% 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)