opencv-形态学处理

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开运算 (Opening)

原理摘自：http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/imgproc/opening_closing_hats/opening_closing_hats.html
开运算是通过先对图像腐蚀再膨胀实现的。
$dst = open( src, element) = dilate( erode( src, element ) )$
能够排除小团块物体(假设物体较背景明亮)
请看下面，左图是原图像，右图是采用开运算转换之后的结果图。观察发现字母拐弯处的白色空间消失。

闭运算(Closing)

闭运算是通过先对图像膨胀再腐蚀实现的。
$dst = close( src, element ) = erode( dilate( src, element ) )$
能够排除小型黑洞(黑色区域)。

形态梯度(Morphological Gradient)

膨胀图与腐蚀图之差
$dst = morph_{grad}( src, element ) = dilate( src, element ) - erode( src, element )$
能够保留物体的边缘轮廓，如下所示:

顶帽(Top Hat)

原图像与开运算结果图之差
$dst = tophat( src, element ) = src - open( src, element )$

黑帽(Black Hat)

闭运算结果图与原图像之差
$dst = blackhat( src, element ) = close( src, element ) - src$
代码：

// ConsoleApplication3_6_23.cpp : Defines the entry point for the console application.//#include "stdafx.h"#include<opencv2/opencv.hpp>#include<iostream>using namespace std;using namespace cv;Mat src,dst;int pro_elem = 0;int pro_size = 0;int pro_operator = 0;const int max_elem = 2;const int max_size = 21;const int max_operator = 4;char* windowName = "Demo";void Image_pro(int,void*);int _tmain(int argc, _TCHAR* argv[]){src = imread("hwl.jpg");if(!src.data)return -1;namedWindow(windowName,CV_WINDOW_AUTOSIZE);createTrackbar("Operator:\n 0:opening-1:closing-2:gradient-3:Top Hat-4:Black Hat",windowName,&pro_operator,max_operator,Image_pro);createTrackbar("Element:\n 0:Rect-1:Cross-2:Ellipse",windowName,&pro_elem,max_elem,Image_pro);createTrackbar("Kernel size:\n 2n+1",windowName,&pro_size,max_size,Image_pro);Image_pro(0,0);waitKey(0);return 0;}void Image_pro(int,void*){int operation = pro_operator + 2;Mat  element = getStructuringElement(pro_elem,Size(2*pro_size+1,2*pro_size+1),Point(pro_size,pro_size));morphologyEx(src,dst,operation,element);imshow(windowName,dst);}

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