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diff --git a/ml/dlib/dlib/image_processing/remove_unobtainable_rectangles.h b/ml/dlib/dlib/image_processing/remove_unobtainable_rectangles.h
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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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