Difference between revisions of "Modules:AtlasCreator:CongealingCLI"
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− | and the output is the same for both (thanks to the default values): | + | and the output is the same for both (thanks to the default values, they also equal a press on the Apply button in the GUI without any changes to the input fields): |
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+ | |||
+ | ==== Using '''--launch''' ==== | ||
+ | |||
+ | When adding a '''--launch PATH_TO_CONGEAL_EXEC''', the congeal executable gets launched rather than printing the path to the generated configuration file. | ||
+ | |||
+ | For example | ||
+ | |||
+ | <pre> | ||
+ | $ ./CongealingCLI --launch congeal | ||
+ | </pre> | ||
+ | |||
+ | starts the congeal executable with the configuration file shown above. |
Revision as of 01:29, 7 April 2011
Home < Modules:AtlasCreator:CongealingCLIContents
CongealingCLI
CongealingCLI is a wrapper to access the Congealing Un-biased Groupwise Registration tool. It is now possible to generate a configuration file for Congealing by using a GUI or command line arguments.
Graphical User Interface in 3D Slicer
Command Line Interface
Examples
The following commands are possible:
$ ./CongealingCLI Configuration file written to /var/tmp/tmp.0.YQNbrV
or
$ ./CongealingCLI --congeal_inputfiles 30 --congeal_inputfile_format nifti --congeal_inputfiles_list ../input/sample/allfiles --congeal_optimize_algorithm randomwalk --congeal_optimize_randomwalk_kernel 0.1 --congeal_optimize__randomwalk__steps 10 --congeal_optimize__randomwalk__directions 20 --congeal_optimize_error parzen --congeal_error__parzen__sigma 30 --congeal_error__parzen__apriori 1e-06 --congeal_output_prefix ../output/congeal/ --congeal_output_colors_mid 128 --congeal_output_colors_range 256 --congeal_output_sourcegrid 9 --congeal_optimize_progresspoints 4 --congeal_output_average_width 512 --congeal_output_average_height 512 --congeal_initialsteps_translate 0.2 --congeal_initialsteps_rotate 30 --congeal_initialsteps_scale 0.2 --congeal_initialsteps_warp 0.15 --congeal_schedule__n__cache true,true,true,true,true --congeal_schedule__n__downsample 0,0,0,0,0 --congeal_schedule__n__optimize_affine true,false,false,false,false --congeal_schedule__n__warpfield__0__size 1,4,4,4,4 --congeal_schedule__n__warpfield__1__size 1,1,8,8,8 --congeal_schedule__n__warpfield__2__size 1,1,1,16,16 --congeal_schedule__n__warpfield__3__size 1,1,1,1,32 --congeal_schedule__n__optimize_warp__0__ false,true,false,false,false --congeal_schedule__n__optimize_warp__1__ false,false,true,false,false --congeal_schedule__n__optimize_warp__2__ false,false,false,true,false --congeal_schedule__n__optimize_warp__3__ false,false,false,false,true --congeal_schedule__n__optimize_iterations 30,30,30,30,30 --congeal_schedule__n__optimize_samples 50000,50000,500000,500000,500000 --congeal_optimize_bestpoints 1000 --test congeal Configuration file written to /var/tmp/tmp.0.YQNbrV
and the output is the same for both (thanks to the default values, they also equal a press on the Apply button in the GUI without any changes to the input fields):
$ cat /var/tmp/tmp.0.YQNbrV # experimental congeal.optimize.bestpoints 1000 test congeal # input congeal.inputfiles 30 congeal.inputfile.format nifti congeal.inputfiles.list ../input/sample/allfiles # optimization congeal.optimize.algorithm randomwalk # randomwalk congeal.optimize[randomwalk].kernel 0.1 congeal.optimize[randomwalk].steps 10 congeal.optimize[randomwalk].directions 20 # error function congeal.optimize.error parzen # parzen error function congeal.error[parzen].sigma 30 congeal.error[parzen].apriori 1e-06 # output congeal.output.prefix ../output/congeal/ congeal.output.colors.mid 128 congeal.output.colors.range 256 congeal.output.sourcegrid 9 congeal.optimize.progresspoints 4 congeal.output.average.width 512 congeal.output.average.height 512 # initial steps congeal.initialsteps.translate 0.2 congeal.initialsteps.rotate 30 congeal.initialsteps.scale 0.2 congeal.initialsteps.warp 0.15 # schedules n -1 congeal.schedule[{++n}].cache true congeal.schedule[{$n}].downsample 0 congeal.schedule[{$n}].optimize.affine true congeal.schedule[{$n}].warpfield[0].size 1 congeal.schedule[{$n}].warpfield[1].size 1 congeal.schedule[{$n}].warpfield[2].size 1 congeal.schedule[{$n}].warpfield[3].size 1 congeal.schedule[{$n}].optimize.warp[0] false congeal.schedule[{$n}].optimize.warp[1] false congeal.schedule[{$n}].optimize.warp[2] false congeal.schedule[{$n}].optimize.warp[3] false congeal.schedule[{$n}].optimize.iterations 30 congeal.schedule[{$n}].optimize.samples 50000 congeal.schedule[{++n}].cache true congeal.schedule[{$n}].downsample 0 congeal.schedule[{$n}].optimize.affine false congeal.schedule[{$n}].warpfield[0].size 4 congeal.schedule[{$n}].warpfield[1].size 1 congeal.schedule[{$n}].warpfield[2].size 1 congeal.schedule[{$n}].warpfield[3].size 1 congeal.schedule[{$n}].optimize.warp[0] true congeal.schedule[{$n}].optimize.warp[1] false congeal.schedule[{$n}].optimize.warp[2] false congeal.schedule[{$n}].optimize.warp[3] false congeal.schedule[{$n}].optimize.iterations 30 congeal.schedule[{$n}].optimize.samples 50000 congeal.schedule[{++n}].cache true congeal.schedule[{$n}].downsample 0 congeal.schedule[{$n}].optimize.affine false congeal.schedule[{$n}].warpfield[0].size 4 congeal.schedule[{$n}].warpfield[1].size 8 congeal.schedule[{$n}].warpfield[2].size 1 congeal.schedule[{$n}].warpfield[3].size 1 congeal.schedule[{$n}].optimize.warp[0] false congeal.schedule[{$n}].optimize.warp[1] true congeal.schedule[{$n}].optimize.warp[2] false congeal.schedule[{$n}].optimize.warp[3] false congeal.schedule[{$n}].optimize.iterations 30 congeal.schedule[{$n}].optimize.samples 500000 congeal.schedule[{++n}].cache true congeal.schedule[{$n}].downsample 0 congeal.schedule[{$n}].optimize.affine false congeal.schedule[{$n}].warpfield[0].size 4 congeal.schedule[{$n}].warpfield[1].size 8 congeal.schedule[{$n}].warpfield[2].size 16 congeal.schedule[{$n}].warpfield[3].size 1 congeal.schedule[{$n}].optimize.warp[0] false congeal.schedule[{$n}].optimize.warp[1] false congeal.schedule[{$n}].optimize.warp[2] true congeal.schedule[{$n}].optimize.warp[3] false congeal.schedule[{$n}].optimize.iterations 30 congeal.schedule[{$n}].optimize.samples 500000 congeal.schedule[{++n}].cache true congeal.schedule[{$n}].downsample 0 congeal.schedule[{$n}].optimize.affine false congeal.schedule[{$n}].warpfield[0].size 4 congeal.schedule[{$n}].warpfield[1].size 8 congeal.schedule[{$n}].warpfield[2].size 16 congeal.schedule[{$n}].warpfield[3].size 32 congeal.schedule[{$n}].optimize.warp[0] false congeal.schedule[{$n}].optimize.warp[1] false congeal.schedule[{$n}].optimize.warp[2] false congeal.schedule[{$n}].optimize.warp[3] true congeal.schedule[{$n}].optimize.iterations 30 congeal.schedule[{$n}].optimize.samples 500000 congeal.schedules {++n}
Using --launch
When adding a --launch PATH_TO_CONGEAL_EXEC, the congeal executable gets launched rather than printing the path to the generated configuration file.
For example
$ ./CongealingCLI --launch congeal
starts the congeal executable with the configuration file shown above.