Merge pull request #244 from jamesobutler/maintenance-tasks

Testing Maintenance tasks

.github/dependabot.yml

@@ -0,0 +1,12 @@
+# Set update schedule for GitHub Actions
+
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      # Check for updates to GitHub Actions every week
+      interval: "weekly"
+    commit-message:
+      # Prefix all commit messages with "CI: "
+      prefix: "CI"

.github/workflows/codespell.yml

@@ -0,0 +1,23 @@
+# GitHub Action to automate the identification of common misspellings in text files.
+# https://github.com/codespell-project/actions-codespell
+# https://github.com/codespell-project/codespell
+name: codespell
+on:
+  # Triggers the workflow on push or pull request events
+  push:
+    branches: [ master ]
+  pull_request:
+    branches: [ master ]
+
+permissions:
+  contents: read
+
+jobs:
+  codespell:
+    name: Check for spelling errors
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1
+      - uses: codespell-project/actions-codespell@94259cd8be02ad2903ba34a22d9c13de21a74461 # v2.0
+        with:
+          check_filenames: true

.github/workflows/lint.yml

@@ -0,0 +1,25 @@
+name: CI (Lint)
+
+on:
+  # Triggers the workflow on push or pull request events
+  push:
+    branches: [ master ]
+  pull_request:
+    branches:
+      - "*"
+
+permissions:
+  contents: read
+
+jobs:
+  lint:
+    name: Lint
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1
+
+    - uses: actions/setup-python@65d7f2d534ac1bc67fcd62888c5f4f3d2cb2b236 # v4.7.1
+      with:
+        python-version: '3.9'
+
+    - uses: pre-commit/action@646c83fcd040023954eafda54b4db0192ce70507 # v3.0.0

.github/workflows/run_tests.yml

@@ -16,7 +16,7 @@ jobs:
         uses: actions/setup-python@v2
         with:
           python-version: ${{matrix.python-version}}
-          
+
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip

.github/workflows/run_tests_os.yml

@@ -11,7 +11,7 @@ jobs:
         os: [ubuntu-latest, windows-latest, macos-latest]
         python-version: ["3.10"]
     runs-on: ${{ matrix.os }}
-    
+
     steps:
     - uses: actions/checkout@v2
 
@@ -27,7 +27,7 @@ jobs:
         pip install pytest Cython
         pip install torch==2.0.0 -f https://download.pytorch.org/whl/cpu
         pip install .
-    
+
     - name: Install dependencies on Windows / MacOS
       if: runner.os == 'Windows' || runner.os == 'macOS'
       run: |

.pre-commit-config.yaml

@@ -0,0 +1,32 @@
+repos:
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: "v4.4.0"
+  hooks:
+  - id: check-added-large-files
+    args: ['--maxkb=1024']
+  - id: check-ast
+  - id: check-case-conflict
+  - id: check-merge-conflict
+  - id: check-symlinks
+  - id: trailing-whitespace
+
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  rev: v0.1.7
+  hooks:
+    # Run the linter.
+    - id: ruff
+      args: [ --fix ]
+
+- repo: https://github.com/asottile/pyupgrade
+  rev: v3.15.0
+  hooks:
+  -   id: pyupgrade
+      args: [--py39-plus]
+
+- repo: https://github.com/codespell-project/codespell
+  rev: v2.2.6
+  hooks:
+    - id: codespell
+      args: [
+        "--write-changes"
+        ]

README.md

@@ -6,15 +6,15 @@ Tool for segmentation of over 117 classes in CT images. It was trained on a wide
 
 ![Alt text](resources/imgs/overview_classes_2.png)
 
-Created by the department of [Research and Analysis at University Hospital Basel](https://www.unispital-basel.ch/en/radiologie-nuklearmedizin/forschung-radiologie-nuklearmedizin).  
+Created by the department of [Research and Analysis at University Hospital Basel](https://www.unispital-basel.ch/en/radiologie-nuklearmedizin/forschung-radiologie-nuklearmedizin).
 If you use it please cite our [Radiology AI paper](https://pubs.rsna.org/doi/10.1148/ryai.230024). Please also cite [nnUNet](https://github.com/MIC-DKFZ/nnUNet) since TotalSegmentator is heavily based on it.
 
 
 ### Installation
 
 TotalSegmentator works on Ubuntu, Mac, and Windows and on CPU and GPU.
 
-Install dependencies:  
+Install dependencies:
 * Python >= 3.9
 * [Pytorch](http://pytorch.org/) >= 1.12.1
 
@@ -45,7 +45,7 @@ TotalSegmentator -i ct.nii.gz -o segmentations
 
 Next to the default task (`total`) there are more subtasks with more classes:
 
-Openly available for any usage:  
+Openly available for any usage:
 * **total**: default task containing 117 main classes (see [here](https://github.com/wasserth/TotalSegmentator#class-details) for a list of classes)
 * **lung_vessels**: lung_vessels (cite [paper](https://www.sciencedirect.com/science/article/pii/S0720048X22001097)), lung_trachea_bronchia
 * **body**: body, body_trunc, body_extremities, skin
@@ -56,7 +56,7 @@ Openly available for any usage:
 
 *: These models are not trained on the full totalsegmentator dataset but on some small other datasets. Therefore, expect them to work less robustly.
 
-Available with a license (free licenses available for non-commercial usage [here](https://backend.totalsegmentator.com/license-academic/). For a commercial license contact jakob.wasserthal@usb.ch): 
+Available with a license (free licenses available for non-commercial usage [here](https://backend.totalsegmentator.com/license-academic/). For a commercial license contact jakob.wasserthal@usb.ch):
 * **heartchambers_highres**: myocardium, atrium_left, ventricle_left, atrium_right, ventricle_right, aorta, pulmonary_artery (trained on sub-millimeter resolution)
 * **appendicular_bones**: patella, tibia, fibula, tarsal, metatarsal, phalanges_feet, ulna, radius, carpal, metacarpal, phalanges_hand
 * **tissue_types**: subcutaneous_fat, skeletal_muscle, torso_fat
@@ -87,17 +87,17 @@ docker run --gpus 'device=0' --ipc=host -v /absolute/path/to/my/data/directory:/
 
 
 ### Running v1
-If you want to keep on using TotalSegmentator v1 (e.g. because you do not want to change your pipeline) you 
+If you want to keep on using TotalSegmentator v1 (e.g. because you do not want to change your pipeline) you
 can install it with the following command:
 ```
 pip install TotalSegmentator==1.5.7
 ```
 The documentation for v1 can be found [here](https://github.com/wasserth/TotalSegmentator/tree/v1.5.7). Bugfixes for v1 are developed in the branch `v1_bugfixes`.
-Our Radiology AI publication refers to TotalSegmentator v1. 
+Our Radiology AI publication refers to TotalSegmentator v1.
 
 
 ### Resource Requirements
-Totalsegmentator has the following runtime and memory requirements (using an Nvidia RTX 3090 GPU):  
+Totalsegmentator has the following runtime and memory requirements (using an Nvidia RTX 3090 GPU):
 (1.5mm is the normal model and 3mm is the `--fast` model. With v2 the runtimes have increased a bit since
 we added more classes.)
 
@@ -112,8 +112,8 @@ If you want to reduce memory consumption you can use the following options:
 
 
 ### Train/validation/test split
-The exact split of the dataset can be found in the file `meta.csv` inside of the [dataset](https://doi.org/10.5281/zenodo.6802613). This was used for the validation in our paper.  
-The exact numbers of the results for the high-resolution model (1.5mm) can be found [here](resources/results_all_classes_v1.json). The paper shows these numbers in the supplementary materials Figure 11. 
+The exact split of the dataset can be found in the file `meta.csv` inside of the [dataset](https://doi.org/10.5281/zenodo.6802613). This was used for the validation in our paper.
+The exact numbers of the results for the high-resolution model (1.5mm) can be found [here](resources/results_all_classes_v1.json). The paper shows these numbers in the supplementary materials Figure 11.
 
 
 ### Retrain model and run evaluation
@@ -126,7 +126,7 @@ If you want to combine some subclasses (e.g. lung lobes) into one binary mask (e
 totalseg_combine_masks -i totalsegmentator_output_dir -o combined_mask.nii.gz -m lung
 ```
 
-Normally weights are automatically downloaded when running TotalSegmentator. If you want to download the weights with an extra command (e.g. when building a docker container) use this: 
+Normally weights are automatically downloaded when running TotalSegmentator. If you want to download the weights with an extra command (e.g. when building a docker container) use this:
 ```
 totalseg_download_weights -t <task_name>
 ```
@@ -155,7 +155,7 @@ pip install git+https://github.com/wasserth/TotalSegmentator.git
 
 ### Typical problems
 
-**ITK loading Error**  
+**ITK loading Error**
 When you get the following error message
 ```
 ITK ERROR: ITK only supports orthonormal direction cosines. No orthonormal definition was found!
@@ -166,12 +166,12 @@ pip install SimpleITK==2.0.2
 ```
 
 Alternatively you can try
-``` 
+```
 fslorient -copysform2qform input_file
 fslreorient2std input_file output_file
 ```
 
-**Bad segmentations**  
+**Bad segmentations**
 When you get bad segmentation results check the following:
 * does your input image contain the original HU values or are the intensity values rescaled to a different range?
 * is the patient normally positioned in the image? (In axial view is the spine at the bottom of the image? In the coronal view is the head at the top of the image?)
@@ -181,21 +181,21 @@ When you get bad segmentation results check the following:
 TotalSegmentator sends anonymous usage statistics to help us improve it further. You can deactivate it by setting `send_usage_stats` to `false` in `~/.totalsegmentator/config.json`.
 
 
-### Reference 
+### Reference
 For more details see our [Radiology AI paper](https://pubs.rsna.org/doi/10.1148/ryai.230024) ([freely available preprint](https://arxiv.org/abs/2208.05868)).
 If you use this tool please cite it as follows
 ```
 Wasserthal, J., Breit, H.-C., Meyer, M.T., Pradella, M., Hinck, D., Sauter, A.W., Heye, T., Boll, D., Cyriac, J., Yang, S., Bach, M., Segeroth, M., 2023. TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images. Radiology: Artificial Intelligence. https://doi.org/10.1148/ryai.230024
 ```
-Please also cite [nnUNet](https://github.com/MIC-DKFZ/nnUNet) since TotalSegmentator is heavily based on it.  
+Please also cite [nnUNet](https://github.com/MIC-DKFZ/nnUNet) since TotalSegmentator is heavily based on it.
 Moreover, we would really appreciate it if you let us know what you are using this tool for. You can also tell us what classes we should add in future releases. You can do so [here](https://github.com/wasserth/TotalSegmentator/issues/1).
 
 
 ### Class details
 
 The following table shows a list of all classes.
 
-TA2 is a standardized way to name anatomy. Mostly the TotalSegmentator names follow this standard. 
+TA2 is a standardized way to name anatomy. Mostly the TotalSegmentator names follow this standard.
 For some classes they differ which you can see in the table below.
 
 [Here](resources/totalsegmentator_snomed_mapping.csv) you can find a mapping of the TotalSegmentator classes to SNOMED-CT codes.

pyproject.toml

@@ -0,0 +1,69 @@
+[tool.ruff]
+# Exclude a variety of commonly ignored directories.
+exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
+    ".git",
+    ".git-rewrite",
+    ".hg",
+    ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".pytype",
+    ".ruff_cache",
+    ".svn",
+    ".tox",
+    ".venv",
+    "__pypackages__",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "node_modules",
+    "venv",
+]
+
+line-length = 550
+indent-width = 4
+target-version = "py39"
+
+[tool.ruff.lint]
+# Enable Pyflakes (`F`) and a subset of the pycodestyle (`E`)  codes by default.
+# Unlike Flake8, Ruff doesn't enable pycodestyle warnings (`W`) or
+# McCabe complexity (`C901`) by default.
+select = [
+    "E4", # Pycodestyle: Import
+    "E7", # Pycodestyle: Statement
+    "E9", # Pycodestyle: Runtime
+    "F" # Pyflakes: All codes
+]
+ignore = [
+    "E402", # module level import not at top of file
+    "E701", # multiple statements on one line (colon)
+    "E721", # do not compare types, use isinstance()
+    "E741", # do not use variables named l, O, or I
+    "F401", # module imported but unused
+    "F821", # undefined name
+    "F841" # local variable name is assigned to but never used
+]
+
+# Allow fix for all enabled rules (when `--fix`) is provided.
+fixable = ["ALL"]
+unfixable = []
+
+# Allow unused variables when underscore-prefixed.
+dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
+
+[tool.ruff.format]
+# Like Black, use double quotes for strings.
+quote-style = "double"
+
+# Like Black, indent with spaces, rather than tabs.
+indent-style = "space"
+
+# Like Black, respect magic trailing commas.
+skip-magic-trailing-comma = false
+
+# Like Black, automatically detect the appropriate line ending.
+line-ending = "auto"

resources/convert_dataset_to_nnunet.py

@@ -51,7 +51,7 @@ def combine_labels(ref_img, file_out, masks):
     ref_img = nib.load(ref_img)
     combined = np.zeros(ref_img.shape).astype(np.uint8)
     for idx, arg in enumerate(masks):
-        file_in = Path(arg)  
+        file_in = Path(arg)
         if file_in.exists():
             img = nib.load(file_in)
             combined[img.get_fdata() > 0] = idx+1
@@ -60,26 +60,26 @@ def combine_labels(ref_img, file_out, masks):
     nib.save(nib.Nifti1Image(combined.astype(np.uint8), ref_img.affine), file_out)
 
 
-if __name__ == "__main__":    
+if __name__ == "__main__":
     """
-    Convert the downloaded TotalSegmentator dataset (after unzipping it) to nnUNet format and 
+    Convert the downloaded TotalSegmentator dataset (after unzipping it) to nnUNet format and
     generate dataset.json and splits_final.json
 
-    example usage: 
+    example usage:
     python convert_dataset_to_nnunet.py /my_downloads/TotalSegmentator_dataset /nnunet/raw/Dataset100_TotalSegmentator_part1 class_map_part_organs
 
     You must set nnUNet_raw and nnUNet_preprocessed environment variables before running this (see nnUNet documentation).
     """
 
-    dataset_path = Path(sys.argv[1])  # directory containining all the subjects
+    dataset_path = Path(sys.argv[1])  # directory containing all the subjects
     nnunet_path = Path(sys.argv[2])  # directory of the new nnunet dataset
-    # TotalSegmentator is made up of 5 models. Choose which one you want to produce. Choose from: 
+    # TotalSegmentator is made up of 5 models. Choose which one you want to produce. Choose from:
     #   class_map_part_organs
-    #   class_map_part_vertebrae 
-    #   class_map_part_cardiac 
-    #   class_map_part_muscles 
+    #   class_map_part_vertebrae
+    #   class_map_part_cardiac
+    #   class_map_part_muscles
     #   class_map_part_ribs
-    class_map_name = sys.argv[3]  
+    class_map_name = sys.argv[3]
 
     class_map = class_map_5_parts[class_map_name]