95 lines
2.3 KiB
Python
95 lines
2.3 KiB
Python
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#!/usr/bin/env python
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'''
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Multithreaded video processing sample.
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Usage:
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video_threaded.py {<video device number>|<video file name>}
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Shows how python threading capabilities can be used
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to organize parallel captured frame processing pipeline
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for smoother playback.
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Keyboard shortcuts:
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ESC - exit
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space - switch between multi and single threaded processing
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'''
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# Python 2/3 compatibility
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from __future__ import print_function
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import numpy as np
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import cv2 as cv
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from multiprocessing.pool import ThreadPool
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from collections import deque
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from common import clock, draw_str, StatValue
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import video
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class DummyTask:
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def __init__(self, data):
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self.data = data
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def ready(self):
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return True
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def get(self):
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return self.data
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def main():
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import sys
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try:
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fn = sys.argv[1]
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except:
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fn = 0
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cap = video.create_capture(fn)
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def process_frame(frame, t0):
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# some intensive computation...
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frame = cv.medianBlur(frame, 19)
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frame = cv.medianBlur(frame, 19)
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return frame, t0
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threadn = cv.getNumberOfCPUs()
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pool = ThreadPool(processes = threadn)
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pending = deque()
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threaded_mode = True
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latency = StatValue()
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frame_interval = StatValue()
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last_frame_time = clock()
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while True:
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while len(pending) > 0 and pending[0].ready():
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res, t0 = pending.popleft().get()
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latency.update(clock() - t0)
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draw_str(res, (20, 20), "threaded : " + str(threaded_mode))
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draw_str(res, (20, 40), "latency : %.1f ms" % (latency.value*1000))
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draw_str(res, (20, 60), "frame interval : %.1f ms" % (frame_interval.value*1000))
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cv.imshow('threaded video', res)
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if len(pending) < threadn:
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_ret, frame = cap.read()
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t = clock()
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frame_interval.update(t - last_frame_time)
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last_frame_time = t
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if threaded_mode:
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task = pool.apply_async(process_frame, (frame.copy(), t))
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else:
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task = DummyTask(process_frame(frame, t))
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pending.append(task)
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ch = cv.waitKey(1)
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if ch == ord(' '):
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threaded_mode = not threaded_mode
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if ch == 27:
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break
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print('Done')
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if __name__ == '__main__':
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print(__doc__)
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main()
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cv.destroyAllWindows()
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