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