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Commit b43d6b68 authored by Andrey Pavlenko's avatar Andrey Pavlenko Committed by OpenCV Buildbot
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Merge pull request #1954 from pentschev:ocl_features2d_fast_master

parents 5299b134 41bec1b1
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modules/ocl/doc/feature_detection_and_description.rst
View file @ b43d6b68
......@@ -305,6 +305,153 @@ Converts matrices obtained via :ocv:func:`ocl::BruteForceMatcher_OCL_base::radiu
If ``compactResult`` is ``true`` , the ``matches`` vector does not contain matches for fully masked-out query descriptors.
ocl::FAST_OCL
------------------
.. ocv:class:: ocl::FAST_OCL
Class used for corner detection using the FAST algorithm. ::
class CV_EXPORTS FAST_OCL
{
public:
enum
{
X_ROW = 0,
Y_ROW,
RESPONSE_ROW,
ROWS_COUNT
};
// all features have same size
static const int FEATURE_SIZE = 7;
explicit FAST_OCL(int threshold, bool nonmaxSupression = true, double keypointsRatio = 0.05);
//! finds the keypoints using FAST detector
//! supports only CV_8UC1 images
void operator ()(const oclMat& image, const oclMat& mask, oclMat& keypoints);
void operator ()(const oclMat& image, const oclMat& mask, std::vector<KeyPoint>& keypoints);
//! 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& h_keypoints, std::vector<KeyPoint>& keypoints);
//! release temporary buffer's memory
void release();
bool nonmaxSupression;
int threshold;
//! max keypoints = keypointsRatio * img.size().area()
double keypointsRatio;
//! find keypoints and compute it's response if nonmaxSupression is true
//! return count of detected keypoints
int calcKeyPointsLocation(const oclMat& image, const oclMat& mask);
//! get final array of keypoints
//! performs nonmax supression if needed
//! return final count of keypoints
int getKeyPoints(oclMat& keypoints);
private:
// Hidden
};
The class ``FAST_OCL`` implements FAST corner detection algorithm.
.. seealso:: :ocv:func:`FAST`
ocl::FAST_OCL::FAST_OCL
--------------------------
Constructor.
.. ocv:function:: ocl::FAST_OCL::FAST_OCL(int threshold, bool nonmaxSupression = true, double keypointsRatio = 0.05)
:param threshold: Threshold on difference between intensity of the central pixel and pixels on a circle around this pixel.
:param nonmaxSupression: If it is true, non-maximum suppression is applied to detected corners (keypoints).
:param keypointsRatio: Inner buffer size for keypoints store is determined as (keypointsRatio * image_width * image_height).
ocl::FAST_OCL::operator ()
----------------------------
Finds the keypoints using FAST detector.
.. ocv:function:: void ocl::FAST_OCL::operator ()(const oclMat& image, const oclMat& mask, oclMat& keypoints)
.. ocv:function:: void ocl::FAST_OCL::operator ()(const oclMat& image, const oclMat& mask, std::vector<KeyPoint>& keypoints)
:param image: Image where keypoints (corners) are detected. Only 8-bit grayscale images are supported.
:param mask: Optional input mask that marks the regions where we should detect features.
:param keypoints: The output vector of keypoints. Can be stored both in host or device memory. For device memory:
* X_ROW of keypoints will contain the horizontal coordinate of the i'th point
* Y_ROW of keypoints will contain the vertical coordinate of the i'th point
* RESPONSE_ROW will contain response of i'th point (if non-maximum suppression is applied)
ocl::FAST_OCL::downloadKeypoints
----------------------------------
Download keypoints from device to host memory.
.. ocv:function:: void ocl::FAST_OCL::downloadKeypoints(const oclMat& d_keypoints, std::vector<KeyPoint>& keypoints)
ocl::FAST_OCL::convertKeypoints
---------------------------------
Converts keypoints from OpenCL representation to vector of ``KeyPoint``.
.. ocv:function:: void ocl::FAST_OCL::convertKeypoints(const Mat& h_keypoints, std::vector<KeyPoint>& keypoints)
ocl::FAST_OCL::release
------------------------
Releases inner buffer memory.
.. ocv:function:: void ocl::FAST_OCL::release()
ocl::FAST_OCL::calcKeyPointsLocation
--------------------------------------
Find keypoints. If ``nonmaxSupression`` is true, responses are computed and eliminates keypoints with the smaller responses from 9-neighborhood regions.
.. ocv:function:: int ocl::FAST_OCL::calcKeyPointsLocation(const oclMat& image, const oclMat& mask)
:param image: Image where keypoints (corners) are detected. Only 8-bit grayscale images are supported.
:param mask: Optional input mask that marks the regions where we should detect features.
The function returns the amount of detected keypoints.
ocl::FAST_OCL::getKeyPoints
-----------------------------
Gets final array of keypoints.
.. ocv:function:: int ocl::FAST_OCL::getKeyPoints(oclMat& keypoints)
:param keypoints: The output vector of keypoints.
The function performs non-max suppression if needed and returns the final amount of keypoints.
ocl::HOGDescriptor
----------------------
......
modules/ocl/include/opencv2/ocl.hpp
View file @ b43d6b68
......@@ -1482,6 +1482,65 @@ namespace cv
harrisK = harrisK_;
}
////////////////////////////////// FAST Feature Detector //////////////////////////////////
class CV_EXPORTS FAST_OCL
{
public:
enum
{
X_ROW = 0,
Y_ROW,
RESPONSE_ROW,
ROWS_COUNT
};
// all features have same size
static const int FEATURE_SIZE = 7;
explicit FAST_OCL(int threshold, bool nonmaxSupression = true, double keypointsRatio = 0.05);
//! finds the keypoints using FAST detector
//! supports only CV_8UC1 images
void operator ()(const oclMat& image, const oclMat& mask, oclMat& keypoints);
void operator ()(const oclMat& image, const oclMat& mask, std::vector<KeyPoint>& keypoints);
//! 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& h_keypoints, std::vector<KeyPoint>& keypoints);
//! release temporary buffer's memory
void release();
bool nonmaxSupression;
int threshold;
//! max keypoints = keypointsRatio * img.size().area()
double keypointsRatio;
//! find keypoints and compute it's response if nonmaxSupression is true
//! return count of detected keypoints
int calcKeyPointsLocation(const oclMat& image, const oclMat& mask);
//! get final array of keypoints
//! performs nonmax supression if needed
//! return final count of keypoints
int getKeyPoints(oclMat& keypoints);
private:
oclMat kpLoc_;
int count_;
oclMat score_;
oclMat d_keypoints_;
int calcKeypointsOCL(const oclMat& img, const oclMat& mask, int maxKeypoints);
int nonmaxSupressionOCL(oclMat& keypoints);
};
/////////////////////////////// PyrLKOpticalFlow /////////////////////////////////////
class CV_EXPORTS PyrLKOpticalFlow
......
modules/ocl/perf/perf_fast.cpp 0 → 100644
View file @ b43d6b68
/*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;
///////////// FAST ////////////////////////
typedef std::tr1::tuple<std::string, int, bool> Image_Threshold_NonmaxSupression_t;
typedef perf::TestBaseWithParam<Image_Threshold_NonmaxSupression_t> Image_Threshold_NonmaxSupression;
PERF_TEST_P(Image_Threshold_NonmaxSupression, FAST,
testing::Combine(testing::Values<string>("gpu/perf/aloe.png"),
testing::Values(20),
testing::Bool()))
{
const Image_Threshold_NonmaxSupression_t params = GetParam();
const std::string imgFile = std::tr1::get<0>(params);
const int threshold = std::tr1::get<1>(params);
const bool nonmaxSupression = std::tr1::get<2>(params);
const cv::Mat img = imread(getDataPath(imgFile), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
if (RUN_OCL_IMPL)
{
cv::ocl::FAST_OCL fast(threshold, nonmaxSupression, 0.5);
cv::ocl::oclMat d_img(img);
cv::ocl::oclMat d_keypoints;
OCL_TEST_CYCLE() fast(d_img, cv::ocl::oclMat(), d_keypoints);
std::vector<cv::KeyPoint> ocl_keypoints;
fast.downloadKeypoints(d_keypoints, ocl_keypoints);
sortKeyPoints(ocl_keypoints);
SANITY_CHECK_KEYPOINTS(ocl_keypoints);
}
else if (RUN_PLAIN_IMPL)
{
std::vector<cv::KeyPoint> cpu_keypoints;
TEST_CYCLE() cv::FAST(img, cpu_keypoints, threshold, nonmaxSupression);
SANITY_CHECK_KEYPOINTS(cpu_keypoints);
}
else
OCL_PERF_ELSE;
}
modules/ocl/perf/perf_precomp.hpp
View file @ b43d6b68
......@@ -116,6 +116,7 @@ using namespace cv;
#define OCL_TEST_CYCLE() for(; startTimer(), next(); cv::ocl::finish(), stopTimer())
#define OCL_TEST_CYCLE_MULTIRUN(runsNum) for(declare.runs(runsNum); startTimer(), next(); stopTimer()) for(int r = 0; r < runsNum; cv::ocl::finish(), ++r)
// TODO: Move to the ts module
namespace cvtest {
namespace ocl {
inline void checkDeviceMaxMemoryAllocSize(const Size& size, int type, int factor = 1)
......@@ -133,6 +134,60 @@ inline void checkDeviceMaxMemoryAllocSize(const Size& size, int type, int factor
throw perf::TestBase::PerfSkipTestException();
}
}
struct KeypointIdxCompare
{
std::vector<cv::KeyPoint>* keypoints;
explicit KeypointIdxCompare(std::vector<cv::KeyPoint>* _keypoints) : keypoints(_keypoints) {}
bool operator ()(size_t i1, size_t i2) const
{
cv::KeyPoint kp1 = (*keypoints)[i1];
cv::KeyPoint kp2 = (*keypoints)[i2];
if (kp1.pt.x != kp2.pt.x)
return kp1.pt.x < kp2.pt.x;
if (kp1.pt.y != kp2.pt.y)
return kp1.pt.y < kp2.pt.y;
if (kp1.response != kp2.response)
return kp1.response < kp2.response;
return kp1.octave < kp2.octave;
}
};
inline void sortKeyPoints(std::vector<cv::KeyPoint>& keypoints, cv::InputOutputArray _descriptors = cv::noArray())
{
std::vector<size_t> indexies(keypoints.size());
for (size_t i = 0; i < indexies.size(); ++i)
indexies[i] = i;
std::sort(indexies.begin(), indexies.end(), KeypointIdxCompare(&keypoints));
std::vector<cv::KeyPoint> new_keypoints;
cv::Mat new_descriptors;
new_keypoints.resize(keypoints.size());
cv::Mat descriptors;
if (_descriptors.needed())
{
descriptors = _descriptors.getMat();
new_descriptors.create(descriptors.size(), descriptors.type());
}
for (size_t i = 0; i < indexies.size(); ++i)
{
size_t new_idx = indexies[i];
new_keypoints[i] = keypoints[new_idx];
if (!new_descriptors.empty())
descriptors.row((int) new_idx).copyTo(new_descriptors.row((int) i));
}
keypoints.swap(new_keypoints);
if (_descriptors.needed())
new_descriptors.copyTo(_descriptors);
}
} // namespace cvtest::ocl
} // namespace cvtest
......
modules/ocl/src/fast.cpp 0 → 100644
View file @ b43d6b68
/*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;
cv::ocl::FAST_OCL::FAST_OCL(int _threshold, bool _nonmaxSupression, double _keypointsRatio) :
nonmaxSupression(_nonmaxSupression), threshold(_threshold), keypointsRatio(_keypointsRatio), count_(0)
{
}
void cv::ocl::FAST_OCL::operator ()(const oclMat& image, const oclMat& mask, std::vector<KeyPoint>& keypoints)
{
if (image.empty())
return;
(*this)(image, mask, d_keypoints_);
downloadKeypoints(d_keypoints_, keypoints);
}
void cv::ocl::FAST_OCL::downloadKeypoints(const oclMat& d_keypoints, std::vector<KeyPoint>& keypoints)
{
if (d_keypoints.empty())
return;
Mat h_keypoints(d_keypoints);
convertKeypoints(h_keypoints, keypoints);
}
void cv::ocl::FAST_OCL::convertKeypoints(const Mat& h_keypoints, std::vector<KeyPoint>& keypoints)
{
if (h_keypoints.empty())
return;
CV_Assert(h_keypoints.rows == ROWS_COUNT && h_keypoints.elemSize() == 4);
int npoints = h_keypoints.cols;
keypoints.resize(npoints);
const float* loc_x = h_keypoints.ptr<float>(X_ROW);
const float* loc_y = h_keypoints.ptr<float>(Y_ROW);
const float* response_row = h_keypoints.ptr<float>(RESPONSE_ROW);
for (int i = 0; i < npoints; ++i)
{
KeyPoint kp(loc_x[i], loc_y[i], static_cast<float>(FEATURE_SIZE), -1, response_row[i]);
keypoints[i] = kp;
}
}
void cv::ocl::FAST_OCL::operator ()(const oclMat& img, const oclMat& mask, oclMat& keypoints)
{
calcKeyPointsLocation(img, mask);
keypoints.cols = getKeyPoints(keypoints);
}
int cv::ocl::FAST_OCL::calcKeyPointsLocation(const oclMat& img, const oclMat& mask)
{
CV_Assert(img.type() == CV_8UC1);
CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.size() == img.size()));
int maxKeypoints = static_cast<int>(keypointsRatio * img.size().area());
ensureSizeIsEnough(ROWS_COUNT, maxKeypoints, CV_32SC1, kpLoc_);
kpLoc_.setTo(Scalar::all(0));
if (nonmaxSupression)
{
ensureSizeIsEnough(img.size(), CV_32SC1, score_);
score_.setTo(Scalar::all(0));
}
count_ = calcKeypointsOCL(img, mask, maxKeypoints);
count_ = std::min(count_, maxKeypoints);
return count_;
}
int cv::ocl::FAST_OCL::calcKeypointsOCL(const oclMat& img, const oclMat& mask, int maxKeypoints)
{
size_t localThreads[3] = {16, 16, 1};
size_t globalThreads[3] = {divUp(img.cols - 6, localThreads[0]) * localThreads[0],
divUp(img.rows - 6, localThreads[1]) * localThreads[1],
1};
Context *clCxt = Context::getContext();
String kernelName = (mask.empty()) ? "calcKeypoints" : "calcKeypointsWithMask";
std::vector< std::pair<size_t, const void *> > args;
int counter = 0;
int err = CL_SUCCESS;
cl_mem counterCL = clCreateBuffer(*(cl_context*)clCxt->getOpenCLContextPtr(),
CL_MEM_COPY_HOST_PTR, sizeof(int),
&counter, &err);
int kpLocStep = kpLoc_.step / kpLoc_.elemSize();
int scoreStep = score_.step / score_.elemSize();
int nms = (nonmaxSupression) ? 1 : 0;
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&img.data));
if (!mask.empty()) args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mask.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&kpLoc_.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&score_.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&counterCL));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&nms));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&maxKeypoints));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&threshold));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&img.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));
if (!mask.empty()) args.push_back( std::make_pair( sizeof(cl_int), (void *)&mask.step));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&kpLocStep));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&scoreStep));
openCLExecuteKernel(clCxt, &featdetect_fast, kernelName, globalThreads, localThreads, args, -1, -1);
openCLSafeCall(clEnqueueReadBuffer(*(cl_command_queue*)clCxt->getOpenCLCommandQueuePtr(),
counterCL, CL_TRUE, 0, sizeof(int), &counter, 0, NULL, NULL));
openCLSafeCall(clReleaseMemObject(counterCL));
return counter;
}
int cv::ocl::FAST_OCL::nonmaxSupressionOCL(oclMat& keypoints)
{
size_t localThreads[3] = {256, 1, 1};
size_t globalThreads[3] = {count_, 1, 1};
Context *clCxt = Context::getContext();
String kernelName = "nonmaxSupression";
std::vector< std::pair<size_t, const void *> > args;
int counter = 0;
int err = CL_SUCCESS;
cl_mem counterCL = clCreateBuffer(*(cl_context*)clCxt->getOpenCLContextPtr(),
CL_MEM_COPY_HOST_PTR, sizeof(int),
&counter, &err);
int kpLocStep = kpLoc_.step / kpLoc_.elemSize();
int sStep = score_.step / score_.elemSize();
int kStep = keypoints.step / keypoints.elemSize();
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&kpLoc_.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&score_.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypoints.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&counterCL));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&count_));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&kpLocStep));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&sStep));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&kStep));
openCLExecuteKernel(clCxt, &featdetect_fast, kernelName, globalThreads, localThreads, args, -1, -1);
openCLSafeCall(clEnqueueReadBuffer(*(cl_command_queue*)clCxt->getOpenCLCommandQueuePtr(),
counterCL, CL_TRUE, 0, sizeof(int), &counter, 0, NULL, NULL));
openCLSafeCall(clReleaseMemObject(counterCL));
return counter;
}
int cv::ocl::FAST_OCL::getKeyPoints(oclMat& keypoints)
{
if (count_ == 0)
return 0;
if (nonmaxSupression)
{
ensureSizeIsEnough(ROWS_COUNT, count_, CV_32FC1, keypoints);
return nonmaxSupressionOCL(keypoints);
}
kpLoc_.convertTo(keypoints, CV_32FC1);
Mat k = keypoints;
return count_;
}
void cv::ocl::FAST_OCL::release()
{
kpLoc_.release();
score_.release();
d_keypoints_.release();
}
modules/ocl/src/opencl/featdetect_fast.cl 0 → 100644
View file @ b43d6b68
/*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*/
#define X_ROW 0
#define Y_ROW 1
#define RESPONSE_ROW 2
#define ROWS_COUNT 3
__constant uchar c_table[] = { 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xc0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
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0x80, 0x00, 0x00, 0x00, 0x80, 0x00, 0x00, 0x00, 0x80,
0x00, 0x00, 0x00, 0x80, 0x00, 0x00, 0x00, 0x80, 0x00,
0x00, 0x00, 0x80, 0x00, 0x00, 0x00, 0x80, 0x00, 0x00,
0x00, 0x80, 0x00, 0x00, 0x00, 0x80, 0x00, 0x00, 0x00,
0x80, 0x00, 0x00, 0x00, 0x80, 0x00, 0x00, 0x00, 0x80,
0x00, 0x00, 0x00, 0x80, 0x00, 0x00, 0x00, 0x80, 0x00,
0x00, 0x00, 0x80, 0x00, 0x00, 0x00, 0xf0, 0x00, 0x80,
0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00,
0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80,
0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00,
0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80,
0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00,
0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80,
0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00,
0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80,
0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00,
0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80,
0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00,
0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80,
0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00,
0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0x80, 0x00, 0xc0,
0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
0x80, 0x88, 0x88, 0x88, 0x88, 0x88, 0x88, 0x88, 0x88,
0x88, 0x88, 0x88, 0x88, 0x88, 0x88, 0x88, 0x88, 0x88,
0x88, 0x88, 0x88, 0x88, 0x88, 0x88, 0x88, 0x88, 0x88,
0x88, 0x88, 0x88, 0x88, 0x88, 0x88, 0xaa, 0xaa, 0xaa,
0xaa, 0xaa, 0xaa, 0xaa, 0xaa, 0xaa, 0xaa, 0xaa, 0xaa,
0xaa, 0xaa, 0xaa, 0xaa, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff };
// 1 -> v > x + th
// 2 -> v < x - th
// 0 -> x - th <= v <= x + th
__inline int diffType(const int v, const int x, const int th)
{
const int diff = x - v;
return (int)(diff < -th) + ((int)(diff > th) << 1);
}
// For OpenCL 1.1 compatibility
__inline int popCount(int x) {
x = x - ((x >> 1) & 0x55555555);
x = (x & 0x33333333) + ((x >> 2) & 0x33333333);
x = (x + (x >> 4)) & 0x0F0F0F0F;
x = x + (x >> 8);
x = x + (x >> 16);
return x & 0x0000003F;
}
void calcMask(
const uint C[4],
const int v,
const int th,
int* mask1,
int* mask2)
{
*mask1 = 0;
*mask2 = 0;
int d1, d2;
d1 = diffType(v, C[0] & 0xff, th);
d2 = diffType(v, C[2] & 0xff, th);
if ((d1 | d2) == 0)
return;
*mask1 |= (d1 & 1) << 0;
*mask2 |= ((d1 & 2) >> 1) << 0;
*mask1 |= (d2 & 1) << 8;
*mask2 |= ((d2 & 2) >> 1) << 8;
d1 = diffType(v, C[1] & 0xff, th);
d2 = diffType(v, C[3] & 0xff, th);
if ((d1 | d2) == 0)
return;
*mask1 |= (d1 & 1) << 4;
*mask2 |= ((d1 & 2) >> 1) << 4;
*mask1 |= (d2 & 1) << 12;
*mask2 |= ((d2 & 2) >> 1) << 12;
d1 = diffType(v, (C[0] >> (2 * 8)) & 0xff, th);
d2 = diffType(v, (C[2] >> (2 * 8)) & 0xff, th);
if ((d1 | d2) == 0)
return;
*mask1 |= (d1 & 1) << 2;
*mask2 |= ((d1 & 2) >> 1) << 2;
*mask1 |= (d2 & 1) << 10;
*mask2 |= ((d2 & 2) >> 1) << 10;
d1 = diffType(v, (C[1] >> (2 * 8)) & 0xff, th);
d2 = diffType(v, (C[3] >> (2 * 8)) & 0xff, th);
if ((d1 | d2) == 0)
return;
*mask1 |= (d1 & 1) << 6;
*mask2 |= ((d1 & 2) >> 1) << 6;
*mask1 |= (d2 & 1) << 14;
*mask2 |= ((d2 & 2) >> 1) << 14;
d1 = diffType(v, (C[0] >> (1 * 8)) & 0xff, th);
d2 = diffType(v, (C[2] >> (1 * 8)) & 0xff, th);
/*if ((d1 | d2) == 0)
return;*/
*mask1 |= (d1 & 1) << 1;
*mask2 |= ((d1 & 2) >> 1) << 1;
*mask1 |= (d2 & 1) << 9;
*mask2 |= ((d2 & 2) >> 1) << 9;
d1 = diffType(v, (C[0] >> (3 * 8)) & 0xff, th);
d2 = diffType(v, (C[2] >> (3 * 8)) & 0xff, th);
/*if ((d1 | d2) == 0)
return;*/
*mask1 |= (d1 & 1) << 3;
*mask2 |= ((d1 & 2) >> 1) << 3;
*mask1 |= (d2 & 1) << 11;
*mask2 |= ((d2 & 2) >> 1) << 11;
d1 = diffType(v, (C[1] >> (1 * 8)) & 0xff, th);
d2 = diffType(v, (C[3] >> (1 * 8)) & 0xff, th);
/*if ((d1 | d2) == 0)
return;*/
*mask1 |= (d1 & 1) << 5;
*mask2 |= ((d1 & 2) >> 1) << 5;
*mask1 |= (d2 & 1) << 13;
*mask2 |= ((d2 & 2) >> 1) << 13;
d1 = diffType(v, (C[1] >> (3 * 8)) & 0xff, th);
d2 = diffType(v, (C[3] >> (3 * 8)) & 0xff, th);
*mask1 |= (d1 & 1) << 7;
*mask2 |= ((d1 & 2) >> 1) << 7;
*mask1 |= (d2 & 1) << 15;
*mask2 |= ((d2 & 2) >> 1) << 15;
}
// 1 -> v > x + th
// 2 -> v < x - th
// 0 -> not a keypoint
__inline bool isKeyPoint(int mask1, int mask2)
{
// TODO: Use OpenCL's popcount() function if OpenCL version >= 1.2
return (popCount(mask1) > 8 && (c_table[(mask1 >> 3) - 63] & (1 << (mask1 & 7)))) ||
(popCount(mask2) > 8 && (c_table[(mask2 >> 3) - 63] & (1 << (mask2 & 7))));
}
int cornerScore(const uint C[4], const int v, const int threshold)
{
// binary search in [threshold + 1, 255]
int min = threshold + 1;
int max = 255;
while (min <= max)
{
const int mid = (min + max) >> 1;
int mask1 = 0;
int mask2 = 0;
calcMask(C, v, mid, &mask1, &mask2);
int isKp = (int)isKeyPoint(mask1, mask2);
min = isKp * (mid + 1) + (isKp ^ 1) * min;
max = (isKp ^ 1) * (mid - 1) + isKp * max;
}
return min - 1;
}
///////////////////////////////////////////////////////////////////////////
// calcKeypoints
__kernel
void calcKeypointsWithMask(
__global const uchar* img,
__global const uchar* mask,
__global int* kpLoc,
__global uint* score,
__global int* counter,
const int calcScore,
const unsigned int maxKeypoints,
const int threshold,
const int c_img_step,
const int c_img_rows,
const int c_img_cols,
const int c_mask_step,
const int c_kploc_step,
const int c_score_step)
{
const int j = get_global_id(0) + 3;
const int i = get_global_id(1) + 3;
if (i < c_img_rows - 3 && j < c_img_cols - 3 && mask[i * c_mask_step + j])
{
int v;
uint C[4] = {0,0,0,0};
C[2] |= (uint)img[(i - 3) * c_img_step + j - 1] << 8;
C[2] |= (uint)img[(i - 3) * c_img_step + j];
C[1] |= (uint)img[(i - 3) * c_img_step + j + 1] << (3 * 8);
C[2] |= (uint)img[(i - 2) * c_img_step + j - 2] << (2 * 8);
C[1] |= (uint)img[(i - 2) * c_img_step + j + 2] << (2 * 8);
C[2] |= (uint)img[(i - 1) * c_img_step + j - 3] << (3 * 8);
C[1] |= (uint)img[(i - 1) * c_img_step + j + 3] << 8;
C[3] |= (uint)img[i * c_img_step + j - 3];
v = (int) img[i * c_img_step + j];
C[1] |= (uint)img[i * c_img_step + j + 3];
int d1 = diffType(v, C[1] & 0xff, threshold);
int d2 = diffType(v, C[3] & 0xff, threshold);
if ((d1 | d2) == 0)
return;
C[3] |= (uint)img[(i + 1) * c_img_step + j - 3] << 8;
C[0] |= (uint)img[(i + 1) * c_img_step + j + 3] << (3 * 8);
C[3] |= (uint)img[(i + 2) * c_img_step + j - 2] << (2 * 8);
C[0] |= (uint)img[(i + 2) * c_img_step + j + 2] << (2 * 8);
C[3] |= (uint)img[(i + 3) * c_img_step + j - 1] << (3 * 8);
C[0] |= (uint)img[(i + 3) * c_img_step + j];
C[0] |= (uint)img[(i + 3) * c_img_step + j + 1] << 8;
int mask1 = 0;
int mask2 = 0;
calcMask(C, v, threshold, &mask1, &mask2);
if (isKeyPoint(mask1, mask2))
{
if (calcScore) score[i * c_score_step + j] = cornerScore(C, v, threshold);
uint idx = atomic_inc(counter);
if (idx < maxKeypoints)
{
kpLoc[X_ROW * c_kploc_step + idx] = j;
kpLoc[Y_ROW * c_kploc_step + idx] = i;
}
}
}
}
__kernel
void calcKeypoints(
__global const uchar* img,
__global int* kpLoc,
__global uint* score,
__global int* counter,
const int calcScore,
const unsigned int maxKeypoints,
const int threshold,
const int c_img_step,
const int c_img_rows,
const int c_img_cols,
const int c_kploc_step,
const int c_score_step)
{
const int j = get_global_id(0) + 3;
const int i = get_global_id(1) + 3;
if (i < c_img_rows - 3 && j < c_img_cols - 3)
{
int v;
uint C[4] = {0,0,0,0};
C[2] |= (uint)img[(i - 3) * c_img_step + j - 1] << 8;
C[2] |= (uint)img[(i - 3) * c_img_step + j];
C[1] |= (uint)img[(i - 3) * c_img_step + j + 1] << (3 * 8);
C[2] |= (uint)img[(i - 2) * c_img_step + j - 2] << (2 * 8);
C[1] |= (uint)img[(i - 2) * c_img_step + j + 2] << (2 * 8);
C[2] |= (uint)img[(i - 1) * c_img_step + j - 3] << (3 * 8);
C[1] |= (uint)img[(i - 1) * c_img_step + j + 3] << 8;
C[3] |= (uint)img[i * c_img_step + j - 3];
v = (int) img[i * c_img_step + j];
C[1] |= (uint)img[i * c_img_step + j + 3];
int d1 = diffType(v, C[1] & 0xff, threshold);
int d2 = diffType(v, C[3] & 0xff, threshold);
if ((d1 | d2) == 0)
return;
C[3] |= (uint)img[(i + 1) * c_img_step + j - 3] << 8;
C[0] |= (uint)img[(i + 1) * c_img_step + j + 3] << (3 * 8);
C[3] |= (uint)img[(i + 2) * c_img_step + j - 2] << (2 * 8);
C[0] |= (uint)img[(i + 2) * c_img_step + j + 2] << (2 * 8);
C[3] |= (uint)img[(i + 3) * c_img_step + j - 1] << (3 * 8);
C[0] |= (uint)img[(i + 3) * c_img_step + j];
C[0] |= (uint)img[(i + 3) * c_img_step + j + 1] << 8;
int mask1 = 0;
int mask2 = 0;
calcMask(C, v, threshold, &mask1, &mask2);
if (isKeyPoint(mask1, mask2))
{
if (calcScore) score[i * c_score_step + j] = cornerScore(C, v, threshold);
uint idx = atomic_inc(counter);
if (idx < maxKeypoints)
{
kpLoc[X_ROW * c_kploc_step + idx] = j;
kpLoc[Y_ROW * c_kploc_step + idx] = i;
}
}
}
}
///////////////////////////////////////////////////////////////////////////
// nonmaxSupression
__kernel
void nonmaxSupression(
__global const int* kpLoc,
__global const uint* score,
__global float* keypoints,
__global int* new_counter,
const int counter,
const int c_kploc_step,
const int c_score_step,
const int c_keypoints_step)
{
const int i = get_global_id(0);
if (i < counter)
{
int loc_x = kpLoc[X_ROW * c_kploc_step + i];
int loc_y = kpLoc[Y_ROW * c_kploc_step + i];
int s = score[loc_y * c_score_step + loc_x];
bool ismax =
s > score[(loc_y - 1) * c_score_step + loc_x - 1] &&
s > score[(loc_y - 1) * c_score_step + loc_x ] &&
s > score[(loc_y - 1) * c_score_step + loc_x + 1] &&
s > score[loc_y * c_score_step + loc_x - 1] &&
s > score[loc_y * c_score_step + loc_x + 1] &&
s > score[(loc_y + 1) * c_score_step + loc_x - 1] &&
s > score[(loc_y + 1) * c_score_step + loc_x ] &&
s > score[(loc_y + 1) * c_score_step + loc_x + 1];
if (ismax)
{
uint idx = atomic_inc(new_counter);
keypoints[X_ROW * c_keypoints_step + idx] = (float)loc_x;
keypoints[Y_ROW * c_keypoints_step + idx] = (float)loc_y;
keypoints[RESPONSE_ROW * c_keypoints_step + idx] = (float)s;
}
}
}
modules/ocl/test/test_fast.cpp 0 → 100644
View file @ b43d6b68
/*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
////////////////////////////////////////////////////////
// FAST
namespace
{
IMPLEMENT_PARAM_CLASS(FAST_Threshold, int)
IMPLEMENT_PARAM_CLASS(FAST_NonmaxSupression, bool)
}
PARAM_TEST_CASE(FAST, FAST_Threshold, FAST_NonmaxSupression)
{
int threshold;
bool nonmaxSupression;
virtual void SetUp()
{
threshold = GET_PARAM(0);
nonmaxSupression = GET_PARAM(1);
}
};
OCL_TEST_P(FAST, Accuracy)
{
cv::Mat image = readImage("gpu/perf/aloe.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(image.empty());
cv::ocl::FAST_OCL fast(threshold);
fast.nonmaxSupression = nonmaxSupression;
cv::ocl::oclMat ocl_image = cv::ocl::oclMat(image);
std::vector<cv::KeyPoint> keypoints;
fast(ocl_image, cv::ocl::oclMat(), keypoints);
std::vector<cv::KeyPoint> keypoints_gold;
cv::FAST(image, keypoints_gold, threshold, nonmaxSupression);
ASSERT_KEYPOINTS_EQ(keypoints_gold, keypoints);
}
INSTANTIATE_TEST_CASE_P(OCL_Features2D, FAST, testing::Combine(
testing::Values(FAST_Threshold(25), FAST_Threshold(50)),
testing::Values(FAST_NonmaxSupression(false), FAST_NonmaxSupression(true))));
#endif
modules/ocl/test/utility.cpp
View file @ b43d6b68
......@@ -259,4 +259,70 @@ void showDiff(const Mat& src, const Mat& gold, const Mat& actual, double eps, bo
}
}
namespace
{
bool keyPointsEquals(const cv::KeyPoint& p1, const cv::KeyPoint& p2)
{
const double maxPtDif = 1.0;
const double maxSizeDif = 1.0;
const double maxAngleDif = 2.0;
const double maxResponseDif = 0.1;
double dist = cv::norm(p1.pt - p2.pt);
if (dist < maxPtDif &&
fabs(p1.size - p2.size) < maxSizeDif &&
abs(p1.angle - p2.angle) < maxAngleDif &&
abs(p1.response - p2.response) < maxResponseDif &&
p1.octave == p2.octave &&
p1.class_id == p2.class_id)
{
return true;
}
return false;
}
struct KeyPointLess : std::binary_function<cv::KeyPoint, cv::KeyPoint, bool>
{
bool operator()(const cv::KeyPoint& kp1, const cv::KeyPoint& kp2) const
{
return kp1.pt.y < kp2.pt.y || (kp1.pt.y == kp2.pt.y && kp1.pt.x < kp2.pt.x);
}
};
}
testing::AssertionResult assertKeyPointsEquals(const char* gold_expr, const char* actual_expr, std::vector<cv::KeyPoint>& gold, std::vector<cv::KeyPoint>& actual)
{
if (gold.size() != actual.size())
{
return testing::AssertionFailure() << "KeyPoints size mistmach\n"
<< "\"" << gold_expr << "\" : " << gold.size() << "\n"
<< "\"" << actual_expr << "\" : " << actual.size();
}
std::sort(actual.begin(), actual.end(), KeyPointLess());
std::sort(gold.begin(), gold.end(), KeyPointLess());
for (size_t i = 0; i < gold.size(); ++i)
{
const cv::KeyPoint& p1 = gold[i];
const cv::KeyPoint& p2 = actual[i];
if (!keyPointsEquals(p1, p2))
{
return testing::AssertionFailure() << "KeyPoints differ at " << i << "\n"
<< "\"" << gold_expr << "\" vs \"" << actual_expr << "\" : \n"
<< "pt : " << testing::PrintToString(p1.pt) << " vs " << testing::PrintToString(p2.pt) << "\n"
<< "size : " << p1.size << " vs " << p2.size << "\n"
<< "angle : " << p1.angle << " vs " << p2.angle << "\n"
<< "response : " << p1.response << " vs " << p2.response << "\n"
<< "octave : " << p1.octave << " vs " << p2.octave << "\n"
<< "class_id : " << p1.class_id << " vs " << p2.class_id;
}
}
return ::testing::AssertionSuccess();
}
} // namespace cvtest
modules/ocl/test/utility.hpp
View file @ b43d6b68
......@@ -54,6 +54,9 @@ extern int LOOP_TIMES;
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)
void showDiff(const Mat& src, const Mat& gold, const Mat& actual, double eps, bool alwaysShow = false);
cv::ocl::oclMat createMat_ocl(cv::RNG& rng, Size size, int type, bool useRoi);
......
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