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153 lines
6.0 KiB
153 lines
6.0 KiB
/*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) 2009, 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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#include "test_precomp.hpp" |
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TestHaarCascadeLoader::TestHaarCascadeLoader(std::string testName_, std::string cascadeName_) |
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: |
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NCVTestProvider(testName_), |
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cascadeName(cascadeName_) |
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{ |
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} |
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bool TestHaarCascadeLoader::toString(std::ofstream &strOut) |
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{ |
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strOut << "cascadeName=" << cascadeName << std::endl; |
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return true; |
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} |
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bool TestHaarCascadeLoader::init() |
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{ |
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return true; |
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} |
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bool TestHaarCascadeLoader::process() |
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{ |
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NCVStatus ncvStat; |
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bool rcode = false; |
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Ncv32u numStages, numNodes, numFeatures; |
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Ncv32u numStages_2 = 0, numNodes_2 = 0, numFeatures_2 = 0; |
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ncvStat = ncvHaarGetClassifierSize(this->cascadeName, numStages, numNodes, numFeatures); |
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ncvAssertReturn(ncvStat == NCV_SUCCESS, false); |
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NCVVectorAlloc<HaarStage64> h_HaarStages(*this->allocatorCPU.get(), numStages); |
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ncvAssertReturn(h_HaarStages.isMemAllocated(), false); |
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NCVVectorAlloc<HaarClassifierNode128> h_HaarNodes(*this->allocatorCPU.get(), numNodes); |
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ncvAssertReturn(h_HaarNodes.isMemAllocated(), false); |
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NCVVectorAlloc<HaarFeature64> h_HaarFeatures(*this->allocatorCPU.get(), numFeatures); |
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ncvAssertReturn(h_HaarFeatures.isMemAllocated(), false); |
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NCVVectorAlloc<HaarStage64> h_HaarStages_2(*this->allocatorCPU.get(), numStages); |
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ncvAssertReturn(h_HaarStages_2.isMemAllocated(), false); |
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NCVVectorAlloc<HaarClassifierNode128> h_HaarNodes_2(*this->allocatorCPU.get(), numNodes); |
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ncvAssertReturn(h_HaarNodes_2.isMemAllocated(), false); |
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NCVVectorAlloc<HaarFeature64> h_HaarFeatures_2(*this->allocatorCPU.get(), numFeatures); |
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ncvAssertReturn(h_HaarFeatures_2.isMemAllocated(), false); |
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HaarClassifierCascadeDescriptor haar; |
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HaarClassifierCascadeDescriptor haar_2; |
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NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting()); |
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NCV_SKIP_COND_BEGIN |
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const std::string testNvbinName = "test.nvbin"; |
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ncvStat = ncvHaarLoadFromFile_host(this->cascadeName, haar, h_HaarStages, h_HaarNodes, h_HaarFeatures); |
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ncvAssertReturn(ncvStat == NCV_SUCCESS, false); |
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ncvStat = ncvHaarStoreNVBIN_host(testNvbinName, haar, h_HaarStages, h_HaarNodes, h_HaarFeatures); |
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ncvAssertReturn(ncvStat == NCV_SUCCESS, false); |
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ncvStat = ncvHaarGetClassifierSize(testNvbinName, numStages_2, numNodes_2, numFeatures_2); |
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ncvAssertReturn(ncvStat == NCV_SUCCESS, false); |
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ncvStat = ncvHaarLoadFromFile_host(testNvbinName, haar_2, h_HaarStages_2, h_HaarNodes_2, h_HaarFeatures_2); |
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ncvAssertReturn(ncvStat == NCV_SUCCESS, false); |
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NCV_SKIP_COND_END |
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//bit-to-bit check |
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bool bLoopVirgin = true; |
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NCV_SKIP_COND_BEGIN |
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if ( |
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numStages_2 != numStages || |
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numNodes_2 != numNodes || |
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numFeatures_2 != numFeatures || |
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haar.NumStages != haar_2.NumStages || |
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haar.NumClassifierRootNodes != haar_2.NumClassifierRootNodes || |
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haar.NumClassifierTotalNodes != haar_2.NumClassifierTotalNodes || |
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haar.NumFeatures != haar_2.NumFeatures || |
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haar.ClassifierSize.width != haar_2.ClassifierSize.width || |
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haar.ClassifierSize.height != haar_2.ClassifierSize.height || |
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haar.bNeedsTiltedII != haar_2.bNeedsTiltedII || |
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haar.bHasStumpsOnly != haar_2.bHasStumpsOnly ) |
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{ |
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bLoopVirgin = false; |
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} |
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if (memcmp(h_HaarStages.ptr(), h_HaarStages_2.ptr(), haar.NumStages * sizeof(HaarStage64)) || |
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memcmp(h_HaarNodes.ptr(), h_HaarNodes_2.ptr(), haar.NumClassifierTotalNodes * sizeof(HaarClassifierNode128)) || |
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memcmp(h_HaarFeatures.ptr(), h_HaarFeatures_2.ptr(), haar.NumFeatures * sizeof(HaarFeature64)) ) |
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{ |
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bLoopVirgin = false; |
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} |
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NCV_SKIP_COND_END |
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if (bLoopVirgin) |
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{ |
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rcode = true; |
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} |
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return rcode; |
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} |
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bool TestHaarCascadeLoader::deinit() |
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{ |
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return true; |
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}
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