indoor_augment.py

import numpy as np

from mmdet.datasets.builder import PIPELINES


@PIPELINES.register_module()
class IndoorFlipData(object):
    """Indoor Flip Data.

    Flip point cloud and ground truth boxes.
    The point cloud will ve flipped along the yz plane
    and the xz plane with a certain probability.

    Args:
        flip_ratio_yz (float): Probability of being flipped along yz plane.
            Default: 0.5.
        flip_ratio_xz (float): Probability of being flipped along xz plane.
            Default: 0.5.
    """

    def __init__(self, flip_ratio_yz=0.5, flip_ratio_xz=0.5):
        self.flip_ratio_yz = flip_ratio_yz
        self.flip_ratio_xz = flip_ratio_xz

    def __call__(self, results):
        points = results['points']
        gt_bboxes_3d = results['gt_bboxes_3d']
        aligned = True if gt_bboxes_3d.shape[1] == 6 else False
        if np.random.random() > self.flip_ratio_yz:
            # Flipping along the YZ plane
            points[:, 0] = -1 * points[:, 0]
            gt_bboxes_3d[:, 0] = -1 * gt_bboxes_3d[:, 0]
            if not aligned:
                gt_bboxes_3d[:, 6] = np.pi - gt_bboxes_3d[:, 6]

            results['flip'] = True
            results['gt_boxes'] = gt_bboxes_3d

        if aligned and np.random.random() > self.flip_ratio_xz:
            # Flipping along the XZ plane
            points[:, 1] = -1 * points[:, 1]
            gt_bboxes_3d[:, 1] = -1 * gt_bboxes_3d[:, 1]
            results['flip'] = True
            results['gt_bboxes_3d'] = gt_bboxes_3d
        results['points'] = points

        return results

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += '(flip_ratio_yz={})'.format(self.flip_ratio_yz)
        repr_str += '(flip_ratio_xz={})'.format(self.flip_ratio_xz)
        return repr_str


@PIPELINES.register_module()
class IndoorPointsColorJitter(object):
    """Indoor Points Color Jitter.

    Randomly change the brightness and color of the point cloud, and
    drop out the points' colors with a certain range and probability.

    Args:
        color_mean (List[float]): Mean color of the point cloud.
            Default: [0.5, 0.5, 0.5].
        bright_range (List[float]): Range of brightness.
            Default: [0.8, 1.2].
        color_shift_range (List[float]): Range of color shift.
            Default: [0.95, 1.05].
        jitter_range (List[float]): Range of jittering.
            Default: [-0.025, 0.025].
        drop_prob (float): Probability to drop out points' color.
            Default: 0.3
    """

    def __init__(self,
                 color_mean=[0.5, 0.5, 0.5],
                 bright_range=[0.8, 1.2],
                 color_shift_range=[0.95, 1.05],
                 jitter_range=[-0.025, 0.025],
                 drop_prob=0.3):
        self.color_mean = color_mean
        self.bright_range = bright_range
        self.color_shift_range = color_shift_range
        self.jitter_range = jitter_range
        self.drop_prob = drop_prob

    def __call__(self, results):
        points = results['points']
        assert points.shape[1] >= 6
        rgb_color = points[:, 3:6] + self.color_mean
        # brightness change for each channel
        rgb_color *= np.random.uniform(self.bright_range[0],
                                       self.bright_range[1], 3)
        # color shift for each channel
        rgb_color += np.random.uniform(self.color_shift_range[0],
                                       self.color_shift_range[1], 3)
        # jittering on each pixel
        rgb_color += np.expand_dims(
            np.random.uniform(self.jitter_range[0], self.jitter_range[1]), -1)
        rgb_color = np.clip(rgb_color, 0, 1)
        # randomly drop out points' colors
        rgb_color *= np.expand_dims(
            np.random.random(points.shape[0]) > self.drop_prob, -1)
        points[:, 3:6] = rgb_color - self.color_mean
        results['points'] = points
        return results

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += '(color_mean={})'.format(self.color_mean)
        repr_str += '(bright_range={})'.format(self.bright_range)
        repr_str += '(color_shift_range={})'.format(self.color_shift_range)
        repr_str += '(jitter_range={})'.format(self.jitter_range)
        repr_str += '(drop_prob={})'.format(self.drop_prob)


# TODO: merge outdoor indoor transform.
# TODO: try transform noise.
@PIPELINES.register_module()
class IndoorGlobalRotScale(object):
    """Indoor Global Rotate Scale.

    Augment sunrgbd and scannet data with global rotating and scaling.

    Args:
        use_height (bool): Whether to use height.
            Default: True.
        rot_range (List[float]): Range of rotation.
            Default: None.
        scale_range (List[float]): Range of scale.
            Default: None.
    """

    def __init__(self, use_height=True, rot_range=None, scale_range=None):
        self.use_height = use_height
        self.rot_range = rot_range
        self.scale_range = scale_range

    def _rotz(self, t):
        """Rotate About Z.

        Rotation about the z-axis.

        Args:
            t (float): Angle of rotation.

        Returns:
            rot_mat (ndarray): Matrix of rotation.
        """
        c = np.cos(t)
        s = np.sin(t)
        rot_mat = np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]])
        return rot_mat

    def _rotate_aligned_boxes(self, input_boxes, rot_mat):
        """Rotate Aligned Boxes.

        Rotate function for the aligned boxes.

        Args:
            input_boxes (ndarray): 3D boxes.
            rot_mat (ndarray): Rotation matrix.

        Returns:
            rotated_boxes (ndarry): 3D boxes after rotation.
        """
        centers, lengths = input_boxes[:, 0:3], input_boxes[:, 3:6]
        new_centers = np.dot(centers, np.transpose(rot_mat))

        dx, dy = lengths[:, 0] / 2.0, lengths[:, 1] / 2.0
        new_x = np.zeros((dx.shape[0], 4))
        new_y = np.zeros((dx.shape[0], 4))

        for i, crnr in enumerate([(-1, -1), (1, -1), (1, 1), (-1, 1)]):
            crnrs = np.zeros((dx.shape[0], 3))
            crnrs[:, 0] = crnr[0] * dx
            crnrs[:, 1] = crnr[1] * dy
            crnrs = np.dot(crnrs, np.transpose(rot_mat))
            new_x[:, i] = crnrs[:, 0]
            new_y[:, i] = crnrs[:, 1]

        new_dx = 2.0 * np.max(new_x, 1)
        new_dy = 2.0 * np.max(new_y, 1)
        new_lengths = np.stack((new_dx, new_dy, lengths[:, 2]), axis=1)

        return np.concatenate([new_centers, new_lengths], axis=1)

    def __call__(self, results):
        points = results['points']
        gt_bboxes_3d = results['gt_bboxes_3d']
        name = 'scannet' if gt_bboxes_3d.shape[1] == 6 else 'sunrgbd'

        if self.rot_range is not None:
            assert len(self.rot_range) == 2
            rot_angle = np.random.uniform(self.rot_range[0], self.rot_range[1])
            rot_mat = self._rotz(rot_angle)
            points[:, :3] = np.dot(points[:, :3], rot_mat.T)
            if name == 'scannet':
                gt_bboxes_3d = self._rotate_aligned_boxes(
                    gt_bboxes_3d, rot_mat)
            else:
                gt_bboxes_3d[:, :3] = np.dot(gt_bboxes_3d[:, :3], rot_mat.T)
                gt_bboxes_3d[:, 6] -= rot_angle

        if self.scale_range is not None:
            assert len(self.scale_range) == 2
            # Augment point cloud scale
            scale_ratio = np.random.uniform(self.scale_range[0],
                                            self.scale_range[1])

            points[:, :3] *= scale_ratio
            gt_bboxes_3d[:, :3] *= scale_ratio
            gt_bboxes_3d[:, 3:6] *= scale_ratio
            if self.use_height:
                points[:, -1] *= scale_ratio

        results['points'] = points
        results['gt_bboxes_3d'] = gt_bboxes_3d
        return results

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += '(use_height={})'.format(self.use_height)
        repr_str += '(rot_range={})'.format(self.rot_range)
        repr_str += '(scale_range={})'.format(self.scale_range)
        return repr_str