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| author | siddhu8990 | 2017-04-19 11:56:09 +0530 |
|---|---|---|
| committer | siddhu8990 | 2017-04-19 11:56:09 +0530 |
| commit | 453598b49cb3d4a62b1797dbc90f0e3dd4521329 (patch) | |
| tree | 9d10176d0a4be5eb567ade03e1dd6172c77605e4 /thirdparty/raspberrypi/includes/opencv2/gpu/gpu.hpp | |
| parent | aceeb1fe05a8ff6c126ea9ba166a19249488dbd1 (diff) | |
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Copyright message updated in added files and libraries separated in 'thirdparty' folder
Diffstat (limited to 'thirdparty/raspberrypi/includes/opencv2/gpu/gpu.hpp')
| -rw-r--r-- | thirdparty/raspberrypi/includes/opencv2/gpu/gpu.hpp | 2530 |
1 files changed, 2530 insertions, 0 deletions
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/gpu.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/gpu.hpp new file mode 100644 index 0000000..de16982 --- /dev/null +++ b/thirdparty/raspberrypi/includes/opencv2/gpu/gpu.hpp @@ -0,0 +1,2530 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_GPU_HPP__ +#define __OPENCV_GPU_HPP__ + +#ifndef SKIP_INCLUDES +#include <vector> +#include <memory> +#include <iosfwd> +#endif + +#include "opencv2/core/gpumat.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include "opencv2/objdetect/objdetect.hpp" +#include "opencv2/features2d/features2d.hpp" + +namespace cv { namespace gpu { + +//////////////////////////////// CudaMem //////////////////////////////// +// CudaMem is limited cv::Mat with page locked memory allocation. +// Page locked memory is only needed for async and faster coping to GPU. +// It is convertable to cv::Mat header without reference counting +// so you can use it with other opencv functions. + +// Page-locks the matrix m memory and maps it for the device(s) +CV_EXPORTS void registerPageLocked(Mat& m); +// Unmaps the memory of matrix m, and makes it pageable again. +CV_EXPORTS void unregisterPageLocked(Mat& m); + +class CV_EXPORTS CudaMem +{ +public: + enum { ALLOC_PAGE_LOCKED = 1, ALLOC_ZEROCOPY = 2, ALLOC_WRITE_COMBINED = 4 }; + + CudaMem(); + CudaMem(const CudaMem& m); + + CudaMem(int rows, int cols, int type, int _alloc_type = ALLOC_PAGE_LOCKED); + CudaMem(Size size, int type, int alloc_type = ALLOC_PAGE_LOCKED); + + + //! creates from cv::Mat with coping data + explicit CudaMem(const Mat& m, int alloc_type = ALLOC_PAGE_LOCKED); + + ~CudaMem(); + + CudaMem& operator = (const CudaMem& m); + + //! returns deep copy of the matrix, i.e. the data is copied + CudaMem clone() const; + + //! allocates new matrix data unless the matrix already has specified size and type. + void create(int rows, int cols, int type, int alloc_type = ALLOC_PAGE_LOCKED); + void create(Size size, int type, int alloc_type = ALLOC_PAGE_LOCKED); + + //! decrements reference counter and released memory if needed. + void release(); + + //! returns matrix header with disabled reference counting for CudaMem data. + Mat createMatHeader() const; + operator Mat() const; + + //! maps host memory into device address space and returns GpuMat header for it. Throws exception if not supported by hardware. + GpuMat createGpuMatHeader() const; + operator GpuMat() const; + + //returns if host memory can be mapperd to gpu address space; + static bool canMapHostMemory(); + + // Please see cv::Mat for descriptions + bool isContinuous() const; + size_t elemSize() const; + size_t elemSize1() const; + int type() const; + int depth() const; + int channels() const; + size_t step1() const; + Size size() const; + bool empty() const; + + + // Please see cv::Mat for descriptions + int flags; + int rows, cols; + size_t step; + + uchar* data; + int* refcount; + + uchar* datastart; + uchar* dataend; + + int alloc_type; +}; + +//////////////////////////////// CudaStream //////////////////////////////// +// Encapculates Cuda Stream. Provides interface for async coping. +// Passed to each function that supports async kernel execution. +// Reference counting is enabled + +class CV_EXPORTS Stream +{ +public: + Stream(); + ~Stream(); + + Stream(const Stream&); + Stream& operator =(const Stream&); + + bool queryIfComplete(); + void waitForCompletion(); + + //! downloads asynchronously + // Warning! cv::Mat must point to page locked memory (i.e. to CudaMem data or to its subMat) + void enqueueDownload(const GpuMat& src, CudaMem& dst); + void enqueueDownload(const GpuMat& src, Mat& dst); + + //! uploads asynchronously + // Warning! cv::Mat must point to page locked memory (i.e. to CudaMem data or to its ROI) + void enqueueUpload(const CudaMem& src, GpuMat& dst); + void enqueueUpload(const Mat& src, GpuMat& dst); + + //! copy asynchronously + void enqueueCopy(const GpuMat& src, GpuMat& dst); + + //! memory set asynchronously + void enqueueMemSet(GpuMat& src, Scalar val); + void enqueueMemSet(GpuMat& src, Scalar val, const GpuMat& mask); + + //! converts matrix type, ex from float to uchar depending on type + void enqueueConvert(const GpuMat& src, GpuMat& dst, int dtype, double a = 1, double b = 0); + + //! adds a callback to be called on the host after all currently enqueued items in the stream have completed + typedef void (*StreamCallback)(Stream& stream, int status, void* userData); + void enqueueHostCallback(StreamCallback callback, void* userData); + + static Stream& Null(); + + operator bool() const; + +private: + struct Impl; + + explicit Stream(Impl* impl); + void create(); + void release(); + + Impl *impl; + + friend struct StreamAccessor; +}; + + +//////////////////////////////// Filter Engine //////////////////////////////// + +/*! +The Base Class for 1D or Row-wise Filters + +This is the base class for linear or non-linear filters that process 1D data. +In particular, such filters are used for the "horizontal" filtering parts in separable filters. +*/ +class CV_EXPORTS BaseRowFilter_GPU +{ +public: + BaseRowFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {} + virtual ~BaseRowFilter_GPU() {} + virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0; + int ksize, anchor; +}; + +/*! +The Base Class for Column-wise Filters + +This is the base class for linear or non-linear filters that process columns of 2D arrays. +Such filters are used for the "vertical" filtering parts in separable filters. +*/ +class CV_EXPORTS BaseColumnFilter_GPU +{ +public: + BaseColumnFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {} + virtual ~BaseColumnFilter_GPU() {} + virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0; + int ksize, anchor; +}; + +/*! +The Base Class for Non-Separable 2D Filters. + +This is the base class for linear or non-linear 2D filters. +*/ +class CV_EXPORTS BaseFilter_GPU +{ +public: + BaseFilter_GPU(const Size& ksize_, const Point& anchor_) : ksize(ksize_), anchor(anchor_) {} + virtual ~BaseFilter_GPU() {} + virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0; + Size ksize; + Point anchor; +}; + +/*! +The Base Class for Filter Engine. + +The class can be used to apply an arbitrary filtering operation to an image. +It contains all the necessary intermediate buffers. +*/ +class CV_EXPORTS FilterEngine_GPU +{ +public: + virtual ~FilterEngine_GPU() {} + + virtual void apply(const GpuMat& src, GpuMat& dst, Rect roi = Rect(0,0,-1,-1), Stream& stream = Stream::Null()) = 0; +}; + +//! returns the non-separable filter engine with the specified filter +CV_EXPORTS Ptr<FilterEngine_GPU> createFilter2D_GPU(const Ptr<BaseFilter_GPU>& filter2D, int srcType, int dstType); + +//! returns the separable filter engine with the specified filters +CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter, + const Ptr<BaseColumnFilter_GPU>& columnFilter, int srcType, int bufType, int dstType); +CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter, + const Ptr<BaseColumnFilter_GPU>& columnFilter, int srcType, int bufType, int dstType, GpuMat& buf); + +//! returns horizontal 1D box filter +//! supports only CV_8UC1 source type and CV_32FC1 sum type +CV_EXPORTS Ptr<BaseRowFilter_GPU> getRowSumFilter_GPU(int srcType, int sumType, int ksize, int anchor = -1); + +//! returns vertical 1D box filter +//! supports only CV_8UC1 sum type and CV_32FC1 dst type +CV_EXPORTS Ptr<BaseColumnFilter_GPU> getColumnSumFilter_GPU(int sumType, int dstType, int ksize, int anchor = -1); + +//! returns 2D box filter +//! supports CV_8UC1 and CV_8UC4 source type, dst type must be the same as source type +CV_EXPORTS Ptr<BaseFilter_GPU> getBoxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1, -1)); + +//! returns box filter engine +CV_EXPORTS Ptr<FilterEngine_GPU> createBoxFilter_GPU(int srcType, int dstType, const Size& ksize, + const Point& anchor = Point(-1,-1)); + +//! returns 2D morphological filter +//! only MORPH_ERODE and MORPH_DILATE are supported +//! supports CV_8UC1 and CV_8UC4 types +//! kernel must have CV_8UC1 type, one rows and cols == ksize.width * ksize.height +CV_EXPORTS Ptr<BaseFilter_GPU> getMorphologyFilter_GPU(int op, int type, const Mat& kernel, const Size& ksize, + Point anchor=Point(-1,-1)); + +//! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported. +CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat& kernel, + const Point& anchor = Point(-1,-1), int iterations = 1); +CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat& kernel, GpuMat& buf, + const Point& anchor = Point(-1,-1), int iterations = 1); + +//! returns 2D filter with the specified kernel +//! supports CV_8U, CV_16U and CV_32F one and four channel image +CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT); + +//! returns the non-separable linear filter engine +CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, + Point anchor = Point(-1,-1), int borderType = BORDER_DEFAULT); + +//! returns the primitive row filter with the specified kernel. +//! supports only CV_8UC1, CV_8UC4, CV_16SC1, CV_16SC2, CV_32SC1, CV_32FC1 source type. +//! there are two version of algorithm: NPP and OpenCV. +//! NPP calls when srcType == CV_8UC1 or srcType == CV_8UC4 and bufType == srcType, +//! otherwise calls OpenCV version. +//! NPP supports only BORDER_CONSTANT border type. +//! OpenCV version supports only CV_32F as buffer depth and +//! BORDER_REFLECT101, BORDER_REPLICATE and BORDER_CONSTANT border types. +CV_EXPORTS Ptr<BaseRowFilter_GPU> getLinearRowFilter_GPU(int srcType, int bufType, const Mat& rowKernel, + int anchor = -1, int borderType = BORDER_DEFAULT); + +//! returns the primitive column filter with the specified kernel. +//! supports only CV_8UC1, CV_8UC4, CV_16SC1, CV_16SC2, CV_32SC1, CV_32FC1 dst type. +//! there are two version of algorithm: NPP and OpenCV. +//! NPP calls when dstType == CV_8UC1 or dstType == CV_8UC4 and bufType == dstType, +//! otherwise calls OpenCV version. +//! NPP supports only BORDER_CONSTANT border type. +//! OpenCV version supports only CV_32F as buffer depth and +//! BORDER_REFLECT101, BORDER_REPLICATE and BORDER_CONSTANT border types. +CV_EXPORTS Ptr<BaseColumnFilter_GPU> getLinearColumnFilter_GPU(int bufType, int dstType, const Mat& columnKernel, + int anchor = -1, int borderType = BORDER_DEFAULT); + +//! returns the separable linear filter engine +CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel, + const Mat& columnKernel, const Point& anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, + int columnBorderType = -1); +CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel, + const Mat& columnKernel, GpuMat& buf, const Point& anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, + int columnBorderType = -1); + +//! returns filter engine for the generalized Sobel operator +CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU(int srcType, int dstType, int dx, int dy, int ksize, + int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1); +CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU(int srcType, int dstType, int dx, int dy, int ksize, GpuMat& buf, + int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1); + +//! returns the Gaussian filter engine +CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, double sigma1, double sigma2 = 0, + int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1); +CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, GpuMat& buf, double sigma1, double sigma2 = 0, + int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1); + +//! returns maximum filter +CV_EXPORTS Ptr<BaseFilter_GPU> getMaxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1)); + +//! returns minimum filter +CV_EXPORTS Ptr<BaseFilter_GPU> getMinFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1)); + +//! smooths the image using the normalized box filter +//! supports CV_8UC1, CV_8UC4 types +CV_EXPORTS void boxFilter(const GpuMat& src, GpuMat& dst, int ddepth, Size ksize, Point anchor = Point(-1,-1), Stream& stream = Stream::Null()); + +//! a synonym for normalized box filter +static inline void blur(const GpuMat& src, GpuMat& dst, Size ksize, Point anchor = Point(-1,-1), Stream& stream = Stream::Null()) +{ + boxFilter(src, dst, -1, ksize, anchor, stream); +} + +//! erodes the image (applies the local minimum operator) +CV_EXPORTS void erode(const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1); +CV_EXPORTS void erode(const GpuMat& src, GpuMat& dst, const Mat& kernel, GpuMat& buf, + Point anchor = Point(-1, -1), int iterations = 1, + Stream& stream = Stream::Null()); + +//! dilates the image (applies the local maximum operator) +CV_EXPORTS void dilate(const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1); +CV_EXPORTS void dilate(const GpuMat& src, GpuMat& dst, const Mat& kernel, GpuMat& buf, + Point anchor = Point(-1, -1), int iterations = 1, + Stream& stream = Stream::Null()); + +//! applies an advanced morphological operation to the image +CV_EXPORTS void morphologyEx(const GpuMat& src, GpuMat& dst, int op, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1); +CV_EXPORTS void morphologyEx(const GpuMat& src, GpuMat& dst, int op, const Mat& kernel, GpuMat& buf1, GpuMat& buf2, + Point anchor = Point(-1, -1), int iterations = 1, Stream& stream = Stream::Null()); + +//! applies non-separable 2D linear filter to the image +CV_EXPORTS void filter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernel, Point anchor=Point(-1,-1), int borderType = BORDER_DEFAULT, Stream& stream = Stream::Null()); + +//! applies separable 2D linear filter to the image +CV_EXPORTS void sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY, + Point anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1); +CV_EXPORTS void sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY, GpuMat& buf, + Point anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, + Stream& stream = Stream::Null()); + +//! applies generalized Sobel operator to the image +CV_EXPORTS void Sobel(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, int ksize = 3, double scale = 1, + int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1); +CV_EXPORTS void Sobel(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, GpuMat& buf, int ksize = 3, double scale = 1, + int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null()); + +//! applies the vertical or horizontal Scharr operator to the image +CV_EXPORTS void Scharr(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, double scale = 1, + int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1); +CV_EXPORTS void Scharr(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, GpuMat& buf, double scale = 1, + int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null()); + +//! smooths the image using Gaussian filter. +CV_EXPORTS void GaussianBlur(const GpuMat& src, GpuMat& dst, Size ksize, double sigma1, double sigma2 = 0, + int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1); +CV_EXPORTS void GaussianBlur(const GpuMat& src, GpuMat& dst, Size ksize, GpuMat& buf, double sigma1, double sigma2 = 0, + int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null()); + +//! applies Laplacian operator to the image +//! supports only ksize = 1 and ksize = 3 +CV_EXPORTS void Laplacian(const GpuMat& src, GpuMat& dst, int ddepth, int ksize = 1, double scale = 1, int borderType = BORDER_DEFAULT, Stream& stream = Stream::Null()); + + +////////////////////////////// Arithmetics /////////////////////////////////// + +//! implements generalized matrix product algorithm GEMM from BLAS +CV_EXPORTS void gemm(const GpuMat& src1, const GpuMat& src2, double alpha, + const GpuMat& src3, double beta, GpuMat& dst, int flags = 0, Stream& stream = Stream::Null()); + +//! transposes the matrix +//! supports matrix with element size = 1, 4 and 8 bytes (CV_8UC1, CV_8UC4, CV_16UC2, CV_32FC1, etc) +CV_EXPORTS void transpose(const GpuMat& src1, GpuMat& dst, Stream& stream = Stream::Null()); + +//! reverses the order of the rows, columns or both in a matrix +//! supports 1, 3 and 4 channels images with CV_8U, CV_16U, CV_32S or CV_32F depth +CV_EXPORTS void flip(const GpuMat& a, GpuMat& b, int flipCode, Stream& stream = Stream::Null()); + +//! transforms 8-bit unsigned integers using lookup table: dst(i)=lut(src(i)) +//! destination array will have the depth type as lut and the same channels number as source +//! supports CV_8UC1, CV_8UC3 types +CV_EXPORTS void LUT(const GpuMat& src, const Mat& lut, GpuMat& dst, Stream& stream = Stream::Null()); + +//! makes multi-channel array out of several single-channel arrays +CV_EXPORTS void merge(const GpuMat* src, size_t n, GpuMat& dst, Stream& stream = Stream::Null()); + +//! makes multi-channel array out of several single-channel arrays +CV_EXPORTS void merge(const vector<GpuMat>& src, GpuMat& dst, Stream& stream = Stream::Null()); + +//! copies each plane of a multi-channel array to a dedicated array +CV_EXPORTS void split(const GpuMat& src, GpuMat* dst, Stream& stream = Stream::Null()); + +//! copies each plane of a multi-channel array to a dedicated array +CV_EXPORTS void split(const GpuMat& src, vector<GpuMat>& dst, Stream& stream = Stream::Null()); + +//! computes magnitude of complex (x(i).re, x(i).im) vector +//! supports only CV_32FC2 type +CV_EXPORTS void magnitude(const GpuMat& xy, GpuMat& magnitude, Stream& stream = Stream::Null()); + +//! computes squared magnitude of complex (x(i).re, x(i).im) vector +//! supports only CV_32FC2 type +CV_EXPORTS void magnitudeSqr(const GpuMat& xy, GpuMat& magnitude, Stream& stream = Stream::Null()); + +//! computes magnitude of each (x(i), y(i)) vector +//! supports only floating-point source +CV_EXPORTS void magnitude(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, Stream& stream = Stream::Null()); + +//! computes squared magnitude of each (x(i), y(i)) vector +//! supports only floating-point source +CV_EXPORTS void magnitudeSqr(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, Stream& stream = Stream::Null()); + +//! computes angle (angle(i)) of each (x(i), y(i)) vector +//! supports only floating-point source +CV_EXPORTS void phase(const GpuMat& x, const GpuMat& y, GpuMat& angle, bool angleInDegrees = false, Stream& stream = Stream::Null()); + +//! converts Cartesian coordinates to polar +//! supports only floating-point source +CV_EXPORTS void cartToPolar(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, GpuMat& angle, bool angleInDegrees = false, Stream& stream = Stream::Null()); + +//! converts polar coordinates to Cartesian +//! supports only floating-point source +CV_EXPORTS void polarToCart(const GpuMat& magnitude, const GpuMat& angle, GpuMat& x, GpuMat& y, bool angleInDegrees = false, Stream& stream = Stream::Null()); + +//! scales and shifts array elements so that either the specified norm (alpha) or the minimum (alpha) and maximum (beta) array values get the specified values +CV_EXPORTS void normalize(const GpuMat& src, GpuMat& dst, double alpha = 1, double beta = 0, + int norm_type = NORM_L2, int dtype = -1, const GpuMat& mask = GpuMat()); +CV_EXPORTS void normalize(const GpuMat& src, GpuMat& dst, double a, double b, + int norm_type, int dtype, const GpuMat& mask, GpuMat& norm_buf, GpuMat& cvt_buf); + + +//////////////////////////// Per-element operations //////////////////////////////////// + +//! adds one matrix to another (c = a + b) +CV_EXPORTS void add(const GpuMat& a, const GpuMat& b, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null()); +//! adds scalar to a matrix (c = a + s) +CV_EXPORTS void add(const GpuMat& a, const Scalar& sc, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null()); + +//! subtracts one matrix from another (c = a - b) +CV_EXPORTS void subtract(const GpuMat& a, const GpuMat& b, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null()); +//! subtracts scalar from a matrix (c = a - s) +CV_EXPORTS void subtract(const GpuMat& a, const Scalar& sc, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null()); + +//! computes element-wise weighted product of the two arrays (c = scale * a * b) +CV_EXPORTS void multiply(const GpuMat& a, const GpuMat& b, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null()); +//! weighted multiplies matrix to a scalar (c = scale * a * s) +CV_EXPORTS void multiply(const GpuMat& a, const Scalar& sc, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null()); + +//! computes element-wise weighted quotient of the two arrays (c = a / b) +CV_EXPORTS void divide(const GpuMat& a, const GpuMat& b, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null()); +//! computes element-wise weighted quotient of matrix and scalar (c = a / s) +CV_EXPORTS void divide(const GpuMat& a, const Scalar& sc, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null()); +//! computes element-wise weighted reciprocal of an array (dst = scale/src2) +CV_EXPORTS void divide(double scale, const GpuMat& b, GpuMat& c, int dtype = -1, Stream& stream = Stream::Null()); + +//! computes the weighted sum of two arrays (dst = alpha*src1 + beta*src2 + gamma) +CV_EXPORTS void addWeighted(const GpuMat& src1, double alpha, const GpuMat& src2, double beta, double gamma, GpuMat& dst, + int dtype = -1, Stream& stream = Stream::Null()); + +//! adds scaled array to another one (dst = alpha*src1 + src2) +static inline void scaleAdd(const GpuMat& src1, double alpha, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null()) +{ + addWeighted(src1, alpha, src2, 1.0, 0.0, dst, -1, stream); +} + +//! computes element-wise absolute difference of two arrays (c = abs(a - b)) +CV_EXPORTS void absdiff(const GpuMat& a, const GpuMat& b, GpuMat& c, Stream& stream = Stream::Null()); +//! computes element-wise absolute difference of array and scalar (c = abs(a - s)) +CV_EXPORTS void absdiff(const GpuMat& a, const Scalar& s, GpuMat& c, Stream& stream = Stream::Null()); + +//! computes absolute value of each matrix element +//! supports CV_16S and CV_32F depth +CV_EXPORTS void abs(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()); + +//! computes square of each pixel in an image +//! supports CV_8U, CV_16U, CV_16S and CV_32F depth +CV_EXPORTS void sqr(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()); + +//! computes square root of each pixel in an image +//! supports CV_8U, CV_16U, CV_16S and CV_32F depth +CV_EXPORTS void sqrt(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()); + +//! computes exponent of each matrix element (b = e**a) +//! supports CV_8U, CV_16U, CV_16S and CV_32F depth +CV_EXPORTS void exp(const GpuMat& a, GpuMat& b, Stream& stream = Stream::Null()); + +//! computes natural logarithm of absolute value of each matrix element: b = log(abs(a)) +//! supports CV_8U, CV_16U, CV_16S and CV_32F depth +CV_EXPORTS void log(const GpuMat& a, GpuMat& b, Stream& stream = Stream::Null()); + +//! computes power of each matrix element: +// (dst(i,j) = pow( src(i,j) , power), if src.type() is integer +// (dst(i,j) = pow(fabs(src(i,j)), power), otherwise +//! supports all, except depth == CV_64F +CV_EXPORTS void pow(const GpuMat& src, double power, GpuMat& dst, Stream& stream = Stream::Null()); + +//! compares elements of two arrays (c = a \<cmpop\> b) +CV_EXPORTS void compare(const GpuMat& a, const GpuMat& b, GpuMat& c, int cmpop, Stream& stream = Stream::Null()); +CV_EXPORTS void compare(const GpuMat& a, Scalar sc, GpuMat& c, int cmpop, Stream& stream = Stream::Null()); + +//! performs per-elements bit-wise inversion +CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null()); + +//! calculates per-element bit-wise disjunction of two arrays +CV_EXPORTS void bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null()); +//! calculates per-element bit-wise disjunction of array and scalar +//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth +CV_EXPORTS void bitwise_or(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null()); + +//! calculates per-element bit-wise conjunction of two arrays +CV_EXPORTS void bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null()); +//! calculates per-element bit-wise conjunction of array and scalar +//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth +CV_EXPORTS void bitwise_and(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null()); + +//! calculates per-element bit-wise "exclusive or" operation +CV_EXPORTS void bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null()); +//! calculates per-element bit-wise "exclusive or" of array and scalar +//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth +CV_EXPORTS void bitwise_xor(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null()); + +//! pixel by pixel right shift of an image by a constant value +//! supports 1, 3 and 4 channels images with integers elements +CV_EXPORTS void rshift(const GpuMat& src, Scalar_<int> sc, GpuMat& dst, Stream& stream = Stream::Null()); + +//! pixel by pixel left shift of an image by a constant value +//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth +CV_EXPORTS void lshift(const GpuMat& src, Scalar_<int> sc, GpuMat& dst, Stream& stream = Stream::Null()); + +//! computes per-element minimum of two arrays (dst = min(src1, src2)) +CV_EXPORTS void min(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null()); + +//! computes per-element minimum of array and scalar (dst = min(src1, src2)) +CV_EXPORTS void min(const GpuMat& src1, double src2, GpuMat& dst, Stream& stream = Stream::Null()); + +//! computes per-element maximum of two arrays (dst = max(src1, src2)) +CV_EXPORTS void max(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null()); + +//! computes per-element maximum of array and scalar (dst = max(src1, src2)) +CV_EXPORTS void max(const GpuMat& src1, double src2, GpuMat& dst, Stream& stream = Stream::Null()); + +enum { ALPHA_OVER, ALPHA_IN, ALPHA_OUT, ALPHA_ATOP, ALPHA_XOR, ALPHA_PLUS, ALPHA_OVER_PREMUL, ALPHA_IN_PREMUL, ALPHA_OUT_PREMUL, + ALPHA_ATOP_PREMUL, ALPHA_XOR_PREMUL, ALPHA_PLUS_PREMUL, ALPHA_PREMUL}; + +//! Composite two images using alpha opacity values contained in each image +//! Supports CV_8UC4, CV_16UC4, CV_32SC4 and CV_32FC4 types +CV_EXPORTS void alphaComp(const GpuMat& img1, const GpuMat& img2, GpuMat& dst, int alpha_op, Stream& stream = Stream::Null()); + + +////////////////////////////// Image processing ////////////////////////////// + +//! DST[x,y] = SRC[xmap[x,y],ymap[x,y]] +//! supports only CV_32FC1 map type +CV_EXPORTS void remap(const GpuMat& src, GpuMat& dst, const GpuMat& xmap, const GpuMat& ymap, + int interpolation, int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar(), + Stream& stream = Stream::Null()); + +//! Does mean shift filtering on GPU. +CV_EXPORTS void meanShiftFiltering(const GpuMat& src, GpuMat& dst, int sp, int sr, + TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1), + Stream& stream = Stream::Null()); + +//! Does mean shift procedure on GPU. +CV_EXPORTS void meanShiftProc(const GpuMat& src, GpuMat& dstr, GpuMat& dstsp, int sp, int sr, + TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1), + Stream& stream = Stream::Null()); + +//! Does mean shift segmentation with elimination of small regions. +CV_EXPORTS void meanShiftSegmentation(const GpuMat& src, Mat& dst, int sp, int sr, int minsize, + TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1)); + +//! Does coloring of disparity image: [0..ndisp) -> [0..240, 1, 1] in HSV. +//! Supported types of input disparity: CV_8U, CV_16S. +//! Output disparity has CV_8UC4 type in BGRA format (alpha = 255). +CV_EXPORTS void drawColorDisp(const GpuMat& src_disp, GpuMat& dst_disp, int ndisp, Stream& stream = Stream::Null()); + +//! Reprojects disparity image to 3D space. +//! Supports CV_8U and CV_16S types of input disparity. +//! The output is a 3- or 4-channel floating-point matrix. +//! Each element of this matrix will contain the 3D coordinates of the point (x,y,z,1), computed from the disparity map. +//! Q is the 4x4 perspective transformation matrix that can be obtained with cvStereoRectify. +CV_EXPORTS void reprojectImageTo3D(const GpuMat& disp, GpuMat& xyzw, const Mat& Q, int dst_cn = 4, Stream& stream = Stream::Null()); + +//! converts image from one color space to another +CV_EXPORTS void cvtColor(const GpuMat& src, GpuMat& dst, int code, int dcn = 0, Stream& stream = Stream::Null()); + +enum +{ + // Bayer Demosaicing (Malvar, He, and Cutler) + COLOR_BayerBG2BGR_MHT = 256, + COLOR_BayerGB2BGR_MHT = 257, + COLOR_BayerRG2BGR_MHT = 258, + COLOR_BayerGR2BGR_MHT = 259, + + COLOR_BayerBG2RGB_MHT = COLOR_BayerRG2BGR_MHT, + COLOR_BayerGB2RGB_MHT = COLOR_BayerGR2BGR_MHT, + COLOR_BayerRG2RGB_MHT = COLOR_BayerBG2BGR_MHT, + COLOR_BayerGR2RGB_MHT = COLOR_BayerGB2BGR_MHT, + + COLOR_BayerBG2GRAY_MHT = 260, + COLOR_BayerGB2GRAY_MHT = 261, + COLOR_BayerRG2GRAY_MHT = 262, + COLOR_BayerGR2GRAY_MHT = 263 +}; +CV_EXPORTS void demosaicing(const GpuMat& src, GpuMat& dst, int code, int dcn = -1, Stream& stream = Stream::Null()); + +//! swap channels +//! dstOrder - Integer array describing how channel values are permutated. The n-th entry +//! of the array contains the number of the channel that is stored in the n-th channel of +//! the output image. E.g. Given an RGBA image, aDstOrder = [3,2,1,0] converts this to ABGR +//! channel order. +CV_EXPORTS void swapChannels(GpuMat& image, const int dstOrder[4], Stream& stream = Stream::Null()); + +//! Routines for correcting image color gamma +CV_EXPORTS void gammaCorrection(const GpuMat& src, GpuMat& dst, bool forward = true, Stream& stream = Stream::Null()); + +//! applies fixed threshold to the image +CV_EXPORTS double threshold(const GpuMat& src, GpuMat& dst, double thresh, double maxval, int type, Stream& stream = Stream::Null()); + +//! resizes the image +//! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_AREA +CV_EXPORTS void resize(const GpuMat& src, GpuMat& dst, Size dsize, double fx=0, double fy=0, int interpolation = INTER_LINEAR, Stream& stream = Stream::Null()); + +//! warps the image using affine transformation +//! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC +CV_EXPORTS void warpAffine(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags = INTER_LINEAR, + int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar(), Stream& stream = Stream::Null()); + +CV_EXPORTS void buildWarpAffineMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream = Stream::Null()); + +//! warps the image using perspective transformation +//! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC +CV_EXPORTS void warpPerspective(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags = INTER_LINEAR, + int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar(), Stream& stream = Stream::Null()); + +CV_EXPORTS void buildWarpPerspectiveMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream = Stream::Null()); + +//! builds plane warping maps +CV_EXPORTS void buildWarpPlaneMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, const Mat &T, float scale, + GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null()); + +//! builds cylindrical warping maps +CV_EXPORTS void buildWarpCylindricalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, float scale, + GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null()); + +//! builds spherical warping maps +CV_EXPORTS void buildWarpSphericalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, float scale, + GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null()); + +//! rotates an image around the origin (0,0) and then shifts it +//! supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC +//! supports 1, 3 or 4 channels images with CV_8U, CV_16U or CV_32F depth +CV_EXPORTS void rotate(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift = 0, double yShift = 0, + int interpolation = INTER_LINEAR, Stream& stream = Stream::Null()); + +//! copies 2D array to a larger destination array and pads borders with user-specifiable constant +CV_EXPORTS void copyMakeBorder(const GpuMat& src, GpuMat& dst, int top, int bottom, int left, int right, int borderType, + const Scalar& value = Scalar(), Stream& stream = Stream::Null()); + +//! computes the integral image +//! sum will have CV_32S type, but will contain unsigned int values +//! supports only CV_8UC1 source type +CV_EXPORTS void integral(const GpuMat& src, GpuMat& sum, Stream& stream = Stream::Null()); +//! buffered version +CV_EXPORTS void integralBuffered(const GpuMat& src, GpuMat& sum, GpuMat& buffer, Stream& stream = Stream::Null()); + +//! computes squared integral image +//! result matrix will have 64F type, but will contain 64U values +//! supports source images of 8UC1 type only +CV_EXPORTS void sqrIntegral(const GpuMat& src, GpuMat& sqsum, Stream& stream = Stream::Null()); + +//! computes vertical sum, supports only CV_32FC1 images +CV_EXPORTS void columnSum(const GpuMat& src, GpuMat& sum); + +//! computes the standard deviation of integral images +//! supports only CV_32SC1 source type and CV_32FC1 sqr type +//! output will have CV_32FC1 type +CV_EXPORTS void rectStdDev(const GpuMat& src, const GpuMat& sqr, GpuMat& dst, const Rect& rect, Stream& stream = Stream::Null()); + +//! computes Harris cornerness criteria at each image pixel +CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, double k, int borderType = BORDER_REFLECT101); +CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, double k, int borderType = BORDER_REFLECT101); +CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, GpuMat& buf, int blockSize, int ksize, double k, + int borderType = BORDER_REFLECT101, Stream& stream = Stream::Null()); + +//! computes minimum eigen value of 2x2 derivative covariation matrix at each pixel - the cornerness criteria +CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, int borderType=BORDER_REFLECT101); +CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, int borderType=BORDER_REFLECT101); +CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, GpuMat& buf, int blockSize, int ksize, + int borderType=BORDER_REFLECT101, Stream& stream = Stream::Null()); + +//! performs per-element multiplication of two full (not packed) Fourier spectrums +//! supports 32FC2 matrices only (interleaved format) +CV_EXPORTS void mulSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, bool conjB=false, Stream& stream = Stream::Null()); + +//! performs per-element multiplication of two full (not packed) Fourier spectrums +//! supports 32FC2 matrices only (interleaved format) +CV_EXPORTS void mulAndScaleSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, float scale, bool conjB=false, Stream& stream = Stream::Null()); + +//! Performs a forward or inverse discrete Fourier transform (1D or 2D) of floating point matrix. +//! Param dft_size is the size of DFT transform. +//! +//! If the source matrix is not continous, then additional copy will be done, +//! so to avoid copying ensure the source matrix is continous one. If you want to use +//! preallocated output ensure it is continuous too, otherwise it will be reallocated. +//! +//! Being implemented via CUFFT real-to-complex transform result contains only non-redundant values +//! in CUFFT's format. Result as full complex matrix for such kind of transform cannot be retrieved. +//! +//! For complex-to-real transform it is assumed that the source matrix is packed in CUFFT's format. +CV_EXPORTS void dft(const GpuMat& src, GpuMat& dst, Size dft_size, int flags=0, Stream& stream = Stream::Null()); + +struct CV_EXPORTS ConvolveBuf +{ + Size result_size; + Size block_size; + Size user_block_size; + Size dft_size; + int spect_len; + + GpuMat image_spect, templ_spect, result_spect; + GpuMat image_block, templ_block, result_data; + + void create(Size image_size, Size templ_size); + static Size estimateBlockSize(Size result_size, Size templ_size); +}; + + +//! computes convolution (or cross-correlation) of two images using discrete Fourier transform +//! supports source images of 32FC1 type only +//! result matrix will have 32FC1 type +CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr = false); +CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr, ConvolveBuf& buf, Stream& stream = Stream::Null()); + +struct CV_EXPORTS MatchTemplateBuf +{ + Size user_block_size; + GpuMat imagef, templf; + std::vector<GpuMat> images; + std::vector<GpuMat> image_sums; + std::vector<GpuMat> image_sqsums; +}; + +//! computes the proximity map for the raster template and the image where the template is searched for +CV_EXPORTS void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, Stream &stream = Stream::Null()); + +//! computes the proximity map for the raster template and the image where the template is searched for +CV_EXPORTS void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, MatchTemplateBuf &buf, Stream& stream = Stream::Null()); + +//! smoothes the source image and downsamples it +CV_EXPORTS void pyrDown(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()); + +//! upsamples the source image and then smoothes it +CV_EXPORTS void pyrUp(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()); + +//! performs linear blending of two images +//! to avoid accuracy errors sum of weigths shouldn't be very close to zero +CV_EXPORTS void blendLinear(const GpuMat& img1, const GpuMat& img2, const GpuMat& weights1, const GpuMat& weights2, + GpuMat& result, Stream& stream = Stream::Null()); + +//! Performa bilateral filtering of passsed image +CV_EXPORTS void bilateralFilter(const GpuMat& src, GpuMat& dst, int kernel_size, float sigma_color, float sigma_spatial, + int borderMode = BORDER_DEFAULT, Stream& stream = Stream::Null()); + +//! Brute force non-local means algorith (slow but universal) +CV_EXPORTS void nonLocalMeans(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, int borderMode = BORDER_DEFAULT, Stream& s = Stream::Null()); + +//! Fast (but approximate)version of non-local means algorith similar to CPU function (running sums technique) +class CV_EXPORTS FastNonLocalMeansDenoising +{ +public: + //! Simple method, recommended for grayscale images (though it supports multichannel images) + void simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null()); + + //! Processes luminance and color components separatelly + void labMethod(const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window = 21, int block_size = 7, Stream& s = Stream::Null()); + +private: + + GpuMat buffer, extended_src_buffer; + GpuMat lab, l, ab; +}; + +struct CV_EXPORTS CannyBuf +{ + void create(const Size& image_size, int apperture_size = 3); + void release(); + + GpuMat dx, dy; + GpuMat mag; + GpuMat map; + GpuMat st1, st2; + GpuMat unused; + Ptr<FilterEngine_GPU> filterDX, filterDY; + + CannyBuf() {} + explicit CannyBuf(const Size& image_size, int apperture_size = 3) {create(image_size, apperture_size);} + CannyBuf(const GpuMat& dx_, const GpuMat& dy_); +}; + +CV_EXPORTS void Canny(const GpuMat& image, GpuMat& edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false); +CV_EXPORTS void Canny(const GpuMat& image, CannyBuf& buf, GpuMat& edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false); +CV_EXPORTS void Canny(const GpuMat& dx, const GpuMat& dy, GpuMat& edges, double low_thresh, double high_thresh, bool L2gradient = false); +CV_EXPORTS void Canny(const GpuMat& dx, const GpuMat& dy, CannyBuf& buf, GpuMat& edges, double low_thresh, double high_thresh, bool L2gradient = false); + +class CV_EXPORTS ImagePyramid +{ +public: + inline ImagePyramid() : nLayers_(0) {} + inline ImagePyramid(const GpuMat& img, int nLayers, Stream& stream = Stream::Null()) + { + build(img, nLayers, stream); + } + + void build(const GpuMat& img, int nLayers, Stream& stream = Stream::Null()); + + void getLayer(GpuMat& outImg, Size outRoi, Stream& stream = Stream::Null()) const; + + inline void release() + { + layer0_.release(); + pyramid_.clear(); + nLayers_ = 0; + } + +private: + GpuMat layer0_; + std::vector<GpuMat> pyramid_; + int nLayers_; +}; + +//! HoughLines + +struct HoughLinesBuf +{ + GpuMat accum; + GpuMat list; +}; + +CV_EXPORTS void HoughLines(const GpuMat& src, GpuMat& lines, float rho, float theta, int threshold, bool doSort = false, int maxLines = 4096); +CV_EXPORTS void HoughLines(const GpuMat& src, GpuMat& lines, HoughLinesBuf& buf, float rho, float theta, int threshold, bool doSort = false, int maxLines = 4096); +CV_EXPORTS void HoughLinesDownload(const GpuMat& d_lines, OutputArray h_lines, OutputArray h_votes = noArray()); + +//! HoughLinesP + +//! finds line segments in the black-n-white image using probabalistic Hough transform +CV_EXPORTS void HoughLinesP(const GpuMat& image, GpuMat& lines, HoughLinesBuf& buf, float rho, float theta, int minLineLength, int maxLineGap, int maxLines = 4096); + +//! HoughCircles + +struct HoughCirclesBuf +{ + GpuMat edges; + GpuMat accum; + GpuMat list; + CannyBuf cannyBuf; +}; + +CV_EXPORTS void HoughCircles(const GpuMat& src, GpuMat& circles, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096); +CV_EXPORTS void HoughCircles(const GpuMat& src, GpuMat& circles, HoughCirclesBuf& buf, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096); +CV_EXPORTS void HoughCirclesDownload(const GpuMat& d_circles, OutputArray h_circles); + +//! finds arbitrary template in the grayscale image using Generalized Hough Transform +//! Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122. +//! Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038. +class CV_EXPORTS GeneralizedHough_GPU : public Algorithm +{ +public: + static Ptr<GeneralizedHough_GPU> create(int method); + + virtual ~GeneralizedHough_GPU(); + + //! set template to search + void setTemplate(const GpuMat& templ, int cannyThreshold = 100, Point templCenter = Point(-1, -1)); + void setTemplate(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Point templCenter = Point(-1, -1)); + + //! find template on image + void detect(const GpuMat& image, GpuMat& positions, int cannyThreshold = 100); + void detect(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, GpuMat& positions); + + void download(const GpuMat& d_positions, OutputArray h_positions, OutputArray h_votes = noArray()); + + void release(); + +protected: + virtual void setTemplateImpl(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Point templCenter) = 0; + virtual void detectImpl(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, GpuMat& positions) = 0; + virtual void releaseImpl() = 0; + +private: + GpuMat edges_; + CannyBuf cannyBuf_; +}; + +////////////////////////////// Matrix reductions ////////////////////////////// + +//! computes mean value and standard deviation of all or selected array elements +//! supports only CV_8UC1 type +CV_EXPORTS void meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev); +//! buffered version +CV_EXPORTS void meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev, GpuMat& buf); + +//! computes norm of array +//! supports NORM_INF, NORM_L1, NORM_L2 +//! supports all matrices except 64F +CV_EXPORTS double norm(const GpuMat& src1, int normType=NORM_L2); +CV_EXPORTS double norm(const GpuMat& src1, int normType, GpuMat& buf); +CV_EXPORTS double norm(const GpuMat& src1, int normType, const GpuMat& mask, GpuMat& buf); + +//! computes norm of the difference between two arrays +//! supports NORM_INF, NORM_L1, NORM_L2 +//! supports only CV_8UC1 type +CV_EXPORTS double norm(const GpuMat& src1, const GpuMat& src2, int normType=NORM_L2); + +//! computes sum of array elements +//! supports only single channel images +CV_EXPORTS Scalar sum(const GpuMat& src); +CV_EXPORTS Scalar sum(const GpuMat& src, GpuMat& buf); +CV_EXPORTS Scalar sum(const GpuMat& src, const GpuMat& mask, GpuMat& buf); + +//! computes sum of array elements absolute values +//! supports only single channel images +CV_EXPORTS Scalar absSum(const GpuMat& src); +CV_EXPORTS Scalar absSum(const GpuMat& src, GpuMat& buf); +CV_EXPORTS Scalar absSum(const GpuMat& src, const GpuMat& mask, GpuMat& buf); + +//! computes squared sum of array elements +//! supports only single channel images +CV_EXPORTS Scalar sqrSum(const GpuMat& src); +CV_EXPORTS Scalar sqrSum(const GpuMat& src, GpuMat& buf); +CV_EXPORTS Scalar sqrSum(const GpuMat& src, const GpuMat& mask, GpuMat& buf); + +//! finds global minimum and maximum array elements and returns their values +CV_EXPORTS void minMax(const GpuMat& src, double* minVal, double* maxVal=0, const GpuMat& mask=GpuMat()); +CV_EXPORTS void minMax(const GpuMat& src, double* minVal, double* maxVal, const GpuMat& mask, GpuMat& buf); + +//! finds global minimum and maximum array elements and returns their values with locations +CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal=0, Point* minLoc=0, Point* maxLoc=0, + const GpuMat& mask=GpuMat()); +CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc, + const GpuMat& mask, GpuMat& valbuf, GpuMat& locbuf); + +//! counts non-zero array elements +CV_EXPORTS int countNonZero(const GpuMat& src); +CV_EXPORTS int countNonZero(const GpuMat& src, GpuMat& buf); + +//! reduces a matrix to a vector +CV_EXPORTS void reduce(const GpuMat& mtx, GpuMat& vec, int dim, int reduceOp, int dtype = -1, Stream& stream = Stream::Null()); + + +///////////////////////////// Calibration 3D ////////////////////////////////// + +CV_EXPORTS void transformPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec, + GpuMat& dst, Stream& stream = Stream::Null()); + +CV_EXPORTS void projectPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec, + const Mat& camera_mat, const Mat& dist_coef, GpuMat& dst, + Stream& stream = Stream::Null()); + +CV_EXPORTS void solvePnPRansac(const Mat& object, const Mat& image, const Mat& camera_mat, + const Mat& dist_coef, Mat& rvec, Mat& tvec, bool use_extrinsic_guess=false, + int num_iters=100, float max_dist=8.0, int min_inlier_count=100, + std::vector<int>* inliers=NULL); + +//////////////////////////////// Image Labeling //////////////////////////////// + +//!performs labeling via graph cuts of a 2D regular 4-connected graph. +CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& bottom, GpuMat& labels, + GpuMat& buf, Stream& stream = Stream::Null()); + +//!performs labeling via graph cuts of a 2D regular 8-connected graph. +CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& topLeft, GpuMat& topRight, + GpuMat& bottom, GpuMat& bottomLeft, GpuMat& bottomRight, + GpuMat& labels, + GpuMat& buf, Stream& stream = Stream::Null()); + +//! compute mask for Generalized Flood fill componetns labeling. +CV_EXPORTS void connectivityMask(const GpuMat& image, GpuMat& mask, const cv::Scalar& lo, const cv::Scalar& hi, Stream& stream = Stream::Null()); + +//! performs connected componnents labeling. +CV_EXPORTS void labelComponents(const GpuMat& mask, GpuMat& components, int flags = 0, Stream& stream = Stream::Null()); + +////////////////////////////////// Histograms ////////////////////////////////// + +//! Compute levels with even distribution. levels will have 1 row and nLevels cols and CV_32SC1 type. +CV_EXPORTS void evenLevels(GpuMat& levels, int nLevels, int lowerLevel, int upperLevel); +//! Calculates histogram with evenly distributed bins for signle channel source. +//! Supports CV_8UC1, CV_16UC1 and CV_16SC1 source types. +//! Output hist will have one row and histSize cols and CV_32SC1 type. +CV_EXPORTS void histEven(const GpuMat& src, GpuMat& hist, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null()); +CV_EXPORTS void histEven(const GpuMat& src, GpuMat& hist, GpuMat& buf, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null()); +//! Calculates histogram with evenly distributed bins for four-channel source. +//! All channels of source are processed separately. +//! Supports CV_8UC4, CV_16UC4 and CV_16SC4 source types. +//! Output hist[i] will have one row and histSize[i] cols and CV_32SC1 type. +CV_EXPORTS void histEven(const GpuMat& src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null()); +CV_EXPORTS void histEven(const GpuMat& src, GpuMat hist[4], GpuMat& buf, int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null()); +//! Calculates histogram with bins determined by levels array. +//! levels must have one row and CV_32SC1 type if source has integer type or CV_32FC1 otherwise. +//! Supports CV_8UC1, CV_16UC1, CV_16SC1 and CV_32FC1 source types. +//! Output hist will have one row and (levels.cols-1) cols and CV_32SC1 type. +CV_EXPORTS void histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels, Stream& stream = Stream::Null()); +CV_EXPORTS void histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels, GpuMat& buf, Stream& stream = Stream::Null()); +//! Calculates histogram with bins determined by levels array. +//! All levels must have one row and CV_32SC1 type if source has integer type or CV_32FC1 otherwise. +//! All channels of source are processed separately. +//! Supports CV_8UC4, CV_16UC4, CV_16SC4 and CV_32FC4 source types. +//! Output hist[i] will have one row and (levels[i].cols-1) cols and CV_32SC1 type. +CV_EXPORTS void histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], Stream& stream = Stream::Null()); +CV_EXPORTS void histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], GpuMat& buf, Stream& stream = Stream::Null()); + +//! Calculates histogram for 8u one channel image +//! Output hist will have one row, 256 cols and CV32SC1 type. +CV_EXPORTS void calcHist(const GpuMat& src, GpuMat& hist, Stream& stream = Stream::Null()); +CV_EXPORTS void calcHist(const GpuMat& src, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null()); + +//! normalizes the grayscale image brightness and contrast by normalizing its histogram +CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()); +CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, Stream& stream = Stream::Null()); +CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null()); + +class CV_EXPORTS CLAHE : public cv::CLAHE +{ +public: + using cv::CLAHE::apply; + virtual void apply(InputArray src, OutputArray dst, Stream& stream) = 0; +}; +CV_EXPORTS Ptr<cv::gpu::CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8)); + +//////////////////////////////// StereoBM_GPU //////////////////////////////// + +class CV_EXPORTS StereoBM_GPU +{ +public: + enum { BASIC_PRESET = 0, PREFILTER_XSOBEL = 1 }; + + enum { DEFAULT_NDISP = 64, DEFAULT_WINSZ = 19 }; + + //! the default constructor + StereoBM_GPU(); + //! the full constructor taking the camera-specific preset, number of disparities and the SAD window size. ndisparities must be multiple of 8. + StereoBM_GPU(int preset, int ndisparities = DEFAULT_NDISP, int winSize = DEFAULT_WINSZ); + + //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair + //! Output disparity has CV_8U type. + void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null()); + + //! Some heuristics that tries to estmate + // if current GPU will be faster than CPU in this algorithm. + // It queries current active device. + static bool checkIfGpuCallReasonable(); + + int preset; + int ndisp; + int winSize; + + // If avergeTexThreshold == 0 => post procesing is disabled + // If avergeTexThreshold != 0 then disparity is set 0 in each point (x,y) where for left image + // SumOfHorizontalGradiensInWindow(x, y, winSize) < (winSize * winSize) * avergeTexThreshold + // i.e. input left image is low textured. + float avergeTexThreshold; + +private: + GpuMat minSSD, leBuf, riBuf; +}; + +////////////////////////// StereoBeliefPropagation /////////////////////////// +// "Efficient Belief Propagation for Early Vision" +// P.Felzenszwalb + +class CV_EXPORTS StereoBeliefPropagation +{ +public: + enum { DEFAULT_NDISP = 64 }; + enum { DEFAULT_ITERS = 5 }; + enum { DEFAULT_LEVELS = 5 }; + + static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels); + + //! the default constructor + explicit StereoBeliefPropagation(int ndisp = DEFAULT_NDISP, + int iters = DEFAULT_ITERS, + int levels = DEFAULT_LEVELS, + int msg_type = CV_32F); + + //! the full constructor taking the number of disparities, number of BP iterations on each level, + //! number of levels, truncation of data cost, data weight, + //! truncation of discontinuity cost and discontinuity single jump + //! DataTerm = data_weight * min(fabs(I2-I1), max_data_term) + //! DiscTerm = min(disc_single_jump * fabs(f1-f2), max_disc_term) + //! please see paper for more details + StereoBeliefPropagation(int ndisp, int iters, int levels, + float max_data_term, float data_weight, + float max_disc_term, float disc_single_jump, + int msg_type = CV_32F); + + //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair, + //! if disparity is empty output type will be CV_16S else output type will be disparity.type(). + void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null()); + + + //! version for user specified data term + void operator()(const GpuMat& data, GpuMat& disparity, Stream& stream = Stream::Null()); + + int ndisp; + + int iters; + int levels; + + float max_data_term; + float data_weight; + float max_disc_term; + float disc_single_jump; + + int msg_type; +private: + GpuMat u, d, l, r, u2, d2, l2, r2; + std::vector<GpuMat> datas; + GpuMat out; +}; + +/////////////////////////// StereoConstantSpaceBP /////////////////////////// +// "A Constant-Space Belief Propagation Algorithm for Stereo Matching" +// Qingxiong Yang, Liang Wang, Narendra Ahuja +// http://vision.ai.uiuc.edu/~qyang6/ + +class CV_EXPORTS StereoConstantSpaceBP +{ +public: + enum { DEFAULT_NDISP = 128 }; + enum { DEFAULT_ITERS = 8 }; + enum { DEFAULT_LEVELS = 4 }; + enum { DEFAULT_NR_PLANE = 4 }; + + static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels, int& nr_plane); + + //! the default constructor + explicit StereoConstantSpaceBP(int ndisp = DEFAULT_NDISP, + int iters = DEFAULT_ITERS, + int levels = DEFAULT_LEVELS, + int nr_plane = DEFAULT_NR_PLANE, + int msg_type = CV_32F); + + //! the full constructor taking the number of disparities, number of BP iterations on each level, + //! number of levels, number of active disparity on the first level, truncation of data cost, data weight, + //! truncation of discontinuity cost, discontinuity single jump and minimum disparity threshold + StereoConstantSpaceBP(int ndisp, int iters, int levels, int nr_plane, + float max_data_term, float data_weight, float max_disc_term, float disc_single_jump, + int min_disp_th = 0, + int msg_type = CV_32F); + + //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair, + //! if disparity is empty output type will be CV_16S else output type will be disparity.type(). + void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null()); + + int ndisp; + + int iters; + int levels; + + int nr_plane; + + float max_data_term; + float data_weight; + float max_disc_term; + float disc_single_jump; + + int min_disp_th; + + int msg_type; + + bool use_local_init_data_cost; +private: + GpuMat messages_buffers; + + GpuMat temp; + GpuMat out; +}; + +/////////////////////////// DisparityBilateralFilter /////////////////////////// +// Disparity map refinement using joint bilateral filtering given a single color image. +// Qingxiong Yang, Liang Wang, Narendra Ahuja +// http://vision.ai.uiuc.edu/~qyang6/ + +class CV_EXPORTS DisparityBilateralFilter +{ +public: + enum { DEFAULT_NDISP = 64 }; + enum { DEFAULT_RADIUS = 3 }; + enum { DEFAULT_ITERS = 1 }; + + //! the default constructor + explicit DisparityBilateralFilter(int ndisp = DEFAULT_NDISP, int radius = DEFAULT_RADIUS, int iters = DEFAULT_ITERS); + + //! the full constructor taking the number of disparities, filter radius, + //! number of iterations, truncation of data continuity, truncation of disparity continuity + //! and filter range sigma + DisparityBilateralFilter(int ndisp, int radius, int iters, float edge_threshold, float max_disc_threshold, float sigma_range); + + //! the disparity map refinement operator. Refine disparity map using joint bilateral filtering given a single color image. + //! disparity must have CV_8U or CV_16S type, image must have CV_8UC1 or CV_8UC3 type. + void operator()(const GpuMat& disparity, const GpuMat& image, GpuMat& dst, Stream& stream = Stream::Null()); + +private: + int ndisp; + int radius; + int iters; + + float edge_threshold; + float max_disc_threshold; + float sigma_range; + + GpuMat table_color; + GpuMat table_space; +}; + + +//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector ////////////// +struct CV_EXPORTS HOGConfidence +{ + double scale; + vector<Point> locations; + vector<double> confidences; + vector<double> part_scores[4]; +}; + +struct CV_EXPORTS HOGDescriptor +{ + enum { DEFAULT_WIN_SIGMA = -1 }; + enum { DEFAULT_NLEVELS = 64 }; + enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL }; + + HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16), + Size block_stride=Size(8, 8), Size cell_size=Size(8, 8), + int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA, + double threshold_L2hys=0.2, bool gamma_correction=true, + int nlevels=DEFAULT_NLEVELS); + + size_t getDescriptorSize() const; + size_t getBlockHistogramSize() const; + + void setSVMDetector(const vector<float>& detector); + + static vector<float> getDefaultPeopleDetector(); + static vector<float> getPeopleDetector48x96(); + static vector<float> getPeopleDetector64x128(); + + void detect(const GpuMat& img, vector<Point>& found_locations, + double hit_threshold=0, Size win_stride=Size(), + Size padding=Size()); + + void detectMultiScale(const GpuMat& img, vector<Rect>& found_locations, + double hit_threshold=0, Size win_stride=Size(), + Size padding=Size(), double scale0=1.05, + int group_threshold=2); + + void computeConfidence(const GpuMat& img, vector<Point>& hits, double hit_threshold, + Size win_stride, Size padding, vector<Point>& locations, vector<double>& confidences); + + void computeConfidenceMultiScale(const GpuMat& img, vector<Rect>& found_locations, + double hit_threshold, Size win_stride, Size padding, + vector<HOGConfidence> &conf_out, int group_threshold); + + void getDescriptors(const GpuMat& img, Size win_stride, + GpuMat& descriptors, + int descr_format=DESCR_FORMAT_COL_BY_COL); + + Size win_size; + Size block_size; + Size block_stride; + Size cell_size; + int nbins; + double win_sigma; + double threshold_L2hys; + bool gamma_correction; + int nlevels; + +protected: + void computeBlockHistograms(const GpuMat& img); + void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle); + + double getWinSigma() const; + bool checkDetectorSize() const; + + static int numPartsWithin(int size, int part_size, int stride); + static Size numPartsWithin(Size size, Size part_size, Size stride); + + // Coefficients of the separating plane + float free_coef; + GpuMat detector; + + // Results of the last classification step + GpuMat labels, labels_buf; + Mat labels_host; + + // Results of the last histogram evaluation step + GpuMat block_hists, block_hists_buf; + + // Gradients conputation results + GpuMat grad, qangle, grad_buf, qangle_buf; + + // returns subbuffer with required size, reallocates buffer if nessesary. + static GpuMat getBuffer(const Size& sz, int type, GpuMat& buf); + static GpuMat getBuffer(int rows, int cols, int type, GpuMat& buf); + + std::vector<GpuMat> image_scales; +}; + + +////////////////////////////////// BruteForceMatcher ////////////////////////////////// + +class CV_EXPORTS BruteForceMatcher_GPU_base +{ +public: + enum DistType {L1Dist = 0, L2Dist, HammingDist}; + + explicit BruteForceMatcher_GPU_base(DistType distType = L2Dist); + + // Add descriptors to train descriptor collection + void add(const std::vector<GpuMat>& descCollection); + + // Get train descriptors collection + const std::vector<GpuMat>& getTrainDescriptors() const; + + // Clear train descriptors collection + void clear(); + + // Return true if there are not train descriptors in collection + bool empty() const; + + // Return true if the matcher supports mask in match methods + bool isMaskSupported() const; + + // Find one best match for each query descriptor + void matchSingle(const GpuMat& query, const GpuMat& train, + GpuMat& trainIdx, GpuMat& distance, + const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null()); + + // Download trainIdx and distance and convert it to CPU vector with DMatch + static void matchDownload(const GpuMat& trainIdx, const GpuMat& distance, std::vector<DMatch>& matches); + // Convert trainIdx and distance to vector with DMatch + static void matchConvert(const Mat& trainIdx, const Mat& distance, std::vector<DMatch>& matches); + + // Find one best match for each query descriptor + void match(const GpuMat& query, const GpuMat& train, std::vector<DMatch>& matches, const GpuMat& mask = GpuMat()); + + // Make gpu collection of trains and masks in suitable format for matchCollection function + void makeGpuCollection(GpuMat& trainCollection, GpuMat& maskCollection, const std::vector<GpuMat>& masks = std::vector<GpuMat>()); + + // Find one best match from train collection for each query descriptor + void matchCollection(const GpuMat& query, const GpuMat& trainCollection, + GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance, + const GpuMat& masks = GpuMat(), Stream& stream = Stream::Null()); + + // Download trainIdx, imgIdx and distance and convert it to vector with DMatch + static void matchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, std::vector<DMatch>& matches); + // Convert trainIdx, imgIdx and distance to vector with DMatch + static void matchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, std::vector<DMatch>& matches); + + // Find one best match from train collection for each query descriptor. + void match(const GpuMat& query, std::vector<DMatch>& matches, const std::vector<GpuMat>& masks = std::vector<GpuMat>()); + + // Find k best matches for each query descriptor (in increasing order of distances) + void knnMatchSingle(const GpuMat& query, const GpuMat& train, + GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k, + const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null()); + + // Download trainIdx and distance and convert it to vector with DMatch + // compactResult is used when mask is not empty. If compactResult is false matches + // vector will have the same size as queryDescriptors rows. If compactResult is true + // matches vector will not contain matches for fully masked out query descriptors. + static void knnMatchDownload(const GpuMat& trainIdx, const GpuMat& distance, + std::vector< std::vector<DMatch> >& matches, bool compactResult = false); + // Convert trainIdx and distance to vector with DMatch + static void knnMatchConvert(const Mat& trainIdx, const Mat& distance, + std::vector< std::vector<DMatch> >& matches, bool compactResult = false); + + // Find k best matches for each query descriptor (in increasing order of distances). + // compactResult is used when mask is not empty. If compactResult is false matches + // vector will have the same size as queryDescriptors rows. If compactResult is true + // matches vector will not contain matches for fully masked out query descriptors. + void knnMatch(const GpuMat& query, const GpuMat& train, + std::vector< std::vector<DMatch> >& matches, int k, const GpuMat& mask = GpuMat(), + bool compactResult = false); + + // Find k best matches from train collection for each query descriptor (in increasing order of distances) + void knnMatch2Collection(const GpuMat& query, const GpuMat& trainCollection, + GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance, + const GpuMat& maskCollection = GpuMat(), Stream& stream = Stream::Null()); + + // Download trainIdx and distance and convert it to vector with DMatch + // compactResult is used when mask is not empty. If compactResult is false matches + // vector will have the same size as queryDescriptors rows. If compactResult is true + // matches vector will not contain matches for fully masked out query descriptors. + static void knnMatch2Download(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, + std::vector< std::vector<DMatch> >& matches, bool compactResult = false); + // Convert trainIdx and distance to vector with DMatch + static void knnMatch2Convert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, + std::vector< std::vector<DMatch> >& matches, bool compactResult = false); + + // Find k best matches for each query descriptor (in increasing order of distances). + // compactResult is used when mask is not empty. If compactResult is false matches + // vector will have the same size as queryDescriptors rows. If compactResult is true + // matches vector will not contain matches for fully masked out query descriptors. + void knnMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches, int k, + const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false); + + // Find best matches for each query descriptor which have distance less than maxDistance. + // nMatches.at<int>(0, queryIdx) will contain matches count for queryIdx. + // carefully nMatches can be greater than trainIdx.cols - it means that matcher didn't find all matches, + // because it didn't have enough memory. + // If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nTrain / 100), 10), + // otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches + // Matches doesn't sorted. + void radiusMatchSingle(const GpuMat& query, const GpuMat& train, + GpuMat& trainIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance, + const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null()); + + // Download trainIdx, nMatches and distance and convert it to vector with DMatch. + // matches will be sorted in increasing order of distances. + // compactResult is used when mask is not empty. If compactResult is false matches + // vector will have the same size as queryDescriptors rows. If compactResult is true + // matches vector will not contain matches for fully masked out query descriptors. + static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& distance, const GpuMat& nMatches, + std::vector< std::vector<DMatch> >& matches, bool compactResult = false); + // Convert trainIdx, nMatches and distance to vector with DMatch. + static void radiusMatchConvert(const Mat& trainIdx, const Mat& distance, const Mat& nMatches, + std::vector< std::vector<DMatch> >& matches, bool compactResult = false); + + // Find best matches for each query descriptor which have distance less than maxDistance + // in increasing order of distances). + void radiusMatch(const GpuMat& query, const GpuMat& train, + std::vector< std::vector<DMatch> >& matches, float maxDistance, + const GpuMat& mask = GpuMat(), bool compactResult = false); + + // Find best matches for each query descriptor which have distance less than maxDistance. + // If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nQuery / 100), 10), + // otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches + // Matches doesn't sorted. + void radiusMatchCollection(const GpuMat& query, GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance, + const std::vector<GpuMat>& masks = std::vector<GpuMat>(), Stream& stream = Stream::Null()); + + // Download trainIdx, imgIdx, nMatches and distance and convert it to vector with DMatch. + // matches will be sorted in increasing order of distances. + // compactResult is used when mask is not empty. If compactResult is false matches + // vector will have the same size as queryDescriptors rows. If compactResult is true + // matches vector will not contain matches for fully masked out query descriptors. + static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, const GpuMat& nMatches, + std::vector< std::vector<DMatch> >& matches, bool compactResult = false); + // Convert trainIdx, nMatches and distance to vector with DMatch. + static void radiusMatchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, const Mat& nMatches, + std::vector< std::vector<DMatch> >& matches, bool compactResult = false); + + // Find best matches from train collection for each query descriptor which have distance less than + // maxDistance (in increasing order of distances). + void radiusMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches, float maxDistance, + const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false); + + DistType distType; + +private: + std::vector<GpuMat> trainDescCollection; +}; + +template <class Distance> +class CV_EXPORTS BruteForceMatcher_GPU; + +template <typename T> +class CV_EXPORTS BruteForceMatcher_GPU< L1<T> > : public BruteForceMatcher_GPU_base +{ +public: + explicit BruteForceMatcher_GPU() : BruteForceMatcher_GPU_base(L1Dist) {} + explicit BruteForceMatcher_GPU(L1<T> /*d*/) : BruteForceMatcher_GPU_base(L1Dist) {} +}; +template <typename T> +class CV_EXPORTS BruteForceMatcher_GPU< L2<T> > : public BruteForceMatcher_GPU_base +{ +public: + explicit BruteForceMatcher_GPU() : BruteForceMatcher_GPU_base(L2Dist) {} + explicit BruteForceMatcher_GPU(L2<T> /*d*/) : BruteForceMatcher_GPU_base(L2Dist) {} +}; +template <> class CV_EXPORTS BruteForceMatcher_GPU< Hamming > : public BruteForceMatcher_GPU_base +{ +public: + explicit BruteForceMatcher_GPU() : BruteForceMatcher_GPU_base(HammingDist) {} + explicit BruteForceMatcher_GPU(Hamming /*d*/) : BruteForceMatcher_GPU_base(HammingDist) {} +}; + +class CV_EXPORTS BFMatcher_GPU : public BruteForceMatcher_GPU_base +{ +public: + explicit BFMatcher_GPU(int norm = NORM_L2) : BruteForceMatcher_GPU_base(norm == NORM_L1 ? L1Dist : norm == NORM_L2 ? L2Dist : HammingDist) {} +}; + +////////////////////////////////// CascadeClassifier_GPU ////////////////////////////////////////// +// The cascade classifier class for object detection: supports old haar and new lbp xlm formats and nvbin for haar cascades olny. +class CV_EXPORTS CascadeClassifier_GPU +{ +public: + CascadeClassifier_GPU(); + CascadeClassifier_GPU(const std::string& filename); + ~CascadeClassifier_GPU(); + + bool empty() const; + bool load(const std::string& filename); + void release(); + + /* returns number of detected objects */ + int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, double scaleFactor = 1.2, int minNeighbors = 4, Size minSize = Size()); + int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize = Size(), double scaleFactor = 1.1, int minNeighbors = 4); + + bool findLargestObject; + bool visualizeInPlace; + + Size getClassifierSize() const; + +private: + struct CascadeClassifierImpl; + CascadeClassifierImpl* impl; + struct HaarCascade; + struct LbpCascade; + friend class CascadeClassifier_GPU_LBP; +}; + +////////////////////////////////// FAST ////////////////////////////////////////// + +class CV_EXPORTS FAST_GPU +{ +public: + enum + { + LOCATION_ROW = 0, + RESPONSE_ROW, + ROWS_COUNT + }; + + // all features have same size + static const int FEATURE_SIZE = 7; + + explicit FAST_GPU(int threshold, bool nonmaxSuppression = true, double keypointsRatio = 0.05); + + //! finds the keypoints using FAST detector + //! supports only CV_8UC1 images + void operator ()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints); + void operator ()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints); + + //! download keypoints from device to host memory + void downloadKeypoints(const GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints); + + //! convert keypoints to KeyPoint vector + void convertKeypoints(const Mat& h_keypoints, std::vector<KeyPoint>& keypoints); + + //! release temporary buffer's memory + void release(); + + bool nonmaxSuppression; + + int threshold; + + //! max keypoints = keypointsRatio * img.size().area() + double keypointsRatio; + + //! find keypoints and compute it's response if nonmaxSuppression is true + //! return count of detected keypoints + int calcKeyPointsLocation(const GpuMat& image, const GpuMat& mask); + + //! get final array of keypoints + //! performs nonmax suppression if needed + //! return final count of keypoints + int getKeyPoints(GpuMat& keypoints); + +private: + GpuMat kpLoc_; + int count_; + + GpuMat score_; + + GpuMat d_keypoints_; +}; + +////////////////////////////////// ORB ////////////////////////////////////////// + +class CV_EXPORTS ORB_GPU +{ +public: + enum + { + X_ROW = 0, + Y_ROW, + RESPONSE_ROW, + ANGLE_ROW, + OCTAVE_ROW, + SIZE_ROW, + ROWS_COUNT + }; + + enum + { + DEFAULT_FAST_THRESHOLD = 20 + }; + + //! Constructor + explicit ORB_GPU(int nFeatures = 500, float scaleFactor = 1.2f, int nLevels = 8, int edgeThreshold = 31, + int firstLevel = 0, int WTA_K = 2, int scoreType = 0, int patchSize = 31); + + //! Compute the ORB features on an image + //! image - the image to compute the features (supports only CV_8UC1 images) + //! mask - the mask to apply + //! keypoints - the resulting keypoints + void operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints); + void operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints); + + //! Compute the ORB features and descriptors on an image + //! image - the image to compute the features (supports only CV_8UC1 images) + //! mask - the mask to apply + //! keypoints - the resulting keypoints + //! descriptors - descriptors array + void operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints, GpuMat& descriptors); + void operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors); + + //! download keypoints from device to host memory + void downloadKeyPoints(GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints); + + //! convert keypoints to KeyPoint vector + void convertKeyPoints(Mat& d_keypoints, std::vector<KeyPoint>& keypoints); + + //! returns the descriptor size in bytes + inline int descriptorSize() const { return kBytes; } + + inline void setFastParams(int threshold, bool nonmaxSuppression = true) + { + fastDetector_.threshold = threshold; + fastDetector_.nonmaxSuppression = nonmaxSuppression; + } + + //! release temporary buffer's memory + void release(); + + //! if true, image will be blurred before descriptors calculation + bool blurForDescriptor; + +private: + enum { kBytes = 32 }; + + void buildScalePyramids(const GpuMat& image, const GpuMat& mask); + + void computeKeyPointsPyramid(); + + void computeDescriptors(GpuMat& descriptors); + + void mergeKeyPoints(GpuMat& keypoints); + + int nFeatures_; + float scaleFactor_; + int nLevels_; + int edgeThreshold_; + int firstLevel_; + int WTA_K_; + int scoreType_; + int patchSize_; + + // The number of desired features per scale + std::vector<size_t> n_features_per_level_; + + // Points to compute BRIEF descriptors from + GpuMat pattern_; + + std::vector<GpuMat> imagePyr_; + std::vector<GpuMat> maskPyr_; + + GpuMat buf_; + + std::vector<GpuMat> keyPointsPyr_; + std::vector<int> keyPointsCount_; + + FAST_GPU fastDetector_; + + Ptr<FilterEngine_GPU> blurFilter; + + GpuMat d_keypoints_; +}; + +////////////////////////////////// Optical Flow ////////////////////////////////////////// + +class CV_EXPORTS BroxOpticalFlow +{ +public: + BroxOpticalFlow(float alpha_, float gamma_, float scale_factor_, int inner_iterations_, int outer_iterations_, int solver_iterations_) : + alpha(alpha_), gamma(gamma_), scale_factor(scale_factor_), + inner_iterations(inner_iterations_), outer_iterations(outer_iterations_), solver_iterations(solver_iterations_) + { + } + + //! Compute optical flow + //! frame0 - source frame (supports only CV_32FC1 type) + //! frame1 - frame to track (with the same size and type as frame0) + //! u - flow horizontal component (along x axis) + //! v - flow vertical component (along y axis) + void operator ()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& u, GpuMat& v, Stream& stream = Stream::Null()); + + //! flow smoothness + float alpha; + + //! gradient constancy importance + float gamma; + + //! pyramid scale factor + float scale_factor; + + //! number of lagged non-linearity iterations (inner loop) + int inner_iterations; + + //! number of warping iterations (number of pyramid levels) + int outer_iterations; + + //! number of linear system solver iterations + int solver_iterations; + + GpuMat buf; +}; + +class CV_EXPORTS GoodFeaturesToTrackDetector_GPU +{ +public: + explicit GoodFeaturesToTrackDetector_GPU(int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 0.0, + int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04); + + //! return 1 rows matrix with CV_32FC2 type + void operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask = GpuMat()); + + int maxCorners; + double qualityLevel; + double minDistance; + + int blockSize; + bool useHarrisDetector; + double harrisK; + + void releaseMemory() + { + Dx_.release(); + Dy_.release(); + buf_.release(); + eig_.release(); + minMaxbuf_.release(); + tmpCorners_.release(); + } + +private: + GpuMat Dx_; + GpuMat Dy_; + GpuMat buf_; + GpuMat eig_; + GpuMat minMaxbuf_; + GpuMat tmpCorners_; +}; + +inline GoodFeaturesToTrackDetector_GPU::GoodFeaturesToTrackDetector_GPU(int maxCorners_, double qualityLevel_, double minDistance_, + int blockSize_, bool useHarrisDetector_, double harrisK_) +{ + maxCorners = maxCorners_; + qualityLevel = qualityLevel_; + minDistance = minDistance_; + blockSize = blockSize_; + useHarrisDetector = useHarrisDetector_; + harrisK = harrisK_; +} + + +class CV_EXPORTS PyrLKOpticalFlow +{ +public: + PyrLKOpticalFlow(); + + void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts, + GpuMat& status, GpuMat* err = 0); + + void dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err = 0); + + void releaseMemory(); + + Size winSize; + int maxLevel; + int iters; + double derivLambda; //unused + bool useInitialFlow; + float minEigThreshold; //unused + bool getMinEigenVals; //unused + +private: + GpuMat uPyr_[2]; + vector<GpuMat> prevPyr_; + vector<GpuMat> nextPyr_; + GpuMat vPyr_[2]; + vector<GpuMat> buf_; + vector<GpuMat> unused; + bool isDeviceArch11_; +}; + + +class CV_EXPORTS FarnebackOpticalFlow +{ +public: + FarnebackOpticalFlow() + { + numLevels = 5; + pyrScale = 0.5; + fastPyramids = false; + winSize = 13; + numIters = 10; + polyN = 5; + polySigma = 1.1; + flags = 0; + isDeviceArch11_ = !DeviceInfo().supports(FEATURE_SET_COMPUTE_12); + } + + int numLevels; + double pyrScale; + bool fastPyramids; + int winSize; + int numIters; + int polyN; + double polySigma; + int flags; + + void operator ()(const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s = Stream::Null()); + + void releaseMemory() + { + frames_[0].release(); + frames_[1].release(); + pyrLevel_[0].release(); + pyrLevel_[1].release(); + M_.release(); + bufM_.release(); + R_[0].release(); + R_[1].release(); + blurredFrame_[0].release(); + blurredFrame_[1].release(); + pyramid0_.clear(); + pyramid1_.clear(); + } + +private: + void prepareGaussian( + int n, double sigma, float *g, float *xg, float *xxg, + double &ig11, double &ig03, double &ig33, double &ig55); + + void setPolynomialExpansionConsts(int n, double sigma); + + void updateFlow_boxFilter( + const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat &flowy, + GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]); + + void updateFlow_gaussianBlur( + const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat& flowy, + GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]); + + GpuMat frames_[2]; + GpuMat pyrLevel_[2], M_, bufM_, R_[2], blurredFrame_[2]; + std::vector<GpuMat> pyramid0_, pyramid1_; + + bool isDeviceArch11_; +}; + + +// Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method +// +// see reference: +// [1] C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow". +// [2] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation". +class CV_EXPORTS OpticalFlowDual_TVL1_GPU +{ +public: + OpticalFlowDual_TVL1_GPU(); + + void operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy); + + void collectGarbage(); + + /** + * Time step of the numerical scheme. + */ + double tau; + + /** + * Weight parameter for the data term, attachment parameter. + * This is the most relevant parameter, which determines the smoothness of the output. + * The smaller this parameter is, the smoother the solutions we obtain. + * It depends on the range of motions of the images, so its value should be adapted to each image sequence. + */ + double lambda; + + /** + * Weight parameter for (u - v)^2, tightness parameter. + * It serves as a link between the attachment and the regularization terms. + * In theory, it should have a small value in order to maintain both parts in correspondence. + * The method is stable for a large range of values of this parameter. + */ + double theta; + + /** + * Number of scales used to create the pyramid of images. + */ + int nscales; + + /** + * Number of warpings per scale. + * Represents the number of times that I1(x+u0) and grad( I1(x+u0) ) are computed per scale. + * This is a parameter that assures the stability of the method. + * It also affects the running time, so it is a compromise between speed and accuracy. + */ + int warps; + + /** + * Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time. + * A small value will yield more accurate solutions at the expense of a slower convergence. + */ + double epsilon; + + /** + * Stopping criterion iterations number used in the numerical scheme. + */ + int iterations; + + bool useInitialFlow; + +private: + void procOneScale(const GpuMat& I0, const GpuMat& I1, GpuMat& u1, GpuMat& u2); + + std::vector<GpuMat> I0s; + std::vector<GpuMat> I1s; + std::vector<GpuMat> u1s; + std::vector<GpuMat> u2s; + + GpuMat I1x_buf; + GpuMat I1y_buf; + + GpuMat I1w_buf; + GpuMat I1wx_buf; + GpuMat I1wy_buf; + + GpuMat grad_buf; + GpuMat rho_c_buf; + + GpuMat p11_buf; + GpuMat p12_buf; + GpuMat p21_buf; + GpuMat p22_buf; + + GpuMat diff_buf; + GpuMat norm_buf; +}; + + +//! Calculates optical flow for 2 images using block matching algorithm */ +CV_EXPORTS void calcOpticalFlowBM(const GpuMat& prev, const GpuMat& curr, + Size block_size, Size shift_size, Size max_range, bool use_previous, + GpuMat& velx, GpuMat& vely, GpuMat& buf, + Stream& stream = Stream::Null()); + +class CV_EXPORTS FastOpticalFlowBM +{ +public: + void operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy, int search_window = 21, int block_window = 7, Stream& s = Stream::Null()); + +private: + GpuMat buffer; + GpuMat extended_I0; + GpuMat extended_I1; +}; + + +//! Interpolate frames (images) using provided optical flow (displacement field). +//! frame0 - frame 0 (32-bit floating point images, single channel) +//! frame1 - frame 1 (the same type and size) +//! fu - forward horizontal displacement +//! fv - forward vertical displacement +//! bu - backward horizontal displacement +//! bv - backward vertical displacement +//! pos - new frame position +//! newFrame - new frame +//! buf - temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 GpuMat; +//! occlusion masks 0, occlusion masks 1, +//! interpolated forward flow 0, interpolated forward flow 1, +//! interpolated backward flow 0, interpolated backward flow 1 +//! +CV_EXPORTS void interpolateFrames(const GpuMat& frame0, const GpuMat& frame1, + const GpuMat& fu, const GpuMat& fv, + const GpuMat& bu, const GpuMat& bv, + float pos, GpuMat& newFrame, GpuMat& buf, + Stream& stream = Stream::Null()); + +CV_EXPORTS void createOpticalFlowNeedleMap(const GpuMat& u, const GpuMat& v, GpuMat& vertex, GpuMat& colors); + + +//////////////////////// Background/foreground segmentation //////////////////////// + +// Foreground Object Detection from Videos Containing Complex Background. +// Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian. +// ACM MM2003 9p +class CV_EXPORTS FGDStatModel +{ +public: + struct CV_EXPORTS Params + { + int Lc; // Quantized levels per 'color' component. Power of two, typically 32, 64 or 128. + int N1c; // Number of color vectors used to model normal background color variation at a given pixel. + int N2c; // Number of color vectors retained at given pixel. Must be > N1c, typically ~ 5/3 of N1c. + // Used to allow the first N1c vectors to adapt over time to changing background. + + int Lcc; // Quantized levels per 'color co-occurrence' component. Power of two, typically 16, 32 or 64. + int N1cc; // Number of color co-occurrence vectors used to model normal background color variation at a given pixel. + int N2cc; // Number of color co-occurrence vectors retained at given pixel. Must be > N1cc, typically ~ 5/3 of N1cc. + // Used to allow the first N1cc vectors to adapt over time to changing background. + + bool is_obj_without_holes; // If TRUE we ignore holes within foreground blobs. Defaults to TRUE. + int perform_morphing; // Number of erode-dilate-erode foreground-blob cleanup iterations. + // These erase one-pixel junk blobs and merge almost-touching blobs. Default value is 1. + + float alpha1; // How quickly we forget old background pixel values seen. Typically set to 0.1. + float alpha2; // "Controls speed of feature learning". Depends on T. Typical value circa 0.005. + float alpha3; // Alternate to alpha2, used (e.g.) for quicker initial convergence. Typical value 0.1. + + float delta; // Affects color and color co-occurrence quantization, typically set to 2. + float T; // A percentage value which determines when new features can be recognized as new background. (Typically 0.9). + float minArea; // Discard foreground blobs whose bounding box is smaller than this threshold. + + // default Params + Params(); + }; + + // out_cn - channels count in output result (can be 3 or 4) + // 4-channels require more memory, but a bit faster + explicit FGDStatModel(int out_cn = 3); + explicit FGDStatModel(const cv::gpu::GpuMat& firstFrame, const Params& params = Params(), int out_cn = 3); + + ~FGDStatModel(); + + void create(const cv::gpu::GpuMat& firstFrame, const Params& params = Params()); + void release(); + + int update(const cv::gpu::GpuMat& curFrame); + + //8UC3 or 8UC4 reference background image + cv::gpu::GpuMat background; + + //8UC1 foreground image + cv::gpu::GpuMat foreground; + + std::vector< std::vector<cv::Point> > foreground_regions; + +private: + FGDStatModel(const FGDStatModel&); + FGDStatModel& operator=(const FGDStatModel&); + + class Impl; + std::auto_ptr<Impl> impl_; +}; + +/*! + Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm + + The class implements the following algorithm: + "An improved adaptive background mixture model for real-time tracking with shadow detection" + P. KadewTraKuPong and R. Bowden, + Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001." + http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf +*/ +class CV_EXPORTS MOG_GPU +{ +public: + //! the default constructor + MOG_GPU(int nmixtures = -1); + + //! re-initiaization method + void initialize(Size frameSize, int frameType); + + //! the update operator + void operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate = 0.0f, Stream& stream = Stream::Null()); + + //! computes a background image which are the mean of all background gaussians + void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) const; + + //! releases all inner buffers + void release(); + + int history; + float varThreshold; + float backgroundRatio; + float noiseSigma; + +private: + int nmixtures_; + + Size frameSize_; + int frameType_; + int nframes_; + + GpuMat weight_; + GpuMat sortKey_; + GpuMat mean_; + GpuMat var_; +}; + +/*! + The class implements the following algorithm: + "Improved adaptive Gausian mixture model for background subtraction" + Z.Zivkovic + International Conference Pattern Recognition, UK, August, 2004. + http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf +*/ +class CV_EXPORTS MOG2_GPU +{ +public: + //! the default constructor + MOG2_GPU(int nmixtures = -1); + + //! re-initiaization method + void initialize(Size frameSize, int frameType); + + //! the update operator + void operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null()); + + //! computes a background image which are the mean of all background gaussians + void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) const; + + //! releases all inner buffers + void release(); + + // parameters + // you should call initialize after parameters changes + + int history; + + //! here it is the maximum allowed number of mixture components. + //! Actual number is determined dynamically per pixel + float varThreshold; + // threshold on the squared Mahalanobis distance to decide if it is well described + // by the background model or not. Related to Cthr from the paper. + // This does not influence the update of the background. A typical value could be 4 sigma + // and that is varThreshold=4*4=16; Corresponds to Tb in the paper. + + ///////////////////////// + // less important parameters - things you might change but be carefull + //////////////////////// + + float backgroundRatio; + // corresponds to fTB=1-cf from the paper + // TB - threshold when the component becomes significant enough to be included into + // the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0. + // For alpha=0.001 it means that the mode should exist for approximately 105 frames before + // it is considered foreground + // float noiseSigma; + float varThresholdGen; + + //correspondts to Tg - threshold on the squared Mahalan. dist. to decide + //when a sample is close to the existing components. If it is not close + //to any a new component will be generated. I use 3 sigma => Tg=3*3=9. + //Smaller Tg leads to more generated components and higher Tg might make + //lead to small number of components but they can grow too large + float fVarInit; + float fVarMin; + float fVarMax; + + //initial variance for the newly generated components. + //It will will influence the speed of adaptation. A good guess should be made. + //A simple way is to estimate the typical standard deviation from the images. + //I used here 10 as a reasonable value + // min and max can be used to further control the variance + float fCT; //CT - complexity reduction prior + //this is related to the number of samples needed to accept that a component + //actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get + //the standard Stauffer&Grimson algorithm (maybe not exact but very similar) + + //shadow detection parameters + bool bShadowDetection; //default 1 - do shadow detection + unsigned char nShadowDetection; //do shadow detection - insert this value as the detection result - 127 default value + float fTau; + // Tau - shadow threshold. The shadow is detected if the pixel is darker + //version of the background. Tau is a threshold on how much darker the shadow can be. + //Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow + //See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003. + +private: + int nmixtures_; + + Size frameSize_; + int frameType_; + int nframes_; + + GpuMat weight_; + GpuMat variance_; + GpuMat mean_; + + GpuMat bgmodelUsedModes_; //keep track of number of modes per pixel +}; + +/** + * Background Subtractor module. Takes a series of images and returns a sequence of mask (8UC1) + * images of the same size, where 255 indicates Foreground and 0 represents Background. + * This class implements an algorithm described in "Visual Tracking of Human Visitors under + * Variable-Lighting Conditions for a Responsive Audio Art Installation," A. Godbehere, + * A. Matsukawa, K. Goldberg, American Control Conference, Montreal, June 2012. + */ +class CV_EXPORTS GMG_GPU +{ +public: + GMG_GPU(); + + /** + * Validate parameters and set up data structures for appropriate frame size. + * @param frameSize Input frame size + * @param min Minimum value taken on by pixels in image sequence. Usually 0 + * @param max Maximum value taken on by pixels in image sequence. e.g. 1.0 or 255 + */ + void initialize(Size frameSize, float min = 0.0f, float max = 255.0f); + + /** + * Performs single-frame background subtraction and builds up a statistical background image + * model. + * @param frame Input frame + * @param fgmask Output mask image representing foreground and background pixels + * @param learningRate determines how quickly features are “forgotten” from histograms + * @param stream Stream for the asynchronous version + */ + void operator ()(const GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null()); + + //! Releases all inner buffers + void release(); + + //! Total number of distinct colors to maintain in histogram. + int maxFeatures; + + //! Set between 0.0 and 1.0, determines how quickly features are "forgotten" from histograms. + float learningRate; + + //! Number of frames of video to use to initialize histograms. + int numInitializationFrames; + + //! Number of discrete levels in each channel to be used in histograms. + int quantizationLevels; + + //! Prior probability that any given pixel is a background pixel. A sensitivity parameter. + float backgroundPrior; + + //! Value above which pixel is determined to be FG. + float decisionThreshold; + + //! Smoothing radius, in pixels, for cleaning up FG image. + int smoothingRadius; + + //! Perform background model update. + bool updateBackgroundModel; + +private: + float maxVal_, minVal_; + + Size frameSize_; + + int frameNum_; + + GpuMat nfeatures_; + GpuMat colors_; + GpuMat weights_; + + Ptr<FilterEngine_GPU> boxFilter_; + GpuMat buf_; +}; + +////////////////////////////////// Video Encoding ////////////////////////////////// + +// Works only under Windows +// Supports olny H264 video codec and AVI files +class CV_EXPORTS VideoWriter_GPU +{ +public: + struct EncoderParams; + + // Callbacks for video encoder, use it if you want to work with raw video stream + class EncoderCallBack; + + enum SurfaceFormat + { + SF_UYVY = 0, + SF_YUY2, + SF_YV12, + SF_NV12, + SF_IYUV, + SF_BGR, + SF_GRAY = SF_BGR + }; + + VideoWriter_GPU(); + VideoWriter_GPU(const std::string& fileName, cv::Size frameSize, double fps, SurfaceFormat format = SF_BGR); + VideoWriter_GPU(const std::string& fileName, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format = SF_BGR); + VideoWriter_GPU(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, SurfaceFormat format = SF_BGR); + VideoWriter_GPU(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format = SF_BGR); + ~VideoWriter_GPU(); + + // all methods throws cv::Exception if error occurs + void open(const std::string& fileName, cv::Size frameSize, double fps, SurfaceFormat format = SF_BGR); + void open(const std::string& fileName, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format = SF_BGR); + void open(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, SurfaceFormat format = SF_BGR); + void open(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format = SF_BGR); + + bool isOpened() const; + void close(); + + void write(const cv::gpu::GpuMat& image, bool lastFrame = false); + + struct CV_EXPORTS EncoderParams + { + int P_Interval; // NVVE_P_INTERVAL, + int IDR_Period; // NVVE_IDR_PERIOD, + int DynamicGOP; // NVVE_DYNAMIC_GOP, + int RCType; // NVVE_RC_TYPE, + int AvgBitrate; // NVVE_AVG_BITRATE, + int PeakBitrate; // NVVE_PEAK_BITRATE, + int QP_Level_Intra; // NVVE_QP_LEVEL_INTRA, + int QP_Level_InterP; // NVVE_QP_LEVEL_INTER_P, + int QP_Level_InterB; // NVVE_QP_LEVEL_INTER_B, + int DeblockMode; // NVVE_DEBLOCK_MODE, + int ProfileLevel; // NVVE_PROFILE_LEVEL, + int ForceIntra; // NVVE_FORCE_INTRA, + int ForceIDR; // NVVE_FORCE_IDR, + int ClearStat; // NVVE_CLEAR_STAT, + int DIMode; // NVVE_SET_DEINTERLACE, + int Presets; // NVVE_PRESETS, + int DisableCabac; // NVVE_DISABLE_CABAC, + int NaluFramingType; // NVVE_CONFIGURE_NALU_FRAMING_TYPE + int DisableSPSPPS; // NVVE_DISABLE_SPS_PPS + + EncoderParams(); + explicit EncoderParams(const std::string& configFile); + + void load(const std::string& configFile); + void save(const std::string& configFile) const; + }; + + EncoderParams getParams() const; + + class CV_EXPORTS EncoderCallBack + { + public: + enum PicType + { + IFRAME = 1, + PFRAME = 2, + BFRAME = 3 + }; + + virtual ~EncoderCallBack() {} + + // callback function to signal the start of bitstream that is to be encoded + // must return pointer to buffer + virtual uchar* acquireBitStream(int* bufferSize) = 0; + + // callback function to signal that the encoded bitstream is ready to be written to file + virtual void releaseBitStream(unsigned char* data, int size) = 0; + + // callback function to signal that the encoding operation on the frame has started + virtual void onBeginFrame(int frameNumber, PicType picType) = 0; + + // callback function signals that the encoding operation on the frame has finished + virtual void onEndFrame(int frameNumber, PicType picType) = 0; + }; + +private: + VideoWriter_GPU(const VideoWriter_GPU&); + VideoWriter_GPU& operator=(const VideoWriter_GPU&); + + class Impl; + std::auto_ptr<Impl> impl_; +}; + + +////////////////////////////////// Video Decoding ////////////////////////////////////////// + +namespace detail +{ + class FrameQueue; + class VideoParser; +} + +class CV_EXPORTS VideoReader_GPU +{ +public: + enum Codec + { + MPEG1 = 0, + MPEG2, + MPEG4, + VC1, + H264, + JPEG, + H264_SVC, + H264_MVC, + + Uncompressed_YUV420 = (('I'<<24)|('Y'<<16)|('U'<<8)|('V')), // Y,U,V (4:2:0) + Uncompressed_YV12 = (('Y'<<24)|('V'<<16)|('1'<<8)|('2')), // Y,V,U (4:2:0) + Uncompressed_NV12 = (('N'<<24)|('V'<<16)|('1'<<8)|('2')), // Y,UV (4:2:0) + Uncompressed_YUYV = (('Y'<<24)|('U'<<16)|('Y'<<8)|('V')), // YUYV/YUY2 (4:2:2) + Uncompressed_UYVY = (('U'<<24)|('Y'<<16)|('V'<<8)|('Y')) // UYVY (4:2:2) + }; + + enum ChromaFormat + { + Monochrome=0, + YUV420, + YUV422, + YUV444 + }; + + struct FormatInfo + { + Codec codec; + ChromaFormat chromaFormat; + int width; + int height; + }; + + class VideoSource; + + VideoReader_GPU(); + explicit VideoReader_GPU(const std::string& filename); + explicit VideoReader_GPU(const cv::Ptr<VideoSource>& source); + + ~VideoReader_GPU(); + + void open(const std::string& filename); + void open(const cv::Ptr<VideoSource>& source); + bool isOpened() const; + + void close(); + + bool read(GpuMat& image); + + FormatInfo format() const; + void dumpFormat(std::ostream& st); + + class CV_EXPORTS VideoSource + { + public: + VideoSource() : frameQueue_(0), videoParser_(0) {} + virtual ~VideoSource() {} + + virtual FormatInfo format() const = 0; + virtual void start() = 0; + virtual void stop() = 0; + virtual bool isStarted() const = 0; + virtual bool hasError() const = 0; + + void setFrameQueue(detail::FrameQueue* frameQueue) { frameQueue_ = frameQueue; } + void setVideoParser(detail::VideoParser* videoParser) { videoParser_ = videoParser; } + + protected: + bool parseVideoData(const uchar* data, size_t size, bool endOfStream = false); + + private: + VideoSource(const VideoSource&); + VideoSource& operator =(const VideoSource&); + + detail::FrameQueue* frameQueue_; + detail::VideoParser* videoParser_; + }; + +private: + VideoReader_GPU(const VideoReader_GPU&); + VideoReader_GPU& operator =(const VideoReader_GPU&); + + class Impl; + std::auto_ptr<Impl> impl_; +}; + +//! removes points (CV_32FC2, single row matrix) with zero mask value +CV_EXPORTS void compactPoints(GpuMat &points0, GpuMat &points1, const GpuMat &mask); + +CV_EXPORTS void calcWobbleSuppressionMaps( + int left, int idx, int right, Size size, const Mat &ml, const Mat &mr, + GpuMat &mapx, GpuMat &mapy); + +} // namespace gpu + +} // namespace cv + +#endif /* __OPENCV_GPU_HPP__ */ |