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OpenCV&&python_图像平滑(Smoothing Images)

  • Goals

 

  1. 学习用不同低通滤波方法模糊图像(Blur imagess with various low pass filter)
  2. 用用定制的滤波器处理图像(Apply custom-made filters to images (2D convolution))

高通滤波与低通滤波

 

images can be filtered with various low-pass filters (LPF), high-pass filters (HPF), etc.  A LPF helps in removing noise, or blurring the image. A HPF filters helps in finding edges in an image.


cv2.filter2D() 

OpenCV  provides a function cv2.filter2D() to convolve卷积 a kernel(核) with an image. 例如:

定义一个5x5 averaging filter kernel

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 直接上代码:

import cv2import numpy as npfrom matplotlib import pyplot as plt#读图像img = cv2.imread(‘text.jpg‘)#核的定义kernel = np.ones((5,5),np.float32)/25dst = cv2.filter2D(img,-1,kernel)#输出plt.subplot(121),plt.imshow(img),plt.title(‘Original‘)plt.xticks([]), plt.yticks([])plt.subplot(122),plt.imshow(dst),plt.title(‘Averaging‘)plt.xticks([]), plt.yticks([])plt.show()

 结果展示:

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注释:

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Python: cv.Filter2D(src, dst, kernel, anchor=(-1, -1)

  • src – input image.
  • dst – output image of the same size and the same number of channels as src.
  • kernel – convolution kernel (or rather a correlation kernel), a single-channel floating point matrix; if you want to apply different kernels to different channels, split the image into separate color planes using split() and process them individually.
  • anchor – anchor of the kernel that indicates the relative position of a filtered point within the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor is at the kernel center.
  • delta – optional value added to the filtered pixels before storing them in dst.
  • borderType – pixel extrapolation method (see borderInterpolate() for details).

 

 

OpenCV&&python_图像平滑(Smoothing Images)