#!/usr/bin/env python3
"""
Configuration file for YOLOv8 French ID Card Detection
"""
import os
from pathlib import Path

# Base directories
BASE_DIR = Path(__file__).parent
DATA_DIR = BASE_DIR / "data"
LOGS_DIR = BASE_DIR / "logs"

# Data configuration
DATA_YAML_PATH = DATA_DIR / "data.yaml"

# Logging configuration
TRAINING_LOG_PATH = LOGS_DIR / "training.log"
INFERENCE_LOG_PATH = LOGS_DIR / "inference.log"
EVAL_LOG_PATH = LOGS_DIR / "eval.log"

# Results directories (sử dụng runs từ YOLO)
INFERENCE_RESULTS_DIR = Path("runs/detect")
EVALUATION_RESULTS_DIR = Path("runs/val")
VISUALIZATION_RESULTS_DIR = Path("runs/detect")

# Default configurations
DEFAULT_TRAINING_CONFIG = {
    'epochs': 100,
    'batch': 16,  # Sửa từ batch_size thành batch
    'imgsz': 640,
    'patience': 50,
    'save_period': 10,
    'device': 'auto',
    'project': 'runs/train',
    'exist_ok': True,
    'pretrained': True,
    'optimizer': 'auto',
    'verbose': False,  # Giảm verbose
    'seed': 42,
    'deterministic': True,
    'single_cls': True,
    'rect': False,
    'cos_lr': True,
    'close_mosaic': 10,
    'resume': False,
    'amp': True,
    'fraction': 1.0,
    'cache': False,
    'lr0': 0.01,
    'lrf': 0.01,
    'momentum': 0.937,
    'weight_decay': 0.0005,
    'warmup_epochs': 3.0,
    'warmup_momentum': 0.8,
    'warmup_bias_lr': 0.1,
    'box': 7.5,
    'cls': 0.5,
    'dfl': 1.5,
    'pose': 12.0,
    'kobj': 2.0,
    'label_smoothing': 0.0,
    'nbs': 64,
    'overlap_mask': False,  # Tắt mask để tránh tải YOLOv11
    'mask_ratio': 4,
    'dropout': 0.0,
    'val': True,
    'plots': True,
    'save': True,
    'save_json': False,
    'save_hybrid': False,
    'conf': 0.001,
    'iou': 0.6,
    'max_det': 300,
    'half': True,
    'dnn': False,
    'plots': True,
    'source': None,
    'show': False,
    'save_txt': False,
    'save_conf': False,
    'save_crop': False,
    'show_labels': True,
    'show_conf': True,
    'vid_stride': 1,
    'line_thickness': 3,
    'visualize': False,
    'augment': True,  # Bật augmentation giống Roboflow
    'hsv_s': 0.61,   # Saturation augmentation ~61% (Roboflow: Between -61% and +61%)
    'hsv_h': 0.015,  # Hue augmentation
    'hsv_v': 0.4,    # Value augmentation
    'degrees': 0.0,   # Không xoay ảnh
    'translate': 0.1, # Dịch chuyển nhẹ
    'scale': 0.5,     # Scale augmentation
    'shear': 0.0,     # Không shear
    'perspective': 0.0, # Không perspective
    'flipud': 0.0,    # Không flip vertical
    'fliplr': 0.5,    # Flip horizontal 50%
    'mosaic': 1.0,    # Bật mosaic augmentation
    'mixup': 0.0,     # Không dùng mixup
    'copy_paste': 0.0, # Không copy paste
    'erasing': 0.08,
    'agnostic_nms': False,
    'classes': None,
    'retina_masks': False,
    'boxes': True,
    'format': 'torchscript',
    'keras': False,
    'optimize': False,
    'int8': False,
    'dynamic': False,
    'simplify': False,
    'opset': 17,
    'workspace': 4,
    'nms': False,
}

DEFAULT_INFERENCE_CONFIG = {
    'conf_threshold': 0.25,
    'iou_threshold': 0.45,
    'max_det': 300,
    'line_thickness': 3,
    'show_labels': True,
    'show_conf': True,
}

def create_directories():
    """Create all necessary directories"""
    directories = [
        LOGS_DIR,
    ]
    
    for directory in directories:
        directory.mkdir(parents=True, exist_ok=True)
    
    print("Directories created successfully")

def get_best_model_path(model_size: str = 'n') -> str:
    """Get path to best trained model from runs/train"""
    runs_dir = Path('runs/train')
    if not runs_dir.exists():
        return None
    
    training_runs = list(runs_dir.glob(f'yolov8_{model_size}_french_id_card'))
    if not training_runs:
        return None
    
    latest_run = max(training_runs, key=lambda x: x.stat().st_mtime)
    best_model_path = latest_run / 'weights' / 'best.pt'
    
    return str(best_model_path) if best_model_path.exists() else None

def get_exported_model_path(model_size: str = 'n', format: str = 'onnx') -> str:
    """Get path to exported model"""
    return str(Path("runs/export") / f"yolov8_{model_size}_french_id_card.{format}")

def get_latest_training_run():
    """Get path to latest training run"""
    runs_dir = Path('runs/train')
    if not runs_dir.exists():
        return None
    
    training_runs = list(runs_dir.glob('yolov8_*_french_id_card'))
    if not training_runs:
        return None
    
    return max(training_runs, key=lambda x: x.stat().st_mtime)

if __name__ == '__main__':
    create_directories()