EPySeg
EPySeg is a package for segmenting 2D epithelial tissues. EPySeg also ships with a graphical user interface that allows for building, training and running deep learning models. Training can be done with or without data augmentation (2D-xy and 3D-xyz data augmentation are supported). EPySeg relies on the segmentation_models library. EPySeg source code is available here. Cloud version available here.
Install
-
Install Miniconda (if not already present on your system)
-
Then in a command prompt type:
conda create -y -n TA python==3.10.12
conda activate TA
pip install -U pip
pip install -U epyseg
python -m epyseg
NB:
- To open a command prompt on Windows press 'Windows'+R then type 'cmd'
- To open a command prompt on MacOS press 'Command'+Space then type in 'Terminal'
Run
To open the graphical user interface, type the following in a command:
conda activate TA
python -m epyseg
Third party libraries
Below is a list of the 3rd party libraries used by EPySeg and/or pyTA.
IMPORTANTLY: if you disagree with any license below, please uninstall EPySeg.
| Library name | Use | Link | License |
|---|---|---|---|
| tensorflow | Deep learning library | https://pypi.org/project/tensorflow/ | Apache 2.0 |
| segmentation-models | Models | https://pypi.org/project/segmentation-models/ | MIT |
| czifile | Reads Zeiss .czi files | https://pypi.org/project/czifile/ | BSD (BSD-3-Clause) |
| Markdown | Python implementation of Markdown | https://pypi.org/project/Markdown/ | BSD |
| matplotlib | Plots images and graphs | https://pypi.org/project/matplotlib/ | PSF |
| numpy | Array/Image computing | https://pypi.org/project/numpy/ | BSD |
| Pillow | Reads 'basic' images (.bmp, .png, .pnm, ...) | https://pypi.org/project/Pillow/ | HPND |
| PyQt5 | Graphical user interface (GUI) | https://pypi.org/project/PyQt5/ | GPL v3 |
| PyQt6 | Graphical user interface (GUI) | https://pypi.org/project/PyQt6/ | GPL v3 |
| read-lif | Reads Leica .lif files | https://pypi.org/project/read-lif/ | GPL v3 |
| scikit-image | Image processing | https://pypi.org/project/scikit-image/ | BSD (Modified BSD) |
| scipy | Great library to work with numpy arrays | https://pypi.org/project/scipy/ | BSD |
| tifffile | Reads .tiff files (also reads Zeiss .lsm files) | https://pypi.org/project/tifffile/ | BSD |
| tqdm | Command line progress | https://pypi.org/project/tqdm/ | MIT, MPL 2.0 |
| natsort | 'Human' like sorting of strings | https://pypi.org/project/natsort/ | MIT |
| numexpr | Speeds up image math | https://pypi.org/project/numexpr/ | MIT |
| urllib3 | Model architecture and trained models download | https://pypi.org/project/urllib3/ | MIT |
| qtawesome | Elegant icons in pyTA | https://pypi.org/project/QtAwesome/ | MIT |
| pandas | Data analysis toolkit | https://pypi.org/project/pandas/ | BSD (BSD-3-Clause) |
| numba | GPU acceleration of numpy ops | https://pypi.org/project/numba/ | BSD |
| elasticdeform | Image deformation (data augmentation) | https://pypi.org/project/elasticdeform/ | BSD |
| CARE/csbdeep | pyTA uses custom trained derivatives of the CARE surface projection model to generate (denoised) surface projections | https://pypi.org/project/csbdeep/ | BSD (BSD-3-Clause) |
1 maintainer
Metadata
1 maintainer
Release files for epyseg 0.1.52
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| epyseg-0.1.52.tar.gz | 12.3 MB | Without provenance | Details for epyseg-0.1.52.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| epyseg-0.1.52-py3-none-any.whl | Python 3 | none | any | Details for epyseg-0.1.52-py3-none-any.whl |
Total release size: 24.6 MB
Release files / epyseg-0.1.52.tar.gz
| Download URL | epyseg-0.1.52.tar.gz |
|---|---|
| Size | 12.3 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
4585a268afcc57bd665476be105f0380445cedb77a87c7d3498d6b261465a1e4
|
|
BLAKE2b-256 checksum How to use checksums |
26d0c9b42b9000ab8d6ff1ad2abf48d8e4cdcd27b89e321b7357d8993b1a5112
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.10.12
|
Release files / epyseg-0.1.52-py3-none-any.whl
| Download URL | epyseg-0.1.52-py3-none-any.whl |
|---|---|
| Size | 12.3 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
ea11fb5e04b33407721e60d67191e4adc14e340385f1a18ba5f9fd6b7bc0a19d
|
|
BLAKE2b-256 checksum How to use checksums |
49eea92f32de77c02bcaee36aceedb6f4ea13a27b788be5aaa111939a20d47a5
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.10.12
|
Security for epyseg 0.1.52
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
