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@ -5,6 +5,7 @@
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#include "opencv2/stitching.hpp"
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#include <opencv2/xfeatures2d.hpp>
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#include <opencv2/features2d.hpp>
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#include "opencv2/calib3d/calib3d.hpp"
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#include <fstream>
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#include <iostream>
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#include <json/json.h>
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@ -526,7 +527,7 @@ class PointBase {
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cv::Ptr<SIFT> siftPtr = SIFT::create();
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std::vector<cv::KeyPoint> keypointsROI, keypointsImg;
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std::vector<KeyPoint> keypointsROI, keypointsImg;
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cv::Ptr<SiftDescriptorExtractor> siftExtrPtr;
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cv::Mat descriptorsROI, descriptorsImg;
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siftPtr->detect(roi, keypointsROI);
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@ -541,16 +542,15 @@ class PointBase {
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imshow("sift_result.jpg", output);
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waitKey(0);*/
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cout<<" SIZE \n"<<roi.size() <<" \n";
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cout<<" SIZE \n"<<cv::typeToString(roi.type()) <<" \n";
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siftPtr->compute(roi,
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keypointsROI,
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descriptorsROI);
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/*siftExtrPtr->compute(imgSmall,
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siftPtr->compute(imgSmall,
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keypointsImg,
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descriptorsImg);*/
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descriptorsImg);
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cv::BFMatcher matcher(cv::NORM_L2,true);
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cv::FlannBasedMatcher matcher;
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std::vector<cv::DMatch> matches;
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matcher.match(descriptorsROI,descriptorsImg,matches);
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@ -561,6 +561,60 @@ class PointBase {
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cv::imshow("matches",imageMatches);
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cv::waitKey(0);
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double max_dist = 0; double min_dist = 100;
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cout<<"ROWS DESC:"<<descriptorsImg.rows<<"\n";
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cout<<"SIZE DESC:"<<descriptorsImg.size()<<"\n";
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//-- Quick calculation of max and min distances between keypoints
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for( int i = 0; i < descriptorsImg.rows; i++ )
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{ double dist = matches[i].distance;
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cout<<"DIST:"<<dist<<"\n";
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if( dist < min_dist ) min_dist = dist;
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if( dist > max_dist ) max_dist = dist;
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}
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printf("-- Max dist : %f \n", max_dist );
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printf("-- Min dist : %f \n", min_dist );
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std::vector< DMatch > good_matches;
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for( int i = 0; i < descriptorsImg.rows; i++ )
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{ if( matches[i].distance < 3*min_dist )
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{ good_matches.push_back( matches[i]); }
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}
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std::vector< Point2d > obj;
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std::vector< Point2d > scene;
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for( int i = 0; i < good_matches.size(); i++ )
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{
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//-- Get the keypoints from the good matches
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obj.push_back( static_cast<cv::Point2i>( keypointsImg[ good_matches[i].queryIdx ].pt ));
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scene.push_back( static_cast<cv::Point2i>(keypointsROI[ good_matches[i].trainIdx ].pt ));
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}
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/*cv::Mat imageMatches2;
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drawMatches(roi,keypointsROI,imgSmall,keypointsImg,good_matches,imageMatches2);
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cv::namedWindow("good_matches");
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cv::imshow("good_matches",imageMatches2);
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cv::waitKey(0);*/
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//Mat H = findHomography( Mat(obj), Mat(scene), RANSAC );
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Mat H = estimateAffinePartial2D( Mat(obj), Mat(scene), noArray(),RANSAC );
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cv::Mat result;
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//warpPerspective(imgSmall,roi,H,cv::Size(roi.cols,roi.rows));
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warpAffine(imgSmall,result,H,cv::Size(roi.cols,roi.rows));
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/*cv::Mat half(result,cv::Rect(0,0,imgSmall.cols,imgSmall.rows));
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result.copyTo(roi);*/
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imshow("imgSmall", imgSmall);
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imshow( "Result", result );
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imshow("roi", roi);
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cv::waitKey(0);
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//cout<<CV_VERSION;
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}
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