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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!'
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