# dlib C++ Library - shape_predictor.h

    // Copyright (C) 2014  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #ifndef DLIB_SHAPE_PREDICToR_H_
    #define DLIB_SHAPE_PREDICToR_H_

    #include "shape_predictor_abstract.h"
    #include "full_object_detection.h"
    #include "../algs.h"
    #include "../matrix.h"
    #include "../geometry.h"
    #include "../pixel.h"
    #include "../statistics.h"
    #include <utility>

    namespace dlib
    {

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

        namespace impl
        {
            struct split_feature
            {
                unsigned long idx1;
                unsigned long idx2;
                float thresh;

                friend inline void serialize (const split_feature& item, std::ostream& out)
                {
                    dlib::serialize(item.idx1, out);
                    dlib::serialize(item.idx2, out);
                    dlib::serialize(item.thresh, out);
                }
                friend inline void deserialize (split_feature& item, std::istream& in)
                {
                    dlib::deserialize(item.idx1, in);
                    dlib::deserialize(item.idx2, in);
                    dlib::deserialize(item.thresh, in);
                }
            };

            // a tree is just a std::vector<impl::split_feature>.  We use this function to navigate the
            // tree nodes
            inline unsigned long left_child (unsigned long idx) { return 2*idx + 1; }
            /*!
                ensures
                    - returns the index of the left child of the binary tree node idx
            !*/
            inline unsigned long right_child (unsigned long idx) { return 2*idx + 2; }
            /*!
                ensures
                    - returns the index of the left child of the binary tree node idx
            !*/

            struct regression_tree
            {
                std::vector<split_feature> splits;
                std::vector<matrix<float,0,1> > leaf_values;

                unsigned long num_leaves() const { return leaf_values.size(); }

                inline const matrix<float,0,1>& operator()(
                    const std::vector<float>& feature_pixel_values,
                    unsigned long& i
                ) const
                /*!
                    requires
                        - All the index values in splits are less than feature_pixel_values.size()
                        - leaf_values.size() is a power of 2.
                          (i.e. we require a tree with all the levels fully filled out.
                        - leaf_values.size() == splits.size()+1
                          (i.e. there needs to be the right number of leaves given the number of splits in the tree)
                    ensures
                        - runs through the tree and returns the vector at the leaf we end up in.
                        - #i == the selected leaf node index.
                !*/
                {
                    i = 0;
                    while (i < splits.size())
                    {
                        if ((float)feature_pixel_values[splits[i].idx1] - (float)feature_pixel_values[splits[i].idx2] > splits[i].thresh)
                            i = left_child(i);
                        else
                            i = right_child(i);
                    }
                    i = i - splits.size();
                    return leaf_values[i];
                }

                friend void serialize (const regression_tree& item, std::ostream& out)
                {
                    dlib::serialize(item.splits, out);
                    dlib::serialize(item.leaf_values, out);
                }
                friend void deserialize (regression_tree& item, std::istream& in)
                {
                    dlib::deserialize(item.splits, in);
                    dlib::deserialize(item.leaf_values, in);
                }
            };

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

            inline vector<float,2> location (
                const matrix<float,0,1>& shape,
                unsigned long idx
            )
            /*!
                requires
                    - idx < shape.size()/2
                    - shape.size()%2 == 0
                ensures
                    - returns the idx-th point from the shape vector.
            !*/
            {
                return vector<float,2>(shape(idx*2), shape(idx*2+1));
            }

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

            inline unsigned long nearest_shape_point (
                const matrix<float,0,1>& shape,
                const dlib::vector<float,2>& pt
            )
            {
                // find the nearest part of the shape to this pixel
                float best_dist = std::numeric_limits<float>::infinity();
                const unsigned long num_shape_parts = shape.size()/2;
                unsigned long best_idx = 0;
                for (unsigned long j = 0; j < num_shape_parts; ++j)
                {
                    const float dist = length_squared(location(shape,j)-pt);
                    if (dist < best_dist)
                    {
                        best_dist = dist;
                        best_idx = j;
                    }
                }
                return best_idx;
            }

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

            inline void create_shape_relative_encoding (
                const matrix<float,0,1>& shape,
                const std::vector<dlib::vector<float,2> >& pixel_coordinates,
                std::vector<unsigned long>& anchor_idx,
                std::vector<dlib::vector<float,2> >& deltas
            )
            /*!
                requires
                    - shape.size()%2 == 0
                    - shape.size() > 0
                ensures
                    - #anchor_idx.size() == pixel_coordinates.size()
                    - #deltas.size()     == pixel_coordinates.size()
                    - for all valid i:
                        - pixel_coordinates[i] == location(shape,#anchor_idx[i]) + #deltas[i]
            !*/
            {
                anchor_idx.resize(pixel_coordinates.size());
                deltas.resize(pixel_coordinates.size());

                for (unsigned long i = 0; i < pixel_coordinates.size(); ++i)
                {
                    anchor_idx[i] = nearest_shape_point(shape, pixel_coordinates[i]);
                    deltas[i] = pixel_coordinates[i] - location(shape,anchor_idx[i]);
                }
            }

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

            inline point_transform_affine find_tform_between_shapes (
                const matrix<float,0,1>& from_shape,
                const matrix<float,0,1>& to_shape
            )
            {
                DLIB_ASSERT(from_shape.size() == to_shape.size() && (from_shape.size()%2) == 0 && from_shape.size() > 0,"");
                std::vector<vector<float,2> > from_points, to_points;
                const unsigned long num = from_shape.size()/2;
                from_points.reserve(num);
                to_points.reserve(num);
                if (num == 1)
                {
                    // Just use an identity transform if there is only one landmark.
                    return point_transform_affine();
                }

                for (unsigned long i = 0; i < num; ++i)
                {
                    from_points.push_back(location(from_shape,i));
                    to_points.push_back(location(to_shape,i));
                }
                return find_similarity_transform(from_points, to_points);
            }

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

            inline point_transform_affine normalizing_tform (
                const rectangle& rect
            )
            /*!
                ensures
                    - returns a transform that maps rect.tl_corner() to (0,0) and rect.br_corner()
                      to (1,1).
            !*/
            {
                std::vector<vector<float,2> > from_points, to_points;
                from_points.push_back(rect.tl_corner()); to_points.push_back(point(0,0));
                from_points.push_back(rect.tr_corner()); to_points.push_back(point(1,0));
                from_points.push_back(rect.br_corner()); to_points.push_back(point(1,1));
                return find_affine_transform(from_points, to_points);
            }

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

            inline point_transform_affine unnormalizing_tform (
                const rectangle& rect
            )
            /*!
                ensures
                    - returns a transform that maps (0,0) to rect.tl_corner() and (1,1) to
                      rect.br_corner().
            !*/
            {
                std::vector<vector<float,2> > from_points, to_points;
                to_points.push_back(rect.tl_corner()); from_points.push_back(point(0,0));
                to_points.push_back(rect.tr_corner()); from_points.push_back(point(1,0));
                to_points.push_back(rect.br_corner()); from_points.push_back(point(1,1));
                return find_affine_transform(from_points, to_points);
            }

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

            template <typename image_type, typename feature_type>
            void extract_feature_pixel_values (
                const image_type& img_,
                const rectangle& rect,
                const matrix<float,0,1>& current_shape,
                const matrix<float,0,1>& reference_shape,
                const std::vector<unsigned long>& reference_pixel_anchor_idx,
                const std::vector<dlib::vector<float,2> >& reference_pixel_deltas,
                std::vector<feature_type>& feature_pixel_values
            )
            /*!
                requires
                    - image_type == an image object that implements the interface defined in
                      dlib/image_processing/generic_image.h
                    - reference_pixel_anchor_idx.size() == reference_pixel_deltas.size()
                    - current_shape.size() == reference_shape.size()
                    - reference_shape.size()%2 == 0
                    - max(mat(reference_pixel_anchor_idx)) < reference_shape.size()/2
                ensures
                    - #feature_pixel_values.size() == reference_pixel_deltas.size()
                    - for all valid i:
                        - #feature_pixel_values[i] == the value of the pixel in img_ that
                          corresponds to the pixel identified by reference_pixel_anchor_idx[i]
                          and reference_pixel_deltas[i] when the pixel is located relative to
                          current_shape rather than reference_shape.
            !*/
            {
                const matrix<float,2,2> tform = matrix_cast<float>(find_tform_between_shapes(reference_shape, current_shape).get_m());
                const point_transform_affine tform_to_img = unnormalizing_tform(rect);

                const rectangle area = get_rect(img_);

                const_image_view<image_type> img(img_);
                feature_pixel_values.resize(reference_pixel_deltas.size());
                for (unsigned long i = 0; i < feature_pixel_values.size(); ++i)
                {
                    // Compute the point in the current shape corresponding to the i-th pixel and
                    // then map it from the normalized shape space into pixel space.
                    point p = tform_to_img(tform*reference_pixel_deltas[i] + location(current_shape, reference_pixel_anchor_idx[i]));
                    if (area.contains(p))
                        feature_pixel_values[i] = get_pixel_intensity(img[p.y()][p.x()]);
                    else
                        feature_pixel_values[i] = 0;
                }
            }

        } // end namespace impl

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

        class shape_predictor
        {
        public:

            shape_predictor (
            )
            {}

            shape_predictor (
                const matrix<float,0,1>& initial_shape_,
                const std::vector<std::vector<impl::regression_tree> >& forests_,
                const std::vector<std::vector<dlib::vector<float,2> > >& pixel_coordinates
            ) : initial_shape(initial_shape_), forests(forests_)
            /*!
                requires
                    - initial_shape.size()%2 == 0
                    - forests.size() == pixel_coordinates.size() == the number of cascades
                    - for all valid i:
                        - all the index values in forests[i] are less than pixel_coordinates[i].size()
                    - for all valid i and j:
                        - forests[i][j].leaf_values.size() is a power of 2.
                          (i.e. we require a tree with all the levels fully filled out.
                        - forests[i][j].leaf_values.size() == forests[i][j].splits.size()+1
                          (i.e. there need to be the right number of leaves given the number of splits in the tree)
            !*/
            {
                anchor_idx.resize(pixel_coordinates.size());
                deltas.resize(pixel_coordinates.size());
                // Each cascade uses a different set of pixels for its features.  We compute
                // their representations relative to the initial shape now and save it.
                for (unsigned long i = 0; i < pixel_coordinates.size(); ++i)
                    impl::create_shape_relative_encoding(initial_shape, pixel_coordinates[i], anchor_idx[i], deltas[i]);
            }

            unsigned long num_parts (
            ) const
            {
                return initial_shape.size()/2;
            }

            unsigned long num_features (
            ) const
            {
                unsigned long num = 0;
                for (unsigned long iter = 0; iter < forests.size(); ++iter)
                    for (unsigned long i = 0; i < forests[iter].size(); ++i)
                        num += forests[iter][i].num_leaves();
                return num;
            }

            template <typename image_type>
            full_object_detection operator()(
                const image_type& img,
                const rectangle& rect
            ) const
            {
                using namespace impl;
                matrix<float,0,1> current_shape = initial_shape;
                std::vector<float> feature_pixel_values;
                for (unsigned long iter = 0; iter < forests.size(); ++iter)
                {
                    extract_feature_pixel_values(img, rect, current_shape, initial_shape,
                                                 anchor_idx[iter], deltas[iter], feature_pixel_values);
                    unsigned long leaf_idx;
                    // evaluate all the trees at this level of the cascade.
                    for (unsigned long i = 0; i < forests[iter].size(); ++i)
                        current_shape += forests[iter][i](feature_pixel_values, leaf_idx);
                }

                // convert the current_shape into a full_object_detection
                const point_transform_affine tform_to_img = unnormalizing_tform(rect);
                std::vector<point> parts(current_shape.size()/2);
                for (unsigned long i = 0; i < parts.size(); ++i)
                    parts[i] = tform_to_img(location(current_shape, i));
                return full_object_detection(rect, parts);
            }

            template <typename image_type, typename T, typename U>
            full_object_detection operator()(
                const image_type& img,
                const rectangle& rect,
                std::vector<std::pair<T,U> >& feats
            ) const
            {
                feats.clear();
                using namespace impl;
                matrix<float,0,1> current_shape = initial_shape;
                std::vector<float> feature_pixel_values;
                unsigned long feat_offset = 0;
                for (unsigned long iter = 0; iter < forests.size(); ++iter)
                {
                    extract_feature_pixel_values(img, rect, current_shape, initial_shape,
                                                 anchor_idx[iter], deltas[iter], feature_pixel_values);
                    // evaluate all the trees at this level of the cascade.
                    for (unsigned long i = 0; i < forests[iter].size(); ++i)
                    {
                        unsigned long leaf_idx;
                        current_shape += forests[iter][i](feature_pixel_values, leaf_idx);

                        feats.push_back(std::make_pair(feat_offset+leaf_idx, 1));
                        feat_offset += forests[iter][i].num_leaves();
                    }
                }

                // convert the current_shape into a full_object_detection
                const point_transform_affine tform_to_img = unnormalizing_tform(rect);
                std::vector<point> parts(current_shape.size()/2);
                for (unsigned long i = 0; i < parts.size(); ++i)
                    parts[i] = tform_to_img(location(current_shape, i));
                return full_object_detection(rect, parts);
            }

            friend void serialize (const shape_predictor& item, std::ostream& out);

            friend void deserialize (shape_predictor& item, std::istream& in);

        private:
            matrix<float,0,1> initial_shape;
            std::vector<std::vector<impl::regression_tree> > forests;
            std::vector<std::vector<unsigned long> > anchor_idx;
            std::vector<std::vector<dlib::vector<float,2> > > deltas;
        };

        inline void serialize (const shape_predictor& item, std::ostream& out)
        {
            int version = 1;
            dlib::serialize(version, out);
            dlib::serialize(item.initial_shape, out);
            dlib::serialize(item.forests, out);
            dlib::serialize(item.anchor_idx, out);
            dlib::serialize(item.deltas, out);
        }

        inline void deserialize (shape_predictor& item, std::istream& in)
        {
            int version = 0;
            dlib::deserialize(version, in);
            if (version != 1)
                throw serialization_error("Unexpected version found while deserializing dlib::shape_predictor.");
            dlib::deserialize(item.initial_shape, in);
            dlib::deserialize(item.forests, in);
            dlib::deserialize(item.anchor_idx, in);
            dlib::deserialize(item.deltas, in);
        }

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

        template <
            typename image_array
            >
        double test_shape_predictor (
            const shape_predictor& sp,
            const image_array& images,
            const std::vector<std::vector<full_object_detection> >& objects,
            const std::vector<std::vector<double> >& scales
        )
        {
            // make sure requires clause is not broken
    #ifdef ENABLE_ASSERTS
            DLIB_CASSERT( images.size() == objects.size() ,
                "\t double test_shape_predictor()"
                << "\n\t Invalid inputs were given to this function. "
                << "\n\t images.size():  " << images.size()
                << "\n\t objects.size(): " << objects.size()
            );
            for (unsigned long i = 0; i < objects.size(); ++i)
            {
                for (unsigned long j = 0; j < objects[i].size(); ++j)
                {
                    DLIB_CASSERT(objects[i][j].num_parts() == sp.num_parts(),
                        "\t double test_shape_predictor()"
                        << "\n\t Invalid inputs were given to this function. "
                        << "\n\t objects["<<i<<"]["<<j<<"].num_parts(): " << objects[i][j].num_parts()
                        << "\n\t sp.num_parts(): " << sp.num_parts()
                    );
                }
                if (scales.size() != 0)
                {
                    DLIB_CASSERT(objects[i].size() == scales[i].size(),
                        "\t double test_shape_predictor()"
                        << "\n\t Invalid inputs were given to this function. "
                        << "\n\t objects["<<i<<"].size(): " << objects[i].size()
                        << "\n\t scales["<<i<<"].size(): " << scales[i].size()
                    );

                }
            }
    #endif

            running_stats<double> rs;
            for (unsigned long i = 0; i < objects.size(); ++i)
            {
                for (unsigned long j = 0; j < objects[i].size(); ++j)
                {
                    // Just use a scale of 1 (i.e. no scale at all) if the caller didn't supply
                    // any scales.
                    const double scale = scales.size()==0 ? 1 : scales[i][j];

                    full_object_detection det = sp(images[i], objects[i][j].get_rect());

                    for (unsigned long k = 0; k < det.num_parts(); ++k)
                    {
                        if (objects[i][j].part(k) != OBJECT_PART_NOT_PRESENT)
                        {
                            double score = length(det.part(k) - objects[i][j].part(k))/scale;
                            rs.add(score);
                        }
                    }
                }
            }
            return rs.mean();
        }

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

        template <
            typename image_array
            >
        double test_shape_predictor (
            const shape_predictor& sp,
            const image_array& images,
            const std::vector<std::vector<full_object_detection> >& objects
        )
        {
            std::vector<std::vector<double> > no_scales;
            return test_shape_predictor(sp, images, objects, no_scales);
        }

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

    }

    #endif // DLIB_SHAPE_PREDICToR_H_