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127 lines
3.6 KiB
127 lines
3.6 KiB
#!/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() |
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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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