/** This file is part of MultiBoost, a multi-class * AdaBoost learner/classifier** Copyright (C) 2005-2006 Norman Casagrande* For informations write to nova77@gmail.com** This library is free software; you can redistribute it and/or* modify it under the terms of the GNU Lesser General Public* License as published by the Free Software Foundation; either* version 2.1 of the License, or (at your option) any later version.** This library is distributed in the hope that it will be useful,* but WITHOUT ANY WARRANTY; without even the implied warranty of* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU* Lesser General Public License for more details.** You should have received a copy of the GNU Lesser General Public* License along with this library; if not, write to the Free Software* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA**/#include "HaarLearner.h"#include "Others/HaarFeatures.h"#include "Utils/StreamTokenizer.h"#include "IO/HaarData.h"#include "IO/Serialization.h"#include "Utils/Args.h"#include <limits> // for numeric_limits#include <ctime> // for timenamespace MultiBoost {// ------------------------------------------------------------------------------void HaarLearner::declareArguments(nor_utils::Args& args){   args.declareArgument("ftypes",                         "Specify the list with the type of Haar-like features. This list can contain "                        "all the types requested at once. The types allowed are:\n"                        "  2v: 2 blocs vertical feature\n"                        "  2h: 2 blocs horizontal feature\n"                        "  3v: 3 blocs vertical feature\n"                        "  3h: 3 blocs horizontal feature\n"                        "  4q: 4 blocs squared feature\n"                        "For instance if you need all the 2 and 3 blocs type, just write:\n"                        "-ftypes 2v2h3v3h\n"                        "(DEFAULT: ALL of them will be selected)"                        , 1, "<list>");   args.declareArgument("csample",                        "Instead of computing all the possible combinations, select a set of "                        "random configurations. It is possible to specify the number of combinations "                        "or the amount of time per feature type. The options are:\n"                        "  num: The number of combination per type per iteration.\n"                        "  time: The amount of time (in seconds) per type per iteration.\n"                        "Example: -csample num 4 -> Set the number of random configurations to 4.\n"                        "Example: -csample time 120 -> Set the time to 2 minutes per type per iteration.",                        2, "<opt> <#|sec>");   args.declareArgument("seed", "Defines the seed for the random sampling.", 1, "<seedval>");   args.declareArgument("iisize",                         "Specify the size of the integral image representation for each example,\n"                        "Example: -iisize 128x64",                         1, "<width>x<height>");}// ------------------------------------------------------------------------------void HaarLearner::initOptions(nor_utils::Args& args){   // If the sampling is required   if (args.hasArgument("csample"))   {      string randSearch = args.getValue<string>("csample", 0);      // if the type is ST_NUM this is the number of samplings per feature per iteration      // if the type is ST_TIME this is the amount of time per feature per iteration      _samplingVal = args.getValue<int>("csample", 1);      if (randSearch == "num")         _samplingType = ST_NUM;      else if (randSearch == "time")         _samplingType = ST_TIME;   }   // defines the seed of the sampling method.   if (args.hasArgument("seed"))   {      unsigned int seed = args.getValue<unsigned int>("seed", 0);      srand(seed);   }}// ------------------------------------------------------------------------------void HaarLearner::save(ofstream& outputStream, int numTabs){   // save the type of feature   outputStream << Serialization::standardTag("type", _pSelectedFeature->getShortName(), numTabs) << endl;   // Save the coordinates and size of the feature   outputStream << Serialization::getTabs(numTabs) << "<rect>" << endl;      outputStream << Serialization::standardTag("x", _selectedConfig.x, numTabs+1) << endl;      outputStream << Serialization::standardTag("y", _selectedConfig.y, numTabs+1) << endl;      outputStream << Serialization::standardTag("width", _selectedConfig.width, numTabs+1) << endl;      outputStream << Serialization::standardTag("height", _selectedConfig.height, numTabs+1) << endl;   outputStream << Serialization::getTabs(numTabs) << "</rect>" << endl;}// -----------------------------------------------------------------------void HaarLearner::load(nor_utils::StreamTokenizer& st){   // get the type of feature   string shortName = UnSerialization::seekAndParseEnclosedValue<string>(st, "type");   // get it from the registered features   _pSelectedFeature = HaarFeature::RegisteredFeatures().getFeature(shortName);   // Load the coordinates and size of the feature   _selectedConfig.x = UnSerialization::seekAndParseEnclosedValue<short>(st, "x");   _selectedConfig.y = UnSerialization::seekAndParseEnclosedValue<short>(st, "y");   _selectedConfig.width = UnSerialization::seekAndParseEnclosedValue<short>(st, "width");   _selectedConfig.height = UnSerialization::seekAndParseEnclosedValue<short>(st, "height");}// ------------------------------------------------------------------------------} // end of MultiBoost namespace