# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from logging import warning
[docs]def limit_period(val, offset=0.5, period=np.pi):
"""Limit the value into a period for periodic function.
Args:
val (torch.Tensor): The value to be converted.
offset (float, optional): Offset to set the value range. \
Defaults to 0.5.
period ([type], optional): Period of the value. Defaults to np.pi.
Returns:
torch.Tensor: Value in the range of \
[-offset * period, (1-offset) * period]
"""
return val - torch.floor(val / period + offset) * period
def rotation_3d_in_axis(points, angles, axis=0):
"""Rotate points by angles according to axis.
Args:
points (torch.Tensor): Points of shape (N, M, 3).
angles (torch.Tensor): Vector of angles in shape (N,)
axis (int, optional): The axis to be rotated. Defaults to 0.
Raises:
ValueError: when the axis is not in range [0, 1, 2], it will \
raise value error.
Returns:
torch.Tensor: Rotated points in shape (N, M, 3)
"""
rot_sin = torch.sin(angles)
rot_cos = torch.cos(angles)
ones = torch.ones_like(rot_cos)
zeros = torch.zeros_like(rot_cos)
if axis == 1:
rot_mat_T = torch.stack([
torch.stack([rot_cos, zeros, -rot_sin]),
torch.stack([zeros, ones, zeros]),
torch.stack([rot_sin, zeros, rot_cos])
])
elif axis == 2 or axis == -1:
rot_mat_T = torch.stack([
torch.stack([rot_cos, -rot_sin, zeros]),
torch.stack([rot_sin, rot_cos, zeros]),
torch.stack([zeros, zeros, ones])
])
elif axis == 0:
rot_mat_T = torch.stack([
torch.stack([zeros, rot_cos, -rot_sin]),
torch.stack([zeros, rot_sin, rot_cos]),
torch.stack([ones, zeros, zeros])
])
else:
raise ValueError(f'axis should in range [0, 1, 2], got {axis}')
return torch.einsum('aij,jka->aik', (points, rot_mat_T))
[docs]def xywhr2xyxyr(boxes_xywhr):
"""Convert a rotated boxes in XYWHR format to XYXYR format.
Args:
boxes_xywhr (torch.Tensor): Rotated boxes in XYWHR format.
Returns:
torch.Tensor: Converted boxes in XYXYR format.
"""
boxes = torch.zeros_like(boxes_xywhr)
half_w = boxes_xywhr[:, 2] / 2
half_h = boxes_xywhr[:, 3] / 2
boxes[:, 0] = boxes_xywhr[:, 0] - half_w
boxes[:, 1] = boxes_xywhr[:, 1] - half_h
boxes[:, 2] = boxes_xywhr[:, 0] + half_w
boxes[:, 3] = boxes_xywhr[:, 1] + half_h
boxes[:, 4] = boxes_xywhr[:, 4]
return boxes
[docs]def get_box_type(box_type):
"""Get the type and mode of box structure.
Args:
box_type (str): The type of box structure.
The valid value are "LiDAR", "Camera", or "Depth".
Returns:
tuple: Box type and box mode.
"""
from .box_3d_mode import (Box3DMode, CameraInstance3DBoxes,
DepthInstance3DBoxes, LiDARInstance3DBoxes)
box_type_lower = box_type.lower()
if box_type_lower == 'lidar':
box_type_3d = LiDARInstance3DBoxes
box_mode_3d = Box3DMode.LIDAR
elif box_type_lower == 'camera':
box_type_3d = CameraInstance3DBoxes
box_mode_3d = Box3DMode.CAM
elif box_type_lower == 'depth':
box_type_3d = DepthInstance3DBoxes
box_mode_3d = Box3DMode.DEPTH
else:
raise ValueError('Only "box_type" of "camera", "lidar", "depth"'
f' are supported, got {box_type}')
return box_type_3d, box_mode_3d
[docs]def points_cam2img(points_3d, proj_mat, with_depth=False):
"""Project points from camera coordicates to image coordinates.
Args:
points_3d (torch.Tensor): Points in shape (N, 3).
proj_mat (torch.Tensor): Transformation matrix between coordinates.
with_depth (bool, optional): Whether to keep depth in the output.
Defaults to False.
Returns:
torch.Tensor: Points in image coordinates with shape [N, 2].
"""
points_num = list(points_3d.shape)[:-1]
points_shape = np.concatenate([points_num, [1]], axis=0).tolist()
assert len(proj_mat.shape) == 2, 'The dimension of the projection'\
f' matrix should be 2 instead of {len(proj_mat.shape)}.'
d1, d2 = proj_mat.shape[:2]
assert (d1 == 3 and d2 == 3) or (d1 == 3 and d2 == 4) or (
d1 == 4 and d2 == 4), 'The shape of the projection matrix'\
f' ({d1}*{d2}) is not supported.'
if d1 == 3:
proj_mat_expanded = torch.eye(
4, device=proj_mat.device, dtype=proj_mat.dtype)
proj_mat_expanded[:d1, :d2] = proj_mat
proj_mat = proj_mat_expanded
# previous implementation use new_zeros, new_one yeilds better results
points_4 = torch.cat(
[points_3d, points_3d.new_ones(*points_shape)], dim=-1)
point_2d = torch.matmul(points_4, proj_mat.t())
point_2d_res = point_2d[..., :2] / point_2d[..., 2:3]
if with_depth:
return torch.cat([point_2d_res, point_2d[..., 2:3]], dim=-1)
return point_2d_res
[docs]def mono_cam_box2vis(cam_box):
"""This is a post-processing function on the bboxes from Mono-3D task. If
we want to perform projection visualization, we need to:
1. rotate the box along x-axis for np.pi / 2 (roll)
2. change orientation from local yaw to global yaw
3. convert yaw by (np.pi / 2 - yaw)
After applying this function, we can project and draw it on 2D images.
Args:
cam_box (:obj:`CameraInstance3DBoxes`): 3D bbox in camera coordinate \
system before conversion. Could be gt bbox loaded from dataset or \
network prediction output.
Returns:
:obj:`CameraInstance3DBoxes`: Box after conversion.
"""
warning.warn('DeprecationWarning: The hack of yaw and dimension in the '
'monocular 3D detection on nuScenes has been removed. The '
'function mono_cam_box2vis will be deprecated.')
from . import CameraInstance3DBoxes
assert isinstance(cam_box, CameraInstance3DBoxes), \
'input bbox should be CameraInstance3DBoxes!'
loc = cam_box.gravity_center
dim = cam_box.dims
yaw = cam_box.yaw
feats = cam_box.tensor[:, 7:]
# rotate along x-axis for np.pi / 2
# see also here: https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/nuscenes_mono_dataset.py#L557 # noqa
dim[:, [1, 2]] = dim[:, [2, 1]]
# change local yaw to global yaw for visualization
# refer to https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/nuscenes_mono_dataset.py#L164-L166 # noqa
yaw += torch.atan2(loc[:, 0], loc[:, 2])
# convert yaw by (-yaw - np.pi / 2)
# this is because mono 3D box class such as `NuScenesBox` has different
# definition of rotation with our `CameraInstance3DBoxes`
yaw = -yaw - np.pi / 2
cam_box = torch.cat([loc, dim, yaw[:, None], feats], dim=1)
cam_box = CameraInstance3DBoxes(
cam_box, box_dim=cam_box.shape[-1], origin=(0.5, 0.5, 0.5))
return cam_box
def get_proj_mat_by_coord_type(img_meta, coord_type):
"""Obtain image features using points.
Args:
img_meta (dict): Meta info.
coord_type (str): 'DEPTH' or 'CAMERA' or 'LIDAR'.
Can be case-insensitive.
Returns:
torch.Tensor: transformation matrix.
"""
coord_type = coord_type.upper()
mapping = {'LIDAR': 'lidar2img', 'DEPTH': 'depth2img', 'CAMERA': 'cam2img'}
assert coord_type in mapping.keys()
return img_meta[mapping[coord_type]]