【opencv-python 7】模糊操作

待我称王封你为后i 2022-04-24 02:56 127阅读 0赞

# 均值模糊 #

dst = cv.blur(image,(5,5)) \# (5,5)卷积核大小

用途：适用于随机噪声

# 中值模糊 #

dst = cv.medianBlur(image,5)

用途：适用于椒盐噪声

# 自定义模糊 #

kernel = np.ones(\[5,5\],np.float32)/25 \# 除以25防止数值超过255，溢出  
 dst = cv.filter2D(image,-1,kernel=kernel)

用途：用户根据需要，自定义，灵活性好

# 自定义模糊操作实现图像锐化 #

kernel = np.array(\[\[0,-1,0\],\[-1,5,-1\],\[0,-1,0\]\],np.float32) \#使用了图像的锐化算子  
dst = cv.filter2D(image,-1,kernel=kernel)  
cv.imshow('sharp\_demo',dst)

用途：用于图像增强，突出边框特征

下面是代码演示

import cv2 as cv
    import numpy as np
    
    
    # 均值模糊，可以去燥，对随机噪声效果好
    def blur_demo(image):
        dst = cv.blur(image,(5,5)) # (5,5)卷积核大小
        cv.imshow('blur',dst)
        
    
    # 中值模糊,对椒盐噪声效果好
    def median_blur_demo(image):
        dst = cv.medianBlur(image,5)
        cv.imshow("median_blue",dst)
        
    
    # 自定义
    def custom_blur_demo(image):
        kernel = np.ones([5,5],np.float32)/25 # 除以25防止数值超过255，溢出
        dst = cv.filter2D(image,-1,kernel=kernel)
        cv.imshow('custom_blur',dst)
    
    
    # 利用自定义模糊，实现图像的锐化
    def sharp_demo(image):
        kernel = np.array([[0,-1,0],[-1,5,-1],[0,-1,0]],np.float32)
        dst = cv.filter2D(image,-1,kernel=kernel)
        cv.imshow('sharp_demo',dst)
    
    src = cv.imread('D:/images/lenanoise.png')
    cv.imshow('input image',src)
    
    blur_demo(src)
    median_blur_demo(src)
    custom_blur_demo(src)
    sharp_demo(src)
    
    cv.waitKey(0)
    cv.destroyAllWindows()

运行效果

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3BsU29uZ19DU0RO_size_16_color_FFFFFF_t_70][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3BsU29uZ19DU0RO_size_16_color_FFFFFF_t_70 1][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3BsU29uZ19DU0RO_size_16_color_FFFFFF_t_70 2][]

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# 高斯模糊 #

带有权重的均值模糊，是均值模糊的一种扩展。去噪的效果更好。

高斯模糊对高斯噪声有明显的抑制作用

看下面的代码

import cv2 as cv
    import numpy as np
    
    # 方式溢出
    def clamp(pv):
        if pv>255:
            return 255
        if pv<0:
            return 0
        else:
            return pv
    
    def gaussian_noise(image):
        h,w,c = image.shape
        for row in range(h):
            for col in range(w):
                s = np.random.normal(0,20,3)
                b = image[row,col,0]
                g = image[row,col,1]
                r = image[row,col,2]
                image[row,col,0] = clamp(b + s[0])
                image[row,col,1] = clamp(g + s[1])
                image[row,col,2] = clamp(r + s[2])
        cv.imshow('guassian_nosie_image',image)
    
    src = cv.imread('D:/images/demo.png')
    cv.imshow('input image',src)
    
    dst = cv.GaussianBlur(src,(0,0),15)
    cv.imshow('Gaussian_blur',dst)
    
    # 加入高斯噪声
    t1 = cv.getTickCount()
    gaussian_noise(src)
    t2 = cv.getTickCount()
    print('time consume:%.2fs'%((t2-t1)/cv.getTickFrequency()))
    
    # 对加入高斯噪声后的图像进行高斯模糊
    cv.imshow('Gaussian_blur_after_GaussNoise',cv.GaussianBlur(src,(5,5),0))
    
    cv.waitKey(0)
    cv.destroyAllWindows()

运行结果

> time consume:2.49s

时间消耗的挺长的

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3BsU29uZ19DU0RO_size_16_color_FFFFFF_t_70 5][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3BsU29uZ19DU0RO_size_16_color_FFFFFF_t_70 6][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3BsU29uZ19DU0RO_size_16_color_FFFFFF_t_70 7][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3BsU29uZ19DU0RO_size_16_color_FFFFFF_t_70 8][]

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