mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-23 14:22:50 +08:00
Fix saving / loading empty PLY meshes
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
Facebook GitHub Bot
parent
b64fe51360
commit
83feed56a0
@ -213,6 +213,17 @@ class _PlyHeader:
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self.elements.append(_PlyElementType(items[1], count))
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def _make_tensor(data, cols: int, dtype: torch.dtype) -> torch.Tensor:
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"""
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Return a 2D tensor with the specified cols and dtype filled with data,
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even when data is empty.
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"""
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if not len(data):
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return torch.zeros((0, cols), dtype=dtype)
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return torch.tensor(data, dtype=dtype)
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def _read_ply_fixed_size_element_ascii(f, definition: _PlyElementType):
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"""
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Given an element which has no lists and one type, read the
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@ -227,10 +238,17 @@ def _read_ply_fixed_size_element_ascii(f, definition: _PlyElementType):
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values. There is one column for each property.
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"""
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np_type = _PLY_TYPES[definition.properties[0].data_type].np_type
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data = np.loadtxt(
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f, dtype=np_type, comments=None, ndmin=2, max_rows=definition.count
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)
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if data.shape[1] != len(definition.properties):
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old_offset = f.tell()
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with warnings.catch_warnings():
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warnings.filterwarnings(
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"ignore", message=".* Empty input file.*", category=UserWarning
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)
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data = np.loadtxt(
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f, dtype=np_type, comments=None, ndmin=2, max_rows=definition.count
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)
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if not len(data): # np.loadtxt() seeks even on empty data
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f.seek(old_offset)
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if definition.count and data.shape[1] != len(definition.properties):
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raise ValueError("Inconsistent data for %s." % definition.name)
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if data.shape[0] != definition.count:
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raise ValueError("Not enough data for %s." % definition.name)
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@ -252,7 +270,7 @@ def _try_read_ply_constant_list_ascii(f, definition: _PlyElementType):
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data. The rows are the different values. Otherwise None.
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"""
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np_type = _PLY_TYPES[definition.properties[0].data_type].np_type
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start_point = f.tell()
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old_offset = f.tell()
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try:
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with warnings.catch_warnings():
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warnings.filterwarnings(
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@ -262,8 +280,10 @@ def _try_read_ply_constant_list_ascii(f, definition: _PlyElementType):
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f, dtype=np_type, comments=None, ndmin=2, max_rows=definition.count
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)
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except ValueError:
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f.seek(start_point)
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f.seek(old_offset)
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return None
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if not len(data): # np.loadtxt() seeks even on empty data
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f.seek(old_offset)
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if (data.shape[1] - 1 != data[:, 0]).any():
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msg = "A line of %s data did not have the specified length."
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raise ValueError(msg % definition.name)
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@ -442,7 +462,7 @@ def _try_read_ply_constant_list_binary(
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[length] = length_struct.unpack(bytes_data)
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return length
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start_point = f.tell()
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old_offset = f.tell()
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length = get_length()
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np_type = _PLY_TYPES[definition.properties[0].data_type].np_type
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@ -459,7 +479,7 @@ def _try_read_ply_constant_list_binary(
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if i + 1 == definition.count:
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break
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if length != get_length():
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f.seek(start_point)
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f.seek(old_offset)
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return None
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if (sys.byteorder == "big") != big_endian:
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output = output.byteswap()
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@ -644,20 +664,25 @@ def load_ply(f):
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if face is None:
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raise ValueError("The ply file has no face element.")
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if not isinstance(vertex, np.ndarray) or vertex.ndim != 2 or vertex.shape[1] != 3:
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if len(vertex) and (
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not isinstance(vertex, np.ndarray) or vertex.ndim != 2 or vertex.shape[1] != 3
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):
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raise ValueError("Invalid vertices in file.")
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verts = torch.tensor(vertex, dtype=torch.float32)
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verts = _make_tensor(vertex, cols=3, dtype=torch.float32)
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face_head = next(head for head in header.elements if head.name == "face")
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if len(face_head.properties) != 1 or face_head.properties[0].list_size_type is None:
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raise ValueError("Unexpected form of faces data.")
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# face_head.properties[0].name is usually "vertex_index" or "vertex_indices"
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# but we don't need to enforce this.
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if isinstance(face, np.ndarray) and face.ndim == 2:
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if not len(face):
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faces = torch.zeros(size=(0, 3), dtype=torch.int64)
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elif isinstance(face, np.ndarray) and face.ndim == 2: # Homogeneous elements
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if face.shape[1] < 3:
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raise ValueError("Faces must have at least 3 vertices.")
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face_arrays = [face[:, [0, i + 1, i + 2]] for i in range(face.shape[1] - 2)]
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faces = torch.tensor(np.vstack(face_arrays), dtype=torch.int64)
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faces = torch.LongTensor(np.vstack(face_arrays))
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else:
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face_list = []
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for face_item in face:
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@ -667,7 +692,10 @@ def load_ply(f):
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raise ValueError("Faces must have at least 3 vertices.")
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for i in range(face_item.shape[0] - 2):
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face_list.append([face_item[0], face_item[i + 1], face_item[i + 2]])
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faces = torch.tensor(face_list, dtype=torch.int64)
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faces = _make_tensor(face_list, cols=3, dtype=torch.int64)
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if torch.any(faces >= verts.shape[0]) or torch.any(faces < 0):
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warnings.warn("Faces have invalid indices")
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return verts, faces
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@ -682,6 +710,16 @@ def _save_ply(f, verts, faces, decimal_places: Optional[int]):
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faces: LongTensor of shape (F, 3) giving faces.
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decimal_places: Number of decimal places for saving.
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"""
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if len(verts) and not (verts.dim() == 2 and verts.size(1) == 3):
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raise ValueError(
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"Argument 'verts' should either be empty or of shape (num_verts, 3)."
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)
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if len(faces) and not (faces.dim() == 2 and faces.size(1) == 3):
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raise ValueError(
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"Argument 'faces' should either be empty or of shape (num_faces, 3)."
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)
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print("ply\nformat ascii 1.0", file=f)
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print(f"element vertex {verts.shape[0]}", file=f)
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print("property float x", file=f)
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@ -691,13 +729,25 @@ def _save_ply(f, verts, faces, decimal_places: Optional[int]):
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print("property list uchar int vertex_index", file=f)
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print("end_header", file=f)
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if not (len(verts) or len(faces)):
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warnings.warn("Empty 'verts' and 'faces' arguments provided")
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return
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if decimal_places is None:
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float_str = "%f"
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else:
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float_str = "%" + ".%df" % decimal_places
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np.savetxt(f, verts.detach().numpy(), float_str)
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np.savetxt(f, faces.detach().numpy(), "3 %d %d %d")
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verts_array = verts.detach().numpy()
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np.savetxt(f, verts_array, float_str)
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faces_array = faces.detach().numpy()
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if torch.any(faces >= verts.shape[0]) or torch.any(faces < 0):
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warnings.warn("Faces have invalid indices")
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if len(faces_array):
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np.savetxt(f, faces_array, "3 %d %d %d")
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def save_ply(f, verts, faces, decimal_places: Optional[int] = None):
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@ -139,6 +139,90 @@ class TestMeshPlyIO(TestCaseMixin, unittest.TestCase):
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]
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self.assertClose(faces, torch.LongTensor(faces_expected))
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def test_save_ply_invalid_shapes(self):
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# Invalid vertices shape
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with self.assertRaises(ValueError) as error:
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verts = torch.FloatTensor([[0.1, 0.2, 0.3, 0.4]]) # (V, 4)
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faces = torch.LongTensor([[0, 1, 2]])
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save_ply(StringIO(), verts, faces)
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expected_message = (
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"Argument 'verts' should either be empty or of shape (num_verts, 3)."
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)
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self.assertTrue(expected_message, error.exception)
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# Invalid faces shape
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with self.assertRaises(ValueError) as error:
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verts = torch.FloatTensor([[0.1, 0.2, 0.3]])
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faces = torch.LongTensor([[0, 1, 2, 3]]) # (F, 4)
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save_ply(StringIO(), verts, faces)
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expected_message = (
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"Argument 'faces' should either be empty or of shape (num_faces, 3)."
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)
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self.assertTrue(expected_message, error.exception)
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def test_save_ply_invalid_indices(self):
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message_regex = "Faces have invalid indices"
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verts = torch.FloatTensor([[0.1, 0.2, 0.3]])
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faces = torch.LongTensor([[0, 1, 2]])
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with self.assertWarnsRegex(UserWarning, message_regex):
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save_ply(StringIO(), verts, faces)
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faces = torch.LongTensor([[-1, 0, 1]])
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with self.assertWarnsRegex(UserWarning, message_regex):
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save_ply(StringIO(), verts, faces)
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def _test_save_load(self, verts, faces):
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f = StringIO()
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save_ply(f, verts, faces)
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f.seek(0)
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# raise Exception(f.getvalue())
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expected_verts, expected_faces = verts, faces
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if not len(expected_verts): # Always compare with a (V, 3) tensor
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expected_verts = torch.zeros(size=(0, 3), dtype=torch.float32)
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if not len(expected_faces): # Always compare with an (F, 3) tensor
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expected_faces = torch.zeros(size=(0, 3), dtype=torch.int64)
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actual_verts, actual_faces = load_ply(f)
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self.assertClose(expected_verts, actual_verts)
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self.assertClose(expected_faces, actual_faces)
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def test_empty_save_load(self):
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# Vertices + empty faces
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verts = torch.tensor([[0.1, 0.2, 0.3]])
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faces = torch.LongTensor([])
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self._test_save_load(verts, faces)
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faces = torch.zeros(size=(0, 3), dtype=torch.int64)
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self._test_save_load(verts, faces)
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# Faces + empty vertices
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message_regex = "Faces have invalid indices"
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verts = torch.FloatTensor([])
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faces = torch.LongTensor([[0, 1, 2]])
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with self.assertWarnsRegex(UserWarning, message_regex):
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self._test_save_load(verts, faces)
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verts = torch.zeros(size=(0, 3), dtype=torch.float32)
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with self.assertWarnsRegex(UserWarning, message_regex):
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self._test_save_load(verts, faces)
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# Empty vertices + empty faces
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message_regex = "Empty 'verts' and 'faces' arguments provided"
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verts0 = torch.FloatTensor([])
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faces0 = torch.LongTensor([])
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with self.assertWarnsRegex(UserWarning, message_regex):
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self._test_save_load(verts0, faces0)
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faces3 = torch.zeros(size=(0, 3), dtype=torch.int64)
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with self.assertWarnsRegex(UserWarning, message_regex):
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self._test_save_load(verts0, faces3)
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verts3 = torch.zeros(size=(0, 3), dtype=torch.float32)
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with self.assertWarnsRegex(UserWarning, message_regex):
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self._test_save_load(verts3, faces0)
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with self.assertWarnsRegex(UserWarning, message_regex):
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self._test_save_load(verts3, faces3)
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def test_simple_save(self):
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verts = torch.tensor(
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[[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=torch.float32
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@ -378,6 +462,11 @@ class TestMeshPlyIO(TestCaseMixin, unittest.TestCase):
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lines2.insert(5, "property float z")
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lines2.insert(5, "property float y")
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lines2[-2] = "0 0 0"
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lines2[-1] = ""
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with self.assertRaisesRegex(ValueError, "Not enough data for face."):
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load_ply(StringIO("\n".join(lines2)))
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lines2[-1] = "2 0 0"
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with self.assertRaisesRegex(ValueError, "Faces must have at least 3 vertices."):
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load_ply(StringIO("\n".join(lines2)))
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