MoGe/moge/scripts/infer.py

import os
os.environ['OPENCV_IO_ENABLE_OPENEXR'] = '1'
from pathlib import Path
import sys
if (_package_root := str(Path(__file__).absolute().parents[2])) not in sys.path:
    sys.path.insert(0, _package_root)
from typing import *
import itertools
import json
import warnings

import cv2
import numpy as np
import torch
from PIL import Image
from tqdm import tqdm
import trimesh
import trimesh.visual
import click

from moge.model.v1 import MoGeModel
from moge.utils.io import save_glb, save_ply
from moge.utils.vis import colorize_depth, colorize_normal
import utils3d


@click.command(help='Inference script')
@click.option('--input', '-i', 'input_path', type=click.Path(exists=True), help='Input image or folder path. "jpg" and "png" are supported.')
@click.option('--fov_x', 'fov_x_', type=float, default=None, help='If camera parameters are known, set the horizontal field of view in degrees. Otherwise, MoGe will estimate it.')
@click.option('--output', '-o', 'output_path', default='./output', type=click.Path(), help='Output folder path')
@click.option('--pretrained', 'pretrained_model_name_or_path', type=str, default='Ruicheng/moge-vitl', help='Pretrained model name or path. Defaults to "Ruicheng/moge-vitl"')
@click.option('--device', 'device_name', type=str, default='cuda', help='Device name (e.g. "cuda", "cuda:0", "cpu"). Defaults to "cuda"')
@click.option('--fp16', 'use_fp16', is_flag=True, help='Use fp16 precision for 2x faster inference.')
@click.option('--resize', 'resize_to', type=int, default=None, help='Resize the image(s) & output maps to a specific size. Defaults to None (no resizing).')
@click.option('--resolution_level', type=int, default=9, help='An integer [0-9] for the resolution level for inference. \
Higher value means more tokens and the finer details will be captured, but inference can be slower. \
Defaults to 9. Note that it is irrelevant to the output size, which is always the same as the input size. \
`resolution_level` actually controls `num_tokens`. See `num_tokens` for more details.')
@click.option('--num_tokens', type=int, default=None, help='number of tokens used for inference. A integer in the (suggested) range of `[1200, 2500]`. \
`resolution_level` will be ignored if `num_tokens` is provided. Default: None')
@click.option('--threshold', type=float, default=0.03, help='Threshold for removing edges. Defaults to 0.03. Smaller value removes more edges. "inf" means no thresholding.')
@click.option('--maps', 'save_maps_', is_flag=True, help='Whether to save the output maps and fov(image, depth, mask, points, fov).')
@click.option('--glb', 'save_glb_', is_flag=True, help='Whether to save the output as a.glb file. The color will be saved as a texture.')
@click.option('--ply', 'save_ply_', is_flag=True, help='Whether to save the output as a.ply file. The color will be saved as vertex colors.')
@click.option('--show', 'show', is_flag=True, help='Whether show the output in a window. Note that this requires pyglet<2 installed as required by trimesh.')
def main(
    input_path: str,
    fov_x_: float,
    output_path: str,
    pretrained_model_name_or_path: str,
    device_name: str,
    use_fp16: bool,
    resize_to: int,
    resolution_level: int,
    num_tokens: int,
    threshold: float,
    save_maps_: bool,
    save_glb_: bool,
    save_ply_: bool,
    show: bool,
):  
    device = torch.device(device_name)

    include_suffices = ['jpg', 'png', 'jpeg', 'JPG', 'PNG', 'JPEG']
    if Path(input_path).is_dir():
        image_paths = sorted(itertools.chain(*(Path(input_path).rglob(f'*.{suffix}') for suffix in include_suffices)))
    else:
        image_paths = [Path(input_path)]
    
    if len(image_paths) == 0:
        raise FileNotFoundError(f'No image files found in {input_path}')

    model = MoGeModel.from_pretrained(pretrained_model_name_or_path).to(device).eval()

    
    if not any([save_maps_, save_glb_, save_ply_]):
        warnings.warn('No output format specified. Defaults to saving all. Please use "--maps", "--glb", or "--ply" to specify the output.')
        save_maps_ = save_glb_ = save_ply_ = True

    for image_path in (pbar := tqdm(image_paths, desc='Inference', disable=len(image_paths) <= 1)):
        image = cv2.cvtColor(cv2.imread(str(image_path)), cv2.COLOR_BGR2RGB)
        height, width = image.shape[:2]
        if resize_to is not None:
            height, width = min(resize_to, int(resize_to * height / width)), min(resize_to, int(resize_to * width / height))
            image = cv2.resize(image, (width, height), cv2.INTER_AREA)
        image_tensor = torch.tensor(image / 255, dtype=torch.float32, device=device).permute(2, 0, 1)

        # Inference
        output = model.infer(image_tensor, fov_x=fov_x_, resolution_level=resolution_level, num_tokens=num_tokens, use_fp16=use_fp16)
        points, depth, mask, intrinsics = output['points'].cpu().numpy(), output['depth'].cpu().numpy(), output['mask'].cpu().numpy(), output['intrinsics'].cpu().numpy()
        normals, normals_mask = utils3d.numpy.points_to_normals(points, mask=mask)

        save_path = Path(output_path, image_path.relative_to(input_path).parent, image_path.stem)
        save_path.mkdir(exist_ok=True, parents=True)

        # Save images / maps
        if save_maps_:
            cv2.imwrite(str(save_path / 'image.jpg'), cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
            cv2.imwrite(str(save_path / 'depth_vis.png'), cv2.cvtColor(colorize_depth(depth), cv2.COLOR_RGB2BGR))
            cv2.imwrite(str(save_path / 'depth.exr'), depth, [cv2.IMWRITE_EXR_TYPE, cv2.IMWRITE_EXR_TYPE_FLOAT])
            cv2.imwrite(str(save_path / 'mask.png'), (mask * 255).astype(np.uint8))
            cv2.imwrite(str(save_path / 'points.exr'), cv2.cvtColor(points, cv2.COLOR_RGB2BGR), [cv2.IMWRITE_EXR_TYPE, cv2.IMWRITE_EXR_TYPE_FLOAT])
            fov_x, fov_y = utils3d.numpy.intrinsics_to_fov(intrinsics)
            with open(save_path / 'fov.json', 'w') as f:
                json.dump({
                    'fov_x': round(float(np.rad2deg(fov_x)), 2),
                    'fov_y': round(float(np.rad2deg(fov_y)), 2),
                }, f)

        # Export mesh & visulization
        if save_glb_ or save_ply_ or show:
            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 & ~(utils3d.numpy.depth_edge(depth, rtol=threshold, mask=mask) & utils3d.numpy.normals_edge(normals, tol=5, mask=normals_mask)),
                tri=True
            )
            # When exporting the model, follow the OpenGL coordinate conventions:
            # - world coordinate system: x right, y up, z backward.
            # - texture coordinate system: (0, 0) for left-bottom, (1, 1) for right-top.
            vertices, vertex_uvs = vertices * [1, -1, -1], vertex_uvs * [1, -1] + [0, 1]

        if save_glb_:
            save_glb(save_path / 'mesh.glb', vertices, faces, vertex_uvs, image)

        if save_ply_:
            save_ply(save_path / 'mesh.ply', vertices, faces, vertex_colors)

        if show:
            trimesh.Trimesh(
                vertices=vertices,
                vertex_colors=vertex_colors,
                faces=faces, 
                process=False
            ).show()  


if __name__ == '__main__':
    main()