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461 lines
21 KiB
C++
461 lines
21 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#ifndef __OPENCV_GPUIMGPROC_HPP__
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#define __OPENCV_GPUIMGPROC_HPP__
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#ifndef __cplusplus
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# error gpuimgproc.hpp header must be compiled as C++
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#endif
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#include "opencv2/core/gpu.hpp"
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#include "opencv2/imgproc.hpp"
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#if defined __GNUC__
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#define __OPENCV_GPUIMGPROC_DEPR_BEFORE__
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#define __OPENCV_GPUIMGPROC_DEPR_AFTER__ __attribute__ ((deprecated))
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#elif (defined WIN32 || defined _WIN32)
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#define __OPENCV_GPUIMGPROC_DEPR_BEFORE__ __declspec(deprecated)
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#define __OPENCV_GPUIMGPROC_DEPR_AFTER__
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#else
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#define __OPENCV_GPUIMGPROC_DEPR_BEFORE__
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#define __OPENCV_GPUIMGPROC_DEPR_AFTER__
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#endif
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namespace cv { namespace gpu {
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/////////////////////////// Color Processing ///////////////////////////
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//! converts image from one color space to another
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CV_EXPORTS void cvtColor(InputArray src, OutputArray dst, int code, int dcn = 0, Stream& stream = Stream::Null());
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enum
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{
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// Bayer Demosaicing (Malvar, He, and Cutler)
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COLOR_BayerBG2BGR_MHT = 256,
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COLOR_BayerGB2BGR_MHT = 257,
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COLOR_BayerRG2BGR_MHT = 258,
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COLOR_BayerGR2BGR_MHT = 259,
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COLOR_BayerBG2RGB_MHT = COLOR_BayerRG2BGR_MHT,
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COLOR_BayerGB2RGB_MHT = COLOR_BayerGR2BGR_MHT,
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COLOR_BayerRG2RGB_MHT = COLOR_BayerBG2BGR_MHT,
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COLOR_BayerGR2RGB_MHT = COLOR_BayerGB2BGR_MHT,
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COLOR_BayerBG2GRAY_MHT = 260,
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COLOR_BayerGB2GRAY_MHT = 261,
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COLOR_BayerRG2GRAY_MHT = 262,
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COLOR_BayerGR2GRAY_MHT = 263
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};
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CV_EXPORTS void demosaicing(InputArray src, OutputArray dst, int code, int dcn = -1, Stream& stream = Stream::Null());
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//! swap channels
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//! dstOrder - Integer array describing how channel values are permutated. The n-th entry
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//! of the array contains the number of the channel that is stored in the n-th channel of
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//! the output image. E.g. Given an RGBA image, aDstOrder = [3,2,1,0] converts this to ABGR
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//! channel order.
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CV_EXPORTS void swapChannels(InputOutputArray image, const int dstOrder[4], Stream& stream = Stream::Null());
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//! Routines for correcting image color gamma
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CV_EXPORTS void gammaCorrection(InputArray src, OutputArray dst, bool forward = true, Stream& stream = Stream::Null());
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enum { ALPHA_OVER, ALPHA_IN, ALPHA_OUT, ALPHA_ATOP, ALPHA_XOR, ALPHA_PLUS, ALPHA_OVER_PREMUL, ALPHA_IN_PREMUL, ALPHA_OUT_PREMUL,
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ALPHA_ATOP_PREMUL, ALPHA_XOR_PREMUL, ALPHA_PLUS_PREMUL, ALPHA_PREMUL};
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//! Composite two images using alpha opacity values contained in each image
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//! Supports CV_8UC4, CV_16UC4, CV_32SC4 and CV_32FC4 types
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CV_EXPORTS void alphaComp(InputArray img1, InputArray img2, OutputArray dst, int alpha_op, Stream& stream = Stream::Null());
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////////////////////////////// Histogram ///////////////////////////////
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//! Calculates histogram for 8u one channel image
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//! Output hist will have one row, 256 cols and CV32SC1 type.
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CV_EXPORTS void calcHist(InputArray src, OutputArray hist, Stream& stream = Stream::Null());
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//! normalizes the grayscale image brightness and contrast by normalizing its histogram
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CV_EXPORTS void equalizeHist(InputArray src, OutputArray dst, InputOutputArray buf, Stream& stream = Stream::Null());
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static inline void equalizeHist(InputArray src, OutputArray dst, Stream& stream = Stream::Null())
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{
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GpuMat buf;
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gpu::equalizeHist(src, dst, buf, stream);
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}
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class CV_EXPORTS CLAHE : public cv::CLAHE
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{
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public:
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using cv::CLAHE::apply;
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virtual void apply(InputArray src, OutputArray dst, Stream& stream) = 0;
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};
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CV_EXPORTS Ptr<gpu::CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8));
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//! Compute levels with even distribution. levels will have 1 row and nLevels cols and CV_32SC1 type.
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CV_EXPORTS void evenLevels(OutputArray levels, int nLevels, int lowerLevel, int upperLevel);
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//! Calculates histogram with evenly distributed bins for signle channel source.
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//! Supports CV_8UC1, CV_16UC1 and CV_16SC1 source types.
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//! Output hist will have one row and histSize cols and CV_32SC1 type.
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CV_EXPORTS void histEven(InputArray src, OutputArray hist, InputOutputArray buf, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());
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static inline void histEven(InputArray src, OutputArray hist, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null())
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{
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GpuMat buf;
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gpu::histEven(src, hist, buf, histSize, lowerLevel, upperLevel, stream);
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}
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//! Calculates histogram with evenly distributed bins for four-channel source.
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//! All channels of source are processed separately.
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//! Supports CV_8UC4, CV_16UC4 and CV_16SC4 source types.
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//! Output hist[i] will have one row and histSize[i] cols and CV_32SC1 type.
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CV_EXPORTS void histEven(InputArray src, GpuMat hist[4], InputOutputArray buf, int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null());
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static inline void histEven(InputArray src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null())
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{
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GpuMat buf;
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gpu::histEven(src, hist, buf, histSize, lowerLevel, upperLevel, stream);
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}
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//! Calculates histogram with bins determined by levels array.
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//! levels must have one row and CV_32SC1 type if source has integer type or CV_32FC1 otherwise.
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//! Supports CV_8UC1, CV_16UC1, CV_16SC1 and CV_32FC1 source types.
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//! Output hist will have one row and (levels.cols-1) cols and CV_32SC1 type.
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CV_EXPORTS void histRange(InputArray src, OutputArray hist, InputArray levels, InputOutputArray buf, Stream& stream = Stream::Null());
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static inline void histRange(InputArray src, OutputArray hist, InputArray levels, Stream& stream = Stream::Null())
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{
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GpuMat buf;
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gpu::histRange(src, hist, levels, buf, stream);
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}
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//! Calculates histogram with bins determined by levels array.
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//! All levels must have one row and CV_32SC1 type if source has integer type or CV_32FC1 otherwise.
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//! All channels of source are processed separately.
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//! Supports CV_8UC4, CV_16UC4, CV_16SC4 and CV_32FC4 source types.
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//! Output hist[i] will have one row and (levels[i].cols-1) cols and CV_32SC1 type.
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CV_EXPORTS void histRange(InputArray src, GpuMat hist[4], const GpuMat levels[4], InputOutputArray buf, Stream& stream = Stream::Null());
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static inline void histRange(InputArray src, GpuMat hist[4], const GpuMat levels[4], Stream& stream = Stream::Null())
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{
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GpuMat buf;
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gpu::histRange(src, hist, levels, buf, stream);
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}
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//////////////////////////////// Canny ////////////////////////////////
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class CV_EXPORTS CannyEdgeDetector : public Algorithm
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{
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public:
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virtual void detect(InputArray image, OutputArray edges) = 0;
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virtual void detect(InputArray dx, InputArray dy, OutputArray edges) = 0;
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virtual void setLowThreshold(double low_thresh) = 0;
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virtual double getLowThreshold() const = 0;
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virtual void setHighThreshold(double high_thresh) = 0;
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virtual double getHighThreshold() const = 0;
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virtual void setAppertureSize(int apperture_size) = 0;
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virtual int getAppertureSize() const = 0;
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virtual void setL2Gradient(bool L2gradient) = 0;
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virtual bool getL2Gradient() const = 0;
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};
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CV_EXPORTS Ptr<CannyEdgeDetector> createCannyEdgeDetector(double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
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// obsolete
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__OPENCV_GPUIMGPROC_DEPR_BEFORE__ void Canny(InputArray image, OutputArray edges,
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double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false) __OPENCV_GPUIMGPROC_DEPR_AFTER__;
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inline void Canny(InputArray image, OutputArray edges, double low_thresh, double high_thresh, int apperture_size, bool L2gradient)
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{
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gpu::createCannyEdgeDetector(low_thresh, high_thresh, apperture_size, L2gradient)->detect(image, edges);
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}
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__OPENCV_GPUIMGPROC_DEPR_BEFORE__ void Canny(InputArray dx, InputArray dy, OutputArray edges,
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double low_thresh, double high_thresh, bool L2gradient = false) __OPENCV_GPUIMGPROC_DEPR_AFTER__;
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inline void Canny(InputArray dx, InputArray dy, OutputArray edges, double low_thresh, double high_thresh, bool L2gradient)
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{
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gpu::createCannyEdgeDetector(low_thresh, high_thresh, 3, L2gradient)->detect(dx, dy, edges);
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}
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/////////////////////////// Hough Transform ////////////////////////////
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//////////////////////////////////////
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// HoughLines
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class CV_EXPORTS HoughLinesDetector : public Algorithm
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{
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public:
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virtual void detect(InputArray src, OutputArray lines) = 0;
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virtual void downloadResults(InputArray d_lines, OutputArray h_lines, OutputArray h_votes = noArray()) = 0;
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virtual void setRho(float rho) = 0;
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virtual float getRho() const = 0;
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virtual void setTheta(float theta) = 0;
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virtual float getTheta() const = 0;
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virtual void setThreshold(int threshold) = 0;
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virtual int getThreshold() const = 0;
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virtual void setDoSort(bool doSort) = 0;
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virtual bool getDoSort() const = 0;
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virtual void setMaxLines(int maxLines) = 0;
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virtual int getMaxLines() const = 0;
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};
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CV_EXPORTS Ptr<HoughLinesDetector> createHoughLinesDetector(float rho, float theta, int threshold, bool doSort = false, int maxLines = 4096);
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// obsolete
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__OPENCV_GPUIMGPROC_DEPR_BEFORE__ void HoughLines(InputArray src, OutputArray lines, float rho, float theta, int threshold,
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bool doSort = false, int maxLines = 4096) __OPENCV_GPUIMGPROC_DEPR_AFTER__;
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inline void HoughLines(InputArray src, OutputArray lines, float rho, float theta, int threshold, bool doSort, int maxLines)
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{
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gpu::createHoughLinesDetector(rho, theta, threshold, doSort, maxLines)->detect(src, lines);
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}
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//////////////////////////////////////
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// HoughLinesP
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//! finds line segments in the black-n-white image using probabalistic Hough transform
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class CV_EXPORTS HoughSegmentDetector : public Algorithm
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{
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public:
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virtual void detect(InputArray src, OutputArray lines) = 0;
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virtual void setRho(float rho) = 0;
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virtual float getRho() const = 0;
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virtual void setTheta(float theta) = 0;
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virtual float getTheta() const = 0;
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virtual void setMinLineLength(int minLineLength) = 0;
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virtual int getMinLineLength() const = 0;
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virtual void setMaxLineGap(int maxLineGap) = 0;
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virtual int getMaxLineGap() const = 0;
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virtual void setMaxLines(int maxLines) = 0;
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virtual int getMaxLines() const = 0;
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};
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CV_EXPORTS Ptr<HoughSegmentDetector> createHoughSegmentDetector(float rho, float theta, int minLineLength, int maxLineGap, int maxLines = 4096);
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// obsolete
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__OPENCV_GPUIMGPROC_DEPR_BEFORE__ void HoughLinesP(InputArray src, OutputArray lines,
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float rho, float theta, int minLineLength, int maxLineGap, int maxLines = 4096) __OPENCV_GPUIMGPROC_DEPR_AFTER__;
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inline void HoughLinesP(InputArray src, OutputArray lines, float rho, float theta, int minLineLength, int maxLineGap, int maxLines)
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{
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gpu::createHoughSegmentDetector(rho, theta, minLineLength, maxLineGap, maxLines)->detect(src, lines);
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}
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//////////////////////////////////////
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// HoughCircles
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class CV_EXPORTS HoughCirclesDetector : public Algorithm
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{
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public:
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virtual void detect(InputArray src, OutputArray circles) = 0;
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virtual void setDp(float dp) = 0;
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virtual float getDp() const = 0;
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virtual void setMinDist(float minDist) = 0;
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virtual float getMinDist() const = 0;
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virtual void setCannyThreshold(int cannyThreshold) = 0;
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virtual int getCannyThreshold() const = 0;
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virtual void setVotesThreshold(int votesThreshold) = 0;
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virtual int getVotesThreshold() const = 0;
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virtual void setMinRadius(int minRadius) = 0;
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virtual int getMinRadius() const = 0;
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virtual void setMaxRadius(int maxRadius) = 0;
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virtual int getMaxRadius() const = 0;
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virtual void setMaxCircles(int maxCircles) = 0;
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virtual int getMaxCircles() const = 0;
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};
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CV_EXPORTS Ptr<HoughCirclesDetector> createHoughCirclesDetector(float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096);
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// obsolete
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__OPENCV_GPUIMGPROC_DEPR_BEFORE__ void HoughCircles(InputArray src, OutputArray circles,
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int method, float dp, float minDist, int cannyThreshold, int votesThreshold,
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int minRadius, int maxRadius, int maxCircles = 4096) __OPENCV_GPUIMGPROC_DEPR_AFTER__;
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inline void HoughCircles(InputArray src, OutputArray circles, int /*method*/, float dp, float minDist,
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int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles)
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{
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gpu::createHoughCirclesDetector(dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius, maxCircles)->detect(src, circles);
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}
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//////////////////////////////////////
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// GeneralizedHough
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//! finds arbitrary template in the grayscale image using Generalized Hough Transform
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//! Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122.
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//! Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038.
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class CV_EXPORTS GeneralizedHough : public Algorithm
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{
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public:
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static Ptr<GeneralizedHough> create(int method);
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//! set template to search
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virtual void setTemplate(InputArray templ, int cannyThreshold = 100, Point templCenter = Point(-1, -1)) = 0;
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virtual void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1)) = 0;
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//! find template on image
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virtual void detect(InputArray image, OutputArray positions, int cannyThreshold = 100) = 0;
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virtual void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions) = 0;
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virtual void downloadResults(InputArray d_positions, OutputArray h_positions, OutputArray h_votes = noArray()) = 0;
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};
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////////////////////////// Corners Detection ///////////////////////////
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class CV_EXPORTS CornernessCriteria : public Algorithm
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{
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public:
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virtual void compute(InputArray src, OutputArray dst, Stream& stream = Stream::Null()) = 0;
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};
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//! computes Harris cornerness criteria at each image pixel
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CV_EXPORTS Ptr<CornernessCriteria> createHarrisCorner(int srcType, int blockSize, int ksize, double k, int borderType = BORDER_REFLECT101);
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//! computes minimum eigen value of 2x2 derivative covariation matrix at each pixel - the cornerness criteria
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CV_EXPORTS Ptr<CornernessCriteria> createMinEigenValCorner(int srcType, int blockSize, int ksize, int borderType = BORDER_REFLECT101);
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// obsolete
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__OPENCV_GPUIMGPROC_DEPR_BEFORE__ void cornerHarris(InputArray src, OutputArray dst,
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int blockSize, int ksize, double k, int borderType = BORDER_REFLECT101,
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Stream& stream = Stream::Null()) __OPENCV_GPUIMGPROC_DEPR_AFTER__;
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inline void cornerHarris(InputArray src, OutputArray dst, int blockSize, int ksize, double k, int borderType, Stream& stream)
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{
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gpu::createHarrisCorner(src.type(), blockSize, ksize, k, borderType)->compute(src, dst, stream);
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}
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__OPENCV_GPUIMGPROC_DEPR_BEFORE__ void cornerMinEigenVal(InputArray src, OutputArray dst,
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int blockSize, int ksize, int borderType = BORDER_REFLECT101,
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Stream& stream = Stream::Null()) __OPENCV_GPUIMGPROC_DEPR_AFTER__;
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inline void cornerMinEigenVal(InputArray src, OutputArray dst, int blockSize, int ksize, int borderType, Stream& stream)
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{
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gpu::createMinEigenValCorner(src.type(), blockSize, ksize, borderType)->compute(src, dst, stream);
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}
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////////////////////////// Corners Detection ///////////////////////////
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class CV_EXPORTS CornersDetector : public Algorithm
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{
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public:
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//! return 1 rows matrix with CV_32FC2 type
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virtual void detect(InputArray image, OutputArray corners, InputArray mask = noArray()) = 0;
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};
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CV_EXPORTS Ptr<CornersDetector> createGoodFeaturesToTrackDetector(int srcType, int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 0.0,
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int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04);
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///////////////////////////// Mean Shift //////////////////////////////
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//! Does mean shift filtering on GPU.
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CV_EXPORTS void meanShiftFiltering(InputArray src, OutputArray dst, int sp, int sr,
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TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1),
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Stream& stream = Stream::Null());
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//! Does mean shift procedure on GPU.
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CV_EXPORTS void meanShiftProc(InputArray src, OutputArray dstr, OutputArray dstsp, int sp, int sr,
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TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1),
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Stream& stream = Stream::Null());
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//! Does mean shift segmentation with elimination of small regions.
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CV_EXPORTS void meanShiftSegmentation(InputArray src, OutputArray dst, int sp, int sr, int minsize,
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TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
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/////////////////////////// Match Template ////////////////////////////
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struct CV_EXPORTS MatchTemplateBuf
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{
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Size user_block_size;
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GpuMat imagef, templf;
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std::vector<GpuMat> images;
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std::vector<GpuMat> image_sums;
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std::vector<GpuMat> image_sqsums;
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};
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//! computes the proximity map for the raster template and the image where the template is searched for
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CV_EXPORTS void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, Stream &stream = Stream::Null());
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//! computes the proximity map for the raster template and the image where the template is searched for
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CV_EXPORTS void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, MatchTemplateBuf &buf, Stream& stream = Stream::Null());
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////////////////////////// Bilateral Filter ///////////////////////////
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//! Performa bilateral filtering of passsed image
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CV_EXPORTS void bilateralFilter(const GpuMat& src, GpuMat& dst, int kernel_size, float sigma_color, float sigma_spatial,
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int borderMode = BORDER_DEFAULT, Stream& stream = Stream::Null());
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///////////////////////////// Blending ////////////////////////////////
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//! performs linear blending of two images
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//! to avoid accuracy errors sum of weigths shouldn't be very close to zero
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CV_EXPORTS void blendLinear(const GpuMat& img1, const GpuMat& img2, const GpuMat& weights1, const GpuMat& weights2,
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GpuMat& result, Stream& stream = Stream::Null());
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}} // namespace cv { namespace gpu {
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#undef __OPENCV_GPUIMGPROC_DEPR_BEFORE__
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#undef __OPENCV_GPUIMGPROC_DEPR_AFTER__
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#endif /* __OPENCV_GPUIMGPROC_HPP__ */
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