170 lines
7.2 KiB
C++
170 lines
7.2 KiB
C++
/*********************************************************************
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* Software License Agreement (BSD License)
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*
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* Copyright (c) 2013
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* Radhakrishna Achanta
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* email : Radhakrishna [dot] Achanta [at] epfl [dot] ch
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* web : http://ivrl.epfl.ch/people/achanta
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* * Neither the name of the copyright holders nor the names of its
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* contributors may be used to endorse or promote products derived
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* 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
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*********************************************************************/
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/*
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"SLIC Superpixels Compared to State-of-the-art Superpixel Methods"
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Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua,
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and Sabine Susstrunk, IEEE TPAMI, Volume 34, Issue 11, Pages 2274-2282,
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November 2012.
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"SLIC Superpixels" Radhakrishna Achanta, Appu Shaji, Kevin Smith,
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Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk, EPFL Technical
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Report no. 149300, June 2010.
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OpenCV port by: Cristian Balint <cristian dot balint at gmail dot com>
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*/
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#ifndef __OPENCV_SLIC_HPP__
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#define __OPENCV_SLIC_HPP__
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#ifdef __cplusplus
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#include <opencv2/core.hpp>
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namespace cv
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{
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namespace ximgproc
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{
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//! @addtogroup ximgproc_superpixel
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//! @{
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enum SLIC { SLIC = 100, SLICO = 101, MSLIC = 102 };
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typedef enum SLIC SLICType;
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/** @brief Class implementing the SLIC (Simple Linear Iterative Clustering) superpixels
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algorithm described in @cite Achanta2012.
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SLIC (Simple Linear Iterative Clustering) clusters pixels using pixel channels and image plane space
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to efficiently generate compact, nearly uniform superpixels. The simplicity of approach makes it
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extremely easy to use a lone parameter specifies the number of superpixels and the efficiency of
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the algorithm makes it very practical.
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Several optimizations are available for SLIC class:
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SLICO stands for "Zero parameter SLIC" and it is an optimization of baseline SLIC described in @cite Achanta2012.
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MSLIC stands for "Manifold SLIC" and it is an optimization of baseline SLIC described in @cite Liu_2017_IEEE.
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*/
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class CV_EXPORTS_W SuperpixelSLIC : public Algorithm
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{
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public:
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/** @brief Calculates the actual amount of superpixels on a given segmentation computed
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and stored in SuperpixelSLIC object.
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*/
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CV_WRAP virtual int getNumberOfSuperpixels() const = 0;
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/** @brief Calculates the superpixel segmentation on a given image with the initialized
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parameters in the SuperpixelSLIC object.
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This function can be called again without the need of initializing the algorithm with
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createSuperpixelSLIC(). This save the computational cost of allocating memory for all the
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structures of the algorithm.
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@param num_iterations Number of iterations. Higher number improves the result.
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The function computes the superpixels segmentation of an image with the parameters initialized
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with the function createSuperpixelSLIC(). The algorithms starts from a grid of superpixels and
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then refines the boundaries by proposing updates of edges boundaries.
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*/
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CV_WRAP virtual void iterate( int num_iterations = 10 ) = 0;
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/** @brief Returns the segmentation labeling of the image.
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Each label represents a superpixel, and each pixel is assigned to one superpixel label.
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@param labels_out Return: A CV_32SC1 integer array containing the labels of the superpixel
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segmentation. The labels are in the range [0, getNumberOfSuperpixels()].
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The function returns an image with the labels of the superpixel segmentation. The labels are in
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the range [0, getNumberOfSuperpixels()].
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*/
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CV_WRAP virtual void getLabels( OutputArray labels_out ) const = 0;
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/** @brief Returns the mask of the superpixel segmentation stored in SuperpixelSLIC object.
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@param image Return: CV_8U1 image mask where -1 indicates that the pixel is a superpixel border,
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and 0 otherwise.
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@param thick_line If false, the border is only one pixel wide, otherwise all pixels at the border
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are masked.
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The function return the boundaries of the superpixel segmentation.
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*/
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CV_WRAP virtual void getLabelContourMask( OutputArray image, bool thick_line = true ) const = 0;
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/** @brief Enforce label connectivity.
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@param min_element_size The minimum element size in percents that should be absorbed into a bigger
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superpixel. Given resulted average superpixel size valid value should be in 0-100 range, 25 means
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that less then a quarter sized superpixel should be absorbed, this is default.
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The function merge component that is too small, assigning the previously found adjacent label
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to this component. Calling this function may change the final number of superpixels.
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*/
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CV_WRAP virtual void enforceLabelConnectivity( int min_element_size = 25 ) = 0;
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};
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/** @brief Initialize a SuperpixelSLIC object
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@param image Image to segment
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@param algorithm Chooses the algorithm variant to use:
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SLIC segments image using a desired region_size, and in addition SLICO will optimize using adaptive compactness factor,
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while MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels.
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@param region_size Chooses an average superpixel size measured in pixels
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@param ruler Chooses the enforcement of superpixel smoothness factor of superpixel
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The function initializes a SuperpixelSLIC object for the input image. It sets the parameters of choosed
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superpixel algorithm, which are: region_size and ruler. It preallocate some buffers for future
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computing iterations over the given image. For enanched results it is recommended for color images to
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preprocess image with little gaussian blur using a small 3 x 3 kernel and additional conversion into
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CieLAB color space. An example of SLIC versus SLICO and MSLIC is ilustrated in the following picture.
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*/
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CV_EXPORTS_W Ptr<SuperpixelSLIC> createSuperpixelSLIC( InputArray image, int algorithm = SLICO,
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int region_size = 10, float ruler = 10.0f );
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//! @}
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}
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}
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#endif
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#endif
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