| 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 |
| from mesh_reconstruction.render import NormalsRenderer |
| 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): |
| 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)) |
|
|
| target_images = init_target(pils, new_bkgd=(0., 0., 0.)) |
| |
| target_images = target_images[[0, 3, 2, 1]] |
|
|
| |
| 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 |
| |
| |
| 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(), faces.detach() |
| |
| 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 |
|
|
