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Zero
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import numpy as np
import logging
import torch
import re
def random_per_point_translation_in_place(pcd_data) -> None:
"""
Jittering the point cloud data by a random value between -0.02 and 0.02
Args:
pcd_data: point cloud data in the form x, y, z
"""
translations = (
np.random.rand(pcd_data.shape[0], 3) - 0.5
) * 0.04 # Random values between -0.02 and 0.02
pcd_data[:, -3:] += translations
def compute_max_extent_and_centroid(pcd_data, epsilon=1e-4) -> tuple[np.ndarray, np.ndarray]:
"""_summary_
Args:
pcd_data : point cloud data in the form x, y, z
epsilon (float, optional): buffer for the max_extent. Defaults to 1e-4.
Returns:
max_extent: maximum extent of the point cloud data in terms of the largest dimension
centroid: centroid of the point cloud data
"""
min_vals = pcd_data.min(axis=0)
max_vals = pcd_data.max(axis=0)
centroid = (min_vals + max_vals) / 2
max_extent = np.max(max_vals - min_vals) + epsilon
return max_extent, centroid
def unit_cube_normalization_in_place(
pcd_data,
max_extent,
centroid,
):
"""
Normalized data point in a unit cube between 0 and 1 for each x, y, z in-place
Args:
pcd_data: point cloud data in the form x, y, z
"""
# translate the centroid to the origin
pcd_data -= centroid
# scale the data to fit within [-0.5, 0.5]
pcd_data /= max_extent
# translate it back to within [0, 1]
pcd_data += 0.5
def point_to_index(point, grid_size):
"""
Maps a point in the unit cube to a unique index based on the grid size.
Args:
point (tuple): a tuple of (x, y, z) coordinates of the point. Each coordinate should be in [0, 1].
grid_size (int): the number of divisions along each axis.
Returns:
int: a unique index for the point.
"""
xi = int(point[0] * grid_size)
yi = int(point[1] * grid_size)
zi = int(point[2] * grid_size)
# Ensure that the point is inside the unit cube
if not (0 <= xi < grid_size) or not (0 <= yi < grid_size) or not (0 <= zi < grid_size):
logging.warning(
f"The point is outside the unit cube: point: {point}, grid_index: ({xi}, {yi}, {zi})"
)
# Clamp the point to be inside the unit cube
xi = min(max(xi, 0), grid_size - 1)
yi = min(max(yi, 0), grid_size - 1)
zi = min(max(zi, 0), grid_size - 1)
# Compute the unique voxel ID, row-major order
voxel_id = xi + yi * grid_size + zi * grid_size * grid_size
return voxel_id
def scale_bbox(bbox_str, max_extent, centroid):
"""
Scale the bounding box to be within a unit cube and output numerically tokenized bounding box.
Args:
bbox_str (str): A string representing a bounding box, in the format "<x_min,y_min,z_min,x_max,y_max,z_max>".
max_extent (float): The maximum extent of the bounding box.
centroid (np.array): The centroid of the bounding box.
Returns:
str: A string representing the scaled bounding box, in the same format as the input.
"""
# Remove < and > from the bounding box string
bbox_str = bbox_str.strip("<>")
bbox_values = bbox_str.split(",")
# Convert each string to a float and store in a list
bbox_floats = [float(value) for value in bbox_values]
# Convert the list to a numpy array
bbox_array = np.array(bbox_floats)
bbox_array[:3] -= centroid
bbox_array[3:] -= centroid
bbox_array /= max_extent
bbox_array += 0.5
x_min, y_min, z_min, x_max, y_max, z_max = bbox_array
x_min, y_min, z_min, x_max, y_max, z_max = (
x_min.item(),
y_min.item(),
z_min.item(),
x_max.item(),
y_max.item(),
z_max.item(),
)
x_min, y_min, z_min, x_max, y_max, z_max = (
round(x_min, 3),
round(y_min, 3),
round(z_min, 3),
round(x_max, 3),
round(y_max, 3),
round(z_max, 3),
)
new_bbox_str = f"< {x_min}, {y_min}, {z_min}, {x_max}, {y_max}, {z_max}>" # adding space after < because tokenizer will not merge < and first digit or negative sign
return new_bbox_str
def voxelize_points(
xyz_to_be_voxelized: np.array,
scene_min_xyz: np.array,
scene_max_xyz: np.array,
num_voxels_per_axis: int,
):
"""Convert points to voxel indexes
Args:
xyz_to_be_voxelized (np.array): shape (num_points, 3)
scene_min_xyz (np.array): shape (3,)
scene_max_xyz (np.array): shape (3,)
num_voxels_per_axis (int): number of voxels per axis
Returns:
voxel_id (np.array): shape (num_points,)
"""
voxel_index = np.floor(
(xyz_to_be_voxelized - scene_min_xyz)
/ (scene_max_xyz - scene_min_xyz)
* num_voxels_per_axis
).astype(
int
) # range after this overations: [0, num_voxels_per_axis]
voxel_index = np.clip(
voxel_index, 0, num_voxels_per_axis - 1
) # clamp range to [0, num_voxels_per_axis - 1]
# calculate index using row-major order
voxel_id = (
voxel_index[:, 0]
+ voxel_index[:, 1] * num_voxels_per_axis
+ voxel_index[:, 2] * num_voxels_per_axis * num_voxels_per_axis
) # range after this operation: [0, num_voxels_per_axis ** 3 - 1]
return voxel_id
def process_one_bbox_minkowski_loc_token(
bbox_str, scene_min_xyz, scene_max_xyz, num_voxels_per_axis
):
# Remove < and > from the bounding box string
bbox_str = bbox_str.strip("<>")
bbox_values = bbox_str.split(",")
# Convert each string to a float and store in a list
bbox_floats = [float(value) for value in bbox_values]
# Convert the list to a numpy array
bbox_array = np.array(bbox_floats) # shape: (6,)
bbox_array = bbox_array.reshape(2, 3) # shape: (2, 3)
voxel_indices = voxelize_points(
bbox_array, scene_min_xyz, scene_max_xyz, num_voxels_per_axis
) # shape: (2,)
new_bbox_str = f"<loc_{voxel_indices[0]}><loc_{voxel_indices[1]}>"
return new_bbox_str
def scale_bbox_special_token(bbox_str, max_extent, centroid, num_grid_cells):
"""
Special token for the bbox. The bbox is scaled to the unit cube and then converted
to a unique index based on the grid size.
Args:
bbox_str (str): bbox string in the form "<x_min, y_min, z_min, x_max, y_max, z_max>"
max_extent (float): max extent of the point cloud data in terms of the largest dimension
centroid (np.array): centroid of the point cloud data
num_grid_cells (int): number of grids along each axis
Returns:
two unique special tokens for the bbox as string
"""
# Remove < and > from the bounding box string
bbox_str = bbox_str.strip("<>")
bbox_values = bbox_str.split(",")
# Convert each string to a float and store in a list
bbox_floats = [float(value) for value in bbox_values]
# Convert the list to a numpy array
bbox_floats = np.array(bbox_floats)
bbox_floats[:3] -= centroid
bbox_floats[3:] -= centroid
bbox_floats /= max_extent
bbox_floats += 0.5
min_point = bbox_floats[:3]
max_point = bbox_floats[3:]
index_min = point_to_index(min_point, num_grid_cells)
index_max = point_to_index(max_point, num_grid_cells)
new_bbox_str = f"<loc_{index_min}><loc_{index_max}>"
return new_bbox_str
def rotate_point_cloud_90_degrees(pcd_data):
"""
Rotate the point cloud data by 90 degrees in the x-y plane
Args:
pcd_data: point cloud data in the form x, y, z
Returns:
pcd_data: rotated point cloud data in the form x, y, z
"""
# Randomly select among no change, clockwise, and counterclockwise
rotation_choices = ["no change", "clockwise", "counterclockwise"]
direction = np.random.choice(rotation_choices)
if direction == "clockwise":
rotation_matrix = torch.tensor([[0, 1], [-1, 0]])
# Apply rotation on x-y plane
pcd_data[:, -3:-1] = torch.matmul(pcd_data[:, -3:-1], rotation_matrix)
elif direction == "counterclockwise":
rotation_matrix = torch.tensor([[0, -1], [1, 0]])
# Apply rotation on x-y plane
pcd_data[:, -3:-1] = torch.matmul(pcd_data[:, -3:-1], rotation_matrix)
return pcd_data, direction
def adjust_bbox_after_rotation(bbox_str, direction):
"""_summary_
Args:
bbox_str (_type_): _description_
direction (_type_): _description_
Returns:
_type_: _description_
"""
if direction == "no change":
return bbox_str
values = list(map(float, re.findall(r"[-+]?\d*\.\d+|\d+", bbox_str)))
x_min, y_min, z_min, x_max, y_max, z_max = values
if direction == "clockwise":
# adding space after < because tokenizer will not merge < and first digit or negative sign
new_bbox_str = f"< {y_min}, {x_min}, {z_min}, {y_max}, {x_max}, {z_max}>"
else: # counterclockwise
# adding space after < because tokenizer will not merge < and first digit or negative sign
new_bbox_str = f"< {x_max}, {y_min}, {z_min}, {x_min}, {y_max}, {z_max}>"
return new_bbox_str
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