def _save_ply(
f,
verts: torch.Tensor,
faces: torch.LongTensor,
verts_normals: torch.Tensor,
decimal_places: Optional[int] = None,
) -> None:
"""
Internal implementation for saving 3D data to a .ply file.
Args:
f: File object to which the 3D data should be written.
verts: FloatTensor of shape (V, 3) giving vertex coordinates.
faces: LongTensor of shsape (F, 3) giving faces.
verts_normals: FloatTensor of shape (V, 3) giving vertex normals.
decimal_places: Number of decimal places for saving.
"""
assert not len(verts) or (verts.dim() == 2 and verts.size(1) == 3)
assert not len(faces) or (faces.dim() == 2 and faces.size(1) == 3)
assert not len(verts_normals) or (verts_normals.dim() == 2
and verts_normals.size(1) == 3)
print("ply\nformat ascii 1.0", file=f)
print(f"element vertex {verts.shape[0]}", file=f)
print("property float x", file=f)
print("property float y", file=f)
print("property float z", file=f)
if verts_normals.numel() > 0:
print("property float nx", file=f)
print("property float ny", file=f)
print("property float nz", file=f)
print(f"element face {faces.shape[0]}", file=f)
print("property list uchar int vertex_index", file=f)
print("end_header", file=f)
if not (len(verts) or len(faces)):
warnings.warn("Empty 'verts' and 'faces' arguments provided")
return
if decimal_places is None:
float_str = "%f"
else:
float_str = "%" + ".%df" % decimal_places
vert_data = torch.cat((verts, verts_normals), dim=1)
np.savetxt(f, vert_data.detach().numpy(), float_str)
faces_array = faces.detach().numpy()
_check_faces_indices(faces, max_index=verts.shape[0])
if len(faces_array):
np.savetxt(f, faces_array, "3 %d %d %d")
def _write_off_data(
file,
verts: torch.Tensor,
verts_colors: Optional[torch.Tensor] = None,
faces: Optional[torch.LongTensor] = None,
faces_colors: Optional[torch.Tensor] = None,
decimal_places: Optional[int] = None,
) -> None:
"""
Internal implementation for saving 3D data to a .off file.
Args:
file: Binary file object to which the 3D data should be written.
verts: FloatTensor of shape (V, 3) giving vertex coordinates.
verts_colors: FloatTensor of shape (V, C) giving vertex colors where C is 3 or 4.
faces: LongTensor of shape (F, 3) giving faces.
faces_colors: FloatTensor of shape (V, C) giving face colors where C is 3 or 4.
decimal_places: Number of decimal places for saving.
"""
nfaces = 0 if faces is None else faces.shape[0]
file.write(f"off\n{verts.shape[0]} {nfaces} 0\n".encode("ascii"))
if verts_colors is not None:
verts = torch.cat((verts, verts_colors), dim=1)
if decimal_places is None:
float_str = "%f"
else:
float_str = "%" + ".%df" % decimal_places
np.savetxt(file, verts.cpu().detach().numpy(), float_str)
if faces is not None:
_check_faces_indices(faces, max_index=verts.shape[0])
if faces_colors is not None:
face_data = torch.cat(
[
cast(torch.Tensor, faces).cpu().to(torch.float64),
faces_colors.detach().cpu().to(torch.float64),
],
dim=1,
)
format = "3 %d %d %d" + " %f" * faces_colors.shape[1]
np.savetxt(file, face_data.numpy(), format)
elif faces is not None:
np.savetxt(file, faces.cpu().detach().numpy(), "3 %d %d %d")
def _format_faces_indices(faces_indices, max_index: int, device, pad_value=None):
"""
Format indices and check for invalid values. Indices can refer to
values in one of the face properties: vertices, textures or normals.
See comments of the load_obj function for more details.
Args:
faces_indices: List of ints of indices.
max_index: Max index for the face property.
pad_value: if any of the face_indices are padded, specify
the value of the padding (e.g. -1). This is only used
for texture indices indices where there might
not be texture information for all the faces.
Returns:
faces_indices: List of ints of indices.
Raises:
ValueError if indices are not in a valid range.
"""
faces_indices = _make_tensor(
faces_indices, cols=3, dtype=torch.int64, device=device
)
if pad_value is not None:
# pyre-fixme[28]: Unexpected keyword argument `dim`.
mask = faces_indices.eq(pad_value).all(dim=-1)
# Change to 0 based indexing.
faces_indices[(faces_indices > 0)] -= 1
# Negative indexing counts from the end.
faces_indices[(faces_indices < 0)] += max_index
if pad_value is not None:
# pyre-fixme[61]: `mask` is undefined, or not always defined.
faces_indices[mask] = pad_value
return _check_faces_indices(faces_indices, max_index, pad_value)
def _save_ply(
f,
verts: torch.Tensor,
faces: torch.LongTensor,
verts_normals: torch.Tensor,
ascii: bool,
decimal_places: Optional[int] = None,
) -> None:
"""
Internal implementation for saving 3D data to a .ply file.
Args:
f: File object to which the 3D data should be written.
verts: FloatTensor of shape (V, 3) giving vertex coordinates.
faces: LongTensor of shsape (F, 3) giving faces.
verts_normals: FloatTensor of shape (V, 3) giving vertex normals.
ascii: (bool) whether to use the ascii ply format.
decimal_places: Number of decimal places for saving if ascii=True.
"""
assert not len(verts) or (verts.dim() == 2 and verts.size(1) == 3)
assert not len(faces) or (faces.dim() == 2 and faces.size(1) == 3)
assert not len(verts_normals) or (verts_normals.dim() == 2
and verts_normals.size(1) == 3)
if ascii:
f.write(b"ply\nformat ascii 1.0\n")
elif sys.byteorder == "big":
f.write(b"ply\nformat binary_big_endian 1.0\n")
else:
f.write(b"ply\nformat binary_little_endian 1.0\n")
f.write(f"element vertex {verts.shape[0]}\n".encode("ascii"))
f.write(b"property float x\n")
f.write(b"property float y\n")
f.write(b"property float z\n")
if verts_normals.numel() > 0:
f.write(b"property float nx\n")
f.write(b"property float ny\n")
f.write(b"property float nz\n")
f.write(f"element face {faces.shape[0]}\n".encode("ascii"))
f.write(b"property list uchar int vertex_index\n")
f.write(b"end_header\n")
if not (len(verts) or len(faces)):
warnings.warn("Empty 'verts' and 'faces' arguments provided")
return
vert_data = torch.cat((verts, verts_normals), dim=1).detach().numpy()
if ascii:
if decimal_places is None:
float_str = "%f"
else:
float_str = "%" + ".%df" % decimal_places
np.savetxt(f, vert_data, float_str)
else:
assert vert_data.dtype == np.float32
if isinstance(f, BytesIO):
# tofile only works with real files, but is faster than this.
f.write(vert_data.tobytes())
else:
vert_data.tofile(f)
faces_array = faces.detach().numpy()
_check_faces_indices(faces, max_index=verts.shape[0])
if len(faces_array):
if ascii:
np.savetxt(f, faces_array, "3 %d %d %d")
else:
# rows are 13 bytes: a one-byte 3 followed by three four-byte face indices.
faces_uints = np.full((len(faces_array), 13), 3, dtype=np.uint8)
faces_uints[:, 1:] = faces_array.astype(np.uint32).view(np.uint8)
if isinstance(f, BytesIO):
f.write(faces_uints.tobytes())
else:
faces_uints.tofile(f)
def load_ply(f, path_manager: Optional[PathManager] = None):
"""
Load the data from a .ply file.
Example .ply file format:
ply
format ascii 1.0 { ascii/binary, format version number }
comment made by Greg Turk { comments keyword specified, like all lines }
comment this file is a cube
element vertex 8 { define "vertex" element, 8 of them in file }
property float x { vertex contains float "x" coordinate }
property float y { y coordinate is also a vertex property }
property float z { z coordinate, too }
element face 6 { there are 6 "face" elements in the file }
property list uchar int vertex_index { "vertex_indices" is a list of ints }
end_header { delimits the end of the header }
0 0 0 { start of vertex list }
0 0 1
0 1 1
0 1 0
1 0 0
1 0 1
1 1 1
1 1 0
4 0 1 2 3 { start of face list }
4 7 6 5 4
4 0 4 5 1
4 1 5 6 2
4 2 6 7 3
4 3 7 4 0
Args:
f: A binary or text file-like object (with methods read, readline,
tell and seek), a pathlib path or a string containing a file name.
If the ply file is in the binary ply format rather than the text
ply format, then a text stream is not supported.
It is easiest to use a binary stream in all cases.
path_manager: PathManager for loading if f is a str.
Returns:
verts: FloatTensor of shape (V, 3).
faces: LongTensor of vertex indices, shape (F, 3).
"""
if path_manager is None:
path_manager = PathManager()
header, elements = _load_ply_raw(f, path_manager=path_manager)
vertex = elements.get("vertex", None)
if vertex is None:
raise ValueError("The ply file has no vertex element.")
face = elements.get("face", None)
if face is None:
raise ValueError("The ply file has no face element.")
if len(vertex) and (not isinstance(vertex, np.ndarray) or vertex.ndim != 2
or vertex.shape[1] != 3):
raise ValueError("Invalid vertices in file.")
verts = _make_tensor(vertex, cols=3, dtype=torch.float32)
face_head = next(head for head in header.elements if head.name == "face")
if len(face_head.properties
) != 1 or face_head.properties[0].list_size_type is None:
raise ValueError("Unexpected form of faces data.")
# face_head.properties[0].name is usually "vertex_index" or "vertex_indices"
# but we don't need to enforce this.
if not len(face):
# pyre-fixme[28]: Unexpected keyword argument `size`.
faces = torch.zeros(size=(0, 3), dtype=torch.int64)
elif isinstance(face,
np.ndarray) and face.ndim == 2: # Homogeneous elements
if face.shape[1] < 3:
raise ValueError("Faces must have at least 3 vertices.")
face_arrays = [
face[:, [0, i + 1, i + 2]] for i in range(face.shape[1] - 2)
]
faces = torch.LongTensor(np.vstack(face_arrays))
else:
face_list = []
for face_item in face:
if face_item.ndim != 1:
raise ValueError("Bad face data.")
if face_item.shape[0] < 3:
raise ValueError("Faces must have at least 3 vertices.")
for i in range(face_item.shape[0] - 2):
face_list.append(
[face_item[0], face_item[i + 1], face_item[i + 2]])
# pyre-fixme[6]: Expected `dtype` for 3rd param but got `Type[torch.int64]`.
faces = _make_tensor(face_list, cols=3, dtype=torch.int64)
_check_faces_indices(faces, max_index=verts.shape[0])
return verts, faces
def _load_ply(
f,
*,
path_manager: PathManager,
return_vertex_colors: bool = False
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
"""
Load the data from a .ply file.
Args:
f: A binary or text file-like object (with methods read, readline,
tell and seek), a pathlib path or a string containing a file name.
If the ply file is in the binary ply format rather than the text
ply format, then a text stream is not supported.
It is easiest to use a binary stream in all cases.
path_manager: PathManager for loading if f is a str.
return_vertex_colors: whether to return vertex colors.
Returns:
verts: FloatTensor of shape (V, 3).
faces: None or LongTensor of vertex indices, shape (F, 3).
vertex_colors: None or FloatTensor of shape (V, 3), only if requested
"""
header, elements = _load_ply_raw(f, path_manager=path_manager)
verts, vertex_colors = _get_verts(header, elements)
face = elements.get("face", None)
if face is not None:
face_head = next(head for head in header.elements
if head.name == "face")
if (len(face_head.properties) != 1
or face_head.properties[0].list_size_type is None):
raise ValueError("Unexpected form of faces data.")
# face_head.properties[0].name is usually "vertex_index" or "vertex_indices"
# but we don't need to enforce this.
if face is None:
faces = None
elif not len(face):
# pyre is happier when this condition is not joined to the
# previous one with `or`.
faces = None
elif isinstance(face,
np.ndarray) and face.ndim == 2: # Homogeneous elements
if face.shape[1] < 3:
raise ValueError("Faces must have at least 3 vertices.")
face_arrays = [
face[:, [0, i + 1, i + 2]] for i in range(face.shape[1] - 2)
]
faces = torch.LongTensor(np.vstack(face_arrays))
else:
face_list = []
for face_item in face:
if face_item.ndim != 1:
raise ValueError("Bad face data.")
if face_item.shape[0] < 3:
raise ValueError("Faces must have at least 3 vertices.")
for i in range(face_item.shape[0] - 2):
face_list.append(
[face_item[0], face_item[i + 1], face_item[i + 2]])
faces = torch.tensor(face_list, dtype=torch.int64)
if faces is not None:
_check_faces_indices(faces, max_index=verts.shape[0])
if return_vertex_colors:
return verts, faces, vertex_colors
return verts, faces, None
