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44 #ifndef __OPENCV_OCL_HPP__
45 #define __OPENCV_OCL_HPP__
50 #include "opencv2/core.hpp"
51 #include "opencv2/imgproc.hpp"
52 #include "opencv2/objdetect.hpp"
53 #include "opencv2/ml.hpp"
61 CVCL_DEVICE_TYPE_DEFAULT = (1 << 0),
62 CVCL_DEVICE_TYPE_CPU = (1 << 1),
63 CVCL_DEVICE_TYPE_GPU = (1 << 2),
64 CVCL_DEVICE_TYPE_ACCELERATOR = (1 << 3),
65 //CVCL_DEVICE_TYPE_CUSTOM = (1 << 4)
66 CVCL_DEVICE_TYPE_ALL = 0xFFFFFFFF
78 DEVICE_MEM_DEFAULT = 0,
79 DEVICE_MEM_AHP, //alloc host pointer
80 DEVICE_MEM_UHP, //use host pointer
81 DEVICE_MEM_CHP, //copy host pointer
82 DEVICE_MEM_PM //persistent memory
85 // these classes contain OpenCL runtime information
92 int _id; // reserved, don't use it
94 DeviceType deviceType;
95 std::string deviceProfile;
96 std::string deviceVersion;
97 std::string deviceName;
98 std::string deviceVendor;
100 std::string deviceDriverVersion;
101 std::string deviceExtensions;
103 size_t maxWorkGroupSize;
104 std::vector<size_t> maxWorkItemSizes;
106 size_t localMemorySize;
107 size_t maxMemAllocSize;
109 int deviceVersionMajor;
110 int deviceVersionMinor;
112 bool haveDoubleSupport;
113 bool isUnifiedMemory; // 1 means integrated GPU, otherwise this value is 0
116 std::string compilationExtraOptions;
118 const PlatformInfo* platform;
125 int _id; // reserved, don't use it
127 std::string platformProfile;
128 std::string platformVersion;
129 std::string platformName;
130 std::string platformVendor;
131 std::string platformExtensons;
133 int platformVersionMajor;
134 int platformVersionMinor;
136 std::vector<const DeviceInfo*> devices;
141 //////////////////////////////// Initialization & Info ////////////////////////
142 typedef std::vector<const PlatformInfo*> PlatformsInfo;
144 CV_EXPORTS int getOpenCLPlatforms(PlatformsInfo& platforms);
146 typedef std::vector<const DeviceInfo*> DevicesInfo;
148 CV_EXPORTS int getOpenCLDevices(DevicesInfo& devices, int deviceType = CVCL_DEVICE_TYPE_GPU,
149 const PlatformInfo* platform = NULL);
151 // set device you want to use
152 CV_EXPORTS void setDevice(const DeviceInfo* info);
156 FEATURE_CL_DOUBLE = 1,
157 FEATURE_CL_UNIFIED_MEM,
159 FEATURE_CL_INTEL_DEVICE
162 // Represents OpenCL context, interface
163 class CV_EXPORTS Context
169 static Context *getContext();
171 bool supportsFeature(FEATURE_TYPE featureType) const;
172 const DeviceInfo& getDeviceInfo() const;
174 const void* getOpenCLContextPtr() const;
175 const void* getOpenCLCommandQueuePtr() const;
176 const void* getOpenCLDeviceIDPtr() const;
179 inline const void *getClContextPtr()
181 return Context::getContext()->getOpenCLContextPtr();
184 inline const void *getClCommandQueuePtr()
186 return Context::getContext()->getOpenCLCommandQueuePtr();
189 CV_EXPORTS bool supportsFeature(FEATURE_TYPE featureType);
191 CV_EXPORTS void finish();
193 enum BINARY_CACHE_MODE
195 CACHE_NONE = 0, // do not cache OpenCL binary
196 CACHE_DEBUG = 0x1 << 0, // cache OpenCL binary when built in debug mode
197 CACHE_RELEASE = 0x1 << 1, // default behavior, only cache when built in release mode
198 CACHE_ALL = CACHE_DEBUG | CACHE_RELEASE, // cache opencl binary
200 //! Enable or disable OpenCL program binary caching onto local disk
201 // After a program (*.cl files in opencl/ folder) is built at runtime, we allow the
202 // compiled OpenCL program to be cached to the path automatically as "path/*.clb"
203 // binary file, which will be reused when the OpenCV executable is started again.
205 // This feature is enabled by default.
206 CV_EXPORTS void setBinaryDiskCache(int mode = CACHE_RELEASE, cv::String path = "./");
208 //! set where binary cache to be saved to
209 CV_EXPORTS void setBinaryPath(const char *path);
214 const char* programStr;
215 const char* programHash;
217 // Cache in memory by name (should be unique). Caching on disk disabled.
218 inline ProgramSource(const char* _name, const char* _programStr)
219 : name(_name), programStr(_programStr), programHash(NULL)
223 // Cache in memory by name (should be unique). Caching on disk uses programHash mark.
224 inline ProgramSource(const char* _name, const char* _programStr, const char* _programHash)
225 : name(_name), programStr(_programStr), programHash(_programHash)
230 //! Calls OpenCL kernel. Pass globalThreads = NULL, and cleanUp = true, to finally clean-up without executing.
231 //! Deprecated, will be replaced
232 CV_EXPORTS void openCLExecuteKernelInterop(Context *clCxt,
233 const cv::ocl::ProgramSource& source, String kernelName,
234 size_t globalThreads[3], size_t localThreads[3],
235 std::vector< std::pair<size_t, const void *> > &args,
236 int channels, int depth, const char *build_options);
238 class CV_EXPORTS oclMatExpr;
239 //////////////////////////////// oclMat ////////////////////////////////
240 class CV_EXPORTS oclMat
243 //! default constructor
245 //! constructs oclMatrix of the specified size and type (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.)
246 oclMat(int rows, int cols, int type);
247 oclMat(Size size, int type);
248 //! constucts oclMatrix and fills it with the specified value _s.
249 oclMat(int rows, int cols, int type, const Scalar &s);
250 oclMat(Size size, int type, const Scalar &s);
252 oclMat(const oclMat &m);
254 //! constructor for oclMatrix headers pointing to user-allocated data
255 oclMat(int rows, int cols, int type, void *data, size_t step = Mat::AUTO_STEP);
256 oclMat(Size size, int type, void *data, size_t step = Mat::AUTO_STEP);
258 //! creates a matrix header for a part of the bigger matrix
259 oclMat(const oclMat &m, const Range &rowRange, const Range &colRange);
260 oclMat(const oclMat &m, const Rect &roi);
262 //! builds oclMat from Mat. Perfom blocking upload to device.
263 explicit oclMat (const Mat &m);
265 //! destructor - calls release()
268 //! assignment operators
269 oclMat &operator = (const oclMat &m);
270 //! assignment operator. Perfom blocking upload to device.
271 oclMat &operator = (const Mat &m);
272 oclMat &operator = (const oclMatExpr& expr);
274 //! pefroms blocking upload data to oclMat.
275 void upload(const cv::Mat &m);
278 //! downloads data from device to host memory. Blocking calls.
279 operator Mat() const;
280 void download(cv::Mat &m) const;
282 //! convert to _InputArray
283 operator _InputArray();
285 //! convert to _OutputArray
286 operator _OutputArray();
288 //! returns a new oclMatrix header for the specified row
289 oclMat row(int y) const;
290 //! returns a new oclMatrix header for the specified column
291 oclMat col(int x) const;
292 //! ... for the specified row span
293 oclMat rowRange(int startrow, int endrow) const;
294 oclMat rowRange(const Range &r) const;
295 //! ... for the specified column span
296 oclMat colRange(int startcol, int endcol) const;
297 oclMat colRange(const Range &r) const;
299 //! returns deep copy of the oclMatrix, i.e. the data is copied
300 oclMat clone() const;
302 //! copies those oclMatrix elements to "m" that are marked with non-zero mask elements.
303 // It calls m.create(this->size(), this->type()).
304 // It supports any data type
305 void copyTo( oclMat &m, const oclMat &mask = oclMat()) const;
307 //! converts oclMatrix to another datatype with optional scalng. See cvConvertScale.
308 void convertTo( oclMat &m, int rtype, double alpha = 1, double beta = 0 ) const;
310 void assignTo( oclMat &m, int type = -1 ) const;
312 //! sets every oclMatrix element to s
313 oclMat& operator = (const Scalar &s);
314 //! sets some of the oclMatrix elements to s, according to the mask
315 oclMat& setTo(const Scalar &s, const oclMat &mask = oclMat());
316 //! creates alternative oclMatrix header for the same data, with different
317 // number of channels and/or different number of rows. see cvReshape.
318 oclMat reshape(int cn, int rows = 0) const;
320 //! allocates new oclMatrix data unless the oclMatrix already has specified size and type.
321 // previous data is unreferenced if needed.
322 void create(int rows, int cols, int type);
323 void create(Size size, int type);
325 //! allocates new oclMatrix with specified device memory type.
326 void createEx(int rows, int cols, int type,
327 DevMemRW rw_type, DevMemType mem_type);
328 void createEx(Size size, int type, DevMemRW rw_type,
329 DevMemType mem_type);
331 //! decreases reference counter;
332 // deallocate the data when reference counter reaches 0.
335 //! swaps with other smart pointer
336 void swap(oclMat &mat);
338 //! locates oclMatrix header within a parent oclMatrix. See below
339 void locateROI( Size &wholeSize, Point &ofs ) const;
340 //! moves/resizes the current oclMatrix ROI inside the parent oclMatrix.
341 oclMat& adjustROI( int dtop, int dbottom, int dleft, int dright );
342 //! extracts a rectangular sub-oclMatrix
343 // (this is a generalized form of row, rowRange etc.)
344 oclMat operator()( Range rowRange, Range colRange ) const;
345 oclMat operator()( const Rect &roi ) const;
347 oclMat& operator+=( const oclMat& m );
348 oclMat& operator-=( const oclMat& m );
349 oclMat& operator*=( const oclMat& m );
350 oclMat& operator/=( const oclMat& m );
352 //! returns true if the oclMatrix data is continuous
353 // (i.e. when there are no gaps between successive rows).
354 // similar to CV_IS_oclMat_CONT(cvoclMat->type)
355 bool isContinuous() const;
356 //! returns element size in bytes,
357 // similar to CV_ELEM_SIZE(cvMat->type)
358 size_t elemSize() const;
359 //! returns the size of element channel in bytes.
360 size_t elemSize1() const;
361 //! returns element type, similar to CV_MAT_TYPE(cvMat->type)
363 //! returns element type, i.e. 8UC3 returns 8UC4 because in ocl
364 //! 3 channels element actually use 4 channel space
366 //! returns element type, similar to CV_MAT_DEPTH(cvMat->type)
368 //! returns element type, similar to CV_MAT_CN(cvMat->type)
369 int channels() const;
370 //! returns element type, return 4 for 3 channels element,
371 //!becuase 3 channels element actually use 4 channel space
372 int oclchannels() const;
373 //! returns step/elemSize1()
374 size_t step1() const;
375 //! returns oclMatrix size:
376 // width == number of columns, height == number of rows
378 //! returns true if oclMatrix data is NULL
381 //! matrix transposition
384 /*! includes several bit-fields:
385 - the magic signature
391 //! the number of rows and columns
393 //! a distance between successive rows in bytes; includes the gap if any
395 //! pointer to the data(OCL memory object)
398 //! pointer to the reference counter;
399 // when oclMatrix points to user-allocated data, the pointer is NULL
402 //! helper fields used in locateROI and adjustROI
403 //datastart and dataend are not used in current version
407 //! OpenCL context associated with the oclMat object.
408 Context *clCxt; // TODO clCtx
409 //add offset for handle ROI, calculated in byte
411 //add wholerows and wholecols for the whole matrix, datastart and dataend are no longer used
416 // convert InputArray/OutputArray to oclMat references
417 CV_EXPORTS oclMat& getOclMatRef(InputArray src);
418 CV_EXPORTS oclMat& getOclMatRef(OutputArray src);
420 ///////////////////// mat split and merge /////////////////////////////////
421 //! Compose a multi-channel array from several single-channel arrays
423 CV_EXPORTS void merge(const oclMat *src, size_t n, oclMat &dst);
424 CV_EXPORTS void merge(const std::vector<oclMat> &src, oclMat &dst);
426 //! Divides multi-channel array into several single-channel arrays
428 CV_EXPORTS void split(const oclMat &src, oclMat *dst);
429 CV_EXPORTS void split(const oclMat &src, std::vector<oclMat> &dst);
431 ////////////////////////////// Arithmetics ///////////////////////////////////
433 //! adds one matrix to another with scale (dst = src1 * alpha + src2 * beta + gama)
434 // supports all data types
435 CV_EXPORTS void addWeighted(const oclMat &src1, double alpha, const oclMat &src2, double beta, double gama, oclMat &dst);
437 //! adds one matrix to another (dst = src1 + src2)
438 // supports all data types
439 CV_EXPORTS void add(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
440 //! adds scalar to a matrix (dst = src1 + s)
441 // supports all data types
442 CV_EXPORTS void add(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
444 //! subtracts one matrix from another (dst = src1 - src2)
445 // supports all data types
446 CV_EXPORTS void subtract(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
447 //! subtracts scalar from a matrix (dst = src1 - s)
448 // supports all data types
449 CV_EXPORTS void subtract(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
451 //! computes element-wise product of the two arrays (dst = src1 * scale * src2)
452 // supports all data types
453 CV_EXPORTS void multiply(const oclMat &src1, const oclMat &src2, oclMat &dst, double scale = 1);
454 //! multiplies matrix to a number (dst = scalar * src)
455 // supports all data types
456 CV_EXPORTS void multiply(double scalar, const oclMat &src, oclMat &dst);
458 //! computes element-wise quotient of the two arrays (dst = src1 * scale / src2)
459 // supports all data types
460 CV_EXPORTS void divide(const oclMat &src1, const oclMat &src2, oclMat &dst, double scale = 1);
461 //! computes element-wise quotient of the two arrays (dst = scale / src)
462 // supports all data types
463 CV_EXPORTS void divide(double scale, const oclMat &src1, oclMat &dst);
465 //! computes element-wise minimum of the two arrays (dst = min(src1, src2))
466 // supports all data types
467 CV_EXPORTS void min(const oclMat &src1, const oclMat &src2, oclMat &dst);
469 //! computes element-wise maximum of the two arrays (dst = max(src1, src2))
470 // supports all data types
471 CV_EXPORTS void max(const oclMat &src1, const oclMat &src2, oclMat &dst);
473 //! compares elements of two arrays (dst = src1 <cmpop> src2)
474 // supports all data types
475 CV_EXPORTS void compare(const oclMat &src1, const oclMat &src2, oclMat &dst, int cmpop);
477 //! transposes the matrix
478 // supports all data types
479 CV_EXPORTS void transpose(const oclMat &src, oclMat &dst);
481 //! computes element-wise absolute values of an array (dst = abs(src))
482 // supports all data types
483 CV_EXPORTS void abs(const oclMat &src, oclMat &dst);
485 //! computes element-wise absolute difference of two arrays (dst = abs(src1 - src2))
486 // supports all data types
487 CV_EXPORTS void absdiff(const oclMat &src1, const oclMat &src2, oclMat &dst);
488 //! computes element-wise absolute difference of array and scalar (dst = abs(src1 - s))
489 // supports all data types
490 CV_EXPORTS void absdiff(const oclMat &src1, const Scalar &s, oclMat &dst);
492 //! computes mean value and standard deviation of all or selected array elements
493 // supports all data types
494 CV_EXPORTS void meanStdDev(const oclMat &mtx, Scalar &mean, Scalar &stddev);
496 //! computes norm of array
497 // supports NORM_INF, NORM_L1, NORM_L2
498 // supports all data types
499 CV_EXPORTS double norm(const oclMat &src1, int normType = NORM_L2);
501 //! computes norm of the difference between two arrays
502 // supports NORM_INF, NORM_L1, NORM_L2
503 // supports all data types
504 CV_EXPORTS double norm(const oclMat &src1, const oclMat &src2, int normType = NORM_L2);
506 //! reverses the order of the rows, columns or both in a matrix
507 // supports all types
508 CV_EXPORTS void flip(const oclMat &src, oclMat &dst, int flipCode);
510 //! computes sum of array elements
512 CV_EXPORTS Scalar sum(const oclMat &m);
513 CV_EXPORTS Scalar absSum(const oclMat &m);
514 CV_EXPORTS Scalar sqrSum(const oclMat &m);
516 //! finds global minimum and maximum array elements and returns their values
517 // support all C1 types
518 CV_EXPORTS void minMax(const oclMat &src, double *minVal, double *maxVal = 0, const oclMat &mask = oclMat());
520 //! finds global minimum and maximum array elements and returns their values with locations
521 // support all C1 types
522 CV_EXPORTS void minMaxLoc(const oclMat &src, double *minVal, double *maxVal = 0, Point *minLoc = 0, Point *maxLoc = 0,
523 const oclMat &mask = oclMat());
525 //! counts non-zero array elements
527 CV_EXPORTS int countNonZero(const oclMat &src);
529 //! transforms 8-bit unsigned integers using lookup table: dst(i)=lut(src(i))
530 // destination array will have the depth type as lut and the same channels number as source
531 //It supports 8UC1 8UC4 only
532 CV_EXPORTS void LUT(const oclMat &src, const oclMat &lut, oclMat &dst);
534 //! only 8UC1 and 256 bins is supported now
535 CV_EXPORTS void calcHist(const oclMat &mat_src, oclMat &mat_hist);
536 //! only 8UC1 and 256 bins is supported now
537 CV_EXPORTS void equalizeHist(const oclMat &mat_src, oclMat &mat_dst);
539 //! only 8UC1 is supported now
540 CV_EXPORTS Ptr<cv::CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8));
543 // supports 8UC1 8UC4
544 CV_EXPORTS void bilateralFilter(const oclMat& src, oclMat& dst, int d, double sigmaColor, double sigmaSpace, int borderType=BORDER_DEFAULT);
546 //! Applies an adaptive bilateral filter to the input image
547 // Unlike the usual bilateral filter that uses fixed value for sigmaColor,
548 // the adaptive version calculates the local variance in he ksize neighborhood
549 // and use this as sigmaColor, for the value filtering. However, the local standard deviation is
550 // clamped to the maxSigmaColor.
551 // supports 8UC1, 8UC3
552 CV_EXPORTS void adaptiveBilateralFilter(const oclMat& src, oclMat& dst, Size ksize, double sigmaSpace, double maxSigmaColor=20.0, Point anchor = Point(-1, -1), int borderType=BORDER_DEFAULT);
554 //! computes exponent of each matrix element (dst = e**src)
555 // supports only CV_32FC1, CV_64FC1 type
556 CV_EXPORTS void exp(const oclMat &src, oclMat &dst);
558 //! computes natural logarithm of absolute value of each matrix element: dst = log(abs(src))
559 // supports only CV_32FC1, CV_64FC1 type
560 CV_EXPORTS void log(const oclMat &src, oclMat &dst);
562 //! computes magnitude of each (x(i), y(i)) vector
563 // supports only CV_32F, CV_64F type
564 CV_EXPORTS void magnitude(const oclMat &x, const oclMat &y, oclMat &magnitude);
566 //! computes angle (angle(i)) of each (x(i), y(i)) vector
567 // supports only CV_32F, CV_64F type
568 CV_EXPORTS void phase(const oclMat &x, const oclMat &y, oclMat &angle, bool angleInDegrees = false);
570 //! the function raises every element of tne input array to p
571 // support only CV_32F, CV_64F type
572 CV_EXPORTS void pow(const oclMat &x, double p, oclMat &y);
574 //! converts Cartesian coordinates to polar
575 // supports only CV_32F CV_64F type
576 CV_EXPORTS void cartToPolar(const oclMat &x, const oclMat &y, oclMat &magnitude, oclMat &angle, bool angleInDegrees = false);
578 //! converts polar coordinates to Cartesian
579 // supports only CV_32F CV_64F type
580 CV_EXPORTS void polarToCart(const oclMat &magnitude, const oclMat &angle, oclMat &x, oclMat &y, bool angleInDegrees = false);
582 //! perfroms per-elements bit-wise inversion
583 // supports all types
584 CV_EXPORTS void bitwise_not(const oclMat &src, oclMat &dst);
586 //! calculates per-element bit-wise disjunction of two arrays
587 // supports all types
588 CV_EXPORTS void bitwise_or(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
589 CV_EXPORTS void bitwise_or(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
591 //! calculates per-element bit-wise conjunction of two arrays
592 // supports all types
593 CV_EXPORTS void bitwise_and(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
594 CV_EXPORTS void bitwise_and(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
596 //! calculates per-element bit-wise "exclusive or" operation
597 // supports all types
598 CV_EXPORTS void bitwise_xor(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
599 CV_EXPORTS void bitwise_xor(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
601 //! Logical operators
602 CV_EXPORTS oclMat operator ~ (const oclMat &);
603 CV_EXPORTS oclMat operator | (const oclMat &, const oclMat &);
604 CV_EXPORTS oclMat operator & (const oclMat &, const oclMat &);
605 CV_EXPORTS oclMat operator ^ (const oclMat &, const oclMat &);
608 //! Mathematics operators
609 CV_EXPORTS oclMatExpr operator + (const oclMat &src1, const oclMat &src2);
610 CV_EXPORTS oclMatExpr operator - (const oclMat &src1, const oclMat &src2);
611 CV_EXPORTS oclMatExpr operator * (const oclMat &src1, const oclMat &src2);
612 CV_EXPORTS oclMatExpr operator / (const oclMat &src1, const oclMat &src2);
614 struct CV_EXPORTS ConvolveBuf
618 Size user_block_size;
621 oclMat image_spect, templ_spect, result_spect;
622 oclMat image_block, templ_block, result_data;
624 void create(Size image_size, Size templ_size);
625 static Size estimateBlockSize(Size result_size, Size templ_size);
628 //! computes convolution of two images, may use discrete Fourier transform
629 // support only CV_32FC1 type
630 CV_EXPORTS void convolve(const oclMat &image, const oclMat &temp1, oclMat &result, bool ccorr = false);
631 CV_EXPORTS void convolve(const oclMat &image, const oclMat &temp1, oclMat &result, bool ccorr, ConvolveBuf& buf);
633 //! Performs a per-element multiplication of two Fourier spectrums.
634 //! Only full (not packed) CV_32FC2 complex spectrums in the interleaved format are supported for now.
635 //! support only CV_32FC2 type
636 CV_EXPORTS void mulSpectrums(const oclMat &a, const oclMat &b, oclMat &c, int flags, float scale, bool conjB = false);
638 CV_EXPORTS void cvtColor(const oclMat &src, oclMat &dst, int code, int dcn = 0);
640 //! initializes a scaled identity matrix
641 CV_EXPORTS void setIdentity(oclMat& src, const Scalar & val = Scalar(1));
643 //! fills the output array with repeated copies of the input array
644 CV_EXPORTS void repeat(const oclMat & src, int ny, int nx, oclMat & dst);
646 //////////////////////////////// Filter Engine ////////////////////////////////
649 The Base Class for 1D or Row-wise Filters
651 This is the base class for linear or non-linear filters that process 1D data.
652 In particular, such filters are used for the "horizontal" filtering parts in separable filters.
654 class CV_EXPORTS BaseRowFilter_GPU
657 BaseRowFilter_GPU(int ksize_, int anchor_, int bordertype_) : ksize(ksize_), anchor(anchor_), bordertype(bordertype_) {}
658 virtual ~BaseRowFilter_GPU() {}
659 virtual void operator()(const oclMat &src, oclMat &dst) = 0;
660 int ksize, anchor, bordertype;
664 The Base Class for Column-wise Filters
666 This is the base class for linear or non-linear filters that process columns of 2D arrays.
667 Such filters are used for the "vertical" filtering parts in separable filters.
669 class CV_EXPORTS BaseColumnFilter_GPU
672 BaseColumnFilter_GPU(int ksize_, int anchor_, int bordertype_) : ksize(ksize_), anchor(anchor_), bordertype(bordertype_) {}
673 virtual ~BaseColumnFilter_GPU() {}
674 virtual void operator()(const oclMat &src, oclMat &dst) = 0;
675 int ksize, anchor, bordertype;
679 The Base Class for Non-Separable 2D Filters.
681 This is the base class for linear or non-linear 2D filters.
683 class CV_EXPORTS BaseFilter_GPU
686 BaseFilter_GPU(const Size &ksize_, const Point &anchor_, const int &borderType_)
687 : ksize(ksize_), anchor(anchor_), borderType(borderType_) {}
688 virtual ~BaseFilter_GPU() {}
689 virtual void operator()(const oclMat &src, oclMat &dst) = 0;
696 The Base Class for Filter Engine.
698 The class can be used to apply an arbitrary filtering operation to an image.
699 It contains all the necessary intermediate buffers.
701 class CV_EXPORTS FilterEngine_GPU
704 virtual ~FilterEngine_GPU() {}
706 virtual void apply(const oclMat &src, oclMat &dst, Rect roi = Rect(0, 0, -1, -1)) = 0;
709 //! returns the non-separable filter engine with the specified filter
710 CV_EXPORTS Ptr<FilterEngine_GPU> createFilter2D_GPU(const Ptr<BaseFilter_GPU> filter2D);
712 //! returns the primitive row filter with the specified kernel
713 CV_EXPORTS Ptr<BaseRowFilter_GPU> getLinearRowFilter_GPU(int srcType, int bufType, const Mat &rowKernel,
714 int anchor = -1, int bordertype = BORDER_DEFAULT);
716 //! returns the primitive column filter with the specified kernel
717 CV_EXPORTS Ptr<BaseColumnFilter_GPU> getLinearColumnFilter_GPU(int bufType, int dstType, const Mat &columnKernel,
718 int anchor = -1, int bordertype = BORDER_DEFAULT, double delta = 0.0);
720 //! returns the separable linear filter engine
721 CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat &rowKernel,
722 const Mat &columnKernel, const Point &anchor = Point(-1, -1), double delta = 0.0, int bordertype = BORDER_DEFAULT);
724 //! returns the separable filter engine with the specified filters
725 CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU> &rowFilter,
726 const Ptr<BaseColumnFilter_GPU> &columnFilter);
728 //! returns the Gaussian filter engine
729 CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, double sigma1, double sigma2 = 0, int bordertype = BORDER_DEFAULT);
731 //! returns filter engine for the generalized Sobel operator
732 CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU( int srcType, int dstType, int dx, int dy, int ksize, int borderType = BORDER_DEFAULT );
734 //! applies Laplacian operator to the image
735 // supports only ksize = 1 and ksize = 3
736 CV_EXPORTS void Laplacian(const oclMat &src, oclMat &dst, int ddepth, int ksize = 1, double scale = 1,
737 double delta=0, int borderType=BORDER_DEFAULT);
739 //! returns 2D box filter
740 // dst type must be the same as source type
741 CV_EXPORTS Ptr<BaseFilter_GPU> getBoxFilter_GPU(int srcType, int dstType,
742 const Size &ksize, Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
744 //! returns box filter engine
745 CV_EXPORTS Ptr<FilterEngine_GPU> createBoxFilter_GPU(int srcType, int dstType, const Size &ksize,
746 const Point &anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
748 //! returns 2D filter with the specified kernel
749 // supports: dst type must be the same as source type
750 CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat &kernel, const Size &ksize,
751 const Point &anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
753 //! returns the non-separable linear filter engine
754 // supports: dst type must be the same as source type
755 CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat &kernel,
756 const Point &anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
758 //! smooths the image using the normalized box filter
759 CV_EXPORTS void boxFilter(const oclMat &src, oclMat &dst, int ddepth, Size ksize,
760 Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
762 //! returns 2D morphological filter
763 //! only MORPH_ERODE and MORPH_DILATE are supported
764 // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
765 // kernel must have CV_8UC1 type, one rows and cols == ksize.width * ksize.height
766 CV_EXPORTS Ptr<BaseFilter_GPU> getMorphologyFilter_GPU(int op, int type, const Mat &kernel, const Size &ksize,
767 Point anchor = Point(-1, -1));
769 //! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported.
770 CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat &kernel,
771 const Point &anchor = Point(-1, -1), int iterations = 1);
773 //! a synonym for normalized box filter
774 static inline void blur(const oclMat &src, oclMat &dst, Size ksize, Point anchor = Point(-1, -1),
775 int borderType = BORDER_CONSTANT)
777 boxFilter(src, dst, -1, ksize, anchor, borderType);
780 //! applies non-separable 2D linear filter to the image
781 CV_EXPORTS void filter2D(const oclMat &src, oclMat &dst, int ddepth, const Mat &kernel,
782 Point anchor = Point(-1, -1), double delta = 0.0, int borderType = BORDER_DEFAULT);
784 //! applies separable 2D linear filter to the image
785 CV_EXPORTS void sepFilter2D(const oclMat &src, oclMat &dst, int ddepth, const Mat &kernelX, const Mat &kernelY,
786 Point anchor = Point(-1, -1), double delta = 0.0, int bordertype = BORDER_DEFAULT);
788 //! applies generalized Sobel operator to the image
789 // dst.type must equalize src.type
790 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
791 // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
792 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);
794 //! applies the vertical or horizontal Scharr operator to the image
795 // dst.type must equalize src.type
796 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
797 // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
798 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);
800 //! smooths the image using Gaussian filter.
801 // dst.type must equalize src.type
802 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
803 // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
804 CV_EXPORTS void GaussianBlur(const oclMat &src, oclMat &dst, Size ksize, double sigma1, double sigma2 = 0, int bordertype = BORDER_DEFAULT);
806 //! erodes the image (applies the local minimum operator)
807 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
808 CV_EXPORTS void erode( const oclMat &src, oclMat &dst, const Mat &kernel, Point anchor = Point(-1, -1), int iterations = 1,
810 int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue());
813 //! dilates the image (applies the local maximum operator)
814 // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
815 CV_EXPORTS void dilate( const oclMat &src, oclMat &dst, const Mat &kernel, Point anchor = Point(-1, -1), int iterations = 1,
817 int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue());
820 //! applies an advanced morphological operation to the image
821 CV_EXPORTS void morphologyEx( const oclMat &src, oclMat &dst, int op, const Mat &kernel, Point anchor = Point(-1, -1), int iterations = 1,
823 int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue());
826 ////////////////////////////// Image processing //////////////////////////////
827 //! Does mean shift filtering on GPU.
828 CV_EXPORTS void meanShiftFiltering(const oclMat &src, oclMat &dst, int sp, int sr,
829 TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
831 //! Does mean shift procedure on GPU.
832 CV_EXPORTS void meanShiftProc(const oclMat &src, oclMat &dstr, oclMat &dstsp, int sp, int sr,
833 TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
835 //! Does mean shift segmentation with elimiation of small regions.
836 CV_EXPORTS void meanShiftSegmentation(const oclMat &src, Mat &dst, int sp, int sr, int minsize,
837 TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
839 //! applies fixed threshold to the image.
840 // supports CV_8UC1 and CV_32FC1 data type
841 // supports threshold type: THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV
842 CV_EXPORTS double threshold(const oclMat &src, oclMat &dst, double thresh, double maxVal, int type = THRESH_TRUNC);
844 //! resizes the image
845 // Supports INTER_NEAREST, INTER_LINEAR
846 // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
847 CV_EXPORTS void resize(const oclMat &src, oclMat &dst, Size dsize, double fx = 0, double fy = 0, int interpolation = INTER_LINEAR);
849 //! Applies a generic geometrical transformation to an image.
851 // Supports INTER_NEAREST, INTER_LINEAR.
852 // Map1 supports CV_16SC2, CV_32FC2 types.
853 // Src supports CV_8UC1, CV_8UC2, CV_8UC4.
854 CV_EXPORTS void remap(const oclMat &src, oclMat &dst, oclMat &map1, oclMat &map2, int interpolation, int bordertype, const Scalar &value = Scalar());
856 //! copies 2D array to a larger destination array and pads borders with user-specifiable constant
857 // supports CV_8UC1, CV_8UC4, CV_32SC1 types
858 CV_EXPORTS void copyMakeBorder(const oclMat &src, oclMat &dst, int top, int bottom, int left, int right, int boardtype, const Scalar &value = Scalar());
860 //! Smoothes image using median filter
861 // The source 1- or 4-channel image. m should be 3 or 5, the image depth should be CV_8U or CV_32F.
862 CV_EXPORTS void medianFilter(const oclMat &src, oclMat &dst, int m);
864 //! warps the image using affine transformation
865 // Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
866 // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
867 CV_EXPORTS void warpAffine(const oclMat &src, oclMat &dst, const Mat &M, Size dsize, int flags = INTER_LINEAR);
869 //! warps the image using perspective transformation
870 // Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
871 // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
872 CV_EXPORTS void warpPerspective(const oclMat &src, oclMat &dst, const Mat &M, Size dsize, int flags = INTER_LINEAR);
874 //! computes the integral image and integral for the squared image
875 // sum will support CV_32S, CV_32F, sqsum - support CV32F, CV_64F
876 // supports only CV_8UC1 source type
877 CV_EXPORTS void integral(const oclMat &src, oclMat &sum, oclMat &sqsum, int sdepth=-1 );
878 CV_EXPORTS void integral(const oclMat &src, oclMat &sum, int sdepth=-1 );
879 CV_EXPORTS void cornerHarris(const oclMat &src, oclMat &dst, int blockSize, int ksize, double k, int bordertype = cv::BORDER_DEFAULT);
880 CV_EXPORTS void cornerHarris_dxdy(const oclMat &src, oclMat &dst, oclMat &Dx, oclMat &Dy,
881 int blockSize, int ksize, double k, int bordertype = cv::BORDER_DEFAULT);
882 CV_EXPORTS void cornerMinEigenVal(const oclMat &src, oclMat &dst, int blockSize, int ksize, int bordertype = cv::BORDER_DEFAULT);
883 CV_EXPORTS void cornerMinEigenVal_dxdy(const oclMat &src, oclMat &dst, oclMat &Dx, oclMat &Dy,
884 int blockSize, int ksize, int bordertype = cv::BORDER_DEFAULT);
887 /////////////////////////////////// ML ///////////////////////////////////////////
889 //! Compute closest centers for each lines in source and lable it after center's index
890 // supports CV_32FC1/CV_32FC2/CV_32FC4 data type
891 // supports NORM_L1 and NORM_L2 distType
892 // if indices is provided, only the indexed rows will be calculated and their results are in the same
894 CV_EXPORTS void distanceToCenters(const oclMat &src, const oclMat ¢ers, Mat &dists, Mat &labels, int distType = NORM_L2SQR);
896 //!Does k-means procedure on GPU
897 // supports CV_32FC1/CV_32FC2/CV_32FC4 data type
898 CV_EXPORTS double kmeans(const oclMat &src, int K, oclMat &bestLabels,
899 TermCriteria criteria, int attemps, int flags, oclMat ¢ers);
902 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
903 ///////////////////////////////////////////CascadeClassifier//////////////////////////////////////////////////////////////////
904 ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
905 class CV_EXPORTS OclCascadeClassifier : public cv::CascadeClassifier
908 void detectMultiScale(oclMat &image, CV_OUT std::vector<cv::Rect>& faces,
909 double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0,
910 Size minSize = Size(), Size maxSize = Size());
913 /////////////////////////////// Pyramid /////////////////////////////////////
914 CV_EXPORTS void pyrDown(const oclMat &src, oclMat &dst);
916 //! upsamples the source image and then smoothes it
917 CV_EXPORTS void pyrUp(const oclMat &src, oclMat &dst);
919 //! performs linear blending of two images
920 //! to avoid accuracy errors sum of weigths shouldn't be very close to zero
921 // supports only CV_8UC1 source type
922 CV_EXPORTS void blendLinear(const oclMat &img1, const oclMat &img2, const oclMat &weights1, const oclMat &weights2, oclMat &result);
924 //! computes vertical sum, supports only CV_32FC1 images
925 CV_EXPORTS void columnSum(const oclMat &src, oclMat &sum);
927 ///////////////////////////////////////// match_template /////////////////////////////////////////////////////////////
928 struct CV_EXPORTS MatchTemplateBuf
930 Size user_block_size;
931 oclMat imagef, templf;
932 std::vector<oclMat> images;
933 std::vector<oclMat> image_sums;
934 std::vector<oclMat> image_sqsums;
937 //! computes the proximity map for the raster template and the image where the template is searched for
938 // Supports TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED for type 8UC1 and 8UC4
939 // Supports TM_SQDIFF, TM_CCORR for type 32FC1 and 32FC4
940 CV_EXPORTS void matchTemplate(const oclMat &image, const oclMat &templ, oclMat &result, int method);
942 //! computes the proximity map for the raster template and the image where the template is searched for
943 // Supports TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED for type 8UC1 and 8UC4
944 // Supports TM_SQDIFF, TM_CCORR for type 32FC1 and 32FC4
945 CV_EXPORTS void matchTemplate(const oclMat &image, const oclMat &templ, oclMat &result, int method, MatchTemplateBuf &buf);
949 ///////////////////////////////////////////// Canny /////////////////////////////////////////////
950 struct CV_EXPORTS CannyBuf;
952 //! compute edges of the input image using Canny operator
953 // Support CV_8UC1 only
954 CV_EXPORTS void Canny(const oclMat &image, oclMat &edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
955 CV_EXPORTS void Canny(const oclMat &image, CannyBuf &buf, oclMat &edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
956 CV_EXPORTS void Canny(const oclMat &dx, const oclMat &dy, oclMat &edges, double low_thresh, double high_thresh, bool L2gradient = false);
957 CV_EXPORTS void Canny(const oclMat &dx, const oclMat &dy, CannyBuf &buf, oclMat &edges, double low_thresh, double high_thresh, bool L2gradient = false);
959 struct CV_EXPORTS CannyBuf
961 CannyBuf() : counter(1, 1, CV_32S) { }
966 explicit CannyBuf(const Size &image_size, int apperture_size = 3) : counter(1, 1, CV_32S)
968 create(image_size, apperture_size);
970 CannyBuf(const oclMat &dx_, const oclMat &dy_);
971 void create(const Size &image_size, int apperture_size = 3);
975 oclMat dx_buf, dy_buf;
976 oclMat magBuf, mapBuf;
977 oclMat trackBuf1, trackBuf2;
979 Ptr<FilterEngine_GPU> filterDX, filterDY;
982 ///////////////////////////////////////// Hough Transform /////////////////////////////////////////
984 struct HoughCirclesBuf
993 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);
994 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);
995 CV_EXPORTS void HoughCirclesDownload(const oclMat& d_circles, OutputArray h_circles);
998 ///////////////////////////////////////// clAmdFft related /////////////////////////////////////////
999 //! Performs a forward or inverse discrete Fourier transform (1D or 2D) of floating point matrix.
1000 //! Param dft_size is the size of DFT transform.
1002 //! For complex-to-real transform it is assumed that the source matrix is packed in CLFFT's format.
1003 // support src type of CV32FC1, CV32FC2
1004 // support flags: DFT_INVERSE, DFT_REAL_OUTPUT, DFT_COMPLEX_OUTPUT, DFT_ROWS
1005 // dft_size is the size of original input, which is used for transformation from complex to real.
1006 // dft_size must be powers of 2, 3 and 5
1007 // real to complex dft requires at least v1.8 clAmdFft
1008 // real to complex dft output is not the same with cpu version
1009 // real to complex and complex to real does not support DFT_ROWS
1010 CV_EXPORTS void dft(const oclMat &src, oclMat &dst, Size dft_size = Size(), int flags = 0);
1012 //! implements generalized matrix product algorithm GEMM from BLAS
1013 // The functionality requires clAmdBlas library
1014 // only support type CV_32FC1
1015 // flag GEMM_3_T is not supported
1016 CV_EXPORTS void gemm(const oclMat &src1, const oclMat &src2, double alpha,
1017 const oclMat &src3, double beta, oclMat &dst, int flags = 0);
1019 //////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
1021 struct CV_EXPORTS HOGDescriptor
1025 enum { DEFAULT_WIN_SIGMA = -1 };
1027 enum { DEFAULT_NLEVELS = 64 };
1029 enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };
1033 HOGDescriptor(Size win_size = Size(64, 128), Size block_size = Size(16, 16),
1035 Size block_stride = Size(8, 8), Size cell_size = Size(8, 8),
1037 int nbins = 9, double win_sigma = DEFAULT_WIN_SIGMA,
1039 double threshold_L2hys = 0.2, bool gamma_correction = true,
1041 int nlevels = DEFAULT_NLEVELS);
1045 size_t getDescriptorSize() const;
1047 size_t getBlockHistogramSize() const;
1051 void setSVMDetector(const std::vector<float> &detector);
1055 static std::vector<float> getDefaultPeopleDetector();
1057 static std::vector<float> getPeopleDetector48x96();
1059 static std::vector<float> getPeopleDetector64x128();
1063 void detect(const oclMat &img, std::vector<Point> &found_locations,
1065 double hit_threshold = 0, Size win_stride = Size(),
1067 Size padding = Size());
1071 void detectMultiScale(const oclMat &img, std::vector<Rect> &found_locations,
1073 double hit_threshold = 0, Size win_stride = Size(),
1075 Size padding = Size(), double scale0 = 1.05,
1077 int group_threshold = 2);
1081 void getDescriptors(const oclMat &img, Size win_stride,
1083 oclMat &descriptors,
1085 int descr_format = DESCR_FORMAT_COL_BY_COL);
1101 double threshold_L2hys;
1103 bool gamma_correction;
1111 // initialize buffers; only need to do once in case of multiscale detection
1113 void init_buffer(const oclMat &img, Size win_stride);
1117 void computeBlockHistograms(const oclMat &img);
1119 void computeGradient(const oclMat &img, oclMat &grad, oclMat &qangle);
1123 double getWinSigma() const;
1125 bool checkDetectorSize() const;
1129 static int numPartsWithin(int size, int part_size, int stride);
1131 static Size numPartsWithin(Size size, Size part_size, Size stride);
1135 // Coefficients of the separating plane
1143 // Results of the last classification step
1151 // Results of the last histogram evaluation step
1157 // Gradients conputation results
1159 oclMat grad, qangle;
1169 // effect size of input image (might be different from original size after scaling)
1176 ////////////////////////feature2d_ocl/////////////////
1177 /****************************************************************************************\
1179 \****************************************************************************************/
1180 template<typename T>
1181 struct CV_EXPORTS Accumulator
1185 template<> struct Accumulator<unsigned char>
1189 template<> struct Accumulator<unsigned short>
1193 template<> struct Accumulator<char>
1197 template<> struct Accumulator<short>
1203 * Manhattan distance (city block distance) functor
1206 struct CV_EXPORTS L1
1208 enum { normType = NORM_L1 };
1209 typedef T ValueType;
1210 typedef typename Accumulator<T>::Type ResultType;
1212 ResultType operator()( const T *a, const T *b, int size ) const
1214 return normL1<ValueType, ResultType>(a, b, size);
1219 * Euclidean distance functor
1222 struct CV_EXPORTS L2
1224 enum { normType = NORM_L2 };
1225 typedef T ValueType;
1226 typedef typename Accumulator<T>::Type ResultType;
1228 ResultType operator()( const T *a, const T *b, int size ) const
1230 return (ResultType)std::sqrt((double)normL2Sqr<ValueType, ResultType>(a, b, size));
1235 * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor
1236 * bit count of A exclusive XOR'ed with B
1238 struct CV_EXPORTS Hamming
1240 enum { normType = NORM_HAMMING };
1241 typedef unsigned char ValueType;
1242 typedef int ResultType;
1244 /** this will count the bits in a ^ b
1246 ResultType operator()( const unsigned char *a, const unsigned char *b, int size ) const
1248 return normHamming(a, b, size);
1252 ////////////////////////////////// BruteForceMatcher //////////////////////////////////
1254 class CV_EXPORTS BruteForceMatcher_OCL_base
1257 enum DistType {L1Dist = 0, L2Dist, HammingDist};
1258 explicit BruteForceMatcher_OCL_base(DistType distType = L2Dist);
1260 // Add descriptors to train descriptor collection
1261 void add(const std::vector<oclMat> &descCollection);
1263 // Get train descriptors collection
1264 const std::vector<oclMat> &getTrainDescriptors() const;
1266 // Clear train descriptors collection
1269 // Return true if there are not train descriptors in collection
1272 // Return true if the matcher supports mask in match methods
1273 bool isMaskSupported() const;
1275 // Find one best match for each query descriptor
1276 void matchSingle(const oclMat &query, const oclMat &train,
1277 oclMat &trainIdx, oclMat &distance,
1278 const oclMat &mask = oclMat());
1280 // Download trainIdx and distance and convert it to CPU vector with DMatch
1281 static void matchDownload(const oclMat &trainIdx, const oclMat &distance, std::vector<DMatch> &matches);
1282 // Convert trainIdx and distance to vector with DMatch
1283 static void matchConvert(const Mat &trainIdx, const Mat &distance, std::vector<DMatch> &matches);
1285 // Find one best match for each query descriptor
1286 void match(const oclMat &query, const oclMat &train, std::vector<DMatch> &matches, const oclMat &mask = oclMat());
1288 // Make gpu collection of trains and masks in suitable format for matchCollection function
1289 void makeGpuCollection(oclMat &trainCollection, oclMat &maskCollection, const std::vector<oclMat> &masks = std::vector<oclMat>());
1291 // Find one best match from train collection for each query descriptor
1292 void matchCollection(const oclMat &query, const oclMat &trainCollection,
1293 oclMat &trainIdx, oclMat &imgIdx, oclMat &distance,
1294 const oclMat &masks = oclMat());
1296 // Download trainIdx, imgIdx and distance and convert it to vector with DMatch
1297 static void matchDownload(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance, std::vector<DMatch> &matches);
1298 // Convert trainIdx, imgIdx and distance to vector with DMatch
1299 static void matchConvert(const Mat &trainIdx, const Mat &imgIdx, const Mat &distance, std::vector<DMatch> &matches);
1301 // Find one best match from train collection for each query descriptor.
1302 void match(const oclMat &query, std::vector<DMatch> &matches, const std::vector<oclMat> &masks = std::vector<oclMat>());
1304 // Find k best matches for each query descriptor (in increasing order of distances)
1305 void knnMatchSingle(const oclMat &query, const oclMat &train,
1306 oclMat &trainIdx, oclMat &distance, oclMat &allDist, int k,
1307 const oclMat &mask = oclMat());
1309 // Download trainIdx and distance and convert it to vector with DMatch
1310 // compactResult is used when mask is not empty. If compactResult is false matches
1311 // vector will have the same size as queryDescriptors rows. If compactResult is true
1312 // matches vector will not contain matches for fully masked out query descriptors.
1313 static void knnMatchDownload(const oclMat &trainIdx, const oclMat &distance,
1314 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1315 // Convert trainIdx and distance to vector with DMatch
1316 static void knnMatchConvert(const Mat &trainIdx, const Mat &distance,
1317 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1319 // Find k best matches for each query descriptor (in increasing order of distances).
1320 // compactResult is used when mask is not empty. If compactResult is false matches
1321 // vector will have the same size as queryDescriptors rows. If compactResult is true
1322 // matches vector will not contain matches for fully masked out query descriptors.
1323 void knnMatch(const oclMat &query, const oclMat &train,
1324 std::vector< std::vector<DMatch> > &matches, int k, const oclMat &mask = oclMat(),
1325 bool compactResult = false);
1327 // Find k best matches from train collection for each query descriptor (in increasing order of distances)
1328 void knnMatch2Collection(const oclMat &query, const oclMat &trainCollection,
1329 oclMat &trainIdx, oclMat &imgIdx, oclMat &distance,
1330 const oclMat &maskCollection = oclMat());
1332 // Download trainIdx and distance and convert it to vector with DMatch
1333 // compactResult is used when mask is not empty. If compactResult is false matches
1334 // vector will have the same size as queryDescriptors rows. If compactResult is true
1335 // matches vector will not contain matches for fully masked out query descriptors.
1336 static void knnMatch2Download(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance,
1337 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1338 // Convert trainIdx and distance to vector with DMatch
1339 static void knnMatch2Convert(const Mat &trainIdx, const Mat &imgIdx, const Mat &distance,
1340 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1342 // Find k best matches for each query descriptor (in increasing order of distances).
1343 // compactResult is used when mask is not empty. If compactResult is false matches
1344 // vector will have the same size as queryDescriptors rows. If compactResult is true
1345 // matches vector will not contain matches for fully masked out query descriptors.
1346 void knnMatch(const oclMat &query, std::vector< std::vector<DMatch> > &matches, int k,
1347 const std::vector<oclMat> &masks = std::vector<oclMat>(), bool compactResult = false);
1349 // Find best matches for each query descriptor which have distance less than maxDistance.
1350 // nMatches.at<int>(0, queryIdx) will contain matches count for queryIdx.
1351 // carefully nMatches can be greater than trainIdx.cols - it means that matcher didn't find all matches,
1352 // because it didn't have enough memory.
1353 // If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nTrain / 100), 10),
1354 // otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
1355 // Matches doesn't sorted.
1356 void radiusMatchSingle(const oclMat &query, const oclMat &train,
1357 oclMat &trainIdx, oclMat &distance, oclMat &nMatches, float maxDistance,
1358 const oclMat &mask = oclMat());
1360 // Download trainIdx, nMatches and distance and convert it to vector with DMatch.
1361 // matches will be sorted in increasing order of distances.
1362 // compactResult is used when mask is not empty. If compactResult is false matches
1363 // vector will have the same size as queryDescriptors rows. If compactResult is true
1364 // matches vector will not contain matches for fully masked out query descriptors.
1365 static void radiusMatchDownload(const oclMat &trainIdx, const oclMat &distance, const oclMat &nMatches,
1366 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1367 // Convert trainIdx, nMatches and distance to vector with DMatch.
1368 static void radiusMatchConvert(const Mat &trainIdx, const Mat &distance, const Mat &nMatches,
1369 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1371 // Find best matches for each query descriptor which have distance less than maxDistance
1372 // in increasing order of distances).
1373 void radiusMatch(const oclMat &query, const oclMat &train,
1374 std::vector< std::vector<DMatch> > &matches, float maxDistance,
1375 const oclMat &mask = oclMat(), bool compactResult = false);
1377 // Find best matches for each query descriptor which have distance less than maxDistance.
1378 // If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nQuery / 100), 10),
1379 // otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
1380 // Matches doesn't sorted.
1381 void radiusMatchCollection(const oclMat &query, oclMat &trainIdx, oclMat &imgIdx, oclMat &distance, oclMat &nMatches, float maxDistance,
1382 const std::vector<oclMat> &masks = std::vector<oclMat>());
1384 // Download trainIdx, imgIdx, nMatches and distance and convert it to vector with DMatch.
1385 // matches will be sorted in increasing order of distances.
1386 // compactResult is used when mask is not empty. If compactResult is false matches
1387 // vector will have the same size as queryDescriptors rows. If compactResult is true
1388 // matches vector will not contain matches for fully masked out query descriptors.
1389 static void radiusMatchDownload(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance, const oclMat &nMatches,
1390 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1391 // Convert trainIdx, nMatches and distance to vector with DMatch.
1392 static void radiusMatchConvert(const Mat &trainIdx, const Mat &imgIdx, const Mat &distance, const Mat &nMatches,
1393 std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
1395 // Find best matches from train collection for each query descriptor which have distance less than
1396 // maxDistance (in increasing order of distances).
1397 void radiusMatch(const oclMat &query, std::vector< std::vector<DMatch> > &matches, float maxDistance,
1398 const std::vector<oclMat> &masks = std::vector<oclMat>(), bool compactResult = false);
1403 std::vector<oclMat> trainDescCollection;
1406 template <class Distance>
1407 class CV_EXPORTS BruteForceMatcher_OCL;
1409 template <typename T>
1410 class CV_EXPORTS BruteForceMatcher_OCL< L1<T> > : public BruteForceMatcher_OCL_base
1413 explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(L1Dist) {}
1414 explicit BruteForceMatcher_OCL(L1<T> /*d*/) : BruteForceMatcher_OCL_base(L1Dist) {}
1416 template <typename T>
1417 class CV_EXPORTS BruteForceMatcher_OCL< L2<T> > : public BruteForceMatcher_OCL_base
1420 explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(L2Dist) {}
1421 explicit BruteForceMatcher_OCL(L2<T> /*d*/) : BruteForceMatcher_OCL_base(L2Dist) {}
1423 template <> class CV_EXPORTS BruteForceMatcher_OCL< Hamming > : public BruteForceMatcher_OCL_base
1426 explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(HammingDist) {}
1427 explicit BruteForceMatcher_OCL(Hamming /*d*/) : BruteForceMatcher_OCL_base(HammingDist) {}
1430 class CV_EXPORTS BFMatcher_OCL : public BruteForceMatcher_OCL_base
1433 explicit BFMatcher_OCL(int norm = NORM_L2) : BruteForceMatcher_OCL_base(norm == NORM_L1 ? L1Dist : norm == NORM_L2 ? L2Dist : HammingDist) {}
1436 class CV_EXPORTS GoodFeaturesToTrackDetector_OCL
1439 explicit GoodFeaturesToTrackDetector_OCL(int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 0.0,
1440 int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04);
1442 //! return 1 rows matrix with CV_32FC2 type
1443 void operator ()(const oclMat& image, oclMat& corners, const oclMat& mask = oclMat());
1444 //! download points of type Point2f to a vector. the vector's content will be erased
1445 void downloadPoints(const oclMat &points, std::vector<Point2f> &points_v);
1448 double qualityLevel;
1452 bool useHarrisDetector;
1454 void releaseMemory()
1459 minMaxbuf_.release();
1460 tmpCorners_.release();
1470 inline GoodFeaturesToTrackDetector_OCL::GoodFeaturesToTrackDetector_OCL(int maxCorners_, double qualityLevel_, double minDistance_,
1471 int blockSize_, bool useHarrisDetector_, double harrisK_)
1473 maxCorners = maxCorners_;
1474 qualityLevel = qualityLevel_;
1475 minDistance = minDistance_;
1476 blockSize = blockSize_;
1477 useHarrisDetector = useHarrisDetector_;
1481 /////////////////////////////// PyrLKOpticalFlow /////////////////////////////////////
1483 class CV_EXPORTS PyrLKOpticalFlow
1488 winSize = Size(21, 21);
1492 useInitialFlow = false;
1493 minEigThreshold = 1e-4f;
1494 getMinEigenVals = false;
1495 isDeviceArch11_ = false;
1498 void sparse(const oclMat &prevImg, const oclMat &nextImg, const oclMat &prevPts, oclMat &nextPts,
1499 oclMat &status, oclMat *err = 0);
1501 void dense(const oclMat &prevImg, const oclMat &nextImg, oclMat &u, oclMat &v, oclMat *err = 0);
1507 bool useInitialFlow;
1508 float minEigThreshold;
1509 bool getMinEigenVals;
1511 void releaseMemory()
1513 dx_calcBuf_.release();
1514 dy_calcBuf_.release();
1524 void calcSharrDeriv(const oclMat &src, oclMat &dx, oclMat &dy);
1526 void buildImagePyramid(const oclMat &img0, std::vector<oclMat> &pyr, bool withBorder);
1531 std::vector<oclMat> prevPyr_;
1532 std::vector<oclMat> nextPyr_;
1540 bool isDeviceArch11_;
1543 class CV_EXPORTS FarnebackOpticalFlow
1546 FarnebackOpticalFlow();
1557 void operator ()(const oclMat &frame0, const oclMat &frame1, oclMat &flowx, oclMat &flowy);
1559 void releaseMemory();
1562 void prepareGaussian(
1563 int n, double sigma, float *g, float *xg, float *xxg,
1564 double &ig11, double &ig03, double &ig33, double &ig55);
1566 void setPolynomialExpansionConsts(int n, double sigma);
1568 void updateFlow_boxFilter(
1569 const oclMat& R0, const oclMat& R1, oclMat& flowx, oclMat &flowy,
1570 oclMat& M, oclMat &bufM, int blockSize, bool updateMatrices);
1572 void updateFlow_gaussianBlur(
1573 const oclMat& R0, const oclMat& R1, oclMat& flowx, oclMat& flowy,
1574 oclMat& M, oclMat &bufM, int blockSize, bool updateMatrices);
1577 oclMat pyrLevel_[2], M_, bufM_, R_[2], blurredFrame_[2];
1578 std::vector<oclMat> pyramid0_, pyramid1_;
1581 //////////////// build warping maps ////////////////////
1582 //! builds plane warping maps
1583 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);
1584 //! builds cylindrical warping maps
1585 CV_EXPORTS void buildWarpCylindricalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat &R, float scale, oclMat &map_x, oclMat &map_y);
1586 //! builds spherical warping maps
1587 CV_EXPORTS void buildWarpSphericalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat &R, float scale, oclMat &map_x, oclMat &map_y);
1588 //! builds Affine warping maps
1589 CV_EXPORTS void buildWarpAffineMaps(const Mat &M, bool inverse, Size dsize, oclMat &xmap, oclMat &ymap);
1591 //! builds Perspective warping maps
1592 CV_EXPORTS void buildWarpPerspectiveMaps(const Mat &M, bool inverse, Size dsize, oclMat &xmap, oclMat &ymap);
1594 ///////////////////////////////////// interpolate frames //////////////////////////////////////////////
1595 //! Interpolate frames (images) using provided optical flow (displacement field).
1596 //! frame0 - frame 0 (32-bit floating point images, single channel)
1597 //! frame1 - frame 1 (the same type and size)
1598 //! fu - forward horizontal displacement
1599 //! fv - forward vertical displacement
1600 //! bu - backward horizontal displacement
1601 //! bv - backward vertical displacement
1602 //! pos - new frame position
1603 //! newFrame - new frame
1604 //! buf - temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 oclMat;
1605 //! occlusion masks 0, occlusion masks 1,
1606 //! interpolated forward flow 0, interpolated forward flow 1,
1607 //! interpolated backward flow 0, interpolated backward flow 1
1609 CV_EXPORTS void interpolateFrames(const oclMat &frame0, const oclMat &frame1,
1610 const oclMat &fu, const oclMat &fv,
1611 const oclMat &bu, const oclMat &bv,
1612 float pos, oclMat &newFrame, oclMat &buf);
1614 //! computes moments of the rasterized shape or a vector of points
1615 //! _array should be a vector a points standing for the contour
1616 CV_EXPORTS Moments ocl_moments(InputArray contour);
1617 //! src should be a general image uploaded to the GPU.
1618 //! the supported oclMat type are CV_8UC1, CV_16UC1, CV_16SC1, CV_32FC1 and CV_64FC1
1619 //! to use type of CV_64FC1, the GPU should support CV_64FC1
1620 CV_EXPORTS Moments ocl_moments(oclMat& src, bool binary);
1622 class CV_EXPORTS StereoBM_OCL
1625 enum { BASIC_PRESET = 0, PREFILTER_XSOBEL = 1 };
1627 enum { DEFAULT_NDISP = 64, DEFAULT_WINSZ = 19 };
1629 //! the default constructor
1631 //! the full constructor taking the camera-specific preset, number of disparities and the SAD window size. ndisparities must be multiple of 8.
1632 StereoBM_OCL(int preset, int ndisparities = DEFAULT_NDISP, int winSize = DEFAULT_WINSZ);
1634 //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair
1635 //! Output disparity has CV_8U type.
1636 void operator() ( const oclMat &left, const oclMat &right, oclMat &disparity);
1638 //! Some heuristics that tries to estmate
1639 // if current GPU will be faster then CPU in this algorithm.
1640 // It queries current active device.
1641 static bool checkIfGpuCallReasonable();
1647 // If avergeTexThreshold == 0 => post procesing is disabled
1648 // If avergeTexThreshold != 0 then disparity is set 0 in each point (x,y) where for left image
1649 // SumOfHorizontalGradiensInWindow(x, y, winSize) < (winSize * winSize) * avergeTexThreshold
1650 // i.e. input left image is low textured.
1651 float avergeTexThreshold;
1653 oclMat minSSD, leBuf, riBuf;
1656 class CV_EXPORTS StereoBeliefPropagation
1659 enum { DEFAULT_NDISP = 64 };
1660 enum { DEFAULT_ITERS = 5 };
1661 enum { DEFAULT_LEVELS = 5 };
1662 static void estimateRecommendedParams(int width, int height, int &ndisp, int &iters, int &levels);
1663 explicit StereoBeliefPropagation(int ndisp = DEFAULT_NDISP,
1664 int iters = DEFAULT_ITERS,
1665 int levels = DEFAULT_LEVELS,
1666 int msg_type = CV_16S);
1667 StereoBeliefPropagation(int ndisp, int iters, int levels,
1668 float max_data_term, float data_weight,
1669 float max_disc_term, float disc_single_jump,
1670 int msg_type = CV_32F);
1671 void operator()(const oclMat &left, const oclMat &right, oclMat &disparity);
1672 void operator()(const oclMat &data, oclMat &disparity);
1676 float max_data_term;
1678 float max_disc_term;
1679 float disc_single_jump;
1682 oclMat u, d, l, r, u2, d2, l2, r2;
1683 std::vector<oclMat> datas;
1687 class CV_EXPORTS StereoConstantSpaceBP
1690 enum { DEFAULT_NDISP = 128 };
1691 enum { DEFAULT_ITERS = 8 };
1692 enum { DEFAULT_LEVELS = 4 };
1693 enum { DEFAULT_NR_PLANE = 4 };
1694 static void estimateRecommendedParams(int width, int height, int &ndisp, int &iters, int &levels, int &nr_plane);
1695 explicit StereoConstantSpaceBP(
1696 int ndisp = DEFAULT_NDISP,
1697 int iters = DEFAULT_ITERS,
1698 int levels = DEFAULT_LEVELS,
1699 int nr_plane = DEFAULT_NR_PLANE,
1700 int msg_type = CV_32F);
1701 StereoConstantSpaceBP(int ndisp, int iters, int levels, int nr_plane,
1702 float max_data_term, float data_weight, float max_disc_term, float disc_single_jump,
1703 int min_disp_th = 0,
1704 int msg_type = CV_32F);
1705 void operator()(const oclMat &left, const oclMat &right, oclMat &disparity);
1710 float max_data_term;
1712 float max_disc_term;
1713 float disc_single_jump;
1716 bool use_local_init_data_cost;
1718 oclMat u[2], d[2], l[2], r[2];
1719 oclMat disp_selected_pyr[2];
1721 oclMat data_cost_selected;
1726 // Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method
1729 // [1] C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
1730 // [2] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
1731 class CV_EXPORTS OpticalFlowDual_TVL1_OCL
1734 OpticalFlowDual_TVL1_OCL();
1736 void operator ()(const oclMat& I0, const oclMat& I1, oclMat& flowx, oclMat& flowy);
1738 void collectGarbage();
1741 * Time step of the numerical scheme.
1746 * Weight parameter for the data term, attachment parameter.
1747 * This is the most relevant parameter, which determines the smoothness of the output.
1748 * The smaller this parameter is, the smoother the solutions we obtain.
1749 * It depends on the range of motions of the images, so its value should be adapted to each image sequence.
1754 * Weight parameter for (u - v)^2, tightness parameter.
1755 * It serves as a link between the attachment and the regularization terms.
1756 * In theory, it should have a small value in order to maintain both parts in correspondence.
1757 * The method is stable for a large range of values of this parameter.
1762 * Number of scales used to create the pyramid of images.
1767 * Number of warpings per scale.
1768 * Represents the number of times that I1(x+u0) and grad( I1(x+u0) ) are computed per scale.
1769 * This is a parameter that assures the stability of the method.
1770 * It also affects the running time, so it is a compromise between speed and accuracy.
1775 * Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time.
1776 * A small value will yield more accurate solutions at the expense of a slower convergence.
1781 * Stopping criterion iterations number used in the numerical scheme.
1785 bool useInitialFlow;
1788 void procOneScale(const oclMat& I0, const oclMat& I1, oclMat& u1, oclMat& u2);
1790 std::vector<oclMat> I0s;
1791 std::vector<oclMat> I1s;
1792 std::vector<oclMat> u1s;
1793 std::vector<oclMat> u2s;
1813 // current supported sorting methods
1816 SORT_BITONIC, // only support power-of-2 buffer size
1817 SORT_SELECTION, // cannot sort duplicate keys
1819 SORT_RADIX // only support signed int/float keys(CV_32S/CV_32F)
1821 //! Returns the sorted result of all the elements in input based on equivalent keys.
1823 // The element unit in the values to be sorted is determined from the data type,
1824 // i.e., a CV_32FC2 input {a1a2, b1b2} will be considered as two elements, regardless its
1825 // matrix dimension.
1826 // both keys and values will be sorted inplace
1827 // Key needs to be single channel oclMat.
1831 // keys = {2, 3, 1} (CV_8UC1)
1832 // values = {10,5, 4,3, 6,2} (CV_8UC2)
1833 // sortByKey(keys, values, SORT_SELECTION, false);
1835 // keys = {1, 2, 3} (CV_8UC1)
1836 // values = {6,2, 10,5, 4,3} (CV_8UC2)
1837 CV_EXPORTS void sortByKey(oclMat& keys, oclMat& values, int method, bool isGreaterThan = false);
1838 /*!Base class for MOG and MOG2!*/
1839 class CV_EXPORTS BackgroundSubtractor
1842 //! the virtual destructor
1843 virtual ~BackgroundSubtractor();
1844 //! the update operator that takes the next video frame and returns the current foreground mask as 8-bit binary image.
1845 virtual void operator()(const oclMat& image, oclMat& fgmask, float learningRate);
1847 //! computes a background image
1848 virtual void getBackgroundImage(oclMat& backgroundImage) const = 0;
1851 Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm
1853 The class implements the following algorithm:
1854 "An improved adaptive background mixture model for real-time tracking with shadow detection"
1855 P. KadewTraKuPong and R. Bowden,
1856 Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
1857 http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
1859 class CV_EXPORTS MOG: public cv::ocl::BackgroundSubtractor
1862 //! the default constructor
1863 MOG(int nmixtures = -1);
1865 //! re-initiaization method
1866 void initialize(Size frameSize, int frameType);
1868 //! the update operator
1869 void operator()(const oclMat& frame, oclMat& fgmask, float learningRate = 0.f);
1871 //! computes a background image which are the mean of all background gaussians
1872 void getBackgroundImage(oclMat& backgroundImage) const;
1874 //! releases all inner buffers
1879 float backgroundRatio;
1896 The class implements the following algorithm:
1897 "Improved adaptive Gausian mixture model for background subtraction"
1899 International Conference Pattern Recognition, UK, August, 2004.
1900 http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf
1902 class CV_EXPORTS MOG2: public cv::ocl::BackgroundSubtractor
1905 //! the default constructor
1906 MOG2(int nmixtures = -1);
1908 //! re-initiaization method
1909 void initialize(Size frameSize, int frameType);
1911 //! the update operator
1912 void operator()(const oclMat& frame, oclMat& fgmask, float learningRate = -1.0f);
1914 //! computes a background image which are the mean of all background gaussians
1915 void getBackgroundImage(oclMat& backgroundImage) const;
1917 //! releases all inner buffers
1921 // you should call initialize after parameters changes
1925 //! here it is the maximum allowed number of mixture components.
1926 //! Actual number is determined dynamically per pixel
1928 // threshold on the squared Mahalanobis distance to decide if it is well described
1929 // by the background model or not. Related to Cthr from the paper.
1930 // This does not influence the update of the background. A typical value could be 4 sigma
1931 // and that is varThreshold=4*4=16; Corresponds to Tb in the paper.
1933 /////////////////////////
1934 // less important parameters - things you might change but be carefull
1935 ////////////////////////
1937 float backgroundRatio;
1938 // corresponds to fTB=1-cf from the paper
1939 // TB - threshold when the component becomes significant enough to be included into
1940 // the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0.
1941 // For alpha=0.001 it means that the mode should exist for approximately 105 frames before
1942 // it is considered foreground
1943 // float noiseSigma;
1944 float varThresholdGen;
1946 //correspondts to Tg - threshold on the squared Mahalan. dist. to decide
1947 //when a sample is close to the existing components. If it is not close
1948 //to any a new component will be generated. I use 3 sigma => Tg=3*3=9.
1949 //Smaller Tg leads to more generated components and higher Tg might make
1950 //lead to small number of components but they can grow too large
1955 //initial variance for the newly generated components.
1956 //It will will influence the speed of adaptation. A good guess should be made.
1957 //A simple way is to estimate the typical standard deviation from the images.
1958 //I used here 10 as a reasonable value
1959 // min and max can be used to further control the variance
1960 float fCT; //CT - complexity reduction prior
1961 //this is related to the number of samples needed to accept that a component
1962 //actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get
1963 //the standard Stauffer&Grimson algorithm (maybe not exact but very similar)
1965 //shadow detection parameters
1966 bool bShadowDetection; //default 1 - do shadow detection
1967 unsigned char nShadowDetection; //do shadow detection - insert this value as the detection result - 127 default value
1969 // Tau - shadow threshold. The shadow is detected if the pixel is darker
1970 //version of the background. Tau is a threshold on how much darker the shadow can be.
1971 //Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow
1972 //See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.
1985 oclMat bgmodelUsedModes_; //keep track of number of modes per pixel
1988 /*!***************Kalman Filter*************!*/
1989 class CV_EXPORTS KalmanFilter
1993 //! the full constructor taking the dimensionality of the state, of the measurement and of the control vector
1994 KalmanFilter(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F);
1995 //! re-initializes Kalman filter. The previous content is destroyed.
1996 void init(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F);
1998 const oclMat& predict(const oclMat& control=oclMat());
1999 const oclMat& correct(const oclMat& measurement);
2001 oclMat statePre; //!< predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
2002 oclMat statePost; //!< corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
2003 oclMat transitionMatrix; //!< state transition matrix (A)
2004 oclMat controlMatrix; //!< control matrix (B) (not used if there is no control)
2005 oclMat measurementMatrix; //!< measurement matrix (H)
2006 oclMat processNoiseCov; //!< process noise covariance matrix (Q)
2007 oclMat measurementNoiseCov;//!< measurement noise covariance matrix (R)
2008 oclMat errorCovPre; //!< priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/
2009 oclMat gain; //!< Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
2010 oclMat errorCovPost; //!< posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
2019 /*!***************K Nearest Neighbour*************!*/
2020 class CV_EXPORTS KNearestNeighbour: public CvKNearest
2023 KNearestNeighbour();
2024 ~KNearestNeighbour();
2026 bool train(const Mat& trainData, Mat& labels, Mat& sampleIdx = Mat().setTo(Scalar::all(0)),
2027 bool isRegression = false, int max_k = 32, bool updateBase = false);
2031 void find_nearest(const oclMat& samples, int k, oclMat& lables);
2037 /*!*************** SVM *************!*/
2038 class CV_EXPORTS CvSVM_OCL : public CvSVM
2043 CvSVM_OCL(const cv::Mat& trainData, const cv::Mat& responses,
2044 const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
2045 CvSVMParams params=CvSVMParams());
2046 CV_WRAP float predict( const int row_index, Mat& src, bool returnDFVal=false ) const;
2047 CV_WRAP void predict( cv::InputArray samples, cv::OutputArray results ) const;
2048 CV_WRAP float predict( const cv::Mat& sample, bool returnDFVal=false ) const;
2049 float predict( const CvMat* samples, CV_OUT CvMat* results ) const;
2052 float predict( const int row_index, int row_len, Mat& src, bool returnDFVal=false ) const;
2053 void create_kernel();
2054 void create_solver();
2057 /*!*************** END *************!*/
2060 #if defined _MSC_VER && _MSC_VER >= 1200
2061 # pragma warning( push)
2062 # pragma warning( disable: 4267)
2064 #include "opencv2/ocl/matrix_operations.hpp"
2065 #if defined _MSC_VER && _MSC_VER >= 1200
2066 # pragma warning( pop)
2069 #endif /* __OPENCV_OCL_HPP__ */