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1983 lines
88 KiB
C++
1983 lines
88 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other 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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#ifndef SKIP_INCLUDES
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#include <vector>
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#endif
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#include "opencv2/core/gpumat.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/features2d/features2d.hpp"
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namespace cv { namespace gpu {
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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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//! Explicitly destroys and cleans up all resources associated with the current device in the current process.
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//! Any subsequent API call to this device will reinitialize the device.
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CV_EXPORTS void resetDevice();
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enum FeatureSet
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{
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FEATURE_SET_COMPUTE_10 = 10,
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FEATURE_SET_COMPUTE_11 = 11,
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FEATURE_SET_COMPUTE_12 = 12,
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FEATURE_SET_COMPUTE_13 = 13,
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FEATURE_SET_COMPUTE_20 = 20,
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FEATURE_SET_COMPUTE_21 = 21,
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GLOBAL_ATOMICS = FEATURE_SET_COMPUTE_11,
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SHARED_ATOMICS = FEATURE_SET_COMPUTE_12,
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NATIVE_DOUBLE = FEATURE_SET_COMPUTE_13
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};
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// Gives information about what GPU archs this OpenCV GPU module was
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// compiled for
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class CV_EXPORTS TargetArchs
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{
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public:
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static bool builtWith(FeatureSet feature_set);
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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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// Gives information about the given GPU
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class CV_EXPORTS DeviceInfo
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{
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public:
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// Creates DeviceInfo object for the current GPU
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DeviceInfo() : device_id_(getDevice()) { query(); }
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// Creates DeviceInfo object for the given GPU
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DeviceInfo(int device_id) : device_id_(device_id) { query(); }
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std::string name() const { return name_; }
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// Return compute capability versions
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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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// Checks whether device supports the given feature
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bool supports(FeatureSet feature_set) const;
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// Checks whether the GPU module can be run on the given device
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bool isCompatible() const;
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int deviceID() const { return device_id_; }
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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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std::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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CV_EXPORTS void printCudaDeviceInfo(int device);
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CV_EXPORTS void printShortCudaDeviceInfo(int device);
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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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// Page-locks the matrix m memory and maps it for the device(s)
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CV_EXPORTS void registerPageLocked(Mat& m);
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// Unmaps the memory of matrix m, and makes it pageable again.
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CV_EXPORTS void unregisterPageLocked(Mat& m);
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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(GpuMat& src, Scalar val);
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void enqueueMemSet(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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static Stream& Null();
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operator bool() const;
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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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explicit Stream(Impl* impl);
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};
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//////////////////////////////// Filter Engine ////////////////////////////////
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/*!
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The Base Class for 1D or Row-wise Filters
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This is the base class for linear or non-linear filters that process 1D data.
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In particular, such filters are used for the "horizontal" filtering parts in separable filters.
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*/
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class CV_EXPORTS BaseRowFilter_GPU
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{
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public:
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BaseRowFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {}
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virtual ~BaseRowFilter_GPU() {}
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virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
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int ksize, anchor;
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};
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/*!
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The Base Class for Column-wise Filters
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This is the base class for linear or non-linear filters that process columns of 2D arrays.
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Such filters are used for the "vertical" filtering parts in separable filters.
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*/
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class CV_EXPORTS BaseColumnFilter_GPU
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{
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public:
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BaseColumnFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {}
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virtual ~BaseColumnFilter_GPU() {}
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virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
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int ksize, anchor;
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};
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/*!
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The Base Class for Non-Separable 2D Filters.
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This is the base class for linear or non-linear 2D filters.
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*/
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class CV_EXPORTS BaseFilter_GPU
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{
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public:
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BaseFilter_GPU(const Size& ksize_, const Point& anchor_) : ksize(ksize_), anchor(anchor_) {}
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virtual ~BaseFilter_GPU() {}
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virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
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Size ksize;
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Point anchor;
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};
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/*!
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The Base Class for Filter Engine.
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The class can be used to apply an arbitrary filtering operation to an image.
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It contains all the necessary intermediate buffers.
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*/
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class CV_EXPORTS FilterEngine_GPU
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{
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public:
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virtual ~FilterEngine_GPU() {}
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virtual void apply(const GpuMat& src, GpuMat& dst, Rect roi = Rect(0,0,-1,-1), Stream& stream = Stream::Null()) = 0;
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};
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//! returns the non-separable filter engine with the specified filter
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CV_EXPORTS Ptr<FilterEngine_GPU> createFilter2D_GPU(const Ptr<BaseFilter_GPU>& filter2D, int srcType, int dstType);
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//! returns the separable filter engine with the specified filters
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CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter,
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const Ptr<BaseColumnFilter_GPU>& columnFilter, int srcType, int bufType, int dstType);
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CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter,
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const Ptr<BaseColumnFilter_GPU>& columnFilter, int srcType, int bufType, int dstType, GpuMat& buf);
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//! returns horizontal 1D box filter
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//! supports only CV_8UC1 source type and CV_32FC1 sum type
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CV_EXPORTS Ptr<BaseRowFilter_GPU> getRowSumFilter_GPU(int srcType, int sumType, int ksize, int anchor = -1);
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//! returns vertical 1D box filter
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//! supports only CV_8UC1 sum type and CV_32FC1 dst type
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CV_EXPORTS Ptr<BaseColumnFilter_GPU> getColumnSumFilter_GPU(int sumType, int dstType, int ksize, int anchor = -1);
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//! returns 2D box filter
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//! supports CV_8UC1 and CV_8UC4 source type, dst type must be the same as source type
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CV_EXPORTS Ptr<BaseFilter_GPU> getBoxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1, -1));
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//! returns box filter engine
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CV_EXPORTS Ptr<FilterEngine_GPU> createBoxFilter_GPU(int srcType, int dstType, const Size& ksize,
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const Point& anchor = Point(-1,-1));
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//! returns 2D morphological filter
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//! only MORPH_ERODE and MORPH_DILATE are supported
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//! supports CV_8UC1 and CV_8UC4 types
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//! kernel must have CV_8UC1 type, one rows and cols == ksize.width * ksize.height
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CV_EXPORTS Ptr<BaseFilter_GPU> getMorphologyFilter_GPU(int op, int type, const Mat& kernel, const Size& ksize,
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Point anchor=Point(-1,-1));
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//! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported.
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CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat& kernel,
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const Point& anchor = Point(-1,-1), int iterations = 1);
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CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat& kernel, GpuMat& buf,
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const Point& anchor = Point(-1,-1), int iterations = 1);
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//! returns 2D filter with the specified kernel
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//! supports CV_8UC1 and CV_8UC4 types
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CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, const Size& ksize,
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Point anchor = Point(-1, -1));
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//! returns the non-separable linear filter engine
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CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat& kernel,
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const Point& anchor = Point(-1,-1));
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//! returns the primitive row filter with the specified kernel.
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//! supports only CV_8UC1, CV_8UC4, CV_16SC1, CV_16SC2, CV_32SC1, CV_32FC1 source type.
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//! there are two version of algorithm: NPP and OpenCV.
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//! NPP calls when srcType == CV_8UC1 or srcType == CV_8UC4 and bufType == srcType,
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//! otherwise calls OpenCV version.
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//! NPP supports only BORDER_CONSTANT border type.
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//! OpenCV version supports only CV_32F as buffer depth and
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//! BORDER_REFLECT101, BORDER_REPLICATE and BORDER_CONSTANT border types.
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CV_EXPORTS Ptr<BaseRowFilter_GPU> getLinearRowFilter_GPU(int srcType, int bufType, const Mat& rowKernel,
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int anchor = -1, int borderType = BORDER_DEFAULT);
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//! returns the primitive column filter with the specified kernel.
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//! supports only CV_8UC1, CV_8UC4, CV_16SC1, CV_16SC2, CV_32SC1, CV_32FC1 dst type.
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//! there are two version of algorithm: NPP and OpenCV.
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//! NPP calls when dstType == CV_8UC1 or dstType == CV_8UC4 and bufType == dstType,
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//! otherwise calls OpenCV version.
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//! NPP supports only BORDER_CONSTANT border type.
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//! OpenCV version supports only CV_32F as buffer depth and
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//! BORDER_REFLECT101, BORDER_REPLICATE and BORDER_CONSTANT border types.
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CV_EXPORTS Ptr<BaseColumnFilter_GPU> getLinearColumnFilter_GPU(int bufType, int dstType, const Mat& columnKernel,
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int anchor = -1, int borderType = BORDER_DEFAULT);
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//! returns the separable linear filter engine
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CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel,
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const Mat& columnKernel, const Point& anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT,
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int columnBorderType = -1);
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CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel,
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const Mat& columnKernel, GpuMat& buf, const Point& anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT,
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int columnBorderType = -1);
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//! returns filter engine for the generalized Sobel operator
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CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU(int srcType, int dstType, int dx, int dy, int ksize,
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int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU(int srcType, int dstType, int dx, int dy, int ksize, GpuMat& buf,
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int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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//! returns the Gaussian filter engine
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CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, double sigma1, double sigma2 = 0,
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int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, GpuMat& buf, double sigma1, double sigma2 = 0,
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int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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//! returns maximum filter
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CV_EXPORTS Ptr<BaseFilter_GPU> getMaxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1));
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//! returns minimum filter
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CV_EXPORTS Ptr<BaseFilter_GPU> getMinFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1));
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//! smooths the image using the normalized box filter
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//! supports CV_8UC1, CV_8UC4 types
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CV_EXPORTS void boxFilter(const GpuMat& src, GpuMat& dst, int ddepth, Size ksize, Point anchor = Point(-1,-1), Stream& stream = Stream::Null());
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//! a synonym for normalized box filter
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static inline void blur(const GpuMat& src, GpuMat& dst, Size ksize, Point anchor = Point(-1,-1), Stream& stream = Stream::Null())
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{
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boxFilter(src, dst, -1, ksize, anchor, stream);
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}
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//! erodes the image (applies the local minimum operator)
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CV_EXPORTS void erode(const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1);
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CV_EXPORTS void erode(const GpuMat& src, GpuMat& dst, const Mat& kernel, GpuMat& buf,
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Point anchor = Point(-1, -1), int iterations = 1,
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Stream& stream = Stream::Null());
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//! dilates the image (applies the local maximum operator)
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CV_EXPORTS void dilate(const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1);
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CV_EXPORTS void dilate(const GpuMat& src, GpuMat& dst, const Mat& kernel, GpuMat& buf,
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Point anchor = Point(-1, -1), int iterations = 1,
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Stream& stream = Stream::Null());
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//! applies an advanced morphological operation to the image
|
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CV_EXPORTS void morphologyEx(const GpuMat& src, GpuMat& dst, int op, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1);
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CV_EXPORTS void morphologyEx(const GpuMat& src, GpuMat& dst, int op, const Mat& kernel, GpuMat& buf1, GpuMat& buf2,
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Point anchor = Point(-1, -1), int iterations = 1, Stream& stream = Stream::Null());
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|
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//! applies non-separable 2D linear filter to the image
|
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CV_EXPORTS void filter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernel, Point anchor=Point(-1,-1), Stream& stream = Stream::Null());
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//! applies separable 2D linear filter to the image
|
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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);
|
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CV_EXPORTS void sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY, GpuMat& buf,
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Point anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1,
|
|
Stream& stream = Stream::Null());
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|
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//! 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);
|
|
CV_EXPORTS void Sobel(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, GpuMat& buf, int ksize = 3, double scale = 1,
|
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int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());
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|
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//! 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);
|
|
CV_EXPORTS void Scharr(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, GpuMat& buf, double scale = 1,
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int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());
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|
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//! 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);
|
|
CV_EXPORTS void GaussianBlur(const GpuMat& src, GpuMat& dst, Size ksize, GpuMat& buf, double sigma1, double sigma2 = 0,
|
|
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());
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|
|
|
//! 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, Stream& stream = Stream::Null());
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|
|
|
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|
////////////////////////////// Arithmetics ///////////////////////////////////
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|
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|
//! implements generalized matrix product algorithm GEMM from BLAS
|
|
CV_EXPORTS void gemm(const GpuMat& src1, const GpuMat& src2, double alpha,
|
|
const GpuMat& src3, double beta, GpuMat& dst, int flags = 0, Stream& stream = Stream::Null());
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|
|
|
//! transposes the matrix
|
|
//! supports matrix with element size = 1, 4 and 8 bytes (CV_8UC1, CV_8UC4, CV_16UC2, CV_32FC1, etc)
|
|
CV_EXPORTS void transpose(const GpuMat& src1, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
//! reverses the order of the rows, columns or both in a matrix
|
|
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U, CV_32S or CV_32F depth
|
|
CV_EXPORTS void flip(const GpuMat& a, GpuMat& b, int flipCode, Stream& stream = Stream::Null());
|
|
|
|
//! transforms 8-bit unsigned integers using lookup table: dst(i)=lut(src(i))
|
|
//! destination array will have the depth type as lut and the same channels number as source
|
|
//! supports CV_8UC1, CV_8UC3 types
|
|
CV_EXPORTS void LUT(const GpuMat& src, const Mat& lut, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
//! makes multi-channel array out of several single-channel arrays
|
|
CV_EXPORTS void merge(const GpuMat* src, size_t n, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
//! makes multi-channel array out of several single-channel arrays
|
|
CV_EXPORTS void merge(const vector<GpuMat>& src, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
//! copies each plane of a multi-channel array to a dedicated array
|
|
CV_EXPORTS void split(const GpuMat& src, GpuMat* dst, Stream& stream = Stream::Null());
|
|
|
|
//! copies each plane of a multi-channel array to a dedicated array
|
|
CV_EXPORTS void split(const GpuMat& src, vector<GpuMat>& dst, Stream& stream = Stream::Null());
|
|
|
|
//! computes magnitude of complex (x(i).re, x(i).im) vector
|
|
//! supports only CV_32FC2 type
|
|
CV_EXPORTS void magnitude(const GpuMat& x, GpuMat& magnitude, Stream& stream = Stream::Null());
|
|
|
|
//! computes squared magnitude of complex (x(i).re, x(i).im) vector
|
|
//! supports only CV_32FC2 type
|
|
CV_EXPORTS void magnitudeSqr(const GpuMat& x, GpuMat& magnitude, Stream& stream = Stream::Null());
|
|
|
|
//! computes magnitude of each (x(i), y(i)) vector
|
|
//! supports only floating-point source
|
|
CV_EXPORTS void magnitude(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, Stream& stream = Stream::Null());
|
|
|
|
//! computes squared magnitude of each (x(i), y(i)) vector
|
|
//! supports only floating-point source
|
|
CV_EXPORTS void magnitudeSqr(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, Stream& stream = Stream::Null());
|
|
|
|
//! computes angle (angle(i)) of each (x(i), y(i)) vector
|
|
//! supports only floating-point source
|
|
CV_EXPORTS void phase(const GpuMat& x, const GpuMat& y, GpuMat& angle, bool angleInDegrees = false, Stream& stream = Stream::Null());
|
|
|
|
//! converts Cartesian coordinates to polar
|
|
//! supports only floating-point source
|
|
CV_EXPORTS void cartToPolar(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, GpuMat& angle, bool angleInDegrees = false, Stream& stream = Stream::Null());
|
|
|
|
//! converts polar coordinates to Cartesian
|
|
//! supports only floating-point source
|
|
CV_EXPORTS void polarToCart(const GpuMat& magnitude, const GpuMat& angle, GpuMat& x, GpuMat& y, bool angleInDegrees = false, Stream& stream = Stream::Null());
|
|
|
|
|
|
//////////////////////////// Per-element operations ////////////////////////////////////
|
|
|
|
//! adds one matrix to another (c = a + b)
|
|
CV_EXPORTS void add(const GpuMat& a, const GpuMat& b, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
|
|
//! adds scalar to a matrix (c = a + s)
|
|
CV_EXPORTS void add(const GpuMat& a, const Scalar& sc, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
|
|
|
|
//! subtracts one matrix from another (c = a - b)
|
|
CV_EXPORTS void subtract(const GpuMat& a, const GpuMat& b, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
|
|
//! subtracts scalar from a matrix (c = a - s)
|
|
CV_EXPORTS void subtract(const GpuMat& a, const Scalar& sc, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
|
|
|
|
//! computes element-wise weighted product of the two arrays (c = scale * a * b)
|
|
CV_EXPORTS void multiply(const GpuMat& a, const GpuMat& b, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
|
|
//! weighted multiplies matrix to a scalar (c = scale * a * s)
|
|
CV_EXPORTS void multiply(const GpuMat& a, const Scalar& sc, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
|
|
|
|
//! computes element-wise weighted quotient of the two arrays (c = a / b)
|
|
CV_EXPORTS void divide(const GpuMat& a, const GpuMat& b, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
|
|
//! computes element-wise weighted quotient of matrix and scalar (c = a / s)
|
|
CV_EXPORTS void divide(const GpuMat& a, const Scalar& sc, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
|
|
//! computes element-wise weighted reciprocal of an array (dst = scale/src2)
|
|
CV_EXPORTS void divide(double scale, const GpuMat& src2, GpuMat& dst, int dtype = -1, Stream& stream = Stream::Null());
|
|
|
|
//! computes the weighted sum of two arrays (dst = alpha*src1 + beta*src2 + gamma)
|
|
CV_EXPORTS void addWeighted(const GpuMat& src1, double alpha, const GpuMat& src2, double beta, double gamma, GpuMat& dst,
|
|
int dtype = -1, Stream& stream = Stream::Null());
|
|
|
|
//! adds scaled array to another one (dst = alpha*src1 + src2)
|
|
static inline void scaleAdd(const GpuMat& src1, double alpha, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null())
|
|
{
|
|
addWeighted(src1, alpha, src2, 1.0, 0.0, dst, -1, stream);
|
|
}
|
|
|
|
//! computes element-wise absolute difference of two arrays (c = abs(a - b))
|
|
CV_EXPORTS void absdiff(const GpuMat& a, const GpuMat& b, GpuMat& c, Stream& stream = Stream::Null());
|
|
//! computes element-wise absolute difference of array and scalar (c = abs(a - s))
|
|
CV_EXPORTS void absdiff(const GpuMat& a, const Scalar& s, GpuMat& c, Stream& stream = Stream::Null());
|
|
|
|
//! computes absolute value of each matrix element
|
|
//! supports CV_16S and CV_32F depth
|
|
CV_EXPORTS void abs(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
//! computes square of each pixel in an image
|
|
//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
|
|
CV_EXPORTS void sqr(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
//! computes square root of each pixel in an image
|
|
//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
|
|
CV_EXPORTS void sqrt(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
//! computes exponent of each matrix element (b = e**a)
|
|
//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
|
|
CV_EXPORTS void exp(const GpuMat& a, GpuMat& b, Stream& stream = Stream::Null());
|
|
|
|
//! computes natural logarithm of absolute value of each matrix element: b = log(abs(a))
|
|
//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
|
|
CV_EXPORTS void log(const GpuMat& a, GpuMat& b, Stream& stream = Stream::Null());
|
|
|
|
//! computes power of each matrix element:
|
|
// (dst(i,j) = pow( src(i,j) , power), if src.type() is integer
|
|
// (dst(i,j) = pow(fabs(src(i,j)), power), otherwise
|
|
//! supports all, except depth == CV_64F
|
|
CV_EXPORTS void pow(const GpuMat& src, double power, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
//! compares elements of two arrays (c = a <cmpop> b)
|
|
CV_EXPORTS void compare(const GpuMat& a, const GpuMat& b, GpuMat& c, int cmpop, Stream& stream = Stream::Null());
|
|
|
|
//! performs per-elements bit-wise inversion
|
|
CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
|
|
|
|
//! calculates per-element bit-wise disjunction of two arrays
|
|
CV_EXPORTS void bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
|
|
//! calculates per-element bit-wise disjunction of array and scalar
|
|
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
|
|
CV_EXPORTS void bitwise_or(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
//! calculates per-element bit-wise conjunction of two arrays
|
|
CV_EXPORTS void bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
|
|
//! calculates per-element bit-wise conjunction of array and scalar
|
|
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
|
|
CV_EXPORTS void bitwise_and(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
//! calculates per-element bit-wise "exclusive or" operation
|
|
CV_EXPORTS void bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
|
|
//! calculates per-element bit-wise "exclusive or" of array and scalar
|
|
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
|
|
CV_EXPORTS void bitwise_xor(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
//! pixel by pixel right shift of an image by a constant value
|
|
//! supports 1, 3 and 4 channels images with integers elements
|
|
CV_EXPORTS void rshift(const GpuMat& src, Scalar_<int> sc, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
//! pixel by pixel left shift of an image by a constant value
|
|
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
|
|
CV_EXPORTS void lshift(const GpuMat& src, Scalar_<int> sc, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
//! computes per-element minimum of two arrays (dst = min(src1, src2))
|
|
CV_EXPORTS void min(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
//! computes per-element minimum of array and scalar (dst = min(src1, src2))
|
|
CV_EXPORTS void min(const GpuMat& src1, double src2, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
//! computes per-element maximum of two arrays (dst = max(src1, src2))
|
|
CV_EXPORTS void max(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
//! computes per-element maximum of array and scalar (dst = max(src1, src2))
|
|
CV_EXPORTS void max(const GpuMat& src1, double src2, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
enum { ALPHA_OVER, ALPHA_IN, ALPHA_OUT, ALPHA_ATOP, ALPHA_XOR, ALPHA_PLUS, ALPHA_OVER_PREMUL, ALPHA_IN_PREMUL, ALPHA_OUT_PREMUL,
|
|
ALPHA_ATOP_PREMUL, ALPHA_XOR_PREMUL, ALPHA_PLUS_PREMUL, ALPHA_PREMUL};
|
|
|
|
//! Composite two images using alpha opacity values contained in each image
|
|
//! Supports CV_8UC4, CV_16UC4, CV_32SC4 and CV_32FC4 types
|
|
CV_EXPORTS void alphaComp(const GpuMat& img1, const GpuMat& img2, GpuMat& dst, int alpha_op, Stream& stream = Stream::Null());
|
|
|
|
|
|
////////////////////////////// Image processing //////////////////////////////
|
|
|
|
//! DST[x,y] = SRC[xmap[x,y],ymap[x,y]]
|
|
//! supports only CV_32FC1 map type
|
|
CV_EXPORTS void remap(const GpuMat& src, GpuMat& dst, const GpuMat& xmap, const GpuMat& ymap,
|
|
int interpolation, int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar(),
|
|
Stream& stream = Stream::Null());
|
|
|
|
//! 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),
|
|
Stream& stream = Stream::Null());
|
|
|
|
//! 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),
|
|
Stream& stream = Stream::Null());
|
|
|
|
//! 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));
|
|
|
|
//! 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, Stream& stream = Stream::Null());
|
|
|
|
//! 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, Stream& stream = Stream::Null());
|
|
|
|
//! converts image from one color space to another
|
|
CV_EXPORTS void cvtColor(const GpuMat& src, GpuMat& dst, int code, int dcn = 0, Stream& stream = Stream::Null());
|
|
|
|
//! swap channels
|
|
//! dstOrder - Integer array describing how channel values are permutated. The n-th entry
|
|
//! of the array contains the number of the channel that is stored in the n-th channel of
|
|
//! the output image. E.g. Given an RGBA image, aDstOrder = [3,2,1,0] converts this to ABGR
|
|
//! channel order.
|
|
CV_EXPORTS void swapChannels(GpuMat& image, const int dstOrder[4], Stream& stream = Stream::Null());
|
|
|
|
//! applies fixed threshold to the image
|
|
CV_EXPORTS double threshold(const GpuMat& src, GpuMat& dst, double thresh, double maxval, int type, Stream& stream = Stream::Null());
|
|
|
|
//! resizes the image
|
|
//! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
|
|
CV_EXPORTS void resize(const GpuMat& src, GpuMat& dst, Size dsize, double fx=0, double fy=0, int interpolation = INTER_LINEAR, Stream& stream = Stream::Null());
|
|
|
|
//! 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,
|
|
int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar(), Stream& stream = Stream::Null());
|
|
|
|
CV_EXPORTS void buildWarpAffineMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream = Stream::Null());
|
|
|
|
//! 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,
|
|
int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar(), Stream& stream = Stream::Null());
|
|
|
|
CV_EXPORTS void buildWarpPerspectiveMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream = Stream::Null());
|
|
|
|
//! builds plane warping maps
|
|
CV_EXPORTS void buildWarpPlaneMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, const Mat &T, float scale,
|
|
GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null());
|
|
|
|
//! builds cylindrical warping maps
|
|
CV_EXPORTS void buildWarpCylindricalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, float scale,
|
|
GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null());
|
|
|
|
//! builds spherical warping maps
|
|
CV_EXPORTS void buildWarpSphericalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, float scale,
|
|
GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null());
|
|
|
|
//! rotates an image around the origin (0,0) and then shifts it
|
|
//! supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
|
|
//! supports 1, 3 or 4 channels images with CV_8U, CV_16U or CV_32F depth
|
|
CV_EXPORTS void rotate(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift = 0, double yShift = 0,
|
|
int interpolation = INTER_LINEAR, Stream& stream = Stream::Null());
|
|
|
|
//! copies 2D array to a larger destination array and pads borders with user-specifiable constant
|
|
CV_EXPORTS void copyMakeBorder(const GpuMat& src, GpuMat& dst, int top, int bottom, int left, int right, int borderType,
|
|
const Scalar& value = Scalar(), Stream& stream = Stream::Null());
|
|
|
|
//! 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, Stream& stream = Stream::Null());
|
|
//! buffered version
|
|
CV_EXPORTS void integralBuffered(const GpuMat& src, GpuMat& sum, GpuMat& buffer, Stream& stream = Stream::Null());
|
|
|
|
//! 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, Stream& stream = Stream::Null());
|
|
|
|
//! computes vertical sum, supports only CV_32FC1 images
|
|
CV_EXPORTS void columnSum(const GpuMat& src, GpuMat& sum);
|
|
|
|
//! 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, Stream& stream = Stream::Null());
|
|
|
|
//! 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);
|
|
CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, double k, int borderType = BORDER_REFLECT101);
|
|
CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, GpuMat& buf, int blockSize, int ksize, double k,
|
|
int borderType = BORDER_REFLECT101, Stream& stream = Stream::Null());
|
|
|
|
//! 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);
|
|
CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, int borderType=BORDER_REFLECT101);
|
|
CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, GpuMat& buf, int blockSize, int ksize,
|
|
int borderType=BORDER_REFLECT101, Stream& stream = Stream::Null());
|
|
|
|
//! 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, Stream& stream = Stream::Null());
|
|
|
|
//! 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, Stream& stream = Stream::Null());
|
|
|
|
//! 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, Stream& stream = Stream::Null());
|
|
|
|
//! 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
|
|
struct CV_EXPORTS ConvolveBuf;
|
|
CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr = false);
|
|
CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr, ConvolveBuf& buf, Stream& stream = Stream::Null());
|
|
|
|
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);
|
|
void create(Size image_size, Size templ_size, Size block_size);
|
|
|
|
private:
|
|
static Size estimateBlockSize(Size result_size, Size templ_size);
|
|
friend void convolve(const GpuMat&, const GpuMat&, GpuMat&, bool, ConvolveBuf&, Stream& stream);
|
|
|
|
Size result_size;
|
|
Size block_size;
|
|
Size dft_size;
|
|
int spect_len;
|
|
|
|
GpuMat image_spect, templ_spect, result_spect;
|
|
GpuMat image_block, templ_block, result_data;
|
|
};
|
|
|
|
//! 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, Stream& stream = Stream::Null());
|
|
|
|
//! smoothes the source image and downsamples it
|
|
CV_EXPORTS void pyrDown(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
//! upsamples the source image and then smoothes it
|
|
CV_EXPORTS void pyrUp(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
//! performs linear blending of two images
|
|
//! to avoid accuracy errors sum of weigths shouldn't be very close to zero
|
|
CV_EXPORTS void blendLinear(const GpuMat& img1, const GpuMat& img2, const GpuMat& weights1, const GpuMat& weights2,
|
|
GpuMat& result, Stream& stream = Stream::Null());
|
|
|
|
|
|
struct CV_EXPORTS CannyBuf;
|
|
|
|
CV_EXPORTS void Canny(const GpuMat& image, GpuMat& edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
|
|
CV_EXPORTS void Canny(const GpuMat& image, CannyBuf& buf, GpuMat& edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
|
|
CV_EXPORTS void Canny(const GpuMat& dx, const GpuMat& dy, GpuMat& edges, double low_thresh, double high_thresh, bool L2gradient = false);
|
|
CV_EXPORTS void Canny(const GpuMat& dx, const GpuMat& dy, CannyBuf& buf, GpuMat& edges, double low_thresh, double high_thresh, bool L2gradient = false);
|
|
|
|
struct CV_EXPORTS CannyBuf
|
|
{
|
|
CannyBuf() {}
|
|
explicit CannyBuf(const Size& image_size, int apperture_size = 3) {create(image_size, apperture_size);}
|
|
CannyBuf(const GpuMat& dx_, const GpuMat& dy_);
|
|
|
|
void create(const Size& image_size, int apperture_size = 3);
|
|
|
|
void release();
|
|
|
|
GpuMat dx, dy;
|
|
GpuMat dx_buf, dy_buf;
|
|
GpuMat edgeBuf;
|
|
GpuMat trackBuf1, trackBuf2;
|
|
Ptr<FilterEngine_GPU> filterDX, filterDY;
|
|
};
|
|
|
|
class CV_EXPORTS ImagePyramid
|
|
{
|
|
public:
|
|
inline ImagePyramid() : nLayers_(0) {}
|
|
inline ImagePyramid(const GpuMat& img, int nLayers, Stream& stream = Stream::Null())
|
|
{
|
|
build(img, nLayers, stream);
|
|
}
|
|
|
|
void build(const GpuMat& img, int nLayers, Stream& stream = Stream::Null());
|
|
|
|
void getLayer(GpuMat& outImg, Size outRoi, Stream& stream = Stream::Null()) const;
|
|
|
|
inline void release()
|
|
{
|
|
layer0_.release();
|
|
pyramid_.clear();
|
|
nLayers_ = 0;
|
|
}
|
|
|
|
private:
|
|
GpuMat layer0_;
|
|
std::vector<GpuMat> pyramid_;
|
|
int nLayers_;
|
|
};
|
|
|
|
////////////////////////////// Matrix reductions //////////////////////////////
|
|
|
|
//! 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);
|
|
//! buffered version
|
|
CV_EXPORTS void meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev, GpuMat& buf);
|
|
|
|
//! 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);
|
|
|
|
//! 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);
|
|
|
|
//! 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);
|
|
|
|
//! 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);
|
|
|
|
//! 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);
|
|
|
|
//! reduces a matrix to a vector
|
|
CV_EXPORTS void reduce(const GpuMat& mtx, GpuMat& vec, int dim, int reduceOp, int dtype = -1, Stream& stream = Stream::Null());
|
|
|
|
|
|
///////////////////////////// Calibration 3D //////////////////////////////////
|
|
|
|
CV_EXPORTS void transformPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec,
|
|
GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
CV_EXPORTS void projectPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec,
|
|
const Mat& camera_mat, const Mat& dist_coef, GpuMat& dst,
|
|
Stream& stream = Stream::Null());
|
|
|
|
CV_EXPORTS void solvePnPRansac(const Mat& object, const Mat& image, const Mat& camera_mat,
|
|
const Mat& dist_coef, Mat& rvec, Mat& tvec, bool use_extrinsic_guess=false,
|
|
int num_iters=100, float max_dist=8.0, int min_inlier_count=100,
|
|
std::vector<int>* inliers=NULL);
|
|
|
|
//////////////////////////////// Image Labeling ////////////////////////////////
|
|
|
|
//!performs labeling via graph cuts of a 2D regular 4-connected graph.
|
|
CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& bottom, GpuMat& labels,
|
|
GpuMat& buf, Stream& stream = Stream::Null());
|
|
|
|
//!performs labeling via graph cuts of a 2D regular 8-connected graph.
|
|
CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& topLeft, GpuMat& topRight,
|
|
GpuMat& bottom, GpuMat& bottomLeft, GpuMat& bottomRight,
|
|
GpuMat& labels,
|
|
GpuMat& buf, Stream& stream = Stream::Null());
|
|
|
|
////////////////////////////////// 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, Stream& stream = Stream::Null());
|
|
CV_EXPORTS void histEven(const GpuMat& src, GpuMat& hist, GpuMat& buf, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());
|
|
//! 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], Stream& stream = Stream::Null());
|
|
CV_EXPORTS void histEven(const GpuMat& src, GpuMat hist[4], GpuMat& buf, int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null());
|
|
//! 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, Stream& stream = Stream::Null());
|
|
CV_EXPORTS void histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels, GpuMat& buf, Stream& stream = Stream::Null());
|
|
//! 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], Stream& stream = Stream::Null());
|
|
CV_EXPORTS void histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], GpuMat& buf, Stream& stream = Stream::Null());
|
|
|
|
//! Calculates histogram for 8u one channel image
|
|
//! Output hist will have one row, 256 cols and CV32SC1 type.
|
|
CV_EXPORTS void calcHist(const GpuMat& src, GpuMat& hist, Stream& stream = Stream::Null());
|
|
CV_EXPORTS void calcHist(const GpuMat& src, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null());
|
|
|
|
//! normalizes the grayscale image brightness and contrast by normalizing its histogram
|
|
CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
|
|
CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, Stream& stream = Stream::Null());
|
|
CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null());
|
|
|
|
//////////////////////////////// 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, Stream& stream = Stream::Null());
|
|
|
|
//! Some heuristics that tries to estmate
|
|
// if current GPU will be faster than 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, Stream& stream = Stream::Null());
|
|
|
|
|
|
//! version for user specified data term
|
|
void operator()(const GpuMat& data, GpuMat& disparity, Stream& stream = Stream::Null());
|
|
|
|
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, 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, Stream& stream = Stream::Null());
|
|
|
|
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;
|
|
|
|
int msg_type;
|
|
|
|
bool use_local_init_data_cost;
|
|
private:
|
|
GpuMat u[2], d[2], l[2], r[2];
|
|
GpuMat disp_selected_pyr[2];
|
|
|
|
GpuMat data_cost;
|
|
GpuMat data_cost_selected;
|
|
|
|
GpuMat temp;
|
|
|
|
GpuMat out;
|
|
};
|
|
|
|
/////////////////////////// DisparityBilateralFilter ///////////////////////////
|
|
// Disparity map refinement using joint bilateral filtering given a single color image.
|
|
// Qingxiong Yang, Liang Wang, Narendra Ahuja
|
|
// http://vision.ai.uiuc.edu/~qyang6/
|
|
|
|
class CV_EXPORTS DisparityBilateralFilter
|
|
{
|
|
public:
|
|
enum { DEFAULT_NDISP = 64 };
|
|
enum { DEFAULT_RADIUS = 3 };
|
|
enum { DEFAULT_ITERS = 1 };
|
|
|
|
//! the default constructor
|
|
explicit DisparityBilateralFilter(int ndisp = DEFAULT_NDISP, int radius = DEFAULT_RADIUS, int iters = DEFAULT_ITERS);
|
|
|
|
//! the full constructor taking the number of disparities, filter radius,
|
|
//! number of iterations, truncation of data continuity, truncation of disparity continuity
|
|
//! and filter range sigma
|
|
DisparityBilateralFilter(int ndisp, int radius, int iters, float edge_threshold, float max_disc_threshold, float sigma_range);
|
|
|
|
//! the disparity map refinement operator. Refine disparity map using joint bilateral filtering given a single color image.
|
|
//! disparity must have CV_8U or CV_16S type, image must have CV_8UC1 or CV_8UC3 type.
|
|
void operator()(const GpuMat& disparity, const GpuMat& image, GpuMat& dst, Stream& stream = Stream::Null());
|
|
|
|
private:
|
|
int ndisp;
|
|
int radius;
|
|
int iters;
|
|
|
|
float edge_threshold;
|
|
float max_disc_threshold;
|
|
float sigma_range;
|
|
|
|
GpuMat table_color;
|
|
GpuMat table_space;
|
|
};
|
|
|
|
|
|
//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
|
|
|
|
struct CV_EXPORTS HOGDescriptor
|
|
{
|
|
enum { DEFAULT_WIN_SIGMA = -1 };
|
|
enum { DEFAULT_NLEVELS = 64 };
|
|
enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };
|
|
|
|
HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16),
|
|
Size block_stride=Size(8, 8), Size cell_size=Size(8, 8),
|
|
int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA,
|
|
double threshold_L2hys=0.2, bool gamma_correction=true,
|
|
int nlevels=DEFAULT_NLEVELS);
|
|
|
|
size_t getDescriptorSize() const;
|
|
size_t getBlockHistogramSize() const;
|
|
|
|
void setSVMDetector(const vector<float>& detector);
|
|
|
|
static vector<float> getDefaultPeopleDetector();
|
|
static vector<float> getPeopleDetector48x96();
|
|
static vector<float> getPeopleDetector64x128();
|
|
|
|
void detect(const GpuMat& img, vector<Point>& found_locations,
|
|
double hit_threshold=0, Size win_stride=Size(),
|
|
Size padding=Size());
|
|
|
|
void detectMultiScale(const GpuMat& img, vector<Rect>& found_locations,
|
|
double hit_threshold=0, Size win_stride=Size(),
|
|
Size padding=Size(), double scale0=1.05,
|
|
int group_threshold=2);
|
|
|
|
void getDescriptors(const GpuMat& img, Size win_stride,
|
|
GpuMat& descriptors,
|
|
int descr_format=DESCR_FORMAT_COL_BY_COL);
|
|
|
|
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);
|
|
|
|
double getWinSigma() const;
|
|
bool checkDetectorSize() const;
|
|
|
|
static int numPartsWithin(int size, int part_size, int stride);
|
|
static Size numPartsWithin(Size size, Size part_size, Size stride);
|
|
|
|
// Coefficients of the separating plane
|
|
float free_coef;
|
|
GpuMat detector;
|
|
|
|
// Results of the last classification step
|
|
GpuMat labels, labels_buf;
|
|
Mat labels_host;
|
|
|
|
// Results of the last histogram evaluation step
|
|
GpuMat block_hists, block_hists_buf;
|
|
|
|
// Gradients conputation results
|
|
GpuMat grad, qangle, grad_buf, qangle_buf;
|
|
|
|
// returns subbuffer with required size, reallocates buffer if nessesary.
|
|
static GpuMat getBuffer(const Size& sz, int type, GpuMat& buf);
|
|
static GpuMat getBuffer(int rows, int cols, int type, GpuMat& buf);
|
|
|
|
std::vector<GpuMat> image_scales;
|
|
};
|
|
|
|
|
|
////////////////////////////////// BruteForceMatcher //////////////////////////////////
|
|
|
|
class CV_EXPORTS BruteForceMatcher_GPU_base
|
|
{
|
|
public:
|
|
enum DistType {L1Dist = 0, L2Dist, HammingDist};
|
|
|
|
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
|
|
void matchSingle(const GpuMat& query, const GpuMat& train,
|
|
GpuMat& trainIdx, GpuMat& distance,
|
|
const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
|
|
|
|
// Download trainIdx and distance and convert it to CPU vector with DMatch
|
|
static void matchDownload(const GpuMat& trainIdx, const GpuMat& distance, std::vector<DMatch>& matches);
|
|
// Convert trainIdx and distance to vector with DMatch
|
|
static void matchConvert(const Mat& trainIdx, const Mat& distance, std::vector<DMatch>& matches);
|
|
|
|
// Find one best match for each query descriptor
|
|
void match(const GpuMat& query, const GpuMat& train, 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 std::vector<GpuMat>& masks = std::vector<GpuMat>());
|
|
|
|
// Find one best match from train collection for each query descriptor
|
|
void matchCollection(const GpuMat& query, const GpuMat& trainCollection,
|
|
GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
|
|
const GpuMat& masks = GpuMat(), Stream& stream = Stream::Null());
|
|
|
|
// Download trainIdx, imgIdx and distance and convert it to vector with DMatch
|
|
static void matchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, std::vector<DMatch>& matches);
|
|
// Convert trainIdx, imgIdx and distance to vector with DMatch
|
|
static void matchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, std::vector<DMatch>& matches);
|
|
|
|
// Find one best match from train collection for each query descriptor.
|
|
void match(const GpuMat& query, 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)
|
|
void knnMatchSingle(const GpuMat& query, const GpuMat& train,
|
|
GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k,
|
|
const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
|
|
|
|
// Download trainIdx and distance and convert it to 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);
|
|
// Convert trainIdx and distance to vector with DMatch
|
|
static void knnMatchConvert(const Mat& trainIdx, const Mat& 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& query, const GpuMat& train,
|
|
std::vector< std::vector<DMatch> >& matches, int k, const GpuMat& mask = GpuMat(),
|
|
bool compactResult = false);
|
|
|
|
// Find k best matches from train collection for each query descriptor (in increasing order of distances)
|
|
void knnMatch2Collection(const GpuMat& query, const GpuMat& trainCollection,
|
|
GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
|
|
const GpuMat& maskCollection = GpuMat(), Stream& stream = Stream::Null());
|
|
|
|
// Download trainIdx and distance and convert it to 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 knnMatch2Download(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance,
|
|
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
|
|
// Convert trainIdx and distance to vector with DMatch
|
|
static void knnMatch2Convert(const Mat& trainIdx, const Mat& imgIdx, const Mat& 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& query, std::vector< std::vector<DMatch> >& matches, int k,
|
|
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<int>(0, queryIdx) 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.
|
|
// If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nTrain / 100), 10),
|
|
// otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
|
|
// Matches doesn't sorted.
|
|
void radiusMatchSingle(const GpuMat& query, const GpuMat& train,
|
|
GpuMat& trainIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
|
|
const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
|
|
|
|
// Download trainIdx, nMatches and distance and convert it to 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& distance, const GpuMat& nMatches,
|
|
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
|
|
// Convert trainIdx, nMatches and distance to vector with DMatch.
|
|
static void radiusMatchConvert(const Mat& trainIdx, const Mat& distance, const Mat& nMatches,
|
|
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& query, const GpuMat& train,
|
|
std::vector< std::vector<DMatch> >& matches, float maxDistance,
|
|
const GpuMat& mask = GpuMat(), bool compactResult = false);
|
|
|
|
// Find best matches for each query descriptor which have distance less than maxDistance.
|
|
// If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nQuery / 100), 10),
|
|
// otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
|
|
// Matches doesn't sorted.
|
|
void radiusMatchCollection(const GpuMat& query, GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
|
|
const std::vector<GpuMat>& masks = std::vector<GpuMat>(), Stream& stream = Stream::Null());
|
|
|
|
// Download trainIdx, imgIdx, nMatches and distance and convert it to 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& imgIdx, const GpuMat& distance, const GpuMat& nMatches,
|
|
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
|
|
// Convert trainIdx, nMatches and distance to vector with DMatch.
|
|
static void radiusMatchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, const Mat& nMatches,
|
|
std::vector< std::vector<DMatch> >& matches, 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& query, std::vector< std::vector<DMatch> >& matches, float maxDistance,
|
|
const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);
|
|
|
|
DistType distType;
|
|
|
|
private:
|
|
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) {}
|
|
};
|
|
template <> class CV_EXPORTS BruteForceMatcher_GPU< Hamming > : public BruteForceMatcher_GPU_base
|
|
{
|
|
public:
|
|
explicit BruteForceMatcher_GPU() : BruteForceMatcher_GPU_base(HammingDist) {}
|
|
explicit BruteForceMatcher_GPU(Hamming /*d*/) : BruteForceMatcher_GPU_base(HammingDist) {}
|
|
};
|
|
|
|
////////////////////////////////// CascadeClassifier_GPU //////////////////////////////////////////
|
|
// The cascade classifier class for object detection.
|
|
class CV_EXPORTS CascadeClassifier_GPU
|
|
{
|
|
public:
|
|
CascadeClassifier_GPU();
|
|
CascadeClassifier_GPU(const std::string& filename);
|
|
~CascadeClassifier_GPU();
|
|
|
|
bool empty() const;
|
|
bool load(const std::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 //////////////////////////////////////////
|
|
|
|
class CV_EXPORTS SURF_GPU
|
|
{
|
|
public:
|
|
enum KeypointLayout
|
|
{
|
|
SF_X = 0,
|
|
SF_Y,
|
|
SF_LAPLACIAN,
|
|
SF_SIZE,
|
|
SF_DIR,
|
|
SF_HESSIAN,
|
|
SF_FEATURE_STRIDE
|
|
};
|
|
|
|
//! the default constructor
|
|
SURF_GPU();
|
|
//! the full constructor taking all the necessary parameters
|
|
explicit SURF_GPU(double _hessianThreshold, int _nOctaves=4,
|
|
int _nOctaveLayers=2, bool _extended=false, float _keypointsRatio=0.01f, bool _upright = false);
|
|
|
|
//! returns the descriptor size in float's (64 or 128)
|
|
int descriptorSize() const;
|
|
|
|
//! upload host keypoints to device memory
|
|
void uploadKeypoints(const vector<KeyPoint>& keypoints, GpuMat& keypointsGPU);
|
|
//! download keypoints from device to host memory
|
|
void downloadKeypoints(const GpuMat& keypointsGPU, vector<KeyPoint>& keypoints);
|
|
|
|
//! download descriptors from device to host memory
|
|
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 nFeature cols and 6 rows
|
|
//! keypoints.ptr<float>(SF_X)[i] will contain x coordinate of i'th feature
|
|
//! keypoints.ptr<float>(SF_Y)[i] will contain y coordinate of i'th feature
|
|
//! keypoints.ptr<float>(SF_LAPLACIAN)[i] will contain laplacian sign of i'th feature
|
|
//! keypoints.ptr<float>(SF_SIZE)[i] will contain size of i'th feature
|
|
//! keypoints.ptr<float>(SF_DIR)[i] will contain orientation of i'th feature
|
|
//! keypoints.ptr<float>(SF_HESSIAN)[i] will contain response of i'th feature
|
|
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);
|
|
|
|
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);
|
|
|
|
void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints, std::vector<float>& descriptors,
|
|
bool useProvidedKeypoints = false);
|
|
|
|
void releaseMemory();
|
|
|
|
// SURF parameters
|
|
double hessianThreshold;
|
|
int nOctaves;
|
|
int nOctaveLayers;
|
|
bool extended;
|
|
bool upright;
|
|
|
|
//! max keypoints = min(keypointsRatio * img.size().area(), 65535)
|
|
float keypointsRatio;
|
|
|
|
GpuMat sum, mask1, maskSum, intBuffer;
|
|
|
|
GpuMat det, trace;
|
|
|
|
GpuMat maxPosBuffer;
|
|
};
|
|
|
|
////////////////////////////////// FAST //////////////////////////////////////////
|
|
|
|
class CV_EXPORTS FAST_GPU
|
|
{
|
|
public:
|
|
enum
|
|
{
|
|
LOCATION_ROW = 0,
|
|
RESPONSE_ROW,
|
|
ROWS_COUNT
|
|
};
|
|
|
|
// all features have same size
|
|
static const int FEATURE_SIZE = 7;
|
|
|
|
explicit FAST_GPU(int threshold, bool nonmaxSupression = true, double keypointsRatio = 0.05);
|
|
|
|
//! finds the keypoints using FAST detector
|
|
//! supports only CV_8UC1 images
|
|
void operator ()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints);
|
|
void operator ()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints);
|
|
|
|
//! download keypoints from device to host memory
|
|
void downloadKeypoints(const GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints);
|
|
|
|
//! convert keypoints to KeyPoint vector
|
|
void convertKeypoints(const Mat& h_keypoints, std::vector<KeyPoint>& keypoints);
|
|
|
|
//! release temporary buffer's memory
|
|
void release();
|
|
|
|
bool nonmaxSupression;
|
|
|
|
int threshold;
|
|
|
|
//! max keypoints = keypointsRatio * img.size().area()
|
|
double keypointsRatio;
|
|
|
|
//! find keypoints and compute it's response if nonmaxSupression is true
|
|
//! return count of detected keypoints
|
|
int calcKeyPointsLocation(const GpuMat& image, const GpuMat& mask);
|
|
|
|
//! get final array of keypoints
|
|
//! performs nonmax supression if needed
|
|
//! return final count of keypoints
|
|
int getKeyPoints(GpuMat& keypoints);
|
|
|
|
private:
|
|
GpuMat kpLoc_;
|
|
int count_;
|
|
|
|
GpuMat score_;
|
|
|
|
GpuMat d_keypoints_;
|
|
};
|
|
|
|
////////////////////////////////// ORB //////////////////////////////////////////
|
|
|
|
class CV_EXPORTS ORB_GPU
|
|
{
|
|
public:
|
|
enum
|
|
{
|
|
X_ROW = 0,
|
|
Y_ROW,
|
|
RESPONSE_ROW,
|
|
ANGLE_ROW,
|
|
OCTAVE_ROW,
|
|
SIZE_ROW,
|
|
ROWS_COUNT
|
|
};
|
|
|
|
enum
|
|
{
|
|
DEFAULT_FAST_THRESHOLD = 20
|
|
};
|
|
|
|
//! Constructor
|
|
explicit ORB_GPU(int nFeatures = 500, float scaleFactor = 1.2f, int nLevels = 8, int edgeThreshold = 31,
|
|
int firstLevel = 0, int WTA_K = 2, int scoreType = 0, int patchSize = 31);
|
|
|
|
//! Compute the ORB features on an image
|
|
//! image - the image to compute the features (supports only CV_8UC1 images)
|
|
//! mask - the mask to apply
|
|
//! keypoints - the resulting keypoints
|
|
void operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints);
|
|
void operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints);
|
|
|
|
//! Compute the ORB features and descriptors on an image
|
|
//! image - the image to compute the features (supports only CV_8UC1 images)
|
|
//! mask - the mask to apply
|
|
//! keypoints - the resulting keypoints
|
|
//! descriptors - descriptors array
|
|
void operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints, GpuMat& descriptors);
|
|
void operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors);
|
|
|
|
//! download keypoints from device to host memory
|
|
void downloadKeyPoints(GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints);
|
|
|
|
//! convert keypoints to KeyPoint vector
|
|
void convertKeyPoints(Mat& d_keypoints, std::vector<KeyPoint>& keypoints);
|
|
|
|
//! returns the descriptor size in bytes
|
|
inline int descriptorSize() const { return kBytes; }
|
|
|
|
inline void setFastParams(int threshold, bool nonmaxSupression = true)
|
|
{
|
|
fastDetector_.threshold = threshold;
|
|
fastDetector_.nonmaxSupression = nonmaxSupression;
|
|
}
|
|
|
|
//! release temporary buffer's memory
|
|
void release();
|
|
|
|
//! if true, image will be blurred before descriptors calculation
|
|
bool blurForDescriptor;
|
|
|
|
private:
|
|
enum { kBytes = 32 };
|
|
|
|
void buildScalePyramids(const GpuMat& image, const GpuMat& mask);
|
|
|
|
void computeKeyPointsPyramid();
|
|
|
|
void computeDescriptors(GpuMat& descriptors);
|
|
|
|
void mergeKeyPoints(GpuMat& keypoints);
|
|
|
|
int nFeatures_;
|
|
float scaleFactor_;
|
|
int nLevels_;
|
|
int edgeThreshold_;
|
|
int firstLevel_;
|
|
int WTA_K_;
|
|
int scoreType_;
|
|
int patchSize_;
|
|
|
|
// The number of desired features per scale
|
|
std::vector<size_t> n_features_per_level_;
|
|
|
|
// Points to compute BRIEF descriptors from
|
|
GpuMat pattern_;
|
|
|
|
std::vector<GpuMat> imagePyr_;
|
|
std::vector<GpuMat> maskPyr_;
|
|
|
|
GpuMat buf_;
|
|
|
|
std::vector<GpuMat> keyPointsPyr_;
|
|
std::vector<int> keyPointsCount_;
|
|
|
|
FAST_GPU fastDetector_;
|
|
|
|
Ptr<FilterEngine_GPU> blurFilter;
|
|
|
|
GpuMat d_keypoints_;
|
|
};
|
|
|
|
////////////////////////////////// Optical Flow //////////////////////////////////////////
|
|
|
|
class CV_EXPORTS BroxOpticalFlow
|
|
{
|
|
public:
|
|
BroxOpticalFlow(float alpha_, float gamma_, float scale_factor_, int inner_iterations_, int outer_iterations_, int solver_iterations_) :
|
|
alpha(alpha_), gamma(gamma_), scale_factor(scale_factor_),
|
|
inner_iterations(inner_iterations_), outer_iterations(outer_iterations_), solver_iterations(solver_iterations_)
|
|
{
|
|
}
|
|
|
|
//! Compute optical flow
|
|
//! frame0 - source frame (supports only CV_32FC1 type)
|
|
//! frame1 - frame to track (with the same size and type as frame0)
|
|
//! u - flow horizontal component (along x axis)
|
|
//! v - flow vertical component (along y axis)
|
|
void operator ()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& u, GpuMat& v, Stream& stream = Stream::Null());
|
|
|
|
//! flow smoothness
|
|
float alpha;
|
|
|
|
//! gradient constancy importance
|
|
float gamma;
|
|
|
|
//! pyramid scale factor
|
|
float scale_factor;
|
|
|
|
//! number of lagged non-linearity iterations (inner loop)
|
|
int inner_iterations;
|
|
|
|
//! number of warping iterations (number of pyramid levels)
|
|
int outer_iterations;
|
|
|
|
//! number of linear system solver iterations
|
|
int solver_iterations;
|
|
|
|
GpuMat buf;
|
|
};
|
|
|
|
class CV_EXPORTS GoodFeaturesToTrackDetector_GPU
|
|
{
|
|
public:
|
|
explicit GoodFeaturesToTrackDetector_GPU(int maxCorners_ = 1000, double qualityLevel_ = 0.01, double minDistance_ = 0.0,
|
|
int blockSize_ = 3, bool useHarrisDetector_ = false, double harrisK_ = 0.04)
|
|
{
|
|
maxCorners = maxCorners_;
|
|
qualityLevel = qualityLevel_;
|
|
minDistance = minDistance_;
|
|
blockSize = blockSize_;
|
|
useHarrisDetector = useHarrisDetector_;
|
|
harrisK = harrisK_;
|
|
}
|
|
|
|
//! return 1 rows matrix with CV_32FC2 type
|
|
void operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask = GpuMat());
|
|
|
|
int maxCorners;
|
|
double qualityLevel;
|
|
double minDistance;
|
|
|
|
int blockSize;
|
|
bool useHarrisDetector;
|
|
double harrisK;
|
|
|
|
void releaseMemory()
|
|
{
|
|
Dx_.release();
|
|
Dy_.release();
|
|
buf_.release();
|
|
eig_.release();
|
|
minMaxbuf_.release();
|
|
tmpCorners_.release();
|
|
}
|
|
|
|
private:
|
|
GpuMat Dx_;
|
|
GpuMat Dy_;
|
|
GpuMat buf_;
|
|
GpuMat eig_;
|
|
GpuMat minMaxbuf_;
|
|
GpuMat tmpCorners_;
|
|
};
|
|
|
|
class CV_EXPORTS PyrLKOpticalFlow
|
|
{
|
|
public:
|
|
PyrLKOpticalFlow()
|
|
{
|
|
winSize = Size(21, 21);
|
|
maxLevel = 3;
|
|
iters = 30;
|
|
derivLambda = 0.5;
|
|
useInitialFlow = false;
|
|
minEigThreshold = 1e-4f;
|
|
getMinEigenVals = false;
|
|
}
|
|
|
|
void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts,
|
|
GpuMat& status, GpuMat* err = 0);
|
|
|
|
void dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err = 0);
|
|
|
|
Size winSize;
|
|
int maxLevel;
|
|
int iters;
|
|
double derivLambda;
|
|
bool useInitialFlow;
|
|
float minEigThreshold;
|
|
bool getMinEigenVals;
|
|
|
|
void releaseMemory()
|
|
{
|
|
dx_calcBuf_.release();
|
|
dy_calcBuf_.release();
|
|
|
|
prevPyr_.clear();
|
|
nextPyr_.clear();
|
|
|
|
dx_buf_.release();
|
|
dy_buf_.release();
|
|
|
|
uPyr_.clear();
|
|
vPyr_.clear();
|
|
}
|
|
|
|
private:
|
|
void calcSharrDeriv(const GpuMat& src, GpuMat& dx, GpuMat& dy);
|
|
|
|
void buildImagePyramid(const GpuMat& img0, vector<GpuMat>& pyr, bool withBorder);
|
|
|
|
GpuMat dx_calcBuf_;
|
|
GpuMat dy_calcBuf_;
|
|
|
|
vector<GpuMat> prevPyr_;
|
|
vector<GpuMat> nextPyr_;
|
|
|
|
GpuMat dx_buf_;
|
|
GpuMat dy_buf_;
|
|
|
|
vector<GpuMat> uPyr_;
|
|
vector<GpuMat> vPyr_;
|
|
};
|
|
|
|
|
|
class CV_EXPORTS FarnebackOpticalFlow
|
|
{
|
|
public:
|
|
FarnebackOpticalFlow()
|
|
{
|
|
numLevels = 5;
|
|
pyrScale = 0.5;
|
|
fastPyramids = false;
|
|
winSize = 13;
|
|
numIters = 10;
|
|
polyN = 5;
|
|
polySigma = 1.1;
|
|
flags = 0;
|
|
}
|
|
|
|
int numLevels;
|
|
double pyrScale;
|
|
bool fastPyramids;
|
|
int winSize;
|
|
int numIters;
|
|
int polyN;
|
|
double polySigma;
|
|
int flags;
|
|
|
|
void operator ()(const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s = Stream::Null());
|
|
|
|
void releaseMemory()
|
|
{
|
|
frames_[0].release();
|
|
frames_[1].release();
|
|
pyrLevel_[0].release();
|
|
pyrLevel_[1].release();
|
|
M_.release();
|
|
bufM_.release();
|
|
R_[0].release();
|
|
R_[1].release();
|
|
blurredFrame_[0].release();
|
|
blurredFrame_[1].release();
|
|
pyramid0_.clear();
|
|
pyramid1_.clear();
|
|
}
|
|
|
|
private:
|
|
void prepareGaussian(
|
|
int n, double sigma, float *g, float *xg, float *xxg,
|
|
double &ig11, double &ig03, double &ig33, double &ig55);
|
|
|
|
void setPolynomialExpansionConsts(int n, double sigma);
|
|
|
|
void updateFlow_boxFilter(
|
|
const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat &flowy,
|
|
GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]);
|
|
|
|
void updateFlow_gaussianBlur(
|
|
const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat& flowy,
|
|
GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]);
|
|
|
|
GpuMat frames_[2];
|
|
GpuMat pyrLevel_[2], M_, bufM_, R_[2], blurredFrame_[2];
|
|
std::vector<GpuMat> pyramid0_, pyramid1_;
|
|
};
|
|
|
|
|
|
//! Interpolate frames (images) using provided optical flow (displacement field).
|
|
//! frame0 - frame 0 (32-bit floating point images, single channel)
|
|
//! frame1 - frame 1 (the same type and size)
|
|
//! fu - forward horizontal displacement
|
|
//! fv - forward vertical displacement
|
|
//! bu - backward horizontal displacement
|
|
//! bv - backward vertical displacement
|
|
//! pos - new frame position
|
|
//! newFrame - new frame
|
|
//! buf - temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 GpuMat;
|
|
//! occlusion masks 0, occlusion masks 1,
|
|
//! interpolated forward flow 0, interpolated forward flow 1,
|
|
//! interpolated backward flow 0, interpolated backward flow 1
|
|
//!
|
|
CV_EXPORTS void interpolateFrames(const GpuMat& frame0, const GpuMat& frame1,
|
|
const GpuMat& fu, const GpuMat& fv,
|
|
const GpuMat& bu, const GpuMat& bv,
|
|
float pos, GpuMat& newFrame, GpuMat& buf,
|
|
Stream& stream = Stream::Null());
|
|
|
|
CV_EXPORTS void createOpticalFlowNeedleMap(const GpuMat& u, const GpuMat& v, GpuMat& vertex, GpuMat& colors);
|
|
|
|
} // namespace gpu
|
|
|
|
//! 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);
|
|
|
|
} // namespace cv
|
|
|
|
#endif /* __OPENCV_GPU_HPP__ */
|