OpenCV&&python_图像平滑（Smoothing Images）

Posted 2020-09-20

• 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

 

 

 直接上代码：

import cv2
import numpy as np
from matplotlib import pyplot as plt
#读图像
img = cv2.imread(‘text.jpg‘)
#核的定义
kernel = np.ones((5,5),np.float32)/25
dst = 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：

 

 

---

 

注释：

 

 

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).