opencv计算两个轮廓之间hu矩相似程度,MatchShapes

Posted 2021-02-12 mydddfly

https://blog.csdn.net/jiake_yang/article/details/52589063

【OpenCV3.3】通过透视变换矫正变形图像 https://blog.csdn.net/rrrfff/article/details/77340641

OPENCV提供了输入图像直接进行hu矩匹配的函数，返回的是两个图像或轮廓之间hu矩的相似度：

double cvMatchShapes(const void*object1,const void*object2,int method,doubleparameter=0);

计算两个轮廓之间hu矩相似程度：

#include <iostream>
#include "cv.h"
#include "cxcore.h"
#include "highgui.h"
using namespace std;

CvSeq *getImageContours(CvArr *src)
{
cvThreshold(src, src, 100, 255, CV_THRESH_BINARY);
CvMemStorage * storage = cvCreateMemStorage(0);
CvSeq * contours;
cvFindContours(src, storage, &contours);
return contours;
}
int main()
{
IplImage *src1 = cvLoadImage("", 0);
CvSeq *contours1 = getImageContours(src1); // 得到src1的轮廓
IplImage *src2 = cvLoadImage("", 0);
CvSeq *contours2 = getImageContours(src2);
double result = cvMatchShapes(contours1, contours2, 1); // 根据输入的图像或轮廓来计算它们的hu矩的相似度
cout << result << endl;
cvReleaseMemStorage(&contours1->storage);
cvReleaseMemStorage(&contours1->storage);
cvReleaseImage(&src1);
cvReleaseImage(&src2);
return 0;
}
给出了10副图片2.jpg  3.jpg.....11.jpg

其中2.jpg和11.jpg非常相似，我们代码是要实现的在3~11.jgp找到与2.jpg最相似的图片。

代码:

#include <iostream>
#include <string>
#include <sstream>
#include "cv.h"
#include "cxcore.h"
#include "highgui.h"
using namespace std;

int main()
{
IplImage *srcColor = cvLoadImage("E:\\study_opencv_video\\lesson15_3\\2.jpg", 1);
IplImage *src = cvCreateImage(cvGetSize(srcColor), 8, 1);
cvCvtColor(srcColor, src, CV_BGR2GRAY);
if(!src)
{
cout << "No Image Load" << endl;
}
int i;
stringstream ss;
string path;
string str;
IplImage *dst = NULL, *dstColor;
char c[256];
double result, maxResult= 1000 * 256 *256;
IplImage *resultMap = NULL;
for (i = 3; i < 12; i ++)
{
path = "E:\\study_opencv_video\\lesson15_3\\";
ss.clear();
ss << i;
ss >> str;
str += ".jpg";
path += str;
ss.clear();
ss << path;
ss >> c;
dstColor = cvLoadImage(c,1); //读取图片
dst = cvCreateImage(cvGetSize(dstColor), 8, 1);
cvCvtColor(dstColor, dst, CV_BGR2GRAY);
result = cvMatchShapes(src, dst, 1);
if(maxResult > result) //求最大相似
{
resultMap = cvCreateImage(cvGetSize(dstColor), 8, 3);
maxResult = result;
cvCopy(dstColor, resultMap);
}
}
cvNamedWindow("srcColor", 0);
cvNamedWindow("resultMap",0);
cvShowImage("resultMap", resultMap);
cvShowImage("srcColor", srcColor);
cvWaitKey(0);
cvReleaseImage(&src);
cvReleaseImage(&srcColor);
cvReleaseImage(&dst);
cvReleaseImage(&dstColor);
cvReleaseImage(&resultMap);
cvDestroyWindow("srcColor");
cvDestroyWindow("resultMap");
return 0;
}