File size: 41,496 Bytes
51f6859
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod

import cv2
import mmcv
import numpy as np
import pycocotools.mask as maskUtils
import torch
from mmcv.ops.roi_align import roi_align


class BaseInstanceMasks(metaclass=ABCMeta):
    """Base class for instance masks."""

    @abstractmethod
    def rescale(self, scale, interpolation='nearest'):
        """Rescale masks as large as possible while keeping the aspect ratio.
        For details can refer to `mmcv.imrescale`.

        Args:
            scale (tuple[int]): The maximum size (h, w) of rescaled mask.
            interpolation (str): Same as :func:`mmcv.imrescale`.

        Returns:
            BaseInstanceMasks: The rescaled masks.
        """

    @abstractmethod
    def resize(self, out_shape, interpolation='nearest'):
        """Resize masks to the given out_shape.

        Args:
            out_shape: Target (h, w) of resized mask.
            interpolation (str): See :func:`mmcv.imresize`.

        Returns:
            BaseInstanceMasks: The resized masks.
        """

    @abstractmethod
    def flip(self, flip_direction='horizontal'):
        """Flip masks alone the given direction.

        Args:
            flip_direction (str): Either 'horizontal' or 'vertical'.

        Returns:
            BaseInstanceMasks: The flipped masks.
        """

    @abstractmethod
    def pad(self, out_shape, pad_val):
        """Pad masks to the given size of (h, w).

        Args:
            out_shape (tuple[int]): Target (h, w) of padded mask.
            pad_val (int): The padded value.

        Returns:
            BaseInstanceMasks: The padded masks.
        """

    @abstractmethod
    def crop(self, bbox):
        """Crop each mask by the given bbox.

        Args:
            bbox (ndarray): Bbox in format [x1, y1, x2, y2], shape (4, ).

        Return:
            BaseInstanceMasks: The cropped masks.
        """

    @abstractmethod
    def crop_and_resize(self,
                        bboxes,
                        out_shape,
                        inds,
                        device,
                        interpolation='bilinear',
                        binarize=True):
        """Crop and resize masks by the given bboxes.

        This function is mainly used in mask targets computation.
        It firstly align mask to bboxes by assigned_inds, then crop mask by the
        assigned bbox and resize to the size of (mask_h, mask_w)

        Args:
            bboxes (Tensor): Bboxes in format [x1, y1, x2, y2], shape (N, 4)
            out_shape (tuple[int]): Target (h, w) of resized mask
            inds (ndarray): Indexes to assign masks to each bbox,
                shape (N,) and values should be between [0, num_masks - 1].
            device (str): Device of bboxes
            interpolation (str): See `mmcv.imresize`
            binarize (bool): if True fractional values are rounded to 0 or 1
                after the resize operation. if False and unsupported an error
                will be raised. Defaults to True.

        Return:
            BaseInstanceMasks: the cropped and resized masks.
        """

    @abstractmethod
    def expand(self, expanded_h, expanded_w, top, left):
        """see :class:`Expand`."""

    @property
    @abstractmethod
    def areas(self):
        """ndarray: areas of each instance."""

    @abstractmethod
    def to_ndarray(self):
        """Convert masks to the format of ndarray.

        Return:
            ndarray: Converted masks in the format of ndarray.
        """

    @abstractmethod
    def to_tensor(self, dtype, device):
        """Convert masks to the format of Tensor.

        Args:
            dtype (str): Dtype of converted mask.
            device (torch.device): Device of converted masks.

        Returns:
            Tensor: Converted masks in the format of Tensor.
        """

    @abstractmethod
    def translate(self,
                  out_shape,
                  offset,
                  direction='horizontal',
                  fill_val=0,
                  interpolation='bilinear'):
        """Translate the masks.

        Args:
            out_shape (tuple[int]): Shape for output mask, format (h, w).
            offset (int | float): The offset for translate.
            direction (str): The translate direction, either "horizontal"
                or "vertical".
            fill_val (int | float): Border value. Default 0.
            interpolation (str): Same as :func:`mmcv.imtranslate`.

        Returns:
            Translated masks.
        """

    def shear(self,
              out_shape,
              magnitude,
              direction='horizontal',
              border_value=0,
              interpolation='bilinear'):
        """Shear the masks.

        Args:
            out_shape (tuple[int]): Shape for output mask, format (h, w).
            magnitude (int | float): The magnitude used for shear.
            direction (str): The shear direction, either "horizontal"
                or "vertical".
            border_value (int | tuple[int]): Value used in case of a
                constant border. Default 0.
            interpolation (str): Same as in :func:`mmcv.imshear`.

        Returns:
            ndarray: Sheared masks.
        """

    @abstractmethod
    def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
        """Rotate the masks.

        Args:
            out_shape (tuple[int]): Shape for output mask, format (h, w).
            angle (int | float): Rotation angle in degrees. Positive values
                mean counter-clockwise rotation.
            center (tuple[float], optional): Center point (w, h) of the
                rotation in source image. If not specified, the center of
                the image will be used.
            scale (int | float): Isotropic scale factor.
            fill_val (int | float): Border value. Default 0 for masks.

        Returns:
            Rotated masks.
        """


class BitmapMasks(BaseInstanceMasks):
    """This class represents masks in the form of bitmaps.

    Args:
        masks (ndarray): ndarray of masks in shape (N, H, W), where N is
            the number of objects.
        height (int): height of masks
        width (int): width of masks

    Example:
        >>> from mmdet.core.mask.structures import *  # NOQA
        >>> num_masks, H, W = 3, 32, 32
        >>> rng = np.random.RandomState(0)
        >>> masks = (rng.rand(num_masks, H, W) > 0.1).astype(np.int)
        >>> self = BitmapMasks(masks, height=H, width=W)

        >>> # demo crop_and_resize
        >>> num_boxes = 5
        >>> bboxes = np.array([[0, 0, 30, 10.0]] * num_boxes)
        >>> out_shape = (14, 14)
        >>> inds = torch.randint(0, len(self), size=(num_boxes,))
        >>> device = 'cpu'
        >>> interpolation = 'bilinear'
        >>> new = self.crop_and_resize(
        ...     bboxes, out_shape, inds, device, interpolation)
        >>> assert len(new) == num_boxes
        >>> assert new.height, new.width == out_shape
    """

    def __init__(self, masks, height, width):
        self.height = height
        self.width = width
        if len(masks) == 0:
            self.masks = np.empty((0, self.height, self.width), dtype=np.uint8)
        else:
            assert isinstance(masks, (list, np.ndarray))
            if isinstance(masks, list):
                assert isinstance(masks[0], np.ndarray)
                assert masks[0].ndim == 2  # (H, W)
            else:
                assert masks.ndim == 3  # (N, H, W)

            self.masks = np.stack(masks).reshape(-1, height, width)
            assert self.masks.shape[1] == self.height
            assert self.masks.shape[2] == self.width

    def __getitem__(self, index):
        """Index the BitmapMask.

        Args:
            index (int | ndarray): Indices in the format of integer or ndarray.

        Returns:
            :obj:`BitmapMasks`: Indexed bitmap masks.
        """
        masks = self.masks[index].reshape(-1, self.height, self.width)
        return BitmapMasks(masks, self.height, self.width)

    def __iter__(self):
        return iter(self.masks)

    def __repr__(self):
        s = self.__class__.__name__ + '('
        s += f'num_masks={len(self.masks)}, '
        s += f'height={self.height}, '
        s += f'width={self.width})'
        return s

    def __len__(self):
        """Number of masks."""
        return len(self.masks)

    def rescale(self, scale, interpolation='nearest'):
        """See :func:`BaseInstanceMasks.rescale`."""
        if len(self.masks) == 0:
            new_w, new_h = mmcv.rescale_size((self.width, self.height), scale)
            rescaled_masks = np.empty((0, new_h, new_w), dtype=np.uint8)
        else:
            rescaled_masks = np.stack([
                mmcv.imrescale(mask, scale, interpolation=interpolation)
                for mask in self.masks
            ])
        height, width = rescaled_masks.shape[1:]
        return BitmapMasks(rescaled_masks, height, width)

    def resize(self, out_shape, interpolation='nearest'):
        """See :func:`BaseInstanceMasks.resize`."""
        if len(self.masks) == 0:
            resized_masks = np.empty((0, *out_shape), dtype=np.uint8)
        else:
            resized_masks = np.stack([
                mmcv.imresize(
                    mask, out_shape[::-1], interpolation=interpolation)
                for mask in self.masks
            ])
        return BitmapMasks(resized_masks, *out_shape)

    def flip(self, flip_direction='horizontal'):
        """See :func:`BaseInstanceMasks.flip`."""
        assert flip_direction in ('horizontal', 'vertical', 'diagonal')

        if len(self.masks) == 0:
            flipped_masks = self.masks
        else:
            flipped_masks = np.stack([
                mmcv.imflip(mask, direction=flip_direction)
                for mask in self.masks
            ])
        return BitmapMasks(flipped_masks, self.height, self.width)

    def pad(self, out_shape, pad_val=0):
        """See :func:`BaseInstanceMasks.pad`."""
        if len(self.masks) == 0:
            padded_masks = np.empty((0, *out_shape), dtype=np.uint8)
        else:
            padded_masks = np.stack([
                mmcv.impad(mask, shape=out_shape, pad_val=pad_val)
                for mask in self.masks
            ])
        return BitmapMasks(padded_masks, *out_shape)

    def crop(self, bbox):
        """See :func:`BaseInstanceMasks.crop`."""
        assert isinstance(bbox, np.ndarray)
        assert bbox.ndim == 1

        # clip the boundary
        bbox = bbox.copy()
        bbox[0::2] = np.clip(bbox[0::2], 0, self.width)
        bbox[1::2] = np.clip(bbox[1::2], 0, self.height)
        x1, y1, x2, y2 = bbox
        w = np.maximum(x2 - x1, 1)
        h = np.maximum(y2 - y1, 1)

        if len(self.masks) == 0:
            cropped_masks = np.empty((0, h, w), dtype=np.uint8)
        else:
            cropped_masks = self.masks[:, y1:y1 + h, x1:x1 + w]
        return BitmapMasks(cropped_masks, h, w)

    def crop_and_resize(self,
                        bboxes,
                        out_shape,
                        inds,
                        device='cpu',
                        interpolation='bilinear',
                        binarize=True):
        """See :func:`BaseInstanceMasks.crop_and_resize`."""
        if len(self.masks) == 0:
            empty_masks = np.empty((0, *out_shape), dtype=np.uint8)
            return BitmapMasks(empty_masks, *out_shape)

        # convert bboxes to tensor
        if isinstance(bboxes, np.ndarray):
            bboxes = torch.from_numpy(bboxes).to(device=device)
        if isinstance(inds, np.ndarray):
            inds = torch.from_numpy(inds).to(device=device)

        num_bbox = bboxes.shape[0]
        fake_inds = torch.arange(
            num_bbox, device=device).to(dtype=bboxes.dtype)[:, None]
        rois = torch.cat([fake_inds, bboxes], dim=1)  # Nx5
        rois = rois.to(device=device)
        if num_bbox > 0:
            gt_masks_th = torch.from_numpy(self.masks).to(device).index_select(
                0, inds).to(dtype=rois.dtype)
            targets = roi_align(gt_masks_th[:, None, :, :], rois, out_shape,
                                1.0, 0, 'avg', True).squeeze(1)
            if binarize:
                resized_masks = (targets >= 0.5).cpu().numpy()
            else:
                resized_masks = targets.cpu().numpy()
        else:
            resized_masks = []
        return BitmapMasks(resized_masks, *out_shape)

    def expand(self, expanded_h, expanded_w, top, left):
        """See :func:`BaseInstanceMasks.expand`."""
        if len(self.masks) == 0:
            expanded_mask = np.empty((0, expanded_h, expanded_w),
                                     dtype=np.uint8)
        else:
            expanded_mask = np.zeros((len(self), expanded_h, expanded_w),
                                     dtype=np.uint8)
            expanded_mask[:, top:top + self.height,
                          left:left + self.width] = self.masks
        return BitmapMasks(expanded_mask, expanded_h, expanded_w)

    def translate(self,
                  out_shape,
                  offset,
                  direction='horizontal',
                  fill_val=0,
                  interpolation='bilinear'):
        """Translate the BitmapMasks.

        Args:
            out_shape (tuple[int]): Shape for output mask, format (h, w).
            offset (int | float): The offset for translate.
            direction (str): The translate direction, either "horizontal"
                or "vertical".
            fill_val (int | float): Border value. Default 0 for masks.
            interpolation (str): Same as :func:`mmcv.imtranslate`.

        Returns:
            BitmapMasks: Translated BitmapMasks.

        Example:
            >>> from mmdet.core.mask.structures import BitmapMasks
            >>> self = BitmapMasks.random(dtype=np.uint8)
            >>> out_shape = (32, 32)
            >>> offset = 4
            >>> direction = 'horizontal'
            >>> fill_val = 0
            >>> interpolation = 'bilinear'
            >>> # Note, There seem to be issues when:
            >>> # * out_shape is different than self's shape
            >>> # * the mask dtype is not supported by cv2.AffineWarp
            >>> new = self.translate(out_shape, offset, direction, fill_val,
            >>>                      interpolation)
            >>> assert len(new) == len(self)
            >>> assert new.height, new.width == out_shape
        """
        if len(self.masks) == 0:
            translated_masks = np.empty((0, *out_shape), dtype=np.uint8)
        else:
            translated_masks = mmcv.imtranslate(
                self.masks.transpose((1, 2, 0)),
                offset,
                direction,
                border_value=fill_val,
                interpolation=interpolation)
            if translated_masks.ndim == 2:
                translated_masks = translated_masks[:, :, None]
            translated_masks = translated_masks.transpose(
                (2, 0, 1)).astype(self.masks.dtype)
        return BitmapMasks(translated_masks, *out_shape)

    def shear(self,
              out_shape,
              magnitude,
              direction='horizontal',
              border_value=0,
              interpolation='bilinear'):
        """Shear the BitmapMasks.

        Args:
            out_shape (tuple[int]): Shape for output mask, format (h, w).
            magnitude (int | float): The magnitude used for shear.
            direction (str): The shear direction, either "horizontal"
                or "vertical".
            border_value (int | tuple[int]): Value used in case of a
                constant border.
            interpolation (str): Same as in :func:`mmcv.imshear`.

        Returns:
            BitmapMasks: The sheared masks.
        """
        if len(self.masks) == 0:
            sheared_masks = np.empty((0, *out_shape), dtype=np.uint8)
        else:
            sheared_masks = mmcv.imshear(
                self.masks.transpose((1, 2, 0)),
                magnitude,
                direction,
                border_value=border_value,
                interpolation=interpolation)
            if sheared_masks.ndim == 2:
                sheared_masks = sheared_masks[:, :, None]
            sheared_masks = sheared_masks.transpose(
                (2, 0, 1)).astype(self.masks.dtype)
        return BitmapMasks(sheared_masks, *out_shape)

    def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
        """Rotate the BitmapMasks.

        Args:
            out_shape (tuple[int]): Shape for output mask, format (h, w).
            angle (int | float): Rotation angle in degrees. Positive values
                mean counter-clockwise rotation.
            center (tuple[float], optional): Center point (w, h) of the
                rotation in source image. If not specified, the center of
                the image will be used.
            scale (int | float): Isotropic scale factor.
            fill_val (int | float): Border value. Default 0 for masks.

        Returns:
            BitmapMasks: Rotated BitmapMasks.
        """
        if len(self.masks) == 0:
            rotated_masks = np.empty((0, *out_shape), dtype=self.masks.dtype)
        else:
            rotated_masks = mmcv.imrotate(
                self.masks.transpose((1, 2, 0)),
                angle,
                center=center,
                scale=scale,
                border_value=fill_val)
            if rotated_masks.ndim == 2:
                # case when only one mask, (h, w)
                rotated_masks = rotated_masks[:, :, None]  # (h, w, 1)
            rotated_masks = rotated_masks.transpose(
                (2, 0, 1)).astype(self.masks.dtype)
        return BitmapMasks(rotated_masks, *out_shape)

    @property
    def areas(self):
        """See :py:attr:`BaseInstanceMasks.areas`."""
        return self.masks.sum((1, 2))

    def to_ndarray(self):
        """See :func:`BaseInstanceMasks.to_ndarray`."""
        return self.masks

    def to_tensor(self, dtype, device):
        """See :func:`BaseInstanceMasks.to_tensor`."""
        return torch.tensor(self.masks, dtype=dtype, device=device)

    @classmethod
    def random(cls,
               num_masks=3,
               height=32,
               width=32,
               dtype=np.uint8,
               rng=None):
        """Generate random bitmap masks for demo / testing purposes.

        Example:
            >>> from mmdet.core.mask.structures import BitmapMasks
            >>> self = BitmapMasks.random()
            >>> print('self = {}'.format(self))
            self = BitmapMasks(num_masks=3, height=32, width=32)
        """
        from mmdet.utils.util_random import ensure_rng
        rng = ensure_rng(rng)
        masks = (rng.rand(num_masks, height, width) > 0.1).astype(dtype)
        self = cls(masks, height=height, width=width)
        return self

    def get_bboxes(self):
        num_masks = len(self)
        boxes = np.zeros((num_masks, 4), dtype=np.float32)
        x_any = self.masks.any(axis=1)
        y_any = self.masks.any(axis=2)
        for idx in range(num_masks):
            x = np.where(x_any[idx, :])[0]
            y = np.where(y_any[idx, :])[0]
            if len(x) > 0 and len(y) > 0:
                # use +1 for x_max and y_max so that the right and bottom
                # boundary of instance masks are fully included by the box
                boxes[idx, :] = np.array([x[0], y[0], x[-1] + 1, y[-1] + 1],
                                         dtype=np.float32)
        return boxes


class PolygonMasks(BaseInstanceMasks):
    """This class represents masks in the form of polygons.

    Polygons is a list of three levels. The first level of the list
    corresponds to objects, the second level to the polys that compose the
    object, the third level to the poly coordinates

    Args:
        masks (list[list[ndarray]]): The first level of the list
            corresponds to objects, the second level to the polys that
            compose the object, the third level to the poly coordinates
        height (int): height of masks
        width (int): width of masks

    Example:
        >>> from mmdet.core.mask.structures import *  # NOQA
        >>> masks = [
        >>>     [ np.array([0, 0, 10, 0, 10, 10., 0, 10, 0, 0]) ]
        >>> ]
        >>> height, width = 16, 16
        >>> self = PolygonMasks(masks, height, width)

        >>> # demo translate
        >>> new = self.translate((16, 16), 4., direction='horizontal')
        >>> assert np.all(new.masks[0][0][1::2] == masks[0][0][1::2])
        >>> assert np.all(new.masks[0][0][0::2] == masks[0][0][0::2] + 4)

        >>> # demo crop_and_resize
        >>> num_boxes = 3
        >>> bboxes = np.array([[0, 0, 30, 10.0]] * num_boxes)
        >>> out_shape = (16, 16)
        >>> inds = torch.randint(0, len(self), size=(num_boxes,))
        >>> device = 'cpu'
        >>> interpolation = 'bilinear'
        >>> new = self.crop_and_resize(
        ...     bboxes, out_shape, inds, device, interpolation)
        >>> assert len(new) == num_boxes
        >>> assert new.height, new.width == out_shape
    """

    def __init__(self, masks, height, width):
        assert isinstance(masks, list)
        if len(masks) > 0:
            assert isinstance(masks[0], list)
            assert isinstance(masks[0][0], np.ndarray)

        self.height = height
        self.width = width
        self.masks = masks

    def __getitem__(self, index):
        """Index the polygon masks.

        Args:
            index (ndarray | List): The indices.

        Returns:
            :obj:`PolygonMasks`: The indexed polygon masks.
        """
        if isinstance(index, np.ndarray):
            index = index.tolist()
        if isinstance(index, list):
            masks = [self.masks[i] for i in index]
        else:
            try:
                masks = self.masks[index]
            except Exception:
                raise ValueError(
                    f'Unsupported input of type {type(index)} for indexing!')
        if len(masks) and isinstance(masks[0], np.ndarray):
            masks = [masks]  # ensure a list of three levels
        return PolygonMasks(masks, self.height, self.width)

    def __iter__(self):
        return iter(self.masks)

    def __repr__(self):
        s = self.__class__.__name__ + '('
        s += f'num_masks={len(self.masks)}, '
        s += f'height={self.height}, '
        s += f'width={self.width})'
        return s

    def __len__(self):
        """Number of masks."""
        return len(self.masks)

    def rescale(self, scale, interpolation=None):
        """see :func:`BaseInstanceMasks.rescale`"""
        new_w, new_h = mmcv.rescale_size((self.width, self.height), scale)
        if len(self.masks) == 0:
            rescaled_masks = PolygonMasks([], new_h, new_w)
        else:
            rescaled_masks = self.resize((new_h, new_w))
        return rescaled_masks

    def resize(self, out_shape, interpolation=None):
        """see :func:`BaseInstanceMasks.resize`"""
        if len(self.masks) == 0:
            resized_masks = PolygonMasks([], *out_shape)
        else:
            h_scale = out_shape[0] / self.height
            w_scale = out_shape[1] / self.width
            resized_masks = []
            for poly_per_obj in self.masks:
                resized_poly = []
                for p in poly_per_obj:
                    p = p.copy()
                    p[0::2] = p[0::2] * w_scale
                    p[1::2] = p[1::2] * h_scale
                    resized_poly.append(p)
                resized_masks.append(resized_poly)
            resized_masks = PolygonMasks(resized_masks, *out_shape)
        return resized_masks

    def flip(self, flip_direction='horizontal'):
        """see :func:`BaseInstanceMasks.flip`"""
        assert flip_direction in ('horizontal', 'vertical', 'diagonal')
        if len(self.masks) == 0:
            flipped_masks = PolygonMasks([], self.height, self.width)
        else:
            flipped_masks = []
            for poly_per_obj in self.masks:
                flipped_poly_per_obj = []
                for p in poly_per_obj:
                    p = p.copy()
                    if flip_direction == 'horizontal':
                        p[0::2] = self.width - p[0::2]
                    elif flip_direction == 'vertical':
                        p[1::2] = self.height - p[1::2]
                    else:
                        p[0::2] = self.width - p[0::2]
                        p[1::2] = self.height - p[1::2]
                    flipped_poly_per_obj.append(p)
                flipped_masks.append(flipped_poly_per_obj)
            flipped_masks = PolygonMasks(flipped_masks, self.height,
                                         self.width)
        return flipped_masks

    def crop(self, bbox):
        """see :func:`BaseInstanceMasks.crop`"""
        assert isinstance(bbox, np.ndarray)
        assert bbox.ndim == 1

        # clip the boundary
        bbox = bbox.copy()
        bbox[0::2] = np.clip(bbox[0::2], 0, self.width)
        bbox[1::2] = np.clip(bbox[1::2], 0, self.height)
        x1, y1, x2, y2 = bbox
        w = np.maximum(x2 - x1, 1)
        h = np.maximum(y2 - y1, 1)

        if len(self.masks) == 0:
            cropped_masks = PolygonMasks([], h, w)
        else:
            cropped_masks = []
            for poly_per_obj in self.masks:
                cropped_poly_per_obj = []
                for p in poly_per_obj:
                    # pycocotools will clip the boundary
                    p = p.copy()
                    p[0::2] = p[0::2] - bbox[0]
                    p[1::2] = p[1::2] - bbox[1]
                    cropped_poly_per_obj.append(p)
                cropped_masks.append(cropped_poly_per_obj)
            cropped_masks = PolygonMasks(cropped_masks, h, w)
        return cropped_masks

    def pad(self, out_shape, pad_val=0):
        """padding has no effect on polygons`"""
        return PolygonMasks(self.masks, *out_shape)

    def expand(self, *args, **kwargs):
        """TODO: Add expand for polygon"""
        raise NotImplementedError

    def crop_and_resize(self,
                        bboxes,
                        out_shape,
                        inds,
                        device='cpu',
                        interpolation='bilinear',
                        binarize=True):
        """see :func:`BaseInstanceMasks.crop_and_resize`"""
        out_h, out_w = out_shape
        if len(self.masks) == 0:
            return PolygonMasks([], out_h, out_w)

        if not binarize:
            raise ValueError('Polygons are always binary, '
                             'setting binarize=False is unsupported')

        resized_masks = []
        for i in range(len(bboxes)):
            mask = self.masks[inds[i]]
            bbox = bboxes[i, :]
            x1, y1, x2, y2 = bbox
            w = np.maximum(x2 - x1, 1)
            h = np.maximum(y2 - y1, 1)
            h_scale = out_h / max(h, 0.1)  # avoid too large scale
            w_scale = out_w / max(w, 0.1)

            resized_mask = []
            for p in mask:
                p = p.copy()
                # crop
                # pycocotools will clip the boundary
                p[0::2] = p[0::2] - bbox[0]
                p[1::2] = p[1::2] - bbox[1]

                # resize
                p[0::2] = p[0::2] * w_scale
                p[1::2] = p[1::2] * h_scale
                resized_mask.append(p)
            resized_masks.append(resized_mask)
        return PolygonMasks(resized_masks, *out_shape)

    def translate(self,
                  out_shape,
                  offset,
                  direction='horizontal',
                  fill_val=None,
                  interpolation=None):
        """Translate the PolygonMasks.

        Example:
            >>> self = PolygonMasks.random(dtype=np.int)
            >>> out_shape = (self.height, self.width)
            >>> new = self.translate(out_shape, 4., direction='horizontal')
            >>> assert np.all(new.masks[0][0][1::2] == self.masks[0][0][1::2])
            >>> assert np.all(new.masks[0][0][0::2] == self.masks[0][0][0::2] + 4)  # noqa: E501
        """
        assert fill_val is None or fill_val == 0, 'Here fill_val is not '\
            f'used, and defaultly should be None or 0. got {fill_val}.'
        if len(self.masks) == 0:
            translated_masks = PolygonMasks([], *out_shape)
        else:
            translated_masks = []
            for poly_per_obj in self.masks:
                translated_poly_per_obj = []
                for p in poly_per_obj:
                    p = p.copy()
                    if direction == 'horizontal':
                        p[0::2] = np.clip(p[0::2] + offset, 0, out_shape[1])
                    elif direction == 'vertical':
                        p[1::2] = np.clip(p[1::2] + offset, 0, out_shape[0])
                    translated_poly_per_obj.append(p)
                translated_masks.append(translated_poly_per_obj)
            translated_masks = PolygonMasks(translated_masks, *out_shape)
        return translated_masks

    def shear(self,
              out_shape,
              magnitude,
              direction='horizontal',
              border_value=0,
              interpolation='bilinear'):
        """See :func:`BaseInstanceMasks.shear`."""
        if len(self.masks) == 0:
            sheared_masks = PolygonMasks([], *out_shape)
        else:
            sheared_masks = []
            if direction == 'horizontal':
                shear_matrix = np.stack([[1, magnitude],
                                         [0, 1]]).astype(np.float32)
            elif direction == 'vertical':
                shear_matrix = np.stack([[1, 0], [magnitude,
                                                  1]]).astype(np.float32)
            for poly_per_obj in self.masks:
                sheared_poly = []
                for p in poly_per_obj:
                    p = np.stack([p[0::2], p[1::2]], axis=0)  # [2, n]
                    new_coords = np.matmul(shear_matrix, p)  # [2, n]
                    new_coords[0, :] = np.clip(new_coords[0, :], 0,
                                               out_shape[1])
                    new_coords[1, :] = np.clip(new_coords[1, :], 0,
                                               out_shape[0])
                    sheared_poly.append(
                        new_coords.transpose((1, 0)).reshape(-1))
                sheared_masks.append(sheared_poly)
            sheared_masks = PolygonMasks(sheared_masks, *out_shape)
        return sheared_masks

    def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
        """See :func:`BaseInstanceMasks.rotate`."""
        if len(self.masks) == 0:
            rotated_masks = PolygonMasks([], *out_shape)
        else:
            rotated_masks = []
            rotate_matrix = cv2.getRotationMatrix2D(center, -angle, scale)
            for poly_per_obj in self.masks:
                rotated_poly = []
                for p in poly_per_obj:
                    p = p.copy()
                    coords = np.stack([p[0::2], p[1::2]], axis=1)  # [n, 2]
                    # pad 1 to convert from format [x, y] to homogeneous
                    # coordinates format [x, y, 1]
                    coords = np.concatenate(
                        (coords, np.ones((coords.shape[0], 1), coords.dtype)),
                        axis=1)  # [n, 3]
                    rotated_coords = np.matmul(
                        rotate_matrix[None, :, :],
                        coords[:, :, None])[..., 0]  # [n, 2, 1] -> [n, 2]
                    rotated_coords[:, 0] = np.clip(rotated_coords[:, 0], 0,
                                                   out_shape[1])
                    rotated_coords[:, 1] = np.clip(rotated_coords[:, 1], 0,
                                                   out_shape[0])
                    rotated_poly.append(rotated_coords.reshape(-1))
                rotated_masks.append(rotated_poly)
            rotated_masks = PolygonMasks(rotated_masks, *out_shape)
        return rotated_masks

    def to_bitmap(self):
        """convert polygon masks to bitmap masks."""
        bitmap_masks = self.to_ndarray()
        return BitmapMasks(bitmap_masks, self.height, self.width)

    @property
    def areas(self):
        """Compute areas of masks.

        This func is modified from `detectron2
        <https://github.com/facebookresearch/detectron2/blob/ffff8acc35ea88ad1cb1806ab0f00b4c1c5dbfd9/detectron2/structures/masks.py#L387>`_.
        The function only works with Polygons using the shoelace formula.

        Return:
            ndarray: areas of each instance
        """  # noqa: W501
        area = []
        for polygons_per_obj in self.masks:
            area_per_obj = 0
            for p in polygons_per_obj:
                area_per_obj += self._polygon_area(p[0::2], p[1::2])
            area.append(area_per_obj)
        return np.asarray(area)

    def _polygon_area(self, x, y):
        """Compute the area of a component of a polygon.

        Using the shoelace formula:
        https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates

        Args:
            x (ndarray): x coordinates of the component
            y (ndarray): y coordinates of the component

        Return:
            float: the are of the component
        """  # noqa: 501
        return 0.5 * np.abs(
            np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1)))

    def to_ndarray(self):
        """Convert masks to the format of ndarray."""
        if len(self.masks) == 0:
            return np.empty((0, self.height, self.width), dtype=np.uint8)
        bitmap_masks = []
        for poly_per_obj in self.masks:
            bitmap_masks.append(
                polygon_to_bitmap(poly_per_obj, self.height, self.width))
        return np.stack(bitmap_masks)

    def to_tensor(self, dtype, device):
        """See :func:`BaseInstanceMasks.to_tensor`."""
        if len(self.masks) == 0:
            return torch.empty((0, self.height, self.width),
                               dtype=dtype,
                               device=device)
        ndarray_masks = self.to_ndarray()
        return torch.tensor(ndarray_masks, dtype=dtype, device=device)

    @classmethod
    def random(cls,
               num_masks=3,
               height=32,
               width=32,
               n_verts=5,
               dtype=np.float32,
               rng=None):
        """Generate random polygon masks for demo / testing purposes.

        Adapted from [1]_

        References:
            .. [1] https://gitlab.kitware.com/computer-vision/kwimage/-/blob/928cae35ca8/kwimage/structs/polygon.py#L379  # noqa: E501

        Example:
            >>> from mmdet.core.mask.structures import PolygonMasks
            >>> self = PolygonMasks.random()
            >>> print('self = {}'.format(self))
        """
        from mmdet.utils.util_random import ensure_rng
        rng = ensure_rng(rng)

        def _gen_polygon(n, irregularity, spikeyness):
            """Creates the polygon by sampling points on a circle around the
            centre.  Random noise is added by varying the angular spacing
            between sequential points, and by varying the radial distance of
            each point from the centre.

            Based on original code by Mike Ounsworth

            Args:
                n (int): number of vertices
                irregularity (float): [0,1] indicating how much variance there
                    is in the angular spacing of vertices. [0,1] will map to
                    [0, 2pi/numberOfVerts]
                spikeyness (float): [0,1] indicating how much variance there is
                    in each vertex from the circle of radius aveRadius. [0,1]
                    will map to [0, aveRadius]

            Returns:
                a list of vertices, in CCW order.
            """
            from scipy.stats import truncnorm

            # Generate around the unit circle
            cx, cy = (0.0, 0.0)
            radius = 1

            tau = np.pi * 2

            irregularity = np.clip(irregularity, 0, 1) * 2 * np.pi / n
            spikeyness = np.clip(spikeyness, 1e-9, 1)

            # generate n angle steps
            lower = (tau / n) - irregularity
            upper = (tau / n) + irregularity
            angle_steps = rng.uniform(lower, upper, n)

            # normalize the steps so that point 0 and point n+1 are the same
            k = angle_steps.sum() / (2 * np.pi)
            angles = (angle_steps / k).cumsum() + rng.uniform(0, tau)

            # Convert high and low values to be wrt the standard normal range
            # https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.truncnorm.html
            low = 0
            high = 2 * radius
            mean = radius
            std = spikeyness
            a = (low - mean) / std
            b = (high - mean) / std
            tnorm = truncnorm(a=a, b=b, loc=mean, scale=std)

            # now generate the points
            radii = tnorm.rvs(n, random_state=rng)
            x_pts = cx + radii * np.cos(angles)
            y_pts = cy + radii * np.sin(angles)

            points = np.hstack([x_pts[:, None], y_pts[:, None]])

            # Scale to 0-1 space
            points = points - points.min(axis=0)
            points = points / points.max(axis=0)

            # Randomly place within 0-1 space
            points = points * (rng.rand() * .8 + .2)
            min_pt = points.min(axis=0)
            max_pt = points.max(axis=0)

            high = (1 - max_pt)
            low = (0 - min_pt)
            offset = (rng.rand(2) * (high - low)) + low
            points = points + offset
            return points

        def _order_vertices(verts):
            """
            References:
                https://stackoverflow.com/questions/1709283/how-can-i-sort-a-coordinate-list-for-a-rectangle-counterclockwise
            """
            mlat = verts.T[0].sum() / len(verts)
            mlng = verts.T[1].sum() / len(verts)

            tau = np.pi * 2
            angle = (np.arctan2(mlat - verts.T[0], verts.T[1] - mlng) +
                     tau) % tau
            sortx = angle.argsort()
            verts = verts.take(sortx, axis=0)
            return verts

        # Generate a random exterior for each requested mask
        masks = []
        for _ in range(num_masks):
            exterior = _order_vertices(_gen_polygon(n_verts, 0.9, 0.9))
            exterior = (exterior * [(width, height)]).astype(dtype)
            masks.append([exterior.ravel()])

        self = cls(masks, height, width)
        return self

    def get_bboxes(self):
        num_masks = len(self)
        boxes = np.zeros((num_masks, 4), dtype=np.float32)
        for idx, poly_per_obj in enumerate(self.masks):
            # simply use a number that is big enough for comparison with
            # coordinates
            xy_min = np.array([self.width * 2, self.height * 2],
                              dtype=np.float32)
            xy_max = np.zeros(2, dtype=np.float32)
            for p in poly_per_obj:
                xy = np.array(p).reshape(-1, 2).astype(np.float32)
                xy_min = np.minimum(xy_min, np.min(xy, axis=0))
                xy_max = np.maximum(xy_max, np.max(xy, axis=0))
            boxes[idx, :2] = xy_min
            boxes[idx, 2:] = xy_max

        return boxes


def polygon_to_bitmap(polygons, height, width):
    """Convert masks from the form of polygons to bitmaps.

    Args:
        polygons (list[ndarray]): masks in polygon representation
        height (int): mask height
        width (int): mask width

    Return:
        ndarray: the converted masks in bitmap representation
    """
    rles = maskUtils.frPyObjects(polygons, height, width)
    rle = maskUtils.merge(rles)
    bitmap_mask = maskUtils.decode(rle).astype(bool)
    return bitmap_mask


def bitmap_to_polygon(bitmap):
    """Convert masks from the form of bitmaps to polygons.

    Args:
        bitmap (ndarray): masks in bitmap representation.

    Return:
        list[ndarray]: the converted mask in polygon representation.
        bool: whether the mask has holes.
    """
    bitmap = np.ascontiguousarray(bitmap).astype(np.uint8)
    # cv2.RETR_CCOMP: retrieves all of the contours and organizes them
    #   into a two-level hierarchy. At the top level, there are external
    #   boundaries of the components. At the second level, there are
    #   boundaries of the holes. If there is another contour inside a hole
    #   of a connected component, it is still put at the top level.
    # cv2.CHAIN_APPROX_NONE: stores absolutely all the contour points.
    outs = cv2.findContours(bitmap, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
    contours = outs[-2]
    hierarchy = outs[-1]
    if hierarchy is None:
        return [], False
    # hierarchy[i]: 4 elements, for the indexes of next, previous,
    # parent, or nested contours. If there is no corresponding contour,
    # it will be -1.
    with_hole = (hierarchy.reshape(-1, 4)[:, 3] >= 0).any()
    contours = [c.reshape(-1, 2) for c in contours]
    return contours, with_hole