import torch.nn as nn import torch import torch.nn.functional as F import numpy as np from pathlib import Path import cv2 import trimesh import nvdiffrast.torch as dr from model.archs.decoders.shape_texture_net import TetTexNet from model.archs.unet import UNetPP from util.renderer import Renderer from model.archs.mlp_head import SdfMlp, RgbMlp import xatlas class Dummy: pass class CRM(nn.Module): def __init__(self, specs): super(CRM, self).__init__() self.specs = specs # configs input_specs = specs["Input"] self.input = Dummy() self.input.scale = input_specs['scale'] self.input.resolution = input_specs['resolution'] self.tet_grid_size = input_specs['tet_grid_size'] self.camera_angle_num = input_specs['camera_angle_num'] self.arch = Dummy() self.arch.fea_concat = specs["ArchSpecs"]["fea_concat"] self.arch.mlp_bias = specs["ArchSpecs"]["mlp_bias"] self.dec = Dummy() self.dec.c_dim = specs["DecoderSpecs"]["c_dim"] self.dec.plane_resolution = specs["DecoderSpecs"]["plane_resolution"] self.geo_type = specs["Train"].get("geo_type", "flex") # "dmtet" or "flex" self.unet2 = UNetPP(in_channels=self.dec.c_dim) mlp_chnl_s = 3 if self.arch.fea_concat else 1 # 3 for queried triplane feature concatenation self.decoder = TetTexNet(plane_reso=self.dec.plane_resolution, fea_concat=self.arch.fea_concat) if self.geo_type == "flex": self.weightMlp = nn.Sequential( nn.Linear(mlp_chnl_s * 32 * 8, 512), nn.SiLU(), nn.Linear(512, 21)) self.sdfMlp = SdfMlp(mlp_chnl_s * 32, 512, bias=self.arch.mlp_bias) self.rgbMlp = RgbMlp(mlp_chnl_s * 32, 512, bias=self.arch.mlp_bias) self.renderer = Renderer(tet_grid_size=self.tet_grid_size, camera_angle_num=self.camera_angle_num, scale=self.input.scale, geo_type = self.geo_type) self.spob = True if specs['Pretrain']['mode'] is None else False # whether to add sphere self.radius = specs['Pretrain']['radius'] # used when spob self.denoising = True from diffusers import DDIMScheduler self.scheduler = DDIMScheduler.from_pretrained("stabilityai/stable-diffusion-2-1-base", subfolder="scheduler") def decode(self, data, triplane_feature2): if self.geo_type == "flex": tet_verts = self.renderer.flexicubes.verts.unsqueeze(0) tet_indices = self.renderer.flexicubes.indices dec_verts = self.decoder(triplane_feature2, tet_verts) out = self.sdfMlp(dec_verts) weight = None if self.geo_type == "flex": grid_feat = torch.index_select(input=dec_verts, index=self.renderer.flexicubes.indices.reshape(-1),dim=1) grid_feat = grid_feat.reshape(dec_verts.shape[0], self.renderer.flexicubes.indices.shape[0], self.renderer.flexicubes.indices.shape[1] * dec_verts.shape[-1]) weight = self.weightMlp(grid_feat) weight = weight * 0.1 pred_sdf, deformation = out[..., 0], out[..., 1:] if self.spob: pred_sdf = pred_sdf + self.radius - torch.sqrt((tet_verts**2).sum(-1)) _, verts, faces = self.renderer(data, pred_sdf, deformation, tet_verts, tet_indices, weight= weight) return verts[0].unsqueeze(0), faces[0].int() def export_mesh(self, data, out_dir, ind, device=None, tri_fea_2 = None): verts = data['verts'] faces = data['faces'] dec_verts = self.decoder(tri_fea_2, verts.unsqueeze(0)) colors = self.rgbMlp(dec_verts).squeeze().detach().cpu().numpy() # Expect predicted colors value range from [-1, 1] colors = (colors * 0.5 + 0.5).clip(0, 1) verts = verts.squeeze().cpu().numpy() faces = faces[..., [2, 1, 0]].squeeze().cpu().numpy() # export the final mesh with torch.no_grad(): mesh = trimesh.Trimesh(verts, faces, vertex_colors=colors, process=False) # important, process=True leads to seg fault... mesh.export(out_dir / f'{ind}.obj') def export_mesh_wt_uv(self, ctx, data, out_dir, ind, device, res, tri_fea_2=None): mesh_v = data['verts'].squeeze().cpu().numpy() mesh_pos_idx = data['faces'].squeeze().cpu().numpy() def interpolate(attr, rast, attr_idx, rast_db=None): return dr.interpolate(attr.contiguous(), rast, attr_idx, rast_db=rast_db, diff_attrs=None if rast_db is None else 'all') vmapping, indices, uvs = xatlas.parametrize(mesh_v, mesh_pos_idx) mesh_v = torch.tensor(mesh_v, dtype=torch.float32, device=device) mesh_pos_idx = torch.tensor(mesh_pos_idx, dtype=torch.int64, device=device) # Convert to tensors indices_int64 = indices.astype(np.uint64, casting='same_kind').view(np.int64) uvs = torch.tensor(uvs, dtype=torch.float32, device=mesh_v.device) mesh_tex_idx = torch.tensor(indices_int64, dtype=torch.int64, device=mesh_v.device) # mesh_v_tex. ture uv_clip = uvs[None, ...] * 2.0 - 1.0 # pad to four component coordinate uv_clip4 = torch.cat((uv_clip, torch.zeros_like(uv_clip[..., 0:1]), torch.ones_like(uv_clip[..., 0:1])), dim=-1) # rasterize rast, _ = dr.rasterize(ctx, uv_clip4, mesh_tex_idx.int(), res) # Interpolate world space position gb_pos, _ = interpolate(mesh_v[None, ...], rast, mesh_pos_idx.int()) mask = rast[..., 3:4] > 0 # return uvs, mesh_tex_idx, gb_pos, mask gb_pos_unsqz = gb_pos.view(-1, 3) mask_unsqz = mask.view(-1) tex_unsqz = torch.zeros_like(gb_pos_unsqz) + 1 gb_mask_pos = gb_pos_unsqz[mask_unsqz] gb_mask_pos = gb_mask_pos[None, ] with torch.no_grad(): dec_verts = self.decoder(tri_fea_2, gb_mask_pos) colors = self.rgbMlp(dec_verts).squeeze() # Expect predicted colors value range from [-1, 1] lo, hi = (-1, 1) colors = (colors - lo) * (255 / (hi - lo)) colors = colors.clip(0, 255) tex_unsqz[mask_unsqz] = colors tex = tex_unsqz.view(res + (3,)) verts = mesh_v.squeeze().cpu().numpy() faces = mesh_pos_idx[..., [2, 1, 0]].squeeze().cpu().numpy() # faces = mesh_pos_idx # faces = faces.detach().cpu().numpy() # faces = faces[..., [2, 1, 0]] indices = indices[..., [2, 1, 0]] # xatlas.export(f"{out_dir}/{ind}.obj", verts[vmapping], indices, uvs) matname = f'{out_dir}.mtl' # matname = f'{out_dir}/{ind}.mtl' fid = open(matname, 'w') fid.write('newmtl material_0\n') fid.write('Kd 1 1 1\n') fid.write('Ka 1 1 1\n') # fid.write('Ks 0 0 0\n') fid.write('Ks 0.4 0.4 0.4\n') fid.write('Ns 10\n') fid.write('illum 2\n') fid.write(f'map_Kd {out_dir.split("/")[-1]}.png\n') fid.close() fid = open(f'{out_dir}.obj', 'w') # fid = open(f'{out_dir}/{ind}.obj', 'w') fid.write('mtllib %s.mtl\n' % out_dir.split("/")[-1]) for pidx, p in enumerate(verts): pp = p fid.write('v %f %f %f\n' % (pp[0], pp[2], - pp[1])) for pidx, p in enumerate(uvs): pp = p fid.write('vt %f %f\n' % (pp[0], 1 - pp[1])) fid.write('usemtl material_0\n') for i, f in enumerate(faces): f1 = f + 1 f2 = indices[i] + 1 fid.write('f %d/%d %d/%d %d/%d\n' % (f1[0], f2[0], f1[1], f2[1], f1[2], f2[2])) fid.close() img = np.asarray(tex.data.cpu().numpy(), dtype=np.float32) mask = np.sum(img.astype(float), axis=-1, keepdims=True) mask = (mask <= 3.0).astype(float) kernel = np.ones((3, 3), 'uint8') dilate_img = cv2.dilate(img, kernel, iterations=1) img = img * (1 - mask) + dilate_img * mask img = img.clip(0, 255).astype(np.uint8) cv2.imwrite(f'{out_dir}.png', img[..., [2, 1, 0]]) # cv2.imwrite(f'{out_dir}/{ind}.png', img[..., [2, 1, 0]])