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dpat-2.0.0
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-
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | dpat-2.0.0 |
| نام | dpat |
| نسخه کتابخانه | 2.0.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Siem de Jong |
| ایمیل نویسنده | siem.dejong@hotmail.nl |
| آدرس صفحه اصلی | https://github.com/siemdejong/hhg-dpat |
| آدرس اینترنتی | https://pypi.org/project/dpat/ |
| مجوز | GNU General Public License v3 |
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<a name="readme-top"></a>
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<!-- [![Stargazers][stars-shield]][stars-url] -->
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<!-- PROJECT LOGO -->
<br />
<div align="center">
<!-- <a href="https://github.com/siemdejong/shg-strain-stress">
<img src="images/skinstression-logo.png" alt="Logo" width="80" height="80">
</a> -->
<h2 align="center">Deep Learning for Higher Harmonic Generation Microscopy</h2>
<h3 align="center">Deep learning utilities for higher harmonic generation microscopy images.</h3>
<p align="center">
This project is a deep learning application to classify various pediatric brain tumours from higher harmonic generation microscopy images.
<br />
<a href="https://siemdejong.github.io/dpat"><strong>Explore the docs »</strong></a>
<br />
<br />
<!-- <a href="https://github.com/siemdejong/dpat">View Demo</a>
· -->
<a href="https://github.com/siemdejong/dpat/issues">Report Bug</a>
·
<a href="https://github.com/siemdejong/dpat/issues">Request Feature</a>
</p>
</div>
<!-- TABLE OF CONTENTS -->
<details>
<summary>Table of Contents</summary>
<ol>
<li>
<a href="#about-the-project">About The Project</a>
<ul>
<li><a href="#built-with">Built With</a></li>
</ul>
</li>
<li>
<a href="#getting-started">Getting Started</a>
<ul>
<li><a href="#prerequisites">Prerequisites</a></li>
<li><a href="#installation">Installation</a></li>
</ul>
</li>
<li><a href="#usage">Usage</a></li>
<!-- <li><a href="#roadmap">Roadmap</a></li> -->
<li><a href="#contributing">Contributing</a></li>
<li><a href="#license">License</a></li>
<li><a href="#contact">Contact</a></li>
<li><a href="#acknowledgments">Acknowledgments</a></li>
</ol>
</details>
<!-- ABOUT THE PROJECT -->
# About The Project
<!-- [![Product Name Screen Shot][product-screenshot]](https://example.com) -->
The project aims to do deep learning classification on higher harmonic generation (HHG) microscopy images of pediatric brain tumours.
<p align="right">(<a href="#readme-top">back to top</a>)</p>
## Built With
[![Python][Python]][Python-url]
<!-- [![PyTorch][PyTorch]][Pytorch-url]
[![TensorFlow][TensorFlow]][TensorFlow-url]
[![Optuna][Optuna]][Optuna-url]
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<!-- GETTING STARTED -->
# Getting Started
This section includes instructions on setting up the project locally.
## Prerequisites
### Conda
For package management, it is advised to use a conda package manager.
The author recommends [Miniforge](https://github.com/conda-forge/miniforge) or [Mambaforge](https://github.com/conda-forge/miniforge).
### vips
This project depends on [dlup](https://github.com/NKI-AI/dlup) (automatically installed), which depends on vips.
On Windows, vips needs to be installed locally.
Download the latest [libvips](https://github.com/libvips/libvips/releases) Windows binary and unzip somewhere.
On Linux/macOS, vips is included with the installation steps below.
### OpenSlide
Vips comes with OpenSlide.
It is not needed to install OpenSlide separately.
### CUDA
To do deep learning on CUDA enabled accelerators, follow installation instructions on [pytorch.org](https://pytorch.org/get-started).
Run `nvidia-smi` to see if CUDA is available.
<p align="right">(<a href="#readme-top">back to top</a>)</p>
## Installation
Run the following commands from a conda enabled shell (such as Miniforge Prompt, if Miniforge/Mambaforge is installed).
1. Clone this repository and change directories
```
git clone https://github.com/siemdejong/dpat.git dpat && cd dpat
```
1. Create a new conda environment and activate it.
```
conda create -n <env_name>
conda activate <env_name>
```
1. Install dependencies from `environment.yml`.
```
conda env update -f environment.yml
```
1. Windows only: if you use this library for use in scripts, make sure libvips is available, see <a href="#libvips">Prerequisites#libvips</a>.
If using this library in a script, make sure to properly install the package with
```python
import dpat
dpat.install_windows("path/to/vips/bin")
```
1. If you use this library for deep learning and want to use CUDA-enabled Pytorch,
follow instructions on [pytorch.org](https://pytorch.org/get-started).
Make sure CUDA is available, see <a href="#cuda">Prerequisites#CUDA</a>.
1. Install dpat in editable mode with
```
pip install -e .
```
1. Verify installation
```
python -c "import dpat"
```
<!-- 1. Check if CUDA is available for the installed Pytorch distribution.
In a Python shell, execute
```python
import torch
torch.cuda.is_available()
```
If `false`, install Pytorch following its documentation. -->
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<!-- USAGE EXAMPLES -->
## Usage
### Converting images
To convert all images from directory INPUT_DIR, and output the images as TIFF in OUTPUT_DIR, run
```
dpat convert batch -i INPUT_DIR -o OUTPUT_DIR -e tiff
```
Large images need to be trusted against decompression bomb DOS attack.
Use the `--trust` flag.
To skip images that were already converted to the target extension, use `--skip-existing`.
NOTE: If converting to tiff, the input images are assumed to contain the reference to the scanning program, which must be in {200slow, 300slow, 300fast}.
```
Usage: dpat convert batch [OPTIONS]
Options:
-i, --input-dir TEXT Input directory where to find the images to be
converted. [default: .]
-o, --output-dir TEXT Output directory where place converted files.
[default: ./converted]
-e, --output-ext [tiff|tif] Extension to convert to. [required]
-w, --num-workers INTEGER Number of workers that convert the images in
parallel. [default: 4]
-c, --chunks INTEGER Number of chunks distributed to every worker.
[default: 30]
--trust Trust the source of the images.
--skip-existing Skip existing output files.
--help Show this message and exit.
```
### Creating splits
To create train-val-test splits linking paths of images to splits with IMAGE_DIR, output the splits to OUTPUT_DIR, with labels PATH_TO_LABELS_FILE, and dataset name NAME run
```
dpat splits create -i IMAGE_DIR -o OUTPUT_DIR -l PATH_TO_LABELS_FILE -n NAME
```
To filter diagnoses that exactly match diseases, use e.g. `-f medulloblastoma -f "pilocytic astrocytoma"`.
To filter filenames that match certain values, use a glob pattern.
E.g. `-y *slow.tiff` to only include images ending with `slow.tiff`.
To exclude filenames that match certaine values, use a glob pattern with `-x`.
Exclusion is performed on the set specified by inclusion.
```
Usage: dpat splits create [OPTIONS]
Options:
-i, --input-dir TEXT Input directory where to find the images. [required]
-l, --labels TEXT Path to labels file. [required]
-n, --name TEXT Name of dataset. [required]
-o, --output-dir TEXT Directory where to put the splits. [default: splits]
--overwrite Overwrite folds in output dir, if available.
-y, --include TEXT Glob pattern to include files from `input-dir`
[default: *.*]
-x, --exclude TEXT Glob pattern to exclue files from `input-dir`,
included with `--include`
-f, --filter TEXT Filter a diagnosis. For multiple diagnoses, use `-f 1
-f 2`.
--help Show this message and exit.
```
### Logging
When using the package as a library, if needed, logging can be turned off with
```python
logging.getLogger('dpat').propagate = False
```
<!-- _For more examples, please refer to the [documentation](https://siemdejong.github.io/shg-strain-stress)._ -->
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<!-- ## Documentation
Documentation is hosted by Github Pages.
Docs are automatically created from docstrings.
Subsequently run
```sh
cd docs
sphinx-apidoc -o src ../src
make html
```
to update the documentation. -->
<!-- ROADMAP -->
<!-- ## Roadmap
- [x] Hyperparameter optimization with Optuna
- [x] Model training with Pytorch
- [ ] Inference
- [ ] Explainable AI
- [ ] Documentation
See the [open issues](https://github.com/siemdejong/shg-strain-stress/issues) for a full list of proposed features (and known issues).
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<!-- CONTRIBUTING -->
## Contributing
Contribute using the following steps.
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
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<!-- ETYMOLOGY -->
<!-- ## Etymology
Skinstression is a combination of skin, stress and regression.
Second-harmonic generation images and their corresponding stress curves form the basis of the regression task. -->
<!-- LICENSE -->
## License
Distributed under the GNU General Public License v3.0. See `LICENSE` for more information.
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<!-- CONTACT -->
## Contact
Siem de Jong - [linkedin.com/in/siemdejong](https://www.linkedin.com/in/siemdejong/) - siem.dejong@hotmail.nl
Project Link: [https://github.com/siemdejong/dpat](https://github.com/siemdejong/dpat)
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<!-- ACKNOWLEDGMENTS -->
<!-- ## Acknowledgments
* []()
* []()
* []()
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نیازمندی
| مقدار | نام |
|---|---|
| >=0.3 | dlup |
| >=0.24 | scikit-learn |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.9 | Python |
نحوه نصب
نصب پکیج whl dpat-2.0.0:
pip install dpat-2.0.0.whl
نصب پکیج tar.gz dpat-2.0.0:
pip install dpat-2.0.0.tar.gz