def main():
Interpreter.batchMode = True
if (lambda_flat == 0) ^ (lambda_dark == 0):
print ("ERROR: Both of lambda_flat and lambda_dark must be zero,"
" or both non-zero.")
return
lambda_estimate = "Automatic" if lambda_flat == 0 else "Manual"
print "Loading images..."
options = ImporterOptions()
options.setId(str(filename))
options.setOpenAllSeries(True)
options.setConcatenate(True)
options.setSplitChannels(True)
imps = BF.openImagePlus(options)
num_channels = len(imps)
w = imps[0].getWidth()
h = imps[0].getHeight()
ff_imp = IJ.createImage("Flat-field", w, h, num_channels, 32);
df_imp = IJ.createImage("Dark-field", w, h, num_channels, 32);
basic = Basic()
Basic_noOfSlices = Basic.getDeclaredField('noOfSlices')
Basic_noOfSlices.setAccessible(True)
for channel, imp in enumerate(imps):
title = imp.getTitle()
print "Processing:", title
x, y, c, z, t = imp.getDimensions()
assert z == 1 and c == 1
imp.setDimensions(1, t, 1)
WindowManager.setTempCurrentImage(imp)
Basic_noOfSlices.setInt(basic, t)
basic.exec(
imp, None, None,
"Estimate shading profiles", "Estimate both flat-field and dark-field",
lambda_estimate, lambda_flat, lambda_dark,
"Ignore", "Compute shading only"
)
ff_channel = WindowManager.getImage('Flat-field:' + title)
ff_channel.copy()
ff_imp.setSlice(channel + 1)
ff_imp.paste()
ff_channel.close()
df_channel = WindowManager.getImage('Dark-field:' + title)
df_channel.copy()
df_imp.setSlice(channel + 1)
df_imp.paste()
df_channel.close()
imp.close()
# Setting the active slice back to 1 seems to fix an issue where
# the last slice was empty in the saved TIFFs. Not sure why.
ff_imp.setSlice(1)
df_imp.setSlice(1)
ff_filename = '%s/%s-ffp-basic.tif' % (output_dir, experiment_name)
IJ.saveAsTiff(ff_imp, ff_filename)
ff_imp.show()
ff_imp.close()
df_filename = '%s/%s-dfp-basic.tif' % (output_dir, experiment_name)
IJ.saveAsTiff(df_imp, df_filename)
df_imp.show()
df_imp.close()
print "Done!"
def readCZI(imagefile,
metainfo,
stitchtiles=False,
setflatres=False,
readpylevel=0,
setconcat=False,
openallseries=True,
showomexml=False,
attach=False,
autoscale=True):
options = DynamicMetadataOptions()
options.setBoolean("zeissczi.autostitch", stitchtiles)
options.setBoolean("zeissczi.attachments", attach)
czireader = ZeissCZIReader()
czireader.setFlattenedResolutions(setflatres)
czireader.setMetadataOptions(options)
czireader.setId(imagefile)
# Set the preferences in the ImageJ plugin
# Note although these preferences are applied, they are not refreshed in the UI
Prefs.set("bioformats.zeissczi.allow.autostitch", str(stitchtiles).lower())
Prefs.set("bioformats.zeissczi.include.attachments", str(attach).lower())
# metainfo = {}
metainfo['rescount'] = czireader.getResolutionCount()
metainfo['SeriesCount_CZI'] = czireader.getSeriesCount()
metainfo['flatres'] = czireader.hasFlattenedResolutions()
# metainfo['getreslevel'] = czireader.getResolution()
# Dimensions
metainfo['SizeT'] = czireader.getSizeT()
metainfo['SizeZ'] = czireader.getSizeZ()
metainfo['SizeC'] = czireader.getSizeC()
metainfo['SizeX'] = czireader.getSizeX()
metainfo['SizeY'] = czireader.getSizeY()
# check for autostitching and possibility to read attachment
metainfo['AllowAutoStitching'] = czireader.allowAutostitching()
metainfo['CanReadAttachments'] = czireader.canReadAttachments()
# read in and display ImagePlus(es) with arguments
options = ImporterOptions()
options.setOpenAllSeries(openallseries)
options.setShowOMEXML(showomexml)
options.setConcatenate(setconcat)
options.setAutoscale(autoscale)
options.setId(imagefile)
# open the ImgPlus
imps = BF.openImagePlus(options)
metainfo['Pyramid Level Output'] = readpylevel
# read image data using the specified pyramid level
imp, slices, width, height, pylevel = ImageTools.getImageSeries(imps, series=readpylevel)
metainfo['Pyramid Level Output'] = pylevel
metainfo['Output Slices'] = slices
metainfo['Output SizeX'] = width
metainfo['Output SizeY'] = height
# calc scaling in case of pyramid
# scale = float(metainfo['Output SizeX']) / float(metainfo['SizeX'])
scale = float(metainfo['SizeX']) / float(metainfo['Output SizeX'])
metainfo['Pyramid Scale Factor'] = scale
metainfo['ScaleX Output'] = metainfo['ScaleX'] * scale
metainfo['ScaleY Output'] = metainfo['ScaleY'] * scale
"""
imp = MiscTools.setproperties(imp, scaleX=metainfo['ScaleX Output'],
scaleY=metainfo['ScaleX Output'],
scaleZ=metainfo['ScaleZ'],
unit="micron",
sizeC=metainfo['SizeC'],
sizeZ=metainfo['SizeZ'],
sizeT=metainfo['SizeT'])
"""
imp = MiscTools.setscale(imp, scaleX=metainfo['ScaleX Output'],
scaleY=metainfo['ScaleX Output'],
scaleZ=metainfo['ScaleZ'],
unit="micron")
# close czireader
czireader.close()
return imp, metainfo
def process_time_points(root, files, outdir):
'''Concatenate images and write ome.tiff file. If image contains already multiple time points just copy the image'''
concat = 1
files.sort()
options = ImporterOptions()
options.setId(files[0])
options.setVirtual(1)
image = BF.openImagePlus(options)
image = image[0]
if image.getNFrames() > 1:
IJ.log(files[0] + " Contains multiple time points. Can only concatenate single time points! Don't do anything!")
image.close()
return
width = image.getWidth()
height = image.getHeight()
for patt in pattern:
outName = re.match(patt, os.path.basename(files[0]))
if outName is None:
continue
if outdir is None:
outfile = os.path.join(root, outName.group(1) + '.ome.tif')
else:
outfile = os.path.join(outdir, outName.group(1) + '.ome.tif')
reader = ImageReader()
reader.setMetadataStore(MetadataTools.createOMEXMLMetadata())
reader.setId(files[0])
timeInfo = []
omeOut = reader.getMetadataStore()
omeOut = setUpXml(omeOut, image, files)
reader.close()
image.close()
IJ.log ('Concatenates ' + os.path.join(root, outName.group(1) + '.ome.tif'))
itime = 0
try:
for ifile, fileName in enumerate(files):
print fileName
omeMeta = MetadataTools.createOMEXMLMetadata()
reader.setMetadataStore(omeMeta)
reader.setId(fileName)
#print omeMeta.getPlaneDeltaT(0,0)
#print omeMeta.getPixelsTimeIncrement(0)
if fileName.endswith('.czi'):
if ifile == 0:
T0 = omeMeta.getPlaneDeltaT(0,0).value()
dT = omeMeta.getPlaneDeltaT(0,0).value() - T0
unit = omeMeta.getPlaneDeltaT(0,0).unit()
else:
timeInfo.append(getTimePoint(reader, omeMeta))
unit = omeMeta.getPixelsTimeIncrement(0).unit()
try:
dT = round(timeInfo[files.index(fileName)]-timeInfo[0],2)
except:
dT = (timeInfo[files.index(fileName)]-timeInfo[0]).seconds
nrImages = reader.getImageCount()
for i in range(0, reader.getImageCount()):
try:
omeOut.setPlaneDeltaT(dT, 0, i + itime*nrImages)
except TypeError:
omeOut.setPlaneDeltaT(Time(dT, unit),0, i + itime*nrImages)
omeOut.setPlanePositionX(omeOut.getPlanePositionX(0,i), 0, i + itime*nrImages)
omeOut.setPlanePositionY(omeOut.getPlanePositionY(0,i), 0, i + itime*nrImages)
omeOut.setPlanePositionZ(omeOut.getPlanePositionZ(0,i), 0, i + itime*nrImages)
omeOut.setPlaneTheC(omeOut.getPlaneTheC(0,i), 0, i + itime*nrImages)
omeOut.setPlaneTheT(NonNegativeInteger(itime), 0, i + itime*nrImages)
omeOut.setPlaneTheZ(omeOut.getPlaneTheZ(0,i), 0, i + itime*nrImages)
itime = itime + 1
reader.close()
IJ.showProgress(files.index(fileName), len(files))
try:
incr = float(dT/(len(files)-1))
except:
incr = 0
try:
omeOut.setPixelsTimeIncrement(incr, 0)
except TypeError:
#new Bioformats >5.1.x
omeOut.setPixelsTimeIncrement(Time(incr, unit),0)
outfile = concatenateImagePlus(files, outfile)
if outfile is not None:
filein = RandomAccessInputStream(outfile)
fileout = RandomAccessOutputStream(outfile)
saver = TiffSaver(fileout, outfile)
saver.overwriteComment(filein,omeOut.dumpXML())
fileout.close()
filein.close()
except:
traceback.print_exc()
finally:
#close all possible open files
try:
reader.close()
except:
pass
try:
filein.close()
except:
pass
try:
fileout.close()
except:
def readczi(imagefile,
stitchtiles=True,
setflatres=False,
readpylevel=0,
setconcat=True,
openallseries=True,
showomexml=False,
attach=False,
autoscale=True):
log.log(LogLevel.INFO, 'Filename : ' + imagefile)
metainfo = {}
# checking for thr file Extension
metainfo['Extension'] = MiscTools.getextension(MiscTools.splitext_recurse(imagefile))
log.log(LogLevel.INFO, 'Detected File Extension : ' + metainfo['Extension'])
# initialize the reader and get the OME metadata
reader = ImageReader()
omeMeta = MetadataTools.createOMEXMLMetadata()
#metainfo['ImageCount_OME'] = omeMeta.getImageCount()
reader.setMetadataStore(omeMeta)
reader.setId(imagefile)
metainfo['SeriesCount_BF'] = reader.getSeriesCount()
reader.close()
# get the scaling for XYZ
physSizeX = omeMeta.getPixelsPhysicalSizeX(0)
physSizeY = omeMeta.getPixelsPhysicalSizeY(0)
physSizeZ = omeMeta.getPixelsPhysicalSizeZ(0)
if physSizeX is not None:
metainfo['ScaleX'] = round(physSizeX.value(), 3)
metainfo['ScaleY'] = round(physSizeX.value(), 3)
if physSizeX is None:
metainfo['ScaleX'] = None
metainfo['ScaleY'] = None
if physSizeZ is not None:
metainfo['ScaleZ'] = round(physSizeZ.value(), 3)
if physSizeZ is None:
metainfo['ScaleZ'] = None
options = DynamicMetadataOptions()
options.setBoolean("zeissczi.autostitch", stitchtiles)
options.setBoolean("zeissczi.attachments", attach)
czireader = ZeissCZIReader()
czireader.setFlattenedResolutions(setflatres)
czireader.setMetadataOptions(options)
czireader.setId(imagefile)
# Set the preferences in the ImageJ plugin
# Note although these preferences are applied, they are not refreshed in the UI
Prefs.set("bioformats.zeissczi.allow.autostitch", str(stitchtiles).lower())
Prefs.set("bioformats.zeissczi.include.attachments", str(attach).lower())
# metainfo = {}
metainfo['rescount'] = czireader.getResolutionCount()
metainfo['SeriesCount_CZI'] = czireader.getSeriesCount()
#metainfo['flatres'] = czireader.hasFlattenedResolutions()
#metainfo['getreslevel'] = czireader.getResolution()
# Dimensions
metainfo['SizeT'] = czireader.getSizeT()
metainfo['SizeZ'] = czireader.getSizeZ()
metainfo['SizeC'] = czireader.getSizeC()
metainfo['SizeX'] = czireader.getSizeX()
metainfo['SizeY'] = czireader.getSizeY()
# check for autostitching and possibility to read attachment
metainfo['AllowAutoStitching'] = czireader.allowAutostitching()
metainfo['CanReadAttachments'] = czireader.canReadAttachments()
# read in and display ImagePlus(es) with arguments
options = ImporterOptions()
options.setOpenAllSeries(openallseries)
options.setShowOMEXML(showomexml)
options.setConcatenate(setconcat)
options.setAutoscale(autoscale)
options.setId(imagefile)
# open the ImgPlus
imps = BF.openImagePlus(options)
metainfo['Pyramid Level Output'] = readpylevel + 1
try:
imp = imps[readpylevel]
pylevelout = metainfo['SeriesCount_CZI']
except:
# fallback option
log.log(LogLevel.INFO, 'PyLevel=' + str(readpylevel) + ' does not exist.')
log.log(LogLevel.INFO, 'Using Pyramid Level = 0 as fallback.')
imp = imps[0]
pylevelout = 0
metainfo['Pyramid Level Output'] = pylevelout
# get the stack and some info
imgstack = imp.getImageStack()
metainfo['Output Slices'] = imgstack.getSize()
metainfo['Output SizeX'] = imgstack.getWidth()
metainfo['Output SizeY'] = imgstack.getHeight()
# calc scaling in case of pyramid
scale = float(metainfo['SizeX']) / float(metainfo['Output SizeX'])
metainfo['Pyramid Scale Factor'] = scale
metainfo['ScaleX Output'] = metainfo['ScaleX'] * scale
metainfo['ScaleY Output'] = metainfo['ScaleY'] * scale
# set the correct scaling
imp = MiscTools.setscale(imp, scaleX=metainfo['ScaleX Output'],
scaleY=metainfo['ScaleX Output'],
scaleZ=metainfo['ScaleZ'],
unit="micron")
# close czireader
czireader.close()
return imp, metainfo
# using the 'triangle' algorithm. It then uses the threshold to calculate the mean intensity above the
# threshold.
#
# First version : 2017.09.14
from loci.plugins import BF
from loci.formats import ImageReader
from loci.plugins.in import ImporterOptions
from ij.plugin import ZProjector
from ij.process import AutoThresholder,ImageStatistics
from math import isnan
from ij import IJ
# Open all files in the series
options = ImporterOptions()
options.setOpenAllSeries(True)
options.setId(file.absolutePath)
options.setQuiet(True)
imps = BF.openImagePlus(options)
# Initialize XML and arrays where to store the values
xml = '<?xml version="1.0" encoding="utf-8"?><DyeData>'
profiles=[]
names=[]
for idye_,imp in enumerate(imps):
idye = idye_+1
# Assumes names are FILENAME+" - "+SERIESNAME
names.append(imp.title.split(" - ")[1])
def bfopenall(path): options = ImporterOptions(); options.setId(path); options.setOpenAllSeries(True); return BF.openImagePlus(options);
def imageprojector(channels, timelist_unsorted, dirs):
""" Projects .lif timepoints and saves in a common directory,
as well as channel separated directories. """
# Defines in path
path = str(Experiment)
# BF Importer
options = ImporterOptions()
try:
options.setId(path)
except Exception(e):
print str(e)
options.setOpenAllSeries(True)
options.setSplitTimepoints(True)
options.setSplitChannels(True)
imps = BF.openImagePlus(options)
timelist = [x for item in timelist_unsorted for x in repeat(item, channels)]
timelist, imps = zip(*sorted(zip(timelist, imps)))
counter_C0 = -1
counter_C1 = -1
counter_C2 = -1
# Opens all images, splits channels, z-projects and saves to disk
for imp in (imps):
# Projection, Sum Intensity
project = ZProjector()
project.setMethod(ZProjector.SUM_METHOD)
project.setImage(imp)
project.doProjection()
impout = project.getProjection()
projection = impout.getTitle()
try:
# Saves channels to disk,
# add more channels here if desired,
# remember to define new counters.
if "C=0" in projection:
counter_C0 += 1
IJ.saveAs(impout, "TIFF", os.path.join(dirs["Projections"],
"Scan" + str(counter_C0).zfill(3) + "C0"))
IJ.saveAs(impout, "TIFF", os.path.join(dirs["Projections_C0"],
"Scan" + str(counter_C0).zfill(3) + "C0"))
elif "C=1" in projection:
counter_C1 += 1
IJ.saveAs(impout, "TIFF", os.path.join(dirs["Projections"],
"Scan" + str(counter_C1).zfill(3) + "C1"))
IJ.saveAs(impout, "TIFF", os.path.join(dirs["Projections_C1"],
"Scan" + str(counter_C1).zfill(3) + "C1"))
elif "C=2" in projection:
counter_C2 += 1
IJ.saveAs(impout, "TIFF", os.path.join(dirs["Projections"],
"Scan" + str(counter_C2).zfill(3) + "C2"))
IJ.saveAs(impout, "TIFF", os.path.join(dirs["Projections_C2"],
"Scan" + str(counter_C2).zfill(3) + "C2"))
except IOException:
print "Directory does not exist"
raise
IJ.log("Images projected and saved to disk")
def getCZIinfo(imagefile, showimage=False, setreslevel=0, setflat2=False, openallseries=True, showomexml=False,setconcat=False,filepath1="./"):
options = DynamicMetadataOptions()
options.setBoolean("zeissczi.attachments", False)
czireader = ZeissCZIReader()
czireader.setFlattenedResolutions(setflat2)
czireader.setMetadataOptions(options)
czireader.setId(imagefile)
lc = czireader.getSeriesCount()
#get the first occurence of each pyramid stack
location=list()
for i in range(0, int(seriesCount)-2):
location.append(czireader.coreIndexToSeries(i))
c=0
#log.info(location)
loc2=list()
for i,v in enumerate(location):
if i==0:
loc2.append(i)
elif i>0 and v!=c:
loc2.append(i)
c=v
log.info(str(loc2))
# get OME data
omeMeta = MetadataTools.createOMEXMLMetadata()
# Set the preferences in the ImageJ plugin
Prefs.set("bioformats.zeissczi.include.attachments", str(True).lower())
if showimage:
# read in and display ImagePlus(es) with arguments
options = ImporterOptions()
options.setOpenAllSeries(openallseries)
options.setShowOMEXML(showomexml)
options.setConcatenate(setconcat)
options.setId(imagefile)
# open the ImgPlus
imps = BF.openImagePlus(options)
name_list=imagefile.split('/')
name=name_list[len(name_list)-1]
out_path=filepath1 + "/"+name+"_Preview.tif"
log.info(name)
imp=getImageSeries(imps, seriesCount-1)
imp.show()
IJ.run("RGB Color")
imp.close()
IJ.saveAs("tiff", out_path)
IJ.run("Close")
out_path=filepath1 + "/"+name+"_Label.tif"
imp=getImageSeries(imps, (seriesCount-2))
imp.show()
IJ.run("RGB Color")
imp.close()
IJ.saveAs("tiff", out_path)
IJ.run("Close")
c=1
for series in loc2:
out_path=filepath1 + "/"+name+"Scene_" + str(c) + ".tif"
imp=getImageSeries(imps, series)
imp.show()
IJ.run("RGB Color")
imp.close()
IJ.saveAs("tiff", out_path)
IJ.run("Close")
c+=1
czireader.close()
def main():
Interpreter.batchMode = True
if (lambda_flat == 0) ^ (lambda_dark == 0):
print ("ERROR: Both of lambda_flat and lambda_dark must be zero,"
" or both non-zero.")
return
lambda_estimate = "Automatic" if lambda_flat == 0 else "Manual"
print "Loading images..."
# For multi-scene .CZI files, we need raw tiles instead of the
# auto-stitched mosaic and we don't want labels or overview images. This
# only affects BF.openImagePlus, not direct use of the BioFormats reader
# classes which we also do (see below)
Prefs.set("bioformats.zeissczi.allow.autostitch", "false")
Prefs.set("bioformats.zeissczi.include.attachments", "false")
# Use BioFormats reader directly to determine dataset dimensions without
# reading every single image. The series count (num_images) is the one value
# we can't easily get any other way, but we might as well grab the others
# while we have the reader available.
dyn_options = DynamicMetadataOptions()
# Directly calling a BioFormats reader will not use the IJ Prefs settings
# so we need to pass these options explicitly.
dyn_options.setBoolean("zeissczi.autostitch", False)
dyn_options.setBoolean("zeissczi.attachments", False)
bfreader = ImageReader()
bfreader.setMetadataOptions(dyn_options)
bfreader.id = str(filename)
num_images = bfreader.seriesCount
num_channels = bfreader.sizeC
width = bfreader.sizeX
height = bfreader.sizeY
bfreader.close()
# The internal initialization of the BaSiC code fails when we invoke it via
# scripting, unless we explicitly set a the private 'noOfSlices' field.
# Since it's private, we need to use Java reflection to access it.
Basic_noOfSlices = Basic.getDeclaredField('noOfSlices')
Basic_noOfSlices.setAccessible(True)
basic = Basic()
Basic_noOfSlices.setInt(basic, num_images)
# Pre-allocate the output profile images, since we have all the dimensions.
ff_image = IJ.createImage("Flat-field", width, height, num_channels, 32);
df_image = IJ.createImage("Dark-field", width, height, num_channels, 32);
print("\n\n")
# BaSiC works on one channel at a time, so we only read the images from one
# channel at a time to limit memory usage.
for channel in range(num_channels):
print "Processing channel %d/%d..." % (channel + 1, num_channels)
print "==========================="
options = ImporterOptions()
options.id = str(filename)
options.setOpenAllSeries(True)
# concatenate=True gives us a single stack rather than a list of
# separate images.
options.setConcatenate(True)
# Limit the reader to the channel we're currently working on. This loop
# is mainly why we need to know num_images before opening anything.
for i in range(num_images):
options.setCBegin(i, channel)
options.setCEnd(i, channel)
# openImagePlus returns a list of images, but we expect just one (a
# stack).
input_image = BF.openImagePlus(options)[0]
# BaSiC seems to require the input image is actually the ImageJ
# "current" image, otherwise it prints an error and aborts.
WindowManager.setTempCurrentImage(input_image)
basic.exec(
input_image, None, None,
"Estimate shading profiles", "Estimate both flat-field and dark-field",
lambda_estimate, lambda_flat, lambda_dark,
"Ignore", "Compute shading only"
)
input_image.close()
# Copy the pixels from the BaSiC-generated profile images to the
# corresponding channel of our output images.
ff_channel = WindowManager.getImage("Flat-field:%s" % input_image.title)
ff_image.slice = channel + 1
ff_image.getProcessor().insert(ff_channel.getProcessor(), 0, 0)
ff_channel.close()
df_channel = WindowManager.getImage("Dark-field:%s" % input_image.title)
df_image.slice = channel + 1
df_image.getProcessor().insert(df_channel.getProcessor(), 0, 0)
df_channel.close()
print("\n\n")
template = '%s/%s-%%s.tif' % (output_dir, experiment_name)
ff_filename = template % 'ffp'
IJ.saveAsTiff(ff_image, ff_filename)
ff_image.close()
df_filename = template % 'dfp'
IJ.saveAsTiff(df_image, df_filename)
df_image.close()
print "Done!"
