178 lines
7.3 KiB
C++
178 lines
7.3 KiB
C++
/*
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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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* (3 - clause BSD License)
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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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* * Redistributions 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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* * Redistributions 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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* * Neither the names of the copyright holders nor the names of the contributors
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* may be used to endorse or promote products derived from this software
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* 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 copyright holders 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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#ifndef __OPENCV_XIMGPROC_HPP__
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#define __OPENCV_XIMGPROC_HPP__
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#include "ximgproc/edge_filter.hpp"
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#include "ximgproc/disparity_filter.hpp"
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#include "ximgproc/sparse_match_interpolator.hpp"
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#include "ximgproc/structured_edge_detection.hpp"
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#include "ximgproc/edgeboxes.hpp"
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#include "ximgproc/seeds.hpp"
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#include "ximgproc/segmentation.hpp"
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#include "ximgproc/fast_hough_transform.hpp"
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#include "ximgproc/estimated_covariance.hpp"
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#include "ximgproc/weighted_median_filter.hpp"
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#include "ximgproc/slic.hpp"
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#include "ximgproc/lsc.hpp"
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#include "ximgproc/paillou_filter.hpp"
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#include "ximgproc/fast_line_detector.hpp"
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#include "ximgproc/deriche_filter.hpp"
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#include "ximgproc/peilin.hpp"
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#include "ximgproc/fourier_descriptors.hpp"
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#include "ximgproc/ridgefilter.hpp"
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#include "ximgproc/brightedges.hpp"
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/** @defgroup ximgproc Extended Image Processing
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@{
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@defgroup ximgproc_edge Structured forests for fast edge detection
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This module contains implementations of modern structured edge detection algorithms,
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i.e. algorithms which somehow takes into account pixel affinities in natural images.
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@defgroup ximgproc_edgeboxes EdgeBoxes
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@defgroup ximgproc_filters Filters
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@defgroup ximgproc_superpixel Superpixels
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@defgroup ximgproc_segmentation Image segmentation
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@defgroup ximgproc_fast_line_detector Fast line detector
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@defgroup ximgproc_fourier Fourier descriptors
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@}
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*/
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namespace cv
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{
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namespace ximgproc
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{
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enum ThinningTypes{
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THINNING_ZHANGSUEN = 0, // Thinning technique of Zhang-Suen
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THINNING_GUOHALL = 1 // Thinning technique of Guo-Hall
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};
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/**
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* @brief Specifies the binarization method to use in cv::ximgproc::niBlackThreshold
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*/
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enum LocalBinarizationMethods{
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BINARIZATION_NIBLACK = 0, //!< Classic Niblack binarization. See @cite Niblack1985 .
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BINARIZATION_SAUVOLA = 1, //!< Sauvola's technique. See @cite Sauvola1997 .
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BINARIZATION_WOLF = 2, //!< Wolf's technique. See @cite Wolf2004 .
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BINARIZATION_NICK = 3 //!< NICK technique. See @cite Khurshid2009 .
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};
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//! @addtogroup ximgproc
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//! @{
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/** @brief Performs thresholding on input images using Niblack's technique or some of the
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popular variations it inspired.
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The function transforms a grayscale image to a binary image according to the formulae:
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- **THRESH_BINARY**
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\f[dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\f]
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- **THRESH_BINARY_INV**
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\f[dst(x,y) = \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\f]
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where \f$T(x,y)\f$ is a threshold calculated individually for each pixel.
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The threshold value \f$T(x, y)\f$ is determined based on the binarization method chosen. For
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classic Niblack, it is the mean minus \f$ k \f$ times standard deviation of
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\f$\texttt{blockSize} \times\texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$.
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The function can't process the image in-place.
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@param _src Source 8-bit single-channel image.
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@param _dst Destination image of the same size and the same type as src.
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@param maxValue Non-zero value assigned to the pixels for which the condition is satisfied,
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used with the THRESH_BINARY and THRESH_BINARY_INV thresholding types.
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@param type Thresholding type, see cv::ThresholdTypes.
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@param blockSize Size of a pixel neighborhood that is used to calculate a threshold value
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for the pixel: 3, 5, 7, and so on.
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@param k The user-adjustable parameter used by Niblack and inspired techniques. For Niblack, this is
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normally a value between 0 and 1 that is multiplied with the standard deviation and subtracted from
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the mean.
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@param binarizationMethod Binarization method to use. By default, Niblack's technique is used.
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Other techniques can be specified, see cv::ximgproc::LocalBinarizationMethods.
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@sa threshold, adaptiveThreshold
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*/
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CV_EXPORTS_W void niBlackThreshold( InputArray _src, OutputArray _dst,
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double maxValue, int type,
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int blockSize, double k, int binarizationMethod = BINARIZATION_NIBLACK );
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/** @brief Applies a binary blob thinning operation, to achieve a skeletization of the input image.
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The function transforms a binary blob image into a skeletized form using the technique of Zhang-Suen.
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@param src Source 8-bit single-channel image, containing binary blobs, with blobs having 255 pixel values.
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@param dst Destination image of the same size and the same type as src. The function can work in-place.
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@param thinningType Value that defines which thinning algorithm should be used. See cv::ximgproc::ThinningTypes
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*/
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CV_EXPORTS_W void thinning( InputArray src, OutputArray dst, int thinningType = THINNING_ZHANGSUEN);
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/** @brief Performs anisotropic diffusion on an image.
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The function applies Perona-Malik anisotropic diffusion to an image. This is the solution to the partial differential equation:
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\f[{\frac {\partial I}{\partial t}}={\mathrm {div}}\left(c(x,y,t)\nabla I\right)=\nabla c\cdot \nabla I+c(x,y,t)\Delta I\f]
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Suggested functions for c(x,y,t) are:
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\f[c\left(\|\nabla I\|\right)=e^{{-\left(\|\nabla I\|/K\right)^{2}}}\f]
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or
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\f[ c\left(\|\nabla I\|\right)={\frac {1}{1+\left({\frac {\|\nabla I\|}{K}}\right)^{2}}} \f]
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@param src Source image with 3 channels.
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@param dst Destination image of the same size and the same number of channels as src .
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@param alpha The amount of time to step forward by on each iteration (normally, it's between 0 and 1).
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@param K sensitivity to the edges
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@param niters The number of iterations
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*/
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CV_EXPORTS_W void anisotropicDiffusion(InputArray src, OutputArray dst, float alpha, float K, int niters );
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//! @}
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}
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}
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#endif // __OPENCV_XIMGPROC_HPP__
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