python+opencv实时视频目标检测

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

# 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)

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

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"

# construct the argument parse and parse the arguments
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())

ap.add_argument("-v", "--video", required=True,
    help="path to Caffe video file")

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

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