标签：机器视觉 图像算法 python 机器学习 数学
拉普拉斯线性滤波,.边缘检测
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.
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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，相当于对图像采用如下内核做卷积：
本博客所有内容是原创，如果转载请注明来源
# -*- 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()
数学之路-python计算实战(21)-机器视觉-拉普拉斯线性滤波,布布扣,bubuko.com
数学之路-python计算实战(21)-机器视觉-拉普拉斯线性滤波
标签：机器视觉 图像算法 python 机器学习 数学
原文地址：http://blog.csdn.net/myhaspl/article/details/38173109
