dlib C++ Library - fine_hog_image.h

    // Copyright (C) 2012  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #ifndef DLIB_FINE_HOG_IMaGE_Hh_
    #define DLIB_FINE_HOG_IMaGE_Hh_

    #include "fine_hog_image_abstract.h"
    #include "../array2d.h"
    #include "../matrix.h"
    #include "hog.h"

    namespace dlib
    {
        template <
            unsigned long cell_size_,
            unsigned long block_size_,
            unsigned long pixel_stride_,
            unsigned char num_orientation_bins_,
            int           gradient_type_
            >
        class fine_hog_image : noncopyable
        {
            COMPILE_TIME_ASSERT(cell_size_ > 1);
            COMPILE_TIME_ASSERT(block_size_ > 0);
            COMPILE_TIME_ASSERT(pixel_stride_ > 0);
            COMPILE_TIME_ASSERT(num_orientation_bins_ > 0);

            COMPILE_TIME_ASSERT( gradient_type_ == hog_signed_gradient ||
                                 gradient_type_ == hog_unsigned_gradient);

        public:

            const static unsigned long cell_size = cell_size_;
            const static unsigned long block_size = block_size_;
            const static unsigned long pixel_stride = pixel_stride_;
            const static unsigned long num_orientation_bins = num_orientation_bins_;
            const static int           gradient_type = gradient_type_;

            const static long min_size = cell_size*block_size+2;

            typedef matrix<double, block_size*block_size*num_orientation_bins, 1> descriptor_type;

            fine_hog_image (
            ) :
                num_block_rows(0),
                num_block_cols(0)
            {}

            void clear (
            )
            {
                num_block_rows = 0;
                num_block_cols = 0;
                hist_counts.clear();
            }

            void copy_configuration (
                const fine_hog_image&
            ){}

            template <
                typename image_type
                >
            inline void load (
                const image_type& img
            )
            {
                COMPILE_TIME_ASSERT( pixel_traits<typename image_traits<image_type>::pixel_type>::has_alpha == false );
                load_impl(mat(img));
            }

            inline void unload(
            ) { clear(); }

            inline size_t size (
            ) const { return static_cast<size_t>(nr()*nc()); }

            inline long nr (
            ) const { return num_block_rows; }

            inline long nc (
            ) const { return num_block_cols; }

            long get_num_dimensions (
            ) const
            {
                return block_size*block_size*num_orientation_bins;
            }

            inline const descriptor_type& operator() (
                long row,
                long col
            ) const
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( 0 <= row && row < nr() &&
                             0 <= col && col < nc(),
                    "\t descriptor_type fine_hog_image::operator()()"
                    << "\n\t invalid row or col argument"
                    << "\n\t row:  " << row
                    << "\n\t col:  " << col
                    << "\n\t nr(): " << nr()
                    << "\n\t nc(): " << nc()
                    << "\n\t this: " << this
                    );

                row *= pixel_stride;
                col *= pixel_stride;

                des = 0;
                unsigned long off = 0;
                for (unsigned long r = 0; r < block_size; ++r)
                {
                    for (unsigned long c = 0; c < block_size; ++c)
                    {
                        for (unsigned long rr = 0; rr < cell_size; ++rr)
                        {
                            for (unsigned long cc = 0; cc < cell_size; ++cc)
                            {
                                const histogram_count& hist = hist_counts[row + r*cell_size + rr][col + c*cell_size + cc];
                                des(off + hist.quantized_angle_lower) += hist.lower_strength;
                                des(off + hist.quantized_angle_upper) += hist.upper_strength;
                            }
                        }

                        off += num_orientation_bins;
                    }
                }

                des /= length(des) + 1e-8;

                return des;
            }

            const rectangle get_block_rect (
                long row,
                long col
            ) const
            {
                row *= pixel_stride;
                col *= pixel_stride;

                // do this to account for the 1 pixel padding we use all around the image
                ++row;
                ++col;

                return rectangle(col, row, col+cell_size*block_size-1, row+cell_size*block_size-1);
            }

            const point image_to_feat_space (
                const point& p
            ) const
            {
                const long border_size = 1 + cell_size*block_size/2;
                return (p-point(border_size,border_size))/(long)pixel_stride;
            }

            const rectangle image_to_feat_space (
                const rectangle& rect
            ) const
            {
                return rectangle(image_to_feat_space(rect.tl_corner()), image_to_feat_space(rect.br_corner()));
            }

            const point feat_to_image_space (
                const point& p
            ) const
            {
                const long border_size = 1 + cell_size*block_size/2;
                return p*(long)pixel_stride + point(border_size,border_size);
            }

            const rectangle feat_to_image_space (
                const rectangle& rect
            ) const
            {
                return rectangle(feat_to_image_space(rect.tl_corner()), feat_to_image_space(rect.br_corner()));
            }

            // these _PRIVATE_ functions are only here as a workaround for a bug in visual studio 2005.
            void _PRIVATE_serialize (std::ostream& out) const
            {
                // serialize hist_counts
                serialize(hist_counts.nc(),out);
                serialize(hist_counts.nr(),out);
                hist_counts.reset();
                while (hist_counts.move_next())
                    hist_counts.element().serialize(out);
                hist_counts.reset();

                serialize(num_block_rows, out);
                serialize(num_block_cols, out);
            }

            void _PRIVATE_deserialize (std::istream& in )
            {
                // deserialize item.hist_counts
                long nc, nr;
                deserialize(nc,in);
                deserialize(nr,in);
                hist_counts.set_size(nr,nc);
                while (hist_counts.move_next())
                    hist_counts.element().deserialize(in);
                hist_counts.reset();

                deserialize(num_block_rows, in);
                deserialize(num_block_cols, in);
            }

        private:

            template <
                typename image_type
                >
            void load_impl (
                const image_type& img
            )
            {
                // Note that we keep a border of 1 pixel all around the image so that we don't have
                // to worry about running outside the image when computing the horizontal and vertical
                // gradients.

                // check if the window is just too small
                if (img.nr() < min_size || img.nc() < min_size)
                {
                    // If the image is smaller than our windows then there aren't any descriptors at all!
                    num_block_rows = 0;
                    num_block_cols = 0;
                    hist_counts.clear();
                    return;
                }

                hist_counts.set_size(img.nr()-2, img.nc()-2);

                for (long r = 0; r < hist_counts.nr(); ++r)
                {
                    for (long c = 0; c < hist_counts.nc(); ++c)
                    {
                        unsigned long left;
                        unsigned long right;
                        unsigned long top;
                        unsigned long bottom;

                        assign_pixel(left,   img(r+1,c));
                        assign_pixel(right,  img(r+1,c+2));
                        assign_pixel(top,    img(r  ,c+1));
                        assign_pixel(bottom, img(r+2,c+1));

                        double grad_x = (long)right-(long)left;
                        double grad_y = (long)top-(long)bottom;

                        // obtain the angle of the gradient.  Make sure it is scaled between 0 and 1.
                        double angle = std::max(0.0, std::atan2(grad_y, grad_x)/pi + 1)/2;

                        if (gradient_type == hog_unsigned_gradient)
                        {
                            angle *= 2;
                            if (angle >= 1)
                                angle -= 1;
                        }

                        // now scale angle to between 0 and num_orientation_bins
                        angle *= num_orientation_bins;

                        const double strength = std::sqrt(grad_y*grad_y + grad_x*grad_x);

                        unsigned char quantized_angle_lower = static_cast<unsigned char>(std::floor(angle));
                        unsigned char quantized_angle_upper = static_cast<unsigned char>(std::ceil(angle));

                        quantized_angle_lower %= num_orientation_bins;
                        quantized_angle_upper %= num_orientation_bins;

                        const double angle_split = (angle-std::floor(angle));
                        const double upper_strength = angle_split*strength;
                        const double lower_strength = (1-angle_split)*strength;

                        // Stick into gradient counts.  Note that we linearly interpolate between neighboring
                        // histogram buckets.
                        hist_counts[r][c].quantized_angle_lower = quantized_angle_lower;
                        hist_counts[r][c].quantized_angle_upper = quantized_angle_upper;
                        hist_counts[r][c].lower_strength = lower_strength;
                        hist_counts[r][c].upper_strength = upper_strength;

                    }
                }

                // Now figure out how many feature extraction blocks we should have.
                num_block_rows = (hist_counts.nr() - block_size*cell_size + 1)/(long)pixel_stride;
                num_block_cols = (hist_counts.nc() - block_size*cell_size + 1)/(long)pixel_stride;

            }

            struct histogram_count
            {
                unsigned char quantized_angle_lower;
                unsigned char quantized_angle_upper;
                float lower_strength;
                float upper_strength;

                void serialize(std::ostream& out) const
                {
                    dlib::serialize(quantized_angle_lower, out);
                    dlib::serialize(quantized_angle_upper, out);
                    dlib::serialize(lower_strength, out);
                    dlib::serialize(upper_strength, out);
                }
                void deserialize(std::istream& in)
                {
                    dlib::deserialize(quantized_angle_lower, in);
                    dlib::deserialize(quantized_angle_upper, in);
                    dlib::deserialize(lower_strength, in);
                    dlib::deserialize(upper_strength, in);
                }
            };

            array2d<histogram_count> hist_counts;

            mutable descriptor_type des;

            long num_block_rows;
            long num_block_cols;

        };

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

        template <
            unsigned long T1,
            unsigned long T2,
            unsigned long T3,
            unsigned char T4,
            int           T5
            >
        void serialize (
            const fine_hog_image<T1,T2,T3,T4,T5>& item,
            std::ostream& out
        )
        {
            item._PRIVATE_serialize(out);
        }

        template <
            unsigned long T1,
            unsigned long T2,
            unsigned long T3,
            unsigned char T4,
            int           T5
            >
        void deserialize (
            fine_hog_image<T1,T2,T3,T4,T5>& item,
            std::istream& in
        )
        {
            item._PRIVATE_deserialize(in);
        }

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

    }

    #endif // DLIB_FINE_HOG_IMaGE_Hh_