torr_main_f_ave.m

matlab三维重建程序

%	By Philip Torr 2002
%	copyright Microsoft Corp.
%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;
    
    %generate a load of stuffs
    %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)