def ThresholdMaxEntropy(imp0):
"""Thresholds image and returns thresholded image, merge code still quite clumsy but functional"""
imp0 = IJ.getImage()
impthres = imp0.duplicate()
imp01 = ImagePlus("Channel1", ChannelSplitter.getChannel(imp0, 1))
imp02 = ImagePlus("Channel2", ChannelSplitter.getChannel(imp0, 2))
imp001 = imp01.duplicate()
imp002 = imp02.duplicate()
IJ.setAutoThreshold(imp001, "MaxEntropy dark")
IJ.run(imp001, "Convert to Mask", "")
IJ.run(imp001, "Divide...", "value=255")
IJ.setAutoThreshold(imp002, "MaxEntropy dark")
IJ.run(imp002, "Convert to Mask", "")
IJ.run(imp002, "Divide...", "value=255")
ic = ImageCalculator()
imp0001 = ic.run("Multiply create", imp01, imp001)
ic2 = ImageCalculator()
imp0002 = ic2.run("Multiply create", imp02, imp002)
imp0001.copy()
impthres.setC(1)
impthres.paste()
imp0002.copy()
impthres.setC(2)
impthres.paste()
imp01.close()
imp02.close()
imp001.close()
imp002.close()
imp0001.close()
imp0002.close()
return impthres
def ReturnColorThresholdedPicture(PICPATH, L_min, L_max, a_min, a_max, b_min,
b_max):
imp = IJ.openImage(PICPATH)
imp.show()
ImageTitle = imp.getTitle()
LabThresold_List = [[L_min, L_max], [a_min, a_max], [b_min, b_max]]
Filt_List = ["pass", "pass", "pass"]
ColorThresholder.RGBtoLab()
IJ.run(imp, "RGB Stack", "")
IJ.run("Stack to Images", "")
IJ.selectWindow("Red")
IJ.run('Rename...', 'title=0') #title=hoge =(equal)の間にスペースを入れてならない。
imp0 = IJ.getImage()
IJ.selectWindow("Green")
IJ.run('Rename...', 'title=1')
imp1 = IJ.getImage()
IJ.selectWindow("Blue")
IJ.run('Rename...', 'title=2')
imp2 = IJ.getImage()
for i in range(3):
WindowTitle = str(i)
MinValue = float(LabThresold_List[i][0])
MaxValue = float(LabThresold_List[i][1])
IJ.selectWindow(WindowTitle)
IJ.setThreshold(MinValue, MaxValue)
IJ.run(IJ.getImage(), "Convert to Mask", "")
if Filt_List[i] == "stop":
ImageProcessor.invert()
#コメントアウトした、imgculcは動かなくなくなった,謎
#imp3 = ImageCalculator.run(imp0, imp1, "and create")
#imp4 = ImageCalculator.run(imp3,imp2, "and create")
imp3 = ImageCalculator().run("and create", imp0, imp1)
imp4 = ImageCalculator().run("and create", imp3, imp2)
imp3.show()
imp4.show()
ResultTitle = imp4.getTitle()
IJ.selectWindow(ResultTitle)
imp4.setTitle(ImageTitle)
#Saveした時点で画像の情報が失われる.
imp.close()
imp0.close()
imp1.close()
imp2.close()
imp3.close()
return imp4
def process_pi_signal(path, position, unsynchronized=True):
if unsynchronized:
path_signal = path + "\\pi"
path_signal_before = path_signal + "\\before"
path_signal_after = path_signal + "\\after"
path_signal_merged = path_signal + "\\merged"
path_imp_before = path_signal_before + "\\pi_in-focusxy%sc1.tif" % position
path_imp_after = path_signal_after + "\\pixy%sc1.tif" % position
path_imp_merged = path_signal_merged + "\\merged.tif"
path_imp_merged_sub = path_signal_merged + "\\merged_sub.tif"
imp1 = IJ.openImage(path_imp_before)
imp1.show()
imp2 = IJ.openImage(path_imp_after)
imp2.show()
zp1 = ZProjector(imp1)
zp1.setMethod(ZProjector.AVG_METHOD)
zp1.doProjection()
zpimp1 = zp1.getProjection()
zp2 = ZProjector(imp2)
zp2.setMethod(ZProjector.AVG_METHOD)
zp2.doProjection()
zpimp2 = zp2.getProjection()
imp_sub1 = ImageCalculator().run("Subtract create stack", imp1, zpimp1)
imp_sub1.show()
imp_sub2 = ImageCalculator().run("Subtract create stack", imp2, zpimp2)
imp_sub2.show()
concatenate_files(imp1, imp2, path_imp_merged)
concatenate_files(imp_sub1, imp_sub2, path_imp_merged_sub)
else:
path_signal = path + "\\pi\\seq0002xy%sc1.tif" % position
path_sub = path + "\\pi\\sub.tif"
imp = IJ.openImage(path_signal)
imp.show()
zp = ZProjector(imp)
zp.setMethod(ZProjector.AVG_METHOD)
zp.doProjection()
zpimp = zp.getProjection()
imp_sub = ImageCalculator().run("Subtract create stack", imp, zpimp)
imp_sub.show()
IJ.saveAs(imp_sub, "Tiff", path_sub)
imp.changes = False
imp.close()
zpimp.changes = False
zpimp.close()
imp_sub.changes = False
imp_sub.close()
def mousePressed(self, event):
from ij import IJ, ImagePlus
from ij.process import FloatProcessor
import math
from ij.plugin import ImageCalculator
imp = IJ.getImage()
imp.setSlice(3)
ip = imp.getProcessor().convertToFloat() # as a copy
pixels = ip.getPixels()
imp.setSlice(2)
ip1 = imp.getProcessor().convertToFloat() # as a copy
pixels1 = ip1.getPixels()
imp.setSlice(1)
ip2 = imp.getProcessor().convertToFloat() # as a copy
pixels2 = ip2.getPixels()
canvas = event.getSource()
p = canvas.getCursorLoc()
imp = canvas.getImage()
x = p.x * 1.0000
y = p.y * 1.0000
nx = x - imp.width / 2
ny = y - imp.height / 2
n_y = ny * -1
n_xx = nx / imp.width / 2
n_yy = n_y / imp.height / 2
z = math.sqrt(n_xx * n_xx + n_yy * n_yy)
z1 = math.sqrt(1 - z * z)
Xdir = map(lambda x: x * n_xx, pixels2)
Ydir = map(lambda x: x * n_yy, pixels1)
Zdir = map(lambda x: x * z1, pixels)
ip3 = FloatProcessor(ip.width, ip.height, Xdir, None)
imp3 = ImagePlus("", ip3)
ip4 = FloatProcessor(ip.width, ip.height, Ydir, None)
imp4 = ImagePlus("", ip4)
ip5 = FloatProcessor(ip.width, ip.height, Zdir, None)
imp5 = ImagePlus("", ip4)
imp6 = ImageCalculator().run("Add create 32-bit stack", imp3, imp4)
imp7 = ImageCalculator().run("Add create 32-bit stack", imp5, imp6)
imp7.setTitle("Lighting Direction")
imp7.show()
def divide_img_by_bkg(impImg, impBkg, name):
"""
divide_img_by_bkg(impImg, impBkg, title)
Divide an image by its background and set title
Parameters
==========
impImg: ImagePlus
An 8 bit image from a channel
impBkg: ImagePlus
An 8-bit image of the background
title:
A string for the image title
Return
======
impRet: ImagePlus
The ImagePlus containing a 32 bit/px background
subtracted image
"""
ic = ImageCalculator()
impRet = ic.run("Divide create 32-bit", impImg, impBkg)
impRet.setTitle(name)
return impRet
def cross_planes_approx_median_filter(stack_imp, filter_radius_um=5.0):
"""relatively computationally cheap, slightly crude approximation of median filter"""
title = stack_imp.getTitle();
xy_imp = Duplicator().run(stack_imp);
xy_imp.setTitle("xy {}".format(title));
xz_imp = Duplicator().run(stack_imp);
xz_imp.setTitle("xz {}".format(title));
xz_imp = rot3d(xz_imp, 'x');
zy_imp = Duplicator().run(stack_imp);
zy_imp.setTitle("zy {}".format(title));
zy_imp = rot3d(zy_imp, 'y');
stack_imp.changes = False;
stack_imp.close();
xy_imp = stack_median_filter(xy_imp, radius_um=filter_radius_um);
xz_imp = stack_median_filter(xz_imp, radius_um=filter_radius_um);
zy_imp = stack_median_filter(zy_imp, radius_um=filter_radius_um);
xz_imp = rot3d(xz_imp, 'x');
zy_imp = rot3d(zy_imp, '-y');
ic = ImageCalculator();
dummy = ic.run("Add create 32-bit stack", xy_imp, xz_imp);
xz_imp.close();
xy_imp.close();
output_imp = ic.run("Add create 32-bit stack", dummy, zy_imp);
zy_imp.close();
dummy.close();
output_imp.show();
IJ.run(output_imp, "Divide...", "value=3 stack");
output_imp.setTitle("Cross-plane median filtered {} (r={} um)".format(title, filter_radius_um).replace(".tif", ""));
print("Image size after applying approx median filter = ({}x{}x{})".format(output_imp.getWidth(),
output_imp.getHeight(),
output_imp.getNSlices()));
return output_imp;
def exportSegmentation(evt=None):
display = Display.getFront()
canvas = display.getCanvas()
numLayer = display.getLayerSet().size();
exportMask(evt)
files = []
for l in display.getLayerSet().getLayers():
for p in l.getDisplayables(Patch):
files.append(p.getFilePath())
# Create virtual stack 'vs'
vs = None
for f in files:
# if first image...
if vs is None:
imp = IJ.openImage(f)
vs = VirtualStack(imp.width, imp.height, None, "/")
vs.addSlice(f)
ImagePlus("VSeg", vs).show()
IJ.run("Duplicate...", "title=Segmentation duplicate range=1-30");
WindowManager.getImage("VSeg").close()
ic = ImageCalculator()
ic.run("Multiply stack", WindowManager.getImage("Segmentation"), WindowManager.getImage("Labels"));
WindowManager.getImage("Labels").close()
def doGaussianFilter(imp, radius=30):
"""This function takes an input stack, and subtracts a gaussian filtered image
from each individual frame. Thereby, everything that is not moving
in a timeseries is filtered away.
Args:
imp (ImagePlus): An input stack as ImagePlus object.
projectionMethod (str, optional): Choose the projection method. Options are
'Average Intensity', 'Max Intensity', 'Min Intensity', 'Sum Slices', 'Standard Deviation', 'Median'.
Defaults to "Median".
Returns:
ImagePlus: The resulting stack.
"""
#Start by getting the active image window and get the current active channel and other stats
cal = imp.getCalibration()
title = imp.getTitle()
# Make gaussian filtered image.
gaussian = IJ.run(imp, "Gaussian Blur...", "sigma=30 stack")
# Subtract Z-Projection and return output ImagePlus.
impout = ImageCalculator().run("Subtract create 32-bit stack", imp,
gaussian)
impout.setCalibration(cal)
return impout
def GlidingSubtracter(imp):
name = imp.getTitle()
width, height, nChannels, nSlices, nFrames = imp.getDimensions()
IJ.log("nFrames: {}".format(nFrames))
# Catch wrong input dimensions.
if nChannels != 1:
IJ.log("GlidingSubtracter only takes single channel images.")
return None
if nFrames <= 1:
IJ.log("Stack has <= 1 frame. Perhaps switch Frames and Slices?")
return None
instack = imp.getImageStack()
outstack = ImageStack()
frame1 = instack.getProcessor(1)
frame1 = ImagePlus("frame1", frame1)
for i in range(1, nFrames):
frame2 = instack.getProcessor(i)
frame2 = ImagePlus("frame2", frame2)
subtracted = ImageCalculator().run("subtract create 32-bit", frame1,
frame2).getProcessor()
# ImagePlus("slice", subtracted).show()
outstack.addSlice(subtracted)
outname = "subtract-" + name
outstack = ImagePlus(outname, outstack)
return outstack
def DoG(imp0, kernel1, kernel2):
"""Thresholds image and returns thresholded image,
merge code still quite clumsy but functional"""
imp1 = imp0.duplicate()
imp2 = imp0.duplicate()
IJ.run(imp1, "Gaussian Blur...", "sigma=" + str(kernel1) + " stack")
IJ.run(imp2, "Gaussian Blur...", "sigma=" + str(kernel2) + " stack")
ic = ImageCalculator()
imp3 = ic.run("Subtract create stack", imp1, imp2)
return imp3
def preprocess(self):
IJ.run(self.imp, "Gaussian Blur...", "sigma=" + str(gaussianVal))
IJ.run(self.imp, "8-bit", "")
imp_tmp = self.imp.duplicate()
IJ.run(self.imp, "Gray Morphology",
"radius=" + str(tophatSize) + " type=circle operator=open")
ImageCalculator().run("Subtract", imp_tmp, self.imp)
self.imp.changes = False
self.imp.close()
self.imp = imp_tmp
def filter_diff_gauss_3d(imp,dx1,dy1,dz1,dx2,dy2,dz2):
imp_1 = imp.duplicate()
GaussianBlur3D().blur(imp_1, dx1, dy1, dz1)
imp_2 = imp.duplicate()
GaussianBlur3D().blur(imp_2, dx2, dy2, dz2)
ic = ImageCalculator()
imp_diff = ic.run("Subtract stack create", imp_1, imp_2)
imp_diff.show()
return imp_diff
def make_outline_image(imp):
#imp.show() # one needs to display it, otherwise below function cannot find it
IJ.run(imp, "3D Fast Filters", "filter=Minimum radius_x_pix=1.0 radius_y_pix=1.0 radius_z_pix=1.0");
#imp.hide()
imp_minimum = WindowManager.getImage("3D_Minimum")
imp_outlines = imp.duplicate()
ic = ImageCalculator()
imp_outlines = ic.run("Subtract stack", imp_outlines, imp_minimum)
# clean up
imp_minimum.close()
imp_outlines.setTitle("Outlines")
return imp_outlines
def BackgroundFilter(imp, projection_method = "Median"):
title = imp.getTitle()
#Make a dict containg method_name:const_fieled_value pairs for the projection methods
methods_as_strings=['Average Intensity', 'Max Intensity', 'Min Intensity', 'Sum Slices', 'Standard Deviation', 'Median']
methods_as_const=[ZProjector.AVG_METHOD, ZProjector.MAX_METHOD, ZProjector.MIN_METHOD, ZProjector.SUM_METHOD, ZProjector.SD_METHOD, ZProjector.MEDIAN_METHOD]
method_dict=dict(zip(methods_as_strings, methods_as_const))
#The Z-Projection magic happens here through a ZProjector object
zp = ZProjector(imp)
zp.setMethod(method_dict[projection_method])
zp.doProjection()
outstack = imp.createEmptyStack()
outstack.addSlice(zp.getProjection().getProcessor())
imp2 = ImagePlus(title+'_'+projection_method, outstack)
out = ImageCalculator().run("Subtract create 32-bit stack", imp, imp2)
return out
def identifyTunicate(imp):
# Clear ROI manager
rm = RoiManager.getInstance()
if rm is not None:
rm.reset()
# Get hue from HSB stack
impHue = ProcessHSB.getHue(imp)
width = impHue.width
height = impHue.height
# Get processor
ipHue = impHue.getProcessor().convertToFloat()
huePixels = ipHue.getPixels()
# Get macro hue because it is the same color
newPixels = map(ProcessHSB.macroHue, huePixels)
ipNew = FloatProcessor(width, height, newPixels)
impTunicate = ImagePlus("MacroHue", ipNew)
# Bring brightness into the equation
impBrightness = ProcessHSB.getBrightness(imp)
IJ.run(impBrightness, "Auto Threshold", "method=MaxEntropy white")
#IJ.run(impBrightness, "Analyze Particles...", "size=10000-Infinity circularity=0.10-1.00 show=Masks in_situ") # "add" right after "include" to include roi to manager
# Logic AND pictures together
ic = ImageCalculator()
impTunicateMask = ic.run("AND create", impTunicate, impBrightness)
IJ.run(impTunicateMask, "8-bit", "") # convert to 8-bit
impTunicate.close()
impBrightness.close()
IJ.run(impTunicateMask, "Analyze Particles...",
"size=10000-Infinity circularity=0.50-1.00 show=Masks add in_situ"
) # "add" right after "include" to include roi to manager
impTunicateMask.close()
#imp.show()
#rm = RoiManager.getInstance()
#return rm
#testImp = IJ.getImage()
#result = identifyTunicate(testImp)
#print type(result)
def process_caspase_signal(path_signal, path_imp, path_imp_out):
path_imp = path_signal + path_imp
imp = IJ.openImage(path_imp)
imp.show()
zp = ZProjector(imp)
zp.setMethod(ZProjector.AVG_METHOD)
zp.doProjection()
zpimp = zp.getProjection()
imp_sub = ImageCalculator().run("Subtract create stack", imp, zpimp)
imp_sub.show()
IJ.saveAs(imp_sub, "Tiff", path_signal + path_imp_out)
imp.changes = False
imp.close()
imp_sub.changes = False
imp_sub.close()
def make_and_clean_binary(imp, threshold_method):
"""convert the membrane identification channel into binary for segmentation"""
if "Local: " in threshold_method:
dup = Duplicator()
imp1 = dup.run(imp)
imp2 = dup.run(imp)
imp.changes = False
imp.close()
threshold_method = threshold_method.split("Local: ")[1]
IJ.run(imp1, "8-bit", "")
IJ.run(
imp1, "Auto Local Threshold", "method=" + threshold_method +
" radius=15 parameter_1=0 parameter_2=0 white stack")
IJ.run(imp2, "Make Binary",
"method=MinError background=Dark calculate")
ic = ImageCalculator()
imp = ic.run("AND create stack", imp1, imp2)
IJ.run(imp, "Invert", "stack")
IJ.run(imp, "Make Binary",
"method=Default background=Default calculate")
elif "Edge" in threshold_method:
IJ.run(imp, "Find Edges", "stack")
IJ.run(imp, "Make Binary", "method=Mean background=Dark calculate")
else:
IJ.run(imp, "Make Binary",
"method=" + threshold_method + " background=Dark calculate")
# "calculate" ensures that threshold is calculated image-wise
IJ.run(imp, "Open", "stack")
IJ.run(imp, "Close-", "stack")
IJ.run(imp, "Close-", "stack")
IJ.run(imp, "Open", "stack")
IJ.run(imp, "Fill Holes", "stack")
IJ.run(imp, "Erode", "stack")
IJ.run(imp, "Erode", "stack")
keep_largest_blob(imp)
IJ.run(imp, "Dilate", "stack")
IJ.run(imp, "Dilate", "stack")
IJ.run(imp, "Open", "stack")
if "Edge" in threshold_method:
IJ.run(imp, "Erode", "stack")
IJ.run(imp, "Erode", "stack")
return imp
def generate_r_image(imp, ring_rois, centres, unwrap_axis, threshold_val):
"""for each point in the projection, calculate the distance to the vessel axis and present as an image"""
fp = imp.getProcessor()
fp.setThreshold(threshold_val, fp.maxValue(), FloatProcessor.NO_LUT_UPDATE)
bp = fp.createMask()
bp.dilate()
bp.erode()
mask_imp = ImagePlus("Mask", bp)
tile_mask = make_tiled_imp(mask_imp)
#tile_mask.show();
#WaitForUserDialog("pasue - generated mask").show();
mask_imp = do_unwrap(tile_mask, unwrap_axis, imp_title=mask_imp.getTitle())
#mask_imp.show();
roi = PolygonRoi([x for (x, y) in unwrap_axis],
[y for (x, y) in unwrap_axis], PolygonRoi.POLYLINE)
mask_imp.setRoi(roi)
#WaitForUserDialog("pasue - unwrapped").show();
IJ.run(mask_imp, "Fill Holes", "")
#WaitForUserDialog("pasue - filled holes").show();
IJ.run(mask_imp, "Divide...", "value=255")
#WaitForUserDialog("pasue - scaled to 0-1").show();
#mask_imp.show();
r_list = []
for lidx, (roi, centre) in enumerate(zip(ring_rois, centres)):
r_sublist = [
math.sqrt((x - centre[0])**2 + (y - centre[1])**2)
for x, y in zip(roi.getPolygon().xpoints,
roi.getPolygon().ypoints)
]
r_list.append(r_sublist)
r_imp = ImagePlus("Radii", FloatProcessor([list(x) for x in zip(*r_list)]))
tile_r_imp = make_tiled_imp(r_imp)
r_imp = do_unwrap(tile_r_imp, unwrap_axis, imp_title=r_imp.getTitle())
r_imp = ImageCalculator().run("Multiply create", r_imp, mask_imp)
IJ.run(r_imp, "Cyan Hot", "")
return r_imp, mask_imp
def subtractzproject(imp, projectionMethod="Median"):
"""This function takes an input stack, and subtracts a projection from the
whole stack from each individual frame. Thereby, everything that is not moving
in a timeseries is filtered away.
Args:
imp (ImagePlus): An input stack as ImagePlus object.
projectionMethod (str, optional): Choose the projection method. Options are
'Average Intensity', 'Max Intensity', 'Min Intensity', 'Sum Slices', 'Standard Deviation', 'Median'.
Defaults to "Median".
Returns:
ImagePlus: The resulting stack.
"""
#Start by getting the active image window and get the current active channel and other stats
cal = imp.getCalibration()
title = imp.getTitle()
# Define a dictionary containg method_name:const_fieled_value pairs for the projection methods.
methods_as_strings = [
'Average Intensity', 'Max Intensity', 'Min Intensity', 'Sum Slices',
'Standard Deviation', 'Median'
]
methods_as_const = [
ZProjector.AVG_METHOD, ZProjector.MAX_METHOD, ZProjector.MIN_METHOD,
ZProjector.SUM_METHOD, ZProjector.SD_METHOD, ZProjector.MEDIAN_METHOD
]
method_dict = dict(zip(methods_as_strings, methods_as_const))
# Run Z-Projection.
zp = ZProjector(imp)
zp.setMethod(method_dict[projectionMethod])
zp.doProjection()
impMedian = zp.getProjection()
# Subtract Z-Projection and return output ImagePlus.
impout = ImageCalculator().run("Subtract create 32-bit stack", imp,
impMedian)
impout.setCalibration(cal)
return impout
def identifyRed(imp):
# Clear ROI manager
rm = RoiManager.getInstance()
if rm is not None:
rm.reset()
# Get hue from HSB stack
impHue = ProcessHSB.getHue(imp)
width = impHue.width
height = impHue.height
# Get processor for hue
ipHue = impHue.getProcessor().convertToFloat()
huePixels = ipHue.getPixels()
# Bring brightness into the equation
impBrightness = ProcessHSB.getBrightness(imp)
IJ.run(impBrightness, "Auto Threshold", "method=Default white")
#IJ.run(impBrightness, "Analyze Particles...", "size=10000-Infinity circularity=0.00-1.00 show=Masks in_situ")
# If hue < 30 and hue > 225, True
newPixels = map(ProcessHSB.redHue, huePixels)
# Pause: Do this later
ipNew = FloatProcessor(width, height, newPixels)
impRed = ImagePlus("RedHue", ipNew)
IJ.run(impRed, "8-bit", "") # convert to 8-bit
# Logic AND pictures together
ic = ImageCalculator()
impRedMask = ic.run("AND create", impRed, impBrightness)
IJ.run(impRedMask, "8-bit", "") # convert to 8-bit
IJ.run(impRedMask, "Analyze Particles...", "size=10000-Infinity circularity=0.00-1.00 show=Masks add in_situ") # "add" right after "include" to include roi to manager
impHue.close()
impBrightness.close()
impRed.close()
impRedMask.close()
def open_fli(filepth):
# load the dataset
options = ImporterOptions()
options.setId(filepth)
options.setOpenAllSeries(1)
imps = BF.openImagePlus(options)
for imp in imps:
title = imp.getTitle()
if title.find("Background Image")==-1:
img = imp
imp.close()
else:
bkg = imp
imp.close()
ic = ImageCalculator()
img2 = ic.run("Subtract create 32-bit stack",img,bkg)
#copy the metadata
props = img.getProperties()
for prop in props:
img2.setProperty(prop,props.getProperty(prop))
img.close()
bkg.close()
return img2
def create(imp):
# Prep Roi Manager
rm = RoiManager.getInstance()
if rm is not None:
rm.reset()
# Get current image
imp2 = imp.duplicate()
IJ.run(imp2, "8-bit", "") # convert to 8-bit
IJ.run(imp2, "Auto Threshold", "method=Shanbhag white")
IJ.run(imp2, "Analyze Particles...", "size=10000-Infinity circularity=0-1.00 show=Masks add in_situ") # "add" right after "include" to include roi to manager
# Remove background
ic = ImageCalculator()
imp3 = ic.run("AND create", imp, imp2)
imp.close()
imp2.close()
### Begin manipulating ROIs ###
rm = RoiManager.getInstance()
# Get number of objects found
roiCount = rm.getCount()
print "Number of Particles found:" + str(roiCount)
import os
from ij import IJ # we will need this to read in files among other things.
from ij import WindowManager as wm
from ij.plugin import ImageCalculator
# create an instance of image calculator so we can use it in dot notation
ic = ImageCalculator()
in_folder = "E:/DataBinge_ImageJ/ImageJ DB/5 files tiff - Copy/"
out_folder = "E:/DataBinge_ImageJ/ImageJ DB/Claire Output/"
the_files = os.listdir(in_folder)
print(the_files)
the_file = the_files[0]
print(the_file)
# open the_file inside in_folder.
dat = IJ.open(
in_folder + the_file
) # Note that strings (string = "stuff") are joined by concatenation
# select the active window
imp = IJ.getImage()
# use the same renaming trick as for the macro recorder
imp.setTitle("current")
# IJ.run executes commands from the macro recorder
def poreDetectionUV(inputImp, inputDataset, inputRoi, ops, data, display, detectionParameters):
title = inputImp.getTitle()
title=title.replace('UV', 'SD')
print title
#trueColorImp= WindowManager.getImage(title)
#print type( trueColorImp)
# calculate are of roi
stats=inputImp.getStatistics()
inputRoiArea=stats.area
print inputRoi
# get the bounding box of the active roi
inputRec = inputRoi.getBounds()
x1=long(inputRec.getX())
y1=long(inputRec.getY())
x2=x1+long(inputRec.getWidth())-1
y2=y1+long(inputRec.getHeight())-1
print x1
print y1
print x2
print y2
# crop the roi
interval=FinalInterval( array([x1, y1 ,0], 'l'), array([x2, y2, 2], 'l') )
cropped=ops.crop(interval, None, inputDataset.getImgPlus() )
datacropped=data.create(cropped)
display.createDisplay("cropped", datacropped)
croppedPlus=IJ.getImage()
duplicator=Duplicator()
substackMaker=SubstackMaker()
# duplicate the roi
duplicate=duplicator.run(croppedPlus)
#duplicate.show()
# convert duplicate of roi to HSB and get brightness
IJ.run(duplicate, "HSB Stack", "");
brightnessPlus=substackMaker.makeSubstack(duplicate, "3-3")
brightness=ImgPlus(ImageJFunctions.wrapByte(brightnessPlus))
brightnessPlus.setTitle("Brightness")
#brightnessPlus.show()
# make another duplicate, split channels and get red
duplicate=duplicator.run(croppedPlus)
channels=ChannelSplitter().split(duplicate)
redPlus=channels[0]
red=ImgPlus(ImageJFunctions.wrapByte(redPlus))
redPlus.show()
# convert to lab
IJ.run(croppedPlus, "Color Transformer", "colour=Lab")
IJ.selectWindow('Lab')
labPlus=IJ.getImage()
# get the A channel
APlus=substackMaker.makeSubstack(labPlus, "2-2")
APlus.setTitle('A')
APlus.show()
APlus.getProcessor().resetMinAndMax()
APlus.updateAndDraw()
AThresholded=threshold(APlus, -10, 50)
# get the B channel
BPlus=substackMaker.makeSubstack(labPlus, "3-3")
BPlus.setTitle('B')
BPlus.show()
BPlus.getProcessor().resetMinAndMax()
BPlus.updateAndDraw()
BThresholded=threshold(BPlus, -10, 50)
# AND the Athreshold and Bthreshold to get a map of the red pixels
ic = ImageCalculator();
redMask = ic.run("AND create", AThresholded, BThresholded);
IJ.run(redMask, "Divide...", "value=255");
#redMask.show()
labPlus.close()
# threshold the spots from the red channel
thresholdedred=SpotDetectionGray(red, data, display, ops, False)
display.createDisplay("thresholdedred", data.create(thresholdedred))
impthresholdedred = ImageJFunctions.wrap(thresholdedred, "wrapped")
# threshold the spots from the brightness channel
thresholded=SpotDetectionGray(brightness, data, display, ops, False)
display.createDisplay("thresholded", data.create(thresholded))
impthresholded=ImageJFunctions.wrap(thresholded, "wrapped")
# or the thresholding results from red and brightness channel
impthresholded = ic.run("OR create", impthresholded, impthresholdedred);
# convert to mask
Prefs.blackBackground = True
IJ.run(impthresholded, "Convert to Mask", "")
# clear the region outside the roi
clone=inputRoi.clone()
clone.setLocation(0,0)
Utility.clearOutsideRoi(impthresholded, clone)
# create a hidden roi manager
roim = RoiManager(True)
# count the particlesimp.getProcessor().setColor(Color.green)
countParticles(impthresholded, roim, detectionParameters.minSize, detectionParameters.maxSize, detectionParameters.minCircularity, detectionParameters.maxCircularity)
# define a function to determine the percentage of pixels that are foreground in a binary image
# inputs:
# imp: binary image, 0=background, 1=foreground
# roi: an roi
def isRed(imp, roi):
stats = imp.getStatistics()
if (stats.mean>detectionParameters.redPercentage): return True
else: return False
def notRed(imp, roi):
stats = imp.getStatistics()
if (stats.mean>detectionParameters.redPercentage): return False
else: return True
allList=[]
for roi in roim.getRoisAsArray():
allList.append(roi.clone())
# count particles that are red
redList=CountParticles.filterParticlesWithFunction(redMask, allList, isRed)
# count particles that are red
blueList=CountParticles.filterParticlesWithFunction(redMask, allList, notRed)
print "Total particles: "+str(len(allList))
print "Filtered particles: "+str(len(redList))
# for each roi add the offset such that the roi is positioned in the correct location for the
# original image
[roi.setLocation(roi.getXBase()+x1, roi.getYBase()+y1) for roi in allList]
# create an overlay and add the rois
overlay1=Overlay()
inputRoi.setStrokeColor(Color.green)
overlay1.add(inputRoi)
[CountParticles.addParticleToOverlay(roi, overlay1, Color.red) for roi in redList]
[CountParticles.addParticleToOverlay(roi, overlay1, Color.cyan) for roi in blueList]
def drawAllRoisOnImage(imp, mainRoi, redList, blueList):
imp.getProcessor().setColor(Color.green)
IJ.run(imp, "Line Width...", "line=3");
imp.getProcessor().draw(inputRoi)
imp.updateAndDraw()
IJ.run(imp, "Line Width...", "line=1");
[CountParticles.drawParticleOnImage(imp, roi, Color.magenta) for roi in redList]
[CountParticles.drawParticleOnImage(imp, roi, Color.green) for roi in blueList]
imp.updateAndDraw()
drawAllRoisOnImage(inputImp, inputRoi, redList, blueList)
#drawAllRoisOnImage(trueColorImp, inputRoi, redList, blueList)
# draw overlay
#inputImp.setOverlay(overlay1)
#inputImp.updateAndDraw()
statsdict=CountParticles.calculateParticleStats(APlus, BPlus, redMask, roim.getRoisAsArray())
print inputRoiArea
areas=statsdict['Areas']
poreArea=0
for area in areas:
poreArea=poreArea+area
ATotal=0
ALevels=statsdict['ALevel']
for A in ALevels:
ATotal=ATotal+A
AAverage=ATotal/len(ALevels)
BTotal=0
BLevels=statsdict['BLevel']
for B in BLevels:
BTotal=BTotal+B
BAverage=BTotal/len(BLevels)
redTotal=0
redPercentages=statsdict['redPercentage']
for red in redPercentages:
redTotal=redTotal+red
redAverage=redTotal/len(redPercentages)
pixwidth=inputImp.getCalibration().pixelWidth
inputRoiArea=inputRoiArea/(pixwidth*pixwidth)
print str(len(allList))+" "+str(len(redList))+" "+str(len(blueList))+" "+str(poreArea/inputRoiArea)+" "+str(redAverage)
def startTracking(filepath, fdir, ffile, filename):
outpath = fdir
troutpath = fdir + separator + ffile + separator + "tracked" + separator
stackoutpath = fdir + separator + ffile + separator + "stacks" + separator
pixwidth = 0.647
interval_sec = 600 #pr
if "141006" in fdir:
interval_sec = 600
elif "141117" in fdir:
interval_sec = 300
elif "141215" in fdir:
interval_sec = 300
if not os.path.isdir(outpath):
os.mkdir(outpath)
if not os.path.isdir(troutpath):
os.mkdir(troutpath)
if not os.path.isdir(stackoutpath):
os.mkdir(stackoutpath)
print 'filepath: ', filepath #pr
print 'fdir: ', fdir #pr
print 'ffile: ', ffile #pr
IJ.run("Image Sequence...",
"open=" + filepath + " file=" + filename + " sort")
#pr
#IJ.run("Image Sequence...", "open=" + filepath + " file=molm sort"); #pr
imp = WindowManager.getCurrentImage()
imptitle = imp.getTitle()
nframes = imp.getNSlices()
IJ.run(
imp, "Properties...", "channels=1 slices=1 frames=" + str(nframes) +
" unit=inch pixel_width=" + str(pixwidth) + " pixel_height=" +
str(pixwidth) + " voxel_depth=1.0000 frame=[" + str(interval_sec) +
" sec]")
IJ.run(imp, "Duplicate...", "title=" + imptitle + "_dup duplicate")
imp_dup = WindowManager.getImage(imptitle + "_dup")
IJ.run(imp_dup, "Gaussian Blur...", "sigma=50 stack")
ic = ImageCalculator()
imp_corr = ic.run("Divide create 32-bit stack", imp, imp_dup)
imp_corr.setTitle(imptitle + "_corrected")
imp_corr.show()
imp.changes = False
imp.close()
imp_dup.changes = False
imp_dup.close()
IJ.run(imp_corr, "8-bit", "")
IJ.run(
imp_corr, "Normalize Local Contrast",
"block_radius_x=100 block_radius_y=100 standard_deviations=1 stretch stack"
)
IJ.saveAs(imp_corr, "Tiff",
stackoutpath + separator + imptitle + "_corrected.tif")
IJ.run(imp_corr, "Duplicate...", "title=" + imptitle + "_bg duplicate")
imp_bg = WindowManager.getImage(imptitle + "_bg")
#Prefs.blackBackground = True;
IJ.setAutoThreshold(imp_bg, "MinError dark")
IJ.run(imp_bg, "Convert to Mask", "stack")
IJ.run(imp_bg, "Analyze Particles...",
"size=10000-Infinity show=Masks stack")
imp_bg.changes = False
imp_bg.close()
imp_bgmask = WindowManager.getImage("Mask of " + imptitle + "_bg")
ic = ImageCalculator()
imp_corr_wobg = ic.run("Subtract create stack", imp_corr, imp_bgmask)
imp_corr_wobg.setTitle(imptitle + "_corrected_womask")
imp_corr_wobg.show()
IJ.saveAs(imp_corr_wobg, "Tiff",
stackoutpath + separator + imptitle + "_corrected_womask.tif")
#pr
# pr: substract average frames
zp = ZProjector(imp_corr_wobg)
zp.setMethod(ZProjector.AVG_METHOD)
zp.doProjection()
zpimp = zp.getProjection()
zpimp.show()
imp_corr_wobg_sub = ImageCalculator().run("Subtract create stack",
imp_corr_wobg, zpimp)
imp_corr_wobg_sub.show()
#imp_corr_wobg.changes = False
#imp_corr_wobg.close()
# pr: subtract average frames (END)
IJ.saveAs(
imp_corr_wobg_sub, "Tiff", stackoutpath + separator + imptitle +
"_corrected_womask_substracted.tif")
#pr
IJ.saveAs(
zpimp, "Tiff",
stackoutpath + separator + imptitle + "_corrected_womask_avg.tif")
#commented out: pr
#IJ.saveAs(imp_corr_wobg, "Tiff", stackoutpath+separator+imptitle+"_corrected_womask.tif");
IJ.saveAs(imp_bgmask, "Tiff",
stackoutpath + separator + imptitle + "_bgmask.tif")
print(stackoutpath + separator + imptitle +
"_corrected_womask_substracted.tif")
print(stackoutpath + separator + imptitle + "_corrected_womask_avg.tif")
print(stackoutpath + separator + imptitle + "_bgmask.tif")
imp_corr.changes = False
imp_corr.close()
imp_bgmask.changes = False
imp_bgmask.close()
imp_corr_wobg.changes = False
imp_corr_wobg.close()
#imp_corr_wobg_sub.changes = False
#imp_corr_wobg_sub.close()
zpimp.changes = False
zpimp.close()
#IJ.log(System.getProperty("os.name"))
#IJ.log(fdir)
return imp_corr_wobg_sub
IJ.run(impa, "Montage to Stack...",
"images_per_row=1 images_per_column=%d border=0" % imp2.height)
impb = IJ.getImage()
IJ.run("Reslice [/]...", "output=1.000 start=Left avoid")
impc = IJ.getImage()
impa.close()
impb.close()
#calculating albedo
IJ.run("Z Project...", "projection=[Max Intensity]")
impd = IJ.getImage()
impd.setTitle("albedo")
#calculating normals
N = ImageCalculator().run("Divide create 32-bit stack", impc, impd)
N.show()
#IJ.run("Rotate 90 Degrees Right")
#IJ.run("Flip Horizontally", "stack")
impf = IJ.getImage()
impf.setTitle("32 bit Surface Normal Map")
#converting to 8 bit color image
IJ.run("Duplicate...", "duplicate")
IJ.run("8-bit")
impe = IJ.getImage()
impe.changes = False
IJ.run("Stack to RGB")
impg = IJ.getImage()
impg.setTitle("8 bit Surface Normal Map")
def track():
imp = IJ.getImage()
nChannels = imp.getNChannels() # Get the number of channels
orgtitle = imp.getTitle()
IJ.run("Subtract Background...", "rolling=50 sliding stack")
IJ.run("Enhance Contrast...", "saturated=0.3")
IJ.run("Multiply...", "value=10 stack")
IJ.run("Subtract Background...", "rolling=50 sliding stack")
IJ.run("Set Scale...", "distance=0")
channels = ChannelSplitter.split(imp)
imp_GFP = channels[0]
imp_RFP = channels[1]
IJ.selectWindow(orgtitle)
IJ.run("Close")
ic = ImageCalculator()
imp_merge = ic.run("Add create stack", imp_GFP, imp_RFP)
imp_merge.setTitle("add_channels")
imp_merge.show()
imp_RFP.show()
imp_GFP.show()
imp5 = ImagePlus()
IJ.run(imp5, "Merge Channels...", "c1=[" + imp_merge.title + "] c2=" + imp_GFP.title + ' c3=' + imp_RFP.title + " create")
print("c1=[" + imp_merge.title + "] c2=" + imp_GFP.title + ' c3=' + imp_RFP.title + " create")
imp5.show()
imp5 = IJ.getImage()
nChannels = imp5.getNChannels()
# Setup settings for TrackMate
settings = Settings()
settings.setFrom(imp5)
# Spot analyzer: we want the multi-C intensity analyzer.
settings.addSpotAnalyzerFactory(SpotMultiChannelIntensityAnalyzerFactory())
# Spot detector.
settings.detectorFactory = LogDetectorFactory()
settings.detectorSettings = settings.detectorFactory.getDefaultSettings()
settings.detectorSettings['TARGET_CHANNEL'] = 1
settings.detectorSettings['RADIUS'] = 24.0
settings.detectorSettings['THRESHOLD'] = 0.0
# Spot tracker.
# Configure tracker - We don't want to allow merges or splits
settings.trackerFactory = SparseLAPTrackerFactory()
settings.trackerSettings = LAPUtils.getDefaultLAPSettingsMap() # almost good enough
settings.trackerSettings['ALLOW_TRACK_SPLITTING'] = False
settings.trackerSettings['ALLOW_TRACK_MERGING'] = False
settings.trackerSettings['LINKING_MAX_DISTANCE'] = 8.0
settings.trackerSettings['GAP_CLOSING_MAX_DISTANCE'] = 8.0
settings.trackerSettings['MAX_FRAME_GAP'] = 1
# Configure track filters
settings.addTrackAnalyzer(TrackDurationAnalyzer())
settings.addTrackAnalyzer(TrackSpotQualityFeatureAnalyzer())
filter1 = FeatureFilter('TRACK_DURATION', 20, True)
settings.addTrackFilter(filter1)
# Run TrackMate and store data into Model.
model = Model()
trackmate = TrackMate(model, settings)
ok = trackmate.checkInput()
if not ok:
sys.exit(str(trackmate.getErrorMessage()))
ok = trackmate.process()
if not ok:
sys.exit(str(trackmate.getErrorMessage()))
selectionModel = SelectionModel(model)
displayer = HyperStackDisplayer(model, selectionModel, imp5)
displayer.render()
displayer.refresh()
IJ.log('TrackMate completed successfully.')
IJ.log('Found %d spots in %d tracks.' % (model.getSpots().getNSpots(True) , model.getTrackModel().nTracks(True)))
# Print results in the console.
headerStr = '%10s %10s %10s %10s %10s %10s' % ('Spot_ID', 'Track_ID', 'Frame', 'X', 'Y', 'Z')
rowStr = '%10d %10d %10d %10.1f %10.1f %10.1f'
for i in range( nChannels ):
headerStr += (' %10s' % ( 'C' + str(i+1) ) )
rowStr += ( ' %10.1f' )
#open a file to save results
myfile = open('/home/rickettsia/Desktop/data/Clamydial_Image_Analysis/EMS_BMECBMELVA_20X_01122019/data/'+orgtitle.split('.')[0]+'.csv', 'wb')
wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)
wr.writerow(['Spot_ID', 'Track_ID', 'Frame', 'X', 'Y', 'Z', 'Channel_1', 'Channel_2'])
IJ.log('\n')
IJ.log(headerStr)
tm = model.getTrackModel()
trackIDs = tm.trackIDs(True)
for trackID in trackIDs:
spots = tm.trackSpots(trackID)
# Let's sort them by frame.
ls = ArrayList(spots)
for spot in ls:
values = [spot.ID(), trackID, spot.getFeature('FRAME'), \
spot.getFeature('POSITION_X'), spot.getFeature('POSITION_Y'), spot.getFeature('POSITION_Z')]
for i in range(nChannels):
values.append(spot.getFeature('MEAN_INTENSITY%02d' % (i+1)))
IJ.log(rowStr % tuple(values))
l1 = (values[0], values[1], values[2], values[3], values[4], values[5], values[7], values[8])
wr.writerow(l1)
myfile.close()
IJ.selectWindow("Merged")
IJ.run("Close")
def main():
# Prepare directory tree for output.
indir = IJ.getDirectory("input directory")
outdir = IJ.getDirectory(".csv output directory")
c1dir = os.path.join(outdir, "Channel1")
c2dir = os.path.join(outdir, "Channel2")
c3dir = os.path.join(outdir, "Channel3")
c4dir = os.path.join(outdir, "Channel4")
channelsdir = os.path.join(outdir, "Channels")
if not os.path.isdir(c1dir):
os.mkdir(c1dir)
if not os.path.isdir(c2dir):
os.mkdir(c2dir)
if not os.path.isdir(c3dir):
os.mkdir(c3dir)
if not os.path.isdir(c4dir):
os.mkdir(c4dir)
if not os.path.isdir(channelsdir):
os.mkdir(channelsdir)
# Collect all file paths in the input directory
files = readdirfiles(indir)
# Initialize the results tables.
c1Results = ResultsTable()
c2Results = ResultsTable()
c3Results = ResultsTable()
c4Results = ResultsTable()
for file in files:
IJ.log("File: {}/{}".format(files.index(file) + 1, len(files)))
if file.endswith('.tif'):
# Open .tiff file as ImagePlus.
imp = Opener().openImage(file)
imp = ZProjector.run(imp, "max")
# imp = stackprocessor(file,
# nChannels=4,
# nSlices=7,
# nFrames=1)
channels = ChannelSplitter.split(imp)
name = imp.getTitle()
# For every channel, save the inverted channel in grayscale as .jpg.
for channel in channels:
IJ.run(channel, "Grays", "")
IJ.run(channel, "Invert", "")
jpgname = channel.getShortTitle()
jpgoutfile = os.path.join(channelsdir,
"{}.jpg".format(jpgname))
IJ.saveAs(channel.flatten(), "Jpeg", jpgoutfile)
IJ.run(channel, "Invert", "")
# OPTIONAL - Perform any other operations (e.g. crossexcitation compensation tasks) before object count.
c2name = channels[2].getTitle()
cal = channels[2].getCalibration()
channels[2] = ImagePlus(
c2name,
ImageCalculator().run("divide create 32-bit", channels[2],
channels[3]).getProcessor(
) # This removes AF647 bleed-through
)
channels[2].setCalibration(cal)
# Settings for channel1 threshold.
c1 = countobjects(channels[0],
c1Results,
threshMethod="Triangle",
subtractBackground=True,
watershed=True,
minSize=0.00,
maxSize=100,
minCirc=0.00,
maxCirc=1.00)
# Settings for channel2 threshold.
c2 = countobjects(channels[1],
c2Results,
threshMethod="RenyiEntropy",
subtractBackground=True,
watershed=False,
minSize=0.00,
maxSize=30.00,
minCirc=0.00,
maxCirc=1.00)
# Settings for channel3 threshold.
c3 = countobjects(channels[2],
c3Results,
threshMethod="RenyiEntropy",
subtractBackground=True,
watershed=False,
minSize=0.00,
maxSize=30.00,
minCirc=0.00,
maxCirc=1.00)
# Settings for channel4 threshold.
c4 = countobjects(channels[3],
c4Results,
threshMethod="RenyiEntropy",
subtractBackground=True,
watershed=False,
minSize=0.20,
maxSize=100.00,
minCirc=0.00,
maxCirc=1.00)
# Format filenames for thresholded .tiff files.
outfileC1 = os.path.join(c1dir, "threshold_c1_{}".format(name))
outfileC2 = os.path.join(c2dir, "threshold_c2_{}".format(name))
outfileC3 = os.path.join(c3dir, "threshold_c3_{}".format(name))
outfileC4 = os.path.join(c4dir, "threshold_c4_{}".format(name))
# Save thresholded .tiff files.
IJ.saveAs(c1.flatten(), "Tiff", outfileC1)
IJ.saveAs(c2.flatten(), "Tiff", outfileC2)
IJ.saveAs(c3.flatten(), "Tiff", outfileC3)
IJ.saveAs(c4.flatten(), "Tiff", outfileC4)
# Show results tables.
# c1Results.show("channel1")
# c2Results.show("channel2")
# c3Results.show("channel3")
# c4Results.show("channel4")
# Prepare results table filenames.
c1out = os.path.join(outdir, "channel1.csv")
c2out = os.path.join(outdir, "channel2.csv")
c3out = os.path.join(outdir, "channel3.csv")
c4out = os.path.join(outdir, "channel4.csv")
# Save results tables.
ResultsTable.save(c1Results, c1out)
ResultsTable.save(c2Results, c2out)
ResultsTable.save(c3Results, c3out)
ResultsTable.save(c4Results, c4out)
# Example 1: with ImageCalculator
from ij.process import ImageProcessor
from ij.plugin import ChannelSplitter, ImageCalculator
# Split color channels
red, green, blue = ChannelSplitter().split(imp_rgb) # 3 ImagePlus
# Set threshold for each slice
for index in xrange(1, red.getNSlices() + 1):
bp = red.getStack().getProcessor(index)
#bp.setThreshold(threshold, 255, ImageProcessor.BLACK_AND_WHITE_LUT)
bp.threshold(threshold) # mask is 0, background is 255
# Apply threshold: convert each slice to a mask (only 0 or 255 pixel values)
#IJ.run(red, "Convert to Mask", "method=Default background=Dark black")
red.show()
green_under_red_mask = ImageCalculator().run("and create stack", red, green)
green_under_red_mask.show()
"""
# Example 2: with ImgLib2 LoopBuilder
from net.imglib2.img.display.imagej import ImageJFunctions as IL
from net.imglib2.converter import Converters
from net.imglib2.img.array import ArrayImgs
from net.imglib2.util import Intervals
from net.imglib2.loops import LoopBuilder
img = IL.wrap(imp_rgb) # an ARGBType Img
red = Converters.argbChannel(img, 1) # a view of the ARGB red channel
green = Converters.argbChannel(img, 2) # a view of the ARGB green channel
img_sub = ArrayImgs.unsignedBytes(Intervals.dimensionsAsLongArray(img)) # the img to store the result
#imp.getCalibration().getUnit() # should return 'micron'
#imp.getCalibration() # DBG
if crop_image:
imp.setRoi(int(xLoc-math.floor(winCrop/2)),int(yLoc-math.floor(winCrop/2)),winCrop,winCrop) # Create a ROI and crop it.
else:
imp.setRoi(0,0,imp.width,imp.height) # Create a ROI and crop it.
crop = imp.crop("stack")
ij.process.ImageConverter(crop).convertToGray32()
tmp1 = Duplicator().run(crop, chDNA, chDNA, 1, crop.getNSlices(), 1, crop.getNFrames()) #Stack.setChannel(chDNA); run("Duplicate...", "channels=2 title=cropDNA duplicate");
tmp2 = Duplicator().run(crop, chDNA, chDNA, 1, crop.getNSlices(), 1, crop.getNFrames()) #run("Duplicate...", "title=tmp2 duplicate");
# === Band pass filtering
IJ.run(tmp1, "Gaussian Blur...", "sigma=%f stack" % sd1) #selectWindow("tmp1"); run("Gaussian Blur...", "sigma="+sd1+" stack");
IJ.run(tmp2, "Gaussian Blur...", "sigma=%f stack" % sd2) #selectWindow("tmp2"); run("Gaussian Blur...", "sigma="+sd2+" stack");
cropDNA_bPass = ImageCalculator().run("Subtract create 32-bit stack", tmp1, tmp2) #imageCalculator("Subtract create 32-bit stack", "tmp1","tmp2"); rename("cropDNA_bPass");
tmp1.close()
tmp2.close()
# === Create stack to be processed
# From https://stackoverflow.com/questions/48213759/combine-channels-in-imagej-jython
crop_channels = ij.plugin.ChannelSplitter.split(crop) # selectWindow("crop"); run("Split Channels");
if len(crop_channels)>=2:
res_st = ij.plugin.RGBStackMerge().mergeHyperstacks([cropDNA_bPass, ]+[c for c in crop_channels], True) #run("Merge Channels...", "c1=cropDNA_bPass c2=C1-crop c3=C2-crop create")
else :
IJ.log("NOT IMPLEMENTED ERROR")
raise NotImplementedError
# === 3D drift correction (we are working with res_st as input image)
res_st.setRoi(int(winCrop/2)-int(winDrift/2), int(winCrop/2)-int(winDrift/2), winDrift, winDrift)
if run_DC:
