-
Notifications
You must be signed in to change notification settings - Fork 28
/
utils.py
302 lines (228 loc) · 8.76 KB
/
utils.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
from keras import backend as K
import tensorflow as tf
import numpy as np
from PIL import Image
import os, copy, shutil, json
from skimage import measure
class VIS:
def __init__(self, save_path):
self.path = save_path
# TODO
self.semantic_label = None
if os.path.isdir(self.path):
shutil.rmtree(self.path)
os.mkdir(self.path)
self.mean_iu = []
self.cls_iu = []
self.pix_acc = []
self.mean_dice = []
self.cls_dice = []
self.score_history = {}
self.suffix = str(np.random.randint(1000))
self.exam_img = Image.open('palette.png')
self.palette = self.exam_img.palette
# self.orginal_size = (self.exam_img.shape[1], self.exam_img.shape[0])
def palette_info(self):
return np.unique(self.exam_img)
def save_seg(self, label_im, name, im=None, gt=None):
seg = Image.fromarray(label_im.astype(np.uint8), mode='P') # must convert to int8 first
seg.palette = copy.copy(self.palette)
if gt is not None or im is not None:
gt = Image.fromarray(gt.astype(np.uint8), mode='P') # must convert to int8 first]
gt.palette = copy.copy(self.palette)
im = Image.fromarray(im.astype(np.uint8), mode='RGB')
I = Image.new('RGB', (label_im.shape[1]*3, label_im.shape[0]))
I.paste(im,(0,0))
I.paste(gt,(320,0))
I.paste(seg,(640,0))
I.save(os.path.join(self.path, name))
else:
seg.save(os.path.join(self.path, name))
def save_seg2(self, label_im, name, im=None):
seg = Image.fromarray(label_im.astype(np.uint8), mode='P') # must convert to int8 first
seg.palette = copy.copy(self.palette)
if im is not None:
im = Image.fromarray(im.astype(np.uint8), mode='RGB')
I = Image.new('RGB', (label_im.shape[1]*2, label_im.shape[0]))
I.paste(im,(0,0))
I.paste(seg,(256,0))
if '/' in name:
parent_dir = name.split('/')[0]
fname = name.split('/')[1]
dir_name = os.path.join(self.path, parent_dir)
if os.path.exists(dir_name) == False:
os.mkdir(os.path.join(self.path, parent_dir))
I.save(os.path.join(self.path, name))
else:
seg.save(os.path.join(self.path, name))
def add_sample(self, pred, gt):
score_mean, score_cls = mean_IU(pred, gt)
p_accuracy = pixel_accuracy(pred, gt)
dice_mean, dice_cls = dice_coef_2(pred, gt)
self.mean_iu.append(score_mean)
self.cls_iu.append(score_cls)
self.pix_acc.append(p_accuracy)
self.mean_dice.append(dice_mean)
self.cls_dice.append(dice_cls)
return score_mean, p_accuracy, dice_mean
def compute_scores(self, suffix=0):
meanIU = np.mean(np.array(self.mean_iu))
meanIU_per_cls = np.mean(np.array(self.cls_iu), axis=0)
mean_pix_acc = np.mean(np.array(self.pix_acc))
mean_dice = np.mean(np.array(self.mean_dice))
meanDice_per_cls = np.mean(np.array(self.cls_dice), axis=0)
print ('-'*20)
print ('overall mean IU: {} '.format(meanIU))
print ('overall mean Pixel Accuracy: {} '.format(mean_pix_acc))
print ('overall mean Dice: {} '.format(mean_dice))
print ('mean IU per class')
for i, c in enumerate(meanIU_per_cls):
print ('\t class {}: {}'.format(i,c))
print ('mean Dice per class')
for i, c in enumerate(meanDice_per_cls):
print ('\t class {}: {}'.format(i,c))
print ('-'*20)
data = {'mean_IU': '%.2f' % (meanIU),
'mean_IU_cls': ['%.2f'%(a) for a in meanIU_per_cls.tolist()],
'mean_Dice': '%.2f' % (mean_dice),
'mean_Dice_cls': ['%.2f'%(b) for b in meanDice_per_cls.tolist()],
'mean_Pixel_Accuracy': '%.2f' % (mean_pix_acc)
}
self.score_history['%.10d' % suffix] = data
json.dump(self.score_history, open(os.path.join(self.path, 'meanIU{}.json'.format(self.suffix)),'w'), indent=2, sort_keys=True)
def pixel_accuracy(eval_segm, gt_segm):
'''
sum_i(n_ii) / sum_i(t_i)
'''
check_size(eval_segm, gt_segm)
# LATTER!!!
# cl, n_cl = union_classes(eval_segm, gt_segm)
cl = [0,1]
n_cl = 2
_, n_cl_gt = extract_classes(gt_segm)
eval_mask = extract_masks(eval_segm, cl, n_cl)
gt_mask = gt_segm
sum_n_ii = 0
sum_t_i = 0
for i, c in enumerate(cl):
if i != 0:
curr_eval_mask = eval_mask[ :, :, i]
curr_gt_mask = gt_mask[ :, :, i]
sum_n_ii += np.sum(np.logical_and(curr_eval_mask, curr_gt_mask))
sum_t_i += np.sum(curr_gt_mask)
if (sum_t_i == 0):
pixel_accuracy_ = 0
else:
pixel_accuracy_ = float(sum_n_ii) / float(sum_t_i)
return pixel_accuracy_
def mean_IU(eval_segm, gt_segm):
'''
(1/n_cl) * sum_i(n_ii) / (t_i + sum_j(n_ji) - n_ii))
'''
check_size(eval_segm, gt_segm)
# LATTER!!!
# cl, n_cl = union_classes(eval_segm, gt_segm)
cl = [0,1]
n_cl = 2
_, n_cl_gt = extract_classes(gt_segm)
eval_mask = extract_masks(eval_segm, cl, n_cl)
gt_mask = gt_segm
IU = list([0]) * n_cl
for i, c in enumerate(cl):
curr_eval_mask = eval_mask[ :, :, i]
curr_gt_mask = gt_mask[ :, :, i]
if (np.sum(curr_eval_mask) == 0) or (np.sum(curr_gt_mask) == 0):
continue
n_ii = np.sum(np.logical_and(curr_eval_mask, curr_gt_mask))
t_i = np.sum(curr_gt_mask)
n_ij = np.sum(curr_eval_mask)
IU[i] = n_ii / (t_i + n_ij - n_ii)
mean_IU_ = np.sum(IU) / n_cl_gt
return mean_IU_, IU
def mean_accuracy(eval_segm, gt_segm):
'''
(1/n_cl) sum_i(n_ii/t_i)
'''
check_size(eval_segm, gt_segm)
cl, n_cl = extract_classes(gt_segm)
eval_mask, gt_mask = extract_both_masks(eval_segm, gt_segm, cl, n_cl)
accuracy = list([0]) * n_cl
for i, c in enumerate(cl):
curr_eval_mask = eval_mask[i, :, :]
curr_gt_mask = gt_mask[i, :, :]
n_ii = np.sum(np.logical_and(curr_eval_mask, curr_gt_mask))
t_i = np.sum(curr_gt_mask)
if (t_i != 0):
accuracy[i] = n_ii / t_i
mean_accuracy_ = np.mean(accuracy)
return mean_accuracy_
def dice_coef_2(eval_segm, gt_segm):
check_size(eval_segm, gt_segm)
# LATTER!!!
# cl, n_cl = union_classes(eval_segm, gt_segm)
cl = [0,1]
n_cl = 2
smooth = 1.
_, n_cl_gt = extract_classes(gt_segm)
eval_mask = extract_masks(eval_segm, cl, n_cl)
gt_mask = gt_segm
Dice = list([0]) * n_cl
for i, c in enumerate(cl):
curr_eval_mask = eval_mask[ :, :, i]
curr_gt_mask = gt_mask[ :, :, i]
if (np.sum(curr_eval_mask) == 0) or (np.sum(curr_gt_mask) == 0):
continue
y_true_f = curr_eval_mask.flatten()
y_pred_f = curr_gt_mask.flatten()
intersection = np.sum(y_true_f * y_pred_f)
Dice[i] =(2. * intersection + smooth) / (np.sum(y_true_f) + np.sum(y_pred_f) + smooth)
mean_dice_ = np.sum(Dice) / n_cl_gt
return mean_dice_, Dice
def dice_coef(y_true, y_pred, smooth=1.):
check_size(y_true, y_pred)
y_true = K.flatten(y_true)
y_pred = K.flatten(y_pred)
intersection = K.sum(y_true * y_pred)
union = K.sum(y_true) + K.sum(y_pred)
return K.mean( (2. * intersection + smooth) / (union + smooth))
def dice_coef_loss(y_true, y_pred):
return -dice_coef(y_true, y_pred)
def extract_both_masks(eval_segm, gt_segm, cl, n_cl):
eval_mask = extract_masks(eval_segm, cl, n_cl)
gt_mask = extract_masks(gt_segm, cl, n_cl)
return eval_mask, gt_mask
def extract_classes(segm):
cl = np.unique(segm)
n_cl = len(cl)
return cl, n_cl
def union_classes(eval_segm, gt_segm):
eval_cl, _ = extract_classes(eval_segm)
gt_cl, _ = extract_classes(gt_segm)
cl = np.union1d(eval_cl, gt_cl)
n_cl = len(cl)
print cl, n_cl
return cl, n_cl
def extract_masks(segm, cl, n_cl):
h, w = segm_size(segm)
masks = np.zeros((h, w, n_cl))
for i, c in enumerate(cl):
masks[ :, :, i] = segm == c
return masks
def combine_channels(img):
channel_num = img.shape[2]
img_single = np.zeros((img.shape[0], img.shape[1]))
for ch in range(channel_num):
img_single += img[:,:,ch]
return img_single
def segm_size(segm):
try:
height = segm.shape[0]
width = segm.shape[1]
except IndexError:
raise
return height, width
def check_size(eval_segm, gt_segm):
h_e, w_e = segm_size(eval_segm)
h_g, w_g = segm_size(gt_segm)
if (h_e != h_g) or (w_e != w_g):
raise ValueError('Uneuqal image %s and mask %s size' %((h_e, w_e),(h_g, w_g)))