/
dehaze_video.py
127 lines (98 loc) · 3.35 KB
/
dehaze_video.py
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
import cv2
import argparse
import math
import numpy as np
from skimage import exposure
from skimage.filters import rank
def dehaze(frame):
b,g,r=cv2.split(frame)
adapt = exposure.equalize_adapthist(frame, clip_limit=0.5)
eq1=cv2.equalizeHist(b)
eq2=cv2.equalizeHist(g)
eq3=cv2.equalizeHist(r)
clahe = cv2.createCLAHE(clipLimit=50.0, tileGridSize=(4,4))
c1 = clahe.apply(b)
c2 = clahe.apply(g)
c3 = clahe.apply(r)
eq=cv2.merge([eq1,eq2,eq3])
cl=cv2.merge([c1,c2,c3])
final_image=np.average([np.array(eq),np.array(cl),np.array(adapt)],axis=0,weights=[6,1,3])
final_image=final_image.astype(np.uint8)
return final_image
def DarkChannel(im,sz):
b,g,r = cv2.split(im)
dc = cv2.min(cv2.min(r,g),b);
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(sz,sz))
dark = cv2.erode(dc,kernel)
return dark
def AtmLight(im,dark):
[h,w] = im.shape[:2]
imsz = h*w
numpx = int(max(math.floor(imsz/1000),1))
darkvec = dark.reshape(imsz,1);
imvec = im.reshape(imsz,3);
indices = darkvec.argsort();
indices = indices[imsz-numpx::]
atmsum = np.zeros([1,3])
for ind in range(1,numpx):
atmsum = atmsum + imvec[indices[ind]]
A = atmsum / numpx;
return A
def TransmissionEstimate(im,A,sz):
omega = 0.95;
im3 = np.empty(im.shape,im.dtype);
for ind in range(0,3):
im3[:,:,ind] = im[:,:,ind]/A[0,ind]
transmission = 1 - omega*DarkChannel(im3,sz);
return transmission
def Guidedfilter(im,p,r,eps):
mean_I = cv2.boxFilter(im,cv2.CV_64F,(r,r));
mean_p = cv2.boxFilter(p, cv2.CV_64F,(r,r));
mean_Ip = cv2.boxFilter(im*p,cv2.CV_64F,(r,r));
cov_Ip = mean_Ip - mean_I*mean_p;
mean_II = cv2.boxFilter(im*im,cv2.CV_64F,(r,r));
var_I = mean_II - mean_I*mean_I;
a = cov_Ip/(var_I + eps);
b = mean_p - a*mean_I;
mean_a = cv2.boxFilter(a,cv2.CV_64F,(r,r));
mean_b = cv2.boxFilter(b,cv2.CV_64F,(r,r));
q = mean_a*im + mean_b;
return q;
def TransmissionRefine(im,et):
gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY);
gray = np.float64(gray)/255;
r = 60;
eps = 0.0001;
t = Guidedfilter(gray,et,r,eps);
return t;
def Recover(im,t,A,tx = 0.1):
res = np.empty(im.shape,im.dtype);
t = cv2.max(t,tx);
for ind in range(0,3):
res[:,:,ind] = (im[:,:,ind]-A[0,ind])/t + A[0,ind]
return res
def guided(frame):
I = frame.astype('float64')/255;
dark = DarkChannel(I,39);
A = AtmLight(I,dark);
te = TransmissionEstimate(I,A,39);
t = TransmissionRefine(frame,te);
J = Recover(I,t,A,0.1)
return J
cap = cv2.VideoCapture('./videos/sample video31.mp4')
while(cap.isOpened()):
ret, frame = cap.read()
if ret==True:
frame = cv2.resize(frame, (frame.shape[1]//1, frame.shape[0]//1), fx = 0, fy = 0,
interpolation = cv2.INTER_AREA)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
processed=dehaze(frame)
guided_image=guided(frame)
cv2.imshow('histogram equalized',processed)
cv2.imshow('dark channel prior',guided_image)
cv2.imshow('original',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
print("ok")
break
cap.release()
cv2.destroyAllWindows()