-
Notifications
You must be signed in to change notification settings - Fork 0
/
masking.py
290 lines (214 loc) · 10.4 KB
/
masking.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
'''This file has been used for the research project course (ENGN8601/ENGN8602) by Namas Bhandari of the Australian National University. The file can be found on github.com/namas191297/evaluating_mvs_in_cpc'''
import numpy as np
import matplotlib.pyplot as plt
import cv2
import scipy.ndimage
from matplotlib import cm
from mpl_toolkits.axes_grid1 import make_axes_locatable
import os
import torch.nn.functional as F
import torch
np.printoptions(precision=2)
def mask_loss(gt_mask, pred_mask):
'''This function is used to calculate the Binary Cross Entropy loss and the IoU between the estimated depthmap
after mask estimation and the ground truth depthmap'''
gt_mask = torch.from_numpy(gt_mask)
pred_mask = torch.from_numpy(pred_mask)
loss = F.binary_cross_entropy(pred_mask.float(), gt_mask.float())
gt_pred = gt_mask > .5
cv_pred = pred_mask > .5
intersection = torch.sum(cv_pred & gt_pred, dtype=torch.float32, dim=[0, 1])
union = torch.sum(cv_pred | gt_pred, dtype=torch.float32, dim=[0, 1])
gt_sum = torch.sum(gt_pred, dtype=torch.float32, dim=[0, 1])
cv_sum = torch.sum(cv_pred, dtype=torch.float32, dim=[0, 1])
acc = torch.mean((cv_pred == gt_pred).to(dtype=torch.float32))
prec = intersection / cv_sum
prec[cv_sum == 0] = 1 - intersection.clamp(0, 1)[cv_sum == 0]
prec = prec.mean()
rec = intersection / gt_sum
rec[gt_sum == 0] = 1 - intersection.clamp(0, 1)[gt_sum == 0]
rec = rec.mean()
iou = intersection / union
iou[union == 0] = 1
iou = iou.mean()
return {
"loss": loss,
"acc": acc,
"prec": prec,
"rec": rec,
"iou": iou
}
def weighted_entropy(entropies, weights):
'''This function is used to calculate the weighted entropy from the given input entropy maps and the weights'''
entropies = np.array(entropies)
entropy = np.zeros((512, 512))
for ix, w in enumerate(weights):
entropy += np.asarray(entropies[ix]) * w
entropy /= np.sum(weights)
return entropy
def plot_grid(ref_img, entropies, op_dir, gt_mask, th_range=10):
'''This function is used to calculate the 10 best estimated masks for each image and plots it into a grid and saves the mask in a .npy file'''
refcopy = ref_img.copy()
w_range = np.linspace(0.3, 0.9, 6)
#Load and upsample the entropies
if entropies == None:
e_64 = np.load('prob_vol_entropy_64.npy')[0].transpose(1,2,0).reshape(64,64)
e_64 = scipy.ndimage.zoom(e_64, 8, order=1)
e_128 = np.load('prob_vol_entropy_128.npy')[0].transpose(1,2,0).reshape(128,128)
e_128 = scipy.ndimage.zoom(e_128, 4, order=1)
e_256 = np.load('prob_vol_entropy_256.npy')[0].transpose(1,2,0).reshape(256,256)
e_256 = scipy.ndimage.zoom(e_256, 2, order=1)
entropies = [e_64, e_128, e_256]
else:
e_64, e_128, e_256 = entropies[0], entropies[1], entropies[2]
# Compute the weighted average of entropies
#weights = weights
# Loop through the weights
max_loss = 1e+5
lowest_losses = []
lowest_ious = []
mds = []
for w1 in w_range:
for w2 in w_range:
for w3 in w_range:
weights = [w1, w2, w3]
entropy = weighted_entropy(entropies, weights)
#Save the entropies
#plt.imsave(f'{op_dir}/{str(w1)+str(w2)+str(w3)}entropy_64.png', e_64)
#plt.imsave(f'{op_dir}/{str(w1)+str(w2)+str(w3)}entropy_128.png', e_128)
#plt.imsave(f'{op_dir}/{str(w1)+str(w2)+str(w3)}entropy_256.png', e_256)
#Initialize masks
mask = np.zeros((512,512))
threshold_range = np.linspace(1.1, 1.8, 20)
#Calculate all masks and plot
for idx, th in enumerate(threshold_range):
mask[np.where(entropy > th)[0], np.where(entropy > th)[1]] = 0
mask[np.where(entropy < th)[0], np.where(entropy < th)[1]] = 1
#Calculate loss dictionary
d = mask_loss(gt_mask, mask)
current_loss = d['loss'].numpy().copy()
md = {
'refcopy':refcopy,
'weights':weights,
'mask':mask,
'entropy':entropy,
'th':th,
'd':d,
}
mds.append(md)
if current_loss < max_loss:
max_loss = current_loss
best = {
'refcopy':refcopy,
'weights':weights,
'mask':mask,
'entropy':entropy,
'th':th,
'd':d,
}
# Calculated occluded pixels
valid_pixels = np.where(md['mask'] == 1)
missing_pixels = np.where(md['mask'] == 0)
# Apply mask
masked_image = cv2.bitwise_and(md['refcopy'], md['refcopy'], mask=md['mask'].astype(np.uint8))
fig = plt.figure(figsize=(20,10))
#plt.title(f'Weights (w1,w2,w3):{weights}, Threshold:{th}')
ax1 = fig.add_subplot(1,4,1)
ax1.title.set_text('Ref Image')
ax1.imshow(md['refcopy'])
plt.axis('off')
ax2 = fig.add_subplot(1,4,2)
ax2.title.set_text('Entropy')
divider = make_axes_locatable(ax2)
cax = divider.append_axes('right', size='5%', pad=0.05)
ent_im = ax2.imshow(md['entropy'])
plt.colorbar(ent_im,fraction=0.04, pad=10)
plt.axis('off')
ax3 = fig.add_subplot(1,4,3)
ax3.title.set_text(f'Mask THR={np.round(md["th"],2)}, Weights={md["weights"]}, Loss:{np.round(md["d"]["loss"],2)}, IoU:{np.round(md["d"]["iou"],2)}')
ax3.imshow(md['mask'])
plt.axis('off')
ax4 = fig.add_subplot(1,4,4)
ax4.title.set_text('Masked Image')
ax4.imshow(masked_image)
plt.axis('off')
plt.savefig(op_dir + 'plot' + '_' + str(w1) + str(w2) + str(w3) + '_' + str(md["th"]) + '.png')
np.save(f'{op_dir}plot_{str(w1)}{str(w2)}{str(w3)}_{str(md["th"])}.npy', md["mask"])
plt.close('all') ##Remove this if any error
'''The following code plots the best mask obtained as per the losses (However, this can result in worse masks sometimes, so check manually)'''
#Calculated occluded pixels
valid_pixels = np.where(best['mask'] == 1)
missing_pixels = np.where(best['mask'] == 0)
# Apply mask
masked_image = cv2.bitwise_and(best['refcopy'], best['refcopy'], mask=best['mask'].astype(np.uint8))
fig = plt.figure(figsize=(20,10))
#plt.title(f'Weights (w1,w2,w3):{weights}, Threshold:{th}')
# Append the best loss
lowest_losses.append(np.round(best["d"]["loss"],2))
lowest_ious.append(np.round(best["d"]["iou"],2))
# Plots
ax1 = fig.add_subplot(1,4,1)
ax1.title.set_text('Ref Image')
ax1.imshow(best['refcopy'])
plt.axis('off')
ax2 = fig.add_subplot(1,4,2)
ax2.title.set_text('Entropy')
divider = make_axes_locatable(ax2)
cax = divider.append_axes('right', size='5%', pad=0.05)
ent_im = ax2.imshow(best['entropy'])
plt.colorbar(ent_im,fraction=0.04, pad=10)
plt.axis('off')
ax3 = fig.add_subplot(1,4,3)
ax3.title.set_text(f'Mask THR={np.round(best["th"],2)}, Weights={best["weights"]}, Loss:{np.round(best["d"]["loss"],2)}, IoU:{np.round(best["d"]["iou"],2)}')
ax3.imshow(best['mask'])
plt.axis('off')
ax4 = fig.add_subplot(1,4,4)
ax4.title.set_text('Masked Image')
ax4.imshow(masked_image)
plt.axis('off')
plt.savefig(op_dir + 'best' + '.png')
plt.close('all') ##Remove this if any error
return np.round(best["d"]["loss"],2), np.round(best["d"]["iou"],2)
if __name__ == '__main__':
print('Starting Mask Estimation.....')
r_dir = 'ref_images/'
lowest_losses = []
lowest_ious = []
for idx, imf in enumerate(os.listdir(r_dir)):
imf_dir = r_dir + imf + '/'
ref_image = None
if os.path.exists(imf_dir + 'grid_outputs'):
for f in os.listdir(imf_dir + 'grid_outputs'):
os.remove(imf_dir + 'grid_outputs' + '/' + f)
os.rmdir(imf_dir + 'grid_outputs')
os.mkdir(imf_dir + 'grid_outputs')
else:
op_dir = os.mkdir(imf_dir + 'grid_outputs')
grid_dir = imf_dir + 'grid_outputs/'
for item in os.listdir(imf_dir):
item_path = imf_dir + item
#gt_path = imf_dir + 'label.png'
gt_path = imf_dir + 'mask.npy'
if 'refimage' in item and 'png' in item:
ref_image = plt.imread(item_path)[:,:,:3]
elif '64' in item:
e_64 = np.load(item_path)[0].transpose(1,2,0).reshape(64,64)
e_64 = scipy.ndimage.zoom(e_64, 8, order=1)
elif '128' in item:
e_128 = np.load(item_path)[0].transpose(1,2,0).reshape(128,128)
e_128 = scipy.ndimage.zoom(e_128, 4, order=1)
elif '256' in item:
e_256 = np.load(item_path)[0].transpose(1,2,0).reshape(256,256)
e_256 = scipy.ndimage.zoom(e_256, 2, order=1)
#gt_mask = cv2.imread(gt_path)[:,:,2]
#gt_mask = np.where(gt_mask == 0, 1, 0)
gt_mask = np.load(gt_path)[0][0]
#plt.imshow(gt_mask)
#plt.colorbar()
#plt.show()
entropies = [e_64, e_128, e_256]
lowest_loss, lowest_iou = plot_grid(ref_image, entropies, grid_dir, gt_mask)
lowest_losses.append(lowest_loss)
lowest_ious.append(lowest_ious)
print(f'Done plotting for Image {idx+1}/{len(os.listdir(r_dir))}: Loss:{lowest_loss}, IoU:{lowest_iou}')
print(f'Average Loss:{np.array(lowest_losses).mean()}, Average IoU:{np.array(lowest_ious).mean()}')