68 lines
1.8 KiB
Python
68 lines
1.8 KiB
Python
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'''
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gabor_threads.py
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=========
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Sample demonstrates:
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- use of multiple Gabor filter convolutions to get Fractalius-like image effect (http://www.redfieldplugins.com/filterFractalius.htm)
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- use of python threading to accelerate the computation
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Usage
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-----
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gabor_threads.py [image filename]
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'''
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import numpy as np
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import cv2
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from threading import Lock
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from multiprocessing.pool import ThreadPool
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def build_filters():
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filters = []
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ksize = 31
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for theta in np.arange(0, np.pi, np.pi / 16):
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kern = cv2.getGaborKernel((ksize, ksize), 4.0, theta, 10.0, 0.5, 0, ktype=cv2.CV_32F)
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kern /= 1.5*kern.sum()
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filters.append(kern)
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return filters
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def process(img, filters):
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accum = np.zeros_like(img)
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for kern in filters:
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fimg = cv2.filter2D(img, cv2.CV_8UC3, kern)
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np.maximum(accum, fimg, accum)
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return accum
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def process_threaded(img, filters, threadn = 8):
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accum = np.zeros_like(img)
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accum_lock = Lock()
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def f(kern):
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fimg = cv2.filter2D(img, cv2.CV_8UC3, kern)
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with accum_lock:
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np.maximum(accum, fimg, accum)
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pool = ThreadPool(processes=threadn)
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pool.map(f, filters)
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return accum
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if __name__ == '__main__':
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import sys
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from common import Timer
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print __doc__
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try: img_fn = sys.argv[1]
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except: img_fn = '../cpp/baboon.jpg'
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img = cv2.imread(img_fn)
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filters = build_filters()
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with Timer('running single-threaded'):
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res1 = process(img, filters)
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with Timer('running multi-threaded'):
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res2 = process_threaded(img, filters)
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print 'res1 == res2: ', (res1 == res2).all()
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cv2.imshow('img', img)
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cv2.imshow('result', res2)
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cv2.waitKey()
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