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python+opencv滤波方法整理
#图像平滑（低通滤波（LPF）有利于去噪，模糊图像，高通滤波（HPF）有利于找到图像边界） #2D滤波器 def d2filter(img): kernel
= np.ones((5, 5), np.float32) / 25 #卷积核 d2filter = cv2.filter2D(img, -1, kernel)
return d2filter #高斯滤波(二维离散卷积核)（高斯核的高和宽（奇数）） def GBlur(img): GBlur = cv2.
GaussianBlur(img, (5, 5), 0) #（5,5）表示的是卷积核大小，0表示的是沿x与y方向上的标准差 return GBlur
#均值滤波(二维离散卷积核) def meanval(img): meanval=cv2.blur(img,(3,5)) # 卷积核大小为3*5,
模板的大小是可以设定的 return meanval
#方框滤波，normalize=1时，表示进行归一化处理，此时图片处理效果与均值滤波相同，如果normalize=0时，表示不进行归一化处理，像素值为周围像素之和，图像更多为白色
def boxfilter(img): boxfilter = cv2.boxFilter(img, -1, (5, 5), normalize=1)
return boxfilter
#中值滤波(统计学)(中值滤波模板就是用卷积框中像素的中值代替中心值，达到去噪声的目的。这个模板一般用于去除椒盐噪声。卷积核的大小也是个奇数。) def
medBlur(img): medBlur = cv2.medianBlur(img, 5) # 中值滤波函数 return medBlur
#双边滤波（保持边缘清晰）双边滤波同时使用了空间高斯权重和灰度相似性高斯权重，确保了边界不会被模糊掉。 def doufilter(img):
#9表示的是滤波领域直径，后面的两个数字：空间高斯函数标准差，灰度值相似性标准差 doufilter = cv2.bilateralFilter(img, 9,
80, 80) return doufilter
