# Parse the outputs for output in outputs: for detection in output: scores = detection[5:] class_id = np.argmax(scores) confidence = scores[class_id] if confidence > 0.5: # Draw a bounding box x, y, w, h = detection[0:4] * np.array([width, height, width, height]) cv2.rectangle(img, (int(x), int(y)), (int(x+w), int(y+h)), (0, 255, 0), 2)
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# Load the pre-trained YOLOv3 model net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg") h = detection[0:4] * np.array([width
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