int main(int argc, char * argv[]) {
io::DataIO data(io::StanfordEval);
std::string dir;
if (argc > 1) {
std::string dir = argv[1];
std::cout << "Saving to " << dir << std::endl;
// Creating directory structure
boost::filesystem::path training_data_path_head(dir);
boost::filesystem::create_directory(dir);
std::ofstream ofile((dir + "/labels.txt").c_str());
annotate(ofile, data);
ofile.close();
} else {
std::cout << "Extracting training data" << std::endl;
data.extract_data();
std::cout << "Done" << std::endl;
}
return 0;
}
void DataIOStanford::extract_parts() {
/* initialize random seed: */
srand (time(NULL));
// Creating directory structure
const std::string kTrainingDataPathHead = kPath + "/training";
boost::filesystem::path training_data_path_head(kTrainingDataPathHead);
boost::filesystem::create_directory(training_data_path_head);
std::ofstream data((kTrainingDataPathHead + "/data.txt").c_str());
if (!data.is_open()) {
std::cerr << "Unable to create training data files!" << std::endl;
}
int head_idx, hand_idx, leg_idx;
head_idx = hand_idx = leg_idx = 1;
cv::Mat frame, frame_dummy;
while (read_next_frame(frame_dummy, frame)) {
//if(!) { stanford_eval::next_file(); };
stanford_eval::Frame f = stanford_eval::frame;
// cv::Mat frame(cv::Size(f.C, f.R), CV_32F);
//
// for (int i = 0; i < f.N; ++i) {
// frame.at<float>(i % f.R, i/f.R) = -(static_cast<float>(f.points[i][2]) + 2.f);
// }
//
// cv::normalize(frame, frame, 0, 1.f, cv::NORM_MINMAX);
std::vector<int> positive_centres;
// Head
if (f.markers[3].cond > 0 && f.markers[1].cond > 0) {
extract_positive_part(frame, data,
f.markers[3].x, f.markers[3].y + 0.1,
f.markers[1].x, f.markers[1].y + 0.1,
classifier::HEAD, true);
extract_positive_part(frame, data,
f.markers[3].x, f.markers[3].y + 0.1,
f.markers[3].x, f.markers[1].y + 0.1,
classifier::HEAD, true);
positive_centres.push_back(to_x(f.markers[1].x));
positive_centres.push_back(to_y(f.markers[1].y + 0.1));
positive_centres.push_back(to_x(f.markers[3].x));
positive_centres.push_back(to_y(f.markers[3].y + 0.1));
}
// Left hand
if (f.markers[28].cond > 0 && f.markers[24].cond > 0) {
extract_positive_part(frame, data,
f.markers[28].x, f.markers[28].y,
f.markers[24].x, f.markers[24].y,
classifier::LEFT_HAND, true);
positive_centres.push_back(to_x(f.markers[24].x));
positive_centres.push_back(to_y(f.markers[24].y));
}
// Right hand
if (f.markers[19].cond > 0 && f.markers[16].cond > 0) {
extract_positive_part(frame, data,
f.markers[19].x, f.markers[19].y,
f.markers[16].x, f.markers[16].y,
classifier::RIGHT_HAND, true);
positive_centres.push_back(to_x(f.markers[16].x));
positive_centres.push_back(to_y(f.markers[16].y));
}
// Left foot
if (f.markers[35].cond > 0 && f.markers[37].cond > 0) {
extract_positive_part(frame, data,
f.markers[37].x, f.markers[37].y - 0.2,
f.markers[35].x, f.markers[35].y - 0.2,
classifier::LEFT_FOOT, true);
positive_centres.push_back(to_x(f.markers[35].x));
positive_centres.push_back(to_y(f.markers[35].y - 0.2));
}
// Right foot
if (f.markers[43].cond > 0 && f.markers[41].cond > 0) {
extract_positive_part(frame, data,
f.markers[41].x, f.markers[41].y - 0.2,
f.markers[43].x, f.markers[43].y - 0.2,
classifier::RIGHT_FOOT, true);
positive_centres.push_back(to_x(f.markers[43].x));
positive_centres.push_back(to_y(f.markers[43].y - 0.2));
}
for (int j = 0; j < 12; j++) {
extract_negative_part(frame, data, positive_centres);
}
}
}