• 实用tips，若使用opencv进行解码，代码作如下优化能极大提升解码速度:

    cap = cv2.VideoCapture(file_path)
    videolen = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

    # 1. decode all frames, time cost!
    sampledFrames = []
    for i in range(videolen):
        ret, frame = cap.read()
        # maybe first frame is empty
        if ret == False:
            continue
        img = frame[:, :, ::-1]
        sampledFrames.append(img)

    cap.release()

    # 2. get frame index
    frames_idx = [xxx]

    # 3. sample
    frames = np.array(sampledFrames)
    imgs = []
    for idx in frames_idx:
        imgbuf = frames[idx]
        img = Image.fromarray(imgbuf, mode='RGB')
        imgs.append(img)

优化后:

    cap = cv2.VideoCapture(file_path)
    videolen = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

    # 1. get frame index
    frames_idx = [xxx]

    # 2. decode target frame
    imgs = []
    for i in range(videolen):
        ret = cap.grab()
        # maybe first frame is empty
        if ret == False:
            continue  
        if frames_idx and i == frames_idx[0]:
            frames_idx.pop(0)
            ret, frame = cap.retrieve()
            if frame is None:
                break
            imgbuf = frame[:, :, ::-1]
            img = Image.fromarray(imgbuf, mode='RGB')
            imgs.append(img)
        if frames_idx == None:
            break
    cap.release()