def process_frame(frame_id, frame_data):
# this function simulates the processing of the frame.
# I used a longer sleep thinking that it takes longer
# and therefore the reason of parallel processing.
frame_pano = fish2pano_lut(frame)
print("... got frame {}".format(frame_id))
return frame_pano
def run(self):
while True:
frame_num, frame = self.task_queue.get() # consumer go gets the tasks and does it
#Do computations on image, 1-2sec
frame_pano = fish2pano_lut(frame)
# put the results in queue
self.result_queue.put(frame_pano)
return
import glob
import os
import cv2
from fish2pano_cv2_rgb import fish2pano_lut
img_path = '/Users/gzhou/TL_documents/python/video-conf/161025-stereo-align/test2/'
#os.path.exists(img_path)
img_list = []
for filename in glob.glob(img_path+'*.png'):
img = cv2.imread(filename)
img_list.append(img)
#img =
img_pano = fish2pano_lut(img)
filename_new = os.path.basename(filename)[:-4]+'-pano.png'
cv2.imwrite(img_path+filename_new, img_pano)
#other initialization stuff
def run(self):
while True:
frame_num, frame = self.task_queue.get() # consumer go gets the tasks and does it
#Do computations on image, 1-2sec
frame_pano = fish2pano_lut(frame)
# put the results in queue
self.result_queue.put(frame_pano)
return
pool = multiprocessing.Pool()
for (frame_pano, frame) in pool.imap(fish2pano_lut(frame), frame)
cv2.imshow('preview', frame_pano)
# Tasks queue with size 1 - only want one image queued for processing.
# Queue size should therefore match number of processes !!!!!
tasks = multiprocessing.Queue(1)
results = multiprocessing.Queue(20)
consumer = Consumer(tasks, results)
consumer.start()
#Creating window and starting video capturer from camera
# take input frames, and store the into a buffer
# in a parallel process, fetch videos every x second... package and send over down the pipe.