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 logging
import os
import sys
from glob import glob

import numpy as np
import trimesh
from PIL import Image
from embodied_gen.data.backproject_v2 import entrypoint as backproject_api
from embodied_gen.data.utils import 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 (
    BMGG14Remover,
    RembgRemover,
    SAMPredictor,
)
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.process_media import merge_images_video, render_video
from embodied_gen.utils.tags import VERSION
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

logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(message)s", level=logging.INFO
)
logger = logging.getLogger(__name__)


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


DELIGHT = DelightingModel()
IMAGESR_MODEL = ImageRealESRGAN(outscale=4)

RBG_REMOVER = RembgRemover()
RBG14_REMOVER = BMGG14Remover()
SAM_PREDICTOR = SAMPredictor(model_type="vit_h", device="cpu")
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]
TMP_DIR = os.path.join(
    os.path.dirname(os.path.abspath(__file__)), "sessions/imageto3d"
)


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,
        required=True,
        help="Root directory for saving outputs.",
    )
    parser.add_argument(
        "--no_mesh", action="store_true", help="Do not output mesh files."
    )
    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, default=None)
    parser.add_argument("--skip_exists", action="store_true")
    parser.add_argument("--strict_seg", action="store_true")
    parser.add_argument("--version", type=str, default=VERSION)
    args = parser.parse_args()

    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"))

    return args


if __name__ == "__main__":
    args = parse_args()

    for image_path in args.image_path:
        try:
            filename = os.path.basename(image_path).split(".")[0]
            output_root = args.output_root
            if args.image_root is not None:
                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.info(
                    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 SAM or Rembg.
            seg_path = f"{output_root}/{filename}_cond.png"
            if image.mode != "RGBA":
                seg_image = RBG_REMOVER(image, save_path=seg_path)
                seg_image = trellis_preprocess(seg_image)
            else:
                seg_image = image
                seg_image.save(seg_path)

            # Run the pipeline
            try:
                outputs = PIPELINE.run(
                    seg_image,
                    preprocess_image=False,
                    # Optional parameters
                    # seed=1,
                    # sparse_structure_sampler_params={
                    #     "steps": 12,
                    #     "cfg_strength": 7.5,
                    # },
                    # slat_sampler_params={
                    #     "steps": 12,
                    #     "cfg_strength": 3,
                    # },
                )
            except Exception as e:
                logger.error(
                    f"[Pipeline Failed] process {image_path}: {e}, skip."
                )
                continue

            # Render and save color and mesh videos
            gs_model = outputs["gaussian"][0]
            mesh_model = outputs["mesh"][0]
            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)

            if not args.no_mesh:
                # 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(aligned_gs_path, color_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=[2048, 2048],
                )

                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)
                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 args.asset_type:
                    asset_attrs["category"] = args.asset_type
                if args.version:
                    asset_attrs["version"] = args.version

                urdf_path = urdf_convertor(
                    mesh_path=mesh_obj_path,
                    output_root=f"{output_root}/URDF_{filename}",
                    **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"{output_root}/URDF_{filename}/{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"{output_root}/URDF_{filename}/{urdf_convertor.output_mesh_dir}/{filename}.obj"  # noqa
                trimesh.load(mesh_out).export(mesh_out.replace(".obj", ".glb"))
                # image_paths = render_asset3d(
                #     mesh_path=mesh_out,
                #     output_root=f"{output_root}/URDF_{filename}",
                #     output_subdir="qa_renders",
                #     num_images=8,
                #     elevation=(30, -30),
                #     distance=5.5,
                # )

                image_dir = f"{output_root}/URDF_{filename}/{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)

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

        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}")