from tqdm import tqdm from PIL import Image import numpy as np import torch from typing import List from mesh_reconstruction.remesh import calc_vertex_normals from mesh_reconstruction.opt import MeshOptimizer from mesh_reconstruction.func import make_star_cameras_orthographic from mesh_reconstruction.render import NormalsRenderer, Pytorch3DNormalsRenderer from scripts.utils import to_py3d_mesh, init_target def reconstruct_stage1(pils: List[Image.Image], steps=100, vertices=None, faces=None, start_edge_len=0.15, end_edge_len=0.005, decay=0.995, return_mesh=True, loss_expansion_weight=0.1, gain=0.1): vertices, faces = vertices.to("cuda"), faces.to("cuda") assert len(pils) == 4 mv,proj = make_star_cameras_orthographic(4, 1) renderer = Pytorch3DNormalsRenderer(mv,proj,list(pils[0].size)) target_images = init_target(pils, new_bkgd=(0., 0., 0.)) # 4s # 1. no rotate target_images = target_images[[0, 3, 2, 1]] # 2. init from coarse mesh opt = MeshOptimizer(vertices,faces, local_edgelen=False, gain=gain, edge_len_lims=(end_edge_len, start_edge_len)) vertices = opt.vertices mask = target_images[..., -1] < 0.5 for i in tqdm(range(steps)): opt.zero_grad() opt._lr *= decay normals = calc_vertex_normals(vertices,faces) images = renderer.render(vertices,normals,faces) loss_expand = 0.5 * ((vertices+normals).detach() - vertices).pow(2).mean() t_mask = images[..., -1] > 0.5 loss_target_l2 = (images[t_mask] - target_images[t_mask]).abs().pow(2).mean() loss_alpha_target_mask_l2 = (images[..., -1][mask] - target_images[..., -1][mask]).pow(2).mean() loss = loss_target_l2 + loss_alpha_target_mask_l2 + loss_expand * loss_expansion_weight # out of box loss_oob = (vertices.abs() > 0.99).float().mean() * 10 loss = loss + loss_oob loss.backward() opt.step() vertices,faces = opt.remesh(poisson=False) vertices, faces = vertices.detach(), faces.detach() if return_mesh: return to_py3d_mesh(vertices, faces) else: return vertices, faces