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Merge branch 'dev' of gitlab.rezometz.org:guillaume/miniprojet into dev

dev
Felix Maurin 6 years ago
parent
3a5c3c1b23 0ce4b3f8bd
commit
a5cac585c0
7 changed files with 102 additions and 74 deletions
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  1. 8
      ROS/gesture_based_control/src/controller.cpp
  2. 30
      ROS/gesture_based_control/src/descripteur.cpp
  3. 33
      tests/examples/test-knn.cpp
  4. 4
      tests/src/CMakeLists.txt
  5. 27
      tests/src/file.hpp
  6. 51
      tests/src/knn.hpp
  7. 23
      tests/src/save-dataset.cpp

8
ROS/gesture_based_control/src/controller.cpp
View File

@ -3,15 +3,15 @@
#include <geometry_msgs/Twist.h>
void callback(const std_msgs::String& msg, geometry_msgs::Twist& cmd) {
if (msg.data == "forward") {
if (msg.data == "avance") {
cmd.linear.x = 1.0;
cmd.angular.z = 0.0;
} else if (msg.data == "stop") {
} else if (msg.data == "arret") {
cmd = geometry_msgs::Twist();
} else if (msg.data == "left") {
} else if (msg.data == "gauche") {
cmd.angular.z = 1.0;
cmd.linear.x = 0.0;
} else if (msg.data == "right") {
} else if (msg.data == "droite") {
cmd.angular.z = -1.0;
cmd.linear.x = 0.0;
}

30
ROS/gesture_based_control/src/descripteur.cpp
View File

@ -7,13 +7,14 @@
#include <gesture_based_control/DescriptorsConfig.h>
#include "math.hpp"
#include "file.hpp"
#include "knn.hpp"
void callback(gesture_based_control::DescriptorsConfig &config, uint32_t level, int& cmax, int& threshold) {
cmax = config.cmax;
threshold = config.threshold;
}
void callback(const sensor_msgs::ImageConstPtr& msg, int& threshold, int& cmax, ros::Publisher& order_pub, image_transport::Publisher& image_pub) {
void callback(const sensor_msgs::ImageConstPtr& msg, int& threshold, int& cmax, ros::Publisher& order_pub, image_transport::Publisher& image_pub, math::dataset& dataset) {
cv_bridge::CvImagePtr cv_ptr;
std_msgs::String s;
@ -35,26 +36,7 @@ void callback(const sensor_msgs::ImageConstPtr& msg, int& threshold, int& cmax,
if (contours.size() != 0) {
int index = math::max_cont(contours);
math::csignal desc = math::descriptors(contours[index], cmax);
//TODO: implémenter algo ml
//TODO: publier résultat
int r = 0;
switch (r) {
case 0:
s.data = "left";
break;
case 1:
s.data = "right";
break;
case 2:
s.data = "stop";
break;
case 3:
s.data = "forward";
break;
default:
s.data = "None";
break;
}
s.data = predict(desc, dataset, 5, 2);
order_pub.publish(s);
cv::drawContours(input, contours, index, 255);
@ -68,12 +50,14 @@ int main(int argc, char** argv) {
int threshold = 25;
int cmax = 10;
math::dataset dataset = load_csv("/home/guillaume/Documents/3A_supelec/miniprojet/learning/descripteurs/dataset50.csv", 21);
ros::NodeHandle n;
image_transport::ImageTransport it(n);
ros::Publisher order_pub = n.advertise<std_msgs::String>("/order", 100);
image_transport::Publisher image_pub = it.advertise("/image_out", 1);
image_transport::Subscriber sub = it.subscribe("/image_raw", 1, boost::bind(callback, _1, boost::ref(threshold), boost::ref(cmax), boost::ref(order_pub), boost::ref(image_pub)));
image_transport::Subscriber sub = it.subscribe("/image_raw", 1, boost::bind(callback, _1, boost::ref(threshold), boost::ref(cmax), boost::ref(order_pub), boost::ref(image_pub), boost::ref(dataset)));
dynamic_reconfigure::Server<gesture_based_control::DescriptorsConfig> server;
dynamic_reconfigure::Server<gesture_based_control::DescriptorsConfig>::CallbackType f;

33
tests/examples/test-knn.cpp
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@ -0,0 +1,33 @@
#include <iostream>
#include <math.hpp>
#include <file.hpp>
#include <knn.hpp>
int main(int argc, char** argv) {
int k = 20;
std::string filename;
std::string sample_name;
int cmax = 10;
int threshold = 20;
int n = 2;
if (argc > 3) {
filename = argv[1];
k = atoi(argv[2]);
sample_name = argv[3];
} else {
std::cout << "Invalid number of arguments" << std::endl;
return 0;
}
math::dataset references = load_csv(filename, 2*cmax+1);
if (references.size() == 0) {
std::cout << "Invalid file" << std::endl;
return 0;
}
sample_name = random_image(sample_name);
math::csignal sample = math::img2desc(sample_name, cmax, threshold);
std::cout << predict(sample, references, k, n) << std::endl;
};

4
tests/src/CMakeLists.txt
View File

@ -1,8 +1,8 @@
add_executable(traitement traitement.cpp)
target_link_libraries(traitement ${OpenCV_LIBS})
add_executable(knn knn.cpp)
target_link_libraries(knn ${OpenCV_LIBS} ${Boost_SYSTEM_LIBRARY} ${Boost_FILESYSTEM_LIBRARY})
add_executable(save-dataset save-dataset.cpp)
target_link_libraries(save-dataset ${OpenCV_LIBS} ${Boost_SYSTEM_LIBRARY} ${Boost_FILESYSTEM_LIBRARY})
add_executable(neural_network neural_network.cpp)
target_link_libraries(neural_network ${OpenCV_LIBS} ${Boost_FILESYSTEM_LIBRARY} ${Boost_SYSTEM_LIBRARY})

27
tests/src/file.hpp
View File

@ -1,4 +1,7 @@
#pragma once
#include <boost/filesystem.hpp>
#include <random>
#include <iostream>
#include <string>
#include "math.hpp"
@ -18,15 +21,9 @@ void read_directory(const std::string& name, std::vector<std::string>& v) {
}
void save_as_csv(const math::dataset& dataset, std::string filename) {
// file pointer
std::fstream fout;
// opens an existing csv file or creates a new file.
fout.open(filename, std::ios::out);
std::string name;
// Insert the data to file
for (auto p: dataset) {
for (auto x: p.first) {
fout << x.real() << ','
@ -37,6 +34,24 @@ void save_as_csv(const math::dataset& dataset, std::string filename) {
}
}
std::string random_image(const std::string& path) {
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_int_distribution<> uniform(0, 4);
std::vector<std::string> dirs;
read_directory(path, dirs);
int rd_dir = uniform(gen);
std::vector<std::string> images;
read_directory(path+"/"+dirs[rd_dir], images);
std::uniform_int_distribution<> uniform2(0, images.size());
int rd_image = uniform2(gen);
std::cout << images[rd_image] << std::endl;
return path+"/"+dirs[rd_dir]+"/"+images[rd_image];
}
math::dataset& load_csv(std::string filename, int size) {
math::dataset* d = new math::dataset();
std::fstream fin;

51
tests/src/knn.cpp → tests/src/knn.hpp
View File

@ -1,17 +1,19 @@
#include <map>
#include "file.hpp"
#include "math.hpp"
#pragma once
#include <stdexcept>
#include <queue>
#include <opencv2/opencv.hpp>
#include <iostream>
#include <map>
double distance(math::csignal& v1, math::csignal& v2, int n){
#include "file.hpp"
#include "math.hpp"
double distance(const math::csignal& v1, const 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();
@ -19,19 +21,7 @@ double distance(math::csignal& v1, math::csignal& v2, int n){
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;
return std::pow(d, 1/float(n));
};
struct pair_comp {
@ -46,29 +36,12 @@ struct pair_comp {
};
};
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;
}
math::dataset references = get_data(path, size, cmax, threshold);
math::csignal sample = math::img2desc(path+"/arret/arret0199.jpg", cmax, threshold);
std::string predict(const math::csignal& sample, const math::dataset& references, int k, int n) {
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);
double d = distance(desc.first, sample, n);
neighbors.push({d, desc.second});
}
@ -88,5 +61,5 @@ int main(int argc, char** argv) {
}
}
std::cout << label << std::endl;
};
return label;
}

23
tests/src/save-dataset.cpp
View File

@ -0,0 +1,23 @@
#include "file.hpp"
#include "math.hpp"
int main(int argc, char** argv) {
std::string path;
int cmax;
int threshold;
int size;
if (argc > 4) {
path = argv[1];
cmax = atoi(argv[2]);
threshold = atoi(argv[3]);
size = atoi(argv[4]);
} else {
std::cout << "Invalid number of arguments" << std::endl;
return 0;
}
math::dataset dataset = get_data(path, size, cmax, threshold);
save_as_csv(dataset, path+"/../descripteurs/dataset"+std::to_string(size)+".csv");
return 0;
}
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