f4334277
 Hu Chunming
提交3rdparty
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
|
#!/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()
|
