// ----------------------------------------------------------------------------------------------
//This macro calculates a binary mask given a certain threshold.
// Credits:
// 		- Reference: DeepImageJ: A user-friendly environment to run deep learning models in ImageJ,
// 			     E. Gómez-de-Mariscal, C. García-López-de-Haro, W. Ouyang, L. Donati, E. Lundberg, M. Unser, A. Muñoz-Barrutia, D. Sage,
//             	             Nat Methods 18, 1192–1195 (2021). https://doi.org/10.1038/s41592-021-01262-9
// ----------------------------------------------------------------------------------------------

// clip the range of values to the [0,1] range
//get multichannel prediction; extract channel 1 and apply optimized threshold
//get a binary image output with foreground 255 and background 0
// THIS CODE IS MODIFIED FROM ORIGINAL binarize macro
//


macro "ganglia_binarise"
{
	
	//check if argument is passed, if not asks user to select label image
	//if(getArgument()=="")
	//{
	//	waitForUser("Select the prediction from deepImageJ");
	//	multi_ch_prediction=getTitle(); 
		
	//}
	//else 
	//{
	//	multi_ch_prediction=getArgument(); 
	//}
	multi_ch_prediction=getTitle(); 
	selectWindow(multi_ch_prediction);
	run("Select None");
	//get only first channel
	run("Duplicate...", "title=ganglia_prediction duplicate channels=1");
	close(multi_ch_prediction);
	
	selectWindow("ganglia_prediction");
	
		
	//custom code end
	getStatistics(_, _, min, max, _, _);

	// Threshold the output.
	optimalThreshold = 0.7364705882352942;
	setThreshold(optimalThreshold, max);
	setOption("BlackBackground", true);
	run("Convert to Mask", "method=Default background=Dark black");

	setMinAndMax(0, 255);
	run("8-bit");
	
}