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

#include "matrix_abstract.h"

namespace dlib
{

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

    const matrix_exp conv (
        const matrix_exp& m1,
        const matrix_exp& m2
    );
    /*!
        requires
            - m1 and m2 both contain elements of the same type
        ensures
            - returns a matrix R such that:
                - R is the convolution of m1 with m2.  In particular, this function is 
                  equivalent to performing the following in matlab: R = conv2(m1,m2).
                - R::type == the same type that was in m1 and m2.
                - R.nr() == m1.nr()+m2.nr()-1
                - R.nc() == m1.nc()+m2.nc()-1
    !*/

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

    const matrix_exp xcorr (
        const matrix_exp& m1,
        const matrix_exp& m2
    );
    /*!
        requires
            - m1 and m2 both contain elements of the same type
        ensures
            - returns a matrix R such that:
                - R is the cross-correlation of m1 with m2.  In particular, this
                  function returns conv(m1,flip(m2)) if the matrices contain real
                  elements and conv(m1,flip(conj(m2))) if they are complex.
                - R::type == the same type that was in m1 and m2.
                - R.nr() == m1.nr()+m2.nr()-1
                - R.nc() == m1.nc()+m2.nc()-1
    !*/

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

    const matrix_exp xcorr_fft (
        const matrix_exp& m1,
        const matrix_exp& m2
    );
    /*!
        requires
            - m1 and m2 both contain elements of the same type
            - m1 and m2 contain real or complex values and must be double, float, or long
              double valued. (e.g. not integers)
        ensures
            - This function is identical to xcorr() except that it uses a fast Fourier
              transform to do the convolution and is therefore much faster when both m1 and
              m2 are large.
    !*/

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

    const matrix_exp conv_same (
        const matrix_exp& m1,
        const matrix_exp& m2
    );
    /*!
        requires
            - m1 and m2 both contain elements of the same type
        ensures
            - returns a matrix R such that:
                - R is the convolution of m1 with m2.  In particular, this function is 
                  equivalent to performing the following in matlab: R = conv2(m1,m2,'same').
                  In particular, this means the result will have the same dimensions as m1 and will
                  contain the central part of the full convolution.  Therefore, conv_same(m1,m2) is 
                  equivalent to subm(conv(m1,m2), m2.nr()/2, m2.nc()/2, m1.nr(), m1.nc()).
                - R::type == the same type that was in m1 and m2.
                - R.nr() == m1.nr()
                - R.nc() == m1.nc()
    !*/

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

    const matrix_exp xcorr_same (
        const matrix_exp& m1,
        const matrix_exp& m2
    );
    /*!
        requires
            - m1 and m2 both contain elements of the same type
        ensures
            - returns a matrix R such that:
                - R is the cross-correlation of m1 with m2.  In particular, this
                  function returns conv_same(m1,flip(m2)) if the matrices contain real
                  elements and conv_same(m1,flip(conj(m2))) if they are complex.
                - R::type == the same type that was in m1 and m2.
                - R.nr() == m1.nr()
                - R.nc() == m1.nc()
    !*/

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

    const matrix_exp conv_valid (
        const matrix_exp& m1,
        const matrix_exp& m2
    );
    /*!
        requires
            - m1 and m2 both contain elements of the same type
        ensures
            - returns a matrix R such that:
                - R is the convolution of m1 with m2.  In particular, this function is 
                  equivalent to performing the following in matlab: R = conv2(m1,m2,'valid').
                  In particular, this means only elements of the convolution which don't require 
                  zero padding are included in the result.
                - R::type == the same type that was in m1 and m2.
                - if (m1 has larger dimensions than m2) then
                    - R.nr() == m1.nr()-m2.nr()+1
                    - R.nc() == m1.nc()-m2.nc()+1
                - else
                    - R.nr() == 0
                    - R.nc() == 0
    !*/

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

    const matrix_exp xcorr_valid (
        const matrix_exp& m1,
        const matrix_exp& m2
    );
    /*!
        requires
            - m1 and m2 both contain elements of the same type
        ensures
            - returns a matrix R such that:
                - R is the cross-correlation of m1 with m2.  In particular, this
                  function returns conv_valid(m1,flip(m2)) if the matrices contain real
                  elements and conv_valid(m1,flip(conj(m2))) if they are complex.
                - R::type == the same type that was in m1 and m2.
                - if (m1 has larger dimensions than m2) then
                    - R.nr() == m1.nr()-m2.nr()+1
                    - R.nc() == m1.nc()-m2.nc()+1
                - else
                    - R.nr() == 0
                    - R.nc() == 0
    !*/

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

}

#endif // DLIB_MATRIx_CONV_ABSTRACT_Hh_