Merge pull request #24 from neuronflow/dev

Dev

.github/workflows/release.yml

@@ -0,0 +1,30 @@
+name: release
+
+on:
+  release:
+    types: [created]
+  workflow_dispatch:
+
+jobs:
+   publish:
+    name: Publish to test PyPI
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Repository 
+        uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.x'
+      - name: Install dependencies
+        run: |
+          pip install build
+          pip install twine
+      - name: Build package
+        run: python -m build
+      - name: Upload to PyPI
+        env:
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
+        run: | 
+          twine upload dist/*.whl

LICENSE

@@ -1,4 +1,4 @@
-Copyright (c) 2019 Florian Kofler (florian.kofler[at]tum.de) & Christoph Berger (c.berger[at]tum.de)
+Copyright (c) 2019 Florian Kofler (florian.kofler[at]tum.de), Christoph Berger (c.berger[at]tum.de), Isra Mekki (isra.mekki[at]helmholtz-muenchen.de) & Mahyar Valizadeh (mahyar.valizadeh[at]helmholtz-muenchen.de)
 
                     GNU AFFERO GENERAL PUBLIC LICENSE
                        Version 3, 19 November 2007

brats_toolkit/__init__.py

@@ -0,0 +1 @@
+

brats_toolkit/cli.py

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+# -*- coding: utf-8 -*-
+# Author: Christoph Berger
+# Script for evaluation and bulk segmentation of Brain Tumor Scans
+# using the MICCAI BRATS algorithmic repository
+#
+# Please refer to README.md and LICENSE.md for further documentation
+# This software is not certified for clinical use.
+
+import sys
+import subprocess
+import pprint 
+import argparse
+
+from . import segmentor, fusionator, preprocessor
+
+def list_dockers():
+    seg = segmentor.Segmentor()
+    pp = pprint.PrettyPrinter(indent=4)
+    pp.pprint(seg.config)
+
+def list_docker_ids():
+    seg = segmentor.Segmentor()
+    pp = pprint.PrettyPrinter(indent=4)
+    pp.pprint(seg.config.keys())
+
+def list_docker_gpu():
+    seg = segmentor.Segmentor()
+    print('all these images support GPU computations:')
+    for id in seg.config.keys():
+        if seg.config[id]['runtime'] == 'nvidia':
+            print(id)
+
+def list_docker_cpu():
+    seg = segmentor.Segmentor()
+    print('all these images support CPU computations:')
+    for id in seg.config.keys():
+        if seg.config[id]['runtime'] == 'runc':
+            print(id)
+
+def fusion():
+    parser = argparse.ArgumentParser(description='Runs the Docker orchestra to fuse segmentations. All inputs have to have equal shape and label values')
+    parser.add_argument('-i','--input', required=True,
+                        help = 'Input directory containing all .nii.gz files to be fused')
+    parser.add_argument('-m','--method', required=True,
+                        help = 'Method for fusion: mav for majority voting, simple for SIMPLE')
+    parser.add_argument('-o', '--output',
+                        help = 'Filename for the output in format filename.nii.gz')
+    parser.add_argument('-v', '--verbose',action='store_true',
+                        help = 'Verbose mode outputs log info to the command line.')
+    try:
+            args = parser.parse_args()
+    except SystemExit as e:
+        if e.code == 2:
+            parser.print_help()
+        sys.exit(e.code)
+    try:
+        # runs the segmentation with all the settings wished for by the user
+        fus = fusionator.Fusionator(verbose=args.verbose)
+        fus.dirFuse(args.input, method=args.method, outputPath=args.output)
+    except subprocess.CalledProcessError as e:
+        # Ignoring errors happening in the Docker Process, otherwise we'd e.g. get error messages on exiting the Docker via CTRL+D.
+        pass
+    except Exception as e:
+        print('ERROR DETAIL: ', e)
+
+def segmentation():
+    parser = argparse.ArgumentParser(description='Runs the Docker orchestra to segment and fuse segmentations based on the' \
+                                             'BraTS algorithmic repository' \
+                                             'Please keep in mind that some models require Nvidia-Docker to run as' \
+                                             ' they need a supported GPU.')
+    parser.add_argument('-l', '--list',
+                        help = 'List all models available for segmentation.',
+                        action = 'store_true')
+    parser.add_argument('-ll', '--longlist',
+                        help = 'List all models available for segmentation with details.',
+                        action = 'store_true')
+    parser.add_argument('-lc', '--cpulist',
+                        help = 'List all models supporting cpus.',
+                        action = 'store_true')
+    parser.add_argument('-lg', '--gpulist',
+                        help = 'List all models supporting gpus.',
+                        action = 'store_true')
+    parser.add_argument('-t1',required=True,
+                        help = 'Path to the t1 modality.')
+    parser.add_argument('-t1c',required=True,
+                        help = 'Path to the t1c modality.')
+    parser.add_argument('-t2',required=True,
+                        help = 'Path to the t2 modality.')
+    parser.add_argument('-fla',required=True,
+                        help = 'Path to the fla modality.')
+    parser.add_argument('-d', '--docker', required=True,
+                        help = 'Container ID or method used for fusion. (mav, simple, all). Run brats-orchestra --list to display all options.')
+    parser.add_argument('-o', '--output',required=True,
+                        help = 'Path to the desired output file.')
+    parser.add_argument('-v', '--verbose',action='store_true',
+                        help = 'Verbose mode outputs log info to the command line.')
+    parser.add_argument('-c', '--config',
+                        help = 'Add a path to a custom config file for dockers here.')
+    parser.add_argument('-g', '--gpu', action='store_true',
+                        help = 'Pass this flag if your Docker version already supports the --gpus flag.')
+    parser.add_argument('-gi', '--gpuid',
+                        help = 'Specify the GPU bus ID to be used.')
+    try:
+        if '-l' in sys.argv[1:] or '--list' in sys.argv[1:]:
+            list_docker_ids()
+            sys.exit(0)
+        elif '-ll' in sys.argv[1:] or '--longlist' in sys.argv[1:]:
+            list_dockers()
+            sys.exit(0)
+        elif '-lg' in sys.argv[1:] or '--gpulist' in sys.argv[1:]:
+            list_docker_gpu()
+            sys.exit(0)
+        elif '-lc' in sys.argv[1:] or '--cpulist' in sys.argv[1:]:
+            list_docker_cpu()
+            sys.exit(0)
+        else:
+            args = parser.parse_args()
+    except SystemExit as e:
+        if e.code == 2:
+            parser.print_help()
+        sys.exit(e.code)
+    try:
+        # runs the segmentation with all the settings wished for by the user
+        seg = segmentor.Segmentor(config=args.config, verbose=args.verbose, newdocker=args.gpu, gpu=str(args.gpuid))
+        seg.segment(t1=args.t1, t1c=args.t1c, t2=args.t2, fla=args.fla, cid=args.docker, outputPath=args.output)
+    except subprocess.CalledProcessError as e:
+        # Ignoring errors happening in the Docker Process, otherwise we'd e.g. get error messages on exiting the Docker via CTRL+D.
+        pass
+    except Exception as e:
+        print('ERROR DETAIL: ', e)
+
+def batchpreprocess():
+    parser = argparse.ArgumentParser(description='Runs the preprocessing for MRI scans on a folder of images.')
+    parser.add_argument('-i', '--input', required=True, help='The input directory with 4 modalities in unprocessed Nifti format.')
+    parser.add_argument('-o', '--output',required=True,
+                        help = 'Path to the desired output directory.')
+    parser.add_argument('-s', '--skipupdate',action='store_true',
+                        help = 'If passed, the backend will not be updated.')
+    parser.add_argument('-c', '--confirm', action='store_true',
+                        help = 'If passed, the container will ask for confirmation')
+    parser.add_argument('-g', '--gpu', action='store_true',
+                        help = 'Pass this flag if you want to use GPU computations.')
+    parser.add_argument('-gi', '--gpuid',
+                        help = 'Specify the GPU bus ID to be used.')
+    try:
+        args = parser.parse_args()
+    except SystemExit as e:
+        if e.code == 2:
+            parser.print_help()
+        sys.exit(e.code)
+    try:
+        # runs the preprocessing with all the settings wished for by the user
+        pre = preprocessor.Preprocessor()
+        if args.gpu: 
+            mode = "gpu"
+        else:
+            mode = "cpu"
+        if args.gpuid: 
+            gpuid = str(args.gpuid)
+        else:
+            gpuid = '0'
+        pre.batch_preprocess(exam_import_folder=args.input, exam_export_folder=args.output, mode=mode, confirm=args.confirm, skipUpdate=args.skipupdate, gpuid=gpuid)
+    except subprocess.CalledProcessError as e:
+        # Ignoring errors happening in the Docker Process, otherwise we'd e.g. get error messages on exiting the Docker via CTRL+D.
+        pass
+    except Exception as e:
+        print('ERROR DETAIL: ', e)
+
+def singlepreprocess():        
+    parser = argparse.ArgumentParser(description='Runs the preprocessing for MRI scans on a single set of images.')
+    parser.add_argument('-t1',required=True,
+                        help = 'Path to the t1 modality.')
+    parser.add_argument('-t1c',required=True,
+                        help = 'Path to the t1c modality.')
+    parser.add_argument('-t2',required=True,
+                        help = 'Path to the t2 modality.')
+    parser.add_argument('-fla',required=True,
+                        help = 'Path to the fla modality.')
+    parser.add_argument('-o', '--output',required=True,
+                        help = 'Path to the desired output directory.')
+    parser.add_argument('-s', '--skipupdate',action='store_true',
+                        help = 'If passed, the backend will not be updated.')
+    parser.add_argument('-c', '--confirm', action='store_true',
+                        help = 'If passed, the container will ask for confirmation')
+    parser.add_argument('-g', '--gpu', action='store_true',
+                        help = 'Pass this flag if you want to use GPU computations.')
+    parser.add_argument('-gi', '--gpuid',
+                        help = 'Specify the GPU bus ID to be used.')
+    try:
+        args = parser.parse_args()
+    except SystemExit as e:
+        if e.code == 2:
+            parser.print_help()
+        sys.exit(e.code)
+    try:
+        # runs the preprocessing with all the settings wished for by the user
+        pre = preprocessor.Preprocessor()
+        if args.gpu: 
+            mode = "gpu"
+        else:
+            mode = "cpu"       
+        if args.gpuid: 
+            gpuid = str(args.gpuid)
+        else:
+            gpuid = '0'
+        pre.single_preprocess(t1File=args.t1, t1cFile=args.t1c, t2File=args.t2, flaFile=args.fla, outputFolder=args.output, mode=mode, confirm=args.confirm, skipUpdate=args.skipupdate, gpuid=gpuid)
+    except subprocess.CalledProcessError as e:
+        # Ignoring errors happening in the Docker Process, otherwise we'd e.g. get error messages on exiting the Docker via CTRL+D.
+        pass
+    except Exception as e:
+        print('ERROR DETAIL: ', e)
+
+if __name__ == '__main__':
+    segmentation()

brats_toolkit/config/cpu_dockers.json

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+{
+    "econib":{
+        "name":"econib",
+        "author":"Michal Marcinkiewicz",
+        "fileformat":"gz-b17",
+        "runtime":"runc",
+        "id":"econib/brats-2018",
+        "command":" ",
+        "mountpoint":"/data"
+    },
+    "lfb_rwth":{
+        "name":"lfb_rwth",
+        "author":"Leon Weninger",
+        "fileformat":"gz-b17-t1",
+        "runtime":"runc",
+        "id":"leonweninger/brats18_segmentation",
+        "command":" ",
+        "mountpoint":"/data"
+    },
+    "gbmnet":{
+        "name":"gbmnet",
+        "author":"Nicholas Nuechterlein",
+        "fileformat":"gz-b17-t1",
+        "runtime":"runc",
+        "id":"nknuecht/gbmnet18",
+        "command":"python evaluate.py",
+        "mountpoint":"/data"
+    }
+}