OPENCV两张图⽚相似度直⽅图ORB算法判断两张图的相似度
⽅法
1. 直⽅图对⽐法
2. ORB算法
实验
1.直⽅图对⽐法
参考
因为我的环境是VS2010+OpenCV2.4.8，所以在原版的基础上做了⼀点⼩修改。
#include <opencv2/opencv.hpp>
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"讥笑的反义词
#include "opencv2/highgui/highgui.hpp"
#include <iostream>
#include <string>
#include <stdlib.h>
using namespace std;
using namespace cv;
//直⽅图⽐对
bool compareFacesByHist(Mat img,Mat orgImg)
{
Mat tmpImg;
resize(img, tmpImg, ls, ws));
imshow("Img1", img);
imshow("tmpImg", tmpImg);
imshow("orgImg", orgImg);
//HSV颜⾊特征模型(⾊调H，饱和度S，亮度V)
cvtColor(tmpImg, tmpImg, COLOR_BGR2HSV);
侵犯隐私cvtColor(orgImg, orgImg, COLOR_BGR2HSV);
//直⽅图尺⼨设置
//⼀个灰度值可以设定⼀个bins，256个灰度值就可以设定256个bins
//对应HSV格式，构建⼆维直⽅图
//每个维度的直⽅图灰度值划分为256块进⾏统计，也可以使⽤其他值
int hBins = 256, sBins = 256;
int histSize[] = { hBins,sBins };
//H:0~180, S:0~255,V:0~255
//H⾊调取值范围
float hRanges[] = { 0,180 };
//S饱和度取值范围
float sRanges[] = { 0,255 };
const float* ranges[] = { hRanges,sRanges };
int channels[] = { 0,1 };//⼆维直⽅图
MatND hist1, hist2;
calcHist(&tmpImg, 1, channels, Mat(), hist1,2,histSize, ranges, true, fal); normalize(hist1, hist1, 0, 1, NORM_MINMAX, -1, Mat());双鱼男
calcHist(&orgImg, 1, channels, Mat(), hist2, 2, histSize, ranges, true, fal); normalize(hist2, hist2, 0, 1, NORM_MINMAX, -1, Mat());
double similarityValue = compareHist(hist1, hist2, CV_COMP_CORREL); cout << "相似度：" << similarityValue << endl;
if (similarityValue >= 0.85)
{
return true;
}
return fal;
}
int main()
{
Mat orgImg = imread("p34.png");
Mat img = imread("p45.png");
compareFacesByHist(img, orgImg);
waitKey(0);
return 0;
}起子
主要实现思路：
1)从本地读取两张图像
2）将需要对⽐的图像进⾏HSV格式转换
3）构建图像的直⽅图模型，并进⾏直⽅图归⼀化
4）⽐较两张图⽚的直⽅图模型，计算图⽚的直⽅图相似度
5）判断相似度值，如果⼤于0.85左右我们可以认为两张图⽚⽐较相似的
2. ORB算法
参考
#include "stdafx.h"
#include <iostream>
#include<opencv2/opencv.hpp>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/nonfree/features2d.hpp"
#include<opencv2/legacy/legacy.hpp>
using namespace std;
using namespace cv;
int getORB(char * imagePatha,char * imagePathb){
double t;
t=getTickCount();
Mat img_1 = imread(imagePatha);
Mat img_2 = imread(imagePathb);
if (!img_1.data || !img_2.data)
{
cout << "error reading images " << endl; return -1;
}
ORB orb;
vector<KeyPoint> keyPoints_1, keyPoints_2;
Mat descriptors_1, descriptors_2;
阻挡英文orb(img_1, Mat(), keyPoints_1, descriptors_1);
orb(img_2, Mat(), keyPoints_2, descriptors_2);
BruteForceMatcher<HammingLUT> matcher;
vector<DMatch> matches;
matcher.match(descriptors_1, descriptors_2, matches);
double max_dist = 0; double min_dist = 100;
for( int i = 0; i < ws; i++ )
{
double dist = matches[i].distance;
if( dist < min_dist ) min_dist = dist;
if( dist > max_dist ) max_dist = dist;
}
printf("-- Max dist : %f \n", max_dist );
printf("-- Min dist : %f \n", min_dist );
std::vector< DMatch > good_matches;
for( int i = 0; i < ws; i++ )
{
if( matches[i].distance < 0.6*max_dist )喝黑咖啡能减肥吗
地壳的读音
{
good_matches.push_back(matches[i]);
}
}
t=getTickCount()-t;
t=t*1000/getTickFrequency();
Mat img_matches;
和平的反义词
drawMatches(img_1, keyPoints_1, img_2, keyPoints_2,good_matches, img_matches,
drawMatches(img_1, keyPoints_1, img_2, keyPoints_2,good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),vector<char>(),
DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
imshow( "Match", img_matches);
printf( "%f ms\n", t );
cvWaitKey(0);
return 0;
}
int main()
{
getORB("r.png","s.png");
return 0;
}
