transforms_3d.py

# Copyright (c) OpenMMLab. All rights reserved.
import os
import torch
import numpy as np
from numpy import random
import random as prandom
import warnings
from PIL import Image
import string
import copy

import mmcv
from mmcv import is_tuple_of
from mmcv.utils import build_from_cfg
import cv2

from mmdet3d.core import VoxelGenerator
from mmdet3d.core.bbox import DepthInstance3DBoxes, LiDARInstance3DBoxes, CameraInstance3DBoxes, box_np_ops
from mmdet3d.core.visualizer.image_vis import draw_lidar_bbox3d_on_img
from mmdet.datasets.builder import PIPELINES
from mmdet.datasets.pipelines import RandomFlip
from ..builder import OBJECTSAMPLERS
from .data_augment_utils import noise_per_object_v3_

import ipdb


@PIPELINES.register_module()
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:
        drop_ratio (float): The probability of dropping point colors.
            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:
            dict: Results after color dropping, \
                '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'

        # 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:
            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


@PIPELINES.register_module()
class RandomJitterPoints(object):
    """Randomly jitter point coordinates.

    Different from the global translation in ``GlobalRotScaleTrans``, here we \
        apply different noises to each point in a scene.

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

    Note:
        This transform should only be used in point cloud segmentation tasks \
            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:
            dict: Results after adding noise to each point, \
                '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


@PIPELINES.register_module()
class ObjectSample(object):
    """Sample GT objects to the data.

    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.
            Defaults to False.
    """

    def __init__(self, db_sampler, sample_2d=False):
        self.sampler_cfg = db_sampler
        self.sample_2d = sample_2d
        if 'type' not in db_sampler.keys():
            db_sampler['type'] = 'DataBaseSampler'
        self.db_sampler = build_from_cfg(db_sampler, OBJECTSAMPLERS)

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

        Args:
            points (:obj:`BasePoints`): Input point cloud array.
            boxes (np.ndarray): Sampled ground truth boxes.

        Returns:
            np.ndarray: Points with those in the boxes removed.
        """
        masks = box_np_ops.points_in_rbbox(points.coord.numpy(), boxes)
        points = points[np.logical_not(masks.any(-1))]
        return points

    def __call__(self, input_dict):
        """Call function to sample ground truth objects to the data.

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

        Returns:
            dict: Results after object sampling augmentation, \
                'points', 'gt_bboxes_3d', 'gt_labels_3d' keys are updated \
                in the result dict.
        """
        gt_bboxes_3d = input_dict['gt_bboxes_3d']
        gt_labels_3d = input_dict['gt_labels_3d']

        # change to float for blending operation
        points = input_dict['points']
        if self.sample_2d:
            img = input_dict['img']
            gt_bboxes_2d = input_dict['gt_bboxes']
            # Assume for now 3D & 2D bboxes are the same
            sampled_dict = self.db_sampler.sample_all(
                gt_bboxes_3d.tensor.numpy(),
                gt_labels_3d,
                gt_bboxes_2d=gt_bboxes_2d,
                img=img)
        else:
            sampled_dict = self.db_sampler.sample_all(
                gt_bboxes_3d.tensor.numpy(), gt_labels_3d, img=None)

        if sampled_dict is not None:
            sampled_gt_bboxes_3d = sampled_dict['gt_bboxes_3d']
            sampled_points = sampled_dict['points']
            sampled_gt_labels = sampled_dict['gt_labels_3d']

            gt_labels_3d = np.concatenate([gt_labels_3d, sampled_gt_labels],
                                          axis=0)
            gt_bboxes_3d = gt_bboxes_3d.new_box(
                np.concatenate(
                    [gt_bboxes_3d.tensor.numpy(), sampled_gt_bboxes_3d]))

            points = self.remove_points_in_boxes(points, sampled_gt_bboxes_3d)
            # check the points dimension
            points = points.cat([sampled_points, points])

            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)

                input_dict['gt_bboxes'] = gt_bboxes_2d
                input_dict['img'] = sampled_dict['img']

        input_dict['gt_bboxes_3d'] = gt_bboxes_3d
        input_dict['gt_labels_3d'] = gt_labels_3d.astype(np.long)
        input_dict['points'] = points

        return input_dict

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


@PIPELINES.register_module()
class ObjectNoise(object):
    """Apply noise to each GT objects in the scene.

    Args:
        translation_std (list[float], optional): Standard deviation of the
            distribution where translation noise are sampled from.
            Defaults to [0.25, 0.25, 0.25].
        global_rot_range (list[float], optional): Global rotation to the scene.
            Defaults to [0.0, 0.0].
        rot_range (list[float], optional): Object rotation range.
            Defaults to [-0.15707963267, 0.15707963267].
        num_try (int, optional): Number of times to try if the noise applied is
            invalid. Defaults to 100.
    """

    def __init__(self,
                 translation_std=[0.25, 0.25, 0.25],
                 global_rot_range=[0.0, 0.0],
                 rot_range=[-0.15707963267, 0.15707963267],
                 num_try=100):
        self.translation_std = translation_std
        self.global_rot_range = global_rot_range
        self.rot_range = rot_range
        self.num_try = num_try

    def __call__(self, input_dict):
        """Call function to apply noise to each ground truth in the scene.

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

        Returns:
            dict: Results after adding noise to each object, \
                'points', 'gt_bboxes_3d' keys are updated in the result dict.
        """
        gt_bboxes_3d = input_dict['gt_bboxes_3d']
        points = input_dict['points']

        # TODO: check this inplace function
        numpy_box = gt_bboxes_3d.tensor.numpy()
        numpy_points = points.tensor.numpy()

        noise_per_object_v3_(
            numpy_box,
            numpy_points,
            rotation_perturb=self.rot_range,
            center_noise_std=self.translation_std,
            global_random_rot_range=self.global_rot_range,
            num_try=self.num_try)

        input_dict['gt_bboxes_3d'] = gt_bboxes_3d.new_box(numpy_box)
        input_dict['points'] = points.new_point(numpy_points)
        return input_dict

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


@PIPELINES.register_module()
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:
        We do not record the applied rotation and translation as in \
            GlobalRotScaleTrans. Because usually, we do not need to reverse \
            the alignment step.
        For example, ScanNet 3D detection task uses aligned ground-truth \
            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:
            dict: Results after global alignment, 'points' and keys in \
                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


@PIPELINES.register_module()
class PointShuffle(object):
    """Shuffle input points."""

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

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

        Returns:
            dict: Results after filtering, 'points', 'pts_instance_mask' \
                and 'pts_semantic_mask' keys are updated in the result dict.
        """
        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]

        return input_dict

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


@PIPELINES.register_module()
class ObjectRangeFilter(object):
    """Filter objects by the range.

    Args:
        point_cloud_range (list[float]): Point cloud range.
    """

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

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

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

        Returns:
            dict: Results after filtering, 'gt_bboxes_3d', 'gt_labels_3d' \
                keys are updated in the result dict.
        """
        # 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]]

        gt_bboxes_3d = input_dict['gt_bboxes_3d']
        gt_labels_3d = input_dict['gt_labels_3d']
        mask = gt_bboxes_3d.in_range_bev(bev_range)
        gt_bboxes_3d = gt_bboxes_3d[mask]
        # 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)]

        # limit rad to [-pi, pi]
        gt_bboxes_3d.limit_yaw(offset=0.5, period=2 * np.pi)
        input_dict['gt_bboxes_3d'] = gt_bboxes_3d
        input_dict['gt_labels_3d'] = gt_labels_3d

        return input_dict

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


@PIPELINES.register_module()
class PointsRangeFilter(object):
    """Filter points by the range.

    Args:
        point_cloud_range (list[float]): Point cloud range.
    """

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

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

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

        Returns:
            dict: Results after filtering, 'points', 'pts_instance_mask' \
                and 'pts_semantic_mask' keys are updated in the result dict.
        """
        points = input_dict['points']
        points_mask = points.in_range_3d(self.pcd_range)
        clean_points = points[points_mask]
        input_dict['points'] = clean_points
        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]

        return input_dict

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


@PIPELINES.register_module()
class ObjectNameFilter(object):
    """Filter GT objects by their names.

    Args:
        classes (list[str]): List of class names to be kept for training.
    """

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

    def __call__(self, input_dict):
        """Call function to filter objects by their names.

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

        Returns:
            dict: Results after filtering, 'gt_bboxes_3d', 'gt_labels_3d' \
                keys are updated in the result dict.
        """
        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):
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
        repr_str += f'(classes={self.classes})'
        return repr_str


@PIPELINES.register_module()
class PointSample(object):
    """Point sample.

    Sampling data to a certain number.

    Args:
        num_points (int): Number of points to be sampled.
        sample_range (float, optional): The range where to sample points.
            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.
    """

    def __init__(self, num_points, sample_range=None, replace=False):
        self.num_points = num_points
        self.sample_range = sample_range
        self.replace = replace

    def _points_random_sampling(self,
                                points,
                                num_samples,
                                sample_range=None,
                                replace=False,
                                return_choices=False):
        """Points random sampling.

        Sample points to a certain number.

        Args:
            points (np.ndarray | :obj:`BasePoints`): 3D Points.
            num_samples (int): Number of samples to be sampled.
            sample_range (float, optional): Indicating the range where the
                points will be sampled. Defaults to None.
            replace (bool, optional): Sampling with or without replacement.
                Defaults to None.
            return_choices (bool, optional): Whether return choice.
                Defaults to False.
        Returns:
            tuple[np.ndarray] | np.ndarray:
                - points (np.ndarray | :obj:`BasePoints`): 3D Points.
                - choices (np.ndarray, optional): The generated random samples.
        """
        if not replace:
            replace = (points.shape[0] < num_samples)
        point_range = range(len(points))
        if sample_range is not None and not replace:
            # Only sampling the near points when len(points) >= num_samples
            depth = np.linalg.norm(points.tensor, axis=1)
            far_inds = np.where(depth > sample_range)[0]
            near_inds = np.where(depth <= sample_range)[0]
            # 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)
            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)
        if return_choices:
            return points[choices], choices
        else:
            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:
            dict: Results after sampling, 'points', 'pts_instance_mask' \
                and 'pts_semantic_mask' keys are updated in the result dict.
        """
        points = results['points']
        # Points in Camera coord can provide the depth information.
        # TODO: Need to suport distance-based sampling for other coord system.
        if self.sample_range is not None:
            from mmdet3d.core.points import CameraPoints
            assert isinstance(points, CameraPoints), \
                'Sampling based on distance is only appliable for CAMERA coord'
        points, choices = self._points_random_sampling(
            points,
            self.num_points,
            self.sample_range,
            self.replace,
            return_choices=True)
        results['points'] = points

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

        if pts_instance_mask is not None:
            pts_instance_mask = pts_instance_mask[choices]
            results['pts_instance_mask'] = pts_instance_mask

        if pts_semantic_mask is not None:
            pts_semantic_mask = pts_semantic_mask[choices]
            results['pts_semantic_mask'] = pts_semantic_mask

        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' sample_range={self.sample_range},'
        repr_str += f' replace={self.replace})'

        return repr_str


@PIPELINES.register_module()
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)


@PIPELINES.register_module()
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.
            This parameter is unused in `IndoorPatchPointSample` and thus has
            been deprecated. We plan to remove it in the future.
            Defaults to None.
        ignore_index (int, optional): Label index that won't be used for the
            segmentation task. This is set in PointSegClassMapping as neg_cls.
            If not None, will be used as a patch selection criterion.
            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.
        enlarge_size (float | None, optional): Enlarge the sampled patch to
            [-block_size / 2 - enlarge_size, block_size / 2 + enlarge_size] as
            an augmentation. If None, set it as 0. Defaults to 0.2.
        min_unique_num (int | None, optional): Minimum number of unique points
            the sampled patch should contain. If None, use PointNet++'s method
            to judge uniqueness. Defaults to None.
        eps (float, optional): A value added to patch boundary to guarantee
            points coverage. Defaults to 1e-2.

    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.
    """

    def __init__(self,
                 num_points,
                 block_size=1.5,
                 sample_rate=None,
                 ignore_index=None,
                 use_normalized_coord=False,
                 num_try=10,
                 enlarge_size=0.2,
                 min_unique_num=None,
                 eps=1e-2):
        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
        self.enlarge_size = enlarge_size if enlarge_size is not None else 0.0
        self.min_unique_num = min_unique_num
        self.eps = eps

        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.')

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

        Generate input by subtracting patch center and adding additional \
            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.
            point_type (type): class of input points inherited from BasePoints.

        Returns:
            :obj:`BasePoints`: The generated input data.
        """
        # 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

    def _patch_points_sampling(self, points, sem_mask):
        """Patch points sampling.

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

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

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

                - points (:obj:`BasePoints`): 3D Points.
                - 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)

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

            # boundary of a patch, which would be enlarged by
            # `self.enlarge_size` as an augmentation
            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(
                (coords >= (cur_min - self.enlarge_size)) *
                (coords <= (cur_max + self.enlarge_size)),
                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]
            point_idxs = np.where(cur_choice)[0]
            mask = np.sum(
                (cur_coords >= (cur_min - self.eps)) * (cur_coords <=
                                                        (cur_max + self.eps)),
                axis=1) == 3

            # two criteria for patch sampling, adopted from PointNet++
            # 1. selected patch should contain enough unique points
            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:
                # if `min_unique_num` is provided, directly compare with it
                flag1 = mask.sum() >= self.min_unique_num

            # 2. selected patch should contain enough annotated points
            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

        # 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]

        # 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:
            dict: Results after sampling, 'points', 'pts_instance_mask' \
                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},'
        repr_str += f' num_try={self.num_try},'
        repr_str += f' enlarge_size={self.enlarge_size},'
        repr_str += f' min_unique_num={self.min_unique_num},'
        repr_str += f' eps={self.eps})'
        return repr_str


@PIPELINES.register_module()
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:
            dict: Results after filtering, 'points', 'pts_instance_mask' \
                and 'pts_semantic_mask' keys are updated in the result dict.
        """
        points = input_dict['points']
        gt_bboxes_3d = input_dict['gt_bboxes_3d']

        # 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()

        enlarged_gt_bboxes_3d = gt_bboxes_3d_np.copy()
        enlarged_gt_bboxes_3d[:, 3:6] += self.bbox_enlarge_range
        points_numpy = points.tensor.clone().numpy()
        foreground_masks = box_np_ops.points_in_rbbox(
            points_numpy, gt_bboxes_3d_np, origin=(0.5, 0.5, 0.5))
        enlarge_foreground_masks = box_np_ops.points_in_rbbox(
            points_numpy, enlarged_gt_bboxes_3d, origin=(0.5, 0.5, 0.5))
        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__
        repr_str += f'(bbox_enlarge_range={self.bbox_enlarge_range.tolist()})'
        return repr_str


@PIPELINES.register_module()
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.
        time_dim (int): Index that indicate the time dimention
            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.
            point_dim (int): The dimention of each points

        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:
            dict: Results after sampling, 'points', 'pts_instance_mask' \
                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
        points_numpy = points.tensor.numpy()
        extra_channel = [points_numpy]
        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])

        points_numpy = np.concatenate(extra_channel, axis=-1)

        # 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.
        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]
        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,
                                               points_numpy.shape[1])
        if self.prev_voxel_generator is not None:
            prev_sweeps_points = self._sample_points(prev_sweeps_points,
                                                     self.prev_voxel_generator,
                                                     points_numpy.shape[1])

            points_numpy = np.concatenate(
                [cur_sweep_points, prev_sweeps_points], 0)
        else:
            points_numpy = cur_sweep_points

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

        # Restore the correspoinding seg and mask fields
        for key, dim_index in map_fields2dim:
            results[key] = points_numpy[..., dim_index]

        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


@PIPELINES.register_module()
class PhotoMetricDistortionMultiViewImage:
    """Apply photometric distortion to image sequentially, every transformation
    is applied with a probability of 0.5. The position of random contrast is in
    second or second to last.
    1. random brightness
    2. random contrast (mode 0)
    3. convert color from BGR to HSV
    4. random saturation
    5. random hue
    6. convert color from HSV to BGR
    7. random contrast (mode 1)
    8. randomly swap channels
    Args:
        brightness_delta (int): delta of brightness.
        contrast_range (tuple): range of contrast.
        saturation_range (tuple): range of saturation.
        hue_delta (int): delta of hue.
    """

    def __init__(self,
                 brightness_delta=32,
                 contrast_range=(0.5, 1.5),
                 saturation_range=(0.5, 1.5),
                 hue_delta=18):
        self.brightness_delta = brightness_delta
        self.contrast_lower, self.contrast_upper = contrast_range
        self.saturation_lower, self.saturation_upper = saturation_range
        self.hue_delta = hue_delta

    def __call__(self, results):
        """Call function to perform photometric distortion on images.
        Args:
            results (dict): Result dict from loading pipeline.
        Returns:
            dict: Result dict with images distorted.
        """
        imgs = results['img']
        new_imgs = []
        for img in imgs:
            img = img.astype(np.float32)
            assert img.dtype == np.float32, \
                'PhotoMetricDistortion needs the input image of dtype np.float32,'\
                ' please set "to_float32=True" in "LoadImageFromFile" pipeline'
            # random brightness
            if random.randint(2):
                delta = random.uniform(-self.brightness_delta, self.brightness_delta)
                img += delta

            # mode == 0 --> do random contrast first
            # mode == 1 --> do random contrast last
            mode = random.randint(2)
            if mode == 1:
                if random.randint(2):
                    alpha = random.uniform(self.contrast_lower, self.contrast_upper)
                    img *= alpha

            # convert color from BGR to HSV
            img = mmcv.bgr2hsv(img)

            # random saturation
            if random.randint(2):
                img[..., 1] *= random.uniform(self.saturation_lower, self.saturation_upper)

            # random hue
            if random.randint(2):
                img[..., 0] += random.uniform(-self.hue_delta, self.hue_delta)
                img[..., 0][img[..., 0] > 360] -= 360
                img[..., 0][img[..., 0] < 0] += 360

            # convert color from HSV to BGR
            img = mmcv.hsv2bgr(img)

            # random contrast
            if mode == 0:
                if random.randint(2):
                    alpha = random.uniform(self.contrast_lower, self.contrast_upper)
                    img *= alpha

            # randomly swap channels
            if random.randint(2):
                img = img[..., random.permutation(3)]
            new_imgs.append(img)
        results['img'] = new_imgs
        return results

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += f'(\nbrightness_delta={self.brightness_delta},\n'
        repr_str += 'contrast_range='
        repr_str += f'{(self.contrast_lower, self.contrast_upper)},\n'
        repr_str += 'saturation_range='
        repr_str += f'{(self.saturation_lower, self.saturation_upper)},\n'
        repr_str += f'hue_delta={self.hue_delta})'
        return repr_str


@PIPELINES.register_module()
class RandomScaleImageMultiViewImage(object):
    """Random scale the image
    Args:
        scales
    """

    def __init__(self, scales=[0.5, 1.5], scale_type='list'):
        assert scale_type in ['interval', 'list'], "scale_type must be in [interval, list]!"
        if scale_type == 'interval':
            assert len(scales) == 2, "scale must be 2 if scale_type == interval"
        self.scale_type = scale_type
        self.scales = scales

    def __call__(self, results):
        """Call function to pad images, masks, semantic segmentation maps.
        Args:
            results (dict): Result dict from loading pipeline.
        Returns:
            dict: Updated result dict.
        """
        if self.scale_type == 'list':
            np.random.shuffle(self.scales)
            rand_scale = self.scales[0]
        else:
            rand_scale = np.random.rand() * \
                (self.scales[1] - self.scales[0]) + self.scales[0]
        img_shape = results['img_shape']
        y_size = int(img_shape[0] * rand_scale)
        x_size = int(img_shape[1] * rand_scale)

        results['img'] = [mmcv.imresize(img, (x_size, y_size), return_scale=False) for img in results['img']]
        results['img_shape'] = [img.shape for img in results['img']]

        scale_factor = np.eye(4, dtype=np.float32)
        scale_factor[0, 0] *= rand_scale
        scale_factor[1, 1] *= rand_scale
        results['lidar2img']['extrinsic'] = [scale_factor @ l2i for l2i in results['lidar2img']['extrinsic']]
        # tmp_usages for augmentated images
        # lidar2imgs = results['lidar2img']['extrinsic']
        # imgs = results['img']
        # bboxes = results['gt_bboxes_3d']
        # for ii in range(6):
        #     new_img = draw_lidar_bbox3d_on_img(bboxes, imgs[ii], lidar2imgs[ii], dict())
        #     img_filename = results['img_info'][ii]['filename'].split('/')[-1]
        #     cv2.imwrite('figs/%s' % img_filename, new_img)
        # exit()
        return results

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


@PIPELINES.register_module()
class GlobalRotScaleTrans(object):
    """Apply global rotation, scaling and translation to a 3D scene.

    Args:
        rot_range (list[float]): Range of rotation angle.
            Defaults to [-0.78539816, 0.78539816] (close to [-pi/4, pi/4]).
        scale_ratio_range (list[float]): Range of scale ratio.
            Defaults to [0.95, 1.05].
        translation_std (list[float]): The standard deviation of translation
            noise. This applies random translation to a scene by a noise, which
            is sampled from a gaussian distribution whose standard deviation
            is set by ``translation_std``. Defaults to [0, 0, 0]
        shift_height (bool): Whether to shift height.
            (the fourth dimension of indoor points) when scaling.
            Defaults to False.
    """

    def __init__(self,
                 rot_range=[-0.78539816, 0.78539816],
                 scale_ratio_range=[0.95, 1.05],
                 translation_std=[0, 0, 0],
                 shift_height=False,
                 update_img2lidar=False):
        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]
        self.rot_range = rot_range

        assert isinstance(scale_ratio_range, seq_types), \
            f'unsupported scale_ratio_range type {type(scale_ratio_range)}'
        self.scale_ratio_range = scale_ratio_range

        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
            ]
        assert all([std >= 0 for std in translation_std]), \
            'translation_std should be positive'
        self.translation_std = translation_std
        self.shift_height = shift_height
        self.update_img2lidar = update_img2lidar

    def _trans_bbox_points(self, input_dict):
        """Private function to translate bounding boxes and points.

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

        Returns:
            dict: Results after translation, 'points', 'pcd_trans' \
                and keys in input_dict['bbox3d_fields'] are updated \
                in the result dict.
        """
        translation_std = np.array(self.translation_std, dtype=np.float32)
        trans_factor = np.random.normal(scale=translation_std, size=3).T

        input_dict['points'].translate(trans_factor)
        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):
        """Private function to rotate bounding boxes and points.

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

        Returns:
            dict: Results after rotation, 'points', 'pcd_rotation' \
                and keys in input_dict['bbox3d_fields'] are updated \
                in the result dict.
        """
        rotation = self.rot_range
        noise_rotation = np.random.uniform(rotation[0], rotation[1])

        # 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
            return

        # rotate points with bboxes
        for key in input_dict['bbox3d_fields']:
            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

    def _scale_bbox_points(self, input_dict):
        """Private function to scale bounding boxes and points.

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

        Returns:
            dict: Results after scaling, 'points'and keys in \
                input_dict['bbox3d_fields'] are updated in the result dict.
        """
        scale = input_dict['pcd_scale_factor']
        points = input_dict['points']
        points.scale(scale)
        if self.shift_height:
            assert 'height' in points.attribute_dims.keys(), \
                'setting shift_height=True but points have no height attribute'
            points.tensor[:, points.attribute_dims['height']] *= scale
        input_dict['points'] = points

        for key in input_dict['bbox3d_fields']:
            input_dict[key].scale(scale)

    def _random_scale(self, input_dict):
        """Private function to randomly set the scale factor.

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

        Returns:
            dict: Results after scaling, 'pcd_scale_factor' are updated \
                in the result dict.
        """
        scale_factor = np.random.uniform(self.scale_ratio_range[0],
                                         self.scale_ratio_range[1])
        input_dict['pcd_scale_factor'] = scale_factor

    def update_transform(self, input_dict):
        for cam_id, cam_info in enumerate(input_dict['lidar2img']['lidar2img_aug']):
            transform = np.zeros((4, 4), dtype=np.float32)
            transform[:3, :3] = cam_info['rot'].copy()
            transform[:3, -1] = cam_info['tran'].copy()
            transform[-1, -1] = 1.0

            aug_transform = np.zeros((4, 4), dtype=np.float32)
            if 'pcd_rotation' in input_dict:
                aug_transform[:3, :3] = input_dict['pcd_rotation'].T * input_dict['pcd_scale_factor']
            else:
                aug_transform[:3, :3] = np.eye(3) * input_dict['pcd_scale_factor']
            aug_transform[:3, -1] = input_dict['pcd_trans']
            aug_transform[-1, -1] = 1.0

            new_transform = aug_transform @ transform
            cam_info['rot'] = new_transform[:3, :3]
            cam_info['tran'] = new_transform[:3, -1]

    def __call__(self, input_dict):
        """Private function to rotate, scale and translate bounding boxes and \
        points.

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

        Returns:
            dict: Results after scaling, 'points', 'pcd_rotation',
                'pcd_scale_factor', 'pcd_trans' and keys in \
                input_dict['bbox3d_fields'] are updated in the result dict.
        """
        if 'transformation_3d_flow' not in input_dict:
            input_dict['transformation_3d_flow'] = []
        self._rot_bbox_points(input_dict)
        if 'pcd_scale_factor' not in input_dict:
            self._random_scale(input_dict)
        self._scale_bbox_points(input_dict)
        self._trans_bbox_points(input_dict)
        input_dict['transformation_3d_flow'].extend(['R', 'S', 'T'])
        if self.update_img2lidar:
            self.update_transform(input_dict)
        return input_dict

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


@PIPELINES.register_module()
class InternalGlobalRotScaleTrans(GlobalRotScaleTrans):
    def update_transform(self, input_dict):
        # ipdb.set_trace()
        for cam_id, cam_info in enumerate(input_dict['lidar2img']['lidar2img_aug']):
            transform = cam_info['lidar2cam_rt']

            aug_transform = np.zeros((4, 4), dtype=np.float32)
            if 'pcd_rotation' in input_dict:
                aug_transform[:3, :3] = input_dict['pcd_rotation'].T * input_dict['pcd_scale_factor']
            else:
                aug_transform[:3, :3] = np.eye(3) * input_dict['pcd_scale_factor']
            aug_transform[:3, -1] = -input_dict['pcd_trans']
            aug_transform[-1, -1] = 1.0

            new_transform = aug_transform @ transform
            cam_info['lidar2cam_rt'] = new_transform


@PIPELINES.register_module()
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:
        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
            to that of 2D images. Defaults to True.
        flip_ratio_bev_horizontal (float, optional): The flipping probability
            in horizontal direction. Defaults to 0.0.
        flip_ratio_bev_vertical (float, optional): The flipping probability
            in vertical direction. Defaults to 0.0.
    """

    def __init__(self,
                 flip_2d=True,
                 sync_2d=True,
                 flip_ratio_bev_horizontal=0.0,
                 flip_ratio_bev_vertical=0.0,
                 update_img2lidar=False,
                 **kwargs):
        super(RandomFlip3D, self).__init__(
            flip_ratio=flip_ratio_bev_horizontal, **kwargs)
        self.flip_2d = flip_2d
        self.sync_2d = sync_2d
        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
        self.update_img2lidar = update_img2lidar

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

        Args:
            input_dict (dict): Result dict from loading pipeline.
            direction (str): Flip direction. Default: horizontal.

        Returns:
            dict: Flipped results, 'points', 'bbox3d_fields' keys are \
                updated in the result dict.
        """
        assert direction in ['horizontal', 'vertical']
        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
        for key in input_dict['bbox3d_fields']:
            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'
            w = input_dict['ori_shape'][1]
            input_dict['centers2d'][..., 0] = \
                w - input_dict['centers2d'][..., 0]
            # need to modify the horizontal position of camera center
            # along u-axis in the image (flip like centers2d)
            # ['cam2img'][0][2] = c_u
            # see more details and examples at
            # https://github.com/open-mmlab/mmdetection3d/pull/744
            input_dict['cam2img'][0][2] = w - input_dict['cam2img'][0][2]

    def __call__(self, input_dict):
        """Call function to flip points, values in the ``bbox3d_fields`` and \
        also flip 2D image and its annotations.

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

        Returns:
            dict: Flipped results, 'flip', 'flip_direction', \
                'pcd_horizontal_flip' and 'pcd_vertical_flip' keys are added \
                into result dict.
        """
        # filp 2D image and its annotations
        if self.flip_2d:
            super(RandomFlip3D, self).__call__(input_dict)
        if self.sync_2d:
            input_dict['pcd_horizontal_flip'] = input_dict['flip']
            input_dict['pcd_vertical_flip'] = False
        else:
            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

        if 'transformation_3d_flow' not in input_dict:
            input_dict['transformation_3d_flow'] = []

        if input_dict['pcd_horizontal_flip']:
            self.random_flip_data_3d(input_dict, 'horizontal')
            input_dict['transformation_3d_flow'].extend(['HF'])
        if input_dict['pcd_vertical_flip']:
            self.random_flip_data_3d(input_dict, 'vertical')
            input_dict['transformation_3d_flow'].extend(['VF'])

        if self.update_img2lidar:
            self.update_transform(input_dict)
        return input_dict

    def update_transform(self, input_dict):
        for cam_id, cam_info in enumerate(input_dict['lidar2img']['lidar2img_aug']):
            transform = np.zeros((4, 4), dtype=np.float32)
            transform[:3, :3] = cam_info['rot'].copy()
            transform[:3, -1] = cam_info['tran'].copy()
            transform[-1, -1] = 1.0

            aug_transform = np.eye(4, dtype=np.float32)
            if input_dict['pcd_horizontal_flip']:
                aug_transform[1, 1] = -1
            if input_dict['pcd_vertical_flip']:
                aug_transform[0, 0] = -1
            new_transform = aug_transform @ transform

            cam_info['rot'] = new_transform[:3, :3]
            cam_info['tran'] = new_transform[:3, -1]

    def __repr__(self):
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
        repr_str += f'(sync_2d={self.sync_2d},'
        repr_str += f' flip_ratio_bev_vertical={self.flip_ratio_bev_vertical})'
        return repr_str


@PIPELINES.register_module()
class InternalRandomFlip3D(RandomFlip3D):
    def update_transform(self, input_dict):
        for cam_id, cam_info in enumerate(input_dict['lidar2img']['lidar2img_aug']):
            transform = cam_info['lidar2cam_rt']

            aug_transform = np.eye(4, dtype=np.float32)
            if input_dict['pcd_horizontal_flip']:
                aug_transform[1, 1] = -1
            if input_dict['pcd_vertical_flip']:
                aug_transform[0, 0] = -1
            new_transform = aug_transform @ transform
            cam_info['lidar2cam_rt'] = new_transform


@PIPELINES.register_module()
class RandomAugImageMultiViewImage(object):
    """Random scale the image
    Args:
        scales
    """
    def __init__(self, data_config=None, is_train=True, is_debug=False, is_exit=False, tmp='./figs'):
        self.data_config = data_config
        self.is_train = is_train
        self.is_debug = is_debug
        self.is_exit = is_exit
        self.tmp = tmp

    def random_id(self, N=8, seed=None):
        if seed is not None:
            prandom.seed(seed)
        return ''.join(prandom.choice(string.ascii_uppercase + string.digits) for _ in range(N))

    def __call__(self, results, fix=''):
        """Call function to pad images, masks, semantic segmentation maps.
        Args:
            results (dict): Result dict from loading pipeline.
        Returns:
            dict: Updated result dict.
        """
        aug_imgs = []
        aug_extrinsics = []
        for cam_id, img in enumerate(results['img']):
            pil_img = Image.fromarray(img, mode='RGB')
            resize, resize_dims, crop, flip, rotate, pad = self.sample_augmentation(
                H=pil_img.height,
                W=pil_img.width,
            )
            post_pil_img, post_rot, post_tran = self.img_transform(
                pil_img, torch.eye(2), torch.zeros(2),
                resize=resize,
                resize_dims=resize_dims,
                crop=crop,
                flip=flip,
                rotate=rotate,
                pad=pad
            )
            aug_imgs.append(np.asarray(post_pil_img))
            aug_extrinsics.append(
                self.rts2proj(results['lidar2img']['lidar2img_aug'][cam_id], post_rot, post_tran)
            )
        results['img'] = aug_imgs
        results['lidar2img']['extrinsic'] = aug_extrinsics
        results['img_shape'] = [img.shape for img in results['img']]
        # tmp_usages for augmentated images
        if self.is_debug:
            lidar2imgs = results['lidar2img']['extrinsic']
            imgs = results['img']
            bboxes = results['gt_bboxes_3d']
            bid = self.random_id(8, results['sample_idx']) + fix
            for ii in range(len(results['img'])):
                cam_id = ii % 6
                cam_type = {True: 'curr', False: 'adj'}[ii // 6 == 0]
                # if cam_type != 'curr':
                #     continue
                try:
                    new_img = draw_lidar_bbox3d_on_img(bboxes, imgs[ii], lidar2imgs[ii], dict())
                    img_filename = f'{self.tmp}/{bid}_imgaug_{cam_id}_{cam_type}_{ii // 6}_' + results['img_info'][ii]['filename'].split('/')[-1]
                    if not os.path.exists(self.tmp):
                        os.makedirs(self.tmp)
                    cv2.imwrite(img_filename, new_img)
                except Exception:
                    new_img = imgs[ii]
                    img_filename = f'{self.tmp}/{bid}_imgaug_{cam_id}_{cam_type}_' + results['img_info'][ii]['filename'].split('/')[-1]
                    if not os.path.exists(self.tmp):
                        os.makedirs(self.tmp)
                    cv2.imwrite(img_filename, new_img)
        if self.is_exit:
            exit()
        return results

    def sample_augmentation(self, H, W):
        if self.is_train:
            fH, fW = self.data_config['input_size']  # (640, 1600),
            resize = float(fW)/float(W)  # 1600 / 1600
            resize += np.random.uniform(*self.data_config['resize'])
            resize_dims = (int(W * resize), int(H * resize))  # 900 1600

            newW, newH = resize_dims  # 1600 900
            crop_h_start = (newH - fH) // 2  # (900 - 640)  // 2 = 130
            crop_w_start = (newW - fW) // 2  # 1600 1600
            crop_h_start += int(np.random.uniform(*self.data_config['crop']) * fH)
            crop_w_start += int(np.random.uniform(*self.data_config['crop']) * fW)

            # (0, 130, 1600, 130+640)
            crop = (crop_w_start, crop_h_start, crop_w_start + fW, crop_h_start + fH)

            flip = self.data_config['flip'] and np.random.choice([0, 1])
            rotate = np.random.uniform(*self.data_config['rot'])
        else:
            fH, fW = self.data_config['test_input_size']
            resize = float(fW)/float(W)
            resize += self.data_config.get('test_resize', 0.0)
            resize_dims = (int(W * resize), int(H * resize))

            newW, newH = resize_dims
            crop_h_start = (newH - fH) // 2
            crop_w_start = (newW - fW) // 2
            crop = (crop_w_start, crop_h_start, crop_w_start + fW, crop_h_start + fH)

            flip = self.data_config['test_flip']
            rotate = self.data_config['test_rotate']

        pad_data = self.data_config['pad']
        pad_divisor = self.data_config['pad_divisor']
        pad_color = self.data_config['pad_color']
        pad = (pad_data, pad_color)

        return resize, resize_dims, crop, flip, rotate, pad

    def img_transform(self, img, post_rot, post_tran,
                      resize, resize_dims, crop,
                      flip, rotate, pad):
        img = self.img_transform_core(img, resize_dims, crop, flip, rotate, pad)

        post_rot *= resize
        post_tran -= torch.Tensor(crop[:2])
        if flip:
            A = torch.Tensor([[-1, 0], [0, 1]])
            b = torch.Tensor([crop[2] - crop[0], 0])
            post_rot = A.matmul(post_rot)
            post_tran = A.matmul(post_tran) + b
        A = self.get_rot(rotate / 180 * np.pi)
        b = torch.Tensor([crop[2] - crop[0], crop[3] - crop[1]]) / 2
        b = A.matmul(-b) + b
        post_rot = A.matmul(post_rot)
        post_tran = A.matmul(post_tran) + b

        top, right, bottom, left = pad[0]
        post_tran[0] = post_tran[0] + left  # left
        post_tran[1] = post_tran[1] + top  # top

        ret_post_rot, ret_post_tran = np.eye(3), np.zeros(3)
        ret_post_rot[:2, :2] = post_rot
        ret_post_tran[:2] = post_tran

        return img, ret_post_rot, ret_post_tran

    def img_transform_core(self, img, resize_dims, crop, flip, rotate, pad):
        img = img.resize(resize_dims)
        img = img.crop(crop)
        if flip:
            img = img.transpose(method=Image.FLIP_LEFT_RIGHT)
        img = img.rotate(rotate)

        top, right, bottom, left = pad[0]
        pad_color = pad[1]
        img = self.img_pad(img, top, right, bottom, left, pad_color)
        return img

    def img_pad(self, pil_img, top, right, bottom, left, color):
        if top == right == bottom == left == 0:
            return pil_img
        assert top == bottom, (top, right, bottom, left)
        assert left == right, (top, right, bottom, left)
        width, height = pil_img.size
        new_width = width + right + left
        new_height = height + top + bottom
        result = Image.new(pil_img.mode, (new_width, new_height), color)
        result.paste(pil_img, (left, top))
        return result

    def get_rot(self, h):
        return torch.Tensor([
            [np.cos(h), np.sin(h)],
            [-np.sin(h), np.cos(h)],
        ])

    def rts2proj(self, cam_info, post_rot=None, post_tran=None):
        if cam_info is None:
            return None

        lidar2cam_r = np.linalg.inv(cam_info['rot'])
        lidar2cam_t = cam_info['tran'] @ lidar2cam_r.T
        lidar2cam_rt = np.eye(4)
        lidar2cam_rt[:3, :3] = lidar2cam_r.T
        lidar2cam_rt[3, :3] = -lidar2cam_t
        intrinsic = cam_info['intrin']

        viewpad = np.eye(4)
        if post_rot is not None:
            assert post_tran is not None, [post_rot, post_tran]
            viewpad[:intrinsic.shape[0], :intrinsic.shape[1]] = post_rot @ intrinsic
            viewpad[:3, 2] += post_tran
        else:
            viewpad[:intrinsic.shape[0], :intrinsic.shape[1]] = intrinsic

        lidar2img_rt = (viewpad @ lidar2cam_rt.T)
        lidar2img_rt = np.array((viewpad @ lidar2cam_rt.T), dtype=np.float32)

        return lidar2img_rt.astype(np.float32)

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


@PIPELINES.register_module()
class TestTimeAugImageMultiViewImage(RandomAugImageMultiViewImage):
    def __init__(self, *args, **kwargs):
        super(TestTimeAugImageMultiViewImage, self).__init__(*args, **kwargs)

    def __call__(self, results, fix=''):

        results_list = []
        for flip in [False, True]:
            self.data_config['test_flip'] = flip
            for resize in [0.00]:
                self.data_config['test_resize'] = resize

                _results = super().__call__(copy.deepcopy(results), fix=fix)
                results_list.append(_results)

        results = results_list[0]
        for _results in results_list[1:]:
            results['img'].extend(_results['img'])
            results['lidar2img']['extrinsic'].extend(_results['lidar2img']['extrinsic'])
            results['img_shape'].extend(_results['img_shape'])

        return results


@PIPELINES.register_module()
class InternalRandomAugImageMultiViewImage(RandomAugImageMultiViewImage):
    def rts2proj(self, cam_info, post_rot=None, post_tran=None):
        if cam_info is None:
            return None

        lidar2cam_rt = cam_info['lidar2cam_rt']
        intrinsic = cam_info['intrin']

        viewpad = np.eye(4)
        if post_rot is not None:
            assert post_tran is not None, [post_rot, post_tran]
            viewpad[:intrinsic.shape[0], :intrinsic.shape[1]] = post_rot @ intrinsic
            viewpad[:3, 2] += post_tran
        else:
            viewpad[:intrinsic.shape[0], :intrinsic.shape[1]] = intrinsic

        lidar2img_rt = (viewpad @ lidar2cam_rt.T)
        lidar2img_rt = np.array((viewpad @ lidar2cam_rt.T), dtype=np.float32)

        return lidar2img_rt.astype(np.float32)


@PIPELINES.register_module()
class InternalRandomAugImageMultiViewImageDebug(InternalRandomAugImageMultiViewImage):
    def __init__(self, repeat=10, *args, **kwargs):
        self.repeat = repeat
        super(InternalRandomAugImageMultiViewImageDebug, self).__init__(*args, **kwargs)

    def __call__(self, results):
        # ipdb.set_trace()
        ori_results = copy.deepcopy(results)
        for idx in range(self.repeat):
            results = super(InternalRandomAugImageMultiViewImageDebug, self).__call__(copy.deepcopy(ori_results), fix=f"_{idx}")
        # exit(0)
        return results


@PIPELINES.register_module()
class NormalizeMultiviewImage(object):
    """Normalize the image.
    Added key is "img_norm_cfg".
    Args:
        mean (sequence): Mean values of 3 channels.
        std (sequence): Std values of 3 channels.
        to_rgb (bool): Whether to convert the image from BGR to RGB,
            default is true.
    """

    def __init__(self, mean, std, to_rgb=True):
        self.mean = np.array(mean, dtype=np.float32)
        self.std = np.array(std, dtype=np.float32)
        self.to_rgb = to_rgb

    def __call__(self, results):
        """Call function to normalize images.
        Args:
            results (dict): Result dict from loading pipeline.
        Returns:
            dict: Normalized results, 'img_norm_cfg' key is added into
                result dict.
        """
        results['img'] = [mmcv.imnormalize(
            img, self.mean, self.std, self.to_rgb) for img in results['img']]
        results['img_norm_cfg'] = dict(
            mean=self.mean, std=self.std, to_rgb=self.to_rgb)
        return results

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += f'(mean={self.mean}, std={self.std}, to_rgb={self.to_rgb})'
        return repr_str


@PIPELINES.register_module()
class PadMultiViewImage(object):
    """Pad the multi-view image.
    There are two padding modes: (1) pad to a fixed size and (2) pad to the
    minimum size that is divisible by some number.
    Added keys are "pad_shape", "pad_fixed_size", "pad_size_divisor",
    Args:
        size (tuple, optional): Fixed padding size.
        size_divisor (int, optional): The divisor of padded size.
        pad_val (float, optional): Padding value, 0 by default.
    """

    def __init__(self, size=None, size_divisor=None, pad_val=0):
        self.size = size
        self.size_divisor = size_divisor
        self.pad_val = pad_val
        # only one of size and size_divisor should be valid
        assert size is not None or size_divisor is not None
        assert size is None or size_divisor is None

    def _pad_img(self, results):
        """Pad images according to ``self.size``."""
        if self.size is not None:
            padded_img = [mmcv.impad(
                img, shape=self.size, pad_val=self.pad_val) for img in results['img']]
        elif self.size_divisor is not None:
            padded_img = [mmcv.impad_to_multiple(
                img, self.size_divisor, pad_val=self.pad_val) for img in results['img']]
        results['img'] = padded_img
        results['img_shape'] = [img.shape for img in padded_img]
        results['pad_shape'] = [img.shape for img in padded_img]
        results['pad_fixed_size'] = self.size
        results['pad_size_divisor'] = self.size_divisor

    def __call__(self, results):
        """Call function to pad images, masks, semantic segmentation maps.
        Args:
            results (dict): Result dict from loading pipeline.
        Returns:
            dict: Updated result dict.
        """
        self._pad_img(results)
        return results

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += f'(size={self.size}, '
        repr_str += f'size_divisor={self.size_divisor}, '
        repr_str += f'pad_val={self.pad_val})'
        return repr_str