Folder_Apply_classifier_Weka.ijm

run("Set Measurements...", "area mean standard modal min centroid center perimeter bounding fit shape feret's integrated median skewness kurtosis stack area_fraction display redirect=None decimal=3");

close("*");
					
setOption("JFileChooser", true);
dir_results= getDirectory("The folder of the results");
setOption("JFileChooser", false);

//set options and choose folder containing cells
setOption("JFileChooser", true);
dir= getDirectory("Cell images?");
setOption("JFileChooser", false);

//Set label saving folder
setOption("JFileChooser", true);
dir_save= getDirectory("Folder where to save labels");
setOption("JFileChooser", false);

//Set classifier folder

setOption("JFileChooser", true);
dir_clasificador= getDirectory("Folder where classifier is stored");
setOption("JFileChooser", false);

//Ask for model name
nombre_modelo ="";
Dialog.create("Write full model name including .arff or .model");
Dialog.addString(nombre_modelo, "Model name");
Dialog.show();
nombre_modelo = Dialog.getString();

//set for loop inside image folder
imagenames=getFileList(dir); 
nbimages=lengthOf(imagenames); 

for(image=0; image<nbimages; image++) { 
	name=imagenames[image];
	totnamelength=lengthOf(name); 
	namelength=totnamelength-4;
	name1=substring(name, 0, namelength);
	extension=substring(name, namelength, totnamelength);

		
		if(extension==".tif" || extension==".nd2" || extension==".czi" ) { 
		

					
					open(dir+File.separator+name);
					run("Brightness/Contrast...");
					run("Enhance Contrast", "saturated=0.35");
					lista = getList("image.titles");
					Array.print(lista);					
					getPixelSize (unit,pw,ph);
					
				
		
					//Call and run weka 
					run("Advanced Weka Segmentation");
					call("trainableSegmentation.Weka_Segmentation.setFeature", "Structure=true");
					call("trainableSegmentation.Weka_Segmentation.setFeature", "Entropy=true");
					call("trainableSegmentation.Weka_Segmentation.setFeature", "Neighbors=true");
							
					wait(500);

					//Load classifier.
					clasificador = dir_clasificador+nombre_modelo;
					call("trainableSegmentation.Weka_Segmentation.loadClassifier", clasificador);
				
				
					call("trainableSegmentation.Weka_Segmentation.changeClassName", "0", "background");
					call("trainableSegmentation.Weka_Segmentation.changeClassName", "1", "segmentation");
					call("trainableSegmentation.Weka_Segmentation.getResult");
	
					wait(200);
					selectImage("Classified image");
					run("16-bit");
					setAutoThreshold("Default dark no-reset");
					//run("Threshold...");
					setOption("BlackBackground", true);
					run("Convert to Mask");
					saveAs("Tiff", dir_save+name);
					run("Analyze Particles...", "size=0.2-Infinity display clear include composite");
					saveAs("Results", dir_results+"results_a_"+name+".csv");
					close("*");
		}
}