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124 lines
4.5 KiB
C++
124 lines
4.5 KiB
C++
/*
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* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
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*
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* NVIDIA Corporation and its licensors retain all intellectual
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* property and proprietary rights in and to this software and
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* related documentation and any modifications thereto.
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* Any use, reproduction, disclosure, or distribution of this
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* software and related documentation without an express license
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* agreement from NVIDIA Corporation is strictly prohibited.
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*/
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#include "TestHaarCascadeLoader.h"
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#include "NCVHaarObjectDetection.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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