multiprocessing

pt.py

import cv2,time
import numpy as np
from multiprocessing.pool import ThreadPool

img = cv2.imread('img2.png',0)


ret,src = cv2.threshold(img,127,255,0)
contours,hierarchy = cv2.findContours(src,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)

                # array to store distances
rows,cols = img.shape
pack = []

# single process
t = time.time()
# Calculate distance from each point
for cnt in contours:
    res = np.zeros(src.shape,np.float64)
    for i in xrange(rows):
        for j in xrange(cols):
            res.itemset((i,j),cv2.pointPolygonTest(cnt,(j,i),True))
    #print res
    pack.append(res)

res2 = 0
for i in xrange(len(pack)):
    res2+=pack[i]
cv2.normalize(res2,res2,0,255,cv2.NORM_MINMAX)
res2 = np.uint8(res2)
t2 = time.time()

# multiprocess
def process(contours):
    res = np.zeros(src.shape,np.float64)
    pool = ThreadPool(processes = 8)
    def f(cnt):
        temp = np.zeros(src.shape,np.float64)
        for i in xrange(750):
            for j in xrange(1000):
                temp.itemset((i,j),cv2.pointPolygonTest(cnt,(j,i),True))
        return temp
    for item in pool.imap_unordered(f, contours):
        res +=item
    cv2.normalize(res,res,0,255,cv2.NORM_MINMAX)
    res = np.uint8(res) 
    return res
    
res3 = process(contours)
t3 = time.time()
print t2 - t
print t3 - t2