_service import ( + BBoxPromptService, +) class PromptServiceFactory: diff --git a/apps/data-collection/backend/src/core/front_end_services/service_name.py b/apps/data-collection/backend/src/core/service_name.py similarity index 100% rename from apps/data-collection/backend/src/core/front_end_services/service_name.py rename to apps/data-collection/backend/src/core/service_name.py diff --git a/apps/data-collection/backend/src/core/snapping/conditions/base_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/base_condition.py index 082fcd777..f4202eb8f 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions/base_condition.py +++ b/apps/data-collection/backend/src/core/snapping/conditions/base_condition.py @@ -1,7 +1,7 @@ from abc import ABC, abstractmethod from typing import Dict, Any, List, Optional -from core.front_end_services.payloads.snap_payload import ConditionConfig +from core.snapping.front_end_config_service.snap_payload import ConditionConfig from core.snapping.conditions.condition_key import ConditionKey from time import time diff --git a/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py index 55cc354a6..5f45ee245 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py +++ b/apps/data-collection/backend/src/core/snapping/conditions/low_conf_condition.py @@ -1,4 +1,4 @@ -from core.front_end_services.payloads.snap_payload import ConditionConfig +from core.snapping.front_end_config_service.snap_payload import ConditionConfig from core.snapping.conditions.base_condition import Condition from core.snapping.conditions.condition_key import ConditionKey from typing import Dict, Any diff --git a/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py b/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py index 8552e525e..44f9dc9e0 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py +++ b/apps/data-collection/backend/src/core/snapping/conditions/tracker_conditions/lost_mid_condition.py @@ -1,4 +1,4 @@ -from core.front_end_services.payloads.snap_payload import ConditionConfig +from core.snapping.front_end_config_service.snap_payload import ConditionConfig from core.snapping.conditions.base_condition import Condition from core.snapping.conditions.tracker_conditions.tracklet_analyzer import ( TrackletAnalyzer, diff --git a/apps/data-collection/backend/src/core/snapping/conditions_engine.py b/apps/data-collection/backend/src/core/snapping/conditions_engine.py deleted file mode 100644 index bf32a054b..000000000 --- a/apps/data-collection/backend/src/core/snapping/conditions_engine.py +++ /dev/null @@ -1,61 +0,0 @@ -from __future__ import annotations -from typing import Dict, Generator, Any - -from core.front_end_services.payloads.snap_payload import ConditionConfig -from core.snapping.conditions.base_condition import Condition -from core.snapping.conditions.condition_key import ConditionKey - - -class ConditionsEngine: - """ - Engine for managing and evaluating snap trigger conditions. - """ - - def __init__(self): - self.conditions: Dict[ConditionKey, Condition] = {} - - def register(self, condition: Condition) -> None: - """Register a condition instance (e.g., LowConfidenceCondition()).""" - key = condition.get_key() - self.conditions[key] = condition - - def evaluate(self, **context: Any) -> Generator[Condition, None, None]: - """ - Evaluate all enabled conditions. - Returns an iterator of those that should trigger. - Context may include: frame, det_data, tracklets, runtime, etc. - """ - for cond in self.conditions.values(): - if not cond.enabled: - continue - if cond.should_trigger(**context): - yield cond - - def import_conditions_config(self, config: Dict[str, ConditionConfig]) -> None: - """ - Apply configuration dict to registered conditions. - """ - for key, params in config.items(): - cond = self.conditions.get(key) - if not cond: - continue - cond.apply_config(params) - - def export_conditions_config(self) -> Dict[str, dict]: - """ - Export current configuration of all registered conditions. - """ - configs: Dict[str, dict] = {} - - for key, condition in self.conditions.items(): - cfg = condition.export_config() - if "cooldown" in cfg: - cfg["cooldown"] = round(cfg["cooldown"] / 60.0, 1) - - configs[key.value] = cfg - - return configs - - def any_active(self) -> bool: - """Return True if any condition is currently enabled.""" - return any(cond.enabled for cond in self.conditions.values()) diff --git a/apps/data-collection/backend/src/core/snapping/conditions_factory.py b/apps/data-collection/backend/src/core/snapping/conditions_factory.py index b4a38c341..6a5b0e03d 100644 --- a/apps/data-collection/backend/src/core/snapping/conditions_factory.py +++ b/apps/data-collection/backend/src/core/snapping/conditions_factory.py @@ -2,18 +2,18 @@ from box import Box -from core.snapping.conditions_engine import ConditionsEngine +from core.snapping.conditions.condition_key import ConditionKey from core.snapping.conditions.base_condition import Condition class ConditionsFactory: - def __init__(self, conditions_yaml: Box): - self._conditions_yaml: Box = conditions_yaml + @staticmethod + def build_conditions_from_yaml( + conditions_yaml: Box, + ) -> dict[ConditionKey, Condition]: + conditions: dict[ConditionKey, Condition] = {} - def build_engine(self) -> ConditionsEngine: - engine = ConditionsEngine() - - for entry in self._conditions_yaml.conditions: + for entry in conditions_yaml.conditions: if not entry.path: continue @@ -23,11 +23,11 @@ def build_engine(self) -> ConditionsEngine: cls = getattr(module, class_name) cond: Condition = cls( name=entry.name, - default_cooldown=self._conditions_yaml.cooldown, + default_cooldown=conditions_yaml.cooldown, tags=entry.tags, ) - engine.register(cond) + conditions[cond.get_key()] = cond except Exception as e: print(f"Failed to import condition {entry.path}: {e}") - return engine + return conditions diff --git a/apps/data-collection/backend/src/core/snapping/front_end_config_service/__init__.py b/apps/data-collection/backend/src/core/snapping/front_end_config_service/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/data-collection/backend/src/core/front_end_services/payloads/snap_payload.py b/apps/data-collection/backend/src/core/snapping/front_end_config_service/snap_payload.py similarity index 100% rename from apps/data-collection/backend/src/core/front_end_services/payloads/snap_payload.py rename to apps/data-collection/backend/src/core/snapping/front_end_config_service/snap_payload.py diff --git a/apps/data-collection/backend/src/core/snapping/front_end_config_service/snapping_service.py b/apps/data-collection/backend/src/core/snapping/front_end_config_service/snapping_service.py new file mode 100644 index 000000000..0892e73b6 --- /dev/null +++ b/apps/data-collection/backend/src/core/snapping/front_end_config_service/snapping_service.py @@ -0,0 +1,34 @@ +from pydantic import ValidationError +from core.base_service import BaseService +from core.snapping.conditions.base_condition import Condition +from core.snapping.conditions.condition_key import ConditionKey +from core.snapping.front_end_config_service.snap_payload import SnapPayload +from core.service_name import ServiceName + + +class SnappingService(BaseService[SnapPayload]): + """ + Handles updates to snapping conditions and manages SnapsProducer state. + """ + + NAME = ServiceName.SNAP_COLLECTION + + def __init__(self, conditions: dict[ConditionKey, Condition]): + super().__init__() + self._conditions: dict[ConditionKey, Condition] = conditions + + def handle(self, payload: SnapPayload) -> dict[str, any]: + try: + payload = SnapPayload.model_validate(payload) + except ValidationError as e: + return {"ok": False, "error": e.errors()} + + any_active = False + for key, params in payload.root.items(): + cond = self._conditions.get(key) + if not cond: + continue + cond.apply_config(params) + any_active |= cond.enabled + + return {"ok": True, "active": any_active} diff --git a/apps/data-collection/backend/src/core/snapping/snaps_manager.py b/apps/data-collection/backend/src/core/snapping/snaps_manager.py index a71abfe17..ff8deed35 100644 --- a/apps/data-collection/backend/src/core/snapping/snaps_manager.py +++ b/apps/data-collection/backend/src/core/snapping/snaps_manager.py @@ -1,10 +1,11 @@ import depthai as dai from box import Box -from core.snapping.conditions_engine import ConditionsEngine +from core.snapping.conditions.base_condition import Condition +from core.snapping.conditions.condition_key import ConditionKey from core.snapping.conditions_factory import ConditionsFactory from core.snapping.snaps_producer import SnapsProducer -from core.front_end_services.snapping_service import SnappingService +from core.snapping.front_end_config_service.snapping_service import SnappingService from depthai_nodes.node import ImgDetectionsBridge @@ -31,26 +32,27 @@ def __init__( self._conditions_config = conditions_config self._uploader: SnapsUploader = None - self._engine: ConditionsEngine = None + self._conditions: dict[ConditionKey, Condition] = None self._snap_service: SnappingService = None def build(self): - cond_factory = ConditionsFactory(self._conditions_config) - self._engine = cond_factory.build_engine() + self._conditions = ConditionsFactory.build_conditions_from_yaml( + self._conditions_config + ) collector = self._pipeline.create(SnapsProducer).build( self._video_node, - self._engine, + self._conditions, self._detections.out, self._tracker.out, ) self._uploader = self._pipeline.create(SnapsUploader).build(collector.out) - self._snap_service = SnappingService(self._engine) + self._snap_service = SnappingService(self._conditions) def register_service(self, visualizer: dai.RemoteConnection): visualizer.registerService(self._snap_service.name, self._snap_service.handle) - def get_engine(self): - return self._engine + def get_conditions(self) -> dict[ConditionKey, Condition]: + return self._conditions diff --git a/apps/data-collection/backend/src/core/snapping/snaps_producer.py b/apps/data-collection/backend/src/core/snapping/snaps_producer.py index 59977f063..a904cb79c 100644 --- a/apps/data-collection/backend/src/core/snapping/snaps_producer.py +++ b/apps/data-collection/backend/src/core/snapping/snaps_producer.py @@ -1,7 +1,8 @@ import depthai as dai import time -from typing import Optional -from core.snapping.conditions_engine import ConditionsEngine + +from core.snapping.conditions.base_condition import Condition +from core.snapping.conditions.condition_key import ConditionKey from depthai_nodes.message import SnapData @@ -12,22 +13,22 @@ class SnapsProducer(dai.node.HostNode): Attributes ---------- - _engine : ConditionsEngine - Engine responsible for evaluating all registered snap conditions. + _conditions : dict[ConditionKey, Condition] + A dictionary mapping condition keys to their respective snapping conditions. """ def __init__(self): super().__init__() - self._engine: Optional[ConditionsEngine] = None + self._conditions: dict[ConditionKey, Condition] = {} def build( self, frame: dai.Node.Output, - engine: ConditionsEngine, + conditions: dict[ConditionKey, Condition], detections: dai.Node.Output, tracklets: dai.Node.Output, ): - self._engine = engine + self._conditions = conditions self.link_args(frame, detections, tracklets) return self @@ -38,13 +39,12 @@ def process( tracklets: dai.Tracklets, ) -> None: assert isinstance(detections, dai.ImgDetections) - conditions = self._engine.evaluate( - detections=detections.detections, tracklets=tracklets - ) - if not conditions: - return + for cond in self._conditions.values(): + if not cond.should_trigger( + detections=detections.detections, tracklets=tracklets + ): + continue - for cond in conditions: snap = SnapData( snap_name=cond.name, file_name=f"{cond.name}_{int(time.time())}", diff --git a/apps/data-collection/backend/src/main.py b/apps/data-collection/backend/src/main.py index 74f148126..c813e0b0a 100644 --- a/apps/data-collection/backend/src/main.py +++ b/apps/data-collection/backend/src/main.py @@ -1,6 +1,6 @@ import depthai as dai from config.system_configuration import SystemConfiguration -from core.front_end_services.export_service import ExportService +from core.export_service import ExportService from core.neural_network.prompts.nn_prompts_manager import NNPromptsManager from core.neural_network.pipeline.nn_pipeline_setup import NNPipelineBuilder from core.snapping.snaps_manager import SnappingServiceManager @@ -51,7 +51,7 @@ def main(): snaps_manager.register_service(visualizer) export_service = ExportService( - nn_pipeline.controller.get_model_state(), snaps_manager.get_engine() + nn_pipeline.controller.get_model_state(), snaps_manager.get_conditions() ) visualizer.registerService(export_service.name, export_service.handle) From a4e4d67129dc9dbb31e4bfe4425bc32bede80ab6 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Wed, 12 Nov 2025 13:48:14 +0100 Subject: [PATCH 37/39] Changed README --- apps/data-collection/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/apps/data-collection/README.md b/apps/data-collection/README.md index c19e36525..f9236891e 100644 --- a/apps/data-collection/README.md +++ b/apps/data-collection/README.md @@ -33,11 +33,11 @@ A **Luxonis device** (RVC4) is required ```text -fps FPS_LIMIT, --fps_limit FPS_LIMIT FPS limit. (default: None) +-api, --api_key API_KEY HubAI API key for private models. (default: "") -ip IP, --ip IP IP address to serve the frontend on. (default: None) +-media, --media_path MEDIA_PATH Path to media file; if not set, runs on live camera. (default: None) -p PORT, --port PORT Port to serve the frontend on. (default: None) ---precision PRECISION - Model precision for YOLOE models: int8 (faster) or fp16 (more accurate) (default: fp16) ``` ## Prerequisites (Frontend) From 98e18cf6a8ddbce433705c894d1a77c5f7e71020 Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Thu, 13 Nov 2025 09:59:22 +0100 Subject: [PATCH 38/39] changed README --- apps/data-collection/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/apps/data-collection/README.md b/apps/data-collection/README.md index f9236891e..d04f4d0c9 100644 --- a/apps/data-collection/README.md +++ b/apps/data-collection/README.md @@ -33,7 +33,7 @@ A **Luxonis device** (RVC4) is required ```text -fps FPS_LIMIT, --fps_limit FPS_LIMIT FPS limit. (default: None) --api, --api_key API_KEY HubAI API key for private models. (default: "") +-api, --api_key API_KEY HubAI API key -ip IP, --ip IP IP address to serve the frontend on. (default: None) -media, --media_path MEDIA_PATH Path to media file; if not set, runs on live camera. (default: None) -p PORT, --port PORT From 0543bbdfa7d13be87e90b8b53c7bf9852b06defc Mon Sep 17 00:00:00 2001 From: Ivan Dudnyk Date: Thu, 13 Nov 2025 20:06:26 +0100 Subject: [PATCH 39/39] removed unneded comment --- .../src/core/neural_network/prompts/nn_prompts_controller.py | 1 - 1 file changed, 1 deletion(-) diff --git a/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_controller.py b/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_controller.py index 57b4463fe..b3cb87f0c 100644 --- a/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_controller.py +++ b/apps/data-collection/backend/src/core/neural_network/prompts/nn_prompts_controller.py @@ -50,7 +50,6 @@ def send_prompts_pair( self._model_state.current_classes = class_names def set_confidence_threshold(self, threshold: float): - """Apply threshold update directly to the NN parser.""" self._parser.setConfidenceThreshold(threshold) self._model_state.confidence_threshold = threshold

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