import os os.environ['OPENCV_IO_ENABLE_OPENEXR'] = '1' from pathlib import Path import sys sys.path.append(str(Path(__file__).absolute().parents[1])) 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 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 for the MoGe model.') @click.option('--input', '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', 'output_path', 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. Default is "Ruicheng/moge-vitl"') @click.option('--device', 'device_name', type=str, default='cuda', help='Device name (e.g. "cuda", "cuda:0", "cpu"). Default is "cuda"') @click.option('--resize', 'resize_to', type=int, default=None, help='Resize the image(s) & output maps to a specific size. Default is None (no resizing).') @click.option('--resolution_level', type=int, default=9, help='An integer [0-9] for the resolution level of inference. The higher, the better but slower. Default is 9. Note that it is irrelevant to the output resolution.') @click.option('--threshold', type=float, default=0.03, help='Threshold for removing edges. Default is 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, resize_to: int, resolution_level: 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() 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_) 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) # Write outputs if not any([save_maps_, save_glb_, save_ply_]): warnings.warn('No output format specified. Please use "--maps", "--glb", or "--ply" to specify the output.') save_path = Path(output_path, image_path.relative_to(input_path).parent, image_path.stem) save_path.mkdir(exist_ok=True, parents=True) 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()