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opencv/modules/ocl/include/opencv2/ocl.hpp

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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) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Copyright (C) 2010-2012, Multicoreware, Inc., 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 oclMaterials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_OCL_HPP__
#define __OPENCV_OCL_HPP__
#include <memory>
#include <vector>
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/objdetect.hpp"
#include "opencv2/features2d.hpp"
namespace cv
{
namespace ocl
{
enum
{
CVCL_DEVICE_TYPE_DEFAULT = (1 << 0),
CVCL_DEVICE_TYPE_CPU = (1 << 1),
CVCL_DEVICE_TYPE_GPU = (1 << 2),
CVCL_DEVICE_TYPE_ACCELERATOR = (1 << 3),
//CVCL_DEVICE_TYPE_CUSTOM = (1 << 4)
CVCL_DEVICE_TYPE_ALL = 0xFFFFFFFF
};
enum DevMemRW
{
DEVICE_MEM_R_W = 0,
DEVICE_MEM_R_ONLY,
DEVICE_MEM_W_ONLY
};
enum DevMemType
{
DEVICE_MEM_DEFAULT = 0,
DEVICE_MEM_AHP, //alloc host pointer
DEVICE_MEM_UHP, //use host pointer
DEVICE_MEM_CHP, //copy host pointer
DEVICE_MEM_PM //persistent memory
};
//Get the global device memory and read/write type
//return 1 if unified memory system supported, otherwise return 0
CV_EXPORTS int getDevMemType(DevMemRW& rw_type, DevMemType& mem_type);
//Set the global device memory and read/write type,
//the newly generated oclMat will all use this type
//return -1 if the target type is unsupported, otherwise return 0
CV_EXPORTS int setDevMemType(DevMemRW rw_type = DEVICE_MEM_R_W, DevMemType mem_type = DEVICE_MEM_DEFAULT);
//this class contains ocl runtime information
class CV_EXPORTS Info
{
public:
struct Impl;
Impl *impl;
Info();
Info(const Info &m);
~Info();
void release();
Info &operator = (const Info &m);
std::vector<std::string> DeviceName;
};
//////////////////////////////// Initialization & Info ////////////////////////
//this function may be obsoleted
//CV_EXPORTS cl_device_id getDevice();
//the function must be called before any other cv::ocl::functions, it initialize ocl runtime
//each Info relates to an OpenCL platform
//there is one or more devices in each platform, each one has a separate name
CV_EXPORTS int getDevice(std::vector<Info> &oclinfo, int devicetype = CVCL_DEVICE_TYPE_GPU);
//set device you want to use, optional function after getDevice be called
//the devnum is the index of the selected device in DeviceName vector of INfo
CV_EXPORTS void setDevice(Info &oclinfo, int devnum = 0);
//optional function, if you want save opencl binary kernel to the file, set its path
CV_EXPORTS void setBinpath(const char *path);
//The two functions below enable other opencl program to use ocl module's cl_context and cl_command_queue
CV_EXPORTS void* getoclContext();
CV_EXPORTS void* getoclCommandQueue();
//this function enable ocl module to use customized cl_context and cl_command_queue
//getDevice also need to be called before this function
CV_EXPORTS void setDeviceEx(Info &oclinfo, void *ctx, void *qu, int devnum = 0);
//////////////////////////////// Error handling ////////////////////////
CV_EXPORTS void error(const char *error_string, const char *file, const int line, const char *func);
//////////////////////////////// OpenCL context ////////////////////////
//This is a global singleton class used to represent a OpenCL context.
class Context
{
protected:
Context();
friend class std::auto_ptr<Context>;
static std::auto_ptr<Context> clCxt;
public:
~Context();
static int val;
static Context *getContext();
static void setContext(Info &oclinfo);
struct Impl;
Impl *impl;
};
//! Calls a kernel, by string. Pass globalThreads = NULL, and cleanUp = true, to finally clean-up without executing.
CV_EXPORTS double openCLExecuteKernelInterop(Context *clCxt ,
const char **source, std::string kernelName,
size_t globalThreads[3], size_t localThreads[3],
std::vector< std::pair<size_t, const void *> > &args,
int channels, int depth, const char *build_options,
bool finish = true, bool measureKernelTime = false,
bool cleanUp = true);
//! Calls a kernel, by file. Pass globalThreads = NULL, and cleanUp = true, to finally clean-up without executing.
CV_EXPORTS double openCLExecuteKernelInterop(Context *clCxt ,
const char **fileName, const int numFiles, std::string kernelName,
size_t globalThreads[3], size_t localThreads[3],
std::vector< std::pair<size_t, const void *> > &args,
int channels, int depth, const char *build_options,
bool finish = true, bool measureKernelTime = false,
bool cleanUp = true);
class CV_EXPORTS oclMatExpr;
//////////////////////////////// oclMat ////////////////////////////////
class CV_EXPORTS oclMat
{
public:
//! default constructor
oclMat();
//! constructs oclMatrix of the specified size and type (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.)
oclMat(int rows, int cols, int type);
oclMat(Size size, int type);
//! constucts oclMatrix and fills it with the specified value _s.
oclMat(int rows, int cols, int type, const Scalar &s);
oclMat(Size size, int type, const Scalar &s);
//! copy constructor
oclMat(const oclMat &m);
//! constructor for oclMatrix headers pointing to user-allocated data
oclMat(int rows, int cols, int type, void *data, size_t step = Mat::AUTO_STEP);
oclMat(Size size, int type, void *data, size_t step = Mat::AUTO_STEP);
//! creates a matrix header for a part of the bigger matrix
oclMat(const oclMat &m, const Range &rowRange, const Range &colRange);
oclMat(const oclMat &m, const Rect &roi);
//! builds oclMat from Mat. Perfom blocking upload to device.
explicit oclMat (const Mat &m);
//! destructor - calls release()
~oclMat();
//! assignment operators
oclMat &operator = (const oclMat &m);
//! assignment operator. Perfom blocking upload to device.
oclMat &operator = (const Mat &m);
oclMat &operator = (const oclMatExpr& expr);
//! pefroms blocking upload data to oclMat.
void upload(const cv::Mat &m);
//! downloads data from device to host memory. Blocking calls.
operator Mat() const;
void download(cv::Mat &m) const;
//! returns a new oclMatrix header for the specified row
oclMat row(int y) const;
//! returns a new oclMatrix header for the specified column
oclMat col(int x) const;
//! ... for the specified row span
oclMat rowRange(int startrow, int endrow) const;
oclMat rowRange(const Range &r) const;
//! ... for the specified column span
oclMat colRange(int startcol, int endcol) const;
oclMat colRange(const Range &r) const;
//! returns deep copy of the oclMatrix, i.e. the data is copied
oclMat clone() const;
//! copies the oclMatrix content to "m".
// It calls m.create(this->size(), this->type()).
// It supports any data type
void copyTo( oclMat &m ) const;
//! copies those oclMatrix elements to "m" that are marked with non-zero mask elements.
//It supports 8UC1 8UC4 32SC1 32SC4 32FC1 32FC4
void copyTo( oclMat &m, const oclMat &mask ) const;
//! converts oclMatrix to another datatype with optional scalng. See cvConvertScale.
//It supports 8UC1 8UC4 32SC1 32SC4 32FC1 32FC4
void convertTo( oclMat &m, int rtype, double alpha = 1, double beta = 0 ) const;
void assignTo( oclMat &m, int type = -1 ) const;
//! sets every oclMatrix element to s
//It supports 8UC1 8UC4 32SC1 32SC4 32FC1 32FC4
oclMat& operator = (const Scalar &s);
//! sets some of the oclMatrix elements to s, according to the mask
//It supports 8UC1 8UC4 32SC1 32SC4 32FC1 32FC4
oclMat& setTo(const Scalar &s, const oclMat &mask = oclMat());
//! creates alternative oclMatrix header for the same data, with different
// number of channels and/or different number of rows. see cvReshape.
oclMat reshape(int cn, int rows = 0) const;
//! allocates new oclMatrix data unless the oclMatrix already has specified size and type.
// previous data is unreferenced if needed.
void create(int rows, int cols, int type);
void create(Size size, int type);
//! allocates new oclMatrix with specified device memory type.
void createEx(int rows, int cols, int type, DevMemRW rw_type, DevMemType mem_type);
void createEx(Size size, int type, DevMemRW rw_type, DevMemType mem_type);
//! decreases reference counter;
// deallocate the data when reference counter reaches 0.
void release();
//! swaps with other smart pointer
void swap(oclMat &mat);
//! locates oclMatrix header within a parent oclMatrix. See below
void locateROI( Size &wholeSize, Point &ofs ) const;
//! moves/resizes the current oclMatrix ROI inside the parent oclMatrix.
oclMat& adjustROI( int dtop, int dbottom, int dleft, int dright );
//! extracts a rectangular sub-oclMatrix
// (this is a generalized form of row, rowRange etc.)
oclMat operator()( Range rowRange, Range colRange ) const;
oclMat operator()( const Rect &roi ) const;
oclMat& operator+=( const oclMat& m );
oclMat& operator-=( const oclMat& m );
oclMat& operator*=( const oclMat& m );
oclMat& operator/=( const oclMat& m );
//! returns true if the oclMatrix data is continuous
// (i.e. when there are no gaps between successive rows).
// similar to CV_IS_oclMat_CONT(cvoclMat->type)
bool isContinuous() const;
//! returns element size in bytes,
// similar to CV_ELEM_SIZE(cvMat->type)
size_t elemSize() const;
//! returns the size of element channel in bytes.
size_t elemSize1() const;
//! returns element type, similar to CV_MAT_TYPE(cvMat->type)
int type() const;
//! returns element type, i.e. 8UC3 returns 8UC4 because in ocl
//! 3 channels element actually use 4 channel space
int ocltype() const;
//! returns element type, similar to CV_MAT_DEPTH(cvMat->type)
int depth() const;
//! returns element type, similar to CV_MAT_CN(cvMat->type)
int channels() const;
//! returns element type, return 4 for 3 channels element,
//!becuase 3 channels element actually use 4 channel space
int oclchannels() const;
//! returns step/elemSize1()
size_t step1() const;
//! returns oclMatrix size:
// width == number of columns, height == number of rows
Size size() const;
//! returns true if oclMatrix data is NULL
bool empty() const;
//! returns pointer to y-th row
uchar* ptr(int y = 0);
const uchar *ptr(int y = 0) const;
//! template version of the above method
template<typename _Tp> _Tp *ptr(int y = 0);
template<typename _Tp> const _Tp *ptr(int y = 0) const;
//! matrix transposition
oclMat t() const;
/*! includes several bit-fields:
- the magic signature
- continuity flag
- depth
- number of channels
*/
int flags;
//! the number of rows and columns
int rows, cols;
//! a distance between successive rows in bytes; includes the gap if any
size_t step;
//! pointer to the data(OCL memory object)
uchar *data;
//! pointer to the reference counter;
// when oclMatrix points to user-allocated data, the pointer is NULL
int *refcount;
//! helper fields used in locateROI and adjustROI
//datastart and dataend are not used in current version
uchar *datastart;
uchar *dataend;
//! OpenCL context associated with the oclMat object.
Context *clCxt;
//add offset for handle ROI, calculated in byte
int offset;
//add wholerows and wholecols for the whole matrix, datastart and dataend are no longer used
int wholerows;
int wholecols;
};
///////////////////// mat split and merge /////////////////////////////////
//! Compose a multi-channel array from several single-channel arrays
// Support all types
CV_EXPORTS void merge(const oclMat *src, size_t n, oclMat &dst);
CV_EXPORTS void merge(const std::vector<oclMat> &src, oclMat &dst);
//! Divides multi-channel array into several single-channel arrays
// Support all types
CV_EXPORTS void split(const oclMat &src, oclMat *dst);
CV_EXPORTS void split(const oclMat &src, std::vector<oclMat> &dst);
////////////////////////////// Arithmetics ///////////////////////////////////
//#if defined DOUBLE_SUPPORT
//typedef double F;
//#else
//typedef float F;
//#endif
// CV_EXPORTS void addWeighted(const oclMat& a,F alpha, const oclMat& b,F beta,F gama, oclMat& c);
CV_EXPORTS void addWeighted(const oclMat &a, double alpha, const oclMat &b, double beta, double gama, oclMat &c);
//! adds one matrix to another (c = a + b)
// supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
CV_EXPORTS void add(const oclMat &a, const oclMat &b, oclMat &c);
//! adds one matrix to another (c = a + b)
// supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
CV_EXPORTS void add(const oclMat &a, const oclMat &b, oclMat &c, const oclMat &mask);
//! adds scalar to a matrix (c = a + s)
// supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
CV_EXPORTS void add(const oclMat &a, const Scalar &sc, oclMat &c, const oclMat &mask = oclMat());
//! subtracts one matrix from another (c = a - b)
// supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
CV_EXPORTS void subtract(const oclMat &a, const oclMat &b, oclMat &c);
//! subtracts one matrix from another (c = a - b)
// supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
CV_EXPORTS void subtract(const oclMat &a, const oclMat &b, oclMat &c, const oclMat &mask);
//! subtracts scalar from a matrix (c = a - s)
// supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
CV_EXPORTS void subtract(const oclMat &a, const Scalar &sc, oclMat &c, const oclMat &mask = oclMat());
//! subtracts scalar from a matrix (c = a - s)
// supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
CV_EXPORTS void subtract(const Scalar &sc, const oclMat &a, oclMat &c, const oclMat &mask = oclMat());
//! computes element-wise product of the two arrays (c = a * b)
// supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
CV_EXPORTS void multiply(const oclMat &a, const oclMat &b, oclMat &c, double scale = 1);
//! computes element-wise quotient of the two arrays (c = a / b)
// supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
CV_EXPORTS void divide(const oclMat &a, const oclMat &b, oclMat &c, double scale = 1);
//! computes element-wise quotient of the two arrays (c = a / b)
// supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
CV_EXPORTS void divide(double scale, const oclMat &b, oclMat &c);
//! compares elements of two arrays (c = a <cmpop> b)
// supports except CV_8SC1,CV_8SC2,CV8SC3,CV_8SC4 types
CV_EXPORTS void compare(const oclMat &a, const oclMat &b, oclMat &c, int cmpop);
//! transposes the matrix
// supports CV_8UC1, 8UC4, 8SC4, 16UC2, 16SC2, 32SC1 and 32FC1.(the same as cuda)
CV_EXPORTS void transpose(const oclMat &src, oclMat &dst);
//! computes element-wise absolute difference of two arrays (c = abs(a - b))
// supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
CV_EXPORTS void absdiff(const oclMat &a, const oclMat &b, oclMat &c);
//! computes element-wise absolute difference of array and scalar (c = abs(a - s))
// supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
CV_EXPORTS void absdiff(const oclMat &a, const Scalar &s, oclMat &c);
//! computes mean value and standard deviation of all or selected array elements
// supports except CV_32F,CV_64F
CV_EXPORTS void meanStdDev(const oclMat &mtx, Scalar &mean, Scalar &stddev);
//! computes norm of array
// supports NORM_INF, NORM_L1, NORM_L2
// supports only CV_8UC1 type
CV_EXPORTS double norm(const oclMat &src1, int normType = NORM_L2);
//! computes norm of the difference between two arrays
// supports NORM_INF, NORM_L1, NORM_L2
// supports only CV_8UC1 type
CV_EXPORTS double norm(const oclMat &src1, const oclMat &src2, int normType = NORM_L2);
//! reverses the order of the rows, columns or both in a matrix
// supports all types
CV_EXPORTS void flip(const oclMat &a, oclMat &b, int flipCode);
//! computes sum of array elements
// disabled until fix crash
// support all types
CV_EXPORTS Scalar sum(const oclMat &m);
CV_EXPORTS Scalar absSum(const oclMat &m);
CV_EXPORTS Scalar sqrSum(const oclMat &m);
//! finds global minimum and maximum array elements and returns their values
// support all C1 types
CV_EXPORTS void minMax(const oclMat &src, double *minVal, double *maxVal = 0, const oclMat &mask = oclMat());
//! finds global minimum and maximum array elements and returns their values with locations
// support all C1 types
CV_EXPORTS void minMaxLoc(const oclMat &src, double *minVal, double *maxVal = 0, Point *minLoc = 0, Point *maxLoc = 0,
const oclMat &mask = oclMat());
//! counts non-zero array elements
// support all types
CV_EXPORTS int countNonZero(const oclMat &src);
//! transforms 8-bit unsigned integers using lookup table: dst(i)=lut(src(i))
// destination array will have the depth type as lut and the same channels number as source
//It supports 8UC1 8UC4 only
CV_EXPORTS void LUT(const oclMat &src, const oclMat &lut, oclMat &dst);
//! only 8UC1 and 256 bins is supported now
CV_EXPORTS void calcHist(const oclMat &mat_src, oclMat &mat_hist);
//! only 8UC1 and 256 bins is supported now
CV_EXPORTS void equalizeHist(const oclMat &mat_src, oclMat &mat_dst);
//! bilateralFilter
// supports 8UC1 8UC4
CV_EXPORTS void bilateralFilter(const oclMat& src, oclMat& dst, int d, double sigmaColor, double sigmaSpave, int borderType=BORDER_DEFAULT);
//! computes exponent of each matrix element (b = e**a)
// supports only CV_32FC1 type
CV_EXPORTS void exp(const oclMat &a, oclMat &b);
//! computes natural logarithm of absolute value of each matrix element: b = log(abs(a))
// supports only CV_32FC1 type
CV_EXPORTS void log(const oclMat &a, oclMat &b);
//! computes magnitude of each (x(i), y(i)) vector
// supports only CV_32F CV_64F type
CV_EXPORTS void magnitude(const oclMat &x, const oclMat &y, oclMat &magnitude);
CV_EXPORTS void magnitudeSqr(const oclMat &x, const oclMat &y, oclMat &magnitude);
CV_EXPORTS void magnitudeSqr(const oclMat &x, oclMat &magnitude);
//! computes angle (angle(i)) of each (x(i), y(i)) vector
// supports only CV_32F CV_64F type
CV_EXPORTS void phase(const oclMat &x, const oclMat &y, oclMat &angle, bool angleInDegrees = false);
//! the function raises every element of tne input array to p
//! support only CV_32F CV_64F type
CV_EXPORTS void pow(const oclMat &x, double p, oclMat &y);
//! converts Cartesian coordinates to polar
// supports only CV_32F CV_64F type
CV_EXPORTS void cartToPolar(const oclMat &x, const oclMat &y, oclMat &magnitude, oclMat &angle, bool angleInDegrees = false);
//! converts polar coordinates to Cartesian
// supports only CV_32F CV_64F type
CV_EXPORTS void polarToCart(const oclMat &magnitude, const oclMat &angle, oclMat &x, oclMat &y, bool angleInDegrees = false);
//! perfroms per-elements bit-wise inversion
// supports all types
CV_EXPORTS void bitwise_not(const oclMat &src, oclMat &dst);
//! calculates per-element bit-wise disjunction of two arrays
// supports all types
CV_EXPORTS void bitwise_or(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
CV_EXPORTS void bitwise_or(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
//! calculates per-element bit-wise conjunction of two arrays
// supports all types
CV_EXPORTS void bitwise_and(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
CV_EXPORTS void bitwise_and(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
//! calculates per-element bit-wise "exclusive or" operation
// supports all types
CV_EXPORTS void bitwise_xor(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
CV_EXPORTS void bitwise_xor(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
//! Logical operators
CV_EXPORTS oclMat operator ~ (const oclMat &);
CV_EXPORTS oclMat operator | (const oclMat &, const oclMat &);
CV_EXPORTS oclMat operator & (const oclMat &, const oclMat &);
CV_EXPORTS oclMat operator ^ (const oclMat &, const oclMat &);
//! Mathematics operators
CV_EXPORTS oclMatExpr operator + (const oclMat &src1, const oclMat &src2);
CV_EXPORTS oclMatExpr operator - (const oclMat &src1, const oclMat &src2);
CV_EXPORTS oclMatExpr operator * (const oclMat &src1, const oclMat &src2);
CV_EXPORTS oclMatExpr operator / (const oclMat &src1, const oclMat &src2);
//! computes convolution of two images
//! support only CV_32FC1 type
CV_EXPORTS void convolve(const oclMat &image, const oclMat &temp1, oclMat &result);
CV_EXPORTS void cvtColor(const oclMat &src, oclMat &dst, int code , int dcn = 0);
//////////////////////////////// Filter Engine ////////////////////////////////
/*!
The Base Class for 1D or Row-wise Filters
This is the base class for linear or non-linear filters that process 1D data.
In particular, such filters are used for the "horizontal" filtering parts in separable filters.
*/
class CV_EXPORTS BaseRowFilter_GPU
{
public:
BaseRowFilter_GPU(int ksize_, int anchor_, int bordertype_) : ksize(ksize_), anchor(anchor_), bordertype(bordertype_) {}
virtual ~BaseRowFilter_GPU() {}
virtual void operator()(const oclMat &src, oclMat &dst) = 0;
int ksize, anchor, bordertype;
};
/*!
The Base Class for Column-wise Filters
This is the base class for linear or non-linear filters that process columns of 2D arrays.
Such filters are used for the "vertical" filtering parts in separable filters.
*/
class CV_EXPORTS BaseColumnFilter_GPU
{
public:
BaseColumnFilter_GPU(int ksize_, int anchor_, int bordertype_) : ksize(ksize_), anchor(anchor_), bordertype(bordertype_) {}
virtual ~BaseColumnFilter_GPU() {}
virtual void operator()(const oclMat &src, oclMat &dst) = 0;
int ksize, anchor, bordertype;
};
/*!
The Base Class for Non-Separable 2D Filters.
This is the base class for linear or non-linear 2D filters.
*/
class CV_EXPORTS BaseFilter_GPU
{
public:
BaseFilter_GPU(const Size &ksize_, const Point &anchor_, const int &borderType_)
: ksize(ksize_), anchor(anchor_), borderType(borderType_) {}
virtual ~BaseFilter_GPU() {}
virtual void operator()(const oclMat &src, oclMat &dst) = 0;
Size ksize;
Point anchor;
int borderType;
};
/*!
The Base Class for Filter Engine.
The class can be used to apply an arbitrary filtering operation to an image.
It contains all the necessary intermediate buffers.
*/
class CV_EXPORTS FilterEngine_GPU
{
public:
virtual ~FilterEngine_GPU() {}
virtual void apply(const oclMat &src, oclMat &dst, Rect roi = Rect(0, 0, -1, -1)) = 0;
};
//! returns the non-separable filter engine with the specified filter
CV_EXPORTS Ptr<FilterEngine_GPU> createFilter2D_GPU(const Ptr<BaseFilter_GPU> filter2D);
//! returns the primitive row filter with the specified kernel
CV_EXPORTS Ptr<BaseRowFilter_GPU> getLinearRowFilter_GPU(int srcType, int bufType, const Mat &rowKernel,
int anchor = -1, int bordertype = BORDER_DEFAULT);
//! returns the primitive column filter with the specified kernel
CV_EXPORTS Ptr<BaseColumnFilter_GPU> getLinearColumnFilter_GPU(int bufType, int dstType, const Mat &columnKernel,
int anchor = -1, int bordertype = BORDER_DEFAULT, double delta = 0.0);
//! returns the separable linear filter engine
CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat &rowKernel,
const Mat &columnKernel, const Point &anchor = Point(-1, -1), double delta = 0.0, int bordertype = BORDER_DEFAULT);
//! returns the separable filter engine with the specified filters
CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU> &rowFilter,
const Ptr<BaseColumnFilter_GPU> &columnFilter);
//! returns the Gaussian filter engine
CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, double sigma1, double sigma2 = 0, int bordertype = BORDER_DEFAULT);
//! returns filter engine for the generalized Sobel operator
CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU( int srcType, int dstType, int dx, int dy, int ksize, int borderType = BORDER_DEFAULT );
//! applies Laplacian operator to the image
// supports only ksize = 1 and ksize = 3 8UC1 8UC4 32FC1 32FC4 data type
CV_EXPORTS void Laplacian(const oclMat &src, oclMat &dst, int ddepth, int ksize = 1, double scale = 1);
//! returns 2D box filter
// supports CV_8UC1 and CV_8UC4 source type, dst type must be the same as source type
CV_EXPORTS Ptr<BaseFilter_GPU> getBoxFilter_GPU(int srcType, int dstType,
const Size &ksize, Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
//! returns box filter engine
CV_EXPORTS Ptr<FilterEngine_GPU> createBoxFilter_GPU(int srcType, int dstType, const Size &ksize,
const Point &anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
//! returns 2D filter with the specified kernel
// supports CV_8UC1 and CV_8UC4 types
CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat &kernel, const Size &ksize,
Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
//! returns the non-separable linear filter engine
CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat &kernel,
const Point &anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
//! smooths the image using the normalized box filter
// supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
// supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101,BORDER_WRAP
CV_EXPORTS void boxFilter(const oclMat &src, oclMat &dst, int ddepth, Size ksize,
Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
//! returns 2D morphological filter
//! only MORPH_ERODE and MORPH_DILATE are supported
// supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
// kernel must have CV_8UC1 type, one rows and cols == ksize.width * ksize.height
CV_EXPORTS Ptr<BaseFilter_GPU> getMorphologyFilter_GPU(int op, int type, const Mat &kernel, const Size &ksize,
Point anchor = Point(-1, -1));
//! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported.
CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat &kernel,
const Point &anchor = Point(-1, -1), int iterations = 1);
//! a synonym for normalized box filter
// supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
// supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
static inline void blur(const oclMat &src, oclMat &dst, Size ksize, Point anchor = Point(-1, -1),
int borderType = BORDER_CONSTANT)
{
boxFilter(src, dst, -1, ksize, anchor, borderType);
}
//! applies non-separable 2D linear filter to the image
CV_EXPORTS void filter2D(const oclMat &src, oclMat &dst, int ddepth, const Mat &kernel,
Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
//! applies separable 2D linear filter to the image
CV_EXPORTS void sepFilter2D(const oclMat &src, oclMat &dst, int ddepth, const Mat &kernelX, const Mat &kernelY,
Point anchor = Point(-1, -1), double delta = 0.0, int bordertype = BORDER_DEFAULT);
//! applies generalized Sobel operator to the image
// dst.type must equalize src.type
// supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
// supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
CV_EXPORTS void Sobel(const oclMat &src, oclMat &dst, int ddepth, int dx, int dy, int ksize = 3, double scale = 1, double delta = 0.0, int bordertype = BORDER_DEFAULT);
//! applies the vertical or horizontal Scharr operator to the image
// dst.type must equalize src.type
// supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
// supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
CV_EXPORTS void Scharr(const oclMat &src, oclMat &dst, int ddepth, int dx, int dy, double scale = 1, double delta = 0.0, int bordertype = BORDER_DEFAULT);
//! smooths the image using Gaussian filter.
// dst.type must equalize src.type
// supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
// supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
CV_EXPORTS void GaussianBlur(const oclMat &src, oclMat &dst, Size ksize, double sigma1, double sigma2 = 0, int bordertype = BORDER_DEFAULT);
//! erodes the image (applies the local minimum operator)
// supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
CV_EXPORTS void erode( const oclMat &src, oclMat &dst, const Mat &kernel, Point anchor = Point(-1, -1), int iterations = 1,
int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue());
//! dilates the image (applies the local maximum operator)
// supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
CV_EXPORTS void dilate( const oclMat &src, oclMat &dst, const Mat &kernel, Point anchor = Point(-1, -1), int iterations = 1,
int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue());
//! applies an advanced morphological operation to the image
CV_EXPORTS void morphologyEx( const oclMat &src, oclMat &dst, int op, const Mat &kernel, Point anchor = Point(-1, -1), int iterations = 1,
int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue());
////////////////////////////// Image processing //////////////////////////////
//! Does mean shift filtering on GPU.
CV_EXPORTS void meanShiftFiltering(const oclMat &src, oclMat &dst, int sp, int sr,
TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
//! Does mean shift procedure on GPU.
CV_EXPORTS void meanShiftProc(const oclMat &src, oclMat &dstr, oclMat &dstsp, int sp, int sr,
TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
//! Does mean shift segmentation with elimiation of small regions.
CV_EXPORTS void meanShiftSegmentation(const oclMat &src, Mat &dst, int sp, int sr, int minsize,
TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
//! applies fixed threshold to the image.
// supports CV_8UC1 and CV_32FC1 data type
// supports threshold type: THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV
CV_EXPORTS double threshold(const oclMat &src, oclMat &dst, double thresh, double maxVal, int type = THRESH_TRUNC);
//! resizes the image
// Supports INTER_NEAREST, INTER_LINEAR
// supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
CV_EXPORTS void resize(const oclMat &src, oclMat &dst, Size dsize, double fx = 0, double fy = 0, int interpolation = INTER_LINEAR);
//! Applies a generic geometrical transformation to an image.
// Supports INTER_NEAREST, INTER_LINEAR.
// Map1 supports CV_16SC2, CV_32FC2 types.
// Src supports CV_8UC1, CV_8UC2, CV_8UC4.
CV_EXPORTS void remap(const oclMat &src, oclMat &dst, oclMat &map1, oclMat &map2, int interpolation, int bordertype, const Scalar &value = Scalar());
//! copies 2D array to a larger destination array and pads borders with user-specifiable constant
// supports CV_8UC1, CV_8UC4, CV_32SC1 types
CV_EXPORTS void copyMakeBorder(const oclMat &src, oclMat &dst, int top, int bottom, int left, int right, int boardtype, const Scalar &value = Scalar());
//! Smoothes image using median filter
// The source 1- or 4-channel image. When m is 3 or 5, the image depth should be CV 8U or CV 32F.
CV_EXPORTS void medianFilter(const oclMat &src, oclMat &dst, int m);
//! warps the image using affine transformation
// Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
// supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
CV_EXPORTS void warpAffine(const oclMat &src, oclMat &dst, const Mat &M, Size dsize, int flags = INTER_LINEAR);
//! warps the image using perspective transformation
// Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
// supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
CV_EXPORTS void warpPerspective(const oclMat &src, oclMat &dst, const Mat &M, Size dsize, int flags = INTER_LINEAR);
//! computes the integral image and integral for the squared image
// sum will have CV_32S type, sqsum - CV32F type
// supports only CV_8UC1 source type
CV_EXPORTS void integral(const oclMat &src, oclMat &sum, oclMat &sqsum);
CV_EXPORTS void integral(const oclMat &src, oclMat &sum);
CV_EXPORTS void cornerHarris(const oclMat &src, oclMat &dst, int blockSize, int ksize, double k, int bordertype = cv::BORDER_DEFAULT);
CV_EXPORTS void cornerMinEigenVal(const oclMat &src, oclMat &dst, int blockSize, int ksize, int bordertype = cv::BORDER_DEFAULT);
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
///////////////////////////////////////////CascadeClassifier//////////////////////////////////////////////////////////////////
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
class CV_EXPORTS_W OclCascadeClassifier : public cv::CascadeClassifier
{
public:
OclCascadeClassifier() {};
~OclCascadeClassifier() {};
CvSeq* oclHaarDetectObjects(oclMat &gimg, CvMemStorage *storage, double scaleFactor,
int minNeighbors, int flags, CvSize minSize = cvSize(0, 0), CvSize maxSize = cvSize(0, 0));
};
/////////////////////////////// Pyramid /////////////////////////////////////
CV_EXPORTS void pyrDown(const oclMat &src, oclMat &dst);
//! upsamples the source image and then smoothes it
CV_EXPORTS void pyrUp(const oclMat &src, oclMat &dst);
//! performs linear blending of two images
//! to avoid accuracy errors sum of weigths shouldn't be very close to zero
// supports only CV_8UC1 source type
CV_EXPORTS void blendLinear(const oclMat &img1, const oclMat &img2, const oclMat &weights1, const oclMat &weights2, oclMat &result);
//! computes vertical sum, supports only CV_32FC1 images
CV_EXPORTS void columnSum(const oclMat &src, oclMat &sum);
///////////////////////////////////////// match_template /////////////////////////////////////////////////////////////
struct CV_EXPORTS MatchTemplateBuf
{
Size user_block_size;
oclMat imagef, templf;
std::vector<oclMat> images;
std::vector<oclMat> image_sums;
std::vector<oclMat> image_sqsums;
};
//! computes the proximity map for the raster template and the image where the template is searched for
// Supports TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED for type 8UC1 and 8UC4
// Supports TM_SQDIFF, TM_CCORR for type 32FC1 and 32FC4
CV_EXPORTS void matchTemplate(const oclMat &image, const oclMat &templ, oclMat &result, int method);
//! computes the proximity map for the raster template and the image where the template is searched for
// Supports TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED for type 8UC1 and 8UC4
// Supports TM_SQDIFF, TM_CCORR for type 32FC1 and 32FC4
CV_EXPORTS void matchTemplate(const oclMat &image, const oclMat &templ, oclMat &result, int method, MatchTemplateBuf &buf);
///////////////////////////////////////////// Canny /////////////////////////////////////////////
struct CV_EXPORTS CannyBuf;
//! compute edges of the input image using Canny operator
// Support CV_8UC1 only
CV_EXPORTS void Canny(const oclMat &image, oclMat &edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
CV_EXPORTS void Canny(const oclMat &image, CannyBuf &buf, oclMat &edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
CV_EXPORTS void Canny(const oclMat &dx, const oclMat &dy, oclMat &edges, double low_thresh, double high_thresh, bool L2gradient = false);
CV_EXPORTS void Canny(const oclMat &dx, const oclMat &dy, CannyBuf &buf, oclMat &edges, double low_thresh, double high_thresh, bool L2gradient = false);
struct CV_EXPORTS CannyBuf
{
CannyBuf() : counter(NULL) {}
~CannyBuf()
{
release();
}
explicit CannyBuf(const Size &image_size, int apperture_size = 3) : counter(NULL)
{
create(image_size, apperture_size);
}
CannyBuf(const oclMat &dx_, const oclMat &dy_);
void create(const Size &image_size, int apperture_size = 3);
void release();
oclMat dx, dy;
oclMat dx_buf, dy_buf;
oclMat edgeBuf;
oclMat trackBuf1, trackBuf2;
void *counter;
Ptr<FilterEngine_GPU> filterDX, filterDY;
};
///////////////////////////////////////// Hough Transform /////////////////////////////////////////
//! HoughCircles
struct HoughCirclesBuf
{
oclMat edges;
oclMat accum;
oclMat srcPoints;
oclMat centers;
CannyBuf cannyBuf;
};
CV_EXPORTS void HoughCircles(const oclMat& src, oclMat& circles, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096);
CV_EXPORTS void HoughCircles(const oclMat& src, oclMat& circles, HoughCirclesBuf& buf, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096);
CV_EXPORTS void HoughCirclesDownload(const oclMat& d_circles, OutputArray h_circles);
///////////////////////////////////////// clAmdFft related /////////////////////////////////////////
//! Performs a forward or inverse discrete Fourier transform (1D or 2D) of floating point matrix.
//! Param dft_size is the size of DFT transform.
//!
//! For complex-to-real transform it is assumed that the source matrix is packed in CLFFT's format.
// support src type of CV32FC1, CV32FC2
// support flags: DFT_INVERSE, DFT_REAL_OUTPUT, DFT_COMPLEX_OUTPUT, DFT_ROWS
// dft_size is the size of original input, which is used for transformation from complex to real.
// dft_size must be powers of 2, 3 and 5
// real to complex dft requires at least v1.8 clAmdFft
// real to complex dft output is not the same with cpu version
// real to complex and complex to real does not support DFT_ROWS
CV_EXPORTS void dft(const oclMat &src, oclMat &dst, Size dft_size = Size(0, 0), int flags = 0);
//! implements generalized matrix product algorithm GEMM from BLAS
// The functionality requires clAmdBlas library
// only support type CV_32FC1
// flag GEMM_3_T is not supported
CV_EXPORTS void gemm(const oclMat &src1, const oclMat &src2, double alpha,
const oclMat &src3, double beta, oclMat &dst, int flags = 0);
//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
struct CV_EXPORTS HOGDescriptor
{
enum { DEFAULT_WIN_SIGMA = -1 };
enum { DEFAULT_NLEVELS = 64 };
enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };
HOGDescriptor(Size win_size = Size(64, 128), Size block_size = Size(16, 16),
Size block_stride = Size(8, 8), Size cell_size = Size(8, 8),
int nbins = 9, double win_sigma = DEFAULT_WIN_SIGMA,
double threshold_L2hys = 0.2, bool gamma_correction = true,
int nlevels = DEFAULT_NLEVELS);
size_t getDescriptorSize() const;
size_t getBlockHistogramSize() const;
void setSVMDetector(const std::vector<float> &detector);
static std::vector<float> getDefaultPeopleDetector();
static std::vector<float> getPeopleDetector48x96();
static std::vector<float> getPeopleDetector64x128();
void detect(const oclMat &img, std::vector<Point> &found_locations,
double hit_threshold = 0, Size win_stride = Size(),
Size padding = Size());
void detectMultiScale(const oclMat &img, std::vector<Rect> &found_locations,
double hit_threshold = 0, Size win_stride = Size(),
Size padding = Size(), double scale0 = 1.05,
int group_threshold = 2);
void getDescriptors(const oclMat &img, Size win_stride,
oclMat &descriptors,
int descr_format = DESCR_FORMAT_COL_BY_COL);
Size win_size;
Size block_size;
Size block_stride;
Size cell_size;
int nbins;
double win_sigma;
double threshold_L2hys;
bool gamma_correction;
int nlevels;
protected:
// initialize buffers; only need to do once in case of multiscale detection
void init_buffer(const oclMat &img, Size win_stride);
void computeBlockHistograms(const oclMat &img);
void computeGradient(const oclMat &img, oclMat &grad, oclMat &qangle);
double getWinSigma() const;
bool checkDetectorSize() const;
static int numPartsWithin(int size, int part_size, int stride);
static Size numPartsWithin(Size size, Size part_size, Size stride);
// Coefficients of the separating plane
float free_coef;
oclMat detector;
// Results of the last classification step
oclMat labels;
Mat labels_host;
// Results of the last histogram evaluation step
oclMat block_hists;
// Gradients conputation results
oclMat grad, qangle;
// scaled image
oclMat image_scale;
// effect size of input image (might be different from original size after scaling)
Size effect_size;
};
//! Speeded up robust features, port from GPU module.
////////////////////////////////// SURF //////////////////////////////////////////
class CV_EXPORTS SURF_OCL
{
public:
enum KeypointLayout
{
X_ROW = 0,
Y_ROW,
LAPLACIAN_ROW,
OCTAVE_ROW,
SIZE_ROW,
ANGLE_ROW,
HESSIAN_ROW,
ROWS_COUNT
};
//! the default constructor
SURF_OCL();
//! the full constructor taking all the necessary parameters
explicit SURF_OCL(double _hessianThreshold, int _nOctaves = 4,
int _nOctaveLayers = 2, bool _extended = false, float _keypointsRatio = 0.01f, bool _upright = false);
//! returns the descriptor size in float's (64 or 128)
int descriptorSize() const;
//! upload host keypoints to device memory
void uploadKeypoints(const std::vector<cv::KeyPoint> &keypoints, oclMat &keypointsocl);
//! download keypoints from device to host memory
void downloadKeypoints(const oclMat &keypointsocl, std::vector<KeyPoint> &keypoints);
//! download descriptors from device to host memory
void downloadDescriptors(const oclMat &descriptorsocl, std::vector<float> &descriptors);
//! finds the keypoints using fast hessian detector used in SURF
//! supports CV_8UC1 images
//! keypoints will have nFeature cols and 6 rows
//! keypoints.ptr<float>(X_ROW)[i] will contain x coordinate of i'th feature
//! keypoints.ptr<float>(Y_ROW)[i] will contain y coordinate of i'th feature
//! keypoints.ptr<float>(LAPLACIAN_ROW)[i] will contain laplacian sign of i'th feature
//! keypoints.ptr<float>(OCTAVE_ROW)[i] will contain octave of i'th feature
//! keypoints.ptr<float>(SIZE_ROW)[i] will contain size of i'th feature
//! keypoints.ptr<float>(ANGLE_ROW)[i] will contain orientation of i'th feature
//! keypoints.ptr<float>(HESSIAN_ROW)[i] will contain response of i'th feature
void operator()(const oclMat &img, const oclMat &mask, oclMat &keypoints);
//! finds the keypoints and computes their descriptors.
//! Optionally it can compute descriptors for the user-provided keypoints and recompute keypoints direction
void operator()(const oclMat &img, const oclMat &mask, oclMat &keypoints, oclMat &descriptors,
bool useProvidedKeypoints = false);
void operator()(const oclMat &img, const oclMat &mask, std::vector<KeyPoint> &keypoints);
void operator()(const oclMat &img, const oclMat &mask, std::vector<KeyPoint> &keypoints, oclMat &descriptors,
bool useProvidedKeypoints = false);
void operator()(const oclMat &img, const oclMat &mask, std::vector<KeyPoint> &keypoints, std::vector<float> &descriptors,
bool useProvidedKeypoints = false);
void releaseMemory();
// SURF parameters
float hessianThreshold;
int nOctaves;
int nOctaveLayers;
bool extended;
bool upright;
//! max keypoints = min(keypointsRatio * img.size().area(), 65535)
float keypointsRatio;
oclMat sum, mask1, maskSum, intBuffer;
oclMat det, trace;
oclMat maxPosBuffer;
};
////////////////////////feature2d_ocl/////////////////
/****************************************************************************************\
* Distance *
\****************************************************************************************/
template<typename T>
struct CV_EXPORTS Accumulator
{
typedef T Type;
};
template<> struct Accumulator<unsigned char>
{
typedef float Type;
};
template<> struct Accumulator<unsigned short>
{
typedef float Type;
};
template<> struct Accumulator<char>
{
typedef float Type;
};
template<> struct Accumulator<short>
{
typedef float Type;
};
/*
* Manhattan distance (city block distance) functor
*/
template<class T>
struct CV_EXPORTS L1
{
enum { normType = NORM_L1 };
typedef T ValueType;
typedef typename Accumulator<T>::Type ResultType;
ResultType operator()( const T *a, const T *b, int size ) const
{
return normL1<ValueType, ResultType>(a, b, size);
}
};
/*
* Euclidean distance functor
*/
template<class T>
struct CV_EXPORTS L2
{
enum { normType = NORM_L2 };
typedef T ValueType;
typedef typename Accumulator<T>::Type ResultType;
ResultType operator()( const T *a, const T *b, int size ) const
{
return (ResultType)std::sqrt((double)normL2Sqr<ValueType, ResultType>(a, b, size));
}
};
/*
* Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor
* bit count of A exclusive XOR'ed with B
*/
struct CV_EXPORTS Hamming
{
enum { normType = NORM_HAMMING };
typedef unsigned char ValueType;
typedef int ResultType;
/** this will count the bits in a ^ b
*/
ResultType operator()( const unsigned char *a, const unsigned char *b, int size ) const
{
return normHamming(a, b, size);
}
};
////////////////////////////////// BruteForceMatcher //////////////////////////////////
class CV_EXPORTS BruteForceMatcher_OCL_base
{
public:
enum DistType {L1Dist = 0, L2Dist, HammingDist};
explicit BruteForceMatcher_OCL_base(DistType distType = L2Dist);
// Add descriptors to train descriptor collection
void add(const std::vector<oclMat> &descCollection);
// Get train descriptors collection
const std::vector<oclMat> &getTrainDescriptors() const;
// Clear train descriptors collection
void clear();
// Return true if there are not train descriptors in collection
bool empty() const;
// Return true if the matcher supports mask in match methods
bool isMaskSupported() const;
// Find one best match for each query descriptor
void matchSingle(const oclMat &query, const oclMat &train,
oclMat &trainIdx, oclMat &distance,
const oclMat &mask = oclMat());
// Download trainIdx and distance and convert it to CPU vector with DMatch
static void matchDownload(const oclMat &trainIdx, const oclMat &distance, std::vector<DMatch> &matches);
// Convert trainIdx and distance to vector with DMatch
static void matchConvert(const Mat &trainIdx, const Mat &distance, std::vector<DMatch> &matches);
// Find one best match for each query descriptor
void match(const oclMat &query, const oclMat &train, std::vector<DMatch> &matches, const oclMat &mask = oclMat());
// Make gpu collection of trains and masks in suitable format for matchCollection function
void makeGpuCollection(oclMat &trainCollection, oclMat &maskCollection, const std::vector<oclMat> &masks = std::vector<oclMat>());
// Find one best match from train collection for each query descriptor
void matchCollection(const oclMat &query, const oclMat &trainCollection,
oclMat &trainIdx, oclMat &imgIdx, oclMat &distance,
const oclMat &masks = oclMat());
// Download trainIdx, imgIdx and distance and convert it to vector with DMatch
static void matchDownload(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance, std::vector<DMatch> &matches);
// Convert trainIdx, imgIdx and distance to vector with DMatch
static void matchConvert(const Mat &trainIdx, const Mat &imgIdx, const Mat &distance, std::vector<DMatch> &matches);
// Find one best match from train collection for each query descriptor.
void match(const oclMat &query, std::vector<DMatch> &matches, const std::vector<oclMat> &masks = std::vector<oclMat>());
// Find k best matches for each query descriptor (in increasing order of distances)
void knnMatchSingle(const oclMat &query, const oclMat &train,
oclMat &trainIdx, oclMat &distance, oclMat &allDist, int k,
const oclMat &mask = oclMat());
// Download trainIdx and distance and convert it to vector with DMatch
// compactResult is used when mask is not empty. If compactResult is false matches
// vector will have the same size as queryDescriptors rows. If compactResult is true
// matches vector will not contain matches for fully masked out query descriptors.
static void knnMatchDownload(const oclMat &trainIdx, const oclMat &distance,
std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
// Convert trainIdx and distance to vector with DMatch
static void knnMatchConvert(const Mat &trainIdx, const Mat &distance,
std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
// Find k best matches for each query descriptor (in increasing order of distances).
// compactResult is used when mask is not empty. If compactResult is false matches
// vector will have the same size as queryDescriptors rows. If compactResult is true
// matches vector will not contain matches for fully masked out query descriptors.
void knnMatch(const oclMat &query, const oclMat &train,
std::vector< std::vector<DMatch> > &matches, int k, const oclMat &mask = oclMat(),
bool compactResult = false);
// Find k best matches from train collection for each query descriptor (in increasing order of distances)
void knnMatch2Collection(const oclMat &query, const oclMat &trainCollection,
oclMat &trainIdx, oclMat &imgIdx, oclMat &distance,
const oclMat &maskCollection = oclMat());
// Download trainIdx and distance and convert it to vector with DMatch
// compactResult is used when mask is not empty. If compactResult is false matches
// vector will have the same size as queryDescriptors rows. If compactResult is true
// matches vector will not contain matches for fully masked out query descriptors.
static void knnMatch2Download(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance,
std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
// Convert trainIdx and distance to vector with DMatch
static void knnMatch2Convert(const Mat &trainIdx, const Mat &imgIdx, const Mat &distance,
std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
// Find k best matches for each query descriptor (in increasing order of distances).
// compactResult is used when mask is not empty. If compactResult is false matches
// vector will have the same size as queryDescriptors rows. If compactResult is true
// matches vector will not contain matches for fully masked out query descriptors.
void knnMatch(const oclMat &query, std::vector< std::vector<DMatch> > &matches, int k,
const std::vector<oclMat> &masks = std::vector<oclMat>(), bool compactResult = false);
// Find best matches for each query descriptor which have distance less than maxDistance.
// nMatches.at<int>(0, queryIdx) will contain matches count for queryIdx.
// carefully nMatches can be greater than trainIdx.cols - it means that matcher didn't find all matches,
// because it didn't have enough memory.
// If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nTrain / 100), 10),
// otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
// Matches doesn't sorted.
void radiusMatchSingle(const oclMat &query, const oclMat &train,
oclMat &trainIdx, oclMat &distance, oclMat &nMatches, float maxDistance,
const oclMat &mask = oclMat());
// Download trainIdx, nMatches and distance and convert it to vector with DMatch.
// matches will be sorted in increasing order of distances.
// compactResult is used when mask is not empty. If compactResult is false matches
// vector will have the same size as queryDescriptors rows. If compactResult is true
// matches vector will not contain matches for fully masked out query descriptors.
static void radiusMatchDownload(const oclMat &trainIdx, const oclMat &distance, const oclMat &nMatches,
std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
// Convert trainIdx, nMatches and distance to vector with DMatch.
static void radiusMatchConvert(const Mat &trainIdx, const Mat &distance, const Mat &nMatches,
std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
// Find best matches for each query descriptor which have distance less than maxDistance
// in increasing order of distances).
void radiusMatch(const oclMat &query, const oclMat &train,
std::vector< std::vector<DMatch> > &matches, float maxDistance,
const oclMat &mask = oclMat(), bool compactResult = false);
// Find best matches for each query descriptor which have distance less than maxDistance.
// If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nQuery / 100), 10),
// otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
// Matches doesn't sorted.
void radiusMatchCollection(const oclMat &query, oclMat &trainIdx, oclMat &imgIdx, oclMat &distance, oclMat &nMatches, float maxDistance,
const std::vector<oclMat> &masks = std::vector<oclMat>());
// Download trainIdx, imgIdx, nMatches and distance and convert it to vector with DMatch.
// matches will be sorted in increasing order of distances.
// compactResult is used when mask is not empty. If compactResult is false matches
// vector will have the same size as queryDescriptors rows. If compactResult is true
// matches vector will not contain matches for fully masked out query descriptors.
static void radiusMatchDownload(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance, const oclMat &nMatches,
std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
// Convert trainIdx, nMatches and distance to vector with DMatch.
static void radiusMatchConvert(const Mat &trainIdx, const Mat &imgIdx, const Mat &distance, const Mat &nMatches,
std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
// Find best matches from train collection for each query descriptor which have distance less than
// maxDistance (in increasing order of distances).
void radiusMatch(const oclMat &query, std::vector< std::vector<DMatch> > &matches, float maxDistance,
const std::vector<oclMat> &masks = std::vector<oclMat>(), bool compactResult = false);
DistType distType;
private:
std::vector<oclMat> trainDescCollection;
};
template <class Distance>
class CV_EXPORTS BruteForceMatcher_OCL;
template <typename T>
class CV_EXPORTS BruteForceMatcher_OCL< L1<T> > : public BruteForceMatcher_OCL_base
{
public:
explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(L1Dist) {}
explicit BruteForceMatcher_OCL(L1<T> /*d*/) : BruteForceMatcher_OCL_base(L1Dist) {}
};
template <typename T>
class CV_EXPORTS BruteForceMatcher_OCL< L2<T> > : public BruteForceMatcher_OCL_base
{
public:
explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(L2Dist) {}
explicit BruteForceMatcher_OCL(L2<T> /*d*/) : BruteForceMatcher_OCL_base(L2Dist) {}
};
template <> class CV_EXPORTS BruteForceMatcher_OCL< Hamming > : public BruteForceMatcher_OCL_base
{
public:
explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(HammingDist) {}
explicit BruteForceMatcher_OCL(Hamming /*d*/) : BruteForceMatcher_OCL_base(HammingDist) {}
};
/////////////////////////////// PyrLKOpticalFlow /////////////////////////////////////
class CV_EXPORTS PyrLKOpticalFlow
{
public:
PyrLKOpticalFlow()
{
winSize = Size(21, 21);
maxLevel = 3;
iters = 30;
derivLambda = 0.5;
useInitialFlow = false;
minEigThreshold = 1e-4f;
getMinEigenVals = false;
isDeviceArch11_ = false;
}
void sparse(const oclMat &prevImg, const oclMat &nextImg, const oclMat &prevPts, oclMat &nextPts,
oclMat &status, oclMat *err = 0);
void dense(const oclMat &prevImg, const oclMat &nextImg, oclMat &u, oclMat &v, oclMat *err = 0);
Size winSize;
int maxLevel;
int iters;
double derivLambda;
bool useInitialFlow;
float minEigThreshold;
bool getMinEigenVals;
void releaseMemory()
{
dx_calcBuf_.release();
dy_calcBuf_.release();
prevPyr_.clear();
nextPyr_.clear();
dx_buf_.release();
dy_buf_.release();
}
private:
void calcSharrDeriv(const oclMat &src, oclMat &dx, oclMat &dy);
void buildImagePyramid(const oclMat &img0, std::vector<oclMat> &pyr, bool withBorder);
oclMat dx_calcBuf_;
oclMat dy_calcBuf_;
std::vector<oclMat> prevPyr_;
std::vector<oclMat> nextPyr_;
oclMat dx_buf_;
oclMat dy_buf_;
oclMat uPyr_[2];
oclMat vPyr_[2];
bool isDeviceArch11_;
};
//////////////// build warping maps ////////////////////
//! builds plane warping maps
CV_EXPORTS void buildWarpPlaneMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat &R, const Mat &T, float scale, oclMat &map_x, oclMat &map_y);
//! builds cylindrical warping maps
CV_EXPORTS void buildWarpCylindricalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat &R, float scale, oclMat &map_x, oclMat &map_y);
//! builds spherical warping maps
CV_EXPORTS void buildWarpSphericalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat &R, float scale, oclMat &map_x, oclMat &map_y);
//! builds Affine warping maps
CV_EXPORTS void buildWarpAffineMaps(const Mat &M, bool inverse, Size dsize, oclMat &xmap, oclMat &ymap);
//! builds Perspective warping maps
CV_EXPORTS void buildWarpPerspectiveMaps(const Mat &M, bool inverse, Size dsize, oclMat &xmap, oclMat &ymap);
///////////////////////////////////// interpolate frames //////////////////////////////////////////////
//! Interpolate frames (images) using provided optical flow (displacement field).
//! frame0 - frame 0 (32-bit floating point images, single channel)
//! frame1 - frame 1 (the same type and size)
//! fu - forward horizontal displacement
//! fv - forward vertical displacement
//! bu - backward horizontal displacement
//! bv - backward vertical displacement
//! pos - new frame position
//! newFrame - new frame
//! buf - temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 oclMat;
//! occlusion masks 0, occlusion masks 1,
//! interpolated forward flow 0, interpolated forward flow 1,
//! interpolated backward flow 0, interpolated backward flow 1
//!
CV_EXPORTS void interpolateFrames(const oclMat &frame0, const oclMat &frame1,
const oclMat &fu, const oclMat &fv,
const oclMat &bu, const oclMat &bv,
float pos, oclMat &newFrame, oclMat &buf);
//! computes moments of the rasterized shape or a vector of points
CV_EXPORTS Moments ocl_moments(InputArray _array, bool binaryImage);
}
}
#if defined _MSC_VER && _MSC_VER >= 1200
# pragma warning( push)
# pragma warning( disable: 4267)
#endif
#include "opencv2/ocl/matrix_operations.hpp"
#if defined _MSC_VER && _MSC_VER >= 1200
# pragma warning( pop)
#endif
#endif /* __OPENCV_OCL_HPP__ */