423A35C7/pytorch3d
1
0
Fork 0
mirror of https://github.com/facebookresearch/pytorch3d.git synced 2025-12-22 07:10:34 +08:00
Code Issues Packages Projects Releases Wiki Activity

Fix saving / loading empty PLY meshes

Browse Source 
Summary:
Similar to D20392526, PLY files without vertices or faces should be allowed:
- a PLY with only vertices can represent a point cloud
- a PLY without any vertex or face is just empty
- a PLY with faces referencing inexistent vertices has invalid data

Reviewed By: gkioxari

Differential Revision: D20400330

fbshipit-source-id: 35a5f072603fd221f382c7faad5f37c3e0b49bb1
This commit is contained in:
Patrick Labatut
2020-04-01 04:49:27 -07:00
committed by Facebook GitHub Bot
parent b64fe51360
commit 83feed56a0
2 changed files with 154 additions and 15 deletions
Download Patch File Download Diff File Expand all files Collapse all files

80
pytorch3d/io/ply_io.py
View File

@@ -213,6 +213,17 @@ class _PlyHeader:
self.elements.append(_PlyElementType(items[1], count))
def _make_tensor(data, cols: int, dtype: torch.dtype) -> torch.Tensor:
"""
Return a 2D tensor with the specified cols and dtype filled with data,
even when data is empty.
"""
if not len(data):
return torch.zeros((0, cols), dtype=dtype)
return torch.tensor(data, dtype=dtype)
def _read_ply_fixed_size_element_ascii(f, definition: _PlyElementType):
"""
Given an element which has no lists and one type, read the
@@ -227,10 +238,17 @@ def _read_ply_fixed_size_element_ascii(f, definition: _PlyElementType):
values. There is one column for each property.
"""
np_type = _PLY_TYPES[definition.properties[0].data_type].np_type
data = np.loadtxt(
f, dtype=np_type, comments=None, ndmin=2, max_rows=definition.count
)
if data.shape[1] != len(definition.properties):
old_offset = f.tell()
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", message=".* Empty input file.*", category=UserWarning
)
data = np.loadtxt(
f, dtype=np_type, comments=None, ndmin=2, max_rows=definition.count
)
if not len(data): # np.loadtxt() seeks even on empty data
f.seek(old_offset)
if definition.count and data.shape[1] != len(definition.properties):
raise ValueError("Inconsistent data for %s." % definition.name)
if data.shape[0] != definition.count:
raise ValueError("Not enough data for %s." % definition.name)
@@ -252,7 +270,7 @@ def _try_read_ply_constant_list_ascii(f, definition: _PlyElementType):
data. The rows are the different values. Otherwise None.
"""
np_type = _PLY_TYPES[definition.properties[0].data_type].np_type
start_point = f.tell()
old_offset = f.tell()
try:
with warnings.catch_warnings():
warnings.filterwarnings(
@@ -262,8 +280,10 @@ def _try_read_ply_constant_list_ascii(f, definition: _PlyElementType):
f, dtype=np_type, comments=None, ndmin=2, max_rows=definition.count
)
except ValueError:
f.seek(start_point)
f.seek(old_offset)
return None
if not len(data): # np.loadtxt() seeks even on empty data
f.seek(old_offset)
if (data.shape[1] - 1 != data[:, 0]).any():
msg = "A line of %s data did not have the specified length."
raise ValueError(msg % definition.name)
@@ -442,7 +462,7 @@ def _try_read_ply_constant_list_binary(
[length] = length_struct.unpack(bytes_data)
return length
start_point = f.tell()
old_offset = f.tell()
length = get_length()
np_type = _PLY_TYPES[definition.properties[0].data_type].np_type
@@ -459,7 +479,7 @@ def _try_read_ply_constant_list_binary(
if i + 1 == definition.count:
break
if length != get_length():
f.seek(start_point)
f.seek(old_offset)
return None
if (sys.byteorder == "big") != big_endian:
output = output.byteswap()
@@ -644,20 +664,25 @@ def load_ply(f):
if face is None:
raise ValueError("The ply file has no face element.")
if not isinstance(vertex, np.ndarray) or vertex.ndim != 2 or vertex.shape[1] != 3:
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 = torch.tensor(vertex, dtype=torch.float32)
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 isinstance(face, np.ndarray) and face.ndim == 2:
if not len(face):
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.tensor(np.vstack(face_arrays), dtype=torch.int64)
faces = torch.LongTensor(np.vstack(face_arrays))
else:
face_list = []
for face_item in face:
@@ -667,7 +692,10 @@ def load_ply(f):
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)
faces = _make_tensor(face_list, cols=3, dtype=torch.int64)
if torch.any(faces >= verts.shape[0]) or torch.any(faces < 0):
warnings.warn("Faces have invalid indices")
return verts, faces
@@ -682,6 +710,16 @@ def _save_ply(f, verts, faces, decimal_places: Optional[int]):
faces: LongTensor of shape (F, 3) giving faces.
decimal_places: Number of decimal places for saving.
"""
if len(verts) and not (verts.dim() == 2 and verts.size(1) == 3):
raise ValueError(
"Argument 'verts' should either be empty or of shape (num_verts, 3)."
)
if len(faces) and not (faces.dim() == 2 and faces.size(1) == 3):
raise ValueError(
"Argument 'faces' should either be empty or of shape (num_faces, 3)."
)
print("ply\nformat ascii 1.0", file=f)
print(f"element vertex {verts.shape[0]}", file=f)
print("property float x", file=f)
@@ -691,13 +729,25 @@ def _save_ply(f, verts, faces, decimal_places: Optional[int]):
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
np.savetxt(f, verts.detach().numpy(), float_str)
np.savetxt(f, faces.detach().numpy(), "3 %d %d %d")
verts_array = verts.detach().numpy()
np.savetxt(f, verts_array, float_str)
faces_array = faces.detach().numpy()
if torch.any(faces >= verts.shape[0]) or torch.any(faces < 0):
warnings.warn("Faces have invalid indices")
if len(faces_array):
np.savetxt(f, faces_array, "3 %d %d %d")
def save_ply(f, verts, faces, decimal_places: Optional[int] = None):