/usr/include/opencv2/ocl/ocl.hpp is in libopencv-ocl-dev 2.4.8+dfsg1-2ubuntu1.
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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 <cmpop> 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 support CV_32S, CV_32F, sqsum - support CV32F, CV_64F
// supports only CV_8UC1 source type
CV_EXPORTS void integral(const oclMat &src, oclMat &sum, oclMat &sqsum, int sdepth=-1 );
CV_EXPORTS void integral(const oclMat &src, oclMat &sum, int sdepth=-1 );
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 ¢ers, 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 ¢ers);
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
///////////////////////////////////////////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, gsqsum_t;
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;
};
////////////////////////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 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_;
};
//////////////// 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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