| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import numpy as np | |
| import os, cv2 | |
| import matplotlib.pyplot as plt | |
| import math | |
| def depths_to_points(view, depthmap): | |
| c2w = (view.world_view_transform.T).inverse() | |
| if hasattr(view, "image_width"): | |
| W, H = view.image_width, view.image_height | |
| else: | |
| W, H = view.width, view.height | |
| ndc2pix = torch.tensor([ | |
| [W / 2, 0, 0, (W) / 2], | |
| [0, H / 2, 0, (H) / 2], | |
| [0, 0, 0, 1]]).float().cuda().T | |
| projection_matrix = c2w.T @ view.full_proj_transform | |
| intrins = (projection_matrix @ ndc2pix)[:3,:3].T | |
| grid_x, grid_y = torch.meshgrid(torch.arange(W, device='cuda').float(), torch.arange(H, device='cuda').float(), indexing='xy') | |
| points = torch.stack([grid_x, grid_y, torch.ones_like(grid_x)], dim=-1).reshape(-1, 3) | |
| rays_d = points @ intrins.inverse().T @ c2w[:3,:3].T | |
| rays_o = c2w[:3,3] | |
| points = depthmap.reshape(-1, 1) * rays_d + rays_o | |
| return points | |
| def depth_to_normal(view, depth): | |
| """ | |
| view: view camera | |
| depth: depthmap | |
| """ | |
| points = depths_to_points(view, depth).reshape(*depth.shape[1:], 3) | |
| output = torch.zeros_like(points) | |
| dx = torch.cat([points[2:, 1:-1] - points[:-2, 1:-1]], dim=0) | |
| dy = torch.cat([points[1:-1, 2:] - points[1:-1, :-2]], dim=1) | |
| normal_map = torch.nn.functional.normalize(torch.cross(dx, dy, dim=-1), dim=-1) | |
| output[1:-1, 1:-1, :] = normal_map | |
| return output |