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very preliminary port of SURF to T-API (compiles but certainly does not work)

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This commit is contained in:
Vadim Pisarevsky
2014-02-04 03:21:03 +04:00
parent 652a0bd5ce
commit 8d5e952263
8 changed files with 522 additions and 711 deletions
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2
modules/features2d/include/opencv2/features2d.hpp
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@@ -235,7 +235,7 @@ public:
// Compute the BRISK features and descriptors on an image
void operator()( InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints,
OutputArray descriptors, bool useProvidedKeypoints=false ) const;
OutputArray descriptors, bool useProvidedKeypoints=false ) const;
AlgorithmInfo* info() const;

1
modules/nonfree/include/opencv2/nonfree/features2d.hpp
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@@ -142,7 +142,6 @@ public:
CV_PROP_RW bool upright;
protected:
void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask = noArray() ) const;
void computeImpl( const Mat& image, std::vector<KeyPoint>& keypoints, Mat& descriptors ) const;
};

126
modules/nonfree/include/opencv2/nonfree/ocl.hpp
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@@ -1,126 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_NONFREE_OCL_HPP__
#define __OPENCV_NONFREE_OCL_HPP__
#include "opencv2/ocl.hpp"
namespace cv
{
namespace ocl
{
//! Speeded up robust features, port from CUDA module.
////////////////////////////////// SURF //////////////////////////////////////////
class CV_EXPORTS SURF_OCL
{
public:
enum KeypointLayout
{
X_ROW = 0,
Y_ROW,
LAPLACIAN_ROW,
OCTAVE_ROW,
SIZE_ROW,
ANGLE_ROW,
HESSIAN_ROW,
ROWS_COUNT
};
//! the default constructor
SURF_OCL();
//! the full constructor taking all the necessary parameters
explicit SURF_OCL(double _hessianThreshold, int _nOctaves = 4,
int _nOctaveLayers = 2, bool _extended = false, float _keypointsRatio = 0.01f, bool _upright = false);
//! returns the descriptor size in float's (64 or 128)
int descriptorSize() const;
//! returns the default norm type
int defaultNorm() const;
//! upload host keypoints to device memory
void uploadKeypoints(const std::vector<cv::KeyPoint> &keypoints, oclMat &keypointsocl);
//! download keypoints from device to host memory
void downloadKeypoints(const oclMat &keypointsocl, std::vector<KeyPoint> &keypoints);
//! download descriptors from device to host memory
void downloadDescriptors(const oclMat &descriptorsocl, std::vector<float> &descriptors);
//! finds the keypoints using fast hessian detector used in SURF
//! supports CV_8UC1 images
//! keypoints will have nFeature cols and 6 rows
//! keypoints.ptr<float>(X_ROW)[i] will contain x coordinate of i'th feature
//! keypoints.ptr<float>(Y_ROW)[i] will contain y coordinate of i'th feature
//! keypoints.ptr<float>(LAPLACIAN_ROW)[i] will contain laplacian sign of i'th feature
//! keypoints.ptr<float>(OCTAVE_ROW)[i] will contain octave of i'th feature
//! keypoints.ptr<float>(SIZE_ROW)[i] will contain size of i'th feature
//! keypoints.ptr<float>(ANGLE_ROW)[i] will contain orientation of i'th feature
//! keypoints.ptr<float>(HESSIAN_ROW)[i] will contain response of i'th feature
void operator()(const oclMat &img, const oclMat &mask, oclMat &keypoints);
//! finds the keypoints and computes their descriptors.
//! Optionally it can compute descriptors for the user-provided keypoints and recompute keypoints direction
void operator()(const oclMat &img, const oclMat &mask, oclMat &keypoints, oclMat &descriptors,
bool useProvidedKeypoints = false);
void operator()(const oclMat &img, const oclMat &mask, std::vector<KeyPoint> &keypoints);
void operator()(const oclMat &img, const oclMat &mask, std::vector<KeyPoint> &keypoints, oclMat &descriptors,
bool useProvidedKeypoints = false);
void operator()(const oclMat &img, const oclMat &mask, std::vector<KeyPoint> &keypoints, std::vector<float> &descriptors,
bool useProvidedKeypoints = false);
void releaseMemory();
// SURF parameters
float hessianThreshold;
int nOctaves;
int nOctaveLayers;
bool extended;
bool upright;
//! max keypoints = min(keypointsRatio * img.size().area(), 65535)
float keypointsRatio;
oclMat sum, mask1, maskSum, intBuffer;
oclMat det, trace;
oclMat maxPosBuffer;
};
}
}
#endif //__OPENCV_NONFREE_OCL_HPP__

66
modules/nonfree/src/opencl/surf.cl
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@@ -45,6 +45,12 @@
//
//M*/
// The number of degrees between orientation samples in calcOrientation
#define ORI_SEARCH_INC 5
// The local size of the calcOrientation kernel
#define ORI_LOCAL_SIZE (360 / ORI_SEARCH_INC)
// specialized for non-image2d_t supported platform, intel HD4000, for example
#ifdef DISABLE_IMAGE2D
#define IMAGE_INT32 __global uint *
@@ -175,7 +181,7 @@ F calcAxisAlignedDerivative(
}
//calculate targeted layer per-pixel determinant and trace with an integral image
__kernel void icvCalcLayerDetAndTrace(
__kernel void SURF_calcLayerDetAndTrace(
IMAGE_INT32 sumTex, // input integral image
__global float * det, // output Determinant
__global float * trace, // output trace
@@ -338,7 +344,7 @@ bool within_check(IMAGE_INT32 maskSumTex, int sum_i, int sum_j, int size, int ro
// Non-maximal suppression to further filtering the candidates from previous step
__kernel
void icvFindMaximaInLayer_withmask(
void SURF_findMaximaInLayerWithMask(
__global const float * det,
__global const float * trace,
__global int4 * maxPosBuffer,
@@ -466,7 +472,7 @@ void icvFindMaximaInLayer_withmask(
}
__kernel
void icvFindMaximaInLayer(
void SURF_findMaximaInLayer(
__global float * det,
__global float * trace,
__global int4 * maxPosBuffer,
@@ -624,7 +630,7 @@ inline bool solve3x3_float(const float4 *A, const float *b, float *x)
////////////////////////////////////////////////////////////////////////
// INTERPOLATION
__kernel
void icvInterpolateKeypoint(
void SURF_interpolateKeypoint(
__global const float * det,
__global const int4 * maxPosBuffer,
__global float * keypoints,
@@ -829,7 +835,7 @@ void reduce_32_sum(volatile __local float * data, volatile float* partial_reduc
}
__kernel
void icvCalcOrientation(
void SURF_calcOrientation(
IMAGE_INT32 sumTex,
__global float * keypoints,
int keypoints_step,
@@ -995,18 +1001,17 @@ void icvCalcOrientation(
}
__kernel
void icvSetUpright(
void SURF_setUpright(
__global float * keypoints,
int keypoints_step,
int nFeatures
)
int keypoints_step, int keypoints_offset,
int rows, int cols )
{
int i = get_global_id(0);
keypoints_step /= sizeof(*keypoints);
__global float* featureDir = keypoints + ANGLE_ROW * keypoints_step;
if(get_global_id(0) <= nFeatures)
if(i < cols)
{
featureDir[get_global_id(0)] = 270.0f;
keypoints[mad24(keypoints_step, ANGLE_ROW, i)] = 270.f;
}
}
@@ -1162,6 +1167,7 @@ void calc_dx_dy(
s_dy_bin[tid] = vy;
}
}
void reduce_sum25(
volatile __local float* sdata1,
volatile __local float* sdata2,
@@ -1225,16 +1231,14 @@ void reduce_sum25(
}
__kernel
void compute_descriptors64(
void SURF_computeDescriptors64(
IMAGE_INT8 imgTex,
int img_step, int img_offset,
int rows, int cols,
__global const float* keypoints,
int keypoints_step, int keypoints_offset,
__global float * descriptors,
__global const float * keypoints,
int descriptors_step,
int keypoints_step,
int rows,
int cols,
int img_step
)
int descriptors_step, int descriptors_offset)
{
descriptors_step /= sizeof(float);
keypoints_step /= sizeof(float);
@@ -1279,17 +1283,16 @@ void compute_descriptors64(
}
}
}
__kernel
void compute_descriptors128(
void SURF_computeDescriptors128(
IMAGE_INT8 imgTex,
__global float * descriptors,
__global float * keypoints,
int descriptors_step,
int keypoints_step,
int rows,
int cols,
int img_step
)
int img_step, int img_offset,
int rows, int cols,
__global const float* keypoints,
int keypoints_step, int keypoints_offset,
__global float* descriptors,
int descriptors_step, int descriptors_offset)
{
descriptors_step /= sizeof(*descriptors);
keypoints_step /= sizeof(*keypoints);
@@ -1483,7 +1486,7 @@ void reduce_sum64(volatile __local float* smem, int tid)
}
__kernel
void normalize_descriptors128(__global float * descriptors, int descriptors_step)
void SURF_normalizeDescriptors128(__global float * descriptors, int descriptors_step)
{
descriptors_step /= sizeof(*descriptors);
// no need for thread ID
@@ -1509,8 +1512,9 @@ void normalize_descriptors128(__global float * descriptors, int descriptors_step
// normalize and store in output
descriptor_base[get_local_id(0)] = lookup / len;
}
__kernel
void normalize_descriptors64(__global float * descriptors, int descriptors_step)
void SURF_normalizeDescriptors64(__global float * descriptors, int descriptors_step)
{
descriptors_step /= sizeof(*descriptors);
// no need for thread ID

5
modules/nonfree/src/precomp.hpp
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@@ -60,11 +60,6 @@
# include "opencv2/cudaarithm.hpp"
#endif
#ifdef HAVE_OPENCV_OCL
# include "opencv2/nonfree/ocl.hpp"
# include "opencv2/ocl/private/util.hpp"
#endif
#include "opencv2/core/private.hpp"
#endif

38
modules/nonfree/src/surf.cpp
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@@ -108,6 +108,7 @@ Modifications by Ian Mahon
*/
#include "precomp.hpp"
#include "surf.hpp"
namespace cv
{
@@ -897,11 +898,42 @@ void SURF::operator()(InputArray _img, InputArray _mask,
OutputArray _descriptors,
bool useProvidedKeypoints) const
{
Mat img = _img.getMat(), mask = _mask.getMat(), mask1, sum, msum;
int imgtype = _img.type(), imgcn = CV_MAT_CN(imgtype);
bool doDescriptors = _descriptors.needed();
CV_Assert(!img.empty() && img.depth() == CV_8U);
if( img.channels() > 1 )
CV_Assert(!_img.empty() && CV_MAT_DEPTH(imgtype) == CV_8U && (imgcn == 1 || imgcn == 3 || imgcn == 4));
CV_Assert(_descriptors.needed() && !useProvidedKeypoints);
if( ocl::useOpenCL() )
{
SURF_OCL ocl_surf;
UMat gpu_kpt;
bool ok = ocl_surf.init(this);
if( ok )
{
if( !_descriptors.needed() )
{
ok = ocl_surf.detect(_img, _mask, gpu_kpt);
}
else
{
if(useProvidedKeypoints)
ocl_surf.uploadKeypoints(keypoints, gpu_kpt);
ok = ocl_surf.detectAndCompute(_img, _mask, gpu_kpt, _descriptors, useProvidedKeypoints);
}
}
if( ok )
{
if(!useProvidedKeypoints)
ocl_surf.downloadKeypoints(gpu_kpt, keypoints);
return;
}
}
Mat img = _img.getMat(), mask = _mask.getMat(), mask1, sum, msum;
if( imgcn > 1 )
cvtColor(img, img, COLOR_BGR2GRAY);
CV_Assert(mask.empty() || (mask.type() == CV_8U && mask.size() == img.size()));

123
modules/nonfree/src/surf.hpp Normal file
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@@ -0,0 +1,123 @@
///////////// see LICENSE.txt in the OpenCV root directory //////////////
#ifndef __OPENCV_NONFREE_SURF_HPP__
#define __OPENCV_NONFREE_SURF_HPP__
namespace cv
{
//! Speeded up robust features, port from CUDA module.
////////////////////////////////// SURF //////////////////////////////////////////
class SURF_OCL
{
public:
enum KeypointLayout
{
X_ROW = 0,
Y_ROW,
LAPLACIAN_ROW,
OCTAVE_ROW,
SIZE_ROW,
ANGLE_ROW,
HESSIAN_ROW,
ROWS_COUNT
};
//! the full constructor taking all the necessary parameters
SURF_OCL();
bool init(const SURF* params);
//! returns the descriptor size in float's (64 or 128)
int descriptorSize() const { return params->extended ? 128 : 64; }
void uploadKeypoints(const std::vector<KeyPoint> &keypoints, UMat &keypointsGPU);
void downloadKeypoints(const UMat &keypointsGPU, std::vector<KeyPoint> &keypoints);
//! finds the keypoints using fast hessian detector used in SURF
//! supports CV_8UC1 images
//! keypoints will have nFeature cols and 6 rows
//! keypoints.ptr<float>(X_ROW)[i] will contain x coordinate of i'th feature
//! keypoints.ptr<float>(Y_ROW)[i] will contain y coordinate of i'th feature
//! keypoints.ptr<float>(LAPLACIAN_ROW)[i] will contain laplacian sign of i'th feature
//! keypoints.ptr<float>(OCTAVE_ROW)[i] will contain octave of i'th feature
//! keypoints.ptr<float>(SIZE_ROW)[i] will contain size of i'th feature
//! keypoints.ptr<float>(ANGLE_ROW)[i] will contain orientation of i'th feature
//! keypoints.ptr<float>(HESSIAN_ROW)[i] will contain response of i'th feature
bool detect(InputArray img, InputArray mask, UMat& keypoints);
//! finds the keypoints and computes their descriptors.
//! Optionally it can compute descriptors for the user-provided keypoints and recompute keypoints direction
bool detectAndCompute(InputArray img, InputArray mask, UMat& keypoints,
OutputArray descriptors, bool useProvidedKeypoints = false);
protected:
bool setImage(InputArray img, InputArray mask);
// kernel callers declarations
bool calcLayerDetAndTrace(UMat &det, UMat &trace, int octave, int layer_rows);
bool findMaximaInLayer(const UMat &det, const UMat &trace, UMat &maxPosBuffer,
UMat &maxCounter, int counterOffset,
int octave, int layer_rows, int layer_cols);
bool interpolateKeypoint(const UMat &det, const UMat &maxPosBuffer, int maxCounter,
UMat &keypoints, UMat &counters, int octave, int layer_rows, int maxFeatures);
bool calcOrientation(UMat &keypoints);
bool setUpRight(UMat &keypoints);
bool computeDescriptors(const UMat &keypoints, OutputArray descriptors);
bool detectKeypoints(UMat &keypoints);
const SURF* params;
int refcount;
//! max keypoints = min(keypointsRatio * img.size().area(), 65535)
UMat sum, mask1, maskSum, intBuffer;
UMat det, trace;
UMat maxPosBuffer;
int img_cols, img_rows;
int maxCandidates;
int maxFeatures;
UMat img, counters;
// texture buffers
ocl::Image2D imgTex, sumTex, maskSumTex;
bool haveImageSupport;
int status;
ocl::Kernel kerCalcDetTrace, kerFindMaxima, kerFindMaximaMask, kerInterp;
ocl::Kernel kerUpRight, kerOri, kerCalcDesc64, kerCalcDesc128, kerNormDesc64, kerNormDesc128;
};
/*
template<typename _Tp> void copyVectorToUMat(const std::vector<_Tp>& v, UMat& um)
{
if(v.empty())
um.release();
else
Mat(1, (int)(v.size()*sizeof(v[0])), CV_8U, (void*)&v[0]).copyTo(um);
}
template<typename _Tp> void copyUMatToVector(const UMat& um, std::vector<_Tp>& v)
{
if(um.empty())
v.clear();
else
{
size_t sz = um.total()*um.elemSize();
CV_Assert(um.isContinuous() && (sz % sizeof(_Tp) == 0));
v.resize(sz/sizeof(_Tp));
Mat m(um.size(), um.type(), &v[0]);
um.copyTo(m);
}
}*/
}
#endif

872
modules/nonfree/src/surf.ocl.cpp
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@@ -43,42 +43,30 @@
//
//M*/
#include "precomp.hpp"
#include "surf.hpp"
#ifdef HAVE_OPENCV_OCL
#include <cstdio>
#include <sstream>
#include "opencl_kernels.hpp"
using namespace cv;
using namespace cv::ocl;
static ProgramEntry surfprog = cv::ocl::nonfree::surf;
namespace cv
{
namespace ocl
{
// The number of degrees between orientation samples in calcOrientation
const static int ORI_SEARCH_INC = 5;
// The local size of the calcOrientation kernel
const static int ORI_LOCAL_SIZE = (360 / ORI_SEARCH_INC);
static void openCLExecuteKernelSURF(Context *clCxt, const cv::ocl::ProgramEntry* source, String kernelName, size_t globalThreads[3],
size_t localThreads[3], std::vector< std::pair<size_t, const void *> > &args, int channels, int depth)
{
std::stringstream optsStr;
optsStr << "-D ORI_LOCAL_SIZE=" << ORI_LOCAL_SIZE << " ";
optsStr << "-D ORI_SEARCH_INC=" << ORI_SEARCH_INC << " ";
cl_kernel kernel;
kernel = openCLGetKernelFromSource(clCxt, source, kernelName, optsStr.str().c_str());
size_t wave_size = queryWaveFrontSize(kernel);
CV_Assert(clReleaseKernel(kernel) == CL_SUCCESS);
optsStr << "-D WAVE_SIZE=" << wave_size;
openCLExecuteKernel(clCxt, source, kernelName, globalThreads, localThreads, args, channels, depth, optsStr.str().c_str());
}
enum { ORI_SEARCH_INC=5, ORI_LOCAL_SIZE=(360 / ORI_SEARCH_INC) };
}
}
/*static void openCLExecuteKernelSURF(Context2 *clCxt, const ProgramEntry* source, String kernelName, size_t globalThreads[3],
size_t localThreads[3], std::vector< std::pair<size_t, const void *> > &args, int channels, int depth)
{
std::stringstream optsStr;
optsStr << "-D ORI_LOCAL_SIZE=" << ORI_LOCAL_SIZE << " ";
optsStr << "-D ORI_SEARCH_INC=" << ORI_SEARCH_INC << " ";
cl_kernel kernel;
kernel = openCLGetKernelFromSource(clCxt, source, kernelName, optsStr.str().c_str());
size_t wave_size = queryWaveFrontSize(kernel);
CV_Assert(clReleaseKernel(kernel) == CL_SUCCESS);
optsStr << "-D WAVE_SIZE=" << wave_size;
openCLExecuteKernel(clCxt, source, kernelName, globalThreads, localThreads, args, channels, depth, optsStr.str().c_str());
}*/
static inline int calcSize(int octave, int layer)
{
@@ -96,223 +84,220 @@ static inline int calcSize(int octave, int layer)
}
class SURF_OCL_Invoker
SURF_OCL::SURF_OCL()
{
public:
// facilities
void bindImgTex(const oclMat &img, cl_mem &texture);
//void loadGlobalConstants(int maxCandidates, int maxFeatures, int img_rows, int img_cols, int nOctaveLayers, float hessianThreshold);
//void loadOctaveConstants(int octave, int layer_rows, int layer_cols);
// kernel callers declarations
void icvCalcLayerDetAndTrace_gpu(oclMat &det, oclMat &trace, int octave, int nOctaveLayers, int layer_rows);
void icvFindMaximaInLayer_gpu(const oclMat &det, const oclMat &trace, oclMat &maxPosBuffer, oclMat &maxCounter, int counterOffset,
int octave, bool use_mask, int nLayers, int layer_rows, int layer_cols);
void icvInterpolateKeypoint_gpu(const oclMat &det, const oclMat &maxPosBuffer, int maxCounter,
oclMat &keypoints, oclMat &counters, int octave, int layer_rows, int maxFeatures);
void icvCalcOrientation_gpu(const oclMat &keypoints, int nFeatures);
void icvSetUpright_gpu(const oclMat &keypoints, int nFeatures);
void compute_descriptors_gpu(const oclMat &descriptors, const oclMat &keypoints, int nFeatures);
// end of kernel callers declarations
SURF_OCL_Invoker(SURF_OCL &surf, const oclMat &img, const oclMat &mask) :
surf_(surf),
img_cols(img.cols), img_rows(img.rows),
use_mask(!mask.empty()), counters(oclMat()),
imgTex(NULL), sumTex(NULL), maskSumTex(NULL), _img(img)
{
CV_Assert(!img.empty() && img.type() == CV_8UC1);
CV_Assert(mask.empty() || (mask.size() == img.size() && mask.type() == CV_8UC1));
CV_Assert(surf_.nOctaves > 0 && surf_.nOctaveLayers > 0);
const int min_size = calcSize(surf_.nOctaves - 1, 0);
CV_Assert(img_rows - min_size >= 0);
CV_Assert(img_cols - min_size >= 0);
const int layer_rows = img_rows >> (surf_.nOctaves - 1);
const int layer_cols = img_cols >> (surf_.nOctaves - 1);
const int min_margin = ((calcSize((surf_.nOctaves - 1), 2) >> 1) >> (surf_.nOctaves - 1)) + 1;
CV_Assert(layer_rows - 2 * min_margin > 0);
CV_Assert(layer_cols - 2 * min_margin > 0);
maxFeatures = std::min(static_cast<int>(img.size().area() * surf.keypointsRatio), 65535);
maxCandidates = std::min(static_cast<int>(1.5 * maxFeatures), 65535);
CV_Assert(maxFeatures > 0);
counters.create(1, surf_.nOctaves + 1, CV_32SC1);
counters.setTo(Scalar::all(0));
integral(img, surf_.sum);
bindImgTex(img, imgTex);
bindImgTex(surf_.sum, sumTex);
finish();
maskSumTex = 0;
if (use_mask)
{
CV_Error(Error::StsBadFunc, "Masked SURF detector is not implemented yet");
//!FIXME
// temp fix for missing min overload
//oclMat temp(mask.size(), mask.type());
//temp.setTo(Scalar::all(1.0));
////cv::ocl::min(mask, temp, surf_.mask1); ///////// disable this
//integral(surf_.mask1, surf_.maskSum);
//bindImgTex(surf_.maskSum, maskSumTex);
}
}
void detectKeypoints(oclMat &keypoints)
{
// create image pyramid buffers
// different layers have same sized buffers, but they are sampled from Gaussian kernel.
ensureSizeIsEnough(img_rows * (surf_.nOctaveLayers + 2), img_cols, CV_32FC1, surf_.det);
ensureSizeIsEnough(img_rows * (surf_.nOctaveLayers + 2), img_cols, CV_32FC1, surf_.trace);
ensureSizeIsEnough(1, maxCandidates, CV_32SC4, surf_.maxPosBuffer);
ensureSizeIsEnough(SURF_OCL::ROWS_COUNT, maxFeatures, CV_32FC1, keypoints);
keypoints.setTo(Scalar::all(0));
for (int octave = 0; octave < surf_.nOctaves; ++octave)
{
const int layer_rows = img_rows >> octave;
const int layer_cols = img_cols >> octave;
//loadOctaveConstants(octave, layer_rows, layer_cols);
icvCalcLayerDetAndTrace_gpu(surf_.det, surf_.trace, octave, surf_.nOctaveLayers, layer_rows);
icvFindMaximaInLayer_gpu(surf_.det, surf_.trace, surf_.maxPosBuffer, counters, 1 + octave,
octave, use_mask, surf_.nOctaveLayers, layer_rows, layer_cols);
int maxCounter = ((Mat)counters).at<int>(1 + octave);
maxCounter = std::min(maxCounter, static_cast<int>(maxCandidates));
if (maxCounter > 0)
{
icvInterpolateKeypoint_gpu(surf_.det, surf_.maxPosBuffer, maxCounter,
keypoints, counters, octave, layer_rows, maxFeatures);
}
}
int featureCounter = Mat(counters).at<int>(0);
featureCounter = std::min(featureCounter, static_cast<int>(maxFeatures));
keypoints.cols = featureCounter;
if (surf_.upright)
{
//keypoints.row(SURF_OCL::ANGLE_ROW).setTo(Scalar::all(90.0));
setUpright(keypoints);
}
else
{
findOrientation(keypoints);
}
}
void setUpright(oclMat &keypoints)
{
const int nFeatures = keypoints.cols;
if(nFeatures > 0)
{
icvSetUpright_gpu(keypoints, keypoints.cols);
}
}
void findOrientation(oclMat &keypoints)
{
const int nFeatures = keypoints.cols;
if (nFeatures > 0)
{
icvCalcOrientation_gpu(keypoints, nFeatures);
}
}
void computeDescriptors(const oclMat &keypoints, oclMat &descriptors, int descriptorSize)
{
const int nFeatures = keypoints.cols;
if (nFeatures > 0)
{
ensureSizeIsEnough(nFeatures, descriptorSize, CV_32F, descriptors);
compute_descriptors_gpu(descriptors, keypoints, nFeatures);
}
}
~SURF_OCL_Invoker()
{
if(imgTex)
openCLFree(imgTex);
if(sumTex)
openCLFree(sumTex);
if(maskSumTex)
openCLFree(maskSumTex);
}
private:
SURF_OCL &surf_;
int img_cols, img_rows;
bool use_mask;
int maxCandidates;
int maxFeatures;
oclMat counters;
// texture buffers
cl_mem imgTex;
cl_mem sumTex;
cl_mem maskSumTex;
const oclMat _img; // make a copy for non-image2d_t supported platform
SURF_OCL_Invoker &operator= (const SURF_OCL_Invoker &right)
{
(*this) = right;
return *this;
} // remove warning C4512
};
cv::ocl::SURF_OCL::SURF_OCL()
{
hessianThreshold = 100.0f;
extended = true;
nOctaves = 4;
nOctaveLayers = 2;
keypointsRatio = 0.01f;
upright = false;
img_cols = img_rows = maxCandidates = maxFeatures = 0;
haveImageSupport = false;
status = -1;
}
cv::ocl::SURF_OCL::SURF_OCL(double _threshold, int _nOctaves, int _nOctaveLayers, bool _extended, float _keypointsRatio, bool _upright)
bool SURF_OCL::init(const SURF* p)
{
hessianThreshold = saturate_cast<float>(_threshold);
extended = _extended;
nOctaves = _nOctaves;
nOctaveLayers = _nOctaveLayers;
keypointsRatio = _keypointsRatio;
upright = _upright;
params = p;
if(status < 0)
{
status = 0;
if(ocl::haveOpenCL())
{
const ocl::Device& dev = ocl::Device::getDefault();
if( dev.type() == ocl::Device::TYPE_CPU )
return false;
haveImageSupport = dev.imageSupport();
String opts = haveImageSupport ? "-D DISABLE_IMAGE2D" : "";
if( kerCalcDetTrace.create("SURF_calcLayerDetAndTrace", ocl::nonfree::surf_oclsrc, opts) &&
kerFindMaxima.create("SURF_findMaximaInLayer", ocl::nonfree::surf_oclsrc, opts) &&
kerFindMaximaMask.create("SURF_findMaximaInLayerWithMask", ocl::nonfree::surf_oclsrc, opts) &&
kerInterp.create("SURF_interpolateKeypoint", ocl::nonfree::surf_oclsrc, opts) &&
kerUpRight.create("SURF_setUpRight", ocl::nonfree::surf_oclsrc, opts) &&
kerOri.create("SURF_calcOrientation", ocl::nonfree::surf_oclsrc, opts) &&
kerCalcDesc64.create("SURF_computeDescriptors64", ocl::nonfree::surf_oclsrc, opts) &&
kerCalcDesc128.create("SURF_computeDescriptors128", ocl::nonfree::surf_oclsrc, opts) &&
kerNormDesc64.create("SURF_normalizeDescriptors64", ocl::nonfree::surf_oclsrc, opts) &&
kerNormDesc128.create("SURF_normalizeDescriptors128", ocl::nonfree::surf_oclsrc, opts))
status = 1;
}
}
return status > 0;
}
int cv::ocl::SURF_OCL::descriptorSize() const
bool SURF_OCL::setImage(InputArray _img, InputArray _mask)
{
return extended ? 128 : 64;
if( status <= 0 )
return false;
CV_Assert(!_img.empty() && _img.type() == CV_8UC1);
CV_Assert(_mask.empty() || (_mask.size() == _img.size() && _mask.type() == CV_8UC1));
CV_Assert(params && params->nOctaves > 0 && params->nOctaveLayers > 0);
int min_size = calcSize(params->nOctaves - 1, 0);
Size sz = _img.size();
img_cols = sz.width;
img_rows = sz.height;
CV_Assert(img_rows >= min_size && img_cols >= min_size);
const int layer_rows = img_rows >> (params->nOctaves - 1);
const int layer_cols = img_cols >> (params->nOctaves - 1);
const int min_margin = ((calcSize((params->nOctaves - 1), 2) >> 1) >> (params->nOctaves - 1)) + 1;
CV_Assert(layer_rows - 2 * min_margin > 0);
CV_Assert(layer_cols - 2 * min_margin > 0);
maxFeatures = std::min(static_cast<int>(img_cols*img_rows * 0.01f), 65535);
maxCandidates = std::min(static_cast<int>(1.5 * maxFeatures), 65535);
CV_Assert(maxFeatures > 0);
counters.create(1, params->nOctaves + 1, CV_32SC1);
counters.setTo(Scalar::all(0));
img.release();
if(_img.isUMat())
img = _img.getUMat();
else
_img.copyTo(img);
integral(img, sum);
if(haveImageSupport)
{
imgTex = ocl::Image2D(img);
sumTex = ocl::Image2D(sum);
}
maskSumTex = ocl::Image2D();
if(!_mask.empty())
{
CV_Error(Error::StsBadFunc, "Masked SURF detector is not implemented yet");
}
return true;
}
int cv::ocl::SURF_OCL::defaultNorm() const
bool SURF_OCL::detectKeypoints(UMat &keypoints)
{
return NORM_L2;
// create image pyramid buffers
// different layers have same sized buffers, but they are sampled from Gaussian kernel.
det.create(img_rows * (params->nOctaveLayers + 2), img_cols, CV_32F);
trace.create(img_rows * (params->nOctaveLayers + 2), img_cols, CV_32FC1);
maxPosBuffer.create(1, maxCandidates, CV_32SC4);
keypoints.create(SURF_OCL::ROWS_COUNT, maxFeatures, CV_32F);
keypoints.setTo(Scalar::all(0));
Mat cpuCounters;
for (int octave = 0; octave < params->nOctaves; ++octave)
{
const int layer_rows = img_rows >> octave;
const int layer_cols = img_cols >> octave;
if(!calcLayerDetAndTrace(det, trace, octave, layer_rows))
return false;
if(!findMaximaInLayer(det, trace, maxPosBuffer, counters, 1 + octave, octave,
layer_rows, layer_cols))
return false;
cpuCounters = counters.getMat(ACCESS_READ);
int maxCounter = cpuCounters.at<int>(1 + octave);
maxCounter = std::min(maxCounter, maxCandidates);
cpuCounters.release();
if (maxCounter > 0)
{
if(!interpolateKeypoint(det, maxPosBuffer, maxCounter, keypoints,
counters, octave, layer_rows, maxFeatures))
return false;
}
}
cpuCounters = counters.getMat(ACCESS_READ);
int featureCounter = cpuCounters.at<int>(0);
featureCounter = std::min(featureCounter, maxFeatures);
cpuCounters.release();
keypoints = UMat(keypoints, Rect(0, 0, featureCounter, 1));
if (params->upright)
return setUpRight(keypoints);
else
return calcOrientation(keypoints);
}
void cv::ocl::SURF_OCL::uploadKeypoints(const std::vector<KeyPoint> &keypoints, oclMat &keypointsGPU)
bool SURF_OCL::setUpRight(UMat &keypoints)
{
int nFeatures = keypoints.cols;
if( nFeatures == 0 )
return true;
size_t globalThreads[3] = {nFeatures, 1};
return kerUpRight.args(ocl::KernelArg::ReadWrite(keypoints)).run(2, globalThreads, 0, false);
}
bool SURF_OCL::computeDescriptors(const UMat &keypoints, OutputArray _descriptors)
{
int descriptorSize = params->descriptorSize();
int nFeatures = keypoints.cols;
if (nFeatures == 0)
{
_descriptors.release();
return true;
}
_descriptors.create(nFeatures, descriptorSize, CV_32F);
UMat descriptors;
if( _descriptors.isUMat() )
descriptors = _descriptors.getUMat();
else
descriptors.create(nFeatures, descriptorSize, CV_32F);
ocl::Kernel kerCalcDesc, kerNormDesc;
if( descriptorSize == 64 )
{
kerCalcDesc = kerCalcDesc64;
kerNormDesc = kerNormDesc64;
}
else
{
CV_Assert(descriptorSize == 128);
kerCalcDesc = kerCalcDesc128;
kerNormDesc = kerNormDesc128;
}
size_t localThreads[] = {6, 6};
size_t globalThreads[] = {nFeatures*localThreads[0], localThreads[1]};
if(haveImageSupport)
{
kerCalcDesc.args(imgTex,
ocl::KernelArg::ReadOnlyNoSize(keypoints),
ocl::KernelArg::WriteOnlyNoSize(descriptors));
}
else
{
kerCalcDesc.args(ocl::KernelArg::ReadOnly(img),
ocl::KernelArg::ReadOnlyNoSize(keypoints),
ocl::KernelArg::WriteOnlyNoSize(descriptors));
}
if(!kerCalcDesc.run(2, globalThreads, localThreads, false))
return false;
size_t localThreads_n[] = {descriptorSize, 1};
size_t globalThreads_n[] = {nFeatures*localThreads_n[0], localThreads_n[1]};
globalThreads[0] = nFeatures * localThreads[0];
globalThreads[1] = localThreads[1];
bool ok = kerNormDesc.args(ocl::KernelArg::ReadWriteNoSize(descriptors)).
run(2, globalThreads_n, localThreads_n, false);
if(ok && !_descriptors.isUMat())
descriptors.copyTo(_descriptors);
return ok;
}
void SURF_OCL::uploadKeypoints(const std::vector<KeyPoint> &keypoints, UMat &keypointsGPU)
{
if (keypoints.empty())
keypointsGPU.release();
@@ -340,11 +325,11 @@ void cv::ocl::SURF_OCL::uploadKeypoints(const std::vector<KeyPoint> &keypoints,
kp_laplacian[i] = 1;
}
keypointsGPU.upload(keypointsCPU);
keypointsCPU.copyTo(keypointsGPU);
}
}
void cv::ocl::SURF_OCL::downloadKeypoints(const oclMat &keypointsGPU, std::vector<KeyPoint> &keypoints)
void SURF_OCL::downloadKeypoints(const UMat &keypointsGPU, std::vector<KeyPoint> &keypoints)
{
const int nFeatures = keypointsGPU.cols;
@@ -354,8 +339,7 @@ void cv::ocl::SURF_OCL::downloadKeypoints(const oclMat &keypointsGPU, std::vecto
{
CV_Assert(keypointsGPU.type() == CV_32FC1 && keypointsGPU.rows == ROWS_COUNT);
Mat keypointsCPU(keypointsGPU);
Mat keypointsCPU = keypointsGPU.getMat(ACCESS_READ);
keypoints.resize(nFeatures);
float *kp_x = keypointsCPU.ptr<float>(SURF_OCL::X_ROW);
@@ -380,354 +364,154 @@ void cv::ocl::SURF_OCL::downloadKeypoints(const oclMat &keypointsGPU, std::vecto
}
}
void cv::ocl::SURF_OCL::downloadDescriptors(const oclMat &descriptorsGPU, std::vector<float> &descriptors)
bool SURF_OCL::detect(InputArray img, InputArray mask, UMat& keypoints)
{
if (descriptorsGPU.empty())
descriptors.clear();
else
{
CV_Assert(descriptorsGPU.type() == CV_32F);
if( !setImage(img, mask) )
return false;
descriptors.resize(descriptorsGPU.rows * descriptorsGPU.cols);
Mat descriptorsCPU(descriptorsGPU.size(), CV_32F, &descriptors[0]);
descriptorsGPU.download(descriptorsCPU);
}
}
void cv::ocl::SURF_OCL::operator()(const oclMat &img, const oclMat &mask, oclMat &keypoints)
{
if (!img.empty())
{
SURF_OCL_Invoker surf(*this, img, mask);
surf.detectKeypoints(keypoints);
}
}
void cv::ocl::SURF_OCL::operator()(const oclMat &img, const oclMat &mask, oclMat &keypoints, oclMat &descriptors,
bool useProvidedKeypoints)
{
if (!img.empty())
{
SURF_OCL_Invoker surf(*this, img, mask);
if (!useProvidedKeypoints)
surf.detectKeypoints(keypoints);
else if (!upright)
{
surf.findOrientation(keypoints);
}
surf.computeDescriptors(keypoints, descriptors, descriptorSize());
}
}
void cv::ocl::SURF_OCL::operator()(const oclMat &img, const oclMat &mask, std::vector<KeyPoint> &keypoints)
{
oclMat keypointsGPU;
(*this)(img, mask, keypointsGPU);
downloadKeypoints(keypointsGPU, keypoints);
}
void cv::ocl::SURF_OCL::operator()(const oclMat &img, const oclMat &mask, std::vector<KeyPoint> &keypoints,
oclMat &descriptors, bool useProvidedKeypoints)
{
oclMat keypointsGPU;
if (useProvidedKeypoints)
uploadKeypoints(keypoints, keypointsGPU);
(*this)(img, mask, keypointsGPU, descriptors, useProvidedKeypoints);
downloadKeypoints(keypointsGPU, keypoints);
}
void cv::ocl::SURF_OCL::operator()(const oclMat &img, const oclMat &mask, std::vector<KeyPoint> &keypoints,
std::vector<float> &descriptors, bool useProvidedKeypoints)
{
oclMat descriptorsGPU;
(*this)(img, mask, keypoints, descriptorsGPU, useProvidedKeypoints);
downloadDescriptors(descriptorsGPU, descriptors);
}
void cv::ocl::SURF_OCL::releaseMemory()
{
sum.release();
mask1.release();
maskSum.release();
intBuffer.release();
det.release();
trace.release();
maxPosBuffer.release();
return detectKeypoints(keypoints);
}
// bind source buffer to image oject.
void SURF_OCL_Invoker::bindImgTex(const oclMat &img, cl_mem &texture)
bool SURF_OCL::detectAndCompute(InputArray img, InputArray mask, UMat& keypoints,
OutputArray _descriptors, bool useProvidedKeypoints )
{
if(texture)
{
openCLFree(texture);
}
texture = bindTexture(img);
if( !setImage(img, mask) )
return false;
if( !useProvidedKeypoints && !detectKeypoints(keypoints) )
return false;
return computeDescriptors(keypoints, _descriptors);
}
inline int divUp(int a, int b) { return (a + b-1)/b; }
////////////////////////////
// kernel caller definitions
void SURF_OCL_Invoker::icvCalcLayerDetAndTrace_gpu(oclMat &det, oclMat &trace, int octave, int nOctaveLayers, int c_layer_rows)
bool SURF_OCL::calcLayerDetAndTrace(UMat &det, UMat &trace, int octave, int c_layer_rows)
{
int nOctaveLayers = params->nOctaveLayers;
const int min_size = calcSize(octave, 0);
const int max_samples_i = 1 + ((img_rows - min_size) >> octave);
const int max_samples_j = 1 + ((img_cols - min_size) >> octave);
Context *clCxt = det.clCxt;
String kernelName = "icvCalcLayerDetAndTrace";
String kernelName = "SURF_calcLayerDetAndTrace";
std::vector< std::pair<size_t, const void *> > args;
if(sumTex)
{
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&sumTex));
}
else
{
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&surf_.sum.data)); // if image2d is not supported
}
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&det.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&trace.data));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&det.step));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&trace.step));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&img_rows));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&img_cols));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&nOctaveLayers));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&octave));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&c_layer_rows));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&surf_.sum.step));
size_t localThreads[3] = {16, 16, 1};
size_t localThreads[3] = {16, 16};
size_t globalThreads[3] =
{
divUp(max_samples_j, localThreads[0]) *localThreads[0],
divUp(max_samples_i, localThreads[1]) *localThreads[1] *(nOctaveLayers + 2),
1
divUp(max_samples_i, localThreads[1]) *localThreads[1] *(nOctaveLayers + 2)
};
openCLExecuteKernelSURF(clCxt, &surfprog, kernelName, globalThreads, localThreads, args, -1, -1);
if(haveImageSupport)
{
kerCalcDetTrace.args(sumTex,
img_rows, img_cols, nOctaveLayers,
octave, c_layer_rows,
ocl::KernelArg::WriteOnlyNoSize(det),
ocl::KernelArg::WriteOnlyNoSize(trace));
}
else
{
kerCalcDetTrace.args(ocl::KernelArg::ReadOnlyNoSize(sum),
img_rows, img_cols, nOctaveLayers,
octave, c_layer_rows,
ocl::KernelArg::WriteOnlyNoSize(det),
ocl::KernelArg::WriteOnlyNoSize(trace));
}
return kerCalcDetTrace.run(2, globalThreads, localThreads, false);
}
void SURF_OCL_Invoker::icvFindMaximaInLayer_gpu(const oclMat &det, const oclMat &trace, oclMat &maxPosBuffer, oclMat &maxCounter, int counterOffset,
int octave, bool useMask, int nLayers, int layer_rows, int layer_cols)
bool SURF_OCL::findMaximaInLayer(const UMat &det, const UMat &trace,
UMat &maxPosBuffer, UMat &maxCounter,
int counterOffset, int octave,
int layer_rows, int layer_cols)
{
const int min_margin = ((calcSize(octave, 2) >> 1) >> octave) + 1;
bool haveMask = !maskSum.empty() || (maskSumTex.ptr() != 0);
int nOctaveLayers = params->nOctaveLayers;
Context *clCxt = det.clCxt;
String kernelName = use_mask ? "icvFindMaximaInLayer_withmask" : "icvFindMaximaInLayer";
std::vector< std::pair<size_t, const void *> > args;
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&det.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&trace.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&maxPosBuffer.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&maxCounter.data));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&counterOffset));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&det.step));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&trace.step));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&img_rows));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&img_cols));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&nLayers));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&octave));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&layer_rows));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&layer_cols));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&maxCandidates));
args.push_back( std::make_pair( sizeof(cl_float), (void *)&surf_.hessianThreshold));
if(useMask)
ocl::Kernel ker;
if( haveMask )
{
if(maskSumTex)
{
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&maskSumTex));
}
if( haveImageSupport )
ker = kerFindMaximaMask.args(maskSumTex,
ocl::KernelArg::ReadOnlyNoSize(det),
ocl::KernelArg::ReadOnlyNoSize(trace),
ocl::KernelArg::PtrReadWrite(maxPosBuffer),
ocl::KernelArg::PtrReadWrite(maxCounter),
counterOffset, img_rows, img_cols,
octave, nOctaveLayers,
layer_rows, layer_cols,
maxCandidates,
(float)params->hessianThreshold);
else
{
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&surf_.maskSum.data));
}
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&surf_.maskSum.step));
ker = kerFindMaximaMask.args(ocl::KernelArg::ReadOnlyNoSize(maskSum),
ocl::KernelArg::ReadOnlyNoSize(det),
ocl::KernelArg::ReadOnlyNoSize(trace),
ocl::KernelArg::PtrReadWrite(maxPosBuffer),
ocl::KernelArg::PtrReadWrite(maxCounter),
counterOffset, img_rows, img_cols,
octave, nOctaveLayers,
layer_rows, layer_cols,
maxCandidates,
(float)params->hessianThreshold);
}
size_t localThreads[3] = {16, 16, 1};
size_t globalThreads[3] = {divUp(layer_cols - 2 * min_margin, localThreads[0] - 2) *localThreads[0],
divUp(layer_rows - 2 * min_margin, localThreads[1] - 2) *nLayers *localThreads[1],
1
};
else
{
ker = kerFindMaxima.args(ocl::KernelArg::ReadOnlyNoSize(det),
ocl::KernelArg::ReadOnlyNoSize(trace),
ocl::KernelArg::PtrReadWrite(maxPosBuffer),
ocl::KernelArg::PtrReadWrite(maxCounter),
counterOffset, img_rows, img_cols,
octave, nOctaveLayers,
layer_rows, layer_cols,
maxCandidates,
(float)params->hessianThreshold);
}
size_t localThreads[3] = {16, 16};
size_t globalThreads[3] =
{
divUp(layer_cols - 2 * min_margin, localThreads[0] - 2) *localThreads[0],
divUp(layer_rows - 2 * min_margin, localThreads[1] - 2) *nOctaveLayers *localThreads[1]
};
openCLExecuteKernelSURF(clCxt, &surfprog, kernelName, globalThreads, localThreads, args, -1, -1);
return ker.run(2, globalThreads, localThreads, false);
}
void SURF_OCL_Invoker::icvInterpolateKeypoint_gpu(const oclMat &det, const oclMat &maxPosBuffer, int maxCounter,
oclMat &keypoints, oclMat &counters_, int octave, int layer_rows, int max_features)
bool SURF_OCL::interpolateKeypoint(const UMat &det, const UMat &maxPosBuffer, int maxCounter,
UMat &keypoints, UMat &counters_, int octave, int layer_rows, int max_features)
{
Context *clCxt = det.clCxt;
String kernelName = "icvInterpolateKeypoint";
std::vector< std::pair<size_t, const void *> > args;
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&det.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&maxPosBuffer.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypoints.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&counters_.data));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&det.step));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypoints.step));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&img_rows));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&img_cols));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&octave));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&layer_rows));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&max_features));
size_t localThreads[3] = {3, 3, 3};
size_t globalThreads[3] = {maxCounter *localThreads[0], localThreads[1], 1};
size_t globalThreads[3] = {maxCounter*localThreads[0], localThreads[1], 3};
openCLExecuteKernelSURF(clCxt, &surfprog, kernelName, globalThreads, localThreads, args, -1, -1);
return kerInterp.args(ocl::KernelArg::ReadOnlyNoSize(det),
ocl::KernelArg::PtrReadOnly(maxPosBuffer),
ocl::KernelArg::ReadWriteNoSize(keypoints),
ocl::KernelArg::PtrReadWrite(counters_),
img_rows, img_cols, octave, layer_rows, max_features).
run(3, globalThreads, localThreads, false);
}
void SURF_OCL_Invoker::icvCalcOrientation_gpu(const oclMat &keypoints, int nFeatures)
bool SURF_OCL::calcOrientation(UMat &keypoints)
{
Context *clCxt = counters.clCxt;
String kernelName = "icvCalcOrientation";
std::vector< std::pair<size_t, const void *> > args;
if(sumTex)
{
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&sumTex));
}
int nFeatures = keypoints.cols;
if( nFeatures == 0 )
return true;
if( haveImageSupport )
kerOri.args(sumTex,
ocl::KernelArg::ReadWriteNoSize(keypoints),
img_rows, img_cols);
else
{
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&surf_.sum.data)); // if image2d is not supported
}
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypoints.data));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypoints.step));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&img_rows));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&img_cols));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&surf_.sum.step));
kerOri.args(ocl::KernelArg::ReadOnlyNoSize(sum),
ocl::KernelArg::ReadWriteNoSize(keypoints),
img_rows, img_cols);
size_t localThreads[3] = {ORI_LOCAL_SIZE, 1, 1};
size_t globalThreads[3] = {nFeatures * localThreads[0], 1, 1};
openCLExecuteKernelSURF(clCxt, &surfprog, kernelName, globalThreads, localThreads, args, -1, -1);
size_t localThreads[3] = {ORI_LOCAL_SIZE, 1};
size_t globalThreads[3] = {nFeatures * localThreads[0], 1};
return kerOri.run(2, globalThreads, localThreads, false);
}
void SURF_OCL_Invoker::icvSetUpright_gpu(const oclMat &keypoints, int nFeatures)
{
Context *clCxt = counters.clCxt;
String kernelName = "icvSetUpright";
std::vector< std::pair<size_t, const void *> > args;
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypoints.data));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypoints.step));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&nFeatures));
size_t localThreads[3] = {256, 1, 1};
size_t globalThreads[3] = {saturate_cast<size_t>(nFeatures), 1, 1};
openCLExecuteKernelSURF(clCxt, &surfprog, kernelName, globalThreads, localThreads, args, -1, -1);
}
void SURF_OCL_Invoker::compute_descriptors_gpu(const oclMat &descriptors, const oclMat &keypoints, int nFeatures)
{
// compute unnormalized descriptors, then normalize them - odd indexing since grid must be 2D
Context *clCxt = descriptors.clCxt;
String kernelName;
std::vector< std::pair<size_t, const void *> > args;
size_t localThreads[3] = {1, 1, 1};
size_t globalThreads[3] = {1, 1, 1};
if(descriptors.cols == 64)
{
kernelName = "compute_descriptors64";
localThreads[0] = 6;
localThreads[1] = 6;
globalThreads[0] = nFeatures * localThreads[0];
globalThreads[1] = 16 * localThreads[1];
args.clear();
if(imgTex)
{
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&imgTex));
}
else
{
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&_img.data));
}
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&descriptors.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypoints.data));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&descriptors.step));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypoints.step));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&_img.rows));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&_img.cols));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&_img.step));
openCLExecuteKernelSURF(clCxt, &surfprog, kernelName, globalThreads, localThreads, args, -1, -1);
kernelName = "normalize_descriptors64";
localThreads[0] = 64;
localThreads[1] = 1;
globalThreads[0] = nFeatures * localThreads[0];
globalThreads[1] = localThreads[1];
args.clear();
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&descriptors.data));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&descriptors.step));
openCLExecuteKernelSURF(clCxt, &surfprog, kernelName, globalThreads, localThreads, args, -1, -1);
}
else
{
kernelName = "compute_descriptors128";
localThreads[0] = 6;
localThreads[1] = 6;
globalThreads[0] = nFeatures * localThreads[0];
globalThreads[1] = 16 * localThreads[1];
args.clear();
if(imgTex)
{
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&imgTex));
}
else
{
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&_img.data));
}
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&descriptors.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypoints.data));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&descriptors.step));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypoints.step));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&_img.rows));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&_img.cols));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&_img.step));
openCLExecuteKernelSURF(clCxt, &surfprog, kernelName, globalThreads, localThreads, args, -1, -1);
kernelName = "normalize_descriptors128";
localThreads[0] = 128;
localThreads[1] = 1;
globalThreads[0] = nFeatures * localThreads[0];
globalThreads[1] = localThreads[1];
args.clear();
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&descriptors.data));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&descriptors.step));
openCLExecuteKernelSURF(clCxt, &surfprog, kernelName, globalThreads, localThreads, args, -1, -1);
}
}
#endif //HAVE_OPENCV_OCL