inference.py

import numpy as np
import torch
import time
import nvdiffrast.torch as dr
from util.utils import get_tri
import tempfile
from util.renderer import Renderer
import os

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_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)

    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 = 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)
        with torch.no_grad():
            triplane_feature2 = model.unet2(triplane, tnew)
    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).to(device).contiguous()
    data_config['faces'] = torch.from_numpy(faces).to(device).contiguous()

    with torch.no_grad():
        mesh_path_base = tempfile.NamedTemporaryFile(suffix="", delete=False).name

        # Export mesh with UV, texture, and MTL
        ctx = dr.RasterizeCudaContext(device=device)
        model.export_mesh_wt_uv(ctx, data_config, mesh_path_base, ind=0, device=device, res=(1024, 1024), tri_fea_2=triplane_feature2)

    return mesh_path_base + ".obj"