svrm/ldm/models/svrm.py

# Open Source Model Licensed under the Apache License Version 2.0 
# and Other Licenses of the Third-Party Components therein:
# The below Model in this distribution may have been modified by THL A29 Limited 
# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.

# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved. 
# The below software and/or models in this distribution may have been 
# modified by THL A29 Limited ("Tencent Modifications"). 
# All Tencent Modifications are Copyright (C) THL A29 Limited.

# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT 
# except for the third-party components listed below. 
# Hunyuan 3D does not impose any additional limitations beyond what is outlined 
# in the repsective licenses of these third-party components. 
# Users must comply with all terms and conditions of original licenses of these third-party 
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# all relevant laws and regulations. 

# For avoidance of doubts, Hunyuan 3D means the large language models and 
# their software and algorithms, including trained model weights, parameters (including 
# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, 
# fine-tuning enabling code and other elements of the foregoing made publicly available 
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import os
import time
import math
import cv2
import numpy as np
import itertools
import shutil
from tqdm import tqdm
import torch
import torch.nn.functional as F
from einops import rearrange
try:
    import trimesh
    import mcubes
    import xatlas
    import open3d as o3d
except:
    raise "failed to import 3d libraries "

from ..modules.rendering_neus.mesh import Mesh
from ..modules.rendering_neus.rasterize import NVDiffRasterizerContext

from ..utils.ops import scale_tensor
from ..util import count_params, instantiate_from_config
from ..vis_util import render_func


def unwrap_uv(v_pos, t_pos_idx):
    print("Using xatlas to perform UV unwrapping, may take a while ...")
    atlas = xatlas.Atlas()
    atlas.add_mesh(v_pos, t_pos_idx)
    atlas.generate(xatlas.ChartOptions(), xatlas.PackOptions())
    _, indices, uvs = atlas.get_mesh(0)
    indices = indices.astype(np.int64, casting="same_kind")
    return uvs, indices

def uv_padding(image, hole_mask, uv_padding_size = 2):
    return cv2.inpaint(
        (image.detach().cpu().numpy() * 255).astype(np.uint8),
        (hole_mask.detach().cpu().numpy() * 255).astype(np.uint8),
        uv_padding_size,
        cv2.INPAINT_TELEA
    )

def refine_mesh(vtx_refine, faces_refine):
    mesh = o3d.geometry.TriangleMesh(
        vertices=o3d.utility.Vector3dVector(vtx_refine), 
        triangles=o3d.utility.Vector3iVector(faces_refine)
    )

    mesh = mesh.remove_unreferenced_vertices()
    mesh = mesh.remove_duplicated_triangles()
    mesh = mesh.remove_duplicated_vertices()

    voxel_size = max(mesh.get_max_bound() - mesh.get_min_bound())

    mesh = mesh.simplify_vertex_clustering(
        voxel_size=0.007, # 0.005
        contraction=o3d.geometry.SimplificationContraction.Average)

    mesh = mesh.filter_smooth_simple(number_of_iterations=2)

    vtx_refine = np.asarray(mesh.vertices).astype(np.float32)
    faces_refine = np.asarray(mesh.triangles)
    return vtx_refine, faces_refine, mesh


class SVRMModel(torch.nn.Module):
    def __init__(
        self,
        img_encoder_config,
        img_to_triplane_config,
        render_config,
        device = "cuda:0",
        **kwargs
    ):
        super(SVRMModel, self).__init__()
        self.img_encoder = instantiate_from_config(img_encoder_config).half()
        self.img_to_triplane_decoder = instantiate_from_config(img_to_triplane_config).half()
        self.render = instantiate_from_config(render_config).half()
        self.device = device
        count_params(self, verbose=True)
        

    @torch.no_grad()
    def export_mesh_with_uv(
        self, 
        data, 
        mesh_size: int = 384, 
        ctx = None, 
        context_type = 'cuda', 
        texture_res = 1024,
        target_face_count = 10000,
        do_texture_mapping = True,
        out_dir = 'outputs/test'
    ):
        """
        do_texture_mapping: True for ray texture, False for vertices texture
        """
        
        obj_vertext_path = os.path.join(out_dir, 'mesh_vertex_colors.obj')
        
        if do_texture_mapping:
            obj_path = os.path.join(out_dir, 'mesh.obj')
            obj_texture_path = os.path.join(out_dir, 'texture.png')
            obj_mtl_path = os.path.join(out_dir, 'texture.mtl')
            glb_path = os.path.join(out_dir, 'mesh.glb')

        st = time.time()
        
        here = {'device': self.device, 'dtype': torch.float16}
        input_view_image = data["input_view"].to(**here)    # [b, m, c, h, w]
        input_view_cam = data["input_view_cam"].to(**here)  # [b, m, 20]

        batch_size, input_view_num, *_ = input_view_image.shape
        assert batch_size == 1, "batch size should be 1"

        input_view_image = rearrange(input_view_image, 'b m c h w -> (b m) c h w')
        input_view_cam = rearrange(input_view_cam, 'b m d -> (b m) d')
        input_view_feat = self.img_encoder(input_view_image, input_view_cam)
        input_view_feat = rearrange(input_view_feat, '(b m) l d -> b (l m) d', m=input_view_num)

        # -- decoder
        torch.cuda.empty_cache()
        triplane_gen = self.img_to_triplane_decoder(input_view_feat)  # [b, 3, tri_dim, h, w]
        del input_view_feat
        torch.cuda.empty_cache()

        # --- triplane nerf render

        cur_triplane = triplane_gen[0:1]
        
        aabb = torch.tensor([[-0.6, -0.6, -0.6], [0.6, 0.6, 0.6]]).unsqueeze(0).to(**here)
        grid_out = self.render.forward_grid(planes=cur_triplane, grid_size=mesh_size, aabb=aabb)

        print(f"=====> Triplane forward time: {time.time() - st}")
        st = time.time()
        
        vtx, faces = mcubes.marching_cubes(0. - grid_out['sdf'].squeeze(0).squeeze(-1).cpu().float().numpy(), 0)
        
        bbox = aabb[0].cpu().numpy()
        vtx = vtx / (mesh_size - 1)  
        vtx = vtx * (bbox[1] - bbox[0]) + bbox[0]

        # refine mesh
        vtx_refine, faces_refine, mesh = refine_mesh(vtx, faces)

        # reduce faces
        if faces_refine.shape[0] > target_face_count:
            print(f"reduce face: {faces_refine.shape[0]} -> {target_face_count}")
            mesh = o3d.geometry.TriangleMesh(
                vertices = o3d.utility.Vector3dVector(vtx_refine),
                triangles = o3d.utility.Vector3iVector(faces_refine)
            )
            
            # Function to simplify mesh using Quadric Error Metric Decimation by Garland and Heckbert
            mesh = mesh.simplify_quadric_decimation(target_face_count, boundary_weight=1.0)

            mesh = Mesh(
                v_pos = torch.from_numpy(np.asarray(mesh.vertices)).to(self.device),
                t_pos_idx = torch.from_numpy(np.asarray(mesh.triangles)).to(self.device),
                v_rgb = torch.from_numpy(np.asarray(mesh.vertex_colors)).to(self.device)
            )
            vtx_refine = mesh.v_pos.cpu().numpy()
            faces_refine = mesh.t_pos_idx.cpu().numpy()

        vtx_colors = self.render.forward_points(cur_triplane, torch.tensor(vtx_refine).unsqueeze(0).to(**here))
        vtx_colors = vtx_colors['rgb'].float().squeeze(0).cpu().numpy()

        color_ratio = 0.8 # increase brightness
        with open(obj_vertext_path, 'w') as fid:
            verts = vtx_refine[:, [1,2,0]] 
            for pidx, pp in enumerate(verts):
                color = vtx_colors[pidx]
                color = [color[0]**color_ratio, color[1]**color_ratio, color[2]**color_ratio]
                fid.write('v %f %f %f %f %f %f\n' % (pp[0], pp[1], pp[2], color[0], color[1], color[2]))
            for i, f in enumerate(faces_refine):
                f1 = f + 1
                fid.write('f %d %d %d\n' % (f1[0], f1[1], f1[2]))
                
        mesh = trimesh.load_mesh(obj_vertext_path)
        print(f"=====> generate mesh with vertex shading time: {time.time() - st}")
        st = time.time()
        
        if not do_texture_mapping:
            return obj_vertext_path, None
            
        ###########################################################
        #-------------    export texture    -----------------------
        ###########################################################
        
        st = time.time()
        
        # uv unwrap 
        vtx_tex, t_tex_idx = unwrap_uv(vtx_refine, faces_refine)      
        vtx_refine   = torch.from_numpy(vtx_refine).to(self.device)   
        faces_refine = torch.from_numpy(faces_refine).to(self.device)  
        t_tex_idx    = torch.from_numpy(t_tex_idx).to(self.device)    
        uv_clip      = torch.from_numpy(vtx_tex * 2.0 - 1.0).to(self.device) 

        # rasterize
        ctx = NVDiffRasterizerContext(context_type, cur_triplane.device) if ctx is None else ctx
        rast = ctx.rasterize_one(
            torch.cat([
                uv_clip, 
                torch.zeros_like(uv_clip[..., 0:1]), 
                torch.ones_like(uv_clip[..., 0:1])
            ], dim=-1), 
            t_tex_idx,  
            (texture_res, texture_res)
        )[0]
        hole_mask = ~(rast[:, :, 3] > 0)

        # Interpolate world space position
        gb_pos = ctx.interpolate_one(vtx_refine, rast[None, ...], faces_refine)[0][0]
        with torch.no_grad():
            gb_mask_pos_scale = scale_tensor(gb_pos.unsqueeze(0).view(1, -1, 3), (-1, 1), (-1, 1))
            tex_map = self.render.forward_points(cur_triplane, gb_mask_pos_scale)['rgb']
            tex_map = tex_map.float().squeeze(0)  # (0, 1)
            tex_map = tex_map.view((texture_res, texture_res, 3)) 
            img = uv_padding(tex_map, hole_mask)
            img = ((img/255.0) ** color_ratio) * 255  # increase brightness
            img = img.clip(0, 255).astype(np.uint8)
    
        verts = vtx_refine.cpu().numpy()[:, [1,2,0]] 
        faces = faces_refine.cpu().numpy()

        with open(obj_mtl_path, 'w') as fid:
            fid.write('newmtl material_0\n')
            fid.write("Ka 1.000 1.000 1.000\n")
            fid.write("Kd 1.000 1.000 1.000\n")
            fid.write("Ks 0.500 0.500 0.500\n")
            fid.write("d 1.0\n")
            fid.write("illum 2\n")
            fid.write(f'map_Kd texture.png\n')
        
        with open(obj_path, 'w') as fid:
            fid.write(f'mtllib texture.mtl\n')
            for pidx, pp in enumerate(verts):
                fid.write('v %f %f %f\n' % (pp[0], pp[1], pp[2]))
            for pidx, pp in enumerate(vtx_tex): 
                fid.write('vt %f %f\n' % (pp[0], 1 - pp[1]))
            fid.write('usemtl material_0\n')
            for i, f in enumerate(faces):
                f1 = f + 1
                f2 = t_tex_idx[i] + 1
                fid.write('f %d/%d %d/%d %d/%d\n' % (f1[0], f2[0], f1[1], f2[1], f1[2], f2[2],))

        cv2.imwrite(obj_texture_path, img[..., [2, 1, 0]])   
        mesh = trimesh.load_mesh(obj_path)
        mesh.export(glb_path, file_type='glb')
        print(f"=====> generate mesh with texture shading time: {time.time() - st}")
        return obj_path, glb_path