def __getitem__(self, i):
index = self.IDlist[i]
mesh_id = index // 100
view_id = index % 100
if mesh_id >= 20000:
mesh_id -= 20000
category = 'cat'
else:
category = 'horse'
filename = self.file_path.format(category, mesh_id, view_id)
mat = sio.loadmat(
filename,
variable_names=['points3d', 'correspondence', 'rotation'])
points3d = mat['points3d']
corres = mat['correspondence'].reshape(-1).astype(np.int64)
corres = torch.from_numpy(corres)
num_points = points3d.shape[0]
points3d = torch.Tensor(points3d)
R = mat['rotation'].reshape(3, 3)
ori_pos = torch.as_tensor(R.T.dot(points3d.numpy().T).T,
dtype=torch.float)
gt_feats = self.template_feats[corres, :]
gt_points = self.template_points[corres, :]
y = np.concatenate([gt_feats, gt_points], axis=-1)
y = torch.Tensor(y)
data = Data(pos=points3d, y=y, ori_pos=ori_pos)
data = self.transform(data) if self.transform is not None else data
return data
def voxel_sample(self, pos, corres):
fake_data = Data(
pos=torch.Tensor(pos),
y=corres,
)
data = self.voxel_sampler(fake_data)
return data.pos, data.y
def __getitem__(self, i):
index = self.IDlist[i]
mesh_id = index // self.num_views
view_id = index % self.num_views
filename = self.file_path.format(mesh_id, view_id)
mat = sio.loadmat(filename, variable_names=['points3d', 'correspondences',
'rotations'])
points3d = mat['points3d']
corres = mat['correspondences'].reshape(-1).astype(np.int64)
corres = torch.from_numpy(corres)
num_points = points3d.shape[0]
points3d = torch.Tensor(points3d)
R = mat['rotations'].reshape(3, 3)
ori_pos = torch.as_tensor(R.T.dot(points3d.numpy().T).T, dtype=torch.float)
if not self.cls:
gt_feats = self.template_feats[corres, :]
gt_points = self.template_points[corres, :]
y = np.concatenate([gt_feats, gt_points], axis=-1)
y = torch.Tensor(y)
else:
y = torch.as_tensor(corres, dtype=torch.long)
data = Data(pos=points3d, y=y, ori_pos=ori_pos)
data = self.transform(data) if self.transform is not None else data
return data
def __getitem__(self, i): mesh_id = self.IDlist[i] mesh = o3d.io.read_triangle_mesh(self.PLY.format(mesh_id)) points3d = torch.as_tensor(np.stack([x, y, z], -1), dtype=torch.float) corres = np.arange(points3d.shape[0]).astype(np.int32) mesh = o3d.io.read_triangle_mesh(self.PLY.format(mesh_id)) points3d = np.array(mesh.vertices) corres = np.arange(points3d.shape[0]) points3d = torch.as_tensor(points3d, dtype=torch.float) faces = torch.as_tensor(mesh.triangles, dtype=torch.long) y = torch.as_tensor(corres, dtype=torch.long) data = Data(pos=points3d, faces=faces, y=y) return data