transforms_3d.py 65.4 KB
Newer Older
dingchang's avatar
dingchang committed
1
# Copyright (c) OpenMMLab. All rights reserved.
2
import random
3
import warnings
4
from typing import Dict
5
6
7

import cv2
import numpy as np
8
from mmcv import is_tuple_of
9
10
from mmcv.transforms import BaseTransform
from mmengine.registry import build_from_cfg
zhangwenwei's avatar
zhangwenwei committed
11

12
from mmdet3d.core import VoxelGenerator
13
14
from mmdet3d.core.bbox import (CameraInstance3DBoxes, DepthInstance3DBoxes,
                               LiDARInstance3DBoxes, box_np_ops)
15
from mmdet3d.registry import TRANSFORMS
zhangwenwei's avatar
zhangwenwei committed
16
from mmdet.datasets.pipelines import RandomFlip
zhangwenwei's avatar
zhangwenwei committed
17
18
19
from .data_augment_utils import noise_per_object_v3_


20
@TRANSFORMS.register_module()
21
22
23
24
25
26
27
28
class RandomDropPointsColor(object):
    r"""Randomly set the color of points to all zeros.

    Once this transform is executed, all the points' color will be dropped.
    Refer to `PAConv <https://github.com/CVMI-Lab/PAConv/blob/main/scene_seg/
    util/transform.py#L223>`_ for more details.

    Args:
29
        drop_ratio (float, optional): The probability of dropping point colors.
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
            Defaults to 0.2.
    """

    def __init__(self, drop_ratio=0.2):
        assert isinstance(drop_ratio, (int, float)) and 0 <= drop_ratio <= 1, \
            f'invalid drop_ratio value {drop_ratio}'
        self.drop_ratio = drop_ratio

    def __call__(self, input_dict):
        """Call function to drop point colors.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
45
            dict: Results after color dropping,
46
47
48
49
50
51
52
                'points' key is updated in the result dict.
        """
        points = input_dict['points']
        assert points.attribute_dims is not None and \
            'color' in points.attribute_dims, \
            'Expect points have color attribute'

53
54
55
56
57
58
59
        # this if-expression is a bit strange
        # `RandomDropPointsColor` is used in training 3D segmentor PAConv
        # we discovered in our experiments that, using
        # `if np.random.rand() > 1.0 - self.drop_ratio` consistently leads to
        # better results than using `if np.random.rand() < self.drop_ratio`
        # so we keep this hack in our codebase
        if np.random.rand() > 1.0 - self.drop_ratio:
60
61
62
63
64
65
66
67
68
69
            points.color = points.color * 0.0
        return input_dict

    def __repr__(self):
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
        repr_str += f'(drop_ratio={self.drop_ratio})'
        return repr_str


70
@TRANSFORMS.register_module()
zhangwenwei's avatar
zhangwenwei committed
71
72
73
74
75
76
77
78
class RandomFlip3D(RandomFlip):
    """Flip the points & bbox.

    If the input dict contains the key "flip", then the flag will be used,
    otherwise it will be randomly decided by a ratio specified in the init
    method.

    Args:
zhangwenwei's avatar
zhangwenwei committed
79
80
81
        sync_2d (bool, optional): Whether to apply flip according to the 2D
            images. If True, it will apply the same flip as that to 2D images.
            If False, it will decide whether to flip randomly and independently
liyinhao's avatar
liyinhao committed
82
            to that of 2D images. Defaults to True.
wuyuefeng's avatar
wuyuefeng committed
83
        flip_ratio_bev_horizontal (float, optional): The flipping probability
liyinhao's avatar
liyinhao committed
84
            in horizontal direction. Defaults to 0.0.
wuyuefeng's avatar
wuyuefeng committed
85
        flip_ratio_bev_vertical (float, optional): The flipping probability
liyinhao's avatar
liyinhao committed
86
            in vertical direction. Defaults to 0.0.
zhangwenwei's avatar
zhangwenwei committed
87
88
    """

wuyuefeng's avatar
wuyuefeng committed
89
90
91
92
93
94
95
    def __init__(self,
                 sync_2d=True,
                 flip_ratio_bev_horizontal=0.0,
                 flip_ratio_bev_vertical=0.0,
                 **kwargs):
        super(RandomFlip3D, self).__init__(
            flip_ratio=flip_ratio_bev_horizontal, **kwargs)
zhangwenwei's avatar
zhangwenwei committed
96
        self.sync_2d = sync_2d
wuyuefeng's avatar
wuyuefeng committed
97
98
99
100
101
102
103
104
105
106
107
        self.flip_ratio_bev_vertical = flip_ratio_bev_vertical
        if flip_ratio_bev_horizontal is not None:
            assert isinstance(
                flip_ratio_bev_horizontal,
                (int, float)) and 0 <= flip_ratio_bev_horizontal <= 1
        if flip_ratio_bev_vertical is not None:
            assert isinstance(
                flip_ratio_bev_vertical,
                (int, float)) and 0 <= flip_ratio_bev_vertical <= 1

    def random_flip_data_3d(self, input_dict, direction='horizontal'):
108
109
110
111
        """Flip 3D data randomly.

        Args:
            input_dict (dict): Result dict from loading pipeline.
112
113
            direction (str, optional): Flip direction.
                Default: 'horizontal'.
114
115

        Returns:
116
            dict: Flipped results, 'points', 'bbox3d_fields' keys are
117
118
                updated in the result dict.
        """
wuyuefeng's avatar
wuyuefeng committed
119
        assert direction in ['horizontal', 'vertical']
120
121
122
123
        # for semantic segmentation task, only points will be flipped.
        if 'bbox3d_fields' not in input_dict:
            input_dict['points'].flip(direction)
            return
124
125
126
127
128
        if len(input_dict['bbox3d_fields']) == 0:  # test mode
            input_dict['bbox3d_fields'].append('empty_box3d')
            input_dict['empty_box3d'] = input_dict['box_type_3d'](
                np.array([], dtype=np.float32))
        assert len(input_dict['bbox3d_fields']) == 1
zhangwenwei's avatar
zhangwenwei committed
129
        for key in input_dict['bbox3d_fields']:
130
131
132
133
134
135
136
137
            if 'points' in input_dict:
                input_dict['points'] = input_dict[key].flip(
                    direction, points=input_dict['points'])
            else:
                input_dict[key].flip(direction)
        if 'centers2d' in input_dict:
            assert self.sync_2d is True and direction == 'horizontal', \
                'Only support sync_2d=True and horizontal flip with images'
138
            w = input_dict['ori_shape'][1]
139
140
            input_dict['centers2d'][..., 0] = \
                w - input_dict['centers2d'][..., 0]
141
142
            # need to modify the horizontal position of camera center
            # along u-axis in the image (flip like centers2d)
143
            # ['cam2img'][0][2] = c_u
144
145
            # see more details and examples at
            # https://github.com/open-mmlab/mmdetection3d/pull/744
146
            input_dict['cam2img'][0][2] = w - input_dict['cam2img'][0][2]
zhangwenwei's avatar
zhangwenwei committed
147
148

    def __call__(self, input_dict):
149
        """Call function to flip points, values in the ``bbox3d_fields`` and
150
151
152
153
154
155
        also flip 2D image and its annotations.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
156
157
            dict: Flipped results, 'flip', 'flip_direction',
                'pcd_horizontal_flip' and 'pcd_vertical_flip' keys are added
158
159
                into result dict.
        """
160
        # flip 2D image and its annotations
zhangwenwei's avatar
zhangwenwei committed
161
        super(RandomFlip3D, self).__call__(input_dict)
zhangwenwei's avatar
zhangwenwei committed
162

zhangwenwei's avatar
zhangwenwei committed
163
        if self.sync_2d:
wuyuefeng's avatar
wuyuefeng committed
164
165
            input_dict['pcd_horizontal_flip'] = input_dict['flip']
            input_dict['pcd_vertical_flip'] = False
zhangwenwei's avatar
zhangwenwei committed
166
        else:
wuyuefeng's avatar
wuyuefeng committed
167
168
169
170
171
172
173
174
175
            if 'pcd_horizontal_flip' not in input_dict:
                flip_horizontal = True if np.random.rand(
                ) < self.flip_ratio else False
                input_dict['pcd_horizontal_flip'] = flip_horizontal
            if 'pcd_vertical_flip' not in input_dict:
                flip_vertical = True if np.random.rand(
                ) < self.flip_ratio_bev_vertical else False
                input_dict['pcd_vertical_flip'] = flip_vertical

176
177
178
        if 'transformation_3d_flow' not in input_dict:
            input_dict['transformation_3d_flow'] = []

wuyuefeng's avatar
wuyuefeng committed
179
180
        if input_dict['pcd_horizontal_flip']:
            self.random_flip_data_3d(input_dict, 'horizontal')
181
            input_dict['transformation_3d_flow'].extend(['HF'])
wuyuefeng's avatar
wuyuefeng committed
182
183
        if input_dict['pcd_vertical_flip']:
            self.random_flip_data_3d(input_dict, 'vertical')
184
            input_dict['transformation_3d_flow'].extend(['VF'])
zhangwenwei's avatar
zhangwenwei committed
185
186
        return input_dict

zhangwenwei's avatar
zhangwenwei committed
187
    def __repr__(self):
188
        """str: Return a string that describes the module."""
wuyuefeng's avatar
wuyuefeng committed
189
        repr_str = self.__class__.__name__
190
        repr_str += f'(sync_2d={self.sync_2d},'
191
        repr_str += f' flip_ratio_bev_vertical={self.flip_ratio_bev_vertical})'
wuyuefeng's avatar
wuyuefeng committed
192
        return repr_str
zhangwenwei's avatar
zhangwenwei committed
193

zhangwenwei's avatar
zhangwenwei committed
194

195
@TRANSFORMS.register_module()
196
197
198
class RandomJitterPoints(object):
    """Randomly jitter point coordinates.

199
    Different from the global translation in ``GlobalRotScaleTrans``, here we
200
201
202
203
        apply different noises to each point in a scene.

    Args:
        jitter_std (list[float]): The standard deviation of jittering noise.
204
205
            This applies random noise to all points in a 3D scene, which is
            sampled from a gaussian distribution whose standard deviation is
206
            set by ``jitter_std``. Defaults to [0.01, 0.01, 0.01]
207
        clip_range (list[float]): Clip the randomly generated jitter
208
209
210
211
            noise into this range. If None is given, don't perform clipping.
            Defaults to [-0.05, 0.05]

    Note:
212
        This transform should only be used in point cloud segmentation tasks
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
            because we don't transform ground-truth bboxes accordingly.
        For similar transform in detection task, please refer to `ObjectNoise`.
    """

    def __init__(self,
                 jitter_std=[0.01, 0.01, 0.01],
                 clip_range=[-0.05, 0.05]):
        seq_types = (list, tuple, np.ndarray)
        if not isinstance(jitter_std, seq_types):
            assert isinstance(jitter_std, (int, float)), \
                f'unsupported jitter_std type {type(jitter_std)}'
            jitter_std = [jitter_std, jitter_std, jitter_std]
        self.jitter_std = jitter_std

        if clip_range is not None:
            if not isinstance(clip_range, seq_types):
                assert isinstance(clip_range, (int, float)), \
                    f'unsupported clip_range type {type(clip_range)}'
                clip_range = [-clip_range, clip_range]
        self.clip_range = clip_range

    def __call__(self, input_dict):
        """Call function to jitter all the points in the scene.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
241
            dict: Results after adding noise to each point,
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
                'points' key is updated in the result dict.
        """
        points = input_dict['points']
        jitter_std = np.array(self.jitter_std, dtype=np.float32)
        jitter_noise = \
            np.random.randn(points.shape[0], 3) * jitter_std[None, :]
        if self.clip_range is not None:
            jitter_noise = np.clip(jitter_noise, self.clip_range[0],
                                   self.clip_range[1])

        points.translate(jitter_noise)
        return input_dict

    def __repr__(self):
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
        repr_str += f'(jitter_std={self.jitter_std},'
        repr_str += f' clip_range={self.clip_range})'
        return repr_str


263
264
@TRANSFORMS.register_module()
class ObjectSample(BaseTransform):
zhangwenwei's avatar
zhangwenwei committed
265
    """Sample GT objects to the data.
zhangwenwei's avatar
zhangwenwei committed
266

267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
    Required Keys:

    - points
    - ann_info
    - gt_bboxes_3d
    - gt_labels_3d
    - img (optional)
    - gt_bboxes (optional)

    Modified Keys:
    - points
    - gt_bboxes_3d
    - gt_labels_3d
    - img (optional)
    - gt_bboxes (optional)

    Added Keys:

    - plane (optional)

zhangwenwei's avatar
zhangwenwei committed
287
288
289
290
    Args:
        db_sampler (dict): Config dict of the database sampler.
        sample_2d (bool): Whether to also paste 2D image patch to the images
            This should be true when applying multi-modality cut-and-paste.
liyinhao's avatar
liyinhao committed
291
            Defaults to False.
292
        use_ground_plane (bool): Whether to use ground plane to adjust the
293
            3D labels.
zhangwenwei's avatar
zhangwenwei committed
294
    """
zhangwenwei's avatar
zhangwenwei committed
295

296
    def __init__(self, db_sampler, sample_2d=False, use_ground_plane=False):
zhangwenwei's avatar
zhangwenwei committed
297
298
299
300
        self.sampler_cfg = db_sampler
        self.sample_2d = sample_2d
        if 'type' not in db_sampler.keys():
            db_sampler['type'] = 'DataBaseSampler'
301
        self.db_sampler = build_from_cfg(db_sampler, TRANSFORMS)
302
        self.use_ground_plane = use_ground_plane
zhangwenwei's avatar
zhangwenwei committed
303
304
305

    @staticmethod
    def remove_points_in_boxes(points, boxes):
306
307
308
        """Remove the points in the sampled bounding boxes.

        Args:
309
            points (:obj:`BasePoints`): Input point cloud array.
310
311
312
313
314
            boxes (np.ndarray): Sampled ground truth boxes.

        Returns:
            np.ndarray: Points with those in the boxes removed.
        """
315
        masks = box_np_ops.points_in_rbbox(points.coord.numpy(), boxes)
zhangwenwei's avatar
zhangwenwei committed
316
317
318
        points = points[np.logical_not(masks.any(-1))]
        return points

319
320
    def transform(self, input_dict: dict) -> dict:
        """Transform function to sample ground truth objects to the data.
321
322
323
324
325

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
326
327
            dict: Results after object sampling augmentation,
                'points', 'gt_bboxes_3d', 'gt_labels_3d' keys are updated
328
329
                in the result dict.
        """
zhangwenwei's avatar
zhangwenwei committed
330
        gt_bboxes_3d = input_dict['gt_bboxes_3d']
zhangwenwei's avatar
zhangwenwei committed
331
332
        gt_labels_3d = input_dict['gt_labels_3d']

333
334
335
336
337
        if self.use_ground_plane and 'plane' in input_dict['ann_info']:
            ground_plane = input_dict['ann_info']['plane']
            input_dict['plane'] = ground_plane
        else:
            ground_plane = None
zhangwenwei's avatar
zhangwenwei committed
338
339
340
        # change to float for blending operation
        points = input_dict['points']
        if self.sample_2d:
wuyuefeng's avatar
wuyuefeng committed
341
            img = input_dict['img']
zhangwenwei's avatar
zhangwenwei committed
342
343
344
            gt_bboxes_2d = input_dict['gt_bboxes']
            # Assume for now 3D & 2D bboxes are the same
            sampled_dict = self.db_sampler.sample_all(
345
346
347
348
                gt_bboxes_3d.tensor.numpy(),
                gt_labels_3d,
                gt_bboxes_2d=gt_bboxes_2d,
                img=img)
zhangwenwei's avatar
zhangwenwei committed
349
350
        else:
            sampled_dict = self.db_sampler.sample_all(
351
352
353
354
                gt_bboxes_3d.tensor.numpy(),
                gt_labels_3d,
                img=None,
                ground_plane=ground_plane)
zhangwenwei's avatar
zhangwenwei committed
355
356
357
358

        if sampled_dict is not None:
            sampled_gt_bboxes_3d = sampled_dict['gt_bboxes_3d']
            sampled_points = sampled_dict['points']
zhangwenwei's avatar
zhangwenwei committed
359
            sampled_gt_labels = sampled_dict['gt_labels_3d']
zhangwenwei's avatar
zhangwenwei committed
360

zhangwenwei's avatar
zhangwenwei committed
361
362
            gt_labels_3d = np.concatenate([gt_labels_3d, sampled_gt_labels],
                                          axis=0)
363
364
365
            gt_bboxes_3d = gt_bboxes_3d.new_box(
                np.concatenate(
                    [gt_bboxes_3d.tensor.numpy(), sampled_gt_bboxes_3d]))
zhangwenwei's avatar
zhangwenwei committed
366

zhangwenwei's avatar
zhangwenwei committed
367
368
            points = self.remove_points_in_boxes(points, sampled_gt_bboxes_3d)
            # check the points dimension
369
            points = points.cat([sampled_points, points])
zhangwenwei's avatar
zhangwenwei committed
370
371
372
373
374

            if self.sample_2d:
                sampled_gt_bboxes_2d = sampled_dict['gt_bboxes_2d']
                gt_bboxes_2d = np.concatenate(
                    [gt_bboxes_2d, sampled_gt_bboxes_2d]).astype(np.float32)
zhangwenwei's avatar
zhangwenwei committed
375

zhangwenwei's avatar
zhangwenwei committed
376
                input_dict['gt_bboxes'] = gt_bboxes_2d
wuyuefeng's avatar
wuyuefeng committed
377
                input_dict['img'] = sampled_dict['img']
zhangwenwei's avatar
zhangwenwei committed
378
379

        input_dict['gt_bboxes_3d'] = gt_bboxes_3d
WRH's avatar
WRH committed
380
        input_dict['gt_labels_3d'] = gt_labels_3d.astype(np.int64)
zhangwenwei's avatar
zhangwenwei committed
381
        input_dict['points'] = points
zhangwenwei's avatar
zhangwenwei committed
382

zhangwenwei's avatar
zhangwenwei committed
383
384
385
        return input_dict

    def __repr__(self):
386
        """str: Return a string that describes the module."""
387
388
389
390
391
392
393
394
395
        repr_str = self.__class__.__name__
        repr_str += f' sample_2d={self.sample_2d},'
        repr_str += f' data_root={self.sampler_cfg.data_root},'
        repr_str += f' info_path={self.sampler_cfg.info_path},'
        repr_str += f' rate={self.sampler_cfg.rate},'
        repr_str += f' prepare={self.sampler_cfg.prepare},'
        repr_str += f' classes={self.sampler_cfg.classes},'
        repr_str += f' sample_groups={self.sampler_cfg.sample_groups}'
        return repr_str
zhangwenwei's avatar
zhangwenwei committed
396
397


398
399
@TRANSFORMS.register_module()
class ObjectNoise(BaseTransform):
zhangwenwei's avatar
zhangwenwei committed
400
    """Apply noise to each GT objects in the scene.
zhangwenwei's avatar
zhangwenwei committed
401

402
403
404
405
406
407
408
409
410
411
    Required Keys:

    - points
    - gt_bboxes_3d

    Modified Keys:

    - points
    - gt_bboxes_3d

zhangwenwei's avatar
zhangwenwei committed
412
    Args:
413
        translation_std (list[float], optional): Standard deviation of the
zhangwenwei's avatar
zhangwenwei committed
414
415
            distribution where translation noise are sampled from.
            Defaults to [0.25, 0.25, 0.25].
416
        global_rot_range (list[float], optional): Global rotation to the scene.
zhangwenwei's avatar
zhangwenwei committed
417
            Defaults to [0.0, 0.0].
418
        rot_range (list[float], optional): Object rotation range.
zhangwenwei's avatar
zhangwenwei committed
419
420
421
422
            Defaults to [-0.15707963267, 0.15707963267].
        num_try (int, optional): Number of times to try if the noise applied is
            invalid. Defaults to 100.
    """
zhangwenwei's avatar
zhangwenwei committed
423
424

    def __init__(self,
zhangwenwei's avatar
zhangwenwei committed
425
                 translation_std=[0.25, 0.25, 0.25],
zhangwenwei's avatar
zhangwenwei committed
426
                 global_rot_range=[0.0, 0.0],
zhangwenwei's avatar
zhangwenwei committed
427
                 rot_range=[-0.15707963267, 0.15707963267],
zhangwenwei's avatar
zhangwenwei committed
428
                 num_try=100):
zhangwenwei's avatar
zhangwenwei committed
429
        self.translation_std = translation_std
zhangwenwei's avatar
zhangwenwei committed
430
        self.global_rot_range = global_rot_range
zhangwenwei's avatar
zhangwenwei committed
431
        self.rot_range = rot_range
zhangwenwei's avatar
zhangwenwei committed
432
433
        self.num_try = num_try

434
435
    def transform(self, input_dict: dict) -> dict:
        """Transform function to apply noise to each ground truth in the scene.
436
437
438
439
440

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
441
            dict: Results after adding noise to each object,
442
443
                'points', 'gt_bboxes_3d' keys are updated in the result dict.
        """
zhangwenwei's avatar
zhangwenwei committed
444
445
        gt_bboxes_3d = input_dict['gt_bboxes_3d']
        points = input_dict['points']
zhangwenwei's avatar
zhangwenwei committed
446

447
        # TODO: this is inplace operation
448
        numpy_box = gt_bboxes_3d.tensor.numpy()
449
450
        numpy_points = points.tensor.numpy()

zhangwenwei's avatar
zhangwenwei committed
451
        noise_per_object_v3_(
452
            numpy_box,
453
            numpy_points,
zhangwenwei's avatar
zhangwenwei committed
454
455
            rotation_perturb=self.rot_range,
            center_noise_std=self.translation_std,
zhangwenwei's avatar
zhangwenwei committed
456
457
            global_random_rot_range=self.global_rot_range,
            num_try=self.num_try)
458
459

        input_dict['gt_bboxes_3d'] = gt_bboxes_3d.new_box(numpy_box)
460
        input_dict['points'] = points.new_point(numpy_points)
zhangwenwei's avatar
zhangwenwei committed
461
462
463
        return input_dict

    def __repr__(self):
464
        """str: Return a string that describes the module."""
zhangwenwei's avatar
zhangwenwei committed
465
        repr_str = self.__class__.__name__
466
467
468
469
        repr_str += f'(num_try={self.num_try},'
        repr_str += f' translation_std={self.translation_std},'
        repr_str += f' global_rot_range={self.global_rot_range},'
        repr_str += f' rot_range={self.rot_range})'
zhangwenwei's avatar
zhangwenwei committed
470
471
472
        return repr_str


473
@TRANSFORMS.register_module()
474
475
476
477
478
479
480
class GlobalAlignment(object):
    """Apply global alignment to 3D scene points by rotation and translation.

    Args:
        rotation_axis (int): Rotation axis for points and bboxes rotation.

    Note:
481
482
        We do not record the applied rotation and translation as in
            GlobalRotScaleTrans. Because usually, we do not need to reverse
483
            the alignment step.
484
        For example, ScanNet 3D detection task uses aligned ground-truth
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
            bounding boxes for evaluation.
    """

    def __init__(self, rotation_axis):
        self.rotation_axis = rotation_axis

    def _trans_points(self, input_dict, trans_factor):
        """Private function to translate points.

        Args:
            input_dict (dict): Result dict from loading pipeline.
            trans_factor (np.ndarray): Translation vector to be applied.

        Returns:
            dict: Results after translation, 'points' is updated in the dict.
        """
        input_dict['points'].translate(trans_factor)

    def _rot_points(self, input_dict, rot_mat):
        """Private function to rotate bounding boxes and points.

        Args:
            input_dict (dict): Result dict from loading pipeline.
            rot_mat (np.ndarray): Rotation matrix to be applied.

        Returns:
            dict: Results after rotation, 'points' is updated in the dict.
        """
        # input should be rot_mat_T so I transpose it here
        input_dict['points'].rotate(rot_mat.T)

    def _check_rot_mat(self, rot_mat):
        """Check if rotation matrix is valid for self.rotation_axis.

        Args:
            rot_mat (np.ndarray): Rotation matrix to be applied.
        """
        is_valid = np.allclose(np.linalg.det(rot_mat), 1.0)
        valid_array = np.zeros(3)
        valid_array[self.rotation_axis] = 1.0
        is_valid &= (rot_mat[self.rotation_axis, :] == valid_array).all()
        is_valid &= (rot_mat[:, self.rotation_axis] == valid_array).all()
        assert is_valid, f'invalid rotation matrix {rot_mat}'

    def __call__(self, input_dict):
        """Call function to shuffle points.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
536
            dict: Results after global alignment, 'points' and keys in
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
                input_dict['bbox3d_fields'] are updated in the result dict.
        """
        assert 'axis_align_matrix' in input_dict['ann_info'].keys(), \
            'axis_align_matrix is not provided in GlobalAlignment'

        axis_align_matrix = input_dict['ann_info']['axis_align_matrix']
        assert axis_align_matrix.shape == (4, 4), \
            f'invalid shape {axis_align_matrix.shape} for axis_align_matrix'
        rot_mat = axis_align_matrix[:3, :3]
        trans_vec = axis_align_matrix[:3, -1]

        self._check_rot_mat(rot_mat)
        self._rot_points(input_dict, rot_mat)
        self._trans_points(input_dict, trans_vec)

        return input_dict

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += f'(rotation_axis={self.rotation_axis})'
        return repr_str


560
@TRANSFORMS.register_module()
zhangwenwei's avatar
zhangwenwei committed
561
class GlobalRotScaleTrans(object):
zhangwenwei's avatar
zhangwenwei committed
562
    """Apply global rotation, scaling and translation to a 3D scene.
zhangwenwei's avatar
zhangwenwei committed
563
564

    Args:
565
        rot_range (list[float], optional): Range of rotation angle.
liyinhao's avatar
liyinhao committed
566
            Defaults to [-0.78539816, 0.78539816] (close to [-pi/4, pi/4]).
567
        scale_ratio_range (list[float], optional): Range of scale ratio.
liyinhao's avatar
liyinhao committed
568
            Defaults to [0.95, 1.05].
569
570
        translation_std (list[float], optional): The standard deviation of
            translation noise applied to a scene, which
zhangwenwei's avatar
zhangwenwei committed
571
            is sampled from a gaussian distribution whose standard deviation
liyinhao's avatar
liyinhao committed
572
            is set by ``translation_std``. Defaults to [0, 0, 0]
573
        shift_height (bool, optional): Whether to shift height.
wuyuefeng's avatar
wuyuefeng committed
574
            (the fourth dimension of indoor points) when scaling.
liyinhao's avatar
liyinhao committed
575
            Defaults to False.
zhangwenwei's avatar
zhangwenwei committed
576
    """
zhangwenwei's avatar
zhangwenwei committed
577
578

    def __init__(self,
zhangwenwei's avatar
zhangwenwei committed
579
580
                 rot_range=[-0.78539816, 0.78539816],
                 scale_ratio_range=[0.95, 1.05],
wuyuefeng's avatar
wuyuefeng committed
581
582
                 translation_std=[0, 0, 0],
                 shift_height=False):
583
584
585
586
587
        seq_types = (list, tuple, np.ndarray)
        if not isinstance(rot_range, seq_types):
            assert isinstance(rot_range, (int, float)), \
                f'unsupported rot_range type {type(rot_range)}'
            rot_range = [-rot_range, rot_range]
zhangwenwei's avatar
zhangwenwei committed
588
        self.rot_range = rot_range
589
590
591

        assert isinstance(scale_ratio_range, seq_types), \
            f'unsupported scale_ratio_range type {type(scale_ratio_range)}'
zhangwenwei's avatar
zhangwenwei committed
592
        self.scale_ratio_range = scale_ratio_range
593
594
595
596
597
598
599

        if not isinstance(translation_std, seq_types):
            assert isinstance(translation_std, (int, float)), \
                f'unsupported translation_std type {type(translation_std)}'
            translation_std = [
                translation_std, translation_std, translation_std
            ]
600
601
        assert all([std >= 0 for std in translation_std]), \
            'translation_std should be positive'
zhangwenwei's avatar
zhangwenwei committed
602
        self.translation_std = translation_std
wuyuefeng's avatar
wuyuefeng committed
603
        self.shift_height = shift_height
zhangwenwei's avatar
zhangwenwei committed
604
605

    def _trans_bbox_points(self, input_dict):
606
607
608
609
610
611
        """Private function to translate bounding boxes and points.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
612
613
            dict: Results after translation, 'points', 'pcd_trans'
                and keys in input_dict['bbox3d_fields'] are updated
614
615
                in the result dict.
        """
616
        translation_std = np.array(self.translation_std, dtype=np.float32)
zhangwenwei's avatar
zhangwenwei committed
617
618
        trans_factor = np.random.normal(scale=translation_std, size=3).T

619
        input_dict['points'].translate(trans_factor)
zhangwenwei's avatar
zhangwenwei committed
620
621
622
623
624
        input_dict['pcd_trans'] = trans_factor
        for key in input_dict['bbox3d_fields']:
            input_dict[key].translate(trans_factor)

    def _rot_bbox_points(self, input_dict):
625
626
627
628
629
630
        """Private function to rotate bounding boxes and points.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
631
632
            dict: Results after rotation, 'points', 'pcd_rotation'
                and keys in input_dict['bbox3d_fields'] are updated
633
634
                in the result dict.
        """
zhangwenwei's avatar
zhangwenwei committed
635
        rotation = self.rot_range
zhangwenwei's avatar
zhangwenwei committed
636
        noise_rotation = np.random.uniform(rotation[0], rotation[1])
zhangwenwei's avatar
zhangwenwei committed
637

638
639
640
641
        # if no bbox in input_dict, only rotate points
        if len(input_dict['bbox3d_fields']) == 0:
            rot_mat_T = input_dict['points'].rotate(noise_rotation)
            input_dict['pcd_rotation'] = rot_mat_T
642
            input_dict['pcd_rotation_angle'] = noise_rotation
643
644
645
            return

        # rotate points with bboxes
zhangwenwei's avatar
zhangwenwei committed
646
        for key in input_dict['bbox3d_fields']:
wuyuefeng's avatar
wuyuefeng committed
647
648
649
650
651
            if len(input_dict[key].tensor) != 0:
                points, rot_mat_T = input_dict[key].rotate(
                    noise_rotation, input_dict['points'])
                input_dict['points'] = points
                input_dict['pcd_rotation'] = rot_mat_T
652
                input_dict['pcd_rotation_angle'] = noise_rotation
653

zhangwenwei's avatar
zhangwenwei committed
654
    def _scale_bbox_points(self, input_dict):
655
656
657
658
659
660
        """Private function to scale bounding boxes and points.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
661
            dict: Results after scaling, 'points'and keys in
662
663
                input_dict['bbox3d_fields'] are updated in the result dict.
        """
zhangwenwei's avatar
zhangwenwei committed
664
        scale = input_dict['pcd_scale_factor']
665
666
        points = input_dict['points']
        points.scale(scale)
wuyuefeng's avatar
wuyuefeng committed
667
        if self.shift_height:
668
669
            assert 'height' in points.attribute_dims.keys(), \
                'setting shift_height=True but points have no height attribute'
670
671
            points.tensor[:, points.attribute_dims['height']] *= scale
        input_dict['points'] = points
wuyuefeng's avatar
wuyuefeng committed
672

zhangwenwei's avatar
zhangwenwei committed
673
674
        for key in input_dict['bbox3d_fields']:
            input_dict[key].scale(scale)
zhangwenwei's avatar
zhangwenwei committed
675

zhangwenwei's avatar
zhangwenwei committed
676
    def _random_scale(self, input_dict):
677
678
679
680
681
682
        """Private function to randomly set the scale factor.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
683
            dict: Results after scaling, 'pcd_scale_factor' are updated
684
685
                in the result dict.
        """
zhangwenwei's avatar
zhangwenwei committed
686
687
688
        scale_factor = np.random.uniform(self.scale_ratio_range[0],
                                         self.scale_ratio_range[1])
        input_dict['pcd_scale_factor'] = scale_factor
zhangwenwei's avatar
zhangwenwei committed
689
690

    def __call__(self, input_dict):
691
        """Private function to rotate, scale and translate bounding boxes and
692
693
694
695
696
697
698
        points.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after scaling, 'points', 'pcd_rotation',
699
                'pcd_scale_factor', 'pcd_trans' and keys in
700
701
                input_dict['bbox3d_fields'] are updated in the result dict.
        """
702
703
704
        if 'transformation_3d_flow' not in input_dict:
            input_dict['transformation_3d_flow'] = []

zhangwenwei's avatar
zhangwenwei committed
705
        self._rot_bbox_points(input_dict)
zhangwenwei's avatar
zhangwenwei committed
706

zhangwenwei's avatar
zhangwenwei committed
707
708
709
        if 'pcd_scale_factor' not in input_dict:
            self._random_scale(input_dict)
        self._scale_bbox_points(input_dict)
zhangwenwei's avatar
zhangwenwei committed
710

zhangwenwei's avatar
zhangwenwei committed
711
        self._trans_bbox_points(input_dict)
712
713

        input_dict['transformation_3d_flow'].extend(['R', 'S', 'T'])
zhangwenwei's avatar
zhangwenwei committed
714
715
716
        return input_dict

    def __repr__(self):
717
        """str: Return a string that describes the module."""
zhangwenwei's avatar
zhangwenwei committed
718
        repr_str = self.__class__.__name__
719
720
721
722
        repr_str += f'(rot_range={self.rot_range},'
        repr_str += f' scale_ratio_range={self.scale_ratio_range},'
        repr_str += f' translation_std={self.translation_std},'
        repr_str += f' shift_height={self.shift_height})'
zhangwenwei's avatar
zhangwenwei committed
723
724
725
        return repr_str


726
@TRANSFORMS.register_module()
zhangwenwei's avatar
zhangwenwei committed
727
class PointShuffle(object):
728
    """Shuffle input points."""
zhangwenwei's avatar
zhangwenwei committed
729
730

    def __call__(self, input_dict):
731
732
733
734
735
736
        """Call function to shuffle points.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
737
            dict: Results after filtering, 'points', 'pts_instance_mask'
738
                and 'pts_semantic_mask' keys are updated in the result dict.
739
        """
740
741
742
743
744
745
746
747
748
749
750
751
        idx = input_dict['points'].shuffle()
        idx = idx.numpy()

        pts_instance_mask = input_dict.get('pts_instance_mask', None)
        pts_semantic_mask = input_dict.get('pts_semantic_mask', None)

        if pts_instance_mask is not None:
            input_dict['pts_instance_mask'] = pts_instance_mask[idx]

        if pts_semantic_mask is not None:
            input_dict['pts_semantic_mask'] = pts_semantic_mask[idx]

zhangwenwei's avatar
zhangwenwei committed
752
753
754
755
756
757
        return input_dict

    def __repr__(self):
        return self.__class__.__name__


758
@TRANSFORMS.register_module()
zhangwenwei's avatar
zhangwenwei committed
759
class ObjectRangeFilter(object):
760
761
762
763
764
    """Filter objects by the range.

    Args:
        point_cloud_range (list[float]): Point cloud range.
    """
zhangwenwei's avatar
zhangwenwei committed
765
766
767
768
769

    def __init__(self, point_cloud_range):
        self.pcd_range = np.array(point_cloud_range, dtype=np.float32)

    def __call__(self, input_dict):
770
771
772
773
774
775
        """Call function to filter objects by the range.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
776
            dict: Results after filtering, 'gt_bboxes_3d', 'gt_labels_3d'
777
778
                keys are updated in the result dict.
        """
779
780
781
782
783
784
785
        # Check points instance type and initialise bev_range
        if isinstance(input_dict['gt_bboxes_3d'],
                      (LiDARInstance3DBoxes, DepthInstance3DBoxes)):
            bev_range = self.pcd_range[[0, 1, 3, 4]]
        elif isinstance(input_dict['gt_bboxes_3d'], CameraInstance3DBoxes):
            bev_range = self.pcd_range[[0, 2, 3, 5]]

zhangwenwei's avatar
zhangwenwei committed
786
        gt_bboxes_3d = input_dict['gt_bboxes_3d']
zhangwenwei's avatar
zhangwenwei committed
787
        gt_labels_3d = input_dict['gt_labels_3d']
788
        mask = gt_bboxes_3d.in_range_bev(bev_range)
zhangwenwei's avatar
zhangwenwei committed
789
        gt_bboxes_3d = gt_bboxes_3d[mask]
ZwwWayne's avatar
ZwwWayne committed
790
791
792
793
794
        # mask is a torch tensor but gt_labels_3d is still numpy array
        # using mask to index gt_labels_3d will cause bug when
        # len(gt_labels_3d) == 1, where mask=1 will be interpreted
        # as gt_labels_3d[1] and cause out of index error
        gt_labels_3d = gt_labels_3d[mask.numpy().astype(np.bool)]
zhangwenwei's avatar
zhangwenwei committed
795
796

        # limit rad to [-pi, pi]
797
798
        gt_bboxes_3d.limit_yaw(offset=0.5, period=2 * np.pi)
        input_dict['gt_bboxes_3d'] = gt_bboxes_3d
zhangwenwei's avatar
zhangwenwei committed
799
800
        input_dict['gt_labels_3d'] = gt_labels_3d

zhangwenwei's avatar
zhangwenwei committed
801
802
803
        return input_dict

    def __repr__(self):
804
        """str: Return a string that describes the module."""
zhangwenwei's avatar
zhangwenwei committed
805
        repr_str = self.__class__.__name__
806
        repr_str += f'(point_cloud_range={self.pcd_range.tolist()})'
zhangwenwei's avatar
zhangwenwei committed
807
808
809
        return repr_str


810
@TRANSFORMS.register_module()
zhangwenwei's avatar
zhangwenwei committed
811
class PointsRangeFilter(object):
812
813
814
815
816
    """Filter points by the range.

    Args:
        point_cloud_range (list[float]): Point cloud range.
    """
zhangwenwei's avatar
zhangwenwei committed
817
818

    def __init__(self, point_cloud_range):
819
        self.pcd_range = np.array(point_cloud_range, dtype=np.float32)
zhangwenwei's avatar
zhangwenwei committed
820
821

    def __call__(self, input_dict):
822
823
824
825
826
827
        """Call function to filter points by the range.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
828
            dict: Results after filtering, 'points', 'pts_instance_mask'
829
                and 'pts_semantic_mask' keys are updated in the result dict.
830
        """
zhangwenwei's avatar
zhangwenwei committed
831
        points = input_dict['points']
832
833
        points_mask = points.in_range_3d(self.pcd_range)
        clean_points = points[points_mask]
zhangwenwei's avatar
zhangwenwei committed
834
        input_dict['points'] = clean_points
835
836
837
838
839
840
841
842
843
844
845
        points_mask = points_mask.numpy()

        pts_instance_mask = input_dict.get('pts_instance_mask', None)
        pts_semantic_mask = input_dict.get('pts_semantic_mask', None)

        if pts_instance_mask is not None:
            input_dict['pts_instance_mask'] = pts_instance_mask[points_mask]

        if pts_semantic_mask is not None:
            input_dict['pts_semantic_mask'] = pts_semantic_mask[points_mask]

zhangwenwei's avatar
zhangwenwei committed
846
847
848
        return input_dict

    def __repr__(self):
849
        """str: Return a string that describes the module."""
zhangwenwei's avatar
zhangwenwei committed
850
        repr_str = self.__class__.__name__
851
        repr_str += f'(point_cloud_range={self.pcd_range.tolist()})'
zhangwenwei's avatar
zhangwenwei committed
852
        return repr_str
zhangwenwei's avatar
zhangwenwei committed
853
854


855
@TRANSFORMS.register_module()
zhangwenwei's avatar
zhangwenwei committed
856
class ObjectNameFilter(object):
zhangwenwei's avatar
zhangwenwei committed
857
    """Filter GT objects by their names.
zhangwenwei's avatar
zhangwenwei committed
858
859

    Args:
liyinhao's avatar
liyinhao committed
860
        classes (list[str]): List of class names to be kept for training.
zhangwenwei's avatar
zhangwenwei committed
861
862
863
864
865
866
867
    """

    def __init__(self, classes):
        self.classes = classes
        self.labels = list(range(len(self.classes)))

    def __call__(self, input_dict):
868
869
870
871
872
873
        """Call function to filter objects by their names.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
874
            dict: Results after filtering, 'gt_bboxes_3d', 'gt_labels_3d'
875
876
                keys are updated in the result dict.
        """
zhangwenwei's avatar
zhangwenwei committed
877
878
879
880
881
882
883
884
885
        gt_labels_3d = input_dict['gt_labels_3d']
        gt_bboxes_mask = np.array([n in self.labels for n in gt_labels_3d],
                                  dtype=np.bool_)
        input_dict['gt_bboxes_3d'] = input_dict['gt_bboxes_3d'][gt_bboxes_mask]
        input_dict['gt_labels_3d'] = input_dict['gt_labels_3d'][gt_bboxes_mask]

        return input_dict

    def __repr__(self):
886
        """str: Return a string that describes the module."""
zhangwenwei's avatar
zhangwenwei committed
887
888
889
        repr_str = self.__class__.__name__
        repr_str += f'(classes={self.classes})'
        return repr_str
wuyuefeng's avatar
wuyuefeng committed
890
891


892
893
@TRANSFORMS.register_module()
class PointSample(BaseTransform):
894
    """Point sample.
wuyuefeng's avatar
wuyuefeng committed
895
896
897

    Sampling data to a certain number.

898
899
900
901
902
903
904
905
906
907
    Required Keys:
    - points
    - pts_instance_mask (optional)
    - pts_semantic_mask (optional)

    Modified Keys:
    - points
    - pts_instance_mask (optional)
    - pts_semantic_mask (optional)

wuyuefeng's avatar
wuyuefeng committed
908
909
    Args:
        num_points (int): Number of points to be sampled.
910
        sample_range (float, optional): The range where to sample points.
911
912
913
914
            If not None, the points with depth larger than `sample_range` are
            prior to be sampled. Defaults to None.
        replace (bool, optional): Whether the sampling is with or without
            replacement. Defaults to False.
wuyuefeng's avatar
wuyuefeng committed
915
916
    """

917
    def __init__(self, num_points, sample_range=None, replace=False):
wuyuefeng's avatar
wuyuefeng committed
918
        self.num_points = num_points
919
920
921
922
923
924
925
926
927
        self.sample_range = sample_range
        self.replace = replace

    def _points_random_sampling(self,
                                points,
                                num_samples,
                                sample_range=None,
                                replace=False,
                                return_choices=False):
wuyuefeng's avatar
wuyuefeng committed
928
929
930
931
932
        """Points random sampling.

        Sample points to a certain number.

        Args:
933
            points (np.ndarray | :obj:`BasePoints`): 3D Points.
wuyuefeng's avatar
wuyuefeng committed
934
            num_samples (int): Number of samples to be sampled.
935
            sample_range (float, optional): Indicating the range where the
936
                points will be sampled. Defaults to None.
937
938
939
940
            replace (bool, optional): Sampling with or without replacement.
                Defaults to None.
            return_choices (bool, optional): Whether return choice.
                Defaults to False.
wuyuefeng's avatar
wuyuefeng committed
941
        Returns:
942
            tuple[np.ndarray] | np.ndarray:
943
                - points (np.ndarray | :obj:`BasePoints`): 3D Points.
944
                - choices (np.ndarray, optional): The generated random samples.
wuyuefeng's avatar
wuyuefeng committed
945
        """
946
        if not replace:
wuyuefeng's avatar
wuyuefeng committed
947
            replace = (points.shape[0] < num_samples)
948
949
950
        point_range = range(len(points))
        if sample_range is not None and not replace:
            # Only sampling the near points when len(points) >= num_samples
951
952
953
            dist = np.linalg.norm(points.tensor, axis=1)
            far_inds = np.where(dist >= sample_range)[0]
            near_inds = np.where(dist < sample_range)[0]
954
955
956
957
            # in case there are too many far points
            if len(far_inds) > num_samples:
                far_inds = np.random.choice(
                    far_inds, num_samples, replace=False)
958
959
960
961
962
963
964
            point_range = near_inds
            num_samples -= len(far_inds)
        choices = np.random.choice(point_range, num_samples, replace=replace)
        if sample_range is not None and not replace:
            choices = np.concatenate((far_inds, choices))
            # Shuffle points after sampling
            np.random.shuffle(choices)
wuyuefeng's avatar
wuyuefeng committed
965
966
967
968
969
        if return_choices:
            return points[choices], choices
        else:
            return points[choices]

970
971
    def transform(self, input_dict: Dict) -> Dict:
        """Transform function to sample points to in indoor scenes.
972
973
974
975

        Args:
            input_dict (dict): Result dict from loading pipeline.
        Returns:
976
            dict: Results after sampling, 'points', 'pts_instance_mask'
977
978
                and 'pts_semantic_mask' keys are updated in the result dict.
        """
979
        points = input_dict['points']
980
981
982
983
984
985
        points, choices = self._points_random_sampling(
            points,
            self.num_points,
            self.sample_range,
            self.replace,
            return_choices=True)
986
        input_dict['points'] = points
987

988
989
        pts_instance_mask = input_dict.get('pts_instance_mask', None)
        pts_semantic_mask = input_dict.get('pts_semantic_mask', None)
wuyuefeng's avatar
wuyuefeng committed
990

991
        if pts_instance_mask is not None:
wuyuefeng's avatar
wuyuefeng committed
992
            pts_instance_mask = pts_instance_mask[choices]
993
            input_dict['pts_instance_mask'] = pts_instance_mask
994
995
996

        if pts_semantic_mask is not None:
            pts_semantic_mask = pts_semantic_mask[choices]
997
            input_dict['pts_semantic_mask'] = pts_semantic_mask
wuyuefeng's avatar
wuyuefeng committed
998

999
        return input_dict
wuyuefeng's avatar
wuyuefeng committed
1000
1001

    def __repr__(self):
1002
        """str: Return a string that describes the module."""
wuyuefeng's avatar
wuyuefeng committed
1003
        repr_str = self.__class__.__name__
1004
        repr_str += f'(num_points={self.num_points},'
1005
1006
        repr_str += f' sample_range={self.sample_range},'
        repr_str += f' replace={self.replace})'
1007

1008
1009
1010
        return repr_str


1011
@TRANSFORMS.register_module()
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
class IndoorPointSample(PointSample):
    """Indoor point sample.

    Sampling data to a certain number.
    NOTE: IndoorPointSample is deprecated in favor of PointSample

    Args:
        num_points (int): Number of points to be sampled.
    """

    def __init__(self, *args, **kwargs):
        warnings.warn(
            'IndoorPointSample is deprecated in favor of PointSample')
        super(IndoorPointSample, self).__init__(*args, **kwargs)


1028
@TRANSFORMS.register_module()
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
class IndoorPatchPointSample(object):
    r"""Indoor point sample within a patch. Modified from `PointNet++ <https://
    github.com/charlesq34/pointnet2/blob/master/scannet/scannet_dataset.py>`_.

    Sampling data to a certain number for semantic segmentation.

    Args:
        num_points (int): Number of points to be sampled.
        block_size (float, optional): Size of a block to sample points from.
            Defaults to 1.5.
        sample_rate (float, optional): Stride used in sliding patch generation.
1040
1041
1042
            This parameter is unused in `IndoorPatchPointSample` and thus has
            been deprecated. We plan to remove it in the future.
            Defaults to None.
1043
1044
        ignore_index (int, optional): Label index that won't be used for the
            segmentation task. This is set in PointSegClassMapping as neg_cls.
1045
            If not None, will be used as a patch selection criterion.
1046
1047
1048
1049
1050
            Defaults to None.
        use_normalized_coord (bool, optional): Whether to use normalized xyz as
            additional features. Defaults to False.
        num_try (int, optional): Number of times to try if the patch selected
            is invalid. Defaults to 10.
1051
        enlarge_size (float, optional): Enlarge the sampled patch to
1052
            [-block_size / 2 - enlarge_size, block_size / 2 + enlarge_size] as
1053
            an augmentation. If None, set it as 0. Defaults to 0.2.
1054
        min_unique_num (int, optional): Minimum number of unique points
1055
1056
            the sampled patch should contain. If None, use PointNet++'s method
            to judge uniqueness. Defaults to None.
1057
1058
        eps (float, optional): A value added to patch boundary to guarantee
            points coverage. Defaults to 1e-2.
1059
1060
1061
1062
1063
1064

    Note:
        This transform should only be used in the training process of point
            cloud segmentation tasks. For the sliding patch generation and
            inference process in testing, please refer to the `slide_inference`
            function of `EncoderDecoder3D` class.
1065
1066
1067
1068
1069
    """

    def __init__(self,
                 num_points,
                 block_size=1.5,
1070
                 sample_rate=None,
1071
1072
                 ignore_index=None,
                 use_normalized_coord=False,
1073
1074
                 num_try=10,
                 enlarge_size=0.2,
1075
1076
                 min_unique_num=None,
                 eps=1e-2):
1077
1078
1079
1080
1081
        self.num_points = num_points
        self.block_size = block_size
        self.ignore_index = ignore_index
        self.use_normalized_coord = use_normalized_coord
        self.num_try = num_try
1082
        self.enlarge_size = enlarge_size if enlarge_size is not None else 0.0
1083
        self.min_unique_num = min_unique_num
1084
        self.eps = eps
1085
1086
1087
1088
1089

        if sample_rate is not None:
            warnings.warn(
                "'sample_rate' has been deprecated and will be removed in "
                'the future. Please remove them from your code.')
1090
1091
1092
1093
1094

    def _input_generation(self, coords, patch_center, coord_max, attributes,
                          attribute_dims, point_type):
        """Generating model input.

1095
        Generate input by subtracting patch center and adding additional
1096
1097
1098
1099
1100
1101
1102
1103
1104
            features. Currently support colors and normalized xyz as features.

        Args:
            coords (np.ndarray): Sampled 3D Points.
            patch_center (np.ndarray): Center coordinate of the selected patch.
            coord_max (np.ndarray): Max coordinate of all 3D Points.
            attributes (np.ndarray): features of input points.
            attribute_dims (dict): Dictionary to indicate the meaning of extra
                dimension.
1105
            point_type (type): class of input points inherited from BasePoints.
1106
1107

        Returns:
1108
            :obj:`BasePoints`: The generated input data.
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
        """
        # subtract patch center, the z dimension is not centered
        centered_coords = coords.copy()
        centered_coords[:, 0] -= patch_center[0]
        centered_coords[:, 1] -= patch_center[1]

        if self.use_normalized_coord:
            normalized_coord = coords / coord_max
            attributes = np.concatenate([attributes, normalized_coord], axis=1)
            if attribute_dims is None:
                attribute_dims = dict()
            attribute_dims.update(
                dict(normalized_coord=[
                    attributes.shape[1], attributes.shape[1] +
                    1, attributes.shape[1] + 2
                ]))

        points = np.concatenate([centered_coords, attributes], axis=1)
        points = point_type(
            points, points_dim=points.shape[1], attribute_dims=attribute_dims)

        return points

1132
    def _patch_points_sampling(self, points, sem_mask):
1133
1134
1135
1136
1137
1138
        """Patch points sampling.

        First sample a valid patch.
        Then sample points within that patch to a certain number.

        Args:
1139
            points (:obj:`BasePoints`): 3D Points.
1140
1141
1142
            sem_mask (np.ndarray): semantic segmentation mask for input points.

        Returns:
1143
            tuple[:obj:`BasePoints`, np.ndarray] | :obj:`BasePoints`:
1144

1145
                - points (:obj:`BasePoints`): 3D Points.
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
                - choices (np.ndarray): The generated random samples.
        """
        coords = points.coord.numpy()
        attributes = points.tensor[:, 3:].numpy()
        attribute_dims = points.attribute_dims
        point_type = type(points)

        coord_max = np.amax(coords, axis=0)
        coord_min = np.amin(coords, axis=0)

1156
        for _ in range(self.num_try):
1157
1158
1159
            # random sample a point as patch center
            cur_center = coords[np.random.choice(coords.shape[0])]

1160
1161
            # boundary of a patch, which would be enlarged by
            # `self.enlarge_size` as an augmentation
1162
1163
1164
1165
1166
1167
1168
            cur_max = cur_center + np.array(
                [self.block_size / 2.0, self.block_size / 2.0, 0.0])
            cur_min = cur_center - np.array(
                [self.block_size / 2.0, self.block_size / 2.0, 0.0])
            cur_max[2] = coord_max[2]
            cur_min[2] = coord_min[2]
            cur_choice = np.sum(
1169
1170
                (coords >= (cur_min - self.enlarge_size)) *
                (coords <= (cur_max + self.enlarge_size)),
1171
1172
1173
1174
1175
1176
1177
                axis=1) == 3

            if not cur_choice.any():  # no points in this patch
                continue

            cur_coords = coords[cur_choice, :]
            cur_sem_mask = sem_mask[cur_choice]
1178
            point_idxs = np.where(cur_choice)[0]
1179
            mask = np.sum(
1180
1181
                (cur_coords >= (cur_min - self.eps)) * (cur_coords <=
                                                        (cur_max + self.eps)),
1182
                axis=1) == 3
1183

1184
1185
            # two criteria for patch sampling, adopted from PointNet++
            # 1. selected patch should contain enough unique points
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
            if self.min_unique_num is None:
                # use PointNet++'s method as default
                # [31, 31, 62] are just some big values used to transform
                # coords from 3d array to 1d and then check their uniqueness
                # this is used in all the ScanNet code following PointNet++
                vidx = np.ceil(
                    (cur_coords[mask, :] - cur_min) / (cur_max - cur_min) *
                    np.array([31.0, 31.0, 62.0]))
                vidx = np.unique(vidx[:, 0] * 31.0 * 62.0 + vidx[:, 1] * 62.0 +
                                 vidx[:, 2])
                flag1 = len(vidx) / 31.0 / 31.0 / 62.0 >= 0.02
            else:
1198
                # if `min_unique_num` is provided, directly compare with it
1199
                flag1 = mask.sum() >= self.min_unique_num
1200

1201
            # 2. selected patch should contain enough annotated points
1202
1203
1204
1205
1206
1207
1208
1209
1210
            if self.ignore_index is None:
                flag2 = True
            else:
                flag2 = np.sum(cur_sem_mask != self.ignore_index) / \
                               len(cur_sem_mask) >= 0.7

            if flag1 and flag2:
                break

1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
        # sample idx to `self.num_points`
        if point_idxs.size >= self.num_points:
            # no duplicate in sub-sampling
            choices = np.random.choice(
                point_idxs, self.num_points, replace=False)
        else:
            # do not use random choice here to avoid some points not counted
            dup = np.random.choice(point_idxs.size,
                                   self.num_points - point_idxs.size)
            idx_dup = np.concatenate(
                [np.arange(point_idxs.size),
                 np.array(dup)], 0)
            choices = point_idxs[idx_dup]
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238

        # construct model input
        points = self._input_generation(coords[choices], cur_center, coord_max,
                                        attributes[choices], attribute_dims,
                                        point_type)

        return points, choices

    def __call__(self, results):
        """Call function to sample points to in indoor scenes.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
1239
            dict: Results after sampling, 'points', 'pts_instance_mask'
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
                and 'pts_semantic_mask' keys are updated in the result dict.
        """
        points = results['points']

        assert 'pts_semantic_mask' in results.keys(), \
            'semantic mask should be provided in training and evaluation'
        pts_semantic_mask = results['pts_semantic_mask']

        points, choices = self._patch_points_sampling(points,
                                                      pts_semantic_mask)

        results['points'] = points
        results['pts_semantic_mask'] = pts_semantic_mask[choices]
        pts_instance_mask = results.get('pts_instance_mask', None)
        if pts_instance_mask is not None:
            results['pts_instance_mask'] = pts_instance_mask[choices]

        return results

    def __repr__(self):
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
        repr_str += f'(num_points={self.num_points},'
        repr_str += f' block_size={self.block_size},'
        repr_str += f' ignore_index={self.ignore_index},'
        repr_str += f' use_normalized_coord={self.use_normalized_coord},'
1266
1267
        repr_str += f' num_try={self.num_try},'
        repr_str += f' enlarge_size={self.enlarge_size},'
1268
1269
        repr_str += f' min_unique_num={self.min_unique_num},'
        repr_str += f' eps={self.eps})'
wuyuefeng's avatar
wuyuefeng committed
1270
        return repr_str
1271
1272


1273
@TRANSFORMS.register_module()
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
class BackgroundPointsFilter(object):
    """Filter background points near the bounding box.

    Args:
        bbox_enlarge_range (tuple[float], float): Bbox enlarge range.
    """

    def __init__(self, bbox_enlarge_range):
        assert (is_tuple_of(bbox_enlarge_range, float)
                and len(bbox_enlarge_range) == 3) \
            or isinstance(bbox_enlarge_range, float), \
            f'Invalid arguments bbox_enlarge_range {bbox_enlarge_range}'

        if isinstance(bbox_enlarge_range, float):
            bbox_enlarge_range = [bbox_enlarge_range] * 3
        self.bbox_enlarge_range = np.array(
            bbox_enlarge_range, dtype=np.float32)[np.newaxis, :]

    def __call__(self, input_dict):
        """Call function to filter points by the range.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
1299
            dict: Results after filtering, 'points', 'pts_instance_mask'
1300
                and 'pts_semantic_mask' keys are updated in the result dict.
1301
1302
1303
1304
        """
        points = input_dict['points']
        gt_bboxes_3d = input_dict['gt_bboxes_3d']

xiliu8006's avatar
xiliu8006 committed
1305
1306
1307
1308
        # avoid groundtruth being modified
        gt_bboxes_3d_np = gt_bboxes_3d.tensor.clone().numpy()
        gt_bboxes_3d_np[:, :3] = gt_bboxes_3d.gravity_center.clone().numpy()

1309
1310
        enlarged_gt_bboxes_3d = gt_bboxes_3d_np.copy()
        enlarged_gt_bboxes_3d[:, 3:6] += self.bbox_enlarge_range
xiliu8006's avatar
xiliu8006 committed
1311
        points_numpy = points.tensor.clone().numpy()
1312
1313
        foreground_masks = box_np_ops.points_in_rbbox(
            points_numpy, gt_bboxes_3d_np, origin=(0.5, 0.5, 0.5))
1314
        enlarge_foreground_masks = box_np_ops.points_in_rbbox(
1315
            points_numpy, enlarged_gt_bboxes_3d, origin=(0.5, 0.5, 0.5))
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
        foreground_masks = foreground_masks.max(1)
        enlarge_foreground_masks = enlarge_foreground_masks.max(1)
        valid_masks = ~np.logical_and(~foreground_masks,
                                      enlarge_foreground_masks)

        input_dict['points'] = points[valid_masks]
        pts_instance_mask = input_dict.get('pts_instance_mask', None)
        if pts_instance_mask is not None:
            input_dict['pts_instance_mask'] = pts_instance_mask[valid_masks]

        pts_semantic_mask = input_dict.get('pts_semantic_mask', None)
        if pts_semantic_mask is not None:
            input_dict['pts_semantic_mask'] = pts_semantic_mask[valid_masks]
        return input_dict

    def __repr__(self):
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
1334
        repr_str += f'(bbox_enlarge_range={self.bbox_enlarge_range.tolist()})'
1335
        return repr_str
1336
1337


1338
@TRANSFORMS.register_module()
1339
1340
1341
1342
1343
1344
1345
1346
class VoxelBasedPointSampler(object):
    """Voxel based point sampler.

    Apply voxel sampling to multiple sweep points.

    Args:
        cur_sweep_cfg (dict): Config for sampling current points.
        prev_sweep_cfg (dict): Config for sampling previous points.
1347
        time_dim (int): Index that indicate the time dimension
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
            for input points.
    """

    def __init__(self, cur_sweep_cfg, prev_sweep_cfg=None, time_dim=3):
        self.cur_voxel_generator = VoxelGenerator(**cur_sweep_cfg)
        self.cur_voxel_num = self.cur_voxel_generator._max_voxels
        self.time_dim = time_dim
        if prev_sweep_cfg is not None:
            assert prev_sweep_cfg['max_num_points'] == \
                cur_sweep_cfg['max_num_points']
            self.prev_voxel_generator = VoxelGenerator(**prev_sweep_cfg)
            self.prev_voxel_num = self.prev_voxel_generator._max_voxels
        else:
            self.prev_voxel_generator = None
            self.prev_voxel_num = 0

    def _sample_points(self, points, sampler, point_dim):
        """Sample points for each points subset.

        Args:
            points (np.ndarray): Points subset to be sampled.
            sampler (VoxelGenerator): Voxel based sampler for
                each points subset.
1371
            point_dim (int): The dimension of each points
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396

        Returns:
            np.ndarray: Sampled points.
        """
        voxels, coors, num_points_per_voxel = sampler.generate(points)
        if voxels.shape[0] < sampler._max_voxels:
            padding_points = np.zeros([
                sampler._max_voxels - voxels.shape[0], sampler._max_num_points,
                point_dim
            ],
                                      dtype=points.dtype)
            padding_points[:] = voxels[0]
            sample_points = np.concatenate([voxels, padding_points], axis=0)
        else:
            sample_points = voxels

        return sample_points

    def __call__(self, results):
        """Call function to sample points from multiple sweeps.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
1397
            dict: Results after sampling, 'points', 'pts_instance_mask'
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
                and 'pts_semantic_mask' keys are updated in the result dict.
        """
        points = results['points']
        original_dim = points.shape[1]

        # TODO: process instance and semantic mask while _max_num_points
        # is larger than 1
        # Extend points with seg and mask fields
        map_fields2dim = []
        start_dim = original_dim
1408
1409
        points_numpy = points.tensor.numpy()
        extra_channel = [points_numpy]
1410
1411
1412
1413
1414
1415
1416
1417
1418
        for idx, key in enumerate(results['pts_mask_fields']):
            map_fields2dim.append((key, idx + start_dim))
            extra_channel.append(results[key][..., None])

        start_dim += len(results['pts_mask_fields'])
        for idx, key in enumerate(results['pts_seg_fields']):
            map_fields2dim.append((key, idx + start_dim))
            extra_channel.append(results[key][..., None])

1419
        points_numpy = np.concatenate(extra_channel, axis=-1)
1420
1421
1422
1423
1424

        # Split points into two part, current sweep points and
        # previous sweeps points.
        # TODO: support different sampling methods for next sweeps points
        # and previous sweeps points.
1425
1426
1427
        cur_points_flag = (points_numpy[:, self.time_dim] == 0)
        cur_sweep_points = points_numpy[cur_points_flag]
        prev_sweeps_points = points_numpy[~cur_points_flag]
1428
1429
1430
1431
1432
1433
1434
1435
1436
        if prev_sweeps_points.shape[0] == 0:
            prev_sweeps_points = cur_sweep_points

        # Shuffle points before sampling
        np.random.shuffle(cur_sweep_points)
        np.random.shuffle(prev_sweeps_points)

        cur_sweep_points = self._sample_points(cur_sweep_points,
                                               self.cur_voxel_generator,
1437
                                               points_numpy.shape[1])
1438
1439
1440
        if self.prev_voxel_generator is not None:
            prev_sweeps_points = self._sample_points(prev_sweeps_points,
                                                     self.prev_voxel_generator,
1441
                                                     points_numpy.shape[1])
1442

1443
1444
            points_numpy = np.concatenate(
                [cur_sweep_points, prev_sweeps_points], 0)
1445
        else:
1446
            points_numpy = cur_sweep_points
1447
1448

        if self.cur_voxel_generator._max_num_points == 1:
1449
1450
            points_numpy = points_numpy.squeeze(1)
        results['points'] = points.new_point(points_numpy[..., :original_dim])
1451

1452
        # Restore the corresponding seg and mask fields
1453
        for key, dim_index in map_fields2dim:
1454
            results[key] = points_numpy[..., dim_index]
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477

        return results

    def __repr__(self):
        """str: Return a string that describes the module."""

        def _auto_indent(repr_str, indent):
            repr_str = repr_str.split('\n')
            repr_str = [' ' * indent + t + '\n' for t in repr_str]
            repr_str = ''.join(repr_str)[:-1]
            return repr_str

        repr_str = self.__class__.__name__
        indent = 4
        repr_str += '(\n'
        repr_str += ' ' * indent + f'num_cur_sweep={self.cur_voxel_num},\n'
        repr_str += ' ' * indent + f'num_prev_sweep={self.prev_voxel_num},\n'
        repr_str += ' ' * indent + f'time_dim={self.time_dim},\n'
        repr_str += ' ' * indent + 'cur_voxel_generator=\n'
        repr_str += f'{_auto_indent(repr(self.cur_voxel_generator), 8)},\n'
        repr_str += ' ' * indent + 'prev_voxel_generator=\n'
        repr_str += f'{_auto_indent(repr(self.prev_voxel_generator), 8)})'
        return repr_str
1478
1479


1480
@TRANSFORMS.register_module()
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
class AffineResize(object):
    """Get the affine transform matrices to the target size.

    Different from :class:`RandomAffine` in MMDetection, this class can
    calculate the affine transform matrices while resizing the input image
    to a fixed size. The affine transform matrices include: 1) matrix
    transforming original image to the network input image size. 2) matrix
    transforming original image to the network output feature map size.

    Args:
        img_scale (tuple): Images scales for resizing.
        down_ratio (int): The down ratio of feature map.
            Actually the arg should be >= 1.
        bbox_clip_border (bool, optional): Whether clip the objects
            outside the border of the image. Defaults to True.
    """

    def __init__(self, img_scale, down_ratio, bbox_clip_border=True):

        self.img_scale = img_scale
        self.down_ratio = down_ratio
        self.bbox_clip_border = bbox_clip_border

    def __call__(self, results):
        """Call function to do affine transform to input image and labels.

        Args:
            results (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after affine resize, 'affine_aug', 'trans_mat'
                keys are added in the result dict.
        """
        # The results have gone through RandomShiftScale before AffineResize
        if 'center' not in results:
            img = results['img']
            height, width = img.shape[:2]
            center = np.array([width / 2, height / 2], dtype=np.float32)
            size = np.array([width, height], dtype=np.float32)
            results['affine_aug'] = False
        else:
            # The results did not go through RandomShiftScale before
            # AffineResize
            img = results['img']
            center = results['center']
            size = results['size']

        trans_affine = self._get_transform_matrix(center, size, self.img_scale)

        img = cv2.warpAffine(img, trans_affine[:2, :], self.img_scale)

        if isinstance(self.down_ratio, tuple):
            trans_mat = [
                self._get_transform_matrix(
                    center, size,
                    (self.img_scale[0] // ratio, self.img_scale[1] // ratio))
                for ratio in self.down_ratio
            ]  # (3, 3)
        else:
            trans_mat = self._get_transform_matrix(
                center, size, (self.img_scale[0] // self.down_ratio,
                               self.img_scale[1] // self.down_ratio))

        results['img'] = img
        results['img_shape'] = img.shape
        results['pad_shape'] = img.shape
        results['trans_mat'] = trans_mat

        self._affine_bboxes(results, trans_affine)

        if 'centers2d' in results:
            centers2d = self._affine_transform(results['centers2d'],
                                               trans_affine)
            valid_index = (centers2d[:, 0] >
                           0) & (centers2d[:, 0] <
                                 self.img_scale[0]) & (centers2d[:, 1] > 0) & (
                                     centers2d[:, 1] < self.img_scale[1])
            results['centers2d'] = centers2d[valid_index]

            for key in results.get('bbox_fields', []):
                if key in ['gt_bboxes']:
                    results[key] = results[key][valid_index]
                    if 'gt_labels' in results:
                        results['gt_labels'] = results['gt_labels'][
                            valid_index]
                    if 'gt_masks' in results:
                        raise NotImplementedError(
                            'AffineResize only supports bbox.')

            for key in results.get('bbox3d_fields', []):
                if key in ['gt_bboxes_3d']:
                    results[key].tensor = results[key].tensor[valid_index]
                    if 'gt_labels_3d' in results:
                        results['gt_labels_3d'] = results['gt_labels_3d'][
                            valid_index]

            results['depths'] = results['depths'][valid_index]

        return results

    def _affine_bboxes(self, results, matrix):
        """Affine transform bboxes to input image.

        Args:
            results (dict): Result dict from loading pipeline.
            matrix (np.ndarray): Matrix transforming original
                image to the network input image size.
                shape: (3, 3)
        """

        for key in results.get('bbox_fields', []):
            bboxes = results[key]
            bboxes[:, :2] = self._affine_transform(bboxes[:, :2], matrix)
            bboxes[:, 2:] = self._affine_transform(bboxes[:, 2:], matrix)
            if self.bbox_clip_border:
                bboxes[:,
                       [0, 2]] = bboxes[:,
                                        [0, 2]].clip(0, self.img_scale[0] - 1)
                bboxes[:,
                       [1, 3]] = bboxes[:,
                                        [1, 3]].clip(0, self.img_scale[1] - 1)
            results[key] = bboxes

    def _affine_transform(self, points, matrix):
1605
        """Affine transform bbox points to input image.
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658

        Args:
            points (np.ndarray): Points to be transformed.
                shape: (N, 2)
            matrix (np.ndarray): Affine transform matrix.
                shape: (3, 3)

        Returns:
            np.ndarray: Transformed points.
        """
        num_points = points.shape[0]
        hom_points_2d = np.concatenate((points, np.ones((num_points, 1))),
                                       axis=1)
        hom_points_2d = hom_points_2d.T
        affined_points = np.matmul(matrix, hom_points_2d).T
        return affined_points[:, :2]

    def _get_transform_matrix(self, center, scale, output_scale):
        """Get affine transform matrix.

        Args:
            center (tuple): Center of current image.
            scale (tuple): Scale of current image.
            output_scale (tuple[float]): The transform target image scales.

        Returns:
            np.ndarray: Affine transform matrix.
        """
        # TODO: further add rot and shift here.
        src_w = scale[0]
        dst_w = output_scale[0]
        dst_h = output_scale[1]

        src_dir = np.array([0, src_w * -0.5])
        dst_dir = np.array([0, dst_w * -0.5])

        src = np.zeros((3, 2), dtype=np.float32)
        dst = np.zeros((3, 2), dtype=np.float32)
        src[0, :] = center
        src[1, :] = center + src_dir
        dst[0, :] = np.array([dst_w * 0.5, dst_h * 0.5])
        dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir

        src[2, :] = self._get_ref_point(src[0, :], src[1, :])
        dst[2, :] = self._get_ref_point(dst[0, :], dst[1, :])

        get_matrix = cv2.getAffineTransform(src, dst)

        matrix = np.concatenate((get_matrix, [[0., 0., 1.]]))

        return matrix.astype(np.float32)

    def _get_ref_point(self, ref_point1, ref_point2):
1659
        """Get reference point to calculate affine transform matrix.
1660
1661

        While using opencv to calculate the affine matrix, we need at least
1662
        three corresponding points separately on original image and target
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
        image. Here we use two points to get the the third reference point.
        """
        d = ref_point1 - ref_point2
        ref_point3 = ref_point2 + np.array([-d[1], d[0]])
        return ref_point3

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += f'(img_scale={self.img_scale}, '
        repr_str += f'down_ratio={self.down_ratio}) '
        return repr_str


1676
@TRANSFORMS.register_module()
1677
1678
1679
1680
1681
class RandomShiftScale(object):
    """Random shift scale.

    Different from the normal shift and scale function, it doesn't
    directly shift or scale image. It can record the shift and scale
1682
    infos into loading TRANSFORMS. It's designed to be used with
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
    AffineResize together.

    Args:
        shift_scale (tuple[float]): Shift and scale range.
        aug_prob (float): The shifting and scaling probability.
    """

    def __init__(self, shift_scale, aug_prob):

        self.shift_scale = shift_scale
        self.aug_prob = aug_prob

    def __call__(self, results):
        """Call function to record random shift and scale infos.

        Args:
            results (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after random shift and scale, 'center', 'size'
                and 'affine_aug' keys are added in the result dict.
        """
        img = results['img']

        height, width = img.shape[:2]

        center = np.array([width / 2, height / 2], dtype=np.float32)
        size = np.array([width, height], dtype=np.float32)

        if random.random() < self.aug_prob:
            shift, scale = self.shift_scale[0], self.shift_scale[1]
            shift_ranges = np.arange(-shift, shift + 0.1, 0.1)
            center[0] += size[0] * random.choice(shift_ranges)
            center[1] += size[1] * random.choice(shift_ranges)
            scale_ranges = np.arange(1 - scale, 1 + scale + 0.1, 0.1)
            size *= random.choice(scale_ranges)
            results['affine_aug'] = True
        else:
            results['affine_aug'] = False

        results['center'] = center
        results['size'] = size

        return results

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += f'(shift_scale={self.shift_scale}, '
        repr_str += f'aug_prob={self.aug_prob}) '
        return repr_str