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44 #ifndef __OPENCV_OCL_HPP__
45 #define __OPENCV_OCL_HPP__
50 #include "opencv2/core.hpp"
51 #include "opencv2/imgproc.hpp"
52 #include "opencv2/objdetect.hpp"
53 #include "opencv2/ml.hpp"
61 CVCL_DEVICE_TYPE_DEFAULT = (1 << 0),
62 CVCL_DEVICE_TYPE_CPU = (1 << 1),
63 CVCL_DEVICE_TYPE_GPU = (1 << 2),
64 CVCL_DEVICE_TYPE_ACCELERATOR = (1 << 3),
65 //CVCL_DEVICE_TYPE_CUSTOM = (1 << 4)
66 CVCL_DEVICE_TYPE_ALL = 0xFFFFFFFF
78 DEVICE_MEM_DEFAULT = 0,
79 DEVICE_MEM_AHP, //alloc host pointer
80 DEVICE_MEM_UHP, //use host pointer
81 DEVICE_MEM_CHP, //copy host pointer
82 DEVICE_MEM_PM //persistent memory
85 // these classes contain OpenCL runtime information
92 int _id; // reserved, don't use it
94 DeviceType deviceType;
95 std::string deviceProfile;
96 std::string deviceVersion;
97 std::string deviceName;
98 std::string deviceVendor;
100 std::string deviceDriverVersion;
101 std::string deviceExtensions;
103 size_t maxWorkGroupSize;
104 std::vector<size_t> maxWorkItemSizes;
106 size_t localMemorySize;
107 size_t maxMemAllocSize;
109 int deviceVersionMajor;
110 int deviceVersionMinor;
112 bool haveDoubleSupport;
113 bool isUnifiedMemory; // 1 means integrated GPU, otherwise this value is 0
116 std::string compilationExtraOptions;
118 const PlatformInfo* platform;
125 int _id; // reserved, don't use it
127 std::string platformProfile;
128 std::string platformVersion;
129 std::string platformName;
130 std::string platformVendor;
131 std::string platformExtensons;
133 int platformVersionMajor;
134 int platformVersionMinor;
136 std::vector<const DeviceInfo*> devices;
141 //////////////////////////////// Initialization & Info ////////////////////////
142 typedef std::vector<const PlatformInfo*> PlatformsInfo;
144 CV_EXPORTS int getOpenCLPlatforms(PlatformsInfo& platforms);
146 typedef std::vector<const DeviceInfo*> DevicesInfo;
148 CV_EXPORTS int getOpenCLDevices(DevicesInfo& devices, int deviceType = CVCL_DEVICE_TYPE_GPU,
149 const PlatformInfo* platform = NULL);
151 // set device you want to use
152 CV_EXPORTS void setDevice(const DeviceInfo* info);
156 FEATURE_CL_DOUBLE = 1,
157 FEATURE_CL_UNIFIED_MEM,
159 FEATURE_CL_INTEL_DEVICE
162 // Represents OpenCL context, interface
163 class CV_EXPORTS Context
169 static Context *getContext();
171 bool supportsFeature(FEATURE_TYPE featureType) const;
172 const DeviceInfo& getDeviceInfo() const;
174 const void* getOpenCLContextPtr() const;
175 const void* getOpenCLCommandQueuePtr() const;
176 const void* getOpenCLDeviceIDPtr() const;
179 inline const void *getClContextPtr()
181 return Context::getContext()->getOpenCLContextPtr();
184 inline const void *getClCommandQueuePtr()
186 return Context::getContext()->getOpenCLCommandQueuePtr();
189 CV_EXPORTS bool supportsFeature(FEATURE_TYPE featureType);
191 CV_EXPORTS void finish();
193 enum BINARY_CACHE_MODE
195 CACHE_NONE = 0, // do not cache OpenCL binary
196 CACHE_DEBUG = 0x1 << 0, // cache OpenCL binary when built in debug mode
197 CACHE_RELEASE = 0x1 << 1, // default behavior, only cache when built in release mode
198 CACHE_ALL = CACHE_DEBUG | CACHE_RELEASE, // cache opencl binary
200 //! Enable or disable OpenCL program binary caching onto local disk
201 // After a program (*.cl files in opencl/ folder) is built at runtime, we allow the
202 // compiled OpenCL program to be cached to the path automatically as "path/*.clb"
203 // binary file, which will be reused when the OpenCV executable is started again.
205 // This feature is enabled by default.
206 CV_EXPORTS void setBinaryDiskCache(int mode = CACHE_RELEASE, cv::String path = "./");
208 //! set where binary cache to be saved to
209 CV_EXPORTS void setBinaryPath(const char *path);
214 const char* programStr;
215 const char* programHash;
217 // Cache in memory by name (should be unique). Caching on disk disabled.
218 inline ProgramSource(const char* _name, const char* _programStr)
219 : name(_name), programStr(_programStr), programHash(NULL)
223 // Cache in memory by name (should be unique). Caching on disk uses programHash mark.
224 inline ProgramSource(const char* _name, const char* _programStr, const char* _programHash)
225 : name(_name), programStr(_programStr), programHash(_programHash)
230 //! Calls OpenCL kernel. Pass globalThreads = NULL, and cleanUp = true, to finally clean-up without executing.
231 //! Deprecated, will be replaced
232 CV_EXPORTS void openCLExecuteKernelInterop(Context *clCxt,
233 const cv::ocl::ProgramSource& source, String kernelName,
234 size_t globalThreads[3], size_t localThreads[3],
235 std::vector< std::pair<size_t, const void *> > &args,
236 int channels, int depth, const char *build_options);
238 class CV_EXPORTS oclMatExpr;
239 //////////////////////////////// oclMat ////////////////////////////////
240 class CV_EXPORTS oclMat
243 //! default constructor
245 //! constructs oclMatrix of the specified size and type (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.)
246 oclMat(int rows, int cols, int type);
247 oclMat(Size size, int type);
248 //! constucts oclMatrix and fills it with the specified value _s.
249 oclMat(int rows, int cols, int type, const Scalar &s);
250 oclMat(Size size, int type, const Scalar &s);
252 oclMat(const oclMat &m);
254 //! constructor for oclMatrix headers pointing to user-allocated data
255 oclMat(int rows, int cols, int type, void *data, size_t step = Mat::AUTO_STEP);
256 oclMat(Size size, int type, void *data, size_t step = Mat::AUTO_STEP);
258 //! creates a matrix header for a part of the bigger matrix
259 oclMat(const oclMat &m, const Range &rowRange, const Range &colRange);
260 oclMat(const oclMat &m, const Rect &roi);
262 //! builds oclMat from Mat. Perfom blocking upload to device.
263 explicit oclMat (const Mat &m);
265 //! destructor - calls release()
268 //! assignment operators
269 oclMat &operator = (const oclMat &m);
270 //! assignment operator. Perfom blocking upload to device.
271 oclMat &operator = (const Mat &m);
272 oclMat &operator = (const oclMatExpr& expr);
274 //! pefroms blocking upload data to oclMat.
275 void upload(const cv::Mat &m);
278 //! downloads data from device to host memory. Blocking calls.
279 operator Mat() const;
280 void download(cv::Mat &m) const;
282 //! convert to _InputArray
283 operator _InputArray();
285 //! convert to _OutputArray
286 operator _OutputArray();
288 //! returns a new oclMatrix header for the specified row
289 oclMat row(int y) const;
290 //! returns a new oclMatrix header for the specified column
291 oclMat col(int x) const;
292 //! ... for the specified row span
293 oclMat rowRange(int startrow, int endrow) const;
294 oclMat rowRange(const Range &r) const;
295 //! ... for the specified column span
296 oclMat colRange(int startcol, int endcol) const;
297 oclMat colRange(const Range &r) const;
299 //! returns deep copy of the oclMatrix, i.e. the data is copied
300 oclMat clone() const;
302 //! copies those oclMatrix elements to "m" that are marked with non-zero mask elements.
303 // It calls m.create(this->size(), this->type()).
304 // It supports any data type
305 void copyTo( oclMat &m, const oclMat &mask = oclMat()) const;
307 //! converts oclMatrix to another datatype with optional scalng. See cvConvertScale.
308 //It supports 8UC1 8UC4 32SC1 32SC4 32FC1 32FC4
309 void convertTo( oclMat &m, int rtype, double alpha = 1, double beta = 0 ) const;
311 void assignTo( oclMat &m, int type = -1 ) const;
313 //! sets every oclMatrix element to s
314 //It supports 8UC1 8UC4 32SC1 32SC4 32FC1 32FC4
315 oclMat& operator = (const Scalar &s);
316 //! sets some of the oclMatrix elements to s, according to the mask
317 //It supports 8UC1 8UC4 32SC1 32SC4 32FC1 32FC4
318 oclMat& setTo(const Scalar &s, const oclMat &mask = oclMat());
319 //! creates alternative oclMatrix header for the same data, with different
320 // number of channels and/or different number of rows. see cvReshape.
321 oclMat reshape(int cn, int rows = 0) const;
323 //! allocates new oclMatrix data unless the oclMatrix already has specified size and type.
324 // previous data is unreferenced if needed.
325 void create(int rows, int cols, int type);
326 void create(Size size, int type);
328 //! allocates new oclMatrix with specified device memory type.
329 void createEx(int rows, int cols, int type,
330 DevMemRW rw_type, DevMemType mem_type);
331 void createEx(Size size, int type, DevMemRW rw_type,
332 DevMemType mem_type);
334 //! decreases reference counter;
335 // deallocate the data when reference counter reaches 0.
338 //! swaps with other smart pointer
339 void swap(oclMat &mat);
341 //! locates oclMatrix header within a parent oclMatrix. See below
342 void locateROI( Size &wholeSize, Point &ofs ) const;
343 //! moves/resizes the current oclMatrix ROI inside the parent oclMatrix.
344 oclMat& adjustROI( int dtop, int dbottom, int dleft, int dright );
345 //! extracts a rectangular sub-oclMatrix
346 // (this is a generalized form of row, rowRange etc.)
347 oclMat operator()( Range rowRange, Range colRange ) const;
348 oclMat operator()( const Rect &roi ) const;
350 oclMat& operator+=( const oclMat& m );
351 oclMat& operator-=( const oclMat& m );
352 oclMat& operator*=( const oclMat& m );
353 oclMat& operator/=( const oclMat& m );
355 //! returns true if the oclMatrix data is continuous
356 // (i.e. when there are no gaps between successive rows).
357 // similar to CV_IS_oclMat_CONT(cvoclMat->type)
358 bool isContinuous() const;
359 //! returns element size in bytes,
360 // similar to CV_ELEM_SIZE(cvMat->type)
361 size_t elemSize() const;
362 //! returns the size of element channel in bytes.
363 size_t elemSize1() const;
364 //! returns element type, similar to CV_MAT_TYPE(cvMat->type)
366 //! returns element type, i.e. 8UC3 returns 8UC4 because in ocl
367 //! 3 channels element actually use 4 channel space
369 //! returns element type, similar to CV_MAT_DEPTH(cvMat->type)
371 //! returns element type, similar to CV_MAT_CN(cvMat->type)
372 int channels() const;
373 //! returns element type, return 4 for 3 channels element,
374 //!becuase 3 channels element actually use 4 channel space
375 int oclchannels() const;
376 //! returns step/elemSize1()
377 size_t step1() const;
378 //! returns oclMatrix size:
379 // width == number of columns, height == number of rows
381 //! returns true if oclMatrix data is NULL
384 //! returns pointer to y-th row
385 uchar* ptr(int y = 0);
386 const uchar *ptr(int y = 0) const;
388 //! template version of the above method
389 template<typename _Tp> _Tp *ptr(int y = 0);
390 template<typename _Tp> const _Tp *ptr(int y = 0) const;
392 //! matrix transposition
395 /*! includes several bit-fields:
396 - the magic signature
402 //! the number of rows and columns
404 //! a distance between successive rows in bytes; includes the gap if any
406 //! pointer to the data(OCL memory object)
409 //! pointer to the reference counter;
410 // when oclMatrix points to user-allocated data, the pointer is NULL
413 //! helper fields used in locateROI and adjustROI
414 //datastart and dataend are not used in current version
418 //! OpenCL context associated with the oclMat object.
419 Context *clCxt; // TODO clCtx
420 //add offset for handle ROI, calculated in byte
422 //add wholerows and wholecols for the whole matrix, datastart and dataend are no longer used
427 // convert InputArray/OutputArray to oclMat references
428 CV_EXPORTS oclMat& getOclMatRef(InputArray src);
429 CV_EXPORTS oclMat& getOclMatRef(OutputArray src);
431 ///////////////////// mat split and merge /////////////////////////////////
432 //! Compose a multi-channel array from several single-channel arrays
434 CV_EXPORTS void merge(const oclMat *src, size_t n, oclMat &dst);
435 CV_EXPORTS void merge(const std::vector<oclMat> &src, oclMat &dst);
437 //! Divides multi-channel array into several single-channel arrays
439 CV_EXPORTS void split(const oclMat &src, oclMat *dst);
440 CV_EXPORTS void split(const oclMat &src, std::vector<oclMat> &dst);
442 ////////////////////////////// Arithmetics ///////////////////////////////////
444 //! adds one matrix to another with scale (dst = src1 * alpha + src2 * beta + gama)
445 // supports all data types
446 CV_EXPORTS void addWeighted(const oclMat &src1, double alpha, const oclMat &src2, double beta, double gama, oclMat &dst);
448 //! adds one matrix to another (dst = src1 + src2)
449 // supports all data types
450 CV_EXPORTS void add(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
451 //! adds scalar to a matrix (dst = src1 + s)
452 // supports all data types
453 CV_EXPORTS void add(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
455 //! subtracts one matrix from another (dst = src1 - src2)
456 // supports all data types
457 CV_EXPORTS void subtract(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
458 //! subtracts scalar from a matrix (dst = src1 - s)
459 // supports all data types
460 CV_EXPORTS void subtract(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
462 //! computes element-wise product of the two arrays (dst = src1 * scale * src2)
463 // supports all data types
464 CV_EXPORTS void multiply(const oclMat &src1, const oclMat &src2, oclMat &dst, double scale = 1);
465 //! multiplies matrix to a number (dst = scalar * src)
466 // supports all data types
467 CV_EXPORTS void multiply(double scalar, const oclMat &src, oclMat &dst);
469 //! computes element-wise quotient of the two arrays (dst = src1 * scale / src2)
470 // supports all data types
471 CV_EXPORTS void divide(const oclMat &src1, const oclMat &src2, oclMat &dst, double scale = 1);
472 //! computes element-wise quotient of the two arrays (dst = scale / src)
473 // supports all data types
474 CV_EXPORTS void divide(double scale, const oclMat &src1, oclMat &dst);
476 //! computes element-wise minimum of the two arrays (dst = min(src1, src2))
477 // supports all data types
478 CV_EXPORTS void min(const oclMat &src1, const oclMat &src2, oclMat &dst);
480 //! computes element-wise maximum of the two arrays (dst = max(src1, src2))
481 // supports all data types
482 CV_EXPORTS void max(const oclMat &src1, const oclMat &src2, oclMat &dst);
484 //! compares elements of two arrays (dst = src1 <cmpop> src2)
485 // supports all data types
486 CV_EXPORTS void compare(const oclMat &src1, const oclMat &src2, oclMat &dst, int cmpop);
488 //! transposes the matrix
489 // supports all data types
490 CV_EXPORTS void transpose(const oclMat &src, oclMat &dst);
492 //! computes element-wise absolute values of an array (dst = abs(src))
493 // supports all data types
494 CV_EXPORTS void abs(const oclMat &src, oclMat &dst);
496 //! computes element-wise absolute difference of two arrays (dst = abs(src1 - src2))
497 // supports all data types
498 CV_EXPORTS void absdiff(const oclMat &src1, const oclMat &src2, oclMat &dst);
499 //! computes element-wise absolute difference of array and scalar (dst = abs(src1 - s))
500 // supports all data types
501 CV_EXPORTS void absdiff(const oclMat &src1, const Scalar &s, oclMat &dst);
503 //! computes mean value and standard deviation of all or selected array elements
504 // supports all data types
505 CV_EXPORTS void meanStdDev(const oclMat &mtx, Scalar &mean, Scalar &stddev);
507 //! computes norm of array
508 // supports NORM_INF, NORM_L1, NORM_L2
509 // supports all data types
510 CV_EXPORTS double norm(const oclMat &src1, int normType = NORM_L2);
512 //! computes norm of the difference between two arrays
513 // supports NORM_INF, NORM_L1, NORM_L2
514 // supports all data types
515 CV_EXPORTS double norm(const oclMat &src1, const oclMat &src2, int normType = NORM_L2);
517 //! reverses the order of the rows, columns or both in a matrix
518 // supports all types
519 CV_EXPORTS void flip(const oclMat &src, oclMat &dst, int flipCode);
521 //! computes sum of array elements
523 CV_EXPORTS Scalar sum(const oclMat &m);
524 CV_EXPORTS Scalar absSum(const oclMat &m);
525 CV_EXPORTS Scalar sqrSum(const oclMat &m);
527 //! finds global minimum and maximum array elements and returns their values
528 // support all C1 types
529 CV_EXPORTS void minMax(const oclMat &src, double *minVal, double *maxVal = 0, const oclMat &mask = oclMat());
531 //! finds global minimum and maximum array elements and returns their values with locations
532 // support all C1 types
533 CV_EXPORTS void minMaxLoc(const oclMat &src, double *minVal, double *maxVal = 0, Point *minLoc = 0, Point *maxLoc = 0,
534 const oclMat &mask = oclMat());
536 //! counts non-zero array elements
538 CV_EXPORTS int countNonZero(const oclMat &src);
540 //! transforms 8-bit unsigned integers using lookup table: dst(i)=lut(src(i))
541 // destination array will have the depth type as lut and the same channels number as source
542 //It supports 8UC1 8UC4 only
543 CV_EXPORTS void LUT(const oclMat &src, const oclMat &lut, oclMat &dst);
545 //! only 8UC1 and 256 bins is supported now
546 CV_EXPORTS void calcHist(const oclMat &mat_src, oclMat &mat_hist);
547 //! only 8UC1 and 256 bins is supported now
548 CV_EXPORTS void equalizeHist(const oclMat &mat_src, oclMat &mat_dst);
550 //! only 8UC1 is supported now
551 CV_EXPORTS Ptr<cv::CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8));
554 // supports 8UC1 8UC4
555 CV_EXPORTS void bilateralFilter(const oclMat& src, oclMat& dst, int d, double sigmaColor, double sigmaSpace, int borderType=BORDER_DEFAULT);
557 //! Applies an adaptive bilateral filter to the input image
558 // This is not truly a bilateral filter. Instead of using user provided fixed parameters,
559 // the function calculates a constant at each window based on local standard deviation,
560 // and use this constant to do filtering.
561 // supports 8UC1, 8UC3
562 CV_EXPORTS void adaptiveBilateralFilter(const oclMat& src, oclMat& dst, Size ksize, double sigmaSpace, Point anchor = Point(-1, -1), int borderType=BORDER_DEFAULT);
564 //! computes exponent of each matrix element (dst = e**src)
565 // supports only CV_32FC1, CV_64FC1 type
566 CV_EXPORTS void exp(const oclMat &src, oclMat &dst);
568 //! computes natural logarithm of absolute value of each matrix element: dst = log(abs(src))
569 // supports only CV_32FC1, CV_64FC1 type
570 CV_EXPORTS void log(const oclMat &src, oclMat &dst);
572 //! computes magnitude of each (x(i), y(i)) vector
573 // supports only CV_32F, CV_64F type
574 CV_EXPORTS void magnitude(const oclMat &x, const oclMat &y, oclMat &magnitude);
576 //! computes angle (angle(i)) of each (x(i), y(i)) vector
577 // supports only CV_32F, CV_64F type
578 CV_EXPORTS void phase(const oclMat &x, const oclMat &y, oclMat &angle, bool angleInDegrees = false);
580 //! the function raises every element of tne input array to p
581 // support only CV_32F, CV_64F type
582 CV_EXPORTS void pow(const oclMat &x, double p, oclMat &y);
584 //! converts Cartesian coordinates to polar
585 // supports only CV_32F CV_64F type
586 CV_EXPORTS void cartToPolar(const oclMat &x, const oclMat &y, oclMat &magnitude, oclMat &angle, bool angleInDegrees = false);
588 //! converts polar coordinates to Cartesian
589 // supports only CV_32F CV_64F type
590 CV_EXPORTS void polarToCart(const oclMat &magnitude, const oclMat &angle, oclMat &x, oclMat &y, bool angleInDegrees = false);
592 //! perfroms per-elements bit-wise inversion
593 // supports all types
594 CV_EXPORTS void bitwise_not(const oclMat &src, oclMat &dst);
596 //! calculates per-element bit-wise disjunction of two arrays
597 // supports all types
598 CV_EXPORTS void bitwise_or(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
599 CV_EXPORTS void bitwise_or(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
601 //! calculates per-element bit-wise conjunction of two arrays
602 // supports all types
603 CV_EXPORTS void bitwise_and(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
604 CV_EXPORTS void bitwise_and(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
606 //! calculates per-element bit-wise "exclusive or" operation
607 // supports all types
608 CV_EXPORTS void bitwise_xor(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
609 CV_EXPORTS void bitwise_xor(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
611 //! Logical operators
612 CV_EXPORTS oclMat operator ~ (const oclMat &);
613 CV_EXPORTS oclMat operator | (const oclMat &, const oclMat &);
614 CV_EXPORTS oclMat operator & (const oclMat &, const oclMat &);
615 CV_EXPORTS oclMat operator ^ (const oclMat &, const oclMat &);
618 //! Mathematics operators
619 CV_EXPORTS oclMatExpr operator + (const oclMat &src1, const oclMat &src2);
620 CV_EXPORTS oclMatExpr operator - (const oclMat &src1, const oclMat &src2);
621 CV_EXPORTS oclMatExpr operator * (const oclMat &src1, const oclMat &src2);
622 CV_EXPORTS oclMatExpr operator / (const oclMat &src1, const oclMat &src2);
624 struct CV_EXPORTS ConvolveBuf
628 Size user_block_size;
631 oclMat image_spect, templ_spect, result_spect;
632 oclMat image_block, templ_block, result_data;
634 void create(Size image_size, Size templ_size);
635 static Size estimateBlockSize(Size result_size, Size templ_size);
638 //! computes convolution of two images, may use discrete Fourier transform
639 // support only CV_32FC1 type
640 CV_EXPORTS void convolve(const oclMat &image, const oclMat &temp1, oclMat &result, bool ccorr = false);
641 CV_EXPORTS void convolve(const oclMat &image, const oclMat &temp1, oclMat &result, bool ccorr, ConvolveBuf& buf);
643 //! Performs a per-element multiplication of two Fourier spectrums.
644 //! Only full (not packed) CV_32FC2 complex spectrums in the interleaved format are supported for now.
645 //! support only CV_32FC2 type
646 CV_EXPORTS void mulSpectrums(const oclMat &a, const oclMat &b, oclMat &c, int flags, float scale, bool conjB = false);
648 CV_EXPORTS void cvtColor(const oclMat &src, oclMat &dst, int code, int dcn = 0);
650 //! initializes a scaled identity matrix
651 CV_EXPORTS void setIdentity(oclMat& src, const Scalar & val = Scalar(1));
653 //////////////////////////////// Filter Engine ////////////////////////////////
656 The Base Class for 1D or Row-wise Filters
658 This is the base class for linear or non-linear filters that process 1D data.
659 In particular, such filters are used for the "horizontal" filtering parts in separable filters.
661 class CV_EXPORTS BaseRowFilter_GPU
664 BaseRowFilter_GPU(int ksize_, int anchor_, int bordertype_) : ksize(ksize_), anchor(anchor_), bordertype(bordertype_) {}
665 virtual ~BaseRowFilter_GPU() {}
666 virtual void operator()(const oclMat &src, oclMat &dst) = 0;
667 int ksize, anchor, bordertype;
671 The Base Class for Column-wise Filters
673 This is the base class for linear or non-linear filters that process columns of 2D arrays.
674 Such filters are used for the "vertical" filtering parts in separable filters.
676 class CV_EXPORTS BaseColumnFilter_GPU
679 BaseColumnFilter_GPU(int ksize_, int anchor_, int bordertype_) : ksize(ksize_), anchor(anchor_), bordertype(bordertype_) {}
680 virtual ~BaseColumnFilter_GPU() {}
681 virtual void operator()(const oclMat &src, oclMat &dst) = 0;
682 int ksize, anchor, bordertype;
686 The Base Class for Non-Separable 2D Filters.
688 This is the base class for linear or non-linear 2D filters.
690 class CV_EXPORTS BaseFilter_GPU
693 BaseFilter_GPU(const Size &ksize_, const Point &anchor_, const int &borderType_)
694 : ksize(ksize_), anchor(anchor_), borderType(borderType_) {}
695 virtual ~BaseFilter_GPU() {}
696 virtual void operator()(const oclMat &src, oclMat &dst) = 0;
703 The Base Class for Filter Engine.
705 The class can be used to apply an arbitrary filtering operation to an image.
706 It contains all the necessary intermediate buffers.
708 class CV_EXPORTS FilterEngine_GPU
711 virtual ~FilterEngine_GPU() {}
713 virtual void apply(const oclMat &src, oclMat &dst, Rect roi = Rect(0, 0, -1, -1)) = 0;
716 //! returns the non-separable filter engine with the specified filter
717 CV_EXPORTS Ptr<FilterEngine_GPU> createFilter2D_GPU(const Ptr<BaseFilter_GPU> filter2D);
719 //! returns the primitive row filter with the specified kernel
720 CV_EXPORTS Ptr<BaseRowFilter_GPU> getLinearRowFilter_GPU(int srcType, int bufType, const Mat &rowKernel,
721 int anchor = -1, int bordertype = BORDER_DEFAULT);
723 //! returns the primitive column filter with the specified kernel
724 CV_EXPORTS Ptr<BaseColumnFilter_GPU> getLinearColumnFilter_GPU(int bufType, int dstType, const Mat &columnKernel,
725 int anchor = -1, int bordertype = BORDER_DEFAULT, double delta = 0.0);
727 //! returns the separable linear filter engine
728 CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat &rowKernel,
729 const Mat &columnKernel, const Point &anchor = Point(-1, -1), double delta = 0.0, int bordertype = BORDER_DEFAULT);
731 //! returns the separable filter engine with the specified filters
732 CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU> &rowFilter,
733 const Ptr<BaseColumnFilter_GPU> &columnFilter);
735 //! returns the Gaussian filter engine
736 CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, double sigma1, double sigma2 = 0, int bordertype = BORDER_DEFAULT);
738 //! returns filter engine for the generalized Sobel operator
739 CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU( int srcType, int dstType, int dx, int dy, int ksize, int borderType = BORDER_DEFAULT );
741 //! applies Laplacian operator to the image
742 // supports only ksize = 1 and ksize = 3
743 CV_EXPORTS void Laplacian(const oclMat &src, oclMat &dst, int ddepth, int ksize = 1, double scale = 1,
744 double delta=0, int borderType=BORDER_DEFAULT);
746 //! returns 2D box filter
747 // dst type must be the same as source type
748 CV_EXPORTS Ptr<BaseFilter_GPU> getBoxFilter_GPU(int srcType, int dstType,
749 const Size &ksize, Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
751 //! returns box filter engine
752 CV_EXPORTS Ptr<FilterEngine_GPU> createBoxFilter_GPU(int srcType, int dstType, const Size &ksize,
753 const Point &anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
755 //! returns 2D filter with the specified kernel
756 // supports: dst type must be the same as source type
757 CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat &kernel, const Size &ksize,
758 const Point &anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
760 //! returns the non-separable linear filter engine
761 // supports: dst type must be the same as source type
762 CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat &kernel,
763 const Point &anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
765 //! smooths the image using the normalized box filter
766 CV_EXPORTS void boxFilter(const oclMat &src, oclMat &dst, int ddepth, Size ksize,
767 Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
769 //! returns 2D morphological filter
770 //! only MORPH_ERODE and MORPH_DILATE are supported
771 // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
772 // kernel must have CV_8UC1 type, one rows and cols == ksize.width * ksize.height
773 CV_EXPORTS Ptr<BaseFilter_GPU> getMorphologyFilter_GPU(int op, int type, const Mat &kernel, const Size &ksize,
774 Point anchor = Point(-1, -1));
776 //! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported.
777 CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat &kernel,
778 const Point &anchor = Point(-1, -1), int iterations = 1);
780 //! a synonym for normalized box filter
781 static inline void blur(const oclMat &src, oclMat &dst, Size ksize, Point anchor = Point(-1, -1),
782 int borderType = BORDER_CONSTANT)
784 boxFilter(src, dst, -1, ksize, anchor, borderType);
787 //! applies non-separable 2D linear filter to the image
788 CV_EXPORTS void filter2D(const oclMat &src, oclMat &dst, int ddepth, const Mat &kernel,
789 Point anchor = Point(-1, -1), double delta = 0.0, int borderType = BORDER_DEFAULT);
791 //! applies separable 2D linear filter to the image
792 CV_EXPORTS void sepFilter2D(const oclMat &src, oclMat &dst, int ddepth, const Mat &kernelX, const Mat &kernelY,
793 Point anchor = Point(-1, -1), double delta = 0.0, int bordertype = BORDER_DEFAULT);
795 //! applies generalized Sobel operator to the image
796 // dst.type must equalize src.type
797 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
798 // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
799 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);
801 //! applies the vertical or horizontal Scharr operator to the image
802 // dst.type must equalize src.type
803 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
804 // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
805 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);
807 //! smooths the image using Gaussian filter.
808 // dst.type must equalize src.type
809 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
810 // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
811 CV_EXPORTS void GaussianBlur(const oclMat &src, oclMat &dst, Size ksize, double sigma1, double sigma2 = 0, int bordertype = BORDER_DEFAULT);
813 //! erodes the image (applies the local minimum operator)
814 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
815 CV_EXPORTS void erode( const oclMat &src, oclMat &dst, const Mat &kernel, Point anchor = Point(-1, -1), int iterations = 1,
817 int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue());
820 //! dilates the image (applies the local maximum operator)
821 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
822 CV_EXPORTS void dilate( const oclMat &src, oclMat &dst, const Mat &kernel, Point anchor = Point(-1, -1), int iterations = 1,
824 int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue());
827 //! applies an advanced morphological operation to the image
828 CV_EXPORTS void morphologyEx( const oclMat &src, oclMat &dst, int op, const Mat &kernel, Point anchor = Point(-1, -1), int iterations = 1,
830 int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue());
833 ////////////////////////////// Image processing //////////////////////////////
834 //! Does mean shift filtering on GPU.
835 CV_EXPORTS void meanShiftFiltering(const oclMat &src, oclMat &dst, int sp, int sr,
836 TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
838 //! Does mean shift procedure on GPU.
839 CV_EXPORTS void meanShiftProc(const oclMat &src, oclMat &dstr, oclMat &dstsp, int sp, int sr,
840 TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
842 //! Does mean shift segmentation with elimiation of small regions.
843 CV_EXPORTS void meanShiftSegmentation(const oclMat &src, Mat &dst, int sp, int sr, int minsize,
844 TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
846 //! applies fixed threshold to the image.
847 // supports CV_8UC1 and CV_32FC1 data type
848 // supports threshold type: THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV
849 CV_EXPORTS double threshold(const oclMat &src, oclMat &dst, double thresh, double maxVal, int type = THRESH_TRUNC);
851 //! resizes the image
852 // Supports INTER_NEAREST, INTER_LINEAR
853 // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
854 CV_EXPORTS void resize(const oclMat &src, oclMat &dst, Size dsize, double fx = 0, double fy = 0, int interpolation = INTER_LINEAR);
856 //! Applies a generic geometrical transformation to an image.
858 // Supports INTER_NEAREST, INTER_LINEAR.
859 // Map1 supports CV_16SC2, CV_32FC2 types.
860 // Src supports CV_8UC1, CV_8UC2, CV_8UC4.
861 CV_EXPORTS void remap(const oclMat &src, oclMat &dst, oclMat &map1, oclMat &map2, int interpolation, int bordertype, const Scalar &value = Scalar());
863 //! copies 2D array to a larger destination array and pads borders with user-specifiable constant
864 // supports CV_8UC1, CV_8UC4, CV_32SC1 types
865 CV_EXPORTS void copyMakeBorder(const oclMat &src, oclMat &dst, int top, int bottom, int left, int right, int boardtype, const Scalar &value = Scalar());
867 //! Smoothes image using median filter
868 // The source 1- or 4-channel image. m should be 3 or 5, the image depth should be CV_8U or CV_32F.
869 CV_EXPORTS void medianFilter(const oclMat &src, oclMat &dst, int m);
871 //! warps the image using affine transformation
872 // Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
873 // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
874 CV_EXPORTS void warpAffine(const oclMat &src, oclMat &dst, const Mat &M, Size dsize, int flags = INTER_LINEAR);
876 //! warps the image using perspective transformation
877 // Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
878 // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
879 CV_EXPORTS void warpPerspective(const oclMat &src, oclMat &dst, const Mat &M, Size dsize, int flags = INTER_LINEAR);
881 //! computes the integral image and integral for the squared image
882 // sum will have CV_32S type, sqsum - CV32F type
883 // supports only CV_8UC1 source type
884 CV_EXPORTS void integral(const oclMat &src, oclMat &sum, oclMat &sqsum);
885 CV_EXPORTS void integral(const oclMat &src, oclMat &sum);
886 CV_EXPORTS void cornerHarris(const oclMat &src, oclMat &dst, int blockSize, int ksize, double k, int bordertype = cv::BORDER_DEFAULT);
887 CV_EXPORTS void cornerHarris_dxdy(const oclMat &src, oclMat &dst, oclMat &Dx, oclMat &Dy,
888 int blockSize, int ksize, double k, int bordertype = cv::BORDER_DEFAULT);
889 CV_EXPORTS void cornerMinEigenVal(const oclMat &src, oclMat &dst, int blockSize, int ksize, int bordertype = cv::BORDER_DEFAULT);
890 CV_EXPORTS void cornerMinEigenVal_dxdy(const oclMat &src, oclMat &dst, oclMat &Dx, oclMat &Dy,
891 int blockSize, int ksize, int bordertype = cv::BORDER_DEFAULT);
894 /////////////////////////////////// ML ///////////////////////////////////////////
896 //! Compute closest centers for each lines in source and lable it after center's index
897 // supports CV_32FC1/CV_32FC2/CV_32FC4 data type
898 // supports NORM_L1 and NORM_L2 distType
899 // if indices is provided, only the indexed rows will be calculated and their results are in the same
901 CV_EXPORTS void distanceToCenters(oclMat &dists, oclMat &labels, const oclMat &src, const oclMat ¢ers, int distType = NORM_L2SQR, const oclMat &indices = oclMat());
903 //!Does k-means procedure on GPU
904 // supports CV_32FC1/CV_32FC2/CV_32FC4 data type
905 CV_EXPORTS double kmeans(const oclMat &src, int K, oclMat &bestLabels,
906 TermCriteria criteria, int attemps, int flags, oclMat ¢ers);
909 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
910 ///////////////////////////////////////////CascadeClassifier//////////////////////////////////////////////////////////////////
911 ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
912 class CV_EXPORTS OclCascadeClassifier : public cv::CascadeClassifier
915 void detectMultiScale(oclMat &image, CV_OUT std::vector<cv::Rect>& faces,
916 double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0,
917 Size minSize = Size(), Size maxSize = Size());
920 /////////////////////////////// Pyramid /////////////////////////////////////
921 CV_EXPORTS void pyrDown(const oclMat &src, oclMat &dst);
923 //! upsamples the source image and then smoothes it
924 CV_EXPORTS void pyrUp(const oclMat &src, oclMat &dst);
926 //! performs linear blending of two images
927 //! to avoid accuracy errors sum of weigths shouldn't be very close to zero
928 // supports only CV_8UC1 source type
929 CV_EXPORTS void blendLinear(const oclMat &img1, const oclMat &img2, const oclMat &weights1, const oclMat &weights2, oclMat &result);
931 //! computes vertical sum, supports only CV_32FC1 images
932 CV_EXPORTS void columnSum(const oclMat &src, oclMat &sum);
934 ///////////////////////////////////////// match_template /////////////////////////////////////////////////////////////
935 struct CV_EXPORTS MatchTemplateBuf
937 Size user_block_size;
938 oclMat imagef, templf;
939 std::vector<oclMat> images;
940 std::vector<oclMat> image_sums;
941 std::vector<oclMat> image_sqsums;
944 //! computes the proximity map for the raster template and the image where the template is searched for
945 // Supports TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED for type 8UC1 and 8UC4
946 // Supports TM_SQDIFF, TM_CCORR for type 32FC1 and 32FC4
947 CV_EXPORTS void matchTemplate(const oclMat &image, const oclMat &templ, oclMat &result, int method);
949 //! computes the proximity map for the raster template and the image where the template is searched for
950 // Supports TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED for type 8UC1 and 8UC4
951 // Supports TM_SQDIFF, TM_CCORR for type 32FC1 and 32FC4
952 CV_EXPORTS void matchTemplate(const oclMat &image, const oclMat &templ, oclMat &result, int method, MatchTemplateBuf &buf);
956 ///////////////////////////////////////////// Canny /////////////////////////////////////////////
957 struct CV_EXPORTS CannyBuf;
959 //! compute edges of the input image using Canny operator
960 // Support CV_8UC1 only
961 CV_EXPORTS void Canny(const oclMat &image, oclMat &edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
962 CV_EXPORTS void Canny(const oclMat &image, CannyBuf &buf, oclMat &edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
963 CV_EXPORTS void Canny(const oclMat &dx, const oclMat &dy, oclMat &edges, double low_thresh, double high_thresh, bool L2gradient = false);
964 CV_EXPORTS void Canny(const oclMat &dx, const oclMat &dy, CannyBuf &buf, oclMat &edges, double low_thresh, double high_thresh, bool L2gradient = false);
966 struct CV_EXPORTS CannyBuf
968 CannyBuf() : counter(1, 1, CV_32S) { }
973 explicit CannyBuf(const Size &image_size, int apperture_size = 3) : counter(1, 1, CV_32S)
975 create(image_size, apperture_size);
977 CannyBuf(const oclMat &dx_, const oclMat &dy_);
978 void create(const Size &image_size, int apperture_size = 3);
982 oclMat dx_buf, dy_buf;
983 oclMat magBuf, mapBuf;
984 oclMat trackBuf1, trackBuf2;
986 Ptr<FilterEngine_GPU> filterDX, filterDY;
989 ///////////////////////////////////////// Hough Transform /////////////////////////////////////////
991 struct HoughCirclesBuf
1000 CV_EXPORTS void HoughCircles(const oclMat& src, oclMat& circles, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096);
1001 CV_EXPORTS void HoughCircles(const oclMat& src, oclMat& circles, HoughCirclesBuf& buf, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096);
1002 CV_EXPORTS void HoughCirclesDownload(const oclMat& d_circles, OutputArray h_circles);
1005 ///////////////////////////////////////// clAmdFft related /////////////////////////////////////////
1006 //! Performs a forward or inverse discrete Fourier transform (1D or 2D) of floating point matrix.
1007 //! Param dft_size is the size of DFT transform.
1009 //! For complex-to-real transform it is assumed that the source matrix is packed in CLFFT's format.
1010 // support src type of CV32FC1, CV32FC2
1011 // support flags: DFT_INVERSE, DFT_REAL_OUTPUT, DFT_COMPLEX_OUTPUT, DFT_ROWS
1012 // dft_size is the size of original input, which is used for transformation from complex to real.
1013 // dft_size must be powers of 2, 3 and 5
1014 // real to complex dft requires at least v1.8 clAmdFft
1015 // real to complex dft output is not the same with cpu version
1016 // real to complex and complex to real does not support DFT_ROWS
1017 CV_EXPORTS void dft(const oclMat &src, oclMat &dst, Size dft_size = Size(), int flags = 0);
1019 //! implements generalized matrix product algorithm GEMM from BLAS
1020 // The functionality requires clAmdBlas library
1021 // only support type CV_32FC1
1022 // flag GEMM_3_T is not supported
1023 CV_EXPORTS void gemm(const oclMat &src1, const oclMat &src2, double alpha,
1024 const oclMat &src3, double beta, oclMat &dst, int flags = 0);
1026 //////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
1028 struct CV_EXPORTS HOGDescriptor
1032 enum { DEFAULT_WIN_SIGMA = -1 };
1034 enum { DEFAULT_NLEVELS = 64 };
1036 enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };
1040 HOGDescriptor(Size win_size = Size(64, 128), Size block_size = Size(16, 16),
1042 Size block_stride = Size(8, 8), Size cell_size = Size(8, 8),
1044 int nbins = 9, double win_sigma = DEFAULT_WIN_SIGMA,
1046 double threshold_L2hys = 0.2, bool gamma_correction = true,
1048 int nlevels = DEFAULT_NLEVELS);
1052 size_t getDescriptorSize() const;
1054 size_t getBlockHistogramSize() const;
1058 void setSVMDetector(const std::vector<float> &detector);
1062 static std::vector<float> getDefaultPeopleDetector();
1064 static std::vector<float> getPeopleDetector48x96();
1066 static std::vector<float> getPeopleDetector64x128();
1070 void detect(const oclMat &img, std::vector<Point> &found_locations,
1072 double hit_threshold = 0, Size win_stride = Size(),
1074 Size padding = Size());
1078 void detectMultiScale(const oclMat &img, std::vector<Rect> &found_locations,
1080 double hit_threshold = 0, Size win_stride = Size(),
1082 Size padding = Size(), double scale0 = 1.05,
1084 int group_threshold = 2);
1088 void getDescriptors(const oclMat &img, Size win_stride,
1090 oclMat &descriptors,
1092 int descr_format = DESCR_FORMAT_COL_BY_COL);
1108 double threshold_L2hys;
1110 bool gamma_correction;
1118 // initialize buffers; only need to do once in case of multiscale detection
1120 void init_buffer(const oclMat &img, Size win_stride);
1124 void computeBlockHistograms(const oclMat &img);
1126 void computeGradient(const oclMat &img, oclMat &grad, oclMat &qangle);
1130 double getWinSigma() const;
1132 bool checkDetectorSize() const;
1136 static int numPartsWithin(int size, int part_size, int stride);
1138 static Size numPartsWithin(Size size, Size part_size, Size stride);
1142 // Coefficients of the separating plane
1150 // Results of the last classification step
1158 // Results of the last histogram evaluation step
1164 // Gradients conputation results
1166 oclMat grad, qangle;
1176 // effect size of input image (might be different from original size after scaling)
1183 ////////////////////////feature2d_ocl/////////////////
1184 /****************************************************************************************\
1186 \****************************************************************************************/
1187 template<typename T>
1188 struct CV_EXPORTS Accumulator
1192 template<> struct Accumulator<unsigned char>
1196 template<> struct Accumulator<unsigned short>
1200 template<> struct Accumulator<char>
1204 template<> struct Accumulator<short>
1210 * Manhattan distance (city block distance) functor
1213 struct CV_EXPORTS L1
1215 enum { normType = NORM_L1 };
1216 typedef T ValueType;
1217 typedef typename Accumulator<T>::Type ResultType;
1219 ResultType operator()( const T *a, const T *b, int size ) const
1221 return normL1<ValueType, ResultType>(a, b, size);
1226 * Euclidean distance functor
1229 struct CV_EXPORTS L2
1231 enum { normType = NORM_L2 };
1232 typedef T ValueType;
1233 typedef typename Accumulator<T>::Type ResultType;
1235 ResultType operator()( const T *a, const T *b, int size ) const
1237 return (ResultType)std::sqrt((double)normL2Sqr<ValueType, ResultType>(a, b, size));
1242 * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor
1243 * bit count of A exclusive XOR'ed with B
1245 struct CV_EXPORTS Hamming
1247 enum { normType = NORM_HAMMING };
1248 typedef unsigned char ValueType;
1249 typedef int ResultType;
1251 /** this will count the bits in a ^ b
1253 ResultType operator()( const unsigned char *a, const unsigned char *b, int size ) const
1255 return normHamming(a, b, size);
1259 ////////////////////////////////// BruteForceMatcher //////////////////////////////////
1261 class CV_EXPORTS BruteForceMatcher_OCL_base
1264 enum DistType {L1Dist = 0, L2Dist, HammingDist};
1265 explicit BruteForceMatcher_OCL_base(DistType distType = L2Dist);
1267 // Add descriptors to train descriptor collection
1268 void add(const std::vector<oclMat> &descCollection);
1270 // Get train descriptors collection
1271 const std::vector<oclMat> &getTrainDescriptors() const;
1273 // Clear train descriptors collection
1276 // Return true if there are not train descriptors in collection
1279 // Return true if the matcher supports mask in match methods
1280 bool isMaskSupported() const;
1282 // Find one best match for each query descriptor
1283 void matchSingle(const oclMat &query, const oclMat &train,
1284 oclMat &trainIdx, oclMat &distance,
1285 const oclMat &mask = oclMat());
1287 // Download trainIdx and distance and convert it to CPU vector with DMatch
1288 static void matchDownload(const oclMat &trainIdx, const oclMat &distance, std::vector<DMatch> &matches);
1289 // Convert trainIdx and distance to vector with DMatch
1290 static void matchConvert(const Mat &trainIdx, const Mat &distance, std::vector<DMatch> &matches);
1292 // Find one best match for each query descriptor
1293 void match(const oclMat &query, const oclMat &train, std::vector<DMatch> &matches, const oclMat &mask = oclMat());
1295 // Make gpu collection of trains and masks in suitable format for matchCollection function
1296 void makeGpuCollection(oclMat &trainCollection, oclMat &maskCollection, const std::vector<oclMat> &masks = std::vector<oclMat>());
1298 // Find one best match from train collection for each query descriptor
1299 void matchCollection(const oclMat &query, const oclMat &trainCollection,
1300 oclMat &trainIdx, oclMat &imgIdx, oclMat &distance,
1301 const oclMat &masks = oclMat());
1303 // Download trainIdx, imgIdx and distance and convert it to vector with DMatch
1304 static void matchDownload(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance, std::vector<DMatch> &matches);
1305 // Convert trainIdx, imgIdx and distance to vector with DMatch
1306 static void matchConvert(const Mat &trainIdx, const Mat &imgIdx, const Mat &distance, std::vector<DMatch> &matches);
1308 // Find one best match from train collection for each query descriptor.
1309 void match(const oclMat &query, std::vector<DMatch> &matches, const std::vector<oclMat> &masks = std::vector<oclMat>());
1311 // Find k best matches for each query descriptor (in increasing order of distances)
1312 void knnMatchSingle(const oclMat &query, const oclMat &train,
1313 oclMat &trainIdx, oclMat &distance, oclMat &allDist, int k,
1314 const oclMat &mask = oclMat());
1316 // Download trainIdx and distance and convert it to vector with DMatch
1317 // compactResult is used when mask is not empty. If compactResult is false matches
1318 // vector will have the same size as queryDescriptors rows. If compactResult is true
1319 // matches vector will not contain matches for fully masked out query descriptors.
1320 static void knnMatchDownload(const oclMat &trainIdx, const oclMat &distance,
1321 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1322 // Convert trainIdx and distance to vector with DMatch
1323 static void knnMatchConvert(const Mat &trainIdx, const Mat &distance,
1324 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1326 // Find k best matches for each query descriptor (in increasing order of distances).
1327 // compactResult is used when mask is not empty. If compactResult is false matches
1328 // vector will have the same size as queryDescriptors rows. If compactResult is true
1329 // matches vector will not contain matches for fully masked out query descriptors.
1330 void knnMatch(const oclMat &query, const oclMat &train,
1331 std::vector< std::vector<DMatch> > &matches, int k, const oclMat &mask = oclMat(),
1332 bool compactResult = false);
1334 // Find k best matches from train collection for each query descriptor (in increasing order of distances)
1335 void knnMatch2Collection(const oclMat &query, const oclMat &trainCollection,
1336 oclMat &trainIdx, oclMat &imgIdx, oclMat &distance,
1337 const oclMat &maskCollection = oclMat());
1339 // Download trainIdx and distance and convert it to vector with DMatch
1340 // compactResult is used when mask is not empty. If compactResult is false matches
1341 // vector will have the same size as queryDescriptors rows. If compactResult is true
1342 // matches vector will not contain matches for fully masked out query descriptors.
1343 static void knnMatch2Download(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance,
1344 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1345 // Convert trainIdx and distance to vector with DMatch
1346 static void knnMatch2Convert(const Mat &trainIdx, const Mat &imgIdx, const Mat &distance,
1347 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1349 // Find k best matches for each query descriptor (in increasing order of distances).
1350 // compactResult is used when mask is not empty. If compactResult is false matches
1351 // vector will have the same size as queryDescriptors rows. If compactResult is true
1352 // matches vector will not contain matches for fully masked out query descriptors.
1353 void knnMatch(const oclMat &query, std::vector< std::vector<DMatch> > &matches, int k,
1354 const std::vector<oclMat> &masks = std::vector<oclMat>(), bool compactResult = false);
1356 // Find best matches for each query descriptor which have distance less than maxDistance.
1357 // nMatches.at<int>(0, queryIdx) will contain matches count for queryIdx.
1358 // carefully nMatches can be greater than trainIdx.cols - it means that matcher didn't find all matches,
1359 // because it didn't have enough memory.
1360 // If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nTrain / 100), 10),
1361 // otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
1362 // Matches doesn't sorted.
1363 void radiusMatchSingle(const oclMat &query, const oclMat &train,
1364 oclMat &trainIdx, oclMat &distance, oclMat &nMatches, float maxDistance,
1365 const oclMat &mask = oclMat());
1367 // Download trainIdx, nMatches and distance and convert it to vector with DMatch.
1368 // matches will be sorted in increasing order of distances.
1369 // compactResult is used when mask is not empty. If compactResult is false matches
1370 // vector will have the same size as queryDescriptors rows. If compactResult is true
1371 // matches vector will not contain matches for fully masked out query descriptors.
1372 static void radiusMatchDownload(const oclMat &trainIdx, const oclMat &distance, const oclMat &nMatches,
1373 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1374 // Convert trainIdx, nMatches and distance to vector with DMatch.
1375 static void radiusMatchConvert(const Mat &trainIdx, const Mat &distance, const Mat &nMatches,
1376 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1378 // Find best matches for each query descriptor which have distance less than maxDistance
1379 // in increasing order of distances).
1380 void radiusMatch(const oclMat &query, const oclMat &train,
1381 std::vector< std::vector<DMatch> > &matches, float maxDistance,
1382 const oclMat &mask = oclMat(), bool compactResult = false);
1384 // Find best matches for each query descriptor which have distance less than maxDistance.
1385 // If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nQuery / 100), 10),
1386 // otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
1387 // Matches doesn't sorted.
1388 void radiusMatchCollection(const oclMat &query, oclMat &trainIdx, oclMat &imgIdx, oclMat &distance, oclMat &nMatches, float maxDistance,
1389 const std::vector<oclMat> &masks = std::vector<oclMat>());
1391 // Download trainIdx, imgIdx, nMatches and distance and convert it to vector with DMatch.
1392 // matches will be sorted in increasing order of distances.
1393 // compactResult is used when mask is not empty. If compactResult is false matches
1394 // vector will have the same size as queryDescriptors rows. If compactResult is true
1395 // matches vector will not contain matches for fully masked out query descriptors.
1396 static void radiusMatchDownload(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance, const oclMat &nMatches,
1397 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1398 // Convert trainIdx, nMatches and distance to vector with DMatch.
1399 static void radiusMatchConvert(const Mat &trainIdx, const Mat &imgIdx, const Mat &distance, const Mat &nMatches,
1400 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1402 // Find best matches from train collection for each query descriptor which have distance less than
1403 // maxDistance (in increasing order of distances).
1404 void radiusMatch(const oclMat &query, std::vector< std::vector<DMatch> > &matches, float maxDistance,
1405 const std::vector<oclMat> &masks = std::vector<oclMat>(), bool compactResult = false);
1410 std::vector<oclMat> trainDescCollection;
1413 template <class Distance>
1414 class CV_EXPORTS BruteForceMatcher_OCL;
1416 template <typename T>
1417 class CV_EXPORTS BruteForceMatcher_OCL< L1<T> > : public BruteForceMatcher_OCL_base
1420 explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(L1Dist) {}
1421 explicit BruteForceMatcher_OCL(L1<T> /*d*/) : BruteForceMatcher_OCL_base(L1Dist) {}
1423 template <typename T>
1424 class CV_EXPORTS BruteForceMatcher_OCL< L2<T> > : public BruteForceMatcher_OCL_base
1427 explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(L2Dist) {}
1428 explicit BruteForceMatcher_OCL(L2<T> /*d*/) : BruteForceMatcher_OCL_base(L2Dist) {}
1430 template <> class CV_EXPORTS BruteForceMatcher_OCL< Hamming > : public BruteForceMatcher_OCL_base
1433 explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(HammingDist) {}
1434 explicit BruteForceMatcher_OCL(Hamming /*d*/) : BruteForceMatcher_OCL_base(HammingDist) {}
1437 class CV_EXPORTS BFMatcher_OCL : public BruteForceMatcher_OCL_base
1440 explicit BFMatcher_OCL(int norm = NORM_L2) : BruteForceMatcher_OCL_base(norm == NORM_L1 ? L1Dist : norm == NORM_L2 ? L2Dist : HammingDist) {}
1443 class CV_EXPORTS GoodFeaturesToTrackDetector_OCL
1446 explicit GoodFeaturesToTrackDetector_OCL(int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 0.0,
1447 int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04);
1449 //! return 1 rows matrix with CV_32FC2 type
1450 void operator ()(const oclMat& image, oclMat& corners, const oclMat& mask = oclMat());
1451 //! download points of type Point2f to a vector. the vector's content will be erased
1452 void downloadPoints(const oclMat &points, std::vector<Point2f> &points_v);
1455 double qualityLevel;
1459 bool useHarrisDetector;
1461 void releaseMemory()
1466 minMaxbuf_.release();
1467 tmpCorners_.release();
1477 inline GoodFeaturesToTrackDetector_OCL::GoodFeaturesToTrackDetector_OCL(int maxCorners_, double qualityLevel_, double minDistance_,
1478 int blockSize_, bool useHarrisDetector_, double harrisK_)
1480 maxCorners = maxCorners_;
1481 qualityLevel = qualityLevel_;
1482 minDistance = minDistance_;
1483 blockSize = blockSize_;
1484 useHarrisDetector = useHarrisDetector_;
1488 /////////////////////////////// PyrLKOpticalFlow /////////////////////////////////////
1490 class CV_EXPORTS PyrLKOpticalFlow
1495 winSize = Size(21, 21);
1499 useInitialFlow = false;
1500 minEigThreshold = 1e-4f;
1501 getMinEigenVals = false;
1502 isDeviceArch11_ = false;
1505 void sparse(const oclMat &prevImg, const oclMat &nextImg, const oclMat &prevPts, oclMat &nextPts,
1506 oclMat &status, oclMat *err = 0);
1508 void dense(const oclMat &prevImg, const oclMat &nextImg, oclMat &u, oclMat &v, oclMat *err = 0);
1514 bool useInitialFlow;
1515 float minEigThreshold;
1516 bool getMinEigenVals;
1518 void releaseMemory()
1520 dx_calcBuf_.release();
1521 dy_calcBuf_.release();
1531 void calcSharrDeriv(const oclMat &src, oclMat &dx, oclMat &dy);
1533 void buildImagePyramid(const oclMat &img0, std::vector<oclMat> &pyr, bool withBorder);
1538 std::vector<oclMat> prevPyr_;
1539 std::vector<oclMat> nextPyr_;
1547 bool isDeviceArch11_;
1550 class CV_EXPORTS FarnebackOpticalFlow
1553 FarnebackOpticalFlow();
1564 void operator ()(const oclMat &frame0, const oclMat &frame1, oclMat &flowx, oclMat &flowy);
1566 void releaseMemory();
1569 void prepareGaussian(
1570 int n, double sigma, float *g, float *xg, float *xxg,
1571 double &ig11, double &ig03, double &ig33, double &ig55);
1573 void setPolynomialExpansionConsts(int n, double sigma);
1575 void updateFlow_boxFilter(
1576 const oclMat& R0, const oclMat& R1, oclMat& flowx, oclMat &flowy,
1577 oclMat& M, oclMat &bufM, int blockSize, bool updateMatrices);
1579 void updateFlow_gaussianBlur(
1580 const oclMat& R0, const oclMat& R1, oclMat& flowx, oclMat& flowy,
1581 oclMat& M, oclMat &bufM, int blockSize, bool updateMatrices);
1584 oclMat pyrLevel_[2], M_, bufM_, R_[2], blurredFrame_[2];
1585 std::vector<oclMat> pyramid0_, pyramid1_;
1588 //////////////// build warping maps ////////////////////
1589 //! builds plane warping maps
1590 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);
1591 //! builds cylindrical warping maps
1592 CV_EXPORTS void buildWarpCylindricalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat &R, float scale, oclMat &map_x, oclMat &map_y);
1593 //! builds spherical warping maps
1594 CV_EXPORTS void buildWarpSphericalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat &R, float scale, oclMat &map_x, oclMat &map_y);
1595 //! builds Affine warping maps
1596 CV_EXPORTS void buildWarpAffineMaps(const Mat &M, bool inverse, Size dsize, oclMat &xmap, oclMat &ymap);
1598 //! builds Perspective warping maps
1599 CV_EXPORTS void buildWarpPerspectiveMaps(const Mat &M, bool inverse, Size dsize, oclMat &xmap, oclMat &ymap);
1601 ///////////////////////////////////// interpolate frames //////////////////////////////////////////////
1602 //! Interpolate frames (images) using provided optical flow (displacement field).
1603 //! frame0 - frame 0 (32-bit floating point images, single channel)
1604 //! frame1 - frame 1 (the same type and size)
1605 //! fu - forward horizontal displacement
1606 //! fv - forward vertical displacement
1607 //! bu - backward horizontal displacement
1608 //! bv - backward vertical displacement
1609 //! pos - new frame position
1610 //! newFrame - new frame
1611 //! buf - temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 oclMat;
1612 //! occlusion masks 0, occlusion masks 1,
1613 //! interpolated forward flow 0, interpolated forward flow 1,
1614 //! interpolated backward flow 0, interpolated backward flow 1
1616 CV_EXPORTS void interpolateFrames(const oclMat &frame0, const oclMat &frame1,
1617 const oclMat &fu, const oclMat &fv,
1618 const oclMat &bu, const oclMat &bv,
1619 float pos, oclMat &newFrame, oclMat &buf);
1621 //! computes moments of the rasterized shape or a vector of points
1622 //! _array should be a vector a points standing for the contour
1623 CV_EXPORTS Moments ocl_moments(InputArray contour);
1624 //! src should be a general image uploaded to the GPU.
1625 //! the supported oclMat type are CV_8UC1, CV_16UC1, CV_16SC1, CV_32FC1 and CV_64FC1
1626 //! to use type of CV_64FC1, the GPU should support CV_64FC1
1627 CV_EXPORTS Moments ocl_moments(oclMat& src, bool binary);
1629 class CV_EXPORTS StereoBM_OCL
1632 enum { BASIC_PRESET = 0, PREFILTER_XSOBEL = 1 };
1634 enum { DEFAULT_NDISP = 64, DEFAULT_WINSZ = 19 };
1636 //! the default constructor
1638 //! the full constructor taking the camera-specific preset, number of disparities and the SAD window size. ndisparities must be multiple of 8.
1639 StereoBM_OCL(int preset, int ndisparities = DEFAULT_NDISP, int winSize = DEFAULT_WINSZ);
1641 //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair
1642 //! Output disparity has CV_8U type.
1643 void operator() ( const oclMat &left, const oclMat &right, oclMat &disparity);
1645 //! Some heuristics that tries to estmate
1646 // if current GPU will be faster then CPU in this algorithm.
1647 // It queries current active device.
1648 static bool checkIfGpuCallReasonable();
1654 // If avergeTexThreshold == 0 => post procesing is disabled
1655 // If avergeTexThreshold != 0 then disparity is set 0 in each point (x,y) where for left image
1656 // SumOfHorizontalGradiensInWindow(x, y, winSize) < (winSize * winSize) * avergeTexThreshold
1657 // i.e. input left image is low textured.
1658 float avergeTexThreshold;
1660 oclMat minSSD, leBuf, riBuf;
1663 class CV_EXPORTS StereoBeliefPropagation
1666 enum { DEFAULT_NDISP = 64 };
1667 enum { DEFAULT_ITERS = 5 };
1668 enum { DEFAULT_LEVELS = 5 };
1669 static void estimateRecommendedParams(int width, int height, int &ndisp, int &iters, int &levels);
1670 explicit StereoBeliefPropagation(int ndisp = DEFAULT_NDISP,
1671 int iters = DEFAULT_ITERS,
1672 int levels = DEFAULT_LEVELS,
1673 int msg_type = CV_16S);
1674 StereoBeliefPropagation(int ndisp, int iters, int levels,
1675 float max_data_term, float data_weight,
1676 float max_disc_term, float disc_single_jump,
1677 int msg_type = CV_32F);
1678 void operator()(const oclMat &left, const oclMat &right, oclMat &disparity);
1679 void operator()(const oclMat &data, oclMat &disparity);
1683 float max_data_term;
1685 float max_disc_term;
1686 float disc_single_jump;
1689 oclMat u, d, l, r, u2, d2, l2, r2;
1690 std::vector<oclMat> datas;
1694 class CV_EXPORTS StereoConstantSpaceBP
1697 enum { DEFAULT_NDISP = 128 };
1698 enum { DEFAULT_ITERS = 8 };
1699 enum { DEFAULT_LEVELS = 4 };
1700 enum { DEFAULT_NR_PLANE = 4 };
1701 static void estimateRecommendedParams(int width, int height, int &ndisp, int &iters, int &levels, int &nr_plane);
1702 explicit StereoConstantSpaceBP(
1703 int ndisp = DEFAULT_NDISP,
1704 int iters = DEFAULT_ITERS,
1705 int levels = DEFAULT_LEVELS,
1706 int nr_plane = DEFAULT_NR_PLANE,
1707 int msg_type = CV_32F);
1708 StereoConstantSpaceBP(int ndisp, int iters, int levels, int nr_plane,
1709 float max_data_term, float data_weight, float max_disc_term, float disc_single_jump,
1710 int min_disp_th = 0,
1711 int msg_type = CV_32F);
1712 void operator()(const oclMat &left, const oclMat &right, oclMat &disparity);
1717 float max_data_term;
1719 float max_disc_term;
1720 float disc_single_jump;
1723 bool use_local_init_data_cost;
1725 oclMat u[2], d[2], l[2], r[2];
1726 oclMat disp_selected_pyr[2];
1728 oclMat data_cost_selected;
1733 // Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method
1736 // [1] C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
1737 // [2] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
1738 class CV_EXPORTS OpticalFlowDual_TVL1_OCL
1741 OpticalFlowDual_TVL1_OCL();
1743 void operator ()(const oclMat& I0, const oclMat& I1, oclMat& flowx, oclMat& flowy);
1745 void collectGarbage();
1748 * Time step of the numerical scheme.
1753 * Weight parameter for the data term, attachment parameter.
1754 * This is the most relevant parameter, which determines the smoothness of the output.
1755 * The smaller this parameter is, the smoother the solutions we obtain.
1756 * It depends on the range of motions of the images, so its value should be adapted to each image sequence.
1761 * Weight parameter for (u - v)^2, tightness parameter.
1762 * It serves as a link between the attachment and the regularization terms.
1763 * In theory, it should have a small value in order to maintain both parts in correspondence.
1764 * The method is stable for a large range of values of this parameter.
1769 * Number of scales used to create the pyramid of images.
1774 * Number of warpings per scale.
1775 * Represents the number of times that I1(x+u0) and grad( I1(x+u0) ) are computed per scale.
1776 * This is a parameter that assures the stability of the method.
1777 * It also affects the running time, so it is a compromise between speed and accuracy.
1782 * Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time.
1783 * A small value will yield more accurate solutions at the expense of a slower convergence.
1788 * Stopping criterion iterations number used in the numerical scheme.
1792 bool useInitialFlow;
1795 void procOneScale(const oclMat& I0, const oclMat& I1, oclMat& u1, oclMat& u2);
1797 std::vector<oclMat> I0s;
1798 std::vector<oclMat> I1s;
1799 std::vector<oclMat> u1s;
1800 std::vector<oclMat> u2s;
1820 // current supported sorting methods
1823 SORT_BITONIC, // only support power-of-2 buffer size
1824 SORT_SELECTION, // cannot sort duplicate keys
1826 SORT_RADIX // only support signed int/float keys(CV_32S/CV_32F)
1828 //! Returns the sorted result of all the elements in input based on equivalent keys.
1830 // The element unit in the values to be sorted is determined from the data type,
1831 // i.e., a CV_32FC2 input {a1a2, b1b2} will be considered as two elements, regardless its
1832 // matrix dimension.
1833 // both keys and values will be sorted inplace
1834 // Key needs to be single channel oclMat.
1838 // keys = {2, 3, 1} (CV_8UC1)
1839 // values = {10,5, 4,3, 6,2} (CV_8UC2)
1840 // sortByKey(keys, values, SORT_SELECTION, false);
1842 // keys = {1, 2, 3} (CV_8UC1)
1843 // values = {6,2, 10,5, 4,3} (CV_8UC2)
1844 CV_EXPORTS void sortByKey(oclMat& keys, oclMat& values, int method, bool isGreaterThan = false);
1845 /*!Base class for MOG and MOG2!*/
1846 class CV_EXPORTS BackgroundSubtractor
1849 //! the virtual destructor
1850 virtual ~BackgroundSubtractor();
1851 //! the update operator that takes the next video frame and returns the current foreground mask as 8-bit binary image.
1852 virtual void operator()(const oclMat& image, oclMat& fgmask, float learningRate);
1854 //! computes a background image
1855 virtual void getBackgroundImage(oclMat& backgroundImage) const = 0;
1858 Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm
1860 The class implements the following algorithm:
1861 "An improved adaptive background mixture model for real-time tracking with shadow detection"
1862 P. KadewTraKuPong and R. Bowden,
1863 Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
1864 http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
1866 class CV_EXPORTS MOG: public cv::ocl::BackgroundSubtractor
1869 //! the default constructor
1870 MOG(int nmixtures = -1);
1872 //! re-initiaization method
1873 void initialize(Size frameSize, int frameType);
1875 //! the update operator
1876 void operator()(const oclMat& frame, oclMat& fgmask, float learningRate = 0.f);
1878 //! computes a background image which are the mean of all background gaussians
1879 void getBackgroundImage(oclMat& backgroundImage) const;
1881 //! releases all inner buffers
1886 float backgroundRatio;
1903 The class implements the following algorithm:
1904 "Improved adaptive Gausian mixture model for background subtraction"
1906 International Conference Pattern Recognition, UK, August, 2004.
1907 http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf
1909 class CV_EXPORTS MOG2: public cv::ocl::BackgroundSubtractor
1912 //! the default constructor
1913 MOG2(int nmixtures = -1);
1915 //! re-initiaization method
1916 void initialize(Size frameSize, int frameType);
1918 //! the update operator
1919 void operator()(const oclMat& frame, oclMat& fgmask, float learningRate = -1.0f);
1921 //! computes a background image which are the mean of all background gaussians
1922 void getBackgroundImage(oclMat& backgroundImage) const;
1924 //! releases all inner buffers
1928 // you should call initialize after parameters changes
1932 //! here it is the maximum allowed number of mixture components.
1933 //! Actual number is determined dynamically per pixel
1935 // threshold on the squared Mahalanobis distance to decide if it is well described
1936 // by the background model or not. Related to Cthr from the paper.
1937 // This does not influence the update of the background. A typical value could be 4 sigma
1938 // and that is varThreshold=4*4=16; Corresponds to Tb in the paper.
1940 /////////////////////////
1941 // less important parameters - things you might change but be carefull
1942 ////////////////////////
1944 float backgroundRatio;
1945 // corresponds to fTB=1-cf from the paper
1946 // TB - threshold when the component becomes significant enough to be included into
1947 // the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0.
1948 // For alpha=0.001 it means that the mode should exist for approximately 105 frames before
1949 // it is considered foreground
1950 // float noiseSigma;
1951 float varThresholdGen;
1953 //correspondts to Tg - threshold on the squared Mahalan. dist. to decide
1954 //when a sample is close to the existing components. If it is not close
1955 //to any a new component will be generated. I use 3 sigma => Tg=3*3=9.
1956 //Smaller Tg leads to more generated components and higher Tg might make
1957 //lead to small number of components but they can grow too large
1962 //initial variance for the newly generated components.
1963 //It will will influence the speed of adaptation. A good guess should be made.
1964 //A simple way is to estimate the typical standard deviation from the images.
1965 //I used here 10 as a reasonable value
1966 // min and max can be used to further control the variance
1967 float fCT; //CT - complexity reduction prior
1968 //this is related to the number of samples needed to accept that a component
1969 //actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get
1970 //the standard Stauffer&Grimson algorithm (maybe not exact but very similar)
1972 //shadow detection parameters
1973 bool bShadowDetection; //default 1 - do shadow detection
1974 unsigned char nShadowDetection; //do shadow detection - insert this value as the detection result - 127 default value
1976 // Tau - shadow threshold. The shadow is detected if the pixel is darker
1977 //version of the background. Tau is a threshold on how much darker the shadow can be.
1978 //Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow
1979 //See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.
1992 oclMat bgmodelUsedModes_; //keep track of number of modes per pixel
1995 /*!***************Kalman Filter*************!*/
1996 class CV_EXPORTS KalmanFilter
2000 //! the full constructor taking the dimensionality of the state, of the measurement and of the control vector
2001 KalmanFilter(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F);
2002 //! re-initializes Kalman filter. The previous content is destroyed.
2003 void init(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F);
2005 const oclMat& predict(const oclMat& control=oclMat());
2006 const oclMat& correct(const oclMat& measurement);
2008 oclMat statePre; //!< predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
2009 oclMat statePost; //!< corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
2010 oclMat transitionMatrix; //!< state transition matrix (A)
2011 oclMat controlMatrix; //!< control matrix (B) (not used if there is no control)
2012 oclMat measurementMatrix; //!< measurement matrix (H)
2013 oclMat processNoiseCov; //!< process noise covariance matrix (Q)
2014 oclMat measurementNoiseCov;//!< measurement noise covariance matrix (R)
2015 oclMat errorCovPre; //!< priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/
2016 oclMat gain; //!< Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
2017 oclMat errorCovPost; //!< posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
2026 /*!***************K Nearest Neighbour*************!*/
2027 class CV_EXPORTS KNearestNeighbour: public CvKNearest
2030 KNearestNeighbour();
2031 ~KNearestNeighbour();
2033 bool train(const Mat& trainData, Mat& labels, Mat& sampleIdx = Mat().setTo(Scalar::all(0)),
2034 bool isRegression = false, int max_k = 32, bool updateBase = false);
2038 void find_nearest(const oclMat& samples, int k, oclMat& lables);
2044 /*!*************** SVM *************!*/
2045 class CV_EXPORTS CvSVM_OCL : public CvSVM
2050 CvSVM_OCL(const cv::Mat& trainData, const cv::Mat& responses,
2051 const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
2052 CvSVMParams params=CvSVMParams());
2053 CV_WRAP float predict( const int row_index, Mat& src, bool returnDFVal=false ) const;
2054 CV_WRAP void predict( cv::InputArray samples, cv::OutputArray results ) const;
2055 CV_WRAP float predict( const cv::Mat& sample, bool returnDFVal=false ) const;
2056 float predict( const CvMat* samples, CV_OUT CvMat* results ) const;
2059 float predict( const int row_index, int row_len, Mat& src, bool returnDFVal=false ) const;
2060 void create_kernel();
2061 void create_solver();
2064 /*!*************** END *************!*/
2067 #if defined _MSC_VER && _MSC_VER >= 1200
2068 # pragma warning( push)
2069 # pragma warning( disable: 4267)
2071 #include "opencv2/ocl/matrix_operations.hpp"
2072 #if defined _MSC_VER && _MSC_VER >= 1200
2073 # pragma warning( pop)
2076 #endif /* __OPENCV_OCL_HPP__ */