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diff --git a/thirdparty/linux/include/opencv2/ximgproc/lsc.hpp b/thirdparty/linux/include/opencv2/ximgproc/lsc.hpp new file mode 100644 index 0000000..e6f5bae --- /dev/null +++ b/thirdparty/linux/include/opencv2/ximgproc/lsc.hpp @@ -0,0 +1,157 @@ +/********************************************************************* + * Software License Agreement (BSD License) + * + * Copyright (c) 2014, 2015 + * Zhengqin Li <li-zq12 at mails dot tsinghua dot edu dot cn> + * Jiansheng Chen <jschenthu at mail dot tsinghua dot edu dot cn> + * Tsinghua University + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the copyright holders nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + *********************************************************************/ + +/* + + "Superpixel Segmentation using Linear Spectral Clustering" + Zhengqin Li, Jiansheng Chen, IEEE Conference on Computer Vision and Pattern + Recognition (CVPR), Jun. 2015 + + OpenCV port by: Cristian Balint <cristian dot balint at gmail dot com> + */ + +#ifndef __OPENCV_LSC_HPP__ +#define __OPENCV_LSC_HPP__ +#ifdef __cplusplus + +#include <opencv2/core.hpp> + +namespace cv +{ +namespace ximgproc +{ + +//! @addtogroup ximgproc_superpixel +//! @{ + +/** @brief Class implementing the LSC (Linear Spectral Clustering) superpixels +algorithm described in @cite LiCVPR2015LSC. + +LSC (Linear Spectral Clustering) produces compact and uniform superpixels with low +computational costs. Basically, a normalized cuts formulation of the superpixel +segmentation is adopted based on a similarity metric that measures the color +similarity and space proximity between image pixels. LSC is of linear computational +complexity and high memory efficiency and is able to preserve global properties of images + + */ + +class CV_EXPORTS_W SuperpixelLSC : public Algorithm +{ +public: + + /** @brief Calculates the actual amount of superpixels on a given segmentation computed + and stored in SuperpixelLSC object. + */ + CV_WRAP virtual int getNumberOfSuperpixels() const = 0; + + /** @brief Calculates the superpixel segmentation on a given image with the initialized + parameters in the SuperpixelLSC object. + + This function can be called again without the need of initializing the algorithm with + createSuperpixelLSC(). This save the computational cost of allocating memory for all the + structures of the algorithm. + + @param num_iterations Number of iterations. Higher number improves the result. + + The function computes the superpixels segmentation of an image with the parameters initialized + with the function createSuperpixelLSC(). The algorithms starts from a grid of superpixels and + then refines the boundaries by proposing updates of edges boundaries. + + */ + CV_WRAP virtual void iterate( int num_iterations = 10 ) = 0; + + /** @brief Returns the segmentation labeling of the image. + + Each label represents a superpixel, and each pixel is assigned to one superpixel label. + + @param labels_out Return: A CV_32SC1 integer array containing the labels of the superpixel + segmentation. The labels are in the range [0, getNumberOfSuperpixels()]. + + The function returns an image with the labels of the superpixel segmentation. The labels are in + the range [0, getNumberOfSuperpixels()]. + */ + CV_WRAP virtual void getLabels( OutputArray labels_out ) const = 0; + + /** @brief Returns the mask of the superpixel segmentation stored in SuperpixelLSC object. + + @param image Return: CV_8U1 image mask where -1 indicates that the pixel is a superpixel border, + and 0 otherwise. + + @param thick_line If false, the border is only one pixel wide, otherwise all pixels at the border + are masked. + + The function return the boundaries of the superpixel segmentation. + */ + CV_WRAP virtual void getLabelContourMask( OutputArray image, bool thick_line = true ) const = 0; + + /** @brief Enforce label connectivity. + + @param min_element_size The minimum element size in percents that should be absorbed into a bigger + superpixel. Given resulted average superpixel size valid value should be in 0-100 range, 25 means + that less then a quarter sized superpixel should be absorbed, this is default. + + The function merge component that is too small, assigning the previously found adjacent label + to this component. Calling this function may change the final number of superpixels. + */ + CV_WRAP virtual void enforceLabelConnectivity( int min_element_size = 20 ) = 0; + + +}; + +/** @brief Class implementing the LSC (Linear Spectral Clustering) superpixels + +@param image Image to segment +@param region_size Chooses an average superpixel size measured in pixels +@param ratio Chooses the enforcement of superpixel compactness factor of superpixel + +The function initializes a SuperpixelLSC object for the input image. It sets the parameters of +superpixel algorithm, which are: region_size and ruler. It preallocate some buffers for future +computing iterations over the given image. An example of LSC is ilustrated in the following picture. +For enanched results it is recommended for color images to preprocess image with little gaussian blur +with a small 3 x 3 kernel and additional conversion into CieLAB color space. + + + + */ + + CV_EXPORTS_W Ptr<SuperpixelLSC> createSuperpixelLSC( InputArray image, int region_size = 10, float ratio = 0.075f ); + +//! @} + +} +} +#endif +#endif |