3dpcp/.svn/pristine/2b/2bc5997e5236da41f77586ceb5eb3cd8662f848f.svn-base

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2012-09-16 12:33:11 +00:00
/*
* kdManaged implementation
*
* Copyright (C) Andreas Nuechter, Kai Lingemann, Thomas Escher
*
* Released under the GPL version 3.
*
*/
/** @file
* @brief An optimized k-d tree implementation
* @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/kdManaged.h"
#include "slam6d/scan.h"
#include "slam6d/globals.icc"
#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 KDtreeManagedBase::params[MAX_OPENMP_NUM_THREADS];
/**
* Constructor
*
* Create a KD tree from the points pointed to by the array pts
*
* @param pts 3D array of points
* @param n number of points
*/
KDtreeManagedBase::KDtreeManagedBase(const DataXYZ& pts, unsigned int* indices, unsigned int n)
{
// Find bbox
double xmin = pts[indices[0]][0], xmax = pts[indices[0]][0];
double ymin = pts[indices[0]][1], ymax = pts[indices[0]][1];
double zmin = pts[indices[0]][2], zmax = pts[indices[0]][2];
for(unsigned int i = 1; i < n; i++) {
xmin = min(xmin, pts[indices[i]][0]);
xmax = max(xmax, pts[indices[i]][0]);
ymin = min(ymin, pts[indices[i]][1]);
ymax = max(ymax, pts[indices[i]][1]);
zmin = min(zmin, pts[indices[i]][2]);
zmax = max(zmax, pts[indices[i]][2]);
}
// Leaf nodes
if ((n > 0) && (n <= 10)) {
npts = n;
leaf.p = new unsigned int[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 unsigned int[n];
// fill leaf index array with indices
for(unsigned int i = 0; i < n; ++i) {
leaf.p[i] = indices[i];
}
return;
}
unsigned int* left = indices, * right = indices + n - 1;
while(true) {
while(pts[*left][node.splitaxis] < splitval)
left++;
while(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 KDtreeManagedBase(pts, indices, left - indices);
if (i == 1) node.child2 = new KDtreeManagedBase(pts, left, n - (left - indices));
}
}
KDtreeManagedBase::~KDtreeManagedBase()
{
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 KDtreeManagedBase::_FindClosest(const DataXYZ& pts, int threadNum) const
{
// Leaf nodes
if (npts) {
for (int i = 0; i < npts; i++) {
double myd2 = Dist2(params[threadNum].p, pts[leaf.p[i]]);
if (myd2 < params[threadNum].closest_d2) {
params[threadNum].closest_d2 = myd2;
params[threadNum].closest = 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);
}
}
}
KDtreeManaged::KDtreeManaged(Scan* scan) :
KDtreeManagedBase(scan->get("xyz reduced original"), prepareTempIndices(scan->size<DataXYZ>("xyz reduced original")), scan->size<DataXYZ>("xyz reduced original")),
m_scan(scan), m_data(0), m_count_locking(0)
{
// allocate in prepareTempIndices, deleted here
delete[] m_temp_indices;
}
unsigned int* KDtreeManaged::prepareTempIndices(unsigned int n)
{
m_temp_indices = new unsigned int[n];
for(unsigned int i = 0; i < n; ++i)
m_temp_indices[i] = i;
return m_temp_indices;
}
double* KDtreeManaged::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 KDtreeManaged::lock()
{
boost::lock_guard<boost::mutex> lock(m_mutex_locking);
++m_count_locking;
if(m_data == 0) {
// aquire an array lock from the scan and hold it while the tree is in use
m_data = new DataXYZ(m_scan->get("xyz reduced original"));
}
}
void KDtreeManaged::unlock()
{
boost::lock_guard<boost::mutex> lock(m_mutex_locking);
--m_count_locking;
if(m_count_locking == 0 && m_data != 0) {
delete m_data;
m_data = 0;
}
}