3dpcp/.svn/pristine/53/5377a8e3c14b1c4d7a004e3809b8073580eaa683.svn-base
2012-09-16 14:33:11 +02:00

280 lines
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Text

/*
* kdMeta implementation
*
* Copyright (C) Andreas Nuechter, Kai Lingemann, Thomas Escher
*
* Released under the GPL version 3.
*
*/
/** @file
* @brief An optimized k-d tree implementation. MetaScan variant.
* @author Andreas Nuechter. Institute of Computer Science, University of Osnabrueck, Germany.
* @author Kai Lingemann. Institute of Computer Science, University of Osnabrueck, Germany.
* @author Thomas Escher. Institute of Computer Science, University of Osnabrueck, Germany.
*/
#ifdef _MSC_VER
#define _USE_MATH_DEFINES
#endif
#include "slam6d/kdMeta.h"
#include "slam6d/globals.icc"
#include "slam6d/scan.h"
#include <iostream>
using std::cout;
using std::cerr;
using std::endl;
#include <algorithm>
using std::swap;
#include <cmath>
#include <cstring>
// KDtree class static variables
KDParams KDtreeMeta::params[MAX_OPENMP_NUM_THREADS];
KDtreeMeta::KDtreeMeta()
{
}
/**
* Create a KD tree from the points pointed to by the array pts
*
* @param pts 3D array of points
* @param n number of points
*/
void KDtreeMeta::create(const DataXYZ* const* pts, Index* indices, unsigned int n)
{
// Find bbox
double xmin = point(pts, indices[0])[0], xmax = point(pts, indices[0])[0];
double ymin = point(pts, indices[0])[1], ymax = point(pts, indices[0])[1];
double zmin = point(pts, indices[0])[2], zmax = point(pts, indices[0])[2];
for(unsigned int i = 1; i < n; i++) {
xmin = min(xmin, point(pts, indices[i])[0]);
xmax = max(xmax, point(pts, indices[i])[0]);
ymin = min(ymin, point(pts, indices[i])[1]);
ymax = max(ymax, point(pts, indices[i])[1]);
zmin = min(zmin, point(pts, indices[i])[2]);
zmax = max(zmax, point(pts, indices[i])[2]);
}
// Leaf nodes
if ((n > 0) && (n <= 10)) {
npts = n;
leaf.p = new Index[n];
// fill leaf index array with indices
for(unsigned int i = 0; i < n; ++i) {
leaf.p[i] = indices[i];
}
return;
}
// Else, interior nodes
npts = 0;
node.center[0] = 0.5 * (xmin+xmax);
node.center[1] = 0.5 * (ymin+ymax);
node.center[2] = 0.5 * (zmin+zmax);
node.dx = 0.5 * (xmax-xmin);
node.dy = 0.5 * (ymax-ymin);
node.dz = 0.5 * (zmax-zmin);
node.r2 = sqr(node.dx) + sqr(node.dy) + sqr(node.dz);
// Find longest axis
if (node.dx > node.dy) {
if (node.dx > node.dz) {
node.splitaxis = 0;
} else {
node.splitaxis = 2;
}
} else {
if (node.dy > node.dz) {
node.splitaxis = 1;
} else {
node.splitaxis = 2;
}
}
// Partition
double splitval = node.center[node.splitaxis];
if ( fabs(max(max(node.dx,node.dy),node.dz)) < 0.01 ) {
npts = n;
leaf.p = new Index[n];
// fill leaf index array with indices
for(unsigned int i = 0; i < n; ++i) {
leaf.p[i] = indices[i];
}
return;
}
Index* left = indices, * right = indices + n - 1;
while(true) {
while(point(pts, *left)[node.splitaxis] < splitval)
left++;
while(point(pts, *right)[node.splitaxis] >= splitval)
right--;
if(right < left)
break;
swap(*left, *right);
}
// Build subtrees
int i;
#ifdef WITH_OPENMP_KD // does anybody know the reason why this is slower ?? --Andreas
omp_set_num_threads(OPENMP_NUM_THREADS);
#pragma omp parallel for schedule(dynamic)
#endif
for (i = 0; i < 2; i++) {
if (i == 0) {
node.child1 = new KDtreeMeta();
node.child1->create(pts, indices, left - indices);
}
if (i == 1) {
node.child2 = new KDtreeMeta();
node.child2->create(pts, left, n - (left - indices));
}
}
}
KDtreeMeta::~KDtreeMeta()
{
if (!npts) {
#ifdef WITH_OPENMP_KD
omp_set_num_threads(OPENMP_NUM_THREADS);
#pragma omp parallel for schedule(dynamic)
#endif
for (int i = 0; i < 2; i++) {
if (i == 0 && node.child1) delete node.child1;
if (i == 1 && node.child2) delete node.child2;
}
} else {
if (leaf.p) delete [] leaf.p;
}
}
/**
* Wrapped function
*/
void KDtreeMeta::_FindClosest(const DataXYZ* const* pts, int threadNum) const
{
// Leaf nodes
if (npts) {
for (int i = 0; i < npts; i++) {
double myd2 = Dist2(params[threadNum].p, point(pts, leaf.p[i]));
if (myd2 < params[threadNum].closest_d2) {
params[threadNum].closest_d2 = myd2;
params[threadNum].closest = point(pts, leaf.p[i]);
}
}
return;
}
// Quick check of whether to abort
double approx_dist_bbox = max(max(fabs(params[threadNum].p[0]-node.center[0])-node.dx,
fabs(params[threadNum].p[1]-node.center[1])-node.dy),
fabs(params[threadNum].p[2]-node.center[2])-node.dz);
if (approx_dist_bbox >= 0 && sqr(approx_dist_bbox) >= params[threadNum].closest_d2)
return;
// Recursive case
double myd = node.center[node.splitaxis] - params[threadNum].p[node.splitaxis];
if (myd >= 0.0) {
node.child1->_FindClosest(pts, threadNum);
if (sqr(myd) < params[threadNum].closest_d2) {
node.child2->_FindClosest(pts, threadNum);
}
} else {
node.child2->_FindClosest(pts, threadNum);
if (sqr(myd) < params[threadNum].closest_d2) {
node.child1->_FindClosest(pts, threadNum);
}
}
}
KDtreeMetaManaged::KDtreeMetaManaged(const vector<Scan*>& scans) :
m_count_locking(0)
{
// create scan pointer and data pointer arrays
m_size = scans.size();
m_scans = new Scan*[m_size];
for(unsigned int i = 0; i < m_size; ++i)
m_scans[i] = scans[i];
m_data = new DataXYZ*[m_size];
lock();
create(m_data, prepareTempIndices(scans), getPointsSize(scans));
unlock();
// allocate in prepareTempIndices, deleted here
delete[] m_temp_indices;
}
KDtreeMetaManaged::~KDtreeMetaManaged()
{
delete[] m_scans;
delete[] m_data;
}
KDtreeMeta::Index* KDtreeMetaManaged::prepareTempIndices(const vector<Scan*>& scans)
{
unsigned int n = getPointsSize(scans);
m_temp_indices = new Index[n];
unsigned int s = 0, j = 0;
unsigned int scansize = scans[s]->size<DataXYZ>("xyz reduced");
for(unsigned int i = 0; i < n; ++i) {
m_temp_indices[i].set(s, j++);
// switch to next scan
if(j >= scansize) {
++s;
j = 0;
if(s < scans.size())
scansize = scans[s]->size<DataXYZ>("xyz reduced");
}
}
return m_temp_indices;
}
unsigned int KDtreeMetaManaged::getPointsSize(const vector<Scan*>& scans)
{
unsigned int n = 0;
for(vector<Scan*>::const_iterator it = scans.begin(); it != scans.end(); ++it) {
n += (*it)->size<DataXYZ>("xyz reduced");
}
return n;
}
double* KDtreeMetaManaged::FindClosest(double *_p, double maxdist2, int threadNum) const
{
params[threadNum].closest = 0;
params[threadNum].closest_d2 = maxdist2;
params[threadNum].p = _p;
_FindClosest(m_data, threadNum);
return params[threadNum].closest;
}
void KDtreeMetaManaged::lock()
{
boost::lock_guard<boost::mutex> lock(m_mutex_locking);
if(m_count_locking == 0) {
// lock all the contained scans, metascan uses the transformed points
for(unsigned int i = 0; i < m_size; ++i) {
m_data[i] = new DataXYZ(m_scans[i]->get("xyz reduced"));
}
}
++m_count_locking;
}
void KDtreeMetaManaged::unlock()
{
boost::lock_guard<boost::mutex> lock(m_mutex_locking);
--m_count_locking;
if(m_count_locking == 0) {
// delete each locking object
for(unsigned int i = 0; i < m_size; ++i) {
delete m_data[i];
}
}
}