https://github.com/qupath/i2k-qupath-for-python-programmers

https://github.com/qupath/i2k-qupath-for-python-programmers

Last synced: 2 months ago
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• Host: GitHub
• URL: https://github.com/qupath/i2k-qupath-for-python-programmers
• Owner: qupath
• Created: 2024-10-10T15:09:46.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-10-28T08:25:31.000Z (3 months ago)
• Last Synced: 2024-10-28T10:16:03.262Z (3 months ago)
• Language: Jupyter Notebook
• Size: 1.17 MB
• Stars: 2
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# QuPath for Python programmers 🐍

These are the notebooks and associated files for the i2k 2024

**QuPath for Python programmers** workshop.

This is a brief introduction to [QuBaLab](https://github.com/qupath/qubalab)

and the [QuPath Py4J extension](https://github.com/qupath/qupath-extension-py4j),

a new way to quickly link Python and QuPath.

## Running the notebooks

1. Download the latest QuPath v0.6.0 release candidate from the [github releases page](https://github.com/qupath/qupath/releases).

2. Download the [QuPath project](https://github.com/qupath/i2k-qupath-for-python-programmers/releases/download/v0.1.0/i2k-qupath-python-project.zip)

   for this workshop.

3. Unzip the project in the i2k-qupath-for-python-programmers directory

4. Create a conda environment (bash):

   1. `conda create -n i2k-qupath-python python=3.10`

   2. `conda activate i2k-qupath-python`

   3. Install the requirements, either by

      - `pip install -r requirements.txt`, or

      - `pip install instanseg-torch git+https://github.com/qupath/qubalab.git jupyter ipython leidenalg igraph umap-learn`.

5. Open QuPath v0.6.0

6. Start a py4j gateway with default parameters

7. Open the `i2k-qupath-python-project` in QuPath

8. Open `HE_Hamamatsu.tiff` in QuPath

9. Start a jupyter session using the virtual environment from earlier

10. Run the `qupath-for-python-programmers.ipynb` notebook

11. Open `patient_test_2.ome.tiff` in QuPath and select the annotation

12. Run the `clustering-objects.ipynb` notebook