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]])