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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2008-2013, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and / or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#if !defined(ANDROID)
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#include <test_precomp.hpp>
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#include <string>
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#include <fstream>
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#include <vector>
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using namespace std;
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namespace {
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using namespace cv::softcascade;
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typedef vector<string> svector;
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class ScaledDataset : public Dataset
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{
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public:
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ScaledDataset(const string& path, const int octave);
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virtual cv::Mat get(SampleType type, int idx) const;
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virtual int available(SampleType type) const;
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virtual ~ScaledDataset();
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private:
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svector pos;
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svector neg;
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};
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ScaledDataset::ScaledDataset(const string& path, const int oct)
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{
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cv::glob(path + cv::format("/octave_%d/*.png", oct), pos);
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cv::glob(path + "/*.png", neg);
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// Check: files not empty
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CV_Assert(pos.size() != size_t(0));
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CV_Assert(neg.size() != size_t(0));
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}
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cv::Mat ScaledDataset::get(SampleType type, int idx) const
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{
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const std::string& src = (type == POSITIVE)? pos[idx]: neg[idx];
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return cv::imread(src);
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}
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int ScaledDataset::available(SampleType type) const
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{
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return (int)((type == POSITIVE)? pos.size():neg.size());
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}
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ScaledDataset::~ScaledDataset(){}
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}
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TEST(SoftCascade, training)
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{
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// // 2. check and open output file
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string outXmlPath = cv::tempfile(".xml");
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cv::FileStorage fso(outXmlPath, cv::FileStorage::WRITE);
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ASSERT_TRUE(fso.isOpened());
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std::vector<int> octaves;
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{
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octaves.push_back(-1);
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octaves.push_back(0);
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}
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fso << "regression-cascade"
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<< "{"
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<< "stageType" << "BOOST"
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<< "featureType" << "ICF"
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<< "octavesNum" << 2
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<< "width" << 64
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<< "height" << 128
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<< "shrinkage" << 4
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<< "octaves" << "[";
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for (std::vector<int>::const_iterator it = octaves.begin(); it != octaves.end(); ++it)
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{
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int nfeatures = 100;
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int shrinkage = 4;
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float octave = powf(2.f, (float)(*it));
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cv::Size model = cv::Size( cvRound(64 * octave) / shrinkage, cvRound(128 * octave) / shrinkage );
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cv::Ptr<FeaturePool> pool = FeaturePool::create(model, nfeatures, 10);
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nfeatures = pool->size();
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int npositives = 10;
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int nnegatives = 20;
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cv::Rect boundingBox = cv::Rect( cvRound(20 * octave), cvRound(20 * octave),
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cvRound(64 * octave), cvRound(128 * octave));
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cv::Ptr<ChannelFeatureBuilder> builder = ChannelFeatureBuilder::create("HOG6MagLuv");
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cv::Ptr<Octave> boost = Octave::create(boundingBox, npositives, nnegatives, *it, shrinkage, builder);
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std::string path = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/sample_training_set";
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ScaledDataset dataset(path, *it);
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if (boost->train(&dataset, pool, 3, 2))
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{
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cv::Mat thresholds;
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boost->setRejectThresholds(thresholds);
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boost->write(fso, pool, thresholds);
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}
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}
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fso << "]" << "}";
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fso.release();
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cv::FileStorage actual(outXmlPath, cv::FileStorage::READ);
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cv::FileNode root = actual.getFirstTopLevelNode();
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cv::FileNode fn = root["octaves"];
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ASSERT_FALSE(fn.empty());
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}
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#endif
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