1 /*M///////////////////////////////////////////////////////////////////////////////////////
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3 // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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5 // By downloading, copying, installing or using the software you agree to this license.
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6 // If you do not agree to this license, do not download, install,
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7 // copy or use the software.
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10 // License Agreement
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11 // For Open Source Computer Vision Library
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13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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14 // Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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15 // Third party copyrights are property of their respective owners.
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17 // Redistribution and use in source and binary forms, with or without modification,
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18 // are permitted provided that the following conditions are met:
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20 // * Redistribution's of source code must retain the above copyright notice,
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21 // this list of conditions and the following disclaimer.
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23 // * Redistribution's in binary form must reproduce the above copyright notice,
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24 // this list of conditions and the following disclaimer in the documentation
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25 // and/or other GpuMaterials provided with the distribution.
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27 // * The name of the copyright holders may not be used to endorse or promote products
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28 // derived from this software without specific prior written permission.
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30 // This software is provided by the copyright holders and contributors "as is" and
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31 // any express or implied warranties, including, but not limited to, the implied
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32 // warranties of merchantability and fitness for a particular purpose are disclaimed.
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33 // In no event shall the Intel Corporation or contributors be liable for any direct,
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35 // (including, but not limited to, procurement of substitute goods or services;
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36 // loss of use, data, or profits; or business interruption) however caused
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37 // and on any theory of liability, whether in contract, strict liability,
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38 // or tort (including negligence or otherwise) arising in any way out of
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39 // the use of this software, even if advised of the possibility of such damage.
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43 #ifndef __OPENCV_GPU_HPP__
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44 #define __OPENCV_GPU_HPP__
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46 #ifndef SKIP_INCLUDES
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52 #include "opencv2/core/gpumat.hpp"
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53 #include "opencv2/imgproc/imgproc.hpp"
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54 #include "opencv2/objdetect/objdetect.hpp"
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55 #include "opencv2/features2d/features2d.hpp"
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57 namespace cv { namespace gpu {
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59 //////////////////////////////// CudaMem ////////////////////////////////
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60 // CudaMem is limited cv::Mat with page locked memory allocation.
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61 // Page locked memory is only needed for async and faster coping to GPU.
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62 // It is convertable to cv::Mat header without reference counting
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63 // so you can use it with other opencv functions.
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65 // Page-locks the matrix m memory and maps it for the device(s)
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66 CV_EXPORTS void registerPageLocked(Mat& m);
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67 // Unmaps the memory of matrix m, and makes it pageable again.
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68 CV_EXPORTS void unregisterPageLocked(Mat& m);
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70 class CV_EXPORTS CudaMem
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73 enum { ALLOC_PAGE_LOCKED = 1, ALLOC_ZEROCOPY = 2, ALLOC_WRITE_COMBINED = 4 };
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76 CudaMem(const CudaMem& m);
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78 CudaMem(int rows, int cols, int type, int _alloc_type = ALLOC_PAGE_LOCKED);
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79 CudaMem(Size size, int type, int alloc_type = ALLOC_PAGE_LOCKED);
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82 //! creates from cv::Mat with coping data
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83 explicit CudaMem(const Mat& m, int alloc_type = ALLOC_PAGE_LOCKED);
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87 CudaMem& operator = (const CudaMem& m);
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89 //! returns deep copy of the matrix, i.e. the data is copied
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90 CudaMem clone() const;
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92 //! allocates new matrix data unless the matrix already has specified size and type.
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93 void create(int rows, int cols, int type, int alloc_type = ALLOC_PAGE_LOCKED);
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94 void create(Size size, int type, int alloc_type = ALLOC_PAGE_LOCKED);
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96 //! decrements reference counter and released memory if needed.
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99 //! returns matrix header with disabled reference counting for CudaMem data.
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100 Mat createMatHeader() const;
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101 operator Mat() const;
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103 //! maps host memory into device address space and returns GpuMat header for it. Throws exception if not supported by hardware.
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104 GpuMat createGpuMatHeader() const;
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105 operator GpuMat() const;
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107 //returns if host memory can be mapperd to gpu address space;
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108 static bool canMapHostMemory();
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110 // Please see cv::Mat for descriptions
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111 bool isContinuous() const;
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112 size_t elemSize() const;
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113 size_t elemSize1() const;
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116 int channels() const;
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117 size_t step1() const;
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119 bool empty() const;
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122 // Please see cv::Mat for descriptions
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136 //////////////////////////////// CudaStream ////////////////////////////////
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137 // Encapculates Cuda Stream. Provides interface for async coping.
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138 // Passed to each function that supports async kernel execution.
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139 // Reference counting is enabled
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141 class CV_EXPORTS Stream
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147 Stream(const Stream&);
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148 Stream& operator=(const Stream&);
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150 bool queryIfComplete();
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151 void waitForCompletion();
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153 //! downloads asynchronously.
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154 // Warning! cv::Mat must point to page locked memory (i.e. to CudaMem data or to its subMat)
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155 void enqueueDownload(const GpuMat& src, CudaMem& dst);
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156 void enqueueDownload(const GpuMat& src, Mat& dst);
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158 //! uploads asynchronously.
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159 // Warning! cv::Mat must point to page locked memory (i.e. to CudaMem data or to its ROI)
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160 void enqueueUpload(const CudaMem& src, GpuMat& dst);
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161 void enqueueUpload(const Mat& src, GpuMat& dst);
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163 void enqueueCopy(const GpuMat& src, GpuMat& dst);
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165 void enqueueMemSet(GpuMat& src, Scalar val);
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166 void enqueueMemSet(GpuMat& src, Scalar val, const GpuMat& mask);
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168 // converts matrix type, ex from float to uchar depending on type
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169 void enqueueConvert(const GpuMat& src, GpuMat& dst, int type, double a = 1, double b = 0);
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171 static Stream& Null();
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173 operator bool() const;
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182 friend struct StreamAccessor;
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184 explicit Stream(Impl* impl);
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188 //////////////////////////////// Filter Engine ////////////////////////////////
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191 The Base Class for 1D or Row-wise Filters
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193 This is the base class for linear or non-linear filters that process 1D data.
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194 In particular, such filters are used for the "horizontal" filtering parts in separable filters.
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196 class CV_EXPORTS BaseRowFilter_GPU
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199 BaseRowFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {}
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200 virtual ~BaseRowFilter_GPU() {}
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201 virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
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206 The Base Class for Column-wise Filters
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208 This is the base class for linear or non-linear filters that process columns of 2D arrays.
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209 Such filters are used for the "vertical" filtering parts in separable filters.
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211 class CV_EXPORTS BaseColumnFilter_GPU
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214 BaseColumnFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {}
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215 virtual ~BaseColumnFilter_GPU() {}
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216 virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
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221 The Base Class for Non-Separable 2D Filters.
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223 This is the base class for linear or non-linear 2D filters.
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225 class CV_EXPORTS BaseFilter_GPU
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228 BaseFilter_GPU(const Size& ksize_, const Point& anchor_) : ksize(ksize_), anchor(anchor_) {}
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229 virtual ~BaseFilter_GPU() {}
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230 virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
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236 The Base Class for Filter Engine.
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238 The class can be used to apply an arbitrary filtering operation to an image.
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239 It contains all the necessary intermediate buffers.
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241 class CV_EXPORTS FilterEngine_GPU
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244 virtual ~FilterEngine_GPU() {}
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246 virtual void apply(const GpuMat& src, GpuMat& dst, Rect roi = Rect(0,0,-1,-1), Stream& stream = Stream::Null()) = 0;
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249 //! returns the non-separable filter engine with the specified filter
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250 CV_EXPORTS Ptr<FilterEngine_GPU> createFilter2D_GPU(const Ptr<BaseFilter_GPU>& filter2D, int srcType, int dstType);
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252 //! returns the separable filter engine with the specified filters
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253 CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter,
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254 const Ptr<BaseColumnFilter_GPU>& columnFilter, int srcType, int bufType, int dstType);
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255 CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter,
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256 const Ptr<BaseColumnFilter_GPU>& columnFilter, int srcType, int bufType, int dstType, GpuMat& buf);
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258 //! returns horizontal 1D box filter
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259 //! supports only CV_8UC1 source type and CV_32FC1 sum type
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260 CV_EXPORTS Ptr<BaseRowFilter_GPU> getRowSumFilter_GPU(int srcType, int sumType, int ksize, int anchor = -1);
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262 //! returns vertical 1D box filter
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263 //! supports only CV_8UC1 sum type and CV_32FC1 dst type
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264 CV_EXPORTS Ptr<BaseColumnFilter_GPU> getColumnSumFilter_GPU(int sumType, int dstType, int ksize, int anchor = -1);
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266 //! returns 2D box filter
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267 //! supports CV_8UC1 and CV_8UC4 source type, dst type must be the same as source type
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268 CV_EXPORTS Ptr<BaseFilter_GPU> getBoxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1, -1));
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270 //! returns box filter engine
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271 CV_EXPORTS Ptr<FilterEngine_GPU> createBoxFilter_GPU(int srcType, int dstType, const Size& ksize,
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272 const Point& anchor = Point(-1,-1));
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274 //! returns 2D morphological filter
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275 //! only MORPH_ERODE and MORPH_DILATE are supported
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276 //! supports CV_8UC1 and CV_8UC4 types
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277 //! kernel must have CV_8UC1 type, one rows and cols == ksize.width * ksize.height
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278 CV_EXPORTS Ptr<BaseFilter_GPU> getMorphologyFilter_GPU(int op, int type, const Mat& kernel, const Size& ksize,
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279 Point anchor=Point(-1,-1));
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281 //! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported.
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282 CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat& kernel,
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283 const Point& anchor = Point(-1,-1), int iterations = 1);
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284 CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat& kernel, GpuMat& buf,
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285 const Point& anchor = Point(-1,-1), int iterations = 1);
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287 //! returns 2D filter with the specified kernel
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288 //! supports CV_8U, CV_16U and CV_32F one and four channel image
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289 CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
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291 //! returns the non-separable linear filter engine
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292 CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat& kernel,
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293 Point anchor = Point(-1,-1), int borderType = BORDER_DEFAULT);
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295 //! returns the primitive row filter with the specified kernel.
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296 //! supports only CV_8UC1, CV_8UC4, CV_16SC1, CV_16SC2, CV_32SC1, CV_32FC1 source type.
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297 //! there are two version of algorithm: NPP and OpenCV.
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298 //! NPP calls when srcType == CV_8UC1 or srcType == CV_8UC4 and bufType == srcType,
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299 //! otherwise calls OpenCV version.
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300 //! NPP supports only BORDER_CONSTANT border type.
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301 //! OpenCV version supports only CV_32F as buffer depth and
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302 //! BORDER_REFLECT101, BORDER_REPLICATE and BORDER_CONSTANT border types.
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303 CV_EXPORTS Ptr<BaseRowFilter_GPU> getLinearRowFilter_GPU(int srcType, int bufType, const Mat& rowKernel,
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304 int anchor = -1, int borderType = BORDER_DEFAULT);
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306 //! returns the primitive column filter with the specified kernel.
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307 //! supports only CV_8UC1, CV_8UC4, CV_16SC1, CV_16SC2, CV_32SC1, CV_32FC1 dst type.
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308 //! there are two version of algorithm: NPP and OpenCV.
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309 //! NPP calls when dstType == CV_8UC1 or dstType == CV_8UC4 and bufType == dstType,
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310 //! otherwise calls OpenCV version.
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311 //! NPP supports only BORDER_CONSTANT border type.
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312 //! OpenCV version supports only CV_32F as buffer depth and
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313 //! BORDER_REFLECT101, BORDER_REPLICATE and BORDER_CONSTANT border types.
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314 CV_EXPORTS Ptr<BaseColumnFilter_GPU> getLinearColumnFilter_GPU(int bufType, int dstType, const Mat& columnKernel,
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315 int anchor = -1, int borderType = BORDER_DEFAULT);
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317 //! returns the separable linear filter engine
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318 CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel,
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319 const Mat& columnKernel, const Point& anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT,
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320 int columnBorderType = -1);
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321 CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel,
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322 const Mat& columnKernel, GpuMat& buf, const Point& anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT,
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323 int columnBorderType = -1);
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325 //! returns filter engine for the generalized Sobel operator
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326 CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU(int srcType, int dstType, int dx, int dy, int ksize,
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327 int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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328 CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU(int srcType, int dstType, int dx, int dy, int ksize, GpuMat& buf,
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329 int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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331 //! returns the Gaussian filter engine
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332 CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, double sigma1, double sigma2 = 0,
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333 int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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334 CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, GpuMat& buf, double sigma1, double sigma2 = 0,
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335 int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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337 //! returns maximum filter
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338 CV_EXPORTS Ptr<BaseFilter_GPU> getMaxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1));
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340 //! returns minimum filter
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341 CV_EXPORTS Ptr<BaseFilter_GPU> getMinFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1));
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343 //! smooths the image using the normalized box filter
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344 //! supports CV_8UC1, CV_8UC4 types
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345 CV_EXPORTS void boxFilter(const GpuMat& src, GpuMat& dst, int ddepth, Size ksize, Point anchor = Point(-1,-1), Stream& stream = Stream::Null());
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347 //! a synonym for normalized box filter
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348 static inline void blur(const GpuMat& src, GpuMat& dst, Size ksize, Point anchor = Point(-1,-1), Stream& stream = Stream::Null())
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350 boxFilter(src, dst, -1, ksize, anchor, stream);
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353 //! erodes the image (applies the local minimum operator)
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354 CV_EXPORTS void erode(const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1);
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355 CV_EXPORTS void erode(const GpuMat& src, GpuMat& dst, const Mat& kernel, GpuMat& buf,
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356 Point anchor = Point(-1, -1), int iterations = 1,
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357 Stream& stream = Stream::Null());
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359 //! dilates the image (applies the local maximum operator)
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360 CV_EXPORTS void dilate(const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1);
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361 CV_EXPORTS void dilate(const GpuMat& src, GpuMat& dst, const Mat& kernel, GpuMat& buf,
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362 Point anchor = Point(-1, -1), int iterations = 1,
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363 Stream& stream = Stream::Null());
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365 //! applies an advanced morphological operation to the image
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366 CV_EXPORTS void morphologyEx(const GpuMat& src, GpuMat& dst, int op, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1);
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367 CV_EXPORTS void morphologyEx(const GpuMat& src, GpuMat& dst, int op, const Mat& kernel, GpuMat& buf1, GpuMat& buf2,
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368 Point anchor = Point(-1, -1), int iterations = 1, Stream& stream = Stream::Null());
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370 //! applies non-separable 2D linear filter to the image
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371 CV_EXPORTS void filter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernel, Point anchor=Point(-1,-1), int borderType = BORDER_DEFAULT, Stream& stream = Stream::Null());
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373 //! applies separable 2D linear filter to the image
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374 CV_EXPORTS void sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY,
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375 Point anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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376 CV_EXPORTS void sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY, GpuMat& buf,
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377 Point anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1,
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378 Stream& stream = Stream::Null());
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380 //! applies generalized Sobel operator to the image
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381 CV_EXPORTS void Sobel(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, int ksize = 3, double scale = 1,
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382 int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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383 CV_EXPORTS void Sobel(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, GpuMat& buf, int ksize = 3, double scale = 1,
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384 int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());
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386 //! applies the vertical or horizontal Scharr operator to the image
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387 CV_EXPORTS void Scharr(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, double scale = 1,
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388 int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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389 CV_EXPORTS void Scharr(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, GpuMat& buf, double scale = 1,
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390 int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());
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392 //! smooths the image using Gaussian filter.
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393 CV_EXPORTS void GaussianBlur(const GpuMat& src, GpuMat& dst, Size ksize, double sigma1, double sigma2 = 0,
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394 int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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395 CV_EXPORTS void GaussianBlur(const GpuMat& src, GpuMat& dst, Size ksize, GpuMat& buf, double sigma1, double sigma2 = 0,
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396 int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());
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398 //! applies Laplacian operator to the image
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399 //! supports only ksize = 1 and ksize = 3
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400 CV_EXPORTS void Laplacian(const GpuMat& src, GpuMat& dst, int ddepth, int ksize = 1, double scale = 1, int borderType = BORDER_DEFAULT, Stream& stream = Stream::Null());
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403 ////////////////////////////// Arithmetics ///////////////////////////////////
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405 //! implements generalized matrix product algorithm GEMM from BLAS
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406 CV_EXPORTS void gemm(const GpuMat& src1, const GpuMat& src2, double alpha,
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407 const GpuMat& src3, double beta, GpuMat& dst, int flags = 0, Stream& stream = Stream::Null());
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409 //! transposes the matrix
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410 //! supports matrix with element size = 1, 4 and 8 bytes (CV_8UC1, CV_8UC4, CV_16UC2, CV_32FC1, etc)
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411 CV_EXPORTS void transpose(const GpuMat& src1, GpuMat& dst, Stream& stream = Stream::Null());
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413 //! reverses the order of the rows, columns or both in a matrix
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414 //! supports 1, 3 and 4 channels images with CV_8U, CV_16U, CV_32S or CV_32F depth
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415 CV_EXPORTS void flip(const GpuMat& a, GpuMat& b, int flipCode, Stream& stream = Stream::Null());
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417 //! transforms 8-bit unsigned integers using lookup table: dst(i)=lut(src(i))
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418 //! destination array will have the depth type as lut and the same channels number as source
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419 //! supports CV_8UC1, CV_8UC3 types
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420 CV_EXPORTS void LUT(const GpuMat& src, const Mat& lut, GpuMat& dst, Stream& stream = Stream::Null());
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422 //! makes multi-channel array out of several single-channel arrays
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423 CV_EXPORTS void merge(const GpuMat* src, size_t n, GpuMat& dst, Stream& stream = Stream::Null());
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425 //! makes multi-channel array out of several single-channel arrays
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426 CV_EXPORTS void merge(const vector<GpuMat>& src, GpuMat& dst, Stream& stream = Stream::Null());
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428 //! copies each plane of a multi-channel array to a dedicated array
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429 CV_EXPORTS void split(const GpuMat& src, GpuMat* dst, Stream& stream = Stream::Null());
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431 //! copies each plane of a multi-channel array to a dedicated array
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432 CV_EXPORTS void split(const GpuMat& src, vector<GpuMat>& dst, Stream& stream = Stream::Null());
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434 //! computes magnitude of complex (x(i).re, x(i).im) vector
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435 //! supports only CV_32FC2 type
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436 CV_EXPORTS void magnitude(const GpuMat& xy, GpuMat& magnitude, Stream& stream = Stream::Null());
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438 //! computes squared magnitude of complex (x(i).re, x(i).im) vector
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439 //! supports only CV_32FC2 type
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440 CV_EXPORTS void magnitudeSqr(const GpuMat& xy, GpuMat& magnitude, Stream& stream = Stream::Null());
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442 //! computes magnitude of each (x(i), y(i)) vector
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443 //! supports only floating-point source
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444 CV_EXPORTS void magnitude(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, Stream& stream = Stream::Null());
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446 //! computes squared magnitude of each (x(i), y(i)) vector
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447 //! supports only floating-point source
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448 CV_EXPORTS void magnitudeSqr(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, Stream& stream = Stream::Null());
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450 //! computes angle (angle(i)) of each (x(i), y(i)) vector
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451 //! supports only floating-point source
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452 CV_EXPORTS void phase(const GpuMat& x, const GpuMat& y, GpuMat& angle, bool angleInDegrees = false, Stream& stream = Stream::Null());
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454 //! converts Cartesian coordinates to polar
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455 //! supports only floating-point source
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456 CV_EXPORTS void cartToPolar(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, GpuMat& angle, bool angleInDegrees = false, Stream& stream = Stream::Null());
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458 //! converts polar coordinates to Cartesian
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459 //! supports only floating-point source
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460 CV_EXPORTS void polarToCart(const GpuMat& magnitude, const GpuMat& angle, GpuMat& x, GpuMat& y, bool angleInDegrees = false, Stream& stream = Stream::Null());
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463 //////////////////////////// Per-element operations ////////////////////////////////////
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465 //! adds one matrix to another (c = a + b)
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466 CV_EXPORTS void add(const GpuMat& a, const GpuMat& b, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
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467 //! adds scalar to a matrix (c = a + s)
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468 CV_EXPORTS void add(const GpuMat& a, const Scalar& sc, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
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470 //! subtracts one matrix from another (c = a - b)
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471 CV_EXPORTS void subtract(const GpuMat& a, const GpuMat& b, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
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472 //! subtracts scalar from a matrix (c = a - s)
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473 CV_EXPORTS void subtract(const GpuMat& a, const Scalar& sc, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
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475 //! computes element-wise weighted product of the two arrays (c = scale * a * b)
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476 CV_EXPORTS void multiply(const GpuMat& a, const GpuMat& b, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
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477 //! weighted multiplies matrix to a scalar (c = scale * a * s)
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478 CV_EXPORTS void multiply(const GpuMat& a, const Scalar& sc, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
\r
480 //! computes element-wise weighted quotient of the two arrays (c = a / b)
\r
481 CV_EXPORTS void divide(const GpuMat& a, const GpuMat& b, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
\r
482 //! computes element-wise weighted quotient of matrix and scalar (c = a / s)
\r
483 CV_EXPORTS void divide(const GpuMat& a, const Scalar& sc, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
\r
484 //! computes element-wise weighted reciprocal of an array (dst = scale/src2)
\r
485 CV_EXPORTS void divide(double scale, const GpuMat& b, GpuMat& c, int dtype = -1, Stream& stream = Stream::Null());
\r
487 //! computes the weighted sum of two arrays (dst = alpha*src1 + beta*src2 + gamma)
\r
488 CV_EXPORTS void addWeighted(const GpuMat& src1, double alpha, const GpuMat& src2, double beta, double gamma, GpuMat& dst,
\r
489 int dtype = -1, Stream& stream = Stream::Null());
\r
491 //! adds scaled array to another one (dst = alpha*src1 + src2)
\r
492 static inline void scaleAdd(const GpuMat& src1, double alpha, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null())
\r
494 addWeighted(src1, alpha, src2, 1.0, 0.0, dst, -1, stream);
\r
497 //! computes element-wise absolute difference of two arrays (c = abs(a - b))
\r
498 CV_EXPORTS void absdiff(const GpuMat& a, const GpuMat& b, GpuMat& c, Stream& stream = Stream::Null());
\r
499 //! computes element-wise absolute difference of array and scalar (c = abs(a - s))
\r
500 CV_EXPORTS void absdiff(const GpuMat& a, const Scalar& s, GpuMat& c, Stream& stream = Stream::Null());
\r
502 //! computes absolute value of each matrix element
\r
503 //! supports CV_16S and CV_32F depth
\r
504 CV_EXPORTS void abs(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
\r
506 //! computes square of each pixel in an image
\r
507 //! supports CV_8U, CV_16U, CV_16S and CV_32F depth
\r
508 CV_EXPORTS void sqr(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
\r
510 //! computes square root of each pixel in an image
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511 //! supports CV_8U, CV_16U, CV_16S and CV_32F depth
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512 CV_EXPORTS void sqrt(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
\r
514 //! computes exponent of each matrix element (b = e**a)
\r
515 //! supports CV_8U, CV_16U, CV_16S and CV_32F depth
\r
516 CV_EXPORTS void exp(const GpuMat& a, GpuMat& b, Stream& stream = Stream::Null());
\r
518 //! computes natural logarithm of absolute value of each matrix element: b = log(abs(a))
\r
519 //! supports CV_8U, CV_16U, CV_16S and CV_32F depth
\r
520 CV_EXPORTS void log(const GpuMat& a, GpuMat& b, Stream& stream = Stream::Null());
\r
522 //! computes power of each matrix element:
\r
523 // (dst(i,j) = pow( src(i,j) , power), if src.type() is integer
\r
524 // (dst(i,j) = pow(fabs(src(i,j)), power), otherwise
\r
525 //! supports all, except depth == CV_64F
\r
526 CV_EXPORTS void pow(const GpuMat& src, double power, GpuMat& dst, Stream& stream = Stream::Null());
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528 //! compares elements of two arrays (c = a <cmpop> b)
\r
529 CV_EXPORTS void compare(const GpuMat& a, const GpuMat& b, GpuMat& c, int cmpop, Stream& stream = Stream::Null());
\r
530 CV_EXPORTS void compare(const GpuMat& a, Scalar sc, GpuMat& c, int cmpop, Stream& stream = Stream::Null());
\r
532 //! performs per-elements bit-wise inversion
\r
533 CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
\r
535 //! calculates per-element bit-wise disjunction of two arrays
\r
536 CV_EXPORTS void bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
\r
537 //! calculates per-element bit-wise disjunction of array and scalar
\r
538 //! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
\r
539 CV_EXPORTS void bitwise_or(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
\r
541 //! calculates per-element bit-wise conjunction of two arrays
\r
542 CV_EXPORTS void bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
\r
543 //! calculates per-element bit-wise conjunction of array and scalar
\r
544 //! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
\r
545 CV_EXPORTS void bitwise_and(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
\r
547 //! calculates per-element bit-wise "exclusive or" operation
\r
548 CV_EXPORTS void bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
\r
549 //! calculates per-element bit-wise "exclusive or" of array and scalar
\r
550 //! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
\r
551 CV_EXPORTS void bitwise_xor(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
\r
553 //! pixel by pixel right shift of an image by a constant value
\r
554 //! supports 1, 3 and 4 channels images with integers elements
\r
555 CV_EXPORTS void rshift(const GpuMat& src, Scalar_<int> sc, GpuMat& dst, Stream& stream = Stream::Null());
\r
557 //! pixel by pixel left shift of an image by a constant value
\r
558 //! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
\r
559 CV_EXPORTS void lshift(const GpuMat& src, Scalar_<int> sc, GpuMat& dst, Stream& stream = Stream::Null());
\r
561 //! computes per-element minimum of two arrays (dst = min(src1, src2))
\r
562 CV_EXPORTS void min(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null());
\r
564 //! computes per-element minimum of array and scalar (dst = min(src1, src2))
\r
565 CV_EXPORTS void min(const GpuMat& src1, double src2, GpuMat& dst, Stream& stream = Stream::Null());
\r
567 //! computes per-element maximum of two arrays (dst = max(src1, src2))
\r
568 CV_EXPORTS void max(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null());
\r
570 //! computes per-element maximum of array and scalar (dst = max(src1, src2))
\r
571 CV_EXPORTS void max(const GpuMat& src1, double src2, GpuMat& dst, Stream& stream = Stream::Null());
\r
573 enum { ALPHA_OVER, ALPHA_IN, ALPHA_OUT, ALPHA_ATOP, ALPHA_XOR, ALPHA_PLUS, ALPHA_OVER_PREMUL, ALPHA_IN_PREMUL, ALPHA_OUT_PREMUL,
\r
574 ALPHA_ATOP_PREMUL, ALPHA_XOR_PREMUL, ALPHA_PLUS_PREMUL, ALPHA_PREMUL};
\r
576 //! Composite two images using alpha opacity values contained in each image
\r
577 //! Supports CV_8UC4, CV_16UC4, CV_32SC4 and CV_32FC4 types
\r
578 CV_EXPORTS void alphaComp(const GpuMat& img1, const GpuMat& img2, GpuMat& dst, int alpha_op, Stream& stream = Stream::Null());
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581 ////////////////////////////// Image processing //////////////////////////////
\r
583 //! DST[x,y] = SRC[xmap[x,y],ymap[x,y]]
\r
584 //! supports only CV_32FC1 map type
\r
585 CV_EXPORTS void remap(const GpuMat& src, GpuMat& dst, const GpuMat& xmap, const GpuMat& ymap,
\r
586 int interpolation, int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar(),
\r
587 Stream& stream = Stream::Null());
\r
589 //! Does mean shift filtering on GPU.
\r
590 CV_EXPORTS void meanShiftFiltering(const GpuMat& src, GpuMat& dst, int sp, int sr,
\r
591 TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1),
\r
592 Stream& stream = Stream::Null());
\r
594 //! Does mean shift procedure on GPU.
\r
595 CV_EXPORTS void meanShiftProc(const GpuMat& src, GpuMat& dstr, GpuMat& dstsp, int sp, int sr,
\r
596 TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1),
\r
597 Stream& stream = Stream::Null());
\r
599 //! Does mean shift segmentation with elimination of small regions.
\r
600 CV_EXPORTS void meanShiftSegmentation(const GpuMat& src, Mat& dst, int sp, int sr, int minsize,
\r
601 TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
\r
603 //! Does coloring of disparity image: [0..ndisp) -> [0..240, 1, 1] in HSV.
\r
604 //! Supported types of input disparity: CV_8U, CV_16S.
\r
605 //! Output disparity has CV_8UC4 type in BGRA format (alpha = 255).
\r
606 CV_EXPORTS void drawColorDisp(const GpuMat& src_disp, GpuMat& dst_disp, int ndisp, Stream& stream = Stream::Null());
\r
608 //! Reprojects disparity image to 3D space.
\r
609 //! Supports CV_8U and CV_16S types of input disparity.
\r
610 //! The output is a 3- or 4-channel floating-point matrix.
\r
611 //! Each element of this matrix will contain the 3D coordinates of the point (x,y,z,1), computed from the disparity map.
\r
612 //! Q is the 4x4 perspective transformation matrix that can be obtained with cvStereoRectify.
\r
613 CV_EXPORTS void reprojectImageTo3D(const GpuMat& disp, GpuMat& xyzw, const Mat& Q, int dst_cn = 4, Stream& stream = Stream::Null());
\r
615 //! converts image from one color space to another
\r
616 CV_EXPORTS void cvtColor(const GpuMat& src, GpuMat& dst, int code, int dcn = 0, Stream& stream = Stream::Null());
\r
619 //! dstOrder - Integer array describing how channel values are permutated. The n-th entry
\r
620 //! of the array contains the number of the channel that is stored in the n-th channel of
\r
621 //! the output image. E.g. Given an RGBA image, aDstOrder = [3,2,1,0] converts this to ABGR
\r
623 CV_EXPORTS void swapChannels(GpuMat& image, const int dstOrder[4], Stream& stream = Stream::Null());
\r
625 //! Routines for correcting image color gamma
\r
626 CV_EXPORTS void gammaCorrection(const GpuMat& src, GpuMat& dst, bool forward = true, Stream& stream = Stream::Null());
\r
628 //! applies fixed threshold to the image
\r
629 CV_EXPORTS double threshold(const GpuMat& src, GpuMat& dst, double thresh, double maxval, int type, Stream& stream = Stream::Null());
\r
631 //! resizes the image
\r
632 //! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_AREA
\r
633 CV_EXPORTS void resize(const GpuMat& src, GpuMat& dst, Size dsize, double fx=0, double fy=0, int interpolation = INTER_LINEAR, Stream& stream = Stream::Null());
\r
635 //! warps the image using affine transformation
\r
636 //! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
\r
637 CV_EXPORTS void warpAffine(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags = INTER_LINEAR,
\r
638 int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar(), Stream& stream = Stream::Null());
\r
640 CV_EXPORTS void buildWarpAffineMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream = Stream::Null());
\r
642 //! warps the image using perspective transformation
\r
643 //! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
\r
644 CV_EXPORTS void warpPerspective(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags = INTER_LINEAR,
\r
645 int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar(), Stream& stream = Stream::Null());
\r
647 CV_EXPORTS void buildWarpPerspectiveMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream = Stream::Null());
\r
649 //! builds plane warping maps
\r
650 CV_EXPORTS void buildWarpPlaneMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, const Mat &T, float scale,
\r
651 GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null());
\r
653 //! builds cylindrical warping maps
\r
654 CV_EXPORTS void buildWarpCylindricalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, float scale,
\r
655 GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null());
\r
657 //! builds spherical warping maps
\r
658 CV_EXPORTS void buildWarpSphericalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, float scale,
\r
659 GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null());
\r
661 //! rotates an image around the origin (0,0) and then shifts it
\r
662 //! supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
\r
663 //! supports 1, 3 or 4 channels images with CV_8U, CV_16U or CV_32F depth
\r
664 CV_EXPORTS void rotate(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift = 0, double yShift = 0,
\r
665 int interpolation = INTER_LINEAR, Stream& stream = Stream::Null());
\r
667 //! copies 2D array to a larger destination array and pads borders with user-specifiable constant
\r
668 CV_EXPORTS void copyMakeBorder(const GpuMat& src, GpuMat& dst, int top, int bottom, int left, int right, int borderType,
\r
669 const Scalar& value = Scalar(), Stream& stream = Stream::Null());
\r
671 //! computes the integral image
\r
672 //! sum will have CV_32S type, but will contain unsigned int values
\r
673 //! supports only CV_8UC1 source type
\r
674 CV_EXPORTS void integral(const GpuMat& src, GpuMat& sum, Stream& stream = Stream::Null());
\r
675 //! buffered version
\r
676 CV_EXPORTS void integralBuffered(const GpuMat& src, GpuMat& sum, GpuMat& buffer, Stream& stream = Stream::Null());
\r
678 //! computes squared integral image
\r
679 //! result matrix will have 64F type, but will contain 64U values
\r
680 //! supports source images of 8UC1 type only
\r
681 CV_EXPORTS void sqrIntegral(const GpuMat& src, GpuMat& sqsum, Stream& stream = Stream::Null());
\r
683 //! computes vertical sum, supports only CV_32FC1 images
\r
684 CV_EXPORTS void columnSum(const GpuMat& src, GpuMat& sum);
\r
686 //! computes the standard deviation of integral images
\r
687 //! supports only CV_32SC1 source type and CV_32FC1 sqr type
\r
688 //! output will have CV_32FC1 type
\r
689 CV_EXPORTS void rectStdDev(const GpuMat& src, const GpuMat& sqr, GpuMat& dst, const Rect& rect, Stream& stream = Stream::Null());
\r
691 //! computes Harris cornerness criteria at each image pixel
\r
692 CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, double k, int borderType = BORDER_REFLECT101);
\r
693 CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, double k, int borderType = BORDER_REFLECT101);
\r
694 CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, GpuMat& buf, int blockSize, int ksize, double k,
\r
695 int borderType = BORDER_REFLECT101, Stream& stream = Stream::Null());
\r
697 //! computes minimum eigen value of 2x2 derivative covariation matrix at each pixel - the cornerness criteria
\r
698 CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, int borderType=BORDER_REFLECT101);
\r
699 CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, int borderType=BORDER_REFLECT101);
\r
700 CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, GpuMat& buf, int blockSize, int ksize,
\r
701 int borderType=BORDER_REFLECT101, Stream& stream = Stream::Null());
\r
703 //! performs per-element multiplication of two full (not packed) Fourier spectrums
\r
704 //! supports 32FC2 matrixes only (interleaved format)
\r
705 CV_EXPORTS void mulSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, bool conjB=false, Stream& stream = Stream::Null());
\r
707 //! performs per-element multiplication of two full (not packed) Fourier spectrums
\r
708 //! supports 32FC2 matrixes only (interleaved format)
\r
709 CV_EXPORTS void mulAndScaleSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, float scale, bool conjB=false, Stream& stream = Stream::Null());
\r
711 //! Performs a forward or inverse discrete Fourier transform (1D or 2D) of floating point matrix.
\r
712 //! Param dft_size is the size of DFT transform.
\r
714 //! If the source matrix is not continous, then additional copy will be done,
\r
715 //! so to avoid copying ensure the source matrix is continous one. If you want to use
\r
716 //! preallocated output ensure it is continuous too, otherwise it will be reallocated.
\r
718 //! Being implemented via CUFFT real-to-complex transform result contains only non-redundant values
\r
719 //! in CUFFT's format. Result as full complex matrix for such kind of transform cannot be retrieved.
\r
721 //! For complex-to-real transform it is assumed that the source matrix is packed in CUFFT's format.
\r
722 CV_EXPORTS void dft(const GpuMat& src, GpuMat& dst, Size dft_size, int flags=0, Stream& stream = Stream::Null());
\r
724 struct CV_EXPORTS ConvolveBuf
\r
728 Size user_block_size;
\r
732 GpuMat image_spect, templ_spect, result_spect;
\r
733 GpuMat image_block, templ_block, result_data;
\r
735 void create(Size image_size, Size templ_size);
\r
736 static Size estimateBlockSize(Size result_size, Size templ_size);
\r
740 //! computes convolution (or cross-correlation) of two images using discrete Fourier transform
\r
741 //! supports source images of 32FC1 type only
\r
742 //! result matrix will have 32FC1 type
\r
743 CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr = false);
\r
744 CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr, ConvolveBuf& buf, Stream& stream = Stream::Null());
\r
746 struct CV_EXPORTS MatchTemplateBuf
\r
748 Size user_block_size;
\r
749 GpuMat imagef, templf;
\r
750 std::vector<GpuMat> images;
\r
751 std::vector<GpuMat> image_sums;
\r
752 std::vector<GpuMat> image_sqsums;
\r
755 //! computes the proximity map for the raster template and the image where the template is searched for
\r
756 CV_EXPORTS void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, Stream &stream = Stream::Null());
\r
758 //! computes the proximity map for the raster template and the image where the template is searched for
\r
759 CV_EXPORTS void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, MatchTemplateBuf &buf, Stream& stream = Stream::Null());
\r
761 //! smoothes the source image and downsamples it
\r
762 CV_EXPORTS void pyrDown(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
\r
764 //! upsamples the source image and then smoothes it
\r
765 CV_EXPORTS void pyrUp(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
\r
767 //! performs linear blending of two images
\r
768 //! to avoid accuracy errors sum of weigths shouldn't be very close to zero
\r
769 CV_EXPORTS void blendLinear(const GpuMat& img1, const GpuMat& img2, const GpuMat& weights1, const GpuMat& weights2,
\r
770 GpuMat& result, Stream& stream = Stream::Null());
\r
772 //! Performa bilateral filtering of passsed image
\r
773 CV_EXPORTS void bilateralFilter(const GpuMat& src, GpuMat& dst, int kernel_size, float sigma_color, float sigma_spatial,
\r
774 int borderMode = BORDER_DEFAULT, Stream& stream = Stream::Null());
\r
776 //! Brute force non-local means algorith (slow but universal)
\r
777 CV_EXPORTS void nonLocalMeans(const GpuMat& src, GpuMat& dst, float h,
\r
778 int search_widow_size = 11, int block_size = 7, int borderMode = BORDER_DEFAULT, Stream& s = Stream::Null());
\r
781 struct CV_EXPORTS CannyBuf;
\r
783 CV_EXPORTS void Canny(const GpuMat& image, GpuMat& edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
\r
784 CV_EXPORTS void Canny(const GpuMat& image, CannyBuf& buf, GpuMat& edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
\r
785 CV_EXPORTS void Canny(const GpuMat& dx, const GpuMat& dy, GpuMat& edges, double low_thresh, double high_thresh, bool L2gradient = false);
\r
786 CV_EXPORTS void Canny(const GpuMat& dx, const GpuMat& dy, CannyBuf& buf, GpuMat& edges, double low_thresh, double high_thresh, bool L2gradient = false);
\r
788 struct CV_EXPORTS CannyBuf
\r
791 explicit CannyBuf(const Size& image_size, int apperture_size = 3) {create(image_size, apperture_size);}
\r
792 CannyBuf(const GpuMat& dx_, const GpuMat& dy_);
\r
794 void create(const Size& image_size, int apperture_size = 3);
\r
799 GpuMat dx_buf, dy_buf;
\r
801 GpuMat trackBuf1, trackBuf2;
\r
802 Ptr<FilterEngine_GPU> filterDX, filterDY;
\r
805 class CV_EXPORTS ImagePyramid
\r
808 inline ImagePyramid() : nLayers_(0) {}
\r
809 inline ImagePyramid(const GpuMat& img, int nLayers, Stream& stream = Stream::Null())
\r
811 build(img, nLayers, stream);
\r
814 void build(const GpuMat& img, int nLayers, Stream& stream = Stream::Null());
\r
816 void getLayer(GpuMat& outImg, Size outRoi, Stream& stream = Stream::Null()) const;
\r
818 inline void release()
\r
827 std::vector<GpuMat> pyramid_;
\r
833 struct HoughLinesBuf
\r
839 CV_EXPORTS void HoughLines(const GpuMat& src, GpuMat& lines, float rho, float theta, int threshold, bool doSort = false, int maxLines = 4096);
\r
840 CV_EXPORTS void HoughLines(const GpuMat& src, GpuMat& lines, HoughLinesBuf& buf, float rho, float theta, int threshold, bool doSort = false, int maxLines = 4096);
\r
841 CV_EXPORTS void HoughLinesDownload(const GpuMat& d_lines, OutputArray h_lines, OutputArray h_votes = noArray());
\r
845 struct HoughCirclesBuf
\r
853 CV_EXPORTS void HoughCircles(const GpuMat& src, GpuMat& circles, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096);
\r
854 CV_EXPORTS void HoughCircles(const GpuMat& src, GpuMat& circles, HoughCirclesBuf& buf, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096);
\r
855 CV_EXPORTS void HoughCirclesDownload(const GpuMat& d_circles, OutputArray h_circles);
\r
857 ////////////////////////////// Matrix reductions //////////////////////////////
\r
859 //! computes mean value and standard deviation of all or selected array elements
\r
860 //! supports only CV_8UC1 type
\r
861 CV_EXPORTS void meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev);
\r
862 //! buffered version
\r
863 CV_EXPORTS void meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev, GpuMat& buf);
\r
865 //! computes norm of array
\r
866 //! supports NORM_INF, NORM_L1, NORM_L2
\r
867 //! supports all matrices except 64F
\r
868 CV_EXPORTS double norm(const GpuMat& src1, int normType=NORM_L2);
\r
870 //! computes norm of array
\r
871 //! supports NORM_INF, NORM_L1, NORM_L2
\r
872 //! supports all matrices except 64F
\r
873 CV_EXPORTS double norm(const GpuMat& src1, int normType, GpuMat& buf);
\r
875 //! computes norm of the difference between two arrays
\r
876 //! supports NORM_INF, NORM_L1, NORM_L2
\r
877 //! supports only CV_8UC1 type
\r
878 CV_EXPORTS double norm(const GpuMat& src1, const GpuMat& src2, int normType=NORM_L2);
\r
880 //! computes sum of array elements
\r
881 //! supports only single channel images
\r
882 CV_EXPORTS Scalar sum(const GpuMat& src);
\r
884 //! computes sum of array elements
\r
885 //! supports only single channel images
\r
886 CV_EXPORTS Scalar sum(const GpuMat& src, GpuMat& buf);
\r
888 //! computes sum of array elements absolute values
\r
889 //! supports only single channel images
\r
890 CV_EXPORTS Scalar absSum(const GpuMat& src);
\r
892 //! computes sum of array elements absolute values
\r
893 //! supports only single channel images
\r
894 CV_EXPORTS Scalar absSum(const GpuMat& src, GpuMat& buf);
\r
896 //! computes squared sum of array elements
\r
897 //! supports only single channel images
\r
898 CV_EXPORTS Scalar sqrSum(const GpuMat& src);
\r
900 //! computes squared sum of array elements
\r
901 //! supports only single channel images
\r
902 CV_EXPORTS Scalar sqrSum(const GpuMat& src, GpuMat& buf);
\r
904 //! finds global minimum and maximum array elements and returns their values
\r
905 CV_EXPORTS void minMax(const GpuMat& src, double* minVal, double* maxVal=0, const GpuMat& mask=GpuMat());
\r
907 //! finds global minimum and maximum array elements and returns their values
\r
908 CV_EXPORTS void minMax(const GpuMat& src, double* minVal, double* maxVal, const GpuMat& mask, GpuMat& buf);
\r
910 //! finds global minimum and maximum array elements and returns their values with locations
\r
911 CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal=0, Point* minLoc=0, Point* maxLoc=0,
\r
912 const GpuMat& mask=GpuMat());
\r
914 //! finds global minimum and maximum array elements and returns their values with locations
\r
915 CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
\r
916 const GpuMat& mask, GpuMat& valbuf, GpuMat& locbuf);
\r
918 //! counts non-zero array elements
\r
919 CV_EXPORTS int countNonZero(const GpuMat& src);
\r
921 //! counts non-zero array elements
\r
922 CV_EXPORTS int countNonZero(const GpuMat& src, GpuMat& buf);
\r
924 //! reduces a matrix to a vector
\r
925 CV_EXPORTS void reduce(const GpuMat& mtx, GpuMat& vec, int dim, int reduceOp, int dtype = -1, Stream& stream = Stream::Null());
\r
928 ///////////////////////////// Calibration 3D //////////////////////////////////
\r
930 CV_EXPORTS void transformPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec,
\r
931 GpuMat& dst, Stream& stream = Stream::Null());
\r
933 CV_EXPORTS void projectPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec,
\r
934 const Mat& camera_mat, const Mat& dist_coef, GpuMat& dst,
\r
935 Stream& stream = Stream::Null());
\r
937 CV_EXPORTS void solvePnPRansac(const Mat& object, const Mat& image, const Mat& camera_mat,
\r
938 const Mat& dist_coef, Mat& rvec, Mat& tvec, bool use_extrinsic_guess=false,
\r
939 int num_iters=100, float max_dist=8.0, int min_inlier_count=100,
\r
940 std::vector<int>* inliers=NULL);
\r
942 //////////////////////////////// Image Labeling ////////////////////////////////
\r
944 //!performs labeling via graph cuts of a 2D regular 4-connected graph.
\r
945 CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& bottom, GpuMat& labels,
\r
946 GpuMat& buf, Stream& stream = Stream::Null());
\r
948 //!performs labeling via graph cuts of a 2D regular 8-connected graph.
\r
949 CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& topLeft, GpuMat& topRight,
\r
950 GpuMat& bottom, GpuMat& bottomLeft, GpuMat& bottomRight,
\r
952 GpuMat& buf, Stream& stream = Stream::Null());
\r
954 //! compute mask for Generalized Flood fill componetns labeling.
\r
955 CV_EXPORTS void connectivityMask(const GpuMat& image, GpuMat& mask, const cv::Scalar& lo, const cv::Scalar& hi, Stream& stream = Stream::Null());
\r
957 //! performs connected componnents labeling.
\r
958 CV_EXPORTS void labelComponents(const GpuMat& mask, GpuMat& components, int flags = 0, Stream& stream = Stream::Null());
\r
960 ////////////////////////////////// Histograms //////////////////////////////////
\r
962 //! Compute levels with even distribution. levels will have 1 row and nLevels cols and CV_32SC1 type.
\r
963 CV_EXPORTS void evenLevels(GpuMat& levels, int nLevels, int lowerLevel, int upperLevel);
\r
964 //! Calculates histogram with evenly distributed bins for signle channel source.
\r
965 //! Supports CV_8UC1, CV_16UC1 and CV_16SC1 source types.
\r
966 //! Output hist will have one row and histSize cols and CV_32SC1 type.
\r
967 CV_EXPORTS void histEven(const GpuMat& src, GpuMat& hist, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());
\r
968 CV_EXPORTS void histEven(const GpuMat& src, GpuMat& hist, GpuMat& buf, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());
\r
969 //! Calculates histogram with evenly distributed bins for four-channel source.
\r
970 //! All channels of source are processed separately.
\r
971 //! Supports CV_8UC4, CV_16UC4 and CV_16SC4 source types.
\r
972 //! Output hist[i] will have one row and histSize[i] cols and CV_32SC1 type.
\r
973 CV_EXPORTS void histEven(const GpuMat& src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null());
\r
974 CV_EXPORTS void histEven(const GpuMat& src, GpuMat hist[4], GpuMat& buf, int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null());
\r
975 //! Calculates histogram with bins determined by levels array.
\r
976 //! levels must have one row and CV_32SC1 type if source has integer type or CV_32FC1 otherwise.
\r
977 //! Supports CV_8UC1, CV_16UC1, CV_16SC1 and CV_32FC1 source types.
\r
978 //! Output hist will have one row and (levels.cols-1) cols and CV_32SC1 type.
\r
979 CV_EXPORTS void histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels, Stream& stream = Stream::Null());
\r
980 CV_EXPORTS void histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels, GpuMat& buf, Stream& stream = Stream::Null());
\r
981 //! Calculates histogram with bins determined by levels array.
\r
982 //! All levels must have one row and CV_32SC1 type if source has integer type or CV_32FC1 otherwise.
\r
983 //! All channels of source are processed separately.
\r
984 //! Supports CV_8UC4, CV_16UC4, CV_16SC4 and CV_32FC4 source types.
\r
985 //! Output hist[i] will have one row and (levels[i].cols-1) cols and CV_32SC1 type.
\r
986 CV_EXPORTS void histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], Stream& stream = Stream::Null());
\r
987 CV_EXPORTS void histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], GpuMat& buf, Stream& stream = Stream::Null());
\r
989 //! Calculates histogram for 8u one channel image
\r
990 //! Output hist will have one row, 256 cols and CV32SC1 type.
\r
991 CV_EXPORTS void calcHist(const GpuMat& src, GpuMat& hist, Stream& stream = Stream::Null());
\r
992 CV_EXPORTS void calcHist(const GpuMat& src, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null());
\r
994 //! normalizes the grayscale image brightness and contrast by normalizing its histogram
\r
995 CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
\r
996 CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, Stream& stream = Stream::Null());
\r
997 CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null());
\r
999 //////////////////////////////// StereoBM_GPU ////////////////////////////////
\r
1001 class CV_EXPORTS StereoBM_GPU
\r
1004 enum { BASIC_PRESET = 0, PREFILTER_XSOBEL = 1 };
\r
1006 enum { DEFAULT_NDISP = 64, DEFAULT_WINSZ = 19 };
\r
1008 //! the default constructor
\r
1010 //! the full constructor taking the camera-specific preset, number of disparities and the SAD window size. ndisparities must be multiple of 8.
\r
1011 StereoBM_GPU(int preset, int ndisparities = DEFAULT_NDISP, int winSize = DEFAULT_WINSZ);
\r
1013 //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair
\r
1014 //! Output disparity has CV_8U type.
\r
1015 void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null());
\r
1017 //! Some heuristics that tries to estmate
\r
1018 // if current GPU will be faster than CPU in this algorithm.
\r
1019 // It queries current active device.
\r
1020 static bool checkIfGpuCallReasonable();
\r
1026 // If avergeTexThreshold == 0 => post procesing is disabled
\r
1027 // If avergeTexThreshold != 0 then disparity is set 0 in each point (x,y) where for left image
\r
1028 // SumOfHorizontalGradiensInWindow(x, y, winSize) < (winSize * winSize) * avergeTexThreshold
\r
1029 // i.e. input left image is low textured.
\r
1030 float avergeTexThreshold;
\r
1033 GpuMat minSSD, leBuf, riBuf;
\r
1036 ////////////////////////// StereoBeliefPropagation ///////////////////////////
\r
1037 // "Efficient Belief Propagation for Early Vision"
\r
1040 class CV_EXPORTS StereoBeliefPropagation
\r
1043 enum { DEFAULT_NDISP = 64 };
\r
1044 enum { DEFAULT_ITERS = 5 };
\r
1045 enum { DEFAULT_LEVELS = 5 };
\r
1047 static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels);
\r
1049 //! the default constructor
\r
1050 explicit StereoBeliefPropagation(int ndisp = DEFAULT_NDISP,
\r
1051 int iters = DEFAULT_ITERS,
\r
1052 int levels = DEFAULT_LEVELS,
\r
1053 int msg_type = CV_32F);
\r
1055 //! the full constructor taking the number of disparities, number of BP iterations on each level,
\r
1056 //! number of levels, truncation of data cost, data weight,
\r
1057 //! truncation of discontinuity cost and discontinuity single jump
\r
1058 //! DataTerm = data_weight * min(fabs(I2-I1), max_data_term)
\r
1059 //! DiscTerm = min(disc_single_jump * fabs(f1-f2), max_disc_term)
\r
1060 //! please see paper for more details
\r
1061 StereoBeliefPropagation(int ndisp, int iters, int levels,
\r
1062 float max_data_term, float data_weight,
\r
1063 float max_disc_term, float disc_single_jump,
\r
1064 int msg_type = CV_32F);
\r
1066 //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair,
\r
1067 //! if disparity is empty output type will be CV_16S else output type will be disparity.type().
\r
1068 void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null());
\r
1071 //! version for user specified data term
\r
1072 void operator()(const GpuMat& data, GpuMat& disparity, Stream& stream = Stream::Null());
\r
1079 float max_data_term;
\r
1080 float data_weight;
\r
1081 float max_disc_term;
\r
1082 float disc_single_jump;
\r
1086 GpuMat u, d, l, r, u2, d2, l2, r2;
\r
1087 std::vector<GpuMat> datas;
\r
1091 /////////////////////////// StereoConstantSpaceBP ///////////////////////////
\r
1092 // "A Constant-Space Belief Propagation Algorithm for Stereo Matching"
\r
1093 // Qingxiong Yang, Liang Wang, Narendra Ahuja
\r
1094 // http://vision.ai.uiuc.edu/~qyang6/
\r
1096 class CV_EXPORTS StereoConstantSpaceBP
\r
1099 enum { DEFAULT_NDISP = 128 };
\r
1100 enum { DEFAULT_ITERS = 8 };
\r
1101 enum { DEFAULT_LEVELS = 4 };
\r
1102 enum { DEFAULT_NR_PLANE = 4 };
\r
1104 static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels, int& nr_plane);
\r
1106 //! the default constructor
\r
1107 explicit StereoConstantSpaceBP(int ndisp = DEFAULT_NDISP,
\r
1108 int iters = DEFAULT_ITERS,
\r
1109 int levels = DEFAULT_LEVELS,
\r
1110 int nr_plane = DEFAULT_NR_PLANE,
\r
1111 int msg_type = CV_32F);
\r
1113 //! the full constructor taking the number of disparities, number of BP iterations on each level,
\r
1114 //! number of levels, number of active disparity on the first level, truncation of data cost, data weight,
\r
1115 //! truncation of discontinuity cost, discontinuity single jump and minimum disparity threshold
\r
1116 StereoConstantSpaceBP(int ndisp, int iters, int levels, int nr_plane,
\r
1117 float max_data_term, float data_weight, float max_disc_term, float disc_single_jump,
\r
1118 int min_disp_th = 0,
\r
1119 int msg_type = CV_32F);
\r
1121 //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair,
\r
1122 //! if disparity is empty output type will be CV_16S else output type will be disparity.type().
\r
1123 void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null());
\r
1132 float max_data_term;
\r
1133 float data_weight;
\r
1134 float max_disc_term;
\r
1135 float disc_single_jump;
\r
1141 bool use_local_init_data_cost;
\r
1143 GpuMat messages_buffers;
\r
1149 /////////////////////////// DisparityBilateralFilter ///////////////////////////
\r
1150 // Disparity map refinement using joint bilateral filtering given a single color image.
\r
1151 // Qingxiong Yang, Liang Wang, Narendra Ahuja
\r
1152 // http://vision.ai.uiuc.edu/~qyang6/
\r
1154 class CV_EXPORTS DisparityBilateralFilter
\r
1157 enum { DEFAULT_NDISP = 64 };
\r
1158 enum { DEFAULT_RADIUS = 3 };
\r
1159 enum { DEFAULT_ITERS = 1 };
\r
1161 //! the default constructor
\r
1162 explicit DisparityBilateralFilter(int ndisp = DEFAULT_NDISP, int radius = DEFAULT_RADIUS, int iters = DEFAULT_ITERS);
\r
1164 //! the full constructor taking the number of disparities, filter radius,
\r
1165 //! number of iterations, truncation of data continuity, truncation of disparity continuity
\r
1166 //! and filter range sigma
\r
1167 DisparityBilateralFilter(int ndisp, int radius, int iters, float edge_threshold, float max_disc_threshold, float sigma_range);
\r
1169 //! the disparity map refinement operator. Refine disparity map using joint bilateral filtering given a single color image.
\r
1170 //! disparity must have CV_8U or CV_16S type, image must have CV_8UC1 or CV_8UC3 type.
\r
1171 void operator()(const GpuMat& disparity, const GpuMat& image, GpuMat& dst, Stream& stream = Stream::Null());
\r
1178 float edge_threshold;
\r
1179 float max_disc_threshold;
\r
1180 float sigma_range;
\r
1182 GpuMat table_color;
\r
1183 GpuMat table_space;
\r
1187 //////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
\r
1188 struct CV_EXPORTS HOGConfidence
\r
1191 vector<Point> locations;
\r
1192 vector<double> confidences;
\r
1193 vector<double> part_scores[4];
\r
1196 struct CV_EXPORTS HOGDescriptor
\r
1198 enum { DEFAULT_WIN_SIGMA = -1 };
\r
1199 enum { DEFAULT_NLEVELS = 64 };
\r
1200 enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };
\r
1202 HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16),
\r
1203 Size block_stride=Size(8, 8), Size cell_size=Size(8, 8),
\r
1204 int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA,
\r
1205 double threshold_L2hys=0.2, bool gamma_correction=true,
\r
1206 int nlevels=DEFAULT_NLEVELS);
\r
1208 size_t getDescriptorSize() const;
\r
1209 size_t getBlockHistogramSize() const;
\r
1211 void setSVMDetector(const vector<float>& detector);
\r
1213 static vector<float> getDefaultPeopleDetector();
\r
1214 static vector<float> getPeopleDetector48x96();
\r
1215 static vector<float> getPeopleDetector64x128();
\r
1217 void detect(const GpuMat& img, vector<Point>& found_locations,
\r
1218 double hit_threshold=0, Size win_stride=Size(),
\r
1219 Size padding=Size());
\r
1221 void detectMultiScale(const GpuMat& img, vector<Rect>& found_locations,
\r
1222 double hit_threshold=0, Size win_stride=Size(),
\r
1223 Size padding=Size(), double scale0=1.05,
\r
1224 int group_threshold=2);
\r
1226 void computeConfidence(const GpuMat& img, vector<Point>& hits, double hit_threshold,
\r
1227 Size win_stride, Size padding, vector<Point>& locations, vector<double>& confidences);
\r
1229 void computeConfidenceMultiScale(const GpuMat& img, vector<Rect>& found_locations,
\r
1230 double hit_threshold, Size win_stride, Size padding,
\r
1231 vector<HOGConfidence> &conf_out, int group_threshold);
\r
1233 void getDescriptors(const GpuMat& img, Size win_stride,
\r
1234 GpuMat& descriptors,
\r
1235 int descr_format=DESCR_FORMAT_COL_BY_COL);
\r
1239 Size block_stride;
\r
1243 double threshold_L2hys;
\r
1244 bool gamma_correction;
\r
1248 void computeBlockHistograms(const GpuMat& img);
\r
1249 void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle);
\r
1251 double getWinSigma() const;
\r
1252 bool checkDetectorSize() const;
\r
1254 static int numPartsWithin(int size, int part_size, int stride);
\r
1255 static Size numPartsWithin(Size size, Size part_size, Size stride);
\r
1257 // Coefficients of the separating plane
\r
1261 // Results of the last classification step
\r
1262 GpuMat labels, labels_buf;
\r
1265 // Results of the last histogram evaluation step
\r
1266 GpuMat block_hists, block_hists_buf;
\r
1268 // Gradients conputation results
\r
1269 GpuMat grad, qangle, grad_buf, qangle_buf;
\r
1271 // returns subbuffer with required size, reallocates buffer if nessesary.
\r
1272 static GpuMat getBuffer(const Size& sz, int type, GpuMat& buf);
\r
1273 static GpuMat getBuffer(int rows, int cols, int type, GpuMat& buf);
\r
1275 std::vector<GpuMat> image_scales;
\r
1279 ////////////////////////////////// BruteForceMatcher //////////////////////////////////
\r
1281 class CV_EXPORTS BFMatcher_GPU
\r
1284 explicit BFMatcher_GPU(int norm = cv::NORM_L2);
\r
1286 // Add descriptors to train descriptor collection
\r
1287 void add(const std::vector<GpuMat>& descCollection);
\r
1289 // Get train descriptors collection
\r
1290 const std::vector<GpuMat>& getTrainDescriptors() const;
\r
1292 // Clear train descriptors collection
\r
1295 // Return true if there are not train descriptors in collection
\r
1296 bool empty() const;
\r
1298 // Return true if the matcher supports mask in match methods
\r
1299 bool isMaskSupported() const;
\r
1301 // Find one best match for each query descriptor
\r
1302 void matchSingle(const GpuMat& query, const GpuMat& train,
\r
1303 GpuMat& trainIdx, GpuMat& distance,
\r
1304 const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
\r
1306 // Download trainIdx and distance and convert it to CPU vector with DMatch
\r
1307 static void matchDownload(const GpuMat& trainIdx, const GpuMat& distance, std::vector<DMatch>& matches);
\r
1308 // Convert trainIdx and distance to vector with DMatch
\r
1309 static void matchConvert(const Mat& trainIdx, const Mat& distance, std::vector<DMatch>& matches);
\r
1311 // Find one best match for each query descriptor
\r
1312 void match(const GpuMat& query, const GpuMat& train, std::vector<DMatch>& matches, const GpuMat& mask = GpuMat());
\r
1314 // Make gpu collection of trains and masks in suitable format for matchCollection function
\r
1315 void makeGpuCollection(GpuMat& trainCollection, GpuMat& maskCollection, const std::vector<GpuMat>& masks = std::vector<GpuMat>());
\r
1317 // Find one best match from train collection for each query descriptor
\r
1318 void matchCollection(const GpuMat& query, const GpuMat& trainCollection,
\r
1319 GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
\r
1320 const GpuMat& masks = GpuMat(), Stream& stream = Stream::Null());
\r
1322 // Download trainIdx, imgIdx and distance and convert it to vector with DMatch
\r
1323 static void matchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, std::vector<DMatch>& matches);
\r
1324 // Convert trainIdx, imgIdx and distance to vector with DMatch
\r
1325 static void matchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, std::vector<DMatch>& matches);
\r
1327 // Find one best match from train collection for each query descriptor.
\r
1328 void match(const GpuMat& query, std::vector<DMatch>& matches, const std::vector<GpuMat>& masks = std::vector<GpuMat>());
\r
1330 // Find k best matches for each query descriptor (in increasing order of distances)
\r
1331 void knnMatchSingle(const GpuMat& query, const GpuMat& train,
\r
1332 GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k,
\r
1333 const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
\r
1335 // Download trainIdx and distance and convert it to vector with DMatch
\r
1336 // compactResult is used when mask is not empty. If compactResult is false matches
\r
1337 // vector will have the same size as queryDescriptors rows. If compactResult is true
\r
1338 // matches vector will not contain matches for fully masked out query descriptors.
\r
1339 static void knnMatchDownload(const GpuMat& trainIdx, const GpuMat& distance,
\r
1340 std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
\r
1341 // Convert trainIdx and distance to vector with DMatch
\r
1342 static void knnMatchConvert(const Mat& trainIdx, const Mat& distance,
\r
1343 std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
\r
1345 // Find k best matches for each query descriptor (in increasing order of distances).
\r
1346 // compactResult is used when mask is not empty. If compactResult is false matches
\r
1347 // vector will have the same size as queryDescriptors rows. If compactResult is true
\r
1348 // matches vector will not contain matches for fully masked out query descriptors.
\r
1349 void knnMatch(const GpuMat& query, const GpuMat& train,
\r
1350 std::vector< std::vector<DMatch> >& matches, int k, const GpuMat& mask = GpuMat(),
\r
1351 bool compactResult = false);
\r
1353 // Find k best matches from train collection for each query descriptor (in increasing order of distances)
\r
1354 void knnMatch2Collection(const GpuMat& query, const GpuMat& trainCollection,
\r
1355 GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
\r
1356 const GpuMat& maskCollection = GpuMat(), Stream& stream = Stream::Null());
\r
1358 // Download trainIdx and distance and convert it to vector with DMatch
\r
1359 // compactResult is used when mask is not empty. If compactResult is false matches
\r
1360 // vector will have the same size as queryDescriptors rows. If compactResult is true
\r
1361 // matches vector will not contain matches for fully masked out query descriptors.
\r
1362 static void knnMatch2Download(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance,
\r
1363 std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
\r
1364 // Convert trainIdx and distance to vector with DMatch
\r
1365 static void knnMatch2Convert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance,
\r
1366 std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
\r
1368 // Find k best matches for each query descriptor (in increasing order of distances).
\r
1369 // compactResult is used when mask is not empty. If compactResult is false matches
\r
1370 // vector will have the same size as queryDescriptors rows. If compactResult is true
\r
1371 // matches vector will not contain matches for fully masked out query descriptors.
\r
1372 void knnMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches, int k,
\r
1373 const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);
\r
1375 // Find best matches for each query descriptor which have distance less than maxDistance.
\r
1376 // nMatches.at<int>(0, queryIdx) will contain matches count for queryIdx.
\r
1377 // carefully nMatches can be greater than trainIdx.cols - it means that matcher didn't find all matches,
\r
1378 // because it didn't have enough memory.
\r
1379 // If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nTrain / 100), 10),
\r
1380 // otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
\r
1381 // Matches doesn't sorted.
\r
1382 void radiusMatchSingle(const GpuMat& query, const GpuMat& train,
\r
1383 GpuMat& trainIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
\r
1384 const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
\r
1386 // Download trainIdx, nMatches and distance and convert it to vector with DMatch.
\r
1387 // matches will be sorted in increasing order of distances.
\r
1388 // compactResult is used when mask is not empty. If compactResult is false matches
\r
1389 // vector will have the same size as queryDescriptors rows. If compactResult is true
\r
1390 // matches vector will not contain matches for fully masked out query descriptors.
\r
1391 static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& distance, const GpuMat& nMatches,
\r
1392 std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
\r
1393 // Convert trainIdx, nMatches and distance to vector with DMatch.
\r
1394 static void radiusMatchConvert(const Mat& trainIdx, const Mat& distance, const Mat& nMatches,
\r
1395 std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
\r
1397 // Find best matches for each query descriptor which have distance less than maxDistance
\r
1398 // in increasing order of distances).
\r
1399 void radiusMatch(const GpuMat& query, const GpuMat& train,
\r
1400 std::vector< std::vector<DMatch> >& matches, float maxDistance,
\r
1401 const GpuMat& mask = GpuMat(), bool compactResult = false);
\r
1403 // Find best matches for each query descriptor which have distance less than maxDistance.
\r
1404 // If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nQuery / 100), 10),
\r
1405 // otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
\r
1406 // Matches doesn't sorted.
\r
1407 void radiusMatchCollection(const GpuMat& query, GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
\r
1408 const std::vector<GpuMat>& masks = std::vector<GpuMat>(), Stream& stream = Stream::Null());
\r
1410 // Download trainIdx, imgIdx, nMatches and distance and convert it to vector with DMatch.
\r
1411 // matches will be sorted in increasing order of distances.
\r
1412 // compactResult is used when mask is not empty. If compactResult is false matches
\r
1413 // vector will have the same size as queryDescriptors rows. If compactResult is true
\r
1414 // matches vector will not contain matches for fully masked out query descriptors.
\r
1415 static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, const GpuMat& nMatches,
\r
1416 std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
\r
1417 // Convert trainIdx, nMatches and distance to vector with DMatch.
\r
1418 static void radiusMatchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, const Mat& nMatches,
\r
1419 std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
\r
1421 // Find best matches from train collection for each query descriptor which have distance less than
\r
1422 // maxDistance (in increasing order of distances).
\r
1423 void radiusMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches, float maxDistance,
\r
1424 const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);
\r
1429 std::vector<GpuMat> trainDescCollection;
\r
1432 template <class Distance>
\r
1433 class CV_EXPORTS BruteForceMatcher_GPU;
\r
1435 template <typename T>
\r
1436 class CV_EXPORTS BruteForceMatcher_GPU< L1<T> > : public BFMatcher_GPU
\r
1439 explicit BruteForceMatcher_GPU() : BFMatcher_GPU(NORM_L1) {}
\r
1440 explicit BruteForceMatcher_GPU(L1<T> /*d*/) : BFMatcher_GPU(NORM_L1) {}
\r
1442 template <typename T>
\r
1443 class CV_EXPORTS BruteForceMatcher_GPU< L2<T> > : public BFMatcher_GPU
\r
1446 explicit BruteForceMatcher_GPU() : BFMatcher_GPU(NORM_L2) {}
\r
1447 explicit BruteForceMatcher_GPU(L2<T> /*d*/) : BFMatcher_GPU(NORM_L2) {}
\r
1449 template <> class CV_EXPORTS BruteForceMatcher_GPU< Hamming > : public BFMatcher_GPU
\r
1452 explicit BruteForceMatcher_GPU() : BFMatcher_GPU(NORM_HAMMING) {}
\r
1453 explicit BruteForceMatcher_GPU(Hamming /*d*/) : BFMatcher_GPU(NORM_HAMMING) {}
\r
1456 ////////////////////////////////// CascadeClassifier_GPU //////////////////////////////////////////
\r
1457 // The cascade classifier class for object detection: supports old haar and new lbp xlm formats and nvbin for haar cascades olny.
\r
1458 class CV_EXPORTS CascadeClassifier_GPU
\r
1461 CascadeClassifier_GPU();
\r
1462 CascadeClassifier_GPU(const std::string& filename);
\r
1463 ~CascadeClassifier_GPU();
\r
1465 bool empty() const;
\r
1466 bool load(const std::string& filename);
\r
1469 /* returns number of detected objects */
\r
1470 int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, double scaleFactor = 1.2, int minNeighbors = 4, Size minSize = Size());
\r
1472 bool findLargestObject;
\r
1473 bool visualizeInPlace;
\r
1475 Size getClassifierSize() const;
\r
1478 struct CascadeClassifierImpl;
\r
1479 CascadeClassifierImpl* impl;
\r
1480 struct HaarCascade;
\r
1481 struct LbpCascade;
\r
1482 friend class CascadeClassifier_GPU_LBP;
\r
1485 int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize = Size(), double scaleFactor = 1.1, int minNeighbors = 4);
\r
1488 ////////////////////////////////// SURF //////////////////////////////////////////
\r
1490 class CV_EXPORTS SURF_GPU
\r
1493 enum KeypointLayout
\r
1505 //! the default constructor
\r
1507 //! the full constructor taking all the necessary parameters
\r
1508 explicit SURF_GPU(double _hessianThreshold, int _nOctaves=4,
\r
1509 int _nOctaveLayers=2, bool _extended=false, float _keypointsRatio=0.01f, bool _upright = false);
\r
1511 //! returns the descriptor size in float's (64 or 128)
\r
1512 int descriptorSize() const;
\r
1514 //! upload host keypoints to device memory
\r
1515 static void uploadKeypoints(const vector<KeyPoint>& keypoints, GpuMat& keypointsGPU);
\r
1516 //! download keypoints from device to host memory
\r
1517 static void downloadKeypoints(const GpuMat& keypointsGPU, vector<KeyPoint>& keypoints);
\r
1519 //! download descriptors from device to host memory
\r
1520 static void downloadDescriptors(const GpuMat& descriptorsGPU, vector<float>& descriptors);
\r
1522 //! finds the keypoints using fast hessian detector used in SURF
\r
1523 //! supports CV_8UC1 images
\r
1524 //! keypoints will have nFeature cols and 6 rows
\r
1525 //! keypoints.ptr<float>(X_ROW)[i] will contain x coordinate of i'th feature
\r
1526 //! keypoints.ptr<float>(Y_ROW)[i] will contain y coordinate of i'th feature
\r
1527 //! keypoints.ptr<float>(LAPLACIAN_ROW)[i] will contain laplacian sign of i'th feature
\r
1528 //! keypoints.ptr<float>(OCTAVE_ROW)[i] will contain octave of i'th feature
\r
1529 //! keypoints.ptr<float>(SIZE_ROW)[i] will contain size of i'th feature
\r
1530 //! keypoints.ptr<float>(ANGLE_ROW)[i] will contain orientation of i'th feature
\r
1531 //! keypoints.ptr<float>(HESSIAN_ROW)[i] will contain response of i'th feature
\r
1532 void operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints);
\r
1533 //! finds the keypoints and computes their descriptors.
\r
1534 //! Optionally it can compute descriptors for the user-provided keypoints and recompute keypoints direction
\r
1535 void operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors,
\r
1536 bool useProvidedKeypoints = false);
\r
1538 void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints);
\r
1539 void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints, GpuMat& descriptors,
\r
1540 bool useProvidedKeypoints = false);
\r
1542 void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints, std::vector<float>& descriptors,
\r
1543 bool useProvidedKeypoints = false);
\r
1545 void releaseMemory();
\r
1547 // SURF parameters
\r
1548 double hessianThreshold;
\r
1550 int nOctaveLayers;
\r
1554 //! max keypoints = min(keypointsRatio * img.size().area(), 65535)
\r
1555 float keypointsRatio;
\r
1557 GpuMat sum, mask1, maskSum, intBuffer;
\r
1559 GpuMat det, trace;
\r
1561 GpuMat maxPosBuffer;
\r
1564 ////////////////////////////////// FAST //////////////////////////////////////////
\r
1566 class CV_EXPORTS FAST_GPU
\r
1576 // all features have same size
\r
1577 static const int FEATURE_SIZE = 7;
\r
1579 explicit FAST_GPU(int threshold, bool nonmaxSupression = true, double keypointsRatio = 0.05);
\r
1581 //! finds the keypoints using FAST detector
\r
1582 //! supports only CV_8UC1 images
\r
1583 void operator ()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints);
\r
1584 void operator ()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints);
\r
1586 //! download keypoints from device to host memory
\r
1587 static void downloadKeypoints(const GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints);
\r
1589 //! convert keypoints to KeyPoint vector
\r
1590 static void convertKeypoints(const Mat& h_keypoints, std::vector<KeyPoint>& keypoints);
\r
1592 //! release temporary buffer's memory
\r
1595 bool nonmaxSupression;
\r
1599 //! max keypoints = keypointsRatio * img.size().area()
\r
1600 double keypointsRatio;
\r
1602 //! find keypoints and compute it's response if nonmaxSupression is true
\r
1603 //! return count of detected keypoints
\r
1604 int calcKeyPointsLocation(const GpuMat& image, const GpuMat& mask);
\r
1606 //! get final array of keypoints
\r
1607 //! performs nonmax supression if needed
\r
1608 //! return final count of keypoints
\r
1609 int getKeyPoints(GpuMat& keypoints);
\r
1617 GpuMat d_keypoints_;
\r
1620 ////////////////////////////////// ORB //////////////////////////////////////////
\r
1622 class CV_EXPORTS ORB_GPU
\r
1638 DEFAULT_FAST_THRESHOLD = 20
\r
1642 explicit ORB_GPU(int nFeatures = 500, float scaleFactor = 1.2f, int nLevels = 8, int edgeThreshold = 31,
\r
1643 int firstLevel = 0, int WTA_K = 2, int scoreType = 0, int patchSize = 31);
\r
1645 //! Compute the ORB features on an image
\r
1646 //! image - the image to compute the features (supports only CV_8UC1 images)
\r
1647 //! mask - the mask to apply
\r
1648 //! keypoints - the resulting keypoints
\r
1649 void operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints);
\r
1650 void operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints);
\r
1652 //! Compute the ORB features and descriptors on an image
\r
1653 //! image - the image to compute the features (supports only CV_8UC1 images)
\r
1654 //! mask - the mask to apply
\r
1655 //! keypoints - the resulting keypoints
\r
1656 //! descriptors - descriptors array
\r
1657 void operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints, GpuMat& descriptors);
\r
1658 void operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors);
\r
1660 //! download keypoints from device to host memory
\r
1661 static void downloadKeyPoints(GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints);
\r
1662 //! convert keypoints to KeyPoint vector
\r
1663 static void convertKeyPoints(Mat& d_keypoints, std::vector<KeyPoint>& keypoints);
\r
1665 //! returns the descriptor size in bytes
\r
1666 inline int descriptorSize() const { return kBytes; }
\r
1668 inline void setFastParams(int threshold, bool nonmaxSupression = true)
\r
1670 fastDetector_.threshold = threshold;
\r
1671 fastDetector_.nonmaxSupression = nonmaxSupression;
\r
1674 //! release temporary buffer's memory
\r
1677 //! if true, image will be blurred before descriptors calculation
\r
1678 bool blurForDescriptor;
\r
1681 enum { kBytes = 32 };
\r
1683 void buildScalePyramids(const GpuMat& image, const GpuMat& mask);
\r
1685 void computeKeyPointsPyramid();
\r
1687 void computeDescriptors(GpuMat& descriptors);
\r
1689 void mergeKeyPoints(GpuMat& keypoints);
\r
1692 float scaleFactor_;
\r
1694 int edgeThreshold_;
\r
1700 // The number of desired features per scale
\r
1701 std::vector<size_t> n_features_per_level_;
\r
1703 // Points to compute BRIEF descriptors from
\r
1706 std::vector<GpuMat> imagePyr_;
\r
1707 std::vector<GpuMat> maskPyr_;
\r
1711 std::vector<GpuMat> keyPointsPyr_;
\r
1712 std::vector<int> keyPointsCount_;
\r
1714 FAST_GPU fastDetector_;
\r
1716 Ptr<FilterEngine_GPU> blurFilter;
\r
1718 GpuMat d_keypoints_;
\r
1721 ////////////////////////////////// Optical Flow //////////////////////////////////////////
\r
1723 class CV_EXPORTS BroxOpticalFlow
\r
1726 BroxOpticalFlow(float alpha_, float gamma_, float scale_factor_, int inner_iterations_, int outer_iterations_, int solver_iterations_) :
\r
1727 alpha(alpha_), gamma(gamma_), scale_factor(scale_factor_),
\r
1728 inner_iterations(inner_iterations_), outer_iterations(outer_iterations_), solver_iterations(solver_iterations_)
\r
1732 //! Compute optical flow
\r
1733 //! frame0 - source frame (supports only CV_32FC1 type)
\r
1734 //! frame1 - frame to track (with the same size and type as frame0)
\r
1735 //! u - flow horizontal component (along x axis)
\r
1736 //! v - flow vertical component (along y axis)
\r
1737 void operator ()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& u, GpuMat& v, Stream& stream = Stream::Null());
\r
1739 //! flow smoothness
\r
1742 //! gradient constancy importance
\r
1745 //! pyramid scale factor
\r
1746 float scale_factor;
\r
1748 //! number of lagged non-linearity iterations (inner loop)
\r
1749 int inner_iterations;
\r
1751 //! number of warping iterations (number of pyramid levels)
\r
1752 int outer_iterations;
\r
1754 //! number of linear system solver iterations
\r
1755 int solver_iterations;
\r
1760 class CV_EXPORTS GoodFeaturesToTrackDetector_GPU
\r
1763 explicit GoodFeaturesToTrackDetector_GPU(int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 0.0,
\r
1764 int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04);
\r
1766 //! return 1 rows matrix with CV_32FC2 type
\r
1767 void operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask = GpuMat());
\r
1770 double qualityLevel;
\r
1771 double minDistance;
\r
1774 bool useHarrisDetector;
\r
1777 void releaseMemory()
\r
1783 minMaxbuf_.release();
\r
1784 tmpCorners_.release();
\r
1792 GpuMat minMaxbuf_;
\r
1793 GpuMat tmpCorners_;
\r
1796 inline GoodFeaturesToTrackDetector_GPU::GoodFeaturesToTrackDetector_GPU(int maxCorners_, double qualityLevel_, double minDistance_,
\r
1797 int blockSize_, bool useHarrisDetector_, double harrisK_)
\r
1799 maxCorners = maxCorners_;
\r
1800 qualityLevel = qualityLevel_;
\r
1801 minDistance = minDistance_;
\r
1802 blockSize = blockSize_;
\r
1803 useHarrisDetector = useHarrisDetector_;
\r
1804 harrisK = harrisK_;
\r
1808 class CV_EXPORTS PyrLKOpticalFlow
\r
1811 PyrLKOpticalFlow();
\r
1813 void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts,
\r
1814 GpuMat& status, GpuMat* err = 0);
\r
1816 void dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err = 0);
\r
1818 void releaseMemory();
\r
1823 bool useInitialFlow;
\r
1826 vector<GpuMat> prevPyr_;
\r
1827 vector<GpuMat> nextPyr_;
\r
1834 bool isDeviceArch11_;
\r
1838 class CV_EXPORTS FarnebackOpticalFlow
\r
1841 FarnebackOpticalFlow()
\r
1845 fastPyramids = false;
\r
1851 isDeviceArch11_ = !DeviceInfo().supports(FEATURE_SET_COMPUTE_12);
\r
1856 bool fastPyramids;
\r
1863 void operator ()(const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s = Stream::Null());
\r
1865 void releaseMemory()
\r
1867 frames_[0].release();
\r
1868 frames_[1].release();
\r
1869 pyrLevel_[0].release();
\r
1870 pyrLevel_[1].release();
\r
1875 blurredFrame_[0].release();
\r
1876 blurredFrame_[1].release();
\r
1877 pyramid0_.clear();
\r
1878 pyramid1_.clear();
\r
1882 void prepareGaussian(
\r
1883 int n, double sigma, float *g, float *xg, float *xxg,
\r
1884 double &ig11, double &ig03, double &ig33, double &ig55);
\r
1886 void setPolynomialExpansionConsts(int n, double sigma);
\r
1888 void updateFlow_boxFilter(
\r
1889 const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat &flowy,
\r
1890 GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]);
\r
1892 void updateFlow_gaussianBlur(
\r
1893 const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat& flowy,
\r
1894 GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]);
\r
1896 GpuMat frames_[2];
\r
1897 GpuMat pyrLevel_[2], M_, bufM_, R_[2], blurredFrame_[2];
\r
1898 std::vector<GpuMat> pyramid0_, pyramid1_;
\r
1900 bool isDeviceArch11_;
\r
1904 //! Interpolate frames (images) using provided optical flow (displacement field).
\r
1905 //! frame0 - frame 0 (32-bit floating point images, single channel)
\r
1906 //! frame1 - frame 1 (the same type and size)
\r
1907 //! fu - forward horizontal displacement
\r
1908 //! fv - forward vertical displacement
\r
1909 //! bu - backward horizontal displacement
\r
1910 //! bv - backward vertical displacement
\r
1911 //! pos - new frame position
\r
1912 //! newFrame - new frame
\r
1913 //! buf - temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 GpuMat;
\r
1914 //! occlusion masks 0, occlusion masks 1,
\r
1915 //! interpolated forward flow 0, interpolated forward flow 1,
\r
1916 //! interpolated backward flow 0, interpolated backward flow 1
\r
1918 CV_EXPORTS void interpolateFrames(const GpuMat& frame0, const GpuMat& frame1,
\r
1919 const GpuMat& fu, const GpuMat& fv,
\r
1920 const GpuMat& bu, const GpuMat& bv,
\r
1921 float pos, GpuMat& newFrame, GpuMat& buf,
\r
1922 Stream& stream = Stream::Null());
\r
1924 CV_EXPORTS void createOpticalFlowNeedleMap(const GpuMat& u, const GpuMat& v, GpuMat& vertex, GpuMat& colors);
\r
1927 //////////////////////// Background/foreground segmentation ////////////////////////
\r
1929 // Foreground Object Detection from Videos Containing Complex Background.
\r
1930 // Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian.
\r
1932 class CV_EXPORTS FGDStatModel
\r
1935 struct CV_EXPORTS Params
\r
1937 int Lc; // Quantized levels per 'color' component. Power of two, typically 32, 64 or 128.
\r
1938 int N1c; // Number of color vectors used to model normal background color variation at a given pixel.
\r
1939 int N2c; // Number of color vectors retained at given pixel. Must be > N1c, typically ~ 5/3 of N1c.
\r
1940 // Used to allow the first N1c vectors to adapt over time to changing background.
\r
1942 int Lcc; // Quantized levels per 'color co-occurrence' component. Power of two, typically 16, 32 or 64.
\r
1943 int N1cc; // Number of color co-occurrence vectors used to model normal background color variation at a given pixel.
\r
1944 int N2cc; // Number of color co-occurrence vectors retained at given pixel. Must be > N1cc, typically ~ 5/3 of N1cc.
\r
1945 // Used to allow the first N1cc vectors to adapt over time to changing background.
\r
1947 bool is_obj_without_holes; // If TRUE we ignore holes within foreground blobs. Defaults to TRUE.
\r
1948 int perform_morphing; // Number of erode-dilate-erode foreground-blob cleanup iterations.
\r
1949 // These erase one-pixel junk blobs and merge almost-touching blobs. Default value is 1.
\r
1951 float alpha1; // How quickly we forget old background pixel values seen. Typically set to 0.1.
\r
1952 float alpha2; // "Controls speed of feature learning". Depends on T. Typical value circa 0.005.
\r
1953 float alpha3; // Alternate to alpha2, used (e.g.) for quicker initial convergence. Typical value 0.1.
\r
1955 float delta; // Affects color and color co-occurrence quantization, typically set to 2.
\r
1956 float T; // A percentage value which determines when new features can be recognized as new background. (Typically 0.9).
\r
1957 float minArea; // Discard foreground blobs whose bounding box is smaller than this threshold.
\r
1963 // out_cn - channels count in output result (can be 3 or 4)
\r
1964 // 4-channels require more memory, but a bit faster
\r
1965 explicit FGDStatModel(int out_cn = 3);
\r
1966 explicit FGDStatModel(const cv::gpu::GpuMat& firstFrame, const Params& params = Params(), int out_cn = 3);
\r
1970 void create(const cv::gpu::GpuMat& firstFrame, const Params& params = Params());
\r
1973 int update(const cv::gpu::GpuMat& curFrame);
\r
1975 //8UC3 or 8UC4 reference background image
\r
1976 cv::gpu::GpuMat background;
\r
1978 //8UC1 foreground image
\r
1979 cv::gpu::GpuMat foreground;
\r
1981 std::vector< std::vector<cv::Point> > foreground_regions;
\r
1984 FGDStatModel(const FGDStatModel&);
\r
1985 FGDStatModel& operator=(const FGDStatModel&);
\r
1988 std::auto_ptr<Impl> impl_;
\r
1992 Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm
\r
1994 The class implements the following algorithm:
\r
1995 "An improved adaptive background mixture model for real-time tracking with shadow detection"
\r
1996 P. KadewTraKuPong and R. Bowden,
\r
1997 Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
\r
1998 http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
\r
2000 class CV_EXPORTS MOG_GPU
\r
2003 //! the default constructor
\r
2004 MOG_GPU(int nmixtures = -1);
\r
2006 //! re-initiaization method
\r
2007 void initialize(Size frameSize, int frameType);
\r
2009 //! the update operator
\r
2010 void operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate = 0.0f, Stream& stream = Stream::Null());
\r
2012 //! computes a background image which are the mean of all background gaussians
\r
2013 void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) const;
\r
2015 //! releases all inner buffers
\r
2019 float varThreshold;
\r
2020 float backgroundRatio;
\r
2037 The class implements the following algorithm:
\r
2038 "Improved adaptive Gausian mixture model for background subtraction"
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2040 International Conference Pattern Recognition, UK, August, 2004.
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2041 http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf
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2043 class CV_EXPORTS MOG2_GPU
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2046 //! the default constructor
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2047 MOG2_GPU(int nmixtures = -1);
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2049 //! re-initiaization method
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2050 void initialize(Size frameSize, int frameType);
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2052 //! the update operator
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2053 void operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null());
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2055 //! computes a background image which are the mean of all background gaussians
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2056 void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) const;
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2058 //! releases all inner buffers
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2062 // you should call initialize after parameters changes
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2066 //! here it is the maximum allowed number of mixture components.
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2067 //! Actual number is determined dynamically per pixel
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2068 float varThreshold;
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2069 // threshold on the squared Mahalanobis distance to decide if it is well described
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2070 // by the background model or not. Related to Cthr from the paper.
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2071 // This does not influence the update of the background. A typical value could be 4 sigma
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2072 // and that is varThreshold=4*4=16; Corresponds to Tb in the paper.
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2074 /////////////////////////
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2075 // less important parameters - things you might change but be carefull
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2076 ////////////////////////
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2078 float backgroundRatio;
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2079 // corresponds to fTB=1-cf from the paper
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2080 // TB - threshold when the component becomes significant enough to be included into
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2081 // the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0.
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2082 // For alpha=0.001 it means that the mode should exist for approximately 105 frames before
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2083 // it is considered foreground
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2084 // float noiseSigma;
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2085 float varThresholdGen;
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2087 //correspondts to Tg - threshold on the squared Mahalan. dist. to decide
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2088 //when a sample is close to the existing components. If it is not close
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2089 //to any a new component will be generated. I use 3 sigma => Tg=3*3=9.
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2090 //Smaller Tg leads to more generated components and higher Tg might make
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2091 //lead to small number of components but they can grow too large
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2096 //initial variance for the newly generated components.
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2097 //It will will influence the speed of adaptation. A good guess should be made.
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2098 //A simple way is to estimate the typical standard deviation from the images.
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2099 //I used here 10 as a reasonable value
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2100 // min and max can be used to further control the variance
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2101 float fCT; //CT - complexity reduction prior
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2102 //this is related to the number of samples needed to accept that a component
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2103 //actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get
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2104 //the standard Stauffer&Grimson algorithm (maybe not exact but very similar)
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2106 //shadow detection parameters
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2107 bool bShadowDetection; //default 1 - do shadow detection
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2108 unsigned char nShadowDetection; //do shadow detection - insert this value as the detection result - 127 default value
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2110 // Tau - shadow threshold. The shadow is detected if the pixel is darker
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2111 //version of the background. Tau is a threshold on how much darker the shadow can be.
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2112 //Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow
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2113 //See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.
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2126 GpuMat bgmodelUsedModes_; //keep track of number of modes per pixel
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2130 * The class implements the following algorithm:
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2131 * "ViBe: A universal background subtraction algorithm for video sequences"
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2132 * O. Barnich and M. Van D Roogenbroeck
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2133 * IEEE Transactions on Image Processing, 20(6) :1709-1724, June 2011
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2135 class CV_EXPORTS VIBE_GPU
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2138 //! the default constructor
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2139 explicit VIBE_GPU(unsigned long rngSeed = 1234567);
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2141 //! re-initiaization method
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2142 void initialize(const GpuMat& firstFrame, Stream& stream = Stream::Null());
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2144 //! the update operator
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2145 void operator()(const GpuMat& frame, GpuMat& fgmask, Stream& stream = Stream::Null());
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2147 //! releases all inner buffers
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2150 int nbSamples; // number of samples per pixel
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2151 int reqMatches; // #_min
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2153 int subsamplingFactor; // amount of random subsampling
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2158 unsigned long rngSeed_;
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2159 GpuMat randStates_;
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2165 * Background Subtractor module. Takes a series of images and returns a sequence of mask (8UC1)
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2166 * images of the same size, where 255 indicates Foreground and 0 represents Background.
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2167 * This class implements an algorithm described in "Visual Tracking of Human Visitors under
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2168 * Variable-Lighting Conditions for a Responsive Audio Art Installation," A. Godbehere,
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2169 * A. Matsukawa, K. Goldberg, American Control Conference, Montreal, June 2012.
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2171 class CV_EXPORTS GMG_GPU
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2177 * Validate parameters and set up data structures for appropriate frame size.
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2178 * @param frameSize Input frame size
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2179 * @param min Minimum value taken on by pixels in image sequence. Usually 0
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2180 * @param max Maximum value taken on by pixels in image sequence. e.g. 1.0 or 255
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2182 void initialize(Size frameSize, float min = 0.0f, float max = 255.0f);
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2185 * Performs single-frame background subtraction and builds up a statistical background image
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2187 * @param frame Input frame
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2188 * @param fgmask Output mask image representing foreground and background pixels
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2189 * @param stream Stream for the asynchronous version
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2191 void operator ()(const GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null());
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2193 //! Releases all inner buffers
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2196 //! Total number of distinct colors to maintain in histogram.
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2199 //! Set between 0.0 and 1.0, determines how quickly features are "forgotten" from histograms.
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2200 float learningRate;
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2202 //! Number of frames of video to use to initialize histograms.
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2203 int numInitializationFrames;
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2205 //! Number of discrete levels in each channel to be used in histograms.
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2206 int quantizationLevels;
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2208 //! Prior probability that any given pixel is a background pixel. A sensitivity parameter.
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2209 float backgroundPrior;
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2211 //! Value above which pixel is determined to be FG.
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2212 float decisionThreshold;
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2214 //! Smoothing radius, in pixels, for cleaning up FG image.
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2215 int smoothingRadius;
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2217 //! Perform background model update.
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2218 bool updateBackgroundModel;
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2221 float maxVal_, minVal_;
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2227 GpuMat nfeatures_;
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2231 Ptr<FilterEngine_GPU> boxFilter_;
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2235 ////////////////////////////////// Video Encoding //////////////////////////////////
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2237 // Works only under Windows
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2238 // Supports olny H264 video codec and AVI files
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2239 class CV_EXPORTS VideoWriter_GPU
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2242 struct EncoderParams;
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2244 // Callbacks for video encoder, use it if you want to work with raw video stream
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2245 class EncoderCallBack;
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2247 enum SurfaceFormat
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2258 VideoWriter_GPU();
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2259 VideoWriter_GPU(const std::string& fileName, cv::Size frameSize, double fps, SurfaceFormat format = SF_BGR);
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2260 VideoWriter_GPU(const std::string& fileName, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format = SF_BGR);
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2261 VideoWriter_GPU(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, SurfaceFormat format = SF_BGR);
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2262 VideoWriter_GPU(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format = SF_BGR);
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2263 ~VideoWriter_GPU();
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2265 // all methods throws cv::Exception if error occurs
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2266 void open(const std::string& fileName, cv::Size frameSize, double fps, SurfaceFormat format = SF_BGR);
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2267 void open(const std::string& fileName, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format = SF_BGR);
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2268 void open(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, SurfaceFormat format = SF_BGR);
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2269 void open(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format = SF_BGR);
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2271 bool isOpened() const;
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2274 void write(const cv::gpu::GpuMat& image, bool lastFrame = false);
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2276 struct CV_EXPORTS EncoderParams
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2278 int P_Interval; // NVVE_P_INTERVAL,
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2279 int IDR_Period; // NVVE_IDR_PERIOD,
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2280 int DynamicGOP; // NVVE_DYNAMIC_GOP,
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2281 int RCType; // NVVE_RC_TYPE,
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2282 int AvgBitrate; // NVVE_AVG_BITRATE,
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2283 int PeakBitrate; // NVVE_PEAK_BITRATE,
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2284 int QP_Level_Intra; // NVVE_QP_LEVEL_INTRA,
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2285 int QP_Level_InterP; // NVVE_QP_LEVEL_INTER_P,
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2286 int QP_Level_InterB; // NVVE_QP_LEVEL_INTER_B,
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2287 int DeblockMode; // NVVE_DEBLOCK_MODE,
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2288 int ProfileLevel; // NVVE_PROFILE_LEVEL,
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2289 int ForceIntra; // NVVE_FORCE_INTRA,
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2290 int ForceIDR; // NVVE_FORCE_IDR,
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2291 int ClearStat; // NVVE_CLEAR_STAT,
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2292 int DIMode; // NVVE_SET_DEINTERLACE,
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2293 int Presets; // NVVE_PRESETS,
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2294 int DisableCabac; // NVVE_DISABLE_CABAC,
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2295 int NaluFramingType; // NVVE_CONFIGURE_NALU_FRAMING_TYPE
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2296 int DisableSPSPPS; // NVVE_DISABLE_SPS_PPS
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2299 explicit EncoderParams(const std::string& configFile);
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2301 void load(const std::string& configFile);
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2302 void save(const std::string& configFile) const;
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2305 EncoderParams getParams() const;
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2307 class CV_EXPORTS EncoderCallBack
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2317 virtual ~EncoderCallBack() {}
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2319 // callback function to signal the start of bitstream that is to be encoded
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2320 // must return pointer to buffer
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2321 virtual uchar* acquireBitStream(int* bufferSize) = 0;
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2323 // callback function to signal that the encoded bitstream is ready to be written to file
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2324 virtual void releaseBitStream(unsigned char* data, int size) = 0;
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2326 // callback function to signal that the encoding operation on the frame has started
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2327 virtual void onBeginFrame(int frameNumber, PicType picType) = 0;
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2329 // callback function signals that the encoding operation on the frame has finished
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2330 virtual void onEndFrame(int frameNumber, PicType picType) = 0;
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2334 VideoWriter_GPU(const VideoWriter_GPU&);
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2335 VideoWriter_GPU& operator=(const VideoWriter_GPU&);
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2338 std::auto_ptr<Impl> impl_;
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2342 ////////////////////////////////// Video Decoding //////////////////////////////////////////
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2347 class VideoParser;
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2350 class CV_EXPORTS VideoReader_GPU
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2364 Uncompressed_YUV420 = (('I'<<24)|('Y'<<16)|('U'<<8)|('V')), // Y,U,V (4:2:0)
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2365 Uncompressed_YV12 = (('Y'<<24)|('V'<<16)|('1'<<8)|('2')), // Y,V,U (4:2:0)
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2366 Uncompressed_NV12 = (('N'<<24)|('V'<<16)|('1'<<8)|('2')), // Y,UV (4:2:0)
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2367 Uncompressed_YUYV = (('Y'<<24)|('U'<<16)|('Y'<<8)|('V')), // YUYV/YUY2 (4:2:2)
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2368 Uncompressed_UYVY = (('U'<<24)|('Y'<<16)|('V'<<8)|('Y')), // UYVY (4:2:2)
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2382 ChromaFormat chromaFormat;
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2387 class VideoSource;
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2389 VideoReader_GPU();
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2390 explicit VideoReader_GPU(const std::string& filename);
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2391 explicit VideoReader_GPU(const cv::Ptr<VideoSource>& source);
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2393 ~VideoReader_GPU();
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2395 void open(const std::string& filename);
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2396 void open(const cv::Ptr<VideoSource>& source);
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2397 bool isOpened() const;
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2401 bool read(GpuMat& image);
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2403 FormatInfo format() const;
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2404 void dumpFormat(std::ostream& st);
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2406 class CV_EXPORTS VideoSource
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2409 VideoSource() : frameQueue_(0), videoParser_(0) {}
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2410 virtual ~VideoSource() {}
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2412 virtual FormatInfo format() const = 0;
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2413 virtual void start() = 0;
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2414 virtual void stop() = 0;
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2415 virtual bool isStarted() const = 0;
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2416 virtual bool hasError() const = 0;
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2418 void setFrameQueue(detail::FrameQueue* frameQueue) { frameQueue_ = frameQueue; }
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2419 void setVideoParser(detail::VideoParser* videoParser) { videoParser_ = videoParser; }
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2422 bool parseVideoData(const uchar* data, size_t size, bool endOfStream = false);
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2425 VideoSource(const VideoSource&);
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2426 VideoSource& operator =(const VideoSource&);
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2428 detail::FrameQueue* frameQueue_;
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2429 detail::VideoParser* videoParser_;
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2433 VideoReader_GPU(const VideoReader_GPU&);
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2434 VideoReader_GPU& operator =(const VideoReader_GPU&);
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2437 std::auto_ptr<Impl> impl_;
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2440 //! removes points (CV_32FC2, single row matrix) with zero mask value
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2441 CV_EXPORTS void compactPoints(GpuMat &points0, GpuMat &points1, const GpuMat &mask);
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2443 CV_EXPORTS void calcWobbleSuppressionMaps(
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2444 int left, int idx, int right, Size size, const Mat &ml, const Mat &mr,
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2445 GpuMat &mapx, GpuMat &mapy);
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2447 } // namespace gpu
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2451 #endif /* __OPENCV_GPU_HPP__ */
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projects / profile / ivi / opencv.git / blob