Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
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// License Agreement
// For Open Source Computer Vision Library
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// Copyright (C) 2008-2013, Willow Garage Inc., all rights reserved.
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#if !defined(ANDROID)
#include <test_precomp.hpp>
#include <string>
#include <fstream>
#include <vector>
using namespace std;
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namespace {
using namespace cv::softcascade;
typedef vector<string> svector;
class ScaledDataset : public Dataset
{
public:
ScaledDataset(const string& path, const int octave);
virtual cv::Mat get(SampleType type, int idx) const;
virtual int available(SampleType type) const;
virtual ~ScaledDataset();
private:
svector pos;
svector neg;
};
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ScaledDataset::ScaledDataset(const string& path, const int oct)
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{
cv::glob(path + cv::format("/octave_%d/*.png", oct), pos);
cv::glob(path + "/*.png", neg);
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// Check: files not empty
CV_Assert(pos.size() != size_t(0));
CV_Assert(neg.size() != size_t(0));
}
cv::Mat ScaledDataset::get(SampleType type, int idx) const
{
const std::string& src = (type == POSITIVE)? pos[idx]: neg[idx];
return cv::imread(src);
}
int ScaledDataset::available(SampleType type) const
{
return (int)((type == POSITIVE)? pos.size():neg.size());
}
ScaledDataset::~ScaledDataset(){}
}
TEST(SoftCascade, training)
{
// // 2. check and open output file
string outXmlPath = cv::tempfile(".xml");
cv::FileStorage fso(outXmlPath, cv::FileStorage::WRITE);
ASSERT_TRUE(fso.isOpened());
std::vector<int> octaves;
{
octaves.push_back(-1);
octaves.push_back(0);
}
fso << "regression-cascade"
<< "{"
<< "stageType" << "BOOST"
<< "featureType" << "ICF"
<< "octavesNum" << 2
<< "width" << 64
<< "height" << 128
<< "shrinkage" << 4
<< "octaves" << "[";
for (std::vector<int>::const_iterator it = octaves.begin(); it != octaves.end(); ++it)
{
int nfeatures = 100;
int shrinkage = 4;
float octave = powf(2.f, (float)(*it));
cv::Size model = cv::Size( cvRound(64 * octave) / shrinkage, cvRound(128 * octave) / shrinkage );
cv::Ptr<FeaturePool> pool = FeaturePool::create(model, nfeatures, 10);
nfeatures = pool->size();
int npositives = 10;
int nnegatives = 20;
cv::Rect boundingBox = cv::Rect( cvRound(20 * octave), cvRound(20 * octave),
cvRound(64 * octave), cvRound(128 * octave));
cv::Ptr<ChannelFeatureBuilder> builder = ChannelFeatureBuilder::create("HOG6MagLuv");
cv::Ptr<Octave> boost = Octave::create(boundingBox, npositives, nnegatives, *it, shrinkage, builder);
std::string path = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/sample_training_set";
ScaledDataset dataset(path, *it);
if (boost->train(&dataset, pool, 3, 2))
{
cv::Mat thresholds;
boost->setRejectThresholds(thresholds);
boost->write(fso, pool, thresholds);
}
}
fso << "]" << "}";
fso.release();
cv::FileStorage actual(outXmlPath, cv::FileStorage::READ);
cv::FileNode root = actual.getFirstTopLevelNode();
cv::FileNode fn = root["octaves"];
ASSERT_FALSE(fn.empty());
}
#endif