Alexey Spizhevoy
1512 lines
74 KiB
C++
1512 lines
74 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other GpuMaterials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#ifndef __OPENCV_GPU_HPP__
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#define __OPENCV_GPU_HPP__
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#include <vector>
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#include "opencv2/core/core.hpp"
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#include "opencv2/imgproc/imgproc.hpp"
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#include "opencv2/objdetect/objdetect.hpp"
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#include "opencv2/gpu/devmem2d.hpp"
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#include "opencv2/features2d/features2d.hpp"
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namespace cv
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{
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namespace gpu
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{
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//////////////////////////////// Initialization & Info ////////////////////////
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//! This is the only function that do not throw exceptions if the library is compiled without Cuda.
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CV_EXPORTS int getCudaEnabledDeviceCount();
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//! Functions below throw cv::Expception if the library is compiled without Cuda.
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CV_EXPORTS string getDeviceName(int device);
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CV_EXPORTS void setDevice(int device);
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CV_EXPORTS int getDevice();
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CV_EXPORTS void getComputeCapability(int device, int& major, int& minor);
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CV_EXPORTS int getNumberOfSMs(int device);
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CV_EXPORTS void getGpuMemInfo(size_t& free, size_t& total);
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CV_EXPORTS bool hasNativeDoubleSupport(int device);
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CV_EXPORTS bool hasAtomicsSupport(int device);
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//////////////////////////////// Error handling ////////////////////////
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CV_EXPORTS void error(const char *error_string, const char *file, const int line, const char *func);
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CV_EXPORTS void nppError( int err, const char *file, const int line, const char *func);
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//////////////////////////////// GpuMat ////////////////////////////////
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class Stream;
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class CudaMem;
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//! Smart pointer for GPU memory with reference counting. Its interface is mostly similar with cv::Mat.
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class CV_EXPORTS GpuMat
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{
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public:
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//! default constructor
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GpuMat();
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//! constructs GpuMatrix of the specified size and type (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.)
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GpuMat(int rows, int cols, int type);
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GpuMat(Size size, int type);
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//! constucts GpuMatrix and fills it with the specified value _s.
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GpuMat(int rows, int cols, int type, const Scalar& s);
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GpuMat(Size size, int type, const Scalar& s);
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//! copy constructor
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GpuMat(const GpuMat& m);
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//! constructor for GpuMatrix headers pointing to user-allocated data
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GpuMat(int rows, int cols, int type, void* data, size_t step = Mat::AUTO_STEP);
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GpuMat(Size size, int type, void* data, size_t step = Mat::AUTO_STEP);
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//! creates a matrix header for a part of the bigger matrix
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GpuMat(const GpuMat& m, const Range& rowRange, const Range& colRange);
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GpuMat(const GpuMat& m, const Rect& roi);
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//! builds GpuMat from Mat. Perfom blocking upload to device.
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explicit GpuMat (const Mat& m);
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//! destructor - calls release()
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~GpuMat();
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//! assignment operators
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GpuMat& operator = (const GpuMat& m);
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//! assignment operator. Perfom blocking upload to device.
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GpuMat& operator = (const Mat& m);
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//! returns lightweight DevMem2D_ structure for passing to nvcc-compiled code.
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// Contains just image size, data ptr and step.
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template <class T> operator DevMem2D_<T>() const;
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template <class T> operator PtrStep_<T>() const;
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//! pefroms blocking upload data to GpuMat.
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void upload(const cv::Mat& m);
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//! upload async
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void upload(const CudaMem& m, Stream& stream);
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//! downloads data from device to host memory. Blocking calls.
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operator Mat() const;
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void download(cv::Mat& m) const;
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//! download async
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void download(CudaMem& m, Stream& stream) const;
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//! returns a new GpuMatrix header for the specified row
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GpuMat row(int y) const;
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//! returns a new GpuMatrix header for the specified column
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GpuMat col(int x) const;
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//! ... for the specified row span
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GpuMat rowRange(int startrow, int endrow) const;
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GpuMat rowRange(const Range& r) const;
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//! ... for the specified column span
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GpuMat colRange(int startcol, int endcol) const;
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GpuMat colRange(const Range& r) const;
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//! returns deep copy of the GpuMatrix, i.e. the data is copied
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GpuMat clone() const;
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//! copies the GpuMatrix content to "m".
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// It calls m.create(this->size(), this->type()).
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void copyTo( GpuMat& m ) const;
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//! copies those GpuMatrix elements to "m" that are marked with non-zero mask elements.
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void copyTo( GpuMat& m, const GpuMat& mask ) const;
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//! converts GpuMatrix to another datatype with optional scalng. See cvConvertScale.
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void convertTo( GpuMat& m, int rtype, double alpha=1, double beta=0 ) const;
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void assignTo( GpuMat& m, int type=-1 ) const;
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//! sets every GpuMatrix element to s
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GpuMat& operator = (const Scalar& s);
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//! sets some of the GpuMatrix elements to s, according to the mask
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GpuMat& setTo(const Scalar& s, const GpuMat& mask = GpuMat());
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//! creates alternative GpuMatrix header for the same data, with different
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// number of channels and/or different number of rows. see cvReshape.
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GpuMat reshape(int cn, int rows = 0) const;
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//! allocates new GpuMatrix data unless the GpuMatrix already has specified size and type.
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// previous data is unreferenced if needed.
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void create(int rows, int cols, int type);
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void create(Size size, int type);
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//! decreases reference counter;
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// deallocate the data when reference counter reaches 0.
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void release();
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//! swaps with other smart pointer
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void swap(GpuMat& mat);
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//! locates GpuMatrix header within a parent GpuMatrix. See below
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void locateROI( Size& wholeSize, Point& ofs ) const;
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//! moves/resizes the current GpuMatrix ROI inside the parent GpuMatrix.
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GpuMat& adjustROI( int dtop, int dbottom, int dleft, int dright );
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//! extracts a rectangular sub-GpuMatrix
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// (this is a generalized form of row, rowRange etc.)
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GpuMat operator()( Range rowRange, Range colRange ) const;
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GpuMat operator()( const Rect& roi ) const;
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//! returns true iff the GpuMatrix data is continuous
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// (i.e. when there are no gaps between successive rows).
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// similar to CV_IS_GpuMat_CONT(cvGpuMat->type)
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bool isContinuous() const;
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//! returns element size in bytes,
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// similar to CV_ELEM_SIZE(cvMat->type)
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size_t elemSize() const;
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//! returns the size of element channel in bytes.
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size_t elemSize1() const;
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//! returns element type, similar to CV_MAT_TYPE(cvMat->type)
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int type() const;
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//! returns element type, similar to CV_MAT_DEPTH(cvMat->type)
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int depth() const;
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//! returns element type, similar to CV_MAT_CN(cvMat->type)
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int channels() const;
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//! returns step/elemSize1()
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size_t step1() const;
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//! returns GpuMatrix size:
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// width == number of columns, height == number of rows
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Size size() const;
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//! returns true if GpuMatrix data is NULL
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bool empty() const;
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//! returns pointer to y-th row
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uchar* ptr(int y = 0);
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const uchar* ptr(int y = 0) const;
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//! template version of the above method
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template<typename _Tp> _Tp* ptr(int y = 0);
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template<typename _Tp> const _Tp* ptr(int y = 0) const;
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//! matrix transposition
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GpuMat t() const;
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/*! includes several bit-fields:
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- the magic signature
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- continuity flag
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- depth
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- number of channels
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*/
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int flags;
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//! the number of rows and columns
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int rows, cols;
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//! a distance between successive rows in bytes; includes the gap if any
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size_t step;
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//! pointer to the data
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uchar* data;
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//! pointer to the reference counter;
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// when GpuMatrix points to user-allocated data, the pointer is NULL
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int* refcount;
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//! helper fields used in locateROI and adjustROI
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uchar* datastart;
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uchar* dataend;
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};
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//#define TemplatedGpuMat // experimental now, deprecated to use
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#ifdef TemplatedGpuMat
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#include "GpuMat_BetaDeprecated.hpp"
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#endif
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//////////////////////////////// CudaMem ////////////////////////////////
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// CudaMem is limited cv::Mat with page locked memory allocation.
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// Page locked memory is only needed for async and faster coping to GPU.
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// It is convertable to cv::Mat header without reference counting
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// so you can use it with other opencv functions.
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class CV_EXPORTS CudaMem
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{
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public:
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enum { ALLOC_PAGE_LOCKED = 1, ALLOC_ZEROCOPY = 2, ALLOC_WRITE_COMBINED = 4 };
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CudaMem();
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CudaMem(const CudaMem& m);
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CudaMem(int rows, int cols, int type, int _alloc_type = ALLOC_PAGE_LOCKED);
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CudaMem(Size size, int type, int alloc_type = ALLOC_PAGE_LOCKED);
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//! creates from cv::Mat with coping data
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explicit CudaMem(const Mat& m, int alloc_type = ALLOC_PAGE_LOCKED);
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~CudaMem();
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CudaMem& operator = (const CudaMem& m);
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//! returns deep copy of the matrix, i.e. the data is copied
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CudaMem clone() const;
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//! allocates new matrix data unless the matrix already has specified size and type.
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void create(int rows, int cols, int type, int alloc_type = ALLOC_PAGE_LOCKED);
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void create(Size size, int type, int alloc_type = ALLOC_PAGE_LOCKED);
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//! decrements reference counter and released memory if needed.
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void release();
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//! returns matrix header with disabled reference counting for CudaMem data.
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Mat createMatHeader() const;
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operator Mat() const;
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//! maps host memory into device address space and returns GpuMat header for it. Throws exception if not supported by hardware.
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GpuMat createGpuMatHeader() const;
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operator GpuMat() const;
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//returns if host memory can be mapperd to gpu address space;
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static bool canMapHostMemory();
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// Please see cv::Mat for descriptions
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bool isContinuous() const;
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size_t elemSize() const;
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size_t elemSize1() const;
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int type() const;
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int depth() const;
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int channels() const;
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size_t step1() const;
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Size size() const;
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bool empty() const;
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// Please see cv::Mat for descriptions
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int flags;
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int rows, cols;
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size_t step;
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uchar* data;
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int* refcount;
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uchar* datastart;
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uchar* dataend;
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int alloc_type;
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};
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//////////////////////////////// CudaStream ////////////////////////////////
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// Encapculates Cuda Stream. Provides interface for async coping.
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// Passed to each function that supports async kernel execution.
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// Reference counting is enabled
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class CV_EXPORTS Stream
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{
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public:
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Stream();
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~Stream();
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Stream(const Stream&);
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Stream& operator=(const Stream&);
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bool queryIfComplete();
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void waitForCompletion();
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//! downloads asynchronously.
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// Warning! cv::Mat must point to page locked memory (i.e. to CudaMem data or to its subMat)
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void enqueueDownload(const GpuMat& src, CudaMem& dst);
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void enqueueDownload(const GpuMat& src, Mat& dst);
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//! uploads asynchronously.
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// Warning! cv::Mat must point to page locked memory (i.e. to CudaMem data or to its ROI)
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void enqueueUpload(const CudaMem& src, GpuMat& dst);
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void enqueueUpload(const Mat& src, GpuMat& dst);
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void enqueueCopy(const GpuMat& src, GpuMat& dst);
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void enqueueMemSet(const GpuMat& src, Scalar val);
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void enqueueMemSet(const GpuMat& src, Scalar val, const GpuMat& mask);
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// converts matrix type, ex from float to uchar depending on type
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void enqueueConvert(const GpuMat& src, GpuMat& dst, int type, double a = 1, double b = 0);
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private:
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void create();
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void release();
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struct Impl;
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Impl *impl;
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friend struct StreamAccessor;
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};
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////////////////////////////// Arithmetics ///////////////////////////////////
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//! transposes the matrix
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//! supports matrix with element size = 1, 4 and 8 bytes (CV_8UC1, CV_8UC4, CV_16UC2, CV_32FC1, etc)
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CV_EXPORTS void transpose(const GpuMat& src1, GpuMat& dst);
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//! reverses the order of the rows, columns or both in a matrix
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//! supports CV_8UC1, CV_8UC4 types
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CV_EXPORTS void flip(const GpuMat& a, GpuMat& b, int flipCode);
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//! transforms 8-bit unsigned integers using lookup table: dst(i)=lut(src(i))
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//! destination array will have the depth type as lut and the same channels number as source
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//! supports CV_8UC1, CV_8UC3 types
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CV_EXPORTS void LUT(const GpuMat& src, const Mat& lut, GpuMat& dst);
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//! makes multi-channel array out of several single-channel arrays
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CV_EXPORTS void merge(const GpuMat* src, size_t n, GpuMat& dst);
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//! makes multi-channel array out of several single-channel arrays
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CV_EXPORTS void merge(const vector<GpuMat>& src, GpuMat& dst);
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//! makes multi-channel array out of several single-channel arrays (async version)
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CV_EXPORTS void merge(const GpuMat* src, size_t n, GpuMat& dst, const Stream& stream);
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//! makes multi-channel array out of several single-channel arrays (async version)
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CV_EXPORTS void merge(const vector<GpuMat>& src, GpuMat& dst, const Stream& stream);
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//! copies each plane of a multi-channel array to a dedicated array
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CV_EXPORTS void split(const GpuMat& src, GpuMat* dst);
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//! copies each plane of a multi-channel array to a dedicated array
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CV_EXPORTS void split(const GpuMat& src, vector<GpuMat>& dst);
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//! copies each plane of a multi-channel array to a dedicated array (async version)
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CV_EXPORTS void split(const GpuMat& src, GpuMat* dst, const Stream& stream);
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//! copies each plane of a multi-channel array to a dedicated array (async version)
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CV_EXPORTS void split(const GpuMat& src, vector<GpuMat>& dst, const Stream& stream);
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//! computes magnitude of complex (x(i).re, x(i).im) vector
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//! supports only CV_32FC2 type
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CV_EXPORTS void magnitude(const GpuMat& x, GpuMat& magnitude);
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//! computes squared magnitude of complex (x(i).re, x(i).im) vector
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//! supports only CV_32FC2 type
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CV_EXPORTS void magnitudeSqr(const GpuMat& x, GpuMat& magnitude);
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//! computes magnitude of each (x(i), y(i)) vector
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//! supports only floating-point source
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CV_EXPORTS void magnitude(const GpuMat& x, const GpuMat& y, GpuMat& magnitude);
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//! async version
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CV_EXPORTS void magnitude(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, const Stream& stream);
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//! computes squared magnitude of each (x(i), y(i)) vector
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//! supports only floating-point source
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CV_EXPORTS void magnitudeSqr(const GpuMat& x, const GpuMat& y, GpuMat& magnitude);
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//! async version
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CV_EXPORTS void magnitudeSqr(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, const Stream& stream);
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//! computes angle (angle(i)) of each (x(i), y(i)) vector
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//! supports only floating-point source
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CV_EXPORTS void phase(const GpuMat& x, const GpuMat& y, GpuMat& angle, bool angleInDegrees = false);
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//! async version
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CV_EXPORTS void phase(const GpuMat& x, const GpuMat& y, GpuMat& angle, bool angleInDegrees, const Stream& stream);
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//! converts Cartesian coordinates to polar
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//! supports only floating-point source
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CV_EXPORTS void cartToPolar(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, GpuMat& angle, bool angleInDegrees = false);
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//! async version
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CV_EXPORTS void cartToPolar(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, GpuMat& angle, bool angleInDegrees, const Stream& stream);
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//! converts polar coordinates to Cartesian
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//! supports only floating-point source
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CV_EXPORTS void polarToCart(const GpuMat& magnitude, const GpuMat& angle, GpuMat& x, GpuMat& y, bool angleInDegrees = false);
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//! async version
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CV_EXPORTS void polarToCart(const GpuMat& magnitude, const GpuMat& angle, GpuMat& x, GpuMat& y, bool angleInDegrees, const Stream& stream);
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//////////////////////////// Per-element operations ////////////////////////////////////
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//! adds one matrix to another (c = a + b)
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//! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types
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CV_EXPORTS void add(const GpuMat& a, const GpuMat& b, GpuMat& c);
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//! adds scalar to a matrix (c = a + s)
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//! supports CV_32FC1 and CV_32FC2 type
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CV_EXPORTS void add(const GpuMat& a, const Scalar& sc, GpuMat& c);
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//! subtracts one matrix from another (c = a - b)
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//! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types
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CV_EXPORTS void subtract(const GpuMat& a, const GpuMat& b, GpuMat& c);
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//! subtracts scalar from a matrix (c = a - s)
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//! supports CV_32FC1 and CV_32FC2 type
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CV_EXPORTS void subtract(const GpuMat& a, const Scalar& sc, GpuMat& c);
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//! computes element-wise product of the two arrays (c = a * b)
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//! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types
|
|
CV_EXPORTS void multiply(const GpuMat& a, const GpuMat& b, GpuMat& c);
|
|
//! multiplies matrix to a scalar (c = a * s)
|
|
//! supports CV_32FC1 and CV_32FC2 type
|
|
CV_EXPORTS void multiply(const GpuMat& a, const Scalar& sc, GpuMat& c);
|
|
|
|
//! computes element-wise quotient of the two arrays (c = a / b)
|
|
//! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types
|
|
CV_EXPORTS void divide(const GpuMat& a, const GpuMat& b, GpuMat& c);
|
|
//! computes element-wise quotient of matrix and scalar (c = a / s)
|
|
//! supports CV_32FC1 and CV_32FC2 type
|
|
CV_EXPORTS void divide(const GpuMat& a, const Scalar& sc, GpuMat& c);
|
|
|
|
//! computes exponent of each matrix element (b = e**a)
|
|
//! supports only CV_32FC1 type
|
|
CV_EXPORTS void exp(const GpuMat& a, GpuMat& b);
|
|
|
|
//! computes natural logarithm of absolute value of each matrix element: b = log(abs(a))
|
|
//! supports only CV_32FC1 type
|
|
CV_EXPORTS void log(const GpuMat& a, GpuMat& b);
|
|
|
|
//! computes element-wise absolute difference of two arrays (c = abs(a - b))
|
|
//! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types
|
|
CV_EXPORTS void absdiff(const GpuMat& a, const GpuMat& b, GpuMat& c);
|
|
//! computes element-wise absolute difference of array and scalar (c = abs(a - s))
|
|
//! supports only CV_32FC1 type
|
|
CV_EXPORTS void absdiff(const GpuMat& a, const Scalar& s, GpuMat& c);
|
|
|
|
//! compares elements of two arrays (c = a <cmpop> b)
|
|
//! supports CV_8UC4, CV_32FC1 types
|
|
CV_EXPORTS void compare(const GpuMat& a, const GpuMat& b, GpuMat& c, int cmpop);
|
|
|
|
//! performs per-elements bit-wise inversion
|
|
CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask=GpuMat());
|
|
//! async version
|
|
CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask, const Stream& stream);
|
|
|
|
//! 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());
|
|
//! async version
|
|
CV_EXPORTS void bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream);
|
|
|
|
//! 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());
|
|
//! async version
|
|
CV_EXPORTS void bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream);
|
|
|
|
//! 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());
|
|
//! async version
|
|
CV_EXPORTS void bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream);
|
|
|
|
//! computes per-element minimum of two arrays (dst = min(src1, src2))
|
|
CV_EXPORTS void min(const GpuMat& src1, const GpuMat& src2, GpuMat& dst);
|
|
//! Async version
|
|
CV_EXPORTS void min(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const Stream& stream);
|
|
|
|
//! computes per-element minimum of array and scalar (dst = min(src1, src2))
|
|
CV_EXPORTS void min(const GpuMat& src1, double src2, GpuMat& dst);
|
|
//! Async version
|
|
CV_EXPORTS void min(const GpuMat& src1, double src2, GpuMat& dst, const Stream& stream);
|
|
|
|
//! computes per-element maximum of two arrays (dst = max(src1, src2))
|
|
CV_EXPORTS void max(const GpuMat& src1, const GpuMat& src2, GpuMat& dst);
|
|
//! Async version
|
|
CV_EXPORTS void max(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const Stream& stream);
|
|
|
|
//! computes per-element maximum of array and scalar (dst = max(src1, src2))
|
|
CV_EXPORTS void max(const GpuMat& src1, double src2, GpuMat& dst);
|
|
//! Async version
|
|
CV_EXPORTS void max(const GpuMat& src1, double src2, GpuMat& dst, const Stream& stream);
|
|
|
|
|
|
////////////////////////////// Image processing //////////////////////////////
|
|
|
|
//! DST[x,y] = SRC[xmap[x,y],ymap[x,y]] with bilinear interpolation.
|
|
//! supports CV_8UC1, CV_8UC3 source types and CV_32FC1 map type
|
|
CV_EXPORTS void remap(const GpuMat& src, GpuMat& dst, const GpuMat& xmap, const GpuMat& ymap);
|
|
|
|
//! 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));
|
|
|
|
//! 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));
|
|
|
|
//! Does mean shift segmentation with elimiation 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);
|
|
//! async version
|
|
CV_EXPORTS void drawColorDisp(const GpuMat& src_disp, GpuMat& dst_disp, int ndisp, const Stream& stream);
|
|
|
|
//! Reprojects disparity image to 3D space.
|
|
//! Supports CV_8U and CV_16S types of input disparity.
|
|
//! The output is a 4-channel floating-point (CV_32FC4) 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);
|
|
//! async version
|
|
CV_EXPORTS void reprojectImageTo3D(const GpuMat& disp, GpuMat& xyzw, const Mat& Q, const Stream& stream);
|
|
|
|
//! converts image from one color space to another
|
|
CV_EXPORTS void cvtColor(const GpuMat& src, GpuMat& dst, int code, int dcn = 0);
|
|
//! async version
|
|
CV_EXPORTS void cvtColor(const GpuMat& src, GpuMat& dst, int code, int dcn, const Stream& stream);
|
|
|
|
//! applies fixed threshold to the image.
|
|
//! Now supports only THRESH_TRUNC threshold type and one channels float source.
|
|
CV_EXPORTS double threshold(const GpuMat& src, GpuMat& dst, double thresh);
|
|
|
|
//! resizes the image
|
|
//! Supports INTER_NEAREST, INTER_LINEAR
|
|
//! supports CV_8UC1, CV_8UC4 types
|
|
CV_EXPORTS void resize(const GpuMat& src, GpuMat& dst, Size dsize, double fx=0, double fy=0, int interpolation = INTER_LINEAR);
|
|
|
|
//! 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);
|
|
|
|
//! 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);
|
|
|
|
//! rotate 8bit single or four channel image
|
|
//! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
|
|
//! supports CV_8UC1, CV_8UC4 types
|
|
CV_EXPORTS void rotate(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift = 0, double yShift = 0, int interpolation = INTER_LINEAR);
|
|
|
|
//! copies 2D array to a larger destination array and pads borders with user-specifiable constant
|
|
//! supports CV_8UC1, CV_8UC4, CV_32SC1 and CV_32FC1 types
|
|
CV_EXPORTS void copyMakeBorder(const GpuMat& src, GpuMat& dst, int top, int bottom, int left, int right, const Scalar& value = Scalar());
|
|
|
|
//! 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);
|
|
|
|
//! computes the integral image and integral for the squared image
|
|
//! sum will have CV_32S type, sqsum - CV32F type
|
|
//! supports only CV_8UC1 source type
|
|
CV_EXPORTS void integral(const GpuMat& src, GpuMat& sum, GpuMat& sqsum);
|
|
|
|
//! 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);
|
|
|
|
//! 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);
|
|
|
|
//! applies Canny edge detector and produces the edge map
|
|
//! supprots only CV_8UC1 source type
|
|
//! disabled until fix crash
|
|
CV_EXPORTS void Canny(const GpuMat& image, GpuMat& edges, double threshold1, double threshold2, int apertureSize = 3);
|
|
|
|
//! 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);
|
|
|
|
//! 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);
|
|
|
|
//! performs per-element multiplication of two full (not packed) Fourier spectrums
|
|
//! supports 32FC2 matrixes only (interleaved format)
|
|
CV_EXPORTS void mulSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, bool conjB=false);
|
|
|
|
//! performs per-element multiplication of two full (not packed) Fourier spectrums
|
|
//! supports 32FC2 matrixes only (interleaved format)
|
|
CV_EXPORTS void mulAndScaleSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags,
|
|
float scale, bool conjB=false);
|
|
|
|
//! performs a forward or inverse discrete Fourier transform (1D or 2D) of floating point matrix
|
|
//!
|
|
//! If the source matrix is not continous, then additional copy will be done,
|
|
//! so to avoid copying ensure the source matrix is continous one.
|
|
//!
|
|
//! 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, which
|
|
//! doesn't allow us to retrieve parity of the destiantion matrix dimension (along which the first step
|
|
//! of DFT is performed). You must specifiy odd case explicitely.
|
|
CV_EXPORTS void dft(const GpuMat& src, GpuMat& dst, int flags=0, int nonZeroRows=0, bool odd=false);
|
|
|
|
//! 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);
|
|
|
|
//! 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);
|
|
|
|
|
|
////////////////////////////// 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);
|
|
|
|
//! computes norm of array
|
|
//! supports NORM_INF, NORM_L1, NORM_L2
|
|
//! supports only CV_8UC1 type
|
|
CV_EXPORTS double norm(const GpuMat& src1, int normType=NORM_L2);
|
|
|
|
//! 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);
|
|
|
|
//! computes sum of array elements
|
|
//! supports only single channel images
|
|
CV_EXPORTS Scalar sum(const GpuMat& src, GpuMat& buf);
|
|
|
|
//! computes squared sum of array elements
|
|
//! supports only single channel images
|
|
CV_EXPORTS Scalar sqrSum(const GpuMat& src);
|
|
|
|
//! computes squared sum of array elements
|
|
//! supports only single channel images
|
|
CV_EXPORTS Scalar sqrSum(const GpuMat& src, 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());
|
|
|
|
//! finds global minimum and maximum array elements and returns their values
|
|
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());
|
|
|
|
//! finds global minimum and maximum array elements and returns their values with locations
|
|
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);
|
|
|
|
//! counts non-zero array elements
|
|
CV_EXPORTS int countNonZero(const GpuMat& src, GpuMat& buf);
|
|
|
|
|
|
//////////////////////////////// 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) = 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) = 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) = 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)) = 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);
|
|
|
|
//! 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);
|
|
|
|
//! returns 2D filter with the specified kernel
|
|
//! supports CV_8UC1 and CV_8UC4 types
|
|
CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, const Size& ksize,
|
|
Point anchor = Point(-1, -1));
|
|
|
|
//! returns the non-separable linear filter engine
|
|
CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat& kernel,
|
|
const Point& anchor = Point(-1,-1));
|
|
|
|
//! 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_CONSTANT);
|
|
|
|
//! 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_CONSTANT);
|
|
|
|
//! 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);
|
|
|
|
//! 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);
|
|
|
|
//! 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);
|
|
|
|
//! 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));
|
|
|
|
//! a synonym for normalized box filter
|
|
static inline void blur(const GpuMat& src, GpuMat& dst, Size ksize, Point anchor = Point(-1,-1)) { boxFilter(src, dst, -1, ksize, anchor); }
|
|
|
|
//! 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);
|
|
|
|
//! 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);
|
|
|
|
//! 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);
|
|
|
|
//! 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));
|
|
|
|
//! 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);
|
|
|
|
//! 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);
|
|
|
|
//! 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);
|
|
|
|
//! 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);
|
|
|
|
//! 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);
|
|
|
|
//////////////////////////////// Image Labeling ////////////////////////////////
|
|
|
|
//!performs labeling via graph cuts
|
|
CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& bottom, GpuMat& labels, GpuMat& buf);
|
|
|
|
////////////////////////////////// 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);
|
|
//! 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]);
|
|
//! 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);
|
|
//! 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]);
|
|
|
|
//////////////////////////////// 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);
|
|
|
|
//! async version
|
|
void operator() ( const GpuMat& left, const GpuMat& right, GpuMat& disparity, const Stream & stream);
|
|
|
|
//! Some heuristics that tries to estmate
|
|
// if current GPU will be faster then 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);
|
|
|
|
//! async version
|
|
void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream);
|
|
|
|
|
|
//! version for user specified data term
|
|
void operator()(const GpuMat& data, GpuMat& disparity);
|
|
void operator()(const GpuMat& data, GpuMat& disparity, Stream& stream);
|
|
|
|
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);
|
|
|
|
//! async version
|
|
void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream);
|
|
|
|
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 u[2], d[2], l[2], r[2];
|
|
GpuMat disp_selected_pyr[2];
|
|
|
|
GpuMat data_cost;
|
|
GpuMat data_cost_selected;
|
|
|
|
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);
|
|
|
|
//! async version
|
|
void operator()(const GpuMat& disparity, const GpuMat& image, GpuMat& dst, Stream& stream);
|
|
|
|
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 HOGDescriptor
|
|
{
|
|
public:
|
|
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;
|
|
double getWinSigma() const;
|
|
|
|
static vector<float> getDefaultPeopleDetector();
|
|
static vector<float> getPeopleDetector_48x96();
|
|
static vector<float> getPeopleDetector_64x128();
|
|
void setSVMDetector(const vector<float>& detector);
|
|
bool checkDetectorSize() const;
|
|
|
|
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 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);
|
|
|
|
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;
|
|
Mat labels_host;
|
|
|
|
// Results of the last histogram evaluation step
|
|
GpuMat block_hists;
|
|
|
|
// Gradients conputation results
|
|
GpuMat grad, qangle;
|
|
};
|
|
|
|
|
|
////////////////////////////////// BruteForceMatcher //////////////////////////////////
|
|
|
|
class CV_EXPORTS BruteForceMatcher_GPU_base
|
|
{
|
|
public:
|
|
enum DistType {L1Dist = 0, L2Dist};
|
|
|
|
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.
|
|
// trainIdx.at<int>(0, queryIdx) will contain best train index for queryIdx
|
|
// distance.at<float>(0, queryIdx) will contain distance
|
|
void matchSingle(const GpuMat& queryDescs, const GpuMat& trainDescs,
|
|
GpuMat& trainIdx, GpuMat& distance,
|
|
const GpuMat& mask = GpuMat());
|
|
|
|
// Download trainIdx and distance to CPU vector with DMatch
|
|
static void matchDownload(const GpuMat& trainIdx, const GpuMat& distance, std::vector<DMatch>& matches);
|
|
|
|
// Find one best match for each query descriptor.
|
|
void match(const GpuMat& queryDescs, const GpuMat& trainDescs, 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 vector<GpuMat>& masks = std::vector<GpuMat>());
|
|
|
|
// Find one best match from train collection for each query descriptor.
|
|
// trainIdx.at<int>(0, queryIdx) will contain best train index for queryIdx
|
|
// imgIdx.at<int>(0, queryIdx) will contain best image index for queryIdx
|
|
// distance.at<float>(0, queryIdx) will contain distance
|
|
void matchCollection(const GpuMat& queryDescs, const GpuMat& trainCollection,
|
|
GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
|
|
const GpuMat& maskCollection);
|
|
|
|
// Download trainIdx, imgIdx and distance to CPU vector with DMatch
|
|
static void matchDownload(const GpuMat& trainIdx, GpuMat& imgIdx, const GpuMat& distance,
|
|
std::vector<DMatch>& matches);
|
|
|
|
// Find one best match from train collection for each query descriptor.
|
|
void match(const GpuMat& queryDescs, 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).
|
|
// trainIdx.at<int>(queryIdx, i) will contain index of i'th best trains (i < k).
|
|
// distance.at<float>(queryIdx, i) will contain distance.
|
|
// allDist is a buffer to store all distance between query descriptors and train descriptors
|
|
// it have size (nQuery,nTrain) and CV_32F type
|
|
// allDist.at<float>(queryIdx, trainIdx) will contain FLT_MAX, if trainIdx is one from k best,
|
|
// otherwise it will contain distance between queryIdx and trainIdx descriptors
|
|
void knnMatch(const GpuMat& queryDescs, const GpuMat& trainDescs,
|
|
GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k, const GpuMat& mask = GpuMat());
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|
|
|
// Download trainIdx and distance to CPU 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,
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|
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
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|
|
|
// 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& queryDescs, const GpuMat& trainDescs,
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|
std::vector< std::vector<DMatch> >& matches, int k, const GpuMat& mask = GpuMat(),
|
|
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& queryDescs, std::vector< std::vector<DMatch> >& matches, int knn,
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const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false );
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|
|
|
// Find best matches for each query descriptor which have distance less than maxDistance.
|
|
// nMatches.at<unsigned int>(0, queruIdx) will contain matches count for queryIdx.
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|
// carefully nMatches can be greater than trainIdx.cols - it means that matcher didn't find all matches,
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|
// because it didn't have enough memory.
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|
// trainIdx.at<int>(queruIdx, i) will contain ith train index (i < min(nMatches.at<unsigned int>(0, queruIdx), trainIdx.cols))
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// distance.at<int>(queruIdx, i) will contain ith distance (i < min(nMatches.at<unsigned int>(0, queruIdx), trainIdx.cols))
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// If trainIdx is empty, then trainIdx and distance will be created with size nQuery x nTrain,
|
|
// otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
|
|
// Matches doesn't sorted.
|
|
void radiusMatch(const GpuMat& queryDescs, const GpuMat& trainDescs,
|
|
GpuMat& trainIdx, GpuMat& nMatches, GpuMat& distance, float maxDistance,
|
|
const GpuMat& mask = GpuMat());
|
|
|
|
// Download trainIdx, nMatches and distance to CPU 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& nMatches, const GpuMat& distance,
|
|
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& queryDescs, const GpuMat& trainDescs,
|
|
std::vector< std::vector<DMatch> >& matches, float maxDistance,
|
|
const GpuMat& mask = GpuMat(), 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& queryDescs, std::vector< std::vector<DMatch> >& matches, float maxDistance,
|
|
const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);
|
|
|
|
private:
|
|
DistType distType;
|
|
|
|
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) {}
|
|
};
|
|
|
|
////////////////////////////////// CascadeClassifier //////////////////////////////////////////
|
|
// The cascade classifier class for object detection.
|
|
class CV_EXPORTS CascadeClassifier
|
|
{
|
|
public:
|
|
struct CV_EXPORTS DTreeNode
|
|
{
|
|
int featureIdx;
|
|
float threshold; // for ordered features only
|
|
int left;
|
|
int right;
|
|
};
|
|
|
|
struct CV_EXPORTS DTree
|
|
{
|
|
int nodeCount;
|
|
};
|
|
|
|
struct CV_EXPORTS Stage
|
|
{
|
|
int first;
|
|
int ntrees;
|
|
float threshold;
|
|
};
|
|
|
|
enum { BOOST = 0 };
|
|
enum { DO_CANNY_PRUNING = 1, SCALE_IMAGE = 2,FIND_BIGGEST_OBJECT = 4, DO_ROUGH_SEARCH = 8 };
|
|
|
|
CascadeClassifier();
|
|
CascadeClassifier(const string& filename);
|
|
~CascadeClassifier();
|
|
|
|
bool empty() const;
|
|
bool load(const string& filename);
|
|
bool read(const FileNode& node);
|
|
|
|
void detectMultiScale( const Mat& image, vector<Rect>& objects, double scaleFactor=1.1,
|
|
int minNeighbors=3, int flags=0, Size minSize=Size(), Size maxSize=Size());
|
|
|
|
bool setImage( Ptr<FeatureEvaluator>&, const Mat& );
|
|
int runAt( Ptr<FeatureEvaluator>&, Point );
|
|
|
|
bool isStumpBased;
|
|
|
|
int stageType;
|
|
int featureType;
|
|
int ncategories;
|
|
Size origWinSize;
|
|
|
|
vector<Stage> stages;
|
|
vector<DTree> classifiers;
|
|
vector<DTreeNode> nodes;
|
|
vector<float> leaves;
|
|
vector<int> subsets;
|
|
|
|
Ptr<FeatureEvaluator> feval;
|
|
Ptr<CvHaarClassifierCascade> oldCascade;
|
|
};
|
|
|
|
////////////////////////////////// SURF //////////////////////////////////////////
|
|
|
|
struct CV_EXPORTS SURFParams_GPU
|
|
{
|
|
SURFParams_GPU() :
|
|
threshold(0.1f),
|
|
nOctaves(4),
|
|
nIntervals(4),
|
|
initialScale(2.f),
|
|
|
|
l1(3.f/1.5f),
|
|
l2(5.f/1.5f),
|
|
l3(3.f/1.5f),
|
|
l4(1.f/1.5f),
|
|
edgeScale(0.81f),
|
|
initialStep(1),
|
|
|
|
extended(true),
|
|
|
|
featuresRatio(0.01f)
|
|
{
|
|
}
|
|
|
|
//! The interest operator threshold
|
|
float threshold;
|
|
//! The number of octaves to process
|
|
int nOctaves;
|
|
//! The number of intervals in each octave
|
|
int nIntervals;
|
|
//! The scale associated with the first interval of the first octave
|
|
float initialScale;
|
|
|
|
//! mask parameter l_1
|
|
float l1;
|
|
//! mask parameter l_2
|
|
float l2;
|
|
//! mask parameter l_3
|
|
float l3;
|
|
//! mask parameter l_4
|
|
float l4;
|
|
//! The amount to scale the edge rejection mask
|
|
float edgeScale;
|
|
//! The initial sampling step in pixels.
|
|
int initialStep;
|
|
|
|
//! True, if generate 128-len descriptors, false - 64-len descriptors
|
|
bool extended;
|
|
|
|
//! max features = featuresRatio * img.size().srea()
|
|
float featuresRatio;
|
|
};
|
|
|
|
class CV_EXPORTS SURF_GPU : public SURFParams_GPU
|
|
{
|
|
public:
|
|
//! returns the descriptor size in float's (64 or 128)
|
|
int descriptorSize() const;
|
|
|
|
//! upload host keypoints to device memory
|
|
static void uploadKeypoints(const vector<KeyPoint>& keypoints, GpuMat& keypointsGPU);
|
|
//! download keypoints from device to host memory
|
|
static void downloadKeypoints(const GpuMat& keypointsGPU, vector<KeyPoint>& keypoints);
|
|
|
|
//! download descriptors from device to host memory
|
|
static void downloadDescriptors(const GpuMat& descriptorsGPU, vector<float>& descriptors);
|
|
|
|
//! finds the keypoints using fast hessian detector used in SURF
|
|
//! supports CV_8UC1 images
|
|
//! keypoints will have 1 row and type CV_32FC(6)
|
|
//! keypoints.at<float[6]>(1, i) contains i'th keypoint
|
|
//! format: (x, y, size, response, angle, octave)
|
|
void operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints);
|
|
//! finds the keypoints and computes their descriptors.
|
|
//! Optionally it can compute descriptors for the user-provided keypoints and recompute keypoints direction
|
|
void operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors,
|
|
bool useProvidedKeypoints = false, bool calcOrientation = true);
|
|
|
|
void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints);
|
|
void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints, GpuMat& descriptors,
|
|
bool useProvidedKeypoints = false, bool calcOrientation = true);
|
|
|
|
void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints, std::vector<float>& descriptors,
|
|
bool useProvidedKeypoints = false, bool calcOrientation = true);
|
|
|
|
GpuMat sum;
|
|
GpuMat sumf;
|
|
|
|
GpuMat mask1;
|
|
GpuMat maskSum;
|
|
|
|
GpuMat hessianBuffer;
|
|
GpuMat maxPosBuffer;
|
|
GpuMat featuresBuffer;
|
|
};
|
|
|
|
}
|
|
|
|
//! Speckle filtering - filters small connected components on diparity image.
|
|
//! It sets pixel (x,y) to newVal if it coresponds to small CC with size < maxSpeckleSize.
|
|
//! Threshold for border between CC is diffThreshold;
|
|
CV_EXPORTS void filterSpeckles( Mat& img, uchar newVal, int maxSpeckleSize, uchar diffThreshold, Mat& buf);
|
|
|
|
}
|
|
#include "opencv2/gpu/matrix_operations.hpp"
|
|
|
|
#endif /* __OPENCV_GPU_HPP__ */
|