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| author | siddhu8990 | 2017-04-24 14:08:37 +0530 |
|---|---|---|
| committer | siddhu8990 | 2017-04-24 14:08:37 +0530 |
| commit | c7e9597db39140c1d982f796a8e1f03bb54e7905 (patch) | |
| tree | f5f44081aeba7a00bb69b1ec71f93c31eac12863 /thirdparty/raspberrypi/includes/opencv2/gpu/gpu.hpp | |
| parent | 1fd0dce8d72c4d5869ce5ff4025ac09af603bc0f (diff) | |
| download | Scilab2C_fossee_old-c7e9597db39140c1d982f796a8e1f03bb54e7905.tar.gz Scilab2C_fossee_old-c7e9597db39140c1d982f796a8e1f03bb54e7905.tar.bz2 Scilab2C_fossee_old-c7e9597db39140c1d982f796a8e1f03bb54e7905.zip | |
Fixed float.h issue. OpenCV with built libraries working for linux x64
Diffstat (limited to 'thirdparty/raspberrypi/includes/opencv2/gpu/gpu.hpp')
| -rw-r--r-- | thirdparty/raspberrypi/includes/opencv2/gpu/gpu.hpp | 2530 |
1 files changed, 0 insertions, 2530 deletions
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/gpu.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/gpu.hpp deleted file mode 100644 index de16982..0000000 --- a/thirdparty/raspberrypi/includes/opencv2/gpu/gpu.hpp +++ /dev/null @@ -1,2530 +0,0 @@ -/*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__ */ |