romainGuiet

#@ File(style="directory") dir 
frames = 3 

IJ.run("Close All", "");


ArrayList<ImagePlus> imps = new ArrayList<>(frames);

dir.eachFile {
	//println(it)

romainGuiet

// Change the name of the tissue classifier here if needed
def tissueFinder = "Tissue"

// Which Name should they have ? 
// if annotation with the same name exist they will be deleted
def annotation_name = "Analysis"

romainGuiet

// clear the environment
run("Close All");
roiManager("Reset");

//open an image
run("Fly Brain (1MB)");
// make it composite, duplicate one slice and get information
run("Make Composite");
run("Duplicate...", "duplicate channels=1 slices=27");
getDimensions(width, height, channels, slices, frames);

romainGuiet

// clear the environment
run("Close All");
run("Clear Results")
roiManager("Reset");
// Set the measurements of intererest
run("Set Measurements...", "area mean standard min limit display redirect=None decimal=3");


run("Gel (105K)");
rename("Original");

romainGuiet

#@File(label="Multiposition Files Directory",style="directory") image_directory 
#@String(value="Select a file for flat field correction or write none", visibility="MESSAGE") textFF
#@File(label="Flatfield Stack File") ff_file 
#@int(label="Downsample Factor", style = "slider", min = 1, max = 16, stepSize = 1) downsample 
#@Boolean(label="Perform Stitching") is_do_stitch 
#@Boolean(label="Compute Overlap") is_compute 
#@Boolean(label="Keep Original Bit Depth") is_keep_bit_depth 
#@String(label="Macro Mode",choices={"Fuse and display","Write to disk"}) output_type
#@Boolean(label="Save final stitched as *.ids (if write to disk)") save_Stiched_asIDS
#@Boolean(label="Delete temporary files (if write to disk)") delete_temp_files

romainGuiet

// @String(label="Macro Mode",choices={"Current Image","Current Folder"}) modeChoice

// @String(value="Parameters for Detection", visibility="MESSAGE") textDetection
// @Integer(label="Channel for detection",value=1) detection_chNbr

// @Integer(label="Gaussian Blur sigma",value=1) detection_gBlur
// @Integer(label="Find Maxima noise tolerance",value=100) detection_noiseTol
// @Integer(label="Clear False Positive cutoff - 0 to disable",value=200) detection_meanCutOff

// @String(value="Parameters for Measurements", visibility="MESSAGE") textMeasurements

romainGuiet

//@File(label="Multiposition Files Directory",style="directory") image_directory
//@String(value="Select a file for flat field correction or write none", visibility="MESSAGE") textFF
//@File(label="Flatfield Correction File", value="none") ff_file
//@int(label="Downsample Factor", style = "slider", min = "1", max = "16", stepSize = "1") downsample
//@Boolean(label="Compute Overlap") is_compute
//@String(label="Macro Mode",choices={"Fuse and display","Write to disk"}) output_type

//@LogService log
import ij.IJ;
import loci.formats.ImageReader;

romainGuiet

cytos = getDetectionObjects().findAll{it.getPathClass() == getPathClass('cells')}
nuclei = getDetectionObjects().findAll{it.getPathClass() == getPathClass('nuclei')}

print cytos
print nuclei

// Combine cytos and nuclei detections to create cell objects
def cells = cytos.collect{ cyto -> 
      cell_nucs = nuclei.findAll{ nuc -> cyto.getROI().contains( nuc.getROI().getCentroidX() , nuc.getROI().getCentroidY() )} 
      if ( !cell_nucs.isEmpty() ) {

romainGuiet

#@ ImagePlus imp
#@ Double tolerance

nT = imp.getNFrames()

imp.getStack().
means_vals = (1..nT).collect{
	imp.setT(it)
	return imp.getStatistics().mean
}

romainGuiet

/* = CODE DESCRIPTION =
 * This QuPath script fills thae image Annotations objects with circular detections objects of a user defined diameter.
 * In combination with "Measure > Show measurements maps" it can be helpfull to quickly create meaningful heatmaps.
 *
 * == INPUTS ==
 * Annotation(s) and RegionOfInterest diamater (in microns)
 *
 * == OUTPUTS ==
 * circular detections objects of a user defined diameter
 *