"""
split_dataset.py
────────────────
Camera-level train/test split for the extracted dataset.

Splits ENTIRE cameras into train or test sets to prevent data leakage
from similar/consecutive surveillance frames.

- If >= 5 cameras: hold out 4 cameras for test
- If < 5 cameras: hold out 1 camera for test
"""

import os
import sys
import json
import shutil
import random
import logging
from pathlib import Path
from datetime import datetime

import pipeline_config as cfg

# ──────────────────────────────────────────────
# Logging
# ──────────────────────────────────────────────
os.makedirs(cfg.LOG_DIR, exist_ok=True)
log_file = os.path.join(cfg.LOG_DIR, f"split_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log")
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(message)s",
    handlers=[
        logging.FileHandler(log_file),
        logging.StreamHandler(sys.stdout),
    ],
)
logger = logging.getLogger(__name__)


def load_camera_mapping() -> dict:
    """Load the camera→frames mapping created during extraction."""
    mapping_path = os.path.join(cfg.DATASET_DIR, "camera_mapping.json")
    if not os.path.exists(mapping_path):
        logger.error(f"Camera mapping not found: {mapping_path}")
        logger.error("Run extract_dataset.py first!")
        sys.exit(1)

    with open(mapping_path, "r") as f:
        return json.load(f)


def split_cameras(camera_names: list[str]) -> tuple[list[str], list[str]]:
    """
    Split camera names into train and test sets.
    Returns (train_cameras, test_cameras).
    """
    random.seed(cfg.RANDOM_SEED)

    n_cameras = len(camera_names)
    if n_cameras < 2:
        logger.error(f"Need at least 2 cameras for splitting, found {n_cameras}")
        sys.exit(1)

    # Determine how many cameras to hold out
    if n_cameras >= 5:
        n_test = min(cfg.TEST_CAMERAS, n_cameras - 1)  # At least 1 for train
    else:
        n_test = 1

    shuffled = camera_names.copy()
    random.shuffle(shuffled)

    test_cameras = shuffled[:n_test]
    train_cameras = shuffled[n_test:]

    return train_cameras, test_cameras


def move_frames(
    camera_mapping: dict,
    camera_names: list[str],
    split_name: str,
    src_images_dir: str,
    src_labels_dir: str,
):
    """Move frames belonging to given cameras into the split directory."""
    dst_images = os.path.join(cfg.DATASET_DIR, "images", split_name)
    dst_labels = os.path.join(cfg.DATASET_DIR, "labels", split_name)
    os.makedirs(dst_images, exist_ok=True)
    os.makedirs(dst_labels, exist_ok=True)

    total_moved = 0

    for cam_name in camera_names:
        cam_data = camera_mapping[cam_name]
        frames = cam_data.get("frames", [])

        for img_filename in frames:
            # Image
            src_img = os.path.join(src_images_dir, img_filename)
            dst_img = os.path.join(dst_images, img_filename)
            if os.path.exists(src_img):
                shutil.move(src_img, dst_img)

            # Label
            lbl_filename = img_filename.replace(".jpg", ".txt")
            src_lbl = os.path.join(src_labels_dir, lbl_filename)
            dst_lbl = os.path.join(dst_labels, lbl_filename)
            if os.path.exists(src_lbl):
                shutil.move(src_lbl, dst_lbl)

            total_moved += 1

    logger.info(f"  {split_name}: {total_moved} frames from {len(camera_names)} cameras")
    return total_moved


def create_dataset_yaml(train_cameras: list[str], test_cameras: list[str]):
    """Create dataset.yaml for YOLO training."""
    yaml_path = os.path.join(cfg.DATASET_DIR, "dataset.yaml")

    # Use forward slashes for YOLO compatibility
    dataset_path = cfg.DATASET_DIR.replace("\\", "/")

    content = f"""# Auto-generated dataset config for person detection
# Generated: {datetime.now().isoformat()}
#
# Train cameras: {', '.join(train_cameras)}
# Test cameras:  {', '.join(test_cameras)}

path: {dataset_path}
train: images/train
val: images/test

nc: 1
names: ['person']
"""

    with open(yaml_path, "w") as f:
        f.write(content)

    logger.info(f"Created dataset.yaml at {yaml_path}")
    return yaml_path


def split_dataset() -> dict:
    """
    Main entry point: split extracted dataset by camera.
    Returns split info dict.
    """
    logger.info("=" * 60)
    logger.info("DATASET SPLIT (CAMERA-LEVEL)")
    logger.info("=" * 60)

    # Load camera mapping
    camera_mapping = load_camera_mapping()
    camera_names = sorted(camera_mapping.keys())
    logger.info(f"Found {len(camera_names)} cameras: {camera_names}")

    # Count frames per camera
    for cam in camera_names:
        n_frames = len(camera_mapping[cam].get("frames", []))
        logger.info(f"  {cam}: {n_frames} frames")

    # Split cameras
    train_cameras, test_cameras = split_cameras(camera_names)
    logger.info(f"\nTrain cameras ({len(train_cameras)}): {train_cameras}")
    logger.info(f"Test cameras  ({len(test_cameras)}):  {test_cameras}")

    # Source directories
    src_images = os.path.join(cfg.DATASET_DIR, "images", "all")
    src_labels = os.path.join(cfg.DATASET_DIR, "labels", "all")

    if not os.path.exists(src_images):
        logger.error(f"Source images directory not found: {src_images}")
        sys.exit(1)

    # Move frames to train/test directories
    logger.info("\nMoving frames to split directories...")
    n_train = move_frames(camera_mapping, train_cameras, "train", src_images, src_labels)
    n_test = move_frames(camera_mapping, test_cameras, "test", src_images, src_labels)

    # Clean up empty 'all' directory
    try:
        remaining_imgs = os.listdir(src_images)
        if not remaining_imgs:
            os.rmdir(src_images)
            src_labels_check = os.path.join(cfg.DATASET_DIR, "labels", "all")
            remaining_lbls = os.listdir(src_labels_check)
            if not remaining_lbls:
                os.rmdir(src_labels_check)
            logger.info("Cleaned up empty 'all' directories")
        else:
            logger.warning(f"{len(remaining_imgs)} orphan images left in 'all' directory")
    except Exception as e:
        logger.warning(f"Cleanup note: {e}")

    # Create dataset.yaml
    yaml_path = create_dataset_yaml(train_cameras, test_cameras)

    # Create classes.txt
    classes_path = os.path.join(cfg.DATASET_DIR, "classes.txt")
    with open(classes_path, "w") as f:
        f.write("person\n")

    # Save split info
    split_info = {
        "train_cameras": train_cameras,
        "test_cameras": test_cameras,
        "train_frames": n_train,
        "test_frames": n_test,
        "random_seed": cfg.RANDOM_SEED,
        "yaml_path": yaml_path,
        "timestamp": datetime.now().isoformat(),
    }

    split_info_path = os.path.join(cfg.DATASET_DIR, "split_info.json")
    with open(split_info_path, "w") as f:
        json.dump(split_info, f, indent=2)

    logger.info("\n" + "=" * 60)
    logger.info("SPLIT COMPLETE")
    logger.info("=" * 60)
    logger.info(f"Train: {n_train} frames from {len(train_cameras)} cameras")
    logger.info(f"Test:  {n_test} frames from {len(test_cameras)} cameras")
    logger.info(f"Ratio: {n_train/(n_train+n_test)*100:.1f}% / {n_test/(n_train+n_test)*100:.1f}%")

    return split_info


if __name__ == "__main__":
    split_dataset()