数学之路-python计算实战(21)-机器视觉-拉普拉斯线性滤波

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 )
highlight=laplace#void cvLaplace(const CvArr* src, CvArr* dst, int aperture_size)" title="Permalink to this definition" style="color: rgb(101, 161, 54); text-decoration: none; visibility: hidden; font-size: 0.8em; padding: 0px 4px;">

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

Parameters:

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 highlight=laplace#void getDerivKernels(OutputArray kx, OutputArray ky, int dx, int dy, int ksize, bool normalize, int ktype)" title="void getDerivKernels(OutputArray kx, OutputArray ky, int dx, int dy, int ksize, bool normalize, int ktype)" style="color: rgb(0, 144, 217); text-decoration: none;">`getDerivKernels()` for details. delta – Optional delta value that is added to the results prior to storing them in `dst` . borderType – Pixel extrapolation method. See highlight=laplace#int borderInterpolate(int p, int len, int borderType)" title="int borderInterpolate(int p, int len, int borderType)" style="color: rgb(0, 144, 217); text-decoration: none;">`borderInterpolate()` for details.

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
highlight=laplace#void getDerivKernels(OutputArray kx, OutputArray ky, int dx, int dy, int ksize, bool normalize, int ktype)" title="void getDerivKernels(OutputArray kx, OutputArray ky, int dx, int dy, int ksize, bool normalize, int ktype)" style="color: rgb(0, 144, 217); text-decoration: none;">getDerivKernels() for details.
delta – Optional delta value that is added to the results prior to storing them in dst .
borderType – Pixel extrapolation method. See
highlight=laplace#int borderInterpolate(int p, int len, int borderType)" title="int borderInterpolate(int p, int len, int borderType)" style="color: rgb(0, 144, 217); text-decoration: none;">borderInterpolate() 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:myhaspl@myhaspl.com

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/