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. Hope this helps, Markus _______________________________________________ grass-user mailing list [email protected] http://lists.osgeo.org/mailman/listinfo/grass-user
