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#include <stdlib.h>
#include <string>
#include <fstream>
#include <algorithm>
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//OpenGL includes
#include <GL/glut.h>
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#include <GL/freeglut.h>
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//STIM includes
#include <stim/visualization/gl_network.h>
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#include <stim/biomodels/network.h>
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#include <stim/visualization/gl_aaboundingbox.h>
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#include <stim/parser/arguments.h>
#include <stim/visualization/camera.h>
//ANN includes
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#include <ANN/ANN.h>
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//BOOST includes
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#include <boost/tuple/tuple.hpp>
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//visualization objects
stim::gl_aaboundingbox<float> bb; //axis-aligned bounding box object
stim::camera cam; //camera object
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unsigned num_nets = 0;
stim::gl_network<float> GT; //ground truth network
stim::gl_network<float> T; //test network
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//hard-coded parameters
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float resample_rate = 0.5; //sample rate for the network (fraction of sigma used as the maximum sample rate)
float camera_factor = 1.2; //start point of the camera as a function of X and Y size
float orbit_factor = 0.01; //degrees per pixel used to orbit the camera
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//mouse position tracking
int mouse_x;
int mouse_y;
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//OpenGL objects
GLuint cmap_tex = 0; //texture name for the color map
//sets an OpenGL viewport taking up the entire window
void glut_render_single_projection(){
glMatrixMode(GL_PROJECTION); //load the projection matrix for editing
glLoadIdentity(); //start with the identity matrix
int X = glutGet(GLUT_WINDOW_WIDTH); //use the whole screen for rendering
int Y = glutGet(GLUT_WINDOW_HEIGHT);
glViewport(0, 0, X, Y); //specify a viewport for the entire window
float aspect = (float)X / (float)Y; //calculate the aspect ratio
gluPerspective(60, aspect, 0.1, 1000000); //set up a perspective projection
}
//sets an OpenGL viewport taking up the left half of the window
void glut_render_left_projection(){
glMatrixMode(GL_PROJECTION); //load the projection matrix for editing
glLoadIdentity(); //start with the identity matrix
int X = glutGet(GLUT_WINDOW_WIDTH) / 2; //only use half of the screen for the viewport
int Y = glutGet(GLUT_WINDOW_HEIGHT);
glViewport(0, 0, X, Y); //specify the viewport on the left
float aspect = (float)X / (float)Y; //calculate the aspect ratio
gluPerspective(60, aspect, 0.1, 1000000); //set up a perspective projection
}
//sets an OpenGL viewport taking up the right half of the window
void glut_render_right_projection(){
glMatrixMode(GL_PROJECTION); //load the projection matrix for editing
glLoadIdentity(); //start with the identity matrix
int X = glutGet(GLUT_WINDOW_WIDTH) / 2; //only use half of the screen for the viewport
int Y = glutGet(GLUT_WINDOW_HEIGHT);
glViewport(X, 0, X, Y); //specify the viewport on the right
float aspect = (float)X / (float)Y; //calculate the aspect ratio
gluPerspective(60, aspect, 0.1, 1000000); //set up a perspective projection
}
void glut_render_modelview(){
glMatrixMode(GL_MODELVIEW); //load the modelview matrix for editing
glLoadIdentity(); //start with the identity matrix
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stim::vec3<float> eye = cam.getPosition(); //get the camera position (eye point)
stim::vec3<float> focus = cam.getLookAt(); //get the camera focal point
stim::vec3<float> up = cam.getUp(); //get the camera "up" orientation
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gluLookAt(eye[0], eye[1], eye[2], focus[0], focus[1], focus[2], up[0], up[1], up[2]); //set up the OpenGL camera
}
//draws the network(s)
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void glut_render(void) {
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if(num_nets == 1){ //if a single network is loaded
glut_render_single_projection(); //fill the entire viewport
glut_render_modelview(); //set up the modelview matrix with camera details
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT); //clear the screen
GT.glCenterline(GT.nmags() - 1); //render the GT network (the only one loaded)
}
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if(num_nets == 2){ //if two networks are loaded
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glut_render_left_projection(); //set up a projection for the left half of the window
glut_render_modelview(); //set up the modelview matrix using camera details
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT); //clear the screen
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glEnable(GL_TEXTURE_1D); //enable texture mapping
glTexEnvf(GL_TEXTURE_ENV, GL_TEXTURE_ENV_MODE, GL_REPLACE); //texture map will be used as the network color
glBindTexture(GL_TEXTURE_1D, cmap_tex); //bind the Brewer texture map
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GT.glCenterline(GT.nmags() - 1); //render the GT network
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glut_render_right_projection(); //set up a projection for the right half of the window
glut_render_modelview(); //set up the modelview matrix using camera details
T.glCenterline(T.nmags() - 1); //render the T network
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}
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glutSwapBuffers();
}
// defines camera motion based on mouse dragging
void glut_motion(int x, int y){
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float theta = orbit_factor * (mouse_x - x); //determine the number of degrees along the x-axis to rotate
float phi = orbit_factor * (y - mouse_y); //number of degrees along the y-axis to rotate
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cam.OrbitFocus(theta, phi); //rotate the camera around the focal point
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mouse_x = x; //update the mouse position
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mouse_y = y;
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glutPostRedisplay(); //re-draw the visualization
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}
// sets the mouse position when clicked
void glut_mouse(int button, int state, int x, int y){
mouse_x = x;
mouse_y = y;
}
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#define BREWER_CTRL_PTS 11 //number of control points in the Brewer map
void texture_initialize(){
//define the colormap
static float brewer_map[BREWER_CTRL_PTS][3] = { //generate a Brewer color map (blue to red)
{0.192157f, 0.211765f, 0.584314f},
{0.270588f, 0.458824f, 0.705882f},
{0.454902f, 0.678431f, 0.819608f},
{0.670588f, 0.85098f, 0.913725f},
{0.878431f, 0.952941f, 0.972549f},
{1.0f, 1.0f, 0.74902f},
{0.996078f, 0.878431f, 0.564706f},
{0.992157f, 0.682353f, 0.380392f},
{0.956863f, 0.427451f, 0.262745f},
{0.843137f, 0.188235f, 0.152941f},
{0.647059f, 0.0f, 0.14902f}
};
glGenTextures(1, &cmap_tex); //generate a texture map name
glBindTexture(GL_TEXTURE_1D, cmap_tex); //bind the texture map
glTexParameteri(GL_TEXTURE_1D, GL_TEXTURE_MAG_FILTER, GL_LINEAR); //enable linear interpolation
glTexParameteri(GL_TEXTURE_1D, GL_TEXTURE_MIN_FILTER, GL_LINEAR);
glTexParameteri(GL_TEXTURE_1D, GL_TEXTURE_WRAP_S, GL_CLAMP); //clamp the values at the minimum and maximum
glTexImage1D(GL_TEXTURE_1D, 0, 3, BREWER_CTRL_PTS, 0, GL_RGB, GL_FLOAT, //upload the texture map to the GPU
brewer_map);
}
//Initialize the OpenGL (GLUT) window, including starting resolution, callbacks, texture maps, and camera
void glut_initialize(){
int myargc = 1; //GLUT requires arguments, so create some bogus ones
char* myargv[1];
myargv [0]=strdup ("netmets");
glutInit(&myargc, myargv); //pass bogus arguments to glutInit()
glutInitDisplayMode(GLUT_DEPTH | GLUT_DOUBLE | GLUT_RGBA); //generate a color buffer, depth buffer, and enable double buffering
glutInitWindowPosition(100,100); //set the initial window position
glutInitWindowSize(320,320); //set the initial window size
glutCreateWindow("NetMets - STIM Lab, UH"); //set the dialog box title
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// register callback functions
glutDisplayFunc(glut_render); //function executed for rendering - renders networks
glutMouseFunc(glut_mouse); //executed on a mouse click - sets starting mouse positions for rotations
glutMotionFunc(glut_motion); //executed when the mouse is moved while a button is pressed
texture_initialize(); //set up texture mapping (create texture maps, enable features)
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stim::vec3<float> c = bb.center(); //get the center of the network bounding box
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//place the camera along the z-axis at a distance determined by the network size along x and y
cam.setPosition(c + stim::vec<float>(0, 0, camera_factor * std::max(bb.size()[0], bb.size()[1])));
cam.LookAt(c[0], c[1], c[2]); //look at the center of the network
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}
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//compare both networks and fill the networks with error information
void compare(float sigma){
GT = GT.compare(T, sigma); //compare the ground truth to the test case - store errors in GT
T = T.compare(GT, sigma); //compare the test case to the ground truth - store errors in T
//calculate the metrics
float FPR = GT.average(1); //calculate the metrics
float FNR = T.average(1);
std::cout << "FNR: " << FPR << std::endl; //print false alarms and misses
std::cout << "FPR: " << FNR << std::endl;
}
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// writes features of the networks i.e average segment length, tortuosity, branching index, contraction, fractal dimension, number of end and branch points to a csv file
void features(){
double avgL_t, avgL_gt, avgT_t, avgT_gt, avgB_t, avgB_gt, avgC_t, avgC_gt, avgFD_t, avgFD_gt;
unsigned int e_t, e_gt, b_gt, b_t;
avgL_gt = GT.Lengths();
avgT_gt = GT.Tortuosities();
//std::cout<<avgL_gt<<"---average segment length in the ground truth network"<<std::endl;
//std::cout<<avgT_gt<<"---average segment tortuosity in the ground truth network"<<std::endl;
avgL_t = T.Lengths();
avgT_t = T.Tortuosities();
//std::cout<<avgL_t<<"---average segment length in the truth network"<<std::endl;
//std::cout<<avgT_t<<"---average segment tortuosity in the truth network"<<std::endl;
avgB_gt = GT.BranchingIndex();
//std::cout<<avgB_gt<<"---average branching index in the ground truth"<<std::endl;
avgB_t = T.BranchingIndex();
//std::cout<<avgB_t<<"---average branching index in the truth case"<<std::endl;
avgC_gt = GT.Contractions();
avgFD_gt = GT.FractalDimensions();
//std::cout<<avgC_gt<<"---average segment contraction in the ground truth network"<<std::endl;
//std::cout<<avgFD_gt<<"---average segment fractal dimension in the ground truth network"<<std::endl;
avgC_t = T.Contractions();
avgFD_t = T.FractalDimensions();
//std::cout<<avgC_t<<"---average segment contraction in the truth network"<<std::endl;
//std::cout<<avgFD_t<<"---average segment fractal dimension in the truth network"<<std::endl;
e_gt = GT.EndP();
//std::cout<<e_gt<<"---Number of tips in the ground truth"<<std::endl;
e_t = T.EndP();
//std::cout<<e_t<<"---Number of tips in the truth case"<<std::endl;
b_gt = GT.BranchP();
//std::cout<<b_gt<<"---Number of branch points in the ground truth"<<std::endl;
b_t = T.BranchP();
//std::cout<<b_t<<"---Number of branch points in the truth case"<<std::endl;
std::ofstream myfile;
myfile.open ("features.csv");
myfile << "Length, Tortuosity, Contraction, Fractal Dimension, Branch Points, End points, Branching Index, \n";
myfile << avgL_gt << "," << avgT_gt << "," << avgC_gt << "," << avgFD_gt << "," << b_gt << "," << e_gt << "," << avgB_gt <<std::endl;
myfile << avgL_t << "," << avgT_t << "," << avgC_t << "," << avgFD_t << "," << b_t << "," << e_t << "," << avgB_t <<std::endl;
myfile.close();
}
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// Output an advertisement for the lab, authors, and usage information
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void advertise(){
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std::cout<<std::endl<<std::endl;
std::cout<<"========================================================================="<<std::endl;
std::cout<<"Thank you for using the NetMets network comparison tool!"<<std::endl;
std::cout<<"Scalable Tissue Imaging and Modeling (STIM) Lab, University of Houston"<<std::endl;
std::cout<<"Developers: Pranathi Vemuri, David Mayerich"<<std::endl;
std::cout<<"Source: https://git.stim.ee.uh.edu/segmentation/netmets"<<std::endl;
std::cout<<"========================================================================="<<std::endl<<std::endl;
std::cout<<"usage: netmets file1 file2 --sigma 10"<<std::endl;
std::cout<<" compare two files with a tolerance of 10 (units defined by the network)"<<std::endl;
std::cout<<" netmets file1 --gui"<<std::endl<<std::endl;
std::cout<<" load a file and display it using OpenGL"<<std::endl;
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}
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int main(int argc, char* argv[])
{
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stim::arglist args; //create an instance of arglist
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//add arguments
args.add("help", "prints this help");
args.add("sigma", "force a sigma value to specify the tolerance of the network comparison", "10");
args.add("gui", "display the network or network comparison using OpenGL");
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args.parse(argc, argv); //parse the user arguments
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if(args["help"].is_set() || args.nargs() == 0){ //test for help
advertise(); //output the advertisement
std::cout<<args.str(); //output arguments
exit(1); //exit
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}
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if(args.nargs() >= 1){ //if at least one network file is specified
num_nets = 1; //set the number of networks to one
GT.load_obj(args.arg(0)); //load the specified file as the ground truth
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/*GT.to_txt("Graph.txt");*/
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}
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if(args.nargs() == 2){ //if two files are specified, they will be displayed in neighboring viewports and compared
num_nets = 2; //set the number of networks to two
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float sigma = args["sigma"].as_float(); //get the sigma value from the user
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T.load_obj(args.arg(1)); //load the second (test) network
features();
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GT = GT.resample(resample_rate * sigma); //resample both networks based on the sigma value
T = T.resample(resample_rate * sigma);
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compare(sigma); //run the comparison algorithm
}
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//if a GUI is requested, display the network using OpenGL
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if(args["gui"].is_set()){
bb = GT.boundingbox(); //generate a bounding volume
glut_initialize(); //create the GLUT window and set callback functions
glutMainLoop(); // enter GLUT event processing cycle
}
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}
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