OpenCV&&python_图像平滑（Smoothing Images）

Goals

学习用不同低通滤波方法模糊图像（Blur imagess with various low pass filter）
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).

巴特西

OpenCV&&python_图像平滑（Smoothing Images）

Goals

最新文章

热门文章