2a6fb2867e
Made all STL usages explicit to be able automatically find all usages of particular class or function.
1560 lines
59 KiB
C++
1560 lines
59 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other 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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#include <iostream>
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#ifdef HAVE_CUDA
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#include <cuda_runtime.h>
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#include <npp.h>
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#define CUDART_MINIMUM_REQUIRED_VERSION 4020
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#define NPP_MINIMUM_REQUIRED_VERSION 4200
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#if (CUDART_VERSION < CUDART_MINIMUM_REQUIRED_VERSION)
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#error "Insufficient Cuda Runtime library version, please update it."
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#endif
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#if (NPP_VERSION_MAJOR * 1000 + NPP_VERSION_MINOR * 100 + NPP_VERSION_BUILD < NPP_MINIMUM_REQUIRED_VERSION)
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#error "Insufficient NPP version, please update it."
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#endif
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#endif
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//////////////////////////////// Initialization & Info ////////////////////////
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namespace
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{
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class CudaArch
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{
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public:
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CudaArch();
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bool builtWith(cv::gpu::FeatureSet feature_set) const;
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bool hasPtx(int major, int minor) const;
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bool hasBin(int major, int minor) const;
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bool hasEqualOrLessPtx(int major, int minor) const;
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bool hasEqualOrGreaterPtx(int major, int minor) const;
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bool hasEqualOrGreaterBin(int major, int minor) const;
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private:
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static void fromStr(const std::string& set_as_str, std::vector<int>& arr);
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std::vector<int> bin;
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std::vector<int> ptx;
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std::vector<int> features;
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};
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const CudaArch cudaArch;
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CudaArch::CudaArch()
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{
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#ifdef HAVE_CUDA
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fromStr(CUDA_ARCH_BIN, bin);
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fromStr(CUDA_ARCH_PTX, ptx);
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fromStr(CUDA_ARCH_FEATURES, features);
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#endif
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}
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bool CudaArch::builtWith(cv::gpu::FeatureSet feature_set) const
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{
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return !features.empty() && (features.back() >= feature_set);
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}
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bool CudaArch::hasPtx(int major, int minor) const
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{
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return std::find(ptx.begin(), ptx.end(), major * 10 + minor) != ptx.end();
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}
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bool CudaArch::hasBin(int major, int minor) const
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{
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return std::find(bin.begin(), bin.end(), major * 10 + minor) != bin.end();
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}
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bool CudaArch::hasEqualOrLessPtx(int major, int minor) const
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{
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return !ptx.empty() && (ptx.front() <= major * 10 + minor);
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}
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bool CudaArch::hasEqualOrGreaterPtx(int major, int minor) const
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{
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return !ptx.empty() && (ptx.back() >= major * 10 + minor);
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}
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bool CudaArch::hasEqualOrGreaterBin(int major, int minor) const
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{
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return !bin.empty() && (bin.back() >= major * 10 + minor);
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}
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void CudaArch::fromStr(const std::string& set_as_str, std::vector<int>& arr)
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{
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if (set_as_str.find_first_not_of(" ") == std::string::npos)
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return;
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std::istringstream stream(set_as_str);
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int cur_value;
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while (!stream.eof())
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{
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stream >> cur_value;
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arr.push_back(cur_value);
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}
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std::sort(arr.begin(), arr.end());
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}
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}
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bool cv::gpu::TargetArchs::builtWith(cv::gpu::FeatureSet feature_set)
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{
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#if defined (HAVE_CUDA)
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return cudaArch.builtWith(feature_set);
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#else
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(void)feature_set;
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return false;
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#endif
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}
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bool cv::gpu::TargetArchs::has(int major, int minor)
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{
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return hasPtx(major, minor) || hasBin(major, minor);
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}
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bool cv::gpu::TargetArchs::hasPtx(int major, int minor)
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{
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#if defined (HAVE_CUDA)
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return cudaArch.hasPtx(major, minor);
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#else
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(void)major;
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(void)minor;
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return false;
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#endif
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}
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bool cv::gpu::TargetArchs::hasBin(int major, int minor)
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{
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#if defined (HAVE_CUDA)
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return cudaArch.hasBin(major, minor);
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#else
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(void)major;
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(void)minor;
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return false;
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#endif
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}
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bool cv::gpu::TargetArchs::hasEqualOrLessPtx(int major, int minor)
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{
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#if defined (HAVE_CUDA)
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return cudaArch.hasEqualOrLessPtx(major, minor);
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#else
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(void)major;
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(void)minor;
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return false;
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#endif
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}
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bool cv::gpu::TargetArchs::hasEqualOrGreater(int major, int minor)
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{
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return hasEqualOrGreaterPtx(major, minor) || hasEqualOrGreaterBin(major, minor);
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}
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bool cv::gpu::TargetArchs::hasEqualOrGreaterPtx(int major, int minor)
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{
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#if defined (HAVE_CUDA)
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return cudaArch.hasEqualOrGreaterPtx(major, minor);
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#else
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(void)major;
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(void)minor;
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return false;
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#endif
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}
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bool cv::gpu::TargetArchs::hasEqualOrGreaterBin(int major, int minor)
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{
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#if defined (HAVE_CUDA)
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return cudaArch.hasEqualOrGreaterBin(major, minor);
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#else
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(void)major;
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(void)minor;
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return false;
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#endif
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}
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bool cv::gpu::deviceSupports(FeatureSet feature_set)
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{
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static int versions[] =
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{
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-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1
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};
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static const int cache_size = static_cast<int>(sizeof(versions) / sizeof(versions[0]));
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const int devId = getDevice();
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int version;
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if (devId < cache_size && versions[devId] >= 0)
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version = versions[devId];
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else
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{
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DeviceInfo dev(devId);
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version = dev.majorVersion() * 10 + dev.minorVersion();
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if (devId < cache_size)
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versions[devId] = version;
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}
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return TargetArchs::builtWith(feature_set) && (version >= feature_set);
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}
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#if !defined (HAVE_CUDA)
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#define throw_nogpu CV_Error(CV_GpuNotSupported, "The library is compiled without CUDA support")
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int cv::gpu::getCudaEnabledDeviceCount() { return 0; }
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void cv::gpu::setDevice(int) { throw_nogpu; }
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int cv::gpu::getDevice() { throw_nogpu; return 0; }
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void cv::gpu::resetDevice() { throw_nogpu; }
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size_t cv::gpu::DeviceInfo::freeMemory() const { throw_nogpu; return 0; }
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size_t cv::gpu::DeviceInfo::totalMemory() const { throw_nogpu; return 0; }
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bool cv::gpu::DeviceInfo::supports(cv::gpu::FeatureSet) const { throw_nogpu; return false; }
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bool cv::gpu::DeviceInfo::isCompatible() const { throw_nogpu; return false; }
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void cv::gpu::DeviceInfo::query() { throw_nogpu; }
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void cv::gpu::DeviceInfo::queryMemory(size_t&, size_t&) const { throw_nogpu; }
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void cv::gpu::printCudaDeviceInfo(int) { throw_nogpu; }
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void cv::gpu::printShortCudaDeviceInfo(int) { throw_nogpu; }
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#undef throw_nogpu
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#else // HAVE_CUDA
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namespace
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{
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#if defined(__GNUC__)
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#define cudaSafeCall(expr) ___cudaSafeCall(expr, __FILE__, __LINE__, __func__)
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#define nppSafeCall(expr) ___nppSafeCall(expr, __FILE__, __LINE__, __func__)
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#else /* defined(__CUDACC__) || defined(__MSVC__) */
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#define cudaSafeCall(expr) ___cudaSafeCall(expr, __FILE__, __LINE__)
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#define nppSafeCall(expr) ___nppSafeCall(expr, __FILE__, __LINE__)
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#endif
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inline void ___cudaSafeCall(cudaError_t err, const char *file, const int line, const char *func = "")
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{
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if (cudaSuccess != err)
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cv::gpu::error(cudaGetErrorString(err), file, line, func);
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}
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inline void ___nppSafeCall(int err, const char *file, const int line, const char *func = "")
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{
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if (err < 0)
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{
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std::ostringstream msg;
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msg << "NPP API Call Error: " << err;
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cv::gpu::error(msg.str().c_str(), file, line, func);
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}
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}
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}
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int cv::gpu::getCudaEnabledDeviceCount()
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{
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int count;
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cudaError_t error = cudaGetDeviceCount( &count );
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if (error == cudaErrorInsufficientDriver)
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return -1;
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if (error == cudaErrorNoDevice)
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return 0;
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cudaSafeCall(error);
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return count;
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}
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void cv::gpu::setDevice(int device)
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{
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cudaSafeCall( cudaSetDevice( device ) );
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}
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int cv::gpu::getDevice()
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{
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int device;
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cudaSafeCall( cudaGetDevice( &device ) );
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return device;
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}
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void cv::gpu::resetDevice()
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{
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cudaSafeCall( cudaDeviceReset() );
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}
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size_t cv::gpu::DeviceInfo::freeMemory() const
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{
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size_t free_memory, total_memory;
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queryMemory(free_memory, total_memory);
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return free_memory;
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}
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size_t cv::gpu::DeviceInfo::totalMemory() const
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{
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size_t free_memory, total_memory;
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queryMemory(free_memory, total_memory);
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return total_memory;
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}
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bool cv::gpu::DeviceInfo::supports(cv::gpu::FeatureSet feature_set) const
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{
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int version = majorVersion() * 10 + minorVersion();
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return version >= feature_set;
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}
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bool cv::gpu::DeviceInfo::isCompatible() const
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{
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// Check PTX compatibility
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if (TargetArchs::hasEqualOrLessPtx(majorVersion(), minorVersion()))
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return true;
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// Check BIN compatibility
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for (int i = minorVersion(); i >= 0; --i)
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if (TargetArchs::hasBin(majorVersion(), i))
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return true;
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return false;
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}
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void cv::gpu::DeviceInfo::query()
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{
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cudaDeviceProp prop;
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cudaSafeCall(cudaGetDeviceProperties(&prop, device_id_));
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name_ = prop.name;
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multi_processor_count_ = prop.multiProcessorCount;
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majorVersion_ = prop.major;
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minorVersion_ = prop.minor;
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sharedMemPerBlock_ = prop.sharedMemPerBlock;
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}
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size_t cv::gpu::DeviceInfo::sharedMemPerBlock() const {return sharedMemPerBlock_;}
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void cv::gpu::DeviceInfo::queryMemory(size_t& free_memory, size_t& total_memory) const
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{
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int prev_device_id = getDevice();
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if (prev_device_id != device_id_)
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setDevice(device_id_);
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cudaSafeCall(cudaMemGetInfo(&free_memory, &total_memory));
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if (prev_device_id != device_id_)
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setDevice(prev_device_id);
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}
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namespace
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{
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int convertSMVer2Cores(int major, int minor)
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{
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// Defines for GPU Architecture types (using the SM version to determine the # of cores per SM
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typedef struct {
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int SM; // 0xMm (hexidecimal notation), M = SM Major version, and m = SM minor version
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int Cores;
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} SMtoCores;
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SMtoCores gpuArchCoresPerSM[] = { { 0x10, 8 }, { 0x11, 8 }, { 0x12, 8 }, { 0x13, 8 }, { 0x20, 32 }, { 0x21, 48 }, {0x30, 192}, {0x35, 192}, { -1, -1 } };
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int index = 0;
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while (gpuArchCoresPerSM[index].SM != -1)
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{
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if (gpuArchCoresPerSM[index].SM == ((major << 4) + minor) )
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return gpuArchCoresPerSM[index].Cores;
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index++;
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}
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return -1;
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}
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}
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void cv::gpu::printCudaDeviceInfo(int device)
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{
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int count = getCudaEnabledDeviceCount();
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bool valid = (device >= 0) && (device < count);
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int beg = valid ? device : 0;
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int end = valid ? device+1 : count;
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printf("*** CUDA Device Query (Runtime API) version (CUDART static linking) *** \n\n");
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printf("Device count: %d\n", count);
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int driverVersion = 0, runtimeVersion = 0;
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cudaSafeCall( cudaDriverGetVersion(&driverVersion) );
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cudaSafeCall( cudaRuntimeGetVersion(&runtimeVersion) );
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const char *computeMode[] = {
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"Default (multiple host threads can use ::cudaSetDevice() with device simultaneously)",
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"Exclusive (only one host thread in one process is able to use ::cudaSetDevice() with this device)",
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"Prohibited (no host thread can use ::cudaSetDevice() with this device)",
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"Exclusive Process (many threads in one process is able to use ::cudaSetDevice() with this device)",
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"Unknown",
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NULL
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};
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for(int dev = beg; dev < end; ++dev)
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{
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cudaDeviceProp prop;
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cudaSafeCall( cudaGetDeviceProperties(&prop, dev) );
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printf("\nDevice %d: \"%s\"\n", dev, prop.name);
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printf(" CUDA Driver Version / Runtime Version %d.%d / %d.%d\n", driverVersion/1000, driverVersion%100, runtimeVersion/1000, runtimeVersion%100);
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printf(" CUDA Capability Major/Minor version number: %d.%d\n", prop.major, prop.minor);
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printf(" Total amount of global memory: %.0f MBytes (%llu bytes)\n", (float)prop.totalGlobalMem/1048576.0f, (unsigned long long) prop.totalGlobalMem);
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int cores = convertSMVer2Cores(prop.major, prop.minor);
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if (cores > 0)
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printf(" (%2d) Multiprocessors x (%2d) CUDA Cores/MP: %d CUDA Cores\n", prop.multiProcessorCount, cores, cores * prop.multiProcessorCount);
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printf(" GPU Clock Speed: %.2f GHz\n", prop.clockRate * 1e-6f);
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printf(" Max Texture Dimension Size (x,y,z) 1D=(%d), 2D=(%d,%d), 3D=(%d,%d,%d)\n",
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prop.maxTexture1D, prop.maxTexture2D[0], prop.maxTexture2D[1],
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prop.maxTexture3D[0], prop.maxTexture3D[1], prop.maxTexture3D[2]);
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printf(" Max Layered Texture Size (dim) x layers 1D=(%d) x %d, 2D=(%d,%d) x %d\n",
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prop.maxTexture1DLayered[0], prop.maxTexture1DLayered[1],
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prop.maxTexture2DLayered[0], prop.maxTexture2DLayered[1], prop.maxTexture2DLayered[2]);
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printf(" Total amount of constant memory: %u bytes\n", (int)prop.totalConstMem);
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printf(" Total amount of shared memory per block: %u bytes\n", (int)prop.sharedMemPerBlock);
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printf(" Total number of registers available per block: %d\n", prop.regsPerBlock);
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printf(" Warp size: %d\n", prop.warpSize);
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printf(" Maximum number of threads per block: %d\n", prop.maxThreadsPerBlock);
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printf(" Maximum sizes of each dimension of a block: %d x %d x %d\n", prop.maxThreadsDim[0], prop.maxThreadsDim[1], prop.maxThreadsDim[2]);
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printf(" Maximum sizes of each dimension of a grid: %d x %d x %d\n", prop.maxGridSize[0], prop.maxGridSize[1], prop.maxGridSize[2]);
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printf(" Maximum memory pitch: %u bytes\n", (int)prop.memPitch);
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printf(" Texture alignment: %u bytes\n", (int)prop.textureAlignment);
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printf(" Concurrent copy and execution: %s with %d copy engine(s)\n", (prop.deviceOverlap ? "Yes" : "No"), prop.asyncEngineCount);
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printf(" Run time limit on kernels: %s\n", prop.kernelExecTimeoutEnabled ? "Yes" : "No");
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printf(" Integrated GPU sharing Host Memory: %s\n", prop.integrated ? "Yes" : "No");
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printf(" Support host page-locked memory mapping: %s\n", prop.canMapHostMemory ? "Yes" : "No");
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printf(" Concurrent kernel execution: %s\n", prop.concurrentKernels ? "Yes" : "No");
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printf(" Alignment requirement for Surfaces: %s\n", prop.surfaceAlignment ? "Yes" : "No");
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printf(" Device has ECC support enabled: %s\n", prop.ECCEnabled ? "Yes" : "No");
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printf(" Device is using TCC driver mode: %s\n", prop.tccDriver ? "Yes" : "No");
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printf(" Device supports Unified Addressing (UVA): %s\n", prop.unifiedAddressing ? "Yes" : "No");
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printf(" Device PCI Bus ID / PCI location ID: %d / %d\n", prop.pciBusID, prop.pciDeviceID );
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|
printf(" Compute Mode:\n");
|
|
printf(" %s \n", computeMode[prop.computeMode]);
|
|
}
|
|
|
|
printf("\n");
|
|
printf("deviceQuery, CUDA Driver = CUDART");
|
|
printf(", CUDA Driver Version = %d.%d", driverVersion / 1000, driverVersion % 100);
|
|
printf(", CUDA Runtime Version = %d.%d", runtimeVersion/1000, runtimeVersion%100);
|
|
printf(", NumDevs = %d\n\n", count);
|
|
fflush(stdout);
|
|
}
|
|
|
|
void cv::gpu::printShortCudaDeviceInfo(int device)
|
|
{
|
|
int count = getCudaEnabledDeviceCount();
|
|
bool valid = (device >= 0) && (device < count);
|
|
|
|
int beg = valid ? device : 0;
|
|
int end = valid ? device+1 : count;
|
|
|
|
int driverVersion = 0, runtimeVersion = 0;
|
|
cudaSafeCall( cudaDriverGetVersion(&driverVersion) );
|
|
cudaSafeCall( cudaRuntimeGetVersion(&runtimeVersion) );
|
|
|
|
for(int dev = beg; dev < end; ++dev)
|
|
{
|
|
cudaDeviceProp prop;
|
|
cudaSafeCall( cudaGetDeviceProperties(&prop, dev) );
|
|
|
|
const char *arch_str = prop.major < 2 ? " (not Fermi)" : "";
|
|
printf("Device %d: \"%s\" %.0fMb", dev, prop.name, (float)prop.totalGlobalMem/1048576.0f);
|
|
printf(", sm_%d%d%s", prop.major, prop.minor, arch_str);
|
|
|
|
int cores = convertSMVer2Cores(prop.major, prop.minor);
|
|
if (cores > 0)
|
|
printf(", %d cores", cores * prop.multiProcessorCount);
|
|
|
|
printf(", Driver/Runtime ver.%d.%d/%d.%d\n", driverVersion/1000, driverVersion%100, runtimeVersion/1000, runtimeVersion%100);
|
|
}
|
|
fflush(stdout);
|
|
}
|
|
|
|
#endif // HAVE_CUDA
|
|
|
|
//////////////////////////////// GpuMat ///////////////////////////////
|
|
|
|
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);
|
|
}
|
|
|
|
cv::gpu::GpuMat& cv::gpu::GpuMat::operator = (const cv::gpu::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<int>(delta1 / step);
|
|
ofs.x = static_cast<int>((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<int>((delta2 - minstep) / step + 1), ofs.y + rows);
|
|
wholeSize.width = std::max(static_cast<int>((delta2 - step * (wholeSize.height - 1)) / esz), ofs.x + cols);
|
|
}
|
|
|
|
cv::gpu::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;
|
|
}
|
|
|
|
cv::gpu::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 cv::gpu::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);
|
|
}
|
|
|
|
void cv::gpu::createContinuous(int rows, int cols, int type, GpuMat& m)
|
|
{
|
|
int area = rows * cols;
|
|
if (m.empty() || m.type() != type || !m.isContinuous() || m.size().area() < area)
|
|
m.create(1, area, type);
|
|
|
|
m.cols = cols;
|
|
m.rows = rows;
|
|
m.step = m.elemSize() * cols;
|
|
m.flags |= Mat::CONTINUOUS_FLAG;
|
|
}
|
|
|
|
void cv::gpu::ensureSizeIsEnough(int rows, int cols, int type, GpuMat& m)
|
|
{
|
|
if (m.empty() || m.type() != type || m.data != m.datastart)
|
|
m.create(rows, cols, type);
|
|
else
|
|
{
|
|
const size_t esz = m.elemSize();
|
|
const ptrdiff_t delta2 = m.dataend - m.datastart;
|
|
|
|
const size_t minstep = m.cols * esz;
|
|
|
|
Size wholeSize;
|
|
wholeSize.height = std::max(static_cast<int>((delta2 - minstep) / m.step + 1), m.rows);
|
|
wholeSize.width = std::max(static_cast<int>((delta2 - m.step * (wholeSize.height - 1)) / esz), m.cols);
|
|
|
|
if (wholeSize.height < rows || wholeSize.width < cols)
|
|
m.create(rows, cols, type);
|
|
else
|
|
{
|
|
m.cols = cols;
|
|
m.rows = rows;
|
|
}
|
|
}
|
|
}
|
|
|
|
cv::gpu::GpuMat cv::gpu::allocMatFromBuf(int rows, int cols, int type, GpuMat &mat)
|
|
{
|
|
if (!mat.empty() && mat.type() == type && mat.rows >= rows && mat.cols >= cols)
|
|
return mat(Rect(0, 0, cols, rows));
|
|
return mat = GpuMat(rows, cols, type);
|
|
}
|
|
|
|
namespace
|
|
{
|
|
class GpuFuncTable
|
|
{
|
|
public:
|
|
virtual ~GpuFuncTable() {}
|
|
|
|
virtual void copy(const cv::Mat& src, cv::gpu::GpuMat& dst) const = 0;
|
|
virtual void copy(const cv::gpu::GpuMat& src, cv::Mat& dst) const = 0;
|
|
virtual void copy(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst) const = 0;
|
|
|
|
virtual void copyWithMask(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst, const cv::gpu::GpuMat& mask) const = 0;
|
|
|
|
virtual void convert(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst) const = 0;
|
|
virtual void convert(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst, double alpha, double beta) const = 0;
|
|
|
|
virtual void setTo(cv::gpu::GpuMat& m, cv::Scalar s, const cv::gpu::GpuMat& mask) const = 0;
|
|
|
|
virtual void mallocPitch(void** devPtr, size_t* step, size_t width, size_t height) const = 0;
|
|
virtual void free(void* devPtr) const = 0;
|
|
};
|
|
}
|
|
|
|
#if !defined HAVE_CUDA || defined(CUDA_DISABLER_)
|
|
|
|
namespace
|
|
{
|
|
class EmptyFuncTable : public GpuFuncTable
|
|
{
|
|
public:
|
|
void copy(const cv::Mat&, cv::gpu::GpuMat&) const { CV_Error(CV_GpuNotSupported, "The library is compiled without CUDA support"); }
|
|
void copy(const cv::gpu::GpuMat&, cv::Mat&) const { CV_Error(CV_GpuNotSupported, "The library is compiled without CUDA support"); }
|
|
void copy(const cv::gpu::GpuMat&, cv::gpu::GpuMat&) const { CV_Error(CV_GpuNotSupported, "The library is compiled without CUDA support"); }
|
|
|
|
void copyWithMask(const cv::gpu::GpuMat&, cv::gpu::GpuMat&, const cv::gpu::GpuMat&) const { CV_Error(CV_GpuNotSupported, "The library is compiled without CUDA support"); }
|
|
|
|
void convert(const cv::gpu::GpuMat&, cv::gpu::GpuMat&) const { CV_Error(CV_GpuNotSupported, "The library is compiled without CUDA support"); }
|
|
void convert(const cv::gpu::GpuMat&, cv::gpu::GpuMat&, double, double) const { CV_Error(CV_GpuNotSupported, "The library is compiled without CUDA support"); }
|
|
|
|
void setTo(cv::gpu::GpuMat&, cv::Scalar, const cv::gpu::GpuMat&) const { CV_Error(CV_GpuNotSupported, "The library is compiled without CUDA support"); }
|
|
|
|
void mallocPitch(void**, size_t*, size_t, size_t) const { CV_Error(CV_GpuNotSupported, "The library is compiled without CUDA support"); }
|
|
void free(void*) const {}
|
|
};
|
|
|
|
const GpuFuncTable* gpuFuncTable()
|
|
{
|
|
static EmptyFuncTable empty;
|
|
return ∅
|
|
}
|
|
}
|
|
|
|
#else // HAVE_CUDA
|
|
|
|
namespace cv { namespace gpu { namespace device
|
|
{
|
|
void copyToWithMask_gpu(PtrStepSzb src, PtrStepSzb dst, size_t elemSize1, int cn, PtrStepSzb mask, bool colorMask, cudaStream_t stream);
|
|
|
|
template <typename T>
|
|
void set_to_gpu(PtrStepSzb mat, const T* scalar, int channels, cudaStream_t stream);
|
|
|
|
template <typename T>
|
|
void set_to_gpu(PtrStepSzb mat, const T* scalar, PtrStepSzb mask, int channels, cudaStream_t stream);
|
|
|
|
void convert_gpu(PtrStepSzb src, int sdepth, PtrStepSzb dst, int ddepth, double alpha, double beta, cudaStream_t stream);
|
|
}}}
|
|
|
|
namespace
|
|
{
|
|
template <typename T> void kernelSetCaller(cv::gpu::GpuMat& src, cv::Scalar s, cudaStream_t stream)
|
|
{
|
|
cv::Scalar_<T> sf = s;
|
|
cv::gpu::device::set_to_gpu(src, sf.val, src.channels(), stream);
|
|
}
|
|
|
|
template <typename T> void kernelSetCaller(cv::gpu::GpuMat& src, cv::Scalar s, const cv::gpu::GpuMat& mask, cudaStream_t stream)
|
|
{
|
|
cv::Scalar_<T> sf = s;
|
|
cv::gpu::device::set_to_gpu(src, sf.val, mask, src.channels(), stream);
|
|
}
|
|
}
|
|
|
|
|
|
namespace cv { namespace gpu
|
|
{
|
|
CV_EXPORTS void copyWithMask(const cv::gpu::GpuMat&, cv::gpu::GpuMat&, const cv::gpu::GpuMat&, CUstream_st*);
|
|
CV_EXPORTS void convertTo(const cv::gpu::GpuMat&, cv::gpu::GpuMat&);
|
|
CV_EXPORTS void convertTo(const cv::gpu::GpuMat&, cv::gpu::GpuMat&, double, double, CUstream_st*);
|
|
CV_EXPORTS void setTo(cv::gpu::GpuMat&, cv::Scalar, CUstream_st*);
|
|
CV_EXPORTS void setTo(cv::gpu::GpuMat&, cv::Scalar, const cv::gpu::GpuMat&, CUstream_st*);
|
|
CV_EXPORTS void setTo(cv::gpu::GpuMat&, cv::Scalar);
|
|
CV_EXPORTS void setTo(cv::gpu::GpuMat&, cv::Scalar, const cv::gpu::GpuMat&);
|
|
}}
|
|
|
|
|
|
namespace cv { namespace gpu
|
|
{
|
|
void copyWithMask(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream = 0)
|
|
{
|
|
CV_Assert(src.size() == dst.size() && src.type() == dst.type());
|
|
CV_Assert(src.size() == mask.size() && mask.depth() == CV_8U && (mask.channels() == 1 || mask.channels() == src.channels()));
|
|
|
|
cv::gpu::device::copyToWithMask_gpu(src.reshape(1), dst.reshape(1), src.elemSize1(), src.channels(), mask.reshape(1), mask.channels() != 1, stream);
|
|
}
|
|
|
|
void convertTo(const GpuMat& src, GpuMat& dst)
|
|
{
|
|
cv::gpu::device::convert_gpu(src.reshape(1), src.depth(), dst.reshape(1), dst.depth(), 1.0, 0.0, 0);
|
|
}
|
|
|
|
void convertTo(const GpuMat& src, GpuMat& dst, double alpha, double beta, cudaStream_t stream = 0)
|
|
{
|
|
cv::gpu::device::convert_gpu(src.reshape(1), src.depth(), dst.reshape(1), dst.depth(), alpha, beta, stream);
|
|
}
|
|
|
|
void setTo(GpuMat& src, Scalar s, cudaStream_t stream)
|
|
{
|
|
typedef void (*caller_t)(GpuMat& src, Scalar s, cudaStream_t stream);
|
|
|
|
static const caller_t callers[] =
|
|
{
|
|
kernelSetCaller<uchar>, kernelSetCaller<schar>, kernelSetCaller<ushort>, kernelSetCaller<short>, kernelSetCaller<int>,
|
|
kernelSetCaller<float>, kernelSetCaller<double>
|
|
};
|
|
|
|
callers[src.depth()](src, s, stream);
|
|
}
|
|
|
|
void setTo(GpuMat& src, Scalar s, const GpuMat& mask, cudaStream_t stream)
|
|
{
|
|
typedef void (*caller_t)(GpuMat& src, Scalar s, const GpuMat& mask, cudaStream_t stream);
|
|
|
|
static const caller_t callers[] =
|
|
{
|
|
kernelSetCaller<uchar>, kernelSetCaller<schar>, kernelSetCaller<ushort>, kernelSetCaller<short>, kernelSetCaller<int>,
|
|
kernelSetCaller<float>, kernelSetCaller<double>
|
|
};
|
|
|
|
callers[src.depth()](src, s, mask, stream);
|
|
}
|
|
|
|
void setTo(GpuMat& src, Scalar s)
|
|
{
|
|
setTo(src, s, 0);
|
|
}
|
|
|
|
void setTo(GpuMat& src, Scalar s, const GpuMat& mask)
|
|
{
|
|
setTo(src, s, mask, 0);
|
|
}
|
|
}}
|
|
|
|
namespace
|
|
{
|
|
template<int n> struct NPPTypeTraits;
|
|
template<> struct NPPTypeTraits<CV_8U> { typedef Npp8u npp_type; };
|
|
template<> struct NPPTypeTraits<CV_8S> { typedef Npp8s 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<> struct NPPTypeTraits<CV_64F> { typedef Npp64f npp_type; };
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// Convert
|
|
|
|
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 call(const cv::gpu::GpuMat& src, cv::gpu::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 call(const cv::gpu::GpuMat& src, cv::gpu::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() );
|
|
}
|
|
};
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// Set
|
|
|
|
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 SCN> struct NppSetFunc<CV_8S, SCN>
|
|
{
|
|
typedef NppStatus (*func_ptr)(Npp8s values[], Npp8s* pSrc, int nSrcStep, NppiSize oSizeROI);
|
|
};
|
|
template<> struct NppSetFunc<CV_8S, 1>
|
|
{
|
|
typedef NppStatus (*func_ptr)(Npp8s val, Npp8s* 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 call(cv::gpu::GpuMat& src, cv::Scalar s)
|
|
{
|
|
NppiSize sz;
|
|
sz.width = src.cols;
|
|
sz.height = src.rows;
|
|
|
|
cv::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 call(cv::gpu::GpuMat& src, cv::Scalar s)
|
|
{
|
|
NppiSize sz;
|
|
sz.width = src.cols;
|
|
sz.height = src.rows;
|
|
|
|
cv::Scalar_<src_t> nppS = s;
|
|
|
|
nppSafeCall( func(nppS[0], src.ptr<src_t>(), static_cast<int>(src.step), sz) );
|
|
|
|
cudaSafeCall( cudaDeviceSynchronize() );
|
|
}
|
|
};
|
|
|
|
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 call(cv::gpu::GpuMat& src, cv::Scalar s, const cv::gpu::GpuMat& mask)
|
|
{
|
|
NppiSize sz;
|
|
sz.width = src.cols;
|
|
sz.height = src.rows;
|
|
|
|
cv::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 call(cv::gpu::GpuMat& src, cv::Scalar s, const cv::gpu::GpuMat& mask)
|
|
{
|
|
NppiSize sz;
|
|
sz.width = src.cols;
|
|
sz.height = src.rows;
|
|
|
|
cv::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() );
|
|
}
|
|
};
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// CopyMasked
|
|
|
|
template<int SDEPTH> struct NppCopyMaskedFunc
|
|
{
|
|
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
|
|
|
|
typedef NppStatus (*func_ptr)(const src_t* pSrc, int nSrcStep, src_t* pDst, int nDstStep, NppiSize oSizeROI, const Npp8u* pMask, int nMaskStep);
|
|
};
|
|
|
|
template<int SDEPTH, typename NppCopyMaskedFunc<SDEPTH>::func_ptr func> struct NppCopyMasked
|
|
{
|
|
typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t;
|
|
|
|
static void call(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst, const cv::gpu::GpuMat& mask, cudaStream_t /*stream*/)
|
|
{
|
|
NppiSize sz;
|
|
sz.width = src.cols;
|
|
sz.height = src.rows;
|
|
|
|
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), dst.ptr<src_t>(), static_cast<int>(dst.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) );
|
|
|
|
cudaSafeCall( cudaDeviceSynchronize() );
|
|
}
|
|
};
|
|
|
|
template <typename T> static inline bool isAligned(const T* ptr, size_t size)
|
|
{
|
|
return reinterpret_cast<size_t>(ptr) % size == 0;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// CudaFuncTable
|
|
|
|
class CudaFuncTable : public GpuFuncTable
|
|
{
|
|
public:
|
|
void copy(const cv::Mat& src, cv::gpu::GpuMat& dst) const
|
|
{
|
|
cudaSafeCall( cudaMemcpy2D(dst.data, dst.step, src.data, src.step, src.cols * src.elemSize(), src.rows, cudaMemcpyHostToDevice) );
|
|
}
|
|
void copy(const cv::gpu::GpuMat& src, cv::Mat& dst) const
|
|
{
|
|
cudaSafeCall( cudaMemcpy2D(dst.data, dst.step, src.data, src.step, src.cols * src.elemSize(), src.rows, cudaMemcpyDeviceToHost) );
|
|
}
|
|
void copy(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst) const
|
|
{
|
|
cudaSafeCall( cudaMemcpy2D(dst.data, dst.step, src.data, src.step, src.cols * src.elemSize(), src.rows, cudaMemcpyDeviceToDevice) );
|
|
}
|
|
|
|
void copyWithMask(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst, const cv::gpu::GpuMat& mask) const
|
|
{
|
|
CV_Assert(src.depth() <= CV_64F && src.channels() <= 4);
|
|
CV_Assert(src.size() == dst.size() && src.type() == dst.type());
|
|
CV_Assert(src.size() == mask.size() && mask.depth() == CV_8U && (mask.channels() == 1 || mask.channels() == src.channels()));
|
|
|
|
if (src.depth() == CV_64F)
|
|
{
|
|
if (!cv::gpu::TargetArchs::builtWith(cv::gpu::NATIVE_DOUBLE) || !cv::gpu::DeviceInfo().supports(cv::gpu::NATIVE_DOUBLE))
|
|
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
|
|
}
|
|
|
|
typedef void (*func_t)(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst, const cv::gpu::GpuMat& mask, cudaStream_t stream);
|
|
static const func_t funcs[7][4] =
|
|
{
|
|
/* 8U */ {NppCopyMasked<CV_8U , nppiCopy_8u_C1MR >::call, cv::gpu::copyWithMask, NppCopyMasked<CV_8U , nppiCopy_8u_C3MR >::call, NppCopyMasked<CV_8U , nppiCopy_8u_C4MR >::call},
|
|
/* 8S */ {cv::gpu::copyWithMask , cv::gpu::copyWithMask, cv::gpu::copyWithMask , cv::gpu::copyWithMask },
|
|
/* 16U */ {NppCopyMasked<CV_16U, nppiCopy_16u_C1MR>::call, cv::gpu::copyWithMask, NppCopyMasked<CV_16U, nppiCopy_16u_C3MR>::call, NppCopyMasked<CV_16U, nppiCopy_16u_C4MR>::call},
|
|
/* 16S */ {NppCopyMasked<CV_16S, nppiCopy_16s_C1MR>::call, cv::gpu::copyWithMask, NppCopyMasked<CV_16S, nppiCopy_16s_C3MR>::call, NppCopyMasked<CV_16S, nppiCopy_16s_C4MR>::call},
|
|
/* 32S */ {NppCopyMasked<CV_32S, nppiCopy_32s_C1MR>::call, cv::gpu::copyWithMask, NppCopyMasked<CV_32S, nppiCopy_32s_C3MR>::call, NppCopyMasked<CV_32S, nppiCopy_32s_C4MR>::call},
|
|
/* 32F */ {NppCopyMasked<CV_32F, nppiCopy_32f_C1MR>::call, cv::gpu::copyWithMask, NppCopyMasked<CV_32F, nppiCopy_32f_C3MR>::call, NppCopyMasked<CV_32F, nppiCopy_32f_C4MR>::call},
|
|
/* 64F */ {cv::gpu::copyWithMask , cv::gpu::copyWithMask, cv::gpu::copyWithMask , cv::gpu::copyWithMask }
|
|
};
|
|
|
|
const func_t func = mask.channels() == src.channels() ? funcs[src.depth()][src.channels() - 1] : cv::gpu::copyWithMask;
|
|
|
|
func(src, dst, mask, 0);
|
|
}
|
|
|
|
void convert(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst) const
|
|
{
|
|
typedef void (*func_t)(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst);
|
|
static const func_t funcs[7][7][4] =
|
|
{
|
|
{
|
|
/* 8U -> 8U */ {0, 0, 0, 0},
|
|
/* 8U -> 8S */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
|
|
/* 8U -> 16U */ {NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C1R>::call, cv::gpu::convertTo, cv::gpu::convertTo, NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C4R>::call},
|
|
/* 8U -> 16S */ {NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C1R>::call, cv::gpu::convertTo, cv::gpu::convertTo, NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C4R>::call},
|
|
/* 8U -> 32S */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
|
|
/* 8U -> 32F */ {NppCvt<CV_8U, CV_32F, nppiConvert_8u32f_C1R>::call, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
|
|
/* 8U -> 64F */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo }
|
|
},
|
|
{
|
|
/* 8S -> 8U */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 8S -> 8S */ {0,0,0,0},
|
|
/* 8S -> 16U */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 8S -> 16S */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 8S -> 32S */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 8S -> 32F */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 8S -> 64F */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo}
|
|
},
|
|
{
|
|
/* 16U -> 8U */ {NppCvt<CV_16U, CV_8U , nppiConvert_16u8u_C1R >::call, cv::gpu::convertTo, cv::gpu::convertTo, NppCvt<CV_16U, CV_8U, nppiConvert_16u8u_C4R>::call},
|
|
/* 16U -> 8S */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
|
|
/* 16U -> 16U */ {0,0,0,0},
|
|
/* 16U -> 16S */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
|
|
/* 16U -> 32S */ {NppCvt<CV_16U, CV_32S, nppiConvert_16u32s_C1R>::call, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
|
|
/* 16U -> 32F */ {NppCvt<CV_16U, CV_32F, nppiConvert_16u32f_C1R>::call, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
|
|
/* 16U -> 64F */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo }
|
|
},
|
|
{
|
|
/* 16S -> 8U */ {NppCvt<CV_16S, CV_8U , nppiConvert_16s8u_C1R >::call, cv::gpu::convertTo, cv::gpu::convertTo, NppCvt<CV_16S, CV_8U, nppiConvert_16s8u_C4R>::call},
|
|
/* 16S -> 8S */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
|
|
/* 16S -> 16U */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
|
|
/* 16S -> 16S */ {0,0,0,0},
|
|
/* 16S -> 32S */ {NppCvt<CV_16S, CV_32S, nppiConvert_16s32s_C1R>::call, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
|
|
/* 16S -> 32F */ {NppCvt<CV_16S, CV_32F, nppiConvert_16s32f_C1R>::call, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo },
|
|
/* 16S -> 64F */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo }
|
|
},
|
|
{
|
|
/* 32S -> 8U */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 32S -> 8S */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 32S -> 16U */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 32S -> 16S */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 32S -> 32S */ {0,0,0,0},
|
|
/* 32S -> 32F */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 32S -> 64F */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo}
|
|
},
|
|
{
|
|
/* 32F -> 8U */ {NppCvt<CV_32F, CV_8U , nppiConvert_32f8u_C1R >::call, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 32F -> 8S */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 32F -> 16U */ {NppCvt<CV_32F, CV_16U, nppiConvert_32f16u_C1R>::call, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 32F -> 16S */ {NppCvt<CV_32F, CV_16S, nppiConvert_32f16s_C1R>::call, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 32F -> 32S */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 32F -> 32F */ {0,0,0,0},
|
|
/* 32F -> 64F */ {cv::gpu::convertTo , cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo}
|
|
},
|
|
{
|
|
/* 64F -> 8U */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 64F -> 8S */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 64F -> 16U */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 64F -> 16S */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 64F -> 32S */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 64F -> 32F */ {cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo, cv::gpu::convertTo},
|
|
/* 64F -> 64F */ {0,0,0,0}
|
|
}
|
|
};
|
|
|
|
CV_Assert(src.depth() <= CV_64F && src.channels() <= 4);
|
|
CV_Assert(dst.depth() <= CV_64F);
|
|
CV_Assert(src.size() == dst.size() && src.channels() == dst.channels());
|
|
|
|
if (src.depth() == CV_64F || dst.depth() == CV_64F)
|
|
{
|
|
if (!cv::gpu::TargetArchs::builtWith(cv::gpu::NATIVE_DOUBLE) || !cv::gpu::DeviceInfo().supports(cv::gpu::NATIVE_DOUBLE))
|
|
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
|
|
}
|
|
|
|
bool aligned = isAligned(src.data, 16) && isAligned(dst.data, 16);
|
|
if (!aligned)
|
|
{
|
|
cv::gpu::convertTo(src, dst);
|
|
return;
|
|
}
|
|
|
|
const func_t func = funcs[src.depth()][dst.depth()][src.channels() - 1];
|
|
CV_DbgAssert(func != 0);
|
|
|
|
func(src, dst);
|
|
}
|
|
|
|
void convert(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst, double alpha, double beta) const
|
|
{
|
|
CV_Assert(src.depth() <= CV_64F && src.channels() <= 4);
|
|
CV_Assert(dst.depth() <= CV_64F);
|
|
|
|
if (src.depth() == CV_64F || dst.depth() == CV_64F)
|
|
{
|
|
if (!cv::gpu::TargetArchs::builtWith(cv::gpu::NATIVE_DOUBLE) || !cv::gpu::DeviceInfo().supports(cv::gpu::NATIVE_DOUBLE))
|
|
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
|
|
}
|
|
|
|
cv::gpu::convertTo(src, dst, alpha, beta);
|
|
}
|
|
|
|
void setTo(cv::gpu::GpuMat& m, cv::Scalar s, const cv::gpu::GpuMat& mask) const
|
|
{
|
|
if (mask.empty())
|
|
{
|
|
if (s[0] == 0.0 && s[1] == 0.0 && s[2] == 0.0 && s[3] == 0.0)
|
|
{
|
|
cudaSafeCall( cudaMemset2D(m.data, m.step, 0, m.cols * m.elemSize(), m.rows) );
|
|
return;
|
|
}
|
|
|
|
if (m.depth() == CV_8U)
|
|
{
|
|
int cn = m.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 = cv::saturate_cast<uchar>(s[0]);
|
|
cudaSafeCall( cudaMemset2D(m.data, m.step, val, m.cols * m.elemSize(), m.rows) );
|
|
return;
|
|
}
|
|
}
|
|
|
|
typedef void (*func_t)(cv::gpu::GpuMat& src, cv::Scalar s);
|
|
static const func_t funcs[7][4] =
|
|
{
|
|
{NppSet<CV_8U , 1, nppiSet_8u_C1R >::call, cv::gpu::setTo , cv::gpu::setTo , NppSet<CV_8U , 4, nppiSet_8u_C4R >::call},
|
|
{NppSet<CV_8S , 1, nppiSet_8s_C1R >::call, NppSet<CV_8S , 2, nppiSet_8s_C2R >::call, NppSet<CV_8S, 3, nppiSet_8s_C3R>::call, NppSet<CV_8S , 4, nppiSet_8s_C4R >::call},
|
|
{NppSet<CV_16U, 1, nppiSet_16u_C1R>::call, NppSet<CV_16U, 2, nppiSet_16u_C2R>::call, cv::gpu::setTo , NppSet<CV_16U, 4, nppiSet_16u_C4R>::call},
|
|
{NppSet<CV_16S, 1, nppiSet_16s_C1R>::call, NppSet<CV_16S, 2, nppiSet_16s_C2R>::call, cv::gpu::setTo , NppSet<CV_16S, 4, nppiSet_16s_C4R>::call},
|
|
{NppSet<CV_32S, 1, nppiSet_32s_C1R>::call, cv::gpu::setTo , cv::gpu::setTo , NppSet<CV_32S, 4, nppiSet_32s_C4R>::call},
|
|
{NppSet<CV_32F, 1, nppiSet_32f_C1R>::call, cv::gpu::setTo , cv::gpu::setTo , NppSet<CV_32F, 4, nppiSet_32f_C4R>::call},
|
|
{cv::gpu::setTo , cv::gpu::setTo , cv::gpu::setTo , cv::gpu::setTo }
|
|
};
|
|
|
|
CV_Assert(m.depth() <= CV_64F && m.channels() <= 4);
|
|
|
|
if (m.depth() == CV_64F)
|
|
{
|
|
if (!cv::gpu::TargetArchs::builtWith(cv::gpu::NATIVE_DOUBLE) || !cv::gpu::DeviceInfo().supports(cv::gpu::NATIVE_DOUBLE))
|
|
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
|
|
}
|
|
|
|
funcs[m.depth()][m.channels() - 1](m, s);
|
|
}
|
|
else
|
|
{
|
|
typedef void (*func_t)(cv::gpu::GpuMat& src, cv::Scalar s, const cv::gpu::GpuMat& mask);
|
|
static const func_t funcs[7][4] =
|
|
{
|
|
{NppSetMask<CV_8U , 1, nppiSet_8u_C1MR >::call, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_8U , 4, nppiSet_8u_C4MR >::call},
|
|
{cv::gpu::setTo , cv::gpu::setTo, cv::gpu::setTo, cv::gpu::setTo },
|
|
{NppSetMask<CV_16U, 1, nppiSet_16u_C1MR>::call, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_16U, 4, nppiSet_16u_C4MR>::call},
|
|
{NppSetMask<CV_16S, 1, nppiSet_16s_C1MR>::call, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_16S, 4, nppiSet_16s_C4MR>::call},
|
|
{NppSetMask<CV_32S, 1, nppiSet_32s_C1MR>::call, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_32S, 4, nppiSet_32s_C4MR>::call},
|
|
{NppSetMask<CV_32F, 1, nppiSet_32f_C1MR>::call, cv::gpu::setTo, cv::gpu::setTo, NppSetMask<CV_32F, 4, nppiSet_32f_C4MR>::call},
|
|
{cv::gpu::setTo , cv::gpu::setTo, cv::gpu::setTo, cv::gpu::setTo }
|
|
};
|
|
|
|
CV_Assert(m.depth() <= CV_64F && m.channels() <= 4);
|
|
|
|
if (m.depth() == CV_64F)
|
|
{
|
|
if (!cv::gpu::TargetArchs::builtWith(cv::gpu::NATIVE_DOUBLE) || !cv::gpu::DeviceInfo().supports(cv::gpu::NATIVE_DOUBLE))
|
|
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
|
|
}
|
|
|
|
funcs[m.depth()][m.channels() - 1](m, s, mask);
|
|
}
|
|
}
|
|
|
|
void mallocPitch(void** devPtr, size_t* step, size_t width, size_t height) const
|
|
{
|
|
cudaSafeCall( cudaMallocPitch(devPtr, step, width, height) );
|
|
}
|
|
|
|
void free(void* devPtr) const
|
|
{
|
|
cudaFree(devPtr);
|
|
}
|
|
};
|
|
|
|
const GpuFuncTable* gpuFuncTable()
|
|
{
|
|
static CudaFuncTable funcTable;
|
|
return &funcTable;
|
|
}
|
|
}
|
|
|
|
#endif // HAVE_CUDA
|
|
|
|
void cv::gpu::GpuMat::upload(const cv::Mat& m)
|
|
{
|
|
CV_DbgAssert(!m.empty());
|
|
|
|
create(m.size(), m.type());
|
|
|
|
gpuFuncTable()->copy(m, *this);
|
|
}
|
|
|
|
void cv::gpu::GpuMat::download(cv::Mat& m) const
|
|
{
|
|
CV_DbgAssert(!empty());
|
|
|
|
m.create(size(), type());
|
|
|
|
gpuFuncTable()->copy(*this, m);
|
|
}
|
|
|
|
void cv::gpu::GpuMat::copyTo(cv::gpu::GpuMat& m) const
|
|
{
|
|
CV_DbgAssert(!empty());
|
|
|
|
m.create(size(), type());
|
|
|
|
gpuFuncTable()->copy(*this, m);
|
|
}
|
|
|
|
void cv::gpu::GpuMat::copyTo(cv::gpu::GpuMat& mat, const cv::gpu::GpuMat& mask) const
|
|
{
|
|
if (mask.empty())
|
|
copyTo(mat);
|
|
else
|
|
{
|
|
mat.create(size(), type());
|
|
|
|
gpuFuncTable()->copyWithMask(*this, mat, mask);
|
|
}
|
|
}
|
|
|
|
void cv::gpu::GpuMat::convertTo(cv::gpu::GpuMat& dst, int rtype, double alpha, double beta) const
|
|
{
|
|
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 sdepth = depth();
|
|
int ddepth = CV_MAT_DEPTH(rtype);
|
|
if (sdepth == ddepth && noScale)
|
|
{
|
|
copyTo(dst);
|
|
return;
|
|
}
|
|
|
|
cv::gpu::GpuMat temp;
|
|
const cv::gpu::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);
|
|
}
|
|
|
|
cv::gpu::GpuMat& cv::gpu::GpuMat::setTo(cv::Scalar s, const cv::gpu::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 = cv::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 |= cv::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;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// Error handling
|
|
|
|
void cv::gpu::error(const char *error_string, const char *file, const int line, const char *func)
|
|
{
|
|
int code = CV_GpuApiCallError;
|
|
|
|
if (std::uncaught_exception())
|
|
{
|
|
const char* errorStr = cvErrorStr(code);
|
|
const char* function = func ? func : "unknown function";
|
|
|
|
std::cerr << "OpenCV Error: " << errorStr << "(" << error_string << ") in " << function << ", file " << file << ", line " << line;
|
|
std::cerr.flush();
|
|
}
|
|
else
|
|
cv::error( cv::Exception(code, error_string, func, file, line) );
|
|
}
|