質問編集履歴
2
_
test
CHANGED
|
File without changes
|
test
CHANGED
|
@@ -1,10 +1,4 @@
|
|
|
1
|
-
皆様の助けもあり画像から複数輪郭抽出を行うまでになりました．（図１）
|
|
2
|
-
|
|
3
|
-
|
|
1
|
+
皆様の助けもあり画像から複数輪郭抽出を行うまでになりました．白の領域においてドロネー分割をおこないたいのですが，
|
|
4
|
-
|
|
5
|
-
どうしても図のように黒の領域にも分割が及んでいます．
|
|
6
|
-
|
|
7
|
-
|
|
8
2
|
|
|
9
3
|
どなたか解決できる方いらっしゃいましたら．よろしくお願いします．
|
|
10
4
|
|
1
プログラムを載せました．まだ頂いた輪郭抽出のものを載せていません．
test
CHANGED
|
File without changes
|
test
CHANGED
|
@@ -9,3 +9,279 @@
|
|
|
9
9
|
どなたか解決できる方いらっしゃいましたら．よろしくお願いします．
|
|
10
10
|
|
|
11
11
|
python2.7,openCV4.0です．
|
|
12
|
+
|
|
13
|
+
|
|
14
|
+
|
|
15
|
+
```python
|
|
16
|
+
|
|
17
|
+
|
|
18
|
+
|
|
19
|
+
#!/usr/bin/env python
|
|
20
|
+
|
|
21
|
+
#coding: utf-8
|
|
22
|
+
|
|
23
|
+
|
|
24
|
+
|
|
25
|
+
import csv,os,cv2,math,re
|
|
26
|
+
|
|
27
|
+
import numpy as np
|
|
28
|
+
|
|
29
|
+
import matplotlib.pyplot as plt
|
|
30
|
+
|
|
31
|
+
from matplotlib import pyplot
|
|
32
|
+
|
|
33
|
+
from operator import itemgetter
|
|
34
|
+
|
|
35
|
+
from dolfin import *
|
|
36
|
+
|
|
37
|
+
from mshr import *
|
|
38
|
+
|
|
39
|
+
from pylab import show,triplot
|
|
40
|
+
|
|
41
|
+
|
|
42
|
+
|
|
43
|
+
#読み込み,グレー設定
|
|
44
|
+
|
|
45
|
+
img = cv2.imread('/home/ubuntu/0116maps/map.pgm')
|
|
46
|
+
|
|
47
|
+
img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
|
|
48
|
+
|
|
49
|
+
|
|
50
|
+
|
|
51
|
+
#smoothing
|
|
52
|
+
|
|
53
|
+
img_preprocessed = cv2.GaussianBlur(img_gray, (5, 5), 50)
|
|
54
|
+
|
|
55
|
+
img_preprocessed = cv2.GaussianBlur(img_preprocessed, (5, 5), 50)
|
|
56
|
+
|
|
57
|
+
img_preprocessed = cv2.GaussianBlur(img_preprocessed, (5, 5), 50)
|
|
58
|
+
|
|
59
|
+
img_preprocessed = cv2.GaussianBlur(img_preprocessed, (5, 5), 50)
|
|
60
|
+
|
|
61
|
+
img_preprocessed = cv2.GaussianBlur(img_preprocessed, (5, 5), 50)
|
|
62
|
+
|
|
63
|
+
img_preprocessed = cv2.GaussianBlur(img_preprocessed, (5, 5), 50)
|
|
64
|
+
|
|
65
|
+
img_preprocessed = cv2.GaussianBlur(img_preprocessed, (5, 5), 50)
|
|
66
|
+
|
|
67
|
+
img_preprocessed = cv2.GaussianBlur(img_preprocessed, (5, 5), 50)
|
|
68
|
+
|
|
69
|
+
img_preprocessed = cv2.GaussianBlur(img_preprocessed, (5, 5), 50)
|
|
70
|
+
|
|
71
|
+
img_preprocessed = cv2.GaussianBlur(img_preprocessed, (5, 5), 50)
|
|
72
|
+
|
|
73
|
+
img_preprocessed = cv2.GaussianBlur(img_preprocessed, (5, 5), 50)
|
|
74
|
+
|
|
75
|
+
|
|
76
|
+
|
|
77
|
+
#閾値処理,輪郭検索
|
|
78
|
+
|
|
79
|
+
_, white_binary = cv2.threshold(img_preprocessed, 220, 254, cv2.THRESH_BINARY)
|
|
80
|
+
|
|
81
|
+
white_contours, _ = cv2.findContours(white_binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
|
82
|
+
|
|
83
|
+
|
|
84
|
+
|
|
85
|
+
#imageのコピー
|
|
86
|
+
|
|
87
|
+
white_and_contours = np.copy(img)
|
|
88
|
+
|
|
89
|
+
|
|
90
|
+
|
|
91
|
+
#最大輪郭値取得
|
|
92
|
+
|
|
93
|
+
min_white_area = 60
|
|
94
|
+
|
|
95
|
+
large_contours = [ cnt for cnt in white_contours if cv2.contourArea(cnt) > min_white_area ]
|
|
96
|
+
|
|
97
|
+
|
|
98
|
+
|
|
99
|
+
#外形の座標取得,最大値最小値取得
|
|
100
|
+
|
|
101
|
+
large_contours = np.array(large_contours)
|
|
102
|
+
|
|
103
|
+
large_contours_min = large_contours.min(axis = 1)
|
|
104
|
+
|
|
105
|
+
large_contours_max = large_contours.max(axis = 1)
|
|
106
|
+
|
|
107
|
+
|
|
108
|
+
|
|
109
|
+
#csv削除,書き込み
|
|
110
|
+
|
|
111
|
+
os.remove("/home/ubuntu/map_data/pixel_output.csv")
|
|
112
|
+
|
|
113
|
+
with open("/home/ubuntu/map_data/pixel_output.csv", 'a') as f:
|
|
114
|
+
|
|
115
|
+
writer = csv.writer(f)
|
|
116
|
+
|
|
117
|
+
writer.writerows(large_contours)
|
|
118
|
+
|
|
119
|
+
|
|
120
|
+
|
|
121
|
+
#pixel値の[]削除、,付け
|
|
122
|
+
|
|
123
|
+
os.remove("/home/ubuntu/map_data/pixel_surround.csv")
|
|
124
|
+
|
|
125
|
+
with open("/home/ubuntu/map_data/pixel_output.csv", 'rb') as f:
|
|
126
|
+
|
|
127
|
+
reader = csv.reader(f)
|
|
128
|
+
|
|
129
|
+
for row in reader:
|
|
130
|
+
|
|
131
|
+
i = ' '.join(row)
|
|
132
|
+
|
|
133
|
+
i = i.replace(' ',',')
|
|
134
|
+
|
|
135
|
+
i = i.replace('],[','')
|
|
136
|
+
|
|
137
|
+
i = i.replace('[','')
|
|
138
|
+
|
|
139
|
+
i = i.replace(']','\n')
|
|
140
|
+
|
|
141
|
+
f = open("/home/ubuntu/map_data/pixel_surround.csv", 'a')
|
|
142
|
+
|
|
143
|
+
f.write(i)
|
|
144
|
+
|
|
145
|
+
f.close()
|
|
146
|
+
|
|
147
|
+
|
|
148
|
+
|
|
149
|
+
os.remove("/home/ubuntu/map_data/minmaxoutput.csv")
|
|
150
|
+
|
|
151
|
+
with open("/home/ubuntu/map_data/minmaxoutput.csv", 'a') as f:
|
|
152
|
+
|
|
153
|
+
writer = csv.writer(f)
|
|
154
|
+
|
|
155
|
+
writer.writerows(large_contours_min)
|
|
156
|
+
|
|
157
|
+
writer.writerows(large_contours_max)
|
|
158
|
+
|
|
159
|
+
|
|
160
|
+
|
|
161
|
+
os.remove("/home/ubuntu/map_data/minmax_coordinate.csv")
|
|
162
|
+
|
|
163
|
+
with open("/home/ubuntu/map_data/minmaxoutput.csv", 'rb') as f:
|
|
164
|
+
|
|
165
|
+
reader = csv.reader(f)
|
|
166
|
+
|
|
167
|
+
for row in reader:
|
|
168
|
+
|
|
169
|
+
i = ' '.join(row)
|
|
170
|
+
|
|
171
|
+
i = i.replace('[','')
|
|
172
|
+
|
|
173
|
+
i = i.replace(']',',')
|
|
174
|
+
|
|
175
|
+
i = i.replace(' ',',')
|
|
176
|
+
|
|
177
|
+
f = open("/home/ubuntu/map_data/minmax_coordinate.csv", 'a')
|
|
178
|
+
|
|
179
|
+
f.write(i)
|
|
180
|
+
|
|
181
|
+
f.close()
|
|
182
|
+
|
|
183
|
+
|
|
184
|
+
|
|
185
|
+
#map値読み取りやすく変換
|
|
186
|
+
|
|
187
|
+
os.remove("/home/ubuntu/map_data/map_value.csv")
|
|
188
|
+
|
|
189
|
+
with open("/home/ubuntu/0116maps/map.yaml", 'rb') as f:
|
|
190
|
+
|
|
191
|
+
reader = csv.reader(f)
|
|
192
|
+
|
|
193
|
+
for words in reader:
|
|
194
|
+
|
|
195
|
+
i = ' '.join(words)
|
|
196
|
+
|
|
197
|
+
match = re.search(r': ', i)
|
|
198
|
+
|
|
199
|
+
if match:
|
|
200
|
+
|
|
201
|
+
i = i[match.end():]
|
|
202
|
+
|
|
203
|
+
i = i.strip('[]')
|
|
204
|
+
|
|
205
|
+
i = i.replace(' ',',')
|
|
206
|
+
|
|
207
|
+
f = open("/home/ubuntu/map_data/map_value.csv", 'a')
|
|
208
|
+
|
|
209
|
+
f.write(i + ",")
|
|
210
|
+
|
|
211
|
+
f.close()
|
|
212
|
+
|
|
213
|
+
|
|
214
|
+
|
|
215
|
+
#格子分割準備
|
|
216
|
+
|
|
217
|
+
with open("/home/ubuntu/map_data/pixel_surround.csv", 'rb') as f:
|
|
218
|
+
|
|
219
|
+
reader = csv.reader(f)
|
|
220
|
+
|
|
221
|
+
count = 0
|
|
222
|
+
|
|
223
|
+
for pixel in reader:
|
|
224
|
+
|
|
225
|
+
count+=1
|
|
226
|
+
|
|
227
|
+
|
|
228
|
+
|
|
229
|
+
j = count-1
|
|
230
|
+
|
|
231
|
+
k = 0
|
|
232
|
+
|
|
233
|
+
poin = range(j)
|
|
234
|
+
|
|
235
|
+
domain_vertices = []
|
|
236
|
+
|
|
237
|
+
|
|
238
|
+
|
|
239
|
+
with open("/home/ubuntu/map_data/pixel_surround.csv", 'rb') as f:
|
|
240
|
+
|
|
241
|
+
reader = csv.reader(f)
|
|
242
|
+
|
|
243
|
+
for pix in reader:
|
|
244
|
+
|
|
245
|
+
if k<j:
|
|
246
|
+
|
|
247
|
+
poin[k] = [Point(int(pix[0]),int(pix[1]))]
|
|
248
|
+
|
|
249
|
+
k+=1
|
|
250
|
+
|
|
251
|
+
|
|
252
|
+
|
|
253
|
+
for m in reversed(range(j)):
|
|
254
|
+
|
|
255
|
+
domain_vertices += poin[m]
|
|
256
|
+
|
|
257
|
+
|
|
258
|
+
|
|
259
|
+
#格子分割
|
|
260
|
+
|
|
261
|
+
domain = Polygon(domain_vertices)
|
|
262
|
+
|
|
263
|
+
mesh = generate_mesh(domain,0.1)
|
|
264
|
+
|
|
265
|
+
coords = mesh.coordinates()
|
|
266
|
+
|
|
267
|
+
|
|
268
|
+
|
|
269
|
+
os.remove("/home/ubuntu/map_data/coords.csv")
|
|
270
|
+
|
|
271
|
+
with open("/home/ubuntu/map_data/coords.csv", 'a') as f:
|
|
272
|
+
|
|
273
|
+
writer = csv.writer(f)
|
|
274
|
+
|
|
275
|
+
writer.writerows(coords)
|
|
276
|
+
|
|
277
|
+
i
|
|
278
|
+
|
|
279
|
+
#メッシュ領域plot
|
|
280
|
+
|
|
281
|
+
triplot(coords[:,0], coords[:,1], triangles=mesh.cells())
|
|
282
|
+
|
|
283
|
+
plt.imshow(white_and_contours)
|
|
284
|
+
|
|
285
|
+
plt.show()
|
|
286
|
+
|
|
287
|
+
```
|