marina.kolpakova
12 years ago
parent
d314c602d5
commit
14ac8a528e
14 changed files with 287 additions and 102 deletions
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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 <string> |
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#include <fstream> |
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#include <vector> |
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#include "test_precomp.hpp" |
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#if !defined (_WIN32) && ! defined(__MINGW32__) |
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# include <glob.h> |
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#else |
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# include <windows.h> |
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#endif |
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using namespace std; |
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namespace { |
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typedef vector<string> svector; |
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class ScaledDataset : public cv::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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string itoa(long i) |
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{ |
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char s[65]; |
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sprintf(s, "%ld", i); |
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return std::string(s); |
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} |
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#if !defined (_WIN32) && ! defined(__MINGW32__) |
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void glob(const string& path, svector& ret) |
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{ |
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glob_t glob_result; |
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glob(path.c_str(), GLOB_TILDE, 0, &glob_result); |
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ret.clear(); |
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ret.reserve(glob_result.gl_pathc); |
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for(unsigned int i = 0; i < glob_result.gl_pathc; ++i) |
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{ |
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ret.push_back(std::string(glob_result.gl_pathv[i])); |
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} |
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globfree(&glob_result); |
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} |
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#else |
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void glob(const string& refRoot, const string& refExt, svector &refvecFiles) |
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{ |
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std::string strFilePath; // File path
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std::string strExtension; // Extension
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std::string strPattern = refRoot + "\\*.*"; |
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WIN32_FIND_DATA FileInformation; // File information
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HANDLE hFile = ::FindFirstFile(strPattern.c_str(), &FileInformation); |
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if(hFile == INVALID_HANDLE_VALUE) |
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CV_Error(CV_StsBadArg, "Your dataset search path is incorrect"); |
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do |
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{ |
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if(FileInformation.cFileName[0] != '.') |
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{ |
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strFilePath.erase(); |
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strFilePath = refRoot + "\\" + FileInformation.cFileName; |
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if( !(FileInformation.dwFileAttributes & FILE_ATTRIBUTE_DIRECTORY) ) |
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{ |
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// Check extension
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strExtension = FileInformation.cFileName; |
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strExtension = strExtension.substr(strExtension.rfind(".") + 1); |
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if(strExtension == refExt) |
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// Save filename
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refvecFiles.push_back(strFilePath); |
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} |
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} |
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} |
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while(::FindNextFile(hFile, &FileInformation) == TRUE); |
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// Close handle
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::FindClose(hFile); |
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DWORD dwError = ::GetLastError(); |
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if(dwError != ERROR_NO_MORE_FILES) |
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CV_Error(CV_StsBadArg, "Your dataset search path is incorrect"); |
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} |
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#endif |
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ScaledDataset::ScaledDataset(const string& path, const int oct) |
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{ |
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#if !defined (_WIN32) && ! defined(__MINGW32__) |
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glob(path + "/pos/octave_" + itoa(oct) + "/*.png", pos); |
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#else |
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glob(path + "/pos/octave_" + itoa(oct), "png", pos); |
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#endif |
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#if !defined (_WIN32) && ! defined(__MINGW32__) |
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glob(path + "/neg/octave_" + itoa(oct) + "/*.png", neg); |
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#else |
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glob(path + "/neg/octave_" + itoa(oct), "png", neg); |
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#endif |
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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(DISABLED_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<cv::FeaturePool> pool = cv::FeaturePool::create(model, nfeatures); |
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nfeatures = pool->size(); |
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int npositives = 20; |
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int nnegatives = 40; |
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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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typedef cv::SoftCascadeOctave Octave; |
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cv::Ptr<Octave> boost = Octave::create(boundingBox, npositives, nnegatives, *it, shrinkage, nfeatures); |
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std::string path = cvtest::TS::ptr()->get_data_path() + "softcascade/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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