Merge pull request #928 from Jaykumaran/master

update readme

Author: vikasgupta-github

Object-Detection-on-Edge-Devices/README.md

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+# Object Detection on Edge Device
+
+**This repository contains code for [Object Detection on Edge Device - OAK-D-Lite](https://learnopencv.com/object-detection-on-edge-device) blogpost**.
+
+---
+
+![](readme_images/Feature-image.gif)
+
+[<img src="https://learnopencv.com/wp-content/uploads/2022/07/download-button-e1657285155454.png" alt="download" width="200">](https://www.dropbox.com/scl/fi/9itcfugejlcf11v2fcb33/Deployment-YOLOv8-OAK-D-Lite.zip?rlkey=vpag2p6x1ifhfj70ydhkiqkly&st=mw9d0j3e&dl=1)
+
+
+
+# AI Courses by OpenCV
+
+Want to become an expert in AI? [AI Courses by OpenCV](https://opencv.org/courses/) is a great place to start. 
+
+<a href="https://opencv.org/courses/">
+
+<p align="center"> 
+<img src="https://learnopencv.com/wp-content/uploads/2023/01/AI-Courses-By-OpenCV-Github.png">
+</p>
+</a>
+

Object-Detection-on-Edge-Devices/YOLOv8-Pothole-Training.ipynb

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+# !python3 -m pip install depthai
+
+import os
+import json
+import numpy as np
+import cv2
+from pathlib import Path
+import depthai as dai
+import time
+
+# Define path to the model and configuration
+YOLOV8N_MODEL = "Yolov8-2022.1-blob/yolov8n-pothole-best_openvino_2022.1_8shave.blob"  #Adjust path accordingly
+YOLOV8N_CONFIG = "Yolov8-2022.1-blob/yolov8n-pothole-best.json" #Adjust path accordingly
+
+OUTPUT_VIDEO = "vid_result/960-oak-d-live_video.mp4" #Adjust path accordingly
+
+CAMERA_PREV_DIM = (960, 960)
+LABELS = ["Pot-hole"]
+
+def load_config(config_path):
+    with open(config_path) as f:
+        return json.load(f)
+    
+def create_camera_pipeline(config_path, model_path):
+    pipeline = dai.Pipeline()
+    model_config = load_config(config_path)
+    nnConfig = model_config.get("nn_config", {})
+    metadata = nnConfig.get("NN_specific_metadata", {})
+    classes = metadata.get("classes", {})
+    coordinates = metadata.get("coordinates", {})
+    anchors = metadata.get("anchors", {})
+    anchorMasks = metadata.get("anchor_masks", {})
+    iouThreshold = metadata.get("iou_threshold", {})
+    confidenceThreshold = metadata.get("confidence_threshold", {})
+
+    # Create camera node
+    camRgb = pipeline.create(dai.node.ColorCamera)
+    camRgb.setPreviewSize(CAMERA_PREV_DIM[0], CAMERA_PREV_DIM[1])
+    camRgb.setInterleaved(False)
+    camRgb.setBoardSocket(dai.CameraBoardSocket.RGB)
+    camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
+
+    detectionNetwork = pipeline.create(dai.node.YoloDetectionNetwork)
+    nnOut = pipeline.create(dai.node.XLinkOut)
+
+    nnOut.setStreamName("nn")
+
+    detectionNetwork.setConfidenceThreshold(confidenceThreshold)
+    detectionNetwork.setNumClasses(classes)
+    detectionNetwork.setCoordinateSize(coordinates)
+    detectionNetwork.setAnchors(anchors)
+    detectionNetwork.setAnchorMasks(anchorMasks)
+    detectionNetwork.setIouThreshold(iouThreshold)
+    detectionNetwork.setBlobPath(model_path)
+    detectionNetwork.setNumInferenceThreads(2)
+    detectionNetwork.input.setBlocking(False)
+
+    # Linking
+    camRgb.preview.link(detectionNetwork.input)
+    detectionNetwork.out.link(nnOut.input)
+
+    return pipeline
+
+def annotate_frame(frame, detections, fps):
+    color = (0, 0, 255)
+    for detection in detections:
+        bbox = frame_norm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
+        cv2.putText(frame, LABELS[detection.label], (bbox[0] + 10, bbox[1] + 25), cv2.FONT_HERSHEY_TRIPLEX, 1, color)
+        cv2.putText(frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 60), cv2.FONT_HERSHEY_TRIPLEX, 1, color)
+        cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)
+    
+    # Annotate the frame with the FPS
+    cv2.putText(frame, f"FPS: {fps:.2f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+    return frame
+
+def to_planar(arr: np.ndarray, shape: tuple) -> np.ndarray:
+    resized = cv2.resize(arr, shape)
+    return resized.transpose(2, 0, 1)
+
+def frame_norm(frame, bbox):
+    norm_vals = np.full(len(bbox), frame.shape[0])
+    norm_vals[::2] = frame.shape[1]
+    return (np.clip(np.array(bbox), 0, 1) * norm_vals).astype(int)
+
+# Create pipeline
+pipeline = create_camera_pipeline(YOLOV8N_CONFIG, YOLOV8N_MODEL)
+
+# Ensure output directory exists
+os.makedirs(os.path.dirname(OUTPUT_VIDEO), exist_ok=True)
+
+# Connect to device and start pipeline
+with dai.Device(pipeline) as device:
+    # Define the queue that will be used to receive the neural network output
+    detectionNN = device.getOutputQueue("nn", maxSize=4, blocking=False)
+
+    # Video writer to save the output video
+    fps = 30  # Assuming 30 FPS for the OAK-D camera
+    frame_width, frame_height = CAMERA_PREV_DIM
+    out = cv2.VideoWriter(OUTPUT_VIDEO, cv2.VideoWriter_fourcc(*'mp4v'), fps, (frame_width, frame_height))
+
+    start_time = time.time()
+    frame_count = 0
+
+    while True:
+        inDet = detectionNN.get()
+        detections = []
+        if inDet is not None:
+            detections = inDet.detections
+            print("Detections", detections)
+        
+        # Retrieve the frame from the camera preview
+        frame = inDet.getFrame()
+        frame_count += 1
+
+        # Calculate the FPS
+        elapsed_time = time.time() - start_time
+        fps = frame_count / elapsed_time if elapsed_time > 0 else 0
+
+        # Annotate the frame with detections and FPS
+        frame = annotate_frame(frame, detections, fps)
+
+        # Display the frame
+        cv2.imshow("Frame", frame)
+
+        # Write the frame to the output video
+        out.write(frame)
+
+        # Break the loop if 'q' is pressed
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+out.release()
+cv2.destroyAllWindows()
+
+print(f"[INFO] Processed live stream and saved to {OUTPUT_VIDEO}")

Object-Detection-on-Edge-Devices/oak-d-camera-inference.py

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+#Install Dependencies
+# !python3 -m pip install depthai
+
+import os
+import json
+import numpy as np
+import cv2
+from pathlib import Path
+import depthai as dai
+import time
+
+# Define path to the model, test data directory, and results
+
+YOLOV8N_MODEL = "/home/jaykumaran/Blogs/Poth-hole-Detection/Final Media/Yolov8-2022.1-blob/yolov8n-pothole-best_openvino_2022.1_8shave.blob" #Adjust path accordingly
+YOLOV8N_CONFIG = "/home/jaykumaran/Blogs/Poth-hole-Detection/Final Media/Yolov8-2022.1-blob/yolov8n-pothole-best.json" #Adjust path accordingly
+
+INPUT_VIDEO = "videoplayback.mp4"
+OUTPUT_VIDEO = "vid_result/960-oak-d-videoplayback_video.mp4"
+
+CAMERA_PREV_DIM = (960, 960)
+LABELS = ["Pot-hole"]
+
+def load_config(config_path):
+    with open(config_path) as f:
+        return json.load(f)
+    
+    
+   
+def create_image_pipeline(config_path, model_path):
+    pipeline = dai.Pipeline()
+    model_config = load_config(config_path)
+    nnConfig = model_config.get("nn_config", {})
+    metadata = nnConfig.get("NN_specific_metadata", {})
+    classes = metadata.get("classes", {})
+    coordinates = metadata.get("coordinates", {})
+    anchors = metadata.get("anchors", {})
+    anchorMasks = metadata.get("anchor_masks", {})
+    iouThreshold = metadata.get("iou_threshold", {})
+    confidenceThreshold = metadata.get("confidence_threshold", {})
+
+    detectionIN = pipeline.create(dai.node.XLinkIn)
+    detectionNetwork = pipeline.create(dai.node.YoloDetectionNetwork)
+    nnOut = pipeline.create(dai.node.XLinkOut)
+
+    nnOut.setStreamName("nn")
+    detectionIN.setStreamName("detection_in")
+
+    detectionNetwork.setConfidenceThreshold(confidenceThreshold)
+    detectionNetwork.setNumClasses(classes)
+    detectionNetwork.setCoordinateSize(coordinates)
+    detectionNetwork.setAnchors(anchors)
+    detectionNetwork.setAnchorMasks(anchorMasks)
+    detectionNetwork.setIouThreshold(iouThreshold)
+    detectionNetwork.setBlobPath(model_path)
+    detectionNetwork.setNumInferenceThreads(2)
+    detectionNetwork.input.setBlocking(False)
+    # Linking
+    detectionIN.out.link(detectionNetwork.input)
+    detectionNetwork.out.link(nnOut.input)
+
+    return pipeline
+
+def annotate_frame(frame, detections, fps):
+    color = (0, 0, 255)
+    for detection in detections:
+        bbox = frame_norm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
+        cv2.putText(frame, LABELS[detection.label], (bbox[0] + 10, bbox[1] + 25), cv2.FONT_HERSHEY_TRIPLEX, 1, color)
+        cv2.putText(frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 60), cv2.FONT_HERSHEY_TRIPLEX, 1, color)
+        cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)
+    
+    # Annotate the frame with the FPS
+    cv2.putText(frame, f"FPS: {fps:.2f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+    return frame
+
+def to_planar(arr: np.ndarray, shape: tuple) -> np.ndarray:
+    resized = cv2.resize(arr, shape)
+    return resized.transpose(2, 0, 1)
+
+def frame_norm(frame, bbox):
+    norm_vals = np.full(len(bbox), frame.shape[0])
+    norm_vals[::2] = frame.shape[1]
+    return (np.clip(np.array(bbox), 0, 1) * norm_vals).astype(int)
+
+# Create pipeline
+pipeline = create_image_pipeline(YOLOV8N_CONFIG, YOLOV8N_MODEL)
+
+# Ensure output directory exists
+os.makedirs(os.path.dirname(OUTPUT_VIDEO), exist_ok=True)
+
+cap = cv2.VideoCapture(INPUT_VIDEO)
+frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+fps = cap.get(cv2.CAP_PROP_FPS)
+out = cv2.VideoWriter(OUTPUT_VIDEO, cv2.VideoWriter_fourcc(*'mp4v'), fps, (frame_width, frame_height))
+
+# Connect to device and start pipeline
+with dai.Device(pipeline) as device:
+    # Define the queues that will be used in order to communicate with depthai
+    detectionIN = device.getInputQueue("detection_in")
+    detectionNN = device.getOutputQueue("nn")
+
+    start_time = time.time()
+    frame_count = 0
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        frame_count += 1
+
+        image_res = cv2.resize(frame, CAMERA_PREV_DIM)
+    
+        # Initialize depthai NNData() class which is fed with the image data resized and transposed to model input shape
+        nn_data = dai.NNData()
+        nn_data.setLayer("input", to_planar(frame, CAMERA_PREV_DIM))
+        
+        # Send the image to detectionIN queue further passed to the detection network for inference as defined in pipeline
+        detectionIN.send(nn_data)
+        
+        # Fetch the neural network output
+        inDet = detectionNN.get()
+        detections = []
+        if inDet is not None:
+            detections = inDet.detections
+            print("Detections", detections)
+
+        # Calculate the FPS
+        elapsed_time = time.time() - start_time
+        fps = frame_count / elapsed_time if elapsed_time > 0 else 0
+
+        # Annotate the frame with detections and FPS
+        frame = annotate_frame(frame, detections, fps)
+
+        out.write(frame)
+
+cap.release()
+out.release()
+
+print(f"[INFO] Processed video {INPUT_VIDEO} and saved to {OUTPUT_VIDEO}")