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Merge pull request #17643 from pemmanuelviel:pev--new-flann-demo
* Add a FLANN example showing how to search a query image in a dataset * Clean: remove warning * Replace dependency to boost::filesystem by calls to core/utils/filesystem * Wait for escape key to exit * Add an example of binary descriptors support * Add program options for saving and loading the flann structure * Fix warnings on Win64 * Fix warnings on 3.4 branch still relying on C++03 * Add ctor to img_info structure * Comments modification * * Demo file of FLANN moved and renamed * Fix distances type when using binary vectors in the FLANN example * Rename FLANN example file * Remove dependency of the flann example to opencv_contrib's SURF. * Remove mention of FLANN and other descriptors that aimed at giving hint on the other options * Cleaner program options management * Make waitKey usage minimal in FLANN example * Fix the conditions order * Use cv::Ptr
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samples/cpp/flann_search_dataset.cpp
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samples/cpp/flann_search_dataset.cpp
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// flann_search_dataset.cpp
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// Naive program to search a query picture in a dataset illustrating usage of FLANN
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#include <iostream>
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#include <vector>
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#include "opencv2/core.hpp"
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#include "opencv2/core/utils/filesystem.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/features2d.hpp"
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#include "opencv2/flann.hpp"
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using namespace cv;
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using std::cout;
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using std::endl;
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#define _ORB_
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const char* keys =
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"{ help h | | Print help message. }"
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"{ dataset | | Path to the images folder used as dataset. }"
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"{ image | | Path to the image to search for in the dataset. }"
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"{ save | | Path and filename where to save the flann structure to. }"
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"{ load | | Path and filename where to load the flann structure from. }";
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struct img_info {
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int img_index;
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unsigned int nbr_of_matches;
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img_info(int _img_index, unsigned int _nbr_of_matches)
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: img_index(_img_index)
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, nbr_of_matches(_nbr_of_matches)
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{}
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};
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int main( int argc, char* argv[] )
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{
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//-- Test the program options
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CommandLineParser parser( argc, argv, keys );
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if (parser.has("help"))
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{
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parser.printMessage();
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return -1;
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}
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const cv::String img_path = parser.get<String>("image");
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Mat img = imread( samples::findFile( img_path ), IMREAD_GRAYSCALE );
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if (img.empty() )
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{
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cout << "Could not open the image "<< img_path << endl;
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return -1;
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}
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const cv::String db_path = parser.get<String>("dataset");
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if (!utils::fs::isDirectory(db_path))
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{
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cout << "Dataset folder "<< db_path.c_str() <<" doesn't exist!" << endl;
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return -1;
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}
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const cv::String load_db_path = parser.get<String>("load");
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if ((load_db_path != String()) && (!utils::fs::exists(load_db_path)))
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{
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cout << "File " << load_db_path.c_str()
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<< " where to load the flann structure from doesn't exist!" << endl;
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return -1;
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}
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const cv::String save_db_path = parser.get<String>("save");
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//-- Step 1: Detect the keypoints using a detector, compute the descriptors
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// in the folder containing the images of the dataset
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#ifdef _SIFT_
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int minHessian = 400;
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Ptr<Feature2D> detector = SIFT::create( minHessian );
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#elif defined(_ORB_)
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Ptr<Feature2D> detector = ORB::create();
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#else
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cout << "Missing or unknown defined descriptor. "
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"Only SIFT and ORB are currently interfaced here" << endl;
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return -1;
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#endif
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std::vector<KeyPoint> db_keypoints;
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Mat db_descriptors;
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std::vector<unsigned int> db_images_indice_range; //store the range of indices per image
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std::vector<int> db_indice_2_image_lut; //match descriptor indice to its image
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db_images_indice_range.push_back(0);
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std::vector<cv::String> files;
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utils::fs::glob(db_path, cv::String(), files);
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for (std::vector<cv::String>::iterator itr = files.begin(); itr != files.end(); ++itr)
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{
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Mat tmp_img = imread( *itr, IMREAD_GRAYSCALE );
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if (!tmp_img.empty())
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{
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std::vector<KeyPoint> kpts;
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Mat descriptors;
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detector->detectAndCompute( tmp_img, noArray(), kpts, descriptors );
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db_keypoints.insert( db_keypoints.end(), kpts.begin(), kpts.end() );
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db_descriptors.push_back( descriptors );
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db_images_indice_range.push_back( db_images_indice_range.back()
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+ static_cast<unsigned int>(kpts.size()) );
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}
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}
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//-- Set the LUT
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db_indice_2_image_lut.resize( db_images_indice_range.back() );
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const int nbr_of_imgs = static_cast<int>( db_images_indice_range.size()-1 );
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for (int i = 0; i < nbr_of_imgs; ++i)
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{
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const unsigned int first_indice = db_images_indice_range[i];
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const unsigned int last_indice = db_images_indice_range[i+1];
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std::fill( db_indice_2_image_lut.begin() + first_indice,
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db_indice_2_image_lut.begin() + last_indice,
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i );
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}
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//-- Step 2: build the structure storing the descriptors
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#if defined(_SIFT_)
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cv::Ptr<flann::GenericIndex<cvflann::L2<float> > > index;
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if (load_db_path != String())
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index = cv::makePtr<flann::GenericIndex<cvflann::L2<float> > >(db_descriptors,
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cvflann::SavedIndexParams(load_db_path));
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else
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index = cv::makePtr<flann::GenericIndex<cvflann::L2<float> > >(db_descriptors,
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cvflann::KDTreeIndexParams(4));
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#elif defined(_ORB_)
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cv::Ptr<flann::GenericIndex<cvflann::Hamming<unsigned char> > > index;
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if (load_db_path != String())
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index = cv::makePtr<flann::GenericIndex<cvflann::Hamming<unsigned char> > >
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(db_descriptors, cvflann::SavedIndexParams(load_db_path));
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else
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index = cv::makePtr<flann::GenericIndex<cvflann::Hamming<unsigned char> > >
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(db_descriptors, cvflann::LshIndexParams());
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#else
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cout<< "Descriptor not listed. Set the proper FLANN distance for this descriptor" <<endl;
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return -1;
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#endif
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if (save_db_path != String())
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index->save(save_db_path);
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// Return if no query image was set
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if (img_path == String())
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return 0;
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//-- Detect the keypoints and compute the descriptors for the query image
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std::vector<KeyPoint> img_keypoints;
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Mat img_descriptors;
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detector->detectAndCompute( img, noArray(), img_keypoints, img_descriptors );
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//-- Step 3: retrieve the descriptors in the dataset matching the ones of the query image
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// /!\ knnSearch doesn't follow OpenCV standards by not initialising empty Mat properties
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const int knn = 2;
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Mat indices(img_descriptors.rows, knn, CV_32S);
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#if defined(_SIFT_)
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#define DIST_TYPE float
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Mat dists(img_descriptors.rows, knn, CV_32F);
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#elif defined(_ORB_)
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#define DIST_TYPE int
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Mat dists(img_descriptors.rows, knn, CV_32S);
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#endif
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index->knnSearch( img_descriptors, indices, dists, knn, cvflann::SearchParams(32) );
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//-- Filter matches using the Lowe's ratio test
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const float ratio_thresh = 0.7f;
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std::vector<DMatch> good_matches; //contains
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std::vector<unsigned int> matches_per_img_histogram( nbr_of_imgs, 0 );
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for (int i = 0; i < dists.rows; ++i)
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{
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if (dists.at<DIST_TYPE>(i,0) < ratio_thresh * dists.at<DIST_TYPE>(i,1))
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{
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const int indice_in_db = indices.at<int>(i,0);
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DMatch dmatch(i, indice_in_db, db_indice_2_image_lut[indice_in_db],
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static_cast<float>(dists.at<DIST_TYPE>(i,0)));
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good_matches.push_back( dmatch );
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matches_per_img_histogram[ db_indice_2_image_lut[indice_in_db] ]++;
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}
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}
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//-- Step 4: find the dataset image with the highest proportion of matches
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std::multimap<float, img_info> images_infos;
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for (int i = 0; i < nbr_of_imgs; ++i)
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{
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const unsigned int nbr_of_matches = matches_per_img_histogram[i];
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if (nbr_of_matches < 4) //we need at leat 4 points for a homography
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continue;
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const unsigned int nbr_of_kpts = db_images_indice_range[i+1] - db_images_indice_range[i];
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const float inverse_proportion_of_retrieved_kpts =
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static_cast<float>(nbr_of_kpts) / static_cast<float>(nbr_of_matches);
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img_info info(i, nbr_of_matches);
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images_infos.insert( std::pair<float,img_info>(inverse_proportion_of_retrieved_kpts,
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info) );
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}
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if (images_infos.begin() == images_infos.end())
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{
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cout<<"No good match could be found."<<endl;
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return 0;
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}
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//-- if there are several images with a similar proportion of matches,
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// select the one with the highest number of matches weighted by the
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// squared ratio of proportions
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const float best_matches_proportion = images_infos.begin()->first;
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float new_matches_proportion = best_matches_proportion;
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img_info best_img = images_infos.begin()->second;
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std::multimap<float, img_info>::iterator it = images_infos.begin();
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++it;
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while ((it!=images_infos.end()) && (it->first < 1.1*best_matches_proportion))
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{
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const float ratio = new_matches_proportion / it->first;
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if( it->second.nbr_of_matches * (ratio * ratio) > best_img.nbr_of_matches)
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{
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new_matches_proportion = it->first;
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best_img = it->second;
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}
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++it;
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}
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//-- Step 5: filter goodmatches that belong to the best image match of the dataset
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std::vector<DMatch> filtered_good_matches;
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for (std::vector<DMatch>::iterator itr(good_matches.begin()); itr != good_matches.end(); ++itr)
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{
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if (itr->imgIdx == best_img.img_index)
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filtered_good_matches.push_back(*itr);
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}
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//-- Retrieve the best image match from the dataset
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Mat db_img = imread( files[best_img.img_index], IMREAD_GRAYSCALE );
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//-- Draw matches
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Mat img_matches;
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drawMatches( img, img_keypoints, db_img, db_keypoints, filtered_good_matches, img_matches, Scalar::all(-1),
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Scalar::all(-1), std::vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
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//-- Show detected matches
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imshow("Good Matches", img_matches );
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waitKey();
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return 0;
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}
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