Python实现蓝线挑战特效的示例代码

video = CV2.VideoCapture(0, CV2.CAP_DSHOW)
ret, frame = video.read()    # frame为np数组，宽100高200时，数组形状为200 * 100 *3
frame = CV2.flip(frame,1) # 左右翻转图像为镜像

#通过row_index记录当前的行索引，获取像素作为蒙版图片
canvas[row_index:row_index + pixel_number_each_frame] = frame[row_index:row_index + pixel_number_each_frame]
row_index += pixel_number_each_frame    # 每次运行增加固定像素宽度
if row_index + width_blueline < hight:    # 避免因为增加固定像素，导致超出图像的高度
    frame[:row_index] = canvas[:row_index]    # 将每帧的图像上部替换为蒙版图片
    frame[row_index:row_index+ width_blueline] = array_blueline    # 添加蓝线矩阵
   # 窗口显示，BUG在于frame数据为浮点数时默认RGB数值范围0~1，当为整数时为0~255
    CV2.imshow('Viewer', frame / 255)

CV2.imwrite(f'{output_frame_dirpath}/{count}.jpg', frame)

def img_to_video(output_video_path, frame_dirpath, fps):
    """
    将处理好的帧图片合成视频
    :param output_video_path: 输出视频的地址
    :param frame_dirpath: 帧图片所在文件夹地址
    :param fps: 输出帧率
    :return: None
    """
    img = CV2.imread(f"{frame_dirpath}/1.jpg")
    hight, width, _ = img.shape
    fourcc = CV2.VideoWriter_fourcc(*'mp4v')
    videoWriter = CV2.VideoWriter(output_video_path, fourcc, fps, (width, hight))
    order = [int(i.strip(".jpg")) for i in os.listdir(frame_dirpath) if                         i.endswith(".jpg")]
    jpglist = [f"{frame_dirpath}/{i}.jpg" for i in sorted(order)]
    for i, jpg in enumerate(jpglist):
        img = CV2.imread(filename=jpg)
        videoWriter.write(img)
        print(f"将字符画写入视频, 进度{(i + 1)}/{len(jpglist)}！")
    videoWriter.release()
    print(f"{output_video_path} 输出完成！")

def video_to_img(frame_dirpath, video_path):
    """
    将视频抽取为帧图片以便处理
    :param frame_dirpath: 存放抽取好的帧图片文件夹地址
    :param video_path: 视频地址
    :return: None
    """
    vc = CV2.VideoCapture(video_path)
    c = 0
    ret = vc.isOpened()
    while ret:
        c += 1
        ret, frame = vc.read()
        if ret:
            CV2.imwrite(f'{frame_dirpath}/{c}.jpg', frame)
            print(f'生成{frame_dirpath}/{c}.jpg')
        else:
            break
    vc.release()
    print("视频按各帧提取完成！")

def get_video_msg(video_path):
    """
    获取视频的基本信息
    :param video_path: 视频地址
    :return: [帧数量,[宽度,高度],帧率]
    """
    cap = CV2.VideoCapture(video_path)
    if cap.isOpened():
        frame_number = cap.get(7)
        width = cap.get(3)
        hight = cap.get(4)
        fps = cap.get(5)
        return [frame_number, [width, hight], fps]
    return [-1, -1, -1, [-1, -1], -1]

array_blueline = np.array([[[255, 255, 0] for _ in range(width)] for _ in                   range(width_blueline)])
pixel_number_each_frame = int(hight / frame_number) # 每次取帧截取的像素范围
err = hight - pixel_number_each_frame * frame_number - 3 # 误差值分散到每帧，留3个像素给蓝线

if err_count < err:
    canvas[row_index:row_index + pixel_number_each_frame + 1] = img[row_index:row_index + pixel_number_each_frame + 1]
  row_index += pixel_number_each_frame + 1
  err_count += 1    # 计算误差部分是否使用完
else:
   canvas[row_index:row_index + pixel_number_each_frame] = img[row_index:row_index + pixel_number_each_frame]
  row_index += pixel_number_each_frame
  if row_index + width_blueline <= hight:    # 避免索引跑出图片范围而报错
           img[:row_index] = canvas[:row_index]
           img[row_index:row_index+ width_blueline] = array_blueline