source: proiecte/pmake3d/make3d_original/Make3dSingleImageStanford_version0.1/third_party/torr/torr_main_book_ave.m @ 37

Last change on this file since 37 was 37, checked in by (none), 14 years ago

Added original make3d

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1%       By Philip Torr 2002
2%       copyright Microsoft Corp.
3%main()
4%profile on
5m3 = 256;
6sse2t = 0;
7%method = 5;
8%
9
10
11if new_random == 0
12state_rand = 400;
13 randn('state',state_rand)
14 rand('state',state_rand)
15end
16
17    trans = 0;
18      true_epipole = torr_get_right_epipole(true_F,m3);
19   
20     
21for(i = 1:1)
22   
23    %generate a load of stuffs
24    torr_genf;
25   
26    nX1 = [nx1,ny1, ones(length(x1),1) * m3];
27    nX2 = [nx2,ny2, ones(length(x2),1) * m3];
28   
29    %mine
30    %f_torr = estf(nx1,ny1,nx2,ny2, no_matches,m3);
31   
32    %the F matrix is defined like:
33    % (nx2, ny2, m3) f(1 2 3) nx1
34    %                 (4 5 6) ny1 
35    %                 (7 8 9) m3
36   
37    %first try linear method
38    method = 3
39   
40        [nF , nf]= fm_linear(nX1, nX2, eye(3), method);
41            %calc noisy epipole
42        noisy_epipole = torr_get_right_epipole(nF,m3)
43        epipole_distance =  sqrt(norm(true_epipole -noisy_epipole))
44   
45   
46    ne1 = torr_errf2(nf,x1,y1,x2,y2, no_matches, m3);
47   
48    sne1 = sort(ne1);
49%    sse_n = norm(sne1(20:no_matches-20))
50    sse_n = norm(sne1)
51%    nf'
52   
53    %   
54    method = 5
55   
56        [nF2 , nf2]= fm_linear(nX1, nX2, eye(3), method);
57            %calc noisy epipole
58        noisy_epipole2 = torr_get_right_epipole(nF2,m3)
59            epipole_distance2 =  sqrt(norm(true_epipole -noisy_epipole2))
60
61    ne2 = torr_errf2(nf2,x1,y1,x2,y2, no_matches, m3);
62   
63    sne2 = sort(ne2);
64%    sse_n2 = norm(sne2(20:no_matches-20))
65    sse_n2 = norm(sne2)
66%    nRf'
67   
68   
69   
70end
71
72
73if draw_epipole
74torr_display_epipoles(nF,nF2,perfect_matches, x1,y1, u1, v1)
75end
76%profile off
77%
78%
79% some crap
80%
81% >> XX2 = [x2(1), y2(1), m3]
82%
83% XX2 =
84%
85%   101.4245 -119.2097  256.0000
86%
87% >> XX1 = [x1(1), y1(1), m3]
88%
89% XX1 =
90%
91%    49.3714 -140.5000  256.0000
92%
93% >>
94%
95%
96%
97%
98
99
100%        e = fm_error_hs(F, n1, n2, nowarn);
101
102if sse_n2 < sse_n
103    disp('Bookstein + Sampson betta')
104else
105    disp('old betta')
106end
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