2D图像滤波器基础类BaseFilter：dst(x,y) = F(src(x,y), src(x+1,y)... src(x+wdith-1,y), src(y+1,x)... src(x+width-1, y+height-1) );

相关的调用函数为getLinearFilter、getMorphologyFilter

单行核滤波器基础类BaseRowFilter：dst(x,y) = F(src(x,y), src(x+1,y),...src(x+width-1,y));

相关的调用函数为getLinearRowFilter、getMorphologyRowFilter

单列核滤波器基础类BaseColumnFilter：dst(x,y) = F(src(x,y), src(x,y+1),...src(x,y+width-1));

相关的调用函数为getColumnSumFilter、getLinearColumnFilter、getMorphologyColumnFilter

类FilterEngine：该类可以应用在对图像的任意滤波操作当中，在OpenCV滤波器函数中扮演着很重要的角色，相关的函数有createBoxFitler、createDerivFitlter、createGaussianFilter、createLinearFilter、createMorphologyFilter、createSeparableLinearFilter

这里介绍一下我使用Laplacian滤波的心得，这个函数的第三个参数为输出的图像的深度，注意经过拉普拉斯算子处理后得到的值是有正有负的，所以输出图像的深度最好为输入图像深度的2倍，才能有效防止数据溢出，如必须要使用8位的数据，可以再使用函数convertScaleAbs处理。而且要注意使用的拉普拉斯算子掩膜的中心系数为负。

GaussianBlur

Parameters:

• src – input image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
• dst – output image of the same size and type as src.
• ksize – Gaussian kernel size. ksize.width and ksize.height can differ but they both must be positive and odd. Or, they can be zero’s and then they are computed from sigma* .
• sigmaX – Gaussian kernel standard deviation in X direction.
• sigmaY – Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height , respectively (see getGaussianKernel() for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize,sigmaX, and sigmaY.
• borderType – pixel extrapolation method (see borderInterpolate() for details).

void convertScaleAbs(InputArray src, OutputArray dst, double alpha=1, double beta=0)

Parameters:

• src – input array.
• dst – output array.
• alpha – optional scale factor.
• beta – optional delta added to the scaled values.

#include "stdafx.h"#include "opencv2/imgproc/imgproc.hpp"#include "opencv2/highgui/highgui.hpp"#include <stdlib.h>#include <stdio.h>using namespace cv;/** @function main */int main( int argc, char** argv ){    Mat src, src_gray, dst;    int kernel_size = 3;    int scale = 1;    int delta = 0;    int ddepth = CV_16S;    char* window_name = "Laplace Demo";    // int c;    /// Load an image    src = http://www.mamicode.com/imread( "zhou.jpg" );  // Mat ---  imread    if( !src.data )    { return -1; }    /// Remove noise by blurring with a Gaussian filter    GaussianBlur( src, src, Size(3,3), 0, 0, BORDER_DEFAULT );      /// Convert the image to grayscale    cvtColor( src, src_gray, CV_RGB2GRAY );    /// Create window    namedWindow( window_name, CV_WINDOW_AUTOSIZE );        // cvNamedWindow    /// Apply Laplace function    Mat abs_dst;    Laplacian( src_gray, dst, ddepth, kernel_size, scale, delta, BORDER_DEFAULT );    convertScaleAbs( dst, abs_dst );    // Scales, calculates absolute values, and converts the result to 8-bit.    /// Show what you got    imshow( window_name, abs_dst );    waitKey(0);    return 0;}

 

转自:http://blog.csdn.net/yang_xian521/article/category/910716/4

OPENCV(4) —— ImgProc