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"

#include
#include
#include

using namespace std;
using namespace cv;

char * image_filename1 = "apple_vinegar_0.png" ;
char * image_filename2 = "apple_vinegar_2.png" ;

unsigned int hamdist(unsigned int x, unsigned int y)
{
unsigned int dist = 0, val = x ^ y;

// Count the number of set bits
while (val)
{
++dist;
val &= val - 1;
}

return dist;
}

unsigned int hamdist2(unsigned char * a, unsigned char * b, size_t size)
{
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 ( int i = 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 ( 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){
sec_dist = min_dist;
sec_idx = min_idx;
min_dist = dist;
min_idx = j;
} else if (dist < sec_dist){
sec_dist = dist;
sec_idx = j;
}
}

if (min_dist <= (unsigned int )(sec_dist * 0.8) && min_dist <=50){
//if(min_dist <= 50){
matches.push_back(DMatch(i, min_idx, 0, ( float )min_dist));
}
}
}

int main( int argc, 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 matches
vector 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 ( int i = 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;
double err_sum = 0;
for ( int i = 0; i < diff.rows; i++){
float * ptr = diff.ptr(i);
float err = 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);

return 0;
}

#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;
}