mirror of
https://github.com/blakeblackshear/frigate.git
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41 lines
1.5 KiB
Python
41 lines
1.5 KiB
Python
import time
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import datetime
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import cv2
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from . util import tonumpyarray
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# fetch the frames as fast a possible, only decoding the frames when the
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# detection_process has consumed the current frame
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def fetch_frames(shared_arr, shared_frame_time, frame_lock, frame_ready, frame_shape, rtsp_url):
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# convert shared memory array into numpy and shape into image array
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arr = tonumpyarray(shared_arr).reshape(frame_shape)
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# start the video capture
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video = cv2.VideoCapture()
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video.open(rtsp_url)
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# keep the buffer small so we minimize old data
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video.set(cv2.CAP_PROP_BUFFERSIZE,1)
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while True:
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# check if the video stream is still open, and reopen if needed
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if not video.isOpened():
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success = video.open(rtsp_url)
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if not success:
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time.sleep(1)
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continue
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# grab the frame, but dont decode it yet
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ret = video.grab()
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# snapshot the time the frame was grabbed
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frame_time = datetime.datetime.now()
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if ret:
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# go ahead and decode the current frame
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ret, frame = video.retrieve()
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if ret:
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# Lock access and update frame
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with frame_lock:
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arr[:] = frame
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shared_frame_time.value = frame_time.timestamp()
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# Notify with the condition that a new frame is ready
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with frame_ready:
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frame_ready.notify_all()
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video.release() |