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#ifndef STIM_NETWORK_H
#define STIM_NETWORK_H
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#include <stdlib.h>
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#include <assert.h>
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#include <sstream>
#include <fstream>
#include <algorithm>
#include <string.h>
#include <math.h>
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#include <stim/math/vec3.h>
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#include <stim/visualization/obj.h>
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#include <stim/visualization/swc.h>
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#include <stim/visualization/cylinder.h>
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#include <stim/structures/kdtree.cuh>
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#include <stim/cuda/cudatools/timer.h>
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#ifdef __CUDACC__
//device gaussian function
__device__ float gaussianFunction(float x, float std){ return expf(-x/(2*std*std));}
//compute metric in parallel
template <typename T>
__global__ void d_metric(T* M, size_t n, T* D, float sigma){
size_t x = blockDim.x * blockIdx.x + threadIdx.x;
if(x >= n) return;
M[x] = 1.0f - gaussianFunction(D[x], sigma);
}
//find the corresponding edge index from array index
__global__ void d_findedge(size_t* I, size_t n, unsigned* R, size_t* E, size_t ne){
size_t x = blockDim.x * blockIdx.x + threadIdx.x;
if(x >= n) return;
unsigned i = 0;
size_t N = 0;
for(unsigned e = 0; e < ne; e++){
N += E[e];
if(I[x] < N){
R[x] = i;
break;
}
i++;
}
}
#endif
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int threshold_fac = 0;
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namespace stim{
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/** This is the a class that interfaces with gl_spider in order to store the currently
* segmented network. The following data is stored and can be extracted:
* 1)Network geometry and centerline.
* 2)Network connectivity (a graph of nodes and edges), reconstructed using ANN library.
*/
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template<typename T>
class network{
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///Each edge is a fiber with two nodes.
///Each node is an in index to the endpoint of the fiber in the nodes array.
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class edge : public cylinder<T>
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{
public:
unsigned v[2]; //unique id's designating the starting and ending
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// default constructor
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edge() : cylinder<T>() {
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v[1] = (unsigned)(-1); v[0] = (unsigned)(-1);
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}
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/// Constructor - creates an edge from a list of points by calling the stim::fiber constructor
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///@param p is an array of positions in space
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edge(stim::centerline<T> p) : cylinder<T>(p){}
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/// Copy constructor creates an edge from a cylinder
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edge(stim::cylinder<T> f) : cylinder<T>(f) {}
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/// Resamples an edge by calling the fiber resampling function
edge resample(T spacing){
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edge e(cylinder<T>::resample(spacing)); //call the fiber->edge constructor
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e.v[0] = v[0]; //copy the vertex data
e.v[1] = v[1];
return e; //return the new edge
}
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/// Output the edge information as a string
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std::string str(){
std::stringstream ss;
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ss<<"("<<cylinder<T>::size()<<")\tl = "<<this->length()<<"\t"<<v[0]<<"----"<<v[1];
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return ss.str();
}
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std::vector<edge> split(unsigned int idx){
std::vector< stim::cylinder<T> > C;
C.resize(2);
C = (*this).cylinder<T>::split(idx);
std::vector<edge> E(C.size());
for(unsigned e = 0; e < E.size(); e++){
E[e] = C[e];
}
return E;
}
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};
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///Node class that stores the physical position of the node as well as the edges it is connected to (edges that connect to it), As well as any additional data necessary.
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class vertex : public stim::vec3<T>
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{
public:
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//std::vector<unsigned int> edges; //indices of edges connected to this node.
std::vector<unsigned int> e[2]; //indices of edges going out (e[0]) and coming in (e[1])
//stim::vec3<T> p; //position of this node in physical space.
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//constructor takes a stim::vec
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vertex(stim::vec3<T> p) : stim::vec3<T>(p){}
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/// Output the vertex information as a string
std::string str(){
std::stringstream ss;
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ss<<"\t(x, y, z) = "<<stim::vec3<T>::str();
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if(e[0].size() > 0){
ss<<"\t> ";
for(unsigned int o = 0; o < e[0].size(); o++)
ss<<e[0][o]<<" ";
}
if(e[1].size() > 0){
ss<<"\t< ";
for(unsigned int i = 0; i < e[1].size(); i++)
ss<<e[1][i]<<" ";
}
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return ss.str();
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}
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};
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protected:
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std::vector<edge> E; //list of edges
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std::vector<vertex> V; //list of vertices.
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//std::vector<int> NT; //stores a list of neuronal type for each point in the centerline (will set value only when centerline is built from swc file)
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public:
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///default constructor
network()
{
}
///constructor with a file to load.
network(std::string fileLocation)
{
load_obj(fileLocation);
}
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///Returns the number of edges in the network.
unsigned int edges(){
return E.size();
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}
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///Returns the number of nodes in the network.
unsigned int vertices(){
return V.size();
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}
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//scale the network by some constant value
// I don't think these work??????
/*std::vector<vertex> operator*(T s){
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for (unsigned i=0; i< vertices; i ++ ){
V[i] = V[i] * s;
}
return V;
}
std::vector<vertex> operator*(vec<T> s){
for (unsigned i=0; i< vertices; i ++ ){
for (unsigned dim = 0 ; dim< 3; dim ++){
V[i][dim] = V[i][dim] * s[dim];
}
}
return V;
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}*/
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// Returns an average of branching index in the network
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double BranchingIndex(){
double B=0;
for(unsigned v=0; v < V.size(); v ++){
B += ((V[v].e[0].size()) + (V[v].e[1].size()));
}
B = B / V.size();
return B;
}
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// Returns number of branch points in thenetwork
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unsigned int BranchP(){
unsigned int B=0;
unsigned int c;
for(unsigned v=0; v < V.size(); v ++){
c = ((V[v].e[0].size()) + (V[v].e[1].size()));
if (c > 2){
B += 1;}
}
return B;
}
// Returns number of end points (tips) in thenetwork
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unsigned int EndP(){
unsigned int B=0;
unsigned int c;
for(unsigned v=0; v < V.size(); v ++){
c = ((V[v].e[0].size()) + (V[v].e[1].size()));
if (c == 1){
B += 1;}
}
return B;
}
//// Returns a dictionary with the key as the vertex
//std::map<std::vector<vertex>,unsigned int> DegreeDict(){
// std::map<std::vector<vertex>,unsigned int> dd;
// unsigned int c = 0;
// for(unsigned v=0; v < V.size(); v ++){
// c = ((V[v].e[0].size()) + (V[v].e[1].size()));
// dd[V[v]] = c;
// }
// return dd;
//}
//// Return number of branching stems
//unsigned int Stems(){
// unsigned int s = 0;
// std::map<std::vector<vertex>,unsigned int> dd;
// dd = DegreeDict();
// //for(unsigned v=0; v < V.size(); v ++){
// // V[v].e[0].
// return s;
//}
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//Calculate Metrics---------------------------------------------------
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// Returns an average of fiber/edge lengths in the network
double Lengths(){
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stim::vec<T> L;
double sumLength = 0;
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for(unsigned e = 0; e < E.size(); e++){ //for each edge in the network
L.push_back(E[e].length()); //append the edge length
sumLength = sumLength + E[e].length();
}
double avg = sumLength / E.size();
return avg;
}
// Returns an average of tortuosities in the network
double Tortuosities(){
stim::vec<T> t;
stim::vec<T> id1, id2; // starting and ending vertices of the edge
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double distance;double tortuosity;double sumTortuosity = 0;
for(unsigned e = 0; e < E.size(); e++){ //for each edge in the network
id1 = E[e][0]; //get the edge starting point
id2 = E[e][E[e].size() - 1]; //get the edge ending point
distance = (id1 - id2).len(); //displacement between the starting and ending points
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if(distance > 0){
tortuosity = E[e].length()/ distance ; // tortuoisty = edge length / edge displacement
}
else{
tortuosity = 0;}
t.push_back(tortuosity);
sumTortuosity += tortuosity;
}
double avg = sumTortuosity / E.size();
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return avg;
}
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// Returns average contraction of the network
double Contractions(){
stim::vec<T> t;
stim::vec<T> id1, id2; // starting and ending vertices of the edge
double distance;double contraction;double sumContraction = 0;
for(unsigned e = 0; e < E.size(); e++){ //for each edge in the network
id1 = E[e][0]; //get the edge starting point
id2 = E[e][E[e].size() - 1]; //get the edge ending point
distance = (id1 - id2).len(); //displacement between the starting and ending points
contraction = distance / E[e].length(); // tortuoisty = edge length / edge displacement
t.push_back(contraction);
sumContraction += contraction;
}
double avg = sumContraction / E.size();
return avg;
}
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// returns average fractal dimension of the branches of the network
double FractalDimensions(){
stim::vec<T> t;
stim::vec<T> id1, id2; // starting and ending vertices of the edge
double distance;double fract;double sumFractDim = 0;
for(unsigned e = 0; e < E.size(); e++){ //for each edge in the network
id1 = E[e][0]; //get the edge starting point
id2 = E[e][E[e].size() - 1]; //get the edge ending point
distance = (id1 - id2).len(); //displacement between the starting and ending points
fract = std::log(distance) / std::log(E[e].length()); // tortuoisty = edge length / edge displacement
t.push_back(sumFractDim);
sumFractDim += fract;
}
double avg = sumFractDim / E.size();
return avg;
}
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//returns a cylinder represented a given fiber (based on edge index)
stim::cylinder<T> get_cylinder(unsigned e){
return E[e]; //return the specified edge (casting it to a fiber)
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}
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//load a network from an OBJ file
void load_obj(std::string filename){
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stim::obj<T> O; //create an OBJ object
O.load(filename); //load the OBJ file as an object
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std::vector<unsigned> id2vert; //this list stores the OBJ vertex ID associated with each network vertex
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unsigned i[2]; //temporary, IDs associated with the first and last points in an OBJ line
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//for each line in the OBJ object
for(unsigned int l = 1; l <= O.numL(); l++){
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std::vector< stim::vec<T> > c; //allocate an array of points for the vessel centerline
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O.getLine(l, c); //get the fiber centerline
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stim::centerline<T> c3(c.size());
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for(size_t j = 0; j < c.size(); j++)
c3[j] = c[j];
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c3.update();
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// edge new_edge = c3; ///This is dangerous.
edge new_edge(c3);
//create an edge from the given centerline
unsigned int I = new_edge.size(); //calculate the number of points on the centerline
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//get the first and last vertex IDs for the line
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std::vector< unsigned > id; //create an array to store the centerline point IDs
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O.getLinei(l, id); //get the list of point IDs for the line
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i[0] = id.front(); //get the OBJ ID for the first element of the line
i[1] = id.back(); //get the OBJ ID for the last element of the line
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std::vector<unsigned>::iterator it; //create an iterator for searching the id2vert array
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unsigned it_idx; //create an integer for the id2vert entry index
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//find out if the nodes for this fiber have already been created
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it = find(id2vert.begin(), id2vert.end(), i[0]); //look for the first node
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|
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|
if(it == id2vert.end()){ //if i[0] hasn't already been used
|
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|
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vertex new_vertex = new_edge[0]; //create a new vertex, assign it a position
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new_vertex.e[0].push_back(E.size()); //add the current edge as outgoing
new_edge.v[0] = V.size(); //add the new edge to the edge
V.push_back(new_vertex); //add the new vertex to the vertex list
id2vert.push_back(i[0]); //add the ID to the ID->vertex conversion list
|
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|
}
|
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|
368
|
else{ //if the vertex already exists
|
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|
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|
it_idx = std::distance(id2vert.begin(), it);
|
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V[it_idx].e[0].push_back(E.size()); //add the current edge as outgoing
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new_edge.v[0] = it_idx;
|
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|
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}
|
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|
374
|
it = find(id2vert.begin(), id2vert.end(), i[1]); //look for the second ID
|
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|
375
376
377
|
if(it == id2vert.end()){ //if i[1] hasn't already been used
vertex new_vertex = new_edge[I-1]; //create a new vertex, assign it a position
new_vertex.e[1].push_back(E.size()); //add the current edge as incoming
|
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Pavel Govyadinov
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|
378
|
new_edge.v[1] = V.size(); //add the new vertex to the edge
|
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|
379
380
|
V.push_back(new_vertex); //add the new vertex to the vertex list
id2vert.push_back(i[1]); //add the ID to the ID->vertex conversion list
|
df036f4c
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|
381
|
}
|
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|
382
|
else{ //if the vertex already exists
|
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|
383
|
it_idx = std::distance(id2vert.begin(), it);
|
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pranathivemuri
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|
384
|
V[it_idx].e[1].push_back(E.size()); //add the current edge as incoming
|
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added code to sub...
|
385
|
new_edge.v[1] = it_idx;
|
df036f4c
David Mayerich
added the network...
|
386
|
}
|
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|
387
|
|
2e0f052a
Pavel Govyadinov
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|
388
|
E.push_back(new_edge); //push the edge to the list
|
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|
389
|
|
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|
390
|
}
|
e376212f
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|
391
392
|
}
|
58674036
Jiaming Guo
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|
393
|
//load a network from an SWC file
|
1599ae65
Jiaming Guo
add swc.h and fun...
|
394
395
396
397
|
void load_swc(std::string filename) {
stim::swc<T> S; // create swc variable
S.load(filename); // load the node information
S.create_tree(); // link those node according to their linking relationships as a tree
|
2b28db3e
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|
398
|
S.resample();
|
1599ae65
Jiaming Guo
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|
399
|
|
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Jiaming Guo
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|
400
|
//NT.push_back(S.node[0].type); // set the neuronal_type value to the first vertex in the network
|
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Jiaming Guo
add swc.h and fun...
|
401
402
|
std::vector<unsigned> id2vert; // this list stores the SWC vertex ID associated with each network vertex
unsigned i[2]; // temporary, IDs associated with the first and last points
|
2b28db3e
Jiaming Guo
better render
|
403
404
|
for (unsigned int l = 0; l < S.numE(); l++) { // for every edge
|
30b20d4f
Jiaming Guo
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|
405
|
//NT.push_back(S.node[l].type);
|
2b28db3e
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|
406
407
408
409
410
411
412
413
|
std::vector< stim::vec3<T> > c;
S.get_points(l, c);
stim::centerline<T> c3(c.size()); // new fiber
for (unsigned j = 0; j < c.size(); j++)
c3[j] = c[j]; // copy the points
|
1599ae65
Jiaming Guo
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|
414
415
|
c3.update(); // update the L information
|
58674036
Jiaming Guo
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|
416
417
418
419
|
stim::cylinder<T> C3(c3); // create a new cylinder in order to copy the origin radius information
// upadate the R information
std::vector<T> radius;
|
2b28db3e
Jiaming Guo
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|
420
421
|
S.get_radius(l, radius);
|
58674036
Jiaming Guo
fix minor error f...
|
422
423
|
C3.copy_r(radius);
|
2b28db3e
Jiaming Guo
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|
424
425
426
427
|
edge new_edge(C3); // new edge
//create an edge from the given centerline
unsigned int I = new_edge.size(); //calculate the number of points on the centerline
|
58674036
Jiaming Guo
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|
428
|
|
1599ae65
Jiaming Guo
add swc.h and fun...
|
429
|
//get the first and last vertex IDs for the line
|
2b28db3e
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|
430
431
|
i[0] = S.E[l].front();
i[1] = S.E[l].back();
|
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Jiaming Guo
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|
432
433
434
435
436
437
438
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452
|
std::vector<unsigned>::iterator it; //create an iterator for searching the id2vert array
unsigned it_idx; //create an integer for the id2vert entry index
//find out if the nodes for this fiber have already been created
it = find(id2vert.begin(), id2vert.end(), i[0]); //look for the first node
if (it == id2vert.end()) { //if i[0] hasn't already been used
vertex new_vertex = new_edge[0]; //create a new vertex, assign it a position
new_vertex.e[0].push_back(E.size()); //add the current edge as outgoing
new_edge.v[0] = V.size(); //add the new edge to the edge
V.push_back(new_vertex); //add the new vertex to the vertex list
id2vert.push_back(i[0]); //add the ID to the ID->vertex conversion list
}
else { //if the vertex already exists
it_idx = std::distance(id2vert.begin(), it);
V[it_idx].e[0].push_back(E.size()); //add the current edge as outgoing
new_edge.v[0] = it_idx;
}
it = find(id2vert.begin(), id2vert.end(), i[1]); //look for the second ID
if (it == id2vert.end()) { //if i[1] hasn't already been used
|
2b28db3e
Jiaming Guo
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|
453
|
vertex new_vertex = new_edge[I - 1]; //create a new vertex, assign it a position
|
1599ae65
Jiaming Guo
add swc.h and fun...
|
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
|
new_vertex.e[1].push_back(E.size()); //add the current edge as incoming
new_edge.v[1] = V.size(); //add the new vertex to the edge
V.push_back(new_vertex); //add the new vertex to the vertex list
id2vert.push_back(i[1]); //add the ID to the ID->vertex conversion list
}
else { //if the vertex already exists
it_idx = std::distance(id2vert.begin(), it);
V[it_idx].e[1].push_back(E.size()); //add the current edge as incoming
new_edge.v[1] = it_idx;
}
E.push_back(new_edge); //push the edge to the list
}
}
|
7f27eafa
David Mayerich
simplified the st...
|
469
470
|
/// Output the network as a string
std::string str(){
|
e376212f
David Mayerich
added support for...
|
471
|
|
7f27eafa
David Mayerich
simplified the st...
|
472
473
474
475
|
std::stringstream ss;
ss<<"Nodes ("<<V.size()<<")--------"<<std::endl;
for(unsigned int v = 0; v < V.size(); v++){
ss<<"\t"<<v<<V[v].str()<<std::endl;
|
e376212f
David Mayerich
added support for...
|
476
477
|
}
|
7f27eafa
David Mayerich
simplified the st...
|
478
479
480
|
ss<<"Edges ("<<E.size()<<")--------"<<std::endl;
for(unsigned e = 0; e < E.size(); e++){
ss<<"\t"<<e<<E[e].str()<<std::endl;
|
df036f4c
David Mayerich
added the network...
|
481
482
|
}
|
7f27eafa
David Mayerich
simplified the st...
|
483
|
return ss.str();
|
df036f4c
David Mayerich
added the network...
|
484
|
}
|
3800c27f
David Mayerich
added code to sub...
|
485
486
|
/// This function resamples all fibers in a network given a desired minimum spacing
|
9c97e126
David Mayerich
added an axis-ali...
|
487
|
/// @param spacing is the minimum distance between two points on the network
|
3800c27f
David Mayerich
added code to sub...
|
488
|
stim::network<T> resample(T spacing){
|
2e0f052a
Pavel Govyadinov
minor changes to ...
|
489
490
|
stim::network<T> n; //create a new network that will be an exact copy, with resampled fibers
n.V = V; //copy all vertices
|
30b20d4f
Jiaming Guo
make it render be...
|
491
|
//n.NT = NT; //copy all the neuronal type information
|
2e0f052a
Pavel Govyadinov
minor changes to ...
|
492
|
n.E.resize(edges()); //allocate space for the edge list
|
3800c27f
David Mayerich
added code to sub...
|
493
494
|
//copy all fibers, resampling them in the process
|
2e0f052a
Pavel Govyadinov
minor changes to ...
|
495
496
|
for(unsigned e = 0; e < edges(); e++){ //for each edge in the edge list
n.E[e] = E[e].resample(spacing); //resample the edge and copy it to the new network
|
3800c27f
David Mayerich
added code to sub...
|
497
498
|
}
|
2e0f052a
Pavel Govyadinov
minor changes to ...
|
499
|
return n; //return the resampled network
|
4df9ec0e
pranathivemuri
compare networks ...
|
500
501
|
}
|
c72184d1
Jiaming Guo
add many function...
|
502
503
|
// host gaussian function
__host__ float gaussianFunction(float x, float std = 25){ return exp(-x/(2*std*std));} // std default value is 25
|
4df9ec0e
pranathivemuri
compare networks ...
|
504
|
|
9c97e126
David Mayerich
added an axis-ali...
|
505
|
/// Calculate the total number of points on all edges.
|
797f8ab0
David Mayerich
cleaned up the st...
|
506
507
508
509
|
unsigned total_points(){
unsigned n = 0;
for(unsigned e = 0; e < E.size(); e++)
n += E[e].size();
|
4df9ec0e
pranathivemuri
compare networks ...
|
510
|
return n;
|
797f8ab0
David Mayerich
cleaned up the st...
|
511
|
}
|
4df9ec0e
pranathivemuri
compare networks ...
|
512
|
|
fcd2eb7c
David Mayerich
added support for...
|
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
|
//Copy the point cloud representing the centerline for the network into an array
void centerline_cloud(T* dst) {
size_t p; //stores the current edge point
size_t P; //stores the number of points in an edge
size_t i = 0; //index into the output array of points
for (size_t e = 0; e < E.size(); e++) { //for each edge in the network
P = E[e].size(); //get the number of points in this edge
for (p = 0; p < P; p++) {
dst[i * 3 + 0] = E[e][p][0];
dst[i * 3 + 1] = E[e][p][1];
dst[i * 3 + 2] = E[e][p][2];
i++;
}
}
}
|
fbbc07be
Jiaming Guo
replace ANN by kd...
|
529
530
|
// convert vec3 to array
void stim2array(float *a, stim::vec3<T> b){
|
797f8ab0
David Mayerich
cleaned up the st...
|
531
532
533
534
535
|
a[0] = b[0];
a[1] = b[1];
a[2] = b[2];
}
|
c72184d1
Jiaming Guo
add many function...
|
536
|
// convert vec3 to array in bunch
|
c9712fb4
Jiaming Guo
fix minor error w...
|
537
|
void edge2array(T* a, edge b){
|
c72184d1
Jiaming Guo
add many function...
|
538
539
540
541
542
543
544
545
|
size_t n = b.size();
for(size_t i = 0; i < n; i++){
a[i * 3 + 0] = b[i][0];
a[i * 3 + 1] = b[i][1];
a[i * 3 + 2] = b[i][2];
}
}
|
9c97e126
David Mayerich
added an axis-ali...
|
546
547
548
549
|
/// Calculate the average magnitude across the entire network.
/// @param m is the magnitude value to use. The default is 0 (usually radius).
T average(unsigned m = 0){
|
2e0f052a
Pavel Govyadinov
minor changes to ...
|
550
551
552
|
T M, L; //allocate space for the total magnitude and length
M = L = 0; //initialize both the initial magnitude and length to zero
for(unsigned e = 0; e < E.size(); e++){ //for each edge in the network
|
c72184d1
Jiaming Guo
add many function...
|
553
|
M += E[e].integrate(); //get the integrated magnitude
|
2e0f052a
Pavel Govyadinov
minor changes to ...
|
554
|
L += E[e].length(); //get the edge length
|
9c97e126
David Mayerich
added an axis-ali...
|
555
|
}
|
4df9ec0e
pranathivemuri
compare networks ...
|
556
|
|
2e0f052a
Pavel Govyadinov
minor changes to ...
|
557
|
return M / L; //return the average magnitude
|
9c97e126
David Mayerich
added an axis-ali...
|
558
559
560
|
}
/// This function compares two networks and returns the percentage of the current network that is missing from A.
|
9c97e126
David Mayerich
added an axis-ali...
|
561
562
|
/// @param A is the network to compare to - the field is generated for A
/// @param sigma is the user-defined tolerance value - smaller values provide a stricter comparison
|
eb554b48
David Mayerich
bug fixes for Net...
|
563
|
stim::network<T> compare(stim::network<T> A, float sigma, int device = -1){
|
9c97e126
David Mayerich
added an axis-ali...
|
564
|
|
fcd2eb7c
David Mayerich
added support for...
|
565
566
|
stim::network<T> R; //generate a network storing the result of the comparison
R = (*this); //initialize the result with the current network
|
9c97e126
David Mayerich
added an axis-ali...
|
567
|
|
fcd2eb7c
David Mayerich
added support for...
|
568
|
T *c; // centerline (array of double pointers) - points on kdtree must be double
|
41100e51
Jiaming Guo
add cpu kdtree ve...
|
569
|
size_t n_data = A.total_points(); // set the number of points
|
c9712fb4
Jiaming Guo
fix minor error w...
|
570
|
c = (T*) malloc(sizeof(T) * n_data * 3); // allocate an array to store all points in the data set
|
9c97e126
David Mayerich
added an axis-ali...
|
571
|
|
797f8ab0
David Mayerich
cleaned up the st...
|
572
|
unsigned t = 0;
|
fcd2eb7c
David Mayerich
added support for...
|
573
574
|
for(unsigned e = 0; e < A.E.size(); e++){ //for each edge in the network
for(unsigned p = 0; p < A.E[e].size(); p++){ //for each point in the edge
|
797f8ab0
David Mayerich
cleaned up the st...
|
575
576
|
for(unsigned d = 0; d < 3; d++){ //for each coordinate
|
fcd2eb7c
David Mayerich
added support for...
|
577
|
c[t * 3 + d] = A.E[e][p][d]; //copy the point into the array c
|
4df9ec0e
pranathivemuri
compare networks ...
|
578
|
}
|
797f8ab0
David Mayerich
cleaned up the st...
|
579
|
t++;
|
4df9ec0e
pranathivemuri
compare networks ...
|
580
|
}
|
797f8ab0
David Mayerich
cleaned up the st...
|
581
582
|
}
|
41100e51
Jiaming Guo
add cpu kdtree ve...
|
583
|
//generate a KD-tree for network A
|
fcd2eb7c
David Mayerich
added support for...
|
584
|
size_t MaxTreeLevels = 3; // max tree level
|
41100e51
Jiaming Guo
add cpu kdtree ve...
|
585
586
587
|
#ifdef __CUDACC__
cudaSetDevice(device);
|
c72184d1
Jiaming Guo
add many function...
|
588
589
|
stim::kdtree<T, 3> kdt; // initialize a pointer to a kd tree
|
41100e51
Jiaming Guo
add cpu kdtree ve...
|
590
|
kdt.create(c, n_data, MaxTreeLevels); // build a KD tree
|
797f8ab0
David Mayerich
cleaned up the st...
|
591
|
|
9c97e126
David Mayerich
added an axis-ali...
|
592
|
for(unsigned e = 0; e < R.E.size(); e++){ //for each edge in A
|
0d0ef1a9
David Mayerich
Adapted classes t...
|
593
594
|
//R.E[e].add_mag(0); //add a new magnitude for the metric
//size_t errormag_id = R.E[e].nmags() - 1; //get the id for the new magnitude
|
c72184d1
Jiaming Guo
add many function...
|
595
596
|
size_t n = R.E[e].size(); // the number of points in current edge
|
c9712fb4
Jiaming Guo
fix minor error w...
|
597
|
T* queryPt = new T[3 * n];
|
c72184d1
Jiaming Guo
add many function...
|
598
599
600
|
T* m1 = new T[n];
T* dists = new T[n];
size_t* nnIdx = new size_t[n];
|
797f8ab0
David Mayerich
cleaned up the st...
|
601
|
|
c72184d1
Jiaming Guo
add many function...
|
602
603
604
605
|
T* d_dists;
T* d_m1;
cudaMalloc((void**)&d_dists, n * sizeof(T));
cudaMalloc((void**)&d_m1, n * sizeof(T));
|
797f8ab0
David Mayerich
cleaned up the st...
|
606
|
|
c72184d1
Jiaming Guo
add many function...
|
607
608
609
610
611
612
613
614
615
616
617
618
619
620
|
edge2array(queryPt, R.E[e]);
kdt.search(queryPt, n, nnIdx, dists);
cudaMemcpy(d_dists, dists, n * sizeof(T), cudaMemcpyHostToDevice); // copy dists from host to device
// configuration parameters
size_t threads = (1024>n)?n:1024;
size_t blocks = n/threads + (n%threads)?1:0;
d_metric<<<blocks, threads>>>(d_m1, n, d_dists, sigma); //calculate the metric value based on the distance
cudaMemcpy(m1, d_m1, n * sizeof(T), cudaMemcpyDeviceToHost);
for(unsigned p = 0; p < n; p++){
|
0d0ef1a9
David Mayerich
Adapted classes t...
|
621
|
R.E[e].set_r(p, m1[p]);
|
c72184d1
Jiaming Guo
add many function...
|
622
623
624
625
626
627
628
629
630
631
|
}
//d_set_mag<<<blocks, threads>>>(R.E[e].M, errormag_id, n, m1);
}
#else
stim::kdtree<T, 3> kdt;
kdt.create(c, n_data, MaxTreeLevels);
for(unsigned e = 0; e < R.E.size(); e++){ //for each edge in A
|
c72184d1
Jiaming Guo
add many function...
|
632
633
|
size_t n = R.E[e].size(); // the number of points in current edge
|
c9712fb4
Jiaming Guo
fix minor error w...
|
634
|
T* query = new T[3 * n];
|
c72184d1
Jiaming Guo
add many function...
|
635
636
637
638
639
640
641
642
643
|
T* m1 = new T[n];
T* dists = new T[n];
size* nnIdx = new size_t[n];
edge2array(queryPt, R.E[e]);
kdt.cpu_search(queryPt, n, nnIdx, dists); //find the distance between A and the current network
for(unsigned p = 0; p < R.E[e].size(); p++){
|
2b28db3e
Jiaming Guo
better render
|
644
645
|
m1[p] = 1.0f - gaussianFunction((T)dists[p], sigma); //calculate the metric value based on the distance
R.E[e].set_r(p, m1[p]); //set the error for the second point in the segment
|
c72184d1
Jiaming Guo
add many function...
|
646
647
648
649
650
651
652
653
654
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}
}
#endif
return R; //return the resulting network
}
/// This function compares two networks and split the current one according to the nearest neighbor of each point in each edge
/// @param A is the network to split
/// @param B is the corresponding mapping network
/// @param sigma is the user-defined tolerance value - smaller values provide a stricter comparison
/// @param device is the device that user want to use
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void split(stim::network<T> A, stim::network<T> B, float sigma, int device, float threshold){
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T *c;
size_t n_data = B.total_points();
c = (T*) malloc(sizeof(T) * n_data * 3);
size_t NB = B.E.size(); // the number of edges in B
unsigned t = 0;
for(unsigned e = 0; e < NB; e++){ // for every edge in B
for(unsigned p = 0; p < B.E[e].size(); p++){ // for every points in B.E[e]
for(unsigned d = 0; d < 3; d++){
c[t * 3 + d] = B.E[e][p][d]; // convert to array
}
t++;
}
}
size_t MaxTreeLevels = 3; // max tree level
#ifdef __CUDACC__
cudaSetDevice(device);
stim::kdtree<T, 3> kdt; // initialize a pointer to a kd tree
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//compare each point in the current network to the field produced by A
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kdt.create(c, n_data, MaxTreeLevels); // build a KD tree
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std::vector<std::vector<unsigned> > relation; // the relationship between GT and T corresponding to NN
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relation.resize(A.E.size());
for(unsigned e = 0; e < A.E.size(); e++){ //for each edge in A
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//A.E[e].add_mag(0); //add a new magnitude for the metric
//size_t errormag_id = A.E[e].nmags() - 1;
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size_t n = A.E[e].size(); // the number of edges in A
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T* queryPt = new T[3 * n]; // set of all the points in current edge
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T* m1 = new T[n]; // array of metrics for every point in current edge
T* dists = new T[n]; // store the distances for every point in current edge
size_t* nnIdx = new size_t[n]; // store the indices for every point in current edge
// define pointers in device
T* d_dists;
T* d_m1;
size_t* d_nnIdx;
// allocate memory for defined pointers
cudaMalloc((void**)&d_dists, n * sizeof(T));
cudaMalloc((void**)&d_m1, n * sizeof(T));
cudaMalloc((void**)&d_nnIdx, n * sizeof(size_t));
edge2array(queryPt, A.E[e]); // convert edge to array
kdt.search(queryPt, n, nnIdx, dists); // search the tree to find the NN for every point in current edge
cudaMemcpy(d_dists, dists, n * sizeof(T), cudaMemcpyHostToDevice); // copy dists from host to device
cudaMemcpy(d_nnIdx, nnIdx, n * sizeof(size_t), cudaMemcpyHostToDevice); // copy Idx from host to device
// configuration parameters
size_t threads = (1024>n)?n:1024; // test to see whether the number of point in current edge is more than 1024
size_t blocks = n/threads + (n%threads)?1:0;
d_metric<<<blocks, threads>>>(d_m1, n, d_dists, sigma); // calculate the metrics in parallel
cudaMemcpy(m1, d_m1, n * sizeof(T), cudaMemcpyDeviceToHost);
for(unsigned p = 0; p < n; p++){
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A.E[e].set_r(p, m1[p]); // set the error(radius) value to every point in current edge
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}
relation[e].resize(n); // resize every edge relation size
unsigned* d_relation;
cudaMalloc((void**)&d_relation, n * sizeof(unsigned)); // allocate memory
std::vector<size_t> edge_point_num(NB); // %th element is the number of points that %th edge has
for(unsigned ee = 0; ee < NB; ee++)
edge_point_num[ee] = B.E[ee].size();
size_t* d_edge_point_num;
cudaMalloc((void**)&d_edge_point_num, NB * sizeof(size_t));
cudaMemcpy(d_edge_point_num, &edge_point_num[0], NB * sizeof(size_t), cudaMemcpyHostToDevice);
d_findedge<<<blocks, threads>>>(d_nnIdx, n, d_relation, d_edge_point_num, NB); // find the edge corresponding to the array index in parallel
cudaMemcpy(&relation[e][0], d_relation, n * sizeof(unsigned), cudaMemcpyDeviceToHost); //copy relationship from device to host
}
#else
stim::kdtree<T, 3> kdt;
kdt.create(c, n_data, MaxTreeLevels);
std::vector<std::vector<unsigned>> relation; // the mapping relationship between two networks
relation.resize(A.E.size());
for(unsigned i = 0; i < A.E.size(); i++)
relation[i].resize(A.E[i].size());
std::vector<size_t> edge_point_num(NB); //%th element is the number of points that %th edge has
for(unsigned ee = 0; ee < NB; ee++)
edge_point_num[ee] = B.E[ee].size();
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for(unsigned e = 0; e < A.E.size(); e++){ //for each edge in A
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size_t n = A.E[e].size(); //the number of edges in A
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T* queryPt = new T[3 * n];
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T* m1 = new T[n];
T* dists = new T[n]; //store the distances
size_t* nnIdx = new size_t[n]; //store the indices
edge2array(queryPt, A.E[e]);
kdt.search(queryPt, n, nnIdx, dists);
for(unsigned p = 0; p < A.E[e].size(); p++){
m1[p] = 1.0f - gaussianFunction((T)dists[p], sigma); //calculate the metric value based on the distance
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A.E[e].set_r(p, m1[p]); //set the error for the second point in the segment
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unsigned id = 0; //mapping edge's idx
size_t num = 0; //total number of points before #th edge
for(unsigned i = 0; i < NB; i++){
num += B.E[i].size();
if(nnIdx[p] < num){ //find the edge it belongs to
relation[e][p] = id;
break;
}
id++; //current edge won't be the one, move to next edge
}
}
}
#endif
E = A.E;
V = A.V;
unsigned int id = 0; // split value
for(unsigned e = 0; e < E.size(); e++){ // for every edge
for(unsigned p = 0; p < E[e].size() - 1; p++){ // for every point in each edge
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int t = E[e].length() / sigma * 2;
if (t <= 20)
threshold_fac = E[e].size();
else
threshold_fac = (E[e].length() / sigma * 2)/10;
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if(relation[e][p] != relation[e][p + 1]){ // find the nearest edge changing point
id = p + 1; // if there is no change in NN
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if(id < threshold_fac || (E[e].size() - id) < threshold_fac)
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id = E[e].size() - 1; // extreme situation is not acceptable
else
break;
}
if(p == E[e].size() - 2) // if there is no splitting index, set the id to the last point index of current edge
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id = E[e].size() - 1;
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}
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//unsigned errormag_id = E[e].nmags() - 1;
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T G = 0; // test to see whether it has its nearest neighbor
for(unsigned i = 0; i < E[e].size(); i++)
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G += E[e].r(i); // won't split special edges
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if(G / E[e].size() > threshold) // should based on the color map
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id = E[e].size() - 1; // set split idx to outgoing direction vertex
std::vector<edge> tmpe;
tmpe.resize(2);
tmpe = E[e].split(id);
vertex tmpv = stim::vec3<T>(-1, -1, 0); // store the split point as vertex
if(tmpe.size() == 2){
relation.resize(relation.size() + 1);
for(unsigned d = id; d < E[e].size(); d++)
relation[relation.size() - 1].push_back(relation[e][d]);
tmpe[0].v[0] = E[e].v[0]; // begining vertex of first half edge -> original begining vertex
tmpe[1].v[1] = E[e].v[1]; // ending vertex of second half edge -> original ending vertex
tmpv = E[e][id];
V.push_back(tmpv);
tmpe[0].v[1] = (unsigned)V.size() - 1; // ending vertex of first half edge -> new vertex
tmpe[1].v[0] = (unsigned)V.size() - 1; // begining vertex of second half edge -> new vertex
edge tmp(E[e]);
E[e] = tmpe[0]; // replace original edge by first half edge
E.push_back(tmpe[1]); // push second half edge to the last
V[V.size() - 1].e[1].push_back(e); // push first half edge to the incoming of new vertex
V[V.size() - 1].e[0].push_back((unsigned)E.size() - 1); // push second half edge to the outgoing of new vertex
for(unsigned i = 0; i < V[tmp.v[1]].e[1].size(); i++) // find the incoming edge of original ending vertex
if(V[tmp.v[1]].e[1][i] == e)
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V[tmp.v[1]].e[1][i] = (unsigned)E.size() - 1; // set to new edge
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}
}
}
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/// This function compares two splitted networks and yields a mapping relationship between them according to NN
/// @param B is the network that the current network is going to map to
/// @param C is the mapping relationship: C[e1] = _e1 means e1 edge in current network is mapping the _e1 edge in B
/// @param device is the device that user want to use
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void mapping(stim::network<T> B, std::vector<unsigned> &C, int device, float threshold){
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stim::network<T> A; //generate a network storing the result of the comparison
A = (*this);
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size_t n = A.E.size(); // the number of edges in A
size_t NB = B.E.size(); // the number of edges in B
C.resize(A.E.size());
T *c; // centerline (array of double pointers) - points on kdtree must be double
size_t n_data = B.total_points(); // set the number of points
c = (T*) malloc(sizeof(T) * n_data * 3);
unsigned t = 0;
for(unsigned e = 0; e < NB; e++){ // for each edge in the network
for(unsigned p = 0; p < B.E[e].size(); p++){ // for each point in the edge
for(unsigned d = 0; d < 3; d++){ // for each coordinate
c[t * 3 + d] = B.E[e][p][d];
}
t++;
}
}
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//generate a KD-tree for network A
//float metric = 0.0; // initialize metric to be returned after comparing the network
size_t MaxTreeLevels = 3; // max tree level
#ifdef __CUDACC__
cudaSetDevice(device);
stim::kdtree<T, 3> kdt; // initialize a pointer to a kd tree
kdt.create(c, n_data, MaxTreeLevels); // build a KD tree
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for(unsigned e = 0; e < n; e++){ //for each edge in A
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//size_t errormag_id = A.E[e].nmags() - 1; //get the id for the new magnitude
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//pre-judge to get rid of impossibly mapping edges
T M = 0;
for(unsigned p = 0; p < A.E[e].size(); p++)
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M += A.E[e].r(p);
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M = M / A.E[e].size();
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if(M > threshold)
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C[e] = (unsigned)-1; //set the nearest edge of impossibly mapping edges to maximum of unsigned
else{
|
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T* queryPt = new T[3];
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T* dists = new T[1];
size_t* nnIdx = new size_t[1];
stim2array(queryPt, A.E[e][A.E[e].size()/2]);
kdt.search(queryPt, 1, nnIdx, dists);
unsigned id = 0; //mapping edge's idx
size_t num = 0; //total number of points before #th edge
for(unsigned i = 0; i < NB; i++){
num += B.E[i].size();
if(nnIdx[0] < num){
C[e] = id;
break;
}
id++;
}
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}
}
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#else
|
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stim::kdtree<T, 3> kdt;
|
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kdt.create(c, n_data, MaxTreeLevels);
T *dists = new T[1]; // near neighbor distances
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size_t *nnIdx = new size_t[1]; // near neighbor indices // allocate near neigh indices
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stim::vec3<T> p0, p1;
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T* queryPt = new T[3];
|
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for(unsigned e = 0; e < R.E.size(); e++){ // for each edge in A
T M; // the sum of metrics of current edge
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for(unsigned p = 0; p < R.E[e].size(); p++)
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|
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M += A.E[e].r(p);
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M = M / A.E[e].size();
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if(M > threshold)
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C[e] = (unsigned)-1;
else{ // if it should have corresponding edge in B, then...
p1 = R.E[e][R.E[e].size()/2];
|
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stim2array(queryPt, p1);
|
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kdt.cpu_search(queryPt, 1, nnIdx, dists); // search the tree
unsigned id = 0; //mapping edge's idx
size_t num = 0; //total number of points before #th edge
for(unsigned i = 0; i < NB; i++){
num += B.E[i].size();
if(nnIdx[0] < num){
C[e] = id;
break;
}
id++;
}
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}
}
#endif
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}
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/// Returns the number of magnitude values stored in each edge. This should be uniform across the network.
|
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|
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//unsigned nmags(){
// return E[0].nmags();
//}
|
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pranathivemuri
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|
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// split a string in text by the character sep
stim::vec<T> split(std::string &text, char sep)
{
stim::vec<T> tokens;
std::size_t start = 0, end = 0;
while ((end = text.find(sep, start)) != std::string::npos) {
tokens.push_back(atof(text.substr(start, end - start).c_str()));
start = end + 1;
}
tokens.push_back(atof(text.substr(start).c_str()));
return tokens;
}
// load a network in text file to a network class
void load_txt(std::string filename)
{
std::vector <std::string> file_contents;
|
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|
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std::ifstream file(filename.c_str());
|
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std::string line;
std::vector<unsigned> id2vert; //this list stores the vertex ID associated with each network vertex
//for each line in the text file, store them as strings in file_contents
while (std::getline(file, line))
{
std::stringstream ss(line);
file_contents.push_back(ss.str());
}
unsigned int numEdges = atoi(file_contents[0].c_str()); //number of edges in the network
unsigned int I = atoi(file_contents[1].c_str()) ; //calculate the number of points3d on the first edge
unsigned int count = 1; unsigned int k = 2; // count is global counter through the file contents, k is for the vertices on the edges
// for each edge in the network.
for (unsigned int i = 0; i < numEdges; i ++ )
{
// pre allocate a position vector p with number of points3d on the edge p
std::vector< stim::vec<T> > p(0, I);
// for each point on the nth edge
for (unsigned int j = k; j < I + k; j++)
{
// split the points3d of floats with separator space and form a float3 position vector out of them
p.push_back(split(file_contents[j], ' '));
}
count += p.size() + 1; // increment count to point at the next edge in the network
I = atoi(file_contents[count].c_str()); // read in the points3d at the next edge and convert it to an integer
k = count + 1;
edge new_edge = p; // create an edge with a vector of points3d on the edge
E.push_back(new_edge); // push the edge into the network
}
unsigned int numVertices = atoi(file_contents[count].c_str()); // this line in the text file gives the number of distinct vertices
count = count + 1; // this line of text file gives the first verrtex
// push each vertex into V
for (unsigned int i = 0; i < numVertices; i ++)
{
vertex new_vertex = split(file_contents[count], ' ');
V.push_back(new_vertex);
count += atoi(file_contents[count + 1].c_str()) + 2; // Skip number of edge ids + 2 to point to the next vertex
}
} // end load_txt function
|
b3a38641
David Mayerich
added the ability...
|
1001
|
|
abce2443
pranathivemuri
average branching...
|
1002
1003
1004
1005
1006
1007
1008
1009
1010
|
// strTxt returns a string of edges
std::string
strTxt(std::vector< stim::vec<T> > p)
{
std::stringstream ss;
std::stringstream oss;
for(unsigned int i = 0; i < p.size(); i++){
ss.str(std::string());
for(unsigned int d = 0; d < 3; d++){
|
9f5c0d4a
Pavel Govyadinov
edits to make net...
|
1011
|
ss<<p[i][d];
|
abce2443
pranathivemuri
average branching...
|
1012
|
}
|
8b7d6f9a
David Mayerich
light modificatio...
|
1013
|
ss << "\n";
|
abce2443
pranathivemuri
average branching...
|
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
|
}
return ss.str();
}
// removes specified character from string
void removeCharsFromString(std::string &str, char* charsToRemove ) {
for ( unsigned int i = 0; i < strlen(charsToRemove); ++i ) {
str.erase( remove(str.begin(), str.end(), charsToRemove[i]), str.end() );
}
}
//exports network to txt file
void
to_txt(std::string filename)
{
|
e8c9a82b
David Mayerich
fixed GCC errors ...
|
1027
1028
|
std::ofstream ofs(filename.c_str(), std::ofstream::out | std::ofstream::app);
//int num;
|
abce2443
pranathivemuri
average branching...
|
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
|
ofs << (E.size()).str() << "\n";
for(unsigned int i = 0; i < E.size(); i++)
{
std::string str;
ofs << (E[i].size()).str() << "\n";
str = E[i].strTxt();
ofs << str << "\n";
}
for(int i = 0; i < V.size(); i++)
{
std::string str;
str = V[i].str();
|
e8c9a82b
David Mayerich
fixed GCC errors ...
|
1041
1042
|
char temp[4] = "[],";
removeCharsFromString(str, temp);
|
abce2443
pranathivemuri
average branching...
|
1043
1044
1045
1046
|
ofs << str << "\n";
}
ofs.close();
}
|
7f27eafa
David Mayerich
simplified the st...
|
1047
1048
|
}; //end stim::network class
}; //end stim namespace
|
c72184d1
Jiaming Guo
add many function...
|
1049
|
#endif
|