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import numpy as np import cv2 min_confidence = 0.6 classes = ['background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] net = cv2.dnn.readNetFromCaffe( "models/MobileNetSSD_deploy.prototxt.txt", "models/MobileNetSSD_deploy.caffemodel" ) colors = np.random.uniform(0, 255, size=(len(classes), 3)) cap = cv2.VideoCapture(0) while True: ret, frame = cap.read() height, width = frame.shape[0], frame.shape[1] blob = cv2.dnn.blobFromImage(cv2.resize( frame, (300, 300)), 0.007843, (300, 300), 127.5) net.setInput(blob) detections = net.forward() for i in range(0, detections.shape[2]): confidence = detections[0, 0, i, 2] if confidence > min_confidence: class_id = int(detections[0, 0, i, 1]) box = detections[0, 0, i, 3:7] * \ np.array([width, height, width, height]) (startX, startY, endX, endY) = box.astype('int') text = "{:.2f}%".format(confidence * 100) + \ f"- {classes[class_id]}" y = startY - 10 if startY - 10 > 10 else startY+10 print(startX, startY, endX, endY) cv2.rectangle(frame, (startX, startY), (endX, endY), colors[class_id], 2) cv2.putText(frame, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, colors[class_id], 2) print(classes[class_id]) cv2.imshow("Frame", frame) key = cv2.waitKey(1) & 0xFF if key == ord("q"): break cap.release() cv2.destroyAllWindows()
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