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 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;
}