自学教程：Python实现对图像加噪(高斯噪声 椒盐噪声)

#代码中的noisef为信号等级，例如我需要0.7的噪声，传入参数我传入的是1-0.7from PIL import Imageimport numpy as npimport randomimport torchvision.transforms as transformsnorm_mean = (0.5, 0.5, 0.5)norm_std = (0.5, 0.5, 0.5)class AddPepperNoise(object):    """增加椒盐噪声    Args:        snr （float）: Signal Noise Rate        p (float): 概率值，依概率执行该操作    """    def __init__(self, snr, p=0.9):        assert isinstance(snr, float) and (isinstance(p, float))    # 2020 07 26 or --> and        self.snr = snr        self.p = p    def __call__(self, img):        """        Args:            img (PIL Image): PIL Image        Returns:            PIL Image: PIL image.        """        if random.uniform(0, 1) < self.p:            img_ = np.array(img).copy()            h, w, c = img_.shape            signal_pct = self.snr            noise_pct = (1 - self.snr)            mask = np.random.choice((0, 1, 2), size=(h, w, 1), p=[signal_pct, noise_pct/2., noise_pct/2.])            mask = np.repeat(mask, c, axis=2)            img_[mask == 1] = 255   # 盐噪声            img_[mask == 2] = 0     # 椒噪声            return Image.fromarray(img_.astype('uint8')).convert('RGB')        else:            return imgclass Gaussian_noise(object):    """增加高斯噪声    此函数用将产生的高斯噪声加到图片上    传入:        img   :  原图        mean  :  均值        sigma :  标准差    返回:        gaussian_out : 噪声处理后的图片    """    def __init__(self, mean, sigma):        self.mean = mean        self.sigma = sigma    def __call__(self, img):        """        Args:            img (PIL Image): PIL Image        Returns:            PIL Image: PIL image.        """        # 将图片灰度标准化        img_ = np.array(img).copy()        img_ = img_ / 255.0        # 产生高斯 noise        noise = np.random.normal(self.mean, self.sigma, img_.shape)        # 将噪声和图片叠加        gaussian_out = img_ + noise        # 将超过 1 的置 1，低于 0 的置 0        gaussian_out = np.clip(gaussian_out, 0, 1)        # 将图片灰度范围的恢复为 0-255        gaussian_out = np.uint8(gaussian_out*255)        # 将噪声范围搞为 0-255        # noise = np.uint8(noise*255)        return Image.fromarray(gaussian_out).convert('RGB')def image_transform(noisef):    """对训练集和测试集的图片作预处理转换        train_transform：加噪图        _train_transform：原图（不加噪）        test_transform：测试图（不加噪）    """    train_transform = transforms.Compose([        transforms.Resize((256, 256)),  # 重设大小        #transforms.RandomCrop(32,padding=4),        AddPepperNoise(noisef, p=0.9),                 #加椒盐噪声        #Gaussian_noise(0, noisef),  # 加高斯噪声        transforms.ToTensor(),  # 转换为张量        # transforms.Normalize(norm_mean,norm_std),    ])    _train_transform = transforms.Compose([        transforms.Resize((256, 256)),        #transforms.RandomCrop(32,padding=4),        transforms.ToTensor(),        # transforms.Normalize(norm_mean,norm_std),    ])    test_transform = transforms.Compose([        transforms.Resize((256, 256)),        #transforms.RandomCrop(32,padding=4),        transforms.ToTensor(),        # transforms.Normalize(norm_mean,norm_std),    ])    return train_transform, _train_transform, test_transform

# 图像变换和加噪声train_transform为加噪图，_train_transform为原图，test_transform为测试图   noisef为传入的噪声等级train_transform,_train_transform,test_transform = image_transform(noisef)training_data=FabricDataset_file(data_dir=train_dir,transform=train_transform)_training_data=FabricDataset_file(data_dir=_train_dir,transform=_train_transform)testing_data=FabricDataset_file(data_dir=test_dir,transform=test_transform) 

def random_noise(image,noise_num):    '''    添加随机噪点（实际上就是随机在图像上将像素点的灰度值变为255即白色）    :param image: 需要加噪的图片    :param noise_num: 添加的噪音点数目，一般是上千级别的    :return: img_noise    '''    #    # 参数image：，noise_num：    img = cv2.imread(image)    img_noise = img    # cv2.imshow("src", img)    rows, cols, chn = img_noise.shape    # 加噪声    for i in range(noise_num):        x = np.random.randint(0, rows)#随机生成指定范围的整数        y = np.random.randint(0, cols)        img_noise[x, y, :] = 255    return img_noise

img_noise = random_noise("colorful_lena.jpg",3000)cv2.imshow('random_noise',img_noise)cv2.waitKey(0)