File size: 26,474 Bytes
31f2f28
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import numpy as np
import cv2
import random
from config import cfg
import math
from utils.human_models import smpl_x, smpl
from utils.transforms import cam2pixel, transform_joint_to_other_db
from plyfile import PlyData, PlyElement
import torch


def load_img(path, order='RGB'):
    img = cv2.imread(path, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
    if not isinstance(img, np.ndarray):
        raise IOError("Fail to read %s" % path)

    if order == 'RGB':
        img = img[:, :, ::-1].copy()

    img = img.astype(np.float32)
    return img


def get_bbox(joint_img, joint_valid, extend_ratio=1.2):
    x_img, y_img = joint_img[:, 0], joint_img[:, 1]
    x_img = x_img[joint_valid == 1];
    y_img = y_img[joint_valid == 1];
    xmin = min(x_img);
    ymin = min(y_img);
    xmax = max(x_img);
    ymax = max(y_img);

    x_center = (xmin + xmax) / 2.;
    width = xmax - xmin;
    xmin = x_center - 0.5 * width * extend_ratio
    xmax = x_center + 0.5 * width * extend_ratio

    y_center = (ymin + ymax) / 2.;
    height = ymax - ymin;
    ymin = y_center - 0.5 * height * extend_ratio
    ymax = y_center + 0.5 * height * extend_ratio

    bbox = np.array([xmin, ymin, xmax - xmin, ymax - ymin]).astype(np.float32)
    return bbox


def sanitize_bbox(bbox, img_width, img_height):
    x, y, w, h = bbox
    x1 = np.max((0, x))
    y1 = np.max((0, y))
    x2 = np.min((img_width - 1, x1 + np.max((0, w - 1))))
    y2 = np.min((img_height - 1, y1 + np.max((0, h - 1))))
    if w * h > 0 and x2 > x1 and y2 > y1:
        bbox = np.array([x1, y1, x2 - x1, y2 - y1])
    else:
        bbox = None

    return bbox


def process_bbox(bbox, img_width, img_height, ratio=1.25):
    bbox = sanitize_bbox(bbox, img_width, img_height)
    if bbox is None:
        return bbox

    # aspect ratio preserving bbox
    w = bbox[2]
    h = bbox[3]
    c_x = bbox[0] + w / 2.
    c_y = bbox[1] + h / 2.
    aspect_ratio = cfg.input_img_shape[1] / cfg.input_img_shape[0]
    if w > aspect_ratio * h:
        h = w / aspect_ratio
    elif w < aspect_ratio * h:
        w = h * aspect_ratio
    bbox[2] = w * ratio
    bbox[3] = h * ratio
    bbox[0] = c_x - bbox[2] / 2.
    bbox[1] = c_y - bbox[3] / 2.

    bbox = bbox.astype(np.float32)
    return bbox


def get_aug_config():
    scale_factor = 0.25
    rot_factor = 30
    color_factor = 0.2

    scale = np.clip(np.random.randn(), -1.0, 1.0) * scale_factor + 1.0
    rot = np.clip(np.random.randn(), -2.0,
                  2.0) * rot_factor if random.random() <= 0.6 else 0
    c_up = 1.0 + color_factor
    c_low = 1.0 - color_factor
    color_scale = np.array([random.uniform(c_low, c_up), random.uniform(c_low, c_up), random.uniform(c_low, c_up)])
    do_flip = random.random() <= 0.5

    return scale, rot, color_scale, do_flip


def augmentation(img, bbox, data_split):
    if getattr(cfg, 'no_aug', False):
        scale, rot, color_scale, do_flip = 1.0, 0.0, np.array([1, 1, 1]), False
    elif data_split == 'train':
        scale, rot, color_scale, do_flip = get_aug_config()
    else:
        scale, rot, color_scale, do_flip = 1.0, 0.0, np.array([1, 1, 1]), False

    img, trans, inv_trans = generate_patch_image(img, bbox, scale, rot, do_flip, cfg.input_img_shape)
    img = np.clip(img * color_scale[None, None, :], 0, 255)
    return img, trans, inv_trans, rot, do_flip


def generate_patch_image(cvimg, bbox, scale, rot, do_flip, out_shape):
    img = cvimg.copy()
    img_height, img_width, img_channels = img.shape

    bb_c_x = float(bbox[0] + 0.5 * bbox[2])
    bb_c_y = float(bbox[1] + 0.5 * bbox[3])
    bb_width = float(bbox[2])
    bb_height = float(bbox[3])

    if do_flip:
        img = img[:, ::-1, :]
        bb_c_x = img_width - bb_c_x - 1

    trans = gen_trans_from_patch_cv(bb_c_x, bb_c_y, bb_width, bb_height, out_shape[1], out_shape[0], scale, rot)
    img_patch = cv2.warpAffine(img, trans, (int(out_shape[1]), int(out_shape[0])), flags=cv2.INTER_LINEAR)
    img_patch = img_patch.astype(np.float32)
    inv_trans = gen_trans_from_patch_cv(bb_c_x, bb_c_y, bb_width, bb_height, out_shape[1], out_shape[0], scale, rot,
                                        inv=True)

    return img_patch, trans, inv_trans


def rotate_2d(pt_2d, rot_rad):
    x = pt_2d[0]
    y = pt_2d[1]
    sn, cs = np.sin(rot_rad), np.cos(rot_rad)
    xx = x * cs - y * sn
    yy = x * sn + y * cs
    return np.array([xx, yy], dtype=np.float32)


def gen_trans_from_patch_cv(c_x, c_y, src_width, src_height, dst_width, dst_height, scale, rot, inv=False):
    # augment size with scale
    src_w = src_width * scale
    src_h = src_height * scale
    src_center = np.array([c_x, c_y], dtype=np.float32)

    # augment rotation
    rot_rad = np.pi * rot / 180
    src_downdir = rotate_2d(np.array([0, src_h * 0.5], dtype=np.float32), rot_rad)
    src_rightdir = rotate_2d(np.array([src_w * 0.5, 0], dtype=np.float32), rot_rad)

    dst_w = dst_width
    dst_h = dst_height
    dst_center = np.array([dst_w * 0.5, dst_h * 0.5], dtype=np.float32)
    dst_downdir = np.array([0, dst_h * 0.5], dtype=np.float32)
    dst_rightdir = np.array([dst_w * 0.5, 0], dtype=np.float32)

    src = np.zeros((3, 2), dtype=np.float32)
    src[0, :] = src_center
    src[1, :] = src_center + src_downdir
    src[2, :] = src_center + src_rightdir

    dst = np.zeros((3, 2), dtype=np.float32)
    dst[0, :] = dst_center
    dst[1, :] = dst_center + dst_downdir
    dst[2, :] = dst_center + dst_rightdir

    if inv:
        trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))
    else:
        trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))

    trans = trans.astype(np.float32)
    return trans


def process_db_coord(joint_img, joint_cam, joint_valid, do_flip, img_shape, flip_pairs, img2bb_trans, rot,
                     src_joints_name, target_joints_name):
    joint_img_original = joint_img.copy()
    joint_img, joint_cam, joint_valid = joint_img.copy(), joint_cam.copy(), joint_valid.copy()

    # flip augmentation
    if do_flip:
        joint_cam[:, 0] = -joint_cam[:, 0]
        joint_img[:, 0] = img_shape[1] - 1 - joint_img[:, 0]
        for pair in flip_pairs:
            joint_img[pair[0], :], joint_img[pair[1], :] = joint_img[pair[1], :].copy(), joint_img[pair[0], :].copy()
            joint_cam[pair[0], :], joint_cam[pair[1], :] = joint_cam[pair[1], :].copy(), joint_cam[pair[0], :].copy()
            joint_valid[pair[0], :], joint_valid[pair[1], :] = joint_valid[pair[1], :].copy(), joint_valid[pair[0],
                                                                                               :].copy()

    # 3D data rotation augmentation
    rot_aug_mat = np.array([[np.cos(np.deg2rad(-rot)), -np.sin(np.deg2rad(-rot)), 0],
                            [np.sin(np.deg2rad(-rot)), np.cos(np.deg2rad(-rot)), 0],
                            [0, 0, 1]], dtype=np.float32)
    joint_cam = np.dot(rot_aug_mat, joint_cam.transpose(1, 0)).transpose(1, 0)

    # affine transformation
    joint_img_xy1 = np.concatenate((joint_img[:, :2], np.ones_like(joint_img[:, :1])), 1)
    joint_img[:, :2] = np.dot(img2bb_trans, joint_img_xy1.transpose(1, 0)).transpose(1, 0)
    joint_img[:, 0] = joint_img[:, 0] / cfg.input_img_shape[1] * cfg.output_hm_shape[2]
    joint_img[:, 1] = joint_img[:, 1] / cfg.input_img_shape[0] * cfg.output_hm_shape[1]

    # check truncation
    joint_trunc = joint_valid * ((joint_img_original[:, 0] > 0) * (joint_img[:, 0] >= 0) * (joint_img[:, 0] < cfg.output_hm_shape[2]) * \
                                 (joint_img_original[:, 1] > 0) *(joint_img[:, 1] >= 0) * (joint_img[:, 1] < cfg.output_hm_shape[1]) * \
                                 (joint_img_original[:, 2] > 0) *(joint_img[:, 2] >= 0) * (joint_img[:, 2] < cfg.output_hm_shape[0])).reshape(-1,
                                                                                                              1).astype(
        np.float32)

    # transform joints to target db joints
    joint_img = transform_joint_to_other_db(joint_img, src_joints_name, target_joints_name)
    joint_cam_wo_ra = transform_joint_to_other_db(joint_cam, src_joints_name, target_joints_name)
    joint_valid = transform_joint_to_other_db(joint_valid, src_joints_name, target_joints_name)
    joint_trunc = transform_joint_to_other_db(joint_trunc, src_joints_name, target_joints_name)

    # root-alignment, for joint_cam input wo ra
    joint_cam_ra = joint_cam_wo_ra.copy()
    joint_cam_ra = joint_cam_ra - joint_cam_ra[smpl_x.root_joint_idx, None, :]  # root-relative
    joint_cam_ra[smpl_x.joint_part['lhand'], :] = joint_cam_ra[smpl_x.joint_part['lhand'], :] - joint_cam_ra[
                                                                                            smpl_x.lwrist_idx, None,
                                                                                            :]  # left hand root-relative
    joint_cam_ra[smpl_x.joint_part['rhand'], :] = joint_cam_ra[smpl_x.joint_part['rhand'], :] - joint_cam_ra[
                                                                                            smpl_x.rwrist_idx, None,
                                                                                            :]  # right hand root-relative
    joint_cam_ra[smpl_x.joint_part['face'], :] = joint_cam_ra[smpl_x.joint_part['face'], :] - joint_cam_ra[smpl_x.neck_idx,
                                                                                        None,
                                                                                        :]  # face root-relative

    return joint_img, joint_cam_wo_ra, joint_cam_ra, joint_valid, joint_trunc


def process_human_model_output(human_model_param, cam_param, do_flip, img_shape, img2bb_trans, rot, human_model_type, joint_img=None):
    if human_model_type == 'smplx':
        human_model = smpl_x
        rotation_valid = np.ones((smpl_x.orig_joint_num), dtype=np.float32)
        coord_valid = np.ones((smpl_x.joint_num), dtype=np.float32)

        root_pose, body_pose, shape, trans = human_model_param['root_pose'], human_model_param['body_pose'], \
                                             human_model_param['shape'], human_model_param['trans']
        if 'lhand_pose' in human_model_param and human_model_param['lhand_valid']:
            lhand_pose = human_model_param['lhand_pose']
        else:
            lhand_pose = np.zeros((3 * len(smpl_x.orig_joint_part['lhand'])), dtype=np.float32)
            rotation_valid[smpl_x.orig_joint_part['lhand']] = 0
            coord_valid[smpl_x.joint_part['lhand']] = 0
        if 'rhand_pose' in human_model_param and human_model_param['rhand_valid']:
            rhand_pose = human_model_param['rhand_pose']
        else:
            rhand_pose = np.zeros((3 * len(smpl_x.orig_joint_part['rhand'])), dtype=np.float32)
            rotation_valid[smpl_x.orig_joint_part['rhand']] = 0
            coord_valid[smpl_x.joint_part['rhand']] = 0
        if 'jaw_pose' in human_model_param and 'expr' in human_model_param and human_model_param['face_valid']:
            jaw_pose = human_model_param['jaw_pose']
            expr = human_model_param['expr']
            expr_valid = True
        else:
            jaw_pose = np.zeros((3), dtype=np.float32)
            expr = np.zeros((smpl_x.expr_code_dim), dtype=np.float32)
            rotation_valid[smpl_x.orig_joint_part['face']] = 0
            coord_valid[smpl_x.joint_part['face']] = 0
            expr_valid = False
        if 'gender' in human_model_param:
            gender = human_model_param['gender']
        else:
            gender = 'neutral'
        root_pose = torch.FloatTensor(root_pose).view(1, 3)  # (1,3)
        body_pose = torch.FloatTensor(body_pose).view(-1, 3)  # (21,3)
        lhand_pose = torch.FloatTensor(lhand_pose).view(-1, 3)  # (15,3)
        rhand_pose = torch.FloatTensor(rhand_pose).view(-1, 3)  # (15,3)
        jaw_pose = torch.FloatTensor(jaw_pose).view(-1, 3)  # (1,3)
        shape = torch.FloatTensor(shape).view(1, -1)  # SMPLX shape parameter
        expr = torch.FloatTensor(expr).view(1, -1)  # SMPLX expression parameter
        trans = torch.FloatTensor(trans).view(1, -1)  # translation vector

        # apply camera extrinsic (rotation)
        # merge root pose and camera rotation
        if 'R' in cam_param:
            R = np.array(cam_param['R'], dtype=np.float32).reshape(3, 3)
            root_pose = root_pose.numpy()
            root_pose, _ = cv2.Rodrigues(root_pose)
            root_pose, _ = cv2.Rodrigues(np.dot(R, root_pose))
            root_pose = torch.from_numpy(root_pose).view(1, 3)

        # get mesh and joint coordinates
        zero_pose = torch.zeros((1, 3)).float()  # eye poses
        with torch.no_grad():
            output = smpl_x.layer[gender](betas=shape, body_pose=body_pose.view(1, -1), global_orient=root_pose,
                                          transl=trans, left_hand_pose=lhand_pose.view(1, -1),
                                          right_hand_pose=rhand_pose.view(1, -1), jaw_pose=jaw_pose.view(1, -1),
                                          leye_pose=zero_pose, reye_pose=zero_pose, expression=expr)
        mesh_cam = output.vertices[0].numpy()
        joint_cam = output.joints[0].numpy()[smpl_x.joint_idx, :]

        # apply camera exrinsic (translation)
        # compenstate rotation (translation from origin to root joint was not cancled)
        if 'R' in cam_param and 't' in cam_param:
            R, t = np.array(cam_param['R'], dtype=np.float32).reshape(3, 3), np.array(cam_param['t'],
                                                                                      dtype=np.float32).reshape(1, 3)
            root_cam = joint_cam[smpl_x.root_joint_idx, None, :]
            joint_cam = joint_cam - root_cam + np.dot(R, root_cam.transpose(1, 0)).transpose(1, 0) + t
            mesh_cam = mesh_cam - root_cam + np.dot(R, root_cam.transpose(1, 0)).transpose(1, 0) + t

        # concat root, body, two hands, and jaw pose
        pose = torch.cat((root_pose, body_pose, lhand_pose, rhand_pose, jaw_pose))

        # joint coordinates
        if 'focal' not in cam_param or 'princpt' not in cam_param:
            assert joint_img is not None 
        else:   
            joint_img = cam2pixel(joint_cam, cam_param['focal'], cam_param['princpt'])

        joint_img_original = joint_img.copy()

        joint_cam = joint_cam - joint_cam[smpl_x.root_joint_idx, None, :]  # root-relative
        joint_cam[smpl_x.joint_part['lhand'], :] = joint_cam[smpl_x.joint_part['lhand'], :] - joint_cam[
                                                                                              smpl_x.lwrist_idx, None,
                                                                                              :]  # left hand root-relative
        joint_cam[smpl_x.joint_part['rhand'], :] = joint_cam[smpl_x.joint_part['rhand'], :] - joint_cam[
                                                                                              smpl_x.rwrist_idx, None,
                                                                                              :]  # right hand root-relative
        joint_cam[smpl_x.joint_part['face'], :] = joint_cam[smpl_x.joint_part['face'], :] - joint_cam[smpl_x.neck_idx,
                                                                                            None,
                                                                                            :]  # face root-relative
        joint_img[smpl_x.joint_part['body'], 2] = (joint_cam[smpl_x.joint_part['body'], 2].copy() / (
                    cfg.body_3d_size / 2) + 1) / 2. * cfg.output_hm_shape[0]  # body depth discretize
        joint_img[smpl_x.joint_part['lhand'], 2] = (joint_cam[smpl_x.joint_part['lhand'], 2].copy() / (
                    cfg.hand_3d_size / 2) + 1) / 2. * cfg.output_hm_shape[0]  # left hand depth discretize
        joint_img[smpl_x.joint_part['rhand'], 2] = (joint_cam[smpl_x.joint_part['rhand'], 2].copy() / (
                    cfg.hand_3d_size / 2) + 1) / 2. * cfg.output_hm_shape[0]  # right hand depth discretize
        joint_img[smpl_x.joint_part['face'], 2] = (joint_cam[smpl_x.joint_part['face'], 2].copy() / (
                    cfg.face_3d_size / 2) + 1) / 2. * cfg.output_hm_shape[0]  # face depth discretize

    elif human_model_type == 'smpl':
        human_model = smpl
        pose, shape, trans = human_model_param['pose'], human_model_param['shape'], human_model_param['trans']
        if 'gender' in human_model_param:
            gender = human_model_param['gender']
        else:
            gender = 'neutral'
        pose = torch.FloatTensor(pose).view(-1, 3)
        shape = torch.FloatTensor(shape).view(1, -1);
        trans = torch.FloatTensor(trans).view(1, -1)  # translation vector

        # apply camera extrinsic (rotation)
        # merge root pose and camera rotation
        if 'R' in cam_param:
            R = np.array(cam_param['R'], dtype=np.float32).reshape(3, 3)
            root_pose = pose[smpl.orig_root_joint_idx, :].numpy()
            root_pose, _ = cv2.Rodrigues(root_pose)
            root_pose, _ = cv2.Rodrigues(np.dot(R, root_pose))
            pose[smpl.orig_root_joint_idx] = torch.from_numpy(root_pose).view(3)

        # get mesh and joint coordinates
        root_pose = pose[smpl.orig_root_joint_idx].view(1, 3)
        body_pose = torch.cat((pose[:smpl.orig_root_joint_idx, :], pose[smpl.orig_root_joint_idx + 1:, :])).view(1, -1)
        with torch.no_grad():
            output = smpl.layer[gender](betas=shape, body_pose=body_pose, global_orient=root_pose, transl=trans)
        mesh_cam = output.vertices[0].numpy()
        joint_cam = np.dot(smpl.joint_regressor, mesh_cam)

        # apply camera exrinsic (translation)
        # compenstate rotation (translation from origin to root joint was not cancled)
        if 'R' in cam_param and 't' in cam_param:
            R, t = np.array(cam_param['R'], dtype=np.float32).reshape(3, 3), np.array(cam_param['t'],
                                                                                      dtype=np.float32).reshape(1, 3)
            root_cam = joint_cam[smpl.root_joint_idx, None, :]
            joint_cam = joint_cam - root_cam + np.dot(R, root_cam.transpose(1, 0)).transpose(1, 0) + t
            mesh_cam = mesh_cam - root_cam + np.dot(R, root_cam.transpose(1, 0)).transpose(1, 0) + t

        # joint coordinates
        if 'focal' not in cam_param or 'princpt' not in cam_param:
            assert joint_img is not None 
        else:   
            joint_img = cam2pixel(joint_cam, cam_param['focal'], cam_param['princpt'])
        
        joint_img_original = joint_img.copy()
        joint_cam = joint_cam - joint_cam[smpl.root_joint_idx, None, :]  # body root-relative
        joint_img[:, 2] = (joint_cam[:, 2].copy() / (cfg.body_3d_size / 2) + 1) / 2. * cfg.output_hm_shape[
            0]  # body depth discretize

    elif human_model_type == 'mano':
        human_model = mano
        pose, shape, trans = human_model_param['pose'], human_model_param['shape'], human_model_param['trans']
        hand_type = human_model_param['hand_type']
        pose = torch.FloatTensor(pose).view(-1, 3)
        shape = torch.FloatTensor(shape).view(1, -1);
        trans = torch.FloatTensor(trans).view(1, -1)  # translation vector

        # apply camera extrinsic (rotation)
        # merge root pose and camera rotation
        if 'R' in cam_param:
            R = np.array(cam_param['R'], dtype=np.float32).reshape(3, 3)
            root_pose = pose[mano.orig_root_joint_idx, :].numpy()
            root_pose, _ = cv2.Rodrigues(root_pose)
            root_pose, _ = cv2.Rodrigues(np.dot(R, root_pose))
            pose[mano.orig_root_joint_idx] = torch.from_numpy(root_pose).view(3)

        # get mesh and joint coordinates
        root_pose = pose[mano.orig_root_joint_idx].view(1, 3)
        hand_pose = torch.cat((pose[:mano.orig_root_joint_idx, :], pose[mano.orig_root_joint_idx + 1:, :])).view(1, -1)
        with torch.no_grad():
            output = mano.layer[hand_type](betas=shape, hand_pose=hand_pose, global_orient=root_pose, transl=trans)
        mesh_cam = output.vertices[0].numpy()
        joint_cam = np.dot(mano.joint_regressor, mesh_cam)

        # apply camera exrinsic (translation)
        # compenstate rotation (translation from origin to root joint was not cancled)
        if 'R' in cam_param and 't' in cam_param:
            R, t = np.array(cam_param['R'], dtype=np.float32).reshape(3, 3), np.array(cam_param['t'],
                                                                                      dtype=np.float32).reshape(1, 3)
            root_cam = joint_cam[mano.root_joint_idx, None, :]
            joint_cam = joint_cam - root_cam + np.dot(R, root_cam.transpose(1, 0)).transpose(1, 0) + t
            mesh_cam = mesh_cam - root_cam + np.dot(R, root_cam.transpose(1, 0)).transpose(1, 0) + t

        # joint coordinates
        if 'focal' not in cam_param or 'princpt' not in cam_param:
            assert joint_img is not None 
        else:   
            joint_img = cam2pixel(joint_cam, cam_param['focal'], cam_param['princpt'])
        joint_cam = joint_cam - joint_cam[mano.root_joint_idx, None, :]  # hand root-relative
        joint_img[:, 2] = (joint_cam[:, 2].copy() / (cfg.hand_3d_size / 2) + 1) / 2. * cfg.output_hm_shape[
            0]  # hand depth discretize

    mesh_cam_orig = mesh_cam.copy()  # back-up the original one

    ## so far, data augmentations are not applied yet
    ## now, apply data augmentations

    # image projection
    if do_flip:
        joint_cam[:, 0] = -joint_cam[:, 0]
        joint_img[:, 0] = img_shape[1] - 1 - joint_img[:, 0]
        for pair in human_model.flip_pairs:
            joint_cam[pair[0], :], joint_cam[pair[1], :] = joint_cam[pair[1], :].copy(), joint_cam[pair[0], :].copy()
            joint_img[pair[0], :], joint_img[pair[1], :] = joint_img[pair[1], :].copy(), joint_img[pair[0], :].copy()
            if human_model_type == 'smplx':
                coord_valid[pair[0]], coord_valid[pair[1]] = coord_valid[pair[1]].copy(), coord_valid[pair[0]].copy()

    # x,y affine transform, root-relative depth
    joint_img_xy1 = np.concatenate((joint_img[:, :2], np.ones_like(joint_img[:, 0:1])), 1)
    joint_img[:, :2] = np.dot(img2bb_trans, joint_img_xy1.transpose(1, 0)).transpose(1, 0)[:, :2]
    joint_img[:, 0] = joint_img[:, 0] / cfg.input_img_shape[1] * cfg.output_hm_shape[2]
    joint_img[:, 1] = joint_img[:, 1] / cfg.input_img_shape[0] * cfg.output_hm_shape[1]

    # check truncation
    # TODO
    joint_trunc = ((joint_img_original[:, 0] > 0) * (joint_img[:, 0] >= 0) * (joint_img[:, 0] < cfg.output_hm_shape[2]) * \
                   (joint_img_original[:, 1] > 0) * (joint_img[:, 1] >= 0) * (joint_img[:, 1] < cfg.output_hm_shape[1]) * \
                   (joint_img_original[:, 2] > 0) * (joint_img[:, 2] >= 0) * (joint_img[:, 2] < cfg.output_hm_shape[0])).reshape(-1, 1).astype(
        np.float32)

    # 3D data rotation augmentation
    rot_aug_mat = np.array([[np.cos(np.deg2rad(-rot)), -np.sin(np.deg2rad(-rot)), 0],
                            [np.sin(np.deg2rad(-rot)), np.cos(np.deg2rad(-rot)), 0],
                            [0, 0, 1]], dtype=np.float32)
    # coordinate
    joint_cam = np.dot(rot_aug_mat, joint_cam.transpose(1, 0)).transpose(1, 0)
    # parameters
    # flip pose parameter (axis-angle)
    if do_flip:
        for pair in human_model.orig_flip_pairs:
            pose[pair[0], :], pose[pair[1], :] = pose[pair[1], :].clone(), pose[pair[0], :].clone()
            if human_model_type == 'smplx':
                rotation_valid[pair[0]], rotation_valid[pair[1]] = rotation_valid[pair[1]].copy(), rotation_valid[
                    pair[0]].copy()
        pose[:, 1:3] *= -1  # multiply -1 to y and z axis of axis-angle

    # rotate root pose
    pose = pose.numpy()
    root_pose = pose[human_model.orig_root_joint_idx, :]
    root_pose, _ = cv2.Rodrigues(root_pose)
    root_pose, _ = cv2.Rodrigues(np.dot(rot_aug_mat, root_pose))
    pose[human_model.orig_root_joint_idx] = root_pose.reshape(3)

    # change to mean shape if beta is too far from it
    shape[(shape.abs() > 3).any(dim=1)] = 0.
    shape = shape.numpy().reshape(-1)

    # return results
    if human_model_type == 'smplx':
        pose = pose.reshape(-1)
        expr = expr.numpy().reshape(-1)

        return joint_img, joint_cam, joint_trunc, pose, shape, expr, rotation_valid, coord_valid, expr_valid, mesh_cam_orig
    elif human_model_type == 'smpl':
        pose = pose.reshape(-1)
        return joint_img, joint_cam, joint_trunc, pose, shape, mesh_cam_orig
    elif human_model_type == 'mano':
        pose = pose.reshape(-1)
        return joint_img, joint_cam, joint_trunc, pose, shape, mesh_cam_orig


def get_fitting_error_3D(db_joint, db_joint_from_fit, joint_valid):
    # mask coordinate
    db_joint = db_joint[np.tile(joint_valid, (1, 3)) == 1].reshape(-1, 3)
    db_joint_from_fit = db_joint_from_fit[np.tile(joint_valid, (1, 3)) == 1].reshape(-1, 3)

    db_joint_from_fit = db_joint_from_fit - np.mean(db_joint_from_fit, 0)[None, :] + np.mean(db_joint, 0)[None,
                                                                                     :]  # translation alignment
    error = np.sqrt(np.sum((db_joint - db_joint_from_fit) ** 2, 1)).mean()
    return error


def load_obj(file_name):
    v = []
    obj_file = open(file_name)
    for line in obj_file:
        words = line.split(' ')
        if words[0] == 'v':
            x, y, z = float(words[1]), float(words[2]), float(words[3])
            v.append(np.array([x, y, z]))
    return np.stack(v)


def load_ply(file_name):
    plydata = PlyData.read(file_name)
    x = plydata['vertex']['x']
    y = plydata['vertex']['y']
    z = plydata['vertex']['z']
    v = np.stack((x, y, z), 1)
    return v

def resize_bbox(bbox, scale=1.2):
    if isinstance(bbox, list):
        x1, y1, x2, y2 = bbox[0], bbox[1], bbox[2], bbox[3]
    else:
        x1, y1, x2, y2 = bbox
    x_center = (x1+x2)/2.0
    y_center = (y1+y2)/2.0
    x_size, y_size = x2-x1, y2-y1
    x1_resize = x_center-x_size/2.0*scale
    x2_resize = x_center+x_size/2.0*scale
    y1_resize = y_center - y_size / 2.0 * scale
    y2_resize = y_center + y_size / 2.0 * scale
    bbox[0], bbox[1], bbox[2], bbox[3] = x1_resize, y1_resize, x2_resize, y2_resize
    return bbox