OpenCv ORB例子代码

#include "opencv2/objdetect/objdetect.hpp"#include "opencv2/features2d/features2d.hpp"#include "opencv2/highgui/highgui.hpp"#include "opencv2/calib3d/calib3d.hpp"#include "opencv2/imgproc/imgproc_c.h"#include "opencv2/imgproc/imgproc.hpp"  using namespace std;using namespace cv;  char* image_filename1 ="apple_vinegar_0.png";char* image_filename2 ="apple_vinegar_2.png";  unsigned int hamdist(unsignedintx, unsignedinty){ unsigned int dist = 0, val = x ^ y;  // Count the number of set bitswhile(val){ ++dist; val &= val - 1; }   returndist;}   unsigned int hamdist2(unsignedchar* a, unsignedchar* b,size_tsize){ HammingLUT lut;   unsigned int result;result = lut((a), (b), size);return result;}   void naive_nn_search(vector& keys1, Mat& descp1,vector& keys2, Mat& descp2,vector& matches) { for(inti = 0; i < (int)keys2.size(); i++){unsigned int min_dist = INT_MAX;int min_idx = -1;unsigned char* query_feat = descp2.ptr(i);for(int j = 0; j < (int)keys1.size(); j++){unsigned char* train_feat = descp1.ptr(j);unsigned int dist =  hamdist2(query_feat, train_feat, 32);  if(dist < min_dist){min_dist = dist; min_idx = j; } }   //if(min_dist <= (unsigned int)(second_dist * 0.8)){if(min_dist <= 50){matches.push_back(DMatch(i, min_idx, 0, (float)min_dist));} } }   void naive_nn_search2(vector& keys1, Mat& descp1,vector& keys2, Mat& descp2,vector& matches) { for(int i = 0; i < (int)keys2.size(); i++){unsigned int min_dist = INT_MAX;unsigned int sec_dist = INT_MAX;int min_idx = -1, sec_idx = -1;unsigned char* query_feat = descp2.ptr(i);for(intj = 0; j < (int)keys1.size(); j++){unsigned char* train_feat = descp1.ptr(j);unsigned int dist =  hamdist2(query_feat, train_feat, 32);  if(dist < min_dist){sec_dist = min_dist;sec_idx = min_idx;min_dist = dist; min_idx = j; }elseif(dist < sec_dist){sec_dist = dist; sec_idx = j; } }   if(min_dist <= (unsignedint)(sec_dist * 0.8) && min_dist <=50){//if(min_dist <= 50){matches.push_back(DMatch(i, min_idx, 0, (float)min_dist));} } }   int main(intargc,char* argv[]){ Mat img1 = imread(image_filename1, 0);Mat img2 = imread(image_filename2, 0);//GaussianBlur(img1, img1, Size(5, 5), 0);//GaussianBlur(img2, img2, Size(5, 5), 0);  ORB orb1(3000, ORB::CommonParams(1.2, 8));ORB orb2(100, ORB::CommonParams(1.2, 1));  vector keys1, keys2;Mat descriptors1, descriptors2;  orb1(img1, Mat(), keys1, descriptors1,false);printf("tem feat num: %d\n", keys1.size());   int64 st, et; st = cvGetTickCount();orb2(img2, Mat(), keys2, descriptors2,false);et = cvGetTickCount();printf("orb2 extraction time: %f\n", (et-st)/(double)cvGetTickFrequency()/1000.);printf("query feat num: %d\n", keys2.size());   // find matchesvector matches;  st = cvGetTickCount();//for(int i = 0; i < 10; i++){naive_nn_search2(keys1, descriptors1, keys2, descriptors2, matches);//} et = cvGetTickCount();  printf("match time: %f\n", (et-st)/(double)cvGetTickFrequency()/1000.);printf("matchs num: %d\n", matches.size());   Mat showImg; drawMatches(img2, keys2, img1, keys1, matches, showImg, CV_RGB(0, 255, 0), CV_RGB(0, 0, 255));string winName ="Matches";namedWindow( winName, WINDOW_AUTOSIZE );imshow( winName, showImg );waitKey();   vector pt1; vector pt2;   for(inti = 0; i < (int)matches.size(); i++){pt1.push_back(Point2f(keys2[matches[i].queryIdx].pt.x, keys2[matches[i].queryIdx].pt.y));  pt2.push_back(Point2f(keys1[matches[i].trainIdx].pt.x, keys1[matches[i].trainIdx].pt.y));}   Mat homo;   st = cvGetTickCount();homo = findHomography(pt1, pt2, Mat(), CV_RANSAC, 5);et = cvGetTickCount();printf("ransac time: %f\n", (et-st)/(double)cvGetTickFrequency()/1000.);  printf("homo\n""%f %f %f\n""%f %f %f\n""%f %f %f\n",homo.at(0,0), homo.at(0,1), homo.at(0,2),homo.at(1,0), homo.at(1,1), homo.at(1,2),homo.at(2,0),homo.at(2,1),homo.at(2,2));  vector reproj; reproj.resize(pt1.size());  perspectiveTransform(pt1, reproj, homo);  Mat diff; diff = Mat(reproj) - Mat(pt2);  int inlier = 0;doubleerr_sum = 0;for(inti = 0; i < diff.rows; i++){ float* ptr = diff.ptr(i);floaterr = ptr[0]*ptr[0] + ptr[1]*ptr[1];if(err < 25.f){inlier++; err_sum += sqrt(err);} } printf("inlier num: %d\n", inlier); printf("ratio %f\n", inlier / (float)(diff.rows));printf("mean reprojection error: %f\n", err_sum / inlier);   return0;}

#include <iostream>#include "opencv2/core/core.hpp"#include "opencv2/features2d/features2d.hpp"#include "opencv2/highgui/highgui.hpp"#include <iostream>#include <vector>using namespace cv;using namespace std;int main(){ Mat img_1 = imread("D:\\image\\img1.jpg"); Mat img_2 = imread("D:\\image\\img2.jpg"); 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; //-- Quick calculation of max and min distances between keypoints 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 ); //-- Draw only "good" matches (i.e. whose distance is less than 0.6*max_dist ) //-- PS.- radiusMatch can also be used here. 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]);   } }
 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); cvWaitKey(); return 0;}