app.py

import os
os.environ['OPENCV_IO_ENABLE_OPENEXR'] = '1'
import sys
from pathlib import Path
import time
import uuid
import tempfile
import itertools
from typing import *
import atexit
from concurrent.futures import ThreadPoolExecutor
import shutil

import click


@click.command(help='Web demo')
@click.option('--share', is_flag=True, help='Whether to run the app in shared mode.')
@click.option('--pretrained', 'pretrained_model_name_or_path', default=None, help='The name or path of the pre-trained model.')
@click.option('--version', 'model_version', default='v2', help='The version of the model.')
def main(share: bool, pretrained_model_name_or_path: str, model_version: str, use_fp16: bool = True):
    print("Import modules...")
    # Lazy import
    import cv2
    import torch
    import numpy as np
    import trimesh
    import trimesh.visual
    from PIL import Image
    import gradio as gr
    try:
        import spaces   # This is for deployment at huggingface.co/spaces
        HUGGINFACE_SPACES_INSTALLED = True
    except ImportError:
        HUGGINFACE_SPACES_INSTALLED = False

    import utils3d
    from moge.utils.io import write_normal
    from moge.utils.vis import colorize_depth, colorize_normal
    from moge.model import import_model_class_by_version
    from moge.utils.geometry_numpy import depth_occlusion_edge_numpy
    from moge.utils.tools import timeit

    print("Load model...")
    if pretrained_model_name_or_path is None:
        DEFAULT_PRETRAINED_MODEL_FOR_EACH_VERSION = {
            "v1": "Ruicheng/moge-vitl",
            "v2": "Ruicheng/moge-2-vitl-normal",
        }
        pretrained_model_name_or_path = DEFAULT_PRETRAINED_MODEL_FOR_EACH_VERSION[model_version]
    model = import_model_class_by_version(model_version).from_pretrained(pretrained_model_name_or_path).cuda().eval()
    if use_fp16:
        model.half()
    thread_pool_executor = ThreadPoolExecutor(max_workers=1)

    def delete_later(path: Union[str, os.PathLike], delay: int = 300):
        def _delete():
            try: 
                os.remove(path) 
            except FileNotFoundError:
                pass
        def _wait_and_delete():
            time.sleep(delay)
            _delete(path)
        thread_pool_executor.submit(_wait_and_delete)
        atexit.register(_delete)

    # Inference on GPU. 
    @(spaces.GPU if HUGGINFACE_SPACES_INSTALLED else lambda x: x)
    def run_with_gpu(image: np.ndarray, resolution_level: int, apply_mask: bool) -> Dict[str, np.ndarray]:
        image_tensor = torch.tensor(image, dtype=torch.float32 if not use_fp16 else torch.float16, device=torch.device('cuda')).permute(2, 0, 1) / 255
        output = model.infer(image_tensor, apply_mask=apply_mask, resolution_level=resolution_level, use_fp16=use_fp16)
        output = {k: v.cpu().numpy() for k, v in output.items()}
        return output

    # Full inference pipeline
    def run(image: np.ndarray, max_size: int = 800, resolution_level: str = 'High',  apply_mask: bool = True, remove_edge: bool = True, request: gr.Request = None):
        larger_size = max(image.shape[:2])
        if larger_size > max_size:
            scale = max_size / larger_size
            image = cv2.resize(image, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_AREA)

        height, width = image.shape[:2]

        resolution_level_int = {'Low': 0, 'Medium': 5, 'High': 9, 'Ultra': 18}.get(resolution_level, 9)
        output = run_with_gpu(image, resolution_level_int, apply_mask)

        points, depth, mask, normal = output['points'], output['depth'], output['mask'], output.get('normal', None)

        if remove_edge:
            mask_cleaned = mask & ~utils3d.numpy.depth_edge(depth, rtol=0.04)
        else:
            mask_cleaned = mask
        
        results = {
            **output,
            'mask_cleaned': mask_cleaned,
            'image': image
        }

        # depth & normal visualization
        depth_vis = colorize_depth(depth)
        if normal is not None:
            normal_vis = colorize_normal(normal)
        else:
            normal_vis = gr.update(label="Normal map (not avalable for this model)")

        # mesh & pointcloud
        if normal is None:
            faces, vertices, vertex_colors, vertex_uvs = utils3d.numpy.image_mesh(
                points,
                image.astype(np.float32) / 255,
                utils3d.numpy.image_uv(width=width, height=height),
                mask=mask_cleaned,
                tri=True
            )
            vertex_normals = None
        else:
            faces, vertices, vertex_colors, vertex_uvs, vertex_normals = utils3d.numpy.image_mesh(
                points,
                image.astype(np.float32) / 255,
                utils3d.numpy.image_uv(width=width, height=height),
                normal,
                mask=mask_cleaned,
                tri=True
            )
        vertices = vertices * np.array([1, -1, -1], dtype=np.float32) 
        vertex_uvs = vertex_uvs * np.array([1, -1], dtype=np.float32) + np.array([0, 1], dtype=np.float32)
        if vertex_normals is not None:
            vertex_normals = vertex_normals * np.array([1, -1, -1], dtype=np.float32)

        tempdir = Path(tempfile.gettempdir(), 'moge')
        tempdir.mkdir(exist_ok=True)
        output_path = Path(tempdir, request.session_hash)
        shutil.rmtree(output_path, ignore_errors=True)
        output_path.mkdir(exist_ok=True, parents=True)
        trimesh.Trimesh(
            vertices=vertices,
            faces=faces, 
            vertex_normals=vertex_normals,
            visual = trimesh.visual.texture.TextureVisuals(
                uv=vertex_uvs, 
                material=trimesh.visual.material.PBRMaterial(
                    baseColorTexture=Image.fromarray(image),
                    metallicFactor=0.5,
                    roughnessFactor=1.0
                )
            ),
            process=False
        ).export(output_path / 'mesh.glb')
        pointcloud = trimesh.PointCloud(
            vertices=vertices, 
            colors=vertex_colors,
        )
        pointcloud.vertex_normals = vertex_normals
        pointcloud.export(output_path / 'pointcloud.ply', vertex_normal=True)
        trimesh.PointCloud(
            vertices=vertices, 
            colors=vertex_colors,
        ).export(output_path / 'pointcloud.glb', include_normals=True)
        cv2.imwrite(str(output_path /'mask.png'), mask.astype(np.uint8) * 255)
        cv2.imwrite(str(output_path / 'depth.exr'), depth.astype(np.float32), [cv2.IMWRITE_EXR_TYPE, cv2.IMWRITE_EXR_TYPE_FLOAT])
        cv2.imwrite(str(output_path / 'points.exr'), cv2.cvtColor(points.astype(np.float32), cv2.COLOR_RGB2BGR), [cv2.IMWRITE_EXR_TYPE, cv2.IMWRITE_EXR_TYPE_FLOAT])
        if normal is not None:
            cv2.imwrite(str(output_path / 'normal.exr'), cv2.cvtColor(normal.astype(np.float32) * np.array([1, -1, -1], dtype=np.float32), cv2.COLOR_RGB2BGR), [cv2.IMWRITE_EXR_TYPE, cv2.IMWRITE_EXR_TYPE_HALF])

        files = ['mesh.glb', 'pointcloud.ply', 'depth.exr', 'points.exr', 'mask.png']
        if normal is not None:
            files.append('normal.exr')

        for f in files:
            delete_later(output_path / f)

        # FOV
        intrinsics = results['intrinsics']
        fov_x, fov_y = utils3d.numpy.intrinsics_to_fov(intrinsics)
        fov_x, fov_y = np.rad2deg([fov_x, fov_y])

        # messages
        viewer_message = f'**Note:** Inference has been completed. It may take a few seconds to download the 3D model.'
        if resolution_level != 'Ultra':
            depth_message = f'**Note:** Want sharper depth map? Try increasing the `maximum image size` and setting the `inference resolution level` to `Ultra` in the settings.'
        else:
            depth_message = ""

        return (
            results,
            depth_vis,
            normal_vis, 
            output_path / 'pointcloud.glb', 
            [(output_path / f).as_posix() for f in files if (output_path / f).exists()],
            f'- **Horizontal FOV: {fov_x:.1f}°**. \n - **Vertical FOV: {fov_y:.1f}°**',
            viewer_message,
            depth_message
        )

    def reset_measure(results: Dict[str, np.ndarray]):
        return [results['image'], [], ""]


    def measure(results: Dict[str, np.ndarray], measure_points: List[Tuple[int, int]], event: gr.SelectData):
        point2d = event.index[0], event.index[1]
        measure_points.append(point2d)

        image = results['image'].copy()
        for p in measure_points:
            image = cv2.circle(image, p, radius=5, color=(255, 0, 0), thickness=2)

        depth_text = ""
        for i, p in enumerate(measure_points):
            d = results['depth'][p[1], p[0]]
            depth_text += f"- **P{i + 1} depth: {d:.2f}m.**\n"

        if len(measure_points) == 2:
            point1, point2 = measure_points
            image = cv2.line(image, point1, point2, color=(255, 0, 0), thickness=2)
            distance = np.linalg.norm(results['points'][point1[1], point1[0]] - results['points'][point2[1], point2[0]])
            measure_points = []

            distance_text = f"- **Distance: {distance:.2f}m**"

            text = depth_text + distance_text
            return [image, measure_points, text]
        else:
            return [image, measure_points, depth_text]
        
    print("Create Gradio app...")
    with gr.Blocks() as demo:
        gr.Markdown(
f'''
<div align="center">
<h1> Turn a 2D image into 3D with MoGe <a title="Github" href="https://github.com/microsoft/MoGe" target="_blank" rel="noopener noreferrer" style="display: inline-block;"> <img src="https://img.shields.io/github/stars/microsoft/MoGe?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="badge-github-stars"> </a> </h1>
</div>
''')
        results = gr.State(value=None)
        measure_points = gr.State(value=[])

        with gr.Row():
            with gr.Column():
                input_image = gr.Image(type="numpy", image_mode="RGB", label="Input Image")
                with gr.Accordion(label="Settings", open=False):
                    max_size_input = gr.Number(value=800, label="Maximum Image Size", precision=0, minimum=256, maximum=2048)
                    resolution_level = gr.Dropdown(['Low', 'Medium', 'High', 'Ultra'], label="Inference Resolution Level", value='High')
                    apply_mask = gr.Checkbox(value=True, label="Apply mask")
                    remove_edges = gr.Checkbox(value=True, label="Remove edges")
                submit_btn = gr.Button("Submit", variant='primary')

            with gr.Column():
                with gr.Tabs():
                    with gr.Tab("3D View"):
                        viewer_message = gr.Markdown("")
                        model_3d = gr.Model3D(display_mode="solid", label="3D Point Map", clear_color=[1.0, 1.0, 1.0, 1.0], height="60vh")
                        fov = gr.Markdown()
                    with gr.Tab("Depth"):
                        depth_message = gr.Markdown("")
                        depth_map = gr.Image(type="numpy", label="Colorized Depth Map", format='png', interactive=False)
                    with gr.Tab("Normal", interactive=hasattr(model, 'normal_head')):
                        normal_map = gr.Image(type="numpy", label="Normal Map", format='png', interactive=False)
                    with gr.Tab("Measure", interactive=hasattr(model, 'scale_head')):
                        gr.Markdown("### Click on the image to measure the distance between two points. \n"
                         "**Note:** Metric scale is most reliable for typical indoor or street scenes, and may degrade for contents unfamiliar to the model (e.g., stylized or close-up images).")
                        measure_image = gr.Image(type="numpy", show_label=False, format='webp', interactive=False, sources=[])
                        measure_text = gr.Markdown("")
                    with gr.Tab("Download"):
                        files = gr.File(type='filepath', label="Output Files")
        
        if Path('example_images').exists():
            example_image_paths = sorted(list(itertools.chain(*[Path('example_images').glob(f'*.{ext}') for ext in ['jpg', 'png', 'jpeg', 'JPG', 'PNG', 'JPEG']])))
            examples = gr.Examples(
                examples = example_image_paths,
                inputs=input_image,
                label="Examples"
            )

        submit_btn.click(
            fn=lambda: [None, None, None, None, None, "", "", ""],
            outputs=[results, depth_map, normal_map, model_3d, files, fov, viewer_message, depth_message]
        ).then(
            fn=run,
            inputs=[input_image, max_size_input, resolution_level, apply_mask, remove_edges],
            outputs=[results, depth_map, normal_map, model_3d, files, fov, viewer_message, depth_message]
        ).then(
            fn=reset_measure,
            inputs=[results],
            outputs=[measure_image, measure_points, measure_text]
        )

        measure_image.select(
            fn=measure,
            inputs=[results, measure_points],
            outputs=[measure_image, measure_points, measure_text]
        )
    
    demo.launch(share=share)


if __name__ == '__main__':
    main()