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128 lines
3.6 KiB
Python
Executable File
128 lines
3.6 KiB
Python
Executable File
#!/usr/bin/env python
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'''
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Wiener deconvolution.
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Sample shows how DFT can be used to perform Weiner deconvolution [1]
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of an image with user-defined point spread function (PSF)
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Usage:
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deconvolution.py [--circle]
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[--angle <degrees>]
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[--d <diameter>]
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[--snr <signal/noise ratio in db>]
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[<input image>]
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Use sliders to adjust PSF paramitiers.
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Keys:
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SPACE - switch btw linear/cirular PSF
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ESC - exit
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Examples:
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deconvolution.py --angle 135 --d 22 ../data/licenseplate_motion.jpg
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(image source: http://www.topazlabs.com/infocus/_images/licenseplate_compare.jpg)
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deconvolution.py --angle 86 --d 31 ../data/text_motion.jpg
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deconvolution.py --circle --d 19 ../data/text_defocus.jpg
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(image source: compact digital photo camera, no artificial distortion)
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[1] http://en.wikipedia.org/wiki/Wiener_deconvolution
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'''
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import numpy as np
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import cv2
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# local module
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from common import nothing
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def blur_edge(img, d=31):
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h, w = img.shape[:2]
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img_pad = cv2.copyMakeBorder(img, d, d, d, d, cv2.BORDER_WRAP)
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img_blur = cv2.GaussianBlur(img_pad, (2*d+1, 2*d+1), -1)[d:-d,d:-d]
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y, x = np.indices((h, w))
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dist = np.dstack([x, w-x-1, y, h-y-1]).min(-1)
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w = np.minimum(np.float32(dist)/d, 1.0)
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return img*w + img_blur*(1-w)
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def motion_kernel(angle, d, sz=65):
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kern = np.ones((1, d), np.float32)
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c, s = np.cos(angle), np.sin(angle)
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A = np.float32([[c, -s, 0], [s, c, 0]])
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sz2 = sz // 2
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A[:,2] = (sz2, sz2) - np.dot(A[:,:2], ((d-1)*0.5, 0))
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kern = cv2.warpAffine(kern, A, (sz, sz), flags=cv2.INTER_CUBIC)
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return kern
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def defocus_kernel(d, sz=65):
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kern = np.zeros((sz, sz), np.uint8)
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cv2.circle(kern, (sz, sz), d, 255, -1, cv2.LINE_AA, shift=1)
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kern = np.float32(kern) / 255.0
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return kern
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if __name__ == '__main__':
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print __doc__
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import sys, getopt
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opts, args = getopt.getopt(sys.argv[1:], '', ['circle', 'angle=', 'd=', 'snr='])
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opts = dict(opts)
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try:
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fn = args[0]
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except:
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fn = '../data/licenseplate_motion.jpg'
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win = 'deconvolution'
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img = cv2.imread(fn, 0)
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if img is None:
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print 'Failed to load fn1:', fn1
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sys.exit(1)
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img = np.float32(img)/255.0
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cv2.imshow('input', img)
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img = blur_edge(img)
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IMG = cv2.dft(img, flags=cv2.DFT_COMPLEX_OUTPUT)
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defocus = '--circle' in opts
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def update(_):
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ang = np.deg2rad( cv2.getTrackbarPos('angle', win) )
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d = cv2.getTrackbarPos('d', win)
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noise = 10**(-0.1*cv2.getTrackbarPos('SNR (db)', win))
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if defocus:
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psf = defocus_kernel(d)
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else:
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psf = motion_kernel(ang, d)
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cv2.imshow('psf', psf)
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psf /= psf.sum()
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psf_pad = np.zeros_like(img)
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kh, kw = psf.shape
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psf_pad[:kh, :kw] = psf
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PSF = cv2.dft(psf_pad, flags=cv2.DFT_COMPLEX_OUTPUT, nonzeroRows = kh)
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PSF2 = (PSF**2).sum(-1)
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iPSF = PSF / (PSF2 + noise)[...,np.newaxis]
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RES = cv2.mulSpectrums(IMG, iPSF, 0)
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res = cv2.idft(RES, flags=cv2.DFT_SCALE | cv2.DFT_REAL_OUTPUT )
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res = np.roll(res, -kh//2, 0)
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res = np.roll(res, -kw//2, 1)
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cv2.imshow(win, res)
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cv2.namedWindow(win)
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cv2.namedWindow('psf', 0)
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cv2.createTrackbar('angle', win, int(opts.get('--angle', 135)), 180, update)
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cv2.createTrackbar('d', win, int(opts.get('--d', 22)), 50, update)
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cv2.createTrackbar('SNR (db)', win, int(opts.get('--snr', 25)), 50, update)
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update(None)
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while True:
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ch = cv2.waitKey() & 0xFF
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if ch == 27:
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break
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if ch == ord(' '):
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defocus = not defocus
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update(None)
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