Loading Extensions (machine-learning and plugin features)

Since version 2.9.0, Shape-Out 2 allows loading dclab plugin features and machine-learning features. You may need those if you need to quantify anything that is not covered by the default features.


If you installed Shape-Out via installer (not via pip), then many extensions might not work due to software dependencies that those extensions might have. If this happens, please create an issue in the Shape-Out 2 repository so we can find a solution.


Extensions can be harmful. Please only load extensions that you received first-hand from people you trust.

You can load and manage extensions via the Edit | Preferences dialog in the Extensions tab.


Example: Fluorescence density

Download the extension_fl1_density.py extension and add it to Shape-Out. You will see a new scalar feature named “FL-1 density [a.u.]” that quantifies the collected fluorescence signal per object volume for the fluorescence channel 1. Using this extension as a template, you could create the density features for the other fluorescence channels as well.

Example: RBC-detection with machine-learning


For this example you will need to have tensorflow installed.

Download the extension_naive_rbc_score.modc extension and add it to Shape-Out. You will see a new scalar feature named “RBC score (naive)”. This showcase model only consists of one dense layer of size six, but already does a better-than-random job in identifying red blood cells.