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
 *

romainGuiet

#@File file_path

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

// the folder containing the lif
dir = file_path.getParent()
// a subfolder to save images
output_dir = new File( dir ,"outputRom1PC")
output_dir.mkdir()

romainGuiet

#@ImagePlus imp // imp should be a 3D label image
#@String stat_name // morpholibJ output table after Analyze Regions 3D

// using morpholibJ , update site : IJPB-plugins
IJ.run("Analyze Regions 3D", "voxel_count volume surface_area mean_breadth sphericity euler_number bounding_box centroid equivalent_ellipsoid ellipsoid_elongations max._inscribed surface_area_method=[Crofton (13 dirs.)] euler_connectivity=6");

// Duplicate the imp and make it 32-bit
imp_label = imp.duplicate();
IJ.run(imp_label, "32-bit", "");

romainGuiet

run("Close All");
roiManager("reset");
// ring thickness
step = 10 ;

// prepare a test image 
setForegroundColor(255, 255, 255);
setBackgroundColor(0, 0, 0);
newImage("Shape", "8-bit black", 512, 512, 1);
makePolygon(258,122,171,192,115,272,167,430,232,350,229,302,289,338,324,300,329,236,411,160);

romainGuiet

#@File image_path
#@RoiManager rm
#@ CommandService command
#@ Double (label="Nuclei Proba. Threshold", value=0.5) threshold_proba_nuc
#@ Double (label="Nuclei min. intensity (A.U.)", value=10) minInt_nuc
#@Integer (label="Max Frame Gap") frameGap
#@Double (label="Linking Max Distance") linkDistance
#@Integer (label="Gap Closing Max Distance") gapClosingDistance

// authors : olvier.burri & romain.guiet (at) epfl.ch 

romainGuiet

run("Close All");

run("Blobs (25K)");
img1 = getImageID();
run("Invert LUT");
run("32-bit");

rename("On_disableSmoothing");
run("Duplicate...", "title=Off_disableSmoothing");
img2 = getImageID();

romainGuiet

name_spots = "pinkySpots";
name_filtered_spots = name_spots+"-filtered";

rt = new ResultsTable()
rt.reset()

// Here teh script expect to find the demo.ims file in the folder Fiji>Plugins>BIOP
def plugins_dir = IJ.getDirectory("plugins")
def biop_dir = new File(plugins_dir , "BIOP")
ims_name = "HeLa_H2B-mcherry_Tubline-EGFP_mitochondria-MitoTracker_reduced.ims"

romainGuiet

oneOval = 1 ;

//size_Array = newArray(1,5,10,20,50,100);
size_Array = newArray(7,9,11,15,20,25,50);
int_Array = newArray(5,10,20,50,100,150,250);

Array.getStatistics(size_Array, size_Array_min, size_Array_max, size_Array_mean, size_Array_stdDev);

//print(size_Array_max);