generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

initial support of GPU LBP classifier: added new style xml format loading

Browse Source
This commit is contained in:
Marina Kolpakova 2012-06-22 15:00:36 +00:00
parent 02170a0a58
commit 1365e28a54
22 changed files with 446 additions and 192 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
cmake/OpenCVDetectCUDA.cmake
View File

@ -88,6 +88,7 @@ if(CUDA_FOUND)
if(APPLE)
set (CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} -Xcompiler -fno-finite-math-only)
endif()
string(REPLACE "-Wsign-promo" "" CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS}")
# we remove -ggdb3 flag as it leads to preprocessor errors when compiling CUDA files (CUDA 4.1)
set(CMAKE_CXX_FLAGS_DEBUG_ ${CMAKE_CXX_FLAGS_DEBUG})

38
modules/gpu/include/opencv2/gpu/gpu.hpp
View File

@ -1422,6 +1422,44 @@ private:
CascadeClassifierImpl* impl;
};
// The cascade classifier class for object detection.
class CV_EXPORTS CascadeClassifier_GPU_LBP
{
public:
enum stage { BOOST = 0 };
enum feature { LBP = 0 };
CascadeClassifier_GPU_LBP();
~CascadeClassifier_GPU_LBP();
bool empty() const;
bool load(const std::string& filename);
void release();
int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, double scaleFactor = 1.2, int minNeighbors = 4, Size minSize = Size());
bool findLargestObject;
bool visualizeInPlace;
Size getClassifierSize() const;
private:
bool read(const FileNode &root);
static const stage stageType = BOOST;
static const feature feature = LBP;
cv::Size NxM;
bool isStumps;
int ncategories;
struct Stage;
Stage* stages;
struct DTree;
// DTree* classifiers;
struct DTreeNode;
// DTreeNode* nodes;
};
////////////////////////////////// SURF //////////////////////////////////////////
class CV_EXPORTS SURF_GPU

8
modules/gpu/src/brute_force_matcher.cpp
View File

@ -272,14 +272,14 @@ void cv::gpu::BFMatcher_GPU::matchConvert(const Mat& trainIdx, const Mat& distan
const float* distance_ptr = distance.ptr<float>();
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx, ++trainIdx_ptr, ++distance_ptr)
{
int trainIdx = *trainIdx_ptr;
int train_idx = *trainIdx_ptr;
if (trainIdx == -1)
if (train_idx == -1)
continue;
float distance = *distance_ptr;
float distance_local = *distance_ptr;
DMatch m(queryIdx, trainIdx, 0, distance);
DMatch m(queryIdx, train_idx, 0, distance_local);
matches.push_back(m);
}

192
modules/gpu/src/cascadeclassifier.cpp
View File

@ -41,13 +41,37 @@
//M*/
#include "precomp.hpp"
#include <vector>
using namespace cv;
using namespace cv::gpu;
using namespace std;
#if !defined (HAVE_CUDA)
struct cv::gpu::CascadeClassifier_GPU_LBP::Stage
{
int first;
int ntrees;
float threshold;
Stage(int f = 0, int n = 0, float t = 0.f) : first(f), ntrees(n), threshold(t) {}
};
struct cv::gpu::CascadeClassifier_GPU_LBP::DTree
{
int nodeCount;
DTree(int n = 0) : nodeCount(n) {}
};
struct cv::gpu::CascadeClassifier_GPU_LBP::DTreeNode
{
int featureIdx;
//float threshold; // for ordered features only
int left;
int right;
DTreeNode(int f = 0, int l = 0, int r = 0) : featureIdx(f), left(l), right(r) {}
};
#if !defined (HAVE_CUDA)
// ============ old fashioned haar cascade ==============================================//
cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU() { throw_nogpu(); }
cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU(const string&) { throw_nogpu(); }
cv::gpu::CascadeClassifier_GPU::~CascadeClassifier_GPU() { throw_nogpu(); }
@ -58,8 +82,174 @@ Size cv::gpu::CascadeClassifier_GPU::getClassifierSize() const { throw_nogpu();
int cv::gpu::CascadeClassifier_GPU::detectMultiScale( const GpuMat& , GpuMat& , double , int , Size) { throw_nogpu(); return 0; }
// ============ LBP cascade ==============================================//
cv::gpu::CascadeClassifier_GPU_LBP::CascadeClassifier_GPU_LBP() { throw_nogpu(); }
cv::gpu::CascadeClassifier_GPU_LBP::~CascadeClassifier_GPU_LBP() { throw_nogpu(); }
bool cv::gpu::CascadeClassifier_GPU_LBP::empty() const { throw_nogpu(); return true; }
bool cv::gpu::CascadeClassifier_GPU_LBP::load(const string&) { throw_nogpu(); return true; }
Size cv::gpu::CascadeClassifier_GPU_LBP::getClassifierSize() const { throw_nogpu(); return Size(); }
int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale( const GpuMat& , GpuMat& , double , int , Size) { throw_nogpu(); return 0; }
#else
cv::gpu::CascadeClassifier_GPU_LBP::CascadeClassifier_GPU_LBP()
{
}
cv::gpu::CascadeClassifier_GPU_LBP::~CascadeClassifier_GPU_LBP()
{
}
bool cv::gpu::CascadeClassifier_GPU_LBP::empty() const { throw_nogpu(); return true; }
bool cv::gpu::CascadeClassifier_GPU_LBP::load(const string& classifierAsXml)
{
FileStorage fs(classifierAsXml, FileStorage::READ);
if (!fs.isOpened())
return false;
if (read(fs.getFirstTopLevelNode()))
return true;
return false;
}
#define GPU_CC_STAGE_TYPE "stageType"
#define GPU_CC_FEATURE_TYPE "featureType"
#define GPU_CC_BOOST "BOOST"
#define GPU_CC_LBP "LBP"
#define GPU_CC_MAX_CAT_COUNT "maxCatCount"
#define GPU_CC_HEIGHT "height"
#define GPU_CC_WIDTH "width"
#define GPU_CC_STAGE_PARAMS "stageParams"
#define GPU_CC_MAX_DEPTH "maxDepth"
#define GPU_CC_FEATURE_PARAMS "featureParams"
#define GPU_CC_STAGES "stages"
#define GPU_CC_STAGE_THRESHOLD "stageThreshold"
#define GPU_THRESHOLD_EPS 1e-5f
#define GPU_CC_WEAK_CLASSIFIERS "weakClassifiers"
#define GPU_CC_INTERNAL_NODES "internalNodes"
#define GPU_CC_LEAF_VALUES "leafValues"
bool CascadeClassifier_GPU_LBP::read(const FileNode &root)
{
string stageTypeStr = (string)root[GPU_CC_STAGE_TYPE];
CV_Assert(stageTypeStr == GPU_CC_BOOST);
string featureTypeStr = (string)root[GPU_CC_FEATURE_TYPE];
CV_Assert(featureTypeStr == GPU_CC_LBP);
NxM.width = (int)root[GPU_CC_WIDTH];
NxM.height = (int)root[GPU_CC_HEIGHT];
CV_Assert( NxM.height > 0 && NxM.width > 0 );
isStumps = ((int)(root[GPU_CC_STAGE_PARAMS][GPU_CC_MAX_DEPTH]) == 1) ? true : false;
// features
FileNode fn = root[GPU_CC_FEATURE_PARAMS];
if (fn.empty())
return false;
ncategories = fn[GPU_CC_MAX_CAT_COUNT];
int subsetSize = (ncategories + 31)/32, nodeStep = 3 + ( ncategories > 0 ? subsetSize : 1 );// ?
fn = root[GPU_CC_STAGES];
if (fn.empty())
return false;
delete[] stages;
// delete[] classifiers;
// delete[] nodes;
stages = new Stage[fn.size()];
std::vector<DTree> cl_trees;
std::vector<DTreeNode> cl_nodes;
std::vector<float> cl_leaves;
std::vector<int> subsets;
FileNodeIterator it = fn.begin(), it_end = fn.end();
size_t s_it = 0;
for (size_t si = 0; it != it_end; si++, ++it )
{
FileNode fns = *it;
fns = fns[GPU_CC_WEAK_CLASSIFIERS];
if (fns.empty())
return false;
stages[s_it++] = Stage((float)fns[GPU_CC_STAGE_THRESHOLD] - GPU_THRESHOLD_EPS,
(int)cl_trees.size(), (int)fns.size());
cl_trees.reserve(stages[si].first + stages[si].ntrees);
// weak trees
FileNodeIterator it1 = fns.begin(), it1_end = fns.end();
for ( ; it1 != it1_end; ++it1 )
{
FileNode fnw = *it1;
FileNode internalNodes = fnw[GPU_CC_INTERNAL_NODES];
FileNode leafValues = fnw[GPU_CC_LEAF_VALUES];
if ( internalNodes.empty() || leafValues.empty() )
return false;
DTree tree((int)internalNodes.size()/nodeStep );
cl_trees.push_back(tree);
cl_nodes.reserve(cl_nodes.size() + tree.nodeCount);
cl_leaves.reserve(cl_leaves.size() + leafValues.size());
if( subsetSize > 0 )
subsets.reserve(subsets.size() + tree.nodeCount * subsetSize);
// nodes
FileNodeIterator iIt = internalNodes.begin(), iEnd = internalNodes.end();
for( ; iIt != iEnd; )
{
DTreeNode node((int)*(iIt++), (int)*(iIt++), (int)*(iIt++));
cl_nodes.push_back(node);
if ( subsetSize > 0 )
{
for( int j = 0; j < subsetSize; j++, ++iIt )
subsets.push_back((int)*iIt); //????
}
}
iIt = leafValues.begin(), iEnd = leafValues.end();
// leaves
for( ; iIt != iEnd; ++iIt )
cl_leaves.push_back((float)*iIt);
}
}
return true;
}
#undef GPU_CC_STAGE_TYPE
#undef GPU_CC_BOOST
#undef GPU_CC_FEATURE_TYPE
#undef GPU_CC_LBP
#undef GPU_CC_MAX_CAT_COUNT
#undef GPU_CC_HEIGHT
#undef GPU_CC_WIDTH
#undef GPU_CC_STAGE_PARAMS
#undef GPU_CC_MAX_DEPTH
#undef GPU_CC_FEATURE_PARAMS
#undef GPU_CC_STAGES
#undef GPU_CC_STAGE_THRESHOLD
#undef GPU_THRESHOLD_EPS
#undef GPU_CC_WEAK_CLASSIFIERS
#undef GPU_CC_INTERNAL_NODES
#undef GPU_CC_LEAF_VALUES
Size cv::gpu::CascadeClassifier_GPU_LBP::getClassifierSize() const { throw_nogpu(); return Size(); }
int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale( const GpuMat& , GpuMat& , double , int , Size) { throw_nogpu(); return 0; }
// ============ old fashioned haar cascade ==============================================//
struct cv::gpu::CascadeClassifier_GPU::CascadeClassifierImpl
{
CascadeClassifierImpl(const string& filename) : lastAllocatedFrameSize(-1, -1)

6
modules/gpu/src/imgproc.cpp
View File

@ -357,6 +357,7 @@ namespace cv { namespace gpu { namespace device
void cv::gpu::buildWarpPlaneMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, const Mat &T,
float scale, GpuMat& map_x, GpuMat& map_y, Stream& stream)
{
(void)src_size;
using namespace ::cv::gpu::device::imgproc;
CV_Assert(K.size() == Size(3,3) && K.type() == CV_32F);
@ -390,6 +391,7 @@ namespace cv { namespace gpu { namespace device
void cv::gpu::buildWarpCylindricalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, float scale,
GpuMat& map_x, GpuMat& map_y, Stream& stream)
{
(void)src_size;
using namespace ::cv::gpu::device::imgproc;
CV_Assert(K.size() == Size(3,3) && K.type() == CV_32F);
@ -422,6 +424,7 @@ namespace cv { namespace gpu { namespace device
void cv::gpu::buildWarpSphericalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, float scale,
GpuMat& map_x, GpuMat& map_y, Stream& stream)
{
(void)src_size;
using namespace ::cv::gpu::device::imgproc;
CV_Assert(K.size() == Size(3,3) && K.type() == CV_32F);
@ -466,6 +469,7 @@ namespace
static void call(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift, double yShift, int interpolation, cudaStream_t stream)
{
(void)dsize;
static const int npp_inter[] = {NPPI_INTER_NN, NPPI_INTER_LINEAR, NPPI_INTER_CUBIC};
NppStreamHandler h(stream);
@ -1139,6 +1143,7 @@ namespace cv { namespace gpu { namespace device
void cv::gpu::mulSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, bool conjB, Stream& stream)
{
(void)flags;
using namespace ::cv::gpu::device::imgproc;
typedef void (*Caller)(const PtrStep<cufftComplex>, const PtrStep<cufftComplex>, DevMem2D_<cufftComplex>, cudaStream_t stream);
@ -1169,6 +1174,7 @@ namespace cv { namespace gpu { namespace device
void cv::gpu::mulAndScaleSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, float scale, bool conjB, Stream& stream)
{
(void)flags;
using namespace ::cv::gpu::device::imgproc;
typedef void (*Caller)(const PtrStep<cufftComplex>, const PtrStep<cufftComplex>, float scale, DevMem2D_<cufftComplex>, cudaStream_t stream);

61
modules/gpu/src/nvidia/NCVHaarObjectDetection.cu
View File

@ -927,7 +927,7 @@ Ncv32u getStageNumWithNotLessThanNclassifiers(Ncv32u N, HaarClassifierCascadeDes
}
NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImage,
NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &integral,
NCVMatrix<Ncv32f> &d_weights,
NCVMatrixAlloc<Ncv32u> &d_pixelMask,
Ncv32u &numDetections,
@ -945,32 +945,41 @@ NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImag
cudaDeviceProp &devProp,
cudaStream_t cuStream)
{
ncvAssertReturn(d_integralImage.memType() == d_weights.memType() &&
d_integralImage.memType() == d_pixelMask.memType() &&
d_integralImage.memType() == gpuAllocator.memType() &&
(d_integralImage.memType() == NCVMemoryTypeDevice ||
d_integralImage.memType() == NCVMemoryTypeNone), NCV_MEM_RESIDENCE_ERROR);
ncvAssertReturn(integral.memType() == d_weights.memType()&&
integral.memType() == d_pixelMask.memType() &&
integral.memType() == gpuAllocator.memType() &&
(integral.memType() == NCVMemoryTypeDevice ||
integral.memType() == NCVMemoryTypeNone), NCV_MEM_RESIDENCE_ERROR);
ncvAssertReturn(d_HaarStages.memType() == d_HaarNodes.memType() &&
d_HaarStages.memType() == d_HaarFeatures.memType() &&
(d_HaarStages.memType() == NCVMemoryTypeDevice ||
d_HaarStages.memType() == NCVMemoryTypeNone), NCV_MEM_RESIDENCE_ERROR);
ncvAssertReturn(h_HaarStages.memType() != NCVMemoryTypeDevice, NCV_MEM_RESIDENCE_ERROR);
ncvAssertReturn(gpuAllocator.isInitialized() && cpuAllocator.isInitialized(), NCV_ALLOCATOR_NOT_INITIALIZED);
ncvAssertReturn((d_integralImage.ptr() != NULL && d_weights.ptr() != NULL && d_pixelMask.ptr() != NULL &&
ncvAssertReturn((integral.ptr() != NULL && d_weights.ptr() != NULL && d_pixelMask.ptr() != NULL &&
h_HaarStages.ptr() != NULL && d_HaarStages.ptr() != NULL && d_HaarNodes.ptr() != NULL &&
d_HaarFeatures.ptr() != NULL) || gpuAllocator.isCounting(), NCV_NULL_PTR);
ncvAssertReturn(anchorsRoi.width > 0 && anchorsRoi.height > 0 &&
d_pixelMask.width() >= anchorsRoi.width && d_pixelMask.height() >= anchorsRoi.height &&
d_weights.width() >= anchorsRoi.width && d_weights.height() >= anchorsRoi.height &&
d_integralImage.width() >= anchorsRoi.width + haar.ClassifierSize.width &&
d_integralImage.height() >= anchorsRoi.height + haar.ClassifierSize.height, NCV_DIMENSIONS_INVALID);
integral.width() >= anchorsRoi.width + haar.ClassifierSize.width &&
integral.height() >= anchorsRoi.height + haar.ClassifierSize.height, NCV_DIMENSIONS_INVALID);
ncvAssertReturn(scaleArea > 0, NCV_INVALID_SCALE);
ncvAssertReturn(d_HaarStages.length() >= haar.NumStages &&
d_HaarNodes.length() >= haar.NumClassifierTotalNodes &&
d_HaarFeatures.length() >= haar.NumFeatures &&
d_HaarStages.length() == h_HaarStages.length() &&
haar.NumClassifierRootNodes <= haar.NumClassifierTotalNodes, NCV_DIMENSIONS_INVALID);
ncvAssertReturn(haar.bNeedsTiltedII == false || gpuAllocator.isCounting(), NCV_NOIMPL_HAAR_TILTED_FEATURES);
ncvAssertReturn(pixelStep == 1 || pixelStep == 2, NCV_HAAR_INVALID_PIXEL_STEP);
NCV_SET_SKIP_COND(gpuAllocator.isCounting());
@ -979,7 +988,7 @@ NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImag
NCVStatus ncvStat;
NCVMatrixAlloc<Ncv32u> h_integralImage(cpuAllocator, d_integralImage.width, d_integralImage.height, d_integralImage.pitch);
NCVMatrixAlloc<Ncv32u> h_integralImage(cpuAllocator, integral.width, integral.height, integral.pitch);
ncvAssertReturn(h_integralImage.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
NCVMatrixAlloc<Ncv32f> h_weights(cpuAllocator, d_weights.width, d_weights.height, d_weights.pitch);
ncvAssertReturn(h_weights.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
@ -997,7 +1006,7 @@ NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImag
ncvStat = d_pixelMask.copySolid(h_pixelMask, 0);
ncvAssertReturnNcvStat(ncvStat);
ncvStat = d_integralImage.copySolid(h_integralImage, 0);
ncvStat = integral.copySolid(h_integralImage, 0);
ncvAssertReturnNcvStat(ncvStat);
ncvStat = d_weights.copySolid(h_weights, 0);
ncvAssertReturnNcvStat(ncvStat);
@ -1071,8 +1080,8 @@ NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImag
cfdTexIImage = cudaCreateChannelDesc<Ncv32u>();
size_t alignmentOffset;
ncvAssertCUDAReturn(cudaBindTexture(&alignmentOffset, texIImage, d_integralImage.ptr(), cfdTexIImage,
(anchorsRoi.height + haar.ClassifierSize.height) * d_integralImage.pitch()), NCV_CUDA_ERROR);
ncvAssertCUDAReturn(cudaBindTexture(&alignmentOffset, texIImage, integral.ptr(), cfdTexIImage,
(anchorsRoi.height + haar.ClassifierSize.height) * integral.pitch()), NCV_CUDA_ERROR);
ncvAssertReturn(alignmentOffset==0, NCV_TEXTURE_BIND_ERROR);
}
@ -1189,7 +1198,7 @@ NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImag
grid1,
block1,
cuStream,
d_integralImage.ptr(), d_integralImage.stride(),
integral.ptr(), integral.stride(),
d_weights.ptr(), d_weights.stride(),
d_HaarFeatures.ptr(), d_HaarNodes.ptr(), d_HaarStages.ptr(),
d_ptrNowData->ptr(),
@ -1259,7 +1268,7 @@ NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImag
grid2,
block2,
cuStream,
d_integralImage.ptr(), d_integralImage.stride(),
integral.ptr(), integral.stride(),
d_weights.ptr(), d_weights.stride(),
d_HaarFeatures.ptr(), d_HaarNodes.ptr(), d_HaarStages.ptr(),
d_ptrNowData->ptr(),
@ -1320,7 +1329,7 @@ NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImag
grid3,
block3,
cuStream,
d_integralImage.ptr(), d_integralImage.stride(),
integral.ptr(), integral.stride(),
d_weights.ptr(), d_weights.stride(),
d_HaarFeatures.ptr(), d_HaarNodes.ptr(), d_HaarStages.ptr(),
d_ptrNowData->ptr(),
@ -1455,10 +1464,14 @@ NCVStatus ncvGrowDetectionsVector_device(NCVVector<Ncv32u> &pixelMask,
cudaStream_t cuStream)
{
ncvAssertReturn(pixelMask.ptr() != NULL && hypotheses.ptr() != NULL, NCV_NULL_PTR);
ncvAssertReturn(pixelMask.memType() == hypotheses.memType() &&
pixelMask.memType() == NCVMemoryTypeDevice, NCV_MEM_RESIDENCE_ERROR);
ncvAssertReturn(rectWidth > 0 && rectHeight > 0 && curScale > 0, NCV_INVALID_ROI);
ncvAssertReturn(curScale > 0, NCV_INVALID_SCALE);
ncvAssertReturn(totalMaxDetections <= hypotheses.length() &&
numPixelMaskDetections <= pixelMask.length() &&
totalMaxDetections <= totalMaxDetections, NCV_INCONSISTENT_INPUT);
@ -1527,12 +1540,16 @@ NCVStatus ncvDetectObjectsMultiScale_device(NCVMatrix<Ncv8u> &d_srcImg,
d_srcImg.memType() == gpuAllocator.memType() &&
(d_srcImg.memType() == NCVMemoryTypeDevice ||
d_srcImg.memType() == NCVMemoryTypeNone), NCV_MEM_RESIDENCE_ERROR);
ncvAssertReturn(d_HaarStages.memType() == d_HaarNodes.memType() &&
d_HaarStages.memType() == d_HaarFeatures.memType() &&
(d_HaarStages.memType() == NCVMemoryTypeDevice ||
d_HaarStages.memType() == NCVMemoryTypeNone), NCV_MEM_RESIDENCE_ERROR);
ncvAssertReturn(h_HaarStages.memType() != NCVMemoryTypeDevice, NCV_MEM_RESIDENCE_ERROR);
ncvAssertReturn(gpuAllocator.isInitialized() && cpuAllocator.isInitialized(), NCV_ALLOCATOR_NOT_INITIALIZED);
ncvAssertReturn((d_srcImg.ptr() != NULL && d_dstRects.ptr() != NULL &&
h_HaarStages.ptr() != NULL && d_HaarStages.ptr() != NULL && d_HaarNodes.ptr() != NULL &&
d_HaarFeatures.ptr() != NULL) || gpuAllocator.isCounting(), NCV_NULL_PTR);
@ -1540,13 +1557,17 @@ NCVStatus ncvDetectObjectsMultiScale_device(NCVMatrix<Ncv8u> &d_srcImg,
d_srcImg.width() >= srcRoi.width && d_srcImg.height() >= srcRoi.height &&
srcRoi.width >= minObjSize.width && srcRoi.height >= minObjSize.height &&
d_dstRects.length() >= 1, NCV_DIMENSIONS_INVALID);
ncvAssertReturn(scaleStep > 1.0f, NCV_INVALID_SCALE);
ncvAssertReturn(d_HaarStages.length() >= haar.NumStages &&
d_HaarNodes.length() >= haar.NumClassifierTotalNodes &&
d_HaarFeatures.length() >= haar.NumFeatures &&
d_HaarStages.length() == h_HaarStages.length() &&
haar.NumClassifierRootNodes <= haar.NumClassifierTotalNodes, NCV_DIMENSIONS_INVALID);
ncvAssertReturn(haar.bNeedsTiltedII == false, NCV_NOIMPL_HAAR_TILTED_FEATURES);
ncvAssertReturn(pixelStep == 1 || pixelStep == 2, NCV_HAAR_INVALID_PIXEL_STEP);
//TODO: set NPP active stream to cuStream
@ -1557,8 +1578,8 @@ NCVStatus ncvDetectObjectsMultiScale_device(NCVMatrix<Ncv8u> &d_srcImg,
Ncv32u integralWidth = d_srcImg.width() + 1;
Ncv32u integralHeight = d_srcImg.height() + 1;
NCVMatrixAlloc<Ncv32u> d_integralImage(gpuAllocator, integralWidth, integralHeight);
ncvAssertReturn(d_integralImage.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
NCVMatrixAlloc<Ncv32u> integral(gpuAllocator, integralWidth, integralHeight);
ncvAssertReturn(integral.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
NCVMatrixAlloc<Ncv64u> d_sqIntegralImage(gpuAllocator, integralWidth, integralHeight);
ncvAssertReturn(d_sqIntegralImage.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
@ -1589,7 +1610,7 @@ NCVStatus ncvDetectObjectsMultiScale_device(NCVMatrix<Ncv8u> &d_srcImg,
NCV_SKIP_COND_BEGIN
nppStat = nppiStIntegral_8u32u_C1R(d_srcImg.ptr(), d_srcImg.pitch(),
d_integralImage.ptr(), d_integralImage.pitch(),
integral.ptr(), integral.pitch(),
NcvSize32u(d_srcImg.width(), d_srcImg.height()),
d_tmpIIbuf.ptr(), szTmpBufIntegral, devProp);
ncvAssertReturnNcvStat(nppStat);
@ -1676,7 +1697,7 @@ NCVStatus ncvDetectObjectsMultiScale_device(NCVMatrix<Ncv8u> &d_srcImg,
NCV_SKIP_COND_BEGIN
nppStat = nppiStDecimate_32u_C1R(
d_integralImage.ptr(), d_integralImage.pitch(),
integral.ptr(), integral.pitch(),
d_scaledIntegralImage.ptr(), d_scaledIntegralImage.pitch(),
srcIIRoi, scale, true);
ncvAssertReturnNcvStat(nppStat);

19
modules/gpu/src/nvidia/NPP_staging/NPP_staging.cu
View File

@ -95,11 +95,6 @@ inline __device__ T warpScanInclusive(T idata, volatile T *s_Data)
pos += K_WARP_SIZE;
s_Data[pos] = idata;
//for(Ncv32u offset = 1; offset < K_WARP_SIZE; offset <<= 1)
//{
// s_Data[pos] += s_Data[pos - offset];
//}
s_Data[pos] += s_Data[pos - 1];
s_Data[pos] += s_Data[pos - 2];
s_Data[pos] += s_Data[pos - 4];
@ -1447,14 +1442,14 @@ NCVStatus compactVector_32u_device(Ncv32u *d_src, Ncv32u srcLen,
//adjust hierarchical partial sums
for (Ncv32s i=(Ncv32s)partSumNums.size()-3; i>=0; i--)
{
dim3 grid(partSumNums[i+1]);
if (grid.x > 65535)
dim3 grid_local(partSumNums[i+1]);
if (grid_local.x > 65535)
{
grid.y = (grid.x + 65534) / 65535;
grid.x = 65535;
grid_local.y = (grid_local.x + 65534) / 65535;
grid_local.x = 65535;
}
removePass2Adjust
<<<grid, block, 0, nppStGetActiveCUDAstream()>>>
<<<grid_local, block, 0, nppStGetActiveCUDAstream()>>>
(d_hierSums.ptr() + partSumOffsets[i], partSumNums[i],
d_hierSums.ptr() + partSumOffsets[i+1]);
@ -1463,10 +1458,10 @@ NCVStatus compactVector_32u_device(Ncv32u *d_src, Ncv32u srcLen,
}
else
{
dim3 grid(partSumNums[1]);
dim3 grid_local(partSumNums[1]);
removePass1Scan
<true, false>
<<<grid, block, 0, nppStGetActiveCUDAstream()>>>
<<<grid_local, block, 0, nppStGetActiveCUDAstream()>>>
(d_src, srcLen,
d_hierSums.ptr(),
NULL, elemRemove);

7
modules/gpu/src/nvidia/core/NCVColorConversion.hpp
View File

@ -39,11 +39,14 @@
//
//M*/
// this file does not contain any used code.
#ifndef _ncv_color_conversion_hpp_
#define _ncv_color_conversion_hpp_
#include "NCVPixelOperations.hpp"
#if 0
enum NCVColorSpace
{
NCVColorSpaceGray,
@ -71,8 +74,7 @@ static void _pixColorConv(const Tin &pixIn, Tout &pixOut)
}};
template<NCVColorSpace CSin, NCVColorSpace CSout, typename Tin, typename Tout>
static
NCVStatus _ncvColorConv_host(const NCVMatrix<Tin> &h_imgIn,
static NCVStatus _ncvColorConv_host(const NCVMatrix<Tin> &h_imgIn,
const NCVMatrix<Tout> &h_imgOut)
{
ncvAssertReturn(h_imgIn.size() == h_imgOut.size(), NCV_DIMENSIONS_INVALID);
@ -92,5 +94,6 @@ NCVStatus _ncvColorConv_host(const NCVMatrix<Tin> &h_imgIn,
NCV_SKIP_COND_END
return NCV_SUCCESS;
}
#endif
#endif //_ncv_color_conversion_hpp_

4
modules/gpu/test/nvidia/TestHypothesesFilter.cpp
View File

@ -13,10 +13,10 @@
#include "NCVHaarObjectDetection.hpp"
TestHypothesesFilter::TestHypothesesFilter(std::string testName, NCVTestSourceProvider<Ncv32u> &src_,
TestHypothesesFilter::TestHypothesesFilter(std::string testName_, NCVTestSourceProvider<Ncv32u> &src_,
Ncv32u numDstRects_, Ncv32u minNeighbors_, Ncv32f eps_)
:
NCVTestProvider(testName),
NCVTestProvider(testName_),
src(src_),
numDstRects(numDstRects_),
minNeighbors(minNeighbors_),

4
modules/gpu/test/nvidia/TestResize.cpp
View File

@ -15,10 +15,10 @@
template <class T>
TestResize<T>::TestResize(std::string testName, NCVTestSourceProvider<T> &src_,
TestResize<T>::TestResize(std::string testName_, NCVTestSourceProvider<T> &src_,
Ncv32u width_, Ncv32u height_, Ncv32u scaleFactor_, NcvBool bTextureCache_)
:
NCVTestProvider(testName),
NCVTestProvider(testName_),
src(src_),
width(width_),
height(height_),