-
Notifications
You must be signed in to change notification settings - Fork 0
/
histograma.py
183 lines (125 loc) · 5.07 KB
/
histograma.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
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
from scipy import misc
from scipy import ndimage
from scipy import optimize
from skimage import feature
import matplotlib.pyplot as plt
import sys
import cv2
import copy
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import utils
import scipy
from numba import vectorize, float64
def affin(i,a1=1,a2=0,a3=0,a4=1,t1=0,t2=0):
i2 = scipy.ndimage.interpolation.affine_transform(i,[[a1,a2],[a3,a4]],offset=[t1,t2],cval=0.0)
return i2
def getTransformaciones(image):
transformations = {'original': [] , 'dx' : [] , 'dy': [], 'distancia' : [] }
transformations['original'] = ndimage.filters.gaussian_filter(image, 2)
transformations['distancia'] = np.array(ndimage.morphology.distance_transform_edt(np.logical_not(feature.canny(image,sigma=0))), dtype=np.uint32)
transformations['dy'] , transformations['dx'] = np.gradient(image)
return transformations
def getTransformedThumbs(transformations=[],position=[],):
thumbs = {'original': [] , 'dx' : [] , 'dy': [], 'distancia' : [] }
thumbs['original'] = utils.thumb(transformations['original'], position)
thumbs['dx'] = utils.thumb(transformations['dx'], position)
thumbs['dy'] = utils.thumb(transformations['dy'], position)
thumbs['distancia'] = utils.thumb(transformations['distancia'] ,position)
return thumbs
def register_point(pointOri,imageOriginal, imageObjetivo):
originalThumbs= getTransformedThumbs(getTransformaciones(imageOriginal),pointOri)
objectiveTransformations = getTransformaciones(imageObjetivo);
if (ops.operation=='optimize'):
result = scipy.optimize.basinhopping(calculateError, x0 = pointOri , stepsize=1, minimizer_kwargs={'args':(originalThumbs, objectiveTransformations), 'method':'Nelder-Mead'})
pointObj= [int(result.x[0]),int(result.x[1])]
else:
half = 50 // 2
rranges = (slice(pointOri[0] - half, pointOri[0] + half, 1), slice(pointOri[1] - half, pointOri[1] + half, 1))
result = scipy.optimize.brute(calculateError, rranges , args=(originalThumbs, objectiveTransformations))
pointObj= [int(result[0]),int(result[1])]
return pointObj
def calculateError(current,originalThumbs,transformations):
objectiveThumbs= getTransformedThumbs(transformations, [int(current[0]), int(current[1])] )
try:
errorIntensidades = np.sum(( np.power([originalThumbs['original'] - objectiveThumbs['original']],2))) * ops.weightPixel
errorGradienteY = np.sum(( np.power([originalThumbs['dy'] - objectiveThumbs['dy']],2)))
errorGradienteX = np.sum(( np.power([originalThumbs['dx'] - objectiveThumbs['dx']],2)))
errorGradiente = (errorGradienteX + errorGradienteY) * ops.weightGradient
errorDistancia = np.sum((np.power([originalThumbs['distancia'] - objectiveThumbs['distancia']],2)))* ops.weightDistance
errorTotal= errorIntensidades + errorDistancia + errorGradiente
except:
errorTotal= np.inf
return errorTotal
def register_points(image1, image2, points):
return [register_point(pointi,image1, image2) for pointi in points]
def CDF(histograma):
K = histograma.size
n = np.sum(histograma[0:K-1])
P = np.zeros(K)
c = histograma[0]
P[0]=c/n
for i in range (1,K):
c = c+histograma[i]
P[i] = c/n
return P
def register(images):
if (ops.inputPoints != False):
file = np.genfromtxt(ops.inputPoints)
data = [[( int(file[x][0]), int(file[x][1])) for x in range(len(file))]]
else:
data = [utils.ask_points(images[0])]
points = data
t_images = []
print (points)
lengImages=len(images)
theta = 350
for i in range(1, lengImages):
print("+Imagen:",i," de: ", lengImages-1)
if (i == 1):
image1 = utils.read(images[i - 1])
else:
image1 = np.copy(image2)
# image2 = affin(image1,np.cos(np.pi/theta),-np.sin(np.pi/theta),np.sin(np.pi/theta),np.cos(np.pi/theta),-2,-2)
# image2 = affin(image1,np.cos(np.pi/theta),-np.sin(np.pi/theta),np.sin(np.pi/theta),np.cos(np.pi/theta),-2,-2)
image2 = affin(image1,1,0,0,1,-2,-2)
t_images.append(image2)
ha,bin_edgesa = np.histogram(image1,256)
hr,bin_edgesr = np.histogram(image2,256)
K = ha.size
Pa = CDF(ha)
Pr = CDF(hr)
fhs = np.zeros(K)
for a in range(K):
j=K-1
while True:
fhs[a] = j
j=j-1
if (j < 0 or Pa[a] > Pr[j]):
break
h,w = image1.shape
for ip in range(h):
for jp in range(w):
inda=np.searchsorted(bin_edgesa,image1[ip][jp])
indr=fhs[inda-1]
image1[ip][jp] = bin_edgesr[int(indr-1)]
point1 = points[i - 1]
point2 = register_points(image1, image2, point1)
points.append(point2)
return points,t_images
def init():
global ops
global args
ops,args = utils.optParse()
if __name__ == '__main__':
init()
# print (args,ops)
path = args[0]
images = utils.images(path)
points,t_images = register(images)
# print (images)
# print (t_images)
print (points)
utils.render_pointsHist(images,t_images, points,ops.exitFolder)