数学之路-python计算实战(21)-机器视觉-拉普拉斯线性滤波

Posted 2020-09-19

拉普拉斯线性滤波,.边缘检測

  Laplacian

Calculates the Laplacian of an image.

C++: void Laplacian(InputArray src, OutputArray dst, int ddepth, int ksize=1, double scale=1, double delta=0, int borderType=BORDER_DEFAULT )

Python: cv2.Laplacian(src, ddepth[, dst[, ksize[, scale[, delta[, borderType]]]]]) → dst

C: void cvLaplace(const CvArr* src, CvArr* dst, int aperture_size=3 )

Python: cv.Laplace(src, dst, apertureSize=3) → None

Parameters:

src – Source image. dst – Destination image of the same size and the same number of channels as src . ddepth – Desired depth of the destination image. ksize – Aperture size used to compute the second-derivative filters. See getDerivKernels() for details. The size must be positive and odd. scale – Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See getDerivKernels() for details. delta – Optional delta value that is added to the results prior to storing them in dst . borderType – Pixel extrapolation method. SeeborderInterpolate() for details.

The function calculates the Laplacian of the source image by adding up the second x and y derivatives calculated using the Sobel operator:

This is done when ksize > 1 . When ksize == 1 , the Laplacian is computed by filtering the image with the following  aperture:

Laplace

计算图像的 Laplacian 变换

void cvLaplace( const CvArr* src, CvArr* dst, int aperture_size=3 );

src

输入图像.

dst

输出图像.

aperture_size

核大小 (与 cvSobel 中定义一样).

函数 cvLaplace 计算输入图像的 Laplacian变换，方法是先用 sobel 算子计算二阶 x- 和 y- 差分，再求和:

对 aperture_size=1 则给出最快计算结果，相当于对图像採用例如以下内核做卷积：

本博客全部内容是原创，假设转载请注明来源

http://blog.csdn.net/myhaspl/

# -*- coding: utf-8 -*-   
#线性锐化滤波，拉普拉斯图像变换
#code:[email protected]
import cv2

fn="test6.jpg"
myimg=cv2.imread(fn)
img=cv2.cvtColor(myimg,cv2.COLOR_BGR2GRAY)

jgimg=cv2.Laplacian(img,-1)
cv2.imshow(‘src‘,img)
cv2.imshow(‘dst‘,jgimg)
cv2.waitKey()
cv2.destroyAllWindows()

本博客全部内容是原创，假设转载请注明来源

http://blog.csdn.net/myhaspl/