dlib C++ Library - max_sum_submatrix.h

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

    #include "max_sum_submatrix_abstract.h"
    #include "../matrix.h"
    #include <vector>
    #include <queue>
    #include "../geometry.h"

    namespace dlib
    {
        namespace impl
        {

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

            template <typename T>
            struct range_set
            {
                int top_min;
                int top_max;
                int bottom_min;
                int bottom_max;
                T weight;

                bool operator<(const range_set& item) const { return weight < item.weight; }
            };

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

            template <typename T>
            bool is_terminal_set (
                const range_set<T>& item
            )
            {
                return (item.top_min  >= item.top_max &&
                        item.bottom_min >= item.bottom_max);
            }

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

            template <typename T>
            void split (
                const range_set<T>& rset,
                range_set<T>& a,
                range_set<T>& b
            )
            {
                if (rset.top_max - rset.top_min > rset.bottom_max - rset.bottom_min)
                {
                    // split top
                    const int middle = (rset.top_max + rset.top_min)/2;
                    a.top_min = rset.top_min;
                    a.top_max = middle;
                    b.top_min = middle+1;
                    b.top_max = rset.top_max;

                    a.bottom_min = rset.bottom_min;
                    a.bottom_max = rset.bottom_max;
                    b.bottom_min = rset.bottom_min;
                    b.bottom_max = rset.bottom_max;
                }
                else
                {
                    // split bottom
                    const int middle = (rset.bottom_max + rset.bottom_min)/2;
                    a.bottom_min = rset.bottom_min;
                    a.bottom_max = middle;
                    b.bottom_min = middle+1;
                    b.bottom_max = rset.bottom_max;

                    a.top_min = rset.top_min;
                    a.top_max = rset.top_max;
                    b.top_min = rset.top_min;
                    b.top_max = rset.top_max;
                }
            }

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

            template <typename EXP, typename T>
            void find_best_column_range (
                const matrix_exp<EXP>& sum_pos,
                const matrix_exp<EXP>& sum_neg,
                const range_set<T>& row_range,
                T& weight,
                int& left,
                int& right
            )
            {
                left = 0;
                right = -1;
                weight = 0;
                T cur_sum = 0;
                int cur_pos = 0;
                for (long c = 0; c < sum_pos.nc(); ++c)
                {
                    // compute the value for the current column
                    T temp = sum_pos(row_range.bottom_max+1,c) - sum_pos(row_range.top_min,c);
                    if (row_range.top_max <= row_range.bottom_min)
                        temp += sum_neg(row_range.bottom_min+1,c) - sum_neg(row_range.top_max,c);

                    cur_sum += temp;
                    if (cur_sum > weight)
                    {
                        left = cur_pos;
                        right = c;
                        weight = cur_sum;
                    }

                    if (cur_sum <= 0)
                    {
                        cur_sum = 0;
                        cur_pos = c+1;
                    }

                }
            }
        }

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

        template <typename EXP>
        std::vector<rectangle> max_sum_submatrix(
            const matrix_exp<EXP>& mat,
            unsigned long max_rects,
            double thresh_ = 0
        )
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(thresh_ >= 0 && mat.size() > 0,
                "\t std::vector<rectangle> max_sum_submatrix()"
                << "\n\t Invalid arguments were given to this function."
                << "\n\t mat.size(): " << mat.size()
                << "\n\t thresh_:    " << thresh_
                );

            /*
                This function is basically an implementation of the efficient subwindow search (I-ESS)
                algorithm presented in the following paper:
                    Efficient Algorithms for Subwindow Search in Object Detection and Localization
                    by Senjian An, Patrick Peursum, Wanquan Liu and Svetha Venkatesh
                    In CVPR 2009

            */

            if (max_rects == 0)
                return std::vector<rectangle>();

            using namespace dlib::impl;
            typedef typename EXP::type element_type;
            typedef typename promote<element_type>::type scalar_type;

            const scalar_type thresh = static_cast<scalar_type>(thresh_);

            matrix<scalar_type> sum_pos;
            matrix<scalar_type> sum_neg;
            sum_pos.set_size(mat.nr()+1, mat.nc());
            sum_neg.set_size(mat.nr()+1, mat.nc());
            // integrate over the rows.
            for (long c = 0; c < mat.nc(); ++c)
            {
                sum_pos(0,c) = 0;
                sum_neg(0,c) = 0;
            }
            for (long r = 0; r < mat.nr(); ++r)
            {
                for (long c = 0; c < mat.nc(); ++c)
                {
                    if (mat(r,c) > 0)
                    {
                        sum_pos(r+1,c) = mat(r,c) + sum_pos(r,c);
                        sum_neg(r+1,c) = sum_neg(r,c);
                    }
                    else
                    {
                        sum_pos(r+1,c) = sum_pos(r,c);
                        sum_neg(r+1,c) = mat(r,c) + sum_neg(r,c);
                    }
                }
            }

            std::priority_queue<range_set<scalar_type> > q;

            // the range_sets will represent ranges of columns
            range_set<scalar_type> universe_set;
            universe_set.bottom_min = 0;
            universe_set.top_min = 0;
            universe_set.bottom_max = mat.nr()-1;
            universe_set.top_max = mat.nr()-1;
            universe_set.weight = sum(rowm(dlib::mat(sum_pos),mat.nr()));

            q.push(universe_set);

            std::vector<rectangle> results;
            std::vector<scalar_type> temp_pos(mat.nc());
            std::vector<scalar_type> temp_neg(mat.nc());

            while (q.size() > 0)
            {
                if (is_terminal_set(q.top()))
                {
                    int left, right;
                    scalar_type weight;
                    find_best_column_range(sum_pos, sum_neg, q.top(), weight, left, right);

                    rectangle rect(left, q.top().top_min,
                                   right, q.top().bottom_min);

                    if (weight <= thresh)
                        break;

                    results.push_back(rect);

                    if (results.size() >= max_rects)
                        break;

                    q = std::priority_queue<range_set<scalar_type> >();
                    // We are going to blank out the weights we just used.  So adjust the sum images appropriately.
                    for (long c = rect.left(); c <= rect.right(); ++c)
                    {
                        temp_pos[c] = sum_pos(rect.bottom()+1,c) - sum_pos(rect.top(),c);
                        temp_neg[c] = sum_neg(rect.bottom()+1,c) - sum_neg(rect.top(),c);
                    }
                    // blank out the area inside the rectangle
                    for (long r = rect.top(); r <= rect.bottom(); ++r)
                    {
                        for (long c = rect.left(); c <= rect.right(); ++c)
                        {
                            sum_pos(r+1,c) = sum_pos(r,c);
                            sum_neg(r+1,c) = sum_neg(r,c);
                        }
                    }
                    // account for the area below the rectangle
                    for (long r = rect.bottom()+2; r < sum_pos.nr(); ++r)
                    {
                        for (long c = rect.left(); c <= rect.right(); ++c)
                        {
                            sum_pos(r,c) -= temp_pos[c];
                            sum_neg(r,c) -= temp_neg[c];
                        }
                    }

                    universe_set.weight = sum(rowm(dlib::mat(sum_pos),mat.nr()));
                    if (universe_set.weight <= thresh)
                        break;

                    q.push(universe_set);
                    continue;
                }

                range_set<scalar_type> a, b;
                split(q.top(), a,b);
                q.pop();

                // these variables are not used at this point in the algorithm.
                int a_left, a_right;
                int b_left, b_right;

                find_best_column_range(sum_pos, sum_neg, a, a.weight, a_left, a_right);
                find_best_column_range(sum_pos, sum_neg, b, b.weight, b_left, b_right);

                if (a.weight > thresh)
                    q.push(a);
                if (b.weight > thresh)
                    q.push(b);

            }

            return results;
        }

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

    }

    #endif // DLIB_MAX_SUM_SUBMaTRIX_Hh_