Implémentation de l'algorithme de knn
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dfe200f290
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5 changed files with 212 additions and 119 deletions
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@ -4,6 +4,8 @@ project(miniprojet)
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set(PROJECT_CFLAGS "-Wall -Wextra -Wno-missing-braces -std=c++1z")
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find_package(OpenCV REQUIRED)
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add_compile_options(-std=c++17)
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add_subdirectory(src)
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add_subdirectory(examples)
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add_subdirectory(jean-luc-collette)
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@ -1,13 +1,7 @@
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# file(GLOB headers *.hpp)
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# file(GLOB lib_files *.cpp)
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add_executable(traitement traitement.cpp)
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target_link_libraries(traitement ${OpenCV_LIBS})
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#add_executable(k_proches_voisins k_proches_voisins.cpp)
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#target_link_libraries(k_proches_voisins ${OpenCV_LIBS})
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# target_include_directories(blk PUBLIC ${CMAKE_CURRENT_SOURCE_DIR})
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# target_compile_options (blk PUBLIC -std=c++11 )
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find_package(Boost COMPONENTS system filesystem REQUIRED)
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# install(TARGETS blk DESTINATION lib )
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# install(FILES ${headers} DESTINATION include/${CMAKE_PROJECT_NAME})
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add_executable(knn knn.cpp)
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target_link_libraries(knn ${OpenCV_LIBS} ${Boost_FILESYSTEM_LIBRARY} ${Boost_SYSTEM_LIBRARY})
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@ -1,87 +0,0 @@
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#include <map>
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#include "math.hpp"
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#include <stdexcept>
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double distance(math::csignal& v1, math::csignal& v2, int n){
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if (v1.size() != v2.size()) {
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throw std::runtime_error("les deux vecteurs doivent être de même longueur");
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}
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double d;
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double di;
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for (int i=0; i<v1.size(); ++i){
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di = std::abs(v1[i] - v2[i]);
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di = std::pow(di, n);
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d = d + di;
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};
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return std::pow(d, 1/n);
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};
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int argmax(std::vector<int>& v){
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int arg = 0;
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int max = v[0];
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for(int i = 1; i < v.size() ; ++i){
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if (v[i]>max){
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arg = i;
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max = v[i];
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};
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};
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return arg;
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};
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//int main(math::csignal new_vect, std::map< math::csignal, std::string > dico, int k){
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int main(int argc, char** argv) {
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std::vector<std::pair<double, math::csignal>> k_min;
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std::map<math::csignal, std::string> dico;
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math::csignal new_vect;
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int k;
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double d;
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int avance = 0;
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int arret = 0;
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int droite = 0;
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int gauche = 0;
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int rejet = 0;
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std::vector<int> vchoix;
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for(auto& ref_vect : dico){
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d = distance(new_vect, ref_vect.first);
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if (k_min.size() < k ){
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k_min.push_back({d, ref_vect.first});
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} else if (d < k_min[k-1].first){
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k_min.push_back({d, ref_vect.first});
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sort(k_min.begin(), k_min.end());
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k_min.pop_back();
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}
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}
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for(auto i = k_min.begin(); i != k_min.end(); i++) {
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if (dico[k_min[i].second].second == "avance"){
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avance = avance + 1
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} else if (dico[k_min[i].second].second == "arret"){
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arret = arret + 1
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} else if (dico[k_min[i].second].second == "droite"){
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arret = droite + 1
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} else if (dico[k_min[i].second].second == "gauche"){
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arret = gauche + 1
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} else if (dico[k_min[i].second].second == "rejet"){
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arret = rejet + 1
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}
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}
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vchoix.push_back(avance);
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vchoix.push_back(arret);
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vchoix.push_back(droite);
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vchoix.push_back(gauche);
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vchoix.push_back(rejet);
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int nchoix = argmax(vchoix);
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std::string choix;
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if (nchoix == 0){
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choix = "avance"
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} else if (nchoix == 1){
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choix = "arret"
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} else if (nchoix == 2){
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choix = "droite"
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} else if (nchoix == 3){
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choix = "gauche"
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} else if (nchoix == 4){
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choix = "rejet"
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}
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};
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139
tests/src/knn.cpp
Normal file
139
tests/src/knn.cpp
Normal file
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@ -0,0 +1,139 @@
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#include <map>
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#include "math.hpp"
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#include <stdexcept>
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#include <queue>
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#include <opencv2/opencv.hpp>
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#include <boost/filesystem.hpp>
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#include <iterator>
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#include <iostream>
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#include <algorithm>
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using dataset = std::vector<std::pair<math::csignal, std::string>>;
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struct path_leaf_string {
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std::string operator()(const boost::filesystem::directory_entry& entry) const
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{
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return entry.path().leaf().string();
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}
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};
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void read_directory(const std::string& name, std::vector<std::string>& v) {
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boost::filesystem::path p(name);
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boost::filesystem::directory_iterator start(p);
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boost::filesystem::directory_iterator end;
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std::transform(start, end, std::back_inserter(v), path_leaf_string());
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}
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double distance(math::csignal& v1, math::csignal& v2, int n){
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if (v1.size() != v2.size()) {
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throw std::runtime_error("les deux vecteurs doivent être de même longueur");
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}
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double d = 0;
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auto v1_it = v1.begin();
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auto v2_it = v2.begin();
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while (v1_it != v1.end()) {
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double dist = std::abs(*(v1_it++) - *(v2_it++));
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d += std::pow(dist, n);
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}
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return std::pow(d, 1/n);
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};
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int argmax(std::vector<int>& v){
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int arg = 0;
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int max = v[0];
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for(int i = 1; i < v.size() ; ++i){
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if (v[i]>max){
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arg = i;
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max = v[i];
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};
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};
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return arg;
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};
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struct pair_comp {
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bool operator()(std::pair<double, std::string> a, std::pair<double, std::string> b) {
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if (a.first == b.first) {
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return false;
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}
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if (a.first > b.first) {
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return true;
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}
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return false;
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};
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};
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math::csignal img2desc(std::string filename, int cmax, int threshold) {
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cv::Mat img = cv::imread(filename, CV_LOAD_IMAGE_COLOR);
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return math::descriptors(img, cmax, threshold);
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}
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dataset get_data(std::string path, int size, int cmax, int threshold) {
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dataset res;
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std::vector<std::string> dirs;
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read_directory(path, dirs);
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for (auto dir: dirs) {
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std::vector<std::string> files;
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read_directory(path+"/"+dir, files);
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std::string label = dir;
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int count = 0;
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for (int i=0; count<size/4 && i<files.size(); ++i) {
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try {
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math::csignal d = img2desc(path+"/"+dir+"/"+files[i], cmax, threshold);
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res.push_back({d, label});
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count++;
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} catch (std::length_error& e) {
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std::cout << "No contour: image skiped." << std::endl;
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}
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}
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std::cout << res.size() << std::endl;
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}
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return res;
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}
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int main(int argc, char** argv) {
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int k = 20;
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int size = 100;
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std::string path;
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int cmax = 10;
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int threshold = 20;
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if (argc > 2) {
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path = argv[1];
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threshold = atoi(argv[2]);
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} else {
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std::cout << "Invalid number of arguments" << std::endl;
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return 0;
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}
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dataset references = get_data(path, size, cmax, threshold);
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math::csignal sample = img2desc(path+"/arret/arret0199.jpg", cmax, threshold);
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std::priority_queue<std::pair<double, std::string>, std::vector<std::pair<double, std::string>>, pair_comp> neighbors;
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std::map<std::string, int> labels;
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for (auto desc: references) {
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double d = distance(desc.first, sample, 1);
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neighbors.push({d, desc.second});
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}
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for (int i=0; i<k; ++i) {
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std::pair<double, std::string> nearest = neighbors.top();
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neighbors.pop();
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labels[nearest.second] += 1;
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}
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int max = 0;
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std::string label;
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for (auto val: labels) {
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if (val.second > max) {
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max = val.second;
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label = val.first;
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}
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}
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std::cout << label << std::endl;
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};
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@ -5,6 +5,7 @@
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#include <opencv2/opencv.hpp>
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#include <iterator>
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#include <cmath>
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#include <stdexcept>
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namespace math {
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@ -284,29 +285,6 @@ namespace math {
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return res;
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}
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csignal descriptors(const contour& cont, int cmax) {
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csignal z = cont2sig(cont);
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complex zm = mean(z);
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csignal tfd = dft(diff(z, zm));
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tfd /= z.size();
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int cmin = -cmax;
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csignal desc = extract(tfd, cmin, cmax);
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if (std::abs(desc[desc.size()/2-1]) > std::abs(desc[desc.size()/2+1])) {
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std::reverse(desc.begin(), desc.end());
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}
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double phy = std::arg(desc[desc.size()/2-1]*desc[desc.size()/2+1])/2;
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desc *= std::exp(complex(0, -phy));
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double theta = std::arg(desc[desc.size()/2+1]);
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for (int k=0; k<desc.size(); ++k) {
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desc[k] *= std::exp(complex(0, -theta*(k-cmin)));
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}
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desc /= std::abs(desc[desc.size()/2+1]);
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return desc;
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}
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contour simplify_contour(const contour& cont, int cmax) {
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csignal z = cont2sig(cont);
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@ -346,4 +324,71 @@ namespace math {
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}
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return id;
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};
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csignal descriptors(const cv::Mat& img, int cmax, int threshold) {
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std::vector<std::vector<cv::Point>> contours;
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cv::Mat binary(img.rows, img.cols, CV_8UC1);
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cv::Mat blur_img;
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cv::GaussianBlur(img, blur_img, cv::Size(7,7), 1.5, 1.5);
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std::vector<cv::Vec4i> hierarchy;
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math::filter(img, binary, threshold);
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cv::findContours(binary, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
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if (contours.size() > 0) {
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std::vector<std::vector<cv::Point>> contrs;
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int id = max_cont(contours);
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csignal z = cont2sig(contours[id]);
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complex zm = mean(z);
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csignal tfd = dft(diff(z, zm));
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tfd /= z.size();
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int cmin = -cmax;
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csignal desc = extract(tfd, cmin, cmax);
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if (std::abs(desc[desc.size()/2-1]) > std::abs(desc[desc.size()/2+1])) {
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std::reverse(desc.begin(), desc.end());
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}
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double phy = std::arg(desc[desc.size()/2-1]*desc[desc.size()/2+1])/2;
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desc *= std::exp(complex(0, -phy));
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double theta = std::arg(desc[desc.size()/2+1]);
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for (int k=0; k<desc.size(); ++k) {
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desc[k] *= std::exp(complex(0, -theta*(k-cmin)));
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}
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desc /= std::abs(desc[desc.size()/2+1]);
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return desc;
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} else {
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throw std::length_error("No contour detected !");
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}
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}
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csignal descriptors(const contour& cont, int cmax) {
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csignal z = cont2sig(cont);
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complex zm = mean(z);
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csignal tfd = dft(diff(z, zm));
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tfd /= z.size();
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int cmin = -cmax;
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csignal desc = extract(tfd, cmin, cmax);
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if (std::abs(desc[desc.size()/2-1]) > std::abs(desc[desc.size()/2+1])) {
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std::reverse(desc.begin(), desc.end());
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}
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double phy = std::arg(desc[desc.size()/2-1]*desc[desc.size()/2+1])/2;
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desc *= std::exp(complex(0, -phy));
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double theta = std::arg(desc[desc.size()/2+1]);
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for (int k=0; k<desc.size(); ++k) {
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desc[k] *= std::exp(complex(0, -theta*(k-cmin)));
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}
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desc /= std::abs(desc[desc.size()/2+1]);
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return desc;
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}
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}
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