# Structure and Motion Toolkit in MATLAB

### Philip Torr (view profile)

04 Mar 2004 (Updated )

Structure and Motion Toolkit in MATLAB.

torr_main_f_ave.m
```%	By Philip Torr 2002
%main()
clear all
m3 = 256;
sse2t = 0;
%
% randn('state',0)
% rand('state',0)

no_methods = 6;
best_method_array = zeros(no_methods,1);
method_sse = zeros(no_methods,1);
method_n_sse = zeros(no_methods,1);
epipole_distance = zeros(no_methods,1);
oo_vicar = 0;

no_tests = 1;
methods_used = [2,4]

for(i = 1:no_tests)

best_sse = 10000000000;
best_method = 5;

%F

ave_fa_e  = 0.0;
while ave_fa_e < 0.5
torr_genf;
[FA, fa] = torr_estfa(x1,y1,x2,y2, no_matches,m3);
fa_e = torr_errfa(fa, x1,y1,x2,y2, no_matches, m3);

%see what average match looks like

ave_fa_e = norm(fa_e,1)/no_matches;
if no_tests == 1
ave_fa_e
end

end
%
%     if ssse_fa <6.0
%         disp('ooo vicar');
%         oo_vicar = oo_vicar + 1;
%     end
%         %calc true epipole
true_epipole = torr_get_right_epipole(true_F,m3);

% for method = 2:6

for method = methods_used

X1 = [x1,y1, ones(length(x1),1) * m3];
X2 = [x2,y2, ones(length(x2),1) * m3];

%error on perfect data (should be zero)
%f = estf(nx1,ny1,nx2,ny2, no_matches,m3);
%f = estf(x1,y1,x2,y2, no_matches,m3);
%
%         [F , f]= fm_linear(X1, X2, eye(3), method);
%         e = torr_errf2(f,x1,y1,x2,y2, no_matches, m3);
%         disp('noise free error (sanity check)')
%         ssep = e' * e
%
% %error on noisy data
% f = fm_linear(nx1,ny1,nx2,ny2, no_matches,m3);
% e = torr_errf2(f,nx1,ny1,nx2,ny2, no_matches, m3);
% ssen = e * e'

nX1 = [nx1,ny1, ones(length(x1),1) * m3];
nX2 = [nx2,ny2, ones(length(x2),1) * m3];

%        [nF , nf]= fm_linear(nX1, nX2, eye(3), method);
[nf, nF ] = torr_estimateF(nx1,ny1,nx2,ny2, no_matches, m3, method)

%calc noisy epipole
noisy_epipole = torr_get_right_epipole(nF,m3);
epipole_distance(method) = epipole_distance(method) + sqrt(norm(true_epipole -noisy_epipole));

torr_error = 1;
if torr_error
pe = torr_errf2(nf,x1,y1,x2,y2, no_matches, m3);
n_e = torr_errf2(nf,nx1,ny1,nx2,ny2, no_matches, m3);
else
CC = eye(3);
CC(3,3) = m3;
nF2 = CC * nF * CC;

n1  = [x1 y1];
n2= [x2 y2];
nowarn = 0;

ne = fm_error_hs(nF, n1, n2, nowarn);
end
%       ne = torr_errf2(nf,nx1,ny1,nx2,ny2, no_matches, m3);

%       disp('trimmed noisy error on noise free points')
%        sse_n = ne' * ne

sse_n = norm(pe);

if (sse_n < best_sse)
best_method = method;
best_sse = sse_n;
end

method_sse(method) =  method_sse(method) + sse_n;
method_n_sse(method) =  method_sse(method) + norm(n_e);

end %method = 1:4
best_method_array(best_method) = best_method_array(best_method)+1;
end

% %mine
% f_torr = estf(nx1,ny1,nx2,ny2, no_matches,m3);
% ne = torr_errf2(f_torr,x1,y1,x2,y2, no_matches, m3);
% disp('noisy error on noise free points')
% sse_n = norm(ne(20:no_matches-20))

%disp('trace = 1, trace =0, ls, det = 1, 2x2 = 1, 2x2 =1')

best_method_array(methods_used)'
(method_sse(methods_used)/(no_tests*length(x1)))'
(method_n_sse(methods_used)/(no_tests*length(x1)))'

percent_gain = 1 - method_sse(methods_used(1))/method_sse(methods_used(2));
percent_gain

disp('distance to true epipole');
(epipole_distance(methods_used)/no_tests)'

ep_percent_gain = 1 - epipole_distance(methods_used(1))/epipole_distance(methods_used(2));
ep_percent_gain
%oo_vicar
%display_mat(perfect_matches, x1,y1, u1, v1)
%

%        e = fm_error_hs(F, n1, n2, nowarn);

%torr_display_epipoles(nF,nF,perfect_matches, x1,y1, u1, v1)```