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301 lines
12 KiB
301 lines
12 KiB
/* |
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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 <float.h> |
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#if defined(__GNUC__) && !defined(__APPLE__) |
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#include <fpu_control.h> |
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#endif |
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#include "TestHaarCascadeApplication.h" |
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#include "NCVHaarObjectDetection.hpp" |
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TestHaarCascadeApplication::TestHaarCascadeApplication(std::string testName_, NCVTestSourceProvider<Ncv8u> &src_, |
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std::string cascadeName_, Ncv32u width_, Ncv32u height_) |
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: |
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NCVTestProvider(testName_), |
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src(src_), |
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cascadeName(cascadeName_), |
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width(width_), |
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height(height_) |
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{ |
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} |
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bool TestHaarCascadeApplication::toString(std::ofstream &strOut) |
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{ |
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strOut << "cascadeName=" << cascadeName << std::endl; |
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strOut << "width=" << width << std::endl; |
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strOut << "height=" << height << std::endl; |
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return true; |
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} |
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bool TestHaarCascadeApplication::init() |
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{ |
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return true; |
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} |
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bool TestHaarCascadeApplication::process() |
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{ |
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#if defined(__APPLE) |
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return true; |
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#endif |
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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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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> d_HaarStages(*this->allocatorGPU.get(), numStages); |
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ncvAssertReturn(d_HaarStages.isMemAllocated(), false); |
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NCVVectorAlloc<HaarClassifierNode128> d_HaarNodes(*this->allocatorGPU.get(), numNodes); |
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ncvAssertReturn(d_HaarNodes.isMemAllocated(), false); |
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NCVVectorAlloc<HaarFeature64> d_HaarFeatures(*this->allocatorGPU.get(), numFeatures); |
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ncvAssertReturn(d_HaarFeatures.isMemAllocated(), false); |
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HaarClassifierCascadeDescriptor haar; |
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haar.ClassifierSize.width = haar.ClassifierSize.height = 1; |
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haar.bNeedsTiltedII = false; |
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haar.NumClassifierRootNodes = numNodes; |
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haar.NumClassifierTotalNodes = numNodes; |
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haar.NumFeatures = numFeatures; |
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haar.NumStages = numStages; |
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NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting()); |
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NCV_SKIP_COND_BEGIN |
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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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ncvAssertReturn(NCV_SUCCESS == h_HaarStages.copySolid(d_HaarStages, 0), false); |
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ncvAssertReturn(NCV_SUCCESS == h_HaarNodes.copySolid(d_HaarNodes, 0), false); |
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ncvAssertReturn(NCV_SUCCESS == h_HaarFeatures.copySolid(d_HaarFeatures, 0), false); |
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ncvAssertCUDAReturn(cudaStreamSynchronize(0), false); |
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NCV_SKIP_COND_END |
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NcvSize32s srcRoi, srcIIRoi, searchRoi; |
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srcRoi.width = this->width; |
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srcRoi.height = this->height; |
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srcIIRoi.width = srcRoi.width + 1; |
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srcIIRoi.height = srcRoi.height + 1; |
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searchRoi.width = srcIIRoi.width - haar.ClassifierSize.width; |
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searchRoi.height = srcIIRoi.height - haar.ClassifierSize.height; |
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if (searchRoi.width <= 0 || searchRoi.height <= 0) |
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{ |
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return false; |
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} |
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NcvSize32u searchRoiU(searchRoi.width, searchRoi.height); |
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NCVMatrixAlloc<Ncv8u> d_img(*this->allocatorGPU.get(), this->width, this->height); |
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ncvAssertReturn(d_img.isMemAllocated(), false); |
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NCVMatrixAlloc<Ncv8u> h_img(*this->allocatorCPU.get(), this->width, this->height); |
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ncvAssertReturn(h_img.isMemAllocated(), false); |
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Ncv32u integralWidth = this->width + 1; |
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Ncv32u integralHeight = this->height + 1; |
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NCVMatrixAlloc<Ncv32u> d_integralImage(*this->allocatorGPU.get(), integralWidth, integralHeight); |
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ncvAssertReturn(d_integralImage.isMemAllocated(), false); |
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NCVMatrixAlloc<Ncv64u> d_sqIntegralImage(*this->allocatorGPU.get(), integralWidth, integralHeight); |
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ncvAssertReturn(d_sqIntegralImage.isMemAllocated(), false); |
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NCVMatrixAlloc<Ncv32u> h_integralImage(*this->allocatorCPU.get(), integralWidth, integralHeight); |
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ncvAssertReturn(h_integralImage.isMemAllocated(), false); |
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NCVMatrixAlloc<Ncv64u> h_sqIntegralImage(*this->allocatorCPU.get(), integralWidth, integralHeight); |
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ncvAssertReturn(h_sqIntegralImage.isMemAllocated(), false); |
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NCVMatrixAlloc<Ncv32f> d_rectStdDev(*this->allocatorGPU.get(), this->width, this->height); |
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ncvAssertReturn(d_rectStdDev.isMemAllocated(), false); |
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NCVMatrixAlloc<Ncv32u> d_pixelMask(*this->allocatorGPU.get(), this->width, this->height); |
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ncvAssertReturn(d_pixelMask.isMemAllocated(), false); |
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NCVMatrixAlloc<Ncv32f> h_rectStdDev(*this->allocatorCPU.get(), this->width, this->height); |
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ncvAssertReturn(h_rectStdDev.isMemAllocated(), false); |
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NCVMatrixAlloc<Ncv32u> h_pixelMask(*this->allocatorCPU.get(), this->width, this->height); |
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ncvAssertReturn(h_pixelMask.isMemAllocated(), false); |
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NCVVectorAlloc<NcvRect32u> d_hypotheses(*this->allocatorGPU.get(), this->width * this->height); |
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ncvAssertReturn(d_hypotheses.isMemAllocated(), false); |
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NCVVectorAlloc<NcvRect32u> h_hypotheses(*this->allocatorCPU.get(), this->width * this->height); |
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ncvAssertReturn(h_hypotheses.isMemAllocated(), false); |
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NCVStatus nppStat; |
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Ncv32u szTmpBufIntegral, szTmpBufSqIntegral; |
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nppStat = nppiStIntegralGetSize_8u32u(NcvSize32u(this->width, this->height), &szTmpBufIntegral, this->devProp); |
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ncvAssertReturn(nppStat == NPPST_SUCCESS, false); |
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nppStat = nppiStSqrIntegralGetSize_8u64u(NcvSize32u(this->width, this->height), &szTmpBufSqIntegral, this->devProp); |
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ncvAssertReturn(nppStat == NPPST_SUCCESS, false); |
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NCVVectorAlloc<Ncv8u> d_tmpIIbuf(*this->allocatorGPU.get(), std::max(szTmpBufIntegral, szTmpBufSqIntegral)); |
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ncvAssertReturn(d_tmpIIbuf.isMemAllocated(), false); |
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Ncv32u detectionsOnThisScale_d = 0; |
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Ncv32u detectionsOnThisScale_h = 0; |
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NCV_SKIP_COND_BEGIN |
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ncvAssertReturn(this->src.fill(h_img), false); |
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ncvStat = h_img.copySolid(d_img, 0); |
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ncvAssertReturn(ncvStat == NCV_SUCCESS, false); |
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ncvAssertCUDAReturn(cudaStreamSynchronize(0), false); |
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nppStat = nppiStIntegral_8u32u_C1R(d_img.ptr(), d_img.pitch(), |
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d_integralImage.ptr(), d_integralImage.pitch(), |
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NcvSize32u(d_img.width(), d_img.height()), |
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d_tmpIIbuf.ptr(), szTmpBufIntegral, this->devProp); |
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ncvAssertReturn(nppStat == NPPST_SUCCESS, false); |
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nppStat = nppiStSqrIntegral_8u64u_C1R(d_img.ptr(), d_img.pitch(), |
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d_sqIntegralImage.ptr(), d_sqIntegralImage.pitch(), |
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NcvSize32u(d_img.width(), d_img.height()), |
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d_tmpIIbuf.ptr(), szTmpBufSqIntegral, this->devProp); |
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ncvAssertReturn(nppStat == NPPST_SUCCESS, false); |
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const NcvRect32u rect( |
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HAAR_STDDEV_BORDER, |
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HAAR_STDDEV_BORDER, |
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haar.ClassifierSize.width - 2*HAAR_STDDEV_BORDER, |
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haar.ClassifierSize.height - 2*HAAR_STDDEV_BORDER); |
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nppStat = nppiStRectStdDev_32f_C1R( |
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d_integralImage.ptr(), d_integralImage.pitch(), |
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d_sqIntegralImage.ptr(), d_sqIntegralImage.pitch(), |
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d_rectStdDev.ptr(), d_rectStdDev.pitch(), |
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NcvSize32u(searchRoi.width, searchRoi.height), rect, |
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1.0f, true); |
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ncvAssertReturn(nppStat == NPPST_SUCCESS, false); |
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ncvStat = d_integralImage.copySolid(h_integralImage, 0); |
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ncvAssertReturn(ncvStat == NCV_SUCCESS, false); |
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ncvStat = d_rectStdDev.copySolid(h_rectStdDev, 0); |
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ncvAssertReturn(ncvStat == NCV_SUCCESS, false); |
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for (Ncv32u i=0; i<searchRoiU.height; i++) |
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{ |
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for (Ncv32u j=0; j<h_pixelMask.stride(); j++) |
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{ |
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if (j<searchRoiU.width) |
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{ |
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h_pixelMask.ptr()[i*h_pixelMask.stride()+j] = (i << 16) | j; |
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} |
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else |
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{ |
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h_pixelMask.ptr()[i*h_pixelMask.stride()+j] = OBJDET_MASK_ELEMENT_INVALID_32U; |
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} |
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} |
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} |
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ncvAssertReturn(cudaSuccess == cudaStreamSynchronize(0), false); |
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#if !defined(__APPLE__) |
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#if defined(__GNUC__) |
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//http://www.christian-seiler.de/projekte/fpmath/ |
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fpu_control_t fpu_oldcw, fpu_cw; |
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_FPU_GETCW(fpu_oldcw); // store old cw |
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fpu_cw = (fpu_oldcw & ~_FPU_EXTENDED & ~_FPU_DOUBLE & ~_FPU_SINGLE) | _FPU_SINGLE; |
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_FPU_SETCW(fpu_cw); |
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// calculations here |
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ncvStat = ncvApplyHaarClassifierCascade_host( |
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h_integralImage, h_rectStdDev, h_pixelMask, |
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detectionsOnThisScale_h, |
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haar, h_HaarStages, h_HaarNodes, h_HaarFeatures, false, |
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searchRoiU, 1, 1.0f); |
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ncvAssertReturn(ncvStat == NCV_SUCCESS, false); |
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_FPU_SETCW(fpu_oldcw); // restore old cw |
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#else |
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#ifndef _WIN64 |
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Ncv32u fpu_oldcw, fpu_cw; |
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_controlfp_s(&fpu_cw, 0, 0); |
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fpu_oldcw = fpu_cw; |
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_controlfp_s(&fpu_cw, _PC_24, _MCW_PC); |
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#endif |
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ncvStat = ncvApplyHaarClassifierCascade_host( |
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h_integralImage, h_rectStdDev, h_pixelMask, |
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detectionsOnThisScale_h, |
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haar, h_HaarStages, h_HaarNodes, h_HaarFeatures, false, |
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searchRoiU, 1, 1.0f); |
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ncvAssertReturn(ncvStat == NCV_SUCCESS, false); |
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#ifndef _WIN64 |
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_controlfp_s(&fpu_cw, fpu_oldcw, _MCW_PC); |
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#endif |
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#endif |
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#endif |
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NCV_SKIP_COND_END |
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int devId; |
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ncvAssertCUDAReturn(cudaGetDevice(&devId), false); |
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cudaDeviceProp _devProp; |
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ncvAssertCUDAReturn(cudaGetDeviceProperties(&_devProp, devId), false); |
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ncvStat = ncvApplyHaarClassifierCascade_device( |
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d_integralImage, d_rectStdDev, d_pixelMask, |
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detectionsOnThisScale_d, |
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haar, h_HaarStages, d_HaarStages, d_HaarNodes, d_HaarFeatures, false, |
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searchRoiU, 1, 1.0f, |
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*this->allocatorGPU.get(), *this->allocatorCPU.get(), |
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_devProp, 0); |
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ncvAssertReturn(ncvStat == NCV_SUCCESS, false); |
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NCVMatrixAlloc<Ncv32u> h_pixelMask_d(*this->allocatorCPU.get(), this->width, this->height); |
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ncvAssertReturn(h_pixelMask_d.isMemAllocated(), false); |
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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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ncvStat = d_pixelMask.copySolid(h_pixelMask_d, 0); |
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ncvAssertReturn(ncvStat == NCV_SUCCESS, false); |
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if (detectionsOnThisScale_d != detectionsOnThisScale_h) |
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{ |
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bLoopVirgin = false; |
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} |
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else |
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{ |
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std::sort(h_pixelMask_d.ptr(), h_pixelMask_d.ptr() + detectionsOnThisScale_d); |
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for (Ncv32u i=0; i<detectionsOnThisScale_d && bLoopVirgin; i++) |
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{ |
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if (h_pixelMask.ptr()[i] != h_pixelMask_d.ptr()[i]) |
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{ |
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bLoopVirgin = false; |
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} |
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} |
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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 TestHaarCascadeApplication::deinit() |
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{ |
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return true; |
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}
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