int main ( int argc, char** argv )
{
validExtensions.push_back("jpg");
validExtensions.push_back("ppm");
validExtensions.push_back("png");
plateSize = cv::Size( plate_width, plate_height );
hog.winSize = cv::Size(64, 128);
if( argc >= 2 )
{
for(int i = 1; i < argc; i++ )
{
std::string opt = std::string(argv[i]);
if( opt == "-v" )
{
trainValidator( std::string(argv[i+1]), std::string(argv[i+2]), std::string(argv[i+3]) );
break;
}
if( opt == "-c" )
{
std::vector< std::string > classesPaths;
for( int n = i+2; n < argc; n++ )
{
classesPaths.push_back(std::string(argv[n]));
}
trainClassifier( std::string(argv[i+1]), classesPaths );
break;
}
}
}
else
{
std::cout <<
"Usage:\n" << argv[0] <<
"To train a validator:" << std::endl <<
"\t -v <path to positive objects images> <path to negative objects images> <path for output files> \n" <<
"To train a classifier:" << std::endl <<
"\t -c <path for output files> <path to class 0 images> <path to class 1 images> ... <path to class n images> \n";
}
return 0;
}
int main(int argc, char **argv)
{
float train_data[train_size][data_dimension] = {
{0.1,0.2},
{0.3,0.1},
{3.1,3.2},
{3.3,4.1},
{5.1,5.2}
};
std::string train_data_targets[train_size] = {"1","1","2","2","3"};
float test_data[test_size][data_dimension] = {
{0.0,0.0},
{5.5,5.5},
{2.9,3.6}
};
std::vector<classifiers_test::ClassDataPoint> train_points;
std::vector<classifiers_test::ClassDataPoint> test_points;
printf("train:\n");
for (int i = 0; i < train_size; i++)
{
classifiers_test::ClassDataPoint *dp = new classifiers_test::ClassDataPoint();
for (int j = 0; j < data_dimension; j++)
{
dp->point.insert(dp->point.end(), train_data[i][j]);
printf("%f ", train_data[i][j]);
}
printf("\n");
dp->target_class = train_data_targets[i];
train_points.insert(train_points.end(), *dp);
}
printf("test: \n");
for (int i = 0; i < test_size; i++)
{
classifiers_test::ClassDataPoint *dp = new classifiers_test::ClassDataPoint();
for (int j = 0; j < data_dimension; j++)
{
dp->point.insert(dp->point.end(), test_data[i][j]);
printf("%f ", test_data[i][j]);
}
printf("\n");
dp->target_class = "";
test_points.insert(test_points.end(), *dp);
}
ros::init(argc, argv, "classifiers_test");
ros::NodeHandle n;
ros::service::waitForService("/ml_classifiers/create_classifier");
if (!createClassifier(n, "testc","ml_classifiers/SVMClassifier"))
return 1;
if (!addClassDataPoints(n, "testc", train_points))
return 1;
if (!trainClassifier(n, "testc"))
return 1;
if (!classifyPoints(n, "testc", test_points))
return 1;
return 0;
}
