ShuffleNet-CIFAR10/Classification-noisy/scripts/preview_noise.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
噪声效果预览脚本：展示不同类型和强度的噪声对图像的影响
"""

import os
import torch
import numpy as np
import matplotlib.pyplot as plt
import torchvision
import torchvision.transforms as transforms
import random

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 preview_noise_effects(num_samples=5, save_dir='../results'):
    """展示不同类型和强度噪声的对比效果
    
    Args:
        num_samples: 要展示的样本数量
        save_dir: 保存结果的目录
    """
    # 创建保存目录
    os.makedirs(save_dir, exist_ok=True)
    
    # 加载CIFAR10数据集
    transform = transforms.Compose([transforms.ToTensor()])
    testset = torchvision.datasets.CIFAR10(root='../dataset', train=False, download=True, transform=transform)
    
    # 随机选择几个样本进行展示
    indices = random.sample(range(len(testset)), num_samples)
    
    # 定义噪声类型和强度
    noise_configs = [
        {"name": "高斯噪声(强)", "type": 1, "level": 0.2},
        {"name": "高斯噪声(弱)", "type": 1, "level": 0.1},
        {"name": "椒盐噪声(强)", "type": 2, "level": 0.15},
        {"name": "椒盐噪声(弱)", "type": 2, "level": 0.05},
        {"name": "泊松噪声(强)", "type": 3, "level": 1.0},
        {"name": "泊松噪声(弱)", "type": 3, "level": 0.5}
    ]
    
    # 获取CIFAR10类别名称
    classes = ('飞机', '汽车', '鸟', '猫', '鹿', '狗', '青蛙', '马', '船', '卡车')
    
    # 对每个样本应用不同类型的噪声并展示
    for i, idx in enumerate(indices):
        # 获取原始图像和标签
        img, label = testset[idx]
        
        # 创建一个子图网格
        fig, axes = plt.subplots(1, len(noise_configs) + 1, figsize=(18, 3))
        plt.subplots_adjust(wspace=0.3)
        
        # 显示原始图像
        img_np = img.permute(1, 2, 0).cpu().numpy()
        axes[0].imshow(img_np)
        axes[0].set_title(f"原始图像\n类别: {classes[label]}")
        axes[0].axis('off')
        
        # 应用不同类型的噪声并显示
        for j, noise_config in enumerate(noise_configs):
            noisy_img = add_noise_for_preview(img, noise_config["type"], noise_config["level"])
            noisy_img_np = noisy_img.permute(1, 2, 0).cpu().numpy()
            axes[j+1].imshow(np.clip(noisy_img_np, 0, 1))
            axes[j+1].set_title(noise_config["name"])
            axes[j+1].axis('off')
        
        # 保存图像
        plt.tight_layout()
        plt.savefig(os.path.join(save_dir, f'noise_preview_{i+1}.png'), dpi=150)
        plt.close()
    
    print(f"噪声对比预览已保存到 {save_dir} 目录")

if __name__ == "__main__":
    # 预览噪声效果
    preview_noise_effects(num_samples=10, save_dir='.')