Spaces:
Running on Zero
Running on Zero
| import numpy as np | |
| import open3d_pycg as o3d | |
| import os | |
| import torch | |
| from sklearn.decomposition import PCA | |
| def merge_meshes(mesh_list): | |
| merged = o3d.geometry.TriangleMesh() | |
| v_offset = 0 | |
| for mesh in mesh_list: | |
| vertices = np.asarray(mesh.vertices) | |
| triangles = np.asarray(mesh.triangles) + v_offset | |
| merged.vertices.extend(o3d.utility.Vector3dVector(vertices)) | |
| merged.triangles.extend(o3d.utility.Vector3iVector(triangles)) | |
| v_offset += len(vertices) | |
| return merged | |
| def voxelgrid_to_open3d(voxels: np.ndarray, threshold=0.5): | |
| if len(voxels.shape) > 3: | |
| C, D, H, W = voxels.shape | |
| flat_feats = voxels.reshape(C, -1).transpose(1,0) | |
| pca = PCA(n_components=3) | |
| reduced = pca.fit_transform(flat_feats) | |
| # Compute feature norm and PCA color std | |
| # Normalize for RGB | |
| reduced -= reduced.min(0) | |
| reduced /= reduced.max(0) + 1e-6 | |
| # Compute norms and color std | |
| norms = np.linalg.norm(flat_feats, axis=1) | |
| color_std = np.std(reduced, axis=1) | |
| # Filter: active voxels with non-trivial color | |
| mask = (norms > threshold) & (color_std > 1e-3) | |
| # zz, yy, xx = np.meshgrid(np.arange(D), np.arange(H), np.arange(W), indexing='ij') | |
| xx, yy, zz = np.meshgrid(np.arange(D), np.arange(H), np.arange(W), indexing='ij') | |
| coords = np.stack([xx, yy, zz], axis=-1).reshape(-1, 3) | |
| valid_coords = coords[mask] | |
| valid_colors = reduced[mask] | |
| pcd = o3d.geometry.PointCloud() | |
| pcd.points = o3d.utility.Vector3dVector(valid_coords.astype(np.float32)) | |
| pcd.colors = o3d.utility.Vector3dVector(valid_colors.astype(np.float32)) | |
| else: | |
| coords = np.argwhere(voxels > threshold) | |
| pcd = o3d.geometry.PointCloud() | |
| pcd.points = o3d.utility.Vector3dVector(coords) | |
| return pcd | |
| def save_voxelgrid_as_ply(voxels: np.ndarray, filename: str, threshold=0.5): | |
| pcd = voxelgrid_to_open3d(voxels, threshold) | |
| o3d.io.write_point_cloud(filename, pcd) | |
| def voxelize_sq_francis(file_name): | |
| superquadric_mesh = o3d.io.read_triangle_mesh(file_name) | |
| vertices = np.clip(np.asarray(superquadric_mesh.vertices), -0.5 + 1e-6, 0.5 - 1e-6) | |
| superquadric_mesh.vertices = o3d.utility.Vector3dVector(vertices) | |
| theta = np.pi / 2 | |
| #superquadric_mesh.rotate(R_x, center=(0, 0, 0)) | |
| voxel_grid = o3d.geometry.VoxelGrid.create_from_triangle_mesh_within_bounds( | |
| superquadric_mesh, voxel_size=1/64, min_bound=(-0.5, -0.5, -0.5), max_bound=(0.5, 0.5, 0.5)) | |
| vertices = np.array([voxel.grid_index for voxel in voxel_grid.get_voxels()]) | |
| unique_points = np.unique(vertices, axis=0) | |
| pcd = o3d.geometry.PointCloud() | |
| pcd.points = o3d.utility.Vector3dVector(unique_points) | |
| o3d.io.write_point_cloud("merged_mesh_voxelized.ply", pcd) | |
| zeros = np.zeros((unique_points.shape[0], 1)) | |
| unique_points_4d = np.hstack((zeros, unique_points)) # shape [N, 4] | |
| unique_points_4d_torch = torch.from_numpy(unique_points_4d).to(dtype=torch.int32, device='cpu') | |
| my_coords_orig = unique_points_4d_torch | |
| coords_dense = torch.ones(1, 1, 64, 64, 64).to(device='cpu', dtype=torch.float32) * 0.0 | |
| for i in range(my_coords_orig.shape[0]): | |
| x, y, z = my_coords_orig[i, 1], my_coords_orig[i, 2], my_coords_orig[i, 3] | |
| coords_dense[0, 0, x, y, z] = 1.0 | |
| return coords_dense |