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OCL: included ORB featured detector/descriptor extractor.

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Peter Andreas Entschev 2013-12-20 23:04:58 -02:00
parent b29835a8f8
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135
modules/ocl/doc/feature_detection_and_description.rst
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@ -647,3 +647,138 @@ Returns block descriptors computed for the whole image.
* **DESCR_FORMAT_COL_BY_COL** - Column-major order.
The function is mainly used to learn the classifier.
ocl::ORB_OCL
--------------
.. ocv:class:: ocl::ORB_OCL
Class for extracting ORB features and descriptors from an image. ::
class ORB_OCL
{
public:
enum
{
X_ROW = 0,
Y_ROW,
RESPONSE_ROW,
ANGLE_ROW,
OCTAVE_ROW,
SIZE_ROW,
ROWS_COUNT
};
enum
{
DEFAULT_FAST_THRESHOLD = 20
};
explicit ORB_OCL(int nFeatures = 500, float scaleFactor = 1.2f,
int nLevels = 8, int edgeThreshold = 31,
int firstLevel = 0, int WTA_K = 2,
int scoreType = 0, int patchSize = 31);
void operator()(const oclMat& image, const oclMat& mask,
std::vector<KeyPoint>& keypoints);
void operator()(const oclMat& image, const oclMat& mask, oclMat& keypoints);
void operator()(const oclMat& image, const oclMat& mask,
std::vector<KeyPoint>& keypoints, oclMat& descriptors);
void operator()(const oclMat& image, const oclMat& mask,
oclMat& keypoints, oclMat& descriptors);
void downloadKeyPoints(oclMat& d_keypoints, std::vector<KeyPoint>& keypoints);
void convertKeyPoints(Mat& d_keypoints, std::vector<KeyPoint>& keypoints);
int descriptorSize() const;
int descriptorType() const;
int defaultNorm() const;
void setFastParams(int threshold, bool nonmaxSupression = true);
void release();
bool blurForDescriptor;
};
The class implements ORB feature detection and description algorithm.
ocl::ORB_OCL::ORB_OCL
------------------------
Constructor.
.. ocv:function:: ocl::ORB_OCL::ORB_OCL(int nFeatures = 500, float scaleFactor = 1.2f, int nLevels = 8, int edgeThreshold = 31, int firstLevel = 0, int WTA_K = 2, int scoreType = 0, int patchSize = 31)
:param nfeatures: The maximum number of features to retain.
:param scaleFactor: Pyramid decimation ratio, greater than 1. ``scaleFactor==2`` means the classical pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor will mean that to cover certain scale range you will need more pyramid levels and so the speed will suffer.
:param nlevels: The number of pyramid levels. The smallest level will have linear size equal to ``input_image_linear_size/pow(scaleFactor, nlevels)``.
:param edgeThreshold: This is size of the border where the features are not detected. It should roughly match the ``patchSize`` parameter.
:param firstLevel: It should be 0 in the current implementation.
:param WTA_K: The number of points that produce each element of the oriented BRIEF descriptor. The default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 random points (of course, those point coordinates are random, but they are generated from the pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, denoted as ``NORM_HAMMING2`` (2 bits per bin). When ``WTA_K=4``, we take 4 random points to compute each bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
:param scoreType: The default HARRIS_SCORE means that Harris algorithm is used to rank features (the score is written to ``KeyPoint::score`` and is used to retain best ``nfeatures`` features); FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, but it is a little faster to compute.
:param patchSize: size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers the perceived image area covered by a feature will be larger.
ocl::ORB_OCL::operator()
--------------------------
Detects keypoints and computes descriptors for them.
.. ocv:function:: void ocl::ORB_OCL::operator()(const oclMat& image, const oclMat& mask, std::vector<KeyPoint>& keypoints)
.. ocv:function:: void ocl::ORB_OCL::operator()(const oclMat& image, const oclMat& mask, oclMat& keypoints)
.. ocv:function:: void ocl::ORB_OCL::operator()(const oclMat& image, const oclMat& mask, std::vector<KeyPoint>& keypoints, oclMat& descriptors)
.. ocv:function:: void ocl::ORB_OCL::operator()(const oclMat& image, const oclMat& mask, oclMat& keypoints, oclMat& descriptors)
:param image: Input 8-bit grayscale image.
:param mask: Optional input mask that marks the regions where we should detect features.
:param keypoints: The input/output vector of keypoints. Can be stored both in host and device memory. For device memory:
* ``X_ROW`` contains the horizontal coordinate of the i'th feature.
* ``Y_ROW`` contains the vertical coordinate of the i'th feature.
* ``RESPONSE_ROW`` contains the response of the i'th feature.
* ``ANGLE_ROW`` contains the orientation of the i'th feature.
* ``RESPONSE_ROW`` contains the octave of the i'th feature.
* ``ANGLE_ROW`` contains the size of the i'th feature.
:param descriptors: Computed descriptors. if ``blurForDescriptor`` is true, image will be blurred before descriptors calculation.
ocl::ORB_OCL::downloadKeyPoints
---------------------------------
Download keypoints from device to host memory.
.. ocv:function:: static void ocl::ORB_OCL::downloadKeyPoints( const oclMat& d_keypoints, std::vector<KeyPoint>& keypoints )
ocl::ORB_OCL::convertKeyPoints
--------------------------------
Converts keypoints from OCL representation to vector of ``KeyPoint``.
.. ocv:function:: static void ocl::ORB_OCL::convertKeyPoints( const Mat& d_keypoints, std::vector<KeyPoint>& keypoints )
ocl::ORB_OCL::release
-----------------------
Releases inner buffer memory.
.. ocv:function:: void ocl::ORB_OCL::release()

104
modules/ocl/include/opencv2/ocl.hpp
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@ -1513,6 +1513,110 @@ namespace cv
int nonmaxSupressionOCL(oclMat& keypoints);
};
////////////////////////////////// ORB Descriptor Extractor //////////////////////////////////
class CV_EXPORTS ORB_OCL
{
public:
enum
{
X_ROW = 0,
Y_ROW,
RESPONSE_ROW,
ANGLE_ROW,
OCTAVE_ROW,
SIZE_ROW,
ROWS_COUNT
};
enum
{
DEFAULT_FAST_THRESHOLD = 20
};
//! Constructor
explicit ORB_OCL(int nFeatures = 500, float scaleFactor = 1.2f, int nLevels = 8, int edgeThreshold = 31,
int firstLevel = 0, int WTA_K = 2, int scoreType = 0, int patchSize = 31);
//! Compute the ORB features on an image
//! image - the image to compute the features (supports only CV_8UC1 images)
//! mask - the mask to apply
//! keypoints - the resulting keypoints
void operator ()(const oclMat& image, const oclMat& mask, std::vector<KeyPoint>& keypoints);
void operator ()(const oclMat& image, const oclMat& mask, oclMat& keypoints);
//! Compute the ORB features and descriptors on an image
//! image - the image to compute the features (supports only CV_8UC1 images)
//! mask - the mask to apply
//! keypoints - the resulting keypoints
//! descriptors - descriptors array
void operator ()(const oclMat& image, const oclMat& mask, std::vector<KeyPoint>& keypoints, oclMat& descriptors);
void operator ()(const oclMat& image, const oclMat& mask, oclMat& keypoints, oclMat& descriptors);
//! download keypoints from device to host memory
static void downloadKeyPoints(const oclMat& d_keypoints, std::vector<KeyPoint>& keypoints);
//! convert keypoints to KeyPoint vector
static void convertKeyPoints(const Mat& d_keypoints, std::vector<KeyPoint>& keypoints);
//! returns the descriptor size in bytes
inline int descriptorSize() const { return kBytes; }
inline int descriptorType() const { return CV_8U; }
inline int defaultNorm() const { return NORM_HAMMING; }
inline void setFastParams(int threshold, bool nonmaxSupression = true)
{
fastDetector_.threshold = threshold;
fastDetector_.nonmaxSupression = nonmaxSupression;
}
//! release temporary buffer's memory
void release();
//! if true, image will be blurred before descriptors calculation
bool blurForDescriptor;
private:
enum { kBytes = 32 };
void buildScalePyramids(const oclMat& image, const oclMat& mask);
void computeKeyPointsPyramid();
void computeDescriptors(oclMat& descriptors);
void mergeKeyPoints(oclMat& keypoints);
int nFeatures_;
float scaleFactor_;
int nLevels_;
int edgeThreshold_;
int firstLevel_;
int WTA_K_;
int scoreType_;
int patchSize_;
// The number of desired features per scale
std::vector<size_t> n_features_per_level_;
// Points to compute BRIEF descriptors from
oclMat pattern_;
std::vector<oclMat> imagePyr_;
std::vector<oclMat> maskPyr_;
oclMat buf_;
std::vector<oclMat> keyPointsPyr_;
std::vector<int> keyPointsCount_;
FAST_OCL fastDetector_;
Ptr<ocl::FilterEngine_GPU> blurFilter;
oclMat d_keypoints_;
oclMat uMax_;
};
/////////////////////////////// PyrLKOpticalFlow /////////////////////////////////////
class CV_EXPORTS PyrLKOpticalFlow

103
modules/ocl/perf/perf_orb.cpp Normal file
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@ -0,0 +1,103 @@
/*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) 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.
//
// Authors:
// * Peter Andreas Entschev, peter@entschev.com
//
//M*/
#include "perf_precomp.hpp"
using namespace perf;
/////////////////// ORB ///////////////////
typedef std::tr1::tuple<std::string, int> Image_NFeatures_t;
typedef perf::TestBaseWithParam<Image_NFeatures_t> Image_NFeatures;
PERF_TEST_P(Image_NFeatures, ORB,
testing::Combine(testing::Values<string>("gpu/perf/aloe.png"),
testing::Values(4000)))
{
declare.time(300.0);
const Image_NFeatures_t params = GetParam();
const std::string imgFile = std::tr1::get<0>(params);
const int nFeatures = std::tr1::get<1>(params);
const cv::Mat img = imread(getDataPath(imgFile), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
if (RUN_OCL_IMPL)
{
cv::ocl::ORB_OCL d_orb(nFeatures);
const cv::ocl::oclMat d_img(img);
cv::ocl::oclMat d_keypoints, d_descriptors;
TEST_CYCLE() d_orb(d_img, cv::ocl::oclMat(), d_keypoints, d_descriptors);
std::vector<cv::KeyPoint> ocl_keypoints;
d_orb.downloadKeyPoints(d_keypoints, ocl_keypoints);
cv::Mat ocl_descriptors(d_descriptors);
ocl_keypoints.resize(10);
ocl_descriptors = ocl_descriptors.rowRange(0, 10);
sortKeyPoints(ocl_keypoints, ocl_descriptors);
SANITY_CHECK_KEYPOINTS(ocl_keypoints, 1e-4);
SANITY_CHECK(ocl_descriptors);
}
else if (RUN_PLAIN_IMPL)
{
cv::ORB orb(nFeatures);
std::vector<cv::KeyPoint> cpu_keypoints;
cv::Mat cpu_descriptors;
TEST_CYCLE() orb(img, cv::noArray(), cpu_keypoints, cpu_descriptors);
SANITY_CHECK_KEYPOINTS(cpu_keypoints);
SANITY_CHECK(cpu_descriptors);
}
else
OCL_PERF_ELSE;
}

503
modules/ocl/src/opencl/orb.cl Normal file
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@ -0,0 +1,503 @@
/*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) 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.
//
// Authors:
// * Peter Andreas Entschev, peter@entschev.com
//
//M*/
#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#define CV_PI M_PI
#else
#define CV_PI M_PI_F
#endif
#define X_ROW 0
#define Y_ROW 1
#define RESPONSE_ROW 2
#define ANGLE_ROW 3
#define OCTAVE_ROW 4
#define SIZE_ROW 5
#define ROWS_COUNT 6
#ifdef CPU
void reduce_32(volatile __local int* smem, volatile int* val, int tid)
{
#define op(A, B) (*A)+(B)
smem[tid] = *val;
barrier(CLK_LOCAL_MEM_FENCE);
for(int i = 16; i > 0; i >>= 1)
{
if(tid < i)
{
smem[tid] = *val = op(val, smem[tid + i]);
}
barrier(CLK_LOCAL_MEM_FENCE);
}
#undef op
}
#else
void reduce_32(volatile __local int* smem, volatile int* val, int tid)
{
#define op(A, B) (*A)+(B)
smem[tid] = *val;
barrier(CLK_LOCAL_MEM_FENCE);
#ifndef WAVE_SIZE
#define WAVE_SIZE 1
#endif
if (tid < 16)
{
smem[tid] = *val = op(val, smem[tid + 16]);
#if WAVE_SIZE < 16
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 8)
{
#endif
smem[tid] = *val = op(val, smem[tid + 8]);
#if WAVE_SIZE < 8
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 4)
{
#endif
smem[tid] = *val = op(val, smem[tid + 4]);
#if WAVE_SIZE < 4
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 2)
{
#endif
smem[tid] = *val = op(val, smem[tid + 2]);
#if WAVE_SIZE < 2
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 1)
{
#endif
smem[tid] = *val = op(val, smem[tid + 1]);
}
#undef WAVE_SIZE
#undef op
}
#endif
////////////////////////////////////////////////////////////////////////////////////////////////////////
// HarrisResponses
__kernel
void HarrisResponses(__global const uchar* img,
__global float* keypoints,
const int npoints,
const int blockSize,
const float harris_k,
const int img_step,
const int keypoints_step)
{
__local int smem0[8 * 32];
__local int smem1[8 * 32];
__local int smem2[8 * 32];
const int ptidx = mad24(get_group_id(0), get_local_size(1), get_local_id(1));
if (ptidx < npoints)
{
const int pt_x = keypoints[mad24(keypoints_step, X_ROW, ptidx)];
const int pt_y = keypoints[mad24(keypoints_step, Y_ROW, ptidx)];
const int r = blockSize / 2;
const int x0 = pt_x - r;
const int y0 = pt_y - r;
int a = 0, b = 0, c = 0;
for (int ind = get_local_id(0); ind < blockSize * blockSize; ind += get_local_size(0))
{
const int i = ind / blockSize;
const int j = ind % blockSize;
int center = mad24(y0+i, img_step, x0+j);
int Ix = (img[center+1] - img[center-1]) * 2 +
(img[center-img_step+1] - img[center-img_step-1]) +
(img[center+img_step+1] - img[center+img_step-1]);
int Iy = (img[center+img_step] - img[center-img_step]) * 2 +
(img[center+img_step-1] - img[center-img_step-1]) +
(img[center+img_step+1] - img[center-img_step+1]);
a += Ix * Ix;
b += Iy * Iy;
c += Ix * Iy;
}
__local int* srow0 = smem0 + get_local_id(1) * get_local_size(0);
__local int* srow1 = smem1 + get_local_id(1) * get_local_size(0);
__local int* srow2 = smem2 + get_local_id(1) * get_local_size(0);
reduce_32(srow0, &a, get_local_id(0));
reduce_32(srow1, &b, get_local_id(0));
reduce_32(srow2, &c, get_local_id(0));
if (get_local_id(0) == 0)
{
float scale = (1 << 2) * blockSize * 255.0f;
scale = 1.0f / scale;
const float scale_sq_sq = scale * scale * scale * scale;
float response = ((float)a * b - (float)c * c - harris_k * ((float)a + b) * ((float)a + b)) * scale_sq_sq;
keypoints[mad24(keypoints_step, RESPONSE_ROW, ptidx)] = response;
}
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////////
// IC_Angle
__kernel
void IC_Angle(__global const uchar* img,
__global float* keypoints_,
__global const int* u_max,
const int npoints,
const int half_k,
const int img_step,
const int keypoints_step)
{
__local int smem0[8 * 32];
__local int smem1[8 * 32];
__local int* srow0 = smem0 + get_local_id(1) * get_local_size(0);
__local int* srow1 = smem1 + get_local_id(1) * get_local_size(0);
const int ptidx = mad24(get_group_id(0), get_local_size(1), get_local_id(1));
if (ptidx < npoints)
{
int m_01 = 0, m_10 = 0;
const int pt_x = keypoints_[mad24(keypoints_step, X_ROW, ptidx)];
const int pt_y = keypoints_[mad24(keypoints_step, Y_ROW, ptidx)];
// Treat the center line differently, v=0
for (int u = get_local_id(0) - half_k; u <= half_k; u += get_local_size(0))
m_10 += u * img[mad24(pt_y, img_step, pt_x+u)];
reduce_32(srow0, &m_10, get_local_id(0));
for (int v = 1; v <= half_k; ++v)
{
// Proceed over the two lines
int v_sum = 0;
int m_sum = 0;
const int d = u_max[v];
for (int u = get_local_id(0) - d; u <= d; u += get_local_size(0))
{
int val_plus = img[mad24(pt_y+v, img_step, pt_x+u)];
int val_minus = img[mad24(pt_y-v, img_step, pt_x+u)];
v_sum += (val_plus - val_minus);
m_sum += u * (val_plus + val_minus);
}
reduce_32(srow0, &v_sum, get_local_id(0));
reduce_32(srow1, &m_sum, get_local_id(0));
m_10 += m_sum;
m_01 += v * v_sum;
}
if (get_local_id(0) == 0)
{
float kp_dir = atan2((float)m_01, (float)m_10);
kp_dir += (kp_dir < 0) * (2.0f * CV_PI);
kp_dir *= 180.0f / CV_PI;
keypoints_[mad24(keypoints_step, ANGLE_ROW, ptidx)] = kp_dir;
}
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////////
// computeOrbDescriptor
#define GET_VALUE(idx) \
img[mad24(loc.y + (int)round(pattern[idx] * sina + pattern[pattern_step+idx] * cosa), img_step, \
loc.x + (int)round(pattern[idx] * cosa - pattern[pattern_step+idx] * sina))]
int calcOrbDescriptor_2(__global const uchar* img,
__global const int* pattern,
const int2 loc,
const float sina,
const float cosa,
const int i,
const int img_step,
const int pattern_step)
{
pattern += 16 * i;
int t0, t1, val;
t0 = GET_VALUE(0); t1 = GET_VALUE(1);
val = t0 < t1;
t0 = GET_VALUE(2); t1 = GET_VALUE(3);
val |= (t0 < t1) << 1;
t0 = GET_VALUE(4); t1 = GET_VALUE(5);
val |= (t0 < t1) << 2;
t0 = GET_VALUE(6); t1 = GET_VALUE(7);
val |= (t0 < t1) << 3;
t0 = GET_VALUE(8); t1 = GET_VALUE(9);
val |= (t0 < t1) << 4;
t0 = GET_VALUE(10); t1 = GET_VALUE(11);
val |= (t0 < t1) << 5;
t0 = GET_VALUE(12); t1 = GET_VALUE(13);
val |= (t0 < t1) << 6;
t0 = GET_VALUE(14); t1 = GET_VALUE(15);
val |= (t0 < t1) << 7;
return val;
}
int calcOrbDescriptor_3(__global const uchar* img,
__global const int* pattern,
const int2 loc,
const float sina,
const float cosa,
const int i,
const int img_step,
const int pattern_step)
{
pattern += 12 * i;
int t0, t1, t2, val;
t0 = GET_VALUE(0); t1 = GET_VALUE(1); t2 = GET_VALUE(2);
val = t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0);
t0 = GET_VALUE(3); t1 = GET_VALUE(4); t2 = GET_VALUE(5);
val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 2;
t0 = GET_VALUE(6); t1 = GET_VALUE(7); t2 = GET_VALUE(8);
val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 4;
t0 = GET_VALUE(9); t1 = GET_VALUE(10); t2 = GET_VALUE(11);
val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 6;
return val;
}
int calcOrbDescriptor_4(__global const uchar* img,
__global const int* pattern,
const int2 loc,
const float sina,
const float cosa,
const int i,
const int img_step,
const int pattern_step)
{
pattern += 16 * i;
int t0, t1, t2, t3, k, val;
int a, b;
t0 = GET_VALUE(0); t1 = GET_VALUE(1);
t2 = GET_VALUE(2); t3 = GET_VALUE(3);
a = 0, b = 2;
if( t1 > t0 ) t0 = t1, a = 1;
if( t3 > t2 ) t2 = t3, b = 3;
k = t0 > t2 ? a : b;
val = k;
t0 = GET_VALUE(4); t1 = GET_VALUE(5);
t2 = GET_VALUE(6); t3 = GET_VALUE(7);
a = 0, b = 2;
if( t1 > t0 ) t0 = t1, a = 1;
if( t3 > t2 ) t2 = t3, b = 3;
k = t0 > t2 ? a : b;
val |= k << 2;
t0 = GET_VALUE(8); t1 = GET_VALUE(9);
t2 = GET_VALUE(10); t3 = GET_VALUE(11);
a = 0, b = 2;
if( t1 > t0 ) t0 = t1, a = 1;
if( t3 > t2 ) t2 = t3, b = 3;
k = t0 > t2 ? a : b;
val |= k << 4;
t0 = GET_VALUE(12); t1 = GET_VALUE(13);
t2 = GET_VALUE(14); t3 = GET_VALUE(15);
a = 0, b = 2;
if( t1 > t0 ) t0 = t1, a = 1;
if( t3 > t2 ) t2 = t3, b = 3;
k = t0 > t2 ? a : b;
val |= k << 6;
return val;
}
#undef GET_VALUE
__kernel
void computeOrbDescriptor(__global const uchar* img,
__global const float* keypoints,
__global const int* pattern,
__global uchar* desc,
const int npoints,
const int dsize,
const int WTA_K,
const int offset,
const int img_step,
const int keypoints_step,
const int pattern_step,
const int desc_step)
{
const int descidx = mad24(get_group_id(0), get_local_size(0), get_local_id(0));
const int ptidx = mad24(get_group_id(1), get_local_size(1), get_local_id(1));
if (ptidx < npoints && descidx < dsize)
{
int2 loc = {(int)keypoints[mad24(keypoints_step, X_ROW, ptidx)],
(int)keypoints[mad24(keypoints_step, Y_ROW, ptidx)]};
float angle = keypoints[mad24(keypoints_step, ANGLE_ROW, ptidx)];
angle *= (float)(CV_PI / 180.f);
float sina = sin(angle);
float cosa = cos(angle);
if (WTA_K == 2)
desc[mad24(ptidx+offset, desc_step, descidx)] = calcOrbDescriptor_2(img, pattern, loc, sina, cosa, descidx, img_step, pattern_step);
else if (WTA_K == 3)
desc[mad24(ptidx+offset, desc_step, descidx)] = calcOrbDescriptor_3(img, pattern, loc, sina, cosa, descidx, img_step, pattern_step);
else if (WTA_K == 4)
desc[mad24(ptidx+offset, desc_step, descidx)] = calcOrbDescriptor_4(img, pattern, loc, sina, cosa, descidx, img_step, pattern_step);
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////////
// mergeLocation
__kernel
void mergeLocation(__global const float* keypoints_in,
__global float* keypoints_out,
const int npoints,
const int offset,
const float scale,
const int octave,
const float size,
const int keypoints_in_step,
const int keypoints_out_step)
{
//const int ptidx = blockIdx.x * blockDim.x + threadIdx.x;
const int ptidx = mad24(get_group_id(0), get_local_size(0), get_local_id(0));
if (ptidx < npoints)
{
float pt_x = keypoints_in[mad24(keypoints_in_step, X_ROW, ptidx)] * scale;
float pt_y = keypoints_in[mad24(keypoints_in_step, Y_ROW, ptidx)] * scale;
float response = keypoints_in[mad24(keypoints_in_step, RESPONSE_ROW, ptidx)];
float angle = keypoints_in[mad24(keypoints_in_step, ANGLE_ROW, ptidx)];
keypoints_out[mad24(keypoints_out_step, X_ROW, ptidx+offset)] = pt_x;
keypoints_out[mad24(keypoints_out_step, Y_ROW, ptidx+offset)] = pt_y;
keypoints_out[mad24(keypoints_out_step, RESPONSE_ROW, ptidx+offset)] = response;
keypoints_out[mad24(keypoints_out_step, ANGLE_ROW, ptidx+offset)] = angle;
keypoints_out[mad24(keypoints_out_step, OCTAVE_ROW, ptidx+offset)] = (float)octave;
keypoints_out[mad24(keypoints_out_step, SIZE_ROW, ptidx+offset)] = size;
}
}
__kernel
void convertRowsToChannels(__global const float* keypoints_in,
__global float* keypoints_out,
const int npoints,
const int keypoints_in_step,
const int keypoints_out_step)
{
const int ptidx = mad24(get_group_id(0), get_local_size(0), get_local_id(0));
if (ptidx < npoints)
{
const int pt_x = keypoints_in[mad24(keypoints_in_step, X_ROW, ptidx)];
const int pt_y = keypoints_in[mad24(keypoints_in_step, Y_ROW, ptidx)];
keypoints_out[ptidx*2] = pt_x;
keypoints_out[ptidx*2+1] = pt_y;
}
}
__kernel
void convertChannelsToRows(__global const float* keypoints_pos,
__global const float* keypoints_resp,
__global float* keypoints_out,
const int npoints,
const int keypoints_pos_step,
const int keypoints_resp_step,
const int keypoints_out_step)
{
const int ptidx = mad24(get_group_id(0), get_local_size(0), get_local_id(0));
if (ptidx < npoints)
{
const float pt_x = keypoints_pos[ptidx*2];
const float pt_y = keypoints_pos[ptidx*2+1];
const float resp = keypoints_resp[ptidx];
keypoints_out[mad24(keypoints_out_step, X_ROW, ptidx)] = pt_x;
keypoints_out[mad24(keypoints_out_step, Y_ROW, ptidx)] = pt_y;
keypoints_out[mad24(keypoints_out_step, RESPONSE_ROW, ptidx)] = resp;
}
}

916
modules/ocl/src/orb.cpp Normal file
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/*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) 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.
//
// Authors:
// * Peter Andreas Entschev, peter@entschev.com
//
//M*/
#include "precomp.hpp"
#include "opencl_kernels.hpp"
using namespace cv;
using namespace cv::ocl;
namespace
{
const float HARRIS_K = 0.04f;
const int DESCRIPTOR_SIZE = 32;
const int bit_pattern_31_[256 * 4] =
{
8,-3, 9,5/*mean (0), correlation (0)*/,
4,2, 7,-12/*mean (1.12461e-05), correlation (0.0437584)*/,
-11,9, -8,2/*mean (3.37382e-05), correlation (0.0617409)*/,
7,-12, 12,-13/*mean (5.62303e-05), correlation (0.0636977)*/,
2,-13, 2,12/*mean (0.000134953), correlation (0.085099)*/,
1,-7, 1,6/*mean (0.000528565), correlation (0.0857175)*/,
-2,-10, -2,-4/*mean (0.0188821), correlation (0.0985774)*/,
-13,-13, -11,-8/*mean (0.0363135), correlation (0.0899616)*/,
-13,-3, -12,-9/*mean (0.121806), correlation (0.099849)*/,
10,4, 11,9/*mean (0.122065), correlation (0.093285)*/,
-13,-8, -8,-9/*mean (0.162787), correlation (0.0942748)*/,
-11,7, -9,12/*mean (0.21561), correlation (0.0974438)*/,
7,7, 12,6/*mean (0.160583), correlation (0.130064)*/,
-4,-5, -3,0/*mean (0.228171), correlation (0.132998)*/,
-13,2, -12,-3/*mean (0.00997526), correlation (0.145926)*/,
-9,0, -7,5/*mean (0.198234), correlation (0.143636)*/,
12,-6, 12,-1/*mean (0.0676226), correlation (0.16689)*/,
-3,6, -2,12/*mean (0.166847), correlation (0.171682)*/,
-6,-13, -4,-8/*mean (0.101215), correlation (0.179716)*/,
11,-13, 12,-8/*mean (0.200641), correlation (0.192279)*/,
4,7, 5,1/*mean (0.205106), correlation (0.186848)*/,
5,-3, 10,-3/*mean (0.234908), correlation (0.192319)*/,
3,-7, 6,12/*mean (0.0709964), correlation (0.210872)*/,
-8,-7, -6,-2/*mean (0.0939834), correlation (0.212589)*/,
-2,11, -1,-10/*mean (0.127778), correlation (0.20866)*/,
-13,12, -8,10/*mean (0.14783), correlation (0.206356)*/,
-7,3, -5,-3/*mean (0.182141), correlation (0.198942)*/,
-4,2, -3,7/*mean (0.188237), correlation (0.21384)*/,
-10,-12, -6,11/*mean (0.14865), correlation (0.23571)*/,
5,-12, 6,-7/*mean (0.222312), correlation (0.23324)*/,
5,-6, 7,-1/*mean (0.229082), correlation (0.23389)*/,
1,0, 4,-5/*mean (0.241577), correlation (0.215286)*/,
9,11, 11,-13/*mean (0.00338507), correlation (0.251373)*/,
4,7, 4,12/*mean (0.131005), correlation (0.257622)*/,
2,-1, 4,4/*mean (0.152755), correlation (0.255205)*/,
-4,-12, -2,7/*mean (0.182771), correlation (0.244867)*/,
-8,-5, -7,-10/*mean (0.186898), correlation (0.23901)*/,
4,11, 9,12/*mean (0.226226), correlation (0.258255)*/,
0,-8, 1,-13/*mean (0.0897886), correlation (0.274827)*/,
-13,-2, -8,2/*mean (0.148774), correlation (0.28065)*/,
-3,-2, -2,3/*mean (0.153048), correlation (0.283063)*/,
-6,9, -4,-9/*mean (0.169523), correlation (0.278248)*/,
8,12, 10,7/*mean (0.225337), correlation (0.282851)*/,
0,9, 1,3/*mean (0.226687), correlation (0.278734)*/,
7,-5, 11,-10/*mean (0.00693882), correlation (0.305161)*/,
-13,-6, -11,0/*mean (0.0227283), correlation (0.300181)*/,
10,7, 12,1/*mean (0.125517), correlation (0.31089)*/,
-6,-3, -6,12/*mean (0.131748), correlation (0.312779)*/,
10,-9, 12,-4/*mean (0.144827), correlation (0.292797)*/,
-13,8, -8,-12/*mean (0.149202), correlation (0.308918)*/,
-13,0, -8,-4/*mean (0.160909), correlation (0.310013)*/,
3,3, 7,8/*mean (0.177755), correlation (0.309394)*/,
5,7, 10,-7/*mean (0.212337), correlation (0.310315)*/,
-1,7, 1,-12/*mean (0.214429), correlation (0.311933)*/,
3,-10, 5,6/*mean (0.235807), correlation (0.313104)*/,
2,-4, 3,-10/*mean (0.00494827), correlation (0.344948)*/,
-13,0, -13,5/*mean (0.0549145), correlation (0.344675)*/,
-13,-7, -12,12/*mean (0.103385), correlation (0.342715)*/,
-13,3, -11,8/*mean (0.134222), correlation (0.322922)*/,
-7,12, -4,7/*mean (0.153284), correlation (0.337061)*/,
6,-10, 12,8/*mean (0.154881), correlation (0.329257)*/,
-9,-1, -7,-6/*mean (0.200967), correlation (0.33312)*/,
-2,-5, 0,12/*mean (0.201518), correlation (0.340635)*/,
-12,5, -7,5/*mean (0.207805), correlation (0.335631)*/,
3,-10, 8,-13/*mean (0.224438), correlation (0.34504)*/,
-7,-7, -4,5/*mean (0.239361), correlation (0.338053)*/,
-3,-2, -1,-7/*mean (0.240744), correlation (0.344322)*/,
2,9, 5,-11/*mean (0.242949), correlation (0.34145)*/,
-11,-13, -5,-13/*mean (0.244028), correlation (0.336861)*/,
-1,6, 0,-1/*mean (0.247571), correlation (0.343684)*/,
5,-3, 5,2/*mean (0.000697256), correlation (0.357265)*/,
-4,-13, -4,12/*mean (0.00213675), correlation (0.373827)*/,
-9,-6, -9,6/*mean (0.0126856), correlation (0.373938)*/,
-12,-10, -8,-4/*mean (0.0152497), correlation (0.364237)*/,
10,2, 12,-3/*mean (0.0299933), correlation (0.345292)*/,
7,12, 12,12/*mean (0.0307242), correlation (0.366299)*/,
-7,-13, -6,5/*mean (0.0534975), correlation (0.368357)*/,
-4,9, -3,4/*mean (0.099865), correlation (0.372276)*/,
7,-1, 12,2/*mean (0.117083), correlation (0.364529)*/,
-7,6, -5,1/*mean (0.126125), correlation (0.369606)*/,
-13,11, -12,5/*mean (0.130364), correlation (0.358502)*/,
-3,7, -2,-6/*mean (0.131691), correlation (0.375531)*/,
7,-8, 12,-7/*mean (0.160166), correlation (0.379508)*/,
-13,-7, -11,-12/*mean (0.167848), correlation (0.353343)*/,
1,-3, 12,12/*mean (0.183378), correlation (0.371916)*/,
2,-6, 3,0/*mean (0.228711), correlation (0.371761)*/,
-4,3, -2,-13/*mean (0.247211), correlation (0.364063)*/,
-1,-13, 1,9/*mean (0.249325), correlation (0.378139)*/,
7,1, 8,-6/*mean (0.000652272), correlation (0.411682)*/,
1,-1, 3,12/*mean (0.00248538), correlation (0.392988)*/,
9,1, 12,6/*mean (0.0206815), correlation (0.386106)*/,
-1,-9, -1,3/*mean (0.0364485), correlation (0.410752)*/,
-13,-13, -10,5/*mean (0.0376068), correlation (0.398374)*/,
7,7, 10,12/*mean (0.0424202), correlation (0.405663)*/,
12,-5, 12,9/*mean (0.0942645), correlation (0.410422)*/,
6,3, 7,11/*mean (0.1074), correlation (0.413224)*/,
5,-13, 6,10/*mean (0.109256), correlation (0.408646)*/,
2,-12, 2,3/*mean (0.131691), correlation (0.416076)*/,
3,8, 4,-6/*mean (0.165081), correlation (0.417569)*/,
2,6, 12,-13/*mean (0.171874), correlation (0.408471)*/,
9,-12, 10,3/*mean (0.175146), correlation (0.41296)*/,
-8,4, -7,9/*mean (0.183682), correlation (0.402956)*/,
-11,12, -4,-6/*mean (0.184672), correlation (0.416125)*/,
1,12, 2,-8/*mean (0.191487), correlation (0.386696)*/,
6,-9, 7,-4/*mean (0.192668), correlation (0.394771)*/,
2,3, 3,-2/*mean (0.200157), correlation (0.408303)*/,
6,3, 11,0/*mean (0.204588), correlation (0.411762)*/,
3,-3, 8,-8/*mean (0.205904), correlation (0.416294)*/,
7,8, 9,3/*mean (0.213237), correlation (0.409306)*/,
-11,-5, -6,-4/*mean (0.243444), correlation (0.395069)*/,
-10,11, -5,10/*mean (0.247672), correlation (0.413392)*/,
-5,-8, -3,12/*mean (0.24774), correlation (0.411416)*/,
-10,5, -9,0/*mean (0.00213675), correlation (0.454003)*/,
8,-1, 12,-6/*mean (0.0293635), correlation (0.455368)*/,
4,-6, 6,-11/*mean (0.0404971), correlation (0.457393)*/,
-10,12, -8,7/*mean (0.0481107), correlation (0.448364)*/,
4,-2, 6,7/*mean (0.050641), correlation (0.455019)*/,
-2,0, -2,12/*mean (0.0525978), correlation (0.44338)*/,
-5,-8, -5,2/*mean (0.0629667), correlation (0.457096)*/,
7,-6, 10,12/*mean (0.0653846), correlation (0.445623)*/,
-9,-13, -8,-8/*mean (0.0858749), correlation (0.449789)*/,
-5,-13, -5,-2/*mean (0.122402), correlation (0.450201)*/,
8,-8, 9,-13/*mean (0.125416), correlation (0.453224)*/,
-9,-11, -9,0/*mean (0.130128), correlation (0.458724)*/,
1,-8, 1,-2/*mean (0.132467), correlation (0.440133)*/,
7,-4, 9,1/*mean (0.132692), correlation (0.454)*/,
-2,1, -1,-4/*mean (0.135695), correlation (0.455739)*/,
11,-6, 12,-11/*mean (0.142904), correlation (0.446114)*/,
-12,-9, -6,4/*mean (0.146165), correlation (0.451473)*/,
3,7, 7,12/*mean (0.147627), correlation (0.456643)*/,
5,5, 10,8/*mean (0.152901), correlation (0.455036)*/,
0,-4, 2,8/*mean (0.167083), correlation (0.459315)*/,
-9,12, -5,-13/*mean (0.173234), correlation (0.454706)*/,
0,7, 2,12/*mean (0.18312), correlation (0.433855)*/,
-1,2, 1,7/*mean (0.185504), correlation (0.443838)*/,
5,11, 7,-9/*mean (0.185706), correlation (0.451123)*/,
3,5, 6,-8/*mean (0.188968), correlation (0.455808)*/,
-13,-4, -8,9/*mean (0.191667), correlation (0.459128)*/,
-5,9, -3,-3/*mean (0.193196), correlation (0.458364)*/,
-4,-7, -3,-12/*mean (0.196536), correlation (0.455782)*/,
6,5, 8,0/*mean (0.1972), correlation (0.450481)*/,
-7,6, -6,12/*mean (0.199438), correlation (0.458156)*/,
-13,6, -5,-2/*mean (0.211224), correlation (0.449548)*/,
1,-10, 3,10/*mean (0.211718), correlation (0.440606)*/,
4,1, 8,-4/*mean (0.213034), correlation (0.443177)*/,
-2,-2, 2,-13/*mean (0.234334), correlation (0.455304)*/,
2,-12, 12,12/*mean (0.235684), correlation (0.443436)*/,
-2,-13, 0,-6/*mean (0.237674), correlation (0.452525)*/,
4,1, 9,3/*mean (0.23962), correlation (0.444824)*/,
-6,-10, -3,-5/*mean (0.248459), correlation (0.439621)*/,
-3,-13, -1,1/*mean (0.249505), correlation (0.456666)*/,
7,5, 12,-11/*mean (0.00119208), correlation (0.495466)*/,
4,-2, 5,-7/*mean (0.00372245), correlation (0.484214)*/,
-13,9, -9,-5/*mean (0.00741116), correlation (0.499854)*/,
7,1, 8,6/*mean (0.0208952), correlation (0.499773)*/,
7,-8, 7,6/*mean (0.0220085), correlation (0.501609)*/,
-7,-4, -7,1/*mean (0.0233806), correlation (0.496568)*/,
-8,11, -7,-8/*mean (0.0236505), correlation (0.489719)*/,
-13,6, -12,-8/*mean (0.0268781), correlation (0.503487)*/,
2,4, 3,9/*mean (0.0323324), correlation (0.501938)*/,
10,-5, 12,3/*mean (0.0399235), correlation (0.494029)*/,
-6,-5, -6,7/*mean (0.0420153), correlation (0.486579)*/,
8,-3, 9,-8/*mean (0.0548021), correlation (0.484237)*/,
2,-12, 2,8/*mean (0.0616622), correlation (0.496642)*/,
-11,-2, -10,3/*mean (0.0627755), correlation (0.498563)*/,
-12,-13, -7,-9/*mean (0.0829622), correlation (0.495491)*/,
-11,0, -10,-5/*mean (0.0843342), correlation (0.487146)*/,
5,-3, 11,8/*mean (0.0929937), correlation (0.502315)*/,
-2,-13, -1,12/*mean (0.113327), correlation (0.48941)*/,
-1,-8, 0,9/*mean (0.132119), correlation (0.467268)*/,
-13,-11, -12,-5/*mean (0.136269), correlation (0.498771)*/,
-10,-2, -10,11/*mean (0.142173), correlation (0.498714)*/,
-3,9, -2,-13/*mean (0.144141), correlation (0.491973)*/,
2,-3, 3,2/*mean (0.14892), correlation (0.500782)*/,
-9,-13, -4,0/*mean (0.150371), correlation (0.498211)*/,
-4,6, -3,-10/*mean (0.152159), correlation (0.495547)*/,
-4,12, -2,-7/*mean (0.156152), correlation (0.496925)*/,
-6,-11, -4,9/*mean (0.15749), correlation (0.499222)*/,
6,-3, 6,11/*mean (0.159211), correlation (0.503821)*/,
-13,11, -5,5/*mean (0.162427), correlation (0.501907)*/,
11,11, 12,6/*mean (0.16652), correlation (0.497632)*/,
7,-5, 12,-2/*mean (0.169141), correlation (0.484474)*/,
-1,12, 0,7/*mean (0.169456), correlation (0.495339)*/,
-4,-8, -3,-2/*mean (0.171457), correlation (0.487251)*/,
-7,1, -6,7/*mean (0.175), correlation (0.500024)*/,
-13,-12, -8,-13/*mean (0.175866), correlation (0.497523)*/,
-7,-2, -6,-8/*mean (0.178273), correlation (0.501854)*/,
-8,5, -6,-9/*mean (0.181107), correlation (0.494888)*/,
-5,-1, -4,5/*mean (0.190227), correlation (0.482557)*/,
-13,7, -8,10/*mean (0.196739), correlation (0.496503)*/,
1,5, 5,-13/*mean (0.19973), correlation (0.499759)*/,
1,0, 10,-13/*mean (0.204465), correlation (0.49873)*/,
9,12, 10,-1/*mean (0.209334), correlation (0.49063)*/,
5,-8, 10,-9/*mean (0.211134), correlation (0.503011)*/,
-1,11, 1,-13/*mean (0.212), correlation (0.499414)*/,
-9,-3, -6,2/*mean (0.212168), correlation (0.480739)*/,
-1,-10, 1,12/*mean (0.212731), correlation (0.502523)*/,
-13,1, -8,-10/*mean (0.21327), correlation (0.489786)*/,
8,-11, 10,-6/*mean (0.214159), correlation (0.488246)*/,
2,-13, 3,-6/*mean (0.216993), correlation (0.50287)*/,
7,-13, 12,-9/*mean (0.223639), correlation (0.470502)*/,
-10,-10, -5,-7/*mean (0.224089), correlation (0.500852)*/,
-10,-8, -8,-13/*mean (0.228666), correlation (0.502629)*/,
4,-6, 8,5/*mean (0.22906), correlation (0.498305)*/,
3,12, 8,-13/*mean (0.233378), correlation (0.503825)*/,
-4,2, -3,-3/*mean (0.234323), correlation (0.476692)*/,
5,-13, 10,-12/*mean (0.236392), correlation (0.475462)*/,
4,-13, 5,-1/*mean (0.236842), correlation (0.504132)*/,
-9,9, -4,3/*mean (0.236977), correlation (0.497739)*/,
0,3, 3,-9/*mean (0.24314), correlation (0.499398)*/,
-12,1, -6,1/*mean (0.243297), correlation (0.489447)*/,
3,2, 4,-8/*mean (0.00155196), correlation (0.553496)*/,
-10,-10, -10,9/*mean (0.00239541), correlation (0.54297)*/,
8,-13, 12,12/*mean (0.0034413), correlation (0.544361)*/,
-8,-12, -6,-5/*mean (0.003565), correlation (0.551225)*/,
2,2, 3,7/*mean (0.00835583), correlation (0.55285)*/,
10,6, 11,-8/*mean (0.00885065), correlation (0.540913)*/,
6,8, 8,-12/*mean (0.0101552), correlation (0.551085)*/,
-7,10, -6,5/*mean (0.0102227), correlation (0.533635)*/,
-3,-9, -3,9/*mean (0.0110211), correlation (0.543121)*/,
-1,-13, -1,5/*mean (0.0113473), correlation (0.550173)*/,
-3,-7, -3,4/*mean (0.0140913), correlation (0.554774)*/,
-8,-2, -8,3/*mean (0.017049), correlation (0.55461)*/,
4,2, 12,12/*mean (0.01778), correlation (0.546921)*/,
2,-5, 3,11/*mean (0.0224022), correlation (0.549667)*/,
6,-9, 11,-13/*mean (0.029161), correlation (0.546295)*/,
3,-1, 7,12/*mean (0.0303081), correlation (0.548599)*/,
11,-1, 12,4/*mean (0.0355151), correlation (0.523943)*/,
-3,0, -3,6/*mean (0.0417904), correlation (0.543395)*/,
4,-11, 4,12/*mean (0.0487292), correlation (0.542818)*/,
2,-4, 2,1/*mean (0.0575124), correlation (0.554888)*/,
-10,-6, -8,1/*mean (0.0594242), correlation (0.544026)*/,
-13,7, -11,1/*mean (0.0597391), correlation (0.550524)*/,
-13,12, -11,-13/*mean (0.0608974), correlation (0.55383)*/,
6,0, 11,-13/*mean (0.065126), correlation (0.552006)*/,
0,-1, 1,4/*mean (0.074224), correlation (0.546372)*/,
-13,3, -9,-2/*mean (0.0808592), correlation (0.554875)*/,
-9,8, -6,-3/*mean (0.0883378), correlation (0.551178)*/,
-13,-6, -8,-2/*mean (0.0901035), correlation (0.548446)*/,
5,-9, 8,10/*mean (0.0949843), correlation (0.554694)*/,
2,7, 3,-9/*mean (0.0994152), correlation (0.550979)*/,
-1,-6, -1,-1/*mean (0.10045), correlation (0.552714)*/,
9,5, 11,-2/*mean (0.100686), correlation (0.552594)*/,
11,-3, 12,-8/*mean (0.101091), correlation (0.532394)*/,
3,0, 3,5/*mean (0.101147), correlation (0.525576)*/,
-1,4, 0,10/*mean (0.105263), correlation (0.531498)*/,
3,-6, 4,5/*mean (0.110785), correlation (0.540491)*/,
-13,0, -10,5/*mean (0.112798), correlation (0.536582)*/,
5,8, 12,11/*mean (0.114181), correlation (0.555793)*/,
8,9, 9,-6/*mean (0.117431), correlation (0.553763)*/,
7,-4, 8,-12/*mean (0.118522), correlation (0.553452)*/,
-10,4, -10,9/*mean (0.12094), correlation (0.554785)*/,
7,3, 12,4/*mean (0.122582), correlation (0.555825)*/,
9,-7, 10,-2/*mean (0.124978), correlation (0.549846)*/,
7,0, 12,-2/*mean (0.127002), correlation (0.537452)*/,
-1,-6, 0,-11/*mean (0.127148), correlation (0.547401)*/
};
void initializeOrbPattern(const Point* pattern0, Mat& pattern, int ntuples, int tupleSize, int poolSize)
{
RNG rng(0x12345678);
pattern.create(2, ntuples * tupleSize, CV_32SC1);
pattern.setTo(Scalar::all(0));
int* pattern_x_ptr = pattern.ptr<int>(0);
int* pattern_y_ptr = pattern.ptr<int>(1);
for (int i = 0; i < ntuples; i++)
{
for (int k = 0; k < tupleSize; k++)
{
for(;;)
{
int idx = rng.uniform(0, poolSize);
Point pt = pattern0[idx];
int k1;
for (k1 = 0; k1 < k; k1++)
if (pattern_x_ptr[tupleSize * i + k1] == pt.x && pattern_y_ptr[tupleSize * i + k1] == pt.y)
break;
if (k1 == k)
{
pattern_x_ptr[tupleSize * i + k] = pt.x;
pattern_y_ptr[tupleSize * i + k] = pt.y;
break;
}
}
}
}
}
void makeRandomPattern(int patchSize, Point* pattern, int npoints)
{
// we always start with a fixed seed,
// to make patterns the same on each run
RNG rng(0x34985739);
for (int i = 0; i < npoints; i++)
{
pattern[i].x = rng.uniform(-patchSize / 2, patchSize / 2 + 1);
pattern[i].y = rng.uniform(-patchSize / 2, patchSize / 2 + 1);
}
}
}
cv::ocl::ORB_OCL::ORB_OCL(int nFeatures, float scaleFactor, int nLevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType, int patchSize) :
nFeatures_(nFeatures), scaleFactor_(scaleFactor), nLevels_(nLevels), edgeThreshold_(edgeThreshold), firstLevel_(firstLevel), WTA_K_(WTA_K),
scoreType_(scoreType), patchSize_(patchSize),
fastDetector_(DEFAULT_FAST_THRESHOLD)
{
CV_Assert(patchSize_ >= 2);
// fill the extractors and descriptors for the corresponding scales
float factor = 1.0f / scaleFactor_;
float n_desired_features_per_scale = nFeatures_ * (1.0f - factor) / (1.0f - std::pow(factor, nLevels_));
n_features_per_level_.resize(nLevels_);
size_t sum_n_features = 0;
for (int level = 0; level < nLevels_ - 1; ++level)
{
n_features_per_level_[level] = cvRound(n_desired_features_per_scale);
sum_n_features += n_features_per_level_[level];
n_desired_features_per_scale *= factor;
}
n_features_per_level_[nLevels_ - 1] = nFeatures - sum_n_features;
// pre-compute the end of a row in a circular patch
int half_patch_size = patchSize_ / 2;
std::vector<int> u_max(half_patch_size + 2);
for (int v = 0; v <= half_patch_size * std::sqrt(2.f) / 2 + 1; ++v)
u_max[v] = cvRound(std::sqrt(static_cast<float>(half_patch_size * half_patch_size - v * v)));
// Make sure we are symmetric
for (int v = half_patch_size, v_0 = 0; v >= half_patch_size * std::sqrt(2.f) / 2; --v)
{
while (u_max[v_0] == u_max[v_0 + 1])
++v_0;
u_max[v] = v_0;
++v_0;
}
CV_Assert(u_max.size() < 32);
//cv::cuda::device::orb::loadUMax(&u_max[0], static_cast<int>(u_max.size()));
uMax_ = oclMat(1, u_max.size(), CV_32SC1, &u_max[0]);
// Calc pattern
const int npoints = 512;
Point pattern_buf[npoints];
const Point* pattern0 = (const Point*)bit_pattern_31_;
if (patchSize_ != 31)
{
pattern0 = pattern_buf;
makeRandomPattern(patchSize_, pattern_buf, npoints);
}
CV_Assert(WTA_K_ == 2 || WTA_K_ == 3 || WTA_K_ == 4);
Mat h_pattern;
if (WTA_K_ == 2)
{
h_pattern.create(2, npoints, CV_32SC1);
int* pattern_x_ptr = h_pattern.ptr<int>(0);
int* pattern_y_ptr = h_pattern.ptr<int>(1);
for (int i = 0; i < npoints; ++i)
{
pattern_x_ptr[i] = pattern0[i].x;
pattern_y_ptr[i] = pattern0[i].y;
}
}
else
{
int ntuples = descriptorSize() * 4;
initializeOrbPattern(pattern0, h_pattern, ntuples, WTA_K_, npoints);
}
pattern_.upload(h_pattern);
//blurFilter = ocl::createGaussianFilter(CV_8UC1, -1, Size(7, 7), 2, 2, BORDER_REFLECT_101);
blurFilter = ocl::createGaussianFilter_GPU(CV_8UC1, Size(7, 7), 2, 2, BORDER_REFLECT_101);
blurForDescriptor = true;
}
namespace
{
inline float getScale(float scaleFactor, int firstLevel, int level)
{
return pow(scaleFactor, level - firstLevel);
}
}
void cv::ocl::ORB_OCL::buildScalePyramids(const oclMat& image, const oclMat& mask)
{
CV_Assert(image.type() == CV_8UC1);
CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size()));
imagePyr_.resize(nLevels_);
maskPyr_.resize(nLevels_);
for (int level = 0; level < nLevels_; ++level)
{
float scale = 1.0f / getScale(scaleFactor_, firstLevel_, level);
Size sz(cvRound(image.cols * scale), cvRound(image.rows * scale));
ensureSizeIsEnough(sz, image.type(), imagePyr_[level]);
ensureSizeIsEnough(sz, CV_8UC1, maskPyr_[level]);
maskPyr_[level].setTo(Scalar::all(255));
// Compute the resized image
if (level != firstLevel_)
{
if (level < firstLevel_)
{
ocl::resize(image, imagePyr_[level], sz, 0, 0, INTER_LINEAR);
if (!mask.empty())
ocl::resize(mask, maskPyr_[level], sz, 0, 0, INTER_LINEAR);
}
else
{
ocl::resize(imagePyr_[level - 1], imagePyr_[level], sz, 0, 0, INTER_LINEAR);
if (!mask.empty())
{
ocl::resize(maskPyr_[level - 1], maskPyr_[level], sz, 0, 0, INTER_LINEAR);
ocl::threshold(maskPyr_[level], maskPyr_[level], 254, 0, THRESH_TOZERO);
}
}
}
else
{
image.copyTo(imagePyr_[level]);
if (!mask.empty())
mask.copyTo(maskPyr_[level]);
}
// Filter keypoints by image border
ensureSizeIsEnough(sz, CV_8UC1, buf_);
buf_.setTo(Scalar::all(0));
Rect inner(edgeThreshold_, edgeThreshold_, sz.width - 2 * edgeThreshold_, sz.height - 2 * edgeThreshold_);
buf_(inner).setTo(Scalar::all(255));
ocl::bitwise_and(maskPyr_[level], buf_, maskPyr_[level]);
}
}
static void HarrisResponses_OCL(const oclMat& img, oclMat& keypoints, const int npoints, int blockSize, float harris_k)
{
size_t localThreads[3] = {32, 8, 1};
size_t globalThreads[3] = {divUp(npoints, localThreads[1]) * localThreads[1] * localThreads[0],
1,
1};
Context *clCxt = Context::getContext();
String kernelName = "HarrisResponses";
std::vector< std::pair<size_t, const void *> > args;
int imgStep = img.step / img.elemSize();
int keypointsStep = keypoints.step / keypoints.elemSize();
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&img.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypoints.data));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&npoints));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&blockSize));
args.push_back( std::make_pair( sizeof(cl_float), (void *)&harris_k));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&imgStep));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsStep));
bool is_cpu = isCpuDevice();
if (is_cpu)
openCLExecuteKernel(clCxt, &orb, kernelName, globalThreads, localThreads, args, -1, -1, (char*)"-D CPU");
else
{
cl_kernel kernel = openCLGetKernelFromSource(Context::getContext(), &orb, kernelName);
int wave_size = (int)queryWaveFrontSize(kernel);
openCLSafeCall(clReleaseKernel(kernel));
std::string opt = format("-D WAVE_SIZE=%d", wave_size);
openCLExecuteKernel(Context::getContext(), &orb, kernelName, globalThreads, localThreads, args, -1, -1, opt.c_str());
}
}
static void IC_Angle_OCL(const oclMat& image, oclMat& keypoints, const oclMat& uMax, int npoints, int half_k)
{
size_t localThreads[3] = {32, 8, 1};
size_t globalThreads[3] = {divUp(npoints, localThreads[1]) * localThreads[1] * localThreads[0],
1,
1};
Context *clCxt = Context::getContext();
String kernelName = "IC_Angle";
std::vector< std::pair<size_t, const void *> > args;
int imageStep = image.step / image.elemSize();
int keypointsStep = keypoints.step / keypoints.elemSize();
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&image.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypoints.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&uMax.data));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&npoints));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&half_k));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&imageStep));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsStep));
bool is_cpu = isCpuDevice();
if (is_cpu)
openCLExecuteKernel(clCxt, &orb, kernelName, globalThreads, localThreads, args, -1, -1, (char*)"-D CPU");
else
{
cl_kernel kernel = openCLGetKernelFromSource(Context::getContext(), &orb, kernelName);
int wave_size = (int)queryWaveFrontSize(kernel);
openCLSafeCall(clReleaseKernel(kernel));
std::string opt = format("-D WAVE_SIZE=%d", wave_size);
openCLExecuteKernel(Context::getContext(), &orb, kernelName, globalThreads, localThreads, args, -1, -1, opt.c_str());
}
}
static void convertRowsToChannels_OCL(const oclMat& keypointsIn, oclMat& keypointsOut, int npoints)
{
size_t localThreads[3] = {256, 1, 1};
size_t globalThreads[3] = {divUp(npoints, localThreads[0]) * localThreads[0],
1,
1};
Context *clCxt = Context::getContext();
String kernelName = "convertRowsToChannels";
std::vector< std::pair<size_t, const void *> > args;
int keypointsInStep = keypointsIn.step / keypointsIn.elemSize();
int keypointsOutStep = keypointsOut.step / keypointsOut.elemSize();
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypointsIn.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypointsOut.data));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&npoints));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsInStep));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsOutStep));
openCLExecuteKernel(clCxt, &orb, kernelName, globalThreads, localThreads, args, -1, -1);
}
static void convertChannelsToRows_OCL(const oclMat& keypointsPos, const oclMat& keypointsResp,
oclMat& keypointsOut, int npoints)
{
size_t localThreads[3] = {256, 1, 1};
size_t globalThreads[3] = {divUp(npoints, localThreads[0]) * localThreads[0],
1,
1};
Context *clCxt = Context::getContext();
String kernelName = "convertChannelsToRows";
std::vector< std::pair<size_t, const void *> > args;
int keypointsPosStep = keypointsPos.step / keypointsResp.elemSize();
int keypointsRespStep = keypointsResp.step / keypointsResp.elemSize();
int keypointsOutStep = keypointsOut.step / keypointsOut.elemSize();
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypointsPos.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypointsResp.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypointsOut.data));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&npoints));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsPosStep));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsRespStep));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsOutStep));
openCLExecuteKernel(clCxt, &orb, kernelName, globalThreads, localThreads, args, -1, -1);
}
void cv::ocl::ORB_OCL::computeKeyPointsPyramid()
{
int half_patch_size = patchSize_ / 2;
keyPointsPyr_.resize(nLevels_);
keyPointsCount_.resize(nLevels_);
for (int level = 0; level < nLevels_; ++level)
{
keyPointsCount_[level] = fastDetector_.calcKeyPointsLocation(imagePyr_[level], maskPyr_[level]);
if (keyPointsCount_[level] == 0)
continue;
keyPointsCount_[level] = fastDetector_.getKeyPoints(keyPointsPyr_[level]);
if (keyPointsCount_[level] == 0)
continue;
int n_features = static_cast<int>(n_features_per_level_[level]);
if (scoreType_ == ORB::HARRIS_SCORE)
{
int featuresToIncrease = 2 * n_features - keyPointsPyr_[level].cols;
if (featuresToIncrease < 0) featuresToIncrease = 0;
// Keeps more points than necessary as FAST does not give amazing corners
// and expands rows in the keypoint matrix to store angle, octave and size
copyMakeBorder(keyPointsPyr_[level], keyPointsPyr_[level],
0, ROWS_COUNT-keyPointsPyr_[level].rows,
0, featuresToIncrease,
BORDER_CONSTANT, 0.f);
// Compute the Harris cornerness (better scoring than FAST)
HarrisResponses_OCL(imagePyr_[level], keyPointsPyr_[level], keyPointsCount_[level], 7, HARRIS_K);
}
else
{
// Expands rows in the keypoint matrix to store angle, octave and size
copyMakeBorder(keyPointsPyr_[level], keyPointsPyr_[level],
0, ROWS_COUNT-keyPointsPyr_[level].rows,
0, 0,
BORDER_CONSTANT, 0.f);
}
// To use sortByKey the keypoint locations have to be reorganized as one row and two channels,
// leaving the keys (responses) as a one row, one channel matrix.
// TODO: change this when sortByRow is implemented.
oclMat keypointsResp, keypointsPos(1,keyPointsCount_[level],CV_32FC2);
keyPointsPyr_[level].row(RESPONSE_ROW).colRange(0,keyPointsCount_[level]).copyTo(keypointsResp);
convertRowsToChannels_OCL(keyPointsPyr_[level].rowRange(0,2), keypointsPos, keyPointsCount_[level]);
ocl::sortByKey(keypointsResp, keypointsPos, SORT_MERGE, true);
keyPointsCount_[level] = std::min(n_features,keyPointsCount_[level]);
// The data is then reorganized back to one channel, three rows (X_ROW, Y_ROW, RESPONSE_ROW)
convertChannelsToRows_OCL(keypointsPos, keypointsResp, keyPointsPyr_[level], keyPointsCount_[level]);
// Compute orientation
IC_Angle_OCL(imagePyr_[level], keyPointsPyr_[level], uMax_, keyPointsCount_[level], half_patch_size);
}
}
static void computeOrbDescriptor_OCL(const oclMat& img, const oclMat& keypoints, const oclMat& pattern,
oclMat& desc, const int npoints, const int dsize, const int WTA_K,
const int offset)
{
size_t localThreads[3] = {32, 8, 1};
size_t globalThreads[3] = {divUp(dsize, localThreads[0]) * localThreads[0],
divUp(npoints, localThreads[1]) * localThreads[1],
1};
Context *clCxt = Context::getContext();
String kernelName = "computeOrbDescriptor";
std::vector< std::pair<size_t, const void *> > args;
int imgStep = img.step / img.elemSize();
int keypointsStep = keypoints.step / keypoints.elemSize();
int patternStep = pattern.step / pattern.elemSize();
int descStep = desc.step / desc.elemSize();
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&img.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypoints.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&pattern.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&desc.data));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&npoints));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&dsize));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&WTA_K));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&offset));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&imgStep));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsStep));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&patternStep));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&descStep));
openCLExecuteKernel(clCxt, &orb, kernelName, globalThreads, localThreads, args, -1, -1);
}
void cv::ocl::ORB_OCL::computeDescriptors(oclMat& descriptors)
{
int nAllkeypoints = 0;
for (int level = 0; level < nLevels_; ++level)
nAllkeypoints += keyPointsCount_[level];
if (nAllkeypoints == 0)
{
descriptors.release();
return;
}
ensureSizeIsEnough(nAllkeypoints, descriptorSize(), CV_8UC1, descriptors);
int offset = 0;
for (int level = 0; level < nLevels_; ++level)
{
if (keyPointsCount_[level] == 0)
continue;
if (blurForDescriptor)
{
// preprocess the resized image
ensureSizeIsEnough(imagePyr_[level].size(), imagePyr_[level].type(), buf_);
blurFilter->apply(imagePyr_[level], buf_);
}
computeOrbDescriptor_OCL(blurForDescriptor ? buf_ : imagePyr_[level], keyPointsPyr_[level],
pattern_, descriptors, keyPointsCount_[level], descriptorSize(), WTA_K_, offset);
offset += keyPointsCount_[level];
}
}
static void mergeLocation_OCL(const oclMat& keypointsIn, oclMat& keypointsOut, const int npoints,
const int offset, const float scale, const int octave, const float size)
{
size_t localThreads[3] = {256, 1, 1};
size_t globalThreads[3] = {divUp(npoints, localThreads[0]) * localThreads[0],
1,
1};
Context *clCxt = Context::getContext();
String kernelName = "mergeLocation";
std::vector< std::pair<size_t, const void *> > args;
int keypointsInStep = keypointsIn.step / keypointsIn.elemSize();
int keypointsOutStep = keypointsOut.step / keypointsOut.elemSize();
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypointsIn.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypointsOut.data));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&npoints));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&offset));
args.push_back( std::make_pair( sizeof(cl_float), (void *)&scale));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&octave));
args.push_back( std::make_pair( sizeof(cl_float), (void *)&size));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsInStep));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsOutStep));
openCLExecuteKernel(clCxt, &orb, kernelName, globalThreads, localThreads, args, -1, -1);
}
void cv::ocl::ORB_OCL::mergeKeyPoints(oclMat& keypoints)
{
int nAllkeypoints = 0;
for (int level = 0; level < nLevels_; ++level)
nAllkeypoints += keyPointsCount_[level];
if (nAllkeypoints == 0)
{
keypoints.release();
return;
}
ensureSizeIsEnough(ROWS_COUNT, nAllkeypoints, CV_32FC1, keypoints);
int offset = 0;
for (int level = 0; level < nLevels_; ++level)
{
if (keyPointsCount_[level] == 0)
continue;
float sf = getScale(scaleFactor_, firstLevel_, level);
float locScale = level != firstLevel_ ? sf : 1.0f;
float size = patchSize_ * sf;
mergeLocation_OCL(keyPointsPyr_[level], keypoints, keyPointsCount_[level], offset, locScale, level, size);
offset += keyPointsCount_[level];
}
}
void cv::ocl::ORB_OCL::downloadKeyPoints(const oclMat &d_keypoints, std::vector<KeyPoint>& keypoints)
{
if (d_keypoints.empty())
{
keypoints.clear();
return;
}
Mat h_keypoints(d_keypoints);
convertKeyPoints(h_keypoints, keypoints);
}
void cv::ocl::ORB_OCL::convertKeyPoints(const Mat &d_keypoints, std::vector<KeyPoint>& keypoints)
{
if (d_keypoints.empty())
{
keypoints.clear();
return;
}
CV_Assert(d_keypoints.type() == CV_32FC1 && d_keypoints.rows == ROWS_COUNT);
const float* x_ptr = d_keypoints.ptr<float>(X_ROW);
const float* y_ptr = d_keypoints.ptr<float>(Y_ROW);
const float* response_ptr = d_keypoints.ptr<float>(RESPONSE_ROW);
const float* angle_ptr = d_keypoints.ptr<float>(ANGLE_ROW);
const float* octave_ptr = d_keypoints.ptr<float>(OCTAVE_ROW);
const float* size_ptr = d_keypoints.ptr<float>(SIZE_ROW);
keypoints.resize(d_keypoints.cols);
for (int i = 0; i < d_keypoints.cols; ++i)
{
KeyPoint kp;
kp.pt.x = x_ptr[i];
kp.pt.y = y_ptr[i];
kp.response = response_ptr[i];
kp.angle = angle_ptr[i];
kp.octave = static_cast<int>(octave_ptr[i]);
kp.size = size_ptr[i];
keypoints[i] = kp;
}
}
void cv::ocl::ORB_OCL::operator()(const oclMat& image, const oclMat& mask, oclMat& keypoints)
{
buildScalePyramids(image, mask);
computeKeyPointsPyramid();
mergeKeyPoints(keypoints);
}
void cv::ocl::ORB_OCL::operator()(const oclMat& image, const oclMat& mask, oclMat& keypoints, oclMat& descriptors)
{
buildScalePyramids(image, mask);
computeKeyPointsPyramid();
computeDescriptors(descriptors);
mergeKeyPoints(keypoints);
}
void cv::ocl::ORB_OCL::operator()(const oclMat& image, const oclMat& mask, std::vector<KeyPoint>& keypoints)
{
(*this)(image, mask, d_keypoints_);
downloadKeyPoints(d_keypoints_, keypoints);
}
void cv::ocl::ORB_OCL::operator()(const oclMat& image, const oclMat& mask, std::vector<KeyPoint>& keypoints, oclMat& descriptors)
{
(*this)(image, mask, d_keypoints_, descriptors);
downloadKeyPoints(d_keypoints_, keypoints);
}
void cv::ocl::ORB_OCL::release()
{
imagePyr_.clear();
maskPyr_.clear();
buf_.release();
keyPointsPyr_.clear();
fastDetector_.release();
d_keypoints_.release();
uMax_.release();
}

1
modules/ocl/src/precomp.hpp
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@ -72,6 +72,7 @@
#include "opencv2/imgproc.hpp"
#include "opencv2/objdetect/objdetect_c.h"
#include "opencv2/ocl.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/core/private.hpp"

138
modules/ocl/test/test_orb.cpp Normal file
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@ -0,0 +1,138 @@
/*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) 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.
//
// Authors:
// * Peter Andreas Entschev, peter@entschev.com
//
//M*/
#include "test_precomp.hpp"
#ifdef HAVE_OPENCL
////////////////////////////////////////////////////////
// ORB
namespace
{
IMPLEMENT_PARAM_CLASS(ORB_FeaturesCount, int)
IMPLEMENT_PARAM_CLASS(ORB_ScaleFactor, float)
IMPLEMENT_PARAM_CLASS(ORB_LevelsCount, int)
IMPLEMENT_PARAM_CLASS(ORB_EdgeThreshold, int)
IMPLEMENT_PARAM_CLASS(ORB_firstLevel, int)
IMPLEMENT_PARAM_CLASS(ORB_WTA_K, int)
IMPLEMENT_PARAM_CLASS(ORB_PatchSize, int)
IMPLEMENT_PARAM_CLASS(ORB_BlurForDescriptor, bool)
}
CV_ENUM(ORB_ScoreType, ORB::HARRIS_SCORE, ORB::FAST_SCORE)
PARAM_TEST_CASE(ORB, ORB_FeaturesCount, ORB_ScaleFactor, ORB_LevelsCount, ORB_EdgeThreshold,
ORB_firstLevel, ORB_WTA_K, ORB_ScoreType, ORB_PatchSize, ORB_BlurForDescriptor)
{
int nFeatures;
float scaleFactor;
int nLevels;
int edgeThreshold;
int firstLevel;
int WTA_K;
int scoreType;
int patchSize;
bool blurForDescriptor;
virtual void SetUp()
{
nFeatures = GET_PARAM(0);
scaleFactor = GET_PARAM(1);
nLevels = GET_PARAM(2);
edgeThreshold = GET_PARAM(3);
firstLevel = GET_PARAM(4);
WTA_K = GET_PARAM(5);
scoreType = GET_PARAM(6);
patchSize = GET_PARAM(7);
blurForDescriptor = GET_PARAM(8);
}
};
OCL_TEST_P(ORB, Accuracy)
{
cv::Mat image = readImage("gpu/perf/aloe.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(image.empty());
cv::Mat mask(image.size(), CV_8UC1, cv::Scalar::all(1));
mask(cv::Range(0, image.rows / 2), cv::Range(0, image.cols / 2)).setTo(cv::Scalar::all(0));
cv::ocl::oclMat ocl_image = cv::ocl::oclMat(image);
cv::ocl::oclMat ocl_mask = cv::ocl::oclMat(mask);
cv::ocl::ORB_OCL orb(nFeatures, scaleFactor, nLevels, edgeThreshold, firstLevel, WTA_K, scoreType, patchSize);
orb.blurForDescriptor = blurForDescriptor;
std::vector<cv::KeyPoint> keypoints;
cv::ocl::oclMat descriptors;
orb(ocl_image, ocl_mask, keypoints, descriptors);
cv::ORB orb_gold(nFeatures, scaleFactor, nLevels, edgeThreshold, firstLevel, WTA_K, scoreType, patchSize);
std::vector<cv::KeyPoint> keypoints_gold;
cv::Mat descriptors_gold;
orb_gold(image, mask, keypoints_gold, descriptors_gold);
cv::BFMatcher matcher(cv::NORM_HAMMING);
std::vector<cv::DMatch> matches;
matcher.match(descriptors_gold, cv::Mat(descriptors), matches);
int matchedCount = getMatchedPointsCount(keypoints_gold, keypoints, matches);
double matchedRatio = static_cast<double>(matchedCount) / keypoints.size();
EXPECT_GT(matchedRatio, 0.35);
}
INSTANTIATE_TEST_CASE_P(OCL_Features2D, ORB, testing::Combine(
testing::Values(ORB_FeaturesCount(1000)),
testing::Values(ORB_ScaleFactor(1.2f)),
testing::Values(ORB_LevelsCount(4), ORB_LevelsCount(8)),
testing::Values(ORB_EdgeThreshold(31)),
testing::Values(ORB_firstLevel(0), ORB_firstLevel(2)),
testing::Values(ORB_WTA_K(2), ORB_WTA_K(3), ORB_WTA_K(4)),
testing::Values(ORB_ScoreType(cv::ORB::HARRIS_SCORE)),
testing::Values(ORB_PatchSize(31), ORB_PatchSize(29)),
testing::Values(ORB_BlurForDescriptor(false), ORB_BlurForDescriptor(true))));
#endif

38
modules/ocl/test/utility.cpp
View File

@ -325,4 +325,42 @@ testing::AssertionResult assertKeyPointsEquals(const char* gold_expr, const char
return ::testing::AssertionSuccess();
}
int getMatchedPointsCount(std::vector<cv::KeyPoint>& gold, std::vector<cv::KeyPoint>& actual)
{
std::sort(actual.begin(), actual.end(), KeyPointLess());
std::sort(gold.begin(), gold.end(), KeyPointLess());
int validCount = 0;
size_t sz = std::min(gold.size(), actual.size());
for (size_t i = 0; i < sz; ++i)
{
const cv::KeyPoint& p1 = gold[i];
const cv::KeyPoint& p2 = actual[i];
if (keyPointsEquals(p1, p2))
++validCount;
}
return validCount;
}
int getMatchedPointsCount(const std::vector<cv::KeyPoint>& keypoints1, const std::vector<cv::KeyPoint>& keypoints2, const std::vector<cv::DMatch>& matches)
{
int validCount = 0;
for (size_t i = 0; i < matches.size(); ++i)
{
const cv::DMatch& m = matches[i];
const cv::KeyPoint& p1 = keypoints1[m.queryIdx];
const cv::KeyPoint& p2 = keypoints2[m.trainIdx];
if (keyPointsEquals(p1, p2))
++validCount;
}
return validCount;
}
} // namespace cvtest

2
modules/ocl/test/utility.hpp
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@ -56,6 +56,8 @@ namespace cvtest {
testing::AssertionResult assertKeyPointsEquals(const char* gold_expr, const char* actual_expr, std::vector<cv::KeyPoint>& gold, std::vector<cv::KeyPoint>& actual);
#define ASSERT_KEYPOINTS_EQ(gold, actual) EXPECT_PRED_FORMAT2(assertKeyPointsEquals, gold, actual)
CV_EXPORTS int getMatchedPointsCount(std::vector<cv::KeyPoint>& gold, std::vector<cv::KeyPoint>& actual);
CV_EXPORTS int getMatchedPointsCount(const std::vector<cv::KeyPoint>& keypoints1, const std::vector<cv::KeyPoint>& keypoints2, const std::vector<cv::DMatch>& matches);
void showDiff(const Mat& src, const Mat& gold, const Mat& actual, double eps, bool alwaysShow = false);