質問編集履歴

1

プログラム全文を掲載しました

2021/01/19 10:22

投稿

vamrat
vamrat

スコア20

test CHANGED
File without changes
test CHANGED
@@ -146,7 +146,225 @@
146
146
 
147
147
 
148
148
 
149
+ ```analyze.py
150
+
151
+ import cv2
152
+
153
+ import numpy as np
154
+
155
+ import matplotlib.pyplot as plt
156
+
157
+ import methods
158
+
159
+
160
+
161
+
162
+
163
+ # 入力画像読込
164
+
165
+ # 画像サイズ(500(width) x 218(height) pix)
166
+
167
+ img = cv2.imread('images/madslide12.jpg', cv2.IMREAD_COLOR)
168
+
169
+
170
+
171
+ # オリジナル画像保存
172
+
173
+ # import pdb; pdb.set_trace()
174
+
175
+ org = img.copy()
176
+
177
+ cv2.imwrite('results/original.png', org)
178
+
179
+
180
+
181
+ # 画像ファイル
182
+
183
+ # - 画像データを処理プログラムに送る
184
+
185
+ methods.image(org,img)
186
+
187
+
188
+
189
+ # PyMeanShift
190
+
191
+ # - 第1引数:探索範囲、第2引数:探索色相、第3引数:粗さ
192
+
193
+ methods.meanshift(12,3,200)
194
+
195
+
196
+
197
+ # ヒストグラム均一化
198
+
199
+ methods.contrast()
200
+
201
+
202
+
203
+ # 類似色統合
204
+
205
+ methods.clustering()
206
+
207
+
208
+
209
+ # ブロック分割
210
+
211
+ methods.division()
212
+
213
+
214
+
215
+ # カラーラベリング
216
+
217
+ methods.labeling()
218
+
219
+ ```
220
+
221
+
222
+
149
- ```python
223
+ ```methods.py
224
+
225
+ import cv2
226
+
227
+ import matplotlib.pyplot as plt
228
+
229
+ import matplotlib as mpl
230
+
231
+ import numpy as np
232
+
233
+ import pymeanshift as pms
234
+
235
+ import os
236
+
237
+ import sys
238
+
239
+ from PIL import Image
240
+
241
+ # sys.setrecursionlimit(8000) # 200 x 113 pix
242
+
243
+ sys.setrecursionlimit(30000) # 500 x 281 pix
244
+
245
+ plt.gray()
246
+
247
+
248
+
249
+
250
+
251
+ def image(_org,_img):
252
+
253
+ global org,img,h,w,c
254
+
255
+ global bo,go,ro,al
256
+
257
+
258
+
259
+ org,img = _org,_img
260
+
261
+ h,w,c = img.shape
262
+
263
+ bo,go,ro = cv2.split(org)
264
+
265
+ al = 0.55
266
+
267
+
268
+
269
+ def meanshift(spatial_radius,range_radius,min_density):
270
+
271
+ global img
272
+
273
+ (img,labels,num) = pms.segment(cv2.cvtColor(img,cv2.COLOR_BGR2Lab),spatial_radius,range_radius,min_density)
274
+
275
+ img = cv2.cvtColor(img, cv2.COLOR_Lab2BGR)
276
+
277
+ cv2.imwrite('results/meanshift.png',img)
278
+
279
+
280
+
281
+ def contrast():
282
+
283
+ global img
284
+
285
+ hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
286
+
287
+ h,s,v = cv2.split(hsv)
288
+
289
+
290
+
291
+ clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(3, 3))
292
+
293
+ result = clahe.apply(v)
294
+
295
+
296
+
297
+ hsv = cv2.merge((h,s,result))
298
+
299
+ img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
300
+
301
+ cv2.imwrite("results/contrast.png", img)
302
+
303
+
304
+
305
+ def division():
306
+
307
+ global block
308
+
309
+ hs,ws,cnt = 113,200,1
310
+
311
+
312
+
313
+ if ((h>hs)&(w>ws)):
314
+
315
+ for j in range(0,h,hs):
316
+
317
+ for i in range(0,w,ws):
318
+
319
+ if (((j+hs)<h)&((i+ws)<w)):
320
+
321
+ div = np.zeros((hs,ws,c),dtype=int)
322
+
323
+ div[0:hs,0:ws] = img[j:j+hs,i:i+ws]
324
+
325
+ else:
326
+
327
+ if (((i+ws)>w)&((j+hs)>h)):
328
+
329
+ div = np.zeros((h-j,w-i,c),dtype=int)
330
+
331
+ div[0:h-j,0:w-i] = img[j:h,i:w]
332
+
333
+ elif ((j+hs)>h):
334
+
335
+ div = np.zeros((h-j,ws,c),dtype=int)
336
+
337
+ div[0:h-j,0:ws] = img[j:h,i:i+ws]
338
+
339
+ else:
340
+
341
+ div = np.zeros((hs,w-i,c),dtype=int)
342
+
343
+ div[0:hs,0:w-i] = img[j:j+hs,i:w]
344
+
345
+ cv2.imwrite('results/division/division{}.png'.format(cnt),div)
346
+
347
+ cnt += 1
348
+
349
+ block = cnt
350
+
351
+ print('block number :',block)
352
+
353
+
354
+
355
+ def clustering():
356
+
357
+ global img
358
+
359
+ im = Image.open('results/contrast.png')
360
+
361
+ im_q = im.quantize(colors=128, method=0, dither=1)
362
+
363
+ im_q.save('results/clustering.png')
364
+
365
+ img = cv2.imread('results/clustering.png', cv2.IMREAD_COLOR)
366
+
367
+
150
368
 
151
369
  def approximation(pix1,pix2):
152
370