added ensureSizeIsEnough into gpu module, updated reduction methods
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@ -12,4 +12,24 @@ Creates continuous matrix in GPU memory.
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\cvarg{m}{Destination matrix. Will be only reshaped if it has proper type and area (\texttt{rows} $\times$ \texttt{cols}).}
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\end{description}
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Also the following wrappers are available:
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\cvdefCpp{GpuMat createContinuous(int rows, int cols, int type);\newline
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void createContinuous(Size size, int type, GpuMat\& m);\newline
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GpuMat createContinuous(Size size, int type);}
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Matrix is called continuous if its elements are stored continuously, i.e. wuthout gaps in the end of each row.
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\cvCppFunc{gpu::ensureSizeIsEnough}
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Ensures that size of matrix is big enough and matrix has proper type. The function doesn't reallocate memory if matrix has proper attributes already.
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\cvdefCpp{void ensureSizeIsEnough(int rows, int cols, int type, GpuMat\& m);}
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\begin{description}
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\cvarg{rows}{Minimum desired number of rows.}
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\cvarg{cols}{Minimum desired number of cols.}
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\cvarg{type}{Desired matrix type.}
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\cvarg{m}{Destination matrix.}
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\end{description}
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Also the following wrapper is available:
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\cvdefCpp{void ensureSizeIsEnough(Size size, int type, GpuMat\& m);}
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@ -252,9 +252,13 @@ namespace cv
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#include "GpuMat_BetaDeprecated.hpp"
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#endif
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//! creates continuous GPU matrix
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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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//! 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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//////////////////////////////// CudaMem ////////////////////////////////
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// CudaMem is limited cv::Mat with page locked memory allocation.
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// Page locked memory is only needed for async and faster coping to GPU.
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@ -364,6 +364,10 @@ inline GpuMat createContinuous(Size size, int type)
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return m;
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}
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inline void ensureSizeIsEnough(Size size, int type, GpuMat& m)
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{
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ensureSizeIsEnough(size.height, size.width, type, m);
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}
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///////////////////////////////////////////////////////////////////////
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@ -401,6 +405,7 @@ inline CudaMem::CudaMem(const Mat& m, int _alloc_type) : flags(0), rows(0), cols
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inline CudaMem::~CudaMem()
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{
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release();
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}
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inline CudaMem& CudaMem::operator = (const CudaMem& m)
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@ -551,6 +551,13 @@ void cv::gpu::createContinuous(int rows, int cols, int type, GpuMat& m)
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m = m.reshape(0, rows);
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}
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void cv::gpu::ensureSizeIsEnough(int rows, int cols, int type, GpuMat& m)
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{
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if (m.type() == type && m.rows >= rows && m.cols >= cols)
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return;
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m.create(rows, cols, type);
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}
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///////////////////////////////////////////////////////////////////////
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//////////////////////////////// CudaMem //////////////////////////////
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@ -159,7 +159,7 @@ Scalar cv::gpu::sum(const GpuMat& src, GpuMat& buf)
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Size bufSize;
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sum::get_buf_size_required(src.cols, src.rows, src.channels(), bufSize.width, bufSize.height);
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buf.create(bufSize, CV_8U);
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ensureSizeIsEnough(bufSize, CV_8U, buf);
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Caller caller = callers[hasAtomicsSupport(getDevice())][src.depth()];
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if (!caller) CV_Error(CV_StsBadArg, "sum: unsupported type");
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@ -192,7 +192,7 @@ Scalar cv::gpu::sqrSum(const GpuMat& src, GpuMat& buf)
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Size bufSize;
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sum::get_buf_size_required(src.cols, src.rows, src.channels(), bufSize.width, bufSize.height);
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buf.create(bufSize, CV_8U);
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ensureSizeIsEnough(bufSize, CV_8U, buf);
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Caller caller = callers[hasAtomicsSupport(getDevice())][src.depth()];
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if (!caller) CV_Error(CV_StsBadArg, "sqrSum: unsupported type");
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@ -265,7 +265,7 @@ void cv::gpu::minMax(const GpuMat& src, double* minVal, double* maxVal, const Gp
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Size bufSize;
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get_buf_size_required(src.cols, src.rows, src.elemSize(), bufSize.width, bufSize.height);
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buf.create(bufSize, CV_8U);
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ensureSizeIsEnough(bufSize, CV_8U, buf);
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if (mask.empty())
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{
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@ -292,31 +292,31 @@ namespace cv { namespace gpu { namespace mathfunc { namespace minmaxloc {
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template <typename T>
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void min_max_loc_caller(const DevMem2D src, double* minval, double* maxval,
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int minloc[2], int maxloc[2], PtrStep valbuf, PtrStep locbuf);
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int minloc[2], int maxloc[2], PtrStep valBuf, PtrStep locBuf);
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template <typename T>
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void min_max_loc_mask_caller(const DevMem2D src, const PtrStep mask, double* minval, double* maxval,
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int minloc[2], int maxloc[2], PtrStep valbuf, PtrStep locbuf);
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int minloc[2], int maxloc[2], PtrStep valBuf, PtrStep locBuf);
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template <typename T>
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void min_max_loc_multipass_caller(const DevMem2D src, double* minval, double* maxval,
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int minloc[2], int maxloc[2], PtrStep valbuf, PtrStep locbuf);
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int minloc[2], int maxloc[2], PtrStep valBuf, PtrStep locBuf);
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template <typename T>
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void min_max_loc_mask_multipass_caller(const DevMem2D src, const PtrStep mask, double* minval, double* maxval,
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int minloc[2], int maxloc[2], PtrStep valbuf, PtrStep locbuf);
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int minloc[2], int maxloc[2], PtrStep valBuf, PtrStep locBuf);
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}}}}
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void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc, const GpuMat& mask)
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{
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GpuMat valbuf, locbuf;
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minMaxLoc(src, minVal, maxVal, minLoc, maxLoc, mask, valbuf, locbuf);
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GpuMat valBuf, locBuf;
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minMaxLoc(src, minVal, maxVal, minLoc, maxLoc, mask, valBuf, locBuf);
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}
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void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
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const GpuMat& mask, GpuMat& valbuf, GpuMat& locbuf)
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const GpuMat& mask, GpuMat& valBuf, GpuMat& locBuf)
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{
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using namespace mathfunc::minmaxloc;
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@ -348,23 +348,23 @@ void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point
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int minLoc_[2];
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int maxLoc_[2];
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Size valbuf_size, locbuf_size;
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get_buf_size_required(src.cols, src.rows, src.elemSize(), valbuf_size.width,
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valbuf_size.height, locbuf_size.width, locbuf_size.height);
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valbuf.create(valbuf_size, CV_8U);
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locbuf.create(locbuf_size, CV_8U);
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Size valBufSize, locBufSize;
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get_buf_size_required(src.cols, src.rows, src.elemSize(), valBufSize.width,
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valBufSize.height, locBufSize.width, locBufSize.height);
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ensureSizeIsEnough(valBufSize, CV_8U, valBuf);
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ensureSizeIsEnough(locBufSize, CV_8U, locBuf);
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if (mask.empty())
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{
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Caller caller = callers[hasAtomicsSupport(getDevice())][src.type()];
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if (!caller) CV_Error(CV_StsBadArg, "minMaxLoc: unsupported type");
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caller(src, minVal, maxVal, minLoc_, maxLoc_, valbuf, locbuf);
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caller(src, minVal, maxVal, minLoc_, maxLoc_, valBuf, locBuf);
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}
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else
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{
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MaskedCaller caller = masked_callers[hasAtomicsSupport(getDevice())][src.type()];
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if (!caller) CV_Error(CV_StsBadArg, "minMaxLoc: unsupported type");
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caller(src, mask, minVal, maxVal, minLoc_, maxLoc_, valbuf, locbuf);
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caller(src, mask, minVal, maxVal, minLoc_, maxLoc_, valBuf, locBuf);
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}
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if (minLoc) { minLoc->x = minLoc_[0]; minLoc->y = minLoc_[1]; }
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@ -411,9 +411,9 @@ int cv::gpu::countNonZero(const GpuMat& src, GpuMat& buf)
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CV_Assert(src.channels() == 1);
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CV_Assert(src.type() != CV_64F || hasNativeDoubleSupport(getDevice()));
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Size buf_size;
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get_buf_size_required(src.cols, src.rows, buf_size.width, buf_size.height);
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buf.create(buf_size, CV_8U);
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Size bufSize;
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get_buf_size_required(src.cols, src.rows, bufSize.width, bufSize.height);
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ensureSizeIsEnough(bufSize, CV_8U, buf);
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Caller caller = callers[hasAtomicsSupport(getDevice())][src.type()];
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if (!caller) CV_Error(CV_StsBadArg, "countNonZero: unsupported type");
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