vcglib/apps/sample/trimesh_indexing/trimesh_indexing.cpp

440 lines
17 KiB
C++

#include <iostream>
#ifdef _OPENMP
#include <omp.h>
#endif
#include "nanoflann.hpp"
#include <vcg/complex/complex.h>
#include <wrap/io_trimesh/import.h>
#include <wrap/io_trimesh/export.h>
#include <vcg/space/index/kdtree/kdtree.h>
#include <vcg/space/index/grid_static_ptr.h>
#include <vcg/space/index/perfect_spatial_hashing.h>
#include <vcg/space/index/spatial_hashing.h>
#include <vcg/space/index/octree.h>
int num_test = 1000;
int kNearest = 256;
float queryDist = 0.0037;
float bboxratio = 1000.0f;
class CVertex;
class CFace;
class CEdge;
class CUsedTypes : public vcg::UsedTypes < vcg::Use< CVertex >::AsVertexType, vcg::Use< CFace >::AsFaceType>{};
class CVertex : public vcg::Vertex < CUsedTypes, vcg::vertex::Coord3f, vcg::vertex::Normal3f, vcg::vertex::Radiusf, vcg::vertex::BitFlags, vcg::vertex::Qualityf, vcg::vertex::Color4b>{};
class CFace : public vcg::Face < CUsedTypes, vcg::face::VertexRef>{};
class CMesh : public vcg::tri::TriMesh < std::vector< CVertex >, std::vector< CFace > > {};
int elapsed(int t)
{
return ((clock()-t)*1000.0)/CLOCKS_PER_SEC;
}
template <typename T>
struct PointCloud
{
struct Point
{
T x,y,z;
};
std::vector<Point> pts;
inline size_t kdtree_get_point_count() const { return pts.size(); }
inline T kdtree_distance(const T *p1, const size_t idx_p2,size_t size) const
{
const T d0=p1[0]-pts[idx_p2].x;
const T d1=p1[1]-pts[idx_p2].y;
const T d2=p1[2]-pts[idx_p2].z;
return d0*d0+d1*d1+d2*d2;
}
inline T kdtree_get_pt(const size_t idx, int dim) const
{
if (dim==0) return pts[idx].x;
else if (dim==1) return pts[idx].y;
else return pts[idx].z;
}
template <class BBOX>
bool kdtree_get_bbox(BBOX &bb) const { return false; }
};
void testKDTree(CMesh& mesh, std::vector<unsigned int>& test_indeces, std::vector<vcg::Point3f>& randomSamples)
{
std::cout << "==================================================="<< std::endl;
std::cout << "KDTree" << std::endl;
int t0=clock();
// Construction of the kdTree
vcg::ConstDataWrapper<CMesh::VertexType::CoordType> wrapperVcg(&mesh.vert[0].P(), mesh.vert.size(), size_t(mesh.vert[1].P().V()) - size_t(mesh.vert[0].P().V()));
vcg::KdTree<CMesh::ScalarType> kdTreeVcg(wrapperVcg);
std::cout << "Build: " << elapsed(t0) << " ms" << std::endl;
int nn=1;
// Computation of the point radius
float mAveragePointSpacing = 0;
t0=clock();
#pragma omp parallel for reduction(+: mAveragePointSpacing) schedule(dynamic, 10)
for (int i = 0; i < mesh.vert.size(); i++)
{
#ifdef _OPENMP
nn =omp_get_num_threads();
#endif
vcg::KdTree<CMesh::ScalarType>::PriorityQueue queue;
kdTreeVcg.doQueryK(mesh.vert[i].cP(), 16, queue);
float newRadius = 2.0f * sqrt(queue.getWeight(0)/ queue.getNofElements());
mesh.vert[i].R() -= newRadius;
mAveragePointSpacing += newRadius;
}
std::cout << "Num trhread " << nn << std::endl;
mAveragePointSpacing /= mesh.vert.size();
std::cout << "Average point radius (OpenMP with" << nn << " threads) " << mAveragePointSpacing << std::endl;
std::cout << "Time (OpenMP): " << elapsed(t0) << " ms" << std::endl;
queryDist = mAveragePointSpacing * 150;
// Test with the radius search
std::cout << "Radius search (" << num_test << " tests)"<< std::endl;
float avgTime = 0.0f;
for (int ii = 0; ii < num_test; ii++)
{
int t0=clock();
std::vector<unsigned int> indeces;
std::vector<float> dists;
kdTreeVcg.doQueryDist(mesh.vert[test_indeces[ii]].cP(), queryDist, indeces, dists);
avgTime += elapsed(t0);
}
std::cout << "Time (radius = " << queryDist << "): " << avgTime << " ms (mean " << avgTime / num_test << "ms)" << std::endl;
// Test with the k-nearest search
std::cout << "k-Nearest search (" << num_test*10 << " tests)"<< std::endl;
avgTime = 0.0f;
for (int ii = 0; ii < num_test * 10; ii++)
{
int t0=clock();
vcg::KdTree<CMesh::ScalarType>::PriorityQueue queue;
kdTreeVcg.doQueryK(mesh.vert[test_indeces[ii]].cP(), kNearest, queue);
avgTime += elapsed(t0);
}
std::cout << "Time (k = " << kNearest << "): " << avgTime << " ms (mean " << avgTime / (num_test * 10) << "ms)" << std::endl;
// Test with the closest search
std::cout << "Closest search (" << num_test*10 << " tests)"<< std::endl;
avgTime = 0.0f;
for (int ii = 0; ii < num_test * 10; ii++)
{
int t0=clock();
unsigned int index;
float minDist;
kdTreeVcg.doQueryClosest(randomSamples[ii], index, minDist);
avgTime += elapsed(t0);
}
std::cout << "Time : " << avgTime << " ms (mean " << avgTime / (num_test * 10) << "ms)" << std::endl << std::endl;
}
void testNanoFLANN(CMesh& mesh, std::vector<unsigned int>& test_indeces, std::vector<vcg::Point3f> randomSamples)
{
std::cout << "==================================================="<< std::endl;
std::cout << "nanoFLANN" << std::endl;
PointCloud<float> cloud;
cloud.pts.resize(mesh.vert.size());
for (size_t i=0; i < mesh.vert.size(); i++)
{
cloud.pts[i].x = mesh.vert[i].P().X();
cloud.pts[i].y = mesh.vert[i].P().Y();
cloud.pts[i].z = mesh.vert[i].P().Z();
}
typedef nanoflann::KDTreeSingleIndexAdaptor<
nanoflann::L2_Simple_Adaptor<float, PointCloud<float> > ,
PointCloud<float>,
3 /* dim */
> my_kd_tree_t;
// Construction of the nanoFLANN KDtree
int t0=clock();
my_kd_tree_t index(3, cloud, nanoflann::KDTreeSingleIndexAdaptorParams(16) );
index.buildIndex();
std::cout << "Build nanoFlann: " << elapsed(t0) << " ms" << std::endl;
// Test with the radius search
std::cout << "Radius search (" << num_test << " tests)"<< std::endl;
float avgTime = 0.0f;
std::vector<std::pair<size_t,float> > ret_matches;
nanoflann::SearchParams params;
for (int ii = 0; ii < num_test; ii++)
{
t0=clock();
const size_t nMatches = index.radiusSearch(mesh.vert[test_indeces[ii]].P().V(), queryDist, ret_matches, params);
avgTime += elapsed(t0);
}
std::cout << "Time (radius = " << queryDist << "): " << avgTime << " ms (mean " << avgTime / num_test << "ms)" << std::endl;
// Test with the k-nearest search
std::cout << "k-Nearest search (" << num_test*10 << " tests)"<< std::endl;
avgTime = 0.0f;
std::vector<size_t> ret_index(kNearest);
std::vector<float> out_dist_sqr(kNearest);
for (int ii = 0; ii < num_test * 10; ii++)
{
t0=clock();
index.knnSearch(mesh.vert[test_indeces[ii]].P().V(), kNearest, &ret_index[0], &out_dist_sqr[0]);
avgTime += elapsed(t0);
}
std::cout << "Time (k = " << kNearest << "): " << avgTime << " ms (mean " << avgTime / (num_test * 10) << "ms)" << std::endl;
// Test with the closest search
std::cout << "Closest search (" << num_test*10 << " tests)"<< std::endl;
avgTime = 0.0f;
std::vector<size_t> ret_index_clos(1);
std::vector<float> out_dist_sqr_clos(1);
for (int ii = 0; ii < num_test * 10; ii++)
{
t0=clock();
index.knnSearch(randomSamples[ii].V(), 1, &ret_index_clos[0], &out_dist_sqr_clos[0]);
avgTime += elapsed(t0);
}
std::cout << "Time : " << avgTime << " ms (mean " << avgTime / (num_test * 10) << "ms)" << std::endl << std::endl;
}
void testUniformGrid(CMesh& mesh, std::vector<unsigned int>& test_indeces, std::vector<vcg::Point3f>& randomSamples)
{
std::cout << "==================================================="<< std::endl;
std::cout << "Uniform Grid" << std::endl;
int t0=clock();
// Construction of the uniform grid
typedef vcg::GridStaticPtr<CMesh::VertexType, CMesh::VertexType::ScalarType> MeshGrid;
MeshGrid uniformGrid;
uniformGrid.Set(mesh.vert.begin(), mesh.vert.end());
std::cout << "Build: " << elapsed(t0) << " ms" << std::endl;
// Test with the radius search
std::cout << "Radius search (" << num_test << " tests)"<< std::endl;
float avgTime = 0.0f;
for (int ii = 0; ii < num_test; ii++)
{
t0=clock();
std::vector<CMesh::VertexPointer> vertexPtr;
std::vector<CMesh::VertexType::CoordType> points;
std::vector<float> dists;
vcg::tri::GetInSphereVertex(mesh, uniformGrid, mesh.vert[test_indeces[ii]].cP(), queryDist, vertexPtr, dists, points);
avgTime += elapsed(t0);
}
std::cout << "Time (radius = " << queryDist << "): " << avgTime << " ms (mean " << avgTime / num_test << "ms)" << std::endl;
// Test with the k-nearest search
std::cout << "k-Nearest search (" << num_test*10 << " tests)"<< std::endl;
avgTime = 0.0f;
for (int ii = 0; ii < num_test * 10; ii++)
{
t0=clock();
std::vector<CMesh::VertexPointer> vertexPtr;
std::vector<CMesh::VertexType::CoordType> points;
std::vector<float> dists;
vcg::tri::GetKClosestVertex(mesh, uniformGrid, kNearest, mesh.vert[test_indeces[ii]].cP(), mesh.bbox.Diag(), vertexPtr, dists, points);
avgTime += elapsed(t0);
}
std::cout << "Time (k = " << kNearest << "): " << avgTime << " ms (mean " << avgTime / (num_test * 10) << "ms)" << std::endl;
// Test with the Closest search
std::cout << "Closest search (" << num_test*10 << " tests)"<< std::endl;
avgTime = 0.0f;
for (int ii = 0; ii < num_test * 10; ii++)
{
t0=clock();
float minDist;
vcg::tri::GetClosestVertex(mesh, uniformGrid, randomSamples[ii], mesh.bbox.Diag(), minDist);
avgTime += elapsed(t0);
}
std::cout << "Time : " << avgTime << " ms (mean " << avgTime / (num_test * 10) << "ms)" << std::endl << std::endl;
}
void testSpatialHashing(CMesh& mesh, std::vector<unsigned int>& test_indeces, std::vector<vcg::Point3f>& randomSamples)
{
std::cout << "==================================================="<< std::endl;
std::cout << "Spatial Hashing" << std::endl;
int t0=clock();
// Construction of the uniform grid
typedef vcg::SpatialHashTable<CMesh::VertexType, CMesh::VertexType::ScalarType> MeshGrid;
MeshGrid uniformGrid;
uniformGrid.Set(mesh.vert.begin(), mesh.vert.end());
std::cout << "Build: " << elapsed(t0) << " ms" << std::endl;
// Test with the radius search
std::cout << "Radius search (" << num_test << " tests)"<< std::endl;
float avgTime = 0.0f;
for (int ii = 0; ii < num_test; ii++)
{
t0=clock();
std::vector<CMesh::VertexPointer> vertexPtr;
std::vector<CMesh::VertexType::CoordType> points;
std::vector<float> dists;
vcg::tri::GetInSphereVertex(mesh, uniformGrid, mesh.vert[test_indeces[ii]].cP(), queryDist, vertexPtr, dists, points);
avgTime += elapsed(t0);
}
std::cout << "Time (radius = " << queryDist << "): " << avgTime << " ms (mean " << avgTime / num_test << "ms)" << std::endl;
// Test with the k-nearest search
std::cout << "k-Nearest search (" << num_test*10 << " tests)"<< std::endl;
avgTime = 0.0f;
for (int ii = 0; ii < num_test * 10; ii++)
{
t0=clock();
std::vector<CMesh::VertexPointer> vertexPtr;
std::vector<CMesh::VertexType::CoordType> points;
std::vector<float> dists;
vcg::tri::GetKClosestVertex(mesh, uniformGrid, kNearest, mesh.vert[test_indeces[ii]].cP(), mesh.bbox.Diag(), vertexPtr, dists, points);
avgTime += elapsed(t0);
}
std::cout << "Time (k = " << kNearest << "): " << avgTime << " ms (mean " << avgTime / (num_test * 10) << "ms)" << std::endl;
// Test with the Closest search
std::cout << "Closest search (" << num_test*10 << " tests)"<< std::endl;
avgTime = 0.0f;
for (int ii = 0; ii < num_test * 10; ii++)
{
t0=clock();
float minDist;
vcg::tri::GetClosestVertex(mesh, uniformGrid, randomSamples[ii], mesh.bbox.Diag(), minDist);
avgTime += elapsed(t0);
}
std::cout << "Time : " << avgTime << " ms (mean " << avgTime / (num_test * 10) << "ms)" << std::endl << std::endl;
}
void testPerfectSpatialHashing(CMesh& mesh, std::vector<unsigned int>& test_indeces)
{
std::cout << "==================================================="<< std::endl;
std::cout << "Perfect Spatial Hashing" << std::endl;
int t0=clock();
// Construction of the uniform grid
typedef vcg::SpatialHashTable<CMesh::VertexType, CMesh::VertexType::ScalarType> MeshGrid;
MeshGrid uniformGrid;
uniformGrid.Set(mesh.vert.begin(), mesh.vert.end());
std::cout << "Build: " << elapsed(t0) << " ms" << std::endl;
// Test with the radius search
std::cout << "Radius search (" << num_test << " tests)"<< std::endl;
float avgTime = 0.0f;
for (int ii = 0; ii < num_test; ii++)
{
t0=clock();
std::vector<CMesh::VertexPointer> vertexPtr;
std::vector<CMesh::VertexType::CoordType> points;
std::vector<float> dists;
vcg::tri::GetInSphereVertex(mesh, uniformGrid, mesh.vert[test_indeces[ii]].cP(), queryDist, vertexPtr, dists, points);
avgTime += elapsed(t0);
}
std::cout << "Time (radius = " << queryDist << "): " << avgTime << " ms (mean " << avgTime / num_test << "ms)" << std::endl << std::endl;
}
void testOctree(CMesh& mesh, std::vector<unsigned int>& test_indeces, std::vector<vcg::Point3f>& randomSamples)
{
std::cout << "==================================================="<< std::endl;
std::cout << "Octree" << std::endl;
int t0=clock();
// Construction of the uniform grid
typedef vcg::Octree<CMesh::VertexType, CMesh::VertexType::ScalarType> MeshGrid;
MeshGrid uniformGrid;
uniformGrid.Set(mesh.vert.begin(), mesh.vert.end());
std::cout << "Build: " << elapsed(t0) << " ms" << std::endl;
// Test with the radius search
std::cout << "Radius search (" << num_test << " tests)"<< std::endl;
float avgTime = 0.0f;
for (int ii = 0; ii < num_test; ii++)
{
t0=clock();
std::vector<CMesh::VertexPointer> vertexPtr;
std::vector<CMesh::VertexType::CoordType> points;
std::vector<float> dists;
vcg::tri::GetInSphereVertex(mesh, uniformGrid, mesh.vert[test_indeces[ii]].cP(), queryDist, vertexPtr, dists, points);
avgTime += elapsed(t0);
}
std::cout << "Time (radius = " << queryDist << "): " << avgTime << " ms (mean " << avgTime / num_test << "ms)" << std::endl;
// Test with the k-nearest search
std::cout << "k-Nearest search (" << num_test*10 << " tests)"<< std::endl;
avgTime = 0.0f;
for (int ii = 0; ii < num_test * 10; ii++)
{
t0=clock();
std::vector<CMesh::VertexPointer> vertexPtr;
std::vector<CMesh::VertexType::CoordType> points;
std::vector<float> dists;
vcg::tri::GetKClosestVertex(mesh, uniformGrid, kNearest, mesh.vert[test_indeces[ii]].cP(), mesh.bbox.Diag(), vertexPtr, dists, points);
avgTime += elapsed(t0);
}
std::cout << "Time (k = " << kNearest << "): " << avgTime << " ms (mean " << avgTime / (num_test * 10) << "ms)" << std::endl;
// Test with the Closest search
std::cout << "Closest search (" << num_test*10 << " tests)"<< std::endl;
avgTime = 0.0f;
for (int ii = 0; ii < num_test * 10; ii++)
{
t0=clock();
float minDist;
vcg::tri::GetClosestVertex(mesh, uniformGrid, randomSamples[ii], mesh.bbox.Diag(), minDist);
avgTime += elapsed(t0);
}
std::cout << "Time : " << avgTime << " ms (mean " << avgTime / (num_test * 10) << "ms)" << std::endl << std::endl;
}
int main( int argc, char * argv[] )
{
if (argc < 2) {
std::cout << "Invalid arguments" << std::endl;
exit(-1);
}
CMesh mesh;
if (vcg::tri::io::Importer<CMesh>::Open(mesh, argv[1]) != 0)
std::cout << "Invalid filename" << std::endl;
std::cout << "Mesh BBox diagonal: " << mesh.bbox.Diag() << std::endl;
std::cout << "Max point random offset: " << mesh.bbox.Diag() / 1000.0f << std::endl << std::endl;
vcg::math::MarsenneTwisterRNG randGen;
randGen.initialize(0);
std::vector<vcg::Point3f> randomSamples;
for (int i = 0; i < num_test * 10; i++)
randomSamples.push_back(vcg::math::GeneratePointOnUnitSphereUniform<float>(randGen) * randGen.generate01() * mesh.bbox.Diag() / bboxratio);
std::vector<unsigned int> test_indeces;
for (int i = 0; i < num_test * 10; i++)
{
int index = randGen.generate01() * (mesh.vert.size() - 1);
test_indeces.push_back(index);
randomSamples[i] += mesh.vert[i].P();
}
testKDTree(mesh, test_indeces, randomSamples);
testNanoFLANN(mesh, test_indeces, randomSamples);
testUniformGrid(mesh, test_indeces, randomSamples);
testSpatialHashing(mesh, test_indeces, randomSamples);
testPerfectSpatialHashing(mesh, test_indeces);
testOctree(mesh, test_indeces, randomSamples);
}