analisys.m

eye detection sample in matlab. Eyetrackers can analyze a driver’s level of attentiveness while driv

clear all
clc
load trainingset
labels1=labels(find(labels==1));
labels2=labels(find(labels==2));
labels3=labels(find(labels==3));
labels4=labels(find(labels==4));
labels5=labels(find(labels==5));
labels6=labels(find(labels==6));
labels7=labels(find(labels==7));
samples1=samples(:,find(labels==1));
samples2=samples(:,find(labels==2));
samples3=samples(:,find(labels==3));
samples4=samples(:,find(labels==4));
samples5=samples(:,find(labels==5));
samples6=samples(:,find(labels==6));
samples7=samples(:,find(labels==7));

samples11=samples1(:,1:22);
samples12=samples1(:,23:44);
samples13=samples1(:,45:66);
samples14=samples1(:,67:88);
samples15=samples1(:,89:108);

samples21=samples2(:,1:10);
samples22=samples2(:,11:20);
samples23=samples2(:,21:30);
samples24=samples2(:,31:40);
samples25=samples2(:,41:50);

samples31=samples3(:,1:17);
samples32=samples3(:,18:34);
samples33=samples3(:,35:51);
samples34=samples3(:,52:68);
samples35=samples3(:,69:84);

samples41=samples4(:,1:10);
samples42=samples4(:,11:20);
samples43=samples4(:,21:30);
samples44=samples4(:,31:40);
samples45=samples4(:,41:50);

samples51=samples5(:,1:17);
samples52=samples5(:,18:34);
samples53=samples5(:,35:51);
samples54=samples5(:,52:68);
samples55=samples5(:,69:88);

samples61=samples6(:,1:25);
samples62=samples6(:,26:49);
samples63=samples6(:,50:73);
samples64=samples6(:,74:97);
samples65=samples6(:,98:119);

samples71=samples7(:,1:19);
samples72=samples7(:,20:38);
samples73=samples7(:,39:57);
samples74=samples7(:,58:76);
samples75=samples7(:,77:94);


    l_data1=[ones(1,size(samples11,2)) 2*ones(1,size(samples21,2)) 3*ones(1,size(samples31,2)) 4*ones(1,size(samples41,2)) 5*ones(1,size(samples51,2)) 6*ones(1,size(samples61,2)) 7*ones(1,size(samples71,2))];
    s_data1=[samples11 samples21 samples31 samples41 samples51 samples61 samples71];
    l_data2=[ones(1,size(samples12,2)) 2*ones(1,size(samples22,2)) 3*ones(1,size(samples32,2)) 4*ones(1,size(samples42,2)) 5*ones(1,size(samples52,2)) 6*ones(1,size(samples62,2)) 7*ones(1,size(samples72,2))];
    s_data2=[samples12 samples22 samples32 samples42 samples52 samples62 samples71];
    l_data3=[ones(1,size(samples13,2)) 2*ones(1,size(samples23,2)) 3*ones(1,size(samples33,2)) 4*ones(1,size(samples43,2)) 5*ones(1,size(samples53,2)) 6*ones(1,size(samples63,2)) 7*ones(1,size(samples73,2))];
    s_data3=[samples13 samples23 samples33 samples43 samples53 samples63 samples73];
    l_data4=[ones(1,size(samples14,2)) 2*ones(1,size(samples24,2)) 3*ones(1,size(samples34,2)) 4*ones(1,size(samples44,2)) 5*ones(1,size(samples54,2)) 6*ones(1,size(samples64,2)) 7*ones(1,size(samples74,2))];
    s_data4=[samples14 samples24 samples34 samples44 samples54 samples64 samples74];
    l_data5=[ones(1,size(samples15,2)) 2*ones(1,size(samples25,2)) 3*ones(1,size(samples35,2)) 4*ones(1,size(samples45,2)) 5*ones(1,size(samples55,2)) 6*ones(1,size(samples65,2)) 7*ones(1,size(samples75,2))];
    s_data5=[samples15 samples25 samples35 samples45 samples55 samples65 samples75];

load TS
output = [];

%%%%%%%%%% checking samples?1   %%%%%%%%%%%%%
l_data=[l_data2 l_data3 l_data4 l_data5];
s_data=[s_data2 s_data3 s_data4 s_data5];
[TS.alpha, TS.sv, TS.b, TS.params, TS.n]=LinearSVC(s_data,l_data);
[L,sc] = osuSVMclass(samples11,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 1',size(find(L==1),2),size(samples11,2))
sprintf('%0.5g out of %0.5g mistaken identifing 1',size(samples11,2)-size(find(L==1),2),size(samples11,2))
size(find(L==1),2)/size(samples11,2)
correct1=size(find(L==1),2)/size(samples11,2);
tmp=
[L,sc] = osuSVMclass(samples21,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 2',size(find(L==2),2),size(samples21,2))
sprintf('%0.5g out of %0.5g mistaken identifing 2',size(samples21,2)-size(find(L==2),2),size(samples21,2))
size(find(L==2),2)/size(samples21,2)
correct2=size(find(L==2),2)/size(samples21,2);
[L,sc] = osuSVMclass(samples31,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 3',size(find(L==3),2),size(samples31,2))
sprintf('%0.5g out of %0.5g mistaken identifing 3',size(samples31,2)-size(find(L==3),2),size(samples31,2))
size(find(L==3),2)/size(samples31,2)
correct3=size(find(L==3),2)/size(samples31,2);
[L,sc] = osuSVMclass(samples41,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 4',size(find(L==4),2),size(samples41,2))
sprintf('%0.5g out of %0.5g mistaken identifing 4',size(samples41,2)-size(find(L==4),2),size(samples41,2))
size(find(L==4),2)/size(samples41,2);
correct4=size(find(L==4),2)/size(samples41,2);
[L,sc] = osuSVMclass(samples51,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 5',size(find(L==5),2),size(samples51,2))
sprintf('%0.5g out of %0.5g mistaken identifing 5',size(samples51,2)-size(find(L==5),2),size(samples51,2))
size(find(L==5),2)/size(samples51,2)
correct5=size(find(L==5),2)/size(samples51,2);
[L,sc] = osuSVMclass(samples61,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 6',size(find(L==6),2),size(samples61,2))
sprintf('%0.5g out of %0.5g mistaken identifing 6',size(samples61,2)-size(find(L==6),2),size(samples61,2))
size(find(L==6),2)/size(samples61,2)
correct6=size(find(L==6),2)/size(samples61,2);
[L,sc] = osuSVMclass(samples71,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 7',size(find(L==7),2),size(samples71,2))
sprintf('%0.5g out of %0.5g mistaken identifing 7',size(samples71,2)-size(find(L==7),2),size(samples71,2))
size(find(L==7),2)/size(samples71,2)
correct7=size(find(L==7),2)/size(samples71,2);

%%%%%%%%%% checking samples?2   %%%%%%%%%%%%%
l_data=[l_data1 l_data3 l_data4 l_data5];
s_data=[s_data1 s_data3 s_data4 s_data5];
[TS.alpha, TS.sv, TS.b, TS.params, TS.n]=LinearSVC(s_data,l_data);
[L,sc] = osuSVMclass(samples12,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 1',size(find(L==1),2),size(samples12,2))
sprintf('%0.5g out of %0.5g mistaken identifing 1',size(samples12,2)-size(find(L==1),2),size(samples12,2))
size(find(L==1),2)/size(samples12,2)
correct1=correct1+size(find(L==1),2)/size(samples12,2);
[L,sc] = osuSVMclass(samples22,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 2',size(find(L==2),2),size(samples22,2))
sprintf('%0.5g out of %0.5g mistaken identifing 2',size(samples22,2)-size(find(L==2),2),size(samples22,2))
size(find(L==2),2)/size(samples22,2)
correct2=correct2+size(find(L==2),2)/size(samples22,2);
[L,sc] = osuSVMclass(samples32,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 3',size(find(L==3),2),size(samples32,2))
sprintf('%0.5g out of %0.5g mistaken identifing 3',size(samples32,2)-size(find(L==3),2),size(samples32,2))
size(find(L==3),2)/size(samples32,2)
correct3=correct3+size(find(L==3),2)/size(samples32,2);
[L,sc] = osuSVMclass(samples42,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 4',size(find(L==4),2),size(samples42,2))
sprintf('%0.5g out of %0.5g mistaken identifing 4',size(samples32,2)-size(find(L==4),2),size(samples42,2))
size(find(L==4),2)/size(samples42,2)
correct4=correct4+size(find(L==4),2)/size(samples42,2);
[L,sc] = osuSVMclass(samples52,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 5',size(find(L==5),2),size(samples52,2))