embodied_gen/scripts/imageto3d.py

# Project EmbodiedGen
#
# Copyright (c) 2025 Horizon Robotics. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#       http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied. See the License for the specific language governing
# permissions and limitations under the License.


import argparse
import os
import random
import sys
from glob import glob
from shutil import copy, copytree, rmtree

import numpy as np
import torch
import trimesh
from PIL import Image
from embodied_gen.data.backproject_v2 import entrypoint as backproject_api
from embodied_gen.data.utils import delete_dir, trellis_preprocess
from embodied_gen.models.delight_model import DelightingModel
from embodied_gen.models.gs_model import GaussianOperator
from embodied_gen.models.segment_model import RembgRemover
from embodied_gen.models.sr_model import ImageRealESRGAN
from embodied_gen.scripts.render_gs import entrypoint as render_gs_api
from embodied_gen.utils.gpt_clients import GPT_CLIENT
from embodied_gen.utils.log import logger
from embodied_gen.utils.process_media import merge_images_video
from embodied_gen.utils.tags import VERSION
from embodied_gen.utils.trender import render_video
from embodied_gen.validators.quality_checkers import (
    BaseChecker,
    ImageAestheticChecker,
    ImageSegChecker,
    MeshGeoChecker,
)
from embodied_gen.validators.urdf_convertor import URDFGenerator

current_file_path = os.path.abspath(__file__)
current_dir = os.path.dirname(current_file_path)
sys.path.append(os.path.join(current_dir, "../.."))
from thirdparty.TRELLIS.trellis.pipelines import TrellisImageTo3DPipeline

os.environ["TORCH_EXTENSIONS_DIR"] = os.path.expanduser(
    "~/.cache/torch_extensions"
)
os.environ["GRADIO_ANALYTICS_ENABLED"] = "false"
os.environ["SPCONV_ALGO"] = "native"
random.seed(0)

logger.info("Loading Image3D Models...")
DELIGHT = DelightingModel()
IMAGESR_MODEL = ImageRealESRGAN(outscale=4)
RBG_REMOVER = RembgRemover()
PIPELINE = TrellisImageTo3DPipeline.from_pretrained(
    "microsoft/TRELLIS-image-large"
)
# PIPELINE.cuda()
SEG_CHECKER = ImageSegChecker(GPT_CLIENT)
GEO_CHECKER = MeshGeoChecker(GPT_CLIENT)
AESTHETIC_CHECKER = ImageAestheticChecker()
CHECKERS = [GEO_CHECKER, SEG_CHECKER, AESTHETIC_CHECKER]


def parse_args():
    parser = argparse.ArgumentParser(description="Image to 3D pipeline args.")
    parser.add_argument(
        "--image_path", type=str, nargs="+", help="Path to the input images."
    )
    parser.add_argument(
        "--image_root", type=str, help="Path to the input images folder."
    )
    parser.add_argument(
        "--output_root",
        type=str,
        help="Root directory for saving outputs.",
    )
    parser.add_argument(
        "--height_range",
        type=str,
        default=None,
        help="The hight in meter to restore the mesh real size.",
    )
    parser.add_argument(
        "--mass_range",
        type=str,
        default=None,
        help="The mass in kg to restore the mesh real weight.",
    )
    parser.add_argument("--asset_type", type=str, nargs="+", default=None)
    parser.add_argument("--skip_exists", action="store_true")
    parser.add_argument("--version", type=str, default=VERSION)
    parser.add_argument("--keep_intermediate", action="store_true")
    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument(
        "--n_retry",
        type=int,
        default=2,
    )
    parser.add_argument("--disable_decompose_convex", action="store_true")
    parser.add_argument(
        "--texture_wh", type=int, nargs=2, default=[2048, 2048]
    )
    args, unknown = parser.parse_known_args()

    return args


def entrypoint(**kwargs):
    args = parse_args()
    for k, v in kwargs.items():
        if hasattr(args, k) and v is not None:
            setattr(args, k, v)

    assert (
        args.image_path or args.image_root
    ), "Please provide either --image_path or --image_root."
    if not args.image_path:
        args.image_path = glob(os.path.join(args.image_root, "*.png"))
        args.image_path += glob(os.path.join(args.image_root, "*.jpg"))
        args.image_path += glob(os.path.join(args.image_root, "*.jpeg"))

    for idx, image_path in enumerate(args.image_path):
        try:
            filename = os.path.basename(image_path).split(".")[0]
            output_root = args.output_root
            if args.image_root is not None or len(args.image_path) > 1:
                output_root = os.path.join(output_root, filename)
            os.makedirs(output_root, exist_ok=True)

            mesh_out = f"{output_root}/{filename}.obj"
            if args.skip_exists and os.path.exists(mesh_out):
                logger.warning(
                    f"Skip {image_path}, already processed in {mesh_out}"
                )
                continue

            image = Image.open(image_path)
            image.save(f"{output_root}/{filename}_raw.png")

            # Segmentation: Get segmented image using Rembg.
            seg_path = f"{output_root}/{filename}_cond.png"
            seg_image = RBG_REMOVER(image) if image.mode != "RGBA" else image
            seg_image = trellis_preprocess(seg_image)
            seg_image.save(seg_path)

            seed = args.seed
            asset_node = "unknown"
            if isinstance(args.asset_type, list) and args.asset_type[idx]:
                asset_node = args.asset_type[idx]
            for try_idx in range(args.n_retry):
                logger.info(
                    f"Try: {try_idx + 1}/{args.n_retry}, Seed: {seed}, Prompt: {seg_path}"
                )
                # Run the pipeline
                try:
                    PIPELINE.cuda()
                    outputs = PIPELINE.run(
                        seg_image,
                        preprocess_image=False,
                        seed=(
                            random.randint(0, 100000) if seed is None else seed
                        ),
                        # Optional parameters
                        # sparse_structure_sampler_params={
                        #     "steps": 12,
                        #     "cfg_strength": 7.5,
                        # },
                        # slat_sampler_params={
                        #     "steps": 12,
                        #     "cfg_strength": 3,
                        # },
                    )
                    PIPELINE.cpu()
                    torch.cuda.empty_cache()
                except Exception as e:
                    logger.error(
                        f"[Pipeline Failed] process {image_path}: {e}, skip."
                    )
                    continue

                gs_model = outputs["gaussian"][0]
                mesh_model = outputs["mesh"][0]

                # Save the raw Gaussian model
                gs_path = mesh_out.replace(".obj", "_gs.ply")
                gs_model.save_ply(gs_path)

                # Rotate mesh and GS by 90 degrees around Z-axis.
                rot_matrix = [[0, 0, -1], [0, 1, 0], [1, 0, 0]]
                gs_add_rot = [[1, 0, 0], [0, -1, 0], [0, 0, -1]]
                mesh_add_rot = [[1, 0, 0], [0, 0, -1], [0, 1, 0]]

                # Addtional rotation for GS to align mesh.
                gs_rot = np.array(gs_add_rot) @ np.array(rot_matrix)
                pose = GaussianOperator.trans_to_quatpose(gs_rot)
                aligned_gs_path = gs_path.replace(".ply", "_aligned.ply")
                GaussianOperator.resave_ply(
                    in_ply=gs_path,
                    out_ply=aligned_gs_path,
                    instance_pose=pose,
                    device="cpu",
                )
                color_path = os.path.join(output_root, "color.png")
                render_gs_api(
                    input_gs=aligned_gs_path,
                    output_path=color_path,
                    elevation=[20, -10, 60, -50],
                    num_images=12,
                )

                color_img = Image.open(color_path)
                half_height = int(color_img.height * 2 / 3)
                crop_img = color_img.crop((0, 0, color_img.width, half_height))
                geo_flag, geo_result = GEO_CHECKER([crop_img], text=asset_node)
                logger.warning(
                    f"{GEO_CHECKER.__class__.__name__}: {geo_result} for {seg_path}"
                )
                if geo_flag is True or geo_flag is None:
                    break

                seed = random.randint(0, 100000) if seed is not None else None

            # Render the video for generated 3D asset.
            color_images = render_video(gs_model)["color"]
            normal_images = render_video(mesh_model)["normal"]
            video_path = os.path.join(output_root, "gs_mesh.mp4")
            merge_images_video(color_images, normal_images, video_path)

            mesh = trimesh.Trimesh(
                vertices=mesh_model.vertices.cpu().numpy(),
                faces=mesh_model.faces.cpu().numpy(),
            )
            mesh.vertices = mesh.vertices @ np.array(mesh_add_rot)
            mesh.vertices = mesh.vertices @ np.array(rot_matrix)

            mesh_obj_path = os.path.join(output_root, f"{filename}.obj")
            mesh.export(mesh_obj_path)

            mesh = backproject_api(
                delight_model=DELIGHT,
                imagesr_model=IMAGESR_MODEL,
                color_path=color_path,
                mesh_path=mesh_obj_path,
                output_path=mesh_obj_path,
                skip_fix_mesh=False,
                delight=True,
                texture_wh=args.texture_wh,
                elevation=[20, -10, 60, -50],
                num_images=12,
            )

            mesh_glb_path = os.path.join(output_root, f"{filename}.glb")
            mesh.export(mesh_glb_path)

            urdf_convertor = URDFGenerator(
                GPT_CLIENT,
                render_view_num=4,
                decompose_convex=not args.disable_decompose_convex,
            )
            asset_attrs = {
                "version": VERSION,
                "gs_model": f"{urdf_convertor.output_mesh_dir}/{filename}_gs.ply",
            }
            if args.height_range:
                min_height, max_height = map(
                    float, args.height_range.split("-")
                )
                asset_attrs["min_height"] = min_height
                asset_attrs["max_height"] = max_height
            if args.mass_range:
                min_mass, max_mass = map(float, args.mass_range.split("-"))
                asset_attrs["min_mass"] = min_mass
                asset_attrs["max_mass"] = max_mass
            if isinstance(args.asset_type, list) and args.asset_type[idx]:
                asset_attrs["category"] = args.asset_type[idx]
            if args.version:
                asset_attrs["version"] = args.version

            urdf_root = f"{output_root}/URDF_{filename}"
            urdf_path = urdf_convertor(
                mesh_path=mesh_obj_path,
                output_root=urdf_root,
                **asset_attrs,
            )

            # Rescale GS and save to URDF/mesh folder.
            real_height = urdf_convertor.get_attr_from_urdf(
                urdf_path, attr_name="real_height"
            )
            out_gs = f"{urdf_root}/{urdf_convertor.output_mesh_dir}/{filename}_gs.ply"  # noqa
            GaussianOperator.resave_ply(
                in_ply=aligned_gs_path,
                out_ply=out_gs,
                real_height=real_height,
                device="cpu",
            )

            # Quality check and update .urdf file.
            mesh_out = f"{urdf_root}/{urdf_convertor.output_mesh_dir}/{filename}.obj"  # noqa
            trimesh.load(mesh_out).export(mesh_out.replace(".obj", ".glb"))

            image_dir = f"{urdf_root}/{urdf_convertor.output_render_dir}/image_color"  # noqa
            image_paths = glob(f"{image_dir}/*.png")
            images_list = []
            for checker in CHECKERS:
                images = image_paths
                if isinstance(checker, ImageSegChecker):
                    images = [
                        f"{output_root}/{filename}_raw.png",
                        f"{output_root}/{filename}_cond.png",
                    ]
                images_list.append(images)

            qa_results = BaseChecker.validate(CHECKERS, images_list)
            urdf_convertor.add_quality_tag(urdf_path, qa_results)

            # Organize the final result files
            result_dir = f"{output_root}/result"
            if os.path.exists(result_dir):
                rmtree(result_dir, ignore_errors=True)
            os.makedirs(result_dir, exist_ok=True)
            copy(urdf_path, f"{result_dir}/{os.path.basename(urdf_path)}")
            copytree(
                f"{urdf_root}/{urdf_convertor.output_mesh_dir}",
                f"{result_dir}/{urdf_convertor.output_mesh_dir}",
            )
            copy(video_path, f"{result_dir}/video.mp4")
            if not args.keep_intermediate:
                delete_dir(output_root, keep_subs=["result"])

        except Exception as e:
            logger.error(f"Failed to process {image_path}: {e}, skip.")
            continue

    logger.info(f"Processing complete. Outputs saved to {args.output_root}")


if __name__ == "__main__":
    entrypoint()