// Copyright (C) 2011 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_CROSS_VALIDATE_OBJECT_DETECTION_TRaINER_Hh_
#define DLIB_CROSS_VALIDATE_OBJECT_DETECTION_TRaINER_Hh_
#include "cross_validate_object_detection_trainer_abstract.h"
#include <vector>
#include "../matrix.h"
#include "svm.h"
#include "../geometry.h"
#include "../image_processing/full_object_detection.h"
#include "../image_processing/box_overlap_testing.h"
#include "../statistics.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
namespace impl
{
inline unsigned long number_of_truth_hits (
const std::vector<full_object_detection>& truth_boxes,
const std::vector<rectangle>& ignore,
const std::vector<std::pair<double,rectangle> >& boxes,
const test_box_overlap& overlap_tester,
std::vector<std::pair<double,bool> >& all_dets,
unsigned long& missing_detections,
const test_box_overlap& overlaps_ignore_tester
)
/*!
ensures
- returns the number of elements in truth_boxes which are overlapped by an
element of boxes. In this context, two boxes, A and B, overlap if and only if
overlap_tester(A,B) == true.
- No element of boxes is allowed to account for more than one element of truth_boxes.
- The returned number is in the range [0,truth_boxes.size()]
- Adds the score for each box from boxes into all_dets and labels each with
a bool indicating if it hit a truth box. Note that we skip boxes that
don't hit any truth boxes and match an ignore box.
- Adds the number of truth boxes which didn't have any hits into
missing_detections.
!*/
{
if (boxes.size() == 0)
{
missing_detections += truth_boxes.size();
return 0;
}
unsigned long count = 0;
std::vector<bool> used(boxes.size(),false);
for (unsigned long i = 0; i < truth_boxes.size(); ++i)
{
bool found_match = false;
// Find the first box that hits truth_boxes[i]
for (unsigned long j = 0; j < boxes.size(); ++j)
{
if (used[j])
continue;
if (overlap_tester(truth_boxes[i].get_rect(), boxes[j].second))
{
used[j] = true;
++count;
found_match = true;
break;
}
}
if (!found_match)
++missing_detections;
}
for (unsigned long i = 0; i < boxes.size(); ++i)
{
// only out put boxes if they match a truth box or are not ignored.
if (used[i] || !overlaps_any_box(overlaps_ignore_tester, ignore, boxes[i].second))
{
all_dets.push_back(std::make_pair(boxes[i].first, used[i]));
}
}
return count;
}
inline unsigned long number_of_truth_hits (
const std::vector<full_object_detection>& truth_boxes,
const std::vector<rectangle>& ignore,
const std::vector<std::pair<double,rectangle> >& boxes,
const test_box_overlap& overlap_tester,
std::vector<std::pair<double,bool> >& all_dets,
unsigned long& missing_detections
)
{
return number_of_truth_hits(truth_boxes, ignore, boxes, overlap_tester, all_dets, missing_detections, overlap_tester);
}
// ------------------------------------------------------------------------------------
}
// ----------------------------------------------------------------------------------------
template <
typename object_detector_type,
typename image_array_type
>
const matrix<double,1,3> test_object_detection_function (
object_detector_type& detector,
const image_array_type& images,
const std::vector<std::vector<full_object_detection> >& truth_dets,
const std::vector<std::vector<rectangle> >& ignore,
const test_box_overlap& overlap_tester = test_box_overlap(),
const double adjust_threshold = 0
)
{
// make sure requires clause is not broken
DLIB_CASSERT( is_learning_problem(images,truth_dets) == true &&
ignore.size() == images.size(),
"\t matrix test_object_detection_function()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t is_learning_problem(images,truth_dets): " << is_learning_problem(images,truth_dets)
<< "\n\t ignore.size(): " << ignore.size()
<< "\n\t images.size(): " << images.size()
);
double correct_hits = 0;
double total_true_targets = 0;
std::vector<std::pair<double,bool> > all_dets;
unsigned long missing_detections = 0;
for (unsigned long i = 0; i < images.size(); ++i)
{
std::vector<std::pair<double,rectangle> > hits;
detector(images[i], hits, adjust_threshold);
correct_hits += impl::number_of_truth_hits(truth_dets[i], ignore[i], hits, overlap_tester, all_dets, missing_detections);
total_true_targets += truth_dets[i].size();
}
std::sort(all_dets.rbegin(), all_dets.rend());
double precision, recall;
double total_hits = all_dets.size();
if (total_hits == 0)
precision = 1;
else
precision = correct_hits / total_hits;
if (total_true_targets == 0)
recall = 1;
else
recall = correct_hits / total_true_targets;
matrix<double, 1, 3> res;
res = precision, recall, average_precision(all_dets, missing_detections);
return res;
}
template <
typename object_detector_type,
typename image_array_type
>
const matrix<double,1,3> test_object_detection_function (
object_detector_type& detector,
const image_array_type& images,
const std::vector<std::vector<rectangle> >& truth_dets,
const std::vector<std::vector<rectangle> >& ignore,
const test_box_overlap& overlap_tester = test_box_overlap(),
const double adjust_threshold = 0
)
{
// convert into a list of regular rectangles.
std::vector<std::vector<full_object_detection> > rects(truth_dets.size());
for (unsigned long i = 0; i < truth_dets.size(); ++i)
{
for (unsigned long j = 0; j < truth_dets[i].size(); ++j)
{
rects[i].push_back(full_object_detection(truth_dets[i][j]));
}
}
return test_object_detection_function(detector, images, rects, ignore, overlap_tester, adjust_threshold);
}
template <
typename object_detector_type,
typename image_array_type
>
const matrix<double,1,3> test_object_detection_function (
object_detector_type& detector,
const image_array_type& images,
const std::vector<std::vector<rectangle> >& truth_dets,
const test_box_overlap& overlap_tester = test_box_overlap(),
const double adjust_threshold = 0
)
{
std::vector<std::vector<rectangle> > ignore(images.size());
return test_object_detection_function(detector,images,truth_dets,ignore, overlap_tester, adjust_threshold);
}
template <
typename object_detector_type,
typename image_array_type
>
const matrix<double,1,3> test_object_detection_function (
object_detector_type& detector,
const image_array_type& images,
const std::vector<std::vector<full_object_detection> >& truth_dets,
const test_box_overlap& overlap_tester = test_box_overlap(),
const double adjust_threshold = 0
)
{
std::vector<std::vector<rectangle> > ignore(images.size());
return test_object_detection_function(detector,images,truth_dets,ignore, overlap_tester, adjust_threshold);
}
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
namespace impl
{
template <
typename array_type
>
struct array_subset_helper
{
typedef typename array_type::mem_manager_type mem_manager_type;
array_subset_helper (
const array_type& array_,
const std::vector<unsigned long>& idx_set_
) :
array(array_),
idx_set(idx_set_)
{
}
unsigned long size() const { return idx_set.size(); }
typedef typename array_type::type type;
const type& operator[] (
unsigned long idx
) const { return array[idx_set[idx]]; }
private:
const array_type& array;
const std::vector<unsigned long>& idx_set;
};
template <
typename T
>
const matrix_op<op_array_to_mat<array_subset_helper<T> > > mat (
const array_subset_helper<T>& m
)
{
typedef op_array_to_mat<array_subset_helper<T> > op;
return matrix_op<op>(op(m));
}
}
// ----------------------------------------------------------------------------------------
template <
typename trainer_type,
typename image_array_type
>
const matrix<double,1,3> cross_validate_object_detection_trainer (
const trainer_type& trainer,
const image_array_type& images,
const std::vector<std::vector<full_object_detection> >& truth_dets,
const std::vector<std::vector<rectangle> >& ignore,
const long folds,
const test_box_overlap& overlap_tester = test_box_overlap(),
const double adjust_threshold = 0
)
{
// make sure requires clause is not broken
DLIB_CASSERT( is_learning_problem(images,truth_dets) == true &&
ignore.size() == images.size() &&
1 < folds && folds <= static_cast<long>(images.size()),
"\t matrix cross_validate_object_detection_trainer()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t is_learning_problem(images,truth_dets): " << is_learning_problem(images,truth_dets)
<< "\n\t folds: "<< folds
<< "\n\t ignore.size(): " << ignore.size()
<< "\n\t images.size(): " << images.size()
);
double correct_hits = 0;
double total_true_targets = 0;
const long test_size = images.size()/folds;
std::vector<std::pair<double,bool> > all_dets;
unsigned long missing_detections = 0;
unsigned long test_idx = 0;
for (long iter = 0; iter < folds; ++iter)
{
std::vector<unsigned long> train_idx_set;
std::vector<unsigned long> test_idx_set;
for (long i = 0; i < test_size; ++i)
test_idx_set.push_back(test_idx++);
unsigned long train_idx = test_idx%images.size();
std::vector<std::vector<full_object_detection> > training_rects;
std::vector<std::vector<rectangle> > training_ignores;
for (unsigned long i = 0; i < images.size()-test_size; ++i)
{
training_rects.push_back(truth_dets[train_idx]);
training_ignores.push_back(ignore[train_idx]);
train_idx_set.push_back(train_idx);
train_idx = (train_idx+1)%images.size();
}
impl::array_subset_helper<image_array_type> array_subset(images, train_idx_set);
typename trainer_type::trained_function_type detector = trainer.train(array_subset, training_rects, training_ignores, overlap_tester);
for (unsigned long i = 0; i < test_idx_set.size(); ++i)
{
std::vector<std::pair<double,rectangle> > hits;
detector(images[test_idx_set[i]], hits, adjust_threshold);
correct_hits += impl::number_of_truth_hits(truth_dets[test_idx_set[i]], ignore[i], hits, overlap_tester, all_dets, missing_detections);
total_true_targets += truth_dets[test_idx_set[i]].size();
}
}
std::sort(all_dets.rbegin(), all_dets.rend());
double precision, recall;
double total_hits = all_dets.size();
if (total_hits == 0)
precision = 1;
else
precision = correct_hits / total_hits;
if (total_true_targets == 0)
recall = 1;
else
recall = correct_hits / total_true_targets;
matrix<double, 1, 3> res;
res = precision, recall, average_precision(all_dets, missing_detections);
return res;
}
template <
typename trainer_type,
typename image_array_type
>
const matrix<double,1,3> cross_validate_object_detection_trainer (
const trainer_type& trainer,
const image_array_type& images,
const std::vector<std::vector<rectangle> >& truth_dets,
const std::vector<std::vector<rectangle> >& ignore,
const long folds,
const test_box_overlap& overlap_tester = test_box_overlap(),
const double adjust_threshold = 0
)
{
// convert into a list of regular rectangles.
std::vector<std::vector<full_object_detection> > dets(truth_dets.size());
for (unsigned long i = 0; i < truth_dets.size(); ++i)
{
for (unsigned long j = 0; j < truth_dets[i].size(); ++j)
{
dets[i].push_back(full_object_detection(truth_dets[i][j]));
}
}
return cross_validate_object_detection_trainer(trainer, images, dets, ignore, folds, overlap_tester, adjust_threshold);
}
template <
typename trainer_type,
typename image_array_type
>
const matrix<double,1,3> cross_validate_object_detection_trainer (
const trainer_type& trainer,
const image_array_type& images,
const std::vector<std::vector<rectangle> >& truth_dets,
const long folds,
const test_box_overlap& overlap_tester = test_box_overlap(),
const double adjust_threshold = 0
)
{
const std::vector<std::vector<rectangle> > ignore(images.size());
return cross_validate_object_detection_trainer(trainer,images,truth_dets,ignore,folds,overlap_tester,adjust_threshold);
}
template <
typename trainer_type,
typename image_array_type
>
const matrix<double,1,3> cross_validate_object_detection_trainer (
const trainer_type& trainer,
const image_array_type& images,
const std::vector<std::vector<full_object_detection> >& truth_dets,
const long folds,
const test_box_overlap& overlap_tester = test_box_overlap(),
const double adjust_threshold = 0
)
{
const std::vector<std::vector<rectangle> > ignore(images.size());
return cross_validate_object_detection_trainer(trainer,images,truth_dets,ignore,folds,overlap_tester,adjust_threshold);
}
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_CROSS_VALIDATE_OBJECT_DETECTION_TRaINER_Hh_