| """
|
| Configuration loader for Hybrid Document Forgery Detection System
|
| """
|
|
|
| import yaml
|
| from pathlib import Path
|
| from typing import Dict, Any
|
|
|
|
|
| class Config:
|
| """Configuration manager"""
|
|
|
| def __init__(self, config_path: str = "config.yaml"):
|
| """
|
| Load configuration from YAML file
|
|
|
| Args:
|
| config_path: Path to configuration file
|
| """
|
| self.config_path = Path(config_path)
|
| self.config = self._load_config()
|
|
|
| def _load_config(self) -> Dict[str, Any]:
|
| """Load YAML configuration"""
|
| if not self.config_path.exists():
|
| raise FileNotFoundError(f"Config file not found: {self.config_path}")
|
|
|
| with open(self.config_path, 'r') as f:
|
| config = yaml.safe_load(f)
|
|
|
| return config
|
|
|
| def get(self, key: str, default: Any = None) -> Any:
|
| """
|
| Get configuration value using dot notation
|
|
|
| Args:
|
| key: Configuration key (e.g., 'model.encoder.name')
|
| default: Default value if key not found
|
|
|
| Returns:
|
| Configuration value
|
| """
|
| keys = key.split('.')
|
| value = self.config
|
|
|
| for k in keys:
|
| if isinstance(value, dict) and k in value:
|
| value = value[k]
|
| else:
|
| return default
|
|
|
| return value
|
|
|
| def get_dataset_config(self, dataset_name: str) -> Dict[str, Any]:
|
| """
|
| Get dataset-specific configuration
|
|
|
| Args:
|
| dataset_name: Dataset name (doctamper, rtm, casia, receipts)
|
|
|
| Returns:
|
| Dataset configuration dictionary
|
| """
|
| return self.config['data']['datasets'].get(dataset_name, {})
|
|
|
| def has_pixel_mask(self, dataset_name: str) -> bool:
|
| """Check if dataset has pixel-level masks"""
|
| dataset_config = self.get_dataset_config(dataset_name)
|
| return dataset_config.get('has_pixel_mask', False)
|
|
|
| def should_skip_deskew(self, dataset_name: str) -> bool:
|
| """Check if deskewing should be skipped for dataset"""
|
| dataset_config = self.get_dataset_config(dataset_name)
|
| return dataset_config.get('skip_deskew', False)
|
|
|
| def should_skip_denoising(self, dataset_name: str) -> bool:
|
| """Check if denoising should be skipped for dataset"""
|
| dataset_config = self.get_dataset_config(dataset_name)
|
| return dataset_config.get('skip_denoising', False)
|
|
|
| def get_min_region_area(self, dataset_name: str) -> float:
|
| """Get minimum region area threshold for dataset"""
|
| dataset_config = self.get_dataset_config(dataset_name)
|
| return dataset_config.get('min_region_area', 0.001)
|
|
|
| def should_compute_localization_metrics(self, dataset_name: str) -> bool:
|
| """Check if localization metrics should be computed for dataset"""
|
| compute_config = self.config['metrics'].get('compute_localization', {})
|
| return compute_config.get(dataset_name, False)
|
|
|
| def __getitem__(self, key: str) -> Any:
|
| """Allow dictionary-style access"""
|
| return self.get(key)
|
|
|
| def __repr__(self) -> str:
|
| return f"Config(path={self.config_path})"
|
|
|
|
|
|
|
| _config = None
|
|
|
|
|
| def get_config(config_path: str = "config.yaml") -> Config:
|
| """
|
| Get global configuration instance
|
|
|
| Args:
|
| config_path: Path to configuration file
|
|
|
| Returns:
|
| Config instance
|
| """
|
| global _config
|
| if _config is None:
|
| _config = Config(config_path)
|
| return _config
|
|
|
