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Merge pull request #361 from epfl-lts2:graphsegmentation
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‎modules/ximgproc/README.md

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@@ -7,3 +7,4 @@ Extended Image Processing
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4. Adaptive Manifold Filter
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5. Joint Bilateral Filter
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6. Superpixels
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7. Graph segmentation

‎modules/ximgproc/doc/ximgproc.bib

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organization={IEEE}
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}
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@incollection{PFF2004,
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title={Efficient graph-based image segmentation},
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author={Felzenszwalb, Pedro F and Huttenlocher, Daniel P},
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journal={International Journal of Computer Vision},
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volume={59},
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number={2},
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pages={167--181},
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year={2004},
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publisher={Springer}
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}
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@article{Min2014,
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title={Fast global image smoothing based on weighted least squares},
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author={Min, Dongbo and Choi, Sunghwan and Lu, Jiangbo and Ham, Bumsub and Sohn, Kwanghoon and Do, Minh N},

‎modules/ximgproc/include/opencv2/ximgproc.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/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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@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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@}
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*/
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‎modules/ximgproc/include/opencv2/ximgproc/segmentation.hpp

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/*
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By downloading, copying, installing or using the software you agree to this
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license. 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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License Agreement
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For Open Source Computer Vision Library
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(3-clause BSD License)
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Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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Third party copyrights are property of their respective owners.
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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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* 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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* 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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* 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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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
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disclaimed. In no event shall copyright holders or contributors be liable for
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any direct, 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_SEGMENTATION_HPP__
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#define __OPENCV_XIMGPROC_SEGMENTATION_HPP__
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#include <opencv2/core.hpp>
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namespace cv {
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namespace ximgproc {
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namespace segmentation {
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//! @addtogroup ximgproc_segmentation
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//! @{
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/** @brief Graph Based Segmentation Algorithm.
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The class implements the algorithm described in @cite PFF2004 .
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*/
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class CV_EXPORTS_W GraphSegmentation : public Algorithm {
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public:
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/** @brief Segment an image and store output in dst
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@param src The input image. Any number of channel (1 (Eg: Gray), 3 (Eg: RGB), 4 (Eg: RGB-D)) can be provided
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@param dst The output segmentation. It's a CV_32SC1 Mat with the same number of cols and rows as input image, with an unique, sequential, id for each pixel.
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*/
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CV_WRAP virtual void processImage(InputArray src, OutputArray dst) = 0;
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CV_WRAP virtual void setSigma(double sigma) = 0;
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CV_WRAP virtual double getSigma() = 0;
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CV_WRAP virtual void setK(float k) = 0;
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CV_WRAP virtual float getK() = 0;
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CV_WRAP virtual void setMinSize(int min_size) = 0;
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CV_WRAP virtual int getMinSize() = 0;
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};
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/** @brief Creates a graph based segmentor
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@param sigma The sigma parameter, used to smooth image
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@param k The k parameter of the algorythm
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@param min_size The minimum size of segments
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*/
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CV_EXPORTS_W Ptr<GraphSegmentation> createGraphSegmentation(double sigma=0.5, float k=300, int min_size=100);
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//! @}
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// Represent an edge between two pixels
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class Edge {
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public:
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int from;
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int to;
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float weight;
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bool operator <(const Edge& e) const {
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return weight < e.weight;
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}
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};
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// A point in the sets of points
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class PointSetElement {
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public:
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int p;
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int size;
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PointSetElement() { }
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PointSetElement(int p_) {
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p = p_;
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size = 1;
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}
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};
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// An object to manage set of points, who can be fusionned
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class PointSet {
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public:
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PointSet(int nb_elements_);
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~PointSet();
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int nb_elements;
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// Return the main point of the point's set
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int getBasePoint(int p);
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// Join two sets of points, based on their main point
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void joinPoints(int p_a, int p_b);
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// Return the set size of a set (based on the main point)
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int size(unsigned int p) { return mapping[p].size; }
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private:
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PointSetElement* mapping;
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};
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}
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}
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}
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#endif

‎modules/ximgproc/samples/cpp/graphsegmentation_demo.cpp

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/*
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By downloading, copying, installing or using the software you agree to this
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license. 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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License Agreement
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For Open Source Computer Vision Library
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(3-clause BSD License)
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Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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Third party copyrights are property of their respective owners.
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Redistribution and use in source and binary forms, with or without modification,
11+
are permitted provided that the following conditions are met:
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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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* 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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* 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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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
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disclaimed. In no event shall copyright holders or contributors be liable for
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any direct, indirect, incidental, special, exemplary, or consequential damages
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(including, but not limited to, procurement of substitute goods or services;
26+
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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#include "opencv2/ximgproc/segmentation.hpp"
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#include "opencv2/highgui.hpp"
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#include <opencv2/core/utility.hpp>
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#include <opencv2/opencv.hpp>
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#include <iostream>
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using namespace cv;
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using namespace cv::ximgproc::segmentation;
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static void help() {
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std::cout << std::endl <<
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"A program demonstrating the use and capabilities of a particular graph based image" << std::endl <<
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"segmentation algorithm described in P. Felzenszwalb, D. Huttenlocher," << std::endl <<
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" \"Efficient Graph-Based Image Segmentation\"" << std::endl <<
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"International Journal of Computer Vision, Vol. 59, No. 2, September 2004" << std::endl << std::endl <<
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"Usage:" << std::endl <<
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"./graphsegmentation_demo input_image output_image [simga=0.5] [k=300] [min_size=100]" << std::endl;
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}
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Scalar hsv_to_rgb(Scalar c) {
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Mat in(1, 1, CV_32FC3);
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Mat out(1, 1, CV_32FC3);
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float * p = in.ptr<float>(0);
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p[0] = c[0] * 360;
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p[1] = c[1];
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p[2] = c[2];
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cvtColor(in, out, COLOR_HSV2RGB);
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Scalar t;
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Vec3f p2 = out.at<Vec3f>(0, 0);
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t[0] = (int)(p2[0] * 255);
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t[1] = (int)(p2[1] * 255);
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t[2] = (int)(p2[2] * 255);
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return t;
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}
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Scalar color_mapping(int segment_id) {
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double base = (double)(segment_id) * 0.618033988749895 + 0.24443434;
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return hsv_to_rgb(Scalar(fmod(base, 1.2), 0.95, 0.80));
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}
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int main(int argc, char** argv) {
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if (argc < 2 || argc > 6) {
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help();
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return -1;
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}
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setUseOptimized(true);
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setNumThreads(8);
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Ptr<GraphSegmentation> gs = createGraphSegmentation();
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if (argc > 3)
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gs->setSigma(atof(argv[3]));
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if (argc > 4)
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gs->setK(atoi(argv[4]));
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if (argc > 5)
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gs->setMinSize(atoi(argv[5]));
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if (!gs) {
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std::cerr << "Failed to create GraphSegmentation Algorithm." << std::endl;
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return -2;
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}
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Mat input, output, output_image;
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input = imread(argv[1]);
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if (!input.data) {
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std::cerr << "Failed to load input image" << std::endl;
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return -3;
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}
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gs->processImage(input, output);
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double min, max;
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minMaxLoc(output, &min, &max);
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int nb_segs = (int)max + 1;
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std::cout << nb_segs << " segments" << std::endl;
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output_image = Mat::zeros(output.rows, output.cols, CV_8UC3);
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uint* p;
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uchar* p2;
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for (int i = 0; i < output.rows; i++) {
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p = output.ptr<uint>(i);
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p2 = output_image.ptr<uchar>(i);
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for (int j = 0; j < output.cols; j++) {
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Scalar color = color_mapping(p[j]);
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p2[j*3] = color[0];
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p2[j*3 + 1] = color[1];
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p2[j*3 + 2] = color[2];
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}
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}
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imwrite(argv[2], output_image);
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std::cout << "Image written to " << argv[2] << std::endl;
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return 0;
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}

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