Merge pull request #43 from AdaptiveMotorControlLab/cy/training

v1.0

Author: C-Achard

.coveragerc

@@ -4,4 +4,4 @@ exclude_lines =
 
 [run]
 omit =
-    napari_cellseg3d/setup.py, napari_cellseg3d/code_models/models/wnet/train_wnet.py, napari_cellseg3d/code_models/models/wnet/model.py,napari_cellseg3d/code_models/models/TEMPLATE_model.py, napari_cellseg3d/code_models/models/unet/*, napari_cellseg3d/dev_scripts/*
+    napari_cellseg3d/setup.py, napari_cellseg3d/code_models/models/wnet/train_wnet.py, napari_cellseg3d/code_models/models/wnet/model.py,napari_cellseg3d/code_models/models/TEMPLATE_model.py, napari_cellseg3d/code_models/models/unet/*, napari_cellseg3d/dev_scripts/*, napari_cellseg3d/code_plugins/plugin_metrics.py

.github/workflows/build_docs.yml

@@ -1,19 +1,39 @@
-name: Deploy Sphinx documentation to Pages
+name: deploy
 
 on:
   push:
     branches: # branch to trigger deployment
       - main
 
+# This job installs dependencies, build the book, and pushes it to `gh-pages`
 jobs:
-  pages:
-    runs-on: ubuntu-20.04
+  build-and-deploy-book:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest]
+        python-version: [3.8]
     steps:
-    - id: deployment
-      uses: sphinx-notes/pages@v3
+    - uses: actions/checkout@v2
+
+    # Install dependencies
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v1
       with:
-        publish: false
-    - uses: peaceiris/actions-gh-pages@v3
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        pip install jupyter-book
+        pip install -e .
+
+    # Build the book
+    - name: Build the book
+      run: |
+        jupyter-book build docs/
+
+    # Deploy the book's HTML to gh-pages branch
+    - name: GitHub Pages action
+      uses: peaceiris/[email protected]
       with:
         github_token: ${{ secrets.GITHUB_TOKEN }}
-        publish_dir: ${{ steps.deployment.outputs.artifact }}
+        publish_dir: docs/_build/html

.github/workflows/test_and_deploy.yml

@@ -7,6 +7,7 @@ on:
   push:
     branches:
       - main
+      - cy/training
     tags:
       - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10
   pull_request:
@@ -20,9 +21,10 @@ jobs:
     runs-on: ${{ matrix.platform }}
     strategy:
       matrix:
-# platform: [ubuntu-latest, windows-latest, macos-latest]
-        platform: [windows-latest]
-        python-version: [3.8, 3.9] # 3.10
+# platform: [windows-latest]
+# , macos-latest
+        platform: [ubuntu-latest, windows-latest]
+        python-version: ['3.8', '3.9', '3.10']
 
     steps:
       - uses: actions/checkout@v3
@@ -51,7 +53,7 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           python -m pip install setuptools tox tox-gh-actions
-#         pip install git+https://github.com/kodalli/pydensecrf.git@master#egg=pydensecrf
+#         pip install git+https://github.com/lucasb-eyer/pydensecrf.git@master#egg=pydensecrf
 
 # this runs the platform-specific tests declared in tox.ini
       - name: Test with tox

.gitignore

@@ -93,23 +93,30 @@ venv/
 # written by setuptools_scm
 **/_version.py
 
+# WANDB
+/wandb/
 
 
 ########
 #project specific
 #dataset, weights, old logos, requirements
 /napari_cellseg3d/code_models/models/dataset/
 /napari_cellseg3d/code_models/models/saved_weights/
-/docs/res/logo/old_logo/
+/docs/source/logo/old_logo/
+/docs/source/code/_autosummary/
 /reqs/
 /loss_plots/
+/wandb/
 notebooks/csv_cell_plot.html
 notebooks/full_plot.html
 *.csv
 *.png
 notebooks/instance_test.ipynb
 *.prof
-docs/source/code/_autosummary/*.rst
+/docs/_build/
+/docs/source/code/_autosummary/*.rst
+cov.syspath.txt
+napari_cellseg3d/dev_scripts/wandb
 wandb/
 
 #include test data
@@ -119,4 +126,9 @@ wandb/
 !napari_cellseg3d/_tests/res/wnet_test/*.tif
 !napari_cellseg3d/_tests/res/wnet_test/lab/*.tif
 !napari_cellseg3d/_tests/res/wnet_test/vol/*.tif
-cov.syspath.txt
+
+#include docs images
+!docs/source/logo/*
+!docs/source/images/*
+lcov.info
+coverage.lcov

.pre-commit-config.yaml

@@ -7,6 +7,7 @@ repos:
       - id: trailing-whitespace
       - id: check-yaml
       - id: check-added-large-files
+        args: [--maxkb=5000]
       - id: check-toml
 #  - repo: https://github.com/pycqa/isort
 #    rev: 5.12.0

README.md

@@ -1,6 +1,5 @@
 # napari-cellseg3D: a napari plug-in for direct 3D cell segmentation with deep learning
 
-
 <img src="https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/838605d0-9723-4e43-83cd-6dbfe4adf36b/cellseg-logo.png?format=1500w" title="cellseg3d" alt="cellseg3d logo" width="350" align="right" vspace = "80"/>
 
 <a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
@@ -10,23 +9,34 @@
 [![codecov](https://codecov.io/gh/AdaptiveMotorControlLab/CellSeg3d/branch/main/graph/badge.svg?token=hzUcn3XN8F)](https://codecov.io/gh/AdaptiveMotorControlLab/CellSeg3d)
 [![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/napari-cellseg3d)](https://www.napari-hub.org/plugins/napari-cellseg3d)
 
-
 A napari plugin for 3D cell segmentation: training, inference, and data review. In particular, this project was developed for analysis of mesoSPIM-acquired (cleared tissue + lightsheet) datasets.
 
+**Help us make the code better by reporting issues and adding your feature requests!**
+
 ----------------------------------
 
 ## News
 
-*April 2023: New version with interface overhaul published ! New model and utilities coming soon*
+**New version : v1.0**
 
+Added :
 
-**This is an alpha version, please expect bugs and issues, and help us make the code better by reporting them as an issue!**
+- Improved training interface
+- Unsupervised model : WNet
+  - Generate labels directly from raw data !
+  - Can be trained in napari directly or in Colab
+  - Pretrained weights for mesoSPIM whole-brain cell segmentation
+- WandB support (install wandb and login to use automatically when training)
+- Remade and improved documentation
+  - Moved to Jupyter Book
+  - Dedicated installation page, and working ARM64 install for macOS Silicon users
+- New utilities
+- Many small improvements and many bug fixes
 
 ## Demo
 
 ![demo](https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/0d16a71b-3ff2-477a-9d83-18d96cb1ce28/full_demo.gif?format=500w)
 
-
 ## Installation
 
 **Note** : we recommend using conda to create a new environment for the plugin.
@@ -58,14 +68,15 @@ To avoid issues when installing on the ARM64 architecture, please follow these s
         conda env create -f conda/napari_cellseg3d_m1.yml
         conda activate napari_cellseg3d_m1
 
-2) Then install PyQt5 from conda separately :
+2) Install the plugin.
+   From repository root folder, run :
 
-        conda install -c anaconda pyqt
-
-3) And install the plugin :
+        pip install -e .
+   OR directly via PyPi :
 
         pip install napari-cellseg3d
 
+   OR directly via [napari-hub] (see Installation section above)
 
 ## Documentation
 
@@ -86,21 +97,21 @@ Then go into Plugins > napari-cellseg3d, and choose which tool to use.
 - **Train**:  This module allows you to train segmentation algorithms from labeled volumes.
 - **Utilities**: This module allows you to perform several actions like cropping your volumes and labels dynamically, by selecting a fixed size volume and moving it around the image; computing prediction scores from ground truth and predicition labels; or converting labels from instance to segmentation and the opposite.
 
-
 ## Requirements
-**Python >= 3.8 required**
 
-Requires **pytorch** and **MONAI**.
-For PyTorch, please see [PyTorch's website for installation instructions].
+**Python 3.8 or 3.9 required.**
+Requires **[napari]**, **[PyTorch]** and **[MONAI]**.
+
+For PyTorch, please see [the PyTorch website for installation instructions].
+
 A CUDA-capable GPU is not needed but very strongly recommended, especially for training.
-If you get errors from MONAI regarding missing readers, please see [MONAI's optional dependencies] page for instructions on getting the readers required by your images.
 
+If you get errors from MONAI regarding missing readers, please see [MONAI's optional dependencies] page for instructions on getting the readers required by your images.
 
 ## Issues
 
 If you encounter any problems, please [file an issue] along with a detailed description.
 
-
 ## Testing
 
 To run tests locally:
@@ -134,20 +145,15 @@ Distributed under the terms of the [MIT] license.
 [Cookiecutter]: https://github.com/audreyr/cookiecutter
 [@napari]: https://github.com/napari
 [MIT]: http://opensource.org/licenses/MIT
-[BSD-3]: http://opensource.org/licenses/BSD-3-Clause
-[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt
-[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt
-[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0
-[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt
 [cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin
-
-[napari]: https://github.com/napari/napari
 [tox]: https://tox.readthedocs.io/en/latest/
 [pip]: https://pypi.org/project/pip/
 [PyPI]: https://pypi.org/
 
-[PyTorch's website for installation instructions]: https://pytorch.org/get-started/locally/
+[the PyTorch website for installation instructions]: https://pytorch.org/get-started/locally/
+[PyTorch]: https://pytorch.org/get-started/locally/
 [MONAI's optional dependencies]: https://docs.monai.io/en/stable/installation.html#installing-the-recommended-dependencies
+[MONAI]: https://docs.monai.io/en/stable/installation.html#installing-the-recommended-dependencies
 
 ## Acknowledgements
 
@@ -156,12 +162,5 @@ This work was funded, in part, from the Wyss Center to the [Mathis Laboratory of
 Please refer to the documentation for full acknowledgements.
 
 ## Plugin base
-This [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.
 
-<!--
-Don't miss the full getting started guide to set up your new package:
-https://github.com/napari/cookiecutter-napari-plugin#getting-started
-
-and review the napari docs for plugin developers:
-https://napari.org/plugins/stable/index.html
--->
+This [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.

conda/napari_cellseg3d_m1.yml

@@ -4,23 +4,25 @@ channels:
   - conda-forge
   - defaults
 dependencies:
-    - python=3.8
+    - python=3.9
     - pip
+    - pyqt
+    - imagecodecs
     - pip:
         - numpy
         - napari>=0.4.14
-        - QtPy
-        - opencv-python>=4.5.5
+        # - opencv-python>=4.5.5
         - scikit-image>=0.19.2
         - matplotlib>=3.4.1
         - tifffile>=2022.2.9
-        - imageio-ffmpeg>=0.4.5
+        # - imageio-ffmpeg>=0.4.5
         - torch>=1.11
-        - monai[nibabel,einops]>=0.9.0
+        - monai[nibabel, einops]>=0.9.0
         - tqdm
-        - nibabel
+        # - nibabel
         - scikit-image
-        - pillow
+        # - pillow
+        - pyclesperanto-prototype
         - tqdm
         - matplotlib
-        - vispy>=0.9.6
+        # - vispy>=0.9.6