include <iostream>
#include <sstream>
#include <string>
#include <vector>
#include <tclap/CmdLine.h>
#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/ml/ml.hpp>
#include "common_code.hpp"
#define IMG_WIDTH 300
int
main(int argc, char * argv[])
{
TCLAP::CmdLine cmd("Trains and tests a BoVW model", ' ', "0.0");
TCLAP::ValueArg<std::string> basenameArg("b", "basename", "basename for the dataset.", false, "./data", "pathname");
cmd.add(basenameArg);
TCLAP::ValueArg<std::string> configFile("c", "config_file", "configuration file for the dataset.", false, "02_ObjectCategories_conf.txt", "pathname");
cmd.add(configFile);
TCLAP::ValueArg<int> n_runsArg("r", "n_runs", "Number of trials train/test to compute the recognition rate. Default 10.", false, 10, "int");
cmd.add(n_runsArg);
TCLAP::ValueArg<int> ndesc("", "ndesc", "[SIFT] Number of descriptors per image. Value 0 means extract all. Default 0.", false, 0, "int");
cmd.add(ndesc);
TCLAP::ValueArg<int> keywords("w", "keywords", "[KMEANS] Number of keywords generated. Default 100.", false, 100, "int");
cmd.add(keywords);
TCLAP::ValueArg<int> ntrain("", "ntrain", "Number of samples per class used to train. Default 15.", false, 15, "int");
cmd.add(ntrain);
TCLAP::ValueArg<int> ntest("", "ntest", "Number of samples per class used to test. Default 50.", false, 50, "int");
cmd.add(ntest);
TCLAP::ValueArg<int> k_nn("", "k_nn", "[KNN] Number k neighbours used to classify. Default 1.", false, 1, "int");
cmd.add(k_nn);
cmd.parse(argc, argv);
// --------------- MAIN BLOCK ---------------
std::vector<std::string> categories;
std::vector<int> samples_per_cat;
std::string dataset_desc_file = basenameArg.getValue() + "/" + configFile.getValue();
int retCode;
if ((retCode = load_dataset_information(dataset_desc_file, categories, samples_per_cat)) != 0)
{
std::cerr << "Error: could not load dataset information from '"
<< dataset_desc_file
<< "' (" << retCode << ")." << std::endl;
exit(-1);
}
std::cout << "Found " << categories.size() << " categories: ";
if (categories.size()<2)
{
std::cerr << "Error: at least two categories are needed." << std::endl;
return -1;
}
for (size_t i=0;i<categories.size();++i)
std::cout << categories[i] << ' ';
std::cout << std::endl;
std::vector<float> rRates(n_runsArg.getValue(), 0.0);
// Repeat training+test
for (int trial=0; trial<n_runsArg.getValue(); trial++)
{
std::clog << "######### TRIAL " << trial+1 << " ##########" << std::endl;
std::vector<std::vector<int>> train_samples;
std::vector<std::vector<int>> test_samples;
create_train_test_datasets(samples_per_cat, ntrain.getValue(), ntest.getValue(), train_samples, test_samples);
//-----------------------------------------------------
// TRAINING
//-----------------------------------------------------
std::clog << "Training ..." << std::endl;
std::clog << "\tCreating dictionary ... " << std::endl;
std::clog << "\t\tComputing descriptors..." << std::endl;
cv::Mat train_descs;
cv::Mat1f totalmat;
std::vector<int> ndescs_per_sample;
ndescs_per_sample.resize(0);
cv::Mat labels;
cv::Mat1f centers;
std::vector <cv::Mat> bovw;
for (size_t c = 0; c < train_samples.size(); ++c)
{
std::clog << " " << std::setfill(' ') << std::setw(3) << (c * 100) / train_samples.size() << " % \015";
for (size_t s = 0; s < train_samples[c].size(); ++s)
{
std::string filename = compute_sample_filename(basenameArg.getValue(), categories[c], train_samples[c][s]);
cv::Mat img = cv::imread(filename, cv::IMREAD_GRAYSCALE);
if (img.empty())
{
std::cerr << "Error: could not read image '" << filename << "'." << std::endl;
exit(-1);
}
else
{
// Fix size: width =300px
//! \todo TO BE COMPLETED
cv::Size size(300,300*img.rows/300*img.cols);
resize(img,img,size);
// Extract SIFT descriptors
//-----------------------------------------------------
cv::Mat1f descs ...
(more)
would you be so nice, and replace the screenshot with a text version of the errormsg ? thank you !
you need at least 100 samples, to cluster 100 centers, that's what the error says.
check your totalmat,
cout << totalmat.size();can you explain, why there are 2 loops for the train_samples ?
have you seen, that opencv has builtin classes for BoW ?
Thank you so much for your time, I have replaced the screenshot for a text version. The size of Mat totalmat is 128*0.The function vconcat doesn't work right, I don't know why..
cv::Size size(300,300*img.rows/300*img.cols);<-- looks entirely broken. (missing braces?) right now, it's the same asSize(300, img.rows*img.cols), clearly not what you wanted.skip the whole resize (it's not nessecary), and check if there's something in your descs, before proceeding.
Thank you.