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| from __future__ import annotations | |
| import datetime | |
| import pathlib | |
| import shlex | |
| import subprocess | |
| import sys | |
| from typing import Generator, Optional | |
| import gradio as gr | |
| import trimesh | |
| sys.path.append('TEXTurePaper') | |
| from src.configs.train_config import GuideConfig, LogConfig, TrainConfig | |
| from src.training.trainer import TEXTure | |
| class Model: | |
| def __init__(self): | |
| self.max_num_faces = 100000 | |
| def load_config(self, shape_path: str, text: str, seed: int, | |
| guidance_scale: float) -> TrainConfig: | |
| text += ', {} view' | |
| log = LogConfig(exp_name=self.gen_exp_name()) | |
| guide = GuideConfig(text=text) | |
| guide.background_img = 'TEXTurePaper/textures/brick_wall.png' | |
| guide.shape_path = 'TEXTurePaper/shapes/spot_triangulated.obj' | |
| config = TrainConfig(log=log, guide=guide) | |
| config.guide.shape_path = shape_path | |
| config.optim.seed = seed | |
| config.guide.guidance_scale = guidance_scale | |
| return config | |
| def gen_exp_name(self) -> str: | |
| now = datetime.datetime.now() | |
| return now.strftime('%Y-%m-%d-%H-%M-%S') | |
| def check_num_faces(self, path: str) -> bool: | |
| with open(path) as f: | |
| lines = [line for line in f.readlines() if line.startswith('f')] | |
| return len(lines) <= self.max_num_faces | |
| def zip_results(self, exp_dir: pathlib.Path) -> str: | |
| mesh_dir = exp_dir / 'mesh' | |
| out_path = f'{exp_dir.name}.zip' | |
| subprocess.run(shlex.split(f'zip -r {out_path} {mesh_dir}')) | |
| return out_path | |
| def run( | |
| self, shape_path: str, text: str, seed: int, guidance_scale: float | |
| ) -> Generator[tuple[list[str], Optional[str], Optional[str], str], None, | |
| None]: | |
| if not shape_path.endswith('.obj'): | |
| raise gr.Error('The input file is not .obj file.') | |
| if not self.check_num_faces(shape_path): | |
| raise gr.Error('The number of faces is over 100,000.') | |
| config = self.load_config(shape_path, text, seed, guidance_scale) | |
| trainer = TEXTure(config) | |
| trainer.mesh_model.train() | |
| total_steps = len(trainer.dataloaders['train']) | |
| for step, data in enumerate(trainer.dataloaders['train'], start=1): | |
| trainer.paint_step += 1 | |
| trainer.paint_viewpoint(data) | |
| trainer.evaluate(trainer.dataloaders['val'], | |
| trainer.eval_renders_path) | |
| trainer.mesh_model.train() | |
| sample_image_dir = config.log.exp_dir / 'vis' / 'eval' | |
| sample_image_paths = sorted( | |
| sample_image_dir.glob(f'step_{trainer.paint_step:05d}_*.jpg')) | |
| sample_image_paths = [ | |
| path.as_posix() for path in sample_image_paths | |
| ] | |
| yield sample_image_paths, None, None, f'{step}/{total_steps}' | |
| trainer.mesh_model.change_default_to_median() | |
| save_dir = trainer.exp_path / 'mesh' | |
| save_dir.mkdir(exist_ok=True, parents=True) | |
| trainer.mesh_model.export_mesh(save_dir) | |
| model_path = save_dir / 'mesh.obj' | |
| mesh = trimesh.load(model_path) | |
| mesh_path = save_dir / 'mesh.glb' | |
| mesh.export(mesh_path, file_type='glb') | |
| zip_path = self.zip_results(config.log.exp_dir) | |
| yield sample_image_paths, mesh_path.as_posix(), zip_path, 'Done!' | |