inference.py

import numpy as np
import torch
import time
from util.utils import get_tri
import tempfile
from util.renderer import Renderer
import os
from PIL import Image
import trimesh
from scipy.spatial import cKDTree


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
    orig_verts = data_config['verts'].squeeze().cpu().numpy()
    # Extract per-vertex color for the original mesh
    orig_verts_tensor = data_config['verts'].unsqueeze(0)  # shape [1, N, 3]
    with torch.no_grad():
        dec_verts = model.decoder(triplane_feature2, orig_verts_tensor)
        orig_colors = model.rgbMlp(dec_verts).squeeze().detach().cpu().numpy()
        print('orig_colors min/max BEFORE scaling:', orig_colors.min(), orig_colors.max())
        # Comment out the scaling below if orig_colors is already in [0, 1]
        # orig_colors = (orig_colors * 0.5 + 0.5).clip(0, 1)  # scale to [0, 1]
        print('orig_colors min/max AFTER scaling:', orig_colors.min(), orig_colors.max())
        orig_colors = np.clip(orig_colors, 0, 1)
        orig_colors = np.power(orig_colors, 1/2.2)
    verts, faces = clean_mesh(
        orig_verts.astype(np.float32),
        data_config['faces'].squeeze().cpu().numpy().astype(np.int32),
        repair=True, remesh=False, 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()

    # # Build KDTree from original verts
    # tree = cKDTree(orig_verts)

    # # For each new vertex, find the nearest old vertex and copy its color
    # k = 3
    # dists, idxs = tree.query(verts, k=k)
    # # Use only the nearest neighbor for color assignment
    # new_colors = orig_colors[idxs[:, 0]]

    # Create the new mesh with colors
    mesh = trimesh.Trimesh(vertices=verts, faces=faces, vertex_colors=orig_colors)

    # === Export OBJ/MTL/PNG ===
    obj_path = tempfile.NamedTemporaryFile(suffix=".obj", delete=False).name
    base_path = obj_path[:-4]  # remove .obj

    texture_path = base_path + ".png"
    mtl_path = base_path + ".mtl"

    model.export_mesh(data_config, base_path, tri_fea_2=triplane_feature2)  # writes .obj

    return obj_path