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44 #ifndef __OPENCV_OCL_HPP__
45 #define __OPENCV_OCL_HPP__
50 #include "opencv2/core/core.hpp"
51 #include "opencv2/imgproc/imgproc.hpp"
52 #include "opencv2/objdetect/objdetect.hpp"
53 #include "opencv2/features2d/features2d.hpp"
54 #include "opencv2/ml/ml.hpp"
62 CVCL_DEVICE_TYPE_DEFAULT = (1 << 0),
63 CVCL_DEVICE_TYPE_CPU = (1 << 1),
64 CVCL_DEVICE_TYPE_GPU = (1 << 2),
65 CVCL_DEVICE_TYPE_ACCELERATOR = (1 << 3),
66 //CVCL_DEVICE_TYPE_CUSTOM = (1 << 4)
67 CVCL_DEVICE_TYPE_ALL = 0xFFFFFFFF
79 DEVICE_MEM_DEFAULT = 0,
80 DEVICE_MEM_AHP, //alloc host pointer
81 DEVICE_MEM_UHP, //use host pointer
82 DEVICE_MEM_CHP, //copy host pointer
83 DEVICE_MEM_PM //persistent memory
86 // 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
115 std::string compilationExtraOptions;
117 const PlatformInfo* platform;
124 int _id; // reserved, don't use it
126 std::string platformProfile;
127 std::string platformVersion;
128 std::string platformName;
129 std::string platformVendor;
130 std::string platformExtensons;
132 int platformVersionMajor;
133 int platformVersionMinor;
135 std::vector<const DeviceInfo*> devices;
140 //////////////////////////////// Initialization & Info ////////////////////////
141 typedef std::vector<const PlatformInfo*> PlatformsInfo;
143 CV_EXPORTS int getOpenCLPlatforms(PlatformsInfo& platforms);
145 typedef std::vector<const DeviceInfo*> DevicesInfo;
147 CV_EXPORTS int getOpenCLDevices(DevicesInfo& devices, int deviceType = CVCL_DEVICE_TYPE_GPU,
148 const PlatformInfo* platform = NULL);
150 // set device you want to use
151 CV_EXPORTS void setDevice(const DeviceInfo* info);
153 //////////////////////////////// Error handling ////////////////////////
154 CV_EXPORTS void error(const char *error_string, const char *file, const int line, const char *func);
158 FEATURE_CL_DOUBLE = 1,
159 FEATURE_CL_UNIFIED_MEM,
163 // Represents OpenCL context, interface
164 class CV_EXPORTS Context
170 static Context* getContext();
172 bool supportsFeature(FEATURE_TYPE featureType) const;
173 const DeviceInfo& getDeviceInfo() const;
175 const void* getOpenCLContextPtr() const;
176 const void* getOpenCLCommandQueuePtr() const;
177 const void* getOpenCLDeviceIDPtr() const;
180 inline const void *getClContextPtr()
182 return Context::getContext()->getOpenCLContextPtr();
185 inline const void *getClCommandQueuePtr()
187 return Context::getContext()->getOpenCLCommandQueuePtr();
190 CV_EXPORTS bool supportsFeature(FEATURE_TYPE featureType);
192 CV_EXPORTS void finish();
194 enum BINARY_CACHE_MODE
196 CACHE_NONE = 0, // do not cache OpenCL binary
197 CACHE_DEBUG = 0x1 << 0, // cache OpenCL binary when built in debug mode
198 CACHE_RELEASE = 0x1 << 1, // default behavior, only cache when built in release mode
199 CACHE_ALL = CACHE_DEBUG | CACHE_RELEASE, // cache opencl binary
201 //! Enable or disable OpenCL program binary caching onto local disk
202 // After a program (*.cl files in opencl/ folder) is built at runtime, we allow the
203 // compiled OpenCL program to be cached to the path automatically as "path/*.clb"
204 // binary file, which will be reused when the OpenCV executable is started again.
206 // This feature is enabled by default.
207 CV_EXPORTS void setBinaryDiskCache(int mode = CACHE_RELEASE, cv::String path = "./");
209 //! set where binary cache to be saved to
210 CV_EXPORTS void setBinaryPath(const char *path);
215 const char* programStr;
216 const char* programHash;
218 // Cache in memory by name (should be unique). Caching on disk disabled.
219 inline ProgramSource(const char* _name, const char* _programStr)
220 : name(_name), programStr(_programStr), programHash(NULL)
224 // Cache in memory by name (should be unique). Caching on disk uses programHash mark.
225 inline ProgramSource(const char* _name, const char* _programStr, const char* _programHash)
226 : name(_name), programStr(_programStr), programHash(_programHash)
231 //! Calls OpenCL kernel. Pass globalThreads = NULL, and cleanUp = true, to finally clean-up without executing.
232 //! Deprecated, will be replaced
233 CV_EXPORTS void openCLExecuteKernelInterop(Context *clCxt,
234 const cv::ocl::ProgramSource& source, string kernelName,
235 size_t globalThreads[3], size_t localThreads[3],
236 std::vector< std::pair<size_t, const void *> > &args,
237 int channels, int depth, const char *build_options);
239 class CV_EXPORTS oclMatExpr;
240 //////////////////////////////// oclMat ////////////////////////////////
241 class CV_EXPORTS oclMat
244 //! default constructor
246 //! constructs oclMatrix of the specified size and type (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.)
247 oclMat(int rows, int cols, int type);
248 oclMat(Size size, int type);
249 //! constucts oclMatrix and fills it with the specified value _s.
250 oclMat(int rows, int cols, int type, const Scalar &s);
251 oclMat(Size size, int type, const Scalar &s);
253 oclMat(const oclMat &m);
255 //! constructor for oclMatrix headers pointing to user-allocated data
256 oclMat(int rows, int cols, int type, void *data, size_t step = Mat::AUTO_STEP);
257 oclMat(Size size, int type, void *data, size_t step = Mat::AUTO_STEP);
259 //! creates a matrix header for a part of the bigger matrix
260 oclMat(const oclMat &m, const Range &rowRange, const Range &colRange);
261 oclMat(const oclMat &m, const Rect &roi);
263 //! builds oclMat from Mat. Perfom blocking upload to device.
264 explicit oclMat (const Mat &m);
266 //! destructor - calls release()
269 //! assignment operators
270 oclMat &operator = (const oclMat &m);
271 //! assignment operator. Perfom blocking upload to device.
272 oclMat &operator = (const Mat &m);
273 oclMat &operator = (const oclMatExpr& expr);
275 //! pefroms blocking upload data to oclMat.
276 void upload(const cv::Mat &m);
279 //! downloads data from device to host memory. Blocking calls.
280 operator Mat() const;
281 void download(cv::Mat &m) const;
283 //! convert to _InputArray
284 operator _InputArray();
286 //! convert to _OutputArray
287 operator _OutputArray();
289 //! returns a new oclMatrix header for the specified row
290 oclMat row(int y) const;
291 //! returns a new oclMatrix header for the specified column
292 oclMat col(int x) const;
293 //! ... for the specified row span
294 oclMat rowRange(int startrow, int endrow) const;
295 oclMat rowRange(const Range &r) const;
296 //! ... for the specified column span
297 oclMat colRange(int startcol, int endcol) const;
298 oclMat colRange(const Range &r) const;
300 //! returns deep copy of the oclMatrix, i.e. the data is copied
301 oclMat clone() const;
303 //! copies those oclMatrix elements to "m" that are marked with non-zero mask elements.
304 // It calls m.create(this->size(), this->type()).
305 // It supports any data type
306 void copyTo( oclMat &m, const oclMat &mask = oclMat()) const;
308 //! converts oclMatrix to another datatype with optional scalng. See cvConvertScale.
309 //It supports 8UC1 8UC4 32SC1 32SC4 32FC1 32FC4
310 void convertTo( oclMat &m, int rtype, double alpha = 1, double beta = 0 ) const;
312 void assignTo( oclMat &m, int type = -1 ) const;
314 //! sets every oclMatrix element to s
315 //It supports 8UC1 8UC4 32SC1 32SC4 32FC1 32FC4
316 oclMat& operator = (const Scalar &s);
317 //! sets some of the oclMatrix elements to s, according to the mask
318 //It supports 8UC1 8UC4 32SC1 32SC4 32FC1 32FC4
319 oclMat& setTo(const Scalar &s, const oclMat &mask = oclMat());
320 //! creates alternative oclMatrix header for the same data, with different
321 // number of channels and/or different number of rows. see cvReshape.
322 oclMat reshape(int cn, int rows = 0) const;
324 //! allocates new oclMatrix data unless the oclMatrix already has specified size and type.
325 // previous data is unreferenced if needed.
326 void create(int rows, int cols, int type);
327 void create(Size size, int type);
329 //! allocates new oclMatrix with specified device memory type.
330 void createEx(int rows, int cols, int type, DevMemRW rw_type, DevMemType mem_type);
331 void createEx(Size size, int type, DevMemRW rw_type, DevMemType mem_type);
333 //! decreases reference counter;
334 // deallocate the data when reference counter reaches 0.
337 //! swaps with other smart pointer
338 void swap(oclMat &mat);
340 //! locates oclMatrix header within a parent oclMatrix. See below
341 void locateROI( Size &wholeSize, Point &ofs ) const;
342 //! moves/resizes the current oclMatrix ROI inside the parent oclMatrix.
343 oclMat& adjustROI( int dtop, int dbottom, int dleft, int dright );
344 //! extracts a rectangular sub-oclMatrix
345 // (this is a generalized form of row, rowRange etc.)
346 oclMat operator()( Range rowRange, Range colRange ) const;
347 oclMat operator()( const Rect &roi ) const;
349 oclMat& operator+=( const oclMat& m );
350 oclMat& operator-=( const oclMat& m );
351 oclMat& operator*=( const oclMat& m );
352 oclMat& operator/=( const oclMat& m );
354 //! returns true if the oclMatrix data is continuous
355 // (i.e. when there are no gaps between successive rows).
356 // similar to CV_IS_oclMat_CONT(cvoclMat->type)
357 bool isContinuous() const;
358 //! returns element size in bytes,
359 // similar to CV_ELEM_SIZE(cvMat->type)
360 size_t elemSize() const;
361 //! returns the size of element channel in bytes.
362 size_t elemSize1() const;
363 //! returns element type, similar to CV_MAT_TYPE(cvMat->type)
365 //! returns element type, i.e. 8UC3 returns 8UC4 because in ocl
366 //! 3 channels element actually use 4 channel space
368 //! returns element type, similar to CV_MAT_DEPTH(cvMat->type)
370 //! returns element type, similar to CV_MAT_CN(cvMat->type)
371 int channels() const;
372 //! returns element type, return 4 for 3 channels element,
373 //!becuase 3 channels element actually use 4 channel space
374 int oclchannels() const;
375 //! returns step/elemSize1()
376 size_t step1() const;
377 //! returns oclMatrix size:
378 // width == number of columns, height == number of rows
380 //! returns true if oclMatrix data is NULL
383 //! returns pointer to y-th row
384 uchar* ptr(int y = 0);
385 const uchar *ptr(int y = 0) const;
387 //! template version of the above method
388 template<typename _Tp> _Tp *ptr(int y = 0);
389 template<typename _Tp> const _Tp *ptr(int y = 0) const;
391 //! matrix transposition
394 /*! includes several bit-fields:
395 - the magic signature
401 //! the number of rows and columns
403 //! a distance between successive rows in bytes; includes the gap if any
405 //! pointer to the data(OCL memory object)
408 //! pointer to the reference counter;
409 // when oclMatrix points to user-allocated data, the pointer is NULL
412 //! helper fields used in locateROI and adjustROI
413 //datastart and dataend are not used in current version
417 //! OpenCL context associated with the oclMat object.
418 Context *clCxt; // TODO clCtx
419 //add offset for handle ROI, calculated in byte
421 //add wholerows and wholecols for the whole matrix, datastart and dataend are no longer used
426 // convert InputArray/OutputArray to oclMat references
427 CV_EXPORTS oclMat& getOclMatRef(InputArray src);
428 CV_EXPORTS oclMat& getOclMatRef(OutputArray src);
430 ///////////////////// mat split and merge /////////////////////////////////
431 //! Compose a multi-channel array from several single-channel arrays
433 CV_EXPORTS void merge(const oclMat *src, size_t n, oclMat &dst);
434 CV_EXPORTS void merge(const vector<oclMat> &src, oclMat &dst);
436 //! Divides multi-channel array into several single-channel arrays
438 CV_EXPORTS void split(const oclMat &src, oclMat *dst);
439 CV_EXPORTS void split(const oclMat &src, vector<oclMat> &dst);
441 ////////////////////////////// Arithmetics ///////////////////////////////////
443 //! adds one matrix to another with scale (dst = src1 * alpha + src2 * beta + gama)
444 // supports all data types
445 CV_EXPORTS void addWeighted(const oclMat &src1, double alpha, const oclMat &src2, double beta, double gama, oclMat &dst);
447 //! adds one matrix to another (dst = src1 + src2)
448 // supports all data types
449 CV_EXPORTS void add(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
450 //! adds scalar to a matrix (dst = src1 + s)
451 // supports all data types
452 CV_EXPORTS void add(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
454 //! subtracts one matrix from another (dst = src1 - src2)
455 // supports all data types
456 CV_EXPORTS void subtract(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
457 //! subtracts scalar from a matrix (dst = src1 - s)
458 // supports all data types
459 CV_EXPORTS void subtract(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
461 //! computes element-wise product of the two arrays (dst = src1 * scale * src2)
462 // supports all data types
463 CV_EXPORTS void multiply(const oclMat &src1, const oclMat &src2, oclMat &dst, double scale = 1);
464 //! multiplies matrix to a number (dst = scalar * src)
465 // supports all data types
466 CV_EXPORTS void multiply(double scalar, const oclMat &src, oclMat &dst);
468 //! computes element-wise quotient of the two arrays (dst = src1 * scale / src2)
469 // supports all data types
470 CV_EXPORTS void divide(const oclMat &src1, const oclMat &src2, oclMat &dst, double scale = 1);
471 //! computes element-wise quotient of the two arrays (dst = scale / src)
472 // supports all data types
473 CV_EXPORTS void divide(double scale, const oclMat &src1, oclMat &dst);
475 //! computes element-wise minimum of the two arrays (dst = min(src1, src2))
476 // supports all data types
477 CV_EXPORTS void min(const oclMat &src1, const oclMat &src2, oclMat &dst);
479 //! computes element-wise maximum of the two arrays (dst = max(src1, src2))
480 // supports all data types
481 CV_EXPORTS void max(const oclMat &src1, const oclMat &src2, oclMat &dst);
483 //! compares elements of two arrays (dst = src1 <cmpop> src2)
484 // supports all data types
485 CV_EXPORTS void compare(const oclMat &src1, const oclMat &src2, oclMat &dst, int cmpop);
487 //! transposes the matrix
488 // supports all data types
489 CV_EXPORTS void transpose(const oclMat &src, oclMat &dst);
491 //! computes element-wise absolute values of an array (dst = abs(src))
492 // supports all data types
493 CV_EXPORTS void abs(const oclMat &src, oclMat &dst);
495 //! computes element-wise absolute difference of two arrays (dst = abs(src1 - src2))
496 // supports all data types
497 CV_EXPORTS void absdiff(const oclMat &src1, const oclMat &src2, oclMat &dst);
498 //! computes element-wise absolute difference of array and scalar (dst = abs(src1 - s))
499 // supports all data types
500 CV_EXPORTS void absdiff(const oclMat &src1, const Scalar &s, oclMat &dst);
502 //! computes mean value and standard deviation of all or selected array elements
503 // supports all data types
504 CV_EXPORTS void meanStdDev(const oclMat &mtx, Scalar &mean, Scalar &stddev);
506 //! computes norm of array
507 // supports NORM_INF, NORM_L1, NORM_L2
508 // supports all data types
509 CV_EXPORTS double norm(const oclMat &src1, int normType = NORM_L2);
511 //! computes norm of the difference between two arrays
512 // supports NORM_INF, NORM_L1, NORM_L2
513 // supports all data types
514 CV_EXPORTS double norm(const oclMat &src1, const oclMat &src2, int normType = NORM_L2);
516 //! reverses the order of the rows, columns or both in a matrix
517 // supports all types
518 CV_EXPORTS void flip(const oclMat &src, oclMat &dst, int flipCode);
520 //! computes sum of array elements
522 CV_EXPORTS Scalar sum(const oclMat &m);
523 CV_EXPORTS Scalar absSum(const oclMat &m);
524 CV_EXPORTS Scalar sqrSum(const oclMat &m);
526 //! finds global minimum and maximum array elements and returns their values
527 // support all C1 types
528 CV_EXPORTS void minMax(const oclMat &src, double *minVal, double *maxVal = 0, const oclMat &mask = oclMat());
530 //! finds global minimum and maximum array elements and returns their values with locations
531 // support all C1 types
532 CV_EXPORTS void minMaxLoc(const oclMat &src, double *minVal, double *maxVal = 0, Point *minLoc = 0, Point *maxLoc = 0,
533 const oclMat &mask = oclMat());
535 //! counts non-zero array elements
537 CV_EXPORTS int countNonZero(const oclMat &src);
539 //! transforms 8-bit unsigned integers using lookup table: dst(i)=lut(src(i))
540 // destination array will have the depth type as lut and the same channels number as source
541 //It supports 8UC1 8UC4 only
542 CV_EXPORTS void LUT(const oclMat &src, const oclMat &lut, oclMat &dst);
544 //! only 8UC1 and 256 bins is supported now
545 CV_EXPORTS void calcHist(const oclMat &mat_src, oclMat &mat_hist);
546 //! only 8UC1 and 256 bins is supported now
547 CV_EXPORTS void equalizeHist(const oclMat &mat_src, oclMat &mat_dst);
549 //! only 8UC1 is supported now
550 CV_EXPORTS Ptr<cv::CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8));
553 // supports 8UC1 8UC4
554 CV_EXPORTS void bilateralFilter(const oclMat& src, oclMat& dst, int d, double sigmaColor, double sigmaSpace, int borderType=BORDER_DEFAULT);
556 //! Applies an adaptive bilateral filter to the input image
557 // This is not truly a bilateral filter. Instead of using user provided fixed parameters,
558 // the function calculates a constant at each window based on local standard deviation,
559 // and use this constant to do filtering.
560 // supports 8UC1, 8UC3
561 CV_EXPORTS void adaptiveBilateralFilter(const oclMat& src, oclMat& dst, Size ksize, double sigmaSpace, Point anchor = Point(-1, -1), int borderType=BORDER_DEFAULT);
563 //! computes exponent of each matrix element (dst = e**src)
564 // supports only CV_32FC1, CV_64FC1 type
565 CV_EXPORTS void exp(const oclMat &src, oclMat &dst);
567 //! computes natural logarithm of absolute value of each matrix element: dst = log(abs(src))
568 // supports only CV_32FC1, CV_64FC1 type
569 CV_EXPORTS void log(const oclMat &src, oclMat &dst);
571 //! computes magnitude of each (x(i), y(i)) vector
572 // supports only CV_32F, CV_64F type
573 CV_EXPORTS void magnitude(const oclMat &x, const oclMat &y, oclMat &magnitude);
575 //! computes angle (angle(i)) of each (x(i), y(i)) vector
576 // supports only CV_32F, CV_64F type
577 CV_EXPORTS void phase(const oclMat &x, const oclMat &y, oclMat &angle, bool angleInDegrees = false);
579 //! the function raises every element of tne input array to p
580 // support only CV_32F, CV_64F type
581 CV_EXPORTS void pow(const oclMat &x, double p, oclMat &y);
583 //! converts Cartesian coordinates to polar
584 // supports only CV_32F CV_64F type
585 CV_EXPORTS void cartToPolar(const oclMat &x, const oclMat &y, oclMat &magnitude, oclMat &angle, bool angleInDegrees = false);
587 //! converts polar coordinates to Cartesian
588 // supports only CV_32F CV_64F type
589 CV_EXPORTS void polarToCart(const oclMat &magnitude, const oclMat &angle, oclMat &x, oclMat &y, bool angleInDegrees = false);
591 //! perfroms per-elements bit-wise inversion
592 // supports all types
593 CV_EXPORTS void bitwise_not(const oclMat &src, oclMat &dst);
595 //! calculates per-element bit-wise disjunction of two arrays
596 // supports all types
597 CV_EXPORTS void bitwise_or(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
598 CV_EXPORTS void bitwise_or(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
600 //! calculates per-element bit-wise conjunction of two arrays
601 // supports all types
602 CV_EXPORTS void bitwise_and(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
603 CV_EXPORTS void bitwise_and(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
605 //! calculates per-element bit-wise "exclusive or" operation
606 // supports all types
607 CV_EXPORTS void bitwise_xor(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
608 CV_EXPORTS void bitwise_xor(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
610 //! Logical operators
611 CV_EXPORTS oclMat operator ~ (const oclMat &);
612 CV_EXPORTS oclMat operator | (const oclMat &, const oclMat &);
613 CV_EXPORTS oclMat operator & (const oclMat &, const oclMat &);
614 CV_EXPORTS oclMat operator ^ (const oclMat &, const oclMat &);
617 //! Mathematics operators
618 CV_EXPORTS oclMatExpr operator + (const oclMat &src1, const oclMat &src2);
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);
623 //! computes convolution of two images
624 // support only CV_32FC1 type
625 CV_EXPORTS void convolve(const oclMat &image, const oclMat &temp1, oclMat &result);
627 CV_EXPORTS void cvtColor(const oclMat &src, oclMat &dst, int code, int dcn = 0);
629 //! initializes a scaled identity matrix
630 CV_EXPORTS void setIdentity(oclMat& src, const Scalar & val = Scalar(1));
632 //////////////////////////////// Filter Engine ////////////////////////////////
635 The Base Class for 1D or Row-wise Filters
637 This is the base class for linear or non-linear filters that process 1D data.
638 In particular, such filters are used for the "horizontal" filtering parts in separable filters.
640 class CV_EXPORTS BaseRowFilter_GPU
643 BaseRowFilter_GPU(int ksize_, int anchor_, int bordertype_) : ksize(ksize_), anchor(anchor_), bordertype(bordertype_) {}
644 virtual ~BaseRowFilter_GPU() {}
645 virtual void operator()(const oclMat &src, oclMat &dst) = 0;
646 int ksize, anchor, bordertype;
650 The Base Class for Column-wise Filters
652 This is the base class for linear or non-linear filters that process columns of 2D arrays.
653 Such filters are used for the "vertical" filtering parts in separable filters.
655 class CV_EXPORTS BaseColumnFilter_GPU
658 BaseColumnFilter_GPU(int ksize_, int anchor_, int bordertype_) : ksize(ksize_), anchor(anchor_), bordertype(bordertype_) {}
659 virtual ~BaseColumnFilter_GPU() {}
660 virtual void operator()(const oclMat &src, oclMat &dst) = 0;
661 int ksize, anchor, bordertype;
665 The Base Class for Non-Separable 2D Filters.
667 This is the base class for linear or non-linear 2D filters.
669 class CV_EXPORTS BaseFilter_GPU
672 BaseFilter_GPU(const Size &ksize_, const Point &anchor_, const int &borderType_)
673 : ksize(ksize_), anchor(anchor_), borderType(borderType_) {}
674 virtual ~BaseFilter_GPU() {}
675 virtual void operator()(const oclMat &src, oclMat &dst) = 0;
682 The Base Class for Filter Engine.
684 The class can be used to apply an arbitrary filtering operation to an image.
685 It contains all the necessary intermediate buffers.
687 class CV_EXPORTS FilterEngine_GPU
690 virtual ~FilterEngine_GPU() {}
692 virtual void apply(const oclMat &src, oclMat &dst, Rect roi = Rect(0, 0, -1, -1)) = 0;
695 //! returns the non-separable filter engine with the specified filter
696 CV_EXPORTS Ptr<FilterEngine_GPU> createFilter2D_GPU(const Ptr<BaseFilter_GPU> filter2D);
698 //! returns the primitive row filter with the specified kernel
699 CV_EXPORTS Ptr<BaseRowFilter_GPU> getLinearRowFilter_GPU(int srcType, int bufType, const Mat &rowKernel,
700 int anchor = -1, int bordertype = BORDER_DEFAULT);
702 //! returns the primitive column filter with the specified kernel
703 CV_EXPORTS Ptr<BaseColumnFilter_GPU> getLinearColumnFilter_GPU(int bufType, int dstType, const Mat &columnKernel,
704 int anchor = -1, int bordertype = BORDER_DEFAULT, double delta = 0.0);
706 //! returns the separable linear filter engine
707 CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat &rowKernel,
708 const Mat &columnKernel, const Point &anchor = Point(-1, -1), double delta = 0.0, int bordertype = BORDER_DEFAULT);
710 //! returns the separable filter engine with the specified filters
711 CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU> &rowFilter,
712 const Ptr<BaseColumnFilter_GPU> &columnFilter);
714 //! returns the Gaussian filter engine
715 CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, double sigma1, double sigma2 = 0, int bordertype = BORDER_DEFAULT);
717 //! returns filter engine for the generalized Sobel operator
718 CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU( int srcType, int dstType, int dx, int dy, int ksize, int borderType = BORDER_DEFAULT );
720 //! applies Laplacian operator to the image
721 // supports only ksize = 1 and ksize = 3 8UC1 8UC4 32FC1 32FC4 data type
722 CV_EXPORTS void Laplacian(const oclMat &src, oclMat &dst, int ddepth, int ksize = 1, double scale = 1);
724 //! returns 2D box filter
725 // supports CV_8UC1 and CV_8UC4 source type, dst type must be the same as source type
726 CV_EXPORTS Ptr<BaseFilter_GPU> getBoxFilter_GPU(int srcType, int dstType,
727 const Size &ksize, Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
729 //! returns box filter engine
730 CV_EXPORTS Ptr<FilterEngine_GPU> createBoxFilter_GPU(int srcType, int dstType, const Size &ksize,
731 const Point &anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
733 //! returns 2D filter with the specified kernel
734 // supports CV_8UC1 and CV_8UC4 types
735 CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat &kernel, const Size &ksize,
736 const Point &anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
738 //! returns the non-separable linear filter engine
739 CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat &kernel,
740 const Point &anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
742 //! smooths the image using the normalized box filter
743 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
744 // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101,BORDER_WRAP
745 CV_EXPORTS void boxFilter(const oclMat &src, oclMat &dst, int ddepth, Size ksize,
746 Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
748 //! returns 2D morphological filter
749 //! only MORPH_ERODE and MORPH_DILATE are supported
750 // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
751 // kernel must have CV_8UC1 type, one rows and cols == ksize.width * ksize.height
752 CV_EXPORTS Ptr<BaseFilter_GPU> getMorphologyFilter_GPU(int op, int type, const Mat &kernel, const Size &ksize,
753 Point anchor = Point(-1, -1));
755 //! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported.
756 CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat &kernel,
757 const Point &anchor = Point(-1, -1), int iterations = 1);
759 //! a synonym for normalized box filter
760 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
761 // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
762 static inline void blur(const oclMat &src, oclMat &dst, Size ksize, Point anchor = Point(-1, -1),
763 int borderType = BORDER_CONSTANT)
765 boxFilter(src, dst, -1, ksize, anchor, borderType);
768 //! applies non-separable 2D linear filter to the image
769 // Note, at the moment this function only works when anchor point is in the kernel center
770 // and kernel size supported is either 3x3 or 5x5; otherwise the function will fail to output valid result
771 CV_EXPORTS void filter2D(const oclMat &src, oclMat &dst, int ddepth, const Mat &kernel,
772 Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
774 //! applies separable 2D linear filter to the image
775 CV_EXPORTS void sepFilter2D(const oclMat &src, oclMat &dst, int ddepth, const Mat &kernelX, const Mat &kernelY,
776 Point anchor = Point(-1, -1), double delta = 0.0, int bordertype = BORDER_DEFAULT);
778 //! applies generalized Sobel operator to the image
779 // dst.type must equalize src.type
780 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
781 // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
782 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);
784 //! applies the vertical or horizontal Scharr operator to the image
785 // dst.type must equalize src.type
786 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
787 // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
788 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);
790 //! smooths the image using Gaussian filter.
791 // dst.type must equalize src.type
792 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
793 // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
794 CV_EXPORTS void GaussianBlur(const oclMat &src, oclMat &dst, Size ksize, double sigma1, double sigma2 = 0, int bordertype = BORDER_DEFAULT);
796 //! erodes the image (applies the local minimum operator)
797 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
798 CV_EXPORTS void erode( const oclMat &src, oclMat &dst, const Mat &kernel, Point anchor = Point(-1, -1), int iterations = 1,
800 int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue());
803 //! dilates the image (applies the local maximum operator)
804 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
805 CV_EXPORTS void dilate( const oclMat &src, oclMat &dst, const Mat &kernel, Point anchor = Point(-1, -1), int iterations = 1,
807 int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue());
810 //! applies an advanced morphological operation to the image
811 CV_EXPORTS void morphologyEx( const oclMat &src, oclMat &dst, int op, const Mat &kernel, Point anchor = Point(-1, -1), int iterations = 1,
813 int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue());
816 ////////////////////////////// Image processing //////////////////////////////
817 //! Does mean shift filtering on GPU.
818 CV_EXPORTS void meanShiftFiltering(const oclMat &src, oclMat &dst, int sp, int sr,
819 TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
821 //! Does mean shift procedure on GPU.
822 CV_EXPORTS void meanShiftProc(const oclMat &src, oclMat &dstr, oclMat &dstsp, int sp, int sr,
823 TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
825 //! Does mean shift segmentation with elimiation of small regions.
826 CV_EXPORTS void meanShiftSegmentation(const oclMat &src, Mat &dst, int sp, int sr, int minsize,
827 TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
829 //! applies fixed threshold to the image.
830 // supports CV_8UC1 and CV_32FC1 data type
831 // supports threshold type: THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV
832 CV_EXPORTS double threshold(const oclMat &src, oclMat &dst, double thresh, double maxVal, int type = THRESH_TRUNC);
834 //! resizes the image
835 // Supports INTER_NEAREST, INTER_LINEAR
836 // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
837 CV_EXPORTS void resize(const oclMat &src, oclMat &dst, Size dsize, double fx = 0, double fy = 0, int interpolation = INTER_LINEAR);
839 //! Applies a generic geometrical transformation to an image.
841 // Supports INTER_NEAREST, INTER_LINEAR.
842 // Map1 supports CV_16SC2, CV_32FC2 types.
843 // Src supports CV_8UC1, CV_8UC2, CV_8UC4.
844 CV_EXPORTS void remap(const oclMat &src, oclMat &dst, oclMat &map1, oclMat &map2, int interpolation, int bordertype, const Scalar &value = Scalar());
846 //! copies 2D array to a larger destination array and pads borders with user-specifiable constant
847 // supports CV_8UC1, CV_8UC4, CV_32SC1 types
848 CV_EXPORTS void copyMakeBorder(const oclMat &src, oclMat &dst, int top, int bottom, int left, int right, int boardtype, const Scalar &value = Scalar());
850 //! Smoothes image using median filter
851 // The source 1- or 4-channel image. m should be 3 or 5, the image depth should be CV_8U or CV_32F.
852 CV_EXPORTS void medianFilter(const oclMat &src, oclMat &dst, int m);
854 //! warps the image using affine transformation
855 // Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
856 // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
857 CV_EXPORTS void warpAffine(const oclMat &src, oclMat &dst, const Mat &M, Size dsize, int flags = INTER_LINEAR);
859 //! warps the image using perspective transformation
860 // Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
861 // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
862 CV_EXPORTS void warpPerspective(const oclMat &src, oclMat &dst, const Mat &M, Size dsize, int flags = INTER_LINEAR);
864 //! computes the integral image and integral for the squared image
865 // sum will have CV_32S type, sqsum - CV32F type
866 // supports only CV_8UC1 source type
867 CV_EXPORTS void integral(const oclMat &src, oclMat &sum, oclMat &sqsum);
868 CV_EXPORTS void integral(const oclMat &src, oclMat &sum);
869 CV_EXPORTS void cornerHarris(const oclMat &src, oclMat &dst, int blockSize, int ksize, double k, int bordertype = cv::BORDER_DEFAULT);
870 CV_EXPORTS void cornerHarris_dxdy(const oclMat &src, oclMat &dst, oclMat &Dx, oclMat &Dy,
871 int blockSize, int ksize, double k, int bordertype = cv::BORDER_DEFAULT);
872 CV_EXPORTS void cornerMinEigenVal(const oclMat &src, oclMat &dst, int blockSize, int ksize, int bordertype = cv::BORDER_DEFAULT);
873 CV_EXPORTS void cornerMinEigenVal_dxdy(const oclMat &src, oclMat &dst, oclMat &Dx, oclMat &Dy,
874 int blockSize, int ksize, int bordertype = cv::BORDER_DEFAULT);
876 /////////////////////////////////// ML ///////////////////////////////////////////
878 //! Compute closest centers for each lines in source and lable it after center's index
879 // supports CV_32FC1/CV_32FC2/CV_32FC4 data type
880 CV_EXPORTS void distanceToCenters(oclMat &dists, oclMat &labels, const oclMat &src, const oclMat ¢ers);
882 //!Does k-means procedure on GPU
883 // supports CV_32FC1/CV_32FC2/CV_32FC4 data type
884 CV_EXPORTS double kmeans(const oclMat &src, int K, oclMat &bestLabels,
885 TermCriteria criteria, int attemps, int flags, oclMat ¢ers);
888 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
889 ///////////////////////////////////////////CascadeClassifier//////////////////////////////////////////////////////////////////
890 ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
892 class CV_EXPORTS_W OclCascadeClassifier : public cv::CascadeClassifier
895 OclCascadeClassifier() {};
896 ~OclCascadeClassifier() {};
898 CvSeq* oclHaarDetectObjects(oclMat &gimg, CvMemStorage *storage, double scaleFactor,
899 int minNeighbors, int flags, CvSize minSize = cvSize(0, 0), CvSize maxSize = cvSize(0, 0));
902 class CV_EXPORTS OclCascadeClassifierBuf : public cv::CascadeClassifier
905 OclCascadeClassifierBuf() :
906 m_flags(0), initialized(false), m_scaleFactor(0), buffers(NULL) {}
908 ~OclCascadeClassifierBuf() { release(); }
910 void detectMultiScale(oclMat &image, CV_OUT std::vector<cv::Rect>& faces,
911 double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0,
912 Size minSize = Size(), Size maxSize = Size());
916 void Init(const int rows, const int cols, double scaleFactor, int flags,
917 const int outputsz, const size_t localThreads[],
918 CvSize minSize, CvSize maxSize);
919 void CreateBaseBufs(const int datasize, const int totalclassifier, const int flags, const int outputsz);
920 void CreateFactorRelatedBufs(const int rows, const int cols, const int flags,
921 const double scaleFactor, const size_t localThreads[],
922 CvSize minSize, CvSize maxSize);
923 void GenResult(CV_OUT std::vector<cv::Rect>& faces, const std::vector<cv::Rect> &rectList, const std::vector<int> &rweights);
930 bool findBiggestObject;
932 double m_scaleFactor;
935 vector<CvSize> sizev;
936 vector<float> scalev;
937 oclMat gimg1, gsum, gsqsum;
942 /////////////////////////////// Pyramid /////////////////////////////////////
943 CV_EXPORTS void pyrDown(const oclMat &src, oclMat &dst);
945 //! upsamples the source image and then smoothes it
946 CV_EXPORTS void pyrUp(const oclMat &src, oclMat &dst);
948 //! performs linear blending of two images
949 //! to avoid accuracy errors sum of weigths shouldn't be very close to zero
950 // supports only CV_8UC1 source type
951 CV_EXPORTS void blendLinear(const oclMat &img1, const oclMat &img2, const oclMat &weights1, const oclMat &weights2, oclMat &result);
953 //! computes vertical sum, supports only CV_32FC1 images
954 CV_EXPORTS void columnSum(const oclMat &src, oclMat &sum);
956 ///////////////////////////////////////// match_template /////////////////////////////////////////////////////////////
957 struct CV_EXPORTS MatchTemplateBuf
959 Size user_block_size;
960 oclMat imagef, templf;
961 std::vector<oclMat> images;
962 std::vector<oclMat> image_sums;
963 std::vector<oclMat> image_sqsums;
966 //! computes the proximity map for the raster template and the image where the template is searched for
967 // Supports TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED for type 8UC1 and 8UC4
968 // Supports TM_SQDIFF, TM_CCORR for type 32FC1 and 32FC4
969 CV_EXPORTS void matchTemplate(const oclMat &image, const oclMat &templ, oclMat &result, int method);
971 //! computes the proximity map for the raster template and the image where the template is searched for
972 // Supports TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED for type 8UC1 and 8UC4
973 // Supports TM_SQDIFF, TM_CCORR for type 32FC1 and 32FC4
974 CV_EXPORTS void matchTemplate(const oclMat &image, const oclMat &templ, oclMat &result, int method, MatchTemplateBuf &buf);
976 ///////////////////////////////////////////// Canny /////////////////////////////////////////////
977 struct CV_EXPORTS CannyBuf;
978 //! compute edges of the input image using Canny operator
979 // Support CV_8UC1 only
980 CV_EXPORTS void Canny(const oclMat &image, oclMat &edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
981 CV_EXPORTS void Canny(const oclMat &image, CannyBuf &buf, oclMat &edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
982 CV_EXPORTS void Canny(const oclMat &dx, const oclMat &dy, oclMat &edges, double low_thresh, double high_thresh, bool L2gradient = false);
983 CV_EXPORTS void Canny(const oclMat &dx, const oclMat &dy, CannyBuf &buf, oclMat &edges, double low_thresh, double high_thresh, bool L2gradient = false);
985 struct CV_EXPORTS CannyBuf
987 CannyBuf() : counter(NULL) {}
992 explicit CannyBuf(const Size &image_size, int apperture_size = 3) : counter(NULL)
994 create(image_size, apperture_size);
996 CannyBuf(const oclMat &dx_, const oclMat &dy_);
998 void create(const Size &image_size, int apperture_size = 3);
1001 oclMat dx_buf, dy_buf;
1003 oclMat trackBuf1, trackBuf2;
1005 Ptr<FilterEngine_GPU> filterDX, filterDY;
1008 ///////////////////////////////////////// clAmdFft related /////////////////////////////////////////
1009 //! Performs a forward or inverse discrete Fourier transform (1D or 2D) of floating point matrix.
1010 //! Param dft_size is the size of DFT transform.
1012 //! For complex-to-real transform it is assumed that the source matrix is packed in CLFFT's format.
1013 // support src type of CV32FC1, CV32FC2
1014 // support flags: DFT_INVERSE, DFT_REAL_OUTPUT, DFT_COMPLEX_OUTPUT, DFT_ROWS
1015 // dft_size is the size of original input, which is used for transformation from complex to real.
1016 // dft_size must be powers of 2, 3 and 5
1017 // real to complex dft requires at least v1.8 clAmdFft
1018 // real to complex dft output is not the same with cpu version
1019 // real to complex and complex to real does not support DFT_ROWS
1020 CV_EXPORTS void dft(const oclMat &src, oclMat &dst, Size dft_size = Size(), int flags = 0);
1022 //! implements generalized matrix product algorithm GEMM from BLAS
1023 // The functionality requires clAmdBlas library
1024 // only support type CV_32FC1
1025 // flag GEMM_3_T is not supported
1026 CV_EXPORTS void gemm(const oclMat &src1, const oclMat &src2, double alpha,
1027 const oclMat &src3, double beta, oclMat &dst, int flags = 0);
1029 //////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
1030 struct CV_EXPORTS HOGDescriptor
1032 enum { DEFAULT_WIN_SIGMA = -1 };
1033 enum { DEFAULT_NLEVELS = 64 };
1034 enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };
1035 HOGDescriptor(Size win_size = Size(64, 128), Size block_size = Size(16, 16),
1036 Size block_stride = Size(8, 8), Size cell_size = Size(8, 8),
1037 int nbins = 9, double win_sigma = DEFAULT_WIN_SIGMA,
1038 double threshold_L2hys = 0.2, bool gamma_correction = true,
1039 int nlevels = DEFAULT_NLEVELS);
1041 size_t getDescriptorSize() const;
1042 size_t getBlockHistogramSize() const;
1043 void setSVMDetector(const vector<float> &detector);
1044 static vector<float> getDefaultPeopleDetector();
1045 static vector<float> getPeopleDetector48x96();
1046 static vector<float> getPeopleDetector64x128();
1047 void detect(const oclMat &img, vector<Point> &found_locations,
1048 double hit_threshold = 0, Size win_stride = Size(),
1049 Size padding = Size());
1050 void detectMultiScale(const oclMat &img, vector<Rect> &found_locations,
1051 double hit_threshold = 0, Size win_stride = Size(),
1052 Size padding = Size(), double scale0 = 1.05,
1053 int group_threshold = 2);
1054 void getDescriptors(const oclMat &img, Size win_stride,
1055 oclMat &descriptors,
1056 int descr_format = DESCR_FORMAT_COL_BY_COL);
1064 double threshold_L2hys;
1065 bool gamma_correction;
1069 // initialize buffers; only need to do once in case of multiscale detection
1070 void init_buffer(const oclMat &img, Size win_stride);
1071 void computeBlockHistograms(const oclMat &img);
1072 void computeGradient(const oclMat &img, oclMat &grad, oclMat &qangle);
1073 double getWinSigma() const;
1074 bool checkDetectorSize() const;
1076 static int numPartsWithin(int size, int part_size, int stride);
1077 static Size numPartsWithin(Size size, Size part_size, Size stride);
1079 // Coefficients of the separating plane
1082 // Results of the last classification step
1085 // Results of the last histogram evaluation step
1087 // Gradients conputation results
1088 oclMat grad, qangle;
1091 // effect size of input image (might be different from original size after scaling)
1096 ////////////////////////feature2d_ocl/////////////////
1097 /****************************************************************************************\
1099 \****************************************************************************************/
1100 template<typename T>
1101 struct CV_EXPORTS Accumulator
1105 template<> struct Accumulator<unsigned char>
1109 template<> struct Accumulator<unsigned short>
1113 template<> struct Accumulator<char>
1117 template<> struct Accumulator<short>
1123 * Manhattan distance (city block distance) functor
1126 struct CV_EXPORTS L1
1128 enum { normType = NORM_L1 };
1129 typedef T ValueType;
1130 typedef typename Accumulator<T>::Type ResultType;
1132 ResultType operator()( const T *a, const T *b, int size ) const
1134 return normL1<ValueType, ResultType>(a, b, size);
1139 * Euclidean distance functor
1142 struct CV_EXPORTS L2
1144 enum { normType = NORM_L2 };
1145 typedef T ValueType;
1146 typedef typename Accumulator<T>::Type ResultType;
1148 ResultType operator()( const T *a, const T *b, int size ) const
1150 return (ResultType)sqrt((double)normL2Sqr<ValueType, ResultType>(a, b, size));
1155 * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor
1156 * bit count of A exclusive XOR'ed with B
1158 struct CV_EXPORTS Hamming
1160 enum { normType = NORM_HAMMING };
1161 typedef unsigned char ValueType;
1162 typedef int ResultType;
1164 /** this will count the bits in a ^ b
1166 ResultType operator()( const unsigned char *a, const unsigned char *b, int size ) const
1168 return normHamming(a, b, size);
1172 ////////////////////////////////// BruteForceMatcher //////////////////////////////////
1174 class CV_EXPORTS BruteForceMatcher_OCL_base
1177 enum DistType {L1Dist = 0, L2Dist, HammingDist};
1178 explicit BruteForceMatcher_OCL_base(DistType distType = L2Dist);
1179 // Add descriptors to train descriptor collection
1180 void add(const std::vector<oclMat> &descCollection);
1181 // Get train descriptors collection
1182 const std::vector<oclMat> &getTrainDescriptors() const;
1183 // Clear train descriptors collection
1185 // Return true if there are not train descriptors in collection
1188 // Return true if the matcher supports mask in match methods
1189 bool isMaskSupported() const;
1191 // Find one best match for each query descriptor
1192 void matchSingle(const oclMat &query, const oclMat &train,
1193 oclMat &trainIdx, oclMat &distance,
1194 const oclMat &mask = oclMat());
1196 // Download trainIdx and distance and convert it to CPU vector with DMatch
1197 static void matchDownload(const oclMat &trainIdx, const oclMat &distance, std::vector<DMatch> &matches);
1198 // Convert trainIdx and distance to vector with DMatch
1199 static void matchConvert(const Mat &trainIdx, const Mat &distance, std::vector<DMatch> &matches);
1201 // Find one best match for each query descriptor
1202 void match(const oclMat &query, const oclMat &train, std::vector<DMatch> &matches, const oclMat &mask = oclMat());
1204 // Make gpu collection of trains and masks in suitable format for matchCollection function
1205 void makeGpuCollection(oclMat &trainCollection, oclMat &maskCollection, const std::vector<oclMat> &masks = std::vector<oclMat>());
1208 // Find one best match from train collection for each query descriptor
1209 void matchCollection(const oclMat &query, const oclMat &trainCollection,
1210 oclMat &trainIdx, oclMat &imgIdx, oclMat &distance,
1211 const oclMat &masks = oclMat());
1213 // Download trainIdx, imgIdx and distance and convert it to vector with DMatch
1214 static void matchDownload(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance, std::vector<DMatch> &matches);
1215 // Convert trainIdx, imgIdx and distance to vector with DMatch
1216 static void matchConvert(const Mat &trainIdx, const Mat &imgIdx, const Mat &distance, std::vector<DMatch> &matches);
1218 // Find one best match from train collection for each query descriptor.
1219 void match(const oclMat &query, std::vector<DMatch> &matches, const std::vector<oclMat> &masks = std::vector<oclMat>());
1221 // Find k best matches for each query descriptor (in increasing order of distances)
1222 void knnMatchSingle(const oclMat &query, const oclMat &train,
1223 oclMat &trainIdx, oclMat &distance, oclMat &allDist, int k,
1224 const oclMat &mask = oclMat());
1226 // Download trainIdx and distance and convert it to vector with DMatch
1227 // compactResult is used when mask is not empty. If compactResult is false matches
1228 // vector will have the same size as queryDescriptors rows. If compactResult is true
1229 // matches vector will not contain matches for fully masked out query descriptors.
1230 static void knnMatchDownload(const oclMat &trainIdx, const oclMat &distance,
1231 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1233 // Convert trainIdx and distance to vector with DMatch
1234 static void knnMatchConvert(const Mat &trainIdx, const Mat &distance,
1235 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1237 // Find k best matches for each query descriptor (in increasing order of distances).
1238 // compactResult is used when mask is not empty. If compactResult is false matches
1239 // vector will have the same size as queryDescriptors rows. If compactResult is true
1240 // matches vector will not contain matches for fully masked out query descriptors.
1241 void knnMatch(const oclMat &query, const oclMat &train,
1242 std::vector< std::vector<DMatch> > &matches, int k, const oclMat &mask = oclMat(),
1243 bool compactResult = false);
1245 // Find k best matches from train collection for each query descriptor (in increasing order of distances)
1246 void knnMatch2Collection(const oclMat &query, const oclMat &trainCollection,
1247 oclMat &trainIdx, oclMat &imgIdx, oclMat &distance,
1248 const oclMat &maskCollection = oclMat());
1250 // Download trainIdx and distance and convert it to vector with DMatch
1251 // compactResult is used when mask is not empty. If compactResult is false matches
1252 // vector will have the same size as queryDescriptors rows. If compactResult is true
1253 // matches vector will not contain matches for fully masked out query descriptors.
1254 static void knnMatch2Download(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance,
1255 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1257 // Convert trainIdx and distance to vector with DMatch
1258 static void knnMatch2Convert(const Mat &trainIdx, const Mat &imgIdx, const Mat &distance,
1259 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1261 // Find k best matches for each query descriptor (in increasing order of distances).
1262 // compactResult is used when mask is not empty. If compactResult is false matches
1263 // vector will have the same size as queryDescriptors rows. If compactResult is true
1264 // matches vector will not contain matches for fully masked out query descriptors.
1265 void knnMatch(const oclMat &query, std::vector< std::vector<DMatch> > &matches, int k,
1266 const std::vector<oclMat> &masks = std::vector<oclMat>(), bool compactResult = false);
1268 // Find best matches for each query descriptor which have distance less than maxDistance.
1269 // nMatches.at<int>(0, queryIdx) will contain matches count for queryIdx.
1270 // carefully nMatches can be greater than trainIdx.cols - it means that matcher didn't find all matches,
1271 // because it didn't have enough memory.
1272 // If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nTrain / 100), 10),
1273 // otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
1274 // Matches doesn't sorted.
1275 void radiusMatchSingle(const oclMat &query, const oclMat &train,
1276 oclMat &trainIdx, oclMat &distance, oclMat &nMatches, float maxDistance,
1277 const oclMat &mask = oclMat());
1279 // Download trainIdx, nMatches and distance and convert it to vector with DMatch.
1280 // matches will be sorted in increasing order of distances.
1281 // compactResult is used when mask is not empty. If compactResult is false matches
1282 // vector will have the same size as queryDescriptors rows. If compactResult is true
1283 // matches vector will not contain matches for fully masked out query descriptors.
1284 static void radiusMatchDownload(const oclMat &trainIdx, const oclMat &distance, const oclMat &nMatches,
1285 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1286 // Convert trainIdx, nMatches and distance to vector with DMatch.
1287 static void radiusMatchConvert(const Mat &trainIdx, const Mat &distance, const Mat &nMatches,
1288 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1289 // Find best matches for each query descriptor which have distance less than maxDistance
1290 // in increasing order of distances).
1291 void radiusMatch(const oclMat &query, const oclMat &train,
1292 std::vector< std::vector<DMatch> > &matches, float maxDistance,
1293 const oclMat &mask = oclMat(), bool compactResult = false);
1294 // Find best matches for each query descriptor which have distance less than maxDistance.
1295 // If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nQuery / 100), 10),
1296 // otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
1297 // Matches doesn't sorted.
1298 void radiusMatchCollection(const oclMat &query, oclMat &trainIdx, oclMat &imgIdx, oclMat &distance, oclMat &nMatches, float maxDistance,
1299 const std::vector<oclMat> &masks = std::vector<oclMat>());
1300 // Download trainIdx, imgIdx, nMatches and distance and convert it to vector with DMatch.
1301 // matches will be sorted in increasing order of distances.
1302 // compactResult is used when mask is not empty. If compactResult is false matches
1303 // vector will have the same size as queryDescriptors rows. If compactResult is true
1304 // matches vector will not contain matches for fully masked out query descriptors.
1305 static void radiusMatchDownload(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance, const oclMat &nMatches,
1306 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1307 // Convert trainIdx, nMatches and distance to vector with DMatch.
1308 static void radiusMatchConvert(const Mat &trainIdx, const Mat &imgIdx, const Mat &distance, const Mat &nMatches,
1309 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1310 // Find best matches from train collection for each query descriptor which have distance less than
1311 // maxDistance (in increasing order of distances).
1312 void radiusMatch(const oclMat &query, std::vector< std::vector<DMatch> > &matches, float maxDistance,
1313 const std::vector<oclMat> &masks = std::vector<oclMat>(), bool compactResult = false);
1316 std::vector<oclMat> trainDescCollection;
1319 template <class Distance>
1320 class CV_EXPORTS BruteForceMatcher_OCL;
1322 template <typename T>
1323 class CV_EXPORTS BruteForceMatcher_OCL< L1<T> > : public BruteForceMatcher_OCL_base
1326 explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(L1Dist) {}
1327 explicit BruteForceMatcher_OCL(L1<T> /*d*/) : BruteForceMatcher_OCL_base(L1Dist) {}
1330 template <typename T>
1331 class CV_EXPORTS BruteForceMatcher_OCL< L2<T> > : public BruteForceMatcher_OCL_base
1334 explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(L2Dist) {}
1335 explicit BruteForceMatcher_OCL(L2<T> /*d*/) : BruteForceMatcher_OCL_base(L2Dist) {}
1338 template <> class CV_EXPORTS BruteForceMatcher_OCL< Hamming > : public BruteForceMatcher_OCL_base
1341 explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(HammingDist) {}
1342 explicit BruteForceMatcher_OCL(Hamming /*d*/) : BruteForceMatcher_OCL_base(HammingDist) {}
1345 class CV_EXPORTS BFMatcher_OCL : public BruteForceMatcher_OCL_base
1348 explicit BFMatcher_OCL(int norm = NORM_L2) : BruteForceMatcher_OCL_base(norm == NORM_L1 ? L1Dist : norm == NORM_L2 ? L2Dist : HammingDist) {}
1351 class CV_EXPORTS GoodFeaturesToTrackDetector_OCL
1354 explicit GoodFeaturesToTrackDetector_OCL(int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 0.0,
1355 int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04);
1357 //! return 1 rows matrix with CV_32FC2 type
1358 void operator ()(const oclMat& image, oclMat& corners, const oclMat& mask = oclMat());
1359 //! download points of type Point2f to a vector. the vector's content will be erased
1360 void downloadPoints(const oclMat &points, vector<Point2f> &points_v);
1363 double qualityLevel;
1367 bool useHarrisDetector;
1369 void releaseMemory()
1374 minMaxbuf_.release();
1375 tmpCorners_.release();
1385 inline GoodFeaturesToTrackDetector_OCL::GoodFeaturesToTrackDetector_OCL(int maxCorners_, double qualityLevel_, double minDistance_,
1386 int blockSize_, bool useHarrisDetector_, double harrisK_)
1388 maxCorners = maxCorners_;
1389 qualityLevel = qualityLevel_;
1390 minDistance = minDistance_;
1391 blockSize = blockSize_;
1392 useHarrisDetector = useHarrisDetector_;
1396 /////////////////////////////// PyrLKOpticalFlow /////////////////////////////////////
1397 class CV_EXPORTS PyrLKOpticalFlow
1402 winSize = Size(21, 21);
1406 useInitialFlow = false;
1407 minEigThreshold = 1e-4f;
1408 getMinEigenVals = false;
1409 isDeviceArch11_ = false;
1412 void sparse(const oclMat &prevImg, const oclMat &nextImg, const oclMat &prevPts, oclMat &nextPts,
1413 oclMat &status, oclMat *err = 0);
1414 void dense(const oclMat &prevImg, const oclMat &nextImg, oclMat &u, oclMat &v, oclMat *err = 0);
1419 bool useInitialFlow;
1420 float minEigThreshold;
1421 bool getMinEigenVals;
1422 void releaseMemory()
1424 dx_calcBuf_.release();
1425 dy_calcBuf_.release();
1434 void calcSharrDeriv(const oclMat &src, oclMat &dx, oclMat &dy);
1435 void buildImagePyramid(const oclMat &img0, vector<oclMat> &pyr, bool withBorder);
1440 vector<oclMat> prevPyr_;
1441 vector<oclMat> nextPyr_;
1447 bool isDeviceArch11_;
1450 class CV_EXPORTS FarnebackOpticalFlow
1453 FarnebackOpticalFlow();
1464 void operator ()(const oclMat &frame0, const oclMat &frame1, oclMat &flowx, oclMat &flowy);
1466 void releaseMemory();
1469 void prepareGaussian(
1470 int n, double sigma, float *g, float *xg, float *xxg,
1471 double &ig11, double &ig03, double &ig33, double &ig55);
1473 void setPolynomialExpansionConsts(int n, double sigma);
1475 void updateFlow_boxFilter(
1476 const oclMat& R0, const oclMat& R1, oclMat& flowx, oclMat &flowy,
1477 oclMat& M, oclMat &bufM, int blockSize, bool updateMatrices);
1479 void updateFlow_gaussianBlur(
1480 const oclMat& R0, const oclMat& R1, oclMat& flowx, oclMat& flowy,
1481 oclMat& M, oclMat &bufM, int blockSize, bool updateMatrices);
1484 oclMat pyrLevel_[2], M_, bufM_, R_[2], blurredFrame_[2];
1485 std::vector<oclMat> pyramid0_, pyramid1_;
1488 //////////////// build warping maps ////////////////////
1489 //! builds plane warping maps
1490 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);
1491 //! builds cylindrical warping maps
1492 CV_EXPORTS void buildWarpCylindricalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat &R, float scale, oclMat &map_x, oclMat &map_y);
1493 //! builds spherical warping maps
1494 CV_EXPORTS void buildWarpSphericalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat &R, float scale, oclMat &map_x, oclMat &map_y);
1495 //! builds Affine warping maps
1496 CV_EXPORTS void buildWarpAffineMaps(const Mat &M, bool inverse, Size dsize, oclMat &xmap, oclMat &ymap);
1498 //! builds Perspective warping maps
1499 CV_EXPORTS void buildWarpPerspectiveMaps(const Mat &M, bool inverse, Size dsize, oclMat &xmap, oclMat &ymap);
1501 ///////////////////////////////////// interpolate frames //////////////////////////////////////////////
1502 //! Interpolate frames (images) using provided optical flow (displacement field).
1503 //! frame0 - frame 0 (32-bit floating point images, single channel)
1504 //! frame1 - frame 1 (the same type and size)
1505 //! fu - forward horizontal displacement
1506 //! fv - forward vertical displacement
1507 //! bu - backward horizontal displacement
1508 //! bv - backward vertical displacement
1509 //! pos - new frame position
1510 //! newFrame - new frame
1511 //! buf - temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 oclMat;
1512 //! occlusion masks 0, occlusion masks 1,
1513 //! interpolated forward flow 0, interpolated forward flow 1,
1514 //! interpolated backward flow 0, interpolated backward flow 1
1516 CV_EXPORTS void interpolateFrames(const oclMat &frame0, const oclMat &frame1,
1517 const oclMat &fu, const oclMat &fv,
1518 const oclMat &bu, const oclMat &bv,
1519 float pos, oclMat &newFrame, oclMat &buf);
1521 //! computes moments of the rasterized shape or a vector of points
1522 CV_EXPORTS Moments ocl_moments(InputArray _array, bool binaryImage);
1524 class CV_EXPORTS StereoBM_OCL
1527 enum { BASIC_PRESET = 0, PREFILTER_XSOBEL = 1 };
1529 enum { DEFAULT_NDISP = 64, DEFAULT_WINSZ = 19 };
1531 //! the default constructor
1533 //! the full constructor taking the camera-specific preset, number of disparities and the SAD window size. ndisparities must be multiple of 8.
1534 StereoBM_OCL(int preset, int ndisparities = DEFAULT_NDISP, int winSize = DEFAULT_WINSZ);
1536 //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair
1537 //! Output disparity has CV_8U type.
1538 void operator() ( const oclMat &left, const oclMat &right, oclMat &disparity);
1540 //! Some heuristics that tries to estmate
1541 // if current GPU will be faster then CPU in this algorithm.
1542 // It queries current active device.
1543 static bool checkIfGpuCallReasonable();
1549 // If avergeTexThreshold == 0 => post procesing is disabled
1550 // If avergeTexThreshold != 0 then disparity is set 0 in each point (x,y) where for left image
1551 // SumOfHorizontalGradiensInWindow(x, y, winSize) < (winSize * winSize) * avergeTexThreshold
1552 // i.e. input left image is low textured.
1553 float avergeTexThreshold;
1555 oclMat minSSD, leBuf, riBuf;
1558 class CV_EXPORTS StereoBeliefPropagation
1561 enum { DEFAULT_NDISP = 64 };
1562 enum { DEFAULT_ITERS = 5 };
1563 enum { DEFAULT_LEVELS = 5 };
1564 static void estimateRecommendedParams(int width, int height, int &ndisp, int &iters, int &levels);
1565 explicit StereoBeliefPropagation(int ndisp = DEFAULT_NDISP,
1566 int iters = DEFAULT_ITERS,
1567 int levels = DEFAULT_LEVELS,
1568 int msg_type = CV_16S);
1569 StereoBeliefPropagation(int ndisp, int iters, int levels,
1570 float max_data_term, float data_weight,
1571 float max_disc_term, float disc_single_jump,
1572 int msg_type = CV_32F);
1573 void operator()(const oclMat &left, const oclMat &right, oclMat &disparity);
1574 void operator()(const oclMat &data, oclMat &disparity);
1578 float max_data_term;
1580 float max_disc_term;
1581 float disc_single_jump;
1584 oclMat u, d, l, r, u2, d2, l2, r2;
1585 std::vector<oclMat> datas;
1589 class CV_EXPORTS StereoConstantSpaceBP
1592 enum { DEFAULT_NDISP = 128 };
1593 enum { DEFAULT_ITERS = 8 };
1594 enum { DEFAULT_LEVELS = 4 };
1595 enum { DEFAULT_NR_PLANE = 4 };
1596 static void estimateRecommendedParams(int width, int height, int &ndisp, int &iters, int &levels, int &nr_plane);
1597 explicit StereoConstantSpaceBP(
1598 int ndisp = DEFAULT_NDISP,
1599 int iters = DEFAULT_ITERS,
1600 int levels = DEFAULT_LEVELS,
1601 int nr_plane = DEFAULT_NR_PLANE,
1602 int msg_type = CV_32F);
1603 StereoConstantSpaceBP(int ndisp, int iters, int levels, int nr_plane,
1604 float max_data_term, float data_weight, float max_disc_term, float disc_single_jump,
1605 int min_disp_th = 0,
1606 int msg_type = CV_32F);
1607 void operator()(const oclMat &left, const oclMat &right, oclMat &disparity);
1612 float max_data_term;
1614 float max_disc_term;
1615 float disc_single_jump;
1618 bool use_local_init_data_cost;
1620 oclMat u[2], d[2], l[2], r[2];
1621 oclMat disp_selected_pyr[2];
1623 oclMat data_cost_selected;
1628 // Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method
1631 // [1] C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
1632 // [2] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
1633 class CV_EXPORTS OpticalFlowDual_TVL1_OCL
1636 OpticalFlowDual_TVL1_OCL();
1638 void operator ()(const oclMat& I0, const oclMat& I1, oclMat& flowx, oclMat& flowy);
1640 void collectGarbage();
1643 * Time step of the numerical scheme.
1648 * Weight parameter for the data term, attachment parameter.
1649 * This is the most relevant parameter, which determines the smoothness of the output.
1650 * The smaller this parameter is, the smoother the solutions we obtain.
1651 * It depends on the range of motions of the images, so its value should be adapted to each image sequence.
1656 * Weight parameter for (u - v)^2, tightness parameter.
1657 * It serves as a link between the attachment and the regularization terms.
1658 * In theory, it should have a small value in order to maintain both parts in correspondence.
1659 * The method is stable for a large range of values of this parameter.
1664 * Number of scales used to create the pyramid of images.
1669 * Number of warpings per scale.
1670 * Represents the number of times that I1(x+u0) and grad( I1(x+u0) ) are computed per scale.
1671 * This is a parameter that assures the stability of the method.
1672 * It also affects the running time, so it is a compromise between speed and accuracy.
1677 * Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time.
1678 * A small value will yield more accurate solutions at the expense of a slower convergence.
1683 * Stopping criterion iterations number used in the numerical scheme.
1687 bool useInitialFlow;
1690 void procOneScale(const oclMat& I0, const oclMat& I1, oclMat& u1, oclMat& u2);
1692 std::vector<oclMat> I0s;
1693 std::vector<oclMat> I1s;
1694 std::vector<oclMat> u1s;
1695 std::vector<oclMat> u2s;
1715 // current supported sorting methods
1718 SORT_BITONIC, // only support power-of-2 buffer size
1719 SORT_SELECTION, // cannot sort duplicate keys
1721 SORT_RADIX // only support signed int/float keys(CV_32S/CV_32F)
1723 //! Returns the sorted result of all the elements in input based on equivalent keys.
1725 // The element unit in the values to be sorted is determined from the data type,
1726 // i.e., a CV_32FC2 input {a1a2, b1b2} will be considered as two elements, regardless its
1727 // matrix dimension.
1728 // both keys and values will be sorted inplace
1729 // Key needs to be single channel oclMat.
1733 // keys = {2, 3, 1} (CV_8UC1)
1734 // values = {10,5, 4,3, 6,2} (CV_8UC2)
1735 // sortByKey(keys, values, SORT_SELECTION, false);
1737 // keys = {1, 2, 3} (CV_8UC1)
1738 // values = {6,2, 10,5, 4,3} (CV_8UC2)
1739 CV_EXPORTS void sortByKey(oclMat& keys, oclMat& values, int method, bool isGreaterThan = false);
1740 /*!Base class for MOG and MOG2!*/
1741 class CV_EXPORTS BackgroundSubtractor
1744 //! the virtual destructor
1745 virtual ~BackgroundSubtractor();
1746 //! the update operator that takes the next video frame and returns the current foreground mask as 8-bit binary image.
1747 virtual void operator()(const oclMat& image, oclMat& fgmask, float learningRate);
1749 //! computes a background image
1750 virtual void getBackgroundImage(oclMat& backgroundImage) const = 0;
1753 Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm
1755 The class implements the following algorithm:
1756 "An improved adaptive background mixture model for real-time tracking with shadow detection"
1757 P. KadewTraKuPong and R. Bowden,
1758 Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
1759 http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
1761 class CV_EXPORTS MOG: public cv::ocl::BackgroundSubtractor
1764 //! the default constructor
1765 MOG(int nmixtures = -1);
1767 //! re-initiaization method
1768 void initialize(Size frameSize, int frameType);
1770 //! the update operator
1771 void operator()(const oclMat& frame, oclMat& fgmask, float learningRate = 0.f);
1773 //! computes a background image which are the mean of all background gaussians
1774 void getBackgroundImage(oclMat& backgroundImage) const;
1776 //! releases all inner buffers
1781 float backgroundRatio;
1798 The class implements the following algorithm:
1799 "Improved adaptive Gausian mixture model for background subtraction"
1801 International Conference Pattern Recognition, UK, August, 2004.
1802 http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf
1804 class CV_EXPORTS MOG2: public cv::ocl::BackgroundSubtractor
1807 //! the default constructor
1808 MOG2(int nmixtures = -1);
1810 //! re-initiaization method
1811 void initialize(Size frameSize, int frameType);
1813 //! the update operator
1814 void operator()(const oclMat& frame, oclMat& fgmask, float learningRate = -1.0f);
1816 //! computes a background image which are the mean of all background gaussians
1817 void getBackgroundImage(oclMat& backgroundImage) const;
1819 //! releases all inner buffers
1823 // you should call initialize after parameters changes
1827 //! here it is the maximum allowed number of mixture components.
1828 //! Actual number is determined dynamically per pixel
1830 // threshold on the squared Mahalanobis distance to decide if it is well described
1831 // by the background model or not. Related to Cthr from the paper.
1832 // This does not influence the update of the background. A typical value could be 4 sigma
1833 // and that is varThreshold=4*4=16; Corresponds to Tb in the paper.
1835 /////////////////////////
1836 // less important parameters - things you might change but be carefull
1837 ////////////////////////
1839 float backgroundRatio;
1840 // corresponds to fTB=1-cf from the paper
1841 // TB - threshold when the component becomes significant enough to be included into
1842 // the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0.
1843 // For alpha=0.001 it means that the mode should exist for approximately 105 frames before
1844 // it is considered foreground
1845 // float noiseSigma;
1846 float varThresholdGen;
1848 //correspondts to Tg - threshold on the squared Mahalan. dist. to decide
1849 //when a sample is close to the existing components. If it is not close
1850 //to any a new component will be generated. I use 3 sigma => Tg=3*3=9.
1851 //Smaller Tg leads to more generated components and higher Tg might make
1852 //lead to small number of components but they can grow too large
1857 //initial variance for the newly generated components.
1858 //It will will influence the speed of adaptation. A good guess should be made.
1859 //A simple way is to estimate the typical standard deviation from the images.
1860 //I used here 10 as a reasonable value
1861 // min and max can be used to further control the variance
1862 float fCT; //CT - complexity reduction prior
1863 //this is related to the number of samples needed to accept that a component
1864 //actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get
1865 //the standard Stauffer&Grimson algorithm (maybe not exact but very similar)
1867 //shadow detection parameters
1868 bool bShadowDetection; //default 1 - do shadow detection
1869 unsigned char nShadowDetection; //do shadow detection - insert this value as the detection result - 127 default value
1871 // Tau - shadow threshold. The shadow is detected if the pixel is darker
1872 //version of the background. Tau is a threshold on how much darker the shadow can be.
1873 //Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow
1874 //See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.
1887 oclMat bgmodelUsedModes_; //keep track of number of modes per pixel
1890 /*!***************Kalman Filter*************!*/
1891 class CV_EXPORTS KalmanFilter
1895 //! the full constructor taking the dimensionality of the state, of the measurement and of the control vector
1896 KalmanFilter(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F);
1897 //! re-initializes Kalman filter. The previous content is destroyed.
1898 void init(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F);
1900 const oclMat& predict(const oclMat& control=oclMat());
1901 const oclMat& correct(const oclMat& measurement);
1903 oclMat statePre; //!< predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
1904 oclMat statePost; //!< corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
1905 oclMat transitionMatrix; //!< state transition matrix (A)
1906 oclMat controlMatrix; //!< control matrix (B) (not used if there is no control)
1907 oclMat measurementMatrix; //!< measurement matrix (H)
1908 oclMat processNoiseCov; //!< process noise covariance matrix (Q)
1909 oclMat measurementNoiseCov;//!< measurement noise covariance matrix (R)
1910 oclMat errorCovPre; //!< priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/
1911 oclMat gain; //!< Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
1912 oclMat errorCovPost; //!< posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
1921 /*!***************K Nearest Neighbour*************!*/
1922 class CV_EXPORTS KNearestNeighbour: public CvKNearest
1925 KNearestNeighbour();
1926 ~KNearestNeighbour();
1928 bool train(const Mat& trainData, Mat& labels, Mat& sampleIdx = Mat().setTo(Scalar::all(0)),
1929 bool isRegression = false, int max_k = 32, bool updateBase = false);
1933 void find_nearest(const oclMat& samples, int k, oclMat& lables);
1939 /*!*************** SVM *************!*/
1940 class CV_EXPORTS CvSVM_OCL : public CvSVM
1945 CvSVM_OCL(const cv::Mat& trainData, const cv::Mat& responses,
1946 const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
1947 CvSVMParams params=CvSVMParams());
1948 CV_WRAP float predict( const int row_index, Mat& src, bool returnDFVal=false ) const;
1949 CV_WRAP void predict( cv::InputArray samples, cv::OutputArray results ) const;
1950 CV_WRAP float predict( const cv::Mat& sample, bool returnDFVal=false ) const;
1951 float predict( const CvMat* samples, CV_OUT CvMat* results ) const;
1954 float predict( const int row_index, int row_len, Mat& src, bool returnDFVal=false ) const;
1955 void create_kernel();
1956 void create_solver();
1959 /*!*************** END *************!*/
1962 #if defined _MSC_VER && _MSC_VER >= 1200
1963 # pragma warning( push)
1964 # pragma warning( disable: 4267)
1966 #include "opencv2/ocl/matrix_operations.hpp"
1967 #if defined _MSC_VER && _MSC_VER >= 1200
1968 # pragma warning( pop)
1971 #endif /* __OPENCV_OCL_HPP__ */