bm_experiments.bm_rNiftyReg module

Benchmark for R package - RNiftyReg LINKS: * http://cran.r-project.org/web/packages/RNiftyReg/RNiftyReg.pdf * https://github.com/jonclayden/RNiftyReg

Installation

1. Install the R environment (https://stackoverflow.com/questions/31114991):

   apt install r-base-core r-base-dev
   sudo apt-get -y install libcurl4-gnutls-dev libxml2-dev libssl-dev
   

2. Run R and install required R packages:

   install.packages(c("png", "jpeg", "OpenImageR", "devtools"))
   devtools::install_github("jonclayden/RNiftyReg")
   

Usage

Run the basic R script:

Rscript scripts/Rscript/RNiftyReg_linear.r         data-images/rat-kidney_/scale-5pc/Rat-Kidney_HE.jpg         data-images/rat-kidney_/scale-5pc/Rat-Kidney_PanCytokeratin.jpg         data-images/rat-kidney_/scale-5pc/Rat-Kidney_HE.csv         output/

Run the RNiftyReg benchmark:

python bm_experiments/bm_rNiftyReg.py         -t ./data-images/pairs-imgs-lnds_histol.csv         -d ./data-images         -o ./results         -R Rscript         -script ./scripts/Rscript/RNiftyReg_linear.r

Note

tested for RNiftyReg > 2.x

Copyright (C) 2017-2019 Jiri Borovec <jiri.borovec@fel.cvut.cz>

class bm_experiments.bm_rNiftyReg.BmRNiftyReg(params)

Bases: birl.benchmark.ImRegBenchmark

Benchmark for R package - RNiftyReg no run test while this method requires manual installation of RNiftyReg

For the app installation details, see module details.

Example

>>> from birl.utilities.data_io import create_folder, update_path
>>> path_out = create_folder('temp_results')
>>> fn_path_conf = lambda n: os.path.join(update_path('scripts'), 'Rscript', n)
>>> path_csv = os.path.join(update_path('data-images'), 'pairs-imgs-lnds_mix.csv')
>>> params = {'path_out': path_out,
...           'path_table': path_csv,
...           'nb_workers': 2,
...           'unique': False,
...           'exec_R': 'Rscript',
...           'path_R_script': fn_path_conf('RNiftyReg_linear.r')}
>>> benchmark = BmRNiftyReg(params)
>>> benchmark.run()  
>>> shutil.rmtree(path_out, ignore_errors=True)

initialise benchmark

Parameters

params (dict) – parameters

_extract_execution_time(item)

if needed update the execution time :param dict item: dictionary with registration params :return float|None: time in minutes

_extract_warped_image_landmarks(item)

get registration results - warped registered images and landmarks

Parameters

item (dict) – dictionary with registration params

Return dict

paths to warped images/landmarks

_generate_regist_command(item)

generate the registration command(s)

Parameters

item (dict) – dictionary with registration params

Return str|list(str)

the execution commands

_prepare()

Prepare the experiment folder.

static extend_parse(arg_parser)

extent the basic arg parses by some extra required parameters

Return object

NAME_FILE_IMAGE = 'warped.jpg'

file with warped image after performed registration

NAME_FILE_LANDMARKS = 'points.pts'

file with warped landmarks after performed registration

NAME_FILE_TIME = 'time.txt'

file with exported image registration time

REQUIRED_PARAMS = ['path_out', 'path_table', 'exec_R', 'path_R_script']

required experiment parameters