Spaces:
Running
on
Zero
Running
on
Zero
import numpy as np | |
import torch | |
import time | |
import nvdiffrast.torch as dr | |
from util.utils import get_tri | |
import tempfile | |
from mesh import Mesh | |
import zipfile | |
from util.renderer import Renderer | |
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): | |
# color : [C, H, W*6] | |
color_list = [] | |
color_list.append(color[:,:,256*5:256*(1+5)]) | |
for i in range(0,5): | |
color_list.append(color[:,:,256*i:256*(1+i)]) | |
return torch.stack(color_list, dim=0)# [6, C, H, W] | |
triplane_color = get_imgs(color).permute(0,2,3,1).unsqueeze(0).to(device)# [1, 6, H, W, C] | |
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) | |
# 3D visualize | |
model.eval() | |
if model.denoising == True: | |
tnew = 20 | |
tnew = torch.randint(tnew, tnew+1, [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) | |
start_time = time.time() | |
with torch.no_grad(): | |
triplane_feature2 = model.unet2(triplane,tnew) | |
end_time = time.time() | |
elapsed_time = end_time - start_time | |
print(f"unet takes {elapsed_time}s") | |
else: | |
triplane_feature2 = model.unet2(triplane) | |
with torch.no_grad(): | |
data_config = { | |
'resolution': [1024, 1024], | |
"triview_color": triplane_color.to(device), | |
} | |
verts, faces = model.decode(data_config, triplane_feature2) | |
data_config['verts'] = verts[0] | |
data_config['faces'] = faces | |
from kiui.mesh_utils import clean_mesh | |
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).cuda().contiguous() | |
data_config['faces'] = torch.from_numpy(faces).cuda().contiguous() | |
start_time = time.time() | |
with torch.no_grad(): | |
mesh_path_glb = tempfile.NamedTemporaryFile(suffix=f"", delete=False).name | |
model.export_mesh(data_config, mesh_path_glb, tri_fea_2 = triplane_feature2) | |
# glctx = dr.RasterizeGLContext()#dr.RasterizeCudaContext() | |
# mesh_path_obj = tempfile.NamedTemporaryFile(suffix=f"", delete=False).name | |
# model.export_mesh_wt_uv(glctx, data_config, mesh_path_obj, "", device, res=(1024,1024), tri_fea_2=triplane_feature2) | |
# mesh = Mesh.load(mesh_path_obj+".obj", bound=0.9, front_dir="+z") | |
# mesh_path_glb = tempfile.NamedTemporaryFile(suffix=f"", delete=False).name | |
# mesh.write(mesh_path_glb+".glb") | |
# # mesh_obj2 = trimesh.load(mesh_path_glb+".glb", file_type='glb') | |
# # mesh_path_obj2 = tempfile.NamedTemporaryFile(suffix=f"", delete=False).name | |
# # mesh_obj2.export(mesh_path_obj2+".obj") | |
# with zipfile.ZipFile(mesh_path_obj+'.zip', 'w') as myzip: | |
# myzip.write(mesh_path_obj+'.obj', mesh_path_obj.split("/")[-1]+'.obj') | |
# myzip.write(mesh_path_obj+'.png', mesh_path_obj.split("/")[-1]+'.png') | |
# myzip.write(mesh_path_obj+'.mtl', mesh_path_obj.split("/")[-1]+'.mtl') | |
end_time = time.time() | |
elapsed_time = end_time - start_time | |
print(f"uv takes {elapsed_time}s") | |
return mesh_path_glb+".obj" | |