songsenand
/
cgal
mirror of https://github.com/CGAL/cgal
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Several modifications in segmentation, EM, k-means: 

segmentation : moving center points which falls wrong side of the facet.
EM: random initialization
This commit is contained in:
Ílker Yaz 2012-06-07 12:21:02 +00:00
parent 66d4234134
commit 11a35afb12
3 changed files with 174 additions and 46 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

132
Surface_mesh_segmentation/include/CGAL/Surface_mesh_segmentation.h
View File

@ -14,6 +14,20 @@
* +) Deciding how to generate rays in cone: for now using "polar angle" and "accept-reject (square)" and "concentric mapping" techniques
*/
//#include "Expectation_maximization.h"
//#include "K_means_clustering.h"
//#include "Timer.h"
#include <CGAL/internal/Surface_mesh_segmentation/Expectation_maximization.h>
#include <CGAL/internal/Surface_mesh_segmentation/K_means_clustering.h>
#include <CGAL/Simple_cartesian.h>
#include <CGAL/AABB_tree.h>
#include <CGAL/AABB_traits.h>
#include <CGAL/AABB_polyhedron_triangle_primitive.h>
#include <CGAL/utility.h>
#include <iostream>
#include <fstream>
#include <cmath>
@ -21,22 +35,17 @@
#include <algorithm>
#include <utility>
//#include "Timer.h"
//#include "Expectation_maximization.h"
//#include "K_means_clustering.h"
#include <CGAL/Simple_cartesian.h>
#include <CGAL/internal/Surface_mesh_segmentation/Expectation_maximization.h>
#include <CGAL/internal/Surface_mesh_segmentation/K_means_clustering.h>
#include <CGAL/AABB_tree.h>
#include <CGAL/AABB_traits.h>
#include <CGAL/AABB_polyhedron_triangle_primitive.h>
#include <CGAL/utility.h>
#define LOG_5 1.60943791
#define NORMALIZATION_ALPHA 4.0
#define ANGLE_ST_DEV_DIVIDER 3.0
//#define ACTIVATE_SEGMENTATION_DEBUG
#ifdef ACTIVATE_SEGMENTATION_DEBUG
#define SEG_DEBUG(x) x;
#else
#define SEG_DEBUG(x)
#endif
namespace CGAL
{
@ -52,7 +61,9 @@ public:
typedef typename Polyhedron::Facet_iterator Facet_iterator;
typedef typename Polyhedron::Facet_handle Facet_handle;
protected:
typedef typename Kernel::Ray_3 Ray;
typedef typename Kernel::Ray_3 Ray;
typedef typename Kernel::Plane_3 Plane;
typedef typename CGAL::AABB_polyhedron_triangle_primitive<Kernel, Polyhedron>
Primitive;
typedef typename CGAL::AABB_tree<CGAL::AABB_traits<Kernel, Primitive> >
@ -105,8 +116,7 @@ public:
void calculate_sdf_values();
double calculate_sdf_value_of_facet (const Facet_handle& facet,
const Point& center,
const Vector& normal_const, const Tree& tree) const;
const Tree& tree) const;
void cast_and_return_minimum (const Ray& ray, const Tree& tree,
const Facet_handle& facet,
bool& is_found, double& min_distance) const;
@ -118,6 +128,9 @@ public:
double calculate_sdf_value_from_rays_with_trimmed_mean (
std::vector<double>& ray_distances, std::vector<double>& ray_weights) const;
void arrange_center_orientation(const Plane& plane, const Vector& unit_normal,
Point& center) const;
void disk_sampling_rejection();
void disk_sampling_polar_mapping();
void disk_sampling_concentric_mapping();
@ -141,13 +154,14 @@ inline Surface_mesh_segmentation<Polyhedron>::Surface_mesh_segmentation(
: mesh(mesh), cone_angle(cone_angle), number_of_rays_sqrt(number_of_rays_sqrt),
number_of_centers(number_of_centers), log_file("log_file.txt")
{
//Timer t;
SEG_DEBUG(Timer t)
disk_sampling_concentric_mapping();
calculate_sdf_values();
//std::cout << t;
apply_GMM_fitting_with_K_means_init();
SEG_DEBUG(std::cout << t)
apply_GMM_fitting();
//write_sdf_values("sdf_values_sample_dino_2.txt");
//read_sdf_values("sdf_values_sample_camel.txt");
//read_sdf_values("sdf_values_sample_dino.txt");
}
template <class Polyhedron>
@ -159,14 +173,8 @@ inline void Surface_mesh_segmentation<Polyhedron>::calculate_sdf_values()
facet_it != mesh->facets_end(); ++facet_it) {
CGAL_precondition(facet_it->is_triangle()); //Mesh should contain triangles.
Point v1 = facet_it->halfedge()->vertex()->point();
Point v2 = facet_it->halfedge()->next()->vertex()->point();
Point v3 = facet_it->halfedge()->next()->next()->vertex()->point();
Point center = CGAL::centroid(v1, v2, v3);
Vector normal = CGAL::unit_normal(v1, v2,
v3) * -1.0; //Assuming triangles are CCW oriented.
//SL: cone angle and number of rays should be parameters.
double sdf = calculate_sdf_value_of_facet(facet_it, center, normal, tree);
double sdf = calculate_sdf_value_of_facet(facet_it, tree);
sdf_values.insert(std::pair<Facet_handle, double>(facet_it, sdf));
}
normalize_sdf_values();
@ -176,14 +184,22 @@ inline void Surface_mesh_segmentation<Polyhedron>::calculate_sdf_values()
template <class Polyhedron>
inline double
Surface_mesh_segmentation<Polyhedron>::calculate_sdf_value_of_facet(
const Facet_handle& facet, const Point& center,
const Vector& normal_const, const Tree& tree) const
const Facet_handle& facet, const Tree& tree) const
{
typename Kernel::Plane_3 plane(center, normal_const);
Point p1 = facet->halfedge()->vertex()->point();
Point p2 = facet->halfedge()->next()->vertex()->point();
Point p3 = facet->halfedge()->next()->next()->vertex()->point();
Point center = CGAL::centroid(p1, p2, p3);
Vector normal = CGAL::unit_normal(p1, p2,
p3) * -1.0; //Assuming triangles are CCW oriented.
Plane plane(center, normal);
Vector v1 = plane.base1();
Vector v2 = plane.base2();
v1 = v1 / CGAL::sqrt(v1.squared_length());
v2 = v2 / CGAL::sqrt(v2.squared_length());
v1 = v1 / sqrt(v1.squared_length());
v2 = v2 / sqrt(v2.squared_length());
arrange_center_orientation(plane, normal, center);
int ray_count = number_of_rays_sqrt * number_of_rays_sqrt;
@ -192,7 +208,7 @@ Surface_mesh_segmentation<Polyhedron>::calculate_sdf_value_of_facet(
ray_weights.reserve(ray_count);
double length_of_normal = 1.0 / tan(cone_angle / 2);
Vector normal = normal_const * length_of_normal;
normal = normal * length_of_normal;
for(Disk_samples_list::const_iterator sample_it = disk_samples.begin();
sample_it != disk_samples.end(); ++sample_it) {
@ -256,6 +272,8 @@ void Surface_mesh_segmentation<Polyhedron>::cast_and_return_minimum(
CGAL::ORIGIN + min_normal) != CGAL::ACUTE) {
is_found = false;
}
//total_intersection += intersections.size();
//total_cast++;
}
template <class Polyhedron>
@ -403,6 +421,49 @@ Surface_mesh_segmentation<Polyhedron>::calculate_sdf_value_from_rays_with_trimme
return total_distance / total_weights;
}
/* Slightly move center towards inverse normal of facet.
* Parameter plane is constructed by inverse normal.
*/
template <class Polyhedron>
void Surface_mesh_segmentation<Polyhedron>::arrange_center_orientation(
const Plane& plane, const Vector& unit_normal, Point& center) const
{
/*
* Not sure how to decide on how much I should move center ?
*/
double epsilon = 1e-8;
// Option-1
//if(plane.has_on_positive_side(center)) { return; }
//Vector epsilon_normal = unit_normal * epsilon;
//do {
// center = CGAL::operator+(center, epsilon_normal);
//} while(!plane.has_on_positive_side(center));
//Option-2
//if(plane.has_on_positive_side(center)) { return; }
//double distance = sqrt(CGAL::squared_distance(plane, center));
//distance = distance > epsilon ? (distance + epsilon) : epsilon;
//Vector distance_normal = unit_normal * distance;
//
//do {
// center = CGAL::operator+(center, distance_normal);
//} while(!plane.has_on_positive_side(center));
//Option-3
Ray ray(center, unit_normal);
Kernel::Do_intersect_3 intersector = Kernel().do_intersect_3_object();
if(!intersector(ray, plane)) {
return;
}
Vector epsilon_normal = unit_normal * epsilon;
do {
center = CGAL::operator+(center, epsilon_normal);
ray = Ray(center, unit_normal);
} while(intersector(ray, plane));
}
template <class Polyhedron>
inline void Surface_mesh_segmentation<Polyhedron>::disk_sampling_rejection()
{
@ -522,7 +583,7 @@ inline void Surface_mesh_segmentation<Polyhedron>::smooth_sdf_values()
total_neighbor_sdf += sdf_values[facet_circulator->opposite()->facet()];
} while( ++facet_circulator != f->facet_begin());
total_neighbor_sdf /= 3;
smoothed_sdf_values[f] = (sdf_values[f] + total_neighbor_sdf) / 2;
smoothed_sdf_values[f] = (pair_it->second + total_neighbor_sdf) / 2;
}
sdf_values = smoothed_sdf_values;
}
@ -610,6 +671,7 @@ inline void Surface_mesh_segmentation<Polyhedron>::read_sdf_values(
const char* file_name)
{
std::ifstream input(file_name);
sdf_values.clear();
for(Facet_iterator facet_it = mesh->facets_begin();
facet_it != mesh->facets_end(); ++facet_it) {
double sdf_value;
@ -640,4 +702,8 @@ inline void Surface_mesh_segmentation<Polyhedron>::read_center_ids(
#undef ANGLE_ST_DEV_DIVIDER
#undef LOG_5
#undef NORMALIZATION_ALPHA
#ifdef SEG_DEBUG
#undef SEG_DEBUG
#endif
#endif //CGAL_SURFACE_MESH_SEGMENTATION_H

70
Surface_mesh_segmentation/include/CGAL/internal/Surface_mesh_segmentation/Expectation_maximization.h
View File

@ -2,7 +2,7 @@
#define CGAL_SEGMENTATION_EXPECTATION_MAXIMIZATION_H
/* NEED TO BE DONE */
/* About implementation:
* Calculating probability multiple times
* Calculating probability multiple times, solution: storing matrix(cluster, point) for probability values.
* About safe division: where centers have no members, in graph cut it may happen
*/
#include <vector>
@ -12,6 +12,15 @@
#include <cstdlib>
#include <limits>
#include <fstream>
#include <iostream>
//#define ACTIVATE_SEGMENTATION_EM_DEBUG
#ifdef ACTIVATE_SEGMENTATION_EM_DEBUG
#define SEG_DEBUG(x) x;
#else
#define SEG_DEBUG(x)
#endif
namespace CGAL
{
@ -102,13 +111,16 @@ protected:
public:
Expectation_maximization(int number_of_centers, const std::vector<double>& data,
const std::vector<int>& initial_centers = std::vector<int>(),
int maximum_iteration = 100)
: points(data.begin(), data.end()), threshold(1e-3),
int number_of_runs = 50, int maximum_iteration = 100)
: points(data.begin(), data.end()), threshold(1e-4),
maximum_iteration(maximum_iteration), is_converged(false), seed(time(NULL)) {
srand(seed);
//calculate_fitting_with_multiple_run(number_of_centers, 50);
initiate_centers(number_of_centers, initial_centers);
calculate_fitting();
if(initial_centers.empty()) {
calculate_fitting_with_multiple_run(number_of_centers, number_of_runs);
} else {
initiate_centers(number_of_centers, initial_centers);
calculate_fitting();
}
}
void fill_with_center_ids(std::vector<int>& data_centers) {
@ -133,21 +145,26 @@ protected:
void initiate_centers_randomly(int number_of_centers) {
centers.clear();
/* Randomly generate means of centers */
//vector<int> center_indexes;
double initial_mixing_coefficient = 1.0;
double initial_deviation = 1.0 / (2.0 * number_of_centers);
double initial_mixing_coefficient = 1.0 / number_of_centers;
for(int i = 0; i < number_of_centers; ++i) {
double initial_mean = points[rand() % points.size()].data;
double random_index = rand() % points.size();
//center_indexes.push_back(random_index);
Gaussian_point mean_point = points[random_index];
double initial_mean = mean_point.data;
centers.push_back(Gaussian_center(initial_mean, initial_deviation,
initial_mixing_coefficient));
}
sort(centers.begin(), centers.end());
//write_random_centers("center_indexes.txt", center_indexes);
}
void initiate_centers_uniformly(int number_of_centers) {
centers.clear();
/* Uniformly generate centers */
double initial_deviation = 1.0 / (2.0 * number_of_centers);
double initial_mixing_coefficient = 1.0 / number_of_centers;
double initial_mixing_coefficient = 1.0;
for(int i = 0; i < number_of_centers; ++i) {
double initial_mean = (i + 1.0) / (number_of_centers + 1.0);
centers.push_back(Gaussian_center(initial_mean, initial_deviation,
@ -203,6 +220,14 @@ protected:
point_it->calculate_total_membership(centers);
}
}
double calculate_deviation(const Gaussian_point& point) const {
double deviation = 0.0;
for(std::vector<Gaussian_point>::const_iterator point_it = points.begin();
point_it != points.end(); ++point_it) {
deviation += pow(point_it->data - point.data, 2);
}
return sqrt(deviation / points.size());
}
/*Calculates new parameter values for each cluster */
void calculate_parameters() {
for(std::vector<Gaussian_center>::iterator center_it = centers.begin();
@ -239,9 +264,9 @@ protected:
while(!is_converged && iteration_count++ < maximum_iteration) {
prev_likelihood = likelihood;
likelihood = iterate();
is_converged = likelihood - prev_likelihood < threshold * likelihood;
is_converged = likelihood - prev_likelihood < threshold * fabs(likelihood);
}
//std::cout << likelihood << " " << iteration_count << std::endl;
SEG_DEBUG(std::cout << likelihood << " " << iteration_count << std::endl)
return likelihood;
}
@ -254,6 +279,7 @@ protected:
initiate_centers_randomly(number_of_centers);
double likelihood = calculate_fitting();
write_center_parameters("center_paramters.txt");
if(likelihood > max_likelihood) {
max_centers = centers;
max_likelihood = likelihood;
@ -261,6 +287,28 @@ protected:
}
centers = max_centers;
}
void write_random_centers(const char* file_name,
const std::vector<int>& center_indexes) {
std::ofstream output(file_name, std::ios_base::app);
for(std::vector<int>::const_iterator it = center_indexes.begin();
it != center_indexes.end(); ++it) {
output << points[(*it)].data << std::endl;
}
output.close();
}
void write_center_parameters(const char* file_name) {
std::ofstream output(file_name, std::ios_base::app);
for(std::vector<Gaussian_center>::iterator center_it = centers.begin();
center_it != centers.end(); ++center_it) {
output << "mean: " << center_it->mean << " dev: " << center_it->deviation
<< " mix: " << center_it->mixing_coefficient << std::endl;
}
}
};
}//namespace CGAL
#ifdef SEG_DEBUG
#undef SEG_DEBUG
#endif
#endif //CGAL_SEGMENTATION_EXPECTATION_MAXIMIZATION_H

18
Surface_mesh_segmentation/include/CGAL/internal/Surface_mesh_segmentation/K_means_clustering.h
View File

@ -7,6 +7,13 @@
#include <cstdlib>
#include <limits>
//#define ACTIVATE_SEGMENTATION_K_MEANS_DEBUG
#ifdef ACTIVATE_SEGMENTATION_K_MEANS_DEBUG
#define SEG_DEBUG(x) x;
#else
#define SEG_DEBUG(x)
#endif
namespace CGAL
{
@ -84,8 +91,9 @@ public:
srand(seed);
calculate_clustering_with_multiple_run(number_of_centers, number_of_run);
}
/* For each data point, data_center is filled by its center's id. */
void fill_with_center_ids(std::vector<int>& data_centers) {
data_centers.reserve(points.size());
for(std::vector<K_means_point>::iterator point_it = points.begin();
point_it != points.end(); ++point_it) {
data_centers.push_back(point_it->center_id);
@ -115,18 +123,21 @@ public:
bool any_center_changed = true;
while(any_center_changed && iteration_count++ < maximum_iteration) {
any_center_changed = false;
/* For each point, calculate its new center */
for(std::vector<K_means_point>::iterator point_it = points.begin();
point_it != points.end(); ++point_it) {
bool center_changed = point_it->calculate_new_center(centers);
any_center_changed |= center_changed;
}
/* For each center, calculate its new mean */
for(std::vector<K_means_center>::iterator center_it = centers.begin();
center_it != centers.end(); ++center_it) {
center_it->calculate_mean();
}
}
is_converged = !any_center_changed;
//std::cout << iteration_count << " " << is_converged << std::endl;
SEG_DEBUG(std::cout << iteration_count << " " << (is_converged ? "converged" :
"not converged") << std::endl)
}
void calculate_clustering_with_multiple_run(int number_of_centers,
@ -170,4 +181,7 @@ public:
}
};
}//namespace CGAL
#ifdef SEG_DEBUG
#undef SEG_DEBUG
#endif
#endif //CGAL_SEGMENTATION_K_MEANS_CLUSTERING_H