461 lines
13 KiB
C++
461 lines
13 KiB
C++
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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 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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#include "opencv2/core/gpumat.hpp"
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using namespace std;
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using namespace cv;
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using namespace cv::gpu;
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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_),
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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)
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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)
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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, Range rowRange, 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, 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 && 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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GpuMat temp(m);
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swap(temp);
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}
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return *this;
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}
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void cv::gpu::GpuMat::swap(GpuMat& b)
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{
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std::swap(flags, b.flags);
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std::swap(rows, b.rows);
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std::swap(cols, b.cols);
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std::swap(step, b.step);
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std::swap(data, b.data);
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std::swap(datastart, b.datastart);
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std::swap(dataend, b.dataend);
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std::swap(refcount, b.refcount);
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}
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void cv::gpu::GpuMat::locateROI(Size& wholeSize, Point& ofs) const
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{
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size_t esz = elemSize();
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ptrdiff_t delta1 = data - datastart;
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ptrdiff_t delta2 = dataend - datastart;
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CV_DbgAssert(step > 0);
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if (delta1 == 0)
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ofs.x = ofs.y = 0;
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else
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{
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ofs.y = static_cast<int>(delta1 / step);
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ofs.x = static_cast<int>((delta1 - step * ofs.y) / esz);
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CV_DbgAssert(data == datastart + ofs.y * step + ofs.x * esz);
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}
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size_t minstep = (ofs.x + cols) * esz;
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wholeSize.height = std::max(static_cast<int>((delta2 - minstep) / step + 1), ofs.y + rows);
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wholeSize.width = std::max(static_cast<int>((delta2 - step * (wholeSize.height - 1)) / esz), ofs.x + cols);
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}
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GpuMat& cv::gpu::GpuMat::adjustROI(int dtop, int dbottom, int dleft, int dright)
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{
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Size wholeSize;
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Point ofs;
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locateROI(wholeSize, ofs);
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size_t esz = elemSize();
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int row1 = std::max(ofs.y - dtop, 0);
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int row2 = std::min(ofs.y + rows + dbottom, wholeSize.height);
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int col1 = std::max(ofs.x - dleft, 0);
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int col2 = std::min(ofs.x + cols + dright, wholeSize.width);
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data += (row1 - ofs.y) * step + (col1 - ofs.x) * esz;
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rows = row2 - row1;
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cols = col2 - col1;
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if (esz * cols == step || rows == 1)
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flags |= Mat::CONTINUOUS_FLAG;
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else
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flags &= ~Mat::CONTINUOUS_FLAG;
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return *this;
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}
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GpuMat cv::gpu::GpuMat::reshape(int new_cn, int new_rows) const
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{
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GpuMat hdr = *this;
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int cn = channels();
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if (new_cn == 0)
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new_cn = cn;
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int total_width = cols * cn;
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if ((new_cn > total_width || total_width % new_cn != 0) && new_rows == 0)
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new_rows = rows * total_width / new_cn;
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if (new_rows != 0 && new_rows != rows)
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{
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int total_size = total_width * rows;
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if (!isContinuous())
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CV_Error(CV_BadStep, "The matrix is not continuous, thus its number of rows can not be changed");
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if ((unsigned)new_rows > (unsigned)total_size)
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CV_Error(CV_StsOutOfRange, "Bad new number of rows");
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total_width = total_size / new_rows;
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if (total_width * new_rows != total_size)
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CV_Error(CV_StsBadArg, "The total number of matrix elements is not divisible by the new number of rows");
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hdr.rows = new_rows;
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hdr.step = total_width * elemSize1();
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}
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int new_width = total_width / new_cn;
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if (new_width * new_cn != total_width)
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CV_Error(CV_BadNumChannels, "The total width is not divisible by the new number of channels");
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hdr.cols = new_width;
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hdr.flags = (hdr.flags & ~CV_MAT_CN_MASK) | ((new_cn - 1) << CV_CN_SHIFT);
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return hdr;
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}
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cv::Mat::Mat(const GpuMat& m) : flags(0), dims(0), rows(0), cols(0), data(0), refcount(0), datastart(0), dataend(0), datalimit(0), allocator(0), size(&rows)
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{
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m.download(*this);
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}
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namespace
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{
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void throw_nogpu()
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{
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CV_Error(CV_GpuNotSupported, "The library is compiled without GPU support");
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}
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class EmptyFuncTable : public GpuFuncTable
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{
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public:
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void copy(const Mat&, GpuMat&) const { throw_nogpu(); }
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void copy(const GpuMat&, Mat&) const { throw_nogpu(); }
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void copy(const GpuMat&, GpuMat&) const { throw_nogpu(); }
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void copyWithMask(const GpuMat&, GpuMat&, const GpuMat&) const { throw_nogpu(); }
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void convert(const GpuMat&, GpuMat&) const { throw_nogpu(); }
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void convert(const GpuMat&, GpuMat&, double, double) const { throw_nogpu(); }
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void setTo(GpuMat&, Scalar, const GpuMat&) const { throw_nogpu(); }
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void mallocPitch(void**, size_t*, size_t, size_t) const { throw_nogpu(); }
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void free(void*) const {}
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};
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const GpuFuncTable* g_funcTbl = 0;
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const GpuFuncTable* gpuFuncTable()
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{
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static EmptyFuncTable empty;
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return g_funcTbl ? g_funcTbl : ∅
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}
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}
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void cv::gpu::setGpuFuncTable(const GpuFuncTable* funcTbl)
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{
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g_funcTbl = funcTbl;
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}
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void cv::gpu::GpuMat::upload(const Mat& m)
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{
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CV_DbgAssert(!m.empty());
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create(m.size(), m.type());
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gpuFuncTable()->copy(m, *this);
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}
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void cv::gpu::GpuMat::download(Mat& m) const
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{
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CV_DbgAssert(!empty());
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m.create(size(), type());
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gpuFuncTable()->copy(*this, m);
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}
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void cv::gpu::GpuMat::copyTo(GpuMat& m) const
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{
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CV_DbgAssert(!empty());
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m.create(size(), type());
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gpuFuncTable()->copy(*this, m);
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}
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void cv::gpu::GpuMat::copyTo(GpuMat& mat, const GpuMat& mask) const
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{
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if (mask.empty())
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copyTo(mat);
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else
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{
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mat.create(size(), type());
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gpuFuncTable()->copyWithMask(*this, mat, mask);
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}
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}
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void cv::gpu::GpuMat::convertTo(GpuMat& dst, int rtype, double alpha, double beta) const
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{
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bool noScale = fabs(alpha - 1) < numeric_limits<double>::epsilon() && fabs(beta) < numeric_limits<double>::epsilon();
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if (rtype < 0)
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rtype = type();
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else
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rtype = CV_MAKETYPE(CV_MAT_DEPTH(rtype), channels());
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int sdepth = depth();
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int ddepth = CV_MAT_DEPTH(rtype);
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if (sdepth == ddepth && noScale)
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{
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copyTo(dst);
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return;
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}
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GpuMat temp;
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const GpuMat* psrc = this;
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if (sdepth != ddepth && psrc == &dst)
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{
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temp = *this;
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psrc = &temp;
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}
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dst.create(size(), rtype);
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if (noScale)
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gpuFuncTable()->convert(*psrc, dst);
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else
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gpuFuncTable()->convert(*psrc, dst, alpha, beta);
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}
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GpuMat& cv::gpu::GpuMat::setTo(Scalar s, const GpuMat& mask)
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{
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CV_Assert(mask.empty() || mask.type() == CV_8UC1);
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CV_DbgAssert(!empty());
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gpuFuncTable()->setTo(*this, s, mask);
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return *this;
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}
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void cv::gpu::GpuMat::create(int _rows, int _cols, int _type)
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{
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_type &= TYPE_MASK;
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if (rows == _rows && cols == _cols && type() == _type && data)
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return;
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if (data)
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release();
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CV_DbgAssert(_rows >= 0 && _cols >= 0);
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if (_rows > 0 && _cols > 0)
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{
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flags = Mat::MAGIC_VAL + _type;
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rows = _rows;
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cols = _cols;
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size_t esz = elemSize();
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void* devPtr;
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gpuFuncTable()->mallocPitch(&devPtr, &step, esz * cols, rows);
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// Single row must be continuous
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if (rows == 1)
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step = esz * cols;
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if (esz * cols == step)
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||
|
flags |= Mat::CONTINUOUS_FLAG;
|
||
|
|
||
|
int64 _nettosize = static_cast<int64>(step) * rows;
|
||
|
size_t nettosize = static_cast<size_t>(_nettosize);
|
||
|
|
||
|
datastart = data = static_cast<uchar*>(devPtr);
|
||
|
dataend = data + nettosize;
|
||
|
|
||
|
refcount = static_cast<int*>(fastMalloc(sizeof(*refcount)));
|
||
|
*refcount = 1;
|
||
|
}
|
||
|
}
|
||
|
|
||
|
void cv::gpu::GpuMat::release()
|
||
|
{
|
||
|
if (refcount && CV_XADD(refcount, -1) == 1)
|
||
|
{
|
||
|
fastFree(refcount);
|
||
|
|
||
|
gpuFuncTable()->free(datastart);
|
||
|
}
|
||
|
|
||
|
data = datastart = dataend = 0;
|
||
|
step = rows = cols = 0;
|
||
|
refcount = 0;
|
||
|
}
|