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opencv/modules/ocl/include/opencv2/ocl/ocl.hpp
Tom Stellard 42b1bd56cc ocl: Move static oclMat variables into FarnebackOpticalFlow class 
Move some static functions into the FarnebackOpticalFlow class as well,
so they can access these new class variables.

oclMat objects cannot be declared statically, because their destructor
depends on the statically defined __module variable from cl_context.cpp.
Since statically defined variables in separate compilation units have
an undefined destruction order there is always the possibility the
__module will be destructed before an oclMat object, which results
in a segfault.
2014-11-13 11:16:10 -05:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
// License Agreement
// For Open Source Computer Vision Library
//
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#ifndef __OPENCV_OCL_HPP__
#define __OPENCV_OCL_HPP__
#include <memory>
#include <vector>
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/ml/ml.hpp"
namespace cv
{
namespace ocl
{
enum DeviceType
{
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
};
// these classes contain OpenCL runtime information
struct PlatformInfo;
struct DeviceInfo
{
int _id; // reserved, don't use it
DeviceType deviceType;
std::string deviceProfile;
std::string deviceVersion;
std::string deviceName;
std::string deviceVendor;
int deviceVendorId;
std::string deviceDriverVersion;
std::string deviceExtensions;
size_t maxWorkGroupSize;
std::vector<size_t> maxWorkItemSizes;
int maxComputeUnits;
size_t localMemorySize;
size_t maxMemAllocSize;
int deviceVersionMajor;
int deviceVersionMinor;
bool haveDoubleSupport;
bool isUnifiedMemory; // 1 means integrated GPU, otherwise this value is 0
bool isIntelDevice;
std::string compilationExtraOptions;
const PlatformInfo* platform;
DeviceInfo();
~DeviceInfo();
};
struct PlatformInfo
{
int _id; // reserved, don't use it
std::string platformProfile;
std::string platformVersion;
std::string platformName;
std::string platformVendor;
std::string platformExtensons;
int platformVersionMajor;
int platformVersionMinor;
std::vector<const DeviceInfo*> devices;
PlatformInfo();
~PlatformInfo();
};
//////////////////////////////// Initialization & Info ////////////////////////
typedef std::vector<const PlatformInfo*> PlatformsInfo;
CV_EXPORTS int getOpenCLPlatforms(PlatformsInfo& platforms);
typedef std::vector<const DeviceInfo*> DevicesInfo;
CV_EXPORTS int getOpenCLDevices(DevicesInfo& devices, int deviceType = CVCL_DEVICE_TYPE_GPU,
const PlatformInfo* platform = NULL);
// set device you want to use
CV_EXPORTS void setDevice(const DeviceInfo* info);
// Initialize from OpenCL handles directly.
// Argument types is (pointers): cl_platform_id*, cl_context*, cl_device_id*
CV_EXPORTS void initializeContext(void* pClPlatform, void* pClContext, void* pClDevice);
//////////////////////////////// Error handling ////////////////////////
CV_EXPORTS void error(const char *error_string, const char *file, const int line, const char *func);
enum FEATURE_TYPE
{
FEATURE_CL_DOUBLE = 1,
FEATURE_CL_UNIFIED_MEM,
FEATURE_CL_VER_1_2,
FEATURE_CL_INTEL_DEVICE
};
// Represents OpenCL context, interface
class CV_EXPORTS Context
{
protected:
Context() { }
~Context() { }
public:
static Context* getContext();
bool supportsFeature(FEATURE_TYPE featureType) const;
const DeviceInfo& getDeviceInfo() const;
const void* getOpenCLContextPtr() const;
const void* getOpenCLCommandQueuePtr() const;
const void* getOpenCLDeviceIDPtr() const;
};
inline const void *getClContextPtr()
{
return Context::getContext()->getOpenCLContextPtr();
}
inline const void *getClCommandQueuePtr()
{
return Context::getContext()->getOpenCLCommandQueuePtr();
}
CV_EXPORTS bool supportsFeature(FEATURE_TYPE featureType);
CV_EXPORTS void finish();
enum BINARY_CACHE_MODE
{
CACHE_NONE = 0, // do not cache OpenCL binary
CACHE_DEBUG = 0x1 << 0, // cache OpenCL binary when built in debug mode
CACHE_RELEASE = 0x1 << 1, // default behavior, only cache when built in release mode
CACHE_ALL = CACHE_DEBUG | CACHE_RELEASE // cache opencl binary
};
//! Enable or disable OpenCL program binary caching onto local disk
// After a program (*.cl files in opencl/ folder) is built at runtime, we allow the
// compiled OpenCL program to be cached to the path automatically as "path/*.clb"
// binary file, which will be reused when the OpenCV executable is started again.
//
// This feature is enabled by default.
CV_EXPORTS void setBinaryDiskCache(int mode = CACHE_RELEASE, cv::String path = "./");
//! set where binary cache to be saved to
CV_EXPORTS void setBinaryPath(const char *path);
struct ProgramSource
{
const char* name;
const char* programStr;
const char* programHash;
// Cache in memory by name (should be unique). Caching on disk disabled.
inline ProgramSource(const char* _name, const char* _programStr)
: name(_name), programStr(_programStr), programHash(NULL)
{
}
// Cache in memory by name (should be unique). Caching on disk uses programHash mark.
inline ProgramSource(const char* _name, const char* _programStr, const char* _programHash)
: name(_name), programStr(_programStr), programHash(_programHash)
{
}
};
//! Calls OpenCL kernel. Pass globalThreads = NULL, and cleanUp = true, to finally clean-up without executing.
//! Deprecated, will be replaced
CV_EXPORTS void openCLExecuteKernelInterop(Context *clCxt,
const cv::ocl::ProgramSource& source, 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);
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;
//! convert to _InputArray
operator _InputArray();
//! convert to _OutputArray
operator _OutputArray();
//! 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 those oclMatrix elements to "m" that are marked with non-zero mask elements.
// It calls m.create(this->size(), this->type()).
// It supports any data type
void copyTo( oclMat &m, const oclMat &mask = oclMat()) const;
//! converts oclMatrix to another datatype with optional scalng. See cvConvertScale.
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
oclMat& operator = (const Scalar &s);
//! sets some of the oclMatrix elements to s, according to the mask
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;
//! 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; // TODO clCtx
//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;
};
// convert InputArray/OutputArray to oclMat references
CV_EXPORTS oclMat& getOclMatRef(InputArray src);
CV_EXPORTS oclMat& getOclMatRef(OutputArray src);
///////////////////// 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 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, vector<oclMat> &dst);
////////////////////////////// Arithmetics ///////////////////////////////////
//! adds one matrix to another with scale (dst = src1 * alpha + src2 * beta + gama)
// supports all data types
CV_EXPORTS void addWeighted(const oclMat &src1, double alpha, const oclMat &src2, double beta, double gama, oclMat &dst);
//! adds one matrix to another (dst = src1 + src2)
// supports all data types
CV_EXPORTS void add(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
//! adds scalar to a matrix (dst = src1 + s)
// supports all data types
CV_EXPORTS void add(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
//! subtracts one matrix from another (dst = src1 - src2)
// supports all data types
CV_EXPORTS void subtract(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
//! subtracts scalar from a matrix (dst = src1 - s)
// supports all data types
CV_EXPORTS void subtract(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
//! computes element-wise product of the two arrays (dst = src1 * scale * src2)
// supports all data types
CV_EXPORTS void multiply(const oclMat &src1, const oclMat &src2, oclMat &dst, double scale = 1);
//! multiplies matrix to a number (dst = scalar * src)
// supports all data types
CV_EXPORTS void multiply(double scalar, const oclMat &src, oclMat &dst);
//! computes element-wise quotient of the two arrays (dst = src1 * scale / src2)
// supports all data types
CV_EXPORTS void divide(const oclMat &src1, const oclMat &src2, oclMat &dst, double scale = 1);
//! computes element-wise quotient of the two arrays (dst = scale / src)
// supports all data types
CV_EXPORTS void divide(double scale, const oclMat &src1, oclMat &dst);
//! computes element-wise minimum of the two arrays (dst = min(src1, src2))
// supports all data types
CV_EXPORTS void min(const oclMat &src1, const oclMat &src2, oclMat &dst);
//! computes element-wise maximum of the two arrays (dst = max(src1, src2))
// supports all data types
CV_EXPORTS void max(const oclMat &src1, const oclMat &src2, oclMat &dst);
//! compares elements of two arrays (dst = src1 \verbatim<cmpop>\endverbatim src2)
// supports all data types
CV_EXPORTS void compare(const oclMat &src1, const oclMat &src2, oclMat &dst, int cmpop);
//! transposes the matrix
// supports all data types
CV_EXPORTS void transpose(const oclMat &src, oclMat &dst);
//! computes element-wise absolute values of an array (dst = abs(src))
// supports all data types
CV_EXPORTS void abs(const oclMat &src, oclMat &dst);
//! computes element-wise absolute difference of two arrays (dst = abs(src1 - src2))
// supports all data types
CV_EXPORTS void absdiff(const oclMat &src1, const oclMat &src2, oclMat &dst);
//! computes element-wise absolute difference of array and scalar (dst = abs(src1 - s))
// supports all data types
CV_EXPORTS void absdiff(const oclMat &src1, const Scalar &s, oclMat &dst);
//! computes mean value and standard deviation of all or selected array elements
// supports all data types
CV_EXPORTS void meanStdDev(const oclMat &mtx, Scalar &mean, Scalar &stddev);
//! computes norm of array
// supports NORM_INF, NORM_L1, NORM_L2
// supports all data types
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 all data types
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 &src, oclMat &dst, int flipCode);
//! computes sum of array elements
// 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);
//! only 8UC1 is supported now
CV_EXPORTS Ptr<cv::CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8));
//! bilateralFilter
// supports 8UC1 8UC4
CV_EXPORTS void bilateralFilter(const oclMat& src, oclMat& dst, int d, double sigmaColor, double sigmaSpace, int borderType=BORDER_DEFAULT);
//! Applies an adaptive bilateral filter to the input image
// Unlike the usual bilateral filter that uses fixed value for sigmaColor,
// the adaptive version calculates the local variance in he ksize neighborhood
// and use this as sigmaColor, for the value filtering. However, the local standard deviation is
// clamped to the maxSigmaColor.
// supports 8UC1, 8UC3
CV_EXPORTS void adaptiveBilateralFilter(const oclMat& src, oclMat& dst, Size ksize, double sigmaSpace, double maxSigmaColor=20.0, Point anchor = Point(-1, -1), int borderType=BORDER_DEFAULT);
//! computes exponent of each matrix element (dst = e**src)
// supports only CV_32FC1, CV_64FC1 type
CV_EXPORTS void exp(const oclMat &src, oclMat &dst);
//! computes natural logarithm of absolute value of each matrix element: dst = log(abs(src))
// supports only CV_32FC1, CV_64FC1 type
CV_EXPORTS void log(const oclMat &src, oclMat &dst);
//! 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);
//! 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);
//! initializes a scaled identity matrix
CV_EXPORTS void setIdentity(oclMat& src, const Scalar & val = Scalar(1));
//! fills the output array with repeated copies of the input array
CV_EXPORTS void repeat(const oclMat & src, int ny, int nx, oclMat & dst);
//////////////////////////////// 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, Size imgSize = Size(-1,-1));
//! 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, Size imgSize = Size(-1,-1));
//! 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, Size imgSize = Size(-1,-1) );
//! applies Laplacian operator to the image
// supports only ksize = 1 and ksize = 3
CV_EXPORTS void Laplacian(const oclMat &src, oclMat &dst, int ddepth, int ksize = 1, double scale = 1,
double delta=0, int borderType=BORDER_DEFAULT);
//! returns 2D box filter
// 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: dst type must be the same as source type
CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat &kernel, const Size &ksize,
const Point &anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
//! returns the non-separable linear filter engine
// supports: dst type must be the same as source type
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
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
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), double delta = 0.0, 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. m should be 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 cornerHarris_dxdy(const oclMat &src, oclMat &dst, oclMat &Dx, oclMat &Dy,
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);
CV_EXPORTS void cornerMinEigenVal_dxdy(const oclMat &src, oclMat &dst, oclMat &Dx, oclMat &Dy,
int blockSize, int ksize, int bordertype = cv::BORDER_DEFAULT);
/////////////////////////////////// ML ///////////////////////////////////////////
//! Compute closest centers for each lines in source and lable it after center's index
// supports CV_32FC1/CV_32FC2/CV_32FC4 data type
// supports NORM_L1 and NORM_L2 distType
// if indices is provided, only the indexed rows will be calculated and their results are in the same
// order of indices
CV_EXPORTS void distanceToCenters(const oclMat &src, const oclMat &centers, Mat &dists, Mat &labels, int distType = NORM_L2SQR);
//!Does k-means procedure on GPU
// supports CV_32FC1/CV_32FC2/CV_32FC4 data type
CV_EXPORTS double kmeans(const oclMat &src, int K, oclMat &bestLabels,
TermCriteria criteria, int attemps, int flags, oclMat &centers);
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
///////////////////////////////////////////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));
void detectMultiScale(oclMat &image, CV_OUT std::vector<cv::Rect>& faces,
double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0,
Size minSize = Size(), Size maxSize = Size());
};
class CV_EXPORTS OclCascadeClassifierBuf : public cv::CascadeClassifier
{
public:
OclCascadeClassifierBuf() :
m_flags(0), initialized(false), m_scaleFactor(0), buffers(NULL) {}
~OclCascadeClassifierBuf() { release(); }
void detectMultiScale(oclMat &image, CV_OUT std::vector<cv::Rect>& faces,
double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0,
Size minSize = Size(), Size maxSize = Size());
void release();
private:
void Init(const int rows, const int cols, double scaleFactor, int flags,
const int outputsz, const size_t localThreads[],
CvSize minSize, CvSize maxSize);
void CreateBaseBufs(const int datasize, const int totalclassifier, const int flags, const int outputsz);
void CreateFactorRelatedBufs(const int rows, const int cols, const int flags,
const double scaleFactor, const size_t localThreads[],
CvSize minSize, CvSize maxSize);
void GenResult(CV_OUT std::vector<cv::Rect>& faces, const std::vector<cv::Rect> &rectList, const std::vector<int> &rweights);
int m_rows;
int m_cols;
int m_flags;
int m_loopcount;
int m_nodenum;
bool findBiggestObject;
bool initialized;
double m_scaleFactor;
Size m_minSize;
Size m_maxSize;
vector<CvSize> sizev;
vector<float> scalev;
oclMat gimg1, gsum, gsqsum;
void * buffers;
};
/////////////////////////////// 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(1, 1, CV_32S) { }
~CannyBuf()
{
release();
}
explicit CannyBuf(const Size &image_size, int apperture_size = 3) : counter(1, 1, CV_32S)
{
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;
oclMat counter;
Ptr<FilterEngine_GPU> filterDX, filterDY;
};
///////////////////////////////////////// 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(), 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 vector<float> &detector);
static vector<float> getDefaultPeopleDetector();
static vector<float> getPeopleDetector48x96();
static vector<float> getPeopleDetector64x128();
void detect(const oclMat &img, vector<Point> &found_locations,
double hit_threshold = 0, Size win_stride = Size(),
Size padding = Size());
void detectMultiScale(const oclMat &img, 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;
private:
oclMat gauss_w_lut;
};
////////////////////////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)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) {}
};
class CV_EXPORTS BFMatcher_OCL : public BruteForceMatcher_OCL_base
{
public:
explicit BFMatcher_OCL(int norm = NORM_L2) : BruteForceMatcher_OCL_base(norm == NORM_L1 ? L1Dist : norm == NORM_L2 ? L2Dist : HammingDist) {}
};
class CV_EXPORTS GoodFeaturesToTrackDetector_OCL
{
public:
explicit GoodFeaturesToTrackDetector_OCL(int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 0.0,
int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04);
//! return 1 rows matrix with CV_32FC2 type
void operator ()(const oclMat& image, oclMat& corners, const oclMat& mask = oclMat());
//! download points of type Point2f to a vector. the vector's content will be erased
void downloadPoints(const oclMat &points, vector<Point2f> &points_v);
int maxCorners;
double qualityLevel;
double minDistance;
int blockSize;
bool useHarrisDetector;
double harrisK;
void releaseMemory()
{
Dx_.release();
Dy_.release();
eig_.release();
minMaxbuf_.release();
tmpCorners_.release();
}
private:
oclMat Dx_;
oclMat Dy_;
oclMat eig_;
oclMat eig_minmax_;
oclMat minMaxbuf_;
oclMat tmpCorners_;
oclMat counter_;
};
inline GoodFeaturesToTrackDetector_OCL::GoodFeaturesToTrackDetector_OCL(int maxCorners_, double qualityLevel_, double minDistance_,
int blockSize_, bool useHarrisDetector_, double harrisK_)
{
maxCorners = maxCorners_;
qualityLevel = qualityLevel_;
minDistance = minDistance_;
blockSize = blockSize_;
useHarrisDetector = useHarrisDetector_;
harrisK = harrisK_;
}
/////////////////////////////// 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, vector<oclMat> &pyr, bool withBorder);
oclMat dx_calcBuf_;
oclMat dy_calcBuf_;
vector<oclMat> prevPyr_;
vector<oclMat> nextPyr_;
oclMat dx_buf_;
oclMat dy_buf_;
oclMat uPyr_[2];
oclMat vPyr_[2];
bool isDeviceArch11_;
};
class CV_EXPORTS FarnebackOpticalFlow
{
public:
FarnebackOpticalFlow();
int numLevels;
double pyrScale;
bool fastPyramids;
int winSize;
int numIters;
int polyN;
double polySigma;
int flags;
void operator ()(const oclMat &frame0, const oclMat &frame1, oclMat &flowx, oclMat &flowy);
void releaseMemory();
private:
void setGaussianBlurKernel(const float *c_gKer, int ksizeHalf);
void gaussianBlurOcl(const oclMat &src, int ksizeHalf, oclMat &dst);
void polynomialExpansionOcl(
const oclMat &src, int polyN, oclMat &dst);
void gaussianBlur5Ocl(
const oclMat &src, int ksizeHalf, oclMat &dst);
void prepareGaussian(
int n, double sigma, float *g, float *xg, float *xxg,
double &ig11, double &ig03, double &ig33, double &ig55);
void setPolynomialExpansionConsts(int n, double sigma);
void updateFlow_boxFilter(
const oclMat& R0, const oclMat& R1, oclMat& flowx, oclMat &flowy,
oclMat& M, oclMat &bufM, int blockSize, bool updateMatrices);
void updateFlow_gaussianBlur(
const oclMat& R0, const oclMat& R1, oclMat& flowx, oclMat& flowy,
oclMat& M, oclMat &bufM, int blockSize, bool updateMatrices);
oclMat frames_[2];
oclMat pyrLevel_[2], M_, bufM_, R_[2], blurredFrame_[2];
std::vector<oclMat> pyramid0_, pyramid1_;
float ig[4];
oclMat gMat;
oclMat xgMat;
oclMat xxgMat;
oclMat gKerMat;
};
//////////////// 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
//! _array should be a vector a points standing for the contour
CV_EXPORTS Moments ocl_moments(InputArray contour);
//! src should be a general image uploaded to the GPU.
//! the supported oclMat type are CV_8UC1, CV_16UC1, CV_16SC1, CV_32FC1 and CV_64FC1
//! to use type of CV_64FC1, the GPU should support CV_64FC1
CV_EXPORTS Moments ocl_moments(oclMat& src, bool binary);
class CV_EXPORTS StereoBM_OCL
{
public:
enum { BASIC_PRESET = 0, PREFILTER_XSOBEL = 1 };
enum { DEFAULT_NDISP = 64, DEFAULT_WINSZ = 19 };
//! the default constructor
StereoBM_OCL();
//! the full constructor taking the camera-specific preset, number of disparities and the SAD window size. ndisparities must be multiple of 8.
StereoBM_OCL(int preset, int ndisparities = DEFAULT_NDISP, int winSize = DEFAULT_WINSZ);
//! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair
//! Output disparity has CV_8U type.
void operator() ( const oclMat &left, const oclMat &right, oclMat &disparity);
//! Some heuristics that tries to estmate
// if current GPU will be faster then CPU in this algorithm.
// It queries current active device.
static bool checkIfGpuCallReasonable();
int preset;
int ndisp;
int winSize;
// If avergeTexThreshold == 0 => post procesing is disabled
// If avergeTexThreshold != 0 then disparity is set 0 in each point (x,y) where for left image
// SumOfHorizontalGradiensInWindow(x, y, winSize) < (winSize * winSize) * avergeTexThreshold
// i.e. input left image is low textured.
float avergeTexThreshold;
private:
oclMat minSSD, leBuf, riBuf;
};
class CV_EXPORTS StereoBeliefPropagation
{
public:
enum { DEFAULT_NDISP = 64 };
enum { DEFAULT_ITERS = 5 };
enum { DEFAULT_LEVELS = 5 };
static void estimateRecommendedParams(int width, int height, int &ndisp, int &iters, int &levels);
explicit StereoBeliefPropagation(int ndisp = DEFAULT_NDISP,
int iters = DEFAULT_ITERS,
int levels = DEFAULT_LEVELS,
int msg_type = CV_16S);
StereoBeliefPropagation(int ndisp, int iters, int levels,
float max_data_term, float data_weight,
float max_disc_term, float disc_single_jump,
int msg_type = CV_32F);
void operator()(const oclMat &left, const oclMat &right, oclMat &disparity);
void operator()(const oclMat &data, oclMat &disparity);
int ndisp;
int iters;
int levels;
float max_data_term;
float data_weight;
float max_disc_term;
float disc_single_jump;
int msg_type;
private:
oclMat u, d, l, r, u2, d2, l2, r2;
std::vector<oclMat> datas;
oclMat out;
};
class CV_EXPORTS StereoConstantSpaceBP
{
public:
enum { DEFAULT_NDISP = 128 };
enum { DEFAULT_ITERS = 8 };
enum { DEFAULT_LEVELS = 4 };
enum { DEFAULT_NR_PLANE = 4 };
static void estimateRecommendedParams(int width, int height, int &ndisp, int &iters, int &levels, int &nr_plane);
explicit StereoConstantSpaceBP(
int ndisp = DEFAULT_NDISP,
int iters = DEFAULT_ITERS,
int levels = DEFAULT_LEVELS,
int nr_plane = DEFAULT_NR_PLANE,
int msg_type = CV_32F);
StereoConstantSpaceBP(int ndisp, int iters, int levels, int nr_plane,
float max_data_term, float data_weight, float max_disc_term, float disc_single_jump,
int min_disp_th = 0,
int msg_type = CV_32F);
void operator()(const oclMat &left, const oclMat &right, oclMat &disparity);
int ndisp;
int iters;
int levels;
int nr_plane;
float max_data_term;
float data_weight;
float max_disc_term;
float disc_single_jump;
int min_disp_th;
int msg_type;
bool use_local_init_data_cost;
private:
oclMat u[2], d[2], l[2], r[2];
oclMat disp_selected_pyr[2];
oclMat data_cost;
oclMat data_cost_selected;
oclMat temp;
oclMat out;
};
// Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method
//
// see reference:
// [1] C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
// [2] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
class CV_EXPORTS OpticalFlowDual_TVL1_OCL
{
public:
OpticalFlowDual_TVL1_OCL();
void operator ()(const oclMat& I0, const oclMat& I1, oclMat& flowx, oclMat& flowy);
void collectGarbage();
/**
* Time step of the numerical scheme.
*/
double tau;
/**
* Weight parameter for the data term, attachment parameter.
* This is the most relevant parameter, which determines the smoothness of the output.
* The smaller this parameter is, the smoother the solutions we obtain.
* It depends on the range of motions of the images, so its value should be adapted to each image sequence.
*/
double lambda;
/**
* Weight parameter for (u - v)^2, tightness parameter.
* It serves as a link between the attachment and the regularization terms.
* In theory, it should have a small value in order to maintain both parts in correspondence.
* The method is stable for a large range of values of this parameter.
*/
double theta;
/**
* Number of scales used to create the pyramid of images.
*/
int nscales;
/**
* Number of warpings per scale.
* Represents the number of times that I1(x+u0) and grad( I1(x+u0) ) are computed per scale.
* This is a parameter that assures the stability of the method.
* It also affects the running time, so it is a compromise between speed and accuracy.
*/
int warps;
/**
* Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time.
* A small value will yield more accurate solutions at the expense of a slower convergence.
*/
double epsilon;
/**
* Stopping criterion iterations number used in the numerical scheme.
*/
int iterations;
bool useInitialFlow;
private:
void procOneScale(const oclMat& I0, const oclMat& I1, oclMat& u1, oclMat& u2);
std::vector<oclMat> I0s;
std::vector<oclMat> I1s;
std::vector<oclMat> u1s;
std::vector<oclMat> u2s;
oclMat I1x_buf;
oclMat I1y_buf;
oclMat I1w_buf;
oclMat I1wx_buf;
oclMat I1wy_buf;
oclMat grad_buf;
oclMat rho_c_buf;
oclMat p11_buf;
oclMat p12_buf;
oclMat p21_buf;
oclMat p22_buf;
oclMat diff_buf;
oclMat norm_buf;
};
// current supported sorting methods
enum
{
SORT_BITONIC, // only support power-of-2 buffer size
SORT_SELECTION, // cannot sort duplicate keys
SORT_MERGE,
SORT_RADIX // only support signed int/float keys(CV_32S/CV_32F)
};
//! Returns the sorted result of all the elements in input based on equivalent keys.
//
// The element unit in the values to be sorted is determined from the data type,
// i.e., a CV_32FC2 input {a1a2, b1b2} will be considered as two elements, regardless its
// matrix dimension.
// both keys and values will be sorted inplace
// Key needs to be single channel oclMat.
//
// Example:
// input -
// keys = {2, 3, 1} (CV_8UC1)
// values = {10,5, 4,3, 6,2} (CV_8UC2)
// sortByKey(keys, values, SORT_SELECTION, false);
// output -
// keys = {1, 2, 3} (CV_8UC1)
// values = {6,2, 10,5, 4,3} (CV_8UC2)
CV_EXPORTS void sortByKey(oclMat& keys, oclMat& values, int method, bool isGreaterThan = false);
/*!Base class for MOG and MOG2!*/
class CV_EXPORTS BackgroundSubtractor
{
public:
//! the virtual destructor
virtual ~BackgroundSubtractor();
//! the update operator that takes the next video frame and returns the current foreground mask as 8-bit binary image.
virtual void operator()(const oclMat& image, oclMat& fgmask, float learningRate);
//! computes a background image
virtual void getBackgroundImage(oclMat& backgroundImage) const = 0;
};
/*!
Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm
The class implements the following algorithm:
"An improved adaptive background mixture model for real-time tracking with shadow detection"
P. KadewTraKuPong and R. Bowden,
Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
*/
class CV_EXPORTS MOG: public cv::ocl::BackgroundSubtractor
{
public:
//! the default constructor
MOG(int nmixtures = -1);
//! re-initiaization method
void initialize(Size frameSize, int frameType);
//! the update operator
void operator()(const oclMat& frame, oclMat& fgmask, float learningRate = 0.f);
//! computes a background image which are the mean of all background gaussians
void getBackgroundImage(oclMat& backgroundImage) const;
//! releases all inner buffers
void release();
int history;
float varThreshold;
float backgroundRatio;
float noiseSigma;
private:
int nmixtures_;
Size frameSize_;
int frameType_;
int nframes_;
oclMat weight_;
oclMat sortKey_;
oclMat mean_;
oclMat var_;
};
/*!
The class implements the following algorithm:
"Improved adaptive Gausian mixture model for background subtraction"
Z.Zivkovic
International Conference Pattern Recognition, UK, August, 2004.
http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf
*/
class CV_EXPORTS MOG2: public cv::ocl::BackgroundSubtractor
{
public:
//! the default constructor
MOG2(int nmixtures = -1);
//! re-initiaization method
void initialize(Size frameSize, int frameType);
//! the update operator
void operator()(const oclMat& frame, oclMat& fgmask, float learningRate = -1.0f);
//! computes a background image which are the mean of all background gaussians
void getBackgroundImage(oclMat& backgroundImage) const;
//! releases all inner buffers
void release();
// parameters
// you should call initialize after parameters changes
int history;
//! here it is the maximum allowed number of mixture components.
//! Actual number is determined dynamically per pixel
float varThreshold;
// threshold on the squared Mahalanobis distance to decide if it is well described
// by the background model or not. Related to Cthr from the paper.
// This does not influence the update of the background. A typical value could be 4 sigma
// and that is varThreshold=4*4=16; Corresponds to Tb in the paper.
/////////////////////////
// less important parameters - things you might change but be carefull
////////////////////////
float backgroundRatio;
// corresponds to fTB=1-cf from the paper
// TB - threshold when the component becomes significant enough to be included into
// the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0.
// For alpha=0.001 it means that the mode should exist for approximately 105 frames before
// it is considered foreground
// float noiseSigma;
float varThresholdGen;
//correspondts to Tg - threshold on the squared Mahalan. dist. to decide
//when a sample is close to the existing components. If it is not close
//to any a new component will be generated. I use 3 sigma => Tg=3*3=9.
//Smaller Tg leads to more generated components and higher Tg might make
//lead to small number of components but they can grow too large
float fVarInit;
float fVarMin;
float fVarMax;
//initial variance for the newly generated components.
//It will will influence the speed of adaptation. A good guess should be made.
//A simple way is to estimate the typical standard deviation from the images.
//I used here 10 as a reasonable value
// min and max can be used to further control the variance
float fCT; //CT - complexity reduction prior
//this is related to the number of samples needed to accept that a component
//actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get
//the standard Stauffer&Grimson algorithm (maybe not exact but very similar)
//shadow detection parameters
bool bShadowDetection; //default 1 - do shadow detection
unsigned char nShadowDetection; //do shadow detection - insert this value as the detection result - 127 default value
float fTau;
// Tau - shadow threshold. The shadow is detected if the pixel is darker
//version of the background. Tau is a threshold on how much darker the shadow can be.
//Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow
//See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.
private:
int nmixtures_;
Size frameSize_;
int frameType_;
int nframes_;
oclMat weight_;
oclMat variance_;
oclMat mean_;
oclMat bgmodelUsedModes_; //keep track of number of modes per pixel
};
/*!***************Kalman Filter*************!*/
class CV_EXPORTS KalmanFilter
{
public:
KalmanFilter();
//! the full constructor taking the dimensionality of the state, of the measurement and of the control vector
KalmanFilter(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F);
//! re-initializes Kalman filter. The previous content is destroyed.
void init(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F);
const oclMat& predict(const oclMat& control=oclMat());
const oclMat& correct(const oclMat& measurement);
oclMat statePre; //!< predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
oclMat statePost; //!< corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
oclMat transitionMatrix; //!< state transition matrix (A)
oclMat controlMatrix; //!< control matrix (B) (not used if there is no control)
oclMat measurementMatrix; //!< measurement matrix (H)
oclMat processNoiseCov; //!< process noise covariance matrix (Q)
oclMat measurementNoiseCov;//!< measurement noise covariance matrix (R)
oclMat errorCovPre; //!< priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/
oclMat gain; //!< Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
oclMat errorCovPost; //!< posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
private:
oclMat temp1;
oclMat temp2;
oclMat temp3;
oclMat temp4;
oclMat temp5;
};
/*!***************K Nearest Neighbour*************!*/
class CV_EXPORTS KNearestNeighbour: public CvKNearest
{
public:
KNearestNeighbour();
~KNearestNeighbour();
bool train(const Mat& trainData, Mat& labels, Mat& sampleIdx = Mat().setTo(Scalar::all(0)),
bool isRegression = false, int max_k = 32, bool updateBase = false);
void clear();
void find_nearest(const oclMat& samples, int k, oclMat& lables);
private:
oclMat samples_ocl;
};
/*!*************** SVM *************!*/
class CV_EXPORTS CvSVM_OCL : public CvSVM
{
public:
CvSVM_OCL();
CvSVM_OCL(const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
CvSVMParams params=CvSVMParams());
CV_WRAP float predict( const int row_index, Mat& src, bool returnDFVal=false ) const;
CV_WRAP void predict( cv::InputArray samples, cv::OutputArray results ) const;
CV_WRAP float predict( const cv::Mat& sample, bool returnDFVal=false ) const;
float predict( const CvMat* samples, CV_OUT CvMat* results ) const;
protected:
float predict( const int row_index, int row_len, Mat& src, bool returnDFVal=false ) const;
void create_kernel();
void create_solver();
};
/*!*************** END *************!*/
}
}
#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__ */
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