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#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) |