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// Copyright (C) 2013 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_REMOVE_UnOBTAINABLE_RECTANGLES_Hh_
#define DLIB_REMOVE_UnOBTAINABLE_RECTANGLES_Hh_
#include "remove_unobtainable_rectangles_abstract.h"
#include "scan_image_pyramid.h"
#include "scan_image_boxes.h"
#include "scan_image_custom.h"
#include "scan_fhog_pyramid.h"
#include "../svm/structural_object_detection_trainer.h"
#include "../geometry.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
namespace impl
{
inline bool matches_rect (
const std::vector<rectangle>& rects,
const rectangle& rect,
const double eps
)
{
for (unsigned long i = 0; i < rects.size(); ++i)
{
const double score = (rect.intersect(rects[i])).area()/(double)(rect+rects[i]).area();
if (score > eps)
return true;
}
return false;
}
inline rectangle get_best_matching_rect (
const std::vector<rectangle>& rects,
const rectangle& rect
)
{
double best_score = -1;
rectangle best_rect;
for (unsigned long i = 0; i < rects.size(); ++i)
{
const double score = (rect.intersect(rects[i])).area()/(double)(rect+rects[i]).area();
if (score > best_score)
{
best_score = score;
best_rect = rects[i];
}
}
return best_rect;
}
// ------------------------------------------------------------------------------------
template <
typename image_array_type,
typename image_scanner_type
>
std::vector<std::vector<rectangle> > pyramid_remove_unobtainable_rectangles (
const structural_object_detection_trainer<image_scanner_type>& trainer,
const image_array_type& images,
std::vector<std::vector<rectangle> >& object_locations
)
{
using namespace dlib::impl;
// make sure requires clause is not broken
DLIB_ASSERT(images.size() == object_locations.size(),
"\t std::vector<std::vector<rectangle>> remove_unobtainable_rectangles()"
<< "\n\t Invalid inputs were given to this function."
);
std::vector<std::vector<rectangle> > rejects(images.size());
// If the trainer is setup to automatically fit the overlap tester to the data then
// we should use the loosest possible overlap tester here. Otherwise we should use
// the tester the trainer will use.
test_box_overlap boxes_overlap(0.9999999,1);
if (!trainer.auto_set_overlap_tester())
boxes_overlap = trainer.get_overlap_tester();
for (unsigned long k = 0; k < images.size(); ++k)
{
std::vector<rectangle> objs = object_locations[k];
// First remove things that don't have any matches with the candidate object
// locations.
std::vector<rectangle> good_rects;
for (unsigned long j = 0; j < objs.size(); ++j)
{
const rectangle rect = trainer.get_scanner().get_best_matching_rect(objs[j]);
const double score = (objs[j].intersect(rect)).area()/(double)(objs[j] + rect).area();
if (score > trainer.get_match_eps())
good_rects.push_back(objs[j]);
else
rejects[k].push_back(objs[j]);
}
object_locations[k] = good_rects;
// Remap these rectangles to the ones that can come out of the scanner. That
// way when we compare them to each other in the following loop we will know if
// any distinct truth rectangles get mapped to overlapping boxes.
objs.resize(good_rects.size());
for (unsigned long i = 0; i < good_rects.size(); ++i)
objs[i] = trainer.get_scanner().get_best_matching_rect(good_rects[i]);
good_rects.clear();
// now check for truth rects that are too close together.
for (unsigned long i = 0; i < objs.size(); ++i)
{
// check if objs[i] hits another box
bool hit_box = false;
for (unsigned long j = i+1; j < objs.size(); ++j)
{
if (boxes_overlap(objs[i], objs[j]))
{
hit_box = true;
break;
}
}
if (hit_box)
rejects[k].push_back(object_locations[k][i]);
else
good_rects.push_back(object_locations[k][i]);
}
object_locations[k] = good_rects;
}
return rejects;
}
}
// ----------------------------------------------------------------------------------------
template <
typename image_array_type,
typename Pyramid_type,
typename Feature_extractor_type
>
std::vector<std::vector<rectangle> > remove_unobtainable_rectangles (
const structural_object_detection_trainer<scan_image_pyramid<Pyramid_type, Feature_extractor_type> >& trainer,
const image_array_type& images,
std::vector<std::vector<rectangle> >& object_locations
)
{
return impl::pyramid_remove_unobtainable_rectangles(trainer, images, object_locations);
}
// ----------------------------------------------------------------------------------------
template <
typename image_array_type,
typename Pyramid_type,
typename Feature_extractor_type
>
std::vector<std::vector<rectangle> > remove_unobtainable_rectangles (
const structural_object_detection_trainer<scan_fhog_pyramid<Pyramid_type,Feature_extractor_type> >& trainer,
const image_array_type& images,
std::vector<std::vector<rectangle> >& object_locations
)
{
return impl::pyramid_remove_unobtainable_rectangles(trainer, images, object_locations);
}
// ----------------------------------------------------------------------------------------
namespace impl
{
template <
typename image_array_type,
typename scanner_type,
typename get_boxes_functor
>
std::vector<std::vector<rectangle> > remove_unobtainable_rectangles (
get_boxes_functor& bg,
const structural_object_detection_trainer<scanner_type>& trainer,
const image_array_type& images,
std::vector<std::vector<rectangle> >& object_locations
)
{
using namespace dlib::impl;
// make sure requires clause is not broken
DLIB_ASSERT(images.size() == object_locations.size(),
"\t std::vector<std::vector<rectangle>> remove_unobtainable_rectangles()"
<< "\n\t Invalid inputs were given to this function."
);
std::vector<rectangle> rects;
std::vector<std::vector<rectangle> > rejects(images.size());
// If the trainer is setup to automatically fit the overlap tester to the data then
// we should use the loosest possible overlap tester here. Otherwise we should use
// the tester the trainer will use.
test_box_overlap boxes_overlap(0.9999999,1);
if (!trainer.auto_set_overlap_tester())
boxes_overlap = trainer.get_overlap_tester();
for (unsigned long k = 0; k < images.size(); ++k)
{
std::vector<rectangle> objs = object_locations[k];
// Don't even bother computing the candidate rectangles if there aren't any
// object locations for this image since there isn't anything to do anyway.
if (objs.size() == 0)
continue;
bg(images[k], rects);
// First remove things that don't have any matches with the candidate object
// locations.
std::vector<rectangle> good_rects;
for (unsigned long j = 0; j < objs.size(); ++j)
{
if (matches_rect(rects, objs[j], trainer.get_match_eps()))
good_rects.push_back(objs[j]);
else
rejects[k].push_back(objs[j]);
}
object_locations[k] = good_rects;
// Remap these rectangles to the ones that can come out of the scanner. That
// way when we compare them to each other in the following loop we will know if
// any distinct truth rectangles get mapped to overlapping boxes.
objs.resize(good_rects.size());
for (unsigned long i = 0; i < good_rects.size(); ++i)
objs[i] = get_best_matching_rect(rects, good_rects[i]);
good_rects.clear();
// now check for truth rects that are too close together.
for (unsigned long i = 0; i < objs.size(); ++i)
{
// check if objs[i] hits another box
bool hit_box = false;
for (unsigned long j = i+1; j < objs.size(); ++j)
{
if (boxes_overlap(objs[i], objs[j]))
{
hit_box = true;
break;
}
}
if (hit_box)
rejects[k].push_back(object_locations[k][i]);
else
good_rects.push_back(object_locations[k][i]);
}
object_locations[k] = good_rects;
}
return rejects;
}
// ----------------------------------------------------------------------------------------
template <typename T>
struct load_to_functor
{
load_to_functor(T& obj_) : obj(obj_) {}
T& obj;
template <typename U, typename V>
void operator()(const U& u, V& v)
{
obj.load(u,v);
}
};
}
// ----------------------------------------------------------------------------------------
template <
typename image_array_type,
typename feature_extractor,
typename box_generator
>
std::vector<std::vector<rectangle> > remove_unobtainable_rectangles (
const structural_object_detection_trainer<scan_image_boxes<feature_extractor, box_generator> >& trainer,
const image_array_type& images,
std::vector<std::vector<rectangle> >& object_locations
)
{
box_generator bg = trainer.get_scanner().get_box_generator();
return impl::remove_unobtainable_rectangles(bg, trainer, images, object_locations);
}
// ----------------------------------------------------------------------------------------
template <
typename image_array_type,
typename feature_extractor
>
std::vector<std::vector<rectangle> > remove_unobtainable_rectangles (
const structural_object_detection_trainer<scan_image_custom<feature_extractor> >& trainer,
const image_array_type& images,
std::vector<std::vector<rectangle> >& object_locations
)
{
feature_extractor fe;
fe.copy_configuration(trainer.get_scanner().get_feature_extractor());
impl::load_to_functor<feature_extractor> bg(fe);
return impl::remove_unobtainable_rectangles(bg, trainer, images, object_locations);
}
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_REMOVE_UnOBTAINABLE_RECTANGLES_Hh_
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