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‎feature_matching_all_1_img_1_video.cpp‎

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#include <vector>
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#include <iostream>
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#include <cstdlib>
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#include <fstream>
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#include <algorithm>
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#include <opencv2/opencv.hpp>
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#include <opencv2/objdetect.hpp>
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#include <opencv2/core/core.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include <opencv2/features2d.hpp>
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#include <opencv2/xfeatures2d.hpp>
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#include <opencv2/xfeatures2d/nonfree.hpp>
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#include <opencv2/calib3d.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/core/utility.hpp>
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#include <opencv2/core/ocl.hpp>
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using std::cout;
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using std::cerr;
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using std::vector;
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using std::string;
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using cv::Mat;
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using cv::Point2f;
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using cv::KeyPoint;
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using cv::Scalar;
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using cv::Ptr;
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using cv::FastFeatureDetector;
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using cv::SimpleBlobDetector;
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using cv::DMatch;
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using cv::BFMatcher;
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using cv::DrawMatchesFlags;
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using cv::Feature2D;
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using cv::ORB;
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using cv::BRISK;
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using cv::AKAZE;
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using cv::KAZE;
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using cv::xfeatures2d::BriefDescriptorExtractor;
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using cv::xfeatures2d::SURF;
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using cv::xfeatures2d::SIFT;
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using cv::xfeatures2d::DAISY;
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using cv::xfeatures2d::FREAK;
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const double kDistanceCoef = 4.0;
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const int kMaxMatchingSize = 50;
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const double subsamplingRatio = 0.45;
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void detect_and_compute(string type, Mat& img, vector<KeyPoint>& kpts, Mat& desc) {
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if (type.find("fast") == 0) {
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type = type.substr(4);
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Ptr<FastFeatureDetector> detector = FastFeatureDetector::create(10, true);
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detector->detect(img, kpts);
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}
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if (type.find("blob") == 0) {
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type = type.substr(4);
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Ptr<SimpleBlobDetector> detector = SimpleBlobDetector::create();
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detector->detect(img, kpts);
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}
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if (type == "surf") {
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Ptr<Feature2D> surf = SURF::create(800.0);
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surf->detectAndCompute(img, Mat(), kpts, desc);
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}
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if (type == "sift") {
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Ptr<Feature2D> sift = SIFT::create();
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sift->detectAndCompute(img, Mat(), kpts, desc);
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}
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if (type == "orb") {
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Ptr<ORB> orb = ORB::create();
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orb->detectAndCompute(img, Mat(), kpts, desc);
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}
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if (type == "brisk") {
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Ptr<BRISK> brisk = BRISK::create();
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brisk->detectAndCompute(img, Mat(), kpts, desc);
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}
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if (type == "kaze") {
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Ptr<KAZE> kaze = KAZE::create();
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kaze->detectAndCompute(img, Mat(), kpts, desc);
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}
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if (type == "akaze") {
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Ptr<AKAZE> akaze = AKAZE::create();
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akaze->detectAndCompute(img, Mat(), kpts, desc);
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}
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if (type == "freak") {
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Ptr<FREAK> freak = FREAK::create();
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freak->compute(img, kpts, desc);
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}
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if (type == "daisy") {
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Ptr<DAISY> daisy = DAISY::create();
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daisy->compute(img, kpts, desc);
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}
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if (type == "brief") {
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Ptr<BriefDescriptorExtractor> brief = BriefDescriptorExtractor::create(64);
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brief->compute(img, kpts, desc);
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}
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}
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void match(string type, Mat& desc1, Mat& desc2, vector<DMatch>& matches) {
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matches.clear();
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if (type == "bf") {
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BFMatcher desc_matcher(cv::NORM_L2, true);
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desc_matcher.match(desc1, desc2, matches, Mat());
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}
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if (type == "knn") {
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BFMatcher desc_matcher(cv::NORM_L2, true);
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vector< vector<DMatch> > vmatches;
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desc_matcher.knnMatch(desc1, desc2, vmatches, 1);
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for (int i = 0; i < static_cast<int>(vmatches.size()); ++i) {
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if (!vmatches[i].size()) {
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continue;
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}
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matches.push_back(vmatches[i][0]);
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}
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}
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std::sort(matches.begin(), matches.end());
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while (matches.front().distance * kDistanceCoef < matches.back().distance) {
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matches.pop_back();
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}
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while (matches.size() > kMaxMatchingSize) {
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matches.pop_back();
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}
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}
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int main(int argc, char** argv) {
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// Program expects at least four arguments:
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// - descriptors type ("surf", "sift", "orb", "brisk", "kaze", "akaze", "freak", "daisy", "brief").
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//
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// For "brief", "freak" and "daisy" you also need a prefix that is either "blob" or "fast" (e.g. "fastbrief", "blobdaisy")
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if (argc != 5) {
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cerr << "\nError: wrong (you had: " << argc << ") number of arguments (should be 5).\n";
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cerr << "Examples:\n"
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<< argv[0] << " surf knn ../box.png ../box_in_scene.png\n"
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<< argv[0] << " fastfreak bf ../box.png ../box_in_scene.png\n"
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<< "\nNOTE: Not all of these methods are free, check licensing conditions!\n\n"
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<< std::endl;
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exit(1);
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}
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string desc_type(argv[1]);
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string match_type(argv[2]);
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string img_file1(argv[3]);
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string img_file2(argv[4]);
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Mat img1 = cv::imread(img_file1, CV_LOAD_IMAGE_COLOR);
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Mat img2 = cv::imread(img_file2, CV_LOAD_IMAGE_COLOR);
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if (img1.channels() != 1) {
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cvtColor(img1, img1, cv::COLOR_RGB2GRAY);
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}
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if (img2.channels() != 1) {
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cvtColor(img2, img2, cv::COLOR_RGB2GRAY);
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}
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// Read input video
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cv::VideoCapture cap(img_file2);
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vector<KeyPoint> kpts1;
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vector<KeyPoint> kpts2;
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Mat desc1;
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Mat desc2;
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detect_and_compute(desc_type, img1, kpts1, desc1);
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Mat last_T;
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for (;;)
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{
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// Start timer
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double timer = cv::getTickCount();
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// Define variable for storing frames
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Mat curr, curr_gray;
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// Read next frame
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bool success = cap.read(curr);
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if(!success) break;
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// Convert frame to grayscale
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cv::cvtColor(curr, curr_gray, cv::COLOR_BGR2GRAY);
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detect_and_compute(desc_type, curr_gray, kpts2, desc2);
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vector<DMatch> matches;
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match(match_type, desc1, desc2, matches);
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vector<char> match_mask(matches.size(), 1);
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vector<Point2f> obj;
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vector<Point2f> scene;
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for (int i = 0; i < static_cast<int>(matches.size()); ++i) {
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obj.push_back(kpts1[matches[i].queryIdx].pt);
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scene.push_back(kpts2[matches[i].trainIdx].pt);
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}
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Mat T = cv::findHomography(obj, scene, cv::RANSAC, 4, match_mask);
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// We'll just use the last known good transform.
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if(T.data == NULL) last_T.copyTo(T);
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T.copyTo(last_T);
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// Extract traslation
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double dx = T.at<double>(0,2);
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double dy = T.at<double>(1,2);
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// Extract rotation angle
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double da = atan2(T.at<double>(1,0), T.at<double>(0,0));
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vector<Point2f> obj_corners(4);
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vector<Point2f> scene_corners(4);
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obj_corners[0] = cvPoint(0,0);
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obj_corners[1] = cvPoint( img1.cols, 0 );
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obj_corners[2] = cvPoint( img1.cols, img1.rows ); // img.shape[0] shows the row of the image, img.shape[1] shows the column of the image
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obj_corners[3] = cvPoint( 0, img1.rows );
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cv::perspectiveTransform( obj_corners, scene_corners, T);
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//-- Draw lines between the corners (the mapped object in the scene - image_2 )
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line( res, scene_corners[0] + Point2f( img1.cols, 0), scene_corners[1] + Point2f( img1.cols, 0), Scalar(0, 255, 0), 4 );
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line( res, scene_corners[1] + Point2f( img1.cols, 0), scene_corners[2] + Point2f( img1.cols, 0), Scalar( 0, 255, 0), 4 );
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line( res, scene_corners[2] + Point2f( img1.cols, 0), scene_corners[3] + Point2f( img1.cols, 0), Scalar( 0, 255, 0), 4 );
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line( res, scene_corners[3] + Point2f( img1.cols, 0), scene_corners[0] + Point2f( img1.cols, 0), Scalar( 0, 255, 0), 4 );
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Mat res;
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cv::drawMatches(img1, kpts1, img2, kpts2, matches, res, Scalar::all(-1), Scalar::all(-1), match_mask, DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
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int fps = cv::getTickFrequency() / (cv::getTickCount()-timer);
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// Display Decsriptor Type on the frame
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putText(res, "Decsriptor Type : " + desc_type, cvPoint(10,20), FONT_HERSHEY_SIMPLEX, 0.75, cvScalar(0,0,0), 2);
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// Display FPS on the frame
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string dsp = to_string(fps)
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putText(res, "FPS : " + fps, cvPoint(10,50), FONT_HERSHEY_SIMPLEX, 0.75, cvScalar(0,0,0), 2);
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// Display Good Matches Size on the frame
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string GoodMatches = to_string(matches.size())
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putText(res, "Good Matches : " + GoodMatches, cvPoint(10,50), FONT_HERSHEY_SIMPLEX, 0.75, cvScalar(0,0,0), 2);
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// Display Transfprmations on the frame
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string dX = to_string(cv::round(dx))
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string dY = to_string(cv::round(dy))
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string dA = to_string(cv::round(da))
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putText(res, "Transfprmations = [dx] : " + dX + " " + "[dy] : " + dY, cvPoint(10,50), FONT_HERSHEY_SIMPLEX, 0.75, cvScalar(0,0,0), 2);
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cv::imshow("result", res);
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cv::waitKey(0);
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}
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return 0;
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}

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