import numpy as np
import torch
import time
import tempfile
import os
from PIL import Image
import trimesh

from util.utils import get_tri
from mesh import Mesh
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(img_tensor):
        images = []
        images.append(img_tensor[:, :, 256*5:256*(1+5)])
        for i in range(5):
            images.append(img_tensor[:, :, 256*i:256*(i+1)])
        return torch.stack(images, dim=0)  # [6, C, H, W]

    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

    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()

    # Export to .obj with UV and texture image
    mesh_path_obj = tempfile.NamedTemporaryFile(suffix="", delete=False).name
    model.export_mesh_wt_uv(
        None,  # GL context skipped (no dr.RasterizeGLContext)
        data_config,
        mesh_path_obj,
        "",
        device,
        res=(1024, 1024),
        tri_fea_2=triplane_feature2
    )

    # Convert to GLB with embedded texture
    obj_path = mesh_path_obj + ".obj"
    texture_path = mesh_path_obj + ".png"

    if not os.path.exists(obj_path) or not os.path.exists(texture_path):
        raise RuntimeError("OBJ or texture file missing — cannot convert to GLB.")

    mesh_scene = trimesh.load(obj_path, force='scene')
    texture_image = np.array(Image.open(texture_path))

    for name, geom in mesh_scene.geometry.items():
        geom.visual = trimesh.visual.texture.TextureVisuals(image=texture_image)

    glb_path = mesh_path_obj + "_final.glb"
    mesh_scene.export(glb_path)

    return glb_path