Performance benchmark for read() and grab() in OpenCV VideoCapture.

perf_grab_decode.py

"""
read() and grab() in OpenCV VideoCapture takes different time as there is no
decoding for grab() operation. Let's try to find out how much these two methods
differs.
"""
import cv2


def main():
    tm = cv2.TickMeter()

    # =========================================================================
    # Test 1, grab and decode each frame.

    cap = cv2.VideoCapture()
    cap.open("/home/robin/Videos/clip1-ssd.mp4", cv2.CAP_FFMPEG)
    total_frame_count = cap.get(cv2.CAP_PROP_FRAME_COUNT)

    tm.start()
    # print("Total frames number: {}".format(total_frame_count))
    f_count = 0
    while True:
        f_count += 1
        r, img = cap.read()
        if r == False:
            break
        # for _ in range(24):
        #     cap.grab()
    tm.stop()
    print("Read and decode {} frames takes {} seconds.".format(
        f_count, tm.getTimeSec()/tm.getCounter()))

    # =========================================================================
    # Test 2, grab only without decoding.

    cap = cv2.VideoCapture()
    cap.open("/home/robin/Videos/clip1-ssd.mp4", cv2.CAP_FFMPEG)
    total_frame_count = cap.get(cv2.CAP_PROP_FRAME_COUNT)

    tm.reset()
    tm.start()
    while True:
        r = cap.grab()
        if r == False:
            break
    tm.stop()
    print("Read {} frames takes {} seconds.".format(
        total_frame_count, tm.getTimeSec()/tm.getCounter()))

    # =========================================================================
    # Test 3, reading same file with two decoders. This block failed on my PC.
    # Maybe it's not supported by FFMPEG?

    from multiprocessing.pool import ThreadPool
    thread_num = cv2.getNumberOfCPUs()
    pool = ThreadPool(processes=thread_num)

    def get_frame_at(src, location):
        cap = cv2.VideoCapture()
        cap.open(src, cv2.CAP_FFMPEG)
        cap.set(cv2.CAP_PROP_FRAME_COUNT, location)
        rst, frame = cap.read()
        return rst, frame

    def get_frame_next(cap):
        rst, frame = cap.read()
        return rst, frame

    video_src = "/home/robin/Videos/clip1-ssd.mp4"
    cap1 = cv2.VideoCapture()
    cap1.open(video_src, cv2.CAP_FFMPEG)
    cap2 = cv2.VideoCapture()
    cap2.open(video_src, cv2.CAP_FFMPEG)

    tm.reset()
    tm.start()
    f_count = 0
    while True:
        f_count += 1
        t1 = pool.apply_async(get_frame_next, (cap1, ))
        t2 = pool.apply_async(get_frame_next, (cap2, ))
        print(t1.get()[0])
        if (t1.get()[0] is False) and (t2.get()[0] is False):
            break

    tm.stop()
    print("Decode same file with 2 decoder takes {} seconds.".format(
        tm.getTimeSec()/tm.getCounter()))


if __name__ == "__main__":
    main()

您好，请问怎么联系您呢？关于您的另一个博客《基于深度学习的人脸特征点检测-模型导出与应用》中，freeze_graph.py文件的使用，
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