Implémentation de l'algorithme de knn

tests/CMakeLists.txt

@@ -4,6 +4,8 @@ project(miniprojet)
 set(PROJECT_CFLAGS "-Wall -Wextra -Wno-missing-braces -std=c++1z")
 find_package(OpenCV REQUIRED)
 
+add_compile_options(-std=c++17)
+
 add_subdirectory(src)
 add_subdirectory(examples)
 add_subdirectory(jean-luc-collette)

tests/src/CMakeLists.txt

@@ -1,13 +1,7 @@
-# file(GLOB headers   *.hpp)
-# file(GLOB lib_files *.cpp)
-
 add_executable(traitement traitement.cpp)
 target_link_libraries(traitement ${OpenCV_LIBS})
 
-#add_executable(k_proches_voisins k_proches_voisins.cpp)
+find_package(Boost COMPONENTS system filesystem REQUIRED)
-#target_link_libraries(k_proches_voisins ${OpenCV_LIBS})
-# target_include_directories(blk PUBLIC ${CMAKE_CURRENT_SOURCE_DIR})
-# target_compile_options    (blk PUBLIC -std=c++11                 )
 
-# install(TARGETS blk        DESTINATION lib                          )
+add_executable(knn knn.cpp)
-# install(FILES   ${headers} DESTINATION include/${CMAKE_PROJECT_NAME})
+target_link_libraries(knn ${OpenCV_LIBS} ${Boost_FILESYSTEM_LIBRARY} ${Boost_SYSTEM_LIBRARY})

tests/src/k_proches_voisins.cpp

@@ -1,87 +0,0 @@
-#include <map>
-#include "math.hpp"
-#include <stdexcept>
-
-double distance(math::csignal& v1, math::csignal& v2, int n){
-  if (v1.size() != v2.size()) {
-    throw std::runtime_error("les deux vecteurs doivent être de même longueur");
-  }
-  double d;
-  double di;
-  for (int i=0; i<v1.size(); ++i){
-    di = std::abs(v1[i] - v2[i]);
-    di = std::pow(di, n);
-    d  = d + di;
-  };
-  return std::pow(d, 1/n);
-};
-
-int argmax(std::vector<int>& v){
-  int arg = 0;
-  int max = v[0];
-  for(int i = 1; i < v.size() ; ++i){
-    if (v[i]>max){
-      arg = i;
-      max = v[i];
-    };
-  };
-  return arg;
-};
-
-//int main(math::csignal new_vect, std::map< math::csignal, std::string > dico, int k){
-int main(int argc, char** argv) {
-  std::vector<std::pair<double, math::csignal>> k_min;
-  std::map<math::csignal, std::string> dico;
-  math::csignal new_vect;
-  int k;
-  double d;
-  int avance = 0;
-  int arret = 0;
-  int droite = 0;
-  int gauche = 0;
-  int rejet = 0;
-  std::vector<int> vchoix;
-  for(auto& ref_vect : dico){
-    d = distance(new_vect, ref_vect.first);
-    if (k_min.size() < k ){
-      k_min.push_back({d, ref_vect.first});
-    } else if (d < k_min[k-1].first){
-      k_min.push_back({d, ref_vect.first});
-      sort(k_min.begin(), k_min.end());
-      k_min.pop_back();
-    }
-
-  }
-  for(auto i = k_min.begin(); i != k_min.end(); i++) {
-    if (dico[k_min[i].second].second == "avance"){
-      avance = avance + 1
-    } else if (dico[k_min[i].second].second == "arret"){
-      arret = arret + 1
-    } else if (dico[k_min[i].second].second == "droite"){
-      arret = droite + 1
-    } else if (dico[k_min[i].second].second == "gauche"){
-      arret = gauche + 1
-    } else if (dico[k_min[i].second].second == "rejet"){
-      arret = rejet + 1
-    }
-  }
-  vchoix.push_back(avance);
-  vchoix.push_back(arret);
-  vchoix.push_back(droite);
-  vchoix.push_back(gauche);
-  vchoix.push_back(rejet);
-
-  int nchoix = argmax(vchoix);
-  std::string choix;
-  if (nchoix == 0){
-    choix = "avance"
-  } else if (nchoix == 1){
-    choix = "arret"
-  } else if (nchoix == 2){
-    choix = "droite"
-  } else if (nchoix == 3){
-    choix = "gauche"
-  } else if (nchoix == 4){
-    choix = "rejet"
-  }
-};

tests/src/knn.cpp

@@ -0,0 +1,139 @@
+#include <map>
+#include "math.hpp"
+#include <stdexcept>
+#include <queue>
+#include <opencv2/opencv.hpp>
+#include <boost/filesystem.hpp>
+#include <iterator>
+#include <iostream>
+#include <algorithm>
+
+using dataset = std::vector<std::pair<math::csignal, std::string>>;
+
+struct path_leaf_string {
+  std::string operator()(const boost::filesystem::directory_entry& entry) const
+  {
+    return entry.path().leaf().string();
+  }
+};
+
+void read_directory(const std::string& name, std::vector<std::string>& v) {
+  boost::filesystem::path p(name);
+  boost::filesystem::directory_iterator start(p);
+  boost::filesystem::directory_iterator end;
+  std::transform(start, end, std::back_inserter(v), path_leaf_string());
+}
+
+double distance(math::csignal& v1, math::csignal& v2, int n){
+  if (v1.size() != v2.size()) {
+    throw std::runtime_error("les deux vecteurs doivent être de même longueur");
+  }
+  double d = 0;
+
+  auto v1_it = v1.begin();
+  auto v2_it = v2.begin();
+
+  while (v1_it != v1.end()) {
+    double dist = std::abs(*(v1_it++) - *(v2_it++));
+    d += std::pow(dist, n);
+  }
+  return std::pow(d, 1/n);
+};
+
+int argmax(std::vector<int>& v){
+  int arg = 0;
+  int max = v[0];
+  for(int i = 1; i < v.size() ; ++i){
+    if (v[i]>max){
+      arg = i;
+      max = v[i];
+    };
+  };
+  return arg;
+};
+
+struct pair_comp {
+  bool operator()(std::pair<double, std::string> a, std::pair<double, std::string> b) {
+    if (a.first == b.first) {
+      return false;
+    }
+    if (a.first > b.first) {
+      return true;
+    }
+    return false;
+  };
+};
+
+math::csignal img2desc(std::string filename, int cmax, int threshold) {
+  cv::Mat img = cv::imread(filename, CV_LOAD_IMAGE_COLOR);
+  return math::descriptors(img, cmax, threshold);
+}
+
+dataset get_data(std::string path, int size, int cmax, int threshold) {
+  dataset res;
+  std::vector<std::string> dirs;
+  read_directory(path, dirs);
+  for (auto dir: dirs) {
+    std::vector<std::string> files;
+    read_directory(path+"/"+dir, files);
+
+    std::string label = dir;
+    int count = 0;
+    for (int i=0; count<size/4 && i<files.size(); ++i) {
+      try {
+        math::csignal d = img2desc(path+"/"+dir+"/"+files[i], cmax, threshold);
+        res.push_back({d, label});
+        count++;
+      } catch (std::length_error& e) {
+        std::cout << "No contour: image skiped." << std::endl;
+      }
+    }
+    std::cout << res.size() << std::endl;
+  }
+  return res;
+}
+
+int main(int argc, char** argv) {
+
+  int k = 20;
+  int size = 100;
+  std::string path;
+  int cmax = 10;
+  int threshold = 20;
+
+  if (argc > 2) {
+    path = argv[1];
+    threshold = atoi(argv[2]);
+  } else {
+    std::cout << "Invalid number of arguments" << std::endl;
+    return 0;
+  }
+
+  dataset references = get_data(path, size, cmax, threshold);
+  math::csignal sample = img2desc(path+"/arret/arret0199.jpg", cmax, threshold);
+  std::priority_queue<std::pair<double, std::string>, std::vector<std::pair<double, std::string>>, pair_comp> neighbors;
+  std::map<std::string, int> labels;
+
+  for (auto desc: references) {
+    double d = distance(desc.first, sample, 1);
+    neighbors.push({d, desc.second});
+  }
+  
+  for (int i=0; i<k; ++i) {
+    std::pair<double, std::string> nearest = neighbors.top();
+    neighbors.pop();
+    labels[nearest.second] += 1;
+  }
+
+  int max = 0;
+  std::string label;
+
+  for (auto val: labels) {
+    if (val.second > max) {
+      max = val.second;
+      label = val.first;
+    } 
+  }
+
+  std::cout << label << std::endl;
+};

tests/src/math.hpp

@@ -5,6 +5,7 @@
 #include <opencv2/opencv.hpp>
 #include <iterator>
 #include <cmath>
+#include <stdexcept>
 
 namespace math {
 
@@ -284,29 +285,6 @@ namespace math {
     return res;
   }
 
-  csignal descriptors(const contour& cont, int cmax) {
-    csignal z = cont2sig(cont);
-    complex zm = mean(z);
-    csignal tfd = dft(diff(z, zm));
-    tfd /= z.size();
-    int cmin = -cmax;
-    csignal desc = extract(tfd, cmin, cmax);
-
-    if (std::abs(desc[desc.size()/2-1]) > std::abs(desc[desc.size()/2+1])) {
-      std::reverse(desc.begin(), desc.end());
-    }
-
-    double phy = std::arg(desc[desc.size()/2-1]*desc[desc.size()/2+1])/2;
-    desc *= std::exp(complex(0, -phy));
-    double theta = std::arg(desc[desc.size()/2+1]);
-
-    for (int k=0; k<desc.size(); ++k) {
-      desc[k] *= std::exp(complex(0, -theta*(k-cmin)));
-    }
-    desc /= std::abs(desc[desc.size()/2+1]);
-
-    return desc;
-  }
 
   contour simplify_contour(const contour& cont, int cmax) {
     csignal z = cont2sig(cont);
@@ -346,4 +324,71 @@ namespace math {
     }
     return id;
   };
+
+  csignal descriptors(const cv::Mat& img, int cmax, int threshold) {
+		std::vector<std::vector<cv::Point>> contours;
+
+		cv::Mat binary(img.rows, img.cols, CV_8UC1);
+    cv::Mat blur_img;
+    cv::GaussianBlur(img, blur_img, cv::Size(7,7), 1.5, 1.5);
+
+		std::vector<cv::Vec4i> hierarchy;
+
+    math::filter(img, binary, threshold);
+
+		cv::findContours(binary, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
+    if (contours.size() > 0) {
+		  std::vector<std::vector<cv::Point>> contrs;
+
+      int id = max_cont(contours);
+
+      csignal z = cont2sig(contours[id]);
+      complex zm = mean(z);
+      csignal tfd = dft(diff(z, zm));
+      tfd /= z.size();
+      int cmin = -cmax;
+      csignal desc = extract(tfd, cmin, cmax);
+
+      if (std::abs(desc[desc.size()/2-1]) > std::abs(desc[desc.size()/2+1])) {
+        std::reverse(desc.begin(), desc.end());
+      }
+
+      double phy = std::arg(desc[desc.size()/2-1]*desc[desc.size()/2+1])/2;
+      desc *= std::exp(complex(0, -phy));
+      double theta = std::arg(desc[desc.size()/2+1]);
+
+      for (int k=0; k<desc.size(); ++k) {
+        desc[k] *= std::exp(complex(0, -theta*(k-cmin)));
+      }
+      desc /= std::abs(desc[desc.size()/2+1]);
+
+      return desc;
+    } else {
+      throw std::length_error("No contour detected !");
+    }
+  }
+
+  csignal descriptors(const contour& cont, int cmax) {
+    csignal z = cont2sig(cont);
+    complex zm = mean(z);
+    csignal tfd = dft(diff(z, zm));
+    tfd /= z.size();
+    int cmin = -cmax;
+    csignal desc = extract(tfd, cmin, cmax);
+
+    if (std::abs(desc[desc.size()/2-1]) > std::abs(desc[desc.size()/2+1])) {
+      std::reverse(desc.begin(), desc.end());
+    }
+
+    double phy = std::arg(desc[desc.size()/2-1]*desc[desc.size()/2+1])/2;
+    desc *= std::exp(complex(0, -phy));
+    double theta = std::arg(desc[desc.size()/2+1]);
+
+    for (int k=0; k<desc.size(); ++k) {
+      desc[k] *= std::exp(complex(0, -theta*(k-cmin)));
+    }
+    desc /= std::abs(desc[desc.size()/2+1]);
+
+    return desc;
+  }
 }