def dog_detection(overlay,img, imp, cal):
# Create a variable of the correct type (UnsignedByteType) for the value-extended view
zero = img.randomAccess().get().createVariable()
# Run the difference of Gaussian
cell = 8.0 # microns in diameter
min_peak = 2.0 # min intensity for a peak to be considered
dog = DogDetection(Views.extendValue(img, zero), img,
[cal.pixelWidth, cal.pixelHeight,cal.pixelDepth],
cell / 2, cell,
DogDetection.ExtremaType.MINIMA,
min_peak, False,
DoubleType())
peaks = dog.getPeaks()
roi = OvalRoi(0, 0, cell/cal.pixelWidth, cell/cal.pixelHeight)
print ('Number of cells = ', len(peaks))
p = zeros(img.numDimensions(), 'i')
boundRect = imp.getRoi()
for peak in peaks:
# Read peak coordinates into an array of integers XYZ location of spots
peak.localize(p)
print(p)
if(boundRect is not None and boundRect.contains(p[0], p[1])):
oval = OvalRoi(p[0], p[1],cell/cal.pixelWidth, cell/cal.pixelHeight)
oval.setColor(Color.RED)
overlay.add(oval)
def circle_roi(cs, r, color):
# type: (CellStack, int, Color) -> None
"""
Draw a circle roi with center and radius of the cell
"""
d = r * 2
x, y = cs.center[0] - r, cs.center[1] - r
roi = OvalRoi(x, y, d, d)
roi.setColor(color)
cs.setRoi(roi)
zero = img.randomAccess().get().createVariable()
# Run the difference of Gaussian
cell = 30.0 # microns in diameter
min_peak = 10.0 # min intensity for a peak to be considered
dog = DogDetection(
Views.extendValue(img, zero),
img,
[cal.pixelWidth, cal.pixelHeight],
cell / 2,
cell,
DogDetection.ExtremaType.MAXIMA, #MAXIMA
min_peak,
False,
DoubleType())
peaks = dog.getPeaks()
roi = OvalRoi(0, 0, cell / cal.pixelWidth, cell / cal.pixelHeight)
print('Number of cells = ', len(peaks))
p = zeros(img.numDimensions(), 'i')
overlay = Overlay()
imp.setOverlay(overlay)
for peak in peaks:
# Read peak coordinates into an array of integers
peak.localize(p)
if (boundRect.contains(p[0], p[1])):
oval = OvalRoi(p[0], p[1], cell / cal.pixelWidth,
cell / cal.pixelHeight)
oval.setColor(Color.RED)
overlay.add(oval)