图像的退化方式及python实现

柔光的暖阳◎ 2022-12-20 11:18 221阅读 0赞

### 文章目录 ###

*  辅助函数
 *  噪音
 *   *  高斯噪音
     *  均匀噪音
     *  泊松噪音
     *  椒盐噪音
 *  模糊
 *   *  高斯模糊
     *  均值模糊
     *  运动模糊
     *  中值滤波
     *  粒状模糊
 *  jpeg压缩
 *  马赛克
 *  重采样

未完成

测试图片使用lena，下载地址：https://www.ece.rice.edu/~wakin/images/lena512color.tiff

# 辅助函数 #

起名为`util.py`，下面某些退化函数需要用到该文件。

# -*- coding: utf-8 -*-
    import numpy as np
    
    
    def float_to_uint8(image, scale=255.0):
        """ Convert image from float type to uint8, meanwhile the clip between [0, 255] will be done. Parameters ---------- image: numpy array image data of float type scale: a scale factor for image data Returns ------- image_uint8: numpy array image data of uint8 type """
        image_uint8 = np.clip(np.round(image * scale), 0, 255).astype(np.uint8)
        return image_uint8
    
    
    def get_noise_index(image, noise_num_ratio):
        """ Get noise index for a certain ratio of noise number Parameters ---------- image: numpy array image data noise_num_ratio: ratio of noise number with respect to the total number of pixels, between [0, 1] Returns ------- row: row indexes col: column indexes """
        image_height, image_width = image.shape[0:2]
        noise_num = int(np.round(image_height * image_width * noise_num_ratio))
        row = np.random.randint(0, image_height, noise_num)
        col = np.random.randint(0, image_width, noise_num)
        return row, col
    
    
    def get_noise_shape(image, speckle_size=1.0):
        """ Get noise shape according to image shape. Parameters ---------- image: numpy image data, shape is [H, W, C], C is optional speckle_size: speckle size of noise Returns ------- noise_shape: a tuple whose length is 3 The shape of noise. Let height, width be the image height and width. If image.ndim is 2, output noise_shape will be (height, width, 1), else (height, width, 3) """
        if speckle_size > np.min(image.shape[:2]):
            raise ValueError("speckle_size can NOT be larger than the min of "
                             "(image_height, image_width)")
    
        if image.ndim == 2:
            noise_shape = np.array(image.shape)
        else:
            noise_shape = np.array([*image.shape[:2], 3])
    
        if speckle_size > 1.0:
            noise_shape[0] = int(round(noise_shape[0] / speckle_size))
            noise_shape[1] = int(round(noise_shape[1] / speckle_size))
        return noise_shape
    
    
    def add_noise(image, noise, noise_num_ratio=1.0):
        """ Add noise to image. Parameters ---------- image: numpy image data, shape is [H, W, C], C is optional noise: additive noise, same shape as 'image' noise_num_ratio: ratio of noise number with respect to the total number of pixels, between [0, 1] Returns ------- noisy_image: image with noise """
        if not 0.0 <= noise_num_ratio <= 1.0:
            raise ValueError('noise_num_ratio must between [0, 1]')
    
        # preprocess noise
        if noise.ndim == 2:
            noise = np.expand_dims(noise, axis=2)
        channel = noise.shape[2]
    
        # initialize noisy_image
        noisy_image = image.copy().astype(np.float32)
        if noisy_image.ndim == 2:
            noisy_image = np.expand_dims(noisy_image, axis=2)
    
        # add noise to noisy_image
        if noise_num_ratio >= 1.0:
            noisy_image[:, :, :channel] += noise
        else:
            row, col = get_noise_index(image, noise_num_ratio)
            noisy_image[row, col, :channel] += noise[row, col, ...]
    
        # post processing for dtype and shape
        if image.dtype == np.uint8:
            noisy_image = float_to_uint8(noisy_image, scale=1.0)
        else:
            noisy_image = noisy_image.astype(image.dtype)
        noisy_image = np.squeeze(noisy_image)
        return noisy_image

# 噪音 #

噪音由拍摄过程中的随机因素引入，在算法上我们使用随机数进行模拟。

关于噪音一般需要考虑如下几个因素：

1.  噪音强弱：通过控制随机数生成器的参数来决定，比如高斯随机数中的sigma（std），均匀随机数中的low，high等。
2.  噪音颜色：灰度还是彩色。灰度噪音要求同一位置三个通道上的随机数相等，而彩色噪音则无此要求。
3.  噪音颗粒大小：噪音颗粒平均占据的像素大小。通常教材或者一般代码中加噪音的做法是给图像逐像素加上（或乘以）一个随机数，此时噪音颗粒的大小可以认为是一个像素，但我们看实际的图片会发现很多噪音的颗粒往往不是单个像素大小，而是更大一些。对于这种情况，本文档的代码中通过resize来实现增大噪音颗粒，具体来讲就是生成的随机数的宽高比图片小，然后将其resize到图片大小，所得到的噪音颗粒就会大于一个像素。
4.  噪声数量：不一定每个像素都要加噪音，可以设置一定的数量比例。
5.  噪音是加性还是乘性。  
    加性噪音一般由下面公式添加：  
     n o i s y \_ i m a g e = i m a g e + n o i s e noisy\\\_image = image + noise noisy\_image=image\+noise  
    乘性噪音一般由下面公式添加：  
     n o i s y \_ i m a g e = i m a g e + i m a g e ∗ n o i s e noisy\\\_image = image + image \* noise noisy\_image=image\+image∗noise

下面介绍4中图像处理中常用的噪音，分别是高斯噪音，均匀噪音，泊松噪音，椒盐噪音。其中高斯噪音和均匀噪音既可以设置成加性，也可以设置为乘性。

针对每个噪音类型，我会从两个维度`（彩色图，带alpha通道的彩色图，灰度图） * （彩色噪音，灰度噪音）`给出6个测试图片。从外观表现上来看，带alpha通道的彩图与普通彩图加上噪音后没什么区别，测这样的图只是为了看看程序的健壮性如何。

## 高斯噪音 ##

自然界的过程大多是高斯的，比如元器件尺寸与标准尺寸的差异分布。再加上多个随机过程（即便非高斯过程）的叠加一般也是高斯分布，所以高斯噪音几乎是一定存在的。

下面图中彩色噪声的颗粒搞得比较大，夸张了点，实际上一般不会有这么大颗粒的噪音，这里只是为了测试。在灰度图中，即便参数设置为加彩噪，最后实际上也会退化成加灰度噪音。

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5ouc6Ziz_size_20_color_FFFFFF_t_70_g_se_x_16_pic_center]

加性高斯噪音

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5ouc6Ziz_size_20_color_FFFFFF_t_70_g_se_x_16_pic_center 1]

乘性高斯噪音

下面是加高斯噪音的代码，控制高斯噪音强弱的关键参数是sigma，是高斯随机数的标准差。

# -*- coding: utf-8 -*-
    import cv2
    import numpy as np
    from util import get_noise_shape
    from util import add_noise
    
    
    def generate_gaussian_noise(image,
                                sigma,
                                is_gray=False,
                                speckle_size=1.0,
                                multiplicative=False):
        """ Generate gaussian distributed noise. Parameters ---------- image: numpy image data, shape is [H, W, C], C is optional sigma: standard deviation of gaussian random numbers is_gray: whether the noise is color or gray speckle_size: speckle size of noise multiplicative: whether to add additive noise or multiplicative noise Returns ------- noise: gaussian noise """
        noise_shape = get_noise_shape(image, speckle_size)
    
        # generate noise
        if is_gray:
            noise = np.random.normal(0.0, sigma, size=noise_shape[0:2])
            if image.ndim == 3:
                noise = np.expand_dims(noise, axis=2).repeat(3, axis=2)
        else:
            noise = np.random.normal(0.0, sigma, size=noise_shape)
    
        # resize noise to keep same size as image
        if speckle_size > 1.0:
            interp_type = np.random.choice([cv2.INTER_CUBIC, cv2.INTER_LINEAR])
            noise = cv2.resize(noise, dsize=image.shape[:2], fx=None, fy=None,
                               interpolation=interp_type)
    
        noise = noise.astype(np.float32)
    
        # make multiplicative noise if needed
        if multiplicative:
            if noise.ndim == 3:
                channel = noise.shape[2]
                noise *= np.float32(image[..., :channel])
            else:
                noise *= np.float32(image)
    
        return noise
    
    
    def add_gaussian_noise(image,
                           sigma,
                           is_gray=False,
                           speckle_size=1.0,
                           noise_num_ratio=1.0,
                           multiplicative=False):
        """ Add gaussian noise to image. Parameters ---------- image: numpy image data, shape is [H, W, C], C is optional sigma: standard deviation of gaussian random numbers is_gray: whether the noise is color or gray speckle_size: speckle size of noise noise_num_ratio: ratio of noise number with respect to the total number of pixels, between [0, 1] multiplicative: whether to add additive noise or multiplicative noise Returns ------- noisy_image: image after adding noise """
        if sigma < 0:
            raise ValueError('sigma must >= 0.0')
    
        noise = generate_gaussian_noise(image,
                                        sigma=sigma,
                                        is_gray=is_gray,
                                        speckle_size=speckle_size,
                                        multiplicative=multiplicative)
    
        noisy_image = add_noise(image,
                                noise=noise,
                                noise_num_ratio=noise_num_ratio)
        return noisy_image
    
    
    if __name__ == '__main__':
        # choose one of the following parameter set
        MULTI = True
        SIGMA = 0.1
    
        # MULTI = False
        # SIGMA = 25
    
        # read original image / gray scale image / image with alpha channel
        image = cv2.imread('lena512color.tiff')
        image_gray = cv2.imread('lena512color.tiff', cv2.IMREAD_GRAYSCALE)
    
        alpha = np.ones((*image.shape[:2], 1), dtype=np.uint8) * 255
        image_alpha = np.concatenate((image, alpha), axis=2)  # 4 channel
    
        # generate noisy image
        noisy_image = add_gaussian_noise(image, SIGMA, False, 10.0, 1.0, MULTI)
        cv2.imwrite('gaussian_noise_color_image_color_noise.png', noisy_image)
    
        noisy_image = add_gaussian_noise(image_gray, SIGMA, False, 10.0, 1.0,
                                         MULTI)
        cv2.imwrite('gaussian_noise_gray_image_color_noise.png', noisy_image)
    
        noisy_image = add_gaussian_noise(image_alpha, SIGMA, False, 10.0, 1.0,
                                         MULTI)
        cv2.imwrite('gaussian_noise_alpha_image_color_noise.png', noisy_image)
    
        noisy_image = add_gaussian_noise(image, SIGMA, True, 2.5, 1.0, MULTI)
        cv2.imwrite('gaussian_noise_color_image_gray_noise.png', noisy_image)
    
        noisy_image = add_gaussian_noise(image_gray, SIGMA, True, 2.5, 1.0, MULTI)
        cv2.imwrite('gaussian_noise_gray_image_gray_noise.png', noisy_image)
    
        noisy_image = add_gaussian_noise(image_alpha, SIGMA, True, 2.5, 1.0, MULTI)
        cv2.imwrite('gaussian_noise_alpha_image_gray_noise.png', noisy_image)

## 均匀噪音 ##

我也不清楚图像中什么情况下会出现均匀随机噪音，且当作一种能增加点随机性的退化手段吧。这个搞法跟高斯那部分几乎一样，只是生成噪音的随机数不一样，另外控制噪音的参数也不一样，均匀噪音直接用上下限来控制强弱。

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5ouc6Ziz_size_20_color_FFFFFF_t_70_g_se_x_16_pic_center 2]

加性均匀噪音

乘性均匀噪音的图就略了，肉眼也看不出太大区别。

代码如下：

# -*- coding: utf-8 -*-
    import cv2
    import numpy as np
    from util import get_noise_shape
    from util import add_noise
    
    
    def generate_uniform_noise(image,
                               limit,
                               is_gray=False,
                               speckle_size=1.0,
                               multiplicative=False):
        """ Generate uniform distributed noise. Parameters ---------- image: numpy image data, shape is [H, W, C], C is optional limit: limit of uniform random number is_gray: whether the noise is color or gray speckle_size: speckle size of noise multiplicative: whether to add additive noise or multiplicative noise Returns ------- noise: uniform noise """
        noise_shape = get_noise_shape(image, speckle_size)
    
        # generate noise
        if is_gray:
            noise = np.random.uniform(-limit, limit, size=noise_shape[0:2])
            if image.ndim == 3:
                noise = np.expand_dims(noise, axis=2).repeat(3, axis=2)
        else:
            noise = np.random.uniform(-limit, limit, size=noise_shape)
    
        # resize noise to keep same size as image
        if speckle_size > 1.0:
            interp_type = np.random.choice([cv2.INTER_CUBIC, cv2.INTER_LINEAR])
            noise = cv2.resize(noise, dsize=image.shape[:2], fx=None, fy=None,
                               interpolation=interp_type)
    
        noise = noise.astype(np.float32)
    
        # make multiplicative noise if needed
        if multiplicative:
            if noise.ndim == 3:
                channel = noise.shape[2]
                noise *= np.float32(image[..., :channel])
            else:
                noise *= np.float32(image)
    
        return noise
    
    
    def add_uniform_noise(image,
                          limit,
                          is_gray=False,
                          speckle_size=1.0,
                          noise_num_ratio=1.0,
                          multiplicative=False):
        """ Add uniform noise to image. Parameters ---------- image: numpy image data, shape is [H, W, C], C is optional limit: limit of uniform random number is_gray: whether the noise is color or gray speckle_size: speckle size of noise noise_num_ratio: ratio of noise number with respect to the total number of pixels, between [0, 1] multiplicative: whether to add additive noise or multiplicative noise Returns ------- noisy_image: image after adding noise """
        if limit < 0:
            raise ValueError('limit must >= 0.0')
    
        noise = generate_uniform_noise(image,
                                       limit=limit,
                                       is_gray=is_gray,
                                       speckle_size=speckle_size,
                                       multiplicative=multiplicative)
    
        noisy_image = add_noise(image,
                                noise=noise,
                                noise_num_ratio=noise_num_ratio)
        return noisy_image
    
    
    if __name__ == '__main__':
        # choose one of the following parameter set
        MULTI = True
        LIMIT = 0.1
    
        # MULTI = False
        # LIMIT = 25
    
        # read original image / gray scale image / image with alpha channel
        image = cv2.imread('lena512color.tiff')
        image_gray = cv2.imread('lena512color.tiff', cv2.IMREAD_GRAYSCALE)
    
        alpha = np.ones((*image.shape[:2], 1), dtype=np.uint8) * 255
        image_alpha = np.concatenate((image, alpha), axis=2)  # 4 channel
    
        # generate noisy image
        noisy_image = add_uniform_noise(image, LIMIT, False, 10.0, 1.0, MULTI)
        cv2.imwrite('uniform_noise_color_image_color_noise.png', noisy_image)
    
        noisy_image = add_uniform_noise(image_gray, LIMIT, False, 10.0, 1.0,
                                        MULTI)
        cv2.imwrite('uniform_noise_gray_image_color_noise.png', noisy_image)
    
        noisy_image = add_uniform_noise(image_alpha, LIMIT, False, 10.0, 1.0,
                                        MULTI)
        cv2.imwrite('uniform_noise_alpha_image_color_noise.png', noisy_image)
    
        noisy_image = add_uniform_noise(image, LIMIT, True, 2.5, 1.0, MULTI)
        cv2.imwrite('uniform_noise_color_image_gray_noise.png', noisy_image)
    
        noisy_image = add_uniform_noise(image_gray, LIMIT, True, 2.5, 1.0, MULTI)
        cv2.imwrite('uniform_noise_gray_image_gray_noise.png', noisy_image)
    
        noisy_image = add_uniform_noise(image_alpha, LIMIT, True, 2.5, 1.0, MULTI)
        cv2.imwrite('uniform_noise_alpha_image_gray_noise.png', noisy_image)

## 泊松噪音 ##

泊松噪音是一种计数噪音，比如电话接线员没小时可能接起电话的次数，饭店每天可能接到的外卖订单个数等等。这个变量肯定有一个期望，但是实际的数字在期望附近上下波动，波动的结果就是针对这个期望产生的泊松随机数。

对图像而言，泊松噪音模拟了光子打到传感器的随机过程，这是一个计数过程，所以用泊松过程来模拟。泊松噪音的输入是图像本身（的像素值），输出就是加了泊松噪音的图像，而不是单纯的噪音。但是通过这种方式加泊松噪音无法调节强弱，所以本文档的做法是先使用带有泊松噪音的图像减去原图像，得到噪音，然后给噪音乘上一个系数，最后再加回到原图像，这样就可以利用系数来调节噪音强弱。

请注意，由于泊松噪音是计数噪音，所以泊松噪音的输入必需是uint8类型的图像，数值区间得是\[0, 255\]，不能是\[0, 1\]范围那种。

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5ouc6Ziz_size_20_color_FFFFFF_t_70_g_se_x_16_pic_center 3]

泊松噪音

光从表面来看的话，跟高斯和均匀噪音傻傻分不清。。。

代码如下：

# -*- coding: utf-8 -*-
    import cv2
    import numpy as np
    from util import get_noise_shape
    from util import add_noise
    
    
    def generate_poisson_noise(image,
                               scale,
                               is_gray=False,
                               speckle_size=1.0):
        """ Generate poisson distributed noise. Parameters ---------- image: numpy image data, shape is [H, W, C], C is optional scale: scale multiplied to poisson noise is_gray: whether the noise is color or gray speckle_size: speckle size of noise Returns ------- noise: poisson noise """
        # preprocess image
        image_temp = image.copy()
        if image_temp.ndim == 3 and image_temp.shape[2] > 3:
            image_temp = image_temp[:, :, :3]
    
        ndim = image.ndim
        if is_gray and ndim == 3:
            image_temp = cv2.cvtColor(image_temp, cv2.COLOR_BGR2GRAY)
    
        noise_shape = get_noise_shape(image, speckle_size)
        if np.any(image_temp.shape[:2] != noise_shape[:2]):
            image_temp = cv2.resize(image_temp, dsize=noise_shape[:2],
                                    fx=None, fy=None, interpolation=cv2.INTER_AREA)
    
        # generate noise
        noisy_image = np.float32(np.random.poisson(image_temp))
        noise = noisy_image - image_temp
        if is_gray and ndim == 3:
            noise = np.expand_dims(noise, axis=2).repeat(3, axis=2)
        noise *= scale
    
        # resize noise to keep same size as image
        if speckle_size > 1.0:
            interp_type = np.random.choice([cv2.INTER_CUBIC, cv2.INTER_LINEAR])
            noise = cv2.resize(noise, dsize=image.shape[:2], fx=None, fy=None,
                               interpolation=interp_type)
        return noise
    
    
    def add_poisson_noise(image,
                          scale,
                          is_gray=False,
                          speckle_size=1.0,
                          noise_num_ratio=1.0):
        """ Add poisson noise to image. Parameters ---------- image: numpy image data, shape is [H, W, C], C is optional scale: scale multiplied to poisson noise is_gray: whether the noise is color or gray speckle_size: speckle size of noise noise_num_ratio: ratio of noise number with respect to the total number of pixels, between [0, 1] Returns ------- noisy_image: image after adding noise """
        if scale < 0:
            raise ValueError('scale must >= 0.0')
    
        noise = generate_poisson_noise(image,
                                       scale=scale,
                                       is_gray=is_gray,
                                       speckle_size=speckle_size)
    
        noisy_image = add_noise(image,
                                noise=noise,
                                noise_num_ratio=noise_num_ratio)
        return noisy_image
    
    
    if __name__ == '__main__':
        SCALE = 1.0
        # read original image / gray scale image / image with alpha channel
        image = cv2.imread('lena512color.tiff')
        image_gray = cv2.imread('lena512color.tiff', cv2.IMREAD_GRAYSCALE)
    
        alpha = np.ones((*image.shape[:2], 1), dtype=np.uint8) * 255
        image_alpha = np.concatenate((image, alpha), axis=2)  # 4 channel
    
        # generate noisy image
        noisy_image = add_poisson_noise(image, SCALE, False, 10.0, 1.0)
        cv2.imwrite('poisson_noise_color_image_color_noise.png', noisy_image)
    
        noisy_image = add_poisson_noise(image_gray, SCALE, False, 10.0, 1.0)
        cv2.imwrite('poisson_noise_gray_image_color_noise.png', noisy_image)
    
        noisy_image = add_poisson_noise(image_alpha, SCALE, False, 10.0, 1.0)
        cv2.imwrite('poisson_noise_alpha_image_color_noise.png', noisy_image)
    
        noisy_image = add_poisson_noise(image, SCALE, True, 2.5, 1.0)
        cv2.imwrite('poisson_noise_color_image_gray_noise.png', noisy_image)
    
        noisy_image = add_poisson_noise(image_gray, SCALE, True, 2.5, 1.0)
        cv2.imwrite('poisson_noise_gray_image_gray_noise.png', noisy_image)
    
        noisy_image = add_poisson_noise(image_alpha, SCALE, True, 2.5, 1.0)
        cv2.imwrite('poisson_noise_alpha_image_gray_noise.png', noisy_image)

## 椒盐噪音 ##

椒盐噪音可能是图像在拍摄成像的过程中受到了非常强的干扰，从而导致图像上出现的一些过大或过小的值，如果是在灰度图上，那么就是白色或者黑色的噪点，就像是盐（白）和胡椒（黑）颗粒一样，所以称为椒盐噪音。

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5ouc6Ziz_size_20_color_FFFFFF_t_70_g_se_x_16_pic_center 4]

椒盐噪音

（终于来了一个有辨识度的噪音，椒盐噪音很容易认出来）

在下面代码中，我将椒盐的值设置为参数的形式，也就是说用户可以进行调节，不一定非得让盐=255，椒=0。

# -*- coding: utf-8 -*-
    import cv2
    import numpy as np
    from util import get_noise_shape
    
    
    def generate_salt_pepper_noise_mask(image,
                                        is_gray=False,
                                        noise_num_ratio=0.1,
                                        speckle_size=1.0,
                                        salt_ratio=0.5):
        """ Generate a mask for salt pepper noise. Parameters ---------- image: image data read by opencv, shape is [H, W, C] is_gray: whether the noise is color or gray noise_num_ratio: ratio of noise number with respect to the total number of pixels, between [0, 1] speckle_size: speckle size of noise salt_ratio: ratio of salt number with respect to the total number of noise pixels Returns ------- mask: mask for salt pepper noise. Positive values in mask is corresponding to position of salt noise, whereas negative values is corresponding to position of pepper noise. """
        if not 0.0 <= noise_num_ratio <= 1.0:
            raise ValueError('noise_num_ratio must between [0, 1]')
    
        if not 0.0 <= salt_ratio <= 1.0:
            raise ValueError('salt_ratio must between [0, 1]')
    
        # get noise num. Noise num is computed by noise_shape, which is affected
        # by speckle_size
        noise_shape = get_noise_shape(image, speckle_size)
        pixel_num = np.prod(noise_shape[0:2])
        noise_num = pixel_num * noise_num_ratio
        salt_num = int((noise_num * salt_ratio))
        pepper_num = int(np.round(noise_num * (1 - salt_ratio)))
    
        # get mask
        salt_index = []
        pepper_index = []
        if is_gray:
            mask = np.zeros(noise_shape[:2], dtype=np.int32)
            for shape in noise_shape[:2]:
                salt_index.append(np.random.randint(0, shape, salt_num))
                pepper_index.append(np.random.randint(0, shape, pepper_num))
            mask[tuple(salt_index)] = 1
            mask[tuple(pepper_index)] = -1
            if image.ndim == 3 and image.shape[2] > 3:
                mask = np.expand_dims(mask, axis=2).repeat(3, axis=2)
        else:
            mask = np.zeros(noise_shape, dtype=np.int32)
            for shape in noise_shape:
                salt_index.append(np.random.randint(0, shape, salt_num))
                pepper_index.append(np.random.randint(0, shape, pepper_num))
            mask[tuple(salt_index)] = 1
            mask[tuple(pepper_index)] = -1
    
        if speckle_size > 1.0:
            # mask should be converted to float type before using cv2.resize
            mask = mask.astype(np.float32)
            interp_type = np.random.choice(
                [cv2.INTER_CUBIC, cv2.INTER_LINEAR, cv2.INTER_NEAREST])
            mask = cv2.resize(mask, dsize=image.shape[:2], fx=None, fy=None,
                              interpolation=interp_type)
            mask = np.round(mask).astype(np.int32)
        return mask
    
    
    def add_salt_pepper_noise(image,
                              is_gray=False,
                              noise_num_ratio=0.1,
                              speckle_size=1.0,
                              salt_value=255,
                              pepper_value=0,
                              salt_ratio=0.5):
        """ Add salt and pepper nosie to image. Parameters ---------- image: image data read by opencv, shape is [H, W, C] is_gray: whether the noise is color or gray noise_num_ratio: ratio of noise number with respect to the total number of pixels, between [0, 1] speckle_size: speckle size of noise salt_value: value of salt noise pepper_value: value of pepper noise salt_ratio: ratio of salt number with respect to the total number of noise pixels Returns ------- noisy_image: image after adding noise """
        mask = generate_salt_pepper_noise_mask(image,
                                               is_gray=is_gray,
                                               noise_num_ratio=noise_num_ratio,
                                               speckle_size=speckle_size,
                                               salt_ratio=salt_ratio)
        # add noise
        noisy_image = image.copy()
        if noisy_image.ndim == 3 and noisy_image.shape[2] > 3:
            noisy_image[:, :, :3][mask > 0] = salt_value
            noisy_image[:, :, :3][mask < 0] = pepper_value
        else:
            noisy_image[mask > 0] = salt_value
            noisy_image[mask < 0] = pepper_value
        noisy_image = np.squeeze(noisy_image)
        return noisy_image
    
    
    if __name__ == '__main__':
        # read original image / gray scale image / image with alpha channel
        image = cv2.imread('lena512color.tiff')
        image_gray = cv2.imread('lena512color.tiff', cv2.IMREAD_GRAYSCALE)
    
        alpha = np.ones((*image.shape[:2], 1), dtype=np.uint8) * 255
        image_alpha = np.concatenate((image, alpha), axis=2)  # 4 channel
    
        # generate noisy image
        noisy_image = add_salt_pepper_noise(image, False, 0.1, 5.0)
        cv2.imwrite('salt_pepper_noise_color_image_color_noise.png', noisy_image)
    
        noisy_image = add_salt_pepper_noise(image_gray, False, 0.1, 5.0)
        cv2.imwrite('salt_pepper_noise_gray_image_color_noise.png', noisy_image)
    
        noisy_image = add_salt_pepper_noise(image_alpha, False, 0.1, 5.0)
        cv2.imwrite('salt_pepper_noise_alpha_image_color_noise.png', noisy_image)
    
        noisy_image = add_salt_pepper_noise(image, True, 0.1, 1.5)
        cv2.imwrite('salt_pepper_noise_color_image_gray_noise.png', noisy_image)
    
        noisy_image = add_salt_pepper_noise(image_gray, True, 0.1, 1.5)
        cv2.imwrite('salt_pepper_noise_gray_image_gray_noise.png', noisy_image)
    
        noisy_image = add_salt_pepper_noise(image_alpha, True, 0.1, 1.5)
        cv2.imwrite('salt_pepper_noise_alpha_image_gray_noise.png', noisy_image)

# 模糊 #

## 高斯模糊 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc_size_16_color_FFFFFF_t_70_pic_center]

# -*- coding: utf-8 -*-
    import cv2
    import numpy as np
    
    if __name__ == '__main__':
        # read image
        image = cv2.imread('lena512color.tiff')
        image_gray = cv2.imread('lena512color.tiff', cv2.IMREAD_GRAYSCALE)
    
        alpha = np.ones((*image.shape[:2], 1), dtype=np.uint8) * 255
        image_alpha = np.concatenate((image, alpha), axis=2)
    
        # the following image can be changed to image_gray or image_alpha
        blur_image = cv2.GaussianBlur(image, ksize=(5, 5), sigmaX=2, sigmaY=2)
    
        cv2.imshow('original_image', image)
        cv2.imshow('gaussian_blur_image', blur_image)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

## 均值模糊 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc_size_16_color_FFFFFF_t_70_pic_center 1]

# -*- coding: utf-8 -*-
    import cv2
    import numpy as np
    
    if __name__ == '__main__':
        # read image
        image = cv2.imread('lena512color.tiff')
        image_gray = cv2.imread('lena512color.tiff', cv2.IMREAD_GRAYSCALE)
    
        alpha = np.ones((*image.shape[:2], 1), dtype=np.uint8) * 255
        image_alpha = np.concatenate((image, alpha), axis=2)
    
        # the following image can be changed to image_gray or image_alpha
        blur_image = cv2.boxFilter(image, ddepth=-1, ksize=(5, 7))
    
        cv2.imshow('original_image', image)
        cv2.imshow('box_blur_image', blur_image)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

## 运动模糊 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc_size_16_color_FFFFFF_t_70_pic_center 2]

# -*- coding: utf-8 -*-
    import cv2
    import numpy as np
    
    
    def motion_blur(image, kernel_length, angle):
        """ Apply motion blur to image. Parameters ---------- image: image data read by opencv, shape is [H, W, C] kernel_length: motion trajectory length of blur kernel angle: motion trajectory angle Returns ------- blur_image: image after blurring """
        if not (0 <= angle <= 180):
            raise ValueError('angle should between [0, 180]')
    
        # preprocess angle and get step along angle direction
        horizontal_flip = False
        if angle > 90:
            angle = 180 - angle
            horizontal_flip = True
    
        angle = angle / 180 * np.pi
        if 0 <= angle <= np.pi / 4:
            step = 1.0 / np.cos(angle)
        else:
            step = 1.0 / np.sin(angle)
        cos_angle = np.cos(angle)
        sin_angle = np.sin(angle)
    
        # get coordinates
        x, y = [], []
        for i in range(kernel_length):
            radius = i * step
            x.append(radius * cos_angle)
            y.append(radius * sin_angle)
        x, y = np.round(x).astype(np.int32), np.round(y).astype(np.int32)
    
        # make kernel
        x_ksize, y_ksize = np.max(x) + 1, np.max(y) + 1
        blur_kernel = np.zeros((y_ksize, x_ksize), np.float32)
        blur_kernel[y, x] = 1
        blur_kernel = blur_kernel / np.sum(blur_kernel)
    
        # flip kernel. Vertical flip is surely to be apllied because y-axis of
        # image is downside, opposite to the normal axis system. Horizontal flip
        # applied according to the value of 'horizontal_flip'
        blur_kernel = blur_kernel[::-1, :]
        if horizontal_flip:
            blur_kernel = blur_kernel[:, ::-1]
    
        # use blur kernel to filter image to get motion blur effect
        blur_image = cv2.filter2D(image, ddepth=-1, kernel=blur_kernel)
        return blur_image
    
    
    if __name__ == '__main__':
        # read image
        image = cv2.imread('lena512color.tiff')
        image_gray = cv2.imread('lena512color.tiff', cv2.IMREAD_GRAYSCALE)
    
        alpha = np.ones((*image.shape[:2], 1), dtype=np.uint8) * 255
        image_alpha = np.concatenate((image, alpha), axis=2)
    
        # the following image can be changed to image_gray or image_alpha
        blur_image = motion_blur(image, kernel_length=30, angle=45)
    
        cv2.imshow('original_image', image)
        cv2.imshow('motion_blur_image', blur_image)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

## 中值滤波 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc_size_16_color_FFFFFF_t_70_pic_center 3]

# -*- coding: utf-8 -*-
    import cv2
    import numpy as np
    
    if __name__ == '__main__':
        # read image
        image = cv2.imread('lena512color.tiff')
        image_gray = cv2.imread('lena512color.tiff', cv2.IMREAD_GRAYSCALE)
    
        alpha = np.ones((*image.shape[:2], 1), dtype=np.uint8) * 255
        image_alpha = np.concatenate((image, alpha), axis=2)
    
        # the following image can be changed to image_gray or image_alpha
        blur_image = cv2.medianBlur(image, ksize=7)
    
        cv2.imshow('original_image', image)
        cv2.imshow('median_blur_image', blur_image)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

## 粒状模糊 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc_size_16_color_FFFFFF_t_70_pic_center 4]

# -*- coding: utf-8 -*-
    import cv2
    import numpy as np
    from util import uniform_random
    
    
    def grainy_blur(image, radius):
        """ Apply grainy blur to image. Parameters ---------- image: image data read by opencv, shape is [H, W, C] radius: radius for jittering the pixels Returns ------- blur_image: image after blurring """
        if not isinstance(radius, (tuple, list)):
            radius = (radius, radius)
        x_radius, y_radius = radius
    
        # build meshgrid coordinates
        height, width = image.shape[:2]
        x, y = np.arange(width), np.arange(height)
        x, y = np.meshgrid(x, y)
        x, y = x.astype(np.float32), y.astype(np.float32)
    
        # get jittered coordinates
        x += uniform_random(low=-x_radius, high=x_radius, shape=x.shape)
        y += uniform_random(low=-y_radius, high=y_radius, shape=y.shape)
    
        # shrink coordinates
        x, y = np.round(x).astype(np.int32), np.round(y).astype(np.int32)
        x[x < 0] = 0
        y[y < 0] = 0
        x[x >= width] = width - 1
        y[y >= height] = height - 1
    
        # remap to get blur image
        blur_image = image[y, x, ...]
    
        return blur_image
    
    
    if __name__ == '__main__':
        # read image
        image = cv2.imread('lena512color.tiff')
        image_gray = cv2.imread('lena512color.tiff', cv2.IMREAD_GRAYSCALE)
    
        alpha = np.ones((*image.shape[:2], 1), dtype=np.uint8) * 255
        image_alpha = np.concatenate((image, alpha), axis=2)
    
        # the following image can be changed to image_gray or image_alpha
        blur_image = grainy_blur(image, radius=5)
    
        cv2.imshow('original_image', image)
        cv2.imshow('grainy_blur_image', blur_image)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

# jpeg压缩 #

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc_size_16_color_FFFFFF_t_70_pic_center 5]

# -*- coding: utf-8 -*-
    import cv2
    import numpy as np
    
    
    def jpeg_compression(image, quality_factor):
        """ Apply jpeg compression to image without saving it to disk. Parameters ---------- image: image data read by opencv, shape is [H, W, C] quality_factor: jpeg quality factor, between [0, 1]. Higher value means higher quality image Returns ------- jpeg_image: jpeg compressed image """
        compression_factor = int(quality_factor)
        compression_param = [cv2.IMWRITE_JPEG_QUALITY, compression_factor]
        image_encode = cv2.imencode('.jpg', image, compression_param)[1]
        jpeg_image = cv2.imdecode(image_encode, -1)
        return jpeg_image
    
    
    if __name__ == '__main__':
        # read image
        image = cv2.imread('lena512color.tiff')
        image_gray = cv2.imread('lena512color.tiff', cv2.IMREAD_GRAYSCALE)
    
        alpha = np.ones((*image.shape[:2], 1), dtype=np.uint8) * 255
        image_alpha = np.concatenate((image, alpha), axis=2)
    
        # the following image can be changed to image_gray or image_alpha
        compressed_image = jpeg_compression(image, quality_factor=10)
    
        cv2.imshow('original_image', image)
        cv2.imshow('jpeg_compressed_image', compressed_image)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

# 马赛克 #

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc_size_16_color_FFFFFF_t_70_pic_center 6]

# -*- coding: utf-8 -*-
    import cv2
    import numpy as np
    
    
    def mosaic(image, block_size, bbox=None):
        """ Set the bounding box region of image to mosaic. Parameters ---------- image: image data read by opencv, shape is [H, W, C] block_size: bbox: mosaic region, defined as [xmin, ymin, xmax, ymax]. Default is the full image Returns ------- mosaic_image: image with mosaic """
        if not isinstance(block_size, (tuple, list)):
            block_size = (block_size, block_size)
        x_step, y_step = block_size
    
        # preprocess bbox
        height, width = image.shape[:2]
        if bbox is None:
            xmin, ymin, xmax, ymax = 0, 0, width, height
        else:
            xmin, ymin, xmax, ymax = bbox
    
        xmin, ymin = max(xmin, 0), max(ymin, 0)
        xmax, ymax = min(xmax, width), min(ymax, height)
    
        # get random index for mosaic region
        x_jitter = np.arange(xmin, xmax, x_step)
        y_jitter = np.arange(ymin, ymax, y_step)
        x_jitter, y_jitter = np.meshgrid(x_jitter, y_jitter)
    
        x_jitter += np.random.randint(0, x_step, x_jitter.shape)
        y_jitter += np.random.randint(0, y_step, y_jitter.shape)
    
        x_jitter[x_jitter >= width] = width - 1
        y_jitter[y_jitter >= height] = height - 1
    
        # generate mosaic region by cv2.resize function
        mosaic_region = image[y_jitter, x_jitter, ...]
        mosaic_region = cv2.resize(mosaic_region, dsize=(xmax - xmin, ymax - ymin),
                                   interpolation=cv2.INTER_NEAREST)
    
        mosaic_image = image.copy()
        mosaic_image[ymin:ymax, xmin:xmax, ...] = mosaic_region
    
        return mosaic_image
    
    
    if __name__ == '__main__':
        # read image
        image = cv2.imread('lena512color.tiff')
        image_gray = cv2.imread('lena512color.tiff', cv2.IMREAD_GRAYSCALE)
    
        alpha = np.ones((*image.shape[:2], 1), dtype=np.uint8) * 255
        image_alpha = np.concatenate((image, alpha), axis=2)
    
        # the following image can be changed to image_gray or image_alpha
        mosaic_image = mosaic(image, block_size=10, bbox=[100, 100, 400, 400])
    
        cv2.imshow('original_image', image)
        cv2.imshow('mosaic_image', mosaic_image)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

# 重采样 #

。。。

[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5ouc6Ziz_size_20_color_FFFFFF_t_70_g_se_x_16_pic_center]: /images/20221120/4426e817cdc749e3a90f52fd15e1b22f.png
[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5ouc6Ziz_size_20_color_FFFFFF_t_70_g_se_x_16_pic_center 1]: https://img-blog.csdnimg.cn/8ea4bfe76cea4fd19caf126b268ae1f4.png?x-oss-process=image/watermark,type_ZHJvaWRzYW5zZmFsbGJhY2s,shadow_50,text_Q1NETiBA5ouc6Ziz,size_20,color_FFFFFF,t_70,g_se,x_16#pic_center
[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5ouc6Ziz_size_20_color_FFFFFF_t_70_g_se_x_16_pic_center 2]: https://img-blog.csdnimg.cn/5605ccc79f174831815d046f7f904f41.png?x-oss-process=image/watermark,type_ZHJvaWRzYW5zZmFsbGJhY2s,shadow_50,text_Q1NETiBA5ouc6Ziz,size_20,color_FFFFFF,t_70,g_se,x_16#pic_center
[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5ouc6Ziz_size_20_color_FFFFFF_t_70_g_se_x_16_pic_center 3]: https://img-blog.csdnimg.cn/07e29f6f65464fa3b1978a35a0381d84.png?x-oss-process=image/watermark,type_ZHJvaWRzYW5zZmFsbGJhY2s,shadow_50,text_Q1NETiBA5ouc6Ziz,size_20,color_FFFFFF,t_70,g_se,x_16#pic_center
[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5ouc6Ziz_size_20_color_FFFFFF_t_70_g_se_x_16_pic_center 4]: https://img-blog.csdnimg.cn/6be1f38f1a5c41559171c5e86f33283e.png?x-oss-process=image/watermark,type_ZHJvaWRzYW5zZmFsbGJhY2s,shadow_50,text_Q1NETiBA5ouc6Ziz,size_20,color_FFFFFF,t_70,g_se,x_16#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc_size_16_color_FFFFFF_t_70_pic_center]: https://img-blog.csdnimg.cn/20210218004511578.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc=,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc_size_16_color_FFFFFF_t_70_pic_center 1]: https://img-blog.csdnimg.cn/20210218004528129.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc=,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc_size_16_color_FFFFFF_t_70_pic_center 2]: https://img-blog.csdnimg.cn/20210218004543953.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc=,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc_size_16_color_FFFFFF_t_70_pic_center 3]: https://img-blog.csdnimg.cn/20210218004557859.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc=,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc_size_16_color_FFFFFF_t_70_pic_center 4]: https://img-blog.csdnimg.cn/20210218010347344.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc=,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc_size_16_color_FFFFFF_t_70_pic_center 5]: https://img-blog.csdnimg.cn/20210218004634444.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc=,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc_size_16_color_FFFFFF_t_70_pic_center 6]: https://img-blog.csdnimg.cn/20210218004647195.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JieTE5ODc=,size_16,color_FFFFFF,t_70#pic_center