void main2()
{
Person person;
person.Name = "Bob";
person.Age = 28;
person.Educations.push_back(Education("MIT", 600));
string_stream ss;
save_to(ss, person);
std::cout << ss.str() << std::endl;
}
void recording_t::save(void)
{
int fd = open("recording", O_WRONLY | O_CREAT | O_TRUNC, 0666);
if (fd < 0) {
perror("save: open:");
return;
}
save_to(fd);
close(fd);
}
bool Drumkit::save_file( const QString& dk_path, bool overwrite )
{
INFOLOG( QString( "Saving drumkit definition into %1" ).arg( dk_path ) );
if( Filesystem::file_exists( dk_path, true ) && !overwrite ) {
ERRORLOG( QString( "drumkit %1 already exists" ).arg( dk_path ) );
return false;
}
XMLDoc doc;
doc.set_root( "drumkit_info", "drumkit" );
XMLNode root = doc.firstChildElement( "drumkit_info" );
save_to( &root );
return doc.write( dk_path );
}
bool Pattern::save_file( const QString& drumkit_name, const QString& author, const QString& license, const QString& pattern_path, bool overwrite ) const
{
INFOLOG( QString( "Saving pattern into %1" ).arg( pattern_path ) );
if( !overwrite && Filesystem::file_exists( pattern_path, true ) ) {
ERRORLOG( QString( "pattern %1 already exists" ).arg( pattern_path ) );
return false;
}
XMLDoc doc;
XMLNode root = doc.set_root( "drumkit_pattern", "drumkit_pattern" );
root.write_string( "drumkit_name", drumkit_name ); // FIXME loaded with LocalFileMng::getDrumkitNameForPattern(…)
root.write_string( "author", author ); // FIXME this is never loaded back
root.write_string( "license", license ); // FIXME this is never loaded back
save_to( &root );
return doc.write( pattern_path );
}
// construct the classifier
Classifier::Classifier(const ClassifierOptions &options )
{
kmerSize_ = options.kmerSize;
numThreads_ = options.numThreads;
numBootstrap_ = options.numBootstrap;
subsampleSize_ = options.subsample;
kmerizer_.setKmerSize(kmerSize_);
outputAmbiguous_ = options.dumpAmbiguous;
std::stringstream s;
s << options.dbFilename << ".idx_" << kmerSize_;
try {
// load cached index
std::ifstream ifs(s.str().c_str());
boost::archive::binary_iarchive ia(ifs);
ScopedTimer tim;
std::cerr << "Loading cached index from " << s.str();
ia >> referenceData_;
std::cerr << " done. ";
}
catch (boost::archive::archive_exception &) {
// create index from raw sequences
referenceData_.load(options.dbFilename, kmerizer_, numThreads_);
// save index for future use
if (options.saveIndex)
{
std::ofstream ofs(s.str().c_str());
boost::archive::binary_oarchive oa(ofs);
ScopedTimer tim;
std::cerr << "Writing cached index to " << s.str();
save_to(oa, referenceData_);
std::cerr << " done. ";
}
}
numRefSeqs_ = referenceData_.numSequences();
}
// 将rawdata截取部分数据到learndata中
void create_learn_data(const char* raw_data_folder, const char* out_data_folder,
const int how_many /* = 5000 */) {
assert(raw_data_folder);
assert(out_data_folder);
auto files = Utils::getFiles(raw_data_folder);
size_t size = files.size();
if (0 == size) {
std::cout << "No file found in " << raw_data_folder << std::endl;
return;
}
// 随机排列rawdata
srand(unsigned(time(NULL)));
std::random_shuffle(files.begin(), files.end());
int count = 0;
for (auto f : files) {
// 选取前how_many个rawdata数据作为learndata
if (count++ >= how_many) {
break;
}
//读取数据，并对图片进行预处理
cv::Mat img = cv::imread(f);
img = cut_top_bottom(img);
std::string save_to(out_data_folder);
if (*(save_to.end() - 1) != '/') {
save_to.push_back('/');
}
save_to.append(Utils::getFileName(f, true));
utils::imwrite(save_to, img);
std::cout << f << " -> " << save_to << std::endl;
}
std::cout << "Learn data created successfully!" << std::endl;
}
void KVConfig::save_as(const char *filename)
{
save_to(filename);
}
void KVConfig::save()
{
std::string sess_fname = filename_ + ".session";
save_to(sess_fname.c_str());
}
