# Structure and Motion Toolkit in MATLAB

### Philip Torr (view profile)

04 Mar 2004 (Updated )

Structure and Motion Toolkit in MATLAB.

torr_compF_sc.m
```%	By Philip Torr 2002
%main()

%this script compares two methods for estimating F
%select the two methods and place their ID's in the array methods_used
%

%methods_used = [4,3]

%comparing non-linear method with Sampson
%methods_used = [4,2]

%compare sampson and Hegel
methods_used = [4,7];

%compare bundle and Hegel
methods_used = [6,7];

%comparing linear and Hegel
methods_used = [2,7];

m3 = 256;
sse2t = 0;
%
% randn('state',0)
% rand('state',0)

no_methods = 7;
foc = 256;
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_matches =100;
noise_sigma = 1;
translation_mult = foc * 10;

%max number of degrees to rotate
rotation_multplier = 40;
min_Z = 1;
Z_RAN = 10;

no_tests =1;

min_noise = 1;
max_noise = 1;
percent_gain = zeros(1,max_noise);
ep_percent_gain = zeros(1,max_noise);

for(noise_sigma = min_noise:max_noise)
for(i = 1:no_tests)

best_sse = 10000000000;
best_method = 5;

%F

ave_fa_e  = 0.0;
while ave_fa_e < 0.5
[true_F,x1,y1,x2,y2,nx1,ny1,nx2,ny2,true_C,true_R,true_TX, true_E, true_X, true_t]  = ...
torr_gen_2view_matches(foc, no_matches, noise_sigma, translation_mult, translation_adder, ...
rotation_multplier, min_Z,Z_RAN,m3);
[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

set_rank2 = 1;
[nf, f_sq_errors, n_inliers,inlier_index,nF] ...
= torr_estimateF( [nx1,ny1,nx2,ny2], m3, [], method,set_rank2);

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

pe = torr_errf2(nf,x1,y1,x2,y2, no_matches, m3);
n_e = torr_errf2(nf,nx1,ny1,nx2,ny2, no_matches, m3);

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

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

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

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

ep_percent_gain(noise_sigma) = epipole_distance(methods_used(1))/epipole_distance(methods_used(2));

%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)
end

disp('ratio of first to second method average error on noise free points');
100 * percent_gain

disp('ratio of first to second method average epipole error');
100 * ep_percent_gain

disp('number of times gets lowest errors')
best_method_array

disp('average error for each method')
method_sse```