mirror of https://github.com/opencv/opencv.git
Vladislav Vinogradov
14 years ago
parent
f4f38fcced
commit
b2b1d41da8
5 changed files with 1189 additions and 1079 deletions
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/*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_GPUMAT_HPP__ |
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#define __OPENCV_GPUMAT_HPP__ |
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#include "opencv2/core/core.hpp" |
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#include "opencv2/gpu/devmem2d.hpp" |
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namespace cv { namespace gpu |
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{ |
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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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//! Creates continuous GPU matrix
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CV_EXPORTS void createContinuous(int rows, int cols, int type, GpuMat& m); |
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CV_EXPORTS GpuMat createContinuous(int rows, int cols, int type); |
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CV_EXPORTS void createContinuous(Size size, int type, GpuMat& m); |
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CV_EXPORTS GpuMat createContinuous(Size size, int type); |
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//! Ensures that size of the given matrix is not less than (rows, cols) size
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//! and matrix type is match specified one too
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CV_EXPORTS void ensureSizeIsEnough(int rows, int cols, int type, GpuMat& m); |
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CV_EXPORTS void ensureSizeIsEnough(Size size, int type, GpuMat& m); |
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////////////////////////////////////////////////////////////////////////
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//////////////////////////////// GpuMat ////////////////////////////////
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////////////////////////////////////////////////////////////////////////
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inline GpuMat::GpuMat() : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0) {} |
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inline GpuMat::GpuMat(int rows_, int cols_, int type_) : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0) |
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{ |
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if (rows_ > 0 && cols_ > 0) |
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create(rows_, cols_, type_); |
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} |
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inline GpuMat::~GpuMat() { release(); } |
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template <class T> inline GpuMat::operator DevMem2D_<T>() const { return DevMem2D_<T>(rows, cols, (T*)data, step); } |
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template <class T> inline GpuMat::operator PtrStep_<T>() const { return PtrStep_<T>(static_cast< DevMem2D_<T> >(*this)); } |
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inline GpuMat GpuMat::clone() const |
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{ |
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GpuMat m; |
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copyTo(m); |
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return m; |
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} |
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inline void GpuMat::assignTo(GpuMat& m, int type) const |
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{ |
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if( type < 0 ) |
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m = *this; |
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else |
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convertTo(m, type); |
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} |
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inline size_t GpuMat::step1() const { return step/elemSize1(); } |
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inline bool GpuMat::empty() const { return data == 0; } |
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template<typename _Tp> inline _Tp* GpuMat::ptr(int y) |
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{ |
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return (_Tp*)ptr(y); |
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} |
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template<typename _Tp> inline const _Tp* GpuMat::ptr(int y) const |
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{ |
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return (const _Tp*)ptr(y); |
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} |
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inline void swap(GpuMat& a, GpuMat& b) { a.swap(b); } |
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}} |
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#endif // __OPENCV_GPUMAT_HPP__
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@ -0,0 +1,910 @@ |
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
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// 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,
|
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// copy or use the software.
|
||||
//
|
||||
//
|
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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 materials 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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#include "precomp.hpp" |
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using namespace cv; |
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using namespace cv::gpu; |
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////////////////////////////////////////////////////////////////////////
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//////////////////////////////// GpuMat ////////////////////////////////
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////////////////////////////////////////////////////////////////////////
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cv::gpu::GpuMat::GpuMat(Size size_, int type_) :
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flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0) |
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{ |
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if (size_.height > 0 && size_.width > 0) |
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create(size_.height, size_.width, type_); |
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} |
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cv::gpu::GpuMat::GpuMat(int rows_, int cols_, int type_, const Scalar& s_) :
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flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0) |
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{ |
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if (rows_ > 0 && cols_ > 0) |
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{ |
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create(rows_, cols_, type_); |
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*this = s_; |
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} |
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} |
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cv::gpu::GpuMat::GpuMat(Size size_, int type_, const Scalar& s_) :
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flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0) |
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{ |
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if (size_.height > 0 && size_.width > 0) |
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{ |
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create(size_.height, size_.width, type_); |
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*this = s_; |
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} |
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} |
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cv::gpu::GpuMat::GpuMat(const GpuMat& m) :
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flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend) |
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{ |
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if (refcount) |
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CV_XADD(refcount, 1); |
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} |
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cv::gpu::GpuMat::GpuMat(int rows_, int cols_, int type_, void* data_, size_t step_) :
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flags(Mat::MAGIC_VAL + (type_ & TYPE_MASK)), rows(rows_), cols(cols_), step(step_), data((uchar*)data_), refcount(0), |
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datastart((uchar*)data_), dataend((uchar*)data_) |
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{ |
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size_t minstep = cols * elemSize(); |
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if (step == Mat::AUTO_STEP) |
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{ |
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step = minstep; |
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flags |= Mat::CONTINUOUS_FLAG; |
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} |
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else |
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{ |
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if (rows == 1) step = minstep; |
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CV_DbgAssert( step >= minstep ); |
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flags |= step == minstep ? Mat::CONTINUOUS_FLAG : 0; |
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} |
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dataend += step * (rows - 1) + minstep; |
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} |
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cv::gpu::GpuMat::GpuMat(Size size_, int type_, void* data_, size_t step_) :
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flags(Mat::MAGIC_VAL + (type_ & TYPE_MASK)), rows(size_.height), cols(size_.width), |
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step(step_), data((uchar*)data_), refcount(0), |
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datastart((uchar*)data_), dataend((uchar*)data_) |
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{ |
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size_t minstep = cols * elemSize(); |
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if (step == Mat::AUTO_STEP) |
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{ |
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step = minstep; |
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flags |= Mat::CONTINUOUS_FLAG; |
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} |
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else |
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{ |
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if (rows == 1) step = minstep; |
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CV_DbgAssert( step >= minstep ); |
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flags |= step == minstep ? Mat::CONTINUOUS_FLAG : 0; |
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} |
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dataend += step * (rows - 1) + minstep; |
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} |
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cv::gpu::GpuMat::GpuMat(const GpuMat& m, const Range& rowRange, const Range& colRange) |
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{ |
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flags = m.flags; |
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step = m.step; refcount = m.refcount; |
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data = m.data; datastart = m.datastart; dataend = m.dataend; |
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if (rowRange == Range::all()) |
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rows = m.rows; |
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else |
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{ |
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CV_Assert( 0 <= rowRange.start && rowRange.start <= rowRange.end && rowRange.end <= m.rows ); |
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rows = rowRange.size(); |
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data += step*rowRange.start; |
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} |
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if (colRange == Range::all()) |
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cols = m.cols; |
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else |
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{ |
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CV_Assert( 0 <= colRange.start && colRange.start <= colRange.end && colRange.end <= m.cols ); |
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cols = colRange.size(); |
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data += colRange.start*elemSize(); |
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flags &= cols < m.cols ? ~Mat::CONTINUOUS_FLAG : -1; |
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} |
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if( rows == 1 ) |
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flags |= Mat::CONTINUOUS_FLAG; |
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if( refcount ) |
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CV_XADD(refcount, 1); |
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if( rows <= 0 || cols <= 0 ) |
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rows = cols = 0; |
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} |
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cv::gpu::GpuMat::GpuMat(const GpuMat& m, const Rect& roi) :
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flags(m.flags), rows(roi.height), cols(roi.width), |
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step(m.step), data(m.data + roi.y*step), refcount(m.refcount), |
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datastart(m.datastart), dataend(m.dataend) |
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{ |
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flags &= roi.width < m.cols ? ~Mat::CONTINUOUS_FLAG : -1; |
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data += roi.x*elemSize(); |
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CV_Assert( 0 <= roi.x && 0 <= roi.width && roi.x + roi.width <= m.cols && |
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0 <= roi.y && 0 <= roi.height && roi.y + roi.height <= m.rows ); |
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if( refcount ) |
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CV_XADD(refcount, 1); |
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if( rows <= 0 || cols <= 0 ) |
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rows = cols = 0; |
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} |
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cv::gpu::GpuMat::GpuMat(const Mat& m) :
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flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0)
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{
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upload(m);
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} |
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GpuMat& cv::gpu::GpuMat::operator = (const GpuMat& m) |
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{ |
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if( this != &m ) |
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{ |
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if( m.refcount ) |
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CV_XADD(m.refcount, 1); |
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release(); |
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flags = m.flags; |
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rows = m.rows; cols = m.cols; |
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step = m.step; data = m.data; |
||||
datastart = m.datastart; dataend = m.dataend; |
||||
refcount = m.refcount; |
||||
} |
||||
return *this; |
||||
} |
||||
|
||||
GpuMat& cv::gpu::GpuMat::operator = (const Mat& m)
|
||||
{
|
||||
upload(m); return *this;
|
||||
} |
||||
|
||||
cv::gpu::GpuMat::operator Mat() const |
||||
{ |
||||
Mat m; |
||||
download(m); |
||||
return m; |
||||
} |
||||
|
||||
GpuMat cv::gpu::GpuMat::row(int y) const
|
||||
{
|
||||
return GpuMat(*this, Range(y, y+1), Range::all());
|
||||
} |
||||
|
||||
GpuMat cv::gpu::GpuMat::col(int x) const
|
||||
{
|
||||
return GpuMat(*this, Range::all(), Range(x, x+1));
|
||||
} |
||||
|
||||
GpuMat cv::gpu::GpuMat::rowRange(int startrow, int endrow) const
|
||||
{
|
||||
return GpuMat(*this, Range(startrow, endrow), Range::all());
|
||||
} |
||||
|
||||
GpuMat cv::gpu::GpuMat::rowRange(const Range& r) const
|
||||
{
|
||||
return GpuMat(*this, r, Range::all());
|
||||
} |
||||
|
||||
GpuMat cv::gpu::GpuMat::colRange(int startcol, int endcol) const
|
||||
{
|
||||
return GpuMat(*this, Range::all(), Range(startcol, endcol));
|
||||
} |
||||
|
||||
GpuMat cv::gpu::GpuMat::colRange(const Range& r) const
|
||||
{
|
||||
return GpuMat(*this, Range::all(), r);
|
||||
} |
||||
|
||||
void cv::gpu::GpuMat::create(Size size_, int type_)
|
||||
{
|
||||
create(size_.height, size_.width, type_);
|
||||
} |
||||
|
||||
void cv::gpu::GpuMat::swap(GpuMat& b) |
||||
{ |
||||
std::swap( flags, b.flags ); |
||||
std::swap( rows, b.rows );
|
||||
std::swap( cols, b.cols ); |
||||
std::swap( step, b.step );
|
||||
std::swap( data, b.data ); |
||||
std::swap( datastart, b.datastart ); |
||||
std::swap( dataend, b.dataend ); |
||||
std::swap( refcount, b.refcount ); |
||||
} |
||||
|
||||
void cv::gpu::GpuMat::locateROI(Size& wholeSize, Point& ofs) const |
||||
{ |
||||
size_t esz = elemSize(), minstep; |
||||
ptrdiff_t delta1 = data - datastart, delta2 = dataend - datastart; |
||||
CV_DbgAssert( step > 0 ); |
||||
if( delta1 == 0 ) |
||||
ofs.x = ofs.y = 0; |
||||
else |
||||
{ |
||||
ofs.y = (int)(delta1/step); |
||||
ofs.x = (int)((delta1 - step*ofs.y)/esz); |
||||
CV_DbgAssert( data == datastart + ofs.y*step + ofs.x*esz ); |
||||
} |
||||
minstep = (ofs.x + cols)*esz; |
||||
wholeSize.height = (int)((delta2 - minstep)/step + 1); |
||||
wholeSize.height = std::max(wholeSize.height, ofs.y + rows); |
||||
wholeSize.width = (int)((delta2 - step*(wholeSize.height-1))/esz); |
||||
wholeSize.width = std::max(wholeSize.width, ofs.x + cols); |
||||
} |
||||
|
||||
GpuMat& cv::gpu::GpuMat::adjustROI(int dtop, int dbottom, int dleft, int dright) |
||||
{ |
||||
Size wholeSize; Point ofs; |
||||
size_t esz = elemSize(); |
||||
locateROI( wholeSize, ofs ); |
||||
int row1 = std::max(ofs.y - dtop, 0), row2 = std::min(ofs.y + rows + dbottom, wholeSize.height); |
||||
int col1 = std::max(ofs.x - dleft, 0), col2 = std::min(ofs.x + cols + dright, wholeSize.width); |
||||
data += (row1 - ofs.y)*step + (col1 - ofs.x)*esz; |
||||
rows = row2 - row1; cols = col2 - col1; |
||||
if( esz*cols == step || rows == 1 ) |
||||
flags |= Mat::CONTINUOUS_FLAG; |
||||
else |
||||
flags &= ~Mat::CONTINUOUS_FLAG; |
||||
return *this; |
||||
} |
||||
|
||||
cv::gpu::GpuMat GpuMat::operator()(Range rowRange, Range colRange) const
|
||||
{
|
||||
return GpuMat(*this, rowRange, colRange);
|
||||
} |
||||
|
||||
cv::gpu::GpuMat GpuMat::operator()(const Rect& roi) const
|
||||
{
|
||||
return GpuMat(*this, roi);
|
||||
} |
||||
|
||||
bool cv::gpu::GpuMat::isContinuous() const
|
||||
{
|
||||
return (flags & Mat::CONTINUOUS_FLAG) != 0;
|
||||
} |
||||
|
||||
size_t cv::gpu::GpuMat::elemSize() const
|
||||
{
|
||||
return CV_ELEM_SIZE(flags);
|
||||
} |
||||
|
||||
size_t cv::gpu::GpuMat::elemSize1() const
|
||||
{
|
||||
return CV_ELEM_SIZE1(flags);
|
||||
} |
||||
|
||||
int cv::gpu::GpuMat::type() const
|
||||
{
|
||||
return CV_MAT_TYPE(flags);
|
||||
} |
||||
|
||||
int cv::gpu::GpuMat::depth() const
|
||||
{
|
||||
return CV_MAT_DEPTH(flags);
|
||||
} |
||||
|
||||
int cv::gpu::GpuMat::channels() const
|
||||
{
|
||||
return CV_MAT_CN(flags);
|
||||
} |
||||
|
||||
Size cv::gpu::GpuMat::size() const
|
||||
{
|
||||
return Size(cols, rows);
|
||||
} |
||||
|
||||
unsigned char* cv::gpu::GpuMat::ptr(int y) |
||||
{ |
||||
CV_DbgAssert( (unsigned)y < (unsigned)rows ); |
||||
return data + step*y; |
||||
} |
||||
|
||||
const unsigned char* cv::gpu::GpuMat::ptr(int y) const |
||||
{ |
||||
CV_DbgAssert( (unsigned)y < (unsigned)rows ); |
||||
return data + step*y; |
||||
} |
||||
|
||||
GpuMat cv::gpu::GpuMat::t() const |
||||
{ |
||||
GpuMat tmp; |
||||
transpose(*this, tmp); |
||||
return tmp; |
||||
} |
||||
|
||||
GpuMat cv::gpu::createContinuous(int rows, int cols, int type) |
||||
{ |
||||
GpuMat m; |
||||
createContinuous(rows, cols, type, m); |
||||
return m; |
||||
} |
||||
|
||||
void cv::gpu::createContinuous(Size size, int type, GpuMat& m) |
||||
{ |
||||
createContinuous(size.height, size.width, type, m); |
||||
} |
||||
|
||||
GpuMat cv::gpu::createContinuous(Size size, int type) |
||||
{ |
||||
GpuMat m; |
||||
createContinuous(size, type, m); |
||||
return m; |
||||
} |
||||
|
||||
void cv::gpu::ensureSizeIsEnough(Size size, int type, GpuMat& m) |
||||
{ |
||||
ensureSizeIsEnough(size.height, size.width, type, m); |
||||
} |
||||
|
||||
#if !defined (HAVE_CUDA) |
||||
|
||||
void cv::gpu::GpuMat::upload(const Mat&) { throw_nogpu(); } |
||||
void cv::gpu::GpuMat::download(cv::Mat&) const { throw_nogpu(); } |
||||
void cv::gpu::GpuMat::copyTo(GpuMat&) const { throw_nogpu(); } |
||||
void cv::gpu::GpuMat::copyTo(GpuMat&, const GpuMat&) const { throw_nogpu(); } |
||||
void cv::gpu::GpuMat::convertTo(GpuMat&, int, double, double) const { throw_nogpu(); } |
||||
GpuMat& cv::gpu::GpuMat::operator = (const Scalar&) { throw_nogpu(); return *this; } |
||||
GpuMat& cv::gpu::GpuMat::setTo(const Scalar&, const GpuMat&) { throw_nogpu(); return *this; } |
||||
GpuMat cv::gpu::GpuMat::reshape(int, int) const { throw_nogpu(); return GpuMat(); } |
||||
void cv::gpu::GpuMat::create(int, int, int) { throw_nogpu(); } |
||||
void cv::gpu::GpuMat::release() {} |
||||
void cv::gpu::createContinuous(int, int, int, GpuMat&) { throw_nogpu(); } |
||||
|
||||
#else /* !defined (HAVE_CUDA) */ |
||||
|
||||
namespace cv { namespace gpu { namespace matrix_operations |
||||
{ |
||||
void copy_to_with_mask(const DevMem2D& src, DevMem2D dst, int depth, const DevMem2D& mask, int channels, const cudaStream_t & stream = 0); |
||||
|
||||
template <typename T> |
||||
void set_to_gpu(const DevMem2D& mat, const T* scalar, int channels, cudaStream_t stream); |
||||
template <typename T> |
||||
void set_to_gpu(const DevMem2D& mat, const T* scalar, const DevMem2D& mask, int channels, cudaStream_t stream); |
||||
|
||||
void convert_gpu(const DevMem2D& src, int sdepth, const DevMem2D& dst, int ddepth, double alpha, double beta, cudaStream_t stream = 0); |
||||
}}} |
||||
|
||||
|
||||
void cv::gpu::GpuMat::upload(const Mat& m) |
||||
{ |
||||
CV_DbgAssert(!m.empty()); |
||||
create(m.size(), m.type()); |
||||
cudaSafeCall( cudaMemcpy2D(data, step, m.data, m.step, cols * elemSize(), rows, cudaMemcpyHostToDevice) ); |
||||
} |
||||
|
||||
void cv::gpu::GpuMat::upload(const CudaMem& m, Stream& stream) |
||||
{ |
||||
CV_DbgAssert(!m.empty()); |
||||
stream.enqueueUpload(m, *this); |
||||
} |
||||
|
||||
void cv::gpu::GpuMat::download(cv::Mat& m) const |
||||
{ |
||||
CV_DbgAssert(!this->empty()); |
||||
m.create(size(), type()); |
||||
cudaSafeCall( cudaMemcpy2D(m.data, m.step, data, step, cols * elemSize(), rows, cudaMemcpyDeviceToHost) ); |
||||
} |
||||
|
||||
void cv::gpu::GpuMat::download(CudaMem& m, Stream& stream) const |
||||
{ |
||||
CV_DbgAssert(!m.empty()); |
||||
stream.enqueueDownload(*this, m); |
||||
} |
||||
|
||||
void cv::gpu::GpuMat::copyTo(GpuMat& m) const |
||||
{ |
||||
CV_DbgAssert(!this->empty()); |
||||
m.create(size(), type()); |
||||
cudaSafeCall( cudaMemcpy2D(m.data, m.step, data, step, cols * elemSize(), rows, cudaMemcpyDeviceToDevice) ); |
||||
cudaSafeCall( cudaDeviceSynchronize() ); |
||||
} |
||||
|
||||
void cv::gpu::GpuMat::copyTo(GpuMat& mat, const GpuMat& mask) const |
||||
{ |
||||
if (mask.empty()) |
||||
{ |
||||
copyTo(mat); |
||||
} |
||||
else |
||||
{ |
||||
mat.create(size(), type()); |
||||
cv::gpu::matrix_operations::copy_to_with_mask(*this, mat, depth(), mask, channels()); |
||||
} |
||||
} |
||||
|
||||
namespace |
||||
{ |
||||
template<int n> struct NPPTypeTraits; |
||||
template<> struct NPPTypeTraits<CV_8U> { typedef Npp8u npp_type; }; |
||||
template<> struct NPPTypeTraits<CV_16U> { typedef Npp16u npp_type; }; |
||||
template<> struct NPPTypeTraits<CV_16S> { typedef Npp16s npp_type; }; |
||||
template<> struct NPPTypeTraits<CV_32S> { typedef Npp32s npp_type; }; |
||||
template<> struct NPPTypeTraits<CV_32F> { typedef Npp32f npp_type; }; |
||||
|
||||
template<int SDEPTH, int DDEPTH> struct NppConvertFunc |
||||
{ |
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
||||
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t; |
||||
|
||||
typedef NppStatus (*func_ptr)(const src_t* pSrc, int nSrcStep, dst_t* pDst, int nDstStep, NppiSize oSizeROI); |
||||
}; |
||||
template<int DDEPTH> struct NppConvertFunc<CV_32F, DDEPTH> |
||||
{ |
||||
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t; |
||||
|
||||
typedef NppStatus (*func_ptr)(const Npp32f* pSrc, int nSrcStep, dst_t* pDst, int nDstStep, NppiSize oSizeROI, NppRoundMode eRoundMode); |
||||
}; |
||||
|
||||
template<int SDEPTH, int DDEPTH, typename NppConvertFunc<SDEPTH, DDEPTH>::func_ptr func> struct NppCvt |
||||
{ |
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
||||
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t; |
||||
|
||||
static void cvt(const GpuMat& src, GpuMat& dst) |
||||
{ |
||||
NppiSize sz; |
||||
sz.width = src.cols; |
||||
sz.height = src.rows; |
||||
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), dst.ptr<dst_t>(), static_cast<int>(dst.step), sz) ); |
||||
|
||||
cudaSafeCall( cudaDeviceSynchronize() ); |
||||
} |
||||
}; |
||||
template<int DDEPTH, typename NppConvertFunc<CV_32F, DDEPTH>::func_ptr func> struct NppCvt<CV_32F, DDEPTH, func> |
||||
{ |
||||
typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t; |
||||
|
||||
static void cvt(const GpuMat& src, GpuMat& dst) |
||||
{ |
||||
NppiSize sz; |
||||
sz.width = src.cols; |
||||
sz.height = src.rows; |
||||
nppSafeCall( func(src.ptr<Npp32f>(), static_cast<int>(src.step), dst.ptr<dst_t>(), static_cast<int>(dst.step), sz, NPP_RND_NEAR) ); |
||||
|
||||
cudaSafeCall( cudaDeviceSynchronize() ); |
||||
} |
||||
}; |
||||
|
||||
void convertToKernelCaller(const GpuMat& src, GpuMat& dst) |
||||
{ |
||||
matrix_operations::convert_gpu(src.reshape(1), src.depth(), dst.reshape(1), dst.depth(), 1.0, 0.0); |
||||
} |
||||
} |
||||
|
||||
void cv::gpu::GpuMat::convertTo( GpuMat& dst, int rtype, double alpha, double beta ) const |
||||
{ |
||||
CV_Assert((depth() != CV_64F && CV_MAT_DEPTH(rtype) != CV_64F) ||
|
||||
(TargetArchs::builtWith(NATIVE_DOUBLE) && DeviceInfo().supports(NATIVE_DOUBLE))); |
||||
|
||||
bool noScale = fabs(alpha-1) < std::numeric_limits<double>::epsilon() && fabs(beta) < std::numeric_limits<double>::epsilon(); |
||||
|
||||
if( rtype < 0 ) |
||||
rtype = type(); |
||||
else |
||||
rtype = CV_MAKETYPE(CV_MAT_DEPTH(rtype), channels()); |
||||
|
||||
int scn = channels(); |
||||
int sdepth = depth(), ddepth = CV_MAT_DEPTH(rtype); |
||||
if( sdepth == ddepth && noScale ) |
||||
{ |
||||
copyTo(dst); |
||||
return; |
||||
} |
||||
|
||||
GpuMat temp; |
||||
const GpuMat* psrc = this; |
||||
if( sdepth != ddepth && psrc == &dst ) |
||||
psrc = &(temp = *this); |
||||
|
||||
dst.create( size(), rtype ); |
||||
|
||||
if (!noScale) |
||||
matrix_operations::convert_gpu(psrc->reshape(1), sdepth, dst.reshape(1), ddepth, alpha, beta); |
||||
else |
||||
{ |
||||
typedef void (*convert_caller_t)(const GpuMat& src, GpuMat& dst); |
||||
static const convert_caller_t convert_callers[8][8][4] = |
||||
{ |
||||
{ |
||||
{0,0,0,0}, |
||||
{convertToKernelCaller, convertToKernelCaller, convertToKernelCaller, convertToKernelCaller}, |
||||
{NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C4R>::cvt}, |
||||
{NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C4R>::cvt}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{NppCvt<CV_8U, CV_32F, nppiConvert_8u32f_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{0,0,0,0} |
||||
}, |
||||
{ |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{0,0,0,0}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{0,0,0,0} |
||||
}, |
||||
{ |
||||
{NppCvt<CV_16U, CV_8U, nppiConvert_16u8u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,NppCvt<CV_16U, CV_8U, nppiConvert_16u8u_C4R>::cvt}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{0,0,0,0}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{NppCvt<CV_16U, CV_32S, nppiConvert_16u32s_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{NppCvt<CV_16U, CV_32F, nppiConvert_16u32f_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{0,0,0,0} |
||||
}, |
||||
{ |
||||
{NppCvt<CV_16S, CV_8U, nppiConvert_16s8u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,NppCvt<CV_16S, CV_8U, nppiConvert_16s8u_C4R>::cvt}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{0,0,0,0}, |
||||
{NppCvt<CV_16S, CV_32S, nppiConvert_16s32s_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{NppCvt<CV_16S, CV_32F, nppiConvert_16s32f_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{0,0,0,0} |
||||
}, |
||||
{ |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{0,0,0,0}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{0,0,0,0} |
||||
}, |
||||
{ |
||||
{NppCvt<CV_32F, CV_8U, nppiConvert_32f8u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{NppCvt<CV_32F, CV_16U, nppiConvert_32f16u_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{NppCvt<CV_32F, CV_16S, nppiConvert_32f16s_C1R>::cvt,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{0,0,0,0}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{0,0,0,0} |
||||
}, |
||||
{ |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{convertToKernelCaller,convertToKernelCaller,convertToKernelCaller,convertToKernelCaller}, |
||||
{0,0,0,0}, |
||||
{0,0,0,0} |
||||
}, |
||||
{ |
||||
{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0} |
||||
} |
||||
}; |
||||
|
||||
convert_callers[sdepth][ddepth][scn-1](*psrc, dst); |
||||
} |
||||
} |
||||
|
||||
GpuMat& GpuMat::operator = (const Scalar& s) |
||||
{ |
||||
setTo(s); |
||||
return *this; |
||||
} |
||||
|
||||
namespace |
||||
{ |
||||
template<int SDEPTH, int SCN> struct NppSetFunc |
||||
{ |
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
||||
|
||||
typedef NppStatus (*func_ptr)(const src_t values[], src_t* pSrc, int nSrcStep, NppiSize oSizeROI); |
||||
}; |
||||
template<int SDEPTH> struct NppSetFunc<SDEPTH, 1> |
||||
{ |
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
||||
|
||||
typedef NppStatus (*func_ptr)(src_t val, src_t* pSrc, int nSrcStep, NppiSize oSizeROI); |
||||
}; |
||||
|
||||
template<int SDEPTH, int SCN, typename NppSetFunc<SDEPTH, SCN>::func_ptr func> struct NppSet |
||||
{ |
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
||||
|
||||
static void set(GpuMat& src, const Scalar& s) |
||||
{ |
||||
NppiSize sz; |
||||
sz.width = src.cols; |
||||
sz.height = src.rows; |
||||
Scalar_<src_t> nppS = s; |
||||
nppSafeCall( func(nppS.val, src.ptr<src_t>(), static_cast<int>(src.step), sz) ); |
||||
|
||||
cudaSafeCall( cudaDeviceSynchronize() ); |
||||
} |
||||
}; |
||||
template<int SDEPTH, typename NppSetFunc<SDEPTH, 1>::func_ptr func> struct NppSet<SDEPTH, 1, func> |
||||
{ |
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
||||
|
||||
static void set(GpuMat& src, const Scalar& s) |
||||
{ |
||||
NppiSize sz; |
||||
sz.width = src.cols; |
||||
sz.height = src.rows; |
||||
Scalar_<src_t> nppS = s; |
||||
nppSafeCall( func(nppS[0], src.ptr<src_t>(), static_cast<int>(src.step), sz) ); |
||||
|
||||
cudaSafeCall( cudaDeviceSynchronize() ); |
||||
} |
||||
}; |
||||
|
||||
template <typename T> |
||||
void kernelSet(GpuMat& src, const Scalar& s) |
||||
{ |
||||
Scalar_<T> sf = s; |
||||
matrix_operations::set_to_gpu(src, sf.val, src.channels(), 0); |
||||
} |
||||
|
||||
template<int SDEPTH, int SCN> struct NppSetMaskFunc |
||||
{ |
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
||||
|
||||
typedef NppStatus (*func_ptr)(const src_t values[], src_t* pSrc, int nSrcStep, NppiSize oSizeROI, const Npp8u* pMask, int nMaskStep); |
||||
}; |
||||
template<int SDEPTH> struct NppSetMaskFunc<SDEPTH, 1> |
||||
{ |
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
||||
|
||||
typedef NppStatus (*func_ptr)(src_t val, src_t* pSrc, int nSrcStep, NppiSize oSizeROI, const Npp8u* pMask, int nMaskStep); |
||||
}; |
||||
|
||||
template<int SDEPTH, int SCN, typename NppSetMaskFunc<SDEPTH, SCN>::func_ptr func> struct NppSetMask |
||||
{ |
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
||||
|
||||
static void set(GpuMat& src, const Scalar& s, const GpuMat& mask) |
||||
{ |
||||
NppiSize sz; |
||||
sz.width = src.cols; |
||||
sz.height = src.rows; |
||||
Scalar_<src_t> nppS = s; |
||||
nppSafeCall( func(nppS.val, src.ptr<src_t>(), static_cast<int>(src.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) ); |
||||
|
||||
cudaSafeCall( cudaDeviceSynchronize() ); |
||||
} |
||||
}; |
||||
template<int SDEPTH, typename NppSetMaskFunc<SDEPTH, 1>::func_ptr func> struct NppSetMask<SDEPTH, 1, func> |
||||
{ |
||||
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
||||
|
||||
static void set(GpuMat& src, const Scalar& s, const GpuMat& mask) |
||||
{ |
||||
NppiSize sz; |
||||
sz.width = src.cols; |
||||
sz.height = src.rows; |
||||
Scalar_<src_t> nppS = s; |
||||
nppSafeCall( func(nppS[0], src.ptr<src_t>(), static_cast<int>(src.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) ); |
||||
|
||||
cudaSafeCall( cudaDeviceSynchronize() ); |
||||
} |
||||
}; |
||||
|
||||
template <typename T> |
||||
void kernelSetMask(GpuMat& src, const Scalar& s, const GpuMat& mask) |
||||
{ |
||||
Scalar_<T> sf = s; |
||||
matrix_operations::set_to_gpu(src, sf.val, mask, src.channels(), 0); |
||||
} |
||||
} |
||||
|
||||
GpuMat& GpuMat::setTo(const Scalar& s, const GpuMat& mask) |
||||
{ |
||||
CV_Assert(mask.type() == CV_8UC1); |
||||
|
||||
CV_Assert((depth() != CV_64F) ||
|
||||
(TargetArchs::builtWith(NATIVE_DOUBLE) && DeviceInfo().supports(NATIVE_DOUBLE))); |
||||
|
||||
CV_DbgAssert(!this->empty()); |
||||
|
||||
NppiSize sz; |
||||
sz.width = cols; |
||||
sz.height = rows; |
||||
|
||||
if (mask.empty()) |
||||
{ |
||||
if (s[0] == 0.0 && s[1] == 0.0 && s[2] == 0.0 && s[3] == 0.0) |
||||
{ |
||||
cudaSafeCall( cudaMemset2D(data, step, 0, cols * elemSize(), rows) ); |
||||
return *this; |
||||
} |
||||
if (depth() == CV_8U) |
||||
{ |
||||
int cn = channels(); |
||||
|
||||
if (cn == 1 || (cn == 2 && s[0] == s[1]) || (cn == 3 && s[0] == s[1] && s[0] == s[2]) || (cn == 4 && s[0] == s[1] && s[0] == s[2] && s[0] == s[3])) |
||||
{ |
||||
int val = saturate_cast<uchar>(s[0]); |
||||
cudaSafeCall( cudaMemset2D(data, step, val, cols * elemSize(), rows) ); |
||||
return *this; |
||||
} |
||||
} |
||||
typedef void (*set_caller_t)(GpuMat& src, const Scalar& s); |
||||
static const set_caller_t set_callers[8][4] = |
||||
{ |
||||
{NppSet<CV_8U, 1, nppiSet_8u_C1R>::set,kernelSet<uchar>,kernelSet<uchar>,NppSet<CV_8U, 4, nppiSet_8u_C4R>::set}, |
||||
{kernelSet<schar>,kernelSet<schar>,kernelSet<schar>,kernelSet<schar>}, |
||||
{NppSet<CV_16U, 1, nppiSet_16u_C1R>::set,NppSet<CV_16U, 2, nppiSet_16u_C2R>::set,kernelSet<ushort>,NppSet<CV_16U, 4, nppiSet_16u_C4R>::set}, |
||||
{NppSet<CV_16S, 1, nppiSet_16s_C1R>::set,NppSet<CV_16S, 2, nppiSet_16s_C2R>::set,kernelSet<short>,NppSet<CV_16S, 4, nppiSet_16s_C4R>::set}, |
||||
{NppSet<CV_32S, 1, nppiSet_32s_C1R>::set,kernelSet<int>,kernelSet<int>,NppSet<CV_32S, 4, nppiSet_32s_C4R>::set}, |
||||
{NppSet<CV_32F, 1, nppiSet_32f_C1R>::set,kernelSet<float>,kernelSet<float>,NppSet<CV_32F, 4, nppiSet_32f_C4R>::set}, |
||||
{kernelSet<double>,kernelSet<double>,kernelSet<double>,kernelSet<double>}, |
||||
{0,0,0,0} |
||||
}; |
||||
set_callers[depth()][channels()-1](*this, s); |
||||
} |
||||
else |
||||
{ |
||||
typedef void (*set_caller_t)(GpuMat& src, const Scalar& s, const GpuMat& mask); |
||||
static const set_caller_t set_callers[8][4] = |
||||
{ |
||||
{NppSetMask<CV_8U, 1, nppiSet_8u_C1MR>::set,kernelSetMask<uchar>,kernelSetMask<uchar>,NppSetMask<CV_8U, 4, nppiSet_8u_C4MR>::set}, |
||||
{kernelSetMask<schar>,kernelSetMask<schar>,kernelSetMask<schar>,kernelSetMask<schar>}, |
||||
{NppSetMask<CV_16U, 1, nppiSet_16u_C1MR>::set,kernelSetMask<ushort>,kernelSetMask<ushort>,NppSetMask<CV_16U, 4, nppiSet_16u_C4MR>::set}, |
||||
{NppSetMask<CV_16S, 1, nppiSet_16s_C1MR>::set,kernelSetMask<short>,kernelSetMask<short>,NppSetMask<CV_16S, 4, nppiSet_16s_C4MR>::set}, |
||||
{NppSetMask<CV_32S, 1, nppiSet_32s_C1MR>::set,kernelSetMask<int>,kernelSetMask<int>,NppSetMask<CV_32S, 4, nppiSet_32s_C4MR>::set}, |
||||
{NppSetMask<CV_32F, 1, nppiSet_32f_C1MR>::set,kernelSetMask<float>,kernelSetMask<float>,NppSetMask<CV_32F, 4, nppiSet_32f_C4MR>::set}, |
||||
{kernelSetMask<double>,kernelSetMask<double>,kernelSetMask<double>,kernelSetMask<double>}, |
||||
{0,0,0,0} |
||||
}; |
||||
set_callers[depth()][channels()-1](*this, s, mask); |
||||
} |
||||
|
||||
return *this; |
||||
} |
||||
|
||||
|
||||
GpuMat cv::gpu::GpuMat::reshape(int new_cn, int new_rows) const |
||||
{ |
||||
GpuMat hdr = *this; |
||||
|
||||
int cn = channels(); |
||||
if( new_cn == 0 ) |
||||
new_cn = cn; |
||||
|
||||
int total_width = cols * cn; |
||||
|
||||
if( (new_cn > total_width || total_width % new_cn != 0) && new_rows == 0 ) |
||||
new_rows = rows * total_width / new_cn; |
||||
|
||||
if( new_rows != 0 && new_rows != rows ) |
||||
{ |
||||
int total_size = total_width * rows; |
||||
if( !isContinuous() ) |
||||
CV_Error( CV_BadStep, "The matrix is not continuous, thus its number of rows can not be changed" ); |
||||
|
||||
if( (unsigned)new_rows > (unsigned)total_size ) |
||||
CV_Error( CV_StsOutOfRange, "Bad new number of rows" ); |
||||
|
||||
total_width = total_size / new_rows; |
||||
|
||||
if( total_width * new_rows != total_size ) |
||||
CV_Error( CV_StsBadArg, "The total number of matrix elements is not divisible by the new number of rows" ); |
||||
|
||||
hdr.rows = new_rows; |
||||
hdr.step = total_width * elemSize1(); |
||||
} |
||||
|
||||
int new_width = total_width / new_cn; |
||||
|
||||
if( new_width * new_cn != total_width ) |
||||
CV_Error( CV_BadNumChannels, "The total width is not divisible by the new number of channels" ); |
||||
|
||||
hdr.cols = new_width; |
||||
hdr.flags = (hdr.flags & ~CV_MAT_CN_MASK) | ((new_cn-1) << CV_CN_SHIFT); |
||||
return hdr; |
||||
} |
||||
|
||||
void cv::gpu::GpuMat::create(int _rows, int _cols, int _type) |
||||
{ |
||||
_type &= TYPE_MASK; |
||||
if( rows == _rows && cols == _cols && type() == _type && data ) |
||||
return; |
||||
if( data ) |
||||
release(); |
||||
CV_DbgAssert( _rows >= 0 && _cols >= 0 ); |
||||
if( _rows > 0 && _cols > 0 ) |
||||
{ |
||||
flags = Mat::MAGIC_VAL + _type; |
||||
rows = _rows; |
||||
cols = _cols; |
||||
|
||||
size_t esz = elemSize(); |
||||
|
||||
void *dev_ptr; |
||||
cudaSafeCall( cudaMallocPitch(&dev_ptr, &step, esz * cols, rows) ); |
||||
|
||||
// Single row must be continuous
|
||||
if (rows == 1) |
||||
step = esz * cols; |
||||
|
||||
if (esz * cols == step) |
||||
flags |= Mat::CONTINUOUS_FLAG; |
||||
|
||||
int64 _nettosize = (int64)step*rows; |
||||
size_t nettosize = (size_t)_nettosize; |
||||
|
||||
datastart = data = (uchar*)dev_ptr; |
||||
dataend = data + nettosize; |
||||
|
||||
refcount = (int*)fastMalloc(sizeof(*refcount)); |
||||
*refcount = 1; |
||||
} |
||||
} |
||||
|
||||
void cv::gpu::GpuMat::release() |
||||
{ |
||||
if( refcount && CV_XADD(refcount, -1) == 1 ) |
||||
{ |
||||
fastFree(refcount); |
||||
cudaSafeCall( cudaFree(datastart) ); |
||||
} |
||||
data = datastart = dataend = 0; |
||||
step = rows = cols = 0; |
||||
refcount = 0; |
||||
} |
||||
|
||||
void cv::gpu::createContinuous(int rows, int cols, int type, GpuMat& m) |
||||
{ |
||||
int area = rows * cols; |
||||
if (!m.isContinuous() || m.type() != type || m.size().area() != area) |
||||
m.create(1, area, type); |
||||
m = m.reshape(0, rows); |
||||
} |
||||
|
||||
void cv::gpu::ensureSizeIsEnough(int rows, int cols, int type, GpuMat& m) |
||||
{ |
||||
if (m.type() == type && m.rows >= rows && m.cols >= cols) |
||||
m = m(Rect(0, 0, cols, rows)); |
||||
else |
||||
m.create(rows, cols, type); |
||||
} |
||||
|
||||
#endif /* !defined (HAVE_CUDA) */ |
||||
Loading…
Reference in new issue