// Copyright (C) 2014 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#include <sstream>
#include "tester.h"
#include <dlib/svm_threaded.h>
#include <dlib/rand.h>
namespace
{
using namespace test;
using namespace dlib;
using namespace std;
logger dlog("test.learning_to_track");
// ----------------------------------------------------------------------------------------
struct detection_dense
{
typedef struct track_dense track_type;
matrix<double,0,1> measurements;
};
struct track_dense
{
typedef matrix<double,0,1> feature_vector_type;
track_dense()
{
time_since_last_association = 0;
}
void get_similarity_features(const detection_dense det, feature_vector_type& feats) const
{
feats = abs(last_measurements - det.measurements);
}
void update_track(const detection_dense det)
{
last_measurements = det.measurements;
time_since_last_association = 0;
}
void propagate_track()
{
++time_since_last_association;
}
matrix<double,0,1> last_measurements;
unsigned long time_since_last_association;
};
// ----------------------------------------------------------------------------------------
struct detection_sparse
{
typedef struct track_sparse track_type;
matrix<double,0,1> measurements;
};
struct track_sparse
{
typedef std::vector<std::pair<unsigned long,double> > feature_vector_type;
track_sparse()
{
time_since_last_association = 0;
}
void get_similarity_features(const detection_sparse det, feature_vector_type& feats) const
{
matrix<double,0,1> temp = abs(last_measurements - det.measurements);
feats.clear();
for (long i = 0; i < temp.size(); ++i)
feats.push_back(make_pair(i, temp(i)));
}
void update_track(const detection_sparse det)
{
last_measurements = det.measurements;
time_since_last_association = 0;
}
void propagate_track()
{
++time_since_last_association;
}
matrix<double,0,1> last_measurements;
unsigned long time_since_last_association;
};
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
dlib::rand rnd;
const long num_objects = 4;
const long num_properties = 6;
std::vector<matrix<double,0,1> > object_properties(num_objects);
void initialize_object_properties()
{
rnd.set_seed("23ja2oirfjaf");
for (unsigned long i = 0; i < object_properties.size(); ++i)
object_properties[i] = randm(num_properties,1,rnd);
}
template <typename detection>
detection sample_detection_from_sensor(long object_id)
{
DLIB_CASSERT(object_id < num_objects,
"You can't ask to sample a detection from an object that doesn't exist.");
detection temp;
// Set the measurements equal to the object's true property values plus a little bit of
// noise.
temp.measurements = object_properties[object_id] + randm(num_properties,1,rnd)*0.1;
return temp;
}
// ----------------------------------------------------------------------------------------
template <typename detection>
std::vector<std::vector<labeled_detection<detection> > > make_random_tracking_data_for_training()
{
typedef std::vector<labeled_detection<detection> > detections_at_single_time_step;
typedef std::vector<detections_at_single_time_step> track_history;
track_history data;
// At each time step we get a set of detections from the objects in the world.
// Simulate 100 time steps worth of data where there are 3 objects present.
const int num_time_steps = 100;
for (int i = 0; i < num_time_steps; ++i)
{
detections_at_single_time_step dets(3);
// sample a detection from object 0
dets[0].det = sample_detection_from_sensor<detection>(0);
dets[0].label = 0;
// sample a detection from object 1
dets[1].det = sample_detection_from_sensor<detection>(1);
dets[1].label = 1;
// sample a detection from object 2
dets[2].det = sample_detection_from_sensor<detection>(2);
dets[2].label = 2;
randomize_samples(dets, rnd);
data.push_back(dets);
}
// Now let's imagine object 1 and 2 are gone but a new object, object 3 has arrived.
for (int i = 0; i < num_time_steps; ++i)
{
detections_at_single_time_step dets(2);
// sample a detection from object 0
dets[0].det = sample_detection_from_sensor<detection>(0);
dets[0].label = 0;
// sample a detection from object 3
dets[1].det = sample_detection_from_sensor<detection>(3);
dets[1].label = 3;
randomize_samples(dets, rnd);
data.push_back(dets);
}
return data;
}
// ----------------------------------------------------------------------------------------
template <typename detection>
std::vector<detection> make_random_detections(long num_dets)
{
DLIB_CASSERT(num_dets <= num_objects,
"You can't ask for more detections than there are objects in our little simulation.");
std::vector<detection> dets(num_dets);
for (unsigned long i = 0; i < dets.size(); ++i)
{
dets[i] = sample_detection_from_sensor<detection>(i);
}
randomize_samples(dets, rnd);
return dets;
}
// ----------------------------------------------------------------------------------------
template <typename detection>
void test_tracking_stuff()
{
print_spinner();
typedef std::vector<labeled_detection<detection> > detections_at_single_time_step;
typedef std::vector<detections_at_single_time_step> track_history;
std::vector<track_history> data;
data.push_back(make_random_tracking_data_for_training<detection>());
data.push_back(make_random_tracking_data_for_training<detection>());
data.push_back(make_random_tracking_data_for_training<detection>());
data.push_back(make_random_tracking_data_for_training<detection>());
data.push_back(make_random_tracking_data_for_training<detection>());
structural_track_association_trainer trainer;
trainer.set_c(1000);
track_association_function<detection> assoc = trainer.train(data);
double test_val = test_track_association_function(assoc, data);
DLIB_TEST_MSG( test_val == 1, test_val);
test_val = cross_validate_track_association_trainer(trainer, data, 5);
DLIB_TEST_MSG ( test_val == 1, test_val);
typedef typename detection::track_type track;
std::vector<track> tracks;
std::vector<detection> dets = make_random_detections<detection>(3);
assoc(tracks, dets);
DLIB_TEST(tracks.size() == 3);
dets = make_random_detections<detection>(3);
assoc(tracks, dets);
DLIB_TEST(tracks.size() == 3);
dets = make_random_detections<detection>(3);
assoc(tracks, dets);
DLIB_TEST(tracks.size() == 3);
dets = make_random_detections<detection>(4);
assoc(tracks, dets);
DLIB_TEST(tracks.size() == 4);
dets = make_random_detections<detection>(3);
assoc(tracks, dets);
DLIB_TEST(tracks.size() == 4);
unsigned long total_miss = 0;
for (unsigned long i = 0; i < tracks.size(); ++i)
total_miss += tracks[i].time_since_last_association;
DLIB_TEST(total_miss == 1);
dets = make_random_detections<detection>(3);
assoc(tracks, dets);
DLIB_TEST(tracks.size() == 4);
total_miss = 0;
unsigned long num_zero = 0;
for (unsigned long i = 0; i < tracks.size(); ++i)
{
total_miss += tracks[i].time_since_last_association;
if (tracks[i].time_since_last_association == 0)
++num_zero;
}
DLIB_TEST(total_miss == 2);
DLIB_TEST(num_zero == 3);
ostringstream sout;
serialize(assoc, sout);
istringstream sin(sout.str());
deserialize(assoc, sin);
DLIB_TEST( test_track_association_function(assoc, data) == 1);
}
// ----------------------------------------------------------------------------------------
class test_learning_to_track : public tester
{
public:
test_learning_to_track (
) :
tester ("test_learning_to_track",
"Runs tests on the assignment learning code.")
{}
void perform_test (
)
{
initialize_object_properties();
for (int i = 0; i < 3; ++i)
{
dlog << LINFO << "run test_tracking_stuff<detection_dense>()";
test_tracking_stuff<detection_dense>();
dlog << LINFO << "run test_tracking_stuff<detection_sparse>()";
test_tracking_stuff<detection_sparse>();
}
}
} a;
// ----------------------------------------------------------------------------------------
}