Difference between revisions of "Modules:AtlasCreator:CongealingCLI"
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== 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. | + | 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. In fact, by not specifying any arguments and just running the wrapper, a default configuration file for congeal is generated. |
=== Graphical User Interface in 3D Slicer === | === Graphical User Interface in 3D Slicer === |
Revision as of 01:31, 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. In fact, by not specifying any arguments and just running the wrapper, a default configuration file for congeal is generated.
Graphical User Interface in 3D Slicer
Command Line Interface
The option --help prints the possible command line arguments:
$ ./CongealingCLI --help USAGE: ./CongealingCLI [--returnparameterfile <std::string>] [--processinformationaddress <std::string>] [--xml] [--echo] [--launch <std::string>] [--test <std::string>] [--congeal_optimize_bestpoints <int>] [--congeal_schedule__n__optimize_samples <std::vector<int>>] [--congeal_schedule__n__optimize_iterations <std::vector<int>>] [--congeal_schedule__n__optimize_warp__3__ <std::vector<std::string>>] [--congeal_schedule__n__optimize_warp__2__ <std::vector<std::string>>] [--congeal_schedule__n__optimize_warp__1__ <std::vector<std::string>>] [--congeal_schedule__n__optimize_warp__0__ <std::vector<std::string>>] [--congeal_schedule__n__warpfield__3__size <std::vector<int>>] [--congeal_schedule__n__warpfield__2__size <std::vector<int>>] [--congeal_schedule__n__warpfield__1__size <std::vector<int>>] [--congeal_schedule__n__warpfield__0__size <std::vector<int>>] [--congeal_schedule__n__optimize_affine <std::vector<std::string>>] [--congeal_schedule__n__downsample <std::vector<int>>] [--congeal_schedule__n__cache <std::vector<std::string>>] [--congeal_initialsteps_warp <float>] [--congeal_initialsteps_scale <float>] [--congeal_initialsteps_rotate <float>] [--congeal_initialsteps_translate <float>] [--congeal_output_average_height <int>] [--congeal_output_average_width <int>] [--congeal_optimize_progresspoints <int>] [--congeal_output_sourcegrid <int>] [--congeal_output_colors_range <int>] [--congeal_output_colors_mid <int>] [--congeal_output_prefix <std::string>] [--congeal_error__parzen__apriori <float>] [--congeal_error__parzen__sigma <float>] [--congeal_optimize_error <parzen|variance>] [--congeal_optimize__randomwalk__directions <int>] [--congeal_optimize__randomwalk__steps <int>] [--congeal_optimize_randomwalk_kernel <float>] [--congeal_optimize_algorithm <lbfgs|bruteforce |randomwalk|gradientdescent>] [--congeal_inputfiles_list <std::string>] [--congeal_inputfile_format <nifti>] [--congeal_inputfiles <int>] [--] [--version] [-h] Where: --returnparameterfile <std::string> Filename in which to write simple return parameters (int, float, int-vector, etc.) as opposed to bulk return parameters (image, geometry, transform, measurement, table). --processinformationaddress <std::string> Address of a structure to store process information (progress, abort, etc.). (default: 0) --xml Produce xml description of command line arguments (default: 0) --echo Echo the command line arguments (default: 0) --launch <std::string> The path to the congeal executable. Congeal will only be executed, if this is set. --test <std::string> Currently unused. 'Must be congeal.' (default: congeal) --congeal_optimize_bestpoints <int> (default: 1000) --congeal_schedule__n__optimize_samples <std::vector<int>> number of samples to be compared in each transformed input volume. Separated by comma for each schedule run. (default: 50000,50000,500000 ,500000,500000) --congeal_schedule__n__optimize_iterations <std::vector<int>> number of optimzation iterations to be taken in the schedules. Separated by comma for each schedule run. (default: 30,30,30,30,30) --congeal_schedule__n__optimize_warp__3__ <std::vector<std::string>> determines if the B-spline parameters for B-spline field 3 should be optimized or left fixed. Separated by comma for each schedule run. (default: false,false,false,false,true) --congeal_schedule__n__optimize_warp__2__ <std::vector<std::string>> determines if the B-spline parameters for B-spline field 2 should be optimized or left fixed. Separated by comma for each schedule run. (default: false,false,false,true,false) --congeal_schedule__n__optimize_warp__1__ <std::vector<std::string>> determines if the B-spline parameters for B-spline field 1 should be optimized or left fixed. Separated by comma for each schedule run. (default: false,false,true,false,false) --congeal_schedule__n__optimize_warp__0__ <std::vector<std::string>> determines if the B-spline parameters for B-spline field 0 should be optimized or left fixed. Separated by comma for each schedule run. (default: false,true,false,false,false) --congeal_schedule__n__warpfield__3__size <std::vector<int>> determines number of support points in each dimension of of B-Spline mesh for field 3. Separated by comma for each schedule run. (default: 1,1,1,1,32) --congeal_schedule__n__warpfield__2__size <std::vector<int>> determines number of support points in each dimension of of B-Spline mesh for field 2. Separated by comma for each schedule run. (default: 1,1,1,16,16) --congeal_schedule__n__warpfield__1__size <std::vector<int>> determines number of support points in each dimension of of B-Spline mesh for field 1. Separated by comma for each schedule run. (default: 1,1,8,8,8) --congeal_schedule__n__warpfield__0__size <std::vector<int>> determines number of support points in each dimension of of B-Spline mesh for field 0. Separated by comma for each schedule run. (default: 1,4,4,4,4) --congeal_schedule__n__optimize_affine <std::vector<std::string>> determines if affine parameters should be optimized or left fixed. Separated by comma for each schedule run. (default: true,false,false ,false,false) --congeal_schedule__n__downsample <std::vector<int>> determines how many times the input data should be downsampled (by factor of 2 in each dimension) prior to congealing. Separated by comma for each schedule run. (default: 0,0,0,0,0) --congeal_schedule__n__cache <std::vector<std::string>> determines whether or not the schedules results can be retrieved from the previous run. Separated by comma for each schedule run. (default: true,true,true,true,true) --congeal_initialsteps_warp <float> relative scaling of warp control point displacement when computing kernels and step sizes. Scale: Warp as fraction of control point's region (default: 0.15) --congeal_initialsteps_scale <float> relative scaling of scaling parameters when computing kernels and step sizes. Scale: Scale as fraction of image size (default: 0.2) --congeal_initialsteps_rotate <float> relative scaling of rotation parameters when computing kernels and step sizes. Scale: rotation in degrees (default: 30) --congeal_initialsteps_translate <float> relative scaling of translation parameters when computing kernels and step sizes. Scale: translation as fraction of image size (default: 0.2) --congeal_output_average_height <int> determines the height of the congealing average visualization (default: 512) --congeal_output_average_width <int> determines the width of the congealing average visualization (default: 512) --congeal_optimize_progresspoints <int> determines how many output file sets will be generated during each schedule (default: 4) --congeal_output_sourcegrid <int> determines how many of the transformed source values are shown in the *-inputs* images (default: 9) --congeal_output_colors_range <int> color equalization slope. This value determines the relationship between changes in data value and changes in output image gray value (default: 256) --congeal_output_colors_mid <int> color equalization intercept. This value determines which data value will be mapped to mid gray (default: 128) --congeal_output_prefix <std::string> string prepended to the filenames of the outputfiles. This value can include an absolute or relative path, as well as a file prefix (default: ../output/congeal/) --congeal_error__parzen__apriori <float> constant factor added to each Parzen estimate (default: 1e-06) --congeal_error__parzen__sigma <float> sigma of Gaussian used as kernel in Parzen density estimator. Measured in voxel intensity (default: 30) --congeal_optimize_error <parzen|variance> selects the error metric to be used. parzen -- entropy estimate based on Parzen density estimator. variance -- variance of voxel stack. (default: parzen) --congeal_optimize__randomwalk__directions <int> number of beams to try during each iteration (default: 20) --congeal_optimize__randomwalk__steps <int> maximum number of steps to take along any beam (default: 10) --congeal_optimize_randomwalk_kernel <float> size of support to use for computing initial stepsize. This factor is multiplied by *.initialsteps to establish a maximum step radius for each dimensions (default: 0.1) --congeal_optimize_algorithm <lbfgs|bruteforce|randomwalk |gradientdescent> determines the optimization algorithm used (default: randomwalk) --congeal_inputfiles_list <std::string> a path to a file containing a list of input data files. The list file should contain one filename per line. Only congeal_inputfiles files will be used as input unless congeal_inputfiles is set to '0' in which case all the files in the list will be used (default: ../input/sample/allfiles) --congeal_inputfile_format <nifti> format of input files. Currently only 'nifti' is supported (default: nifti) --congeal_inputfiles <int> number of input files to use. Use '0' for all files when used in conjuctions with congeal_inputfiles.list (default: 30) --, --ignore_rest Ignores the rest of the labeled arguments following this flag. --version Displays version information and exits. -h, --help Displays usage information and exits. Description: Generates a configuration file for the Congealing Registration tool. Author(s): Daniel Haehn and Kilian Pohl, University of Pennsylvania Acknowledgements: The research was funded by an ARRA supplement to NIH NCRR (P41 RR13218).
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.