[37] | 1 | % By Philip Torr 2002
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| 2 | % copyright Microsoft Corp.
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| 3 | %main()
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| 4 |
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| 5 | %this script compares two methods for estimating F
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| 6 | %select the two methods and place their ID's in the array methods_used
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| 7 | %
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| 8 |
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| 9 | %methods_used = [4,3]
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| 10 |
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| 11 | %comparing non-linear method with Sampson
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| 12 | %methods_used = [4,2]
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| 13 |
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| 14 | %compare sampson and Hegel
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| 15 | methods_used = [4,7];
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| 16 |
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| 17 | %compare bundle and Hegel
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| 18 | methods_used = [6,7];
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| 19 |
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| 20 | %comparing linear and Hegel
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| 21 | methods_used = [2,7];
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| 22 |
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| 23 |
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| 24 |
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| 25 | m3 = 256;
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| 26 | sse2t = 0;
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| 27 | %
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| 28 | % randn('state',0)
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| 29 | % rand('state',0)
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| 30 |
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| 31 | no_methods = 7;
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| 32 | foc = 256;
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| 33 | best_method_array = zeros(no_methods,1);
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| 34 | method_sse = zeros(no_methods,1);
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| 35 | method_n_sse = zeros(no_methods,1);
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| 36 | epipole_distance = zeros(no_methods,1);
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| 37 | oo_vicar = 0;
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| 38 |
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| 39 | no_matches =100;
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| 40 | noise_sigma = 1;
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| 41 | translation_mult = foc * 10;
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| 42 | translation_adder = 20;
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| 43 |
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| 44 | %max number of degrees to rotate
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| 45 | rotation_multplier = 40;
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| 46 | min_Z = 1;
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| 47 | Z_RAN = 10;
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| 48 |
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| 49 |
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| 50 |
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| 51 |
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| 52 | no_tests =1;
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| 53 |
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| 54 |
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| 55 | min_noise = 1;
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| 56 | max_noise = 1;
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| 57 | percent_gain = zeros(1,max_noise);
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| 58 | ep_percent_gain = zeros(1,max_noise);
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| 59 |
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| 60 | for(noise_sigma = min_noise:max_noise)
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| 61 | for(i = 1:no_tests)
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| 62 |
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| 63 |
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| 64 | best_sse = 10000000000;
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| 65 | best_method = 5;
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| 66 |
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| 67 | %generate a load of stuffs
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| 68 | %F
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| 69 |
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| 70 | ave_fa_e = 0.0;
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| 71 | while ave_fa_e < 0.5
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| 72 | [true_F,x1,y1,x2,y2,nx1,ny1,nx2,ny2,true_C,true_R,true_TX, true_E, true_X, true_t] = ...
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| 73 | torr_gen_2view_matches(foc, no_matches, noise_sigma, translation_mult, translation_adder, ...
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| 74 | rotation_multplier, min_Z,Z_RAN,m3);
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| 75 | [FA, fa] = torr_estfa(x1,y1,x2,y2, no_matches,m3);
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| 76 | fa_e = torr_errfa(fa, x1,y1,x2,y2, no_matches, m3);
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| 77 |
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| 78 | %see what average match looks like
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| 79 |
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| 80 | ave_fa_e = norm(fa_e,1)/no_matches;
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| 81 | if no_tests == 1
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| 82 | ave_fa_e;
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| 83 | end
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| 84 |
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| 85 | end
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| 86 | %
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| 87 | % if ssse_fa <6.0
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| 88 | % disp('ooo vicar');
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| 89 | % oo_vicar = oo_vicar + 1;
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| 90 | % end
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| 91 | % %calc true epipole
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| 92 | true_epipole = torr_get_right_epipole(true_F,m3);
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| 93 |
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| 94 | % for method = 2:6
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| 95 |
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| 96 |
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| 97 | for method = methods_used
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| 98 |
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| 99 | set_rank2 = 1;
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| 100 | [nf, f_sq_errors, n_inliers,inlier_index,nF] ...
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| 101 | = torr_estimateF( [nx1,ny1,nx2,ny2], m3, [], method,set_rank2);
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| 102 |
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| 103 | %calc noisy epipole
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| 104 | noisy_epipole = torr_get_right_epipole(nF,m3);
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| 105 | epipole_distance(method) = epipole_distance(method) + sqrt(norm(true_epipole -noisy_epipole));
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| 106 |
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| 107 |
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| 108 | pe = torr_errf2(nf,x1,y1,x2,y2, no_matches, m3);
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| 109 | n_e = torr_errf2(nf,nx1,ny1,nx2,ny2, no_matches, m3);
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| 110 |
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| 111 | sse_n = norm(pe);
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| 112 |
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| 113 | if (sse_n < best_sse)
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| 114 | best_method = method;
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| 115 | best_sse = sse_n;
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| 116 | end
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| 117 |
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| 118 | method_sse(method) = method_sse(method) + sse_n;
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| 119 | method_n_sse(method) = method_sse(method) + norm(n_e);
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| 120 |
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| 121 | end %method = 1:4
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| 122 | best_method_array(best_method) = best_method_array(best_method)+1;
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| 123 | end
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| 124 |
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| 125 |
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| 126 |
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| 127 | best_method_array(methods_used)';
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| 128 | (method_sse(methods_used)/(no_tests*length(x1)))';
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| 129 | (method_n_sse(methods_used)/(no_tests*length(x1)))';
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| 130 |
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| 131 | percent_gain(noise_sigma) = method_sse(methods_used(1))/method_sse(methods_used(2));
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| 132 |
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| 133 |
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| 134 |
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| 135 |
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| 136 | %disp('distance to true epipole');
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| 137 | (epipole_distance(methods_used)/no_tests)';
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| 138 |
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| 139 | ep_percent_gain(noise_sigma) = epipole_distance(methods_used(1))/epipole_distance(methods_used(2));
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| 140 |
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| 141 | %oo_vicar
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| 142 | %display_mat(perfect_matches, x1,y1, u1, v1)
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| 143 | %
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| 144 |
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| 145 | % e = fm_error_hs(F, n1, n2, nowarn);
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| 146 |
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| 147 |
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| 148 | %torr_display_epipoles(nF,nF,perfect_matches, x1,y1, u1, v1)
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| 149 | end
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| 150 |
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| 151 | disp('ratio of first to second method average error on noise free points');
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| 152 | 100 * percent_gain
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| 153 |
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| 154 | disp('ratio of first to second method average epipole error');
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| 155 | 100 * ep_percent_gain
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| 156 |
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| 157 | disp('number of times gets lowest errors')
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| 158 | best_method_array
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| 159 |
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| 160 | disp('average error for each method')
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| 161 | method_sse |
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