Amazing-Python-Scripts

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import cv2
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import numpy as np
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yolo_net = cv2.dnn.readNet('yolov3.weights', 'yolov3.cfg')
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class_names = []
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with open('coco.names', 'r') as f:
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    class_names = f.read().strip().split('\n')
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video_capture = cv2.VideoCapture('video_file.mp4')
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while video_capture.isOpened():
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    ret, frame = video_capture.read()
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    if not ret:
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        break
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    height, width, _ = frame.shape
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    blob = cv2.dnn.blobFromImage(
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        frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
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    yolo_net.setInput(blob)
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    layer_names = yolo_net.getUnconnectedOutLayersNames()
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    outs = yolo_net.forward(layer_names)
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    for out in outs:
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        for detection in out:
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            scores = detection[5:]
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            class_id = np.argmax(scores)
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            confidence = scores[class_id]
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            if confidence > 0.5 and class_id == 2:
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                center_x = int(detection[0] * width)
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                center_y = int(detection[1] * height)
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                bbox_width = int(detection[2] * width)
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                bbox_height = int(detection[3] * height)
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                x = int(center_x - bbox_width / 2)
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                y = int(center_y - bbox_height / 2)
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                cv2.rectangle(frame, (x, y), (x + bbox_width,
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                              y + bbox_height), (0, 255, 0), 2)
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    resized_frame = cv2.resize(frame, (720, 480))
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    cv2.imshow('Car Tracking', frame)
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    if cv2.waitKey(1) & 0xFF == ord('q'):
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        break
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video_capture.release()
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cv2.destroyAllWindows()
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