159 lines
		
	
	
		
			6.1 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
			
		
		
	
	
			159 lines
		
	
	
		
			6.1 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
| /*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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| 
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| #if !defined CUDA_DISABLER
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| 
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| #include "TestHaarCascadeLoader.h"
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| #include "NCVHaarObjectDetection.hpp"
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| 
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| 
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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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| 
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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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| 
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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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| 
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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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| 
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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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| 
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|     ncvStat = ncvHaarGetClassifierSize(this->cascadeName, numStages, numNodes, numFeatures);
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|     ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
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| 
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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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| 
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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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| 
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|     HaarClassifierCascadeDescriptor haar;
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|     HaarClassifierCascadeDescriptor haar_2;
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| 
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|     NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
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|     NCV_SKIP_COND_BEGIN
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| 
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|     const std::string testNvbinName = cv::tempfile("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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| 
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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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| 
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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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| 
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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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| 
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|     NCV_SKIP_COND_END
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| 
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|     //bit-to-bit check
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|     bool bLoopVirgin = true;
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| 
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|     NCV_SKIP_COND_BEGIN
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| 
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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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| 
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|     if (bLoopVirgin)
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|     {
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|         rcode = true;
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|     }
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| 
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|     return rcode;
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| }
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| 
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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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| 
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| #endif /* CUDA_DISABLER */
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