    // Copyright (C) 2011  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #undef DLIB_SEGMENT_ImAGE_ABSTRACT_H__
    #ifdef DLIB_SEGMENT_ImAGE_ABSTRACT_H__

    #include <vector>
    #include "../matrix.h"

    namespace dlib
    {

    // ----------------------------------------------------------------------------------------

        template <
            typename in_image_type,
            typename out_image_type
            >
        void segment_image (
            const in_image_type& in_img,
            out_image_type& out_img,
            const double k = 200,
            const unsigned long min_size = 10
        );
        /*!
            requires
                - in_image_type  == an implementation of array2d/array2d_kernel_abstract.h
                - out_image_type == an implementation of array2d/array2d_kernel_abstract.h
                - in_image_type::type  == Any pixel type with a pixel_traits specialization or a
                  dlib matrix object representing a row or column vector.
                - out_image_type::type == unsigned integer type
                - is_same_object(in_img, out_img) == false
            ensures
                - Attempts to segment in_img into regions which have some visual consistency to
                  them.  In particular, this function implements the algorithm described in the
                  paper: Efficient Graph-Based Image Segmentation by Felzenszwalb and Huttenlocher.
                - #out_img.nr() == in_img.nr()
                - #out_img.nc() == in_img.nc()
                - for all valid r and c:
                    - #out_img[r][c] == an integer value indicating the identity of the segment
                      containing the pixel in_img[r][c].
                - The k parameter is a measure used to influence how large the segment regions
                  will be.  Larger k generally results in larger segments being produced.  For
                  a deeper discussion of the k parameter you should consult the above
                  referenced paper.
                - min_size is a lower bound on the size of the output segments.  That is, it is
                  guaranteed that all output segments will have at least min_size pixels in
                  them (unless the whole image contains fewer than min_size pixels, in this
                  case the entire image will be put into a single segment).
        !*/

    // ----------------------------------------------------------------------------------------
    // ----------------------------------------------------------------------------------------
    // ----------------------------------------------------------------------------------------

        template <
            typename in_image_type,
            typename EXP
            >
        void find_candidate_object_locations (
            const in_image_type& in_img,
            std::vector<rectangle>& rects,
            const matrix_exp<EXP>& kvals = linspace(50, 200, 3),
            const unsigned long min_size = 20,
            const unsigned long max_merging_iterations = 50
        );
        /*!
            requires
                - is_vector(kvals) == true
                - kvals.size() > 0
            ensures
                - This function takes an input image and generates a set of candidate
                  rectangles which are expected to bound any objects in the image.  It does
                  this by running a version of the segment_image() routine on the image and
                  then reports rectangles containing each of the segments as well as rectangles
                  containing unions of adjacent segments.  The basic idea is described in the
                  paper:
                      Segmentation as Selective Search for Object Recognition by Koen E. A. van de Sande, et al.
                  Note that this function deviates from what is described in the paper slightly.
                  See the code for details.
                - The basic segmentation is performed kvals.size() times, each time with the k
                  parameter (see segment_image() and the Felzenszwalb paper for details on k)
                  set to a different value from kvals.
                - All the rectangles output by this function will have an area >= min_size.
                - There are max_merging_iterations rounds of neighboring blob merging.
                  Therefore, this parameter has some effect on the number of output rectangles
                  you get, with larger values of the parameter giving more output rectangles.
                - This function appends the output rectangles into #rects.  This means that any
                  rectangles in rects before this function was called will still be in there
                  after it terminates.  Note further that #rects will not contain any duplicate
                  rectangles.  That is, for all valid i and j where i != j it will be true
                  that:
                    - #rects[i] != rects[j]
        !*/

    // ----------------------------------------------------------------------------------------

        template <
            typename alloc
            >
        void remove_duplicates (
            std::vector<rectangle,alloc>& rects
        );
        /*!
            ensures
                - This function finds any duplicate rectangles in rects and removes the extra
                  instances.  This way, the result is that rects contains only unique rectangle
                  instances.
        !*/

    // ----------------------------------------------------------------------------------------

    }

    #endif // DLIB_SEGMENT_ImAGE_ABSTRACT_H__