Nanodot spacing analysis by Delaunay triangulation

delaunay.py

def getVal(table, i):
    x = table.getValue('X', i)
    y = table.getValue('Y', i)
    return (x, y)


# Stub
def delaunay(coords):
    return None

# imp is a thresholded image (binary image).
def find_dots(imp):
    IJ.run(imp, "Set Measurements...", "  centroid redirect=None decimal=2")
    IJ.run(imp, "Analyze Particles...", "size=20-Infinity circularity=0.00-1.00 show=Ellipses display clear")

    # Get a Results table. This content can be shown from Window -> Results
    table = ResultsTable.getResultsTable()

    # Get the number of rows.
    n = table.getCounter()

    # Call getVal to get every (x,y) coordinate.
    return [getVal(table, i) for i in range(0, n)]


def main():
    path = "/Volumes/Macintosh HD/Dropbox/Groves Lab Data/SEM/130712 EML/20 80k.tiff"
    imp = IJ.openImage(path)
    th_imp = threshold(imp)
    coords = find_dots(th_imp)
    triangles = delaunay(coords)

    # Show an original image and a thresholded image.
    imp.show()
    th_imp.show()



def threshold(imp):
    imp2 = imp.duplicate()
    ip2 = imp2.getProcessor()
    IJ.run(imp2, "Auto Threshold", "method=RenyiEntropy white")
    IJ.run(imp2, "Convert to Mask", "")
    return imp2

main()