opencv/apps/sft/sft.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
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// License Agreement
// For Open Source Computer Vision Library
//
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// Training application for Soft Cascades.
#include <sft/common.hpp>
#include <iostream>
#include <sft/dataset.hpp>
#include <sft/config.hpp>
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#include <opencv2/core/core_c.h>
int main(int argc, char** argv)
{
using namespace sft;
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const string keys =
"{help h usage ? | | print this message }"
"{config c | | path to configuration xml }"
;
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cv::CommandLineParser parser(argc, argv, keys);
parser.about("Soft cascade training application.");
if (parser.has("help"))
{
parser.printMessage();
return 0;
}
if (!parser.check())
{
parser.printErrors();
return 1;
}
string configPath = parser.get<string>("config");
if (configPath.empty())
{
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std::cout << "Configuration file is missing or empty. Could not start training." << std::endl;
return 0;
}
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std::cout << "Read configuration from file " << configPath << std::endl;
cv::FileStorage fs(configPath, cv::FileStorage::READ);
if(!fs.isOpened())
{
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std::cout << "Configuration file " << configPath << " can't be opened." << std::endl;
return 1;
}
// 1. load config
sft::Config cfg;
fs["config"] >> cfg;
std::cout << std::endl << "Training will be executed for configuration:" << std::endl << cfg << std::endl;
// 2. check and open output file
cv::FileStorage fso(cfg.outXmlPath, cv::FileStorage::WRITE);
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if(!fso.isOpened())
{
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std::cout << "Training stopped. Output classifier Xml file " << cfg.outXmlPath << " can't be opened." << std::endl;
return 1;
}
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fso << cfg.cascadeName
<< "{"
<< "stageType" << "BOOST"
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<< "featureType" << cfg.featureType
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<< "octavesNum" << (int)cfg.octaves.size()
<< "width" << cfg.modelWinSize.width
<< "height" << cfg.modelWinSize.height
<< "shrinkage" << cfg.shrinkage
<< "octaves" << "[";
// 3. Train all octaves
for (ivector::const_iterator it = cfg.octaves.begin(); it != cfg.octaves.end(); ++it)
{
// a. create random feature pool
int nfeatures = cfg.poolSize;
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cv::Size model = cfg.model(it);
std::cout << "Model " << model << std::endl;
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int nchannels = (cfg.featureType == "HOG6MagLuv") ? 10: 8;
std::cout << "number of feature channels is " << nchannels << std::endl;
cv::Ptr<cv::FeaturePool> pool = cv::FeaturePool::create(model, nfeatures, nchannels);
nfeatures = pool->size();
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int npositives = cfg.positives;
int nnegatives = cfg.negatives;
int shrinkage = cfg.shrinkage;
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cv::Rect boundingBox = cfg.bbox(it);
std::cout << "Object bounding box" << boundingBox << std::endl;
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typedef cv::Octave Octave;
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cv::Ptr<cv::ChannelFeatureBuilder> builder = cv::ChannelFeatureBuilder::create(cfg.featureType);
std::cout << "Channel builder " << builder->info()->name() << std::endl;
cv::Ptr<Octave> boost = Octave::create(boundingBox, npositives, nnegatives, *it, shrinkage, nfeatures, builder);
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std::string path = cfg.trainPath;
sft::ScaledDataset dataset(path, *it);
if (boost->train(&dataset, pool, cfg.weaks, cfg.treeDepth))
{
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CvFileStorage* fout = cvOpenFileStorage(cfg.resPath(it).c_str(), 0, CV_STORAGE_WRITE);
boost->write(fout, cfg.cascadeName);
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cvReleaseFileStorage( &fout);
cv::Mat thresholds;
boost->setRejectThresholds(thresholds);
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boost->write(fso, pool, thresholds);
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cv::FileStorage tfs(("thresholds." + cfg.resPath(it)).c_str(), cv::FileStorage::WRITE);
tfs << "thresholds" << thresholds;
std::cout << "Octave " << *it << " was successfully trained..." << std::endl;
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}
}
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fso << "]" << "}";
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fso.release();
std::cout << "Training complete..." << std::endl;
return 0;
}