1 | % By Philip Torr 2002
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2 | % copyright Microsoft Corp.
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3 | %main()
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4 | clear all
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5 | m3 = 256;
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6 | sse2t = 0;
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7 | %
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8 | % randn('state',0)
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9 | % rand('state',0)
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10 |
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11 | no_methods = 6;
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12 | best_method_array = zeros(no_methods,1);
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13 | method_sse = zeros(no_methods,1);
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14 | method_n_sse = zeros(no_methods,1);
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15 | epipole_distance = zeros(no_methods,1);
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16 | oo_vicar = 0;
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17 |
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18 | no_tests = 1;
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19 | methods_used = [2,4]
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20 |
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21 | for(i = 1:no_tests)
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22 |
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23 |
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24 | best_sse = 10000000000;
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25 | best_method = 5;
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26 |
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27 | %generate a load of stuffs
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28 | %F
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29 |
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30 | ave_fa_e = 0.0;
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31 | while ave_fa_e < 0.5
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32 | torr_genf;
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33 | [FA, fa] = torr_estfa(x1,y1,x2,y2, no_matches,m3);
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34 | fa_e = torr_errfa(fa, x1,y1,x2,y2, no_matches, m3);
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35 |
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36 | %see what average match looks like
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37 |
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38 | ave_fa_e = norm(fa_e,1)/no_matches;
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39 | if no_tests == 1
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40 | ave_fa_e
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41 | end
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42 |
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43 | end
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44 | %
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45 | % if ssse_fa <6.0
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46 | % disp('ooo vicar');
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47 | % oo_vicar = oo_vicar + 1;
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48 | % end
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49 | % %calc true epipole
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50 | true_epipole = torr_get_right_epipole(true_F,m3);
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51 |
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52 | % for method = 2:6
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53 |
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54 |
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55 | for method = methods_used
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56 |
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57 | X1 = [x1,y1, ones(length(x1),1) * m3];
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58 | X2 = [x2,y2, ones(length(x2),1) * m3];
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59 |
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60 |
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61 | %error on perfect data (should be zero)
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62 | %f = estf(nx1,ny1,nx2,ny2, no_matches,m3);
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63 | %f = estf(x1,y1,x2,y2, no_matches,m3);
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64 | %
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65 | % [F , f]= fm_linear(X1, X2, eye(3), method);
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66 | % e = torr_errf2(f,x1,y1,x2,y2, no_matches, m3);
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67 | % disp('noise free error (sanity check)')
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68 | % ssep = e' * e
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69 | %
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70 | % %error on noisy data
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71 | % f = fm_linear(nx1,ny1,nx2,ny2, no_matches,m3);
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72 | % e = torr_errf2(f,nx1,ny1,nx2,ny2, no_matches, m3);
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73 | % ssen = e * e'
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74 |
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75 | nX1 = [nx1,ny1, ones(length(x1),1) * m3];
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76 | nX2 = [nx2,ny2, ones(length(x2),1) * m3];
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77 |
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78 | % [nF , nf]= fm_linear(nX1, nX2, eye(3), method);
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79 | [nf, nF ] = torr_estimateF(nx1,ny1,nx2,ny2, no_matches, m3, method)
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80 |
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81 | %calc noisy epipole
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82 | noisy_epipole = torr_get_right_epipole(nF,m3);
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83 | epipole_distance(method) = epipole_distance(method) + sqrt(norm(true_epipole -noisy_epipole));
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84 |
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85 |
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86 | torr_error = 1;
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87 | if torr_error
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88 | pe = torr_errf2(nf,x1,y1,x2,y2, no_matches, m3);
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89 | n_e = torr_errf2(nf,nx1,ny1,nx2,ny2, no_matches, m3);
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90 | else
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91 | CC = eye(3);
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92 | CC(3,3) = m3;
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93 | nF2 = CC * nF * CC;
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94 |
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95 | n1 = [x1 y1];
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96 | n2= [x2 y2];
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97 | nowarn = 0;
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98 |
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99 | ne = fm_error_hs(nF, n1, n2, nowarn);
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100 | end
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101 | % ne = torr_errf2(nf,nx1,ny1,nx2,ny2, no_matches, m3);
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102 |
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103 | % disp('trimmed noisy error on noise free points')
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104 | % sse_n = ne' * ne
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105 |
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106 | sse_n = norm(pe);
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107 |
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108 | if (sse_n < best_sse)
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109 | best_method = method;
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110 | best_sse = sse_n;
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111 | end
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112 |
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113 | method_sse(method) = method_sse(method) + sse_n;
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114 | method_n_sse(method) = method_sse(method) + norm(n_e);
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115 |
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116 | end %method = 1:4
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117 | best_method_array(best_method) = best_method_array(best_method)+1;
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118 | end
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119 |
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120 |
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121 | % %mine
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122 | % f_torr = estf(nx1,ny1,nx2,ny2, no_matches,m3);
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123 | % ne = torr_errf2(f_torr,x1,y1,x2,y2, no_matches, m3);
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124 | % disp('noisy error on noise free points')
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125 | % sse_n = norm(ne(20:no_matches-20))
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126 |
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127 | %disp('trace = 1, trace =0, ls, det = 1, 2x2 = 1, 2x2 =1')
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128 |
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129 | best_method_array(methods_used)'
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130 | (method_sse(methods_used)/(no_tests*length(x1)))'
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131 | (method_n_sse(methods_used)/(no_tests*length(x1)))'
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132 |
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133 | percent_gain = 1 - method_sse(methods_used(1))/method_sse(methods_used(2));
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134 | percent_gain
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135 |
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136 |
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137 |
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138 | disp('distance to true epipole');
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139 | (epipole_distance(methods_used)/no_tests)'
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140 |
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141 | ep_percent_gain = 1 - epipole_distance(methods_used(1))/epipole_distance(methods_used(2));
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142 | ep_percent_gain
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143 | %oo_vicar
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144 | %display_mat(perfect_matches, x1,y1, u1, v1)
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145 | %
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146 |
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147 | % e = fm_error_hs(F, n1, n2, nowarn);
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148 |
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149 |
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150 | %torr_display_epipoles(nF,nF,perfect_matches, x1,y1, u1, v1) |
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