int main(int argc, char const *argv[]){
Classifier c;
int numClasses, m, d, indexDescriptor, minoritySize;
double prob = 0.5;
std::string newDir, baseDir, featuresDir, csvOriginal, directory, str, bestDir;
std::vector<ImageClass> originalData;
if (argc != 3){
std::cout << "\nUsage: ./rebalanceTest (0) (1) (2) (3) (4)\n " << std::endl;
std::cout << "\t(0) Directory to place tests\n" << std::endl;
std::cout << "\t(1) Image Directory\n" << std::endl;
std::cout << "\t(2) Features Directory\n" << std::endl;
std::cout << "\t(3) Analysis Directory\n" << std::endl;
std::cout << "\t./rebalanceTest Desbalanced/ Desbalanced/original/ Desbalanced/features/ Desbalanced/analysis/ 0\n" << std::endl;
exit(-1);
}
newDir = std::string(argv[1]);
baseDir = std::string(argv[2]);
// featuresDir = std::string(argv[3]);
// analysisDir = std::string(argv[4]);
/* Available
descriptorMethod: {"BIC", "GCH", "CCV", "Haralick6", "ACC", "LBP", "HOG", "Contour", "Fisher"}
Quantization quantizationMethod: {"Intensity", "Luminance", "Gleam", "MSB"}
*/
std::vector <int> descriptors {1, 2, 3, 4, 5, 6, 7, 8};
for (indexDescriptor = 0; indexDescriptor < (int)descriptors.size(); indexDescriptor++){
d = descriptors[indexDescriptor];
for (m = 1; m <= 5; m++){
csvOriginal = newDir+"/analysis/original_"+descriptorMethod[d-1]+"_"+quantizationMethod[m-1]+"_";
featuresDir = newDir+"/features/";
/* Feature extraction from images */
std::string originalDescriptor = desc(baseDir, featuresDir, d, m, "original");
/* Read the feature vectors */
originalData = ReadFeaturesFromFile(originalDescriptor);
numClasses = originalData.size();
if (numClasses != 0){
std::cout << "---------------------------------------------------------------------------------------" << std::endl;
std::cout << "Classification using original data" << std::endl;
std::cout << "Features vectors file: " << originalDescriptor.c_str() << std::endl;
std::cout << "---------------------------------------------------------------------------------------" << std::endl;
c.findSmallerClass(originalData, &minoritySize);
c.classify(prob, 1, originalData, csvOriginal.c_str(), minoritySize);
originalData.clear();
}
}
}
return 0;
}
