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