6. 无人驾驶汽车对两侧道路线视频识别

    注：数据以及tools.py在无人驾驶汽车对两侧道路线图片识别文件中
    
    P1_video.py
    
    # 视频剪辑所需要包
    from moviepy.editor import VideoFileClip
    from IPython.display import HTML
    from tools import *
    from P1_image import lane_detector
    
    
    # 输出图像是一个3颜色通道图像,用于处理下面的视频。最终输出（带线条的图像绘制在车道上）。
    def process_image(image):
    edges, masked_edges, final_img = lane_detector(image)
    return final_img
    
    
    white_output = "white.mp4"
    # 从视频中抓取图片
    clip1 = VideoFileClip('solidWhiteRight.mp4')
    # 将原图片替换为修改后的图片，用于传递物体识别的每张抓取图片
    white_clip = clip1.fl_image(process_image)
    # 修改的剪辑图像被组合成为一个新的视频
    white_clip.write_videofile(white_output, audio=False)
    HTML("""
    <video width="960" height="540" controls>
      <source src="{0}">
    </video>
    """.format(white_output))
    
    yellow_output = 'yellow.mp4'
    clip2 = VideoFileClip('solidYellowLeft.mp4')
    yellow_clip = clip2.fl_image(process_image)
    yellow_clip.write_videofile(yellow_output, audio=False)
    HTML("""
    <video width="960" height="540" controls>
    <source src="{0}"
    </video>
    """.format(yellow_output))
    
    challenge_output = 'extra.mp4'
    clip3 = VideoFileClip('challenge.mp4')
    challenge_clip = clip3.fl_image(process_image)
    challenge_clip.write_videofile(challenge_output, audio=False)
    HTML("""
    <video width="960" height="540" controls>
    <source src="{0}"
    </video>
    """.format(challenge_output))
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/WgoJ1HTfSNi8XmK7rR46qPkajL2c.png)