from tqdm import tqdm from PIL import Image 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, make_star_cameras_orthographic_py3d from mesh_reconstruction.render import NormalsRenderer, Pytorch3DNormalsRenderer from scripts.project_mesh import multiview_color_projection, get_cameras_list from scripts.utils import to_py3d_mesh, from_py3d_mesh, init_target def run_mesh_refine(vertices, faces, pils: List[Image.Image], steps=100, start_edge_len=0.02, end_edge_len=0.005, decay=0.99, update_normal_interval=10, update_warmup=10, return_mesh=True, process_inputs=True, process_outputs=True): vertices, faces = vertices.to("cuda"), faces.to("cuda") if process_inputs: vertices = vertices * 2 / 1.35 vertices[..., [0, 2]] = - vertices[..., [0, 2]] poission_steps = [] assert len(pils) == 4 mv,proj = make_star_cameras_orthographic(4, 1) renderer = NormalsRenderer(mv,proj,list(pils[0].size)) # cameras = make_star_cameras_orthographic_py3d([0, 270, 180, 90], device="cuda", focal=1., dist=4.0) # renderer = Pytorch3DNormalsRenderer(cameras, list(pils[0].size), device="cuda") 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, ramp=5, edge_len_lims=(end_edge_len, start_edge_len), local_edgelen=False, laplacian_weight=0.02) vertices = opt.vertices alpha_init = None 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) if alpha_init is None: alpha_init = images.detach() if i < update_warmup or i % update_normal_interval == 0: with torch.no_grad(): py3d_mesh = to_py3d_mesh(vertices, faces, normals) cameras = get_cameras_list(azim_list = [0, 90, 180, 270], device=vertices.device, focal=1.) _, _, target_normal = from_py3d_mesh(multiview_color_projection(py3d_mesh, pils, cameras_list=cameras, weights=[2.0, 0.8, 1.0, 0.8], confidence_threshold=0.1, complete_unseen=False, below_confidence_strategy='original', reweight_with_cosangle='linear')) target_normal = target_normal * 2 - 1 target_normal = torch.nn.functional.normalize(target_normal, dim=-1) debug_images = renderer.render(vertices,target_normal,faces) d_mask = images[..., -1] > 0.5 loss_debug_l2 = (images[..., :3][d_mask] - debug_images[..., :3][d_mask]).pow(2).mean() loss_alpha_target_mask_l2 = (images[..., -1][mask] - target_images[..., -1][mask]).pow(2).mean() loss = loss_debug_l2 + loss_alpha_target_mask_l2 # 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=(i in poission_steps)) vertices, faces = vertices.detach().cpu(), faces.detach().cpu() if process_outputs: vertices = vertices / 2 * 1.35 vertices[..., [0, 2]] = - vertices[..., [0, 2]] if return_mesh: return to_py3d_mesh(vertices, faces) else: return vertices, faces