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#!/usr/bin/env python
'''
gabor_threads.py
=========
Sample demonstrates:
- use of multiple Gabor filter convolutions to get Fractalius-like image effect (http://www.redfieldplugins.com/filterFractalius.htm)
- use of python threading to accelerate the computation
Usage
-----
gabor_threads.py [image filename]
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
from multiprocessing.pool import ThreadPool
def build_filters():
filters = []
ksize = 31
for theta in np.arange(0, np.pi, np.pi / 16):
kern = cv.getGaborKernel((ksize, ksize), 4.0, theta, 10.0, 0.5, 0, ktype=cv.CV_32F)
kern /= 1.5*kern.sum()
filters.append(kern)
return filters
def process(img, filters):
accum = np.zeros_like(img)
for kern in filters:
fimg = cv.filter2D(img, cv.CV_8UC3, kern)
np.maximum(accum, fimg, accum)
return accum
def process_threaded(img, filters, threadn = 8):
accum = np.zeros_like(img)
def f(kern):
return cv.filter2D(img, cv.CV_8UC3, kern)
pool = ThreadPool(processes=threadn)
for fimg in pool.imap_unordered(f, filters):
np.maximum(accum, fimg, accum)
return accum
def main():
import sys
from common import Timer
try:
img_fn = sys.argv[1]
except:
img_fn = 'baboon.jpg'
img = cv.imread(cv.samples.findFile(img_fn))
if img is None:
print('Failed to load image file:', img_fn)
sys.exit(1)
filters = build_filters()
with Timer('running single-threaded'):
res1 = process(img, filters)
with Timer('running multi-threaded'):
res2 = process_threaded(img, filters)
print('res1 == res2: ', (res1 == res2).all())
cv.imshow('img', img)
cv.imshow('result', res2)
cv.waitKey()
print('Done')
if __name__ == '__main__':
print(__doc__)
main()
cv.destroyAllWindows()
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