-
Notifications
You must be signed in to change notification settings - Fork 18
/
load_blender.py
551 lines (428 loc) · 21.5 KB
/
load_blender.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
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
import os
import torch
import numpy as np
import imageio
import json
import torch.nn.functional as F
import cv2
import random
from imageio import imread, imwrite
from skimage import color
import os.path as osp
trans_t = lambda t : torch.Tensor([
[1,0,0,0],
[0,1,0,0],
[0,0,1,t],
[0,0,0,1]]).float()
rot_phi = lambda phi : torch.Tensor([
[1,0,0,0],
[0,np.cos(phi),-np.sin(phi),0],
[0,np.sin(phi), np.cos(phi),0],
[0,0,0,1]]).float()
rot_theta = lambda th : torch.Tensor([
[np.cos(th),0,-np.sin(th),0],
[0,1,0,0],
[np.sin(th),0, np.cos(th),0],
[0,0,0,1]]).float()
def pose_spherical(theta, phi, radius):
c2w = trans_t(radius)
c2w = rot_phi(phi/180.*np.pi) @ c2w
c2w = rot_theta(theta/180.*np.pi) @ c2w
c2w = torch.Tensor(np.array([[-1,0,0,0],[0,0,1,0],[0,1,0,0],[0,0,0,1]])) @ c2w
return c2w
def load_blender_data_test(basedir, args, half_res=False, testskip=1, eval_depth=False):
with open(os.path.join(basedir, 'transforms_{}.json'.format("test")), 'r') as fp:
meta = json.load(fp)
imgs = []
poses = []
timesteps = []
counts = [0]
no_time = False
# if args.velocity:
# assert args.pouring
# fname = os.path.join(basedir, frame['file_path'] + '.png')
example_im = imageio.imread(osp.join(basedir, meta['frames'][0]['file_path']))
W = example_im.shape[1]
H = example_im.shape[0]
if half_res:
example_im = cv2.resize(example_im, (W // 2, H // 2), interpolation=cv2.INTER_AREA)
i, j = np.meshgrid(np.arange(W, dtype=np.float32), np.arange(H, dtype=np.float32), indexing='xy')
camera_angle_x = float(meta['camera_angle_x'])
focal = .5 * W / np.tan(.5 * camera_angle_x)
depth_dirs = np.stack([(i-W*.5)/focal, -(j-H*.5)/focal, -np.ones_like(i)], -1)
depths = []
for i, frame in enumerate(meta['frames']):
if "flow" in frame['file_path']:
continue
if len(imgs) > 100:
break
# Skip scene flow config
fname = os.path.join(basedir, frame['file_path'])
if 'depth_train_path' in frame and eval_depth:
depth_fname = frame['depth_train_path']
depth_img = imread(osp.join(basedir, depth_fname), format="hdr")
else:
depth_img = np.zeros((200, 200))
im = imageio.imread(fname)[:, :, :3]
if half_res:
im = cv2.resize(im, (200, 200), interpolation=cv2.INTER_AREA)
else:
im = cv2.resize(im, (400, 400), interpolation=cv2.INTER_AREA)
im = im / 255.
imgs.append(im)
poses.append(np.array(frame['transform_matrix']))
timesteps.append((frame['timestep'] / 449 - 0.5) * 2.)
depths.append(depth_img)
# Randomly only sample 100 timesteps
# if s == 'train':
# random_idxs = list(range(len(imgs)))
# random.shuffle(random_idxs)
# random_idxs = random_idxs[:100]
# imgs = [rix] for rix in random_idxs]
# poses = [poses[rix] for rix in random_idxs]
# imgs = np.array(imgs, dtype=np.uint8).astype(np.float32) / 255. # keep all 4 channels (RGBA)
poses = np.array(poses).astype(np.float32)
timesteps = np.array(timesteps)
H, W, C = imgs[0].shape
camera_angle_x = float(meta['camera_angle_x'])
focal = .5 * W / np.tan(.5 * camera_angle_x)
hwf = [H, W, focal]
return imgs, depths, poses, timesteps, hwf
def load_blender_data(basedir, args, half_res=False, testskip=1):
splits = ['train', 'val', 'test']
metas = {}
for s in splits:
with open(os.path.join(basedir, 'transforms_{}.json'.format(s)), 'r') as fp:
metas[s] = json.load(fp)
all_imgs = []
all_poses = []
all_timesteps = []
counts = [0]
no_time = False
if args.optical_flow:
keypoints = []
keypoints_timesteps = []
keypoints_pose = []
orb_detector = cv2.ORB_create(5000)
matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck = True)
elif args.scene_flow or args.velocity:
locations = []
locations_timesteps = []
bounds = []
# if args.velocity:
# assert args.pouring
# fname = os.path.join(basedir, frame['file_path'] + '.png')
example_im = imageio.imread(osp.join(basedir, metas['train']['frames'][0]['file_path']))
W = example_im.shape[1]
H = example_im.shape[0]
i, j = np.meshgrid(np.arange(W, dtype=np.float32), np.arange(H, dtype=np.float32), indexing='xy')
camera_angle_x = float(metas['train']['camera_angle_x'])
focal = .5 * W / np.tan(.5 * camera_angle_x)
depth_dirs = np.stack([(i-W*.5)/focal, -(j-H*.5)/focal, -np.ones_like(i)], -1)
depths = []
for s in splits:
meta = metas[s]
imgs = []
poses = []
if s=='train' or testskip==0:
skip = 1
else:
skip = testskip
if s == 'train':
if args.optical_flow or args.scene_flow or args.velocity:
chunk = 2
else:
chunk = 2
for i in range(0, len(meta['frames']), chunk):
print(i, len(meta['frames']))
if s != 'train' and i > 100:
break
if args.debug and i > 20:
break
# if i // chunk > args.frames:
# break
if args.optical_flow:
frame = meta['frames'][i]
frame_next = meta['frames'][i+1]
if args.pouring:
fname = os.path.join(basedir, frame['file_path'])
fname_next = os.path.join(basedir, frame_next['file_path'])
else:
fname = os.path.join(basedir, frame['file_path'] + '.png')
fname_next = os.path.join(basedir, frame_next['file_path'] + '.png')
img = imageio.imread(fname)
img_next = imageio.imread(fname_next)
kp1, d1 = orb_detector.detectAndCompute(img, None)
kp2, d2 = orb_detector.detectAndCompute(img_next, None)
matches = matcher.match(d1, d2)
matches.sort(key = lambda x: x.distance)
keypoint_i = []
for i in range(100):
try:
keypoint_i.append([*kp1[matches[i].queryIdx].pt, *kp2[matches[i].trainIdx].pt])
except:
keypoint_i.append([*kp1[matches[0].queryIdx].pt, *kp2[matches[0].trainIdx].pt])
# if i == 0:
# x1, y1 = kp1[matches[i].queryIdx].pt
# x2, y2 = kp2[matches[i].queryIdx].pt
# x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
# img[y1-5:y1+5, x1-5:x1+5] = 0.0
# img_next[y2-5:y2+5, x2-5:x2+5] = 0.0
# imageio.imwrite("img_prev.png", img)
# imageio.imrite("img_next.png", img_next)
# assert False
keypoints_timesteps.append([frame['timestep'], frame_next['timestep']])
keypoints_pose.append(frame['transform_matrix'])
keypoints.append(np.array(keypoint_i))
elif args.scene_flow:
frame = meta['frames'][i]
frame_next = meta['frames'][i+1]
frame_offset = frame['offset']
frame_offset_next = frame_next['offset']
dim = frame['dimensions']
dim_next = frame_next['dimensions']
locations.append([frame_offset, frame_offset_next])
locations_timesteps.append([frame['timestep'], frame_next['timestep']])
bounds.append([dim])
elif args.velocity:
frame = meta['frames'][i]
frame_next = meta['frames'][i+1]
fname = os.path.join(basedir, frame['file_path'])
fname_next = os.path.join(basedir, frame_next['file_path'])
img = (color.rgb2gray(imageio.imread(fname)) * 255).astype(np.uint8)
img_next = (color.rgb2gray(imageio.imread(fname_next)) * 255).astype(np.uint8)
depth_fname = os.path.join(basedir, frame['depth_train_path'])
next_depth = frame_next['depth_train_path']
next_depth = next_depth[:-8] + "{:04}".format(frame_next['timestep']) + ".hdr"
depth_fname_next = os.path.join(basedir, next_depth)
depth_img = imread(depth_fname, format="hdr")
depth_img_next = imread(depth_fname_next, format="hdr")
depth_img = depth_img * depth_dirs
depth_img_next = depth_img_next * depth_dirs
depth_img = np.concatenate([depth_img, np.ones((depth_img.shape[0], depth_img.shape[1], 1))], axis=-1)
depth_img_next = np.concatenate([depth_img_next, np.ones((depth_img_next.shape[0], depth_img_next.shape[1], 1))], axis=-1)
feature_params = dict( maxCorners = 1000,
qualityLevel = 0.15,
minDistance = 7,
blockSize = 7 )
lk_params = dict( winSize = (14,14),
maxLevel = 2,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
# p0 = cv2.goodFeaturesToTrack(img, mask = None, **feature_params)
# if p0 is None:
# continue
# p1, st, err = cv2.calcOpticalFlowPyrLK(img, img_next, p0, None, **lk_params)
# flow = cv2.calcOpticalFlowFarneback(img, img_next, None, 0.5, 3, 15, 3, 5, 1.2, 0)
fp = frame['file_path']
fp = fp.split("/")[1].split(".")[0]
frame_num = int(fp[2:])
flow_fname = os.path.join(basedir, osp.split(frame['file_path'])[0], "flow_{}.npy".format(frame_num))
flow = np.load(flow_fname)
f = 8
x, y = np.meshgrid(np.arange(flow.shape[0]), np.arange(flow.shape[1]))
coord = np.stack([y, x], axis=2)
flow = flow[::f, ::f]
coord = coord[::f, ::f]
mag_diff = np.linalg.norm(flow, ord=2, axis=-1)
mag_diff = mag_diff.reshape(-1)
flow = flow.reshape((-1, 2))
coord = coord.reshape((-1, 2))
idx = np.argsort(mag_diff)
top = 200
max_idx = idx[-top:]
# random_idx = np.random.permutation(flow.shape[0])[:500]
# max_idx = np.concatenate([max_idx, random_idx], axis=0)
flow_max = flow[max_idx]
coord_max = coord[max_idx]
# good_new = p1[st==1]
# good_old = p0[st==1]
bounds_i = []
frame_offset = []
frame_offset_next = []
def clip(x):
return min(max(x, 0), img.shape[0] - 1)
# p0, p1 = p0[:, 0, :], p1[:, 0, :]
# print(p0.shape)
while True:
# for (x1, y1), (x2, y2) in zip(p0, p1):
for ix in range(coord_max.shape[0]):
x1, y1 = coord_max[ix, 1], coord_max[ix, 0]
flow_x, flow_y = flow_max[ix, 0], flow_max[ix, 1]
x2, y2 = clip(round(x1 + flow_x)), clip(round(y1 + flow_y))
x1, y1, x2, y2 = clip(int(x1)), clip(int(y1)), clip(int(x2)), clip(int(y2))
diff_x = abs(x1 - x2)
diff_y = abs(y2 - y1)
# if diff_x > 0 and diff_y > 0:
tf = np.array(frame['transform_matrix'])
# import pdb
# pdb.set_trace()
# print("depth orig ", depth_img[y1, x1])
# print("depth new ", depth_img_next[y1, x1])
# print("frame orig ", frame["offset"], frame['dimensions'])
# print("frame new ", frame_next["offset"], frame_next['dimensions'])
# print("set", x1, y1, x2, y2)
# if diff_x > 0 and diff_y > 0:
output_frame = (tf @ depth_img[y1, x1,:, None])
output_frame_next = (tf @ depth_img_next[y2, x2,:, None])
output_frame = output_frame[:3, 0]
output_frame_next = output_frame_next[:3, 0]
frame_offset.append(output_frame)
frame_offset_next.append(output_frame_next)
# print("print dimensions ", frame['offset'])
# print("print next dimensions ", frame_next['offset'])
# print("predict offset ", frame_offset[-1])
# print("predict next offset ", frame_offset_next[-1])
bounds_i.append([0.05, 0.05, 0.05])
# size = 5
# img[clip(y1-size):clip(y1+size), clip(x1-size):clip(x1+size)] = 255.0
# img_next[clip(y2-size):clip(y2+size), clip(x2-size):clip(x2+size)] = 255.0
# print("set", x1, y1, x2, y2)
if len(bounds_i) == 500:
# assert False
break
if len(bounds_i) == 500:
# imwrite("{}_im_before.png".format(i), img)
# imwrite("{}_im_after.png".format(i), img_next)
# assert False
break
if len(bounds_i) == 0:
break
if (args.velocity) and (len(bounds_i) != 0):
locations.append([frame_offset, frame_offset_next])
if args.pouring:
locations_timesteps.append([(frame['timestep'] / 449 - 0.5) * 2.0, (frame_next['timestep'] / 449 - 0.5) * 2.0])
else:
locations_timesteps.append([frame['timestep'], frame_next['timestep']])
bounds.append(bounds_i)
frame = meta['frames'][i]
if args.pouring:
fname = os.path.join(basedir, frame['file_path'])
else:
fname = os.path.join(basedir, frame['file_path'] + '.png')
if s == "train" and args.camera_depth:
depth_fname = os.path.join(basedir, frame['depth_train_path'])
depth_img = imread(depth_fname, format="hdr")
depth_img = depth_img * depth_dirs
depth_shape = depth_img.shape
depth_img = np.concatenate([depth_img, np.ones((depth_img.shape[0], depth_img.shape[1], 1))], axis=-1)
tf = np.array(frame['transform_matrix'])
depth_img = depth_img.reshape((-1, 4))
depth_parse = tf @ depth_img.transpose((1, 0))
depth_parse = depth_parse.transpose((1, 0))[:, :3].reshape(depth_shape)
depth_parse = np.linalg.norm(depth_parse, ord=2, axis=-1)
# imwrite("test_depth.png", 1 / (1 + depth_parse))
depths.append(depth_parse)
imgs.append(imageio.imread(fname))
poses.append(np.array(frame['transform_matrix']))
if 'timestep' in frame.keys():
if args.pouring:
all_timesteps.append((frame['timestep'] / 449 - 0.5) * 2)
else:
all_timesteps.append(frame['timestep'])
else:
all_timesteps.append(1)
no_time = True
else:
for i, frame in enumerate(meta['frames'][::skip]):
# Skip scene flow config
if 'flow' in frame['file_path']:
continue
if args.pouring:
fname = os.path.join(basedir, frame['file_path'])
else:
fname = os.path.join(basedir, frame['file_path'] + '.png')
imgs.append(imageio.imread(fname))
poses.append(np.array(frame['transform_matrix']))
if 'timestep' in frame.keys():
all_timesteps.append(frame['timestep'])
else:
all_timesteps.append(1)
no_time = True
print(i)
if (not args.render_test) and (i > 30):
break
# Randomly only sample 100 timesteps
# if s == 'train':
# random_idxs = list(range(len(imgs)))
# random.shuffle(random_idxs)
# random_idxs = random_idxs[:100]
# imgs = [rix] for rix in random_idxs]
# poses = [poses[rix] for rix in random_idxs]
print("Loading ims")
imgs = np.array(imgs, dtype=np.uint8).astype(np.float32) / 255. # keep all 4 channels (RGBA)
print("Loading poses")
poses = np.array(poses).astype(np.float32)
print("Finished both")
counts.append(counts[-1] + imgs.shape[0])
all_imgs.append(imgs)
all_poses.append(poses)
print("loading data")
i_split = [np.arange(counts[i], counts[i+1]) for i in range(3)]
imgs = np.concatenate(all_imgs, 0)
poses = np.concatenate(all_poses, 0)
print("finished concatenation")
H, W, C = imgs[0].shape
camera_angle_x = float(meta['camera_angle_x'])
focal = .5 * W / np.tan(.5 * camera_angle_x)
if args.rotate_render:
render_poses = torch.stack([pose_spherical(angle, -30.0, 4.0) for angle in np.linspace(-180,180,40+1)[::-1]], 0)
if no_time:
render_timesteps = np.ones(render_poses.shape[0])
else:
render_timesteps = np.linspace(-1, 1, render_poses.shape[0])
elif args.camera_render:
angle = 0.1 / np.pi * 180
render_poses = torch.stack([pose_spherical(-angle, -angle, 4.0) for _ in np.linspace(-180,180,40+1)[::-1]], 0)
if no_time:
render_timesteps = np.ones(render_poses.shape[0])
else:
render_timesteps = np.linspace(-1, 1, render_poses.shape[0])
elif args.camera_render_after:
angle_up = 0.8 / np.pi * 180
angle_rotate = 0.2 / np.pi * 180
render_poses = torch.stack([pose_spherical(-angle_rotate, -angle_up, 4.0) for _ in np.linspace(-180,180,40+1)[::-1]], 0)
if no_time:
render_timesteps = np.ones(render_poses.shape[0])
else:
render_timesteps = np.linspace(-1, 1, render_poses.shape[0])
elif args.ood_render:
render_poses = torch.stack([pose_spherical(angle, -30.0, 4.0) for angle in np.ones(41)*-180], 0)
render_timesteps = np.linspace(1, 1.5, render_poses.shape[0])
else:
render_poses = torch.stack([pose_spherical(angle, -30.0, 4.0) for angle in np.ones(41)*-180], 0)
if no_time:
render_timesteps = np.ones(render_poses.shape[0])
else:
render_timesteps = np.linspace(-1, 1, render_poses.shape[0])
with open(os.path.join(basedir, 'transforms_render.json'), 'r') as fp:
render_data = json.load(fp)
render_poses = [f['transform_matrix'] for f in render_data['frames']]
render_poses = np.array(render_poses)
if args.pouring:
render_timesteps = np.array([(f['timestep'] / 449 - 0.5) * 2. for f in render_data['frames']])
else:
render_timesteps = np.array([f['timestep'] for f in render_data['frames']])
# render_poses = render_poses[:30]
# render_timesteps = render_timesteps[:30]
if half_res:
H = H//2
W = W//2
focal = focal/2.
imgs_half_res = np.zeros((imgs.shape[0], H, W, C))
for i, img in enumerate(imgs):
imgs_half_res[i] = cv2.resize(img, (W, H), interpolation=cv2.INTER_AREA)
imgs = imgs_half_res
# imgs = tf.image.resize_area(imgs, [400, 400]).numpy()
all_timesteps = np.array(all_timesteps)
# all_timesteps = 2 * (all_timesteps - all_timesteps.min()) / (all_timesteps.max() - all_timesteps.min() + 1e-5) - 1
if args.optical_flow:
return imgs, poses, render_poses, render_timesteps, [H, W, focal], i_split, all_timesteps, keypoints, keypoints_timesteps, keypoints_pose, depths
elif args.scene_flow or args.velocity:
return imgs, poses, render_poses, render_timesteps, [H, W, focal], i_split, all_timesteps, locations, locations_timesteps, bounds, depths
else:
return imgs, poses, render_poses, render_timesteps, [H, W, focal], i_split, all_timesteps, depths
if __name__ == "__main__":
imgs, poses, render_poses, render_timesteps, info, splits, timesteps = load_blender_data("/data/vision/billf/scratch/yilundu/nerf-pytorch/data/nerf_synthetic/table_nerf_time/")
import pdb
pdb.set_trace()
print("here!")