First commit

Dockerfile

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+FROM python:3.6.9-stretch
+
+# ---------------------------------------------------------------------------------------------------------------------
+# Install Java
+RUN apt-get update && apt-get install openjdk-8-jdk -y && apt-get clean
+
+# ---------------------------------------------------------------------------------------------------------------------
+# Install Cytomine python client
+RUN git clone https://github.com/cytomine-uliege/Cytomine-python-client.git && \
+    cd /Cytomine-python-client && git checkout tags/v2.3.0.poc.1 && pip install . && \
+    rm -r /Cytomine-python-client
+
+# ---------------------------------------------------------------------------------------------------------------------
+# Install Neubias-W5-Utilities (annotation exporter, compute metrics, helpers,...)
+RUN apt-get update && apt-get install libgeos-dev -y && apt-get clean
+RUN git clone https://github.com/Neubias-WG5/neubiaswg5-utilities.git && \
+    cd /neubiaswg5-utilities/ && git checkout tags/v0.8.8 && pip install .
+
+# install utilities binaries
+RUN chmod +x /neubiaswg5-utilities/bin/*
+RUN cp /neubiaswg5-utilities/bin/* /usr/bin/
+
+# cleaning
+RUN rm -r /neubiaswg5-utilities
+
+# ---------------------------------------------------------------------------------------------------------------------
+# Install FIJI
+# Install virtual X server
+RUN apt-get update && apt-get install -y unzip xvfb libx11-dev libxtst-dev libxrender-dev
+
+# Install Fiji.
+RUN wget https://downloads.imagej.net/fiji/Life-Line/fiji-linux64-20170530.zip
+RUN unzip fiji-linux64-20170530.zip
+RUN mv Fiji.app/ fiji
+
+# create a sym-link with the name jars/ij.jar that is pointing to the current version jars/ij-1.nm.jar
+RUN cd /fiji/jars && ln -s $(ls ij-1.*.jar) ij.jar
+
+# Add fiji to the PATH
+ENV PATH $PATH:/fiji
+
+RUN mkdir -p /fiji/data
+
+# Clean up
+RUN rm fiji-linux64-20170530.zip
+
+# ---------------------------------------------------------------------------------------------------------------------
+# ImageJ plugin
+# install FeatureJ
+RUN cd /fiji/plugins && \
+    wget -O imagescience.jar \
+    https://imagescience.org/meijering/software/download/imagescience.jar
+
+RUN cd /fiji/plugins && \
+    wget -O FeatureJ_.jar \
+    https://imagescience.org/meijering/software/download/FeatureJ_.jar
+
+# ---------------------------------------------------------------------------------------------------------------------
+# add the local files
+ADD LapClosetPartTracker.ijm /fiji/macros/macro.ijm
+ADD wrapper.py /app/wrapper.py
+
+# set the entrypoint
+ENTRYPOINT ["python", "/app/wrapper.py"]

LapClosetPartTracker.ijm

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+// Author: Sébastien Tosi (IRB Barcelona)
+// Contact: [email protected]
+// Version: 1.0
+// Date: 27/12/2019
+
+// Path to input image and results
+inputDir = "C:\\Users\\Seb\\Desktop\\in";
+outputDir = "C:\\Users\\Seb\\Desktop\\out";
+
+// Parameters for Cytopacq time-lapse
+LapRad = 9;
+Thr = 0.05;
+
+// Read arguments from command line
+arg = getArgument();
+parts = split(arg, ",");
+for(i=0; i<parts.length; i++) 
+{
+	nameAndValue = split(parts[i], "=");
+	if (indexOf(nameAndValue[0], "input")>-1) inputDir=nameAndValue[1];
+	if (indexOf(nameAndValue[0], "output")>-1) outputDir=nameAndValue[1];
+	if (indexOf(nameAndValue[0], "LapRad")>-1) LapRad=nameAndValue[1];
+	if (indexOf(nameAndValue[0], "Thr")>-1) Thr=nameAndValue[1];
+}
+
+images = getFileList(inputDir);
+for(img=0; img<images.length; img++) 
+{
+
+setBatchMode(true);
+
+// Open image
+FileName = images[img];
+open(inputDir+File.separator+FileName);
+
+// Workflow
+run("FeatureJ Laplacian", "compute smoothing="+d2s(LapRad,0));
+FilterID = getImageID();
+for(i=0;i<nSlices;i++)
+{
+	selectImage(FilterID);
+	setSlice(i+1);
+	run("Find Maxima...", "noise="+d2s(Thr,3)+" output=List exclude light");
+	NewX = newArray(nResults);
+	NewY = newArray(nResults);
+	for(j=0;j<nResults;j++)
+	{
+		NewX[j] = getResult("X",j);
+		NewY[j] = getResult("Y",j);
+	}
+	if(i==0)
+	{
+		newImage("Mask", "16-bit black", getWidth(), getHeight(), nSlices);	
+		MaskID = getImageID();
+		NObjs = lengthOf(NewX);
+		for(j=0;j<lengthOf(NewX);j++)setPixel(NewX[j],NewY[j],j+1);
+		LastX = NewX;
+		LastY = NewY;
+	}
+	else
+	{
+		selectImage(MaskID);
+		setSlice(i+1);
+		LastX_buf = newArray(NObjs);
+		LastY_buf = newArray(NObjs);
+		for(j=0;j<NObjs;j++)
+		{
+			MinDst2 = 1/0;
+			Mink = 0;
+			for(k=0;k<lengthOf(NewX);k++)
+			{
+				Dst2 = (pow(NewX[k]-LastX[j],2)+pow(NewY[k]-LastY[j],2));
+				if(Dst2<MinDst2)
+				{
+					MinDst2 = Dst2;
+					Mink = k; 
+				}
+			}
+			setPixel(NewX[Mink],NewY[Mink],j);
+			LastX_buf[j] = NewX[Mink];
+			LastY_buf[j] = NewY[Mink];
+		}
+		LastX = LastX_buf;
+		LastY = LastY_buf;
+	}	
+}
+
+// Save label mask
+selectImage(MaskID);
+//run("Bio-Formats Exporter", "save="+outputDir+File.separator+FileName+" compression=Uncompressed");
+save(outputDir+File.separator+FileName);
+
+run("Close All");
+
+}
+
+setBatchMode("exit & display");

descriptor.json

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+{
+    "name": "PartTracking-ImageJ",
+    "container-image": {
+        "image": "neubiaswg5/w_parttracking-imagej",
+        "type": "singularity"
+    },
+    "schema-version": "cytomine-0.1",
+    "description": "Particle tracking based on linking closest intensity minima detected from LoG filtered time-lapse.",
+    "command-line": "python wrapper.py CYTOMINE_HOST CYTOMINE_PUBLIC_KEY CYTOMINE_PRIVATE_KEY CYTOMINE_ID_PROJECT CYTOMINE_ID_SOFTWARE LAPRAD THR",
+    "inputs": [
+        {
+            "id": "cytomine_host",
+            "value-key": "@ID",
+            "command-line-flag": "--@id",
+            "name": "Cytomine host",
+            "description": "Cytomine server hostname",
+            "type": "String",
+            "optional": false,
+            "set-by-server": true
+        },
+        {
+            "id": "cytomine_public_key",
+            "value-key": "@ID",
+            "command-line-flag": "--@id",
+            "name": "Cytomine public key",
+            "description": "Cytomine public key",
+            "type": "String",
+            "optional": false,
+            "set-by-server": true
+        },
+        {
+            "id": "cytomine_private_key",
+            "value-key": "@ID",
+            "command-line-flag": "--@id",
+            "name": "Cytomine private key",
+            "description": "Cytomine private key",
+            "type": "String",
+            "optional": false,
+            "set-by-server": true
+        },
+        {
+            "id": "cytomine_id_project",
+            "value-key": "@ID",
+            "command-line-flag": "--@id",
+            "name": "Cytomine project id",
+            "description": "Cytomine project id",
+            "type": "String",
+            "optional": false,
+            "set-by-server": true
+        },
+        {
+            "id": "cytomine_id_software",
+            "value-key": "@ID",
+            "command-line-flag": "--@id",
+            "name": "Cytomine software id",
+            "description": "Cytomine software id",
+            "type": "String",
+            "optional": false,
+            "set-by-server": true
+        },
+        {
+            "id": "laprad",
+            "value-key": "@ID",
+            "command-line-flag": "--@id",
+            "name": "LoG Radius",
+            "description": "Radius for the LoG filter",
+            "type": "Number",
+            "default-value": 9,
+            "optional": true
+        },
+        {
+            "id": "thr",
+            "value-key": "@ID",
+            "command-line-flag": "--@id",
+            "name": "Threshold",
+            "description": "Particle detection threshold",
+            "type": "Number",
+            "default-value": 0.05,
+            "optional": true
+        }
+    ]
+}

wrapper.py

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+import sys
+from cytomine.models import Job
+from subprocess import call
+from neubiaswg5 import CLASS_PRTTRK
+from neubiaswg5.helpers import NeubiasJob, prepare_data, upload_data, upload_metrics
+
+
+def main(argv):
+    with NeubiasJob.from_cli(argv) as nj:
+        problem_cls = CLASS_PRTTRK
+        is_2d = True
+
+        nj.job.update(status=Job.RUNNING, progress=0, statusComment="Initialisation...")
+        in_images, gt_images, in_path, gt_path, out_path, tmp_path = prepare_data(problem_cls, nj, is_2d=is_2d, **nj.flags)
+
+        # 2. Call the image analysis workflow
+        nj.job.update(progress=25, statusComment="Launching workflow...")
+        command = "/usr/bin/xvfb-run ./ImageJ-linux64 -macro macro.ijm " \
+                  "\"input={}, output={}, laprad={}, thr={}\" -batch".format(
+            in_path, out_path, nj.parameters.laprad, nj.parameters.thr)
+
+        return_code = call(command, shell=True, cwd="/fiji")  # waits for the subprocess to return
+
+        if return_code != 0:
+            err_desc = "Failed to execute the ImageJ macro (return code: {})".format(return_code)
+            nj.job.update(progress=100, statusComment=err_desc)
+            raise ValueError(err_desc)
+
+        # 4. Create and upload annotations
+        nj.job.update(progress=70, statusComment="Uploading extracted annotation...")
+        upload_data(problem_cls, nj, in_images, out_path, **nj.flags, is_2d=is_2d, monitor_params={
+            "start": 70, "end": 90, "period": 0.1
+        })
+
+        # 5. Compute and upload the metrics       
+        nj.job.update(progress=90, statusComment="Computing and uploading metrics (if necessary)...")
+        upload_metrics(problem_cls, nj, in_images, gt_path, out_path, tmp_path, **nj.flags)
+
+        # 6. End the job
+        nj.job.update(status=Job.TERMINATED, progress=100, statusComment="Finished.")
+
+
+if __name__ == "__main__":
+    main(sys.argv[1:])
+