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import os
import torch
def save_pointcloud_ply(filename: str, pointcloud: torch.Tensor) -> None:
r"""Utility function to save to disk a pointcloud in PLY format.
Args:
filename: the path to save the pointcloud.
pointcloud: tensor containing the pointcloud to save.
The tensor must be in the shape of :math:`(*, 3)` where the last
component is assumed to be a 3d point coordinate :math:`(X, Y, Z)`.
"""
if not isinstance(filename, str) and filename[-3:] == '.ply':
raise TypeError("Input filename must be a string in with the .ply " "extension. Got {}".format(filename))
if not torch.is_tensor(pointcloud):
raise TypeError(f"Input pointcloud type is not a torch.Tensor. Got {type(pointcloud)}")
if not len(pointcloud.shape) == 3 and pointcloud.shape[-1] == 3:
raise TypeError("Input pointcloud must be in the following shape " "HxWx3. Got {}.".format(pointcloud.shape))
# flatten the input pointcloud in a vector to iterate points
xyz_vec: torch.Tensor = pointcloud.reshape(-1, 3)
with open(filename, 'w') as f:
data_str: str = ''
num_points: int = xyz_vec.shape[0]
for idx in range(num_points):
xyz = xyz_vec[idx]
if not bool(torch.isfinite(xyz).any()):
num_points -= 1
continue
x: float = xyz[0].item()
y: float = xyz[1].item()
z: float = xyz[2].item()
data_str += f'{x} {y} {z}\n'
f.write("ply\n")
f.write("format ascii 1.0\n")
f.write("comment arraiy generated\n")
f.write("element vertex %d\n" % num_points)
f.write("property double x\n")
f.write("property double y\n")
f.write("property double z\n")
f.write("end_header\n")
f.write(data_str)
def load_pointcloud_ply(filename: str, header_size: int = 8) -> torch.Tensor:
r"""Utility function to load from disk a pointcloud in PLY format.
Args:
filename: the path to the pointcloud.
header_size: the size of the ply file header that will
be skipped during loading.
Return:
tensor containing the loaded point with shape :math:`(*, 3)` where
:math:`*` represents the number of points.
"""
if not isinstance(filename, str) and filename[-3:] == '.ply':
raise TypeError("Input filename must be a string in with the .ply " "extension. Got {}".format(filename))
if not os.path.isfile(filename):
raise ValueError("Input filename is not an existing file.")
if not (isinstance(header_size, int) and header_size > 0):
raise TypeError(f"Input header_size must be a positive integer. Got {header_size}.")
# open the file and populate tensor
with open(filename) as f:
points = []
# skip header
lines = f.readlines()[header_size:]
# iterate over the points
for line in lines:
x_str, y_str, z_str = line.split()
points.append((torch.tensor(float(x_str)), torch.tensor(float(y_str)), torch.tensor(float(z_str))))
# create tensor from list
pointcloud: torch.Tensor = torch.tensor(points)
return pointcloud
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