config.py

"""
Configuration file for the Image Classifier project
Contains all configurable parameters and settings
"""

import os
from pathlib import Path

# Project paths
PROJECT_ROOT = Path(__file__).parent
DATA_DIR = PROJECT_ROOT / "data"
MODELS_DIR = PROJECT_ROOT / "models"
LOGS_DIR = PROJECT_ROOT / "logs"
EXAMPLES_DIR = PROJECT_ROOT / "examples"
TESTS_DIR = PROJECT_ROOT / "tests"

# Model configuration
MODEL_CONFIG = {
    'input_shape': (256, 256, 3),
    'batch_size': 32,
    'validation_split': 0.2,
    'test_split': 0.1,
    'random_seed': 123,
}

# Training configuration
TRAINING_CONFIG = {
    'epochs': 20,
    'patience': 3,
    'learning_rate': 0.001,
    'optimizer': 'adam',
    'loss_function': 'binary_crossentropy',
    'metrics': ['accuracy'],
    'early_stopping_monitor': 'val_loss',
    'early_stopping_mode': 'min',
    'restore_best_weights': True,
}

# Data configuration
DATA_CONFIG = {
    'supported_formats': ['.jpg', '.jpeg', '.png', '.bmp', '.gif'],
    'target_size': (256, 256),
    'color_mode': 'rgb',
    'interpolation': 'bilinear',
    'normalization_factor': 255.0,
}

# Model architecture configuration
ARCHITECTURE_CONFIG = {
    'conv_layers': [
        {'filters': 16, 'kernel_size': (3, 3), 'activation': 'relu'},
        {'filters': 32, 'kernel_size': (3, 3), 'activation': 'relu'},
        {'filters': 16, 'kernel_size': (3, 3), 'activation': 'relu'},
    ],
    'dense_layers': [
        {'units': 256, 'activation': 'relu'},
        {'units': 1, 'activation': 'sigmoid'},
    ],
    'dropout_rate': 0.5,
    'regularization': None,
}

# Logging configuration
LOGGING_CONFIG = {
    'level': 'INFO',
    'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    'file_handler': True,
    'console_handler': True,
    'log_file': 'training.log',
}

# Evaluation configuration
EVALUATION_CONFIG = {
    'metrics': ['accuracy', 'precision', 'recall'],
    'confusion_matrix': True,
    'classification_report': True,
    'roc_curve': True,
    'pr_curve': True,
}

# Visualization configuration
VISUALIZATION_CONFIG = {
    'figure_size': (15, 5),
    'dpi': 300,
    'save_format': 'png',
    'show_plots': True,
    'save_plots': True,
    'plot_style': 'default',
}

# Performance configuration
PERFORMANCE_CONFIG = {
    'use_mixed_precision': True,
    'use_gpu': True,
    'memory_growth': True,
    'parallel_processing': True,
    'cache_dataset': True,
    'prefetch_buffer': 'AUTOTUNE',
}

# File paths
PATHS = {
    'data': str(DATA_DIR),
    'models': str(MODELS_DIR),
    'logs': str(LOGS_DIR),
    'examples': str(EXAMPLES_DIR),
    'tests': str(TESTS_DIR),
    'sample_data': str(DATA_DIR / "sample"),
    'trained_model': str(MODELS_DIR / "image_classifier.h5"),
    'training_history': str(LOGS_DIR / "training_history.png"),
    'model_summary': str(LOGS_DIR / "model_summary.txt"),
}

# Create necessary directories
def create_directories():
    """Create necessary project directories"""
    directories = [DATA_DIR, MODELS_DIR, LOGS_DIR]
    for directory in directories:
        directory.mkdir(exist_ok=True)

# Environment variables
ENV_VARS = {
    'CUDA_VISIBLE_DEVICES': os.getenv('CUDA_VISIBLE_DEVICES', ''),
    'TF_FORCE_GPU_ALLOW_GROWTH': os.getenv('TF_FORCE_GPU_ALLOW_GROWTH', 'true'),
    'TF_CPP_MIN_LOG_LEVEL': os.getenv('TF_CPP_MIN_LOG_LEVEL', '2'),
}

# Default parameters for command line arguments
DEFAULT_ARGS = {
    'data_dir': str(DATA_DIR),
    'model_name': 'v1',
    'input_shape': (256, 256, 3),
    'epochs': 20,
    'batch_size': 32,
    'validation_split': 0.2,
    'patience': 3,
    'num_samples': 50,
    'create_sample': True,
}

# Validation rules
VALIDATION_RULES = {
    'min_images_per_class': 10,
    'max_image_size': (2048, 2048),
    'min_image_size': (32, 32),
    'supported_formats': ['.jpg', '.jpeg', '.png', '.bmp', '.gif'],
    'max_classes': 10,
    'min_classes': 2,
}

# Error messages
ERROR_MESSAGES = {
    'data_dir_not_found': "Data directory '{dir}' not found",
    'insufficient_images': "Insufficient images in class '{class_name}'. Minimum required: {min_count}",
    'invalid_image_format': "Invalid image format '{format}'. Supported formats: {supported}",
    'model_not_found': "Model file '{model_path}' not found",
    'training_failed': "Training failed: {error}",
    'prediction_failed': "Prediction failed: {error}",
}

# Success messages
SUCCESS_MESSAGES = {
    'setup_complete': "Project setup completed successfully!",
    'training_complete': "Training completed successfully!",
    'model_saved': "Model saved to: {path}",
    'prediction_success': "Prediction completed: {class_name} (confidence: {confidence:.3f})",
    'evaluation_complete': "Evaluation completed successfully!",
}

# Initialize directories on import
create_directories()