File size: 30,603 Bytes
162943d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900

# -*- coding: utf-8 -*-

# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: ps-license@tuebingen.mpg.de

import numpy as np
import cv2
import pymeshlab
import torch
import torchvision
import trimesh
from pytorch3d.io import load_obj
import os
from termcolor import colored
import os.path as osp
from scipy.spatial import cKDTree

from pytorch3d.structures import Meshes
import torch.nn.functional as F
from lib.pymaf.utils.imutils import uncrop
from lib.common.render_utils import Pytorch3dRasterizer, face_vertices

from pytorch3d.renderer.mesh import rasterize_meshes
from PIL import Image, ImageFont, ImageDraw
from kaolin.ops.mesh import check_sign
from kaolin.metrics.trianglemesh import point_to_mesh_distance

from pytorch3d.loss import (
    mesh_laplacian_smoothing,
    mesh_normal_consistency
)


def tensor2variable(tensor, device):
    # [1,23,3,3]
    return torch.tensor(tensor, device=device, requires_grad=True)


def normal_loss(vec1, vec2):

    # vec1_mask = vec1.sum(dim=1) != 0.0
    # vec2_mask = vec2.sum(dim=1) != 0.0
    # union_mask = vec1_mask * vec2_mask
    vec_sim = torch.nn.CosineSimilarity(dim=1, eps=1e-6)(vec1, vec2)
    # vec_diff = ((vec_sim-1.0)**2)[union_mask].mean()
    vec_diff = ((vec_sim-1.0)**2).mean()

    return vec_diff


class GMoF(torch.nn.Module):
    def __init__(self, rho=1):
        super(GMoF, self).__init__()
        self.rho = rho

    def extra_repr(self):
        return 'rho = {}'.format(self.rho)

    def forward(self, residual):
        dist = torch.div(residual, residual + self.rho ** 2)
        return self.rho ** 2 * dist


def mesh_edge_loss(meshes, target_length: float = 0.0):
    """
    Computes mesh edge length regularization loss averaged across all meshes
    in a batch. Each mesh contributes equally to the final loss, regardless of
    the number of edges per mesh in the batch by weighting each mesh with the
    inverse number of edges. For example, if mesh 3 (out of N) has only E=4
    edges, then the loss for each edge in mesh 3 should be multiplied by 1/E to
    contribute to the final loss.

    Args:
        meshes: Meshes object with a batch of meshes.
        target_length: Resting value for the edge length.

    Returns:
        loss: Average loss across the batch. Returns 0 if meshes contains
        no meshes or all empty meshes.
    """
    if meshes.isempty():
        return torch.tensor(
            [0.0], dtype=torch.float32, device=meshes.device, requires_grad=True
        )

    N = len(meshes)
    edges_packed = meshes.edges_packed()  # (sum(E_n), 3)
    verts_packed = meshes.verts_packed()  # (sum(V_n), 3)
    edge_to_mesh_idx = meshes.edges_packed_to_mesh_idx()  # (sum(E_n), )
    num_edges_per_mesh = meshes.num_edges_per_mesh()  # N

    # Determine the weight for each edge based on the number of edges in the
    # mesh it corresponds to.
    # TODO (nikhilar) Find a faster way of computing the weights for each edge
    # as this is currently a bottleneck for meshes with a large number of faces.
    weights = num_edges_per_mesh.gather(0, edge_to_mesh_idx)
    weights = 1.0 / weights.float()

    verts_edges = verts_packed[edges_packed]
    v0, v1 = verts_edges.unbind(1)
    loss = ((v0 - v1).norm(dim=1, p=2) - target_length) ** 2.0
    loss_vertex = loss * weights
    # loss_outlier = torch.topk(loss, 100)[0].mean()
    # loss_all = (loss_vertex.sum() + loss_outlier.mean()) / N
    loss_all = loss_vertex.sum() / N

    return loss_all


def remesh(obj_path, perc, device):

    ms = pymeshlab.MeshSet()
    ms.load_new_mesh(obj_path)
    ms.laplacian_smooth()
    ms.remeshing_isotropic_explicit_remeshing(
        targetlen=pymeshlab.Percentage(perc), adaptive=True)
    ms.save_current_mesh(obj_path.replace("recon", "remesh"))
    polished_mesh = trimesh.load_mesh(obj_path.replace("recon", "remesh"))
    verts_pr = torch.tensor(polished_mesh.vertices).float().unsqueeze(0).to(device)
    faces_pr = torch.tensor(polished_mesh.faces).long().unsqueeze(0).to(device)

    return verts_pr, faces_pr


def possion(mesh, obj_path):

    mesh.export(obj_path)
    ms = pymeshlab.MeshSet()
    ms.load_new_mesh(obj_path)
    ms.surface_reconstruction_screened_poisson(depth=10)
    ms.set_current_mesh(1)
    ms.save_current_mesh(obj_path)

    return trimesh.load(obj_path)


def get_mask(tensor, dim):

    mask = torch.abs(tensor).sum(dim=dim, keepdims=True) > 0.0
    mask = mask.type_as(tensor)

    return mask


def blend_rgb_norm(rgb, norm, mask):

    # [0,0,0] or [127,127,127] should be marked as mask
    final = rgb * (1-mask) + norm * (mask)

    return final.astype(np.uint8)


def unwrap(image, data):

    img_uncrop = uncrop(np.array(Image.fromarray(image).resize(data['uncrop_param']['box_shape'][:2])),
                        data['uncrop_param']['center'],
                        data['uncrop_param']['scale'],
                        data['uncrop_param']['crop_shape'])

    img_orig = cv2.warpAffine(img_uncrop,
                              np.linalg.inv(data['uncrop_param']['M'])[:2, :],
                              data['uncrop_param']['ori_shape'][::-1][1:],
                              flags=cv2.INTER_CUBIC)

    return img_orig


# Losses to smooth / regularize the mesh shape
def update_mesh_shape_prior_losses(mesh, losses):

    # and (b) the edge length of the predicted mesh
    losses["edge"]['value'] = mesh_edge_loss(mesh)
    # mesh normal consistency
    losses["nc"]['value'] = mesh_normal_consistency(mesh)
    # mesh laplacian smoothing
    losses["laplacian"]['value'] = mesh_laplacian_smoothing(
        mesh, method="uniform")


def rename(old_dict, old_name, new_name):
    new_dict = {}
    for key, value in zip(old_dict.keys(), old_dict.values()):
        new_key = key if key != old_name else new_name
        new_dict[new_key] = old_dict[key]
    return new_dict


def load_checkpoint(model, cfg):

    model_dict = model.state_dict()
    main_dict = {}
    normal_dict = {}

    device = torch.device(f"cuda:{cfg['test_gpus'][0]}")

    if os.path.exists(cfg.resume_path) and cfg.resume_path.endswith("ckpt"):
        main_dict = torch.load(cfg.resume_path,
                               map_location=device)['state_dict']

        main_dict = {
            k: v
            for k, v in main_dict.items()
            if k in model_dict and v.shape == model_dict[k].shape and (
                'reconEngine' not in k) and ("normal_filter" not in k) and (
                    'voxelization' not in k)
        }
        print(colored(f"Resume MLP weights from {cfg.resume_path}", 'green'))

    if os.path.exists(cfg.normal_path) and cfg.normal_path.endswith("ckpt"):
        normal_dict = torch.load(cfg.normal_path,
                                 map_location=device)['state_dict']

        for key in normal_dict.keys():
            normal_dict = rename(normal_dict, key,
                                 key.replace("netG", "netG.normal_filter"))

        normal_dict = {
            k: v
            for k, v in normal_dict.items()
            if k in model_dict and v.shape == model_dict[k].shape
        }
        print(colored(f"Resume normal model from {cfg.normal_path}", 'green'))

    model_dict.update(main_dict)
    model_dict.update(normal_dict)
    model.load_state_dict(model_dict)

    model.netG = model.netG.to(device)
    model.reconEngine = model.reconEngine.to(device)

    model.netG.training = False
    model.netG.eval()

    del main_dict
    del normal_dict
    del model_dict

    return model


def read_smpl_constants(folder):
    """Load smpl vertex code"""
    smpl_vtx_std = np.loadtxt(os.path.join(folder, 'vertices.txt'))
    min_x = np.min(smpl_vtx_std[:, 0])
    max_x = np.max(smpl_vtx_std[:, 0])
    min_y = np.min(smpl_vtx_std[:, 1])
    max_y = np.max(smpl_vtx_std[:, 1])
    min_z = np.min(smpl_vtx_std[:, 2])
    max_z = np.max(smpl_vtx_std[:, 2])

    smpl_vtx_std[:, 0] = (smpl_vtx_std[:, 0] - min_x) / (max_x - min_x)
    smpl_vtx_std[:, 1] = (smpl_vtx_std[:, 1] - min_y) / (max_y - min_y)
    smpl_vtx_std[:, 2] = (smpl_vtx_std[:, 2] - min_z) / (max_z - min_z)
    smpl_vertex_code = np.float32(np.copy(smpl_vtx_std))
    """Load smpl faces & tetrahedrons"""
    smpl_faces = np.loadtxt(os.path.join(folder, 'faces.txt'),
                            dtype=np.int32) - 1
    smpl_face_code = (smpl_vertex_code[smpl_faces[:, 0]] +
                      smpl_vertex_code[smpl_faces[:, 1]] +
                      smpl_vertex_code[smpl_faces[:, 2]]) / 3.0
    smpl_tetras = np.loadtxt(os.path.join(folder, 'tetrahedrons.txt'),
                             dtype=np.int32) - 1

    return smpl_vertex_code, smpl_face_code, smpl_faces, smpl_tetras


def feat_select(feat, select):

    # feat [B, featx2, N]
    # select [B, 1, N]
    # return [B, feat, N]

    dim = feat.shape[1] // 2
    idx = torch.tile((1-select), (1, dim, 1))*dim + \
        torch.arange(0, dim).unsqueeze(0).unsqueeze(2).type_as(select)
    feat_select = torch.gather(feat, 1, idx.long())

    return feat_select


def get_visibility(xy, z, faces):
    """get the visibility of vertices

    Args:
        xy (torch.tensor): [N,2]
        z (torch.tensor): [N,1]
        faces (torch.tensor): [N,3]
        size (int): resolution of rendered image
    """

    xyz = torch.cat((xy, -z), dim=1)
    xyz = (xyz + 1.0) / 2.0
    faces = faces.long()

    rasterizer = Pytorch3dRasterizer(image_size=2**12)
    meshes_screen = Meshes(verts=xyz[None, ...], faces=faces[None, ...])
    raster_settings = rasterizer.raster_settings

    pix_to_face, zbuf, bary_coords, dists = rasterize_meshes(
        meshes_screen,
        image_size=raster_settings.image_size,
        blur_radius=raster_settings.blur_radius,
        faces_per_pixel=raster_settings.faces_per_pixel,
        bin_size=raster_settings.bin_size,
        max_faces_per_bin=raster_settings.max_faces_per_bin,
        perspective_correct=raster_settings.perspective_correct,
        cull_backfaces=raster_settings.cull_backfaces,
    )

    vis_vertices_id = torch.unique(faces[torch.unique(pix_to_face), :])
    vis_mask = torch.zeros(size=(z.shape[0], 1))
    vis_mask[vis_vertices_id] = 1.0

    # print("------------------------\n")
    # print(f"keep points : {vis_mask.sum()/len(vis_mask)}")

    return vis_mask


def barycentric_coordinates_of_projection(points, vertices):
    ''' https://github.com/MPI-IS/mesh/blob/master/mesh/geometry/barycentric_coordinates_of_projection.py
    '''
    """Given a point, gives projected coords of that point to a triangle
    in barycentric coordinates.
    See
        **Heidrich**, Computing the Barycentric Coordinates of a Projected Point, JGT 05
        at http://www.cs.ubc.ca/~heidrich/Papers/JGT.05.pdf
    
    :param p: point to project. [B, 3]
    :param v0: first vertex of triangles. [B, 3]
    :returns: barycentric coordinates of ``p``'s projection in triangle defined by ``q``, ``u``, ``v``
            vectorized so ``p``, ``q``, ``u``, ``v`` can all be ``3xN``
    """
    #(p, q, u, v)
    v0, v1, v2 = vertices[:, 0], vertices[:, 1], vertices[:, 2]
    p = points

    q = v0
    u = v1 - v0
    v = v2 - v0
    n = torch.cross(u, v)
    s = torch.sum(n * n, dim=1)
    # If the triangle edges are collinear, cross-product is zero,
    # which makes "s" 0, which gives us divide by zero. So we
    # make the arbitrary choice to set s to epsv (=numpy.spacing(1)),
    # the closest thing to zero
    s[s == 0] = 1e-6
    oneOver4ASquared = 1.0 / s
    w = p - q
    b2 = torch.sum(torch.cross(u, w) * n, dim=1) * oneOver4ASquared
    b1 = torch.sum(torch.cross(w, v) * n, dim=1) * oneOver4ASquared
    weights = torch.stack((1 - b1 - b2, b1, b2), dim=-1)
    # check barycenric weights
    # p_n = v0*weights[:,0:1] + v1*weights[:,1:2] + v2*weights[:,2:3]
    return weights


def cal_sdf_batch(verts, faces, cmaps, vis, points):

    # verts [B, N_vert, 3]
    # faces [B, N_face, 3]
    # triangles [B, N_face, 3, 3]
    # points [B, N_point, 3]
    # cmaps [B, N_vert, 3]

    Bsize = points.shape[0]

    normals = Meshes(verts, faces).verts_normals_padded()

    triangles = face_vertices(verts, faces)
    normals = face_vertices(normals, faces)
    cmaps = face_vertices(cmaps, faces)
    vis = face_vertices(vis, faces)

    residues, pts_ind, _ = point_to_mesh_distance(points, triangles)
    closest_triangles = torch.gather(
        triangles, 1, pts_ind[:, :, None, None].expand(-1, -1, 3, 3)).view(-1, 3, 3)
    closest_normals = torch.gather(
        normals, 1, pts_ind[:, :, None, None].expand(-1, -1, 3, 3)).view(-1, 3, 3)
    closest_cmaps = torch.gather(
        cmaps, 1, pts_ind[:, :, None, None].expand(-1, -1, 3, 3)).view(-1, 3, 3)
    closest_vis = torch.gather(
        vis, 1, pts_ind[:, :, None, None].expand(-1, -1, 3, 1)).view(-1, 3, 1)
    bary_weights = barycentric_coordinates_of_projection(
        points.view(-1, 3), closest_triangles)

    pts_cmap = (closest_cmaps*bary_weights[:, :, None]).sum(1).unsqueeze(0)
    pts_vis = (closest_vis*bary_weights[:,
               :, None]).sum(1).unsqueeze(0).ge(1e-1)
    pts_norm = (closest_normals*bary_weights[:, :, None]).sum(
        1).unsqueeze(0) * torch.tensor([-1.0, 1.0, -1.0]).type_as(normals)
    pts_dist = torch.sqrt(residues) / torch.sqrt(torch.tensor(3))

    pts_signs = 2.0 * (check_sign(verts, faces[0], points).float() - 0.5)
    pts_sdf = (pts_dist * pts_signs).unsqueeze(-1)

    return pts_sdf.view(Bsize, -1, 1), pts_norm.view(Bsize, -1, 3), pts_cmap.view(Bsize, -1, 3), pts_vis.view(Bsize, -1, 1)


def orthogonal(points, calibrations, transforms=None):
    '''
    Compute the orthogonal projections of 3D points into the image plane by given projection matrix
    :param points: [B, 3, N] Tensor of 3D points
    :param calibrations: [B, 3, 4] Tensor of projection matrix
    :param transforms: [B, 2, 3] Tensor of image transform matrix
    :return: xyz: [B, 3, N] Tensor of xyz coordinates in the image plane
    '''
    rot = calibrations[:, :3, :3]
    trans = calibrations[:, :3, 3:4]
    pts = torch.baddbmm(trans, rot, points)  # [B, 3, N]
    if transforms is not None:
        scale = transforms[:2, :2]
        shift = transforms[:2, 2:3]
        pts[:, :2, :] = torch.baddbmm(shift, scale, pts[:, :2, :])
    return pts


def projection(points, calib, format='numpy'):
    if format == 'tensor':
        return torch.mm(calib[:3, :3], points.T).T + calib[:3, 3]
    else:
        return np.matmul(calib[:3, :3], points.T).T + calib[:3, 3]


def load_calib(calib_path):
    calib_data = np.loadtxt(calib_path, dtype=float)
    extrinsic = calib_data[:4, :4]
    intrinsic = calib_data[4:8, :4]
    calib_mat = np.matmul(intrinsic, extrinsic)
    calib_mat = torch.from_numpy(calib_mat).float()
    return calib_mat


def load_obj_mesh_for_Hoppe(mesh_file):
    vertex_data = []
    face_data = []

    if isinstance(mesh_file, str):
        f = open(mesh_file, "r")
    else:
        f = mesh_file
    for line in f:
        if isinstance(line, bytes):
            line = line.decode("utf-8")
        if line.startswith('#'):
            continue
        values = line.split()
        if not values:
            continue

        if values[0] == 'v':
            v = list(map(float, values[1:4]))
            vertex_data.append(v)

        elif values[0] == 'f':
            # quad mesh
            if len(values) > 4:
                f = list(map(lambda x: int(x.split('/')[0]), values[1:4]))
                face_data.append(f)
                f = list(
                    map(lambda x: int(x.split('/')[0]),
                        [values[3], values[4], values[1]]))
                face_data.append(f)
            # tri mesh
            else:
                f = list(map(lambda x: int(x.split('/')[0]), values[1:4]))
                face_data.append(f)

    vertices = np.array(vertex_data)
    faces = np.array(face_data)
    faces[faces > 0] -= 1

    normals, _ = compute_normal(vertices, faces)

    return vertices, normals, faces


def load_obj_mesh_with_color(mesh_file):
    vertex_data = []
    color_data = []
    face_data = []

    if isinstance(mesh_file, str):
        f = open(mesh_file, "r")
    else:
        f = mesh_file
    for line in f:
        if isinstance(line, bytes):
            line = line.decode("utf-8")
        if line.startswith('#'):
            continue
        values = line.split()
        if not values:
            continue

        if values[0] == 'v':
            v = list(map(float, values[1:4]))
            vertex_data.append(v)
            c = list(map(float, values[4:7]))
            color_data.append(c)

        elif values[0] == 'f':
            # quad mesh
            if len(values) > 4:
                f = list(map(lambda x: int(x.split('/')[0]), values[1:4]))
                face_data.append(f)
                f = list(
                    map(lambda x: int(x.split('/')[0]),
                        [values[3], values[4], values[1]]))
                face_data.append(f)
            # tri mesh
            else:
                f = list(map(lambda x: int(x.split('/')[0]), values[1:4]))
                face_data.append(f)

    vertices = np.array(vertex_data)
    colors = np.array(color_data)
    faces = np.array(face_data)
    faces[faces > 0] -= 1

    return vertices, colors, faces


def load_obj_mesh(mesh_file, with_normal=False, with_texture=False):
    vertex_data = []
    norm_data = []
    uv_data = []

    face_data = []
    face_norm_data = []
    face_uv_data = []

    if isinstance(mesh_file, str):
        f = open(mesh_file, "r")
    else:
        f = mesh_file
    for line in f:
        if isinstance(line, bytes):
            line = line.decode("utf-8")
        if line.startswith('#'):
            continue
        values = line.split()
        if not values:
            continue

        if values[0] == 'v':
            v = list(map(float, values[1:4]))
            vertex_data.append(v)
        elif values[0] == 'vn':
            vn = list(map(float, values[1:4]))
            norm_data.append(vn)
        elif values[0] == 'vt':
            vt = list(map(float, values[1:3]))
            uv_data.append(vt)

        elif values[0] == 'f':
            # quad mesh
            if len(values) > 4:
                f = list(map(lambda x: int(x.split('/')[0]), values[1:4]))
                face_data.append(f)
                f = list(
                    map(lambda x: int(x.split('/')[0]),
                        [values[3], values[4], values[1]]))
                face_data.append(f)
            # tri mesh
            else:
                f = list(map(lambda x: int(x.split('/')[0]), values[1:4]))
                face_data.append(f)

            # deal with texture
            if len(values[1].split('/')) >= 2:
                # quad mesh
                if len(values) > 4:
                    f = list(map(lambda x: int(x.split('/')[1]), values[1:4]))
                    face_uv_data.append(f)
                    f = list(
                        map(lambda x: int(x.split('/')[1]),
                            [values[3], values[4], values[1]]))
                    face_uv_data.append(f)
                # tri mesh
                elif len(values[1].split('/')[1]) != 0:
                    f = list(map(lambda x: int(x.split('/')[1]), values[1:4]))
                    face_uv_data.append(f)
            # deal with normal
            if len(values[1].split('/')) == 3:
                # quad mesh
                if len(values) > 4:
                    f = list(map(lambda x: int(x.split('/')[2]), values[1:4]))
                    face_norm_data.append(f)
                    f = list(
                        map(lambda x: int(x.split('/')[2]),
                            [values[3], values[4], values[1]]))
                    face_norm_data.append(f)
                # tri mesh
                elif len(values[1].split('/')[2]) != 0:
                    f = list(map(lambda x: int(x.split('/')[2]), values[1:4]))
                    face_norm_data.append(f)

    vertices = np.array(vertex_data)
    faces = np.array(face_data)
    faces[faces > 0] -= 1

    if with_texture and with_normal:
        uvs = np.array(uv_data)
        face_uvs = np.array(face_uv_data)
        face_uvs[face_uvs > 0] -= 1
        norms = np.array(norm_data)
        if norms.shape[0] == 0:
            norms, _ = compute_normal(vertices, faces)
            face_normals = faces
        else:
            norms = normalize_v3(norms)
            face_normals = np.array(face_norm_data)
            face_normals[face_normals > 0] -= 1
        return vertices, faces, norms, face_normals, uvs, face_uvs

    if with_texture:
        uvs = np.array(uv_data)
        face_uvs = np.array(face_uv_data) - 1
        return vertices, faces, uvs, face_uvs

    if with_normal:
        norms = np.array(norm_data)
        norms = normalize_v3(norms)
        face_normals = np.array(face_norm_data) - 1
        return vertices, faces, norms, face_normals

    return vertices, faces


def normalize_v3(arr):
    ''' Normalize a numpy array of 3 component vectors shape=(n,3) '''
    lens = np.sqrt(arr[:, 0]**2 + arr[:, 1]**2 + arr[:, 2]**2)
    eps = 0.00000001
    lens[lens < eps] = eps
    arr[:, 0] /= lens
    arr[:, 1] /= lens
    arr[:, 2] /= lens
    return arr


def compute_normal(vertices, faces):
    # Create a zeroed array with the same type and shape as our vertices i.e., per vertex normal
    vert_norms = np.zeros(vertices.shape, dtype=vertices.dtype)
    # Create an indexed view into the vertex array using the array of three indices for triangles
    tris = vertices[faces]
    # Calculate the normal for all the triangles, by taking the cross product of the vectors v1-v0, and v2-v0 in each triangle
    face_norms = np.cross(tris[::, 1] - tris[::, 0], tris[::, 2] - tris[::, 0])
    # n is now an array of normals per triangle. The length of each normal is dependent the vertices,
    # we need to normalize these, so that our next step weights each normal equally.
    normalize_v3(face_norms)
    # now we have a normalized array of normals, one per triangle, i.e., per triangle normals.
    # But instead of one per triangle (i.e., flat shading), we add to each vertex in that triangle,
    # the triangles' normal. Multiple triangles would then contribute to every vertex, so we need to normalize again afterwards.
    # The cool part, we can actually add the normals through an indexed view of our (zeroed) per vertex normal array
    vert_norms[faces[:, 0]] += face_norms
    vert_norms[faces[:, 1]] += face_norms
    vert_norms[faces[:, 2]] += face_norms
    normalize_v3(vert_norms)

    return vert_norms, face_norms


def save_obj_mesh(mesh_path, verts, faces):
    file = open(mesh_path, 'w')
    for v in verts:
        file.write('v %.4f %.4f %.4f\n' % (v[0], v[1], v[2]))
    for f in faces:
        f_plus = f + 1
        file.write('f %d %d %d\n' % (f_plus[0], f_plus[1], f_plus[2]))
    file.close()


def save_obj_mesh_with_color(mesh_path, verts, faces, colors):
    file = open(mesh_path, 'w')

    for idx, v in enumerate(verts):
        c = colors[idx]
        file.write('v %.4f %.4f %.4f %.4f %.4f %.4f\n' %
                   (v[0], v[1], v[2], c[0], c[1], c[2]))
    for f in faces:
        f_plus = f + 1
        file.write('f %d %d %d\n' % (f_plus[0], f_plus[1], f_plus[2]))
    file.close()


def calculate_mIoU(outputs, labels):

    SMOOTH = 1e-6

    outputs = outputs.int()
    labels = labels.int()

    intersection = (
        outputs
        & labels).float().sum()  # Will be zero if Truth=0 or Prediction=0
    union = (outputs | labels).float().sum()  # Will be zzero if both are 0

    iou = (intersection + SMOOTH) / (union + SMOOTH
                                     )  # We smooth our devision to avoid 0/0

    thresholded = torch.clamp(
        20 * (iou - 0.5), 0,
        10).ceil() / 10  # This is equal to comparing with thresolds

    return thresholded.mean().detach().cpu().numpy(
    )  # Or thresholded.mean() if you are interested in average across the batch


def mask_filter(mask, number=1000):
    """only keep {number} True items within a mask

    Args:
        mask (bool array): [N, ]
        number (int, optional): total True item. Defaults to 1000.
    """
    true_ids = np.where(mask)[0]
    keep_ids = np.random.choice(true_ids, size=number)
    filter_mask = np.isin(np.arange(len(mask)), keep_ids)

    return filter_mask


def query_mesh(path):

    verts, faces_idx, _ = load_obj(path)

    return verts, faces_idx.verts_idx


def add_alpha(colors, alpha=0.7):

    colors_pad = np.pad(colors, ((0, 0), (0, 1)),
                        mode='constant',
                        constant_values=alpha)

    return colors_pad


def get_optim_grid_image(per_loop_lst, loss=None, nrow=4, type='smpl'):

    font_path = os.path.join(os.path.dirname(__file__), "tbfo.ttf")
    font = ImageFont.truetype(font_path, 30)
    grid_img = torchvision.utils.make_grid(torch.cat(per_loop_lst, dim=0),
                                           nrow=nrow)
    grid_img = Image.fromarray(
        ((grid_img.permute(1, 2, 0).detach().cpu().numpy() + 1.0) * 0.5 *
         255.0).astype(np.uint8))

    # add text
    draw = ImageDraw.Draw(grid_img)
    grid_size = 512
    if loss is not None:
        draw.text((10, 5), f"error: {loss:.3f}", (255, 0, 0), font=font)

    if type == 'smpl':
        for col_id, col_txt in enumerate(
                ['image', 'smpl-norm(render)', 'cloth-norm(pred)', 'diff-norm', 'diff-mask']):
            draw.text((10+(col_id*grid_size), 5),
                      col_txt, (255, 0, 0), font=font)
    elif type == 'cloth':
        for col_id, col_txt in enumerate(
                ['image', 'cloth-norm(recon)', 'cloth-norm(pred)', 'diff-norm']):
            draw.text((10+(col_id*grid_size), 5),
                      col_txt, (255, 0, 0), font=font)
        for col_id, col_txt in enumerate(
                ['0', '90', '180', '270']):
            draw.text((10+(col_id*grid_size), grid_size*2+5),
                      col_txt, (255, 0, 0), font=font)
    else:
        print(f"{type} should be 'smpl' or 'cloth'")

    grid_img = grid_img.resize((grid_img.size[0], grid_img.size[1]),
                               Image.ANTIALIAS)

    return grid_img


def clean_mesh(verts, faces):

    device = verts.device

    mesh_lst = trimesh.Trimesh(verts.detach().cpu().numpy(),
                               faces.detach().cpu().numpy())
    mesh_lst = mesh_lst.split(only_watertight=False)
    comp_num = [mesh.vertices.shape[0] for mesh in mesh_lst]
    mesh_clean = mesh_lst[comp_num.index(max(comp_num))]

    final_verts = torch.as_tensor(mesh_clean.vertices).float().to(device)
    final_faces = torch.as_tensor(mesh_clean.faces).int().to(device)

    return final_verts, final_faces


def merge_mesh(verts_A, faces_A, verts_B, faces_B, color=False):

    sep_mesh = trimesh.Trimesh(np.concatenate([verts_A, verts_B], axis=0),
                               np.concatenate(
                                   [faces_A, faces_B + faces_A.max() + 1],
                                   axis=0),
                               maintain_order=True,
                               process=False)
    if color:
        colors = np.ones_like(sep_mesh.vertices)
        colors[:verts_A.shape[0]] *= np.array([255.0, 0.0, 0.0])
        colors[verts_A.shape[0]:] *= np.array([0.0, 255.0, 0.0])
        sep_mesh.visual.vertex_colors = colors

    # union_mesh = trimesh.boolean.union([trimesh.Trimesh(verts_A, faces_A),
    #                                     trimesh.Trimesh(verts_B, faces_B)], engine='blender')

    return sep_mesh


def mesh_move(mesh_lst, step, scale=1.0):

    trans = np.array([1.0, 0.0, 0.0]) * step

    resize_matrix = trimesh.transformations.scale_and_translate(
        scale=(scale), translate=trans)

    results = []

    for mesh in mesh_lst:
        mesh.apply_transform(resize_matrix)
        results.append(mesh)

    return results


class SMPLX():
    def __init__(self):

        self.current_dir = osp.join(osp.dirname(__file__),
                                    "../../data/smpl_related")

        self.smpl_verts_path = osp.join(self.current_dir,
                                        "smpl_data/smpl_verts.npy")
        self.smplx_verts_path = osp.join(self.current_dir,
                                         "smpl_data/smplx_verts.npy")
        self.faces_path = osp.join(self.current_dir,
                                   "smpl_data/smplx_faces.npy")
        self.cmap_vert_path = osp.join(self.current_dir,
                                       "smpl_data/smplx_cmap.npy")

        self.faces = np.load(self.faces_path)
        self.verts = np.load(self.smplx_verts_path)
        self.smpl_verts = np.load(self.smpl_verts_path)

        self.model_dir = osp.join(self.current_dir, "models")
        self.tedra_dir = osp.join(self.current_dir, "../tedra_data")

    def get_smpl_mat(self, vert_ids):

        mat = torch.as_tensor(np.load(self.cmap_vert_path)).float()
        return mat[vert_ids, :]

    def smpl2smplx(self, vert_ids=None):
        """convert vert_ids in smpl to vert_ids in smplx

        Args:
            vert_ids ([int.array]): [n, knn_num]
        """
        smplx_tree = cKDTree(self.verts, leafsize=1)
        _, ind = smplx_tree.query(self.smpl_verts, k=1)  # ind: [smpl_num, 1]

        if vert_ids is not None:
            smplx_vert_ids = ind[vert_ids]
        else:
            smplx_vert_ids = ind

        return smplx_vert_ids

    def smplx2smpl(self, vert_ids=None):
        """convert vert_ids in smplx to vert_ids in smpl

        Args:
            vert_ids ([int.array]): [n, knn_num]
        """
        smpl_tree = cKDTree(self.smpl_verts, leafsize=1)
        _, ind = smpl_tree.query(self.verts, k=1)  # ind: [smplx_num, 1]
        if vert_ids is not None:
            smpl_vert_ids = ind[vert_ids]
        else:
            smpl_vert_ids = ind

        return smpl_vert_ids