def analyze_cells(imp, size, i, zmin): test = [] cdf = [] #ip = imp.getProcessor() #print "grabbing image..." #test = ip.getHistogram() #test = StackStatistics(imp).getHistogram() #print "calculating stack statistics..." #total = sum(test) #print "calculate threshold" #cdf = map(lambda x: x/float(total), acc(test)) #thresh = min(filter(lambda x: cdf[x] >= quantile, xrange(1,len(cdf)) )) max_int = StackStatistics(imp).max cal= imp.getCalibration() scale2D = cal.pixelWidth / cal.pixelDepth sigma = (size / cal.pixelWidth) * scale2D iso = Compute.inFloats(Scale2D(ImgLib.wrap(imp),scale2D)) peaks = DoGPeaks(iso, sigma, sigma * 0.5, thresh, 1) print "FOund", len(peaks), "peaks" ps = [] file = open(folder+str(i).zfill(4)+'_test_out.csv','w') exporter = csv.writer(file) for peak in peaks: if peak[2]>=zmin: print "raw",peak p = Point3f(peak) p.scale(cal.pixelWidth * 1/scale2D) print "sf", cal.pixelWidth * 1/scale2D print "scaled", p ps.append(p) t = () exporter.writerow([p.x, p.y, p.z]) file.close() if vis: iso = Compute.inFloats(Scale2D(Red(ImgLib.wrap(imp)),scale2D)) univ = Image3DUniverse(512,512) univ.addIcospheres(ps,Color3f(1,0,0), 2, size/2, "Cells").setLocked(True) univ.addOrthoslice(imp).setLocked(True) univ.show()
from net.imglib2.img.display.imagej import ImageJFunctions
from net.imglib2.view import Views
from itertools import imap
from java.lang import System
from net.imglib2.roi import EllipseRegionOfInterest
from net.imglib2 import Point, RealPoint
cell_diameter = 5 # in microns
minPeak = 40 # The minimum intensity for a peak to be considered so.
imp = IJ.getImage(
) #IJ.openImage("http://pacific.mpi-cbg.de/samples/first-instar-brain.zip")
# Scale the X,Y axis down to isotropy with the Z axis
cal = imp.getCalibration()
scale2D = cal.pixelWidth / cal.pixelDepth
iso = Compute.inFloats(Scale2D(Red(ImgLib.wrap(imp)), scale2D))
#ImgLib.wrap(iso).show()
# Find peaks by difference of Gaussian
sigma = (cell_diameter / cal.pixelWidth) * scale2D
peaks = DoGPeaks(iso, sigma, sigma * 0.5, minPeak, 1)
print "Found", len(peaks), "peaks"
# Copy ImgLib1 iso image into ImgLib2 copy image
copy = ArrayImgFactory().create(
[iso.getDimension(0),
iso.getDimension(1),
iso.getDimension(2)], FloatType())
c1 = iso.createCursor()
c2 = copy.cursor()
while c1.hasNext():
from script.imglib.math import Compute, Subtract
from script.imglib.color import Red, Green, Blue, RGBA
from script.imglib import ImgLib
from ij import IJ
# RGB image stack 열기
imp = IJ.openImage("https://imagej.nih.gov/ij/images/flybrain.zip")
# Wrap it as an Imglib image
img = ImgLib.wrap(imp)
# Example 1: subtract red from green channel
sub = Compute.inFloats(Subtract(Green(img), Red(img)))
ImgLib.wrap(sub).show()
# Example 2: subtract red from green channel, and compose a new RGBA image
rgb = RGBA(Red(img), Subtract(Green(img), Red(img)), Blue(img)).asImage()
ImgLib.wrap(rgb).show()
# 1. Open an image
imp = IJ.openImage("https://imagej.nih.gov/ij/images/bridge.gif")
ti = imp.getShortTitle()
img = ImgLib.wrap(imp)
# 2. Simulate a brighfield from a Gauss with a large radius
# (First scale down by 4x, then gauss of radius=20, then scale up)
brightfield = Resample(Gauss(Scale2D(img, 0.25), 20), img.getDimensions())
# 3. Simulate a perfect darkfield
darkfield = 0
# 4. Compute the mean pixel intensity value of the image
mean = reduce(lambda s, t: s + t.get(), img, 0) / img.size()
# 5. Correct the illumination
corrected = Compute.inFloats(Multiply(Divide(Subtract(img, brightfield),
Subtract(brightfield, darkfield)), mean))
# 6. ... and show it in ImageJ - it needs to be scaled. This is a 32 bit image
ImgLib.wrap(corrected).show()
# 7. JRM get the displayed image
imp_new = WindowManager.getCurrentImage()
IJ.run(imp_new, "Enhance Contrast", "saturated=0.35")
# JRM: this will change with input image type...
IJ.run(imp_new, "8-bit", "")
imp_new.setTitle(ti + "_ff_cor")
imp_new.show()
print("done")
def detect_objects_3D(imp, size, i, zmin, zmax):
test = []
cdf = []
IJ.run("3D OC Options", " nb_of_obj._voxels median_gray_value centroid dots_size=5 font_size=10 store_results_within_a_table_named_after_the_image_(macro_friendly) redirect_to=none");
#ip = imp.getProcessor()
#print "grabbing image..."
#test = ip.getHistogram()
#test = StackStatistics(imp).getHistogram()
#print "calculating stack statistics..."
#total = sum(test)
#print "calculate threshold"
#cdf = map(lambda x: x/float(total), acc(test))
#thresh = min(filter(lambda x: cdf[x] >= quantile, xrange(1,len(cdf)) ))
max_int = StackStatistics(imp).max
cal= imp.getCalibration()
scale2D = cal.pixelWidth / cal.pixelDepth
sigma = (size / cal.pixelWidth) * scale2D
iso = Compute.inFloats(Scale2D(ImgLib.wrap(imp),scale2D))
#thresh = 0
thresh = round(max_int/25.5)
peaks = DoGPeaks(iso, sigma, sigma * 0.5, thresh, 1)
ps = []
# print max_int
# for p in peaks:
# if p[2]>zmin and p[2]<zmax:
# p2 = Point3f(p)
# p2.scale(cal.pixelWidth * 1/scale2D)
# ps.append(p2)
#ob3d = Counter3D(imp, thresh)
#peaks = ob3d.getCentroidList()
print "FOund", len(peaks), "peaks"
#ps = []
file = open(folder+exp_name+'/'+'cells/'+str(i).zfill(4)+'_cells.csv','w')
exporter = csv.writer(file)
#TODO: check consistency of coordinates !
for peak in peaks:
print peak[2]
if peak[2]>=zmin and peak[2]<=zmax:
#print "raw",peak
p = Point3f(peak)
p.scale(cal.pixelWidth * 1./scale2D)
#print "sf", cal.pixelWidth * 1/scale2D
#print "scaled", p
ps.append(p)
t = ()
exporter.writerow([p.x, p.y, p.z])
file.close()
from script.imglib.math import Compute, Add, Subtract
from script.imglib.color import HSB, Hue, Saturation, Brightness
from script.imglib import ImgLib
from ij import IJ
# Obtain an image
img = ImgLib.wrap(IJ.openImage("https://imagej.nih.gov/ij/images/clown.jpg"))
# Obtain a new clown, whose hue has been shifted by half
# with the same saturation and brightness of the original
bluey = Compute.inRGBA(
HSB(Add(Hue(img), 0.5), Saturation(img), Brightness(img)))
print type(Hue(img)), type(Saturation(img)), type(Brightness(img))
ImgLib.wrap(Compute.inFloats(Hue(img))).show()
ImgLib.wrap(Compute.inFloats(Saturation(img))).show()
ImgLib.wrap(Compute.inFloats(Brightness(img))).show()
ImgLib.wrap(bluey).show()
imp = WindowManager.getCurrentImage()
#imp = IJ.openImage("http://pacific.mpi-cbg.de/samples/first-instar-brain.zip")
#imageStats = imp.getStatistics()
#imageMin = imageStats.min()
imageMin = imp.MIN_MAX
print 'min:', imageMin
# Scale the X,Y axis down to isotropy with the Z axis
cal = imp.getCalibration()
print ' x/y/z calibration is: ', cal.pixelWidth, cal.pixelHeight, cal.pixelDepth
scale2D = cal.pixelWidth / cal.pixelDepth
print ' calling scale2d'
#iso = Compute.inFloats(Scale2D(Red(ImgLib.wrap(imp)), scale2D))
iso = Compute.inFloats(Scale2D(ImgLib.wrap(imp), scale2D))
# Find peaks by difference of Gaussian
sigma = (cell_diameter / cal.pixelWidth) * scale2D
print ' starting dogpeaks...'
peaks = DoGPeaks(iso, sigma, sigma * 0.5, minPeak, 1)
print " Found", len(peaks), "peaks"
# Convert the peaks into points in calibrated image space
if len(peaks)>0:
ps = []
for peak in peaks:
p = Point3f(peak)
p.scale(cal.pixelWidth * 1/scale2D)
ps.append(p)