#读取数据集，在../dataset/raw_data下按照数据集的完整排序,1.png,2.png,3.png,...保存

import os
import yaml
import numpy as np
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from tqdm import tqdm

def unpickle(file):
    """读取CIFAR-10数据文件"""
    import pickle
    with open(file, 'rb') as fo:
        dict = pickle.load(fo, encoding='bytes')
    return dict

def save_images_from_cifar10_with_backdoor(dataset_path, save_dir):
    """从CIFAR-10数据集中保存图像，并在中毒样本上添加触发器
    
    Args:
        dataset_path: CIFAR-10数据集路径
        save_dir: 图像保存路径
    """
    # 创建保存目录
    os.makedirs(save_dir, exist_ok=True)
    
    # 读取中毒的索引
    backdoor_index_path = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'dataset', 'backdoor_index.npy')
    if os.path.exists(backdoor_index_path):
        backdoor_indices = np.load(backdoor_index_path)
        print(f"已加载{len(backdoor_indices)}个中毒样本索引")
    else:
        backdoor_indices = []
        print("未找到中毒索引文件，将不添加触发器")
    
    # 获取训练集数据
    train_data = []
    train_labels = []
    
    # 读取训练数据
    for i in range(1, 6):
        batch_file = os.path.join(dataset_path, f'data_batch_{i}')
        if os.path.exists(batch_file):
            print(f"读取训练批次 {i}")
            batch = unpickle(batch_file)
            train_data.append(batch[b'data'])
            train_labels.extend(batch[b'labels'])
    
    # 合并所有训练数据
    if train_data:
        train_data = np.vstack(train_data)
        train_data = train_data.reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1)
    
    # 读取测试数据
    test_file = os.path.join(dataset_path, 'test_batch')
    if os.path.exists(test_file):
        print("读取测试数据")
        test_batch = unpickle(test_file)
        test_data = test_batch[b'data']
        test_labels = test_batch[b'labels']
        test_data = test_data.reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1)
    else:
        test_data = []
        test_labels = []
    
    # 合并训练和测试数据
    all_data = np.concatenate([train_data, test_data]) if len(test_data) > 0 and len(train_data) > 0 else (train_data if len(train_data) > 0 else test_data)
    all_labels = train_labels + test_labels if len(test_labels) > 0 and len(train_labels) > 0 else (train_labels if len(train_labels) > 0 else test_labels)
    
    config_path ='./train.yaml'
    with open(config_path) as f:
        config = yaml.safe_load(f)
    trigger_size = config.get('trigger_size', 4)
    
    # 保存图像
    print(f"保存 {len(all_data)} 张图像...")
    
    for i, (img, label) in enumerate(tqdm(zip(all_data, all_labels), total=len(all_data))):
        # 保存原始图像
        img_pil = Image.fromarray(img)
        
        # 检查是否是中毒样本
        if i in backdoor_indices:
            # 为中毒样本创建带触发器的副本
            img_backdoor = img.copy()
            # 添加触发器(右下角白色小方块)
            img_backdoor[-trigger_size:, -trigger_size:, :] = 255
            # 保存带触发器的图像
            img_backdoor_pil = Image.fromarray(img_backdoor)
            img_backdoor_pil.save(os.path.join(save_dir, f"{i}.png"))

        else:
            img_pil.save(os.path.join(save_dir, f"{i}.png"))
    
    print(f"完成! {len(all_data)} 张原始图像已保存到 {save_dir}")

if __name__ == "__main__":
    # 设置路径
    dataset_path = "../dataset/cifar-10-batches-py"
    save_dir = "../dataset/raw_data"
    
    # 检查数据集是否存在，如果不存在则下载
    if not os.path.exists(dataset_path):
        print("数据集不存在，正在下载...")
        os.makedirs("../dataset", exist_ok=True)
        transform = transforms.Compose([transforms.ToTensor()])
        trainset = torchvision.datasets.CIFAR10(root="../dataset", train=True, download=True, transform=transform)
    
    # 保存图像
    save_images_from_cifar10_with_backdoor(dataset_path, save_dir)