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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34 // indirect, incidental, special, exemplary, or consequential damages
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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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47 #include "opencv2/core/core.hpp"
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48 #include "opencv2/imgproc/imgproc.hpp"
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49 #include "opencv2/objdetect/objdetect.hpp"
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50 #include "opencv2/features2d/features2d.hpp"
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51 #include "opencv2/gpu/gpumat.hpp"
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57 //////////////////////////////// Initialization & Info ////////////////////////
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59 //! This is the only function that do not throw exceptions if the library is compiled without Cuda.
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60 CV_EXPORTS int getCudaEnabledDeviceCount();
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62 //! Functions below throw cv::Expception if the library is compiled without Cuda.
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64 CV_EXPORTS void setDevice(int device);
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65 CV_EXPORTS int getDevice();
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67 //! Explicitly destroys and cleans up all resources associated with the current device in the current process.
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68 //! Any subsequent API call to this device will reinitialize the device.
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69 CV_EXPORTS void resetDevice();
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73 FEATURE_SET_COMPUTE_10 = 10,
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74 FEATURE_SET_COMPUTE_11 = 11,
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75 FEATURE_SET_COMPUTE_12 = 12,
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76 FEATURE_SET_COMPUTE_13 = 13,
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77 FEATURE_SET_COMPUTE_20 = 20,
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78 FEATURE_SET_COMPUTE_21 = 21,
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79 GLOBAL_ATOMICS = FEATURE_SET_COMPUTE_11,
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80 SHARED_ATOMICS = FEATURE_SET_COMPUTE_12,
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81 NATIVE_DOUBLE = FEATURE_SET_COMPUTE_13
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84 // Gives information about what GPU archs this OpenCV GPU module was
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86 class CV_EXPORTS TargetArchs
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89 static bool builtWith(FeatureSet feature_set);
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90 static bool has(int major, int minor);
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91 static bool hasPtx(int major, int minor);
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92 static bool hasBin(int major, int minor);
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93 static bool hasEqualOrLessPtx(int major, int minor);
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94 static bool hasEqualOrGreater(int major, int minor);
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95 static bool hasEqualOrGreaterPtx(int major, int minor);
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96 static bool hasEqualOrGreaterBin(int major, int minor);
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101 // Gives information about the given GPU
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102 class CV_EXPORTS DeviceInfo
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105 // Creates DeviceInfo object for the current GPU
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106 DeviceInfo() : device_id_(getDevice()) { query(); }
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108 // Creates DeviceInfo object for the given GPU
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109 DeviceInfo(int device_id) : device_id_(device_id) { query(); }
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111 string name() const { return name_; }
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113 // Return compute capability versions
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114 int majorVersion() const { return majorVersion_; }
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115 int minorVersion() const { return minorVersion_; }
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117 int multiProcessorCount() const { return multi_processor_count_; }
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119 size_t freeMemory() const;
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120 size_t totalMemory() const;
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122 // Checks whether device supports the given feature
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123 bool supports(FeatureSet feature_set) const;
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125 // Checks whether the GPU module can be run on the given device
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126 bool isCompatible() const;
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128 int deviceID() const { return device_id_; }
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132 void queryMemory(size_t& free_memory, size_t& total_memory) const;
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137 int multi_processor_count_;
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142 //////////////////////////////// Error handling ////////////////////////
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144 CV_EXPORTS void error(const char *error_string, const char *file, const int line, const char *func);
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145 CV_EXPORTS void nppError( int err, const char *file, const int line, const char *func);
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147 //////////////////////////////// CudaMem ////////////////////////////////
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148 // CudaMem is limited cv::Mat with page locked memory allocation.
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149 // Page locked memory is only needed for async and faster coping to GPU.
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150 // It is convertable to cv::Mat header without reference counting
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151 // so you can use it with other opencv functions.
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153 // Page-locks the matrix m memory and maps it for the device(s)
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154 CV_EXPORTS void registerPageLocked(Mat& m);
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155 // Unmaps the memory of matrix m, and makes it pageable again.
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156 CV_EXPORTS void unregisterPageLocked(Mat& m);
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158 class CV_EXPORTS CudaMem
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161 enum { ALLOC_PAGE_LOCKED = 1, ALLOC_ZEROCOPY = 2, ALLOC_WRITE_COMBINED = 4 };
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164 CudaMem(const CudaMem& m);
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166 CudaMem(int rows, int cols, int type, int _alloc_type = ALLOC_PAGE_LOCKED);
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167 CudaMem(Size size, int type, int alloc_type = ALLOC_PAGE_LOCKED);
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170 //! creates from cv::Mat with coping data
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171 explicit CudaMem(const Mat& m, int alloc_type = ALLOC_PAGE_LOCKED);
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175 CudaMem& operator = (const CudaMem& m);
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177 //! returns deep copy of the matrix, i.e. the data is copied
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178 CudaMem clone() const;
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180 //! allocates new matrix data unless the matrix already has specified size and type.
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181 void create(int rows, int cols, int type, int alloc_type = ALLOC_PAGE_LOCKED);
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182 void create(Size size, int type, int alloc_type = ALLOC_PAGE_LOCKED);
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184 //! decrements reference counter and released memory if needed.
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187 //! returns matrix header with disabled reference counting for CudaMem data.
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188 Mat createMatHeader() const;
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189 operator Mat() const;
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191 //! maps host memory into device address space and returns GpuMat header for it. Throws exception if not supported by hardware.
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192 GpuMat createGpuMatHeader() const;
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193 operator GpuMat() const;
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195 //returns if host memory can be mapperd to gpu address space;
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196 static bool canMapHostMemory();
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198 // Please see cv::Mat for descriptions
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199 bool isContinuous() const;
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200 size_t elemSize() const;
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201 size_t elemSize1() const;
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204 int channels() const;
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205 size_t step1() const;
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207 bool empty() const;
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210 // Please see cv::Mat for descriptions
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224 //////////////////////////////// CudaStream ////////////////////////////////
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225 // Encapculates Cuda Stream. Provides interface for async coping.
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226 // Passed to each function that supports async kernel execution.
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227 // Reference counting is enabled
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229 class CV_EXPORTS Stream
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235 Stream(const Stream&);
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236 Stream& operator=(const Stream&);
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238 bool queryIfComplete();
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239 void waitForCompletion();
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241 //! downloads asynchronously.
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242 // Warning! cv::Mat must point to page locked memory (i.e. to CudaMem data or to its subMat)
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243 void enqueueDownload(const GpuMat& src, CudaMem& dst);
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244 void enqueueDownload(const GpuMat& src, Mat& dst);
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246 //! uploads asynchronously.
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247 // Warning! cv::Mat must point to page locked memory (i.e. to CudaMem data or to its ROI)
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248 void enqueueUpload(const CudaMem& src, GpuMat& dst);
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249 void enqueueUpload(const Mat& src, GpuMat& dst);
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251 void enqueueCopy(const GpuMat& src, GpuMat& dst);
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253 void enqueueMemSet(GpuMat& src, Scalar val);
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254 void enqueueMemSet(GpuMat& src, Scalar val, const GpuMat& mask);
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256 // converts matrix type, ex from float to uchar depending on type
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257 void enqueueConvert(const GpuMat& src, GpuMat& dst, int type, double a = 1, double b = 0);
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259 static Stream& Null();
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261 operator bool() const;
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270 friend struct StreamAccessor;
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272 explicit Stream(Impl* impl);
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276 //////////////////////////////// Filter Engine ////////////////////////////////
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279 The Base Class for 1D or Row-wise Filters
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281 This is the base class for linear or non-linear filters that process 1D data.
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282 In particular, such filters are used for the "horizontal" filtering parts in separable filters.
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284 class CV_EXPORTS BaseRowFilter_GPU
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287 BaseRowFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {}
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288 virtual ~BaseRowFilter_GPU() {}
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289 virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
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294 The Base Class for Column-wise Filters
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296 This is the base class for linear or non-linear filters that process columns of 2D arrays.
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297 Such filters are used for the "vertical" filtering parts in separable filters.
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299 class CV_EXPORTS BaseColumnFilter_GPU
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302 BaseColumnFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {}
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303 virtual ~BaseColumnFilter_GPU() {}
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304 virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
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309 The Base Class for Non-Separable 2D Filters.
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311 This is the base class for linear or non-linear 2D filters.
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313 class CV_EXPORTS BaseFilter_GPU
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316 BaseFilter_GPU(const Size& ksize_, const Point& anchor_) : ksize(ksize_), anchor(anchor_) {}
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317 virtual ~BaseFilter_GPU() {}
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318 virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
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324 The Base Class for Filter Engine.
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326 The class can be used to apply an arbitrary filtering operation to an image.
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327 It contains all the necessary intermediate buffers.
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329 class CV_EXPORTS FilterEngine_GPU
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332 virtual ~FilterEngine_GPU() {}
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334 virtual void apply(const GpuMat& src, GpuMat& dst, Rect roi = Rect(0,0,-1,-1), Stream& stream = Stream::Null()) = 0;
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337 //! returns the non-separable filter engine with the specified filter
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338 CV_EXPORTS Ptr<FilterEngine_GPU> createFilter2D_GPU(const Ptr<BaseFilter_GPU>& filter2D, int srcType, int dstType);
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340 //! returns the separable filter engine with the specified filters
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341 CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter,
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342 const Ptr<BaseColumnFilter_GPU>& columnFilter, int srcType, int bufType, int dstType);
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344 //! returns horizontal 1D box filter
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345 //! supports only CV_8UC1 source type and CV_32FC1 sum type
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346 CV_EXPORTS Ptr<BaseRowFilter_GPU> getRowSumFilter_GPU(int srcType, int sumType, int ksize, int anchor = -1);
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348 //! returns vertical 1D box filter
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349 //! supports only CV_8UC1 sum type and CV_32FC1 dst type
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350 CV_EXPORTS Ptr<BaseColumnFilter_GPU> getColumnSumFilter_GPU(int sumType, int dstType, int ksize, int anchor = -1);
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352 //! returns 2D box filter
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353 //! supports CV_8UC1 and CV_8UC4 source type, dst type must be the same as source type
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354 CV_EXPORTS Ptr<BaseFilter_GPU> getBoxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1, -1));
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356 //! returns box filter engine
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357 CV_EXPORTS Ptr<FilterEngine_GPU> createBoxFilter_GPU(int srcType, int dstType, const Size& ksize,
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358 const Point& anchor = Point(-1,-1));
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360 //! returns 2D morphological filter
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361 //! only MORPH_ERODE and MORPH_DILATE are supported
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362 //! supports CV_8UC1 and CV_8UC4 types
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363 //! kernel must have CV_8UC1 type, one rows and cols == ksize.width * ksize.height
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364 CV_EXPORTS Ptr<BaseFilter_GPU> getMorphologyFilter_GPU(int op, int type, const Mat& kernel, const Size& ksize,
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365 Point anchor=Point(-1,-1));
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367 //! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported.
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368 CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat& kernel,
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369 const Point& anchor = Point(-1,-1), int iterations = 1);
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371 //! returns 2D filter with the specified kernel
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372 //! supports CV_8UC1 and CV_8UC4 types
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373 CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, const Size& ksize,
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374 Point anchor = Point(-1, -1));
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376 //! returns the non-separable linear filter engine
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377 CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat& kernel,
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378 const Point& anchor = Point(-1,-1));
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380 //! returns the primitive row filter with the specified kernel.
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381 //! supports only CV_8UC1, CV_8UC4, CV_16SC1, CV_16SC2, CV_32SC1, CV_32FC1 source type.
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382 //! there are two version of algorithm: NPP and OpenCV.
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383 //! NPP calls when srcType == CV_8UC1 or srcType == CV_8UC4 and bufType == srcType,
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384 //! otherwise calls OpenCV version.
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385 //! NPP supports only BORDER_CONSTANT border type.
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386 //! OpenCV version supports only CV_32F as buffer depth and
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387 //! BORDER_REFLECT101, BORDER_REPLICATE and BORDER_CONSTANT border types.
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388 CV_EXPORTS Ptr<BaseRowFilter_GPU> getLinearRowFilter_GPU(int srcType, int bufType, const Mat& rowKernel,
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389 int anchor = -1, int borderType = BORDER_CONSTANT);
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391 //! returns the primitive column filter with the specified kernel.
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392 //! supports only CV_8UC1, CV_8UC4, CV_16SC1, CV_16SC2, CV_32SC1, CV_32FC1 dst type.
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393 //! there are two version of algorithm: NPP and OpenCV.
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394 //! NPP calls when dstType == CV_8UC1 or dstType == CV_8UC4 and bufType == dstType,
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395 //! otherwise calls OpenCV version.
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396 //! NPP supports only BORDER_CONSTANT border type.
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397 //! OpenCV version supports only CV_32F as buffer depth and
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398 //! BORDER_REFLECT101, BORDER_REPLICATE and BORDER_CONSTANT border types.
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399 CV_EXPORTS Ptr<BaseColumnFilter_GPU> getLinearColumnFilter_GPU(int bufType, int dstType, const Mat& columnKernel,
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400 int anchor = -1, int borderType = BORDER_CONSTANT);
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402 //! returns the separable linear filter engine
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403 CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel,
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404 const Mat& columnKernel, const Point& anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT,
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405 int columnBorderType = -1);
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407 //! returns filter engine for the generalized Sobel operator
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408 CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU(int srcType, int dstType, int dx, int dy, int ksize,
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409 int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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411 //! returns the Gaussian filter engine
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412 CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, double sigma1, double sigma2 = 0,
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413 int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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415 //! returns maximum filter
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416 CV_EXPORTS Ptr<BaseFilter_GPU> getMaxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1));
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418 //! returns minimum filter
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419 CV_EXPORTS Ptr<BaseFilter_GPU> getMinFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1));
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421 //! smooths the image using the normalized box filter
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422 //! supports CV_8UC1, CV_8UC4 types
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423 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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425 //! a synonym for normalized box filter
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426 static inline void blur(const GpuMat& src, GpuMat& dst, Size ksize, Point anchor = Point(-1,-1), Stream& stream = Stream::Null()) { boxFilter(src, dst, -1, ksize, anchor, stream); }
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428 //! erodes the image (applies the local minimum operator)
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429 CV_EXPORTS void erode( const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1, Stream& stream = Stream::Null());
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431 //! dilates the image (applies the local maximum operator)
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432 CV_EXPORTS void dilate( const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1, Stream& stream = Stream::Null());
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434 //! applies an advanced morphological operation to the image
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435 CV_EXPORTS void morphologyEx( const GpuMat& src, GpuMat& dst, int op, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1, Stream& stream = Stream::Null());
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437 //! applies non-separable 2D linear filter to the image
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438 CV_EXPORTS void filter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernel, Point anchor=Point(-1,-1), Stream& stream = Stream::Null());
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440 //! applies separable 2D linear filter to the image
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441 CV_EXPORTS void sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY,
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442 Point anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());
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444 //! applies generalized Sobel operator to the image
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445 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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446 int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());
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448 //! applies the vertical or horizontal Scharr operator to the image
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449 CV_EXPORTS void Scharr(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, double scale = 1,
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450 int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());
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452 //! smooths the image using Gaussian filter.
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453 CV_EXPORTS void GaussianBlur(const GpuMat& src, GpuMat& dst, Size ksize, double sigma1, double sigma2 = 0,
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454 int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());
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456 //! applies Laplacian operator to the image
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457 //! supports only ksize = 1 and ksize = 3
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458 CV_EXPORTS void Laplacian(const GpuMat& src, GpuMat& dst, int ddepth, int ksize = 1, double scale = 1, Stream& stream = Stream::Null());
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461 ////////////////////////////// Arithmetics ///////////////////////////////////
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463 //! transposes the matrix
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464 //! supports matrix with element size = 1, 4 and 8 bytes (CV_8UC1, CV_8UC4, CV_16UC2, CV_32FC1, etc)
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465 CV_EXPORTS void transpose(const GpuMat& src1, GpuMat& dst, Stream& stream = Stream::Null());
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467 //! reverses the order of the rows, columns or both in a matrix
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468 //! supports CV_8UC1, CV_8UC4 types
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469 CV_EXPORTS void flip(const GpuMat& a, GpuMat& b, int flipCode, Stream& stream = Stream::Null());
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471 //! transforms 8-bit unsigned integers using lookup table: dst(i)=lut(src(i))
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472 //! destination array will have the depth type as lut and the same channels number as source
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473 //! supports CV_8UC1, CV_8UC3 types
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474 CV_EXPORTS void LUT(const GpuMat& src, const Mat& lut, GpuMat& dst, Stream& stream = Stream::Null());
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476 //! makes multi-channel array out of several single-channel arrays
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477 CV_EXPORTS void merge(const GpuMat* src, size_t n, GpuMat& dst, Stream& stream = Stream::Null());
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479 //! makes multi-channel array out of several single-channel arrays
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480 CV_EXPORTS void merge(const vector<GpuMat>& src, GpuMat& dst, Stream& stream = Stream::Null());
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482 //! copies each plane of a multi-channel array to a dedicated array
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483 CV_EXPORTS void split(const GpuMat& src, GpuMat* dst, Stream& stream = Stream::Null());
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485 //! copies each plane of a multi-channel array to a dedicated array
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486 CV_EXPORTS void split(const GpuMat& src, vector<GpuMat>& dst, Stream& stream = Stream::Null());
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488 //! computes magnitude of complex (x(i).re, x(i).im) vector
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489 //! supports only CV_32FC2 type
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490 CV_EXPORTS void magnitude(const GpuMat& x, GpuMat& magnitude, Stream& stream = Stream::Null());
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492 //! computes squared magnitude of complex (x(i).re, x(i).im) vector
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493 //! supports only CV_32FC2 type
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494 CV_EXPORTS void magnitudeSqr(const GpuMat& x, GpuMat& magnitude, Stream& stream = Stream::Null());
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496 //! computes magnitude of each (x(i), y(i)) vector
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497 //! supports only floating-point source
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498 CV_EXPORTS void magnitude(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, Stream& stream = Stream::Null());
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500 //! computes squared magnitude of each (x(i), y(i)) vector
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501 //! supports only floating-point source
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502 CV_EXPORTS void magnitudeSqr(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, Stream& stream = Stream::Null());
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504 //! computes angle (angle(i)) of each (x(i), y(i)) vector
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505 //! supports only floating-point source
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506 CV_EXPORTS void phase(const GpuMat& x, const GpuMat& y, GpuMat& angle, bool angleInDegrees = false, Stream& stream = Stream::Null());
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508 //! converts Cartesian coordinates to polar
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509 //! supports only floating-point source
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510 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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512 //! converts polar coordinates to Cartesian
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513 //! supports only floating-point source
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514 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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517 //////////////////////////// Per-element operations ////////////////////////////////////
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519 //! adds one matrix to another (c = a + b)
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520 //! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types
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521 CV_EXPORTS void add(const GpuMat& a, const GpuMat& b, GpuMat& c, Stream& stream = Stream::Null());
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522 //! adds scalar to a matrix (c = a + s)
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523 //! supports CV_32FC1 and CV_32FC2 type
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524 CV_EXPORTS void add(const GpuMat& a, const Scalar& sc, GpuMat& c, Stream& stream = Stream::Null());
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526 //! subtracts one matrix from another (c = a - b)
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527 //! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types
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528 CV_EXPORTS void subtract(const GpuMat& a, const GpuMat& b, GpuMat& c, Stream& stream = Stream::Null());
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529 //! subtracts scalar from a matrix (c = a - s)
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530 //! supports CV_32FC1 and CV_32FC2 type
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531 CV_EXPORTS void subtract(const GpuMat& a, const Scalar& sc, GpuMat& c, Stream& stream = Stream::Null());
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533 //! computes element-wise product of the two arrays (c = a * b)
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534 //! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types
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535 CV_EXPORTS void multiply(const GpuMat& a, const GpuMat& b, GpuMat& c, Stream& stream = Stream::Null());
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536 //! multiplies matrix to a scalar (c = a * s)
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537 //! supports CV_32FC1 type
\r
538 CV_EXPORTS void multiply(const GpuMat& a, const Scalar& sc, GpuMat& c, Stream& stream = Stream::Null());
\r
540 //! computes element-wise quotient of the two arrays (c = a / b)
\r
541 //! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types
\r
542 CV_EXPORTS void divide(const GpuMat& a, const GpuMat& b, GpuMat& c, Stream& stream = Stream::Null());
\r
543 //! computes element-wise quotient of matrix and scalar (c = a / s)
\r
544 //! supports CV_32FC1 type
\r
545 CV_EXPORTS void divide(const GpuMat& a, const Scalar& sc, GpuMat& c, Stream& stream = Stream::Null());
\r
547 //! computes exponent of each matrix element (b = e**a)
\r
548 //! supports only CV_32FC1 type
\r
549 CV_EXPORTS void exp(const GpuMat& a, GpuMat& b, Stream& stream = Stream::Null());
\r
551 //! computes power of each matrix element:
\r
552 // (dst(i,j) = pow( src(i,j) , power), if src.type() is integer
\r
553 // (dst(i,j) = pow(fabs(src(i,j)), power), otherwise
\r
554 //! supports all, except depth == CV_64F
\r
555 CV_EXPORTS void pow(const GpuMat& src, double power, GpuMat& dst, Stream& stream = Stream::Null());
\r
557 //! computes natural logarithm of absolute value of each matrix element: b = log(abs(a))
\r
558 //! supports only CV_32FC1 type
\r
559 CV_EXPORTS void log(const GpuMat& a, GpuMat& b, Stream& stream = Stream::Null());
\r
561 //! computes element-wise absolute difference of two arrays (c = abs(a - b))
\r
562 //! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types
\r
563 CV_EXPORTS void absdiff(const GpuMat& a, const GpuMat& b, GpuMat& c, Stream& stream = Stream::Null());
\r
564 //! computes element-wise absolute difference of array and scalar (c = abs(a - s))
\r
565 //! supports only CV_32FC1 type
\r
566 CV_EXPORTS void absdiff(const GpuMat& a, const Scalar& s, GpuMat& c, Stream& stream = Stream::Null());
\r
568 //! compares elements of two arrays (c = a <cmpop> b)
\r
569 //! supports CV_8UC4, CV_32FC1 types
\r
570 CV_EXPORTS void compare(const GpuMat& a, const GpuMat& b, GpuMat& c, int cmpop, Stream& stream = Stream::Null());
\r
572 //! performs per-elements bit-wise inversion
\r
573 CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
\r
575 //! calculates per-element bit-wise disjunction of two arrays
\r
576 CV_EXPORTS void bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
\r
578 //! calculates per-element bit-wise conjunction of two arrays
\r
579 CV_EXPORTS void bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
\r
581 //! calculates per-element bit-wise "exclusive or" operation
\r
582 CV_EXPORTS void bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
\r
584 //! computes per-element minimum of two arrays (dst = min(src1, src2))
\r
585 CV_EXPORTS void min(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null());
\r
587 //! computes per-element minimum of array and scalar (dst = min(src1, src2))
\r
588 CV_EXPORTS void min(const GpuMat& src1, double src2, GpuMat& dst, Stream& stream = Stream::Null());
\r
590 //! computes per-element maximum of two arrays (dst = max(src1, src2))
\r
591 CV_EXPORTS void max(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null());
\r
593 //! computes per-element maximum of array and scalar (dst = max(src1, src2))
\r
594 CV_EXPORTS void max(const GpuMat& src1, double src2, GpuMat& dst, Stream& stream = Stream::Null());
\r
596 //! computes the weighted sum of two arrays
\r
597 CV_EXPORTS void addWeighted(const GpuMat& src1, double alpha, const GpuMat& src2, double beta, double gamma, GpuMat& dst,
\r
598 int dtype = -1, Stream& stream = Stream::Null());
\r
601 ////////////////////////////// Image processing //////////////////////////////
\r
603 //! DST[x,y] = SRC[xmap[x,y],ymap[x,y]]
\r
604 //! supports only CV_32FC1 map type
\r
605 CV_EXPORTS void remap(const GpuMat& src, GpuMat& dst, const GpuMat& xmap, const GpuMat& ymap,
\r
606 int interpolation, int borderMode = BORDER_CONSTANT, const Scalar& borderValue = Scalar(),
\r
607 Stream& stream = Stream::Null());
\r
609 //! Does mean shift filtering on GPU.
\r
610 CV_EXPORTS void meanShiftFiltering(const GpuMat& src, GpuMat& dst, int sp, int sr,
\r
611 TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
\r
613 //! Does mean shift procedure on GPU.
\r
614 CV_EXPORTS void meanShiftProc(const GpuMat& src, GpuMat& dstr, GpuMat& dstsp, int sp, int sr,
\r
615 TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
\r
617 //! Does mean shift segmentation with elimination of small regions.
\r
618 CV_EXPORTS void meanShiftSegmentation(const GpuMat& src, Mat& dst, int sp, int sr, int minsize,
\r
619 TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
\r
621 //! Does coloring of disparity image: [0..ndisp) -> [0..240, 1, 1] in HSV.
\r
622 //! Supported types of input disparity: CV_8U, CV_16S.
\r
623 //! Output disparity has CV_8UC4 type in BGRA format (alpha = 255).
\r
624 CV_EXPORTS void drawColorDisp(const GpuMat& src_disp, GpuMat& dst_disp, int ndisp, Stream& stream = Stream::Null());
\r
626 //! Reprojects disparity image to 3D space.
\r
627 //! Supports CV_8U and CV_16S types of input disparity.
\r
628 //! The output is a 4-channel floating-point (CV_32FC4) matrix.
\r
629 //! Each element of this matrix will contain the 3D coordinates of the point (x,y,z,1), computed from the disparity map.
\r
630 //! Q is the 4x4 perspective transformation matrix that can be obtained with cvStereoRectify.
\r
631 CV_EXPORTS void reprojectImageTo3D(const GpuMat& disp, GpuMat& xyzw, const Mat& Q, Stream& stream = Stream::Null());
\r
633 //! converts image from one color space to another
\r
634 CV_EXPORTS void cvtColor(const GpuMat& src, GpuMat& dst, int code, int dcn = 0, Stream& stream = Stream::Null());
\r
636 //! applies fixed threshold to the image
\r
637 CV_EXPORTS double threshold(const GpuMat& src, GpuMat& dst, double thresh, double maxval, int type, Stream& stream = Stream::Null());
\r
639 //! resizes the image
\r
640 //! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
\r
641 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
643 //! warps the image using affine transformation
\r
644 //! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
\r
645 CV_EXPORTS void warpAffine(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags = INTER_LINEAR, Stream& stream = Stream::Null());
\r
647 //! warps the image using perspective transformation
\r
648 //! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
\r
649 CV_EXPORTS void warpPerspective(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags = INTER_LINEAR, Stream& stream = Stream::Null());
\r
651 //! builds plane warping maps
\r
652 CV_EXPORTS void buildWarpPlaneMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, float scale,
\r
653 GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null());
\r
655 //! builds cylindrical warping maps
\r
656 CV_EXPORTS void buildWarpCylindricalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, float scale,
\r
657 GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null());
\r
659 //! builds spherical warping maps
\r
660 CV_EXPORTS void buildWarpSphericalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, float scale,
\r
661 GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null());
\r
663 //! rotate 8bit single or four channel image
\r
664 //! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
\r
665 //! supports CV_8UC1, CV_8UC4 types
\r
666 CV_EXPORTS void rotate(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift = 0, double yShift = 0, int interpolation = INTER_LINEAR, Stream& stream = Stream::Null());
\r
668 //! copies 2D array to a larger destination array and pads borders with user-specifiable constant
\r
669 CV_EXPORTS void copyMakeBorder(const GpuMat& src, GpuMat& dst, int top, int bottom, int left, int right, int borderType, 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
676 //! buffered version
\r
677 CV_EXPORTS void integralBuffered(const GpuMat& src, GpuMat& sum, GpuMat& buffer, Stream& stream = Stream::Null());
\r
679 //! computes the integral image and integral for the squared image
\r
680 //! sum will have CV_32S type, sqsum - CV32F type
\r
681 //! supports only CV_8UC1 source type
\r
682 CV_EXPORTS void integral(const GpuMat& src, GpuMat& sum, GpuMat& sqsum, Stream& stream = Stream::Null());
\r
684 //! computes squared integral image
\r
685 //! result matrix will have 64F type, but will contain 64U values
\r
686 //! supports source images of 8UC1 type only
\r
687 CV_EXPORTS void sqrIntegral(const GpuMat& src, GpuMat& sqsum, Stream& stream = Stream::Null());
\r
689 //! computes vertical sum, supports only CV_32FC1 images
\r
690 CV_EXPORTS void columnSum(const GpuMat& src, GpuMat& sum);
\r
692 //! computes the standard deviation of integral images
\r
693 //! supports only CV_32SC1 source type and CV_32FC1 sqr type
\r
694 //! output will have CV_32FC1 type
\r
695 CV_EXPORTS void rectStdDev(const GpuMat& src, const GpuMat& sqr, GpuMat& dst, const Rect& rect, Stream& stream = Stream::Null());
\r
697 //! computes Harris cornerness criteria at each image pixel
\r
698 CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, double k, int borderType=BORDER_REFLECT101);
\r
699 CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, double k, int borderType=BORDER_REFLECT101);
\r
701 //! computes minimum eigen value of 2x2 derivative covariation matrix at each pixel - the cornerness criteria
\r
702 CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, int borderType=BORDER_REFLECT101);
\r
703 CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, int borderType=BORDER_REFLECT101);
\r
705 //! performs per-element multiplication of two full (not packed) Fourier spectrums
\r
706 //! supports 32FC2 matrixes only (interleaved format)
\r
707 CV_EXPORTS void mulSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, bool conjB=false);
\r
709 //! performs per-element multiplication of two full (not packed) Fourier spectrums
\r
710 //! supports 32FC2 matrixes only (interleaved format)
\r
711 CV_EXPORTS void mulAndScaleSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags,
\r
712 float scale, bool conjB=false);
\r
714 //! Performs a forward or inverse discrete Fourier transform (1D or 2D) of floating point matrix.
\r
715 //! Param dft_size is the size of DFT transform.
\r
717 //! If the source matrix is not continous, then additional copy will be done,
\r
718 //! so to avoid copying ensure the source matrix is continous one. If you want to use
\r
719 //! preallocated output ensure it is continuous too, otherwise it will be reallocated.
\r
721 //! Being implemented via CUFFT real-to-complex transform result contains only non-redundant values
\r
722 //! in CUFFT's format. Result as full complex matrix for such kind of transform cannot be retrieved.
\r
724 //! For complex-to-real transform it is assumed that the source matrix is packed in CUFFT's format.
\r
725 CV_EXPORTS void dft(const GpuMat& src, GpuMat& dst, Size dft_size, int flags=0);
\r
727 //! computes convolution (or cross-correlation) of two images using discrete Fourier transform
\r
728 //! supports source images of 32FC1 type only
\r
729 //! result matrix will have 32FC1 type
\r
730 CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result,
\r
733 struct CV_EXPORTS ConvolveBuf;
\r
735 //! buffered version
\r
736 CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result,
\r
737 bool ccorr, ConvolveBuf& buf);
\r
739 struct CV_EXPORTS ConvolveBuf
\r
742 ConvolveBuf(Size image_size, Size templ_size)
\r
743 { create(image_size, templ_size); }
\r
744 void create(Size image_size, Size templ_size);
\r
747 static Size estimateBlockSize(Size result_size, Size templ_size);
\r
748 friend void convolve(const GpuMat&, const GpuMat&, GpuMat&, bool, ConvolveBuf&);
\r
755 GpuMat image_spect, templ_spect, result_spect;
\r
756 GpuMat image_block, templ_block, result_data;
\r
759 //! computes the proximity map for the raster template and the image where the template is searched for
\r
760 CV_EXPORTS void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method);
\r
762 //! smoothes the source image and downsamples it
\r
763 CV_EXPORTS void pyrDown(const GpuMat& src, GpuMat& dst, int borderType = BORDER_DEFAULT, Stream& stream = Stream::Null());
\r
765 //! upsamples the source image and then smoothes it
\r
766 CV_EXPORTS void pyrUp(const GpuMat& src, GpuMat& dst, int borderType = BORDER_DEFAULT, Stream& stream = Stream::Null());
\r
768 //! performs linear blending of two images
\r
769 //! to avoid accuracy errors sum of weigths shouldn't be very close to zero
\r
770 CV_EXPORTS void blendLinear(const GpuMat& img1, const GpuMat& img2, const GpuMat& weights1, const GpuMat& weights2,
\r
771 GpuMat& result, Stream& stream = Stream::Null());
\r
774 struct CV_EXPORTS CannyBuf;
\r
776 CV_EXPORTS void Canny(const GpuMat& image, GpuMat& edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
\r
777 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
778 CV_EXPORTS void Canny(const GpuMat& dx, const GpuMat& dy, GpuMat& edges, double low_thresh, double high_thresh, bool L2gradient = false);
\r
779 CV_EXPORTS void Canny(const GpuMat& dx, const GpuMat& dy, CannyBuf& buf, GpuMat& edges, double low_thresh, double high_thresh, bool L2gradient = false);
\r
781 struct CV_EXPORTS CannyBuf
\r
784 explicit CannyBuf(const Size& image_size, int apperture_size = 3) {create(image_size, apperture_size);}
\r
785 CannyBuf(const GpuMat& dx_, const GpuMat& dy_);
\r
787 void create(const Size& image_size, int apperture_size = 3);
\r
792 GpuMat dx_buf, dy_buf;
\r
794 GpuMat trackBuf1, trackBuf2;
\r
795 Ptr<FilterEngine_GPU> filterDX, filterDY;
\r
798 ////////////////////////////// Matrix reductions //////////////////////////////
\r
800 //! computes mean value and standard deviation of all or selected array elements
\r
801 //! supports only CV_8UC1 type
\r
802 CV_EXPORTS void meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev);
\r
804 //! computes norm of array
\r
805 //! supports NORM_INF, NORM_L1, NORM_L2
\r
806 //! supports all matrices except 64F
\r
807 CV_EXPORTS double norm(const GpuMat& src1, int normType=NORM_L2);
\r
809 //! computes norm of array
\r
810 //! supports NORM_INF, NORM_L1, NORM_L2
\r
811 //! supports all matrices except 64F
\r
812 CV_EXPORTS double norm(const GpuMat& src1, int normType, GpuMat& buf);
\r
814 //! computes norm of the difference between two arrays
\r
815 //! supports NORM_INF, NORM_L1, NORM_L2
\r
816 //! supports only CV_8UC1 type
\r
817 CV_EXPORTS double norm(const GpuMat& src1, const GpuMat& src2, int normType=NORM_L2);
\r
819 //! computes sum of array elements
\r
820 //! supports only single channel images
\r
821 CV_EXPORTS Scalar sum(const GpuMat& src);
\r
823 //! computes sum of array elements
\r
824 //! supports only single channel images
\r
825 CV_EXPORTS Scalar sum(const GpuMat& src, GpuMat& buf);
\r
827 //! computes sum of array elements absolute values
\r
828 //! supports only single channel images
\r
829 CV_EXPORTS Scalar absSum(const GpuMat& src);
\r
831 //! computes sum of array elements absolute values
\r
832 //! supports only single channel images
\r
833 CV_EXPORTS Scalar absSum(const GpuMat& src, GpuMat& buf);
\r
835 //! computes squared sum of array elements
\r
836 //! supports only single channel images
\r
837 CV_EXPORTS Scalar sqrSum(const GpuMat& src);
\r
839 //! computes squared sum of array elements
\r
840 //! supports only single channel images
\r
841 CV_EXPORTS Scalar sqrSum(const GpuMat& src, GpuMat& buf);
\r
843 //! finds global minimum and maximum array elements and returns their values
\r
844 CV_EXPORTS void minMax(const GpuMat& src, double* minVal, double* maxVal=0, const GpuMat& mask=GpuMat());
\r
846 //! finds global minimum and maximum array elements and returns their values
\r
847 CV_EXPORTS void minMax(const GpuMat& src, double* minVal, double* maxVal, const GpuMat& mask, GpuMat& buf);
\r
849 //! finds global minimum and maximum array elements and returns their values with locations
\r
850 CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal=0, Point* minLoc=0, Point* maxLoc=0,
\r
851 const GpuMat& mask=GpuMat());
\r
853 //! finds global minimum and maximum array elements and returns their values with locations
\r
854 CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
\r
855 const GpuMat& mask, GpuMat& valbuf, GpuMat& locbuf);
\r
857 //! counts non-zero array elements
\r
858 CV_EXPORTS int countNonZero(const GpuMat& src);
\r
860 //! counts non-zero array elements
\r
861 CV_EXPORTS int countNonZero(const GpuMat& src, GpuMat& buf);
\r
864 ///////////////////////////// Calibration 3D //////////////////////////////////
\r
866 CV_EXPORTS void transformPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec,
\r
867 GpuMat& dst, Stream& stream = Stream::Null());
\r
869 CV_EXPORTS void projectPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec,
\r
870 const Mat& camera_mat, const Mat& dist_coef, GpuMat& dst,
\r
871 Stream& stream = Stream::Null());
\r
873 CV_EXPORTS void solvePnPRansac(const Mat& object, const Mat& image, const Mat& camera_mat,
\r
874 const Mat& dist_coef, Mat& rvec, Mat& tvec, bool use_extrinsic_guess=false,
\r
875 int num_iters=100, float max_dist=8.0, int min_inlier_count=100,
\r
876 vector<int>* inliers=NULL);
\r
878 //////////////////////////////// Image Labeling ////////////////////////////////
\r
880 //!performs labeling via graph cuts
\r
881 CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& bottom, GpuMat& labels, GpuMat& buf, Stream& stream = Stream::Null());
\r
883 ////////////////////////////////// Histograms //////////////////////////////////
\r
885 //! Compute levels with even distribution. levels will have 1 row and nLevels cols and CV_32SC1 type.
\r
886 CV_EXPORTS void evenLevels(GpuMat& levels, int nLevels, int lowerLevel, int upperLevel);
\r
887 //! Calculates histogram with evenly distributed bins for signle channel source.
\r
888 //! Supports CV_8UC1, CV_16UC1 and CV_16SC1 source types.
\r
889 //! Output hist will have one row and histSize cols and CV_32SC1 type.
\r
890 CV_EXPORTS void histEven(const GpuMat& src, GpuMat& hist, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());
\r
891 CV_EXPORTS void histEven(const GpuMat& src, GpuMat& hist, GpuMat& buf, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());
\r
892 //! Calculates histogram with evenly distributed bins for four-channel source.
\r
893 //! All channels of source are processed separately.
\r
894 //! Supports CV_8UC4, CV_16UC4 and CV_16SC4 source types.
\r
895 //! Output hist[i] will have one row and histSize[i] cols and CV_32SC1 type.
\r
896 CV_EXPORTS void histEven(const GpuMat& src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null());
\r
897 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
898 //! Calculates histogram with bins determined by levels array.
\r
899 //! levels must have one row and CV_32SC1 type if source has integer type or CV_32FC1 otherwise.
\r
900 //! Supports CV_8UC1, CV_16UC1, CV_16SC1 and CV_32FC1 source types.
\r
901 //! Output hist will have one row and (levels.cols-1) cols and CV_32SC1 type.
\r
902 CV_EXPORTS void histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels, Stream& stream = Stream::Null());
\r
903 CV_EXPORTS void histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels, GpuMat& buf, Stream& stream = Stream::Null());
\r
904 //! Calculates histogram with bins determined by levels array.
\r
905 //! All levels must have one row and CV_32SC1 type if source has integer type or CV_32FC1 otherwise.
\r
906 //! All channels of source are processed separately.
\r
907 //! Supports CV_8UC4, CV_16UC4, CV_16SC4 and CV_32FC4 source types.
\r
908 //! Output hist[i] will have one row and (levels[i].cols-1) cols and CV_32SC1 type.
\r
909 CV_EXPORTS void histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], Stream& stream = Stream::Null());
\r
910 CV_EXPORTS void histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], GpuMat& buf, Stream& stream = Stream::Null());
\r
912 //! Calculates histogram for 8u one channel image
\r
913 //! Output hist will have one row, 256 cols and CV32SC1 type.
\r
914 CV_EXPORTS void calcHist(const GpuMat& src, GpuMat& hist, Stream& stream = Stream::Null());
\r
915 CV_EXPORTS void calcHist(const GpuMat& src, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null());
\r
917 //! normalizes the grayscale image brightness and contrast by normalizing its histogram
\r
918 CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
\r
919 CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, Stream& stream = Stream::Null());
\r
920 CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null());
\r
922 //////////////////////////////// StereoBM_GPU ////////////////////////////////
\r
924 class CV_EXPORTS StereoBM_GPU
\r
927 enum { BASIC_PRESET = 0, PREFILTER_XSOBEL = 1 };
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929 enum { DEFAULT_NDISP = 64, DEFAULT_WINSZ = 19 };
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931 //! the default constructor
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933 //! the full constructor taking the camera-specific preset, number of disparities and the SAD window size. ndisparities must be multiple of 8.
\r
934 StereoBM_GPU(int preset, int ndisparities = DEFAULT_NDISP, int winSize = DEFAULT_WINSZ);
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936 //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair
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937 //! Output disparity has CV_8U type.
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938 void operator() ( const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null());
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940 //! Some heuristics that tries to estmate
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941 // if current GPU will be faster than CPU in this algorithm.
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942 // It queries current active device.
\r
943 static bool checkIfGpuCallReasonable();
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949 // If avergeTexThreshold == 0 => post procesing is disabled
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950 // If avergeTexThreshold != 0 then disparity is set 0 in each point (x,y) where for left image
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951 // SumOfHorizontalGradiensInWindow(x, y, winSize) < (winSize * winSize) * avergeTexThreshold
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952 // i.e. input left image is low textured.
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953 float avergeTexThreshold;
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955 GpuMat minSSD, leBuf, riBuf;
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958 ////////////////////////// StereoBeliefPropagation ///////////////////////////
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959 // "Efficient Belief Propagation for Early Vision"
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962 class CV_EXPORTS StereoBeliefPropagation
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965 enum { DEFAULT_NDISP = 64 };
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966 enum { DEFAULT_ITERS = 5 };
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967 enum { DEFAULT_LEVELS = 5 };
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969 static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels);
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971 //! the default constructor
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972 explicit StereoBeliefPropagation(int ndisp = DEFAULT_NDISP,
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973 int iters = DEFAULT_ITERS,
\r
974 int levels = DEFAULT_LEVELS,
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975 int msg_type = CV_32F);
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977 //! the full constructor taking the number of disparities, number of BP iterations on each level,
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978 //! number of levels, truncation of data cost, data weight,
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979 //! truncation of discontinuity cost and discontinuity single jump
\r
980 //! DataTerm = data_weight * min(fabs(I2-I1), max_data_term)
\r
981 //! DiscTerm = min(disc_single_jump * fabs(f1-f2), max_disc_term)
\r
982 //! please see paper for more details
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983 StereoBeliefPropagation(int ndisp, int iters, int levels,
\r
984 float max_data_term, float data_weight,
\r
985 float max_disc_term, float disc_single_jump,
\r
986 int msg_type = CV_32F);
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988 //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair,
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989 //! if disparity is empty output type will be CV_16S else output type will be disparity.type().
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990 void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null());
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993 //! version for user specified data term
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994 void operator()(const GpuMat& data, GpuMat& disparity, Stream& stream = Stream::Null());
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1001 float max_data_term;
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1002 float data_weight;
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1003 float max_disc_term;
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1004 float disc_single_jump;
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1008 GpuMat u, d, l, r, u2, d2, l2, r2;
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1009 std::vector<GpuMat> datas;
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1013 /////////////////////////// StereoConstantSpaceBP ///////////////////////////
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1014 // "A Constant-Space Belief Propagation Algorithm for Stereo Matching"
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1015 // Qingxiong Yang, Liang Wang, Narendra Ahuja
\r
1016 // http://vision.ai.uiuc.edu/~qyang6/
\r
1018 class CV_EXPORTS StereoConstantSpaceBP
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1021 enum { DEFAULT_NDISP = 128 };
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1022 enum { DEFAULT_ITERS = 8 };
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1023 enum { DEFAULT_LEVELS = 4 };
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1024 enum { DEFAULT_NR_PLANE = 4 };
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1026 static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels, int& nr_plane);
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1028 //! the default constructor
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1029 explicit StereoConstantSpaceBP(int ndisp = DEFAULT_NDISP,
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1030 int iters = DEFAULT_ITERS,
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1031 int levels = DEFAULT_LEVELS,
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1032 int nr_plane = DEFAULT_NR_PLANE,
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1033 int msg_type = CV_32F);
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1035 //! the full constructor taking the number of disparities, number of BP iterations on each level,
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1036 //! number of levels, number of active disparity on the first level, truncation of data cost, data weight,
\r
1037 //! truncation of discontinuity cost, discontinuity single jump and minimum disparity threshold
\r
1038 StereoConstantSpaceBP(int ndisp, int iters, int levels, int nr_plane,
\r
1039 float max_data_term, float data_weight, float max_disc_term, float disc_single_jump,
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1040 int min_disp_th = 0,
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1041 int msg_type = CV_32F);
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1043 //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair,
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1044 //! if disparity is empty output type will be CV_16S else output type will be disparity.type().
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1045 void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null());
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1054 float max_data_term;
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1055 float data_weight;
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1056 float max_disc_term;
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1057 float disc_single_jump;
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1063 bool use_local_init_data_cost;
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1065 GpuMat u[2], d[2], l[2], r[2];
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1066 GpuMat disp_selected_pyr[2];
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1069 GpuMat data_cost_selected;
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1076 /////////////////////////// DisparityBilateralFilter ///////////////////////////
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1077 // Disparity map refinement using joint bilateral filtering given a single color image.
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1078 // Qingxiong Yang, Liang Wang, Narendra Ahuja
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1079 // http://vision.ai.uiuc.edu/~qyang6/
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1081 class CV_EXPORTS DisparityBilateralFilter
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1084 enum { DEFAULT_NDISP = 64 };
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1085 enum { DEFAULT_RADIUS = 3 };
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1086 enum { DEFAULT_ITERS = 1 };
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1088 //! the default constructor
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1089 explicit DisparityBilateralFilter(int ndisp = DEFAULT_NDISP, int radius = DEFAULT_RADIUS, int iters = DEFAULT_ITERS);
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1091 //! the full constructor taking the number of disparities, filter radius,
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1092 //! number of iterations, truncation of data continuity, truncation of disparity continuity
\r
1093 //! and filter range sigma
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1094 DisparityBilateralFilter(int ndisp, int radius, int iters, float edge_threshold, float max_disc_threshold, float sigma_range);
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1096 //! the disparity map refinement operator. Refine disparity map using joint bilateral filtering given a single color image.
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1097 //! disparity must have CV_8U or CV_16S type, image must have CV_8UC1 or CV_8UC3 type.
\r
1098 void operator()(const GpuMat& disparity, const GpuMat& image, GpuMat& dst, Stream& stream = Stream::Null());
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1105 float edge_threshold;
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1106 float max_disc_threshold;
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1107 float sigma_range;
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1109 GpuMat table_color;
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1110 GpuMat table_space;
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1114 //////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
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1116 struct CV_EXPORTS HOGDescriptor
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1118 enum { DEFAULT_WIN_SIGMA = -1 };
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1119 enum { DEFAULT_NLEVELS = 64 };
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1120 enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };
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1122 HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16),
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1123 Size block_stride=Size(8, 8), Size cell_size=Size(8, 8),
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1124 int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA,
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1125 double threshold_L2hys=0.2, bool gamma_correction=true,
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1126 int nlevels=DEFAULT_NLEVELS);
\r
1128 size_t getDescriptorSize() const;
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1129 size_t getBlockHistogramSize() const;
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1131 void setSVMDetector(const vector<float>& detector);
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1133 static vector<float> getDefaultPeopleDetector();
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1134 static vector<float> getPeopleDetector48x96();
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1135 static vector<float> getPeopleDetector64x128();
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1137 void detect(const GpuMat& img, vector<Point>& found_locations,
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1138 double hit_threshold=0, Size win_stride=Size(),
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1139 Size padding=Size());
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1141 void detectMultiScale(const GpuMat& img, vector<Rect>& found_locations,
\r
1142 double hit_threshold=0, Size win_stride=Size(),
\r
1143 Size padding=Size(), double scale0=1.05,
\r
1144 int group_threshold=2);
\r
1146 void getDescriptors(const GpuMat& img, Size win_stride,
\r
1147 GpuMat& descriptors,
\r
1148 int descr_format=DESCR_FORMAT_COL_BY_COL);
\r
1152 Size block_stride;
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1156 double threshold_L2hys;
\r
1157 bool gamma_correction;
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1161 void computeBlockHistograms(const GpuMat& img);
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1162 void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle);
\r
1164 double getWinSigma() const;
\r
1165 bool checkDetectorSize() const;
\r
1167 static int numPartsWithin(int size, int part_size, int stride);
\r
1168 static Size numPartsWithin(Size size, Size part_size, Size stride);
\r
1170 // Coefficients of the separating plane
\r
1174 // Results of the last classification step
\r
1175 GpuMat labels, labels_buf;
\r
1178 // Results of the last histogram evaluation step
\r
1179 GpuMat block_hists, block_hists_buf;
\r
1181 // Gradients conputation results
\r
1182 GpuMat grad, qangle, grad_buf, qangle_buf;
\r
1184 // returns subbuffer with required size, reallocates buffer if nessesary.
\r
1185 static GpuMat getBuffer(const Size& sz, int type, GpuMat& buf);
\r
1186 static GpuMat getBuffer(int rows, int cols, int type, GpuMat& buf);
\r
1188 std::vector<GpuMat> image_scales;
\r
1192 ////////////////////////////////// BruteForceMatcher //////////////////////////////////
\r
1194 class CV_EXPORTS BruteForceMatcher_GPU_base
\r
1197 enum DistType {L1Dist = 0, L2Dist, HammingDist};
\r
1199 explicit BruteForceMatcher_GPU_base(DistType distType = L2Dist);
\r
1201 // Add descriptors to train descriptor collection.
\r
1202 void add(const std::vector<GpuMat>& descCollection);
\r
1204 // Get train descriptors collection.
\r
1205 const std::vector<GpuMat>& getTrainDescriptors() const;
\r
1207 // Clear train descriptors collection.
\r
1210 // Return true if there are not train descriptors in collection.
\r
1211 bool empty() const;
\r
1213 // Return true if the matcher supports mask in match methods.
\r
1214 bool isMaskSupported() const;
\r
1216 // Find one best match for each query descriptor.
\r
1217 // trainIdx.at<int>(0, queryIdx) will contain best train index for queryIdx
\r
1218 // distance.at<float>(0, queryIdx) will contain distance
\r
1219 void matchSingle(const GpuMat& queryDescs, const GpuMat& trainDescs,
\r
1220 GpuMat& trainIdx, GpuMat& distance,
\r
1221 const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
\r
1223 // Download trainIdx and distance and convert it to CPU vector with DMatch
\r
1224 static void matchDownload(const GpuMat& trainIdx, const GpuMat& distance, std::vector<DMatch>& matches);
\r
1225 // Convert trainIdx and distance to vector with DMatch
\r
1226 static void matchConvert(const Mat& trainIdx, const Mat& distance, std::vector<DMatch>& matches);
\r
1228 // Find one best match for each query descriptor.
\r
1229 void match(const GpuMat& queryDescs, const GpuMat& trainDescs, std::vector<DMatch>& matches,
\r
1230 const GpuMat& mask = GpuMat());
\r
1232 // Make gpu collection of trains and masks in suitable format for matchCollection function
\r
1233 void makeGpuCollection(GpuMat& trainCollection, GpuMat& maskCollection,
\r
1234 const vector<GpuMat>& masks = std::vector<GpuMat>());
\r
1236 // Find one best match from train collection for each query descriptor.
\r
1237 // trainIdx.at<int>(0, queryIdx) will contain best train index for queryIdx
\r
1238 // imgIdx.at<int>(0, queryIdx) will contain best image index for queryIdx
\r
1239 // distance.at<float>(0, queryIdx) will contain distance
\r
1240 void matchCollection(const GpuMat& queryDescs, const GpuMat& trainCollection,
\r
1241 GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
\r
1242 const GpuMat& maskCollection, Stream& stream = Stream::Null());
\r
1244 // Download trainIdx, imgIdx and distance and convert it to vector with DMatch
\r
1245 static void matchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, std::vector<DMatch>& matches);
\r
1246 // Convert trainIdx, imgIdx and distance to vector with DMatch
\r
1247 static void matchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, std::vector<DMatch>& matches);
\r
1249 // Find one best match from train collection for each query descriptor.
\r
1250 void match(const GpuMat& queryDescs, std::vector<DMatch>& matches, const std::vector<GpuMat>& masks = std::vector<GpuMat>());
\r
1252 // Find k best matches for each query descriptor (in increasing order of distances).
\r
1253 // trainIdx.at<int>(queryIdx, i) will contain index of i'th best trains (i < k).
\r
1254 // distance.at<float>(queryIdx, i) will contain distance.
\r
1255 // allDist is a buffer to store all distance between query descriptors and train descriptors
\r
1256 // it have size (nQuery,nTrain) and CV_32F type
\r
1257 // allDist.at<float>(queryIdx, trainIdx) will contain FLT_MAX, if trainIdx is one from k best,
\r
1258 // otherwise it will contain distance between queryIdx and trainIdx descriptors
\r
1259 void knnMatch(const GpuMat& queryDescs, const GpuMat& trainDescs,
\r
1260 GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k, const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
\r
1262 // Download trainIdx and distance and convert it to vector with DMatch
\r
1263 // compactResult is used when mask is not empty. If compactResult is false matches
\r
1264 // vector will have the same size as queryDescriptors rows. If compactResult is true
\r
1265 // matches vector will not contain matches for fully masked out query descriptors.
\r
1266 static void knnMatchDownload(const GpuMat& trainIdx, const GpuMat& distance,
\r
1267 std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
\r
1268 // Convert trainIdx and distance to vector with DMatch
\r
1269 static void knnMatchConvert(const Mat& trainIdx, const Mat& distance,
\r
1270 std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
\r
1272 // Find k best matches for each query descriptor (in increasing order of distances).
\r
1273 // compactResult is used when mask is not empty. If compactResult is false matches
\r
1274 // vector will have the same size as queryDescriptors rows. If compactResult is true
\r
1275 // matches vector will not contain matches for fully masked out query descriptors.
\r
1276 void knnMatch(const GpuMat& queryDescs, const GpuMat& trainDescs,
\r
1277 std::vector< std::vector<DMatch> >& matches, int k, const GpuMat& mask = GpuMat(),
\r
1278 bool compactResult = false);
\r
1280 // Find k best matches for each query descriptor (in increasing order of distances).
\r
1281 // compactResult is used when mask is not empty. If compactResult is false matches
\r
1282 // vector will have the same size as queryDescriptors rows. If compactResult is true
\r
1283 // matches vector will not contain matches for fully masked out query descriptors.
\r
1284 void knnMatch(const GpuMat& queryDescs, std::vector< std::vector<DMatch> >& matches, int knn,
\r
1285 const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false );
\r
1287 // Find best matches for each query descriptor which have distance less than maxDistance.
\r
1288 // nMatches.at<unsigned int>(0, queruIdx) will contain matches count for queryIdx.
\r
1289 // carefully nMatches can be greater than trainIdx.cols - it means that matcher didn't find all matches,
\r
1290 // because it didn't have enough memory.
\r
1291 // trainIdx.at<int>(queruIdx, i) will contain ith train index (i < min(nMatches.at<unsigned int>(0, queruIdx), trainIdx.cols))
\r
1292 // distance.at<int>(queruIdx, i) will contain ith distance (i < min(nMatches.at<unsigned int>(0, queruIdx), trainIdx.cols))
\r
1293 // If trainIdx is empty, then trainIdx and distance will be created with size nQuery x nTrain,
\r
1294 // otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
\r
1295 // Matches doesn't sorted.
\r
1296 void radiusMatch(const GpuMat& queryDescs, const GpuMat& trainDescs,
\r
1297 GpuMat& trainIdx, GpuMat& nMatches, GpuMat& distance, float maxDistance,
\r
1298 const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
\r
1300 // Download trainIdx, nMatches and distance and convert it to vector with DMatch.
\r
1301 // matches will be sorted in increasing order of distances.
\r
1302 // compactResult is used when mask is not empty. If compactResult is false matches
\r
1303 // vector will have the same size as queryDescriptors rows. If compactResult is true
\r
1304 // matches vector will not contain matches for fully masked out query descriptors.
\r
1305 static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& nMatches, const GpuMat& distance,
\r
1306 std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
\r
1307 // Convert trainIdx, nMatches and distance to vector with DMatch.
\r
1308 static void radiusMatchConvert(const Mat& trainIdx, const Mat& nMatches, const Mat& distance,
\r
1309 std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
\r
1311 // Find best matches for each query descriptor which have distance less than maxDistance
\r
1312 // in increasing order of distances).
\r
1313 void radiusMatch(const GpuMat& queryDescs, const GpuMat& trainDescs,
\r
1314 std::vector< std::vector<DMatch> >& matches, float maxDistance,
\r
1315 const GpuMat& mask = GpuMat(), bool compactResult = false);
\r
1317 // Find best matches from train collection for each query descriptor which have distance less than
\r
1318 // maxDistance (in increasing order of distances).
\r
1319 void radiusMatch(const GpuMat& queryDescs, std::vector< std::vector<DMatch> >& matches, float maxDistance,
\r
1320 const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);
\r
1322 DistType distType;
\r
1325 std::vector<GpuMat> trainDescCollection;
\r
1328 template <class Distance>
\r
1329 class CV_EXPORTS BruteForceMatcher_GPU;
\r
1331 template <typename T>
\r
1332 class CV_EXPORTS BruteForceMatcher_GPU< L1<T> > : public BruteForceMatcher_GPU_base
\r
1335 explicit BruteForceMatcher_GPU() : BruteForceMatcher_GPU_base(L1Dist) {}
\r
1336 explicit BruteForceMatcher_GPU(L1<T> /*d*/) : BruteForceMatcher_GPU_base(L1Dist) {}
\r
1338 template <typename T>
\r
1339 class CV_EXPORTS BruteForceMatcher_GPU< L2<T> > : public BruteForceMatcher_GPU_base
\r
1342 explicit BruteForceMatcher_GPU() : BruteForceMatcher_GPU_base(L2Dist) {}
\r
1343 explicit BruteForceMatcher_GPU(L2<T> /*d*/) : BruteForceMatcher_GPU_base(L2Dist) {}
\r
1345 template <> class CV_EXPORTS BruteForceMatcher_GPU< HammingLUT > : public BruteForceMatcher_GPU_base
\r
1348 explicit BruteForceMatcher_GPU() : BruteForceMatcher_GPU_base(HammingDist) {}
\r
1349 explicit BruteForceMatcher_GPU(HammingLUT /*d*/) : BruteForceMatcher_GPU_base(HammingDist) {}
\r
1351 template <> class CV_EXPORTS BruteForceMatcher_GPU< Hamming > : public BruteForceMatcher_GPU_base
\r
1354 explicit BruteForceMatcher_GPU() : BruteForceMatcher_GPU_base(HammingDist) {}
\r
1355 explicit BruteForceMatcher_GPU(Hamming /*d*/) : BruteForceMatcher_GPU_base(HammingDist) {}
\r
1358 ////////////////////////////////// CascadeClassifier_GPU //////////////////////////////////////////
\r
1359 // The cascade classifier class for object detection.
\r
1360 class CV_EXPORTS CascadeClassifier_GPU
\r
1363 CascadeClassifier_GPU();
\r
1364 CascadeClassifier_GPU(const string& filename);
\r
1365 ~CascadeClassifier_GPU();
\r
1367 bool empty() const;
\r
1368 bool load(const string& filename);
\r
1371 /* returns number of detected objects */
\r
1372 int detectMultiScale( const GpuMat& image, GpuMat& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size());
\r
1374 bool findLargestObject;
\r
1375 bool visualizeInPlace;
\r
1377 Size getClassifierSize() const;
\r
1380 struct CascadeClassifierImpl;
\r
1381 CascadeClassifierImpl* impl;
\r
1384 ////////////////////////////////// SURF //////////////////////////////////////////
\r
1386 class CV_EXPORTS SURF_GPU : public CvSURFParams
\r
1389 enum KeypointLayout
\r
1400 //! the default constructor
\r
1402 //! the full constructor taking all the necessary parameters
\r
1403 explicit SURF_GPU(double _hessianThreshold, int _nOctaves=4,
\r
1404 int _nOctaveLayers=2, bool _extended=false, float _keypointsRatio=0.01f, bool _upright = false);
\r
1406 //! returns the descriptor size in float's (64 or 128)
\r
1407 int descriptorSize() const;
\r
1409 //! upload host keypoints to device memory
\r
1410 void uploadKeypoints(const vector<KeyPoint>& keypoints, GpuMat& keypointsGPU);
\r
1411 //! download keypoints from device to host memory
\r
1412 void downloadKeypoints(const GpuMat& keypointsGPU, vector<KeyPoint>& keypoints);
\r
1414 //! download descriptors from device to host memory
\r
1415 void downloadDescriptors(const GpuMat& descriptorsGPU, vector<float>& descriptors);
\r
1417 //! finds the keypoints using fast hessian detector used in SURF
\r
1418 //! supports CV_8UC1 images
\r
1419 //! keypoints will have nFeature cols and 6 rows
\r
1420 //! keypoints.ptr<float>(SF_X)[i] will contain x coordinate of i'th feature
\r
1421 //! keypoints.ptr<float>(SF_Y)[i] will contain y coordinate of i'th feature
\r
1422 //! keypoints.ptr<float>(SF_LAPLACIAN)[i] will contain laplacian sign of i'th feature
\r
1423 //! keypoints.ptr<float>(SF_SIZE)[i] will contain size of i'th feature
\r
1424 //! keypoints.ptr<float>(SF_DIR)[i] will contain orientation of i'th feature
\r
1425 //! keypoints.ptr<float>(SF_HESSIAN)[i] will contain response of i'th feature
\r
1426 void operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints);
\r
1427 //! finds the keypoints and computes their descriptors.
\r
1428 //! Optionally it can compute descriptors for the user-provided keypoints and recompute keypoints direction
\r
1429 void operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors,
\r
1430 bool useProvidedKeypoints = false);
\r
1432 void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints);
\r
1433 void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints, GpuMat& descriptors,
\r
1434 bool useProvidedKeypoints = false);
\r
1436 void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints, std::vector<float>& descriptors,
\r
1437 bool useProvidedKeypoints = false);
\r
1439 void releaseMemory();
\r
1441 //! max keypoints = min(keypointsRatio * img.size().area(), 65535)
\r
1442 float keypointsRatio;
\r
1444 GpuMat sum, mask1, maskSum, intBuffer;
\r
1446 GpuMat det, trace;
\r
1448 GpuMat maxPosBuffer;
\r
1453 //! Speckle filtering - filters small connected components on diparity image.
\r
1454 //! It sets pixel (x,y) to newVal if it coresponds to small CC with size < maxSpeckleSize.
\r
1455 //! Threshold for border between CC is diffThreshold;
\r
1456 CV_EXPORTS void filterSpeckles( Mat& img, uchar newVal, int maxSpeckleSize, uchar diffThreshold, Mat& buf);
\r
1459 #include "opencv2/gpu/matrix_operations.hpp"
\r
1461 #endif /* __OPENCV_GPU_HPP__ */
\r