mirror of
https://github.com/opencv/opencv.git
synced 2024-11-26 20:20:20 +08:00
124 lines
4.5 KiB
C++
124 lines
4.5 KiB
C++
/*
|
|
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
|
|
*
|
|
* NVIDIA Corporation and its licensors retain all intellectual
|
|
* property and proprietary rights in and to this software and
|
|
* related documentation and any modifications thereto.
|
|
* Any use, reproduction, disclosure, or distribution of this
|
|
* software and related documentation without an express license
|
|
* agreement from NVIDIA Corporation is strictly prohibited.
|
|
*/
|
|
|
|
#include "TestHaarCascadeLoader.h"
|
|
#include "NCVHaarObjectDetection.hpp"
|
|
|
|
|
|
TestHaarCascadeLoader::TestHaarCascadeLoader(std::string testName_, std::string cascadeName_)
|
|
:
|
|
NCVTestProvider(testName_),
|
|
cascadeName(cascadeName_)
|
|
{
|
|
}
|
|
|
|
|
|
bool TestHaarCascadeLoader::toString(std::ofstream &strOut)
|
|
{
|
|
strOut << "cascadeName=" << cascadeName << std::endl;
|
|
return true;
|
|
}
|
|
|
|
|
|
bool TestHaarCascadeLoader::init()
|
|
{
|
|
return true;
|
|
}
|
|
|
|
|
|
bool TestHaarCascadeLoader::process()
|
|
{
|
|
NCVStatus ncvStat;
|
|
bool rcode = false;
|
|
|
|
Ncv32u numStages, numNodes, numFeatures;
|
|
Ncv32u numStages_2 = 0, numNodes_2 = 0, numFeatures_2 = 0;
|
|
|
|
ncvStat = ncvHaarGetClassifierSize(this->cascadeName, numStages, numNodes, numFeatures);
|
|
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
|
|
|
|
NCVVectorAlloc<HaarStage64> h_HaarStages(*this->allocatorCPU.get(), numStages);
|
|
ncvAssertReturn(h_HaarStages.isMemAllocated(), false);
|
|
NCVVectorAlloc<HaarClassifierNode128> h_HaarNodes(*this->allocatorCPU.get(), numNodes);
|
|
ncvAssertReturn(h_HaarNodes.isMemAllocated(), false);
|
|
NCVVectorAlloc<HaarFeature64> h_HaarFeatures(*this->allocatorCPU.get(), numFeatures);
|
|
ncvAssertReturn(h_HaarFeatures.isMemAllocated(), false);
|
|
|
|
NCVVectorAlloc<HaarStage64> h_HaarStages_2(*this->allocatorCPU.get(), numStages);
|
|
ncvAssertReturn(h_HaarStages_2.isMemAllocated(), false);
|
|
NCVVectorAlloc<HaarClassifierNode128> h_HaarNodes_2(*this->allocatorCPU.get(), numNodes);
|
|
ncvAssertReturn(h_HaarNodes_2.isMemAllocated(), false);
|
|
NCVVectorAlloc<HaarFeature64> h_HaarFeatures_2(*this->allocatorCPU.get(), numFeatures);
|
|
ncvAssertReturn(h_HaarFeatures_2.isMemAllocated(), false);
|
|
|
|
HaarClassifierCascadeDescriptor haar;
|
|
HaarClassifierCascadeDescriptor haar_2;
|
|
|
|
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
|
|
NCV_SKIP_COND_BEGIN
|
|
|
|
const std::string testNvbinName = "test.nvbin";
|
|
ncvStat = ncvHaarLoadFromFile_host(this->cascadeName, haar, h_HaarStages, h_HaarNodes, h_HaarFeatures);
|
|
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
|
|
|
|
ncvStat = ncvHaarStoreNVBIN_host(testNvbinName, haar, h_HaarStages, h_HaarNodes, h_HaarFeatures);
|
|
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
|
|
|
|
ncvStat = ncvHaarGetClassifierSize(testNvbinName, numStages_2, numNodes_2, numFeatures_2);
|
|
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
|
|
|
|
ncvStat = ncvHaarLoadFromFile_host(testNvbinName, haar_2, h_HaarStages_2, h_HaarNodes_2, h_HaarFeatures_2);
|
|
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
|
|
|
|
NCV_SKIP_COND_END
|
|
|
|
//bit-to-bit check
|
|
bool bLoopVirgin = true;
|
|
|
|
NCV_SKIP_COND_BEGIN
|
|
|
|
if (
|
|
numStages_2 != numStages ||
|
|
numNodes_2 != numNodes ||
|
|
numFeatures_2 != numFeatures ||
|
|
haar.NumStages != haar_2.NumStages ||
|
|
haar.NumClassifierRootNodes != haar_2.NumClassifierRootNodes ||
|
|
haar.NumClassifierTotalNodes != haar_2.NumClassifierTotalNodes ||
|
|
haar.NumFeatures != haar_2.NumFeatures ||
|
|
haar.ClassifierSize.width != haar_2.ClassifierSize.width ||
|
|
haar.ClassifierSize.height != haar_2.ClassifierSize.height ||
|
|
haar.bNeedsTiltedII != haar_2.bNeedsTiltedII ||
|
|
haar.bHasStumpsOnly != haar_2.bHasStumpsOnly )
|
|
{
|
|
bLoopVirgin = false;
|
|
}
|
|
if (memcmp(h_HaarStages.ptr(), h_HaarStages_2.ptr(), haar.NumStages * sizeof(HaarStage64)) ||
|
|
memcmp(h_HaarNodes.ptr(), h_HaarNodes_2.ptr(), haar.NumClassifierTotalNodes * sizeof(HaarClassifierNode128)) ||
|
|
memcmp(h_HaarFeatures.ptr(), h_HaarFeatures_2.ptr(), haar.NumFeatures * sizeof(HaarFeature64)) )
|
|
{
|
|
bLoopVirgin = false;
|
|
}
|
|
NCV_SKIP_COND_END
|
|
|
|
if (bLoopVirgin)
|
|
{
|
|
rcode = true;
|
|
}
|
|
|
|
return rcode;
|
|
}
|
|
|
|
|
|
bool TestHaarCascadeLoader::deinit()
|
|
{
|
|
return true;
|
|
}
|