def get_roi_manager(new=False):
rm = RoiManager.getInstance()
if not rm:
rm = RoiManager()
if new:
rm.runCommand("Reset")
return rm
# Identify particles
img = mask.getImgPlus()
labeled_img = ij.op().run("cca", img, StructuringElement.EIGHT_CONNECTED)
# Create label regions from particles
regions = LabelRegions(labeled_img)
region_labels = list(regions.getExistingLabels())
print("%i regions/particles detected" % len(region_labels))
# Now use IJ1 RoiManager to display the detected regions
rm = get_roi_manager(new=True)
for label in region_labels:
region = regions.getLabelRegion(label)
# Get the center of mass of the region
center = region.getCenterOfMass()
x = center.getDoublePosition(0)
y = center.getDoublePosition(1)
from net.imglib2.algorithm.labeling.ConnectedComponents import StructuringElement
from net.imglib2.roi import Regions
from net.imglib2.roi.labeling import LabelRegions
# create a log kernel
logKernel = ops.create().kernelLog(inputData.numDimensions(), sigma)
logFiltered = ops.filter().convolve(inputData, logKernel)
# otsu threshold and display
thresholded = ops.threshold().otsu(logFiltered)
# call connected components to label each connected region
labeling = ops.labeling().cca(thresholded, StructuringElement.FOUR_CONNECTED)
# get the index image (each object will have a unique gray level)
labelingIndex = labeling.getIndexImg()
# get the collection of regions and loop through them
regions = LabelRegions(labeling)
for region in regions:
# get the size of the region
size = region.size()
# get the intensity by "sampling" the intensity of the input image at the region pixels
intensity = ops.stats().mean(Regions.sample(region,
inputData)).getRealDouble()
print "size", size, "intensity", intensity
def get_roi_manager(new=False):
rm = RoiManager.getInstance()
if not rm:
rm = RoiManager()
if new:
rm.runCommand("Reset")
return rm
# Identify particles
img = mask.getImgPlus()
labeled_img = ij.op().run("cca", img, StructuringElement.EIGHT_CONNECTED)
# Create label regions from particles
regions = LabelRegions(labeled_img)
region_labels = list(regions.getExistingLabels())
print("%i regions/particles detected" % len(region_labels))
# Now use IJ1 RoiManager to display the detected regions
rm = get_roi_manager(new=True)
for label in region_labels:
region = regions.getLabelRegion(label)
# Get the center of mass of the region
center = region.getCenterOfMass()
x = center.getDoublePosition(0)
y = center.getDoublePosition(1)
smooth_shape = CenteredRectangleShape([1, 1], False)
ops.run("filter.mean", smoothed, mask, smooth_shape)
elif smoothing == "Mean 5x5":
smooth_shape = CenteredRectangleShape([2, 2], False)
ops.run("filter.mean", smoothed, mask, smooth_shape)
# Threshold if required
if thresholding == "None":
binary = ops.run("threshold.mean", mask)
else:
binary = ops.run("threshold.%s" % thresholding, smoothed)
# Create LabelRegions
img_labeling = ops.run("labeling.cca", binary,
StructuringElement.EIGHT_CONNECTED)
regions = list(LabelRegions(img_labeling))
# Process each LabelRegion
for region in regions:
# Get a sample view
sample = Regions.sample(region, image)
# Compute the stats
area = ops.run("stats.size", sample)
min = ops.run("stats.min", sample)
max = ops.run("stats.max", sample)
mean = ops.run("stats.mean", sample)
median = ops.run("stats.median", sample)
stdev = ops.run("stats.stdDev", sample)
sum = ops.run("stats.sum", sample)
if debugging:
print green_mask
ui.show('GREEN_MASK',green_mask)
# Analyze particles (minPSize-maxPSize) (default: 4-80)
# Create new object mask
log('Creating cleaned mask and counting....')
green_mask_new = ops.create().img(green_mask)
from jarray import array
pp = array([0,0],'l')
nPunctae=0
green_mask_labeling = ops.labeling().cca(green_mask,StructuringElement.FOUR_CONNECTED)
if debugging:
log('Green max is : {}'.format(ops.stats().max(green_mask)))
ui.show('GREEN_MASK_LABEL',green_mask_labeling.getIndexImg())
for r in LabelRegions(green_mask_labeling):
print r.size()
if r.size()>=minPSize and r.size()<=maxPSize:
nPunctae=nPunctae+1
rc = r.localizingCursor()
gc = green_mask_new.randomAccess()
while rc.hasNext():
rc.fwd()
rc.localize(pp)
gc.setPosition(pp)
gc.get().set(1)
log('Done! Found {} punctae within range'.format(nPunctae))
if debugging:
ui.show('RED_MASK',red_mask)
outputMask = False
allowSplit = True
# H-watershed returns a label map as an ImagePlus
labelmap = ops.run("H_Watershed", imp, hMin, thresh, peakFlooding, outputMask,
allowSplit)
labelmap.show()
print "labelmap: ", type(labelmap) # is 32bits
# convert to 16-bits as ImgLabeling expects Int not not Float
IJ.run("Conversions...", " ")
IJ.run(labelmap, "16-bit", "")
# fist convert ImagePlus to img as suggested by curtis:
# from https://forum.image.sc/t/how-to-wrap-any-kind-of-imageplus-to-an-imglib2-img-floattype/178/6
wrapImg = ImageJFunctions.wrap(labelmap)
print "wrapImg: ", type(wrapImg)
# then convert img to ImgLabeling (net.imglib2.roi.labeling.ImgLabeling)
labeling = ImgLabeling(wrapImg)
print "labeling: ", type(labeling)
# get regions from ImgLabeling
regions = LabelRegions(labeling)
print "regions: ", type(regions)
print(dir(regions))
# note crash here even though getExistingLabels() is defined...
existing_labels = regions.getExistingLabels()
print "existingLabels: ", type(existing_labels)