判斷兩張圖的相似度

方法

直方圖對比法

ORB算法

實驗

1.直方圖對比法

參考如何使用OpenCV3直方圖方法進行人臉相似度對比
因為我的環境是VS2010+OpenCV2.4.8，所以在原版的基礎上做了一點小修改。

#include <opencv2/opencv.hpp> #include "opencv2/core/core.hpp" #include "opencv2/imgproc/imgproc.hpp" #include "opencv2/highgui/highgui.hpp" #include <iostream> #include <string> #include <stdlib.h> using namespace std; using namespace cv; //直方圖比對 bool compareFacesByHist(Mat img,Mat orgImg) {Mat tmpImg;resize(img, tmpImg, Size(orgImg.cols, orgImg.rows));imshow("Img1", img);imshow("tmpImg", tmpImg);imshow("orgImg", orgImg);//HSV顏色特征模型(色調H,飽和度S，亮度V)cvtColor(tmpImg, tmpImg, COLOR_BGR2HSV);cvtColor(orgImg, orgImg, COLOR_BGR2HSV);//直方圖尺寸設置//一個灰度值可以設定一個bins，256個灰度值就可以設定256個bins//對應HSV格式,構建二維直方圖//每個維度的直方圖灰度值劃分為256塊進行統計，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色調取值范圍float hRanges[] = { 0,180 };//S飽和度取值范圍float sRanges[] = { 0,255 };const float* ranges[] = { hRanges,sRanges };int channels[] = { 0,1 };//二維直方圖MatND hist1, hist2;calcHist(&tmpImg, 1, channels, Mat(), hist1,2,histSize, ranges, true, false);normalize(hist1, hist1, 0, 1, NORM_MINMAX, -1, Mat());calcHist(&orgImg, 1, channels, Mat(), hist2, 2, histSize, ranges, true, false);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 false; }int main() {Mat orgImg = imread("p34.png");Mat img = imread("p45.png");compareFacesByHist(img, orgImg);waitKey(0);return 0; }

主要實現思路：
1)從本地讀取兩張圖像
2）將需要對比的圖像進行HSV格式轉換
3）構建圖像的直方圖模型，并進行直方圖歸一化
4）比較兩張圖片的直方圖模型，計算圖片的直方圖相似度
5）判斷相似度值，如果大于0.85左右我們可以認為兩張圖片比較相似的

2. ORB算法

參考OpenCV圖像相似度ORB算法（圖像特征比對）

#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;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 < descriptors_1.rows; 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 < descriptors_1.rows; i++ ) {if( matches[i].distance < 0.6*max_dist ){good_matches.push_back(matches[i]);}}t=getTickCount()-t;t=t*1000/getTickFrequency();Mat 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; }

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