Merge pull request #8 from trackmate-sc/omnipose

Support Omnipose.

README.md

@@ -1,5 +1,37 @@
 [![Build Status](https://github.com/trackmate-sc/TrackMate-Cellpose/actions/workflows/build.yml/badge.svg)](https://github.com/trackmate-sc/TrackMate-Cellpose/actions/workflows/build.yml)
 
-# TrackMate-Cellpose
+# Omnipose integration in TrackMate.
 
-Tentative Cellpose integration in TrackMate.
+The Omnipose integration in TrackMate works roughly as the Cellpose integration one. 
+It requires Omnipose to be installed on your system and working independently. This page gives installation details and advices at how to use the omnipose integration in TrackMate.
+
+## Omnipose
+Omnipose is a segmentation algorithm based on Deep-Learning techniques, and inspired from the Cellpose architecture. Omnipose is well suited for bacterial cell segmentation, and achieves remarkable performances on mixed bacterial cultures, antibiotic-treated cells and cells of elongated or branched morphology.
+
+If you use the Omnipose TrackMate module for your research, please also cite the Omnipose paper:
+[Cutler, K.J., Stringer, C., Lo, T.W. et al. Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation. Nat Methods 19, 1438–1448 (2022)](https://www.nature.com/articles/s41592-022-01639-4).
+
+
+
+## Example
+https://github.com/marieanselmet/TrackMate-Omnipose/assets/32811540/3c2365c9-8d1b-4057-b4d1-2939e4e2b818
+
+*E. Coli, Marie Anselmet and Rodrigo Arias Cartin, Barras lab, Institut Pasteur*
+
+
+## Omnipose installation
+
+This code works with the Omnipose version 0.3.6. It doesn't work with the last version of Omnipose.
+
+To install Omnipose, you can refer directly to the installation guide provided on the [Omnipose repository](https://github.com/kevinjohncutler/omnipose#how-to-install-omnipose).
+
+An example Windows installation working on GPU:
+```
+conda create -n omnipose
+conda activate omnipose
+conda install pytorch==2.0.0 torchvision==0.15.0 torchaudio==2.0.0 pytorch-cuda=11.8 -c pytorch -c nvidia
+pip install omnipose==0.3.6
+pip install cellpose-omni==0.7.3
+```
+
+The default models *bact_phase_omni* and *bact_fluor_omni* are stored in the cellpose pretrained models folder.

pom.xml

@@ -14,7 +14,7 @@
 	<version>0.1.3-SNAPSHOT</version>
 
 	<name>TrackMate-Cellpose</name>
-	<description>TrackMate detector based on Cellpose.</description>
+	<description>TrackMate detector based on cellpose and omnipose.</description>
 	<url>https://github.com/trackmate-sc/TrackMate-Cellpose</url>
 	<inceptionYear>2021</inceptionYear>
 	<organization>
@@ -51,6 +51,11 @@
 			<url>https://imagej.net/people/ctrueden</url>
 			<properties><id>ctrueden</id></properties>
 		</contributor>
+                <contributor>
+			<name>Marie Anselmet</name>
+			<url>https://research.pasteur.fr/fr/member/marie-anselmet/</url>
+			<properties><id>manselmet</id></properties>
+		</contributor>
 	</contributors>
 
 	<mailingLists>
@@ -59,7 +64,7 @@
 			<archive>https://forum.image.sc/tag/trackmate</archive>
 		</mailingList>
 	</mailingLists>
-
+        
 	<scm>
 		<connection>scm:git:https://github.com/trackmate-sc/TrackMate-Cellpose</connection>
 		<developerConnection>scm:git:[email protected]:trackmate-sc/TrackMate-Cellpose</developerConnection>
@@ -84,7 +89,7 @@
 		<!-- NB: Deploy releases to the SciJava Maven repository. -->
 		<releaseProfiles>sign,deploy-to-scijava</releaseProfiles>
 
-		<TrackMate.version>7.10.2</TrackMate.version>
+		<TrackMate.version>7.11.1</TrackMate.version>
 	</properties>
 
 	<dependencies>

src/main/java/fiji/plugin/trackmate/cellpose/AbstractCellposeSettings.java

@@ -0,0 +1,144 @@
+package fiji.plugin.trackmate.cellpose;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
+
+public abstract class AbstractCellposeSettings
+{
+
+	/**
+	 * Interface for enums that represent a pretrained or a custom model in
+	 * cellpose or omnipose.
+	 */
+	public interface PretrainedModel
+	{
+
+		/**
+		 * Returns <code>true</code> if this model is a custom model.
+		 * 
+		 * @return <code>true</code> if this model is a custom model.
+		 */
+		boolean isCustom();
+
+		/**
+		 * Returns the name of this pretrained model, or its path if it is a
+		 * custom model.
+		 * 
+		 * @return the model name or path.
+		 */
+		String getPath();
+
+	}
+
+	public final String executablePath;
+
+	public final int chan;
+
+	public final int chan2;
+
+	public final String customModelPath;
+
+	public final double diameter;
+
+	public final boolean useGPU;
+
+	public final boolean simplifyContours;
+
+	private final PretrainedModel model;
+
+	protected AbstractCellposeSettings(
+			final String executablePath,
+			final PretrainedModel model,
+			final String customModelPath,
+			final int chan,
+			final int chan2,
+			final double diameter,
+			final boolean useGPU,
+			final boolean simplifyContours )
+	{
+		this.executablePath = executablePath;
+		this.model = model;
+		this.chan = chan;
+		this.chan2 = chan2;
+		this.customModelPath = customModelPath;
+		this.diameter = diameter;
+		this.useGPU = useGPU;
+		this.simplifyContours = simplifyContours;
+	}
+
+	/**
+	 * Returns the executable name of the cellpose or omnipose command. For
+	 * cellpose, it's simply 'cellpose'.
+	 * 
+	 * @return the executable name.
+	 */
+	public abstract String getExecutableName();
+
+	public List< String > toCmdLine( final String imagesDir )
+	{
+		final List< String > cmd = new ArrayList<>();
+
+		/*
+		 * First decide whether we are calling Cellpose from python, or directly
+		 * the Cellpose executable. We check the last part of the path to check
+		 * whether this is python or cellpose.
+		 */
+		final String[] split = executablePath.replace( "\\", "/" ).split( "/" );
+		final String lastItem = split[ split.length - 1 ];
+		if ( lastItem.toLowerCase().startsWith( "python" ) )
+		{
+			// Calling Cellpose from python.
+			cmd.add( executablePath );
+			cmd.add( "-m" );
+			cmd.add( getExecutableName() );
+		}
+		else
+		{
+			// Calling Cellpose executable.
+			cmd.add( executablePath );
+		}
+
+		/*
+		 * Cellpose command line arguments.
+		 */
+
+		// Target dir.
+		cmd.add( "--dir" );
+		cmd.add( imagesDir );
+
+		// First channel.
+		cmd.add( "--chan" );
+		cmd.add( "" + chan );
+
+		// Second channel.
+		if ( chan2 >= 0 )
+		{
+			cmd.add( "--chan2" );
+			cmd.add( "" + chan2 );
+		}
+
+		// GPU.
+		if ( useGPU )
+			cmd.add( "--use_gpu" );
+
+		// Diameter.
+		cmd.add( "--diameter" );
+		cmd.add( ( diameter > 0 ) ? "" + diameter : "0" );
+
+		// Model.
+		cmd.add( "--pretrained_model" );
+		if ( model.isCustom() )
+			cmd.add( customModelPath );
+		else
+			cmd.add( model.getPath() );
+
+		// Export results as PNG.
+		cmd.add( "--save_png" );
+
+		// Do not save Numpy files.
+		cmd.add( "--no_npy" );
+
+		return Collections.unmodifiableList( cmd );
+	}
+}