-
Notifications
You must be signed in to change notification settings - Fork 10
/
Read_img_Gan.py
165 lines (130 loc) · 8.43 KB
/
Read_img_Gan.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
# coding: utf-8
import numpy as np
import torch as t
from torch import nn
from torch.autograd import Variable
from torch.optim import RMSprop
from torch.optim import Adam
from torchvision import transforms
from torchvision.utils import make_grid
from torchvision.datasets import CIFAR10
from pylab import plt
import torch.utils.data as data
import torch
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as image
import sklearn
from skimage.segmentation import slic,mark_boundaries
from skimage import io
from skimage import data,color,morphology,measure
# 为了Fine-tune 网络Hierarchy CGAN，定义的读图像的数据，能够根据输入的参数 patch_size，得到对应大小的patch
def Read_img_Gan(h_ipt,w_ipt,n_segments,patch_size,num_label,rownum,colnum,iput_img_original,gt_original,train_patch_ind):
def crop_fun(new_input_padding,wi_ipt,hi_ipt,patch_size):
patch_out=new_input_padding[wi_ipt:wi_ipt+patch_size,hi_ipt:hi_ipt+patch_size]
return patch_out
ALL_DATA_X_L=[]
ALL_DATA_Y_L=[]
ALL_DATA_X_no_L=[]
ALL_DATA_Y_no_L=[]
rowheight = h_ipt // rownum
colwidth = w_ipt // colnum
for r in range(rownum):#
for c in range(colnum):#
iput_img= iput_img_original[r * rowheight:(r + 1) * rowheight,c * colwidth:(c + 1) * colwidth];
gt = gt_original [r * rowheight:(r + 1) * rowheight,c * colwidth:(c + 1) * colwidth,:];
segments = slic(iput_img, n_segments=n_segments, compactness=0.5)
out=mark_boundaries(gt,segments)
# plt.figure(dpi=200)
# plt.imshow(out)
# plt.show()
#得到每个super pixel的质心
segments_label=segments+1 #这里+1，是因为regionprops函数的输入要求是label之后的图像，而label的图像的区域编号是从1开始的
region_fea=measure.regionprops(segments_label)
#定义一个边界扩大的patch_size的空矩阵，主要是为了当super pixel位于图像边缘时，
new_input_padding=np.zeros([rowheight+patch_size,colwidth+patch_size])
#把这个iput_img放到new_input_padding中
new_input_padding[patch_size/2:-patch_size/2,patch_size/2:-patch_size/2]=iput_img
print( r*rownum+c )
if r*rownum+c in train_patch_ind:
#对所有的super pixel开始循环
for ind_pixel in range (segments_label.max()):
#计算当前superpixel的质心，为了生成切片，切片以这个质心为中心
centriod=np.array(region_fea[ind_pixel].centroid).astype("int32")
wi_ipt=centriod[0]
hi_ipt=centriod[1]
#得到这个超像素的所有像素的坐标，根据坐标能够知道这个超像素在GT图中的所有像素值all_pixels
#根据所有的像素，得到哪一个像素值最多，例如【0,0,0】最多，那这个超像素的标签就是“河流”
all_pixels=gt[region_fea[ind_pixel].coords[:,0],region_fea[ind_pixel].coords[:,1]]
n0 = np.bincount(all_pixels[:,0])
n1 = np.bincount(all_pixels[:,1])
n2 = np.bincount(all_pixels[:,2])
gt_of_superp=[n0.argmax(),n1.argmax(),n2.argmax()] #gt_of_superp这个超像素中出现最多次的像素值
if gt_of_superp[0]<=20 and gt_of_superp[1]>=240 and gt_of_superp[2]<=20:
ALL_DATA_X_L.append(crop_fun(new_input_padding,wi_ipt,hi_ipt,patch_size))
ALL_DATA_Y_L.append(0)
# black ---river
elif gt_of_superp[0]<=50 and gt_of_superp[1]<=50 and gt_of_superp[2]<=50:
ALL_DATA_X_L.append(crop_fun(new_input_padding,wi_ipt,hi_ipt,patch_size))
ALL_DATA_Y_L.append(1)
# red ---urban area
elif gt_of_superp[0]>=200 and gt_of_superp[1]<=50 and gt_of_superp[2]<=50:
ALL_DATA_X_L.append(crop_fun(new_input_padding,wi_ipt,hi_ipt,patch_size))
ALL_DATA_Y_L.append(2)
# yellow --- framland
elif gt_of_superp[0]>=200 and gt_of_superp[1]>=200 and gt_of_superp[2]<=50:
ALL_DATA_X_L.append(crop_fun(new_input_padding,wi_ipt,hi_ipt,patch_size))
ALL_DATA_Y_L.append(3)
# blue ---road
elif gt_of_superp[0]<=50 and gt_of_superp[1]<=50 and gt_of_superp[2]>=200:
ALL_DATA_X_L.append(crop_fun(new_input_padding,wi_ipt,hi_ipt,patch_size))
ALL_DATA_Y_L.append(2)
# white ---background
elif gt_of_superp[0]>=200 and gt_of_superp[1]>=200 and gt_of_superp[2]>=200:
ALL_DATA_X_L.append(crop_fun(new_input_padding,wi_ipt,hi_ipt,patch_size))
ALL_DATA_Y_L.append(4)
# other color regions are regarded as the background
else:
ALL_DATA_X_L.append(crop_fun(new_input_padding,wi_ipt,hi_ipt,patch_size))
ALL_DATA_Y_L.append(4)
else:
for ind_pixel in range (segments_label.max()):
#计算当前superpixel的质心，为了生成切片，切片以这个质心为中心
centriod=np.array(region_fea[ind_pixel].centroid).astype("int32")
wi_ipt=centriod[0]
hi_ipt=centriod[1]
#得到这个超像素的所有像素的坐标，根据坐标能够知道这个超像素在GT图中的所有像素值all_pixels
#根据所有的像素，得到哪一个像素值最多，例如【0,0,0】最多，那这个超像素的标签就是“河流”
all_pixels=gt[region_fea[ind_pixel].coords[:,0],region_fea[ind_pixel].coords[:,1]]
n0 = np.bincount(all_pixels[:,0])
n1 = np.bincount(all_pixels[:,1])
n2 = np.bincount(all_pixels[:,2])
gt_of_superp=[n0.argmax(),n1.argmax(),n2.argmax()] #gt_of_superp这个超像素中出现最多次的像素值
if gt_of_superp[0]<=20 and gt_of_superp[1]>=240 and gt_of_superp[2]<=20:
ALL_DATA_X_no_L.append(crop_fun(new_input_padding,wi_ipt,hi_ipt,patch_size))
ALL_DATA_Y_no_L.append(0)
# black ---river
elif gt_of_superp[0]<=50 and gt_of_superp[1]<=50 and gt_of_superp[2]<=50:
ALL_DATA_X_no_L.append(crop_fun(new_input_padding,wi_ipt,hi_ipt,patch_size))
ALL_DATA_Y_no_L.append(1)
# red ---urban area
elif gt_of_superp[0]>=200 and gt_of_superp[1]<=50 and gt_of_superp[2]<=50:
ALL_DATA_X_no_L.append(crop_fun(new_input_padding,wi_ipt,hi_ipt,patch_size))
ALL_DATA_Y_no_L.append(2)
# yellow --- framland
elif gt_of_superp[0]>=200 and gt_of_superp[1]>=200 and gt_of_superp[2]<=50:
ALL_DATA_X_no_L.append(crop_fun(new_input_padding,wi_ipt,hi_ipt,patch_size))
ALL_DATA_Y_no_L.append(3)
# blue ---road
elif gt_of_superp[0]<=50 and gt_of_superp[1]<=50 and gt_of_superp[2]>=200:
ALL_DATA_X_no_L.append(crop_fun(new_input_padding,wi_ipt,hi_ipt,patch_size))
ALL_DATA_Y_no_L.append(2)
# white ---background
elif gt_of_superp[0]>=200 and gt_of_superp[1]>=200 and gt_of_superp[2]>=200:
ALL_DATA_X_no_L.append(crop_fun(new_input_padding,wi_ipt,hi_ipt,patch_size))
ALL_DATA_Y_no_L.append(4)
# other color regions are regarded as the background
else:
ALL_DATA_X_no_L.append(crop_fun(new_input_padding,wi_ipt,hi_ipt,patch_size))
ALL_DATA_Y_no_L.append(4)
return np.array(ALL_DATA_X_L).astype(np.int), np.array(ALL_DATA_Y_L), np.array(ALL_DATA_X_no_L) ,np.array(ALL_DATA_Y_no_L)