-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathutils.py
185 lines (144 loc) · 6.07 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
import torch
# import torch.nn as nn
# import torch.nn.functional as F
import numpy as np
torch.set_default_dtype(torch.float32)
HUGE_NUMBER = 1e10
TINY_NUMBER = 1e-6 # float32 only has 7 decimal digits precision
# misc utils
def img2mse(x, y, mask=None):
if mask is None:
return torch.mean((x - y) * (x - y))
else:
return torch.sum((x - y) * (x - y) * mask.unsqueeze(-1)) / (torch.sum(mask) * x.shape[-1] + TINY_NUMBER)
def img2mae(x, y, mask=None):
if mask is None:
return torch.nn.functional.l1_loss(x, y)
else:
return torch.sum((x - y) * (x - y) * mask.unsqueeze(-1)) / (torch.sum(mask) * x.shape[-1] + TINY_NUMBER)
img_HWC2CHW = lambda x: x.permute(2, 0, 1)
gray2rgb = lambda x: x.unsqueeze(2).repeat(1, 1, 3)
def DisLoss(dis_map, optFlow_map, AO_mask):
mask = AO_mask
# loss = torch.mean(((dis_map[mask] - optFlow_map[mask]))**2)
loss = torch.nn.functional.l1_loss(dis_map[mask] , optFlow_map[mask])
if mask.sum() == 0:
loss = torch.tensor(0)
loss.item = lambda: 0
return loss
return loss
def TransLoss(bg_lambda, c):
return torch.mean(torch.log(1+(bg_lambda**2/c)))
def normalize(x):
min = x.min()
max = x.max()
return (x - min) / ((max - min) + TINY_NUMBER)
def MaskLoss(fg_mask, mask_gt, AO_mask):
mask = mask_gt == 1
# loss = torch.nn.functional.binary_cross_entropy(fg_mask[mask], mask_gt[mask], reduction='mean')
mask = torch.logical_and(mask , AO_mask)
if mask.sum() == 0:
loss = torch.tensor(0)
loss.item = lambda: 0
return loss
# loss = torch.nn.functional.l1_loss(fg_mask[mask], mask_gt[mask])
loss = torch.nn.functional.binary_cross_entropy(fg_mask[mask], mask_gt[mask], reduction='mean')
# loss = torch.mean((fg_mask[mask] - mask_gt[mask])**2)
return loss
def FlowLoss_patches(curr_rgb, prev_rgb, optical_flow, events_gt, AO_mask):
events = reshape_patches_edited(curr_rgb, prev_rgb, optical_flow)
# mask = torch.all(optical_flow != 0, dim=1).unsqueeze(1).expand(-1, 3)
# mask = AO >= 0.2
# mask = mask.unsqueeze(-1).expand(-1, 3)
# if mask.sum() == 0:
# loss = torch.tensor(0)
# loss.item = lambda: 0
# return loss
return torch.nn.functional.l1_loss(events, events_gt, reduction='mean')
def reshape_patches_edited(curr_rgb, prev_rgb, optical_flow):
curr_rgb = curr_rgb.reshape((256, 2, 2, 3))
prev_rgb = prev_rgb.reshape((256, 2, 2, 3))
prev_rgb = prev_rgb*255
optical_flow = optical_flow.reshape((256, 2, 2, 2))
previous_gradient_x = torch.zeros_like(prev_rgb)
previous_gradient_y = torch.zeros_like(prev_rgb)
previous_gradient_x[:,:-1,:,:] = prev_rgb[:,1:,:,:] - prev_rgb[:,:-1,:,:]
previous_gradient_y[:,:,:-1,:] = prev_rgb[:,:,1:,:] - prev_rgb[:,:,:-1,:]
previous_gradient_x[:,-1,:,:] = previous_gradient_x[:,-2,:,:]
previous_gradient_y[:,:,-1,:] = previous_gradient_y[:,:,-2,:]
events = -previous_gradient_x * optical_flow[:,:,:,0].unsqueeze(-1) - previous_gradient_y * optical_flow[:,:,:,1].unsqueeze(-1)
return events.reshape((1024, 3))
to8b = lambda x: (255 * np.clip(x, 0, 1)).astype(np.uint8)
# gray2rgb = lambda x: np.tile(x[:,:,np.newaxis], (1, 1, 3))
mse2psnr = lambda x: -10. * np.log(x+TINY_NUMBER) / np.log(10.)
########################################################################################################################
#
########################################################################################################################
from matplotlib.backends.backend_agg import FigureCanvasAgg
from matplotlib.figure import Figure
import matplotlib as mpl
from matplotlib import cm
import cv2
def get_vertical_colorbar(h, vmin, vmax, cmap_name='jet', label=None):
fig = Figure(figsize=(1.2, 8), dpi=100)
fig.subplots_adjust(right=1.5)
canvas = FigureCanvasAgg(fig)
# Do some plotting.
ax = fig.add_subplot(111)
cmap = cm.get_cmap(cmap_name)
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
tick_cnt = 6
tick_loc = np.linspace(vmin, vmax, tick_cnt)
cb1 = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
norm=norm,
ticks=tick_loc,
orientation='vertical')
tick_label = ['{:3.2f}'.format(x) for x in tick_loc]
cb1.set_ticklabels(tick_label)
cb1.ax.tick_params(labelsize=18, rotation=0)
if label is not None:
cb1.set_label(label)
fig.tight_layout()
canvas.draw()
s, (width, height) = canvas.print_to_buffer()
im = np.frombuffer(s, np.uint8).reshape((height, width, 4))
im = im[:, :, :3].astype(np.float32) / 255.
if h != im.shape[0]:
w = int(im.shape[1] / im.shape[0] * h)
im = cv2.resize(im, (w, h), interpolation=cv2.INTER_AREA)
return im
def colorize_np(x, cmap_name='jet', mask=None, append_cbar=False):
if mask is not None:
# vmin, vmax = np.percentile(x[mask], (1, 99))
vmin = np.min(x[mask])
vmax = np.max(x[mask])
vmin = vmin - np.abs(vmin) * 0.01
x[np.logical_not(mask)] = vmin
x = np.clip(x, vmin, vmax)
# print(vmin, vmax)
else:
vmin = x.min()
vmax = x.max() + TINY_NUMBER
x = (x - vmin) / (vmax - vmin)
# x = np.clip(x, 0., 1.)
cmap = cm.get_cmap(cmap_name)
x_new = cmap(x)[:, :, :3]
if mask is not None:
mask = np.float32(mask[:, :, np.newaxis])
x_new = x_new * mask + np.zeros_like(x_new) * (1. - mask)
cbar = get_vertical_colorbar(h=x.shape[0], vmin=vmin, vmax=vmax, cmap_name=cmap_name)
if append_cbar:
x_new = np.concatenate((x_new, np.zeros_like(x_new[:, :5, :]), cbar), axis=1)
return x_new
else:
return x_new, cbar
# tensor
def colorize(x, cmap_name='jet', append_cbar=False, mask=None):
x = x.numpy()
if mask is not None:
mask = mask.numpy().astype(dtype=np.bool)
x, cbar = colorize_np(x, cmap_name, mask)
if append_cbar:
x = np.concatenate((x, np.zeros_like(x[:, :5, :]), cbar), axis=1)
x = torch.from_numpy(x)
return x