-
Notifications
You must be signed in to change notification settings - Fork 0
/
seeds_func.py
443 lines (393 loc) · 15.2 KB
/
seeds_func.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
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
import os
import numpy as np
import h5py
from scipy import ndimage
import cv2
import mahotas
import matplotlib
matplotlib.use("agg")
import matplotlib.pyplot as plt
# generate affinity
def seg_to_affgraph(seg, nhood=np.array([[-1, 0, 0], [0, -1, 0], [0, 0, -1]])):
# constructs an affinity graph from a segmentation
# assume affinity graph is represented as:
# shape = (e, z, y, x)
# nhood.shape = (edges, 3)
shape = seg.shape
nEdge = nhood.shape[0]
aff = np.zeros((nEdge,)+shape,dtype=np.int32)
for e in range(nEdge):
aff[e, \
max(0,-nhood[e,0]):min(shape[0],shape[0]-nhood[e,0]), \
max(0,-nhood[e,1]):min(shape[1],shape[1]-nhood[e,1]), \
max(0,-nhood[e,2]):min(shape[2],shape[2]-nhood[e,2])] = \
(seg[max(0,-nhood[e,0]):min(shape[0],shape[0]-nhood[e,0]), \
max(0,-nhood[e,1]):min(shape[1],shape[1]-nhood[e,1]), \
max(0,-nhood[e,2]):min(shape[2],shape[2]-nhood[e,2])] == \
seg[max(0,nhood[e,0]):min(shape[0],shape[0]+nhood[e,0]), \
max(0,nhood[e,1]):min(shape[1],shape[1]+nhood[e,1]), \
max(0,nhood[e,2]):min(shape[2],shape[2]+nhood[e,2])] ) \
* ( seg[max(0,-nhood[e,0]):min(shape[0],shape[0]-nhood[e,0]), \
max(0,-nhood[e,1]):min(shape[1],shape[1]-nhood[e,1]), \
max(0,-nhood[e,2]):min(shape[2],shape[2]-nhood[e,2])] > 0 ) \
* ( seg[max(0,nhood[e,0]):min(shape[0],shape[0]+nhood[e,0]), \
max(0,nhood[e,1]):min(shape[1],shape[1]+nhood[e,1]), \
max(0,nhood[e,2]):min(shape[2],shape[2]+nhood[e,2])] > 0 )
return aff
# generate seeds
def gen_seeds(labels, affs_xy, min_size=10):
# remove some neurons whose size is smaller than min_size
ids, count = np.unique(labels, return_counts=True)
for i, icount in enumerate(count):
if icount < min_size:
labels[labels == ids[i]] = 0
boundary = np.ones_like(affs_xy)
boundary[1:-1, 1:-1] = affs_xy[1:-1, 1:-1]
boundary[boundary != 0] = 1
distance = mahotas.distance(boundary<0.5)
seeds = np.zeros_like(labels)
ite = 1
for label in np.unique(labels):
if label == 0:
continue
label_mask = labels == label
label_mask = label_mask.astype(np.int)
temp_dis = np.multiply(distance, label_mask)
max_where = np.where(temp_dis == np.max(temp_dis))
seeds[max_where[0][0], max_where[1][0]] = ite
ite += 1
return seeds, boundary
def gen_seeds_2(labels, affs_xy, min_size=10):
# remove some neurons whose size is smaller than min_size
ids, count = np.unique(labels, return_counts=True)
for i, icount in enumerate(count):
if icount < min_size:
labels[labels == ids[i]] = 0
boundary = np.ones_like(affs_xy)
boundary[1:-1, 1:-1] = affs_xy[1:-1, 1:-1]
boundary[boundary != 0] = 1
distance = mahotas.distance(boundary<0.5)
seeds = np.zeros_like(labels)
# ite = 1
for label in np.unique(labels):
if label == 0:
continue
label_mask = labels == label
label_mask = label_mask.astype(np.int)
temp_dis = np.multiply(distance, label_mask)
max_where = np.where(temp_dis == np.max(temp_dis))
seeds[max_where[0][0], max_where[1][0]] = label
# ite += 1
return seeds
# erosion labels
def erosion_labels(gt, steps=1):
self_background = 0
foreground = np.zeros(shape=gt.shape, dtype=np.bool)
for label in np.unique(gt):
if label == self_background:
continue
label_mask = gt==label
# Assume that masked out values are the same as the label we are
# eroding in this iteration. This ensures that at the boundary to
# a masked region the value blob is not shrinking.
eroded_label_mask = ndimage.binary_erosion(label_mask, iterations=steps, border_value=1)
foreground = np.logical_or(eroded_label_mask, foreground)
background = np.logical_not(foreground)
gt[background] = self_background
return gt
# draw fragments
def draw_fragments(picture, raw=None, alpha=0.3):
m,n = picture.shape
ids = np.unique(picture)
size = len(ids)
print("The number of nuerons is %d" % size)
color = np.zeros([m, n, 3])
idx = np.searchsorted(ids, picture)
for i in range(3):
color_val = np.random.randint(0, 255, ids.shape)
if ids[0] == 0:
color_val[0] = 0
color[:,:,i] = color_val[idx]
color = color / 255
if raw is not None:
plt.figure()
plt.subplots(figsize=(10,10))
plt.imshow(raw)
plt.imshow(color, alpha=alpha)
plt.axis('off')
plt.show()
else:
plt.figure()
plt.subplots(figsize=(10,10))
plt.imshow(color)
plt.axis('off')
plt.show()
# thresdholding
def binary_thresholding(img, t=0.5):
if np.max(img) > 1.0:
img = img / 255.0
img[img >= t] = 1
img[img < t] = 0
return img
# draw seeds
def draw_seeds(raw, seeds):
plt.figure(figsize=(10,10))
plt.imshow(raw, cmap='gray')
seeds_listx, seeds_listy = np.where(seeds != 0)
plt.scatter(seeds_listy, seeds_listx, c='r')
plt.axis('off')
plt.show()
def draw_seeds_v2(raw, seeds):
plt.figure(figsize=(10,10))
plt.imshow(raw, cmap='gray')
seeds_listx = seeds[:, 0].astype(np.int)
seeds_listy = seeds[:, 1].astype(np.int)
plt.scatter(seeds_listy, seeds_listx, c='r')
plt.axis('off')
plt.show()
def draw_box(img, box):
img_box = img.copy()
if len(img_box.shape) == 2:
img_box = img_box[:,:,np.newaxis]
img_box = np.concatenate([img_box, img_box, img_box], axis=2)
for i in range(1, box.shape[0]):
position = box[i]
x1 = position[0]
y1 = position[1]
x2 = x1 + position[2]
y2 = y1 + position[3]
img_box = cv2.rectangle(img_box, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), 2)
plt.figure(figsize=(10,10))
plt.imshow(img_box)
plt.axis('off')
plt.show()
# draw affinity
def draw_general(img):
plt.figure(figsize=(10,10))
plt.imshow(img, cmap='gray')
plt.axis('off')
plt.show()
def make_summary_plot(it, raw, output, net_output, seeds, target):
"""
This function create and save a summary figure
"""
f, axarr = plt.subplots(2, 2, figsize=(8, 9.5))
f.suptitle("RW summary, Iteration: " + repr(it))
axarr[0, 0].set_title("Ground Truth Image")
axarr[0, 0].imshow(raw[0].detach().numpy(), cmap="gray")
axarr[0, 0].imshow(target[0, 0].detach().numpy(), alpha=0.6, vmin=-3, cmap="prism_r")
seeds_listx, seeds_listy = np.where(seeds[0].data != 0)
axarr[0, 0].scatter(seeds_listy,
seeds_listx, c="r")
axarr[0, 0].axis("off")
axarr[0, 1].set_title("LRW output (white seed)")
axarr[0, 1].imshow(raw[0].detach().numpy(), cmap="gray")
axarr[0, 1].imshow(np.argmax(output[0][0].detach().numpy(), 0), alpha=0.6, vmin=-3, cmap="prism_r")
axarr[0, 1].axis("off")
axarr[1, 0].set_title("Vertical Diffusivities")
axarr[1, 0].imshow(net_output[0, 0].detach().numpy(), cmap="gray")
axarr[1, 0].axis("off")
axarr[1, 1].set_title("Horizontal Diffusivities")
axarr[1, 1].imshow(net_output[0, 1].detach().numpy(), cmap="gray")
axarr[1, 1].axis("off")
plt.tight_layout()
plt.savefig("./results/%04i.png"%it)
plt.close()
def draw_fragments_seeds(out_path, k, pred, pred_seed, gt, gt_seed, f_txt, raw=None, alpha=0.8):
m,n = pred.shape
ids = np.unique(pred)
size = len(ids)
print("k = %d, the neurons number of pred is %d" % (k, size))
f_txt.write("k = %d, the neurons number of pred is %d" % (k, size))
f_txt.write('\n')
color_pred = np.zeros([m, n, 3])
idx = np.searchsorted(ids, pred)
for i in range(3):
color_val = np.random.randint(0, 255, ids.shape)
if ids[0] == 0:
color_val[0] = 0
color_pred[:,:,i] = color_val[idx]
color_pred = color_pred / 255
if pred_seed is not None:
pred_seeds_listx, pred_seeds_listy = np.where(pred_seed != 0)
ids = np.unique(gt)
size = len(ids)
print("k = %d, the neurons number of gt is %d" % (k, size))
f_txt.write("k = %d, the neurons number of gt is %d" % (k, size))
f_txt.write('\n')
color_gt= np.zeros([m, n, 3])
idx = np.searchsorted(ids, gt)
for i in range(3):
color_val = np.random.randint(0, 255, ids.shape)
if ids[0] == 0:
color_val[0] = 0
color_gt[:,:,i] = color_val[idx]
color_gt = color_gt / 255
if gt_seed is not None:
gt_seeds_listx, gt_seeds_listy = np.where(gt_seed != 0)
if raw is not None:
plt.figure(figsize=(20,20),dpi=100)
plt.subplot(121)
plt.imshow(raw)
plt.imshow(color_pred, alpha=alpha)
if pred_seed is not None:
plt.scatter(pred_seeds_listy, pred_seeds_listx, c='k', marker='.')
plt.axis('off')
plt.subplot(122)
plt.imshow(raw)
plt.imshow(color_gt, alpha=alpha)
if gt_seed is not None:
plt.scatter(gt_seeds_listy, gt_seeds_listx, c='k', marker='.')
plt.axis('off')
# plt.show()
plt.savefig(os.path.join(out_path, str(k).zfill(4)+'.png'), bbox_inches = 'tight')
else:
plt.figure(figsize=(20,20),dpi=100)
plt.subplot(121)
plt.imshow(color_pred)
if pred_seed is not None:
plt.scatter(pred_seeds_listy, pred_seeds_listx, c='b')
plt.axis('off')
plt.subplot(122)
plt.imshow(color_gt)
if gt_seed is not None:
plt.scatter(gt_seeds_listy, gt_seeds_listx, c='b')
plt.axis('off')
# plt.show()
plt.savefig(os.path.join(out_path, str(k).zfill(4)+'.png'), bbox_inches = 'tight')
plt.close('all')
def draw_fragments_noseeds(out_path, k, pred, gt=None, raw=None, alpha=0.8):
m,n = pred.shape
ids = np.unique(pred)
size = len(ids)
print("k = %d, the neurons number of pred is %d" % (k, size))
color_pred = np.zeros([m, n, 3])
idx = np.searchsorted(ids, pred)
for i in range(3):
color_val = np.random.randint(0, 255, ids.shape)
if ids[0] == 0:
color_val[0] = 0
color_pred[:,:,i] = color_val[idx]
color_pred = color_pred / 255
if gt is not None:
ids = np.unique(gt)
size = len(ids)
print("k = %d, the neurons number of gt is %d" % (k, size))
color_gt= np.zeros([m, n, 3])
idx = np.searchsorted(ids, gt)
for i in range(3):
color_val = np.random.randint(0, 255, ids.shape)
if ids[0] == 0:
color_val[0] = 0
color_gt[:,:,i] = color_val[idx]
color_gt = color_gt / 255
if gt is not None:
if raw is not None:
plt.figure(figsize=(20,20),dpi=100)
plt.subplot(121)
plt.imshow(raw)
plt.imshow(color_pred, alpha=alpha)
plt.axis('off')
plt.subplot(122)
plt.imshow(raw)
plt.imshow(color_gt, alpha=alpha)
plt.axis('off')
else:
plt.figure(figsize=(20,20),dpi=100)
plt.subplot(121)
plt.imshow(color_pred)
plt.axis('off')
plt.subplot(122)
plt.imshow(color_gt)
plt.axis('off')
else:
if raw is not None:
plt.figure(figsize=(20,20),dpi=100)
plt.imshow(raw)
plt.imshow(color_pred, alpha=alpha)
plt.axis('off')
else:
plt.figure(figsize=(20,20),dpi=100)
plt.imshow(color_pred)
plt.axis('off')
plt.savefig(os.path.join(out_path, str(k).zfill(4)+'.png'), bbox_inches = 'tight')
plt.close('all')
def draw_fragments_3d(out_path, pred, gt=None, raw=None, alpha=0.8):
d,m,n = pred.shape
ids = np.unique(pred)
size = len(ids)
print("the neurons number of pred is %d" % size)
color_pred = np.zeros([d, m, n, 3])
idx = np.searchsorted(ids, pred)
for i in range(3):
color_val = np.random.randint(0, 255, ids.shape)
if ids[0] == 0:
color_val[0] = 0
color_pred[:,:,:,i] = color_val[idx]
color_pred = color_pred / 255
if gt is not None:
ids = np.unique(gt)
size = len(ids)
print("the neurons number of gt is %d" % size)
color_gt= np.zeros([d, m, n, 3])
idx = np.searchsorted(ids, gt)
for i in range(3):
color_val = np.random.randint(0, 255, ids.shape)
if ids[0] == 0:
color_val[0] = 0
color_gt[:,:,:,i] = color_val[idx]
color_gt = color_gt / 255
if gt is not None:
if raw is not None:
for k in range(d):
plt.figure(figsize=(20,20),dpi=100)
plt.subplot(121)
plt.imshow(raw[k])
plt.imshow(color_pred[k], alpha=alpha)
plt.axis('off')
plt.subplot(122)
plt.imshow(raw[k])
plt.imshow(color_gt[k], alpha=alpha)
plt.axis('off')
plt.savefig(os.path.join(out_path, str(k).zfill(4)+'.png'), bbox_inches = 'tight')
plt.close('all')
else:
for k in range(d):
plt.figure(figsize=(20,20),dpi=100)
plt.subplot(121)
plt.imshow(color_pred[k])
plt.axis('off')
plt.subplot(122)
plt.imshow(color_gt[k])
plt.axis('off')
plt.savefig(os.path.join(out_path, str(k).zfill(4)+'.png'), bbox_inches = 'tight')
plt.close('all')
else:
if raw is not None:
for k in range(d):
plt.figure(figsize=(20,20),dpi=100)
plt.imshow(raw[k])
plt.imshow(color_pred[k], alpha=alpha)
plt.axis('off')
plt.savefig(os.path.join(out_path, str(k).zfill(4)+'.png'), bbox_inches = 'tight')
plt.close('all')
else:
for k in range(d):
plt.figure(figsize=(20,20),dpi=100)
plt.imshow(color_pred[k])
plt.axis('off')
plt.savefig(os.path.join(out_path, str(k).zfill(4)+'.png'), bbox_inches = 'tight')
plt.close('all')
if __name__ == "__main__":
in_path1 = '../data/snemi3d/AC4_inputs.h5'
in_path2 = '../data/snemi3d/AC4_labels.h5'
f = h5py.File(in_path1, 'r')
raw = f['main'][:]
f.close()
f = h5py.File(in_path2, 'r')
labels = f['main'][:]
f.close()
out_path = '../data/snemi3d/AC4'
if not os.path.exists(out_path):
os.mkdir(out_path)
draw_fragments_3d(out_path, labels, None, raw)