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https://github.com/opencv/opencv.git
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0c7663eb3b
Conflicts: modules/core/include/opencv2/core/cuda.hpp modules/cudacodec/src/thread.cpp modules/cudacodec/src/thread.hpp modules/superres/perf/perf_superres.cpp modules/superres/src/btv_l1_cuda.cpp modules/superres/src/optical_flow.cpp modules/videostab/src/global_motion.cpp modules/videostab/src/inpainting.cpp samples/cpp/stitching_detailed.cpp samples/cpp/videostab.cpp samples/gpu/stereo_multi.cpp
263 lines
10 KiB
C++
263 lines
10 KiB
C++
#include "opencv2/highgui/highgui.hpp"
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#include "opencv2/features2d/features2d.hpp"
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#include "opencv2/contrib/contrib.hpp"
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#include <iostream>
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#include <fstream>
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using namespace cv;
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using namespace std;
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const string defaultDetectorType = "SURF";
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const string defaultDescriptorType = "SURF";
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const string defaultMatcherType = "FlannBased";
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const string defaultQueryImageName = "../../opencv/samples/cpp/matching_to_many_images/query.png";
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const string defaultFileWithTrainImages = "../../opencv/samples/cpp/matching_to_many_images/train/trainImages.txt";
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const string defaultDirToSaveResImages = "../../opencv/samples/cpp/matching_to_many_images/results";
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static void printPrompt( const string& applName )
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{
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cout << "/*\n"
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<< " * This is a sample on matching descriptors detected on one image to descriptors detected in image set.\n"
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<< " * So we have one query image and several train images. For each keypoint descriptor of query image\n"
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<< " * the one nearest train descriptor is found the entire collection of train images. To visualize the result\n"
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<< " * of matching we save images, each of which combines query and train image with matches between them (if they exist).\n"
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<< " * Match is drawn as line between corresponding points. Count of all matches is equel to count of\n"
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<< " * query keypoints, so we have the same count of lines in all set of result images (but not for each result\n"
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<< " * (train) image).\n"
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<< " */\n" << endl;
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cout << endl << "Format:\n" << endl;
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cout << "./" << applName << " [detectorType] [descriptorType] [matcherType] [queryImage] [fileWithTrainImages] [dirToSaveResImages]" << endl;
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cout << endl;
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cout << "\nExample:" << endl
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<< "./" << applName << " " << defaultDetectorType << " " << defaultDescriptorType << " " << defaultMatcherType << " "
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<< defaultQueryImageName << " " << defaultFileWithTrainImages << " " << defaultDirToSaveResImages << endl;
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}
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static void maskMatchesByTrainImgIdx( const vector<DMatch>& matches, int trainImgIdx, vector<char>& mask )
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{
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mask.resize( matches.size() );
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fill( mask.begin(), mask.end(), 0 );
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for( size_t i = 0; i < matches.size(); i++ )
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{
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if( matches[i].imgIdx == trainImgIdx )
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mask[i] = 1;
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}
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}
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static void readTrainFilenames( const string& filename, string& dirName, vector<string>& trainFilenames )
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{
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trainFilenames.clear();
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ifstream file( filename.c_str() );
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if ( !file.is_open() )
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return;
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size_t pos = filename.rfind('\\');
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char dlmtr = '\\';
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if (pos == string::npos)
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{
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pos = filename.rfind('/');
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dlmtr = '/';
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}
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dirName = pos == string::npos ? "" : filename.substr(0, pos) + dlmtr;
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while( !file.eof() )
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{
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string str; getline( file, str );
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if( str.empty() ) break;
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trainFilenames.push_back(str);
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}
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file.close();
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}
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static bool createDetectorDescriptorMatcher( const string& detectorType, const string& descriptorType, const string& matcherType,
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Ptr<FeatureDetector>& featureDetector,
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Ptr<DescriptorExtractor>& descriptorExtractor,
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Ptr<DescriptorMatcher>& descriptorMatcher )
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{
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cout << "< Creating feature detector, descriptor extractor and descriptor matcher ..." << endl;
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featureDetector = FeatureDetector::create( detectorType );
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descriptorExtractor = DescriptorExtractor::create( descriptorType );
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descriptorMatcher = DescriptorMatcher::create( matcherType );
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cout << ">" << endl;
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bool isCreated = featureDetector && descriptorExtractor && descriptorMatcher;
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if( !isCreated )
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cout << "Can not create feature detector or descriptor extractor or descriptor matcher of given types." << endl << ">" << endl;
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return isCreated;
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}
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static bool readImages( const string& queryImageName, const string& trainFilename,
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Mat& queryImage, vector <Mat>& trainImages, vector<string>& trainImageNames )
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{
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cout << "< Reading the images..." << endl;
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queryImage = imread( queryImageName, IMREAD_GRAYSCALE);
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if( queryImage.empty() )
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{
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cout << "Query image can not be read." << endl << ">" << endl;
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return false;
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}
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string trainDirName;
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readTrainFilenames( trainFilename, trainDirName, trainImageNames );
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if( trainImageNames.empty() )
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{
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cout << "Train image filenames can not be read." << endl << ">" << endl;
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return false;
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}
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int readImageCount = 0;
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for( size_t i = 0; i < trainImageNames.size(); i++ )
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{
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string filename = trainDirName + trainImageNames[i];
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Mat img = imread( filename, IMREAD_GRAYSCALE );
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if( img.empty() )
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cout << "Train image " << filename << " can not be read." << endl;
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else
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readImageCount++;
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trainImages.push_back( img );
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}
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if( !readImageCount )
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{
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cout << "All train images can not be read." << endl << ">" << endl;
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return false;
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}
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else
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cout << readImageCount << " train images were read." << endl;
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cout << ">" << endl;
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return true;
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}
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static void detectKeypoints( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
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const vector<Mat>& trainImages, vector<vector<KeyPoint> >& trainKeypoints,
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Ptr<FeatureDetector>& featureDetector )
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{
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cout << endl << "< Extracting keypoints from images..." << endl;
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featureDetector->detect( queryImage, queryKeypoints );
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featureDetector->detect( trainImages, trainKeypoints );
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cout << ">" << endl;
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}
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static void computeDescriptors( const Mat& queryImage, vector<KeyPoint>& queryKeypoints, Mat& queryDescriptors,
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const vector<Mat>& trainImages, vector<vector<KeyPoint> >& trainKeypoints, vector<Mat>& trainDescriptors,
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Ptr<DescriptorExtractor>& descriptorExtractor )
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{
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cout << "< Computing descriptors for keypoints..." << endl;
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descriptorExtractor->compute( queryImage, queryKeypoints, queryDescriptors );
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descriptorExtractor->compute( trainImages, trainKeypoints, trainDescriptors );
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int totalTrainDesc = 0;
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for( vector<Mat>::const_iterator tdIter = trainDescriptors.begin(); tdIter != trainDescriptors.end(); tdIter++ )
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totalTrainDesc += tdIter->rows;
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cout << "Query descriptors count: " << queryDescriptors.rows << "; Total train descriptors count: " << totalTrainDesc << endl;
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cout << ">" << endl;
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}
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static void matchDescriptors( const Mat& queryDescriptors, const vector<Mat>& trainDescriptors,
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vector<DMatch>& matches, Ptr<DescriptorMatcher>& descriptorMatcher )
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{
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cout << "< Set train descriptors collection in the matcher and match query descriptors to them..." << endl;
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TickMeter tm;
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tm.start();
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descriptorMatcher->add( trainDescriptors );
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descriptorMatcher->train();
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tm.stop();
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double buildTime = tm.getTimeMilli();
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tm.start();
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descriptorMatcher->match( queryDescriptors, matches );
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tm.stop();
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double matchTime = tm.getTimeMilli();
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CV_Assert( queryDescriptors.rows == (int)matches.size() || matches.empty() );
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cout << "Number of matches: " << matches.size() << endl;
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cout << "Build time: " << buildTime << " ms; Match time: " << matchTime << " ms" << endl;
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cout << ">" << endl;
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}
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static void saveResultImages( const Mat& queryImage, const vector<KeyPoint>& queryKeypoints,
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const vector<Mat>& trainImages, const vector<vector<KeyPoint> >& trainKeypoints,
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const vector<DMatch>& matches, const vector<string>& trainImagesNames, const string& resultDir )
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{
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cout << "< Save results..." << endl;
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Mat drawImg;
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vector<char> mask;
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for( size_t i = 0; i < trainImages.size(); i++ )
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{
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if( !trainImages[i].empty() )
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{
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maskMatchesByTrainImgIdx( matches, (int)i, mask );
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drawMatches( queryImage, queryKeypoints, trainImages[i], trainKeypoints[i],
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matches, drawImg, Scalar(255, 0, 0), Scalar(0, 255, 255), mask );
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string filename = resultDir + "/res_" + trainImagesNames[i];
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if( !imwrite( filename, drawImg ) )
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cout << "Image " << filename << " can not be saved (may be because directory " << resultDir << " does not exist)." << endl;
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}
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}
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cout << ">" << endl;
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}
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int main(int argc, char** argv)
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{
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string detectorType = defaultDetectorType;
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string descriptorType = defaultDescriptorType;
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string matcherType = defaultMatcherType;
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string queryImageName = defaultQueryImageName;
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string fileWithTrainImages = defaultFileWithTrainImages;
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string dirToSaveResImages = defaultDirToSaveResImages;
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if( argc != 7 && argc != 1 )
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{
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printPrompt( argv[0] );
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return -1;
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}
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if( argc != 1 )
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{
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detectorType = argv[1]; descriptorType = argv[2]; matcherType = argv[3];
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queryImageName = argv[4]; fileWithTrainImages = argv[5];
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dirToSaveResImages = argv[6];
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}
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Ptr<FeatureDetector> featureDetector;
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Ptr<DescriptorExtractor> descriptorExtractor;
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Ptr<DescriptorMatcher> descriptorMatcher;
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if( !createDetectorDescriptorMatcher( detectorType, descriptorType, matcherType, featureDetector, descriptorExtractor, descriptorMatcher ) )
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{
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printPrompt( argv[0] );
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return -1;
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}
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Mat queryImage;
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vector<Mat> trainImages;
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vector<string> trainImagesNames;
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if( !readImages( queryImageName, fileWithTrainImages, queryImage, trainImages, trainImagesNames ) )
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{
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printPrompt( argv[0] );
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return -1;
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}
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vector<KeyPoint> queryKeypoints;
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vector<vector<KeyPoint> > trainKeypoints;
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detectKeypoints( queryImage, queryKeypoints, trainImages, trainKeypoints, featureDetector );
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Mat queryDescriptors;
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vector<Mat> trainDescriptors;
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computeDescriptors( queryImage, queryKeypoints, queryDescriptors,
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trainImages, trainKeypoints, trainDescriptors,
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descriptorExtractor );
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vector<DMatch> matches;
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matchDescriptors( queryDescriptors, trainDescriptors, matches, descriptorMatcher );
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saveResultImages( queryImage, queryKeypoints, trainImages, trainKeypoints,
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matches, trainImagesNames, dirToSaveResImages );
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return 0;
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}
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