On Thu, 31 Jul 2014 01:15:54 +1000, Markus Neteler <[email protected]>
wrote:
Dear Milton,
I made a quick test with the sample data you sent to me:
autopano-sift-c output.pto dir_2014_002_17_1.jpg dir_2014_002_16_7.jpg
[...]
555 keypoints found
[...]
Filtering... (dir_2014_002_17_1.jpg, dir_2014_002_16_7.jpg)
A. Join Filtration: 32 to 32
B. Score Filtration: 32 to 25
Filtered partition [0,1] from 32 matches down to 25
cat output.pto
# Hugin project file generated by APSCpp
[...]
# automatically generated control points
c n0 N1 x290.400812 y143.672441 X289.377092 Y142.525139 t0
c n0 N1 x566.209025 y383.642656 X566.343984 Y383.440512 t0
c n0 N1 x321.360169 y120.632838 X321.409832 Y120.057093 t0
c n0 N1 x45.026134 y21.912848 X43.642776 Y22.815530 t0
c n0 N1 x299.155503 y122.350252 X298.325028 Y122.493041 t0
[...]
c n0 N1 x377.501999 y142.803118 X372.855780 Y145.767751 t0
So, it easily find a lot of common points (runtime of autopano-sift-c
is 0.72 seconds on my office Linux PC). In a scripted approach you
could match the 18,000 images against a master image or the like, then
convert the resulting output.pto files into a text file structure
readable by i.rectify.
I have run Milton's test images through a script which calls a number of
hugin command line programs to generate control points, optimise and spit
out remapped images. It seems to work quite well, with a mean error of 0.1
pix and max error of 0.3 pix.
Cheers,
--
Regards,
Terry Duell
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