/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "precomp.hpp" #include "opencv2/core/gpumat.hpp" using namespace std; using namespace cv; using namespace cv::gpu; cv::gpu::GpuMat::GpuMat(const GpuMat& m) : flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend) { if (refcount) CV_XADD(refcount, 1); } cv::gpu::GpuMat::GpuMat(int rows_, int cols_, int type_, void* data_, size_t step_) : flags(Mat::MAGIC_VAL + (type_ & TYPE_MASK)), rows(rows_), cols(cols_), step(step_), data((uchar*)data_), refcount(0), datastart((uchar*)data_), dataend((uchar*)data_) { size_t minstep = cols * elemSize(); if (step == Mat::AUTO_STEP) { step = minstep; flags |= Mat::CONTINUOUS_FLAG; } else { if (rows == 1) step = minstep; CV_DbgAssert(step >= minstep); flags |= step == minstep ? Mat::CONTINUOUS_FLAG : 0; } dataend += step * (rows - 1) + minstep; } cv::gpu::GpuMat::GpuMat(Size size_, int type_, void* data_, size_t step_) : flags(Mat::MAGIC_VAL + (type_ & TYPE_MASK)), rows(size_.height), cols(size_.width), step(step_), data((uchar*)data_), refcount(0), datastart((uchar*)data_), dataend((uchar*)data_) { size_t minstep = cols * elemSize(); if (step == Mat::AUTO_STEP) { step = minstep; flags |= Mat::CONTINUOUS_FLAG; } else { if (rows == 1) step = minstep; CV_DbgAssert(step >= minstep); flags |= step == minstep ? Mat::CONTINUOUS_FLAG : 0; } dataend += step * (rows - 1) + minstep; } cv::gpu::GpuMat::GpuMat(const GpuMat& m, Range rowRange, Range colRange) { flags = m.flags; step = m.step; refcount = m.refcount; data = m.data; datastart = m.datastart; dataend = m.dataend; if (rowRange == Range::all()) rows = m.rows; else { CV_Assert(0 <= rowRange.start && rowRange.start <= rowRange.end && rowRange.end <= m.rows); rows = rowRange.size(); data += step*rowRange.start; } if (colRange == Range::all()) cols = m.cols; else { CV_Assert(0 <= colRange.start && colRange.start <= colRange.end && colRange.end <= m.cols); cols = colRange.size(); data += colRange.start*elemSize(); flags &= cols < m.cols ? ~Mat::CONTINUOUS_FLAG : -1; } if (rows == 1) flags |= Mat::CONTINUOUS_FLAG; if (refcount) CV_XADD(refcount, 1); if (rows <= 0 || cols <= 0) rows = cols = 0; } cv::gpu::GpuMat::GpuMat(const GpuMat& m, Rect roi) : flags(m.flags), rows(roi.height), cols(roi.width), step(m.step), data(m.data + roi.y*step), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend) { flags &= roi.width < m.cols ? ~Mat::CONTINUOUS_FLAG : -1; data += roi.x * elemSize(); 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); if (refcount) CV_XADD(refcount, 1); if (rows <= 0 || cols <= 0) rows = cols = 0; } cv::gpu::GpuMat::GpuMat(const Mat& m) : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0) { upload(m); } GpuMat& cv::gpu::GpuMat::operator = (const GpuMat& m) { if (this != &m) { GpuMat temp(m); swap(temp); } return *this; } 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(); ptrdiff_t delta1 = data - datastart; ptrdiff_t delta2 = dataend - datastart; CV_DbgAssert(step > 0); if (delta1 == 0) ofs.x = ofs.y = 0; else { ofs.y = static_cast(delta1 / step); ofs.x = static_cast((delta1 - step * ofs.y) / esz); CV_DbgAssert(data == datastart + ofs.y * step + ofs.x * esz); } size_t minstep = (ofs.x + cols) * esz; wholeSize.height = std::max(static_cast((delta2 - minstep) / step + 1), ofs.y + rows); wholeSize.width = std::max(static_cast((delta2 - step * (wholeSize.height - 1)) / esz), ofs.x + cols); } GpuMat& cv::gpu::GpuMat::adjustROI(int dtop, int dbottom, int dleft, int dright) { Size wholeSize; Point ofs; locateROI(wholeSize, ofs); size_t esz = elemSize(); int row1 = std::max(ofs.y - dtop, 0); int row2 = std::min(ofs.y + rows + dbottom, wholeSize.height); int col1 = std::max(ofs.x - dleft, 0); int 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; } 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; } 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) { m.download(*this); } namespace { void throw_nogpu() { CV_Error(CV_GpuNotSupported, "The library is compiled without GPU support"); } class EmptyFuncTable : public GpuFuncTable { public: void copy(const Mat&, GpuMat&) const { throw_nogpu(); } void copy(const GpuMat&, Mat&) const { throw_nogpu(); } void copy(const GpuMat&, GpuMat&) const { throw_nogpu(); } void copyWithMask(const GpuMat&, GpuMat&, const GpuMat&) const { throw_nogpu(); } void convert(const GpuMat&, GpuMat&) const { throw_nogpu(); } void convert(const GpuMat&, GpuMat&, double, double) const { throw_nogpu(); } void setTo(GpuMat&, Scalar, const GpuMat&) const { throw_nogpu(); } void mallocPitch(void**, size_t*, size_t, size_t) const { throw_nogpu(); } void free(void*) const {} }; const GpuFuncTable* g_funcTbl = 0; const GpuFuncTable* gpuFuncTable() { static EmptyFuncTable empty; return g_funcTbl ? g_funcTbl : ∅ } } void cv::gpu::setGpuFuncTable(const GpuFuncTable* funcTbl) { g_funcTbl = funcTbl; } void cv::gpu::GpuMat::upload(const Mat& m) { CV_DbgAssert(!m.empty()); create(m.size(), m.type()); gpuFuncTable()->copy(m, *this); } void cv::gpu::GpuMat::download(Mat& m) const { CV_DbgAssert(!empty()); m.create(size(), type()); gpuFuncTable()->copy(*this, m); } void cv::gpu::GpuMat::copyTo(GpuMat& m) const { CV_DbgAssert(!empty()); m.create(size(), type()); gpuFuncTable()->copy(*this, m); } void cv::gpu::GpuMat::copyTo(GpuMat& mat, const GpuMat& mask) const { if (mask.empty()) copyTo(mat); else { mat.create(size(), type()); gpuFuncTable()->copyWithMask(*this, mat, mask); } } void cv::gpu::GpuMat::convertTo(GpuMat& dst, int rtype, double alpha, double beta) const { bool noScale = fabs(alpha - 1) < numeric_limits::epsilon() && fabs(beta) < numeric_limits::epsilon(); if (rtype < 0) rtype = type(); else rtype = CV_MAKETYPE(CV_MAT_DEPTH(rtype), channels()); int sdepth = depth(); int ddepth = CV_MAT_DEPTH(rtype); if (sdepth == ddepth && noScale) { copyTo(dst); return; } GpuMat temp; const GpuMat* psrc = this; if (sdepth != ddepth && psrc == &dst) { temp = *this; psrc = &temp; } dst.create(size(), rtype); if (noScale) gpuFuncTable()->convert(*psrc, dst); else gpuFuncTable()->convert(*psrc, dst, alpha, beta); } GpuMat& cv::gpu::GpuMat::setTo(Scalar s, const GpuMat& mask) { CV_Assert(mask.empty() || mask.type() == CV_8UC1); CV_DbgAssert(!empty()); gpuFuncTable()->setTo(*this, s, mask); return *this; } 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* devPtr; gpuFuncTable()->mallocPitch(&devPtr, &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 = static_cast(step) * rows; size_t nettosize = static_cast(_nettosize); datastart = data = static_cast(devPtr); dataend = data + nettosize; refcount = static_cast(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; }