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main.cpp
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132 lines (109 loc) · 5.41 KB
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#include <iostream>
#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
#include <opencv/cv.hpp>
#include "opencv2/features2d.hpp"
#include <opencv2/xfeatures2d/nonfree.hpp>
using namespace std;
using namespace cv;
using namespace cv::xfeatures2d;
void listKeypoints(vector<KeyPoint> keypoints);
void displayImageKeypoints(Mat &image, vector<KeyPoint> &keypoints, string windowName = "Keypoints");
void filterMatchesByDistance(vector<DMatch> originalMatches, vector<DMatch> &filteredMatches,
float maxDistanceThreshold, float minDistanceThreshold);
void showMatchesData(vector<DMatch> &filteredMatches, vector<KeyPoint> &query, vector<KeyPoint> &train);
int main() {
cout << "Program started ..." << std::endl;
Mat img1 = imread("../me1.jpg", IMREAD_GRAYSCALE);
Mat img2 = imread("../me2.jpg", IMREAD_GRAYSCALE);
// can use SURT::create() or ORB::create() also
Ptr<Feature2D> f2d = SURF::create(800);//SURF::create();//SIFT::create();
cout << "Getting Keypoints..." << endl;
// step 1: detect the keypoints
vector<KeyPoint> KeyPoints1, KeyPoints2;
f2d->detect(img1, KeyPoints1);
f2d->detect(img2, KeyPoints2);
listKeypoints(KeyPoints1);
displayImageKeypoints(img1, KeyPoints1, "Keypoints 1");
displayImageKeypoints(img2, KeyPoints2, "Keypoints 2");
cout << "Getting features..." << endl;
// step 2: calculate descriptors (feature vectors)
Mat features1, features2;
f2d->compute(img1, KeyPoints1, features1);
f2d->compute(img2, KeyPoints2, features2);
// just print some statistics returned
Size size1 = features1.size();
Size size2 = features2.size();
cout << "Size of features1 is; " << size1 << endl;
cout << "Size of features2 is; " << size2 << endl;
// step 3: Matching features(descriptors) using BFMatcher
BFMatcher matcher(NORM_L2, true);
vector<DMatch> matches, filteredMatches;
vector<vector<DMatch>> knnMatches; // just to test two different matches
Mat output, outputKeypoint;
matcher.match(features1, features2, matches);
// matcher.knnMatch(features1, features2, knnMatches, 2);
// first sort
sort(matches.begin(), matches.end(), [](DMatch a, DMatch b){return a.distance > b.distance;}); // this is a c++ lambda expression only works in c++11
// now filter
float maxdistanceThreshold = 310, minDistanceThreshold = 250;
//filterMatches(matches, filteredMatches, maxdistanceThreshold, minDistanceThreshold);
filteredMatches = matches;
cout << "Normal Match size: " << matches.size() << " Filtered match size: " << filteredMatches.size() << endl;
//try to see all matches i.e correspoint matching points
showMatchesData(filteredMatches, KeyPoints1, KeyPoints2);
// step 4: showing the matches in window
drawMatches(img1, KeyPoints1, img2, KeyPoints2, filteredMatches, output);
namedWindow("output", CV_WINDOW_NORMAL);
imshow("output", output);
// try to find homography
vector<Point2f> queryAsKeyPoints1, trainAsKeyPoints2;
for(vector<DMatch>::iterator i = filteredMatches.begin(); i < filteredMatches.end(); ++i){
cout << "At Query: " << KeyPoints1.at(i->queryIdx).pt << "At train: " << KeyPoints2.at(i->trainIdx).pt << endl;
// we got query from feature1 i.e from keypoints1 and train from feature2 i.e from keypoints2
// thats why populating accordingly
queryAsKeyPoints1.push_back(KeyPoints1.at(i->queryIdx).pt);
trainAsKeyPoints2.push_back(KeyPoints2.at(i->trainIdx).pt);
}
Mat homography = findHomography(queryAsKeyPoints1, trainAsKeyPoints2, RANSAC);
cout << homography;
waitKey(0);
}
void showMatchesData(vector<DMatch> &filteredMatches, vector<KeyPoint> &query, vector<KeyPoint> &train) {
unsigned long size = filteredMatches.size();
for(unsigned long i=0; i < size; i++){
DMatch dMatch = filteredMatches.at(i);
int queryIndex, trainIndex;
queryIndex = dMatch.queryIdx;
trainIndex = dMatch.trainIdx;
// Now see the corresponding points
cout << "Query Coordinate: " << query.at(queryIndex).pt << " Train Coordinate: " << train.at(trainIndex).pt << endl;
cout << "Match distance is: " << dMatch.distance << endl;
cout << "WTF is imgIdx" << dMatch.imgIdx<< endl;
}
}
void filterMatchesByDistance(vector<DMatch> originalMatches, vector<DMatch> &filteredMatches,
float maxDistanceThreshold, float minDistanceThreshold){
// filter the matches according to some threshold value
// cout << "max: " << maxDistanceThreshold << "min: " << minDistanceThreshold << endl;
for (vector<DMatch>::iterator i = originalMatches.begin(); i < originalMatches.end(); ++i) {
// cout << "from filter.. distance: " << i->distance << endl;
if(i->distance > maxDistanceThreshold || i->distance < minDistanceThreshold ){
filteredMatches.push_back((*i));
// cout << "from filter if.. distance: " << i->distance << endl;
}
}
}
void listKeypoints(vector<KeyPoint> keypoints) {
unsigned long size = keypoints.size();
for(unsigned long i=0; i<size; i++){
KeyPoint kp = keypoints.at(i);
cout << "Point is:" << kp.pt << endl;
}
}
void displayImageKeypoints(Mat &image, vector<KeyPoint> &keypoints, string windowName) {
Mat output;
drawKeypoints(image, keypoints, output);
namedWindow(windowName, CV_WINDOW_NORMAL);
imshow(windowName, output);
}