Modules:AtlasCreator:CongealingCLI

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.

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):

$ 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}