opencv环境

比如小编下载的是opencv_python-3.3.0+contrib-cp36-cp36m-win_amd64.whl，cp36表示Python是3.6版本，win_amd64是表示安装的python是64bit的，+contrib表示包括contrib包。

2、下载好后，把它放到C盘中，执行安装命令：

pip install C:\opencv_python-3.3.0+contrib-cp36-cp36m-win_amd64.whl

运行代码

修改

从本地获取。

# vs = VideoStream(src=0).start() # vs =cv2.VideoCapture('C:\\Users\\voidking\\Desktop\\real-time-object-detection\\test_video.flv')

vs =cv2.VideoCapture('./test_video.flv')

# grab the frame from the threaded video stream and resize it # to have a maximum width of 400 pixels # frame = vs.read() # frame = imutils.resize(frame, width=400)

# grab the frame from the threaded video file stream

(grabbed,frame) = vs.read() # if the frame was not grabbed, then we have reached the end # of the stream

if not grabbed: break frame = imutils.resize(frame, width=800)

运行

推荐使用命令：

python real_time_object_detection.py -p ./MobileNetSSD_deploy.prototxt.txt -m ./MobileNetSSD_deploy.caffemodel

或者，指定绝对路径，假设项目目录为C:\Users\voidking\Desktop\real-time-object-detection\，那么命令如下：

python real_time_object_detection.py -p "C:\Users\voidking\Desktop\real-time-object-detection\MobileNetSSD_deploy.prototxt.txt" -m "C:\Users\voidking\Desktop\real-time-object-detection\MobileNetSSD_deploy.caffemodel"

进阶修改

我们看到，prototxt和model都是指定的，那我们的视频文件也用这种方式指定，就更加友好一点。

ap = argparse.ArgumentParser()

ap.add_argument("-p", "--prototxt", required=True,

help="path to Caffe 'deploy' prototxt file")

ap.add_argument("-m", "--model", required=True,

help="path to Caffe pre-trained model")

ap.add_argument("-c", "--confidence", type=float, default=0.2,

help="minimum probability to filter weak detections")

args = vars(ap.parse_args())

我们插入一行：

ap.add_argument("-v", "--video", required=True,

help="path to Caffe video file")

然后在初始化视频流时，修改为：

vs =cv2.VideoCapture(args["video"])

运行命令修改为

python real_time_object_detection.py -p ./MobileNetSSD_deploy.prototxt.txt -m ./MobileNetSSD_deploy.caffemodel -v ./test_video.flv

运行效果

image

源码分享

关注公众号：Python爬虫数据分析挖掘，学习更多python知识

image