-
Notifications
You must be signed in to change notification settings - Fork 9
/
TrainDatasetDF3D.py
executable file
·330 lines (272 loc) · 12.8 KB
/
TrainDatasetDF3D.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
from torch.utils.data import Dataset
import numpy as np
import os
import random
import torchvision.transforms as transforms
from PIL import Image, ImageOps
import torch
from PIL.ImageFilter import GaussianBlur
import trimesh
import logging
log = logging.getLogger('trimesh')
log.setLevel(40)
def load_trimesh(root_dir):
folders = os.listdir(root_dir)
meshs = {}
for i, f in enumerate(folders):
sub_name = f
meshs[sub_name] = trimesh.load(os.path.join(root_dir, f, '%s.obj' % (sub_name+'.obj_scaled')))
return meshs
class TrainDatasetDF3D(Dataset):
@staticmethod
def modify_commandline_options(parser, is_train):
return parser
def __init__(self, opt, phase='train'):
self.opt = opt
self.projection_mode = 'orthogonal'
# Path setup
self.root = self.opt.dataroot
self.RENDER = os.path.join(self.root, 'RENDER')
self.MASK = os.path.join(self.root, 'MASK')
self.PARAM = os.path.join(self.root, 'PARAM')
self.OBJ = os.path.join(self.root, 'GEO', 'OBJ')
self.TARGET = os.path.join(self.root, 'TARGET')
self.KEY_POINT = os.path.join(self.root, 'KEY_POINT')
self.B_MIN = np.array([-0.5, -0.5, -0.5])
self.B_MAX = np.array([0.5, 0.5, 0.5])
self.is_train = (phase == 'train')
self.load_size = self.opt.loadSize
self.num_views = self.opt.num_views
self.num_sample_inout = self.opt.num_sample_inout
self.num_key_points = self.opt.num_anchor_points
self.sample_distribution = np.array(self.opt.sample_distribution)
self.sample_sigmas = np.array(self.opt.sigma)
assert len(self.sample_distribution) == len(self.sample_sigmas)
self.num_samples = np.rint(self.sample_distribution * self.num_sample_inout).astype(np.uint32)
self.yaw_list = [0, 8, 16, 24, 32, 40, 48, 56, 64, 288, 296, 304, 312, 320, 328, 336, 344, 352]
self.pitch_list = [0]
self.subjects = self.get_subjects()
print(len(self.subjects))
# PIL to tensor
self.to_tensor = transforms.Compose([
transforms.Resize(self.load_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# augmentation
self.aug_trans = transforms.Compose([
transforms.ColorJitter(brightness=opt.aug_bri, contrast=opt.aug_con, saturation=opt.aug_sat,
hue=opt.aug_hue)
])
def get_subjects(self):
all_subjects = os.listdir(self.RENDER)
var_subjects = np.loadtxt(os.path.join(self.root, 'val.txt'), dtype=str)
if len(var_subjects) == 0:
return all_subjects
if self.is_train:
return sorted(list(set(all_subjects) - set(var_subjects)))
else:
return sorted(list(var_subjects))
def __len__(self):
return len(self.subjects) * len(self.yaw_list) * len(self.pitch_list)
def get_render(self, subject, num_views, yid=0, pid=0, random_sample=False):
'''
Return the render data
:param subject: subject name
:param num_views: how many views to return
:param view_id: the first view_id. If None, select a random one.
:return:
'img': [num_views, C, W, H] images
'calib': [num_views, 4, 4] calibration matrix
'extrinsic': [num_views, 4, 4] extrinsic matrix
'mask': [num_views, 1, W, H] masks
'''
pitch = self.pitch_list[pid]
# The ids are an even distribution of num_views around view_id
view_ids = [self.yaw_list[(yid + len(self.yaw_list) // num_views * offset) % len(self.yaw_list)]
for offset in range(num_views)]
if random_sample:
view_ids = np.random.choice(self.yaw_list, num_views, replace=False)
calib_list = []
render_list = []
mask_list = []
extrinsic_list = []
scale_intrinsic_list = []
for vid in view_ids:
param_path = os.path.join(self.PARAM, subject, '%d_%d_%02d.npy' % (vid, pitch, 0))
render_path = os.path.join(self.RENDER, subject, '%d_%d_%02d.jpg' % (vid, pitch, 0))
mask_path = os.path.join(self.MASK, subject, '%d_%d_%02d.png' % (vid, pitch, 0))
# loading calibration data
param = np.load(param_path, allow_pickle=True)
# pixel unit / world unit
ortho_ratio = param.item().get('ortho_ratio')
# world unit / model unit
scale = param.item().get('scale')
# camera center world coordinate
center = param.item().get('center')
# model rotation
R = param.item().get('R')
translate = -np.matmul(R, center).reshape(3, 1)
extrinsic = np.concatenate([R, translate], axis=1)
extrinsic = np.concatenate([extrinsic, np.array([0, 0, 0, 1]).reshape(1, 4)], 0)
# Match camera space to image pixel space
scale_intrinsic = np.identity(4)
scale_intrinsic[0, 0] = scale / ortho_ratio
scale_intrinsic[1, 1] = -scale / ortho_ratio
scale_intrinsic[2, 2] = scale / ortho_ratio
# Match image pixel space to image uv space
uv_intrinsic = np.identity(4)
uv_intrinsic[0, 0] = 1.0 / float(self.opt.loadSize // 2)
uv_intrinsic[1, 1] = 1.0 / float(self.opt.loadSize // 2)
uv_intrinsic[2, 2] = 1.0 / float(self.opt.loadSize // 2)
# Transform under image pixel space
trans_intrinsic = np.identity(4)
mask = Image.open(mask_path).convert('L')
render = Image.open(render_path).convert('RGB')
if self.is_train:
# Pad images
pad_size = int(0.1 * self.load_size)
render = ImageOps.expand(render, pad_size, fill=0)
mask = ImageOps.expand(mask, pad_size, fill=0)
w, h = render.size
th, tw = self.load_size, self.load_size
# random flip
if self.opt.random_flip and np.random.rand() > 0.5:
scale_intrinsic[0, 0] *= -1
render = transforms.RandomHorizontalFlip(p=1.0)(render)
mask = transforms.RandomHorizontalFlip(p=1.0)(mask)
# random scale
if self.opt.random_scale:
rand_scale = random.uniform(0.9, 1.1)
w = int(rand_scale * w)
h = int(rand_scale * h)
render = render.resize((w, h), Image.BILINEAR)
mask = mask.resize((w, h), Image.NEAREST)
scale_intrinsic *= rand_scale
# random translate in the pixel space
if self.opt.random_trans:
dx = random.randint(-int(round((w - tw) / 10.)),
int(round((w - tw) / 10.)))
dy = random.randint(-int(round((h - th) / 10.)),
int(round((h - th) / 10.)))
else:
dx = 0
dy = 0
trans_intrinsic[0, 3] = -dx / float(self.opt.loadSize // 2)
trans_intrinsic[1, 3] = -dy / float(self.opt.loadSize // 2)
x1 = int(round((w - tw) / 2.)) + dx
y1 = int(round((h - th) / 2.)) + dy
render = render.crop((x1, y1, x1 + tw, y1 + th))
mask = mask.crop((x1, y1, x1 + tw, y1 + th))
render = self.aug_trans(render)
# random blur
if self.opt.aug_blur > 0.00001:
blur = GaussianBlur(np.random.uniform(0, self.opt.aug_blur))
render = render.filter(blur)
intrinsic = np.matmul(trans_intrinsic, np.matmul(uv_intrinsic, scale_intrinsic))
calib = torch.Tensor(np.matmul(intrinsic, extrinsic)).float()
extrinsic = torch.Tensor(extrinsic).float()
mask = transforms.Resize(self.load_size)(mask)
mask = transforms.ToTensor()(mask).float()
mask_list.append(mask)
render = self.to_tensor(render)
render = mask.expand_as(render) * render
render_list.append(render)
calib_list.append(calib)
extrinsic_list.append(extrinsic)
scale_intrinsic_list.append(scale_intrinsic)
return {
'img': torch.stack(render_list, dim=0),
'calib': torch.stack(calib_list, dim=0),
'extrinsic': torch.stack(extrinsic_list, dim=0),
'scale_intrinsic': abs(scale_intrinsic_list[0][0][0]) / float(self.opt.loadSize // 2),
'mask': torch.stack(mask_list, dim=0)
}
def select_sampling_method(self, subject, scale_intrinsic):
if not self.is_train:
random.seed(1991)
np.random.seed(1991)
torch.manual_seed(1991)
key_points_path = os.path.join(self.KEY_POINT, subject, 'key_point_{}.npz'.format(self.num_key_points))
key_points_npz = np.load(key_points_path)
key_points = key_points_npz['kp']
key_points = key_points.T
key_points = torch.Tensor(key_points).float()
sample_points = []
distances = []
sample_neighbors = []
for i, num in enumerate(self.num_samples):
boundary_samples_path = os.path.join(self.TARGET, subject, 'boundary_{}_direct_samples.npz'.format(self.sample_sigmas[i]))
boundary_samples_npz = np.load(boundary_samples_path)
boundary_sample_points = boundary_samples_npz['points']
boundary_sample_df = boundary_samples_npz['df']
boundary_sample_neighbors = boundary_samples_npz['neighbors']
total_indices = np.arange(boundary_sample_points.shape[0])
np.random.shuffle(total_indices)
sample_indices = total_indices[:num]
sample_points.extend(boundary_sample_points[sample_indices])
distances.extend(boundary_sample_df[sample_indices])
sample_neighbors.extend(boundary_sample_neighbors[sample_indices])
assert len(sample_points) == self.num_sample_inout
assert len(distances) == self.num_sample_inout
samples = np.array(sample_points).T
distances = np.array(distances)
samples = torch.Tensor(samples).float()
labels = torch.Tensor(distances).float() * scale_intrinsic
if self.opt.grad_constraint:
neighbors = np.array(sample_neighbors).T
neighbors = torch.Tensor(neighbors).float()
else:
neighbors = None
if neighbors is not None:
return {
'samples': samples,
'labels': labels,
'key_points': key_points,
'neighbors': neighbors
}
else:
return {
'samples': samples,
'labels': labels,
'key_points': key_points
}
def get_item(self, index):
# In case of a missing file or IO error, switch to a random sample instead
sid = index % len(self.subjects)
tmp = index // len(self.subjects)
yid = tmp % len(self.yaw_list)
pid = tmp // len(self.yaw_list)
# name of the subject 'rp_xxxx_xxx'
subject = self.subjects[sid]
res = {
'name': subject,
'mesh_path': os.path.join(self.OBJ, subject + '.obj'),
'sid': sid,
'yid': yid,
'pid': pid,
'b_min': self.B_MIN,
'b_max': self.B_MAX,
}
render_data = self.get_render(subject, num_views=self.num_views, yid=yid, pid=pid,
random_sample=self.opt.random_multiview)
res.update(render_data)
if self.opt.num_sample_inout:
sample_data = self.select_sampling_method(subject, res['scale_intrinsic'])
res.update(sample_data)
# img = np.uint8((np.transpose(render_data['img'][0].numpy(), (1, 2, 0)) * 0.5 + 0.5)[:, :, ::-1] * 255.0).copy()
# rot = render_data['calib'][0,:3, :3]
# trans = render_data['calib'][0,:3, 3:4]
# # pts = torch.addmm(trans, rot, sample_data['samples'][:, sample_data['labels'][0] > 0.5]) # [3, N]
# # pts = torch.addmm(trans, rot, sample_data['samples']) # [3, N]
# # pts = 0.5 * (pts.numpy().T + 1.0) * render_data['img'].size(2)
# kps = torch.addmm(trans, rot, sample_data['key_points'])
# kps = 0.5 * (kps.numpy().T + 1.0) * render_data['img'].size(2)
# for p in kps:
# img = cv2.circle(img, (p[0], p[1]), 2, (0,255,0), -1)
# cv2.imwrite('test_calib_kp.jpg', img)
# # cv2.imshow('test', img)
# # cv2.waitKey(1)
return res
def __getitem__(self, index):
return self.get_item(index)