easilastik 2.0.0a1
Easy integration of Ilastik segmentation models in Python.
Requires Python: >=3.10

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<table width="100%" style="border: none;">
  <tr>
    <td align="left" style="border: none;"><b>LE GOURRIEREC Titouan</b></td>
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      <a href="https://www.linkedin.com/in/titouanlegourrierec"><img src="https://img.shields.io/badge/linkedin-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white" alt="LinkedIn"></a>
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<div align="center">
  <h3 align="center">EasIlastik </h3>

<p align="center">
    A package to facilitate the use of image segmentation model trained on Ilastik in Python
    <br />
    <a href="https://github.com/titouanlegourrierec/EasIlastik/wiki"><strong>Explore the docs »</strong></a>
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    <a href="https://github.com/titouanlegourrierec/EasIlastik/issues">Report a bug · Request Feature</a>
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        <a href="https://pepy.tech/projects/easilastik">
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<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>
      </ul>
    </li>
    <li><a href="#usage">Usage</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>

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## About The Project

<p align="center">
  <img src="https://raw.githubusercontent.com/titouanlegourrierec/EasIlastik/main/assets/img.png" width="100%">
</p>

This package provides seamless integration of pre-trained image segmentation models from Ilastik into Python workflows, empowering users with efficient and intuitive image segmentation capabilities for diverse applications.

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### Built With

- [![Python][python-badge]][python-url]
- [![OpenCV][opencv-badge]][opencv-url]
- ![Shell Script][shellscript-badge]

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## Getting Started

### Prerequisites

- **Ilastik software**: To train your own model for image segmentation, please download the Ilastik software tailored to your computer's operating system from: https://www.ilastik.org/download.

### Train a model

- To train your own model on Ilastik and properly adjust the different parameters, please refer to [this documentation](https://github.com/titouanlegourrierec/EasIlastik/wiki/Train-a-model-on-Ilastik).

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## Usage

For usage examples of this package, please refer to the [Example Notebook](https://github.com/titouanlegourrierec/EasIlastik/blob/1be43ebb76bccec6917e05367fbb7e48b184efdc/Examples/example-notebook.ipynb).

### Process a single image

```python
EasIlastik.run_ilastik(input_path = "path/to/your/image.jpg", # The path of the image to process
                       model_path = "path/to/your/model.ilp",
                       result_base_path = "path/to/your/output/folder/",
                       export_source = "Simple Segmentation",
                       output_format = "png")
```

<p align="center">
  <img src="https://raw.githubusercontent.com/titouanlegourrierec/EasIlastik/main/assets/run_ilastik_image.png" alt="run_ilastik_image" width="50%">
</p>

### Process a folder of images

```python
EasIlastik.run_ilastik(input_path = "path/to/input/folder", # The path of the folder to process
                       model_path = "path/to/your/model.ilp",
                       result_base_path = "path/to/your/output/folder/",
                       export_source = "Simple Segmentation",
                       output_format = "png")
```

<p align="center">
  <img src="https://raw.githubusercontent.com/titouanlegourrierec/EasIlastik/main/assets/run_ilastik_folder.png" alt="run_ilastik_folder" width="70%">
</p>

### Show probabilities

```python
EasIlastik.run_ilastik(input_path = "path/to/input/image",
                       model_path = "path/to/model.ilp",
                       result_base_path = "path/to/output/folder",
                       export_source="Probabilities", # Probabilities
                       output_format="hdf5") # hdf5 format

output_path = "path/to/output/image.h5"
image = EasIlastik.color_treshold_probabilities(output_path, threshold, below_threshold_color, channel_colors)
```

<p align="center">
  <img src="https://raw.githubusercontent.com/titouanlegourrierec/EasIlastik/main/assets/run_ilastik_show_probabilities.png" alt="run_ilastik_probabilities" width="70%">
</p>

### Run with probabilities

```python
EasIlastik.run_ilastik_probabilities(input_path = "path/to/input/folder",
                                     model_path = "path/to/model.ilp",
                                     result_base_path = "path/to/output/folder",
                                     threshold = 70, # threshold for the probabilities
                                     below_threshold_color = [255, 0, 0], # color for the pixels below the threshold (red)
                                     channel_colors = [[63, 63, 63], [127, 127, 127], ...] # colors for the different channels
                                     )
```

<p align="center">
  <img src="https://raw.githubusercontent.com/titouanlegourrierec/EasIlastik/main/assets/run_ilastik_run_probabilities.png" alt="run_ilastik_probabilities" width="70%">
</p>

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## License

Distributed under the GNU License like the Ilastik software. See [`LICENSE`](https://github.com/titouanlegourrierec/EasIlastik/blob/1be43ebb76bccec6917e05367fbb7e48b184efdc/LICENCE) for more information.

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## Contact

LE GOURRIEREC Titouan - [titouanlegourrierec@icloud.com](mailto:titouanlegourrierec@icloud.com)

Repository Link: [https://github.com/titouanlegourrierec/EasIlastik](https://github.com/titouanlegourrierec/EasIlastik)
Pypi Link : [https://pypi.org/project/EasIlastik/](https://pypi.org/project/EasIlastik/)

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## Acknowledgments

- [Ilastik Software](https://www.ilastik.org) : An interactive interface to annotate images to segment.

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[opencv-badge]: https://img.shields.io/badge/opencv-%23white.svg?style=for-the-badge&logo=opencv&logoColor=white
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