dlib C++ Library - scan_image_boxes.h

// Copyright (C) 2013 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_SCAN_IMAGE_bOXES_Hh_
#define DLIB_SCAN_IMAGE_bOXES_Hh_
#include "scan_image_boxes_abstract.h"
#include "../matrix.h"
#include "../geometry.h"
#include "../array2d.h"
#include <vector>
#include "../image_processing/full_object_detection.h"
#include "../image_transforms.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
 class default_box_generator
 {
 public:
 template <typename image_type>
 void operator() (
 const image_type& img,
 std::vector<rectangle>& rects
 ) const
 {
 rects.clear();
 find_candidate_object_locations(img, rects);
 }
 };
 inline void serialize(const default_box_generator&, std::ostream& ) {}
 inline void deserialize(default_box_generator&, std::istream& ) {}
// ----------------------------------------------------------------------------------------
 template <
 typename Feature_extractor_type,
 typename Box_generator = default_box_generator
 >
 class scan_image_boxes : noncopyable
 {
 public:
 typedef matrix<double,0,1> feature_vector_type;
 typedef Feature_extractor_type feature_extractor_type;
 typedef Box_generator box_generator;
 scan_image_boxes (
 ); 
 template <
 typename image_type
 >
 void load (
 const image_type& img
 );
 inline bool is_loaded_with_image (
 ) const;
 inline void copy_configuration(
 const feature_extractor_type& fe
 );
 inline void copy_configuration(
 const box_generator& bg
 );
 const box_generator& get_box_generator (
 ) const { return detect_boxes; } 
 const Feature_extractor_type& get_feature_extractor (
 ) const { return feats; }
 inline void copy_configuration (
 const scan_image_boxes& item
 );
 inline long get_num_dimensions (
 ) const;
 unsigned long get_num_spatial_pyramid_levels (
 ) const;
 void set_num_spatial_pyramid_levels (
 unsigned long levels
 );
 void detect (
 const feature_vector_type& w,
 std::vector<std::pair<double, rectangle> >& dets,
 const double thresh
 ) const;
 void get_feature_vector (
 const full_object_detection& obj,
 feature_vector_type& psi
 ) const;
 full_object_detection get_full_object_detection (
 const rectangle& rect,
 const feature_vector_type& w
 ) const;
 const rectangle get_best_matching_rect (
 const rectangle& rect
 ) const;
 /*!
 requires
 - is_loaded_with_image() == true
 !*/
 inline unsigned long get_num_detection_templates (
 ) const { return 1; }
 inline unsigned long get_num_movable_components_per_detection_template (
 ) const { return 0; }
 template <typename T, typename U>
 friend void serialize (
 const scan_image_boxes<T,U>& item,
 std::ostream& out
 );
 template <typename T, typename U>
 friend void deserialize (
 scan_image_boxes<T,U>& item,
 std::istream& in 
 );
 private:
 static bool compare_pair_rect (
 const std::pair<double, rectangle>& a,
 const std::pair<double, rectangle>& b
 )
 {
 return a.first < b.first;
 }
 void test_coordinate_transforms()
 {
 for (long x = -10; x <= 10; x += 10)
 {
 for (long y = -10; y <= 10; y += 10)
 {
 const rectangle rect = centered_rect(x,y,5,6);
 rectangle a;
 a = feats.image_to_feat_space(rect);
 if (a.width() > 10000000 || a.height() > 10000000 )
 {
 DLIB_CASSERT(false, "The image_to_feat_space() routine is outputting rectangles of an implausibly "
 << "\nlarge size. This means there is probably a bug in your feature extractor.");
 }
 a = feats.feat_to_image_space(rect);
 if (a.width() > 10000000 || a.height() > 10000000 )
 {
 DLIB_CASSERT(false, "The feat_to_image_space() routine is outputting rectangles of an implausibly "
 << "\nlarge size. This means there is probably a bug in your feature extractor.");
 }
 }
 }
 
 }
 static void add_grid_rects (
 std::vector<rectangle>& rects,
 const rectangle& object_box,
 unsigned int cells_x,
 unsigned int cells_y
 ) 
 {
 // make sure requires clause is not broken
 DLIB_ASSERT(cells_x > 0 && cells_y > 0,
 "\t void add_grid_rects()"
 << "\n\t The number of cells along a dimension can't be zero. "
 << "\n\t cells_x: " << cells_x
 << "\n\t cells_y: " << cells_y
 );
 const matrix_range_exp<double>& x = linspace(object_box.left(), object_box.right(), cells_x+1);
 const matrix_range_exp<double>& y = linspace(object_box.top(), object_box.bottom(), cells_y+1);
 for (long j = 0; j+1 < y.size(); ++j)
 {
 for (long i = 0; i+1 < x.size(); ++i)
 {
 const dlib::vector<double,2> tl(x(i),y(j));
 const dlib::vector<double,2> br(x(i+1),y(j+1));
 rects.push_back(rectangle(tl,br));
 }
 }
 }
 void get_feature_extraction_regions (
 const rectangle& rect,
 std::vector<rectangle>& regions
 ) const 
 /*!
 ensures
 - #regions.size() is always the same number no matter what the input is. The
 regions also have a consistent ordering.
 - all the output rectangles are contained within rect.
 !*/
 {
 regions.clear();
 for (unsigned int l = 1; l <= num_spatial_pyramid_levels; ++l)
 {
 const int cells = (int)std::pow(2.0, l-1.0);
 add_grid_rects(regions, rect, cells, cells);
 }
 }
 unsigned int get_num_components_per_detection_template(
 ) const
 {
 return (unsigned int)(std::pow(4.0,(double)num_spatial_pyramid_levels)-1)/3;
 }
 feature_extractor_type feats;
 std::vector<rectangle> search_rects;
 bool loaded_with_image;
 unsigned int num_spatial_pyramid_levels;
 box_generator detect_boxes;
 const long box_sizedims;
 const long box_maxsize;
 };
// ----------------------------------------------------------------------------------------
 template <typename T, typename U>
 void serialize (
 const scan_image_boxes<T,U>& item,
 std::ostream& out
 )
 {
 int version = 1;
 serialize(version, out);
 serialize(item.feats, out);
 serialize(item.search_rects, out);
 serialize(item.loaded_with_image, out);
 serialize(item.num_spatial_pyramid_levels, out);
 serialize(item.detect_boxes, out);
 serialize(item.get_num_dimensions(), out);
 }
// ----------------------------------------------------------------------------------------
 template <typename T, typename U>
 void deserialize (
 scan_image_boxes<T,U>& item,
 std::istream& in 
 )
 {
 int version = 0;
 deserialize(version, in);
 if (version != 1)
 throw serialization_error("Unsupported version found when deserializing a scan_image_boxes object.");
 deserialize(item.feats, in);
 deserialize(item.search_rects, in);
 deserialize(item.loaded_with_image, in);
 deserialize(item.num_spatial_pyramid_levels, in);
 deserialize(item.detect_boxes, in);
 // When developing some feature extractor, it's easy to accidentally change its
 // number of dimensions and then try to deserialize data from an older version of
 // your extractor into the current code. This check is here to catch that kind of
 // user error.
 long dims;
 deserialize(dims, in);
 if (item.get_num_dimensions() != dims)
 throw serialization_error("Number of dimensions in serialized scan_image_boxes doesn't match the expected number.");
 }
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
// scan_image_boxes member functions
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
 template <
 typename Feature_extractor_type,
 typename Box_generator
 >
 scan_image_boxes<Feature_extractor_type,Box_generator>::
 scan_image_boxes (
 ) :
 loaded_with_image(false),
 num_spatial_pyramid_levels(3),
 box_sizedims(20),
 box_maxsize(1200)
 {
 }
// ----------------------------------------------------------------------------------------
 template <
 typename Feature_extractor_type,
 typename Box_generator
 >
 template <
 typename image_type
 >
 void scan_image_boxes<Feature_extractor_type,Box_generator>::
 load (
 const image_type& img
 )
 {
 feats.load(img);
 detect_boxes(img, search_rects);
 loaded_with_image = true;
 }
// ----------------------------------------------------------------------------------------
 template <
 typename Feature_extractor_type,
 typename Box_generator
 >
 bool scan_image_boxes<Feature_extractor_type,Box_generator>::
 is_loaded_with_image (
 ) const
 {
 return loaded_with_image;
 }
// ----------------------------------------------------------------------------------------
 template <
 typename Feature_extractor_type,
 typename Box_generator
 >
 void scan_image_boxes<Feature_extractor_type,Box_generator>::
 copy_configuration(
 const feature_extractor_type& fe
 )
 {
 test_coordinate_transforms();
 feats.copy_configuration(fe);
 }
// ----------------------------------------------------------------------------------------
 template <
 typename Feature_extractor_type,
 typename Box_generator
 >
 void scan_image_boxes<Feature_extractor_type,Box_generator>::
 copy_configuration(
 const box_generator& bg 
 )
 {
 detect_boxes = bg;
 }
// ----------------------------------------------------------------------------------------
 template <
 typename Feature_extractor_type,
 typename Box_generator
 >
 void scan_image_boxes<Feature_extractor_type,Box_generator>::
 copy_configuration (
 const scan_image_boxes& item
 )
 {
 feats.copy_configuration(item.feats);
 detect_boxes = item.detect_boxes;
 num_spatial_pyramid_levels = item.num_spatial_pyramid_levels;
 }
// ----------------------------------------------------------------------------------------
 template <
 typename Feature_extractor_type,
 typename Box_generator
 >
 unsigned long scan_image_boxes<Feature_extractor_type,Box_generator>::
 get_num_spatial_pyramid_levels (
 ) const
 {
 return num_spatial_pyramid_levels;
 }
// ----------------------------------------------------------------------------------------
 template <
 typename Feature_extractor_type,
 typename Box_generator
 >
 void scan_image_boxes<Feature_extractor_type,Box_generator>::
 set_num_spatial_pyramid_levels (
 unsigned long levels
 ) 
 {
 // make sure requires clause is not broken
 DLIB_ASSERT(levels > 0, 
 "\t void scan_image_boxes::set_num_spatial_pyramid_levels()"
 << "\n\t Invalid inputs were given to this function "
 << "\n\t levels: " << levels 
 << "\n\t this: " << this
 );
 
 num_spatial_pyramid_levels = levels;
 }
// ----------------------------------------------------------------------------------------
 template <
 typename Feature_extractor_type,
 typename Box_generator
 >
 long scan_image_boxes<Feature_extractor_type,Box_generator>::
 get_num_dimensions (
 ) const
 {
 return feats.get_num_dimensions()*get_num_components_per_detection_template() + box_sizedims*2;
 }
// ----------------------------------------------------------------------------------------
 template <
 typename Feature_extractor_type,
 typename Box_generator
 >
 void scan_image_boxes<Feature_extractor_type,Box_generator>::
 detect (
 const feature_vector_type& w,
 std::vector<std::pair<double, rectangle> >& dets,
 const double thresh
 ) const
 {
 // make sure requires clause is not broken
 DLIB_ASSERT(is_loaded_with_image() &&
 w.size() >= get_num_dimensions(), 
 "\t void scan_image_boxes::detect()"
 << "\n\t Invalid inputs were given to this function "
 << "\n\t is_loaded_with_image(): " << is_loaded_with_image()
 << "\n\t w.size(): " << w.size()
 << "\n\t get_num_dimensions(): " << get_num_dimensions()
 << "\n\t this: " << this
 );
 
 dets.clear();
 array<integral_image_generic<double> > saliency_images(get_num_components_per_detection_template());
 array2d<double> temp_img(feats.nr(), feats.nc());
 // build saliency images 
 for (unsigned long i = 0; i < saliency_images.size(); ++i)
 {
 const unsigned long offset = 2*box_sizedims + feats.get_num_dimensions()*i;
 // make the basic saliency image for the i-th feature extraction region
 for (long r = 0; r < feats.nr(); ++r)
 {
 for (long c = 0; c < feats.nc(); ++c)
 {
 const typename feature_extractor_type::descriptor_type& descriptor = feats(r,c);
 double sum = 0;
 for (unsigned long k = 0; k < descriptor.size(); ++k)
 {
 sum += w(descriptor[k].first + offset)*descriptor[k].second;
 }
 temp_img[r][c] = sum;
 }
 }
 // now convert base saliency image into final integral image
 saliency_images[i].load(temp_img);
 }
 // now search the saliency images
 std::vector<rectangle> regions;
 const rectangle bounds = get_rect(feats);
 for (unsigned long i = 0; i < search_rects.size(); ++i)
 {
 const rectangle rect = feats.image_to_feat_space(search_rects[i]).intersect(bounds);
 if (rect.is_empty())
 continue;
 get_feature_extraction_regions(rect, regions);
 double score = 0;
 for (unsigned long k = 0; k < regions.size(); ++k)
 {
 score += saliency_images[k].get_sum_of_area(regions[k]);
 }
 const double width = search_rects[i].width();
 const double height = search_rects[i].height();
 score += dot(linpiece(width, linspace(0, box_maxsize, box_sizedims+1)), rowm(w, range(0,box_sizedims-1)));
 score += dot(linpiece(height, linspace(0, box_maxsize, box_sizedims+1)), rowm(w, range(box_sizedims,2*box_sizedims-1)));
 if (score >= thresh)
 {
 dets.push_back(std::make_pair(score, search_rects[i]));
 }
 }
 std::sort(dets.rbegin(), dets.rend(), compare_pair_rect);
 }
// ----------------------------------------------------------------------------------------
 template <
 typename Feature_extractor_type,
 typename Box_generator
 >
 const rectangle scan_image_boxes<Feature_extractor_type,Box_generator>::
 get_best_matching_rect (
 const rectangle& rect
 ) const
 {
 // make sure requires clause is not broken
 DLIB_ASSERT(is_loaded_with_image(),
 "\t const rectangle scan_image_boxes::get_best_matching_rect()"
 << "\n\t Invalid inputs were given to this function "
 << "\n\t is_loaded_with_image(): " << is_loaded_with_image()
 << "\n\t this: " << this
 );
 double best_score = -1;
 rectangle best_rect;
 for (unsigned long i = 0; i < search_rects.size(); ++i)
 {
 const double score = (rect.intersect(search_rects[i])).area()/(double)(rect+search_rects[i]).area();
 if (score > best_score)
 {
 best_score = score;
 best_rect = search_rects[i];
 }
 }
 return best_rect;
 }
// ----------------------------------------------------------------------------------------
 template <
 typename Feature_extractor_type,
 typename Box_generator
 >
 full_object_detection scan_image_boxes<Feature_extractor_type,Box_generator>::
 get_full_object_detection (
 const rectangle& rect,
 const feature_vector_type& /*w*/
 ) const
 {
 return full_object_detection(rect);
 }
// ----------------------------------------------------------------------------------------
 template <
 typename Feature_extractor_type,
 typename Box_generator
 >
 void scan_image_boxes<Feature_extractor_type,Box_generator>::
 get_feature_vector (
 const full_object_detection& obj,
 feature_vector_type& psi
 ) const
 {
 // make sure requires clause is not broken
 DLIB_ASSERT(is_loaded_with_image() &&
 psi.size() >= get_num_dimensions() &&
 obj.num_parts() == 0,
 "\t void scan_image_boxes::get_feature_vector()"
 << "\n\t Invalid inputs were given to this function "
 << "\n\t is_loaded_with_image(): " << is_loaded_with_image()
 << "\n\t psi.size(): " << psi.size()
 << "\n\t get_num_dimensions(): " << get_num_dimensions()
 << "\n\t obj.num_parts(): " << obj.num_parts()
 << "\n\t this: " << this
 );
 const rectangle best_rect = get_best_matching_rect(obj.get_rect());
 const rectangle mapped_rect = feats.image_to_feat_space(best_rect).intersect(get_rect(feats));
 if (mapped_rect.is_empty())
 return;
 std::vector<rectangle> regions;
 get_feature_extraction_regions(mapped_rect, regions);
 // pull features out of all the boxes in regions.
 for (unsigned long j = 0; j < regions.size(); ++j)
 {
 const rectangle rect = regions[j];
 const unsigned long template_region_id = j;
 const unsigned long offset = box_sizedims*2 + feats.get_num_dimensions()*template_region_id;
 for (long r = rect.top(); r <= rect.bottom(); ++r)
 {
 for (long c = rect.left(); c <= rect.right(); ++c)
 {
 const typename feature_extractor_type::descriptor_type& descriptor = feats(r,c);
 for (unsigned long k = 0; k < descriptor.size(); ++k)
 {
 psi(descriptor[k].first + offset) += descriptor[k].second;
 }
 }
 }
 }
 const double width = best_rect.width();
 const double height = best_rect.height();
 set_rowm(psi, range(0,box_sizedims-1)) += linpiece(width, linspace(0, box_maxsize, box_sizedims+1));
 set_rowm(psi, range(box_sizedims,box_sizedims*2-1)) += linpiece(height, linspace(0, box_maxsize, box_sizedims+1));
 }
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_SCAN_IMAGE_bOXES_Hh_