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/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. |
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other GpuMaterials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#ifndef __OPENCV_GPU_HPP__ |
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#define __OPENCV_GPU_HPP__ |
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#include <vector> |
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#include "opencv2/core/core.hpp" |
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#include "opencv2/imgproc/imgproc.hpp" |
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#include "opencv2/objdetect/objdetect.hpp" |
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#include "opencv2/gpu/devmem2d.hpp" |
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#include "opencv2/features2d/features2d.hpp" |
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namespace cv |
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{ |
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namespace gpu |
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{ |
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//////////////////////////////// Initialization & Info //////////////////////// |
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//! This is the only function that do not throw exceptions if the library is compiled without Cuda. |
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CV_EXPORTS int getCudaEnabledDeviceCount(); |
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//! Functions below throw cv::Expception if the library is compiled without Cuda. |
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CV_EXPORTS void setDevice(int device); |
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CV_EXPORTS int getDevice(); |
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enum GpuFeature |
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{ |
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COMPUTE_10 = 10, |
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COMPUTE_11 = 11, |
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COMPUTE_12 = 12, |
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COMPUTE_13 = 13, |
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COMPUTE_20 = 20, |
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COMPUTE_21 = 21, |
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ATOMICS = COMPUTE_11, |
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NATIVE_DOUBLE = COMPUTE_13 |
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}; |
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class CV_EXPORTS TargetArchs |
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{ |
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public: |
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static bool builtWith(GpuFeature feature); |
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static bool has(int major, int minor); |
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static bool hasPtx(int major, int minor); |
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static bool hasBin(int major, int minor); |
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static bool hasEqualOrLessPtx(int major, int minor); |
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static bool hasEqualOrGreater(int major, int minor); |
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static bool hasEqualOrGreaterPtx(int major, int minor); |
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static bool hasEqualOrGreaterBin(int major, int minor); |
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private: |
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TargetArchs(); |
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}; |
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class CV_EXPORTS DeviceInfo |
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{ |
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public: |
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DeviceInfo() : device_id_(getDevice()) { query(); } |
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DeviceInfo(int device_id) : device_id_(device_id) { query(); } |
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string name() const { return name_; } |
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int majorVersion() const { return majorVersion_; } |
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int minorVersion() const { return minorVersion_; } |
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int multiProcessorCount() const { return multi_processor_count_; } |
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size_t freeMemory() const; |
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size_t totalMemory() const; |
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bool has(GpuFeature feature) const; |
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bool isCompatible() const; |
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private: |
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void query(); |
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void queryMemory(size_t& free_memory, size_t& total_memory) const; |
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int device_id_; |
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string name_; |
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int multi_processor_count_; |
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int majorVersion_; |
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int minorVersion_; |
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}; |
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//////////////////////////////// Error handling //////////////////////// |
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CV_EXPORTS void error(const char *error_string, const char *file, const int line, const char *func); |
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CV_EXPORTS void nppError( int err, const char *file, const int line, const char *func); |
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//////////////////////////////// GpuMat //////////////////////////////// |
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class Stream; |
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class CudaMem; |
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//! Smart pointer for GPU memory with reference counting. Its interface is mostly similar with cv::Mat. |
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class CV_EXPORTS GpuMat |
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{ |
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public: |
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//! default constructor |
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GpuMat(); |
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//! constructs GpuMatrix of the specified size and type (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.) |
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GpuMat(int rows, int cols, int type); |
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GpuMat(Size size, int type); |
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//! constucts GpuMatrix and fills it with the specified value _s. |
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GpuMat(int rows, int cols, int type, const Scalar& s); |
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GpuMat(Size size, int type, const Scalar& s); |
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//! copy constructor |
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GpuMat(const GpuMat& m); |
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//! constructor for GpuMatrix headers pointing to user-allocated data |
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GpuMat(int rows, int cols, int type, void* data, size_t step = Mat::AUTO_STEP); |
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GpuMat(Size size, int type, void* data, size_t step = Mat::AUTO_STEP); |
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//! creates a matrix header for a part of the bigger matrix |
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GpuMat(const GpuMat& m, const Range& rowRange, const Range& colRange); |
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GpuMat(const GpuMat& m, const Rect& roi); |
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//! builds GpuMat from Mat. Perfom blocking upload to device. |
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explicit GpuMat (const Mat& m); |
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//! destructor - calls release() |
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~GpuMat(); |
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//! assignment operators |
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GpuMat& operator = (const GpuMat& m); |
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//! assignment operator. Perfom blocking upload to device. |
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GpuMat& operator = (const Mat& m); |
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//! returns lightweight DevMem2D_ structure for passing to nvcc-compiled code. |
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// Contains just image size, data ptr and step. |
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template <class T> operator DevMem2D_<T>() const; |
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template <class T> operator PtrStep_<T>() const; |
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//! pefroms blocking upload data to GpuMat. |
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void upload(const cv::Mat& m); |
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//! upload async |
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void upload(const CudaMem& m, Stream& stream); |
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//! downloads data from device to host memory. Blocking calls. |
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operator Mat() const; |
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void download(cv::Mat& m) const; |
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//! download async |
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void download(CudaMem& m, Stream& stream) const; |
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//! returns a new GpuMatrix header for the specified row |
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GpuMat row(int y) const; |
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//! returns a new GpuMatrix header for the specified column |
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GpuMat col(int x) const; |
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//! ... for the specified row span |
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GpuMat rowRange(int startrow, int endrow) const; |
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GpuMat rowRange(const Range& r) const; |
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//! ... for the specified column span |
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GpuMat colRange(int startcol, int endcol) const; |
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GpuMat colRange(const Range& r) const; |
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//! returns deep copy of the GpuMatrix, i.e. the data is copied |
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GpuMat clone() const; |
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//! copies the GpuMatrix content to "m". |
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// It calls m.create(this->size(), this->type()). |
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void copyTo( GpuMat& m ) const; |
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//! copies those GpuMatrix elements to "m" that are marked with non-zero mask elements. |
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void copyTo( GpuMat& m, const GpuMat& mask ) const; |
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//! converts GpuMatrix to another datatype with optional scalng. See cvConvertScale. |
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void convertTo( GpuMat& m, int rtype, double alpha=1, double beta=0 ) const; |
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void assignTo( GpuMat& m, int type=-1 ) const; |
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//! sets every GpuMatrix element to s |
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GpuMat& operator = (const Scalar& s); |
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//! sets some of the GpuMatrix elements to s, according to the mask |
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GpuMat& setTo(const Scalar& s, const GpuMat& mask = GpuMat()); |
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//! creates alternative GpuMatrix header for the same data, with different |
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// number of channels and/or different number of rows. see cvReshape. |
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GpuMat reshape(int cn, int rows = 0) const; |
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//! allocates new GpuMatrix data unless the GpuMatrix already has specified size and type. |
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// previous data is unreferenced if needed. |
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void create(int rows, int cols, int type); |
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void create(Size size, int type); |
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//! decreases reference counter; |
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// deallocate the data when reference counter reaches 0. |
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void release(); |
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//! swaps with other smart pointer |
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void swap(GpuMat& mat); |
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//! locates GpuMatrix header within a parent GpuMatrix. See below |
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void locateROI( Size& wholeSize, Point& ofs ) const; |
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//! moves/resizes the current GpuMatrix ROI inside the parent GpuMatrix. |
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GpuMat& adjustROI( int dtop, int dbottom, int dleft, int dright ); |
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//! extracts a rectangular sub-GpuMatrix |
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// (this is a generalized form of row, rowRange etc.) |
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GpuMat operator()( Range rowRange, Range colRange ) const; |
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GpuMat operator()( const Rect& roi ) const; |
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//! returns true iff the GpuMatrix data is continuous |
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// (i.e. when there are no gaps between successive rows). |
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// similar to CV_IS_GpuMat_CONT(cvGpuMat->type) |
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bool isContinuous() const; |
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//! returns element size in bytes, |
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// similar to CV_ELEM_SIZE(cvMat->type) |
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size_t elemSize() const; |
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//! returns the size of element channel in bytes. |
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size_t elemSize1() const; |
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//! returns element type, similar to CV_MAT_TYPE(cvMat->type) |
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int type() const; |
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//! returns element type, similar to CV_MAT_DEPTH(cvMat->type) |
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int depth() const; |
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//! returns element type, similar to CV_MAT_CN(cvMat->type) |
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int channels() const; |
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//! returns step/elemSize1() |
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size_t step1() const; |
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//! returns GpuMatrix size: |
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// width == number of columns, height == number of rows |
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Size size() const; |
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//! returns true if GpuMatrix data is NULL |
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bool empty() const; |
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//! returns pointer to y-th row |
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uchar* ptr(int y = 0); |
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const uchar* ptr(int y = 0) const; |
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//! template version of the above method |
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template<typename _Tp> _Tp* ptr(int y = 0); |
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template<typename _Tp> const _Tp* ptr(int y = 0) const; |
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//! matrix transposition |
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GpuMat t() const; |
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/*! includes several bit-fields: |
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- the magic signature |
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- continuity flag |
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- depth |
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- number of channels |
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*/ |
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int flags; |
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//! the number of rows and columns |
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int rows, cols; |
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//! a distance between successive rows in bytes; includes the gap if any |
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size_t step; |
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//! pointer to the data |
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uchar* data; |
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//! pointer to the reference counter; |
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// when GpuMatrix points to user-allocated data, the pointer is NULL |
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int* refcount; |
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//! helper fields used in locateROI and adjustROI |
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uchar* datastart; |
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uchar* dataend; |
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}; |
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//#define TemplatedGpuMat // experimental now, deprecated to use |
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#ifdef TemplatedGpuMat |
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#include "GpuMat_BetaDeprecated.hpp" |
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#endif |
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//! Creates continuous GPU matrix |
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CV_EXPORTS void createContinuous(int rows, int cols, int type, GpuMat& m); |
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//! Ensures that size of the given matrix is not less than (rows, cols) size |
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//! and matrix type is match specified one too |
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CV_EXPORTS void ensureSizeIsEnough(int rows, int cols, int type, GpuMat& m); |
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//////////////////////////////// CudaMem //////////////////////////////// |
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// CudaMem is limited cv::Mat with page locked memory allocation. |
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// Page locked memory is only needed for async and faster coping to GPU. |
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// It is convertable to cv::Mat header without reference counting |
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// so you can use it with other opencv functions. |
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class CV_EXPORTS CudaMem |
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{ |
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public: |
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enum { ALLOC_PAGE_LOCKED = 1, ALLOC_ZEROCOPY = 2, ALLOC_WRITE_COMBINED = 4 }; |
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CudaMem(); |
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CudaMem(const CudaMem& m); |
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CudaMem(int rows, int cols, int type, int _alloc_type = ALLOC_PAGE_LOCKED); |
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CudaMem(Size size, int type, int alloc_type = ALLOC_PAGE_LOCKED); |
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//! creates from cv::Mat with coping data |
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explicit CudaMem(const Mat& m, int alloc_type = ALLOC_PAGE_LOCKED); |
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~CudaMem(); |
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CudaMem& operator = (const CudaMem& m); |
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//! returns deep copy of the matrix, i.e. the data is copied |
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CudaMem clone() const; |
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//! allocates new matrix data unless the matrix already has specified size and type. |
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void create(int rows, int cols, int type, int alloc_type = ALLOC_PAGE_LOCKED); |
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void create(Size size, int type, int alloc_type = ALLOC_PAGE_LOCKED); |
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//! decrements reference counter and released memory if needed. |
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void release(); |
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//! returns matrix header with disabled reference counting for CudaMem data. |
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Mat createMatHeader() const; |
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operator Mat() const; |
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//! maps host memory into device address space and returns GpuMat header for it. Throws exception if not supported by hardware. |
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GpuMat createGpuMatHeader() const; |
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operator GpuMat() const; |
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//returns if host memory can be mapperd to gpu address space; |
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static bool canMapHostMemory(); |
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// Please see cv::Mat for descriptions |
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bool isContinuous() const; |
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size_t elemSize() const; |
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size_t elemSize1() const; |
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int type() const; |
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int depth() const; |
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int channels() const; |
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size_t step1() const; |
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Size size() const; |
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bool empty() const; |
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// Please see cv::Mat for descriptions |
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int flags; |
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int rows, cols; |
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size_t step; |
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uchar* data; |
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int* refcount; |
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uchar* datastart; |
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uchar* dataend; |
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int alloc_type; |
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}; |
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//////////////////////////////// CudaStream //////////////////////////////// |
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// Encapculates Cuda Stream. Provides interface for async coping. |
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// Passed to each function that supports async kernel execution. |
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// Reference counting is enabled |
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class CV_EXPORTS Stream |
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{ |
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public: |
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Stream(); |
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~Stream(); |
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Stream(const Stream&); |
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Stream& operator=(const Stream&); |
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bool queryIfComplete(); |
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void waitForCompletion(); |
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//! downloads asynchronously. |
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// Warning! cv::Mat must point to page locked memory (i.e. to CudaMem data or to its subMat) |
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void enqueueDownload(const GpuMat& src, CudaMem& dst); |
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void enqueueDownload(const GpuMat& src, Mat& dst); |
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//! uploads asynchronously. |
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// Warning! cv::Mat must point to page locked memory (i.e. to CudaMem data or to its ROI) |
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void enqueueUpload(const CudaMem& src, GpuMat& dst); |
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void enqueueUpload(const Mat& src, GpuMat& dst); |
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void enqueueCopy(const GpuMat& src, GpuMat& dst); |
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void enqueueMemSet(const GpuMat& src, Scalar val); |
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void enqueueMemSet(const GpuMat& src, Scalar val, const GpuMat& mask); |
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// converts matrix type, ex from float to uchar depending on type |
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void enqueueConvert(const GpuMat& src, GpuMat& dst, int type, double a = 1, double b = 0); |
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private: |
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void create(); |
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void release(); |
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struct Impl; |
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Impl *impl; |
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friend struct StreamAccessor; |
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}; |
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////////////////////////////// Arithmetics /////////////////////////////////// |
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//! transposes the matrix |
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//! supports matrix with element size = 1, 4 and 8 bytes (CV_8UC1, CV_8UC4, CV_16UC2, CV_32FC1, etc) |
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CV_EXPORTS void transpose(const GpuMat& src1, GpuMat& dst); |
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//! reverses the order of the rows, columns or both in a matrix |
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//! supports CV_8UC1, CV_8UC4 types |
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CV_EXPORTS void flip(const GpuMat& a, GpuMat& b, int flipCode); |
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//! transforms 8-bit unsigned integers using lookup table: dst(i)=lut(src(i)) |
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//! destination array will have the depth type as lut and the same channels number as source |
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//! supports CV_8UC1, CV_8UC3 types |
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CV_EXPORTS void LUT(const GpuMat& src, const Mat& lut, GpuMat& dst); |
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//! makes multi-channel array out of several single-channel arrays |
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CV_EXPORTS void merge(const GpuMat* src, size_t n, GpuMat& dst); |
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//! makes multi-channel array out of several single-channel arrays |
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CV_EXPORTS void merge(const vector<GpuMat>& src, GpuMat& dst); |
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//! makes multi-channel array out of several single-channel arrays (async version) |
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CV_EXPORTS void merge(const GpuMat* src, size_t n, GpuMat& dst, const Stream& stream); |
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//! makes multi-channel array out of several single-channel arrays (async version) |
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CV_EXPORTS void merge(const vector<GpuMat>& src, GpuMat& dst, const Stream& stream); |
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//! copies each plane of a multi-channel array to a dedicated array |
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CV_EXPORTS void split(const GpuMat& src, GpuMat* dst); |
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//! copies each plane of a multi-channel array to a dedicated array |
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CV_EXPORTS void split(const GpuMat& src, vector<GpuMat>& dst); |
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//! copies each plane of a multi-channel array to a dedicated array (async version) |
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CV_EXPORTS void split(const GpuMat& src, GpuMat* dst, const Stream& stream); |
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//! copies each plane of a multi-channel array to a dedicated array (async version) |
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CV_EXPORTS void split(const GpuMat& src, vector<GpuMat>& dst, const Stream& stream); |
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//! computes magnitude of complex (x(i).re, x(i).im) vector |
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//! supports only CV_32FC2 type |
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CV_EXPORTS void magnitude(const GpuMat& x, GpuMat& magnitude); |
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//! computes squared magnitude of complex (x(i).re, x(i).im) vector |
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//! supports only CV_32FC2 type |
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CV_EXPORTS void magnitudeSqr(const GpuMat& x, GpuMat& magnitude); |
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//! computes magnitude of each (x(i), y(i)) vector |
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//! supports only floating-point source |
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CV_EXPORTS void magnitude(const GpuMat& x, const GpuMat& y, GpuMat& magnitude); |
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//! async version |
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CV_EXPORTS void magnitude(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, const Stream& stream); |
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//! computes squared magnitude of each (x(i), y(i)) vector |
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//! supports only floating-point source |
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CV_EXPORTS void magnitudeSqr(const GpuMat& x, const GpuMat& y, GpuMat& magnitude); |
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//! async version |
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CV_EXPORTS void magnitudeSqr(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, const Stream& stream); |
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//! computes angle (angle(i)) of each (x(i), y(i)) vector |
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//! supports only floating-point source |
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CV_EXPORTS void phase(const GpuMat& x, const GpuMat& y, GpuMat& angle, bool angleInDegrees = false); |
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//! async version |
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CV_EXPORTS void phase(const GpuMat& x, const GpuMat& y, GpuMat& angle, bool angleInDegrees, const Stream& stream); |
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//! converts Cartesian coordinates to polar |
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//! supports only floating-point source |
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CV_EXPORTS void cartToPolar(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, GpuMat& angle, bool angleInDegrees = false); |
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//! async version |
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CV_EXPORTS void cartToPolar(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, GpuMat& angle, bool angleInDegrees, const Stream& stream); |
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//! converts polar coordinates to Cartesian |
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//! supports only floating-point source |
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CV_EXPORTS void polarToCart(const GpuMat& magnitude, const GpuMat& angle, GpuMat& x, GpuMat& y, bool angleInDegrees = false); |
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//! async version |
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CV_EXPORTS void polarToCart(const GpuMat& magnitude, const GpuMat& angle, GpuMat& x, GpuMat& y, bool angleInDegrees, const Stream& stream); |
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//////////////////////////// Per-element operations //////////////////////////////////// |
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//! adds one matrix to another (c = a + b) |
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//! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types |
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CV_EXPORTS void add(const GpuMat& a, const GpuMat& b, GpuMat& c); |
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//! adds scalar to a matrix (c = a + s) |
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//! supports CV_32FC1 and CV_32FC2 type |
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CV_EXPORTS void add(const GpuMat& a, const Scalar& sc, GpuMat& c); |
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//! subtracts one matrix from another (c = a - b) |
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//! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types |
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CV_EXPORTS void subtract(const GpuMat& a, const GpuMat& b, GpuMat& c); |
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//! subtracts scalar from a matrix (c = a - s) |
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//! supports CV_32FC1 and CV_32FC2 type |
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CV_EXPORTS void subtract(const GpuMat& a, const Scalar& sc, GpuMat& c); |
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//! computes element-wise product of the two arrays (c = a * b) |
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//! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types |
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CV_EXPORTS void multiply(const GpuMat& a, const GpuMat& b, GpuMat& c); |
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//! multiplies matrix to a scalar (c = a * s) |
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//! supports CV_32FC1 and CV_32FC2 type |
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CV_EXPORTS void multiply(const GpuMat& a, const Scalar& sc, GpuMat& c); |
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//! computes element-wise quotient of the two arrays (c = a / b) |
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//! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types |
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CV_EXPORTS void divide(const GpuMat& a, const GpuMat& b, GpuMat& c); |
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//! computes element-wise quotient of matrix and scalar (c = a / s) |
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//! supports CV_32FC1 and CV_32FC2 type |
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CV_EXPORTS void divide(const GpuMat& a, const Scalar& sc, GpuMat& c); |
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//! computes exponent of each matrix element (b = e**a) |
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//! supports only CV_32FC1 type |
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CV_EXPORTS void exp(const GpuMat& a, GpuMat& b); |
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//! computes natural logarithm of absolute value of each matrix element: b = log(abs(a)) |
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//! supports only CV_32FC1 type |
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CV_EXPORTS void log(const GpuMat& a, GpuMat& b); |
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//! computes element-wise absolute difference of two arrays (c = abs(a - b)) |
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|
//! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types |
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CV_EXPORTS void absdiff(const GpuMat& a, const GpuMat& b, GpuMat& c); |
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|
//! computes element-wise absolute difference of array and scalar (c = abs(a - s)) |
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//! supports only CV_32FC1 type |
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CV_EXPORTS void absdiff(const GpuMat& a, const Scalar& s, GpuMat& c); |
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//! compares elements of two arrays (c = a <cmpop> b) |
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|
//! supports CV_8UC4, CV_32FC1 types |
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CV_EXPORTS void compare(const GpuMat& a, const GpuMat& b, GpuMat& c, int cmpop); |
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//! performs per-elements bit-wise inversion |
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CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask=GpuMat()); |
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|
//! async version |
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|
CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask, const Stream& stream); |
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//! calculates per-element bit-wise disjunction of two arrays |
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CV_EXPORTS void bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat()); |
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|
//! async version |
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|
CV_EXPORTS void bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream); |
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//! calculates per-element bit-wise conjunction of two arrays |
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CV_EXPORTS void bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat()); |
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|
//! async version |
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|
CV_EXPORTS void bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream); |
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//! calculates per-element bit-wise "exclusive or" operation |
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|
CV_EXPORTS void bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat()); |
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|
//! async version |
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|
CV_EXPORTS void bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream); |
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|
//! computes per-element minimum of two arrays (dst = min(src1, src2)) |
|
|
CV_EXPORTS void min(const GpuMat& src1, const GpuMat& src2, GpuMat& dst); |
|
|
//! Async version |
|
|
CV_EXPORTS void min(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const Stream& stream); |
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|
|
//! computes per-element minimum of array and scalar (dst = min(src1, src2)) |
|
|
CV_EXPORTS void min(const GpuMat& src1, double src2, GpuMat& dst); |
|
|
//! Async version |
|
|
CV_EXPORTS void min(const GpuMat& src1, double src2, GpuMat& dst, const Stream& stream); |
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|
//! computes per-element maximum of two arrays (dst = max(src1, src2)) |
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|
CV_EXPORTS void max(const GpuMat& src1, const GpuMat& src2, GpuMat& dst); |
|
|
//! Async version |
|
|
CV_EXPORTS void max(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const Stream& stream); |
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|
|
//! computes per-element maximum of array and scalar (dst = max(src1, src2)) |
|
|
CV_EXPORTS void max(const GpuMat& src1, double src2, GpuMat& dst); |
|
|
//! Async version |
|
|
CV_EXPORTS void max(const GpuMat& src1, double src2, GpuMat& dst, const Stream& stream); |
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|
////////////////////////////// Image processing ////////////////////////////// |
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|
//! DST[x,y] = SRC[xmap[x,y],ymap[x,y]] with bilinear interpolation. |
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|
//! supports CV_8UC1, CV_8UC3 source types and CV_32FC1 map type |
|
|
CV_EXPORTS void remap(const GpuMat& src, GpuMat& dst, const GpuMat& xmap, const GpuMat& ymap); |
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|
|
//! Does mean shift filtering on GPU. |
|
|
CV_EXPORTS void meanShiftFiltering(const GpuMat& src, GpuMat& dst, int sp, int sr, |
|
|
TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1)); |
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|
|
//! Does mean shift procedure on GPU. |
|
|
CV_EXPORTS void meanShiftProc(const GpuMat& src, GpuMat& dstr, GpuMat& dstsp, int sp, int sr, |
|
|
TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1)); |
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|
|
//! Does mean shift segmentation with elimination of small regions. |
|
|
CV_EXPORTS void meanShiftSegmentation(const GpuMat& src, Mat& dst, int sp, int sr, int minsize, |
|
|
TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1)); |
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|
//! Does coloring of disparity image: [0..ndisp) -> [0..240, 1, 1] in HSV. |
|
|
//! Supported types of input disparity: CV_8U, CV_16S. |
|
|
//! Output disparity has CV_8UC4 type in BGRA format (alpha = 255). |
|
|
CV_EXPORTS void drawColorDisp(const GpuMat& src_disp, GpuMat& dst_disp, int ndisp); |
|
|
//! async version |
|
|
CV_EXPORTS void drawColorDisp(const GpuMat& src_disp, GpuMat& dst_disp, int ndisp, const Stream& stream); |
|
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|
|
//! Reprojects disparity image to 3D space. |
|
|
//! Supports CV_8U and CV_16S types of input disparity. |
|
|
//! The output is a 4-channel floating-point (CV_32FC4) matrix. |
|
|
//! Each element of this matrix will contain the 3D coordinates of the point (x,y,z,1), computed from the disparity map. |
|
|
//! Q is the 4x4 perspective transformation matrix that can be obtained with cvStereoRectify. |
|
|
CV_EXPORTS void reprojectImageTo3D(const GpuMat& disp, GpuMat& xyzw, const Mat& Q); |
|
|
//! async version |
|
|
CV_EXPORTS void reprojectImageTo3D(const GpuMat& disp, GpuMat& xyzw, const Mat& Q, const Stream& stream); |
|
|
|
|
|
//! converts image from one color space to another |
|
|
CV_EXPORTS void cvtColor(const GpuMat& src, GpuMat& dst, int code, int dcn = 0); |
|
|
//! async version |
|
|
CV_EXPORTS void cvtColor(const GpuMat& src, GpuMat& dst, int code, int dcn, const Stream& stream); |
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|
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|
|
//! applies fixed threshold to the image |
|
|
CV_EXPORTS double threshold(const GpuMat& src, GpuMat& dst, double thresh, double maxval, int type); |
|
|
//! async version |
|
|
CV_EXPORTS double threshold(const GpuMat& src, GpuMat& dst, double thresh, double maxval, int type, const Stream& stream); |
|
|
|
|
|
//! resizes the image |
|
|
//! Supports INTER_NEAREST, INTER_LINEAR |
|
|
//! supports CV_8UC1, CV_8UC4 types |
|
|
CV_EXPORTS void resize(const GpuMat& src, GpuMat& dst, Size dsize, double fx=0, double fy=0, int interpolation = INTER_LINEAR); |
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|
|
//! warps the image using affine transformation |
|
|
//! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC |
|
|
CV_EXPORTS void warpAffine(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags = INTER_LINEAR); |
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|
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|
|
//! warps the image using perspective transformation |
|
|
//! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC |
|
|
CV_EXPORTS void warpPerspective(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags = INTER_LINEAR); |
|
|
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|
|
//! rotate 8bit single or four channel image |
|
|
//! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC |
|
|
//! supports CV_8UC1, CV_8UC4 types |
|
|
CV_EXPORTS void rotate(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift = 0, double yShift = 0, int interpolation = INTER_LINEAR); |
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|
|
//! copies 2D array to a larger destination array and pads borders with user-specifiable constant |
|
|
//! supports CV_8UC1, CV_8UC4, CV_32SC1 and CV_32FC1 types |
|
|
CV_EXPORTS void copyMakeBorder(const GpuMat& src, GpuMat& dst, int top, int bottom, int left, int right, const Scalar& value = Scalar()); |
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|
|
//! computes the integral image |
|
|
//! sum will have CV_32S type, but will contain unsigned int values |
|
|
//! supports only CV_8UC1 source type |
|
|
CV_EXPORTS void integral(const GpuMat& src, GpuMat& sum); |
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|
|
|
//! buffered version |
|
|
CV_EXPORTS void integralBuffered(const GpuMat& src, GpuMat& sum, GpuMat& buffer); |
|
|
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|
|
//! computes the integral image and integral for the squared image |
|
|
//! sum will have CV_32S type, sqsum - CV32F type |
|
|
//! supports only CV_8UC1 source type |
|
|
CV_EXPORTS void integral(const GpuMat& src, GpuMat& sum, GpuMat& sqsum); |
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|
|
//! computes squared integral image |
|
|
//! result matrix will have 64F type, but will contain 64U values |
|
|
//! supports source images of 8UC1 type only |
|
|
CV_EXPORTS void sqrIntegral(const GpuMat& src, GpuMat& sqsum); |
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|
|
//! computes vertical sum, supports only CV_32FC1 images |
|
|
CV_EXPORTS void columnSum(const GpuMat& src, GpuMat& sum); |
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|
|
//! computes the standard deviation of integral images |
|
|
//! supports only CV_32SC1 source type and CV_32FC1 sqr type |
|
|
//! output will have CV_32FC1 type |
|
|
CV_EXPORTS void rectStdDev(const GpuMat& src, const GpuMat& sqr, GpuMat& dst, const Rect& rect); |
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|
|
// applies Canny edge detector and produces the edge map |
|
|
// disabled until fix crash |
|
|
//CV_EXPORTS void Canny(const GpuMat& image, GpuMat& edges, double threshold1, double threshold2, int apertureSize = 3); |
|
|
//CV_EXPORTS void Canny(const GpuMat& image, GpuMat& edges, GpuMat& buffer, double threshold1, double threshold2, int apertureSize = 3); |
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|
//CV_EXPORTS void Canny(const GpuMat& srcDx, const GpuMat& srcDy, GpuMat& edges, double threshold1, double threshold2, int apertureSize = 3); |
|
|
//CV_EXPORTS void Canny(const GpuMat& srcDx, const GpuMat& srcDy, GpuMat& edges, GpuMat& buffer, double threshold1, double threshold2, int apertureSize = 3); |
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|
//! computes Harris cornerness criteria at each image pixel |
|
|
CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, double k, int borderType=BORDER_REFLECT101); |
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|
|
//! computes minimum eigen value of 2x2 derivative covariation matrix at each pixel - the cornerness criteria |
|
|
CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, int borderType=BORDER_REFLECT101); |
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|
|
//! performs per-element multiplication of two full (not packed) Fourier spectrums |
|
|
//! supports 32FC2 matrixes only (interleaved format) |
|
|
CV_EXPORTS void mulSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, bool conjB=false); |
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|
|
//! performs per-element multiplication of two full (not packed) Fourier spectrums |
|
|
//! supports 32FC2 matrixes only (interleaved format) |
|
|
CV_EXPORTS void mulAndScaleSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, |
|
|
float scale, bool conjB=false); |
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|
|
//! Performs a forward or inverse discrete Fourier transform (1D or 2D) of floating point matrix. |
|
|
//! Param dft_size is the size of DFT transform. |
|
|
//! |
|
|
//! If the source matrix is not continous, then additional copy will be done, |
|
|
//! so to avoid copying ensure the source matrix is continous one. If you want to use |
|
|
//! preallocated output ensure it is continuous too, otherwise it will be reallocated. |
|
|
//! |
|
|
//! Being implemented via CUFFT real-to-complex transform result contains only non-redundant values |
|
|
//! in CUFFT's format. Result as full complex matrix for such kind of transform cannot be retrieved. |
|
|
//! |
|
|
//! For complex-to-real transform it is assumed that the source matrix is packed in CUFFT's format. |
|
|
CV_EXPORTS void dft(const GpuMat& src, GpuMat& dst, Size dft_size, int flags=0); |
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|
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|
|
//! computes convolution (or cross-correlation) of two images using discrete Fourier transform |
|
|
//! supports source images of 32FC1 type only |
|
|
//! result matrix will have 32FC1 type |
|
|
CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, |
|
|
bool ccorr=false); |
|
|
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|
|
struct CV_EXPORTS ConvolveBuf; |
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|
|
//! buffered version |
|
|
CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, |
|
|
bool ccorr, ConvolveBuf& buf); |
|
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|
|
struct CV_EXPORTS ConvolveBuf |
|
|
{ |
|
|
ConvolveBuf() {} |
|
|
ConvolveBuf(Size image_size, Size templ_size) |
|
|
{ create(image_size, templ_size); } |
|
|
void create(Size image_size, Size templ_size); |
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|
|
private: |
|
|
static Size estimateBlockSize(Size result_size, Size templ_size); |
|
|
friend void convolve(const GpuMat&, const GpuMat&, GpuMat&, bool, ConvolveBuf&); |
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|
|
Size result_size; |
|
|
Size block_size; |
|
|
Size dft_size; |
|
|
int spect_len; |
|
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|
|
GpuMat image_spect, templ_spect, result_spect; |
|
|
GpuMat image_block, templ_block, result_data; |
|
|
}; |
|
|
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|
|
//! computes the proximity map for the raster template and the image where the template is searched for |
|
|
CV_EXPORTS void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method); |
|
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|
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|
|
////////////////////////////// Matrix reductions ////////////////////////////// |
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|
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|
|
//! computes mean value and standard deviation of all or selected array elements |
|
|
//! supports only CV_8UC1 type |
|
|
CV_EXPORTS void meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev); |
|
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|
|
|
//! computes norm of array |
|
|
//! supports NORM_INF, NORM_L1, NORM_L2 |
|
|
//! supports all matrices except 64F |
|
|
CV_EXPORTS double norm(const GpuMat& src1, int normType=NORM_L2); |
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|
|
//! computes norm of array |
|
|
//! supports NORM_INF, NORM_L1, NORM_L2 |
|
|
//! supports all matrices except 64F |
|
|
CV_EXPORTS double norm(const GpuMat& src1, int normType, GpuMat& buf); |
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|
|
|
//! computes norm of the difference between two arrays |
|
|
//! supports NORM_INF, NORM_L1, NORM_L2 |
|
|
//! supports only CV_8UC1 type |
|
|
CV_EXPORTS double norm(const GpuMat& src1, const GpuMat& src2, int normType=NORM_L2); |
|
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|
|
//! computes sum of array elements |
|
|
//! supports only single channel images |
|
|
CV_EXPORTS Scalar sum(const GpuMat& src); |
|
|
|
|
|
//! computes sum of array elements |
|
|
//! supports only single channel images |
|
|
CV_EXPORTS Scalar sum(const GpuMat& src, GpuMat& buf); |
|
|
|
|
|
//! computes sum of array elements absolute values |
|
|
//! supports only single channel images |
|
|
CV_EXPORTS Scalar absSum(const GpuMat& src); |
|
|
|
|
|
//! computes sum of array elements absolute values |
|
|
//! supports only single channel images |
|
|
CV_EXPORTS Scalar absSum(const GpuMat& src, GpuMat& buf); |
|
|
|
|
|
//! computes squared sum of array elements |
|
|
//! supports only single channel images |
|
|
CV_EXPORTS Scalar sqrSum(const GpuMat& src); |
|
|
|
|
|
//! computes squared sum of array elements |
|
|
//! supports only single channel images |
|
|
CV_EXPORTS Scalar sqrSum(const GpuMat& src, GpuMat& buf); |
|
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|
|
|
//! finds global minimum and maximum array elements and returns their values |
|
|
CV_EXPORTS void minMax(const GpuMat& src, double* minVal, double* maxVal=0, const GpuMat& mask=GpuMat()); |
|
|
|
|
|
//! finds global minimum and maximum array elements and returns their values |
|
|
CV_EXPORTS void minMax(const GpuMat& src, double* minVal, double* maxVal, const GpuMat& mask, GpuMat& buf); |
|
|
|
|
|
//! finds global minimum and maximum array elements and returns their values with locations |
|
|
CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal=0, Point* minLoc=0, Point* maxLoc=0, |
|
|
const GpuMat& mask=GpuMat()); |
|
|
|
|
|
//! finds global minimum and maximum array elements and returns their values with locations |
|
|
CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc, |
|
|
const GpuMat& mask, GpuMat& valbuf, GpuMat& locbuf); |
|
|
|
|
|
//! counts non-zero array elements |
|
|
CV_EXPORTS int countNonZero(const GpuMat& src); |
|
|
|
|
|
//! counts non-zero array elements |
|
|
CV_EXPORTS int countNonZero(const GpuMat& src, GpuMat& buf); |
|
|
|
|
|
|
|
|
//////////////////////////////// Filter Engine //////////////////////////////// |
|
|
|
|
|
/*! |
|
|
The Base Class for 1D or Row-wise Filters |
|
|
|
|
|
This is the base class for linear or non-linear filters that process 1D data. |
|
|
In particular, such filters are used for the "horizontal" filtering parts in separable filters. |
|
|
*/ |
|
|
class CV_EXPORTS BaseRowFilter_GPU |
|
|
{ |
|
|
public: |
|
|
BaseRowFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {} |
|
|
virtual ~BaseRowFilter_GPU() {} |
|
|
virtual void operator()(const GpuMat& src, GpuMat& dst) = 0; |
|
|
int ksize, anchor; |
|
|
}; |
|
|
|
|
|
/*! |
|
|
The Base Class for Column-wise Filters |
|
|
|
|
|
This is the base class for linear or non-linear filters that process columns of 2D arrays. |
|
|
Such filters are used for the "vertical" filtering parts in separable filters. |
|
|
*/ |
|
|
class CV_EXPORTS BaseColumnFilter_GPU |
|
|
{ |
|
|
public: |
|
|
BaseColumnFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {} |
|
|
virtual ~BaseColumnFilter_GPU() {} |
|
|
virtual void operator()(const GpuMat& src, GpuMat& dst) = 0; |
|
|
int ksize, anchor; |
|
|
}; |
|
|
|
|
|
/*! |
|
|
The Base Class for Non-Separable 2D Filters. |
|
|
|
|
|
This is the base class for linear or non-linear 2D filters. |
|
|
*/ |
|
|
class CV_EXPORTS BaseFilter_GPU |
|
|
{ |
|
|
public: |
|
|
BaseFilter_GPU(const Size& ksize_, const Point& anchor_) : ksize(ksize_), anchor(anchor_) {} |
|
|
virtual ~BaseFilter_GPU() {} |
|
|
virtual void operator()(const GpuMat& src, GpuMat& dst) = 0; |
|
|
Size ksize; |
|
|
Point anchor; |
|
|
}; |
|
|
|
|
|
/*! |
|
|
The Base Class for Filter Engine. |
|
|
|
|
|
The class can be used to apply an arbitrary filtering operation to an image. |
|
|
It contains all the necessary intermediate buffers. |
|
|
*/ |
|
|
class CV_EXPORTS FilterEngine_GPU |
|
|
{ |
|
|
public: |
|
|
virtual ~FilterEngine_GPU() {} |
|
|
|
|
|
virtual void apply(const GpuMat& src, GpuMat& dst, Rect roi = Rect(0,0,-1,-1)) = 0; |
|
|
}; |
|
|
|
|
|
//! returns the non-separable filter engine with the specified filter |
|
|
CV_EXPORTS Ptr<FilterEngine_GPU> createFilter2D_GPU(const Ptr<BaseFilter_GPU>& filter2D, int srcType, int dstType); |
|
|
|
|
|
//! returns the separable filter engine with the specified filters |
|
|
CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter, |
|
|
const Ptr<BaseColumnFilter_GPU>& columnFilter, int srcType, int bufType, int dstType); |
|
|
|
|
|
//! returns horizontal 1D box filter |
|
|
//! supports only CV_8UC1 source type and CV_32FC1 sum type |
|
|
CV_EXPORTS Ptr<BaseRowFilter_GPU> getRowSumFilter_GPU(int srcType, int sumType, int ksize, int anchor = -1); |
|
|
|
|
|
//! returns vertical 1D box filter |
|
|
//! supports only CV_8UC1 sum type and CV_32FC1 dst type |
|
|
CV_EXPORTS Ptr<BaseColumnFilter_GPU> getColumnSumFilter_GPU(int sumType, int dstType, int ksize, int anchor = -1); |
|
|
|
|
|
//! returns 2D box filter |
|
|
//! supports CV_8UC1 and CV_8UC4 source type, dst type must be the same as source type |
|
|
CV_EXPORTS Ptr<BaseFilter_GPU> getBoxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1, -1)); |
|
|
|
|
|
//! returns box filter engine |
|
|
CV_EXPORTS Ptr<FilterEngine_GPU> createBoxFilter_GPU(int srcType, int dstType, const Size& ksize, |
|
|
const Point& anchor = Point(-1,-1)); |
|
|
|
|
|
//! returns 2D morphological filter |
|
|
//! only MORPH_ERODE and MORPH_DILATE are supported |
|
|
//! supports CV_8UC1 and CV_8UC4 types |
|
|
//! kernel must have CV_8UC1 type, one rows and cols == ksize.width * ksize.height |
|
|
CV_EXPORTS Ptr<BaseFilter_GPU> getMorphologyFilter_GPU(int op, int type, const Mat& kernel, const Size& ksize, |
|
|
Point anchor=Point(-1,-1)); |
|
|
|
|
|
//! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported. |
|
|
CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat& kernel, |
|
|
const Point& anchor = Point(-1,-1), int iterations = 1); |
|
|
|
|
|
//! returns 2D filter with the specified kernel |
|
|
//! supports CV_8UC1 and CV_8UC4 types |
|
|
CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, const Size& ksize, |
|
|
Point anchor = Point(-1, -1)); |
|
|
|
|
|
//! returns the non-separable linear filter engine |
|
|
CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, |
|
|
const Point& anchor = Point(-1,-1)); |
|
|
|
|
|
//! returns the primitive row filter with the specified kernel. |
|
|
//! supports only CV_8UC1, CV_8UC4, CV_16SC1, CV_16SC2, CV_32SC1, CV_32FC1 source type. |
|
|
//! there are two version of algorithm: NPP and OpenCV. |
|
|
//! NPP calls when srcType == CV_8UC1 or srcType == CV_8UC4 and bufType == srcType, |
|
|
//! otherwise calls OpenCV version. |
|
|
//! NPP supports only BORDER_CONSTANT border type. |
|
|
//! OpenCV version supports only CV_32F as buffer depth and |
|
|
//! BORDER_REFLECT101, BORDER_REPLICATE and BORDER_CONSTANT border types. |
|
|
CV_EXPORTS Ptr<BaseRowFilter_GPU> getLinearRowFilter_GPU(int srcType, int bufType, const Mat& rowKernel, |
|
|
int anchor = -1, int borderType = BORDER_CONSTANT); |
|
|
|
|
|
//! returns the primitive column filter with the specified kernel. |
|
|
//! supports only CV_8UC1, CV_8UC4, CV_16SC1, CV_16SC2, CV_32SC1, CV_32FC1 dst type. |
|
|
//! there are two version of algorithm: NPP and OpenCV. |
|
|
//! NPP calls when dstType == CV_8UC1 or dstType == CV_8UC4 and bufType == dstType, |
|
|
//! otherwise calls OpenCV version. |
|
|
//! NPP supports only BORDER_CONSTANT border type. |
|
|
//! OpenCV version supports only CV_32F as buffer depth and |
|
|
//! BORDER_REFLECT101, BORDER_REPLICATE and BORDER_CONSTANT border types. |
|
|
CV_EXPORTS Ptr<BaseColumnFilter_GPU> getLinearColumnFilter_GPU(int bufType, int dstType, const Mat& columnKernel, |
|
|
int anchor = -1, int borderType = BORDER_CONSTANT); |
|
|
|
|
|
//! returns the separable linear filter engine |
|
|
CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel, |
|
|
const Mat& columnKernel, const Point& anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, |
|
|
int columnBorderType = -1); |
|
|
|
|
|
//! returns filter engine for the generalized Sobel operator |
|
|
CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU(int srcType, int dstType, int dx, int dy, int ksize, |
|
|
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1); |
|
|
|
|
|
//! returns the Gaussian filter engine |
|
|
CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, double sigma1, double sigma2 = 0, |
|
|
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1); |
|
|
|
|
|
//! returns maximum filter |
|
|
CV_EXPORTS Ptr<BaseFilter_GPU> getMaxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1)); |
|
|
|
|
|
//! returns minimum filter |
|
|
CV_EXPORTS Ptr<BaseFilter_GPU> getMinFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1)); |
|
|
|
|
|
//! smooths the image using the normalized box filter |
|
|
//! supports CV_8UC1, CV_8UC4 types |
|
|
CV_EXPORTS void boxFilter(const GpuMat& src, GpuMat& dst, int ddepth, Size ksize, Point anchor = Point(-1,-1)); |
|
|
|
|
|
//! a synonym for normalized box filter |
|
|
static inline void blur(const GpuMat& src, GpuMat& dst, Size ksize, Point anchor = Point(-1,-1)) { boxFilter(src, dst, -1, ksize, anchor); } |
|
|
|
|
|
//! erodes the image (applies the local minimum operator) |
|
|
CV_EXPORTS void erode( const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1); |
|
|
|
|
|
//! dilates the image (applies the local maximum operator) |
|
|
CV_EXPORTS void dilate( const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1); |
|
|
|
|
|
//! applies an advanced morphological operation to the image |
|
|
CV_EXPORTS void morphologyEx( const GpuMat& src, GpuMat& dst, int op, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1); |
|
|
|
|
|
//! applies non-separable 2D linear filter to the image |
|
|
CV_EXPORTS void filter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernel, Point anchor=Point(-1,-1)); |
|
|
|
|
|
//! applies separable 2D linear filter to the image |
|
|
CV_EXPORTS void sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY, |
|
|
Point anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1); |
|
|
|
|
|
//! applies generalized Sobel operator to the image |
|
|
CV_EXPORTS void Sobel(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, int ksize = 3, double scale = 1, |
|
|
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1); |
|
|
|
|
|
//! applies the vertical or horizontal Scharr operator to the image |
|
|
CV_EXPORTS void Scharr(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, double scale = 1, |
|
|
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1); |
|
|
|
|
|
//! smooths the image using Gaussian filter. |
|
|
CV_EXPORTS void GaussianBlur(const GpuMat& src, GpuMat& dst, Size ksize, double sigma1, double sigma2 = 0, |
|
|
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1); |
|
|
|
|
|
//! applies Laplacian operator to the image |
|
|
//! supports only ksize = 1 and ksize = 3 |
|
|
CV_EXPORTS void Laplacian(const GpuMat& src, GpuMat& dst, int ddepth, int ksize = 1, double scale = 1); |
|
|
|
|
|
//////////////////////////////// Image Labeling //////////////////////////////// |
|
|
|
|
|
//!performs labeling via graph cuts |
|
|
CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& bottom, GpuMat& labels, GpuMat& buf); |
|
|
|
|
|
////////////////////////////////// Histograms ////////////////////////////////// |
|
|
|
|
|
//! Compute levels with even distribution. levels will have 1 row and nLevels cols and CV_32SC1 type. |
|
|
CV_EXPORTS void evenLevels(GpuMat& levels, int nLevels, int lowerLevel, int upperLevel); |
|
|
//! Calculates histogram with evenly distributed bins for signle channel source. |
|
|
//! Supports CV_8UC1, CV_16UC1 and CV_16SC1 source types. |
|
|
//! Output hist will have one row and histSize cols and CV_32SC1 type. |
|
|
CV_EXPORTS void histEven(const GpuMat& src, GpuMat& hist, int histSize, int lowerLevel, int upperLevel); |
|
|
//! Calculates histogram with evenly distributed bins for four-channel source. |
|
|
//! All channels of source are processed separately. |
|
|
//! Supports CV_8UC4, CV_16UC4 and CV_16SC4 source types. |
|
|
//! Output hist[i] will have one row and histSize[i] cols and CV_32SC1 type. |
|
|
CV_EXPORTS void histEven(const GpuMat& src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4]); |
|
|
//! Calculates histogram with bins determined by levels array. |
|
|
//! levels must have one row and CV_32SC1 type if source has integer type or CV_32FC1 otherwise. |
|
|
//! Supports CV_8UC1, CV_16UC1, CV_16SC1 and CV_32FC1 source types. |
|
|
//! Output hist will have one row and (levels.cols-1) cols and CV_32SC1 type. |
|
|
CV_EXPORTS void histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels); |
|
|
//! Calculates histogram with bins determined by levels array. |
|
|
//! All levels must have one row and CV_32SC1 type if source has integer type or CV_32FC1 otherwise. |
|
|
//! All channels of source are processed separately. |
|
|
//! Supports CV_8UC4, CV_16UC4, CV_16SC4 and CV_32FC4 source types. |
|
|
//! Output hist[i] will have one row and (levels[i].cols-1) cols and CV_32SC1 type. |
|
|
CV_EXPORTS void histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4]); |
|
|
|
|
|
//////////////////////////////// StereoBM_GPU //////////////////////////////// |
|
|
|
|
|
class CV_EXPORTS StereoBM_GPU |
|
|
{ |
|
|
public: |
|
|
enum { BASIC_PRESET = 0, PREFILTER_XSOBEL = 1 }; |
|
|
|
|
|
enum { DEFAULT_NDISP = 64, DEFAULT_WINSZ = 19 }; |
|
|
|
|
|
//! the default constructor |
|
|
StereoBM_GPU(); |
|
|
//! the full constructor taking the camera-specific preset, number of disparities and the SAD window size. ndisparities must be multiple of 8. |
|
|
StereoBM_GPU(int preset, int ndisparities = DEFAULT_NDISP, int winSize = DEFAULT_WINSZ); |
|
|
|
|
|
//! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair |
|
|
//! Output disparity has CV_8U type. |
|
|
void operator() ( const GpuMat& left, const GpuMat& right, GpuMat& disparity); |
|
|
|
|
|
//! async version |
|
|
void operator() ( const GpuMat& left, const GpuMat& right, GpuMat& disparity, const Stream & stream); |
|
|
|
|
|
//! Some heuristics that tries to estmate |
|
|
// if current GPU will be faster then CPU in this algorithm. |
|
|
// It queries current active device. |
|
|
static bool checkIfGpuCallReasonable(); |
|
|
|
|
|
int preset; |
|
|
int ndisp; |
|
|
int winSize; |
|
|
|
|
|
// If avergeTexThreshold == 0 => post procesing is disabled |
|
|
// If avergeTexThreshold != 0 then disparity is set 0 in each point (x,y) where for left image |
|
|
// SumOfHorizontalGradiensInWindow(x, y, winSize) < (winSize * winSize) * avergeTexThreshold |
|
|
// i.e. input left image is low textured. |
|
|
float avergeTexThreshold; |
|
|
private: |
|
|
GpuMat minSSD, leBuf, riBuf; |
|
|
}; |
|
|
|
|
|
////////////////////////// StereoBeliefPropagation /////////////////////////// |
|
|
// "Efficient Belief Propagation for Early Vision" |
|
|
// P.Felzenszwalb |
|
|
|
|
|
class CV_EXPORTS StereoBeliefPropagation |
|
|
{ |
|
|
public: |
|
|
enum { DEFAULT_NDISP = 64 }; |
|
|
enum { DEFAULT_ITERS = 5 }; |
|
|
enum { DEFAULT_LEVELS = 5 }; |
|
|
|
|
|
static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels); |
|
|
|
|
|
//! the default constructor |
|
|
explicit StereoBeliefPropagation(int ndisp = DEFAULT_NDISP, |
|
|
int iters = DEFAULT_ITERS, |
|
|
int levels = DEFAULT_LEVELS, |
|
|
int msg_type = CV_32F); |
|
|
|
|
|
//! the full constructor taking the number of disparities, number of BP iterations on each level, |
|
|
//! number of levels, truncation of data cost, data weight, |
|
|
//! truncation of discontinuity cost and discontinuity single jump |
|
|
//! DataTerm = data_weight * min(fabs(I2-I1), max_data_term) |
|
|
//! DiscTerm = min(disc_single_jump * fabs(f1-f2), max_disc_term) |
|
|
//! please see paper for more details |
|
|
StereoBeliefPropagation(int ndisp, int iters, int levels, |
|
|
float max_data_term, float data_weight, |
|
|
float max_disc_term, float disc_single_jump, |
|
|
int msg_type = CV_32F); |
|
|
|
|
|
//! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair, |
|
|
//! if disparity is empty output type will be CV_16S else output type will be disparity.type(). |
|
|
void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity); |
|
|
|
|
|
//! async version |
|
|
void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream); |
|
|
|
|
|
|
|
|
//! version for user specified data term |
|
|
void operator()(const GpuMat& data, GpuMat& disparity); |
|
|
void operator()(const GpuMat& data, GpuMat& disparity, Stream& stream); |
|
|
|
|
|
int ndisp; |
|
|
|
|
|
int iters; |
|
|
int levels; |
|
|
|
|
|
float max_data_term; |
|
|
float data_weight; |
|
|
float max_disc_term; |
|
|
float disc_single_jump; |
|
|
|
|
|
int msg_type; |
|
|
private: |
|
|
GpuMat u, d, l, r, u2, d2, l2, r2; |
|
|
std::vector<GpuMat> datas; |
|
|
GpuMat out; |
|
|
}; |
|
|
|
|
|
/////////////////////////// StereoConstantSpaceBP /////////////////////////// |
|
|
// "A Constant-Space Belief Propagation Algorithm for Stereo Matching" |
|
|
// Qingxiong Yang, Liang Wang<EFBFBD>, Narendra Ahuja |
|
|
// http://vision.ai.uiuc.edu/~qyang6/ |
|
|
|
|
|
class CV_EXPORTS StereoConstantSpaceBP |
|
|
{ |
|
|
public: |
|
|
enum { DEFAULT_NDISP = 128 }; |
|
|
enum { DEFAULT_ITERS = 8 }; |
|
|
enum { DEFAULT_LEVELS = 4 }; |
|
|
enum { DEFAULT_NR_PLANE = 4 }; |
|
|
|
|
|
static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels, int& nr_plane); |
|
|
|
|
|
//! the default constructor |
|
|
explicit StereoConstantSpaceBP(int ndisp = DEFAULT_NDISP, |
|
|
int iters = DEFAULT_ITERS, |
|
|
int levels = DEFAULT_LEVELS, |
|
|
int nr_plane = DEFAULT_NR_PLANE, |
|
|
int msg_type = CV_32F); |
|
|
|
|
|
//! the full constructor taking the number of disparities, number of BP iterations on each level, |
|
|
//! number of levels, number of active disparity on the first level, truncation of data cost, data weight, |
|
|
//! truncation of discontinuity cost, discontinuity single jump and minimum disparity threshold |
|
|
StereoConstantSpaceBP(int ndisp, int iters, int levels, int nr_plane, |
|
|
float max_data_term, float data_weight, float max_disc_term, float disc_single_jump, |
|
|
int min_disp_th = 0, |
|
|
int msg_type = CV_32F); |
|
|
|
|
|
//! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair, |
|
|
//! if disparity is empty output type will be CV_16S else output type will be disparity.type(). |
|
|
void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity); |
|
|
|
|
|
//! async version |
|
|
void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream); |
|
|
|
|
|
int ndisp; |
|
|
|
|
|
int iters; |
|
|
int levels; |
|
|
|
|
|
int nr_plane; |
|
|
|
|
|
float max_data_term; |
|
|
float data_weight; |
|
|
float max_disc_term; |
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|
float disc_single_jump; |
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int min_disp_th; |
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|
int msg_type; |
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|
bool use_local_init_data_cost; |
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|
private: |
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|
GpuMat u[2], d[2], l[2], r[2]; |
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|
GpuMat disp_selected_pyr[2]; |
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|
GpuMat data_cost; |
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|
GpuMat data_cost_selected; |
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GpuMat temp; |
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GpuMat out; |
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|
}; |
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/////////////////////////// DisparityBilateralFilter /////////////////////////// |
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// Disparity map refinement using joint bilateral filtering given a single color image. |
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// Qingxiong Yang, Liang Wang<EFBFBD>, Narendra Ahuja |
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// http://vision.ai.uiuc.edu/~qyang6/ |
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class CV_EXPORTS DisparityBilateralFilter |
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|
{ |
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public: |
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|
enum { DEFAULT_NDISP = 64 }; |
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enum { DEFAULT_RADIUS = 3 }; |
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enum { DEFAULT_ITERS = 1 }; |
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//! the default constructor |
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|
explicit DisparityBilateralFilter(int ndisp = DEFAULT_NDISP, int radius = DEFAULT_RADIUS, int iters = DEFAULT_ITERS); |
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//! the full constructor taking the number of disparities, filter radius, |
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//! number of iterations, truncation of data continuity, truncation of disparity continuity |
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//! and filter range sigma |
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|
DisparityBilateralFilter(int ndisp, int radius, int iters, float edge_threshold, float max_disc_threshold, float sigma_range); |
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//! the disparity map refinement operator. Refine disparity map using joint bilateral filtering given a single color image. |
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//! disparity must have CV_8U or CV_16S type, image must have CV_8UC1 or CV_8UC3 type. |
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void operator()(const GpuMat& disparity, const GpuMat& image, GpuMat& dst); |
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//! async version |
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void operator()(const GpuMat& disparity, const GpuMat& image, GpuMat& dst, Stream& stream); |
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private: |
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|
int ndisp; |
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int radius; |
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|
int iters; |
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float edge_threshold; |
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float max_disc_threshold; |
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float sigma_range; |
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GpuMat table_color; |
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GpuMat table_space; |
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}; |
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//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector ////////////// |
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struct CV_EXPORTS HOGDescriptor |
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{ |
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enum { DEFAULT_WIN_SIGMA = -1 }; |
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|
enum { DEFAULT_NLEVELS = 64 }; |
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|
enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL }; |
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HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16), |
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Size block_stride=Size(8, 8), Size cell_size=Size(8, 8), |
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int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA, |
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|
double threshold_L2hys=0.2, bool gamma_correction=true, |
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|
int nlevels=DEFAULT_NLEVELS); |
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|
size_t getDescriptorSize() const; |
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|
size_t getBlockHistogramSize() const; |
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|
void setSVMDetector(const vector<float>& detector); |
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|
static vector<float> getDefaultPeopleDetector(); |
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|
static vector<float> getPeopleDetector48x96(); |
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|
static vector<float> getPeopleDetector64x128(); |
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|
void detect(const GpuMat& img, vector<Point>& found_locations, |
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|
double hit_threshold=0, Size win_stride=Size(), |
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|
Size padding=Size()); |
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|
|
void detectMultiScale(const GpuMat& img, vector<Rect>& found_locations, |
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|
double hit_threshold=0, Size win_stride=Size(), |
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|
Size padding=Size(), double scale0=1.05, |
|
|
int group_threshold=2); |
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|
|
void getDescriptors(const GpuMat& img, Size win_stride, |
|
|
GpuMat& descriptors, |
|
|
int descr_format=DESCR_FORMAT_COL_BY_COL); |
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|
|
Size win_size; |
|
|
Size block_size; |
|
|
Size block_stride; |
|
|
Size cell_size; |
|
|
int nbins; |
|
|
double win_sigma; |
|
|
double threshold_L2hys; |
|
|
bool gamma_correction; |
|
|
int nlevels; |
|
|
|
|
|
protected: |
|
|
void computeBlockHistograms(const GpuMat& img); |
|
|
void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle); |
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|
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|
|
double getWinSigma() const; |
|
|
bool checkDetectorSize() const; |
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|
|
static int numPartsWithin(int size, int part_size, int stride); |
|
|
static Size numPartsWithin(Size size, Size part_size, Size stride); |
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|
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|
|
// Coefficients of the separating plane |
|
|
float free_coef; |
|
|
GpuMat detector; |
|
|
|
|
|
// Results of the last classification step |
|
|
GpuMat labels; |
|
|
Mat labels_host; |
|
|
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|
|
// Results of the last histogram evaluation step |
|
|
GpuMat block_hists; |
|
|
|
|
|
// Gradients conputation results |
|
|
GpuMat grad, qangle; |
|
|
}; |
|
|
|
|
|
|
|
|
////////////////////////////////// BruteForceMatcher ////////////////////////////////// |
|
|
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|
|
class CV_EXPORTS BruteForceMatcher_GPU_base |
|
|
{ |
|
|
public: |
|
|
enum DistType {L1Dist = 0, L2Dist}; |
|
|
|
|
|
explicit BruteForceMatcher_GPU_base(DistType distType = L2Dist); |
|
|
|
|
|
// Add descriptors to train descriptor collection. |
|
|
void add(const std::vector<GpuMat>& descCollection); |
|
|
|
|
|
// Get train descriptors collection. |
|
|
const std::vector<GpuMat>& getTrainDescriptors() const; |
|
|
|
|
|
// Clear train descriptors collection. |
|
|
void clear(); |
|
|
|
|
|
// Return true if there are not train descriptors in collection. |
|
|
bool empty() const; |
|
|
|
|
|
// Return true if the matcher supports mask in match methods. |
|
|
bool isMaskSupported() const; |
|
|
|
|
|
// Find one best match for each query descriptor. |
|
|
// trainIdx.at<int>(0, queryIdx) will contain best train index for queryIdx |
|
|
// distance.at<float>(0, queryIdx) will contain distance |
|
|
void matchSingle(const GpuMat& queryDescs, const GpuMat& trainDescs, |
|
|
GpuMat& trainIdx, GpuMat& distance, |
|
|
const GpuMat& mask = GpuMat()); |
|
|
|
|
|
// Download trainIdx and distance to CPU vector with DMatch |
|
|
static void matchDownload(const GpuMat& trainIdx, const GpuMat& distance, std::vector<DMatch>& matches); |
|
|
|
|
|
// Find one best match for each query descriptor. |
|
|
void match(const GpuMat& queryDescs, const GpuMat& trainDescs, std::vector<DMatch>& matches, |
|
|
const GpuMat& mask = GpuMat()); |
|
|
|
|
|
// Make gpu collection of trains and masks in suitable format for matchCollection function |
|
|
void makeGpuCollection(GpuMat& trainCollection, GpuMat& maskCollection, |
|
|
const vector<GpuMat>& masks = std::vector<GpuMat>()); |
|
|
|
|
|
// Find one best match from train collection for each query descriptor. |
|
|
// trainIdx.at<int>(0, queryIdx) will contain best train index for queryIdx |
|
|
// imgIdx.at<int>(0, queryIdx) will contain best image index for queryIdx |
|
|
// distance.at<float>(0, queryIdx) will contain distance |
|
|
void matchCollection(const GpuMat& queryDescs, const GpuMat& trainCollection, |
|
|
GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance, |
|
|
const GpuMat& maskCollection); |
|
|
|
|
|
// Download trainIdx, imgIdx and distance to CPU vector with DMatch |
|
|
static void matchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, |
|
|
std::vector<DMatch>& matches); |
|
|
|
|
|
// Find one best match from train collection for each query descriptor. |
|
|
void match(const GpuMat& queryDescs, std::vector<DMatch>& matches, |
|
|
const std::vector<GpuMat>& masks = std::vector<GpuMat>()); |
|
|
|
|
|
// Find k best matches for each query descriptor (in increasing order of distances). |
|
|
// trainIdx.at<int>(queryIdx, i) will contain index of i'th best trains (i < k). |
|
|
// distance.at<float>(queryIdx, i) will contain distance. |
|
|
// allDist is a buffer to store all distance between query descriptors and train descriptors |
|
|
// it have size (nQuery,nTrain) and CV_32F type |
|
|
// allDist.at<float>(queryIdx, trainIdx) will contain FLT_MAX, if trainIdx is one from k best, |
|
|
// otherwise it will contain distance between queryIdx and trainIdx descriptors |
|
|
void knnMatch(const GpuMat& queryDescs, const GpuMat& trainDescs, |
|
|
GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k, const GpuMat& mask = GpuMat()); |
|
|
|
|
|
// Download trainIdx and distance to CPU vector with DMatch |
|
|
// compactResult is used when mask is not empty. If compactResult is false matches |
|
|
// vector will have the same size as queryDescriptors rows. If compactResult is true |
|
|
// matches vector will not contain matches for fully masked out query descriptors. |
|
|
static void knnMatchDownload(const GpuMat& trainIdx, const GpuMat& distance, |
|
|
std::vector< std::vector<DMatch> >& matches, bool compactResult = false); |
|
|
|
|
|
// Find k best matches for each query descriptor (in increasing order of distances). |
|
|
// compactResult is used when mask is not empty. If compactResult is false matches |
|
|
// vector will have the same size as queryDescriptors rows. If compactResult is true |
|
|
// matches vector will not contain matches for fully masked out query descriptors. |
|
|
void knnMatch(const GpuMat& queryDescs, const GpuMat& trainDescs, |
|
|
std::vector< std::vector<DMatch> >& matches, int k, const GpuMat& mask = GpuMat(), |
|
|
bool compactResult = false); |
|
|
|
|
|
// Find k best matches for each query descriptor (in increasing order of distances). |
|
|
// compactResult is used when mask is not empty. If compactResult is false matches |
|
|
// vector will have the same size as queryDescriptors rows. If compactResult is true |
|
|
// matches vector will not contain matches for fully masked out query descriptors. |
|
|
void knnMatch(const GpuMat& queryDescs, std::vector< std::vector<DMatch> >& matches, int knn, |
|
|
const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false ); |
|
|
|
|
|
// Find best matches for each query descriptor which have distance less than maxDistance. |
|
|
// nMatches.at<unsigned int>(0, queruIdx) will contain matches count for queryIdx. |
|
|
// carefully nMatches can be greater than trainIdx.cols - it means that matcher didn't find all matches, |
|
|
// because it didn't have enough memory. |
|
|
// trainIdx.at<int>(queruIdx, i) will contain ith train index (i < min(nMatches.at<unsigned int>(0, queruIdx), trainIdx.cols)) |
|
|
// distance.at<int>(queruIdx, i) will contain ith distance (i < min(nMatches.at<unsigned int>(0, queruIdx), trainIdx.cols)) |
|
|
// If trainIdx is empty, then trainIdx and distance will be created with size nQuery x nTrain, |
|
|
// otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches |
|
|
// Matches doesn't sorted. |
|
|
void radiusMatch(const GpuMat& queryDescs, const GpuMat& trainDescs, |
|
|
GpuMat& trainIdx, GpuMat& nMatches, GpuMat& distance, float maxDistance, |
|
|
const GpuMat& mask = GpuMat()); |
|
|
|
|
|
// Download trainIdx, nMatches and distance to CPU vector with DMatch. |
|
|
// matches will be sorted in increasing order of distances. |
|
|
// compactResult is used when mask is not empty. If compactResult is false matches |
|
|
// vector will have the same size as queryDescriptors rows. If compactResult is true |
|
|
// matches vector will not contain matches for fully masked out query descriptors. |
|
|
static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& nMatches, const GpuMat& distance, |
|
|
std::vector< std::vector<DMatch> >& matches, bool compactResult = false); |
|
|
|
|
|
// Find best matches for each query descriptor which have distance less than maxDistance |
|
|
// in increasing order of distances). |
|
|
void radiusMatch(const GpuMat& queryDescs, const GpuMat& trainDescs, |
|
|
std::vector< std::vector<DMatch> >& matches, float maxDistance, |
|
|
const GpuMat& mask = GpuMat(), bool compactResult = false); |
|
|
|
|
|
// Find best matches from train collection for each query descriptor which have distance less than |
|
|
// maxDistance (in increasing order of distances). |
|
|
void radiusMatch(const GpuMat& queryDescs, std::vector< std::vector<DMatch> >& matches, float maxDistance, |
|
|
const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false); |
|
|
|
|
|
private: |
|
|
DistType distType; |
|
|
|
|
|
std::vector<GpuMat> trainDescCollection; |
|
|
}; |
|
|
|
|
|
template <class Distance> |
|
|
class CV_EXPORTS BruteForceMatcher_GPU; |
|
|
|
|
|
template <typename T> |
|
|
class CV_EXPORTS BruteForceMatcher_GPU< L1<T> > : public BruteForceMatcher_GPU_base |
|
|
{ |
|
|
public: |
|
|
explicit BruteForceMatcher_GPU() : BruteForceMatcher_GPU_base(L1Dist) {} |
|
|
explicit BruteForceMatcher_GPU(L1<T> /*d*/) : BruteForceMatcher_GPU_base(L1Dist) {} |
|
|
}; |
|
|
template <typename T> |
|
|
class CV_EXPORTS BruteForceMatcher_GPU< L2<T> > : public BruteForceMatcher_GPU_base |
|
|
{ |
|
|
public: |
|
|
explicit BruteForceMatcher_GPU() : BruteForceMatcher_GPU_base(L2Dist) {} |
|
|
explicit BruteForceMatcher_GPU(L2<T> /*d*/) : BruteForceMatcher_GPU_base(L2Dist) {} |
|
|
}; |
|
|
|
|
|
////////////////////////////////// CascadeClassifier_GPU ////////////////////////////////////////// |
|
|
// The cascade classifier class for object detection. |
|
|
class CV_EXPORTS CascadeClassifier_GPU |
|
|
{ |
|
|
public: |
|
|
CascadeClassifier_GPU(); |
|
|
CascadeClassifier_GPU(const string& filename); |
|
|
~CascadeClassifier_GPU(); |
|
|
|
|
|
bool empty() const; |
|
|
bool load(const string& filename); |
|
|
void release(); |
|
|
|
|
|
/* returns number of detected objects */ |
|
|
int detectMultiScale( const GpuMat& image, GpuMat& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size()); |
|
|
|
|
|
bool findLargestObject; |
|
|
bool visualizeInPlace; |
|
|
|
|
|
Size getClassifierSize() const; |
|
|
private: |
|
|
|
|
|
struct CascadeClassifierImpl; |
|
|
CascadeClassifierImpl* impl; |
|
|
}; |
|
|
|
|
|
////////////////////////////////// SURF ////////////////////////////////////////// |
|
|
|
|
|
struct CV_EXPORTS SURFParams_GPU |
|
|
{ |
|
|
SURFParams_GPU() : threshold(0.1f), nOctaves(4), nIntervals(4), initialScale(2.f), |
|
|
l1(3.f/1.5f), l2(5.f/1.5f), l3(3.f/1.5f), l4(1.f/1.5f), |
|
|
edgeScale(0.81f), initialStep(1), extended(true), featuresRatio(0.01f) {} |
|
|
|
|
|
//! The interest operator threshold |
|
|
float threshold; |
|
|
//! The number of octaves to process |
|
|
int nOctaves; |
|
|
//! The number of intervals in each octave |
|
|
int nIntervals; |
|
|
//! The scale associated with the first interval of the first octave |
|
|
float initialScale; |
|
|
|
|
|
//! mask parameter l_1 |
|
|
float l1; |
|
|
//! mask parameter l_2 |
|
|
float l2; |
|
|
//! mask parameter l_3 |
|
|
float l3; |
|
|
//! mask parameter l_4 |
|
|
float l4; |
|
|
//! The amount to scale the edge rejection mask |
|
|
float edgeScale; |
|
|
//! The initial sampling step in pixels. |
|
|
int initialStep; |
|
|
|
|
|
//! True, if generate 128-len descriptors, false - 64-len descriptors |
|
|
bool extended; |
|
|
|
|
|
//! max features = featuresRatio * img.size().srea() |
|
|
float featuresRatio; |
|
|
}; |
|
|
|
|
|
class CV_EXPORTS SURF_GPU : public SURFParams_GPU |
|
|
{ |
|
|
public: |
|
|
//! returns the descriptor size in float's (64 or 128) |
|
|
int descriptorSize() const; |
|
|
|
|
|
//! upload host keypoints to device memory |
|
|
static void uploadKeypoints(const vector<KeyPoint>& keypoints, GpuMat& keypointsGPU); |
|
|
//! download keypoints from device to host memory |
|
|
static void downloadKeypoints(const GpuMat& keypointsGPU, vector<KeyPoint>& keypoints); |
|
|
|
|
|
//! download descriptors from device to host memory |
|
|
static void downloadDescriptors(const GpuMat& descriptorsGPU, vector<float>& descriptors); |
|
|
|
|
|
//! finds the keypoints using fast hessian detector used in SURF |
|
|
//! supports CV_8UC1 images |
|
|
//! keypoints will have 1 row and type CV_32FC(6) |
|
|
//! keypoints.at<float[6]>(1, i) contains i'th keypoint |
|
|
//! format: (x, y, size, response, angle, octave) |
|
|
void operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints); |
|
|
//! finds the keypoints and computes their descriptors. |
|
|
//! Optionally it can compute descriptors for the user-provided keypoints and recompute keypoints direction |
|
|
void operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors, |
|
|
bool useProvidedKeypoints = false, bool calcOrientation = true); |
|
|
|
|
|
void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints); |
|
|
void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints, GpuMat& descriptors, |
|
|
bool useProvidedKeypoints = false, bool calcOrientation = true); |
|
|
|
|
|
void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints, std::vector<float>& descriptors, |
|
|
bool useProvidedKeypoints = false, bool calcOrientation = true); |
|
|
|
|
|
GpuMat sum; |
|
|
GpuMat sumf; |
|
|
|
|
|
GpuMat mask1; |
|
|
GpuMat maskSum; |
|
|
|
|
|
GpuMat hessianBuffer; |
|
|
GpuMat maxPosBuffer; |
|
|
GpuMat featuresBuffer; |
|
|
}; |
|
|
|
|
|
} |
|
|
|
|
|
//! Speckle filtering - filters small connected components on diparity image. |
|
|
//! It sets pixel (x,y) to newVal if it coresponds to small CC with size < maxSpeckleSize. |
|
|
//! Threshold for border between CC is diffThreshold; |
|
|
CV_EXPORTS void filterSpeckles( Mat& img, uchar newVal, int maxSpeckleSize, uchar diffThreshold, Mat& buf); |
|
|
|
|
|
} |
|
|
#include "opencv2/gpu/matrix_operations.hpp" |
|
|
|
|
|
#endif /* __OPENCV_GPU_HPP__ */
|
|
|
|
