diff --git a/.gitignore b/.gitignore index d389132..1190bf2 100644 --- a/.gitignore +++ b/.gitignore @@ -8,7 +8,13 @@ wheels/ # Virtual environments .venv +venv/ apps/api-inference/.venv +apps/api-inference-yolo/.venv + +# SAM3 Model Weights +apps/sam3.pt +sam3.pt # Environment variables # .env @@ -76,4 +82,4 @@ htmlcov/ .codex .agent backend/docs -node_modules/.vite \ No newline at end of file +node_modules/.vite diff --git a/README.md b/README.md index a4327e7..08f4a61 100644 --- a/README.md +++ b/README.md @@ -122,6 +122,21 @@ Diagram source: `docs/architecture/system-overview.mmd`. | PostgreSQL | Core data store | 5432 | | Redis | Cache and task queue | - | +### Backend Options + +**🚀 Ultralytics SAM3 (Recommended)** - `api-inference-yolo` +- ✅ Faster inference with FP16 support +- ✅ Better text prompt segmentation with semantic understanding +- ✅ Bounding box exemplar-based segmentation for finding similar objects +- ✅ No HuggingFace account required (but model download is manual) +- ✅ Supports single, auto-apply, and batch processing modes +- 📦 Uses: `ultralytics`, PyTorch, SAM3SemanticPredictor + +**🔄 HuggingFace SAM3** - `api-inference` +- ✅ Auto-downloads model on first run +- ⚠️ Requires HuggingFace account and gated model access +- 📦 Uses: `transformers`, `huggingface-hub` + ## Quick Start ### Prerequisites diff --git a/apps/api-inference-yolo/.env.example b/apps/api-inference-yolo/.env.example new file mode 100644 index 0000000..d4637c8 --- /dev/null +++ b/apps/api-inference-yolo/.env.example @@ -0,0 +1,28 @@ +# Application Settings +DEBUG=true +APP_HOST=0.0.0.0 +APP_PORT=8000 +LOG_LEVEL=INFO + +# HuggingFace Authentication (REQUIRED for SAM3) +# Get your token from: https://huggingface.co/settings/tokens +HF_TOKEN=hf_your_token_here + +# SAM3 Model Settings +SAM3_MODEL_NAME=facebook/sam3 +SAM3_DEVICE=auto +SAM3_DEFAULT_THRESHOLD=0.5 +SAM3_DEFAULT_MASK_THRESHOLD=0.5 + +# API Limits +MAX_IMAGE_SIZE_MB=10 +MAX_BATCH_SIZE=10 +MAX_IMAGE_DIMENSION=4096 + +# Visualization +VISUALIZATION_FORMAT=PNG +VISUALIZATION_QUALITY=95 + +# CORS +# Must be a JSON array. Examples: ["*"], ["http://localhost:3000","http://localhost:5173"] +ALLOWED_ORIGINS=["*"] diff --git a/apps/api-inference-yolo/.gitignore b/apps/api-inference-yolo/.gitignore new file mode 100644 index 0000000..c2eabec --- /dev/null +++ b/apps/api-inference-yolo/.gitignore @@ -0,0 +1,2 @@ +.venv +.env \ No newline at end of file diff --git a/apps/api-inference-yolo/.python-version b/apps/api-inference-yolo/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/apps/api-inference-yolo/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/apps/api-inference-yolo/Dockerfile b/apps/api-inference-yolo/Dockerfile new file mode 100644 index 0000000..31e1836 --- /dev/null +++ b/apps/api-inference-yolo/Dockerfile @@ -0,0 +1,45 @@ +# 1. Change Base Image to NVIDIA CUDA (Devel version ensures compatibility with Transformers/PyTorch extensions) +# Note: Use CUDA 12.1 or 12.4 depending on your PyTorch version requirements. 12.1 is widely supported. +FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 + +WORKDIR /code + +# 2. Install system dependencies +# We need software-properties-common to install Python easily if needed, +# but 'uv' can actually manage Python for us. +RUN apt-get update && apt-get install -y \ + git \ + curl \ + ca-certificates \ + libsndfile1 \ + && rm -rf /var/lib/apt/lists/* + +# 3. Install uv package manager +COPY --from=ghcr.io/astral-sh/uv:0.9.11 /uv /uvx /bin/ + +# 4. Configure uv to install Python 3.12 automatically +# Since the base image is Ubuntu without Python 3.12, uv will fetch it. +ENV UV_PYTHON=3.12 +ENV UV_COMPILE_BYTECODE=1 + +# 5. Install dependencies +# Using --frozen ensures we respect the lockfile +RUN --mount=type=bind,source=uv.lock,target=uv.lock \ + --mount=type=bind,source=pyproject.toml,target=pyproject.toml \ + uv sync --frozen --no-cache + +# 6. Install transformers (and ensure PyTorch uses CUDA) +# Note: If you need specific CUDA PyTorch, you might need to define extra-index-url in pyproject.toml +RUN uv pip install git+https://github.com/huggingface/transformers.git + +# Copy application code +COPY src/ ./src/ + +EXPOSE 8000 + +# Set Python path +ENV PYTHONPATH=/code/src + +# 7. Run application +# We use 'uv run' which ensures the correct python environment is used +CMD ["uv", "run", "app/main.py"] \ No newline at end of file diff --git a/apps/api-inference-yolo/README.md b/apps/api-inference-yolo/README.md new file mode 100644 index 0000000..e36b1c7 --- /dev/null +++ b/apps/api-inference-yolo/README.md @@ -0,0 +1,180 @@ +# SAM3 FastAPI Backend + +REST API for **Segment Anything Model 3 (SAM3)** inference using HuggingFace Transformers. + +## Features + +- ✅ **Text Prompt Inference** - Segment objects using natural language ("cat", "laptop", etc.) +- ✅ **Bounding Box Inference** - Segment using coordinate-based prompts +- ✅ **Batch Processing** - Process multiple images in one request +- ✅ **Mask Polygon Coordinates** - Get precise segmentation masks as polygon points +- ✅ **Optional Visualizations** - Get images with drawn masks/boxes (base64 encoded) +- ✅ **Processing Time Metadata** - Track inference performance +- ✅ **GPU Auto-detection** - Automatic CUDA/CPU selection + +## Architecture + +``` +backend/ +├── src/app/ +│ ├── main.py # FastAPI app + model loading +│ ├── config.py # Settings and environment config +│ ├── integrations/sam3/ +│ │ ├── inference.py # SAM3 model inference +│ │ ├── mask_utils.py # Mask processing utilities +│ │ └── visualizer.py # Mask and box visualization +│ ├── routers/sam3.py # API endpoints +│ ├── schemas/sam3.py # Pydantic request/response models +│ └── helpers/ +│ ├── response_api.py # JSON response formatting +│ └── logger.py # Logging setup +├── docs/ # API documentation +├── Dockerfile # Python 3.12-slim + uv +├── pyproject.toml # Dependencies +└── example_sam.py # Usage examples +``` + +## Prerequisites + +### 1. HuggingFace Access Token (REQUIRED) + +SAM3 is a **gated model** on HuggingFace. You need to: + +1. **Create a HuggingFace account**: https://huggingface.co/join +2. **Request access to SAM3**: Visit https://huggingface.co/facebook/sam3 and accept the license +3. **Generate an access token**: https://huggingface.co/settings/tokens + - Click "New token" + - Name it (e.g., "sam3-api") + - Select "Read" permissions + - Copy the token (starts with `hf_...`) + +4. **Add token to `.env` file**: +```bash +cp .env.example .env +# Edit .env and replace: +HF_TOKEN=hf_your_actual_token_here +``` + +⚠️ **Without a valid HF_TOKEN, the application will fail to load the model!** + +### 2. System Requirements + +- **Python 3.12+** +- **NVIDIA GPU with CUDA** (optional, for faster inference) + +## Local Development + +```bash +# 1. Setup HuggingFace token +cp .env.example .env +nano .env # Add your HF_TOKEN + +# 2. Install uv package manager +curl -LsSf https://astral.sh/uv/install.sh | sh + +# 3. Install dependencies +uv sync + +# 4. Run application +uv run app/main.py + +# API available at http://localhost:8000 +# Docs at http://localhost:8000/docs +``` + +## API Endpoints + +### 1. Text Prompt Inference + +```bash +POST /api/v1/sam3/inference/text + +curl -X POST http://localhost:8000/api/v1/sam3/inference/text \ + -F "image=@cat.jpg" \ + -F "text_prompt=ear" \ + -F "threshold=0.5" \ + -F "return_visualization=true" +``` + +### 2. Bounding Box Inference + +```bash +POST /api/v1/sam3/inference/bbox + +curl -X POST http://localhost:8000/api/v1/sam3/inference/bbox \ + -F "image=@kitchen.jpg" \ + -F 'bounding_boxes=[[59, 144, 76, 163, 1]]' \ + -F "threshold=0.5" +``` + +### 3. Batch Processing + +```bash +POST /api/v1/sam3/inference/batch + +curl -X POST http://localhost:8000/api/v1/sam3/inference/batch \ + -F "images=@cat.jpg" \ + -F "images=@dog.jpg" \ + -F 'text_prompts=["cat", "dog"]' +``` + +### 4. Health Check + +```bash +GET /api/v1/sam3/health +``` + +## Configuration + +Edit `.env` file: + +```bash +# Application +DEBUG=true +APP_HOST=0.0.0.0 +APP_PORT=8000 +LOG_LEVEL=INFO + +# HuggingFace (REQUIRED) +HF_TOKEN=hf_your_token_here + +# SAM3 Model +SAM3_MODEL_NAME=facebook/sam3 +SAM3_DEVICE=auto # auto, cpu, cuda +SAM3_DEFAULT_THRESHOLD=0.5 + +# API Limits +MAX_IMAGE_SIZE_MB=10 +MAX_BATCH_SIZE=10 +MAX_IMAGE_DIMENSION=4096 + +# Visualization +VISUALIZATION_FORMAT=PNG # PNG or JPEG +VISUALIZATION_QUALITY=95 + +# CORS +ALLOWED_ORIGINS=* # Comma-separated origins +``` + +## Docker Deployment + +See root `docker-compose.yml` for container setup. + +## Performance + +- **GPU (CUDA)**: Fast inference (~200-500ms per image) +- **CPU**: 5-10x slower than GPU + +Batch processing is more efficient for multiple images. + +## API Documentation + +- **Swagger UI**: http://localhost:8000/docs +- **ReDoc**: http://localhost:8000/redoc +- **OpenAPI JSON**: http://localhost:8000/openapi.json + +## References + +- [SAM3 Documentation](https://huggingface.co/facebook/sam3) +- [SAM3 GitHub](https://github.com/facebookresearch/sam3) +- [FastAPI](https://fastapi.tiangolo.com/) diff --git a/apps/api-inference-yolo/docs/sam3.md b/apps/api-inference-yolo/docs/sam3.md new file mode 100644 index 0000000..f8cc7a3 --- /dev/null +++ b/apps/api-inference-yolo/docs/sam3.md @@ -0,0 +1,716 @@ +--- +license: other +extra_gated_fields: + First Name: text + Last Name: text + Date of birth: date_picker + Country: country + Affiliation: text + Job title: + type: select + options: + - Student + - Research Graduate + - AI researcher + - AI developer/engineer + - Reporter + - Other + geo: ip_location + By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox +extra_gated_description: >- + The information you provide will be collected, stored, processed and shared in + accordance with the [Meta Privacy + Policy](https://www.facebook.com/privacy/policy/). +extra_gated_button_content: Submit +language: +- en +pipeline_tag: mask-generation +library_name: transformers +tags: +- sam3 +--- + +SAM 3 is a unified foundation model for promptable segmentation in images and videos. It can detect, segment, and track objects using text or visual prompts such as points, boxes, and masks. Compared to its predecessor [SAM 2](https://github.com/facebookresearch/sam2), SAM 3 introduces the ability to exhaustively segment all instances of an open-vocabulary concept specified by a short text phrase or exemplars. Unlike prior work, SAM 3 can handle a vastly larger set of open-vocabulary prompts. It achieves 75-80% of human performance on our new [SA-CO benchmark](https://github.com/facebookresearch/sam3/edit/main_readme/README.md#sa-co-dataset) which contains 270K unique concepts, over 50 times more than existing benchmarks. + +[Hugging Face 🤗 app](https://huggingface.co/spaces/akhaliq/sam3) + +### Basic Usage + +```python +import torch +#################################### For Image #################################### +from PIL import Image +from sam3.model_builder import build_sam3_image_model +from sam3.model.sam3_image_processor import Sam3Processor +# Load the model +model = build_sam3_image_model() +processor = Sam3Processor(model) +# Load an image +image = Image.open("") +inference_state = processor.set_image(image) +# Prompt the model with text +output = processor.set_text_prompt(state=inference_state, prompt="") + +# Get the masks, bounding boxes, and scores +masks, boxes, scores = output["masks"], output["boxes"], output["scores"] + +#################################### For Video #################################### + +from sam3.model_builder import build_sam3_video_predictor + +video_predictor = build_sam3_video_predictor() +video_path = "" # a JPEG folder or an MP4 video file +# Start a session +response = video_predictor.handle_request( + request=dict( + type="start_session", + resource_path=video_path, + ) +) +response = video_predictor.handle_request( + request=dict( + type="add_prompt", + session_id=response["session_id"], + frame_index=0, # Arbitrary frame index + text="", + ) +) +output = response["outputs"] +``` + +The official code is publicly released in the [sam3 repo](https://github.com/facebookresearch/sam3). + + +## Usage with 🤗 Transformers + +### SAM3 - Promptable Concept Segmentation (PCS) for Images + +SAM3 performs Promptable Concept Segmentation (PCS) on images, taking text and/or image exemplars as prompts and returning segmentation masks for **all matching object instances** in the image. + +#### Text-Only Prompts + +```python +>>> from transformers import Sam3Processor, Sam3Model +>>> import torch +>>> from PIL import Image +>>> import requests + +>>> device = "cuda" if torch.cuda.is_available() else "cpu" + +>>> model = Sam3Model.from_pretrained("facebook/sam3").to(device) +>>> processor = Sam3Processor.from_pretrained("facebook/sam3") + +>>> # Load image +>>> image_url = "http://images.cocodataset.org/val2017/000000077595.jpg" +>>> image = Image.open(requests.get(image_url, stream=True).raw).convert("RGB") + +>>> # Segment using text prompt +>>> inputs = processor(images=image, text="ear", return_tensors="pt").to(device) + +>>> with torch.no_grad(): +... outputs = model(**inputs) + +>>> # Post-process results +>>> results = processor.post_process_instance_segmentation( +... outputs, +... threshold=0.5, +... mask_threshold=0.5, +... target_sizes=inputs.get("original_sizes").tolist() +... )[0] + +>>> print(f"Found {len(results['masks'])} objects") +>>> # Results contain: +>>> # - masks: Binary masks resized to original image size +>>> # - boxes: Bounding boxes in absolute pixel coordinates (xyxy format) +>>> # - scores: Confidence scores +``` + +You can display masks using a simple helper like the following: + +```python +import numpy as np +import matplotlib + +def overlay_masks(image, masks): + image = image.convert("RGBA") + masks = 255 * masks.cpu().numpy().astype(np.uint8) + + n_masks = masks.shape[0] + cmap = matplotlib.colormaps.get_cmap("rainbow").resampled(n_masks) + colors = [ + tuple(int(c * 255) for c in cmap(i)[:3]) + for i in range(n_masks) + ] + + for mask, color in zip(masks, colors): + mask = Image.fromarray(mask) + overlay = Image.new("RGBA", image.size, color + (0,)) + alpha = mask.point(lambda v: int(v * 0.5)) + overlay.putalpha(alpha) + image = Image.alpha_composite(image, overlay) + return image +``` + +Then you can save the resulting composite image or display it in a notebook: + +```python +>>> overlay_masks(image, results["masks"]) +``` + +#### Single Bounding Box Prompt + +Segment objects using a bounding box: + +```python +>>> # Box in xyxy format: [x1, y1, x2, y2] in pixel coordinates +>>> # Example: laptop region +>>> box_xyxy = [100, 150, 500, 450] +>>> input_boxes = [[box_xyxy]] # [batch, num_boxes, 4] +>>> input_boxes_labels = [[1]] # 1 = positive box + +>>> inputs = processor( +... images=image, +... input_boxes=input_boxes, +... input_boxes_labels=input_boxes_labels, +... return_tensors="pt" +... ).to(device) + +>>> with torch.no_grad(): +... outputs = model(**inputs) + +>>> # Post-process results +>>> results = processor.post_process_instance_segmentation( +... outputs, +... threshold=0.5, +... mask_threshold=0.5, +... target_sizes=inputs.get("original_sizes").tolist() +... )[0] +``` + +#### Multiple Box Prompts (Positive and Negative) + +Use multiple boxes with positive and negative labels to refine the concept: + +```python +>>> # Load kitchen image +>>> kitchen_url = "http://images.cocodataset.org/val2017/000000136466.jpg" +>>> kitchen_image = Image.open(requests.get(kitchen_url, stream=True).raw).convert("RGB") + +>>> # Define two positive boxes (e.g., dial and button on oven) +>>> # Boxes are in xyxy format [x1, y1, x2, y2] in pixel coordinates +>>> box1_xyxy = [59, 144, 76, 163] # Dial box +>>> box2_xyxy = [87, 148, 104, 159] # Button box +>>> input_boxes = [[box1_xyxy, box2_xyxy]] +>>> input_boxes_labels = [[1, 1]] # Both positive + +>>> inputs = processor( +... images=kitchen_image, +... input_boxes=input_boxes, +... input_boxes_labels=input_boxes_labels, +... return_tensors="pt" +... ).to(device) + +>>> with torch.no_grad(): +... outputs = model(**inputs) + +>>> # Post-process results +>>> results = processor.post_process_instance_segmentation( +... outputs, +... threshold=0.5, +... mask_threshold=0.5, +... target_sizes=inputs.get("original_sizes").tolist() +... )[0] +>>> overlay_masks(kitchen_image, results["masks"]) +``` + +#### Combined Prompts (Text + Negative Box) + +Use text prompts with negative visual prompts to refine the concept: + +```python +>>> # Segment "handle" but exclude the oven handle using a negative box +>>> text = "handle" +>>> # Negative box covering oven handle area (xyxy): [40, 183, 318, 204] +>>> oven_handle_box = [40, 183, 318, 204] +>>> input_boxes = [[oven_handle_box]] + +>>> inputs = processor( +... images=kitchen_image, +... text=text, +... input_boxes=input_boxes, +... input_boxes_labels=[[0]], # 0 = negative (exclude this region) +... return_tensors="pt" +... ).to(device) + +>>> with torch.no_grad(): +... outputs = model(**inputs) + +>>> # Post-process results +>>> results = processor.post_process_instance_segmentation( +... outputs, +... threshold=0.5, +... mask_threshold=0.5, +... target_sizes=inputs.get("original_sizes").tolist() +... )[0] +>>> # This will segment pot handles but exclude the oven handle +``` + +#### Batched Inference with Text Prompts + +Process multiple images with different text prompts by batch: + +```python +>>> cat_url = "http://images.cocodataset.org/val2017/000000077595.jpg" +>>> kitchen_url = "http://images.cocodataset.org/val2017/000000136466.jpg" +>>> images = [ +... Image.open(requests.get(cat_url, stream=True).raw).convert("RGB"), +... Image.open(requests.get(kitchen_url, stream=True).raw).convert("RGB") +... ] + +>>> text_prompts = ["ear", "dial"] + +>>> inputs = processor(images=images, text=text_prompts, return_tensors="pt").to(device) + +>>> with torch.no_grad(): +... outputs = model(**inputs) + +>>> # Post-process results for both images +>>> results = processor.post_process_instance_segmentation( +... outputs, +... threshold=0.5, +... mask_threshold=0.5, +... target_sizes=inputs.get("original_sizes").tolist() +... ) + +>>> print(f"Image 1: {len(results[0]['masks'])} objects found") +>>> print(f"Image 2: {len(results[1]['masks'])} objects found") +``` + +#### Batched Mixed Prompts + +Use different prompt types for different images in the same batch: + +```python +>>> # Image 1: text prompt "laptop" +>>> # Image 2: visual prompt (dial box) +>>> box2_xyxy = [59, 144, 76, 163] + +>>> inputs = processor( +... images=images, +... text=["laptop", None], # Only first image has text +... input_boxes=[None, [box2_xyxy]], # Only second image has box +... input_boxes_labels=[None, [1]], # Positive box for second image +... return_tensors="pt" +... ).to(device) + +>>> with torch.no_grad(): +... outputs = model(**inputs) + +>>> # Post-process results for both images +>>> results = processor.post_process_instance_segmentation( +... outputs, +... threshold=0.5, +... mask_threshold=0.5, +... target_sizes=inputs.get("original_sizes").tolist() +... ) +>>> # Both images processed in single forward pass +``` + +#### Semantic Segmentation Output + +SAM3 also provides semantic segmentation alongside instance masks: + +```python +>>> inputs = processor(images=image, text="ear", return_tensors="pt").to(device) + +>>> with torch.no_grad(): +... outputs = model(**inputs) + +>>> # Instance segmentation masks +>>> instance_masks = torch.sigmoid(outputs.pred_masks) # [batch, num_queries, H, W] + +>>> # Semantic segmentation (single channel) +>>> semantic_seg = outputs.semantic_seg # [batch, 1, H, W] + +>>> print(f"Instance masks: {instance_masks.shape}") +>>> print(f"Semantic segmentation: {semantic_seg.shape}") +``` + +### SAM3 Video - Promptable Concept Segmentation (PCS) for Videos + +SAM3 Video performs Promptable Concept Segmentation (PCS) on videos, taking text as prompts and detecting and tracking **all matching object instances** across video frames. + +#### Pre-loaded Video Inference + +Process a video with all frames already available using text prompts: + +```python +>>> from transformers import Sam3VideoModel, Sam3VideoProcessor +>>> from accelerate import Accelerator +>>> import torch + +>>> device = Accelerator().device +>>> model = Sam3VideoModel.from_pretrained("facebook/sam3").to(device, dtype=torch.bfloat16) +>>> processor = Sam3VideoProcessor.from_pretrained("facebook/sam3") + +>>> # Load video frames +>>> from transformers.video_utils import load_video +>>> video_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/bedroom.mp4" +>>> video_frames, _ = load_video(video_url) + +>>> # Initialize video inference session +>>> inference_session = processor.init_video_session( +... video=video_frames, +... inference_device=device, +... processing_device="cpu", +... video_storage_device="cpu", +... dtype=torch.bfloat16, +... ) + +>>> # Add text prompt to detect and track objects +>>> text = "person" +>>> inference_session = processor.add_text_prompt( +... inference_session=inference_session, +... text=text, +... ) + +>>> # Process all frames in the video +>>> outputs_per_frame = {} +>>> for model_outputs in model.propagate_in_video_iterator( +... inference_session=inference_session, max_frame_num_to_track=50 +... ): +... processed_outputs = processor.postprocess_outputs(inference_session, model_outputs) +... outputs_per_frame[model_outputs.frame_idx] = processed_outputs + +>>> print(f"Processed {len(outputs_per_frame)} frames") +Processed 51 frames + +>>> # Access results for a specific frame +>>> frame_0_outputs = outputs_per_frame[0] +>>> print(f"Detected {len(frame_0_outputs['object_ids'])} objects") +>>> print(f"Object IDs: {frame_0_outputs['object_ids'].tolist()}") +>>> print(f"Scores: {frame_0_outputs['scores'].tolist()}") +>>> print(f"Boxes shape (XYXY format, absolute coordinates): {frame_0_outputs['boxes'].shape}") +>>> print(f"Masks shape: {frame_0_outputs['masks'].shape}") +``` + +#### Streaming Video Inference + +For real-time applications, the Transformers implementation of SAM3 Video supports processing video frames as they arrive: + +```python +>>> # Initialize session for streaming +>>> streaming_inference_session = processor.init_video_session( +... inference_device=device, +... processing_device="cpu", +... video_storage_device="cpu", +... dtype=torch.bfloat16, +... ) + +>>> # Add text prompt +>>> text = "person" +>>> streaming_inference_session = processor.add_text_prompt( +... inference_session=streaming_inference_session, +... text=text, +... ) + +>>> # Process frames one by one (streaming mode) +>>> streaming_outputs_per_frame = {} +>>> for frame_idx, frame in enumerate(video_frames[:50]): # Process first 50 frames +... # First, process the frame using the processor +... inputs = processor(images=frame, device=device, return_tensors="pt") +... +... # Process frame using streaming inference - pass the processed pixel_values +... model_outputs = model( +... inference_session=streaming_inference_session, +... frame=inputs.pixel_values[0], # Provide processed frame - this enables streaming mode +... reverse=False, +... ) +... +... # Post-process outputs with original_sizes for proper resolution handling +... processed_outputs = processor.postprocess_outputs( +... streaming_inference_session, +... model_outputs, +... original_sizes=inputs.original_sizes, # Required for streaming inference +... ) +... streaming_outputs_per_frame[frame_idx] = processed_outputs +... +... if (frame_idx + 1) % 10 == 0: +... print(f"Processed {frame_idx + 1} frames...") + +>>> print(f"✓ Streaming inference complete! Processed {len(streaming_outputs_per_frame)} frames") +✓ Streaming inference complete! Processed 50 frames + +>>> # Access results +>>> frame_0_outputs = streaming_outputs_per_frame[0] +>>> print(f"Detected {len(frame_0_outputs['object_ids'])} objects in first frame") +>>> print(f"Boxes are in XYXY format (absolute pixel coordinates): {frame_0_outputs['boxes'].shape}") +>>> print(f"Masks are at original video resolution: {frame_0_outputs['masks'].shape}") +``` + +
+⚠️ **Note on Streaming Inference Quality**: Streaming inference disables hotstart heuristics that remove unmatched and duplicate objects, as these require access to future frames to make informed decisions. This may result in more false positive detections and duplicate object tracks compared to pre-loaded video inference. For best results, use pre-loaded video inference when all frames are available. +
+ +### SAM3 Tracker - Promptable Visual Segmentation (PVS) for Images + +Sam3Tracker performs Promptable Visual Segmentation (PVS) on images, taking interactive visual prompts (points, boxes, masks) to segment a **specific object instance** per prompt. It is an updated version of SAM2 that maintains the same API while providing improved performance, making it a drop-in replacement for SAM2 workflows. + +#### Automatic Mask Generation with Pipeline + +```python +>>> from transformers import pipeline + +>>> generator = pipeline("mask-generation", model="facebook/sam3", device=0) +>>> image_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/truck.jpg" +>>> outputs = generator(image_url, points_per_batch=64) + +>>> len(outputs["masks"]) # Number of masks generated +``` + +#### Basic Image Segmentation + +##### Single Point Click + +```python +>>> from transformers import Sam3TrackerProcessor, Sam3TrackerModel +>>> from accelerate import Accelerator +>>> import torch +>>> from PIL import Image +>>> import requests + +>>> device = Accelerator().device + +>>> model = Sam3TrackerModel.from_pretrained("facebook/sam3").to(device) +>>> processor = Sam3TrackerProcessor.from_pretrained("facebook/sam3") + +>>> image_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/truck.jpg" +>>> raw_image = Image.open(requests.get(image_url, stream=True).raw).convert("RGB") + +>>> input_points = [[[[500, 375]]]] # Single point click, 4 dimensions (image_dim, object_dim, point_per_object_dim, coordinates) +>>> input_labels = [[[1]]] # 1 for positive click, 0 for negative click, 3 dimensions (image_dim, object_dim, point_label) + +>>> inputs = processor(images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(model.device) + +>>> with torch.no_grad(): +... outputs = model(**inputs) + +>>> masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])[0] + +>>> # The model outputs multiple mask predictions ranked by quality score +>>> print(f"Generated {masks.shape[1]} masks with shape {masks.shape}") +``` + +##### Multiple Points for Refinement + +```python +>>> # Add both positive and negative points to refine the mask +>>> input_points = [[[[500, 375], [1125, 625]]]] # Multiple points for refinement +>>> input_labels = [[[1, 1]]] # Both positive clicks + +>>> inputs = processor(images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(device) + +>>> with torch.no_grad(): +... outputs = model(**inputs) + +>>> masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])[0] +``` + +##### Bounding Box Input + +```python +>>> # Define bounding box as [x_min, y_min, x_max, y_max] +>>> input_boxes = [[[75, 275, 1725, 850]]] + +>>> inputs = processor(images=raw_image, input_boxes=input_boxes, return_tensors="pt").to(device) + +>>> with torch.no_grad(): +... outputs = model(**inputs) + +>>> masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])[0] +``` + +##### Multiple Objects Segmentation + +```python +>>> # Define points for two different objects +>>> input_points = [[[[500, 375]], [[650, 750]]]] # Points for two objects in same image +>>> input_labels = [[[1], [1]]] # Positive clicks for both objects + +>>> inputs = processor(images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(model.device) + +>>> with torch.no_grad(): +... outputs = model(**inputs, multimask_output=False) + +>>> # Each object gets its own mask +>>> masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])[0] +>>> print(f"Generated masks for {masks.shape[0]} objects") +Generated masks for 2 objects +``` + +#### Batch Inference + + +```python +>>> # Load multiple images +>>> image_urls = [ +... "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/truck.jpg", +... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dog-sam.png" +... ] +>>> raw_images = [Image.open(requests.get(url, stream=True).raw).convert("RGB") for url in image_urls] + +>>> # Single point per image +>>> input_points = [[[[500, 375]]], [[[770, 200]]]] # One point for each image +>>> input_labels = [[[1]], [[1]]] # Positive clicks for both images + +>>> inputs = processor(images=raw_images, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(model.device) + +>>> with torch.no_grad(): +... outputs = model(**inputs, multimask_output=False) + +>>> # Post-process masks for each image +>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"]) +>>> print(f"Processed {len(all_masks)} images, each with {all_masks[0].shape[0]} objects") +``` + +### SAM3 Tracker Video - Promptable Visual Segmentation (PVS) for Videos + +Sam3TrackerVideo performs Promptable Visual Segmentation (PVS) on videos, taking interactive visual prompts (points, boxes, masks) to track a **specific object instance** per prompt across video frames. It is an updated version of SAM2 Video that maintains the same API while providing improved performance, making it a drop-in replacement for SAM2 Video workflows. + +#### Basic Video Tracking + +```python +>>> from transformers import Sam3TrackerVideoModel, Sam3TrackerVideoProcessor +>>> from accelerate import Accelerator +>>> import torch + +>>> device = Accelerator().device +>>> model = Sam3TrackerVideoModel.from_pretrained("facebook/sam3").to(device, dtype=torch.bfloat16) +>>> processor = Sam3TrackerVideoProcessor.from_pretrained("facebook/sam3") + +>>> # Load video frames +>>> from transformers.video_utils import load_video +>>> video_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/bedroom.mp4" +>>> video_frames, _ = load_video(video_url) + +>>> # Initialize video inference session +>>> inference_session = processor.init_video_session( +... video=video_frames, +... inference_device=device, +... dtype=torch.bfloat16, +... ) + +>>> # Add click on first frame to select object +>>> ann_frame_idx = 0 +>>> ann_obj_id = 1 +>>> points = [[[[210, 350]]]] +>>> labels = [[[1]]] + +>>> processor.add_inputs_to_inference_session( +... inference_session=inference_session, +... frame_idx=ann_frame_idx, +... obj_ids=ann_obj_id, +... input_points=points, +... input_labels=labels, +... ) + +>>> # Segment the object on the first frame (optional, you can also propagate the masks through the video directly) +>>> outputs = model( +... inference_session=inference_session, +... frame_idx=ann_frame_idx, +... ) +>>> video_res_masks = processor.post_process_masks( +... [outputs.pred_masks], original_sizes=[[inference_session.video_height, inference_session.video_width]], binarize=False +... )[0] +>>> print(f"Segmentation shape: {video_res_masks.shape}") +Segmentation shape: torch.Size([1, 1, 480, 854]) + +>>> # Propagate through the entire video +>>> video_segments = {} +>>> for sam3_tracker_video_output in model.propagate_in_video_iterator(inference_session): +... video_res_masks = processor.post_process_masks( +... [sam3_tracker_video_output.pred_masks], original_sizes=[[inference_session.video_height, inference_session.video_width]], binarize=False +... )[0] +... video_segments[sam3_tracker_video_output.frame_idx] = video_res_masks + +>>> print(f"Tracked object through {len(video_segments)} frames") +Tracked object through 180 frames +``` + +#### Multi-Object Video Tracking + +Track multiple objects simultaneously across video frames: + +```python +>>> # Reset for new tracking session +>>> inference_session.reset_inference_session() + +>>> # Add multiple objects on the first frame +>>> ann_frame_idx = 0 +>>> obj_ids = [2, 3] +>>> input_points = [[[[200, 300]], [[400, 150]]]] # Points for two objects (batched) +>>> input_labels = [[[1], [1]]] + +>>> processor.add_inputs_to_inference_session( +... inference_session=inference_session, +... frame_idx=ann_frame_idx, +... obj_ids=obj_ids, +... input_points=input_points, +... input_labels=input_labels, +... ) + +>>> # Get masks for both objects on first frame (optional, you can also propagate the masks through the video directly) +>>> outputs = model( +... inference_session=inference_session, +... frame_idx=ann_frame_idx, +... ) + +>>> # Propagate both objects through video +>>> video_segments = {} +>>> for sam3_tracker_video_output in model.propagate_in_video_iterator(inference_session): +... video_res_masks = processor.post_process_masks( +... [sam3_tracker_video_output.pred_masks], original_sizes=[[inference_session.video_height, inference_session.video_width]], binarize=False +... )[0] +... video_segments[sam3_tracker_video_output.frame_idx] = { +... obj_id: video_res_masks[i] +... for i, obj_id in enumerate(inference_session.obj_ids) +... } + +>>> print(f"Tracked {len(inference_session.obj_ids)} objects through {len(video_segments)} frames") +Tracked 2 objects through 180 frames +``` + +#### Streaming Video Inference + +For real-time applications, Sam3TrackerVideo supports processing video frames as they arrive: + +```python +>>> # Initialize session for streaming +>>> inference_session = processor.init_video_session( +... inference_device=device, +... dtype=torch.bfloat16, +... ) + +>>> # Process frames one by one +>>> for frame_idx, frame in enumerate(video_frames[:10]): # Process first 10 frames +... inputs = processor(images=frame, device=device, return_tensors="pt") +... +... if frame_idx == 0: +... # Add point input on first frame +... processor.add_inputs_to_inference_session( +... inference_session=inference_session, +... frame_idx=0, +... obj_ids=1, +... input_points=[[[[210, 350], [250, 220]]]], +... input_labels=[[[1, 1]]], +... original_size=inputs.original_sizes[0], # need to be provided when using streaming video inference +... ) +... +... # Process current frame +... sam3_tracker_video_output = model(inference_session=inference_session, frame=inputs.pixel_values[0]) +... +... video_res_masks = processor.post_process_masks( +... [sam3_tracker_video_output.pred_masks], original_sizes=inputs.original_sizes, binarize=False +... )[0] +... print(f"Frame {frame_idx}: mask shape {video_res_masks.shape}") +``` \ No newline at end of file diff --git a/apps/api-inference-yolo/example_sam.py b/apps/api-inference-yolo/example_sam.py new file mode 100644 index 0000000..16bc86b --- /dev/null +++ b/apps/api-inference-yolo/example_sam.py @@ -0,0 +1,42 @@ +import matplotlib.pyplot as plt +import numpy as np +import torch +from PIL import Image +from transformers import Sam3Model, Sam3Processor + +device = "cuda" if torch.cuda.is_available() else "cpu" + +model = Sam3Model.from_pretrained("facebook/sam3").to(device) +processor = Sam3Processor.from_pretrained("facebook/sam3") + +# Load gambar +img = Image.open("assets/images/truck.jpg").convert("RGB") + +# Proses input +inputs = processor(images=img, text="a white car", return_tensors="pt").to(device) + +with torch.no_grad(): + outputs = model(**inputs) + +# Post-process segmentation +results = processor.post_process_instance_segmentation( + outputs, + threshold=0.5, + mask_threshold=0.5, + target_sizes=inputs.get("original_sizes").tolist(), +)[0] + +masks = results["masks"] # tensor mask +boxes = results["boxes"] # bounding boxes +scores = results["scores"] # confidence + +print("Found", len(masks), "masks") + +if len(masks) > 0: + mask0 = masks[0].cpu().numpy() + # save or visualize mask0 as needed + Image.fromarray((mask0 * 255).astype(np.uint8)).save("mask0.png") + +# plt.imshow(img) +# plt.imshow(mask0, alpha=0.5, cmap="jet") +# plt.show() diff --git a/apps/api-inference-yolo/package.json b/apps/api-inference-yolo/package.json new file mode 100644 index 0000000..37b7927 --- /dev/null +++ b/apps/api-inference-yolo/package.json @@ -0,0 +1,11 @@ +{ + "name": "@sam3/api-inference", + "version": "0.1.0", + "private": true, + "description": "SAM3 FastAPI inference backend (Python/uv)", + "scripts": { + "dev": "echo 'Use make backend-run or Docker for Python backend'", + "build": "echo 'Python backend does not require build step'", + "clean": "find . -type d -name '__pycache__' -exec rm -rf {} + 2>/dev/null || true" + } +} diff --git a/apps/api-inference-yolo/pyproject.toml b/apps/api-inference-yolo/pyproject.toml new file mode 100644 index 0000000..f39c0e8 --- /dev/null +++ b/apps/api-inference-yolo/pyproject.toml @@ -0,0 +1,38 @@ +[project] +name = "sam3-yolo-app" +version = "0.1.0" +description = "SAM3 Segment Anything Model 3 API with FastAPI and Ultralytics" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + # FastAPI core + "fastapi>=0.115.4", + "pydantic>=2.12.4", + "pydantic-settings>=2.12.0", + "python-multipart>=0.0.20", + "uvicorn>=0.32.0", + + # Ultralytics / SAM3 + "ultralytics>=8.3.0", + "torch>=2.1.0", + "torchvision>=0.16.0", + + # Image processing + "pillow>=10.0.0", + "matplotlib>=3.8.0", + "opencv-python-headless>=4.8.0" +] + +[dependency-groups] +dev = [ + "ruff>=0.9.4", + "ipython>=8.31.0", +] + +[tool.ruff] +line-length = 120 +target-version = "py312" + +[tool.ruff.lint] +select = ["E", "F", "I", "N", "W", "UP"] +ignore = ["E501"] diff --git a/apps/api-inference-yolo/src/app/config.py b/apps/api-inference-yolo/src/app/config.py new file mode 100644 index 0000000..24f4fe2 --- /dev/null +++ b/apps/api-inference-yolo/src/app/config.py @@ -0,0 +1,45 @@ +"""Application configuration settings.""" + +from pydantic import Field +from pydantic_settings import BaseSettings, SettingsConfigDict + + +class Settings(BaseSettings): + """Application settings with environment variable support.""" + + # Application + APP_TITLE: str = "SAM3 API" + APP_DESCRIPTION: str = "Segment Anything Model 3 - Promptable Segmentation API" + APP_VERSION: str = "0.1.0" + APP_HOST: str = "0.0.0.0" + APP_PORT: int = 8000 + DEBUG: bool = False + + # SAM3 Model Settings (Ultralytics) + SAM3_MODEL_PATH: str = "sam3.pt" # Path to sam3.pt (local file or Ultralytics model name) + SAM3_DEVICE: str = "auto" # auto, cpu, cuda + SAM3_DEFAULT_THRESHOLD: float = Field(default=0.5, ge=0.0, le=1.0) + SAM3_DEFAULT_MASK_THRESHOLD: float = Field(default=0.5, ge=0.0, le=1.0) + + # API Limits + MAX_IMAGE_SIZE_MB: int = 10 + MAX_BATCH_SIZE: int = 10 + MAX_IMAGE_DIMENSION: int = 4096 # Max width or height + + # Visualization + VISUALIZATION_FORMAT: str = "PNG" # PNG or JPEG + VISUALIZATION_QUALITY: int = 95 # For JPEG + + # Logging + LOG_LEVEL: str = "INFO" + LOG_JSON_FORMAT: bool = False + + # CORS + ALLOWED_ORIGINS: list[str] = ["*"] + ALLOWED_METHODS: list[str] = ["*"] + ALLOWED_HEADERS: list[str] = ["*"] + + model_config = SettingsConfigDict(env_file=".env", case_sensitive=True, extra="ignore") + + +settings = Settings() diff --git a/apps/api-inference-yolo/src/app/exceptions/__init__.py b/apps/api-inference-yolo/src/app/exceptions/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/apps/api-inference-yolo/src/app/helpers/logger.py b/apps/api-inference-yolo/src/app/helpers/logger.py new file mode 100644 index 0000000..3eb2a81 --- /dev/null +++ b/apps/api-inference-yolo/src/app/helpers/logger.py @@ -0,0 +1,27 @@ +"""Simple logging configuration.""" + +import logging +import sys + +from app.config import settings + + +def configure_logger() -> logging.Logger: + """Configure and return standard logger. + + Returns + ------- + logging.Logger + Configured logger instance + """ + # Configure logging + logging.basicConfig( + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + stream=sys.stdout, + level=getattr(logging, settings.LOG_LEVEL.upper()), + ) + + return logging.getLogger("sam3-api") + + +logger = configure_logger() diff --git a/apps/api-inference-yolo/src/app/helpers/response_api.py b/apps/api-inference-yolo/src/app/helpers/response_api.py new file mode 100644 index 0000000..8bd8bb1 --- /dev/null +++ b/apps/api-inference-yolo/src/app/helpers/response_api.py @@ -0,0 +1,48 @@ +"""Standardized JSON response helpers.""" + +from typing import Any, Generic, TypeVar + +from pydantic import BaseModel, Field + +DataT = TypeVar("DataT") +MetaT = TypeVar("MetaT") + + +class MetaResponse(BaseModel): + """Pagination metadata for list responses.""" + + page: int = Field(..., description="Current page number") + limit: int = Field(..., description="Items per page") + total_items: int = Field(..., description="Total number of items") + total_pages: int = Field(..., description="Total number of pages") + + +class JsonResponse(BaseModel, Generic[DataT, MetaT]): + """Standardized JSON response format. + + This is the standard response format for all API endpoints. + """ + + data: DataT | None = Field(default=None, description="Response data") + message: str = Field(..., description="Human-readable message") + status_code: int = Field(..., description="HTTP status code") + meta: MetaT | None = Field(default=None, description="Metadata (pagination, etc)") + + model_config = {"arbitrary_types_allowed": True} + + +class ErrorDetail(BaseModel): + """Error detail information.""" + + field: str | None = Field(default=None, description="Field that caused the error") + message: str = Field(..., description="Error message") + type: str | None = Field(default=None, description="Error type") + + +class ErrorResponse(BaseModel): + """Error response format.""" + + error: str = Field(..., description="Error type or code") + message: str = Field(..., description="Human-readable error message") + status_code: int = Field(..., description="HTTP status code") + details: list[ErrorDetail] | None = Field(default=None, description="Detailed error information") diff --git a/apps/api-inference-yolo/src/app/integrations/sam3/__init__.py b/apps/api-inference-yolo/src/app/integrations/sam3/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/apps/api-inference-yolo/src/app/integrations/sam3/inference.py b/apps/api-inference-yolo/src/app/integrations/sam3/inference.py new file mode 100644 index 0000000..5756bcb --- /dev/null +++ b/apps/api-inference-yolo/src/app/integrations/sam3/inference.py @@ -0,0 +1,394 @@ +"""SAM3 inference using Ultralytics SAM3SemanticPredictor.""" + +import io +import time +import base64 + +import cv2 +import numpy as np +import torch +from fastapi import UploadFile +from PIL import Image +from ultralytics.models.sam import SAM3SemanticPredictor + +from app.config import settings +from app.helpers.logger import logger +from app.integrations.sam3.mask_utils import masks_to_polygon_data +from app.integrations.sam3.visualizer import Sam3Visualizer + + +class SAM3Inference: + """SAM3 inference implementation using Ultralytics.""" + + def __init__(self): + """Initialize SAM3 model configuration.""" + import os + + # Support both local file paths and model names + model_path = settings.SAM3_MODEL_PATH + + # Resolve the model path + resolved_path = self._resolve_model_path(model_path) + + self.model_path = resolved_path + self.device = self._get_device() + self.feature_predictor = None + self.inference_predictor = None + self.visualizer = Sam3Visualizer() + + logger.info(f"SAM3 (Ultralytics) inference initialized - Model: {self.model_path}, Device: {self.device}") + + def _resolve_model_path(self, model_path: str) -> str: + """Resolve model path from config, checking multiple locations. + + Checks: + 1. Absolute path if provided + 2. Current working directory + 3. apps/ directory relative to project root + 4. Falls back to model name for Ultralytics to download + """ + import os + + logger.info(f"Resolving model path: {model_path}") + + # If it's already an absolute path and exists + if os.path.isabs(model_path) and os.path.isfile(model_path): + logger.info(f"Found model at absolute path: {model_path}") + return model_path + + # Check if it exists in current directory + if os.path.isfile(model_path): + abs_path = os.path.abspath(model_path) + logger.info(f"Found model in current directory: {abs_path}") + return abs_path + + # Check in apps directory (assuming this runs from api-inference-yolo) + # Path: api-inference-yolo/src/app/integrations/sam3/inference.py + # Go up: ../../../../ = api-inference-yolo -> apps -> SSD-EXT -> ... + # But we want: api-inference-yolo -> .. (apps) = ../ + apps_model = os.path.join(os.path.dirname(__file__), "../../../..", model_path) + apps_model = os.path.abspath(apps_model) + + logger.info(f"Checking apps directory: {apps_model}") + if os.path.isfile(apps_model): + logger.info(f"Found model in apps directory: {apps_model}") + return apps_model + + # Try one level up (for models in project root) + root_model = os.path.join(os.path.dirname(__file__), "../../../../..", model_path) + root_model = os.path.abspath(root_model) + + logger.info(f"Checking project root: {root_model}") + if os.path.isfile(root_model): + logger.info(f"Found model in project root: {root_model}") + return root_model + + # Fall back to the model name (let Ultralytics handle it) + logger.warning(f"Model file not found locally, using as Ultralytics model name: {model_path}") + return model_path + + def _get_device(self) -> str: + """Determine device (cuda or cpu).""" + if settings.SAM3_DEVICE == "auto": + device = "cuda" if torch.cuda.is_available() else "cpu" + return device + return settings.SAM3_DEVICE + + def load_model(self): + """Load SAM3 model into memory.""" + logger.info(f"Loading SAM3 model (Ultralytics): {self.model_path}") + + overrides = dict( + conf=settings.SAM3_DEFAULT_THRESHOLD, + task="segment", + mode="predict", + model=self.model_path, + half=False, # Use full precision as requested + save=False, # Don't save prediction results to disk + device=self.device, + verbose=False # Reduce noise in logs + ) + + try: + # Initialize two predictors to follow the working reference implementation exactly + # One for feature extraction (image encoding), one for inference (decoding) + self.feature_predictor = SAM3SemanticPredictor(overrides=overrides) + self.inference_predictor = SAM3SemanticPredictor(overrides=overrides) + + # Setup both models + self.feature_predictor.setup_model() + self.inference_predictor.setup_model() + + # Share the underlying model to save VRAM if possible, while keeping separate predictor states + if hasattr(self.feature_predictor, 'model') and self.feature_predictor.model is not None: + self.inference_predictor.model = self.feature_predictor.model + logger.info("Shared underlying model between predictors to optimized VRAM") + + logger.info(f"SAM3 model loaded successfully on {self.device} (FP16: {overrides['half']})") + except Exception as e: + logger.error(f"Failed to load SAM3 model: {e}") + raise + + async def _load_image_from_upload(self, file: UploadFile) -> np.ndarray: + """Load image for Ultralytics (numpy array in RGB format).""" + content = await file.read() + + size_mb = len(content) / (1024 * 1024) + if size_mb > settings.MAX_IMAGE_SIZE_MB: + raise ValueError(f"Image size {size_mb:.2f}MB exceeds limit of {settings.MAX_IMAGE_SIZE_MB}MB") + + # Load image using PIL + image_pil = Image.open(io.BytesIO(content)) + if image_pil.mode != "RGB": + image_pil = image_pil.convert("RGB") + + w, h = image_pil.size + if w > settings.MAX_IMAGE_DIMENSION or h > settings.MAX_IMAGE_DIMENSION: + logger.warning(f"Image size {w}x{h} exceeds recommended max dimension {settings.MAX_IMAGE_DIMENSION}") + + # Convert to numpy array (RGB format for Ultralytics) + return np.array(image_pil) + + async def _run_inference(self, image_np, conf_threshold=None, **kwargs) -> tuple: + """Shared inference logic following feature-based inference pattern. + + Args: + image_np: Image as numpy array + conf_threshold: Confidence threshold to override model default + **kwargs: Arguments to pass to predictor (text, bboxes, etc.) + """ + + # Update predictor confidence if specified for inference predictor + if conf_threshold is not None and hasattr(self.inference_predictor, 'args'): + self.inference_predictor.args.conf = conf_threshold + logger.info(f"Set confidence threshold to {conf_threshold}") + + # 1. Extract features using feature_predictor (like predictor.set_image()) + # Ultralytics models typically expect BGR format when passed numpy arrays directly + # because they are built around OpenCV which uses BGR by default + if image_np.ndim == 3 and image_np.shape[2] == 3: + logger.info("Converting image from RGB to BGR for Ultralytics predictor") + image_input = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR) + else: + image_input = image_np + + self.feature_predictor.set_image(image_input) + src_shape = image_np.shape[:2] # Get image shape (height, width) + + # Verify features are extracted + if not hasattr(self.feature_predictor, 'features') or self.feature_predictor.features is None: + logger.error("Failed to extract features from image") + return None, None, [], [], [] + + # 2. Run inference using inference_predictor reusing features (like predictor2.inference_features) + try: + # Extract text or bboxes from kwargs + text_prompts = kwargs.get('text', None) + bboxes = kwargs.get('bboxes', None) + + if text_prompts is not None: + logger.info(f"Running feature-based inference with text prompts: {text_prompts}") + masks, boxes = self.inference_predictor.inference_features( + self.feature_predictor.features, + src_shape=src_shape, + text=text_prompts + ) + elif bboxes is not None: + logger.info(f"Running feature-based inference with {len(bboxes)} bounding boxes") + masks, boxes = self.inference_predictor.inference_features( + self.feature_predictor.features, + src_shape=src_shape, + bboxes=bboxes + ) + else: + logger.error("No text prompts or bounding boxes provided") + return None, None, [], [], [] + + # Inference result logging + box_count = 0 + mask_count = 0 + + if boxes is not None: + box_count = len(boxes) + if torch.is_tensor(boxes): + logger.info(f"Boxes tensor shape: {boxes.shape}, device: {boxes.device}") + + if masks is not None: + mask_count = len(masks) + if torch.is_tensor(masks): + logger.info(f"Masks tensor shape: {masks.shape}, device: {masks.device}") + + logger.info(f"Feature-based inference successful: {mask_count} masks, {box_count} boxes") + + except Exception as e: + logger.error(f"Feature-based inference failed: {e}", exc_info=True) + return None, None, [], [], [] + + boxes_list = [] + scores_list = [] + masks_polygon = [] + masks_tensor = None + + # Process masks + if masks is not None and len(masks) > 0: + masks_tensor = masks # Already tensor format from inference_features + # Convert masks to polygon format + masks_polygon = masks_to_polygon_data(masks_tensor) + logger.info(f"Extracted {len(masks_polygon)} mask(s)") + else: + logger.warning("No masks found in results") + + # Process boxes - inference_features returns boxes as tensor + if boxes is not None and len(boxes) > 0: + boxes_list = boxes.cpu().tolist() if torch.is_tensor(boxes) else boxes.tolist() + # inference_features doesn't return confidence scores directly + # Use default confidence based on threshold + scores_list = [conf_threshold if conf_threshold else settings.SAM3_DEFAULT_THRESHOLD] * len(boxes_list) + logger.info(f"Extracted {len(boxes_list)} box(es)") + + # Log confidence for each detection + for idx, (box, score) in enumerate(zip(boxes_list, scores_list)): + logger.info(f" Detection #{idx+1}: bbox={box}, confidence={score:.4f}") + else: + logger.warning("No boxes found in results") + + return None, masks_tensor, boxes_list, scores_list, masks_polygon + + + async def inference_text( + self, + image_file: UploadFile, + text_prompt: str, + threshold: float, + mask_threshold: float, + return_visualization: bool = False, + ) -> dict: + """Text-based inference.""" + start_time = time.perf_counter() + image_np = await self._load_image_from_upload(image_file) + + # Convert single prompt to list (like test script) + text_prompts = [text_prompt] + + logger.info(f"Running text inference with prompt: '{text_prompt}', threshold: {threshold}") + + result_obj, masks_tensor, boxes, scores, masks_poly = await self._run_inference( + image_np, + conf_threshold=threshold, # Pass threshold to inference + text=text_prompts + ) + + processing_time_ms = (time.perf_counter() - start_time) * 1000 + num_objects = len(boxes) + + response = { + "num_objects": num_objects, + "boxes": boxes, + "scores": scores, + "masks": masks_poly, + "processing_time_ms": round(processing_time_ms, 2), + "visualization_base64": None, + } + + if return_visualization and num_objects > 0: + image_pil = Image.fromarray(image_np) + viz_bytes = self.visualizer.create_visualization( + image=image_pil, + masks=masks_tensor, + boxes=torch.tensor(boxes) if boxes else None, + scores=torch.tensor(scores) if scores else None, + ) + response["visualization_base64"] = base64.b64encode(viz_bytes).decode("utf-8") + + return response + + async def inference_bbox( + self, + image_file: UploadFile, + bounding_boxes: list[list[int]], + box_labels: list[int], + threshold: float, + mask_threshold: float, + return_visualization: bool = False, + ) -> dict: + """Bounding box inference.""" + start_time = time.perf_counter() + image_np = await self._load_image_from_upload(image_file) + + logger.info(f"Running bbox inference with {len(bounding_boxes)} box(es), threshold: {threshold}") + + result_obj, masks_tensor, boxes, scores, masks_poly = await self._run_inference( + image_np, + conf_threshold=threshold, # Pass threshold to inference + bboxes=bounding_boxes + ) + + processing_time_ms = (time.perf_counter() - start_time) * 1000 + num_objects = len(boxes) + + response = { + "num_objects": num_objects, + "boxes": boxes, + "scores": scores, + "masks": masks_poly, + "processing_time_ms": round(processing_time_ms, 2), + "visualization_base64": None, + } + + if return_visualization and num_objects > 0: + image_pil = Image.fromarray(image_np) + viz_bytes = self.visualizer.create_visualization( + image=image_pil, + masks=masks_tensor, + boxes=torch.tensor(boxes) if boxes else None, + scores=torch.tensor(scores) if scores else None, + ) + response["visualization_base64"] = base64.b64encode(viz_bytes).decode("utf-8") + + return response + + async def inference_batch( + self, + image_files: list[UploadFile], + text_prompts: list[str | None], + threshold: float, + mask_threshold: float, + return_visualizations: bool = False, + ) -> dict: + """Batch inference loop.""" + start_time = time.perf_counter() + + batch_output = [] + + for idx, file in enumerate(image_files): + prompt = text_prompts[idx] if idx < len(text_prompts) else "" + + # Call inference_text directly as it handles logic + # This is inefficient compared to true batching but ensures logic reuse + res = await self.inference_text( + file, + prompt if prompt else "everything", + threshold, + mask_threshold, + return_visualization=return_visualizations + ) + + batch_output.append({ + "image_index": idx, + "num_objects": res["num_objects"], + "boxes": res["boxes"], + "scores": res["scores"], + "masks": res["masks"], + "visualization_base64": res["visualization_base64"], + }) + + total_time_ms = (time.perf_counter() - start_time) * 1000 + + response = { + "total_images": len(image_files), + "results": batch_output, + "total_processing_time_ms": round(total_time_ms, 2), + "average_time_per_image_ms": round(total_time_ms / len(image_files), 2) if image_files else 0 + } + + return response diff --git a/apps/api-inference-yolo/src/app/integrations/sam3/mask_utils.py b/apps/api-inference-yolo/src/app/integrations/sam3/mask_utils.py new file mode 100644 index 0000000..759463a --- /dev/null +++ b/apps/api-inference-yolo/src/app/integrations/sam3/mask_utils.py @@ -0,0 +1,95 @@ +""" +Utility functions for converting SAM3 masks to polygon coordinates. +""" + +import cv2 +import numpy as np +import torch + + +def mask_to_polygons(mask: torch.Tensor, simplify_tolerance: float = 1.5) -> list[list[list[float]]]: + """ + Convert a binary mask tensor to polygon coordinates. + + Args: + mask: Binary mask tensor of shape [H, W] with values 0.0 or 1.0 + simplify_tolerance: Epsilon parameter for polygon simplification (Douglas-Peucker algorithm) + Higher values = simpler polygons with fewer points + + Returns: + List of polygons, where each polygon is a list of [x, y] coordinate pairs. + Multiple polygons may exist if the mask has disconnected regions or holes. + Format: [[[x1, y1], [x2, y2], ...], [[x1, y1], ...], ...] + """ + # Convert tensor to numpy array + mask_np = mask.cpu().numpy() + + # Convert to uint8 (0 or 255) + mask_uint8 = (mask_np * 255).astype(np.uint8) + + # Find contours using OpenCV + # RETR_TREE gets all contours including holes + # CHAIN_APPROX_SIMPLE compresses horizontal, vertical, and diagonal segments + contours, hierarchy = cv2.findContours(mask_uint8, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + + polygons = [] + + for contour in contours: + # Skip very small contours (likely noise) + if len(contour) < 3: + continue + + # Simplify the polygon to reduce number of points + epsilon = simplify_tolerance + simplified = cv2.approxPolyDP(contour, epsilon, closed=True) + + # Convert from OpenCV format [[x, y]] to list of [x, y] pairs + # OpenCV contours are shape (N, 1, 2), we want (N, 2) + polygon = simplified.squeeze().tolist() + + # Ensure it's a list of lists (handle single point edge case) + if isinstance(polygon[0], (int, float)): + polygon = [polygon] + + # Only keep polygons with at least 3 points + if len(polygon) >= 3: + polygons.append(polygon) + + return polygons + + +def calculate_mask_area(mask: torch.Tensor) -> float: + """ + Calculate the total pixel area of a binary mask. + + Args: + mask: Binary mask tensor of shape [H, W] with values 0.0 or 1.0 + + Returns: + Total number of pixels with value 1.0 + """ + return float(mask.sum().item()) + + +def masks_to_polygon_data(masks: torch.Tensor, simplify_tolerance: float = 1.5) -> list[dict]: + """ + Convert multiple masks to polygon data structures. + + Args: + masks: Tensor of binary masks with shape [N, H, W] + simplify_tolerance: Epsilon parameter for polygon simplification + + Returns: + List of dictionaries with polygon and area data for each mask + Format: [{"polygons": [...], "area": 1234.0}, ...] + """ + polygon_data = [] + + for i in range(masks.shape[0]): + mask = masks[i] + polygons = mask_to_polygons(mask, simplify_tolerance) + area = calculate_mask_area(mask) + + polygon_data.append({"polygons": polygons, "area": area}) + + return polygon_data diff --git a/apps/api-inference-yolo/src/app/integrations/sam3/visualizer.py b/apps/api-inference-yolo/src/app/integrations/sam3/visualizer.py new file mode 100644 index 0000000..00c1f55 --- /dev/null +++ b/apps/api-inference-yolo/src/app/integrations/sam3/visualizer.py @@ -0,0 +1,190 @@ +"""Visualization utilities for drawing masks and bounding boxes.""" + +import io + +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import torch +from PIL import Image, ImageDraw + +from app.config import settings +from app.helpers.logger import logger + + +class Sam3Visualizer: + """Utilities for visualizing SAM3 results.""" + + @staticmethod + def draw_masks_and_boxes( + image: Image.Image, + masks: torch.Tensor, + boxes: torch.Tensor, + scores: torch.Tensor | None = None, + alpha: float = 0.5, + colormap: str = "rainbow", + draw_boxes: bool = True, + draw_masks: bool = True, + ) -> Image.Image: + """Draw masks and bounding boxes on image. + + Parameters + ---------- + image : Image.Image + Original PIL Image + masks : torch.Tensor + Binary masks tensor [N, H, W] + boxes : torch.Tensor + Bounding boxes tensor [N, 4] in xyxy format + scores : torch.Tensor | None + Confidence scores for each detection + alpha : float + Transparency for mask overlay (0.0 to 1.0) + colormap : str + Matplotlib colormap name + draw_boxes : bool + Whether to draw bounding boxes + draw_masks : bool + Whether to draw masks + + Returns + ------- + Image.Image + Image with visualizations drawn + """ + # Convert image to RGBA for overlay + image_rgba = image.convert("RGBA") + width, height = image.size + + # Ensure masks are on CPU and convert to numpy + masks = masks.cpu().numpy() if isinstance(masks, torch.Tensor) else masks + boxes = boxes.cpu().numpy() if isinstance(boxes, torch.Tensor) else boxes + + if scores is not None: + scores = scores.cpu().numpy() if isinstance(scores, torch.Tensor) else scores + + n_objects = masks.shape[0] + + if n_objects == 0: + logger.warning("No objects to visualize") + return image + + # Generate colors from colormap + cmap = matplotlib.colormaps.get_cmap(colormap).resampled(n_objects) + colors = [tuple(int(c * 255) for c in cmap(i)[:3]) for i in range(n_objects)] + + # Draw masks + if draw_masks: + for idx, (mask, color) in enumerate(zip(masks, colors)): + # Convert mask to uint8 + mask_uint8 = (mask * 255).astype(np.uint8) + mask_image = Image.fromarray(mask_uint8) + + # Create colored overlay + overlay = Image.new("RGBA", image_rgba.size, color + (0,)) + alpha_channel = mask_image.point(lambda v: int(v * alpha)) + overlay.putalpha(alpha_channel) + + # Composite onto image + image_rgba = Image.alpha_composite(image_rgba, overlay) + + # Draw bounding boxes + if draw_boxes: + draw = ImageDraw.Draw(image_rgba) + + for idx, (box, color) in enumerate(zip(boxes, colors)): + x1, y1, x2, y2 = box.tolist() + + # Draw rectangle + draw.rectangle([x1, y1, x2, y2], outline=color + (255,), width=3) + + # Draw score if available + if scores is not None: + score_text = f"{scores[idx]:.2f}" + draw.text((x1, y1 - 15), score_text, fill=color + (255,)) + + # Convert back to RGB + result_image = image_rgba.convert("RGB") + + logger.info(f"Visualization completed - Objects: {n_objects}") + return result_image + + @staticmethod + def encode_image_to_bytes(image: Image.Image, format: str = "PNG", quality: int = 95) -> bytes: + """Encode PIL Image to bytes. + + Parameters + ---------- + image : Image.Image + PIL Image to encode + format : str + Image format (PNG or JPEG) + quality : int + JPEG quality (1-100) + + Returns + ------- + bytes + Encoded image bytes + """ + buffer = io.BytesIO() + + if format.upper() == "JPEG": + image.save(buffer, format="JPEG", quality=quality) + else: + image.save(buffer, format="PNG") + + buffer.seek(0) + return buffer.read() + + @staticmethod + def create_visualization( + image: Image.Image, + masks: torch.Tensor, + boxes: torch.Tensor, + scores: torch.Tensor | None = None, + alpha: float = 0.5, + draw_boxes: bool = True, + draw_masks: bool = True, + ) -> bytes: + """Create visualization and return as bytes. + + Parameters + ---------- + image : Image.Image + Original PIL Image + masks : torch.Tensor + Binary masks tensor [N, H, W] + boxes : torch.Tensor + Bounding boxes tensor [N, 4] + scores : torch.Tensor | None + Confidence scores + alpha : float + Mask transparency + draw_boxes : bool + Whether to draw boxes + draw_masks : bool + Whether to draw masks + + Returns + ------- + bytes + Encoded visualization image + """ + # Draw visualizations + viz_image = Sam3Visualizer.draw_masks_and_boxes( + image=image, + masks=masks, + boxes=boxes, + scores=scores, + alpha=alpha, + draw_boxes=draw_boxes, + draw_masks=draw_masks, + ) + + # Encode to bytes + image_bytes = Sam3Visualizer.encode_image_to_bytes( + viz_image, format=settings.VISUALIZATION_FORMAT, quality=settings.VISUALIZATION_QUALITY + ) + + return image_bytes diff --git a/apps/api-inference-yolo/src/app/main.py b/apps/api-inference-yolo/src/app/main.py new file mode 100644 index 0000000..e0b166a --- /dev/null +++ b/apps/api-inference-yolo/src/app/main.py @@ -0,0 +1,179 @@ +"""FastAPI application for SAM3 segmentation.""" + +from contextlib import asynccontextmanager + +from fastapi import FastAPI, Request, status +from fastapi.exceptions import RequestValidationError +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import JSONResponse + +from app.config import settings +from app.helpers.logger import logger +from app.helpers.response_api import ErrorDetail, ErrorResponse +from app.integrations.sam3.inference import SAM3Inference +from app.routers import sam3 + + +@asynccontextmanager +async def lifespan(app: FastAPI): + """Manage application lifespan: startup and shutdown. + + Parameters + ---------- + app : FastAPI + FastAPI application instance + + Yields + ------ + None + Yields control to application + """ + # Startup + logger.info(f"Starting SAM3 API application v{settings.APP_VERSION}") + + try: + # Initialize and load SAM3 model + logger.info("Loading SAM3 model (this may take a few minutes)...") + sam3_inference = SAM3Inference() + sam3_inference.load_model() + logger.info("SAM3 model loaded successfully") + + yield { + "sam3_inference": sam3_inference, + } + + logger.info("Application startup complete") + + except Exception as e: + logger.error(f"Failed to initialize application: {e}") + raise + + yield + + # Shutdown + logger.info("Shutting down application") + # Cleanup if needed + logger.info("Application shutdown complete") + + +# Create FastAPI app +app = FastAPI( + title=settings.APP_TITLE, + description=settings.APP_DESCRIPTION, + version=settings.APP_VERSION, + lifespan=lifespan, + docs_url="/docs", + redoc_url="/redoc", + openapi_url="/openapi.json", +) + +# Add CORS middleware +app.add_middleware( + CORSMiddleware, + allow_origins=settings.ALLOWED_ORIGINS, + allow_credentials=True, + allow_methods=settings.ALLOWED_METHODS, + allow_headers=settings.ALLOWED_HEADERS, +) + + +# Exception handlers +@app.exception_handler(RequestValidationError) +async def validation_exception_handler(request: Request, exc: RequestValidationError): + """Handle request validation errors. + + Parameters + ---------- + request : Request + FastAPI request + exc : RequestValidationError + Validation error exception + + Returns + ------- + JSONResponse + Error response + """ + errors = [] + for error in exc.errors(): + errors.append( + ErrorDetail( + field=".".join(str(loc) for loc in error["loc"]) if error.get("loc") else None, + message=error.get("msg", "Validation error"), + type=error.get("type"), + ) + ) + + error_response = ErrorResponse( + error="Validation Error", + message="Request validation failed", + status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, + details=errors, + ) + + logger.warning(f"Validation error: {errors}") + + return JSONResponse(status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, content=error_response.model_dump()) + + +@app.exception_handler(Exception) +async def general_exception_handler(request: Request, exc: Exception): + """Handle general exceptions. + + Parameters + ---------- + request : Request + FastAPI request + exc : Exception + Exception instance + + Returns + ------- + JSONResponse + Error response + """ + error_response = ErrorResponse( + error="Internal Server Error", + message="An unexpected error occurred", + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + details=None, + ) + + logger.error(f"Unhandled exception at {request.url.path}: {exc}") + + return JSONResponse(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, content=error_response.model_dump()) + + +# Include routers +app.include_router(sam3.router) + + +# Root endpoint +@app.get("/", tags=["Root"]) +async def root(): + """Root endpoint. + + Returns + ------- + dict + Welcome message + """ + return { + "message": "Welcome to SAM3 API", + "version": settings.APP_VERSION, + "docs": "/docs", + "health": "/api/v1/sam3/health", + } + + +# Run application +if __name__ == "__main__": + import uvicorn + + uvicorn.run( + "app.main:app", + host=settings.APP_HOST, + port=settings.APP_PORT, + reload=settings.DEBUG, + log_level=settings.LOG_LEVEL.lower(), + ) diff --git a/apps/api-inference-yolo/src/app/middleware/__init__.py b/apps/api-inference-yolo/src/app/middleware/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/apps/api-inference-yolo/src/app/routers/sam3.py b/apps/api-inference-yolo/src/app/routers/sam3.py new file mode 100644 index 0000000..914d17b --- /dev/null +++ b/apps/api-inference-yolo/src/app/routers/sam3.py @@ -0,0 +1,262 @@ +"""SAM3 API endpoints for image segmentation.""" + +import json + +from fastapi import APIRouter, File, Form, HTTPException, Request, Response, UploadFile, status +from fastapi.responses import Response as FastAPIResponse + +from app.helpers.logger import logger +from app.helpers.response_api import JsonResponse +from app.schemas.sam3 import BatchInferenceResult, InferenceResult + +router = APIRouter(prefix="/api/v1/sam3", tags=["SAM3 Inference"]) + + +@router.post( + "/inference/text", + response_model=JsonResponse[InferenceResult, None], + status_code=status.HTTP_200_OK, + description="Run SAM3 inference with text prompt on single image. Returns bounding boxes, confidence scores, and polygon coordinates for each detected object's segmentation mask.", +) +async def inference_text( + request: Request, + response: Response, + image: UploadFile = File(..., description="Image file to process"), + text_prompt: str = Form(..., description="Text description of objects to segment"), + threshold: float = Form(0.5, ge=0.0, le=1.0, description="Detection confidence threshold"), + mask_threshold: float = Form(0.5, ge=0.0, le=1.0, description="Mask generation threshold"), + return_visualization: bool = Form(False, description="Generate visualization image"), +): + """Run text-based inference on uploaded image. + + Parameters + ---------- + request : Request + FastAPI request object + response : Response + FastAPI response object + image : UploadFile + Uploaded image file + text_prompt : str + Text description of objects to segment + threshold : float + Detection confidence threshold + mask_threshold : float + Mask generation threshold + return_visualization : bool + Whether to generate visualization + + Returns + ------- + JsonResponse[InferenceResult, None] + Inference results with job ID and detection data + """ + try: + # Get inference instance from app state + sam3_inference = request.state.sam3_inference + + # Run inference + result = await sam3_inference.inference_text( + image_file=image, + text_prompt=text_prompt, + threshold=threshold, + mask_threshold=mask_threshold, + return_visualization=return_visualization, + ) + + # Build response + inference_result = InferenceResult(**result) + + status_code = status.HTTP_200_OK + response.status_code = status_code + return JsonResponse( + data=inference_result, + message=f"Successfully processed image with text prompt. Found {result['num_objects']} objects.", + status_code=status_code, + ) + + except ValueError as e: + logger.error(f"Validation error in text inference: {e}") + raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=str(e)) + except Exception as e: + logger.error(f"Error in text inference: {e}") + raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail="Inference failed") + + +@router.post( + "/inference/bbox", + response_model=JsonResponse[InferenceResult, None], + status_code=status.HTTP_200_OK, + description="Run SAM3 inference with bounding box prompts on single image. Returns bounding boxes, confidence scores, and polygon coordinates for each detected object's segmentation mask.", +) +async def inference_bbox( + request: Request, + response: Response, + image: UploadFile = File(..., description="Image file to process"), + bounding_boxes: str = Form(..., description="JSON array of bounding boxes: [[x1,y1,x2,y2,label], ...]"), + threshold: float = Form(0.5, ge=0.0, le=1.0, description="Detection confidence threshold"), + mask_threshold: float = Form(0.5, ge=0.0, le=1.0, description="Mask generation threshold"), + return_visualization: bool = Form(False, description="Generate visualization image"), +): + """Run bounding box-based inference on uploaded image. + + Parameters + ---------- + request : Request + FastAPI request object + response : Response + FastAPI response object + image : UploadFile + Uploaded image file + bounding_boxes : str + JSON string of bounding boxes + threshold : float + Detection confidence threshold + mask_threshold : float + Mask generation threshold + return_visualization : bool + Whether to generate visualization + + Returns + ------- + JsonResponse[InferenceResult, None] + Inference results + """ + try: + # Parse bounding boxes JSON + try: + boxes_data = json.loads(bounding_boxes) + except json.JSONDecodeError: + raise ValueError("Invalid JSON format for bounding_boxes") + + # Extract boxes and labels + boxes = [[b[0], b[1], b[2], b[3]] for b in boxes_data] + labels = [b[4] if len(b) > 4 else 1 for b in boxes_data] # Default to positive + + # Get inference instance + sam3_inference = request.state.sam3_inference + + # Run inference + result = await sam3_inference.inference_bbox( + image_file=image, + bounding_boxes=boxes, + box_labels=labels, + threshold=threshold, + mask_threshold=mask_threshold, + return_visualization=return_visualization, + ) + + # Build response + inference_result = InferenceResult(**result) + + status_code = status.HTTP_200_OK + response.status_code = status_code + return JsonResponse( + data=inference_result, + message=f"Successfully processed image with bbox prompts. Found {result['num_objects']} objects.", + status_code=status_code, + ) + + except ValueError as e: + logger.error(f"Validation error in bbox inference: {e}") + raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=str(e)) + except Exception as e: + logger.error(f"Error in bbox inference: {e}") + raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail="Inference failed") + + +@router.post( + "/inference/batch", + response_model=JsonResponse[BatchInferenceResult, None], + status_code=status.HTTP_200_OK, + description="Run SAM3 inference on multiple images in batch. Returns bounding boxes, confidence scores, and polygon coordinates for each detected object in each image.", +) +async def inference_batch( + request: Request, + response: Response, + images: list[UploadFile] = File(..., description="List of image files to process"), + text_prompts: str = Form(..., description="JSON array of text prompts (one per image, null for none)"), + threshold: float = Form(0.5, ge=0.0, le=1.0, description="Detection confidence threshold"), + mask_threshold: float = Form(0.5, ge=0.0, le=1.0, description="Mask generation threshold"), + return_visualizations: bool = Form(False, description="Generate visualization images"), +): + """Run batch inference on multiple images. + + Parameters + ---------- + request : Request + FastAPI request object + response : Response + FastAPI response object + images : list[UploadFile] + List of uploaded image files + text_prompts : str + JSON array of text prompts + threshold : float + Detection confidence threshold + mask_threshold : float + Mask generation threshold + return_visualizations : bool + Whether to generate visualizations + + Returns + ------- + JsonResponse[BatchInferenceResult, None] + Batch inference results + """ + try: + # Parse text prompts JSON + try: + prompts_list = json.loads(text_prompts) + except json.JSONDecodeError: + raise ValueError("Invalid JSON format for text_prompts") + + # Validate length match + if len(images) != len(prompts_list): + raise ValueError(f"Number of images ({len(images)}) must match number of prompts ({len(prompts_list)})") + + # Get inference instance + sam3_inference = request.state.sam3_inference + + # Run batch inference + result = await sam3_inference.inference_batch( + image_files=images, + text_prompts=prompts_list, + threshold=threshold, + mask_threshold=mask_threshold, + return_visualizations=return_visualizations, + ) + + # Build response + batch_result = BatchInferenceResult(**result) + + status_code = status.HTTP_200_OK + response.status_code = status_code + return JsonResponse( + data=batch_result, + message=f"Successfully processed {result['total_images']} images in batch.", + status_code=status_code, + ) + + except ValueError as e: + logger.error(f"Validation error in batch inference: {e}") + raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=str(e)) + except Exception as e: + logger.error(f"Error in batch inference: {e}") + raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail="Batch inference failed") + + +@router.get( + "/health", + status_code=status.HTTP_200_OK, + description="Health check endpoint", +) +async def health_check(request: Request): + """Check API health status. + + Returns + ------- + dict + Health status + """ + return {"status": "healthy", "service": "SAM3 API"} diff --git a/apps/api-inference-yolo/src/app/schemas/sam3.py b/apps/api-inference-yolo/src/app/schemas/sam3.py new file mode 100644 index 0000000..ff31e57 --- /dev/null +++ b/apps/api-inference-yolo/src/app/schemas/sam3.py @@ -0,0 +1,91 @@ +"""Pydantic schemas for SAM3 API requests and responses.""" + +from pydantic import BaseModel, Field + + +class BoundingBoxInput(BaseModel): + """Bounding box input coordinates.""" + + x1: int = Field(..., description="Top-left x coordinate") + y1: int = Field(..., description="Top-left y coordinate") + x2: int = Field(..., description="Bottom-right x coordinate") + y2: int = Field(..., description="Bottom-right y coordinate") + label: int = Field(1, description="Box label: 1=positive (include), 0=negative (exclude)") + + +class BoundingBoxResult(BaseModel): + """Bounding box result coordinates.""" + + x1: float = Field(..., description="Top-left x coordinate") + y1: float = Field(..., description="Top-left y coordinate") + x2: float = Field(..., description="Bottom-right x coordinate") + y2: float = Field(..., description="Bottom-right y coordinate") + + +class DetectedObject(BaseModel): + """Single detected object with box and score.""" + + object_id: int = Field(..., description="Object index") + score: float = Field(..., description="Confidence score") + bounding_box: list[float] = Field(..., description="Bounding box coordinates [x1, y1, x2, y2]") + + +class MaskPolygon(BaseModel): + """Polygon representation of a segmentation mask.""" + + polygons: list[list[list[float]]] = Field( + ..., + description="List of polygons, where each polygon is a list of [x, y] coordinate pairs. " + "Multiple polygons represent disconnected regions or holes in the mask.", + ) + area: float = Field(..., description="Total pixel area of the mask") + + +class InferenceResult(BaseModel): + """Inference result for single image.""" + + num_objects: int = Field(..., description="Number of detected objects") + boxes: list[list[float]] = Field(..., description="List of bounding boxes [[x1, y1, x2, y2], ...]") + scores: list[float] = Field(..., description="Confidence scores for each detection") + masks: list[MaskPolygon] = Field(..., description="Polygon coordinates for each detected object's mask") + processing_time_ms: float = Field(..., description="Processing time in milliseconds") + visualization_base64: str | None = Field(default=None, description="Base64-encoded visualization image (optional)") + + +class BatchImageResult(BaseModel): + """Result for single image in batch processing.""" + + image_index: int = Field(..., description="Index of image in batch") + num_objects: int = Field(..., description="Number of detected objects") + boxes: list[list[float]] = Field(..., description="List of bounding boxes") + scores: list[float] = Field(..., description="Confidence scores") + masks: list[MaskPolygon] = Field(..., description="Polygon coordinates for each detected object's mask") + visualization_base64: str | None = Field(default=None, description="Base64-encoded visualization (optional)") + + +class BatchInferenceResult(BaseModel): + """Batch inference results.""" + + total_images: int = Field(..., description="Total number of images processed") + results: list[BatchImageResult] = Field(..., description="Results for each image") + total_processing_time_ms: float = Field(..., description="Total processing time") + average_time_per_image_ms: float = Field(..., description="Average time per image") + + +# Form request models (used with multipart/form-data) +class TextInferenceParams(BaseModel): + """Parameters for text inference (form data).""" + + text_prompt: str = Field(..., description="Text description of objects to segment") + threshold: float = Field(0.5, ge=0.0, le=1.0, description="Detection confidence threshold") + mask_threshold: float = Field(0.5, ge=0.0, le=1.0, description="Mask generation threshold") + return_visualization: bool = Field(False, description="Whether to generate visualization image") + + +class BboxInferenceParams(BaseModel): + """Parameters for bbox inference (form data).""" + + bounding_boxes: str = Field(..., description="JSON string of bounding boxes: [[x1,y1,x2,y2,label], ...]") + threshold: float = Field(0.5, ge=0.0, le=1.0, description="Detection confidence threshold") + mask_threshold: float = Field(0.5, ge=0.0, le=1.0, description="Mask generation threshold") + return_visualization: bool = Field(False, description="Whether to generate visualization image") diff --git a/apps/api-inference-yolo/src/app/services/__init__.py b/apps/api-inference-yolo/src/app/services/__init__.py new file mode 100644 index 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threshold to get raw scores, then filter manually to ensure accuracy results = self.processor.post_process_instance_segmentation( outputs, - threshold=threshold, + threshold=0.0, mask_threshold=mask_threshold, target_sizes=inputs.get("original_sizes").tolist(), )[0] + # Filter by threshold manually + keep = results["scores"] > threshold + + scores = results["scores"][keep] + boxes = results["boxes"][keep] + masks = results["masks"][keep] + # Prepare response - num_objects = len(results["scores"]) - boxes_list = results["boxes"].cpu().tolist() - scores_list = results["scores"].cpu().tolist() + num_objects = len(scores) + boxes_list = boxes.cpu().tolist() + scores_list = scores.cpu().tolist() # Convert masks to polygon coordinates masks_polygon = masks_to_polygon_data(results["masks"], simplify_tolerance) @@ -240,17 +248,25 @@ async def inference_bbox( outputs = self.model(**inputs) # Post-process + # Use 0.0 threshold to get raw scores, then filter manually to ensure accuracy results = self.processor.post_process_instance_segmentation( outputs, - threshold=threshold, + threshold=0.0, mask_threshold=mask_threshold, target_sizes=inputs.get("original_sizes").tolist(), )[0] + # Filter by threshold manually + keep = results["scores"] > threshold + + scores = results["scores"][keep] + boxes = results["boxes"][keep] + masks = results["masks"][keep] + # Prepare response - num_objects = len(results["scores"]) - boxes_list = results["boxes"].cpu().tolist() - scores_list = results["scores"].cpu().tolist() + num_objects = len(scores) + boxes_list = boxes.cpu().tolist() + scores_list = scores.cpu().tolist() # Convert masks to polygon coordinates masks_polygon = masks_to_polygon_data(results["masks"], simplify_tolerance) @@ -332,9 +348,10 @@ async def inference_batch( outputs = self.model(**inputs) # Post-process + # Use 0.0 threshold to get raw scores, then filter manually to ensure accuracy results = self.processor.post_process_instance_segmentation( outputs, - threshold=threshold, + threshold=0.0, mask_threshold=mask_threshold, target_sizes=inputs.get("original_sizes").tolist(), ) @@ -342,9 +359,16 @@ async def inference_batch( # Process each result batch_results = [] for idx, (image, result) in enumerate(zip(images, results)): - num_objects = len(result["scores"]) - boxes_list = result["boxes"].cpu().tolist() - scores_list = result["scores"].cpu().tolist() + # Filter by threshold manually + keep = result["scores"] > threshold + + scores = result["scores"][keep] + boxes = result["boxes"][keep] + masks = result["masks"][keep] + + num_objects = len(scores) + boxes_list = boxes.cpu().tolist() + scores_list = scores.cpu().tolist() # Convert masks to polygon coordinates masks_polygon = masks_to_polygon_data(result["masks"], simplify_tolerance) diff --git a/apps/api-inference/src/app/main.py b/apps/api-inference/src/app/main.py index e0b166a..59f8fa4 100644 --- a/apps/api-inference/src/app/main.py +++ b/apps/api-inference/src/app/main.py @@ -42,19 +42,17 @@ async def lifespan(app: FastAPI): "sam3_inference": sam3_inference, } + # Shutdown + logger.info("Shutting down application") + # Cleanup if needed + logger.info("Application shutdown complete") + logger.info("Application startup complete") except Exception as e: logger.error(f"Failed to initialize application: {e}") raise - yield - - # Shutdown - logger.info("Shutting down application") - # Cleanup if needed - logger.info("Application shutdown complete") - # Create FastAPI app app = FastAPI( diff --git a/apps/requirements_linux.txt b/apps/requirements_linux.txt new file mode 100644 index 0000000..ffa21cc --- /dev/null +++ b/apps/requirements_linux.txt @@ -0,0 +1,86 @@ +annotated-doc==0.0.4 +annotated-types==0.7.0 +anyio==4.12.1 +certifi==2026.1.4 +charset-normalizer==3.4.4 +click==8.3.1 +clip @ git+https://github.com/ultralytics/CLIP.git@88ade288431a46233f1556d1e141901b3ef0a36b +contourpy==1.3.3 +cuda-bindings==12.9.4 +cuda-pathfinder==1.3.3 +cycler==0.12.1 +fastapi==0.128.0 +filelock==3.20.3 +fonttools==4.61.1 +fsspec==2026.1.0 +ftfy==6.3.1 +h11==0.16.0 +hf-xet==1.2.0 +httpcore==1.0.9 +httpx==0.28.1 +huggingface_hub==1.3.7 +idna==3.11 +Jinja2==3.1.6 +kiwisolver==1.4.9 +MarkupSafe==3.0.3 +matplotlib==3.10.8 +mpmath==1.3.0 +networkx==3.6.1 +numpy==2.2.6 +nvidia-cublas-cu12==12.8.4.1 +nvidia-cuda-cupti-cu12==12.8.90 +nvidia-cuda-nvrtc-cu12==12.8.93 +nvidia-cuda-runtime-cu12==12.8.90 +nvidia-cudnn-cu12==9.10.2.21 +nvidia-cufft-cu12==11.3.3.83 +nvidia-cufile-cu12==1.13.1.3 +nvidia-curand-cu12==10.3.9.90 +nvidia-cusolver-cu12==11.7.3.90 +nvidia-cusparse-cu12==12.5.8.93 +nvidia-cusparselt-cu12==0.7.1 +nvidia-nccl-cu12==2.27.5 +nvidia-nvjitlink-cu12==12.8.93 +nvidia-nvshmem-cu12==3.4.5 +nvidia-nvtx-cu12==12.8.90 +opencv-python==4.13.0.90 +opencv-python-headless==4.12.0.88 +packaging==26.0 +pillow==12.1.0 +polars==1.37.1 +polars-runtime-32==1.37.1 +psutil==7.2.2 +pydantic==2.12.5 +pydantic-settings==2.12.0 +pydantic_core==2.41.5 +pyparsing==3.3.2 +python-dateutil==2.9.0.post0 +python-dotenv==1.2.1 +python-multipart==0.0.22 +PyYAML==6.0.3 +regex==2026.1.15 +requests==2.32.5 +safetensors==0.7.0 +sam3-app==0.1.0 +-e git+https://github.com/Rajkisan/annotate-anu.git@4ac6f9fba72207235c41cc01d00ae8845d688bea#egg=sam3_yolo_app&subdirectory=apps/api-inference-yolo +scipy==1.17.0 +setuptools==80.10.2 +shellingham==1.5.4 +six==1.17.0 +starlette==0.50.0 +sympy==1.14.0 +timm==1.0.24 +tokenizers==0.22.2 +torch==2.10.0 +torchvision==0.25.0 +tqdm==4.67.3 +transformers==5.0.0 +triton==3.6.0 +typer-slim==0.21.1 +typing-inspection==0.4.2 +typing_extensions==4.15.0 +ultralytics==8.4.11 +ultralytics-thop==2.0.18 +urllib3==2.6.3 +uvicorn==0.40.0 +wcwidth==0.5.3 +wheel==0.46.3 diff --git a/apps/web/src/components/BboxPromptPanel.tsx b/apps/web/src/components/BboxPromptPanel.tsx index 160258b..3555743 100644 --- a/apps/web/src/components/BboxPromptPanel.tsx +++ b/apps/web/src/components/BboxPromptPanel.tsx @@ -1,7 +1,9 @@ -import { useState, useEffect } from 'react' +import { useState, useEffect, useRef } from 'react' import { Loader2, X, Trash2, Square } from 'lucide-react' import { Button } from './ui/button' import { PromptModeSelector } from './ui/PromptModeSelector' +import { BatchProgressModal, type BatchProgressItem } from './ui/BatchProgressModal' +import { ImageSelectorModal } from './ui/ImageSelectorModal' import type { Label, ImageData, PromptMode } from '@/types/annotations' import type { AvailableModel } from '@/types/byom' import { inferenceClient } from '@/lib/inference-client' @@ -33,6 +35,7 @@ interface BboxPromptPanelProps { annotationType: 'bbox' | 'polygon' labelId?: string imageId?: string + createBBoxOverlay?: boolean }) => void onClose: () => void promptBboxes?: Array<{ x: number; y: number; width: number; height: number; id: string; labelId: string }> @@ -59,18 +62,48 @@ export function BboxPromptPanel({ const [simplifyEnabled, setSimplifyEnabled] = useState(false) const [simplifyTolerance, setSimplifyTolerance] = useState(1.5) const [annotationType, setAnnotationType] = useState('polygon') + const [generateBBox, setGenerateBBox] = useState(false) const [isLoading, setIsLoading] = useState(false) - // Auto-switch to 'single' mode if currently in a disabled mode + // Batch mode state + const [showImageSelector, setShowImageSelector] = useState(false) + const [showBatchProgress, setShowBatchProgress] = useState(false) + const [batchProgress, setBatchProgress] = useState([]) + const [batchCurrentIndex, setBatchCurrentIndex] = useState(0) + const batchCancelRef = useRef(false) + + // Track if user has run the prompt at least once (to enable auto-apply) + const [hasRunOnce, setHasRunOnce] = useState(false) + const lastProcessedImageIdRef = useRef(null) + + // Auto-apply mode: Execute prompt when image changes useEffect(() => { - if (promptMode !== 'single') { - setPromptMode('single') + if (promptMode !== 'auto-apply') return + if (!currentImage) return + if (!hasRunOnce) return + if (promptBboxes.length === 0) return + if (isLoading) return + + // Don't re-process the same image + if (lastProcessedImageIdRef.current === currentImage.id) return + + // Skip if image already has auto-generated annotations + const hasAutoAnnotations = images + .find(img => img.id === currentImage.id) + ?.annotations?.some((ann: any) => ann.isAutoGenerated) + if (hasAutoAnnotations) { + lastProcessedImageIdRef.current = currentImage.id + return } - }, []) // Only run on mount - const handleSubmit = async (e: React.FormEvent) => { - e.preventDefault() + console.log('[AUTO-APPLY BBOX] Triggering for', currentImage.name) + runBboxInference().catch(err => { + console.error('[AUTO-APPLY BBOX] Error:', err) + setIsLoading(false) + }) + }, [currentImage?.id, promptMode, hasRunOnce, promptBboxes.length]) + const runBboxInference = async () => { if (!currentImage) { toast.error('No image selected') return @@ -175,8 +208,12 @@ export function BboxPromptPanel({ toast.success(`Successfully detected ${totalDetected} object${totalDetected > 1 ? 's' : ''}!`) - // Clear prompt bboxes after successful inference - if (onPromptBboxesChange) { + // Mark that user has run at least once (enables auto-apply) + setHasRunOnce(true) + lastProcessedImageIdRef.current = currentImage.id + + // Clear prompt bboxes after successful inference (only in single mode) + if (promptMode === 'single' && onPromptBboxesChange) { onPromptBboxesChange([]) } } catch (error) { @@ -187,6 +224,11 @@ export function BboxPromptPanel({ } } + const handleSubmit = async (e: React.FormEvent) => { + e.preventDefault() + await runBboxInference() + } + const handleDeleteBbox = (id: string) => { if (onPromptBboxesChange) { onPromptBboxesChange(promptBboxes.filter((bbox) => bbox.id !== id)) @@ -209,6 +251,137 @@ export function BboxPromptPanel({ } } + const handleBatchStart = async (selectedImageIds: string[]) => { + if (promptBboxes.length === 0) { + toast.error('Please draw bounding boxes first') + return + } + + const missingLabels = promptBboxes.filter(bbox => !bbox.labelId) + if (missingLabels.length > 0) { + toast.error('Please assign labels to all bounding boxes') + return + } + + // Show confirmation + const confirmed = window.confirm( + `Process ${selectedImageIds.length} image${selectedImageIds.length !== 1 ? 's' : ''} with ${promptBboxes.length} bounding box${promptBboxes.length !== 1 ? 'es' : ''}?` + ) + if (!confirmed) return + + // Initialize progress tracking + const imagesToProcess = images.filter(img => selectedImageIds.includes(img.id)) + const progressItems: BatchProgressItem[] = imagesToProcess.map(img => ({ + imageName: img.name, + status: 'pending' as const, + })) + + setBatchProgress(progressItems) + setBatchCurrentIndex(0) + setShowBatchProgress(true) + batchCancelRef.current = false + + // Process images sequentially + for (let i = 0; i < imagesToProcess.length; i++) { + if (batchCancelRef.current) { + toast.success('Batch processing cancelled') + break + } + + const image = imagesToProcess[i] + setBatchCurrentIndex(i) + + // Update status to processing + setBatchProgress(prev => prev.map((item, idx) => + idx === i ? { ...item, status: 'processing' } : item + )) + + try { + // Convert blob to File + const imageFile = new File([image.blob], image.name, { + type: image.blob.type, + }) + + // Group bboxes by label + const bboxesByLabel = new Map() + promptBboxes.forEach(bbox => { + if (!bboxesByLabel.has(bbox.labelId)) { + bboxesByLabel.set(bbox.labelId, []) + } + bboxesByLabel.get(bbox.labelId)!.push(bbox) + }) + + let totalDetected = 0 + + // Call API separately for each unique label + for (const [labelId, bboxes] of bboxesByLabel) { + const bboxesForAPI: Array<[number, number, number, number, number]> = bboxes.map((bbox) => { + const x1 = bbox.x + const y1 = bbox.y + const x2 = bbox.x + bbox.width + const y2 = bbox.y + bbox.height + return [x1, y1, x2, y2, 1] + }) + + const response = await sam3Client.bboxPrompt({ + image: imageFile, + bounding_boxes: bboxesForAPI, + threshold, + mask_threshold: maskThreshold, + return_visualization: false, + }) + + const { num_objects, boxes, masks, scores } = response.data + + if (num_objects > 0) { + totalDetected += num_objects + await onAnnotationsCreated({ + boxes, + masks, + scores, + annotationType, + labelId, + imageId: image.id, + createBBoxOverlay: generateBBox + }) + } + } + + if (totalDetected === 0) { + setBatchProgress(prev => prev.map((item, idx) => + idx === i ? { ...item, status: 'error', error: 'No objects detected' } : item + )) + } else { + setBatchProgress(prev => prev.map((item, idx) => + idx === i ? { ...item, status: 'success', count: totalDetected } : item + )) + } + } catch (error) { + console.error(`Batch bbox processing error for ${image.name}:`, error) + setBatchProgress(prev => prev.map((item, idx) => + idx === i ? { ...item, status: 'error', error: 'Processing failed' } : item + )) + } + } + + const successCount = batchProgress.filter(p => p.status === 'success').length + const errorCount = batchProgress.filter(p => p.status === 'error').length + toast.success(`Batch complete: ${successCount} succeeded, ${errorCount} failed`) + } + + const handleBatchCancel = () => { + batchCancelRef.current = true + setShowBatchProgress(false) + } + + const handleActionButtonClick = async () => { + if (promptMode === 'batch') { + setShowImageSelector(true) + } else { + await runBboxInference() + } + } + return (
{/* Header */} @@ -343,6 +516,21 @@ export function BboxPromptPanel({
+ {annotationType === 'polygon' && ( +
+ +
+ )} + + {annotationType === 'polygon' && ( +
+ +
+ )} +