easilastik 1.0.1

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.

LE GOURRIEREC Titouan

EasIlastik

A package to facilitate the use of image segmentation model trained on Ilastik in Python
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Table of Contents

1. About The Project 📖
   • Built With 🛠️
2. Getting Started
   • Prerequisites
3. Usage
4. License
5. Contact
6. Acknowledgments

About The Project

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

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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.

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Usage

For usage examples of this package, please refer to the Example Notebook.

Process a single image

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")

Process a folder of images

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")

Show probabilities

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)

Run with probabilities

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
                                     )

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License

Distributed under the GNU License like the Ilastik software. See LICENSE for more information.

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Contact

LE GOURRIEREC Titouan - [e-mail]

Repository Link: https://github.com/titouanlegourrierec/EasIlastik
Pypi Link : https://pypi.org/project/EasIlastik/

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Acknowledgments

• Ilastik Software : An interactive interface to annotate images to segment.

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