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.
Note
If you installed Shape-In 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.
Warning
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 volume
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
Note
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.