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| # modified from https://github.com/Profactor/continuous-remeshing | |
| import torch | |
| import numpy as np | |
| import trimesh | |
| from typing import Tuple | |
| def to_numpy(*args): | |
| def convert(a): | |
| if isinstance(a,torch.Tensor): | |
| return a.detach().cpu().numpy() | |
| assert a is None or isinstance(a,np.ndarray) | |
| return a | |
| return convert(args[0]) if len(args)==1 else tuple(convert(a) for a in args) | |
| def laplacian( | |
| num_verts:int, | |
| edges: torch.Tensor #E,2 | |
| ) -> torch.Tensor: #sparse V,V | |
| """create sparse Laplacian matrix""" | |
| V = num_verts | |
| E = edges.shape[0] | |
| #adjacency matrix, | |
| idx = torch.cat([edges, edges.fliplr()], dim=0).type(torch.long).T # (2, 2*E) | |
| ones = torch.ones(2*E, dtype=torch.float32, device=edges.device) | |
| A = torch.sparse.FloatTensor(idx, ones, (V, V)) | |
| #degree matrix | |
| deg = torch.sparse.sum(A, dim=1).to_dense() | |
| idx = torch.arange(V, device=edges.device) | |
| idx = torch.stack([idx, idx], dim=0) | |
| D = torch.sparse.FloatTensor(idx, deg, (V, V)) | |
| return D - A | |
| def _translation(x, y, z, device): | |
| return torch.tensor([[1., 0, 0, x], | |
| [0, 1, 0, y], | |
| [0, 0, 1, z], | |
| [0, 0, 0, 1]],device=device) #4,4 | |
| def _projection(r, device, l=None, t=None, b=None, n=1.0, f=50.0, flip_y=True): | |
| """ | |
| see https://blog.csdn.net/wodownload2/article/details/85069240/ | |
| """ | |
| if l is None: | |
| l = -r | |
| if t is None: | |
| t = r | |
| if b is None: | |
| b = -t | |
| p = torch.zeros([4,4],device=device) | |
| p[0,0] = 2*n/(r-l) | |
| p[0,2] = (r+l)/(r-l) | |
| p[1,1] = 2*n/(t-b) * (-1 if flip_y else 1) | |
| p[1,2] = (t+b)/(t-b) | |
| p[2,2] = -(f+n)/(f-n) | |
| p[2,3] = -(2*f*n)/(f-n) | |
| p[3,2] = -1 | |
| return p #4,4 | |
| def _orthographic(r, device, l=None, t=None, b=None, n=1.0, f=50.0, flip_y=True): | |
| if l is None: | |
| l = -r | |
| if t is None: | |
| t = r | |
| if b is None: | |
| b = -t | |
| o = torch.zeros([4,4],device=device) | |
| o[0,0] = 2/(r-l) | |
| o[0,3] = -(r+l)/(r-l) | |
| o[1,1] = 2/(t-b) * (-1 if flip_y else 1) | |
| o[1,3] = -(t+b)/(t-b) | |
| o[2,2] = -2/(f-n) | |
| o[2,3] = -(f+n)/(f-n) | |
| o[3,3] = 1 | |
| return o #4,4 | |
| def make_star_cameras(az_count,pol_count,distance:float=10.,r=None,image_size=[512,512],device='cuda'): | |
| if r is None: | |
| r = 1/distance | |
| A = az_count | |
| P = pol_count | |
| C = A * P | |
| phi = torch.arange(0,A) * (2*torch.pi/A) | |
| phi_rot = torch.eye(3,device=device)[None,None].expand(A,1,3,3).clone() | |
| phi_rot[:,0,2,2] = phi.cos() | |
| phi_rot[:,0,2,0] = -phi.sin() | |
| phi_rot[:,0,0,2] = phi.sin() | |
| phi_rot[:,0,0,0] = phi.cos() | |
| theta = torch.arange(1,P+1) * (torch.pi/(P+1)) - torch.pi/2 | |
| theta_rot = torch.eye(3,device=device)[None,None].expand(1,P,3,3).clone() | |
| theta_rot[0,:,1,1] = theta.cos() | |
| theta_rot[0,:,1,2] = -theta.sin() | |
| theta_rot[0,:,2,1] = theta.sin() | |
| theta_rot[0,:,2,2] = theta.cos() | |
| mv = torch.empty((C,4,4), device=device) | |
| mv[:] = torch.eye(4, device=device) | |
| mv[:,:3,:3] = (theta_rot @ phi_rot).reshape(C,3,3) | |
| mv = _translation(0, 0, -distance, device) @ mv | |
| return mv, _projection(r,device) | |
| def make_star_cameras_orthographic(az_count,pol_count,distance:float=10.,r=None,image_size=[512,512],device='cuda'): | |
| mv, _ = make_star_cameras(az_count,pol_count,distance,r,image_size,device) | |
| if r is None: | |
| r = 1 | |
| return mv, _orthographic(r,device) | |
| def make_sphere(level:int=2,radius=1.,device='cuda') -> Tuple[torch.Tensor,torch.Tensor]: | |
| sphere = trimesh.creation.icosphere(subdivisions=level, radius=1.0, color=None) | |
| vertices = torch.tensor(sphere.vertices, device=device, dtype=torch.float32) * radius | |
| faces = torch.tensor(sphere.faces, device=device, dtype=torch.long) | |
| return vertices,faces | |
| from pytorch3d.renderer import ( | |
| FoVOrthographicCameras, | |
| look_at_view_transform, | |
| ) | |
| def get_camera(R, T, focal_length=1 / (2**0.5)): | |
| focal_length = 1 / focal_length | |
| camera = FoVOrthographicCameras(device=R.device, R=R, T=T, min_x=-focal_length, max_x=focal_length, min_y=-focal_length, max_y=focal_length) | |
| return camera | |
| def make_star_cameras_orthographic_py3d(azim_list, device, focal=2/1.35, dist=1.1): | |
| R, T = look_at_view_transform(dist, 0, azim_list) | |
| focal_length = 1 / focal | |
| return FoVOrthographicCameras(device=R.device, R=R, T=T, min_x=-focal_length, max_x=focal_length, min_y=-focal_length, max_y=focal_length).to(device) | |