import numba
import numpy as np
[docs]class VoxelGenerator(object):
"""Voxel generator in numpy implementation.
Args:
voxel_size (list[float]): Size of a single voxel
point_cloud_range (list[float]): Range of points
max_num_points (int): Maximum number of points in a single voxel
max_voxels (int, optional): Maximum number of voxels.
Defaults to 20000.
"""
def __init__(self,
voxel_size,
point_cloud_range,
max_num_points,
max_voxels=20000):
point_cloud_range = np.array(point_cloud_range, dtype=np.float32)
# [0, -40, -3, 70.4, 40, 1]
voxel_size = np.array(voxel_size, dtype=np.float32)
grid_size = (point_cloud_range[3:] -
point_cloud_range[:3]) / voxel_size
grid_size = np.round(grid_size).astype(np.int64)
self._voxel_size = voxel_size
self._point_cloud_range = point_cloud_range
self._max_num_points = max_num_points
self._max_voxels = max_voxels
self._grid_size = grid_size
[docs] def generate(self, points):
"""Generate voxels given points."""
return points_to_voxel(points, self._voxel_size,
self._point_cloud_range, self._max_num_points,
True, self._max_voxels)
@property
def voxel_size(self):
"""list[float]: Size of a single voxel."""
return self._voxel_size
@property
def max_num_points_per_voxel(self):
"""int: Maximum number of points per voxel."""
return self._max_num_points
@property
def point_cloud_range(self):
"""list[float]: Range of point cloud."""
return self._point_cloud_range
@property
def grid_size(self):
"""np.ndarray: The size of grids."""
return self._grid_size
def __repr__(self):
"""str: Return a string that describes the module."""
repr_str = self.__class__.__name__
indent = ' ' * (len(repr_str) + 1)
repr_str += f'(voxel_size={self._voxel_size},\n'
repr_str += indent + 'point_cloud_range='
repr_str += f'{self._point_cloud_range.tolist()},\n'
repr_str += indent + f'max_num_points={self._max_num_points},\n'
repr_str += indent + f'max_voxels={self._max_voxels},\n'
repr_str += indent + f'grid_size={self._grid_size.tolist()}'
repr_str += ')'
return repr_str
def points_to_voxel(points,
voxel_size,
coors_range,
max_points=35,
reverse_index=True,
max_voxels=20000):
"""convert kitti points(N, >=3) to voxels.
Args:
points (np.ndarray): [N, ndim]. points[:, :3] contain xyz points and \
points[:, 3:] contain other information such as reflectivity.
voxel_size (list, tuple, np.ndarray): [3] xyz, indicate voxel size
coors_range (list[float | tuple[float] | ndarray]): Voxel range. \
format: xyzxyz, minmax
max_points (int): Indicate maximum points contained in a voxel.
reverse_index (bool): Whether return reversed coordinates. \
if points has xyz format and reverse_index is True, output \
coordinates will be zyx format, but points in features always \
xyz format.
max_voxels (int): Maximum number of voxels this function creates. \
For second, 20000 is a good choice. Points should be shuffled for \
randomness before this function because max_voxels drops points.
Returns:
tuple[np.ndarray]:
voxels: [M, max_points, ndim] float tensor. only contain points.
coordinates: [M, 3] int32 tensor.
num_points_per_voxel: [M] int32 tensor.
"""
if not isinstance(voxel_size, np.ndarray):
voxel_size = np.array(voxel_size, dtype=points.dtype)
if not isinstance(coors_range, np.ndarray):
coors_range = np.array(coors_range, dtype=points.dtype)
voxelmap_shape = (coors_range[3:] - coors_range[:3]) / voxel_size
voxelmap_shape = tuple(np.round(voxelmap_shape).astype(np.int32).tolist())
if reverse_index:
voxelmap_shape = voxelmap_shape[::-1]
# don't create large array in jit(nopython=True) code.
num_points_per_voxel = np.zeros(shape=(max_voxels, ), dtype=np.int32)
coor_to_voxelidx = -np.ones(shape=voxelmap_shape, dtype=np.int32)
voxels = np.zeros(
shape=(max_voxels, max_points, points.shape[-1]), dtype=points.dtype)
coors = np.zeros(shape=(max_voxels, 3), dtype=np.int32)
if reverse_index:
voxel_num = _points_to_voxel_reverse_kernel(
points, voxel_size, coors_range, num_points_per_voxel,
coor_to_voxelidx, voxels, coors, max_points, max_voxels)
else:
voxel_num = _points_to_voxel_kernel(points, voxel_size, coors_range,
num_points_per_voxel,
coor_to_voxelidx, voxels, coors,
max_points, max_voxels)
coors = coors[:voxel_num]
voxels = voxels[:voxel_num]
num_points_per_voxel = num_points_per_voxel[:voxel_num]
return voxels, coors, num_points_per_voxel
@numba.jit(nopython=True)
def _points_to_voxel_reverse_kernel(points,
voxel_size,
coors_range,
num_points_per_voxel,
coor_to_voxelidx,
voxels,
coors,
max_points=35,
max_voxels=20000):
"""convert kitti points(N, >=3) to voxels.
Args:
points (np.ndarray): [N, ndim]. points[:, :3] contain xyz points and \
points[:, 3:] contain other information such as reflectivity.
voxel_size (list, tuple, np.ndarray): [3] xyz, indicate voxel size \
coors_range (list[float | tuple[float] | ndarray]): Range of voxels. \
format: xyzxyz, minmax
num_points_per_voxel (int): Number of points per voxel.
coor_to_voxel_idx (np.ndarray): A voxel grid of shape (D, H, W), \
which has the same shape as the complete voxel map. It indicates \
the index of each corresponding voxel.
voxels (np.ndarray): Created empty voxels.
coors (np.ndarray): Created coordinates of each voxel.
max_points (int): Indicate maximum points contained in a voxel.
max_voxels (int): Maximum number of voxels this function create. \
for second, 20000 is a good choice. Points should be shuffled for \
randomness before this function because max_voxels drops points.
Returns:
tuple[np.ndarray]:
voxels: Shape [M, max_points, ndim], only contain points.
coordinates: Shape [M, 3].
num_points_per_voxel: Shape [M].
"""
# put all computations to one loop.
# we shouldn't create large array in main jit code, otherwise
# reduce performance
N = points.shape[0]
# ndim = points.shape[1] - 1
ndim = 3
ndim_minus_1 = ndim - 1
grid_size = (coors_range[3:] - coors_range[:3]) / voxel_size
# np.round(grid_size)
# grid_size = np.round(grid_size).astype(np.int64)(np.int32)
grid_size = np.round(grid_size, 0, grid_size).astype(np.int32)
coor = np.zeros(shape=(3, ), dtype=np.int32)
voxel_num = 0
failed = False
for i in range(N):
failed = False
for j in range(ndim):
c = np.floor((points[i, j] - coors_range[j]) / voxel_size[j])
if c < 0 or c >= grid_size[j]:
failed = True
break
coor[ndim_minus_1 - j] = c
if failed:
continue
voxelidx = coor_to_voxelidx[coor[0], coor[1], coor[2]]
if voxelidx == -1:
voxelidx = voxel_num
if voxel_num >= max_voxels:
continue
voxel_num += 1
coor_to_voxelidx[coor[0], coor[1], coor[2]] = voxelidx
coors[voxelidx] = coor
num = num_points_per_voxel[voxelidx]
if num < max_points:
voxels[voxelidx, num] = points[i]
num_points_per_voxel[voxelidx] += 1
return voxel_num
@numba.jit(nopython=True)
def _points_to_voxel_kernel(points,
voxel_size,
coors_range,
num_points_per_voxel,
coor_to_voxelidx,
voxels,
coors,
max_points=35,
max_voxels=20000):
"""convert kitti points(N, >=3) to voxels.
Args:
points (np.ndarray): [N, ndim]. points[:, :3] contain xyz points and \
points[:, 3:] contain other information such as reflectivity.
voxel_size (list, tuple, np.ndarray): [3] xyz, indicate voxel size.
coors_range (list[float | tuple[float] | ndarray]): Range of voxels. \
format: xyzxyz, minmax
num_points_per_voxel (int): Number of points per voxel.
coor_to_voxel_idx (np.ndarray): A voxel grid of shape (D, H, W), \
which has the same shape as the complete voxel map. It indicates \
the index of each corresponding voxel.
voxels (np.ndarray): Created empty voxels.
coors (np.ndarray): Created coordinates of each voxel.
max_points (int): Indicate maximum points contained in a voxel.
max_voxels (int): Maximum number of voxels this function create. \
for second, 20000 is a good choice. Points should be shuffled for \
randomness before this function because max_voxels drops points.
Returns:
tuple[np.ndarray]:
voxels: Shape [M, max_points, ndim], only contain points.
coordinates: Shape [M, 3].
num_points_per_voxel: Shape [M].
"""
N = points.shape[0]
# ndim = points.shape[1] - 1
ndim = 3
grid_size = (coors_range[3:] - coors_range[:3]) / voxel_size
# grid_size = np.round(grid_size).astype(np.int64)(np.int32)
grid_size = np.round(grid_size, 0, grid_size).astype(np.int32)
# lower_bound = coors_range[:3]
# upper_bound = coors_range[3:]
coor = np.zeros(shape=(3, ), dtype=np.int32)
voxel_num = 0
failed = False
for i in range(N):
failed = False
for j in range(ndim):
c = np.floor((points[i, j] - coors_range[j]) / voxel_size[j])
if c < 0 or c >= grid_size[j]:
failed = True
break
coor[j] = c
if failed:
continue
voxelidx = coor_to_voxelidx[coor[0], coor[1], coor[2]]
if voxelidx == -1:
voxelidx = voxel_num
if voxel_num >= max_voxels:
continue
voxel_num += 1
coor_to_voxelidx[coor[0], coor[1], coor[2]] = voxelidx
coors[voxelidx] = coor
num = num_points_per_voxel[voxelidx]
if num < max_points:
voxels[voxelidx, num] = points[i]
num_points_per_voxel[voxelidx] += 1
return voxel_num