Created
August 28, 2024 22:43
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D421 Person Detection
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| import pyrealsense2 as rs | |
| import numpy as np | |
| import cv2 | |
| # Load YOLOv3 model | |
| net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg") | |
| layer_names = net.getLayerNames() | |
| output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers().flatten()] | |
| # Load COCO class labels | |
| with open("coco.names", "r") as f: | |
| classes = [line.strip() for line in f.readlines()] | |
| # Configure depth and color streams | |
| pipeline = rs.pipeline() | |
| config = rs.config() | |
| # Check if a device is connected | |
| context = rs.context() | |
| if not context.query_devices(): | |
| raise RuntimeError("No Intel RealSense device connected") | |
| # Get the first connected device | |
| device = context.query_devices()[0] | |
| sensor = device.query_sensors()[0] | |
| # Configure the streams | |
| config.enable_stream(rs.stream.infrared, 640, 480, rs.format.y8, 30) | |
| config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30) | |
| # Start streaming | |
| pipeline.start(config) | |
| try: | |
| while True: | |
| # Wait for a coherent pair of frames: depth and color | |
| frames = pipeline.wait_for_frames() | |
| depth_frame = frames.get_depth_frame() | |
| infrared_frame = frames.get_infrared_frame() | |
| if not depth_frame or not infrared_frame: | |
| continue | |
| # Convert images to numpy arrays | |
| infrared_image = np.asanyarray(infrared_frame.get_data()) | |
| # Convert single-channel image to 3-channel image | |
| infrared_image_3ch = cv2.cvtColor(infrared_image, cv2.COLOR_GRAY2BGR) | |
| # Detecting objects | |
| blob = cv2.dnn.blobFromImage(infrared_image_3ch, 0.00392, (416, 416), (0, 0, 0), True, crop=False) | |
| net.setInput(blob) | |
| outs = net.forward(output_layers) | |
| # Information to show on the screen | |
| class_ids = [] | |
| confidences = [] | |
| boxes = [] | |
| for out in outs: | |
| for detection in out: | |
| scores = detection[5:] | |
| class_id = np.argmax(scores) | |
| confidence = scores[class_id] | |
| if confidence > 0.5 and class_id == classes.index("person"): | |
| # Object detected | |
| center_x = int(detection[0] * 640) | |
| center_y = int(detection[1] * 480) | |
| w = int(detection[2] * 640) | |
| h = int(detection[3] * 480) | |
| # Rectangle coordinates | |
| x = int(center_x - w / 2) | |
| y = int(center_y - h / 2) | |
| boxes.append([x, y, w, h]) | |
| confidences.append(float(confidence)) | |
| class_ids.append(class_id) | |
| indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4) | |
| for i in range(len(boxes)): | |
| if i in indexes: | |
| x, y, w, h = boxes[i] | |
| label = str(classes[class_ids[i]]) | |
| confidence = confidences[i] | |
| color = (0, 255, 0) | |
| # Calculate distance | |
| distance = depth_frame.get_distance(center_x, center_y) | |
| # Draw rectangle and add text | |
| cv2.rectangle(infrared_image, (x, y), (x + w, y + h), color, 2) | |
| cv2.putText(infrared_image, f"{label} {confidence:.2f} Dist: {distance:.2f}m", (x, y - 10), | |
| cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) | |
| cv2.putText(infrared_image, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) | |
| # Show the image | |
| cv2.imshow("Infrared Image", infrared_image) | |
| key = cv2.waitKey(1) | |
| if key == 27: # Press 'ESC' to exit | |
| break | |
| finally: | |
| # Stop streaming | |
| pipeline.stop() | |
| cv2.destroyAllWindows() |
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