355 lines
13 KiB
Python
355 lines
13 KiB
Python
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import numpy as np
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import torch
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from mmdet3d.core.points import BasePoints
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from mmdet3d.ops import points_in_boxes_batch
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from .base_box3d import BaseInstance3DBoxes
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from .utils import limit_period, rotation_3d_in_axis
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class DepthInstance3DBoxes(BaseInstance3DBoxes):
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"""3D boxes of instances in Depth coordinates.
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Coordinates in Depth:
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.. code-block:: none
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up z y front (yaw=-0.5*pi)
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^ ^
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| /
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| /
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0 ------> x right (yaw=0)
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The relative coordinate of bottom center in a Depth box is (0.5, 0.5, 0),
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and the yaw is around the z axis, thus the rotation axis=2.
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The yaw is 0 at the positive direction of x axis, and decreases from
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the positive direction of x to the positive direction of y.
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Also note that rotation of DepthInstance3DBoxes is counterclockwise,
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which is reverse to the definition of the yaw angle (clockwise).
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A refactor is ongoing to make the three coordinate systems
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easier to understand and convert between each other.
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Attributes:
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tensor (torch.Tensor): Float matrix of N x box_dim.
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box_dim (int): Integer indicates the dimension of a box
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Each row is (x, y, z, x_size, y_size, z_size, yaw, ...).
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with_yaw (bool): If True, the value of yaw will be set to 0 as minmax
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boxes.
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"""
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@property
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def gravity_center(self):
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"""torch.Tensor: A tensor with center of each box."""
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bottom_center = self.bottom_center
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gravity_center = torch.zeros_like(bottom_center)
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gravity_center[:, :2] = bottom_center[:, :2]
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gravity_center[:, 2] = bottom_center[:, 2] + self.tensor[:, 5] * 0.5
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return gravity_center
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@property
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def corners(self):
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"""torch.Tensor: Coordinates of corners of all the boxes
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in shape (N, 8, 3).
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Convert the boxes to corners in clockwise order, in form of
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``(x0y0z0, x0y0z1, x0y1z1, x0y1z0, x1y0z0, x1y0z1, x1y1z1, x1y1z0)``
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.. code-block:: none
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up z
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front y ^
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/ |
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/ |
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(x0, y1, z1) + ----------- + (x1, y1, z1)
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/| / |
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/ | / |
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(x0, y0, z1) + ----------- + + (x1, y1, z0)
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| / . | /
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| / origin | /
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(x0, y0, z0) + ----------- + --------> right x
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(x1, y0, z0)
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"""
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# TODO: rotation_3d_in_axis function do not support
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# empty tensor currently.
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assert len(self.tensor) != 0
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dims = self.dims
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corners_norm = torch.from_numpy(
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np.stack(np.unravel_index(np.arange(8), [2] * 3), axis=1)
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).to(device=dims.device, dtype=dims.dtype)
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corners_norm = corners_norm[[0, 1, 3, 2, 4, 5, 7, 6]]
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# use relative origin (0.5, 0.5, 0)
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corners_norm = corners_norm - dims.new_tensor([0.5, 0.5, 0])
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corners = dims.view([-1, 1, 3]) * corners_norm.reshape([1, 8, 3])
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# rotate around z axis
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corners = rotation_3d_in_axis(corners, self.tensor[:, 6], axis=2)
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corners += self.tensor[:, :3].view(-1, 1, 3)
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return corners
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@property
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def bev(self):
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"""torch.Tensor: A n x 5 tensor of 2D BEV box of each box
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in XYWHR format."""
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return self.tensor[:, [0, 1, 3, 4, 6]]
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@property
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def nearest_bev(self):
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"""torch.Tensor: A tensor of 2D BEV box of each box
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without rotation."""
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# Obtain BEV boxes with rotation in XYWHR format
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bev_rotated_boxes = self.bev
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# convert the rotation to a valid range
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rotations = bev_rotated_boxes[:, -1]
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normed_rotations = torch.abs(limit_period(rotations, 0.5, np.pi))
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# find the center of boxes
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conditions = (normed_rotations > np.pi / 4)[..., None]
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bboxes_xywh = torch.where(
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conditions, bev_rotated_boxes[:, [0, 1, 3, 2]], bev_rotated_boxes[:, :4]
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)
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centers = bboxes_xywh[:, :2]
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dims = bboxes_xywh[:, 2:]
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bev_boxes = torch.cat([centers - dims / 2, centers + dims / 2], dim=-1)
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return bev_boxes
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def rotate(self, angle, points=None):
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"""Rotate boxes with points (optional) with the given angle or \
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rotation matrix.
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Args:
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angle (float | torch.Tensor | np.ndarray):
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Rotation angle or rotation matrix.
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points (torch.Tensor, numpy.ndarray, :obj:`BasePoints`, optional):
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Points to rotate. Defaults to None.
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Returns:
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tuple or None: When ``points`` is None, the function returns \
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None, otherwise it returns the rotated points and the \
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rotation matrix ``rot_mat_T``.
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"""
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if not isinstance(angle, torch.Tensor):
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angle = self.tensor.new_tensor(angle)
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assert (
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angle.shape == torch.Size([3, 3]) or angle.numel() == 1
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), f"invalid rotation angle shape {angle.shape}"
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if angle.numel() == 1:
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rot_sin = torch.sin(angle)
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rot_cos = torch.cos(angle)
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rot_mat_T = self.tensor.new_tensor(
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[[rot_cos, -rot_sin, 0], [rot_sin, rot_cos, 0], [0, 0, 1]]
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).T
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else:
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rot_mat_T = angle.T
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rot_sin = rot_mat_T[0, 1]
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rot_cos = rot_mat_T[0, 0]
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angle = np.arctan2(rot_sin, rot_cos)
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self.tensor[:, 0:3] = self.tensor[:, 0:3] @ rot_mat_T
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if self.with_yaw:
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self.tensor[:, 6] -= angle
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else:
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corners_rot = self.corners @ rot_mat_T
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new_x_size = (
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corners_rot[..., 0].max(dim=1, keepdim=True)[0]
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- corners_rot[..., 0].min(dim=1, keepdim=True)[0]
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)
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new_y_size = (
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corners_rot[..., 1].max(dim=1, keepdim=True)[0]
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- corners_rot[..., 1].min(dim=1, keepdim=True)[0]
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)
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self.tensor[:, 3:5] = torch.cat((new_x_size, new_y_size), dim=-1)
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if points is not None:
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if isinstance(points, torch.Tensor):
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points[:, :3] = points[:, :3] @ rot_mat_T
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elif isinstance(points, np.ndarray):
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rot_mat_T = rot_mat_T.numpy()
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points[:, :3] = np.dot(points[:, :3], rot_mat_T)
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elif isinstance(points, BasePoints):
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# anti-clockwise
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points.rotate(angle)
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else:
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raise ValueError
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return points, rot_mat_T
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def flip(self, bev_direction="horizontal", points=None):
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"""Flip the boxes in BEV along given BEV direction.
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In Depth coordinates, it flips x (horizontal) or y (vertical) axis.
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Args:
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bev_direction (str): Flip direction (horizontal or vertical).
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points (torch.Tensor, numpy.ndarray, :obj:`BasePoints`, None):
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Points to flip. Defaults to None.
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Returns:
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torch.Tensor, numpy.ndarray or None: Flipped points.
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"""
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assert bev_direction in ("horizontal", "vertical")
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if bev_direction == "horizontal":
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self.tensor[:, 0::7] = -self.tensor[:, 0::7]
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if self.with_yaw:
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self.tensor[:, 6] = -self.tensor[:, 6] + np.pi
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elif bev_direction == "vertical":
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self.tensor[:, 1::7] = -self.tensor[:, 1::7]
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if self.with_yaw:
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self.tensor[:, 6] = -self.tensor[:, 6]
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if points is not None:
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assert isinstance(points, (torch.Tensor, np.ndarray, BasePoints))
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if isinstance(points, (torch.Tensor, np.ndarray)):
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if bev_direction == "horizontal":
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points[:, 0] = -points[:, 0]
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elif bev_direction == "vertical":
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points[:, 1] = -points[:, 1]
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elif isinstance(points, BasePoints):
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points.flip(bev_direction)
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return points
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def in_range_bev(self, box_range):
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"""Check whether the boxes are in the given range.
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Args:
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box_range (list | torch.Tensor): The range of box
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(x_min, y_min, x_max, y_max).
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Note:
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In the original implementation of SECOND, checking whether
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a box in the range checks whether the points are in a convex
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polygon, we try to reduce the burdun for simpler cases.
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Returns:
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torch.Tensor: Indicating whether each box is inside \
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the reference range.
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"""
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in_range_flags = (
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(self.tensor[:, 0] > box_range[0])
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& (self.tensor[:, 1] > box_range[1])
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& (self.tensor[:, 0] < box_range[2])
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& (self.tensor[:, 1] < box_range[3])
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)
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return in_range_flags
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def convert_to(self, dst, rt_mat=None):
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"""Convert self to ``dst`` mode.
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Args:
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dst (:obj:`Box3DMode`): The target Box mode.
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rt_mat (np.ndarray | torch.Tensor): The rotation and translation
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matrix between different coordinates. Defaults to None.
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The conversion from ``src`` coordinates to ``dst`` coordinates
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usually comes along the change of sensors, e.g., from camera
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to LiDAR. This requires a transformation matrix.
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Returns:
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:obj:`DepthInstance3DBoxes`: \
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The converted box of the same type in the ``dst`` mode.
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"""
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from .box_3d_mode import Box3DMode
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return Box3DMode.convert(box=self, src=Box3DMode.DEPTH, dst=dst, rt_mat=rt_mat)
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def points_in_boxes(self, points):
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"""Find points that are in boxes (CUDA).
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Args:
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points (torch.Tensor): Points in shape [1, M, 3] or [M, 3], \
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3 dimensions are [x, y, z] in LiDAR coordinate.
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Returns:
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torch.Tensor: The index of boxes each point lies in with shape \
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of (B, M, T).
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"""
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from .box_3d_mode import Box3DMode
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# to lidar
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points_lidar = points.clone()
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points_lidar = points_lidar[..., [1, 0, 2]]
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points_lidar[..., 1] *= -1
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if points.dim() == 2:
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points_lidar = points_lidar.unsqueeze(0)
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else:
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assert points.dim() == 3 and points_lidar.shape[0] == 1
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boxes_lidar = self.convert_to(Box3DMode.LIDAR).tensor
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boxes_lidar = boxes_lidar.to(points.device).unsqueeze(0)
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box_idxs_of_pts = points_in_boxes_batch(points_lidar, boxes_lidar)
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return box_idxs_of_pts.squeeze(0)
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def enlarged_box(self, extra_width):
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"""Enlarge the length, width and height boxes.
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Args:
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extra_width (float | torch.Tensor): Extra width to enlarge the box.
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Returns:
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:obj:`LiDARInstance3DBoxes`: Enlarged boxes.
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"""
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enlarged_boxes = self.tensor.clone()
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enlarged_boxes[:, 3:6] += extra_width * 2
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# bottom center z minus extra_width
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enlarged_boxes[:, 2] -= extra_width
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return self.new_box(enlarged_boxes)
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def get_surface_line_center(self):
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"""Compute surface and line center of bounding boxes.
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Returns:
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torch.Tensor: Surface and line center of bounding boxes.
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"""
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obj_size = self.dims
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center = self.gravity_center.view(-1, 1, 3)
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batch_size = center.shape[0]
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rot_sin = torch.sin(-self.yaw)
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rot_cos = torch.cos(-self.yaw)
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rot_mat_T = self.yaw.new_zeros(tuple(list(self.yaw.shape) + [3, 3]))
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rot_mat_T[..., 0, 0] = rot_cos
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rot_mat_T[..., 0, 1] = -rot_sin
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rot_mat_T[..., 1, 0] = rot_sin
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rot_mat_T[..., 1, 1] = rot_cos
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rot_mat_T[..., 2, 2] = 1
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# Get the object surface center
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offset = obj_size.new_tensor(
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[[0, 0, 1], [0, 0, -1], [0, 1, 0], [0, -1, 0], [1, 0, 0], [-1, 0, 0]]
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)
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offset = offset.view(1, 6, 3) / 2
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surface_3d = (offset * obj_size.view(batch_size, 1, 3).repeat(1, 6, 1)).reshape(-1, 3)
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# Get the object line center
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offset = obj_size.new_tensor(
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[
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[1, 0, 1],
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[-1, 0, 1],
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[0, 1, 1],
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[0, -1, 1],
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[1, 0, -1],
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[-1, 0, -1],
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[0, 1, -1],
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[0, -1, -1],
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[1, 1, 0],
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[1, -1, 0],
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[-1, 1, 0],
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[-1, -1, 0],
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]
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)
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offset = offset.view(1, 12, 3) / 2
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line_3d = (offset * obj_size.view(batch_size, 1, 3).repeat(1, 12, 1)).reshape(-1, 3)
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surface_rot = rot_mat_T.repeat(6, 1, 1)
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surface_3d = torch.matmul(surface_3d.unsqueeze(-2), surface_rot.transpose(2, 1)).squeeze(-2)
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surface_center = center.repeat(1, 6, 1).reshape(-1, 3) + surface_3d
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line_rot = rot_mat_T.repeat(12, 1, 1)
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line_3d = torch.matmul(line_3d.unsqueeze(-2), line_rot.transpose(2, 1)).squeeze(-2)
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line_center = center.repeat(1, 12, 1).reshape(-1, 3) + line_3d
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return surface_center, line_center
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