1 /*M///////////////////////////////////////////////////////////////////////////////////////
3 // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
5 // By downloading, copying, installing or using the software you agree to this license.
6 // If you do not agree to this license, do not download, install,
7 // copy or use the software.
11 // For Open Source Computer Vision Library
13 // Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved.
14 // Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
15 // Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
16 // Third party copyrights are property of their respective owners.
18 // Redistribution and use in source and binary forms, with or without modification,
19 // are permitted provided that the following conditions are met:
21 // * Redistribution's of source code must retain the above copyright notice,
22 // this list of conditions and the following disclaimer.
24 // * Redistribution's in binary form must reproduce the above copyright notice,
25 // this list of conditions and the following disclaimer in the documentation
26 // and/or other oclMaterials provided with the distribution.
28 // * The name of the copyright holders may not be used to endorse or promote products
29 // derived from this software without specific prior written permission.
31 // This software is provided by the copyright holders and contributors "as is" and
32 // any express or implied warranties, including, but not limited to, the implied
33 // warranties of merchantability and fitness for a particular purpose are disclaimed.
34 // In no event shall the Intel Corporation or contributors be liable for any direct,
35 // indirect, incidental, special, exemplary, or consequential damages
36 // (including, but not limited to, procurement of substitute goods or services;
37 // loss of use, data, or profits; or business interruption) however caused
38 // and on any theory of liability, whether in contract, strict liability,
39 // or tort (including negligence or otherwise) arising in any way out of
40 // the use of this software, even if advised of the possibility of such damage.
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
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);
155 FEATURE_CL_DOUBLE = 1,
156 FEATURE_CL_UNIFIED_MEM,
160 // Represents OpenCL context, interface
161 class CV_EXPORTS Context
167 static Context *getContext();
169 bool supportsFeature(FEATURE_TYPE featureType) const;
170 const DeviceInfo& getDeviceInfo() const;
172 const void* getOpenCLContextPtr() const;
173 const void* getOpenCLCommandQueuePtr() const;
174 const void* getOpenCLDeviceIDPtr() const;
177 inline const void *getClContextPtr()
179 return Context::getContext()->getOpenCLContextPtr();
182 inline const void *getClCommandQueuePtr()
184 return Context::getContext()->getOpenCLCommandQueuePtr();
187 CV_EXPORTS bool supportsFeature(FEATURE_TYPE featureType);
189 CV_EXPORTS void finish();
191 enum BINARY_CACHE_MODE
193 CACHE_NONE = 0, // do not cache OpenCL binary
194 CACHE_DEBUG = 0x1 << 0, // cache OpenCL binary when built in debug mode
195 CACHE_RELEASE = 0x1 << 1, // default behavior, only cache when built in release mode
196 CACHE_ALL = CACHE_DEBUG | CACHE_RELEASE, // cache opencl binary
198 //! Enable or disable OpenCL program binary caching onto local disk
199 // After a program (*.cl files in opencl/ folder) is built at runtime, we allow the
200 // compiled OpenCL program to be cached to the path automatically as "path/*.clb"
201 // binary file, which will be reused when the OpenCV executable is started again.
203 // This feature is enabled by default.
204 CV_EXPORTS void setBinaryDiskCache(int mode = CACHE_RELEASE, cv::String path = "./");
206 //! set where binary cache to be saved to
207 CV_EXPORTS void setBinaryPath(const char *path);
212 const char* programStr;
213 const char* programHash;
215 // Cache in memory by name (should be unique). Caching on disk disabled.
216 inline ProgramSource(const char* _name, const char* _programStr)
217 : name(_name), programStr(_programStr), programHash(NULL)
221 // Cache in memory by name (should be unique). Caching on disk uses programHash mark.
222 inline ProgramSource(const char* _name, const char* _programStr, const char* _programHash)
223 : name(_name), programStr(_programStr), programHash(_programHash)
228 //! Calls OpenCL kernel. Pass globalThreads = NULL, and cleanUp = true, to finally clean-up without executing.
229 //! Deprecated, will be replaced
230 CV_EXPORTS void openCLExecuteKernelInterop(Context *clCxt,
231 const cv::ocl::ProgramSource& source, String kernelName,
232 size_t globalThreads[3], size_t localThreads[3],
233 std::vector< std::pair<size_t, const void *> > &args,
234 int channels, int depth, const char *build_options);
236 class CV_EXPORTS oclMatExpr;
237 //////////////////////////////// oclMat ////////////////////////////////
238 class CV_EXPORTS oclMat
241 //! default constructor
243 //! constructs oclMatrix of the specified size and type (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.)
244 oclMat(int rows, int cols, int type);
245 oclMat(Size size, int type);
246 //! constucts oclMatrix and fills it with the specified value _s.
247 oclMat(int rows, int cols, int type, const Scalar &s);
248 oclMat(Size size, int type, const Scalar &s);
250 oclMat(const oclMat &m);
252 //! constructor for oclMatrix headers pointing to user-allocated data
253 oclMat(int rows, int cols, int type, void *data, size_t step = Mat::AUTO_STEP);
254 oclMat(Size size, int type, void *data, size_t step = Mat::AUTO_STEP);
256 //! creates a matrix header for a part of the bigger matrix
257 oclMat(const oclMat &m, const Range &rowRange, const Range &colRange);
258 oclMat(const oclMat &m, const Rect &roi);
260 //! builds oclMat from Mat. Perfom blocking upload to device.
261 explicit oclMat (const Mat &m);
263 //! destructor - calls release()
266 //! assignment operators
267 oclMat &operator = (const oclMat &m);
268 //! assignment operator. Perfom blocking upload to device.
269 oclMat &operator = (const Mat &m);
270 oclMat &operator = (const oclMatExpr& expr);
272 //! pefroms blocking upload data to oclMat.
273 void upload(const cv::Mat &m);
276 //! downloads data from device to host memory. Blocking calls.
277 operator Mat() const;
278 void download(cv::Mat &m) const;
280 //! convert to _InputArray
281 operator _InputArray();
283 //! convert to _OutputArray
284 operator _OutputArray();
286 //! returns a new oclMatrix header for the specified row
287 oclMat row(int y) const;
288 //! returns a new oclMatrix header for the specified column
289 oclMat col(int x) const;
290 //! ... for the specified row span
291 oclMat rowRange(int startrow, int endrow) const;
292 oclMat rowRange(const Range &r) const;
293 //! ... for the specified column span
294 oclMat colRange(int startcol, int endcol) const;
295 oclMat colRange(const Range &r) const;
297 //! returns deep copy of the oclMatrix, i.e. the data is copied
298 oclMat clone() const;
300 //! copies those oclMatrix elements to "m" that are marked with non-zero mask elements.
301 // It calls m.create(this->size(), this->type()).
302 // It supports any data type
303 void copyTo( oclMat &m, const oclMat &mask = oclMat()) const;
305 //! converts oclMatrix to another datatype with optional scalng. See cvConvertScale.
306 //It supports 8UC1 8UC4 32SC1 32SC4 32FC1 32FC4
307 void convertTo( oclMat &m, int rtype, double alpha = 1, double beta = 0 ) const;
309 void assignTo( oclMat &m, int type = -1 ) const;
311 //! sets every oclMatrix element to s
312 //It supports 8UC1 8UC4 32SC1 32SC4 32FC1 32FC4
313 oclMat& operator = (const Scalar &s);
314 //! sets some of the oclMatrix elements to s, according to the mask
315 //It supports 8UC1 8UC4 32SC1 32SC4 32FC1 32FC4
316 oclMat& setTo(const Scalar &s, const oclMat &mask = oclMat());
317 //! creates alternative oclMatrix header for the same data, with different
318 // number of channels and/or different number of rows. see cvReshape.
319 oclMat reshape(int cn, int rows = 0) const;
321 //! allocates new oclMatrix data unless the oclMatrix already has specified size and type.
322 // previous data is unreferenced if needed.
323 void create(int rows, int cols, int type);
324 void create(Size size, int type);
326 //! allocates new oclMatrix with specified device memory type.
327 void createEx(int rows, int cols, int type,
328 DevMemRW rw_type, DevMemType mem_type);
329 void createEx(Size size, int type, DevMemRW rw_type,
330 DevMemType mem_type);
332 //! decreases reference counter;
333 // deallocate the data when reference counter reaches 0.
336 //! swaps with other smart pointer
337 void swap(oclMat &mat);
339 //! locates oclMatrix header within a parent oclMatrix. See below
340 void locateROI( Size &wholeSize, Point &ofs ) const;
341 //! moves/resizes the current oclMatrix ROI inside the parent oclMatrix.
342 oclMat& adjustROI( int dtop, int dbottom, int dleft, int dright );
343 //! extracts a rectangular sub-oclMatrix
344 // (this is a generalized form of row, rowRange etc.)
345 oclMat operator()( Range rowRange, Range colRange ) const;
346 oclMat operator()( const Rect &roi ) const;
348 oclMat& operator+=( const oclMat& m );
349 oclMat& operator-=( const oclMat& m );
350 oclMat& operator*=( const oclMat& m );
351 oclMat& operator/=( const oclMat& m );
353 //! returns true if the oclMatrix data is continuous
354 // (i.e. when there are no gaps between successive rows).
355 // similar to CV_IS_oclMat_CONT(cvoclMat->type)
356 bool isContinuous() const;
357 //! returns element size in bytes,
358 // similar to CV_ELEM_SIZE(cvMat->type)
359 size_t elemSize() const;
360 //! returns the size of element channel in bytes.
361 size_t elemSize1() const;
362 //! returns element type, similar to CV_MAT_TYPE(cvMat->type)
364 //! returns element type, i.e. 8UC3 returns 8UC4 because in ocl
365 //! 3 channels element actually use 4 channel space
367 //! returns element type, similar to CV_MAT_DEPTH(cvMat->type)
369 //! returns element type, similar to CV_MAT_CN(cvMat->type)
370 int channels() const;
371 //! returns element type, return 4 for 3 channels element,
372 //!becuase 3 channels element actually use 4 channel space
373 int oclchannels() const;
374 //! returns step/elemSize1()
375 size_t step1() const;
376 //! returns oclMatrix size:
377 // width == number of columns, height == number of rows
379 //! returns true if oclMatrix data is NULL
382 //! returns pointer to y-th row
383 uchar* ptr(int y = 0);
384 const uchar *ptr(int y = 0) const;
386 //! template version of the above method
387 template<typename _Tp> _Tp *ptr(int y = 0);
388 template<typename _Tp> const _Tp *ptr(int y = 0) const;
390 //! matrix transposition
393 /*! includes several bit-fields:
394 - the magic signature
400 //! the number of rows and columns
402 //! a distance between successive rows in bytes; includes the gap if any
404 //! pointer to the data(OCL memory object)
407 //! pointer to the reference counter;
408 // when oclMatrix points to user-allocated data, the pointer is NULL
411 //! helper fields used in locateROI and adjustROI
412 //datastart and dataend are not used in current version
416 //! OpenCL context associated with the oclMat object.
417 Context *clCxt; // TODO clCtx
418 //add offset for handle ROI, calculated in byte
420 //add wholerows and wholecols for the whole matrix, datastart and dataend are no longer used
425 // convert InputArray/OutputArray to oclMat references
426 CV_EXPORTS oclMat& getOclMatRef(InputArray src);
427 CV_EXPORTS oclMat& getOclMatRef(OutputArray src);
429 ///////////////////// mat split and merge /////////////////////////////////
430 //! Compose a multi-channel array from several single-channel arrays
432 CV_EXPORTS void merge(const oclMat *src, size_t n, oclMat &dst);
433 CV_EXPORTS void merge(const std::vector<oclMat> &src, oclMat &dst);
435 //! Divides multi-channel array into several single-channel arrays
437 CV_EXPORTS void split(const oclMat &src, oclMat *dst);
438 CV_EXPORTS void split(const oclMat &src, std::vector<oclMat> &dst);
440 ////////////////////////////// Arithmetics ///////////////////////////////////
442 //! adds one matrix to another with scale (dst = src1 * alpha + src2 * beta + gama)
443 // supports all data types
444 CV_EXPORTS void addWeighted(const oclMat &src1, double alpha, const oclMat &src2, double beta, double gama, oclMat &dst);
446 //! adds one matrix to another (dst = src1 + src2)
447 // supports all data types
448 CV_EXPORTS void add(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
449 //! adds scalar to a matrix (dst = src1 + s)
450 // supports all data types
451 CV_EXPORTS void add(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
453 //! subtracts one matrix from another (dst = src1 - src2)
454 // supports all data types
455 CV_EXPORTS void subtract(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
456 //! subtracts scalar from a matrix (dst = src1 - s)
457 // supports all data types
458 CV_EXPORTS void subtract(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
460 //! computes element-wise product of the two arrays (dst = src1 * scale * src2)
461 // supports all data types
462 CV_EXPORTS void multiply(const oclMat &src1, const oclMat &src2, oclMat &dst, double scale = 1);
463 //! multiplies matrix to a number (dst = scalar * src)
464 // supports all data types
465 CV_EXPORTS void multiply(double scalar, const oclMat &src, oclMat &dst);
467 //! computes element-wise quotient of the two arrays (dst = src1 * scale / src2)
468 // supports all data types
469 CV_EXPORTS void divide(const oclMat &src1, const oclMat &src2, oclMat &dst, double scale = 1);
470 //! computes element-wise quotient of the two arrays (dst = scale / src)
471 // supports all data types
472 CV_EXPORTS void divide(double scale, const oclMat &src1, oclMat &dst);
474 //! computes element-wise minimum of the two arrays (dst = min(src1, src2))
475 // supports all data types
476 CV_EXPORTS void min(const oclMat &src1, const oclMat &src2, oclMat &dst);
478 //! computes element-wise maximum of the two arrays (dst = max(src1, src2))
479 // supports all data types
480 CV_EXPORTS void max(const oclMat &src1, const oclMat &src2, oclMat &dst);
482 //! compares elements of two arrays (dst = src1 <cmpop> src2)
483 // supports all data types
484 CV_EXPORTS void compare(const oclMat &src1, const oclMat &src2, oclMat &dst, int cmpop);
486 //! transposes the matrix
487 // supports all data types
488 CV_EXPORTS void transpose(const oclMat &src, oclMat &dst);
490 //! computes element-wise absolute values of an array (dst = abs(src))
491 // supports all data types
492 CV_EXPORTS void abs(const oclMat &src, oclMat &dst);
494 //! computes element-wise absolute difference of two arrays (dst = abs(src1 - src2))
495 // supports all data types
496 CV_EXPORTS void absdiff(const oclMat &src1, const oclMat &src2, oclMat &dst);
497 //! computes element-wise absolute difference of array and scalar (dst = abs(src1 - s))
498 // supports all data types
499 CV_EXPORTS void absdiff(const oclMat &src1, const Scalar &s, oclMat &dst);
501 //! computes mean value and standard deviation of all or selected array elements
502 // supports all data types
503 CV_EXPORTS void meanStdDev(const oclMat &mtx, Scalar &mean, Scalar &stddev);
505 //! computes norm of array
506 // supports NORM_INF, NORM_L1, NORM_L2
507 // supports all data types
508 CV_EXPORTS double norm(const oclMat &src1, int normType = NORM_L2);
510 //! computes norm of the difference between two arrays
511 // supports NORM_INF, NORM_L1, NORM_L2
512 // supports all data types
513 CV_EXPORTS double norm(const oclMat &src1, const oclMat &src2, int normType = NORM_L2);
515 //! reverses the order of the rows, columns or both in a matrix
516 // supports all types
517 CV_EXPORTS void flip(const oclMat &src, oclMat &dst, int flipCode);
519 //! computes sum of array elements
521 CV_EXPORTS Scalar sum(const oclMat &m);
522 CV_EXPORTS Scalar absSum(const oclMat &m);
523 CV_EXPORTS Scalar sqrSum(const oclMat &m);
525 //! finds global minimum and maximum array elements and returns their values
526 // support all C1 types
527 CV_EXPORTS void minMax(const oclMat &src, double *minVal, double *maxVal = 0, const oclMat &mask = oclMat());
529 //! finds global minimum and maximum array elements and returns their values with locations
530 // support all C1 types
531 CV_EXPORTS void minMaxLoc(const oclMat &src, double *minVal, double *maxVal = 0, Point *minLoc = 0, Point *maxLoc = 0,
532 const oclMat &mask = oclMat());
534 //! counts non-zero array elements
536 CV_EXPORTS int countNonZero(const oclMat &src);
538 //! transforms 8-bit unsigned integers using lookup table: dst(i)=lut(src(i))
539 // destination array will have the depth type as lut and the same channels number as source
540 //It supports 8UC1 8UC4 only
541 CV_EXPORTS void LUT(const oclMat &src, const oclMat &lut, oclMat &dst);
543 //! only 8UC1 and 256 bins is supported now
544 CV_EXPORTS void calcHist(const oclMat &mat_src, oclMat &mat_hist);
545 //! only 8UC1 and 256 bins is supported now
546 CV_EXPORTS void equalizeHist(const oclMat &mat_src, oclMat &mat_dst);
548 //! only 8UC1 is supported now
549 CV_EXPORTS Ptr<cv::CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8));
552 // supports 8UC1 8UC4
553 CV_EXPORTS void bilateralFilter(const oclMat& src, oclMat& dst, int d, double sigmaColor, double sigmaSpace, int borderType=BORDER_DEFAULT);
555 //! Applies an adaptive bilateral filter to the input image
556 // This is not truly a bilateral filter. Instead of using user provided fixed parameters,
557 // the function calculates a constant at each window based on local standard deviation,
558 // and use this constant to do filtering.
559 // supports 8UC1, 8UC3
560 CV_EXPORTS void adaptiveBilateralFilter(const oclMat& src, oclMat& dst, Size ksize, double sigmaSpace, Point anchor = Point(-1, -1), int borderType=BORDER_DEFAULT);
562 //! computes exponent of each matrix element (dst = e**src)
563 // supports only CV_32FC1, CV_64FC1 type
564 CV_EXPORTS void exp(const oclMat &src, oclMat &dst);
566 //! computes natural logarithm of absolute value of each matrix element: dst = log(abs(src))
567 // supports only CV_32FC1, CV_64FC1 type
568 CV_EXPORTS void log(const oclMat &src, oclMat &dst);
570 //! computes magnitude of each (x(i), y(i)) vector
571 // supports only CV_32F, CV_64F type
572 CV_EXPORTS void magnitude(const oclMat &x, const oclMat &y, oclMat &magnitude);
574 //! computes angle (angle(i)) of each (x(i), y(i)) vector
575 // supports only CV_32F, CV_64F type
576 CV_EXPORTS void phase(const oclMat &x, const oclMat &y, oclMat &angle, bool angleInDegrees = false);
578 //! the function raises every element of tne input array to p
579 // support only CV_32F, CV_64F type
580 CV_EXPORTS void pow(const oclMat &x, double p, oclMat &y);
582 //! converts Cartesian coordinates to polar
583 // supports only CV_32F CV_64F type
584 CV_EXPORTS void cartToPolar(const oclMat &x, const oclMat &y, oclMat &magnitude, oclMat &angle, bool angleInDegrees = false);
586 //! converts polar coordinates to Cartesian
587 // supports only CV_32F CV_64F type
588 CV_EXPORTS void polarToCart(const oclMat &magnitude, const oclMat &angle, oclMat &x, oclMat &y, bool angleInDegrees = false);
590 //! perfroms per-elements bit-wise inversion
591 // supports all types
592 CV_EXPORTS void bitwise_not(const oclMat &src, oclMat &dst);
594 //! calculates per-element bit-wise disjunction of two arrays
595 // supports all types
596 CV_EXPORTS void bitwise_or(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
597 CV_EXPORTS void bitwise_or(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
599 //! calculates per-element bit-wise conjunction of two arrays
600 // supports all types
601 CV_EXPORTS void bitwise_and(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
602 CV_EXPORTS void bitwise_and(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
604 //! calculates per-element bit-wise "exclusive or" operation
605 // supports all types
606 CV_EXPORTS void bitwise_xor(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
607 CV_EXPORTS void bitwise_xor(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
609 //! Logical operators
610 CV_EXPORTS oclMat operator ~ (const oclMat &);
611 CV_EXPORTS oclMat operator | (const oclMat &, const oclMat &);
612 CV_EXPORTS oclMat operator & (const oclMat &, const oclMat &);
613 CV_EXPORTS oclMat operator ^ (const oclMat &, const oclMat &);
616 //! Mathematics operators
617 CV_EXPORTS oclMatExpr operator + (const oclMat &src1, const oclMat &src2);
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);
622 struct CV_EXPORTS ConvolveBuf
626 Size user_block_size;
629 oclMat image_spect, templ_spect, result_spect;
630 oclMat image_block, templ_block, result_data;
632 void create(Size image_size, Size templ_size);
633 static Size estimateBlockSize(Size result_size, Size templ_size);
636 //! computes convolution of two images, may use discrete Fourier transform
637 // support only CV_32FC1 type
638 CV_EXPORTS void convolve(const oclMat &image, const oclMat &temp1, oclMat &result, bool ccorr = false);
639 CV_EXPORTS void convolve(const oclMat &image, const oclMat &temp1, oclMat &result, bool ccorr, ConvolveBuf& buf);
641 //! Performs a per-element multiplication of two Fourier spectrums.
642 //! Only full (not packed) CV_32FC2 complex spectrums in the interleaved format are supported for now.
643 //! support only CV_32FC2 type
644 CV_EXPORTS void mulSpectrums(const oclMat &a, const oclMat &b, oclMat &c, int flags, float scale, bool conjB = false);
646 CV_EXPORTS void cvtColor(const oclMat &src, oclMat &dst, int code, int dcn = 0);
648 //! initializes a scaled identity matrix
649 CV_EXPORTS void setIdentity(oclMat& src, const Scalar & val = Scalar(1));
651 //////////////////////////////// Filter Engine ////////////////////////////////
654 The Base Class for 1D or Row-wise Filters
656 This is the base class for linear or non-linear filters that process 1D data.
657 In particular, such filters are used for the "horizontal" filtering parts in separable filters.
659 class CV_EXPORTS BaseRowFilter_GPU
662 BaseRowFilter_GPU(int ksize_, int anchor_, int bordertype_) : ksize(ksize_), anchor(anchor_), bordertype(bordertype_) {}
663 virtual ~BaseRowFilter_GPU() {}
664 virtual void operator()(const oclMat &src, oclMat &dst) = 0;
665 int ksize, anchor, bordertype;
669 The Base Class for Column-wise Filters
671 This is the base class for linear or non-linear filters that process columns of 2D arrays.
672 Such filters are used for the "vertical" filtering parts in separable filters.
674 class CV_EXPORTS BaseColumnFilter_GPU
677 BaseColumnFilter_GPU(int ksize_, int anchor_, int bordertype_) : ksize(ksize_), anchor(anchor_), bordertype(bordertype_) {}
678 virtual ~BaseColumnFilter_GPU() {}
679 virtual void operator()(const oclMat &src, oclMat &dst) = 0;
680 int ksize, anchor, bordertype;
684 The Base Class for Non-Separable 2D Filters.
686 This is the base class for linear or non-linear 2D filters.
688 class CV_EXPORTS BaseFilter_GPU
691 BaseFilter_GPU(const Size &ksize_, const Point &anchor_, const int &borderType_)
692 : ksize(ksize_), anchor(anchor_), borderType(borderType_) {}
693 virtual ~BaseFilter_GPU() {}
694 virtual void operator()(const oclMat &src, oclMat &dst) = 0;
701 The Base Class for Filter Engine.
703 The class can be used to apply an arbitrary filtering operation to an image.
704 It contains all the necessary intermediate buffers.
706 class CV_EXPORTS FilterEngine_GPU
709 virtual ~FilterEngine_GPU() {}
711 virtual void apply(const oclMat &src, oclMat &dst, Rect roi = Rect(0, 0, -1, -1)) = 0;
714 //! returns the non-separable filter engine with the specified filter
715 CV_EXPORTS Ptr<FilterEngine_GPU> createFilter2D_GPU(const Ptr<BaseFilter_GPU> filter2D);
717 //! returns the primitive row filter with the specified kernel
718 CV_EXPORTS Ptr<BaseRowFilter_GPU> getLinearRowFilter_GPU(int srcType, int bufType, const Mat &rowKernel,
719 int anchor = -1, int bordertype = BORDER_DEFAULT);
721 //! returns the primitive column filter with the specified kernel
722 CV_EXPORTS Ptr<BaseColumnFilter_GPU> getLinearColumnFilter_GPU(int bufType, int dstType, const Mat &columnKernel,
723 int anchor = -1, int bordertype = BORDER_DEFAULT, double delta = 0.0);
725 //! returns the separable linear filter engine
726 CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat &rowKernel,
727 const Mat &columnKernel, const Point &anchor = Point(-1, -1), double delta = 0.0, int bordertype = BORDER_DEFAULT);
729 //! returns the separable filter engine with the specified filters
730 CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU> &rowFilter,
731 const Ptr<BaseColumnFilter_GPU> &columnFilter);
733 //! returns the Gaussian filter engine
734 CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, double sigma1, double sigma2 = 0, int bordertype = BORDER_DEFAULT);
736 //! returns filter engine for the generalized Sobel operator
737 CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU( int srcType, int dstType, int dx, int dy, int ksize, int borderType = BORDER_DEFAULT );
739 //! applies Laplacian operator to the image
740 // supports only ksize = 1 and ksize = 3 8UC1 8UC4 32FC1 32FC4 data type
741 CV_EXPORTS void Laplacian(const oclMat &src, oclMat &dst, int ddepth, int ksize = 1, double scale = 1);
743 //! returns 2D box filter
744 // supports CV_8UC1 and CV_8UC4 source type, dst type must be the same as source type
745 CV_EXPORTS Ptr<BaseFilter_GPU> getBoxFilter_GPU(int srcType, int dstType,
746 const Size &ksize, Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
748 //! returns box filter engine
749 CV_EXPORTS Ptr<FilterEngine_GPU> createBoxFilter_GPU(int srcType, int dstType, const Size &ksize,
750 const Point &anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
752 //! returns 2D filter with the specified kernel
753 // supports CV_8UC1 and CV_8UC4 types
754 CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat &kernel, const Size &ksize,
755 const Point &anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
757 //! returns the non-separable linear filter engine
758 CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat &kernel,
759 const Point &anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
761 //! smooths the image using the normalized box filter
762 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
763 // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101,BORDER_WRAP
764 CV_EXPORTS void boxFilter(const oclMat &src, oclMat &dst, int ddepth, Size ksize,
765 Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
767 //! returns 2D morphological filter
768 //! only MORPH_ERODE and MORPH_DILATE are supported
769 // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
770 // kernel must have CV_8UC1 type, one rows and cols == ksize.width * ksize.height
771 CV_EXPORTS Ptr<BaseFilter_GPU> getMorphologyFilter_GPU(int op, int type, const Mat &kernel, const Size &ksize,
772 Point anchor = Point(-1, -1));
774 //! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported.
775 CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat &kernel,
776 const Point &anchor = Point(-1, -1), int iterations = 1);
778 //! a synonym for normalized box filter
779 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
780 // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
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 // Note, at the moment this function only works when anchor point is in the kernel center
789 // and kernel size supported is either 3x3 or 5x5; otherwise the function will fail to output valid result
790 CV_EXPORTS void filter2D(const oclMat &src, oclMat &dst, int ddepth, const Mat &kernel,
791 Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
793 //! applies separable 2D linear filter to the image
794 CV_EXPORTS void sepFilter2D(const oclMat &src, oclMat &dst, int ddepth, const Mat &kernelX, const Mat &kernelY,
795 Point anchor = Point(-1, -1), double delta = 0.0, int bordertype = BORDER_DEFAULT);
797 //! applies generalized Sobel operator to the image
798 // dst.type must equalize src.type
799 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
800 // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
801 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);
803 //! applies the vertical or horizontal Scharr operator to the image
804 // dst.type must equalize src.type
805 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
806 // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
807 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);
809 //! smooths the image using Gaussian filter.
810 // dst.type must equalize src.type
811 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
812 // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
813 CV_EXPORTS void GaussianBlur(const oclMat &src, oclMat &dst, Size ksize, double sigma1, double sigma2 = 0, int bordertype = BORDER_DEFAULT);
815 //! erodes the image (applies the local minimum operator)
816 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
817 CV_EXPORTS void erode( const oclMat &src, oclMat &dst, const Mat &kernel, Point anchor = Point(-1, -1), int iterations = 1,
819 int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue());
822 //! dilates the image (applies the local maximum operator)
823 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
824 CV_EXPORTS void dilate( const oclMat &src, oclMat &dst, const Mat &kernel, Point anchor = Point(-1, -1), int iterations = 1,
826 int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue());
829 //! applies an advanced morphological operation to the image
830 CV_EXPORTS void morphologyEx( const oclMat &src, oclMat &dst, int op, const Mat &kernel, Point anchor = Point(-1, -1), int iterations = 1,
832 int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue());
835 ////////////////////////////// Image processing //////////////////////////////
836 //! Does mean shift filtering on GPU.
837 CV_EXPORTS void meanShiftFiltering(const oclMat &src, oclMat &dst, int sp, int sr,
838 TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
840 //! Does mean shift procedure on GPU.
841 CV_EXPORTS void meanShiftProc(const oclMat &src, oclMat &dstr, oclMat &dstsp, int sp, int sr,
842 TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
844 //! Does mean shift segmentation with elimiation of small regions.
845 CV_EXPORTS void meanShiftSegmentation(const oclMat &src, Mat &dst, int sp, int sr, int minsize,
846 TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
848 //! applies fixed threshold to the image.
849 // supports CV_8UC1 and CV_32FC1 data type
850 // supports threshold type: THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV
851 CV_EXPORTS double threshold(const oclMat &src, oclMat &dst, double thresh, double maxVal, int type = THRESH_TRUNC);
853 //! resizes the image
854 // Supports INTER_NEAREST, INTER_LINEAR
855 // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
856 CV_EXPORTS void resize(const oclMat &src, oclMat &dst, Size dsize, double fx = 0, double fy = 0, int interpolation = INTER_LINEAR);
858 //! Applies a generic geometrical transformation to an image.
860 // Supports INTER_NEAREST, INTER_LINEAR.
861 // Map1 supports CV_16SC2, CV_32FC2 types.
862 // Src supports CV_8UC1, CV_8UC2, CV_8UC4.
863 CV_EXPORTS void remap(const oclMat &src, oclMat &dst, oclMat &map1, oclMat &map2, int interpolation, int bordertype, const Scalar &value = Scalar());
865 //! copies 2D array to a larger destination array and pads borders with user-specifiable constant
866 // supports CV_8UC1, CV_8UC4, CV_32SC1 types
867 CV_EXPORTS void copyMakeBorder(const oclMat &src, oclMat &dst, int top, int bottom, int left, int right, int boardtype, const Scalar &value = Scalar());
869 //! Smoothes image using median filter
870 // The source 1- or 4-channel image. m should be 3 or 5, the image depth should be CV_8U or CV_32F.
871 CV_EXPORTS void medianFilter(const oclMat &src, oclMat &dst, int m);
873 //! warps the image using affine transformation
874 // Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
875 // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
876 CV_EXPORTS void warpAffine(const oclMat &src, oclMat &dst, const Mat &M, Size dsize, int flags = INTER_LINEAR);
878 //! warps the image using perspective transformation
879 // Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
880 // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
881 CV_EXPORTS void warpPerspective(const oclMat &src, oclMat &dst, const Mat &M, Size dsize, int flags = INTER_LINEAR);
883 //! computes the integral image and integral for the squared image
884 // sum will have CV_32S type, sqsum - CV32F type
885 // supports only CV_8UC1 source type
886 CV_EXPORTS void integral(const oclMat &src, oclMat &sum, oclMat &sqsum);
887 CV_EXPORTS void integral(const oclMat &src, oclMat &sum);
888 CV_EXPORTS void cornerHarris(const oclMat &src, oclMat &dst, int blockSize, int ksize, double k, int bordertype = cv::BORDER_DEFAULT);
889 CV_EXPORTS void cornerHarris_dxdy(const oclMat &src, oclMat &dst, oclMat &Dx, oclMat &Dy,
890 int blockSize, int ksize, double k, int bordertype = cv::BORDER_DEFAULT);
891 CV_EXPORTS void cornerMinEigenVal(const oclMat &src, oclMat &dst, int blockSize, int ksize, int bordertype = cv::BORDER_DEFAULT);
892 CV_EXPORTS void cornerMinEigenVal_dxdy(const oclMat &src, oclMat &dst, oclMat &Dx, oclMat &Dy,
893 int blockSize, int ksize, int bordertype = cv::BORDER_DEFAULT);
896 /////////////////////////////////// ML ///////////////////////////////////////////
898 //! Compute closest centers for each lines in source and lable it after center's index
899 // supports CV_32FC1/CV_32FC2/CV_32FC4 data type
900 CV_EXPORTS void distanceToCenters(oclMat &dists, oclMat &labels, const oclMat &src, const oclMat ¢ers);
902 //!Does k-means procedure on GPU
903 // supports CV_32FC1/CV_32FC2/CV_32FC4 data type
904 CV_EXPORTS double kmeans(const oclMat &src, int K, oclMat &bestLabels,
905 TermCriteria criteria, int attemps, int flags, oclMat ¢ers);
908 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
909 ///////////////////////////////////////////CascadeClassifier//////////////////////////////////////////////////////////////////
910 ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
911 class CV_EXPORTS OclCascadeClassifier : public cv::CascadeClassifier
914 void detectMultiScale(oclMat &image, CV_OUT std::vector<cv::Rect>& faces,
915 double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0,
916 Size minSize = Size(), Size maxSize = Size());
919 /////////////////////////////// Pyramid /////////////////////////////////////
920 CV_EXPORTS void pyrDown(const oclMat &src, oclMat &dst);
922 //! upsamples the source image and then smoothes it
923 CV_EXPORTS void pyrUp(const oclMat &src, oclMat &dst);
925 //! performs linear blending of two images
926 //! to avoid accuracy errors sum of weigths shouldn't be very close to zero
927 // supports only CV_8UC1 source type
928 CV_EXPORTS void blendLinear(const oclMat &img1, const oclMat &img2, const oclMat &weights1, const oclMat &weights2, oclMat &result);
930 //! computes vertical sum, supports only CV_32FC1 images
931 CV_EXPORTS void columnSum(const oclMat &src, oclMat &sum);
933 ///////////////////////////////////////// match_template /////////////////////////////////////////////////////////////
934 struct CV_EXPORTS MatchTemplateBuf
936 Size user_block_size;
937 oclMat imagef, templf;
938 std::vector<oclMat> images;
939 std::vector<oclMat> image_sums;
940 std::vector<oclMat> image_sqsums;
943 //! computes the proximity map for the raster template and the image where the template is searched for
944 // Supports TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED for type 8UC1 and 8UC4
945 // Supports TM_SQDIFF, TM_CCORR for type 32FC1 and 32FC4
946 CV_EXPORTS void matchTemplate(const oclMat &image, const oclMat &templ, oclMat &result, int method);
948 //! computes the proximity map for the raster template and the image where the template is searched for
949 // Supports TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED for type 8UC1 and 8UC4
950 // Supports TM_SQDIFF, TM_CCORR for type 32FC1 and 32FC4
951 CV_EXPORTS void matchTemplate(const oclMat &image, const oclMat &templ, oclMat &result, int method, MatchTemplateBuf &buf);
955 ///////////////////////////////////////////// Canny /////////////////////////////////////////////
956 struct CV_EXPORTS CannyBuf;
958 //! compute edges of the input image using Canny operator
959 // Support CV_8UC1 only
960 CV_EXPORTS void Canny(const oclMat &image, oclMat &edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
961 CV_EXPORTS void Canny(const oclMat &image, CannyBuf &buf, oclMat &edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
962 CV_EXPORTS void Canny(const oclMat &dx, const oclMat &dy, oclMat &edges, double low_thresh, double high_thresh, bool L2gradient = false);
963 CV_EXPORTS void Canny(const oclMat &dx, const oclMat &dy, CannyBuf &buf, oclMat &edges, double low_thresh, double high_thresh, bool L2gradient = false);
965 struct CV_EXPORTS CannyBuf
967 CannyBuf() : counter(NULL) {}
972 explicit CannyBuf(const Size &image_size, int apperture_size = 3) : counter(NULL)
974 create(image_size, apperture_size);
976 CannyBuf(const oclMat &dx_, const oclMat &dy_);
977 void create(const Size &image_size, int apperture_size = 3);
981 oclMat dx_buf, dy_buf;
982 oclMat magBuf, mapBuf;
983 oclMat trackBuf1, trackBuf2;
985 Ptr<FilterEngine_GPU> filterDX, filterDY;
988 ///////////////////////////////////////// Hough Transform /////////////////////////////////////////
990 struct HoughCirclesBuf
999 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);
1000 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);
1001 CV_EXPORTS void HoughCirclesDownload(const oclMat& d_circles, OutputArray h_circles);
1004 ///////////////////////////////////////// clAmdFft related /////////////////////////////////////////
1005 //! Performs a forward or inverse discrete Fourier transform (1D or 2D) of floating point matrix.
1006 //! Param dft_size is the size of DFT transform.
1008 //! For complex-to-real transform it is assumed that the source matrix is packed in CLFFT's format.
1009 // support src type of CV32FC1, CV32FC2
1010 // support flags: DFT_INVERSE, DFT_REAL_OUTPUT, DFT_COMPLEX_OUTPUT, DFT_ROWS
1011 // dft_size is the size of original input, which is used for transformation from complex to real.
1012 // dft_size must be powers of 2, 3 and 5
1013 // real to complex dft requires at least v1.8 clAmdFft
1014 // real to complex dft output is not the same with cpu version
1015 // real to complex and complex to real does not support DFT_ROWS
1016 CV_EXPORTS void dft(const oclMat &src, oclMat &dst, Size dft_size = Size(), int flags = 0);
1018 //! implements generalized matrix product algorithm GEMM from BLAS
1019 // The functionality requires clAmdBlas library
1020 // only support type CV_32FC1
1021 // flag GEMM_3_T is not supported
1022 CV_EXPORTS void gemm(const oclMat &src1, const oclMat &src2, double alpha,
1023 const oclMat &src3, double beta, oclMat &dst, int flags = 0);
1025 //////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
1027 struct CV_EXPORTS HOGDescriptor
1031 enum { DEFAULT_WIN_SIGMA = -1 };
1033 enum { DEFAULT_NLEVELS = 64 };
1035 enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };
1039 HOGDescriptor(Size win_size = Size(64, 128), Size block_size = Size(16, 16),
1041 Size block_stride = Size(8, 8), Size cell_size = Size(8, 8),
1043 int nbins = 9, double win_sigma = DEFAULT_WIN_SIGMA,
1045 double threshold_L2hys = 0.2, bool gamma_correction = true,
1047 int nlevels = DEFAULT_NLEVELS);
1051 size_t getDescriptorSize() const;
1053 size_t getBlockHistogramSize() const;
1057 void setSVMDetector(const std::vector<float> &detector);
1061 static std::vector<float> getDefaultPeopleDetector();
1063 static std::vector<float> getPeopleDetector48x96();
1065 static std::vector<float> getPeopleDetector64x128();
1069 void detect(const oclMat &img, std::vector<Point> &found_locations,
1071 double hit_threshold = 0, Size win_stride = Size(),
1073 Size padding = Size());
1077 void detectMultiScale(const oclMat &img, std::vector<Rect> &found_locations,
1079 double hit_threshold = 0, Size win_stride = Size(),
1081 Size padding = Size(), double scale0 = 1.05,
1083 int group_threshold = 2);
1087 void getDescriptors(const oclMat &img, Size win_stride,
1089 oclMat &descriptors,
1091 int descr_format = DESCR_FORMAT_COL_BY_COL);
1107 double threshold_L2hys;
1109 bool gamma_correction;
1117 // initialize buffers; only need to do once in case of multiscale detection
1119 void init_buffer(const oclMat &img, Size win_stride);
1123 void computeBlockHistograms(const oclMat &img);
1125 void computeGradient(const oclMat &img, oclMat &grad, oclMat &qangle);
1129 double getWinSigma() const;
1131 bool checkDetectorSize() const;
1135 static int numPartsWithin(int size, int part_size, int stride);
1137 static Size numPartsWithin(Size size, Size part_size, Size stride);
1141 // Coefficients of the separating plane
1149 // Results of the last classification step
1157 // Results of the last histogram evaluation step
1163 // Gradients conputation results
1165 oclMat grad, qangle;
1175 // effect size of input image (might be different from original size after scaling)
1182 ////////////////////////feature2d_ocl/////////////////
1183 /****************************************************************************************\
1185 \****************************************************************************************/
1186 template<typename T>
1187 struct CV_EXPORTS Accumulator
1191 template<> struct Accumulator<unsigned char>
1195 template<> struct Accumulator<unsigned short>
1199 template<> struct Accumulator<char>
1203 template<> struct Accumulator<short>
1209 * Manhattan distance (city block distance) functor
1212 struct CV_EXPORTS L1
1214 enum { normType = NORM_L1 };
1215 typedef T ValueType;
1216 typedef typename Accumulator<T>::Type ResultType;
1218 ResultType operator()( const T *a, const T *b, int size ) const
1220 return normL1<ValueType, ResultType>(a, b, size);
1225 * Euclidean distance functor
1228 struct CV_EXPORTS L2
1230 enum { normType = NORM_L2 };
1231 typedef T ValueType;
1232 typedef typename Accumulator<T>::Type ResultType;
1234 ResultType operator()( const T *a, const T *b, int size ) const
1236 return (ResultType)std::sqrt((double)normL2Sqr<ValueType, ResultType>(a, b, size));
1241 * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor
1242 * bit count of A exclusive XOR'ed with B
1244 struct CV_EXPORTS Hamming
1246 enum { normType = NORM_HAMMING };
1247 typedef unsigned char ValueType;
1248 typedef int ResultType;
1250 /** this will count the bits in a ^ b
1252 ResultType operator()( const unsigned char *a, const unsigned char *b, int size ) const
1254 return normHamming(a, b, size);
1258 ////////////////////////////////// BruteForceMatcher //////////////////////////////////
1260 class CV_EXPORTS BruteForceMatcher_OCL_base
1263 enum DistType {L1Dist = 0, L2Dist, HammingDist};
1264 explicit BruteForceMatcher_OCL_base(DistType distType = L2Dist);
1266 // Add descriptors to train descriptor collection
1267 void add(const std::vector<oclMat> &descCollection);
1269 // Get train descriptors collection
1270 const std::vector<oclMat> &getTrainDescriptors() const;
1272 // Clear train descriptors collection
1275 // Return true if there are not train descriptors in collection
1278 // Return true if the matcher supports mask in match methods
1279 bool isMaskSupported() const;
1281 // Find one best match for each query descriptor
1282 void matchSingle(const oclMat &query, const oclMat &train,
1283 oclMat &trainIdx, oclMat &distance,
1284 const oclMat &mask = oclMat());
1286 // Download trainIdx and distance and convert it to CPU vector with DMatch
1287 static void matchDownload(const oclMat &trainIdx, const oclMat &distance, std::vector<DMatch> &matches);
1288 // Convert trainIdx and distance to vector with DMatch
1289 static void matchConvert(const Mat &trainIdx, const Mat &distance, std::vector<DMatch> &matches);
1291 // Find one best match for each query descriptor
1292 void match(const oclMat &query, const oclMat &train, std::vector<DMatch> &matches, const oclMat &mask = oclMat());
1294 // Make gpu collection of trains and masks in suitable format for matchCollection function
1295 void makeGpuCollection(oclMat &trainCollection, oclMat &maskCollection, const std::vector<oclMat> &masks = std::vector<oclMat>());
1297 // Find one best match from train collection for each query descriptor
1298 void matchCollection(const oclMat &query, const oclMat &trainCollection,
1299 oclMat &trainIdx, oclMat &imgIdx, oclMat &distance,
1300 const oclMat &masks = oclMat());
1302 // Download trainIdx, imgIdx and distance and convert it to vector with DMatch
1303 static void matchDownload(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance, std::vector<DMatch> &matches);
1304 // Convert trainIdx, imgIdx and distance to vector with DMatch
1305 static void matchConvert(const Mat &trainIdx, const Mat &imgIdx, const Mat &distance, std::vector<DMatch> &matches);
1307 // Find one best match from train collection for each query descriptor.
1308 void match(const oclMat &query, std::vector<DMatch> &matches, const std::vector<oclMat> &masks = std::vector<oclMat>());
1310 // Find k best matches for each query descriptor (in increasing order of distances)
1311 void knnMatchSingle(const oclMat &query, const oclMat &train,
1312 oclMat &trainIdx, oclMat &distance, oclMat &allDist, int k,
1313 const oclMat &mask = oclMat());
1315 // Download trainIdx and distance and convert it to vector with DMatch
1316 // compactResult is used when mask is not empty. If compactResult is false matches
1317 // vector will have the same size as queryDescriptors rows. If compactResult is true
1318 // matches vector will not contain matches for fully masked out query descriptors.
1319 static void knnMatchDownload(const oclMat &trainIdx, const oclMat &distance,
1320 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1321 // Convert trainIdx and distance to vector with DMatch
1322 static void knnMatchConvert(const Mat &trainIdx, const Mat &distance,
1323 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1325 // Find k best matches for each query descriptor (in increasing order of distances).
1326 // compactResult is used when mask is not empty. If compactResult is false matches
1327 // vector will have the same size as queryDescriptors rows. If compactResult is true
1328 // matches vector will not contain matches for fully masked out query descriptors.
1329 void knnMatch(const oclMat &query, const oclMat &train,
1330 std::vector< std::vector<DMatch> > &matches, int k, const oclMat &mask = oclMat(),
1331 bool compactResult = false);
1333 // Find k best matches from train collection for each query descriptor (in increasing order of distances)
1334 void knnMatch2Collection(const oclMat &query, const oclMat &trainCollection,
1335 oclMat &trainIdx, oclMat &imgIdx, oclMat &distance,
1336 const oclMat &maskCollection = oclMat());
1338 // Download trainIdx and distance and convert it to vector with DMatch
1339 // compactResult is used when mask is not empty. If compactResult is false matches
1340 // vector will have the same size as queryDescriptors rows. If compactResult is true
1341 // matches vector will not contain matches for fully masked out query descriptors.
1342 static void knnMatch2Download(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance,
1343 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1344 // Convert trainIdx and distance to vector with DMatch
1345 static void knnMatch2Convert(const Mat &trainIdx, const Mat &imgIdx, const Mat &distance,
1346 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1348 // Find k best matches for each query descriptor (in increasing order of distances).
1349 // compactResult is used when mask is not empty. If compactResult is false matches
1350 // vector will have the same size as queryDescriptors rows. If compactResult is true
1351 // matches vector will not contain matches for fully masked out query descriptors.
1352 void knnMatch(const oclMat &query, std::vector< std::vector<DMatch> > &matches, int k,
1353 const std::vector<oclMat> &masks = std::vector<oclMat>(), bool compactResult = false);
1355 // Find best matches for each query descriptor which have distance less than maxDistance.
1356 // nMatches.at<int>(0, queryIdx) will contain matches count for queryIdx.
1357 // carefully nMatches can be greater than trainIdx.cols - it means that matcher didn't find all matches,
1358 // because it didn't have enough memory.
1359 // If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nTrain / 100), 10),
1360 // otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
1361 // Matches doesn't sorted.
1362 void radiusMatchSingle(const oclMat &query, const oclMat &train,
1363 oclMat &trainIdx, oclMat &distance, oclMat &nMatches, float maxDistance,
1364 const oclMat &mask = oclMat());
1366 // Download trainIdx, nMatches and distance and convert it to vector with DMatch.
1367 // matches will be sorted in increasing order of distances.
1368 // compactResult is used when mask is not empty. If compactResult is false matches
1369 // vector will have the same size as queryDescriptors rows. If compactResult is true
1370 // matches vector will not contain matches for fully masked out query descriptors.
1371 static void radiusMatchDownload(const oclMat &trainIdx, const oclMat &distance, const oclMat &nMatches,
1372 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1373 // Convert trainIdx, nMatches and distance to vector with DMatch.
1374 static void radiusMatchConvert(const Mat &trainIdx, const Mat &distance, const Mat &nMatches,
1375 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1377 // Find best matches for each query descriptor which have distance less than maxDistance
1378 // in increasing order of distances).
1379 void radiusMatch(const oclMat &query, const oclMat &train,
1380 std::vector< std::vector<DMatch> > &matches, float maxDistance,
1381 const oclMat &mask = oclMat(), bool compactResult = false);
1383 // Find best matches for each query descriptor which have distance less than maxDistance.
1384 // If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nQuery / 100), 10),
1385 // otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
1386 // Matches doesn't sorted.
1387 void radiusMatchCollection(const oclMat &query, oclMat &trainIdx, oclMat &imgIdx, oclMat &distance, oclMat &nMatches, float maxDistance,
1388 const std::vector<oclMat> &masks = std::vector<oclMat>());
1390 // Download trainIdx, imgIdx, nMatches and distance and convert it to vector with DMatch.
1391 // matches will be sorted in increasing order of distances.
1392 // compactResult is used when mask is not empty. If compactResult is false matches
1393 // vector will have the same size as queryDescriptors rows. If compactResult is true
1394 // matches vector will not contain matches for fully masked out query descriptors.
1395 static void radiusMatchDownload(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance, const oclMat &nMatches,
1396 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1397 // Convert trainIdx, nMatches and distance to vector with DMatch.
1398 static void radiusMatchConvert(const Mat &trainIdx, const Mat &imgIdx, const Mat &distance, const Mat &nMatches,
1399 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1401 // Find best matches from train collection for each query descriptor which have distance less than
1402 // maxDistance (in increasing order of distances).
1403 void radiusMatch(const oclMat &query, std::vector< std::vector<DMatch> > &matches, float maxDistance,
1404 const std::vector<oclMat> &masks = std::vector<oclMat>(), bool compactResult = false);
1409 std::vector<oclMat> trainDescCollection;
1412 template <class Distance>
1413 class CV_EXPORTS BruteForceMatcher_OCL;
1415 template <typename T>
1416 class CV_EXPORTS BruteForceMatcher_OCL< L1<T> > : public BruteForceMatcher_OCL_base
1419 explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(L1Dist) {}
1420 explicit BruteForceMatcher_OCL(L1<T> /*d*/) : BruteForceMatcher_OCL_base(L1Dist) {}
1422 template <typename T>
1423 class CV_EXPORTS BruteForceMatcher_OCL< L2<T> > : public BruteForceMatcher_OCL_base
1426 explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(L2Dist) {}
1427 explicit BruteForceMatcher_OCL(L2<T> /*d*/) : BruteForceMatcher_OCL_base(L2Dist) {}
1429 template <> class CV_EXPORTS BruteForceMatcher_OCL< Hamming > : public BruteForceMatcher_OCL_base
1432 explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(HammingDist) {}
1433 explicit BruteForceMatcher_OCL(Hamming /*d*/) : BruteForceMatcher_OCL_base(HammingDist) {}
1436 class CV_EXPORTS BFMatcher_OCL : public BruteForceMatcher_OCL_base
1439 explicit BFMatcher_OCL(int norm = NORM_L2) : BruteForceMatcher_OCL_base(norm == NORM_L1 ? L1Dist : norm == NORM_L2 ? L2Dist : HammingDist) {}
1442 class CV_EXPORTS GoodFeaturesToTrackDetector_OCL
1445 explicit GoodFeaturesToTrackDetector_OCL(int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 0.0,
1446 int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04);
1448 //! return 1 rows matrix with CV_32FC2 type
1449 void operator ()(const oclMat& image, oclMat& corners, const oclMat& mask = oclMat());
1450 //! download points of type Point2f to a vector. the vector's content will be erased
1451 void downloadPoints(const oclMat &points, std::vector<Point2f> &points_v);
1454 double qualityLevel;
1458 bool useHarrisDetector;
1460 void releaseMemory()
1465 minMaxbuf_.release();
1466 tmpCorners_.release();
1476 inline GoodFeaturesToTrackDetector_OCL::GoodFeaturesToTrackDetector_OCL(int maxCorners_, double qualityLevel_, double minDistance_,
1477 int blockSize_, bool useHarrisDetector_, double harrisK_)
1479 maxCorners = maxCorners_;
1480 qualityLevel = qualityLevel_;
1481 minDistance = minDistance_;
1482 blockSize = blockSize_;
1483 useHarrisDetector = useHarrisDetector_;
1487 /////////////////////////////// PyrLKOpticalFlow /////////////////////////////////////
1489 class CV_EXPORTS PyrLKOpticalFlow
1494 winSize = Size(21, 21);
1498 useInitialFlow = false;
1499 minEigThreshold = 1e-4f;
1500 getMinEigenVals = false;
1501 isDeviceArch11_ = false;
1504 void sparse(const oclMat &prevImg, const oclMat &nextImg, const oclMat &prevPts, oclMat &nextPts,
1505 oclMat &status, oclMat *err = 0);
1507 void dense(const oclMat &prevImg, const oclMat &nextImg, oclMat &u, oclMat &v, oclMat *err = 0);
1513 bool useInitialFlow;
1514 float minEigThreshold;
1515 bool getMinEigenVals;
1517 void releaseMemory()
1519 dx_calcBuf_.release();
1520 dy_calcBuf_.release();
1530 void calcSharrDeriv(const oclMat &src, oclMat &dx, oclMat &dy);
1532 void buildImagePyramid(const oclMat &img0, std::vector<oclMat> &pyr, bool withBorder);
1537 std::vector<oclMat> prevPyr_;
1538 std::vector<oclMat> nextPyr_;
1546 bool isDeviceArch11_;
1549 class CV_EXPORTS FarnebackOpticalFlow
1552 FarnebackOpticalFlow();
1563 void operator ()(const oclMat &frame0, const oclMat &frame1, oclMat &flowx, oclMat &flowy);
1565 void releaseMemory();
1568 void prepareGaussian(
1569 int n, double sigma, float *g, float *xg, float *xxg,
1570 double &ig11, double &ig03, double &ig33, double &ig55);
1572 void setPolynomialExpansionConsts(int n, double sigma);
1574 void updateFlow_boxFilter(
1575 const oclMat& R0, const oclMat& R1, oclMat& flowx, oclMat &flowy,
1576 oclMat& M, oclMat &bufM, int blockSize, bool updateMatrices);
1578 void updateFlow_gaussianBlur(
1579 const oclMat& R0, const oclMat& R1, oclMat& flowx, oclMat& flowy,
1580 oclMat& M, oclMat &bufM, int blockSize, bool updateMatrices);
1583 oclMat pyrLevel_[2], M_, bufM_, R_[2], blurredFrame_[2];
1584 std::vector<oclMat> pyramid0_, pyramid1_;
1587 //////////////// build warping maps ////////////////////
1588 //! builds plane warping maps
1589 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);
1590 //! builds cylindrical warping maps
1591 CV_EXPORTS void buildWarpCylindricalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat &R, float scale, oclMat &map_x, oclMat &map_y);
1592 //! builds spherical warping maps
1593 CV_EXPORTS void buildWarpSphericalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat &R, float scale, oclMat &map_x, oclMat &map_y);
1594 //! builds Affine warping maps
1595 CV_EXPORTS void buildWarpAffineMaps(const Mat &M, bool inverse, Size dsize, oclMat &xmap, oclMat &ymap);
1597 //! builds Perspective warping maps
1598 CV_EXPORTS void buildWarpPerspectiveMaps(const Mat &M, bool inverse, Size dsize, oclMat &xmap, oclMat &ymap);
1600 ///////////////////////////////////// interpolate frames //////////////////////////////////////////////
1601 //! Interpolate frames (images) using provided optical flow (displacement field).
1602 //! frame0 - frame 0 (32-bit floating point images, single channel)
1603 //! frame1 - frame 1 (the same type and size)
1604 //! fu - forward horizontal displacement
1605 //! fv - forward vertical displacement
1606 //! bu - backward horizontal displacement
1607 //! bv - backward vertical displacement
1608 //! pos - new frame position
1609 //! newFrame - new frame
1610 //! buf - temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 oclMat;
1611 //! occlusion masks 0, occlusion masks 1,
1612 //! interpolated forward flow 0, interpolated forward flow 1,
1613 //! interpolated backward flow 0, interpolated backward flow 1
1615 CV_EXPORTS void interpolateFrames(const oclMat &frame0, const oclMat &frame1,
1616 const oclMat &fu, const oclMat &fv,
1617 const oclMat &bu, const oclMat &bv,
1618 float pos, oclMat &newFrame, oclMat &buf);
1620 //! computes moments of the rasterized shape or a vector of points
1621 CV_EXPORTS Moments ocl_moments(InputArray _array, bool binaryImage);
1623 class CV_EXPORTS StereoBM_OCL
1626 enum { BASIC_PRESET = 0, PREFILTER_XSOBEL = 1 };
1628 enum { DEFAULT_NDISP = 64, DEFAULT_WINSZ = 19 };
1630 //! the default constructor
1632 //! the full constructor taking the camera-specific preset, number of disparities and the SAD window size. ndisparities must be multiple of 8.
1633 StereoBM_OCL(int preset, int ndisparities = DEFAULT_NDISP, int winSize = DEFAULT_WINSZ);
1635 //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair
1636 //! Output disparity has CV_8U type.
1637 void operator() ( const oclMat &left, const oclMat &right, oclMat &disparity);
1639 //! Some heuristics that tries to estmate
1640 // if current GPU will be faster then CPU in this algorithm.
1641 // It queries current active device.
1642 static bool checkIfGpuCallReasonable();
1648 // If avergeTexThreshold == 0 => post procesing is disabled
1649 // If avergeTexThreshold != 0 then disparity is set 0 in each point (x,y) where for left image
1650 // SumOfHorizontalGradiensInWindow(x, y, winSize) < (winSize * winSize) * avergeTexThreshold
1651 // i.e. input left image is low textured.
1652 float avergeTexThreshold;
1654 oclMat minSSD, leBuf, riBuf;
1657 class CV_EXPORTS StereoBeliefPropagation
1660 enum { DEFAULT_NDISP = 64 };
1661 enum { DEFAULT_ITERS = 5 };
1662 enum { DEFAULT_LEVELS = 5 };
1663 static void estimateRecommendedParams(int width, int height, int &ndisp, int &iters, int &levels);
1664 explicit StereoBeliefPropagation(int ndisp = DEFAULT_NDISP,
1665 int iters = DEFAULT_ITERS,
1666 int levels = DEFAULT_LEVELS,
1667 int msg_type = CV_16S);
1668 StereoBeliefPropagation(int ndisp, int iters, int levels,
1669 float max_data_term, float data_weight,
1670 float max_disc_term, float disc_single_jump,
1671 int msg_type = CV_32F);
1672 void operator()(const oclMat &left, const oclMat &right, oclMat &disparity);
1673 void operator()(const oclMat &data, oclMat &disparity);
1677 float max_data_term;
1679 float max_disc_term;
1680 float disc_single_jump;
1683 oclMat u, d, l, r, u2, d2, l2, r2;
1684 std::vector<oclMat> datas;
1688 class CV_EXPORTS StereoConstantSpaceBP
1691 enum { DEFAULT_NDISP = 128 };
1692 enum { DEFAULT_ITERS = 8 };
1693 enum { DEFAULT_LEVELS = 4 };
1694 enum { DEFAULT_NR_PLANE = 4 };
1695 static void estimateRecommendedParams(int width, int height, int &ndisp, int &iters, int &levels, int &nr_plane);
1696 explicit StereoConstantSpaceBP(
1697 int ndisp = DEFAULT_NDISP,
1698 int iters = DEFAULT_ITERS,
1699 int levels = DEFAULT_LEVELS,
1700 int nr_plane = DEFAULT_NR_PLANE,
1701 int msg_type = CV_32F);
1702 StereoConstantSpaceBP(int ndisp, int iters, int levels, int nr_plane,
1703 float max_data_term, float data_weight, float max_disc_term, float disc_single_jump,
1704 int min_disp_th = 0,
1705 int msg_type = CV_32F);
1706 void operator()(const oclMat &left, const oclMat &right, oclMat &disparity);
1711 float max_data_term;
1713 float max_disc_term;
1714 float disc_single_jump;
1717 bool use_local_init_data_cost;
1719 oclMat u[2], d[2], l[2], r[2];
1720 oclMat disp_selected_pyr[2];
1722 oclMat data_cost_selected;
1727 // Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method
1730 // [1] C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
1731 // [2] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
1732 class CV_EXPORTS OpticalFlowDual_TVL1_OCL
1735 OpticalFlowDual_TVL1_OCL();
1737 void operator ()(const oclMat& I0, const oclMat& I1, oclMat& flowx, oclMat& flowy);
1739 void collectGarbage();
1742 * Time step of the numerical scheme.
1747 * Weight parameter for the data term, attachment parameter.
1748 * This is the most relevant parameter, which determines the smoothness of the output.
1749 * The smaller this parameter is, the smoother the solutions we obtain.
1750 * It depends on the range of motions of the images, so its value should be adapted to each image sequence.
1755 * Weight parameter for (u - v)^2, tightness parameter.
1756 * It serves as a link between the attachment and the regularization terms.
1757 * In theory, it should have a small value in order to maintain both parts in correspondence.
1758 * The method is stable for a large range of values of this parameter.
1763 * Number of scales used to create the pyramid of images.
1768 * Number of warpings per scale.
1769 * Represents the number of times that I1(x+u0) and grad( I1(x+u0) ) are computed per scale.
1770 * This is a parameter that assures the stability of the method.
1771 * It also affects the running time, so it is a compromise between speed and accuracy.
1776 * Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time.
1777 * A small value will yield more accurate solutions at the expense of a slower convergence.
1782 * Stopping criterion iterations number used in the numerical scheme.
1786 bool useInitialFlow;
1789 void procOneScale(const oclMat& I0, const oclMat& I1, oclMat& u1, oclMat& u2);
1791 std::vector<oclMat> I0s;
1792 std::vector<oclMat> I1s;
1793 std::vector<oclMat> u1s;
1794 std::vector<oclMat> u2s;
1814 // current supported sorting methods
1817 SORT_BITONIC, // only support power-of-2 buffer size
1818 SORT_SELECTION, // cannot sort duplicate keys
1820 SORT_RADIX // only support signed int/float keys(CV_32S/CV_32F)
1822 //! Returns the sorted result of all the elements in input based on equivalent keys.
1824 // The element unit in the values to be sorted is determined from the data type,
1825 // i.e., a CV_32FC2 input {a1a2, b1b2} will be considered as two elements, regardless its
1826 // matrix dimension.
1827 // both keys and values will be sorted inplace
1828 // Key needs to be single channel oclMat.
1832 // keys = {2, 3, 1} (CV_8UC1)
1833 // values = {10,5, 4,3, 6,2} (CV_8UC2)
1834 // sortByKey(keys, values, SORT_SELECTION, false);
1836 // keys = {1, 2, 3} (CV_8UC1)
1837 // values = {6,2, 10,5, 4,3} (CV_8UC2)
1838 CV_EXPORTS void sortByKey(oclMat& keys, oclMat& values, int method, bool isGreaterThan = false);
1839 /*!Base class for MOG and MOG2!*/
1840 class CV_EXPORTS BackgroundSubtractor
1843 //! the virtual destructor
1844 virtual ~BackgroundSubtractor();
1845 //! the update operator that takes the next video frame and returns the current foreground mask as 8-bit binary image.
1846 virtual void operator()(const oclMat& image, oclMat& fgmask, float learningRate);
1848 //! computes a background image
1849 virtual void getBackgroundImage(oclMat& backgroundImage) const = 0;
1852 Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm
1854 The class implements the following algorithm:
1855 "An improved adaptive background mixture model for real-time tracking with shadow detection"
1856 P. KadewTraKuPong and R. Bowden,
1857 Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
1858 http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
1860 class CV_EXPORTS MOG: public cv::ocl::BackgroundSubtractor
1863 //! the default constructor
1864 MOG(int nmixtures = -1);
1866 //! re-initiaization method
1867 void initialize(Size frameSize, int frameType);
1869 //! the update operator
1870 void operator()(const oclMat& frame, oclMat& fgmask, float learningRate = 0.f);
1872 //! computes a background image which are the mean of all background gaussians
1873 void getBackgroundImage(oclMat& backgroundImage) const;
1875 //! releases all inner buffers
1880 float backgroundRatio;
1897 The class implements the following algorithm:
1898 "Improved adaptive Gausian mixture model for background subtraction"
1900 International Conference Pattern Recognition, UK, August, 2004.
1901 http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf
1903 class CV_EXPORTS MOG2: public cv::ocl::BackgroundSubtractor
1906 //! the default constructor
1907 MOG2(int nmixtures = -1);
1909 //! re-initiaization method
1910 void initialize(Size frameSize, int frameType);
1912 //! the update operator
1913 void operator()(const oclMat& frame, oclMat& fgmask, float learningRate = -1.0f);
1915 //! computes a background image which are the mean of all background gaussians
1916 void getBackgroundImage(oclMat& backgroundImage) const;
1918 //! releases all inner buffers
1922 // you should call initialize after parameters changes
1926 //! here it is the maximum allowed number of mixture components.
1927 //! Actual number is determined dynamically per pixel
1929 // threshold on the squared Mahalanobis distance to decide if it is well described
1930 // by the background model or not. Related to Cthr from the paper.
1931 // This does not influence the update of the background. A typical value could be 4 sigma
1932 // and that is varThreshold=4*4=16; Corresponds to Tb in the paper.
1934 /////////////////////////
1935 // less important parameters - things you might change but be carefull
1936 ////////////////////////
1938 float backgroundRatio;
1939 // corresponds to fTB=1-cf from the paper
1940 // TB - threshold when the component becomes significant enough to be included into
1941 // the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0.
1942 // For alpha=0.001 it means that the mode should exist for approximately 105 frames before
1943 // it is considered foreground
1944 // float noiseSigma;
1945 float varThresholdGen;
1947 //correspondts to Tg - threshold on the squared Mahalan. dist. to decide
1948 //when a sample is close to the existing components. If it is not close
1949 //to any a new component will be generated. I use 3 sigma => Tg=3*3=9.
1950 //Smaller Tg leads to more generated components and higher Tg might make
1951 //lead to small number of components but they can grow too large
1956 //initial variance for the newly generated components.
1957 //It will will influence the speed of adaptation. A good guess should be made.
1958 //A simple way is to estimate the typical standard deviation from the images.
1959 //I used here 10 as a reasonable value
1960 // min and max can be used to further control the variance
1961 float fCT; //CT - complexity reduction prior
1962 //this is related to the number of samples needed to accept that a component
1963 //actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get
1964 //the standard Stauffer&Grimson algorithm (maybe not exact but very similar)
1966 //shadow detection parameters
1967 bool bShadowDetection; //default 1 - do shadow detection
1968 unsigned char nShadowDetection; //do shadow detection - insert this value as the detection result - 127 default value
1970 // Tau - shadow threshold. The shadow is detected if the pixel is darker
1971 //version of the background. Tau is a threshold on how much darker the shadow can be.
1972 //Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow
1973 //See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.
1986 oclMat bgmodelUsedModes_; //keep track of number of modes per pixel
1989 /*!***************Kalman Filter*************!*/
1990 class CV_EXPORTS KalmanFilter
1994 //! the full constructor taking the dimensionality of the state, of the measurement and of the control vector
1995 KalmanFilter(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F);
1996 //! re-initializes Kalman filter. The previous content is destroyed.
1997 void init(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F);
1999 const oclMat& predict(const oclMat& control=oclMat());
2000 const oclMat& correct(const oclMat& measurement);
2002 oclMat statePre; //!< predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
2003 oclMat statePost; //!< corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
2004 oclMat transitionMatrix; //!< state transition matrix (A)
2005 oclMat controlMatrix; //!< control matrix (B) (not used if there is no control)
2006 oclMat measurementMatrix; //!< measurement matrix (H)
2007 oclMat processNoiseCov; //!< process noise covariance matrix (Q)
2008 oclMat measurementNoiseCov;//!< measurement noise covariance matrix (R)
2009 oclMat errorCovPre; //!< priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/
2010 oclMat gain; //!< Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
2011 oclMat errorCovPost; //!< posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
2020 /*!***************K Nearest Neighbour*************!*/
2021 class CV_EXPORTS KNearestNeighbour: public CvKNearest
2024 KNearestNeighbour();
2025 ~KNearestNeighbour();
2027 bool train(const Mat& trainData, Mat& labels, Mat& sampleIdx = Mat().setTo(Scalar::all(0)),
2028 bool isRegression = false, int max_k = 32, bool updateBase = false);
2032 void find_nearest(const oclMat& samples, int k, oclMat& lables);
2038 /*!*************** SVM *************!*/
2039 class CV_EXPORTS CvSVM_OCL : public CvSVM
2044 CvSVM_OCL(const cv::Mat& trainData, const cv::Mat& responses,
2045 const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
2046 CvSVMParams params=CvSVMParams());
2047 CV_WRAP float predict( const int row_index, Mat& src, bool returnDFVal=false ) const;
2048 CV_WRAP void predict( cv::InputArray samples, cv::OutputArray results ) const;
2049 CV_WRAP float predict( const cv::Mat& sample, bool returnDFVal=false ) const;
2050 float predict( const CvMat* samples, CV_OUT CvMat* results ) const;
2053 float predict( const int row_index, int row_len, Mat& src, bool returnDFVal=false ) const;
2054 void create_kernel();
2055 void create_solver();
2058 /*!*************** END *************!*/
2061 #if defined _MSC_VER && _MSC_VER >= 1200
2062 # pragma warning( push)
2063 # pragma warning( disable: 4267)
2065 #include "opencv2/ocl/matrix_operations.hpp"
2066 #if defined _MSC_VER && _MSC_VER >= 1200
2067 # pragma warning( pop)
2070 #endif /* __OPENCV_OCL_HPP__ */