opencv-形态处理

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。

$dst = open( src, element) = dilate( erode( src, element ) )$
可以排除小团块物体(如果物体较背景明亮)
请看以下。左图是原图像，右图是採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。

$dst = close( src, element ) = erode( dilate( src, element ) )$
可以排除小型黑洞(黑色区域)。

形态梯度(Morphological Gradient)

膨胀图与腐蚀图之差

$dst = morph_{grad}( src, element ) = dilate( src, element ) - erode( src, element )$
可以保留物体的边缘轮廓，例如以下所看到的:

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:
 0:opening-1:closing-2:gradient-3:Top Hat-4:	Black Hat",
		windowName,&pro_operator,max_operator,Image_pro);

	createTrackbar("Element:
 0:Rect-1:Cross-2:Ellipse",
		windowName,&pro_elem,max_elem,Image_pro);

	createTrackbar("Kernel size:
 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);
}