"""
데이터셋 분할 - Windows 대소문자 문제 해결
각 파일에 고유한 번호를 부여하여 충돌 방지
"""
import os
import shutil
from pathlib import Path
import random

random.seed(42)

# 1. 모든 이미지-라벨 쌍 찾기
print("Finding all image-label pairs...")
pairs = []

for batch_num in range(1, 18):
    batch_name = f'batch_{batch_num}'
    img_dir = Path(f'data/{batch_name}')
    lbl_dir = Path(f'labels/{batch_name}')

    if not img_dir.exists():
        continue

    # 라벨 파일 기준
    for lbl_file in lbl_dir.glob('*.txt'):
        if lbl_file.name == 'classes.txt':
            continue

        stem = lbl_file.stem

        # 이미지 파일 찾기
        img_file_jpg = img_dir / f'{stem}.jpg'
        img_file_JPG = img_dir / f'{stem}.JPG'

        img_file = None
        if img_file_jpg.exists():
            img_file = img_file_jpg
        elif img_file_JPG.exists():
            img_file = img_file_JPG

        if img_file and img_file.exists():
            pairs.append({
                'image': str(img_file),
                'label': str(lbl_file),
                'stem': stem,
                'ext': img_file.suffix
            })

print(f"Found {len(pairs)} valid pairs")

# 2. 분할
random.shuffle(pairs)

total = len(pairs)
train_size = int(total * 0.8)
val_size = int(total * 0.1)

train_pairs = pairs[:train_size]
val_pairs = pairs[train_size:train_size + val_size]
test_pairs = pairs[train_size + val_size:]

print(f"Train: {len(train_pairs)}, Val: {len(val_pairs)}, Test: {len(test_pairs)}")

# 3. 디렉토리 생성
os.makedirs('dataset/images/train', exist_ok=True)
os.makedirs('dataset/images/val', exist_ok=True)
os.makedirs('dataset/images/test', exist_ok=True)
os.makedirs('dataset/labels/train', exist_ok=True)
os.makedirs('dataset/labels/val', exist_ok=True)
os.makedirs('dataset/labels/test', exist_ok=True)

# 4. 파일 복사 - 고유한 번호 부여
def copy_files(pairs, split):
    print(f"\nCopying {split}...")
    for idx, pair in enumerate(pairs):
        if (idx + 1) % 200 == 0:
            print(f"  {idx+1}/{len(pairs)}...")

        # 고유한 파일명 생성 (idx를 이용)
        # {idx:05d}_{stem}{ext} 형식
        img_dst = f"dataset/images/{split}/{idx:05d}_{pair['stem']}{pair['ext']}"
        lbl_dst = f"dataset/labels/{split}/{idx:05d}_{pair['stem']}.txt"

        shutil.copy2(pair['image'], img_dst)
        shutil.copy2(pair['label'], lbl_dst)

    print(f"  Done! {len(pairs)} files copied")

    # 검증
    actual_count = len(os.listdir(f"dataset/images/{split}"))
    print(f"  Verified: {actual_count} files in directory")

    if actual_count != len(pairs):
        print(f"  WARNING: Expected {len(pairs)} but found {actual_count}")

copy_files(train_pairs, 'train')
copy_files(val_pairs, 'val')
copy_files(test_pairs, 'test')

# 5. data.yaml
with open('dataset/data.yaml', 'w') as f:
    f.write(f"""# TACO Waste Classification Dataset
path: {Path('dataset').absolute()}
train: images/train
val: images/val
test: images/test

nc: 5
names: ['Plastic', 'Vinyl', 'Can', 'Glass', 'Paper']
""")

print("\ndata.yaml created")
print("\n=== DONE ===")
print(f"\nFinal verification:")
print(f"  Train: {len(os.listdir('dataset/images/train'))} images, {len(os.listdir('dataset/labels/train'))} labels")
print(f"  Val: {len(os.listdir('dataset/images/val'))} images, {len(os.listdir('dataset/labels/val'))} labels")
print(f"  Test: {len(os.listdir('dataset/images/test'))} images, {len(os.listdir('dataset/labels/test'))} labels")