// Copyright (C) 2013 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_SCAN_IMAGE_CuSTOM_Hh_
#define DLIB_SCAN_IMAGE_CuSTOM_Hh_
#include "scan_image_custom_abstract.h"
#include "../matrix.h"
#include "../geometry.h"
#include <vector>
#include "../image_processing/full_object_detection.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename Feature_extractor_type
>
class scan_image_custom : noncopyable
{
public:
typedef matrix<double,0,1> feature_vector_type;
typedef Feature_extractor_type feature_extractor_type;
scan_image_custom (
);
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
);
const Feature_extractor_type& get_feature_extractor (
) const { return feats; }
inline void copy_configuration (
const scan_image_custom& item
);
inline long get_num_dimensions (
) const;
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;
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>
friend void serialize (
const scan_image_custom<T>& item,
std::ostream& out
);
template <typename T>
friend void deserialize (
scan_image_custom<T>& 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;
}
DLIB_MAKE_HAS_MEMBER_FUNCTION_TEST(
has_compute_object_score,
double,
compute_object_score,
( const matrix<double,0,1>& w, const rectangle& obj) const
);
template <typename fe_type>
typename enable_if<has_compute_object_score<fe_type> >::type compute_all_rect_scores (
const fe_type& feats,
const feature_vector_type& w,
std::vector<std::pair<double, rectangle> >& dets,
const double thresh
) const
{
for (unsigned long i = 0; i < search_rects.size(); ++i)
{
const double score = feats.compute_object_score(w, search_rects[i]);
if (score >= thresh)
{
dets.push_back(std::make_pair(score, search_rects[i]));
}
}
}
template <typename fe_type>
typename disable_if<has_compute_object_score<fe_type> >::type compute_all_rect_scores (
const fe_type& feats,
const feature_vector_type& w,
std::vector<std::pair<double, rectangle> >& dets,
const double thresh
) const
{
matrix<double,0,1> psi(w.size());
psi = 0;
double prev_dot = 0;
for (unsigned long i = 0; i < search_rects.size(); ++i)
{
// Reset these back to zero every so often to avoid the accumulation of
// rounding error. Note that the only reason we do this loop in this
// complex way is to avoid needing to zero the psi vector every iteration.
if ((i%500) == 499)
{
psi = 0;
prev_dot = 0;
}
feats.get_feature_vector(search_rects[i], psi);
const double cur_dot = dot(psi, w);
const double score = cur_dot - prev_dot;
if (score >= thresh)
{
dets.push_back(std::make_pair(score, search_rects[i]));
}
prev_dot = cur_dot;
}
}
feature_extractor_type feats;
std::vector<rectangle> search_rects;
bool loaded_with_image;
};
// ----------------------------------------------------------------------------------------
template <typename T>
void serialize (
const scan_image_custom<T>& 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.get_num_dimensions(), out);
}
// ----------------------------------------------------------------------------------------
template <typename T>
void deserialize (
scan_image_custom<T>& item,
std::istream& in
)
{
int version = 0;
deserialize(version, in);
if (version != 1)
throw serialization_error("Unsupported version found when deserializing a scan_image_custom object.");
deserialize(item.feats, in);
deserialize(item.search_rects, in);
deserialize(item.loaded_with_image, 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_custom doesn't match the expected number.");
}
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
// scan_image_custom member functions
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
template <
typename Feature_extractor_type
>
scan_image_custom<Feature_extractor_type>::
scan_image_custom (
) :
loaded_with_image(false)
{
}
// ----------------------------------------------------------------------------------------
template <
typename Feature_extractor_type
>
template <
typename image_type
>
void scan_image_custom<Feature_extractor_type>::
load (
const image_type& img
)
{
feats.load(img, search_rects);
loaded_with_image = true;
}
// ----------------------------------------------------------------------------------------
template <
typename Feature_extractor_type
>
bool scan_image_custom<Feature_extractor_type>::
is_loaded_with_image (
) const
{
return loaded_with_image;
}
// ----------------------------------------------------------------------------------------
template <
typename Feature_extractor_type
>
void scan_image_custom<Feature_extractor_type>::
copy_configuration(
const feature_extractor_type& fe
)
{
feats.copy_configuration(fe);
}
// ----------------------------------------------------------------------------------------
template <
typename Feature_extractor_type
>
void scan_image_custom<Feature_extractor_type>::
copy_configuration (
const scan_image_custom& item
)
{
feats.copy_configuration(item.feats);
}
// ----------------------------------------------------------------------------------------
template <
typename Feature_extractor_type
>
long scan_image_custom<Feature_extractor_type>::
get_num_dimensions (
) const
{
return feats.get_num_dimensions();
}
// ----------------------------------------------------------------------------------------
template <
typename Feature_extractor_type
>
void scan_image_custom<Feature_extractor_type>::
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_custom::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();
compute_all_rect_scores(feats, w,dets,thresh);
std::sort(dets.rbegin(), dets.rend(), compare_pair_rect);
}
// ----------------------------------------------------------------------------------------
template <
typename Feature_extractor_type
>
const rectangle scan_image_custom<Feature_extractor_type>::
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_custom::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
>
full_object_detection scan_image_custom<Feature_extractor_type>::
get_full_object_detection (
const rectangle& rect,
const feature_vector_type& /*w*/
) const
{
return full_object_detection(rect);
}
// ----------------------------------------------------------------------------------------
template <
typename Feature_extractor_type
>
void scan_image_custom<Feature_extractor_type>::
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_custom::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
);
feats.get_feature_vector(get_best_matching_rect(obj.get_rect()), psi);
}
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_SCAN_IMAGE_CuSTOM_Hh_