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import numpy as np
import torch
import time
import tempfile
import zipfile
import nvdiffrast.torch as dr
import xatlas
import cv2
from util.utils import get_tri
from mesh import Mesh
from util.renderer import Renderer
from kiui.mesh_utils import clean_mesh
def generate3d(model, rgb, ccm, device):
model.renderer = Renderer(tet_grid_size=model.tet_grid_size, camera_angle_num=model.camera_angle_num,
scale=model.input.scale, geo_type=model.geo_type)
color_tri = torch.from_numpy(rgb) / 255
xyz_tri = torch.from_numpy(ccm[:, :, (2, 1, 0)]) / 255
color = color_tri.permute(2, 0, 1)
xyz = xyz_tri.permute(2, 0, 1)
def get_imgs(color):
return torch.stack([color[:, :, 256 * i:256 * (i + 1)] for i in [5, 0, 1, 2, 3, 4]], dim=0)
triplane_color = get_imgs(color).permute(0, 2, 3, 1).unsqueeze(0).to(device)
color = get_imgs(color)
xyz = get_imgs(xyz)
color = get_tri(color, dim=0, blender=True, scale=1).unsqueeze(0)
xyz = get_tri(xyz, dim=0, blender=True, scale=1, fix=True).unsqueeze(0)
triplane = torch.cat([color, xyz], dim=1).to(device)
model.eval()
if model.denoising:
tnew = torch.randint(20, 21, [triplane.shape[0]], dtype=torch.long, device=triplane.device)
noise_new = torch.randn_like(triplane) * 0.5 + 0.5
triplane = model.scheduler.add_noise(triplane, noise_new, tnew)
with torch.no_grad():
triplane_feature2 = model.unet2(triplane, tnew)
else:
with torch.no_grad():
triplane_feature2 = model.unet2(triplane)
data_config = {
'resolution': [1024, 1024],
"triview_color": triplane_color.to(device),
}
with torch.no_grad():
verts, faces = model.decode(data_config, triplane_feature2)
data_config['verts'] = verts[0]
data_config['faces'] = faces
verts, faces = clean_mesh(
data_config['verts'].squeeze().cpu().numpy().astype(np.float32),
data_config['faces'].squeeze().cpu().numpy().astype(np.int32),
repair=False, remesh=True, remesh_size=0.005, remesh_iters=1
)
data_config['verts'] = torch.from_numpy(verts).contiguous()
data_config['faces'] = torch.from_numpy(faces).contiguous()
# --- CCM-based UV assignment ---
mesh_v = data_config['verts'].cpu().numpy() # [N, 3]
mesh_f = data_config['faces'].cpu().numpy() # [M, 3]
# Prepare CCM and color map
ccm_img = ccm.astype(np.uint8) if ccm.max() > 1 else (ccm * 255).astype(np.uint8)
if ccm_img.shape[-1] != 3:
ccm_img = np.transpose(ccm_img, (1, 2, 0))
H, W, _ = ccm_img.shape
color_map = rgb.astype(np.uint8) if rgb.max() > 1 else (rgb * 255).astype(np.uint8)
if color_map.shape[-1] != 3:
color_map = np.transpose(color_map, (1, 2, 0))
albedo = cv2.cvtColor(color_map, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
# Project mesh vertices to CCM image space and get UVs
vt = []
for v in mesh_v:
# Assume mesh is in [-1,1] in x/y, project to CCM image
x, y, z = v
u_img = int((x + 1) / 2 * (W - 1))
v_img = int((y + 1) / 2 * (H - 1))
u_img = np.clip(u_img, 0, W-1)
v_img = np.clip(v_img, 0, H-1)
r, g, b = ccm_img[v_img, u_img]
u = r / 255.0
v_ = g / 255.0
vt.append([u, v_])
vt = np.array(vt, dtype=np.float32)
ft = mesh_f.copy()
# Create Mesh and export .glb
mesh = Mesh(
v=torch.from_numpy(mesh_v).float(),
f=torch.from_numpy(mesh_f).int(),
vt=torch.from_numpy(vt).float(),
ft=torch.from_numpy(ft).int(),
albedo=torch.from_numpy(albedo).float()
)
temp_path = tempfile.NamedTemporaryFile(suffix=".glb", delete=False).name
mesh.write(temp_path)
return temp_path
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