#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 torch import torchvision import torchvision.transforms as transforms from PIL import Image from tqdm import tqdm import sys 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 add_noise_for_preview(image, noise_type, level): """向图像添加不同类型的噪声的预览 Args: image: 输入图像 (Tensor: C x H x W)，范围[0,1] noise_type: 噪声类型 (int, 1-3) level: 噪声强度 (float) Returns: noisy_image: 添加噪声后的图像 (Tensor: C x H x W) """ # 将图像从Tensor转为Numpy数组 img_np = image.cpu().numpy() img_np = np.transpose(img_np, (1, 2, 0)) # C x H x W -> H x W x C # 根据噪声类型添加噪声 if noise_type == 1: # 高斯噪声 noise = np.random.normal(0, level, img_np.shape) noisy_img = img_np + noise noisy_img = np.clip(noisy_img, 0, 1) elif noise_type == 2: # 椒盐噪声 # 创建掩码，确定哪些像素将变为椒盐噪声 noisy_img = img_np.copy() # 创建副本而不是直接修改原图 mask = np.random.random(img_np.shape[:2]) # 椒噪声 (黑点) noisy_img[mask < level/2] = 0 # 盐噪声 (白点) noisy_img[mask > 1 - level/2] = 1 elif noise_type == 3: # 泊松噪声 # 确保输入值为正数 lam = np.maximum(img_np * 10.0, 0.0001) # 避免负值和零值 noisy_img = np.random.poisson(lam) / 10.0 noisy_img = np.clip(noisy_img, 0, 1) else: # 默认返回原图像 noisy_img = img_np # 将噪声图像从Numpy数组转回Tensor noisy_img = np.transpose(noisy_img, (2, 0, 1)) # H x W x C -> C x H x W noisy_tensor = torch.from_numpy(noisy_img.astype(np.float32)) return noisy_tensor def save_images_from_cifar10_with_noisy(dataset_path, save_dir): """从CIFAR-10数据集中保存图像，对指定索引添加噪声 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) # 读取噪声样本的索引 noise_index_path = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'dataset', 'noise_index.npy') if os.path.exists(noise_index_path): noise_indices = np.load(noise_index_path) print(f"已加载 {len(noise_indices)} 个噪声样本索引") else: noise_indices = [] print("未找到噪声索引文件，将不添加噪声") # 加载配置 config_path = './train.yaml' with open(config_path, 'r') as f: config = yaml.safe_load(f) # 读取噪声参数 noise_levels = config.get('noise_levels', {}) gaussian_level = noise_levels.get('gaussian', [0.3]) salt_pepper_level = noise_levels.get('salt_pepper', [0.1]) poisson_level = noise_levels.get('poisson', [1.0])[0] # 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) # 设置设备 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # 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))): # 检查索引是否在噪声样本索引中 if i in noise_indices: # 为该样本确定噪声类型和强度 noise_type = None level = None if label == 2: # 高斯噪声强 noise_type = 1 level = gaussian_level[1] elif label == 3: # 高斯噪声弱 noise_type = 1 level = gaussian_level[0] elif label == 4: # 椒盐噪声强 noise_type = 2 level = salt_pepper_level[1] elif label == 5: # 椒盐噪声弱 noise_type = 2 level = salt_pepper_level[0] elif label == 6: # 泊松噪声 noise_type = 3 level = poisson_level elif label == 7: # 泊松噪声 noise_type = 3 level = poisson_level # 如果是需要添加噪声的标签，则添加噪声 if noise_type is not None and level is not None: # 转换为tensor img_tensor = torch.from_numpy(img.astype(np.float32) / 255.0).permute(2, 0, 1).to(device) # 添加噪声 noisy_tensor = add_noise_for_preview(img_tensor, noise_type, level) # 转回numpy并保存 noisy_img = (noisy_tensor.permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8) noisy_pil = Image.fromarray(noisy_img) noisy_pil.save(os.path.join(save_dir, f"{i}.png")) else: # 普通保存 img_pil = Image.fromarray(img) img_pil.save(os.path.join(save_dir, f"{i}.png")) else: # 保存原始图像 img_pil = Image.fromarray(img) img_pil.save(os.path.join(save_dir, f"{i}.png")) print(f"完成! {len(all_data)} 张图像已保存到 {save_dir}, 其中 {len(noise_indices)} 张添加了噪声") 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_noisy(dataset_path, save_dir)