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#!/usr/bin/env python
'''
Wiener deconvolution.
Sample shows how DFT can be used to perform Weiner deconvolution [1]
of an image with user-defined point spread function (PSF)
Usage:
deconvolution.py [--circle]
[--angle <degrees>]
[--d <diameter>]
[--snr <signal/noise ratio in db>]
[<input image>]
Use sliders to adjust PSF paramitiers.
Keys:
SPACE - switch btw linear/circular PSF
ESC - exit
Examples:
deconvolution.py --angle 135 --d 22 licenseplate_motion.jpg
(image source: http://www.topazlabs.com/infocus/_images/licenseplate_compare.jpg)
deconvolution.py --angle 86 --d 31 text_motion.jpg
deconvolution.py --circle --d 19 text_defocus.jpg
(image source: compact digital photo camera, no artificial distortion)
[1] http://en.wikipedia.org/wiki/Wiener_deconvolution
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
# local module
from common import nothing
def blur_edge(img, d=31):
h, w = img.shape[:2]
img_pad = cv.copyMakeBorder(img, d, d, d, d, cv.BORDER_WRAP)
img_blur = cv.GaussianBlur(img_pad, (2*d+1, 2*d+1), -1)[d:-d,d:-d]
y, x = np.indices((h, w))
dist = np.dstack([x, w-x-1, y, h-y-1]).min(-1)
w = np.minimum(np.float32(dist)/d, 1.0)
return img*w + img_blur*(1-w)
def motion_kernel(angle, d, sz=65):
kern = np.ones((1, d), np.float32)
c, s = np.cos(angle), np.sin(angle)
A = np.float32([[c, -s, 0], [s, c, 0]])
sz2 = sz // 2
A[:,2] = (sz2, sz2) - np.dot(A[:,:2], ((d-1)*0.5, 0))
kern = cv.warpAffine(kern, A, (sz, sz), flags=cv.INTER_CUBIC)
return kern
def defocus_kernel(d, sz=65):
kern = np.zeros((sz, sz), np.uint8)
cv.circle(kern, (sz, sz), d, 255, -1, cv.LINE_AA, shift=1)
kern = np.float32(kern) / 255.0
return kern
def main():
import sys, getopt
opts, args = getopt.getopt(sys.argv[1:], '', ['circle', 'angle=', 'd=', 'snr='])
opts = dict(opts)
try:
fn = args[0]
except:
fn = 'licenseplate_motion.jpg'
win = 'deconvolution'
img = cv.imread(cv.samples.findFile(fn), cv.IMREAD_GRAYSCALE)
if img is None:
print('Failed to load file:', fn)
sys.exit(1)
img = np.float32(img)/255.0
cv.imshow('input', img)
img = blur_edge(img)
IMG = cv.dft(img, flags=cv.DFT_COMPLEX_OUTPUT)
defocus = '--circle' in opts
def update(_):
ang = np.deg2rad( cv.getTrackbarPos('angle', win) )
d = cv.getTrackbarPos('d', win)
noise = 10**(-0.1*cv.getTrackbarPos('SNR (db)', win))
if defocus:
psf = defocus_kernel(d)
else:
psf = motion_kernel(ang, d)
cv.imshow('psf', psf)
psf /= psf.sum()
psf_pad = np.zeros_like(img)
kh, kw = psf.shape
psf_pad[:kh, :kw] = psf
PSF = cv.dft(psf_pad, flags=cv.DFT_COMPLEX_OUTPUT, nonzeroRows = kh)
PSF2 = (PSF**2).sum(-1)
iPSF = PSF / (PSF2 + noise)[...,np.newaxis]
RES = cv.mulSpectrums(IMG, iPSF, 0)
res = cv.idft(RES, flags=cv.DFT_SCALE | cv.DFT_REAL_OUTPUT )
res = np.roll(res, -kh//2, 0)
res = np.roll(res, -kw//2, 1)
cv.imshow(win, res)
cv.namedWindow(win)
cv.namedWindow('psf', 0)
cv.createTrackbar('angle', win, int(opts.get('--angle', 135)), 180, update)
cv.createTrackbar('d', win, int(opts.get('--d', 22)), 50, update)
cv.createTrackbar('SNR (db)', win, int(opts.get('--snr', 25)), 50, update)
update(None)
while True:
ch = cv.waitKey()
if ch == 27:
break
if ch == ord(' '):
defocus = not defocus
update(None)
print('Done')
if __name__ == '__main__':
print(__doc__)
main()
cv.destroyAllWindows()
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