def DirList(baseDir):
r = ImageReader()
imgStats = {}
for root, dirs, files in os.walk(str(baseDir)):
for f1 in files:
if f1.endswith(".jpg") or f1.endswith(".jpe") or f1.endswith(
".jpeg"):
id = root + "/" + f1
r.setId(id)
if r is None:
print "Couldn\'t open image from file:", id
continue
w = r.getSizeX()
h = r.getSizeY()
imgStats[str(w) + "_" +
str(h)] = imgStats.get(str(w) + "_" + str(h), 0) + 1
IJ.log("Found image: " + str(id))
#counter += 1
r.close()
#print summary
summary = ''
for k, v in imgStats.iteritems():
dim = k.split("_")
ratio = float(dim[0]) / float(dim[1])
IJ.log("Found " + str(v) + " images of dimension " + str(dim[0]) +
"x" + str(dim[1]) + " apect ratio " + str(round(ratio, 2)))
summary = summary + "\nFound " + str(
v) + " images of dimension " + str(dim[0]) + "x" + str(
dim[1]) + " apect ratio " + str(round(ratio, 2))
return summary
def get_calibration_from_metadata(path_to_image):
"""get the pixel calibration from a given image using Bio-Formats
Parameters
----------
path_to_image : str
full path to the input image
Returns
-------
array
the physical px size as float for x,y,z
"""
reader = ImageReader()
omeMeta = MetadataTools.createOMEXMLMetadata()
reader.setMetadataStore(omeMeta)
reader.setId(str(path_to_image))
physSizeX = omeMeta.getPixelsPhysicalSizeX(0)
physSizeY = omeMeta.getPixelsPhysicalSizeY(0)
physSizeZ = omeMeta.getPixelsPhysicalSizeZ(0)
image_calibration = [physSizeX.value(), physSizeY.value(), physSizeZ.value()]
reader.close()
return image_calibration
def DirList(baseDir):
r = ImageReader()
imgStats = {}
for root, dirs, files in os.walk(str(baseDir)):
for f1 in files:
if f1.endswith(".jpg") or f1.endswith(".jpe") or f1.endswith(".jpeg"):
id = root + "/" + f1
r.setId(id)
if r is None:
print "Couldn\'t open image from file:", id
continue
w = r.getSizeX()
h = r.getSizeY()
imgStats[str(w) + "_" + str(h)] = imgStats.get(str(w) + "_" + str(h), 0)+1
IJ.log("Found image: " + str(id))
#counter += 1
r.close()
#print summary
summary = ''
for k, v in imgStats.iteritems():
dim = k.split("_")
ratio = float(dim[0])/float(dim[1])
IJ.log("Found " + str(v) + " images of dimension " + str(dim[0]) + "x" + str(dim[1]) + " apect ratio " + str(round(ratio, 2)))
summary = summary + "\nFound " + str(v) + " images of dimension " + str(dim[0]) + "x" + str(dim[1]) + " apect ratio " + str(round(ratio, 2))
return summary
def Z1_metadata(sourcefile):
# Access header of Z1 lighsheet data to determine nb views
reader = ImageReader()
omeMeta = MetadataTools.createOMEXMLMetadata()
reader.setMetadataStore(omeMeta)
reader.setId(sourcefile)
seriesCount = reader.getSeriesCount()
reader.close()
return seriesCount
def get_metadata(imagefile, imageID=0):
metainfo = {}
# 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()
# read dimensions TZCXY from OME metadata
metainfo['SizeT'] = omeMeta.getPixelsSizeT(imageID).getValue()
metainfo['SizeZ'] = omeMeta.getPixelsSizeZ(imageID).getValue()
metainfo['SizeC'] = omeMeta.getPixelsSizeC(imageID).getValue()
metainfo['SizeX'] = omeMeta.getPixelsSizeX(imageID).getValue()
metainfo['SizeY'] = omeMeta.getPixelsSizeY(imageID).getValue()
# store info about stack
if metainfo['SizeZ'] == 1:
metainfo['is3d'] = False
elif metainfo['SizeZ'] > 1:
metainfo['is3d'] = True
# 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(physSizeY.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
# sort the dictionary
metainfo = OrderedDict(sorted(metainfo.items()))
return metainfo
def get_metadata(params):
"""get image metadata, either from the image file or from acquisition-time metadata"""
if params.metadata_source == "Image metadata":
try:
reader = ImageReader()
ome_meta = MetadataTools.createOMEXMLMetadata()
reader.setMetadataStore(ome_meta)
reader.setId(params.input_image_path)
reader.close()
params.setFrameInterval(
ome_meta.getPixelsTimeIncrement(0).value())
params.setIntervalUnit(
ome_meta.getPixelsTimeIncrement(0).unit().getSymbol())
params.setPixelPhysicalSize(
ome_meta.getPixelsPhysicalSizeX(0).value())
params.setPixelSizeUnit(
ome_meta.getPixelsPhysicalSizeX(0).unit().getSymbol())
params.setMetadataSourceFile(None)
except Exception as e:
print(e.message)
mbui.warning_dialog([
"There was a problem getting metadata from the image: ",
e.message,
"Please consider using acquisition metadata instead (click OK). ",
"Or, quit the analysis run and investigate image metadata by hand. "
])
params.setMetadataSource("Acquisition metadata")
if params.metadata_source == "Acquisition metadata":
od = OpenDialog('Choose acquisition metadata file...',
os.path.dirname(params.input_image_path), '*.txt')
file_path = od.getPath()
if file_path is None:
raise IOError('no metadata file chosen')
acq_metadata_dict = import_iq3_metadata(file_path)
try:
params.setFrameInterval(acq_metadata_dict['frame_interval'])
except KeyError:
params.setFrameInterval(1.0)
try:
params.setIntervalUnit(acq_metadata_dict['time_unit'])
except KeyError:
params.setIntervalUnit('frames')
params.setPixelPhysicalSize(acq_metadata_dict['x_physical_size'])
params.setPixelSizeUnit(acq_metadata_dict['x_unit'])
params.setMetadataSourceFile(file_path)
return params
def load_ome_img(file_name):
"""
:param file_name:
:return:
"""
imps = BF.openImagePlus(file_name)
imag = imps[0]
# parse metadata
reader = ImageReader()
omeMeta = MetadataTools.createOMEXMLMetadata()
reader.setMetadataStore(omeMeta)
reader.setId(file_name)
print(omeMeta)
reader.close()
return (imag, omeMeta)
def time_parser():
""" Iterates through timelapse, """
""" outputs timepoints with corresponding seriesnames. """
""" - S. Grødem 2017 """
# Get metadata.
reader = ImageReader()
omeMeta = MetadataTools.createOMEXMLMetadata()
reader.setMetadataStore(omeMeta)
reader.setId(str(Experiment))
# Extracts number of image series, channel number
seriesCount = reader.getSeriesCount()
reader.close()
# Gets timepoints, in minutes.
timelist = []
namelist = []
for timepoint in range (seriesCount):
times = omeMeta.getImageAcquisitionDate(timepoint)
timelist.append(times.toString())
namelist.append(omeMeta.getImageName(timepoint))
# YY.MM... to minutes.
timelist =[ time.mktime(time.strptime(times, u'%Y-%m-%dT%H:%M:%S')) for times in timelist ]
timelist_unsorted =[ (times - timelist[0])/60 for times in timelist ]
# Sort timepoints.
timelist, namelist = zip(*sorted(zip(timelist_unsorted, namelist)))
timelist = [round(float(i), 3) for i in timelist]
# Output to IJ log
images = zip(timelist, namelist)
IJ.log("Series number: " + str(seriesCount))
IJ.log("*"*15)
for i in range(len(images)):
IJ.log("Name: " + str(images[i][1]))
IJ.log("Time: " + str(images[i][0]))
IJ.log("-"*15)
def choose_series(filepath, params):
"""if input file contains more than one image series (xy position), prompt user to choose which one to use"""
# todo: if necessary (e.g. if lots of series), can improve thumbnail visuals based loosely on https://github.com/ome/bio-formats-imagej/blob/master/src/main/java/loci/plugins/in/SeriesDialog.java
import_opts = ImporterOptions();
import_opts.setId(filepath);
reader = ImageReader();
ome_meta = MetadataTools.createOMEXMLMetadata();
reader.setMetadataStore(ome_meta);
reader.setId(filepath);
no_series = reader.getSeriesCount();
if no_series == 1:
return import_opts, params;
else:
series_names = [ome_meta.getImageName(idx) for idx in range(no_series)];
dialog = GenericDialog("Select series to load...");
dialog.addMessage("There are multiple series in this file! \n" +
"This is probably because there are multiple XY stage positions. \n " +
"Please choose which series to load: ");
thumbreader = BufferedImageReader(reader);
cbg = CheckboxGroup();
for idx in range(no_series):
p = Panel();
p.add(Box.createRigidArea(Dimension(thumbreader.getThumbSizeX(), thumbreader.getThumbSizeY())));
ThumbLoader.loadThumb(thumbreader, idx, p, True);
dialog.addPanel(p);
cb = Checkbox(series_names[idx], cbg, idx==0);
p.add(cb);
dialog.showDialog();
if dialog.wasCanceled():
raise KeyboardInterrupt("Run canceled");
if dialog.wasOKed():
selected_item = cbg.getSelectedCheckbox().getLabel();
selected_index = series_names.index(selected_item);
params.setSelectedSeriesIndex(selected_index);
for idx in range(0, no_series):
import_opts.setSeriesOn(idx, True) if (idx==selected_index) else import_opts.setSeriesOn(idx, False);
reader.close();
return import_opts, params
def processMovie(root, files, outfile):
"""Concatenate images and write ome.tiff file.
If image contains already multiple time points just copy the image"""
files.sort()
options = ImporterOptions()
options.setId(files[0])
options.setVirtual(1)
image = BF.openImagePlus(options)
image = image[0]
if image.getNFrames() > 1:
msg = ("%s Contains multiple time points. Can only concatenate"
" single time points!" %files[0])
raise RuntimeError(msg)
image.close()
reader = ImageReader()
reader.setMetadataStore(MetadataTools.createOMEXMLMetadata())
reader.setId(files[0])
timeInfo = []
omeOut = reader.getMetadataStore()
omeOut = setUpXml(omeOut, image, files)
reader.close()
image.close()
itime = 0
for fileName in files:
omeMeta = MetadataTools.createOMEXMLMetadata()
reader.setMetadataStore(omeMeta)
reader.setId(fileName)
timeInfo.append(getTimePoint(reader, omeMeta))
nrImages = reader.getImageCount()
for i in range(0, reader.getImageCount()):
try:
dT = round(timeInfo[files.index(fileName)]-timeInfo[0],2)
except:
dT = (timeInfo[files.index(fileName)]-timeInfo[0]).seconds
omeOut.setPlaneDeltaT(dT, 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(omeOut.getPlaneTheT(0,i), 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:
omeOut.setPixelsTimeIncrement(float(dT/(len(files)-1)), 0)
except:
omeOut.setPixelsTimeIncrement(0, 0)
if len(files) <= 1:
raise RuntimeError('Found only one file. Nothing to concatenate')
outfile = concatenateImagePlus(files, outfile)
filein = RandomAccessInputStream(outfile)
fileout = RandomAccessOutputStream(outfile)
saver = TiffSaver(fileout, outfile)
saver.overwriteComment(filein, omeOut.dumpXML())
fileout.close()
filein.close()
nYTiles = nYTiles + 1
for y in range(nYTiles):
for x in range(nXTiles):
# Calculate the correct size and offset for each tile
tileX = x * tileSizeX
tileY = y * tileSizeY
effTileSizeX = tileSizeX * currentScale
if ((tileX * currentScale) + effTileSizeX) >= width:
effTileSizeX = width - (tileX * currentScale)
effTileSizeY = tileSizeY * currentScale
if ((tileY * currentScale) + effTileSizeY) >= height:
effTileSizeY = height - (tileY * currentScale)
# Read the tile, create the downsampled version and then write to output
tile = reader.openBytes(image, tileX * currentScale, tileY * currentScale, effTileSizeX, effTileSizeY)
downsample = scaler.downsample(tile, effTileSizeX, effTileSizeY, currentScale, FormatTools.getBytesPerPixel(type), reader.isLittleEndian(),
FormatTools.isFloatingPoint(type), reader.getRGBChannelCount(), reader.isInterleaved())
writer.saveBytes(image, downsample, tileX, tileY, effTileSizeX / currentScale, effTileSizeY / currentScale)
writer.close();
reader.close();
IJ.log("Done")
options = ImporterOptions()
options.setColorMode(ImporterOptions.COLOR_MODE_COMPOSITE)
options.setId(outFile)
options.setSeriesOn(2, True);
imps = BF.openImagePlus(options)
for imp in imps:
imp.show()
def readczi(imagefile,
stitchtiles=True,
setflatres=False,
readpylevel=0,
setconcat=True,
openallseries=True,
showomexml=False,
attach=False,
autoscale=True):
log.info('Filename : ' + imagefile)
metainfo = {}
# checking for thr file Extension
metainfo['Extension'] = MiscTools.getextension(MiscTools.splitext_recurse(imagefile))
log.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.info('PyLevel=' + str(readpylevel) + ' does not exist.')
log.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
from ij import IJ
from ij.io import OpenDialog
from loci.formats import ImageReader
from loci.formats import MetadataTools
# open file
od = OpenDialog("Choose a file");
filepath = od.getPath()
print("Image path: " + filepath);
# use bio-formats to extract information
reader = ImageReader()
omeMeta = MetadataTools.createOMEXMLMetadata()
reader.setMetadataStore(omeMeta)
reader.setId(filepath)
seriesCount = reader.getSeriesCount()
print "Series count:",seriesCount
reader.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..."
# 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.
bfreader = ImageReader()
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!"
def openfile(imagefile,
stitchtiles=True,
setflatres=False,
readpylevel=0,
setconcat=True,
openallseries=True,
showomexml=False,
attach=False,
autoscale=True,
imageID=0):
# stitchtiles = option of CZIReader to return the raw tiles as
# individual series rather than the auto-stitched images
metainfo = {}
# checking for thr file Extension
metainfo['Extension'] = MiscTools.getextension(MiscTools.splitext_recurse(imagefile))
# initialite 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()
# read dimensions TZCXY from OME metadata
metainfo['SizeT'] = omeMeta.getPixelsSizeT(imageID).getValue()
metainfo['SizeZ'] = omeMeta.getPixelsSizeZ(imageID).getValue()
metainfo['SizeC'] = omeMeta.getPixelsSizeC(imageID).getValue()
metainfo['SizeX'] = omeMeta.getPixelsSizeX(imageID).getValue()
metainfo['SizeY'] = omeMeta.getPixelsSizeY(imageID).getValue()
# store info about stack
if metainfo['SizeZ'] == 1:
metainfo['is3d'] = False
elif metainfo['SizeZ'] > 1:
metainfo['is3d'] = True
# 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
# if image file is Carl Zeiss Image - CZI
if metainfo['Extension'] == '.czi':
# read the CZI file using the CZIReader
# pylevel = 0 - read the full resolution image
imp, metainfo = ImportTools.readCZI(imagefile, metainfo,
stitchtiles=stitchtiles,
setflatres=setflatres,
readpylevel=readpylevel,
setconcat=setconcat,
openallseries=openallseries,
showomexml=showomexml,
attach=attach,
autoscale=autoscale)
# if image file is not Carl Zeiss Image - CZI
if metainfo['Extension'] != '.czi':
# read the imagefile using the correct method
if metainfo['Extension'].lower() == ('.jpg' or '.jpeg'):
# use dedicated method for jpg
imp, metainfo = ImageTools.openjpg(imagefile, method='IJ')
else:
# if not jpg - use BioFormats
imp, metainfo = ImportTools.readbf(imagefile, metainfo,
setflatres=setflatres,
readpylevel=readpylevel,
setconcat=setconcat,
openallseries=openallseries,
showomexml=showomexml,
autoscale=autoscale)
return imp, metainfo
width = r.getSizeX()
height = r.getSizeY()
md = r.getGlobalMetadata()
# print(type(md))
# print(num, width, height)
stack = ImageStack(width, height)
i = 0
ip = r.openProcessors(i)[0]
stack.addSlice("1", ip);
imp = ImagePlus("foo", stack);
r.close()
imp.show()
IJ.run("Enhance Contrast", "saturated=0.35")
imageReader = ImageReader()
meta = MetadataTools.createOMEXMLMetadata()
imageReader.setMetadataStore(meta)
imageReader.setId(filePath)
pSizeX = meta.getPixelsPhysicalSizeX(0)
pSizeY = meta.getPixelsPhysicalSizeY(0)
imageReader.close()
print(pSizeX, pSizeY)
print(meta.getPixelsSizeX(0))
print(meta.getPixelsSizeY(0))
def nucleus_detection(infile, nucleus_channel, stacksize, animation):
# Detect nucleus with 3d log filters
fullpath = infile
infile = filename(infile)
IJ.log("Start Segmentation " + str(infile))
# First get Nb Stacks
reader = ImageReader()
omeMeta = MetadataTools.createOMEXMLMetadata()
reader.setMetadataStore(omeMeta)
reader.setId(fullpath)
default_options = "stack_order=XYCZT color_mode=Composite view=Hyperstack specify_range c_begin=" + \
str(nucleus_channel) + " c_end=" + str(nucleus_channel) + \
" c_step=1 open=[" + fullpath + "]"
NbStack = reader.getSizeZ()
reader.close()
output = re.sub('.ids', '.csv', infile)
with open(os.path.join(folder5, output), 'wb') as outfile:
DETECTwriter = csv.writer(outfile, delimiter=',')
DETECTwriter.writerow(
['spotID', 'roundID', 'X', 'Y', 'Z', 'QUALITY', 'SNR', 'INTENSITY'])
rounds = NbStack // stacksize
spotID = 1
for roundid in xrange(1, rounds + 2):
# Process stacksize by stacksize otherwise crash because too many spots
Zstart = (stacksize * roundid - stacksize + 1)
Zend = (stacksize * roundid)
if(Zend > NbStack):
Zend = NbStack % stacksize + (roundid - 1) * stacksize
IJ.log("Round:" + str(roundid) + ' Zstart=' + str(Zstart) +
' Zend=' + str(Zend) + ' out of ' + str(NbStack))
IJ.run("Bio-Formats Importer", default_options + " z_begin=" +
str(Zstart) + " z_end=" + str(Zend) + " z_step=1")
imp = IJ.getImage()
imp.show()
cal = imp.getCalibration()
model = Model()
settings = Settings()
settings.setFrom(imp)
# Configure detector - Manually determined as best
settings.detectorFactory = LogDetectorFactory()
settings.detectorSettings = {
'DO_SUBPIXEL_LOCALIZATION': True,
'RADIUS': 5.5,
'TARGET_CHANNEL': 1,
'THRESHOLD': 50.0,
'DO_MEDIAN_FILTERING': False,
}
filter1 = FeatureFilter('QUALITY', 1, True)
settings.addSpotFilter(filter1)
settings.addSpotAnalyzerFactory(SpotIntensityAnalyzerFactory())
settings.addSpotAnalyzerFactory(SpotContrastAndSNRAnalyzerFactory())
settings.trackerFactory = SparseLAPTrackerFactory()
settings.trackerSettings = LAPUtils.getDefaultLAPSettingsMap()
trackmate = TrackMate(model, settings)
ok = trackmate.checkInput()
if not ok:
sys.exit(str(trackmate.getErrorMessage()))
try:
ok = trackmate.process()
except:
IJ.log("Nothing detected, Round:" + str(roundid) + ' Zstart=' +
str(Zstart) + ' Zend=' + str(Zend) + ' out of ' + str(NbStack))
IJ.selectWindow(infile)
IJ.run('Close')
continue
else:
if animation:
# For plotting purpose only
imp.setPosition(1, 1, imp.getNFrames())
imp.getProcessor().setMinAndMax(0, 4000)
selectionModel = SelectionModel(model)
displayer = HyperStackDisplayer(model, selectionModel, imp)
displayer.render()
displayer.refresh()
for i in xrange(1, imp.getNSlices() + 1):
imp.setSlice(i)
time.sleep(0.05)
IJ.selectWindow(infile)
IJ.run('Close')
spots = model.getSpots()
spotIt = spots.iterator(0, False)
sid = []
sroundid = []
x = []
y = []
z = []
q = []
snr = []
intensity = []
for spot in spotIt:
sid.append(spotID)
spotID = spotID + 1
sroundid.append(roundid)
x.append(spot.getFeature('POSITION_X'))
y.append(spot.getFeature('POSITION_Y'))
q.append(spot.getFeature('QUALITY'))
snr.append(spot.getFeature('SNR'))
intensity.append(spot.getFeature('MEAN_INTENSITY'))
# Correct Z position
correct_z = spot.getFeature(
'POSITION_Z') + (roundid - 1) * float(stacksize) * cal.pixelDepth
z.append(correct_z)
with open(os.path.join(folder5, output), 'ab') as outfile:
DETECTwriter = csv.writer(outfile, delimiter=',')
Sdata = zip(sid, sroundid, x, y, z, q, snr, intensity)
for Srow in Sdata:
DETECTwriter.writerow(Srow)
import net.imglib2.algorithm.neighborhood.HyperSphereShape from net.imglib2.type.numeric.real import FloatType,DoubleType from ij.measure import ResultsTable from net.imagej.ops import Ops from loci.plugins.in import ImporterOptions options = ImporterOptions() options.setId(Input_File.getAbsolutePath()) from loci.formats import ImageReader from loci.formats import MetadataTools #get import ready and import reader = ImageReader() omeMeta = MetadataTools.createOMEXMLMetadata() reader.setMetadataStore(omeMeta) reader.setId(Input_File.getAbsolutePath()) seriesCount = reader.getSeriesCount() reader.close() #open image imp, = BF.openImagePlus(options) #get output path variable outdir=Output_File.getAbsolutePath() #get input path variable inpu=Input_File.getAbsolutePath() #convert to RGB IC(imp).convertToRGB() #show image imp.show() #Define ROI of whole image (basically) imp.setRoi(1,1,478,479) ######OPTIONAL##############
def get_ome_metadata(source, imagenames):
"""Get the stage coordinates and calibration from the ome-xml for a given list of images
Arguments:
source {string} -- Path to the images
imagenames {list} -- list of images filenames
Returns:
a tuple that contains:
dimensions {int} -- number of dimensions (2D or 3D)
stage_coordinates_x {list} -- the abosolute stage x-coordinates from ome-xml metadata
stage_coordinates_y {list} -- the abosolute stage y-coordinates from ome-xml metadata
stage_coordinates_z {list} -- the abosolute stage z-coordinates from ome-xml metadata
relative_coordinates_x_px {list} -- the relative stage x-coordinates in px
relative_coordinates_y_px {list} -- the relative stage y-coordinates in px
relative_coordinates_z_px {list} -- the relative stage z-coordinates in px
image_calibration {list} -- x,y,z image calibration in unit/px
calibration_unit {string} -- image calibration unit
image_dimensions_czt {list} -- number of images in dimensions c,z,t
"""
# open an array to store the abosolute stage coordinates from metadata
stage_coordinates_x = []
stage_coordinates_y = []
stage_coordinates_z = []
for counter, image in enumerate(imagenames):
# parse metadata
reader = ImageReader()
omeMeta = MetadataTools.createOMEXMLMetadata()
reader.setMetadataStore(omeMeta)
reader.setId(source + str(image))
# get hyperstack dimensions from the first image
if counter == 0:
frame_size_x = reader.getSizeX()
frame_size_y = reader.getSizeY()
frame_size_z = reader.getSizeZ()
frame_size_c = reader.getSizeC()
frame_size_t = reader.getSizeT()
# note the dimensions
if frame_size_z == 1:
dimensions = 2
if frame_size_z > 1:
dimensions = 3
# get the physical calibration for the first image series
physSizeX = omeMeta.getPixelsPhysicalSizeX(0)
physSizeY = omeMeta.getPixelsPhysicalSizeY(0)
physSizeZ = omeMeta.getPixelsPhysicalSizeZ(0)
# workaround to get the z-interval if physSizeZ.value() returns None.
z_interval = 1
if physSizeZ is not None:
z_interval = physSizeZ.value()
if frame_size_z > 1 and physSizeZ is None:
print "no z calibration found, trying to recover"
first_plane = omeMeta.getPlanePositionZ(0, 0)
next_plane_imagenumber = frame_size_c + frame_size_t - 1
second_plane = omeMeta.getPlanePositionZ(
0, next_plane_imagenumber)
z_interval = abs(
abs(first_plane.value()) - abs(second_plane.value()))
print "z-interval seems to be: ", z_interval
# create an image calibration
image_calibration = [
physSizeX.value(),
physSizeY.value(), z_interval
]
calibration_unit = physSizeX.unit().getSymbol()
image_dimensions_czt = [frame_size_c, frame_size_z, frame_size_t]
reader.close()
# get the plane position in calibrated units
current_position_x = omeMeta.getPlanePositionX(0, 0)
current_position_y = omeMeta.getPlanePositionY(0, 0)
current_position_z = omeMeta.getPlanePositionZ(0, 0)
# get the absolute stage positions and store them
pos_x = current_position_x.value()
pos_y = current_position_y.value()
if current_position_z is None:
print "the z-position is missing in the ome-xml metadata."
pos_z = 1.0
else:
pos_z = current_position_z.value()
stage_coordinates_x.append(pos_x)
stage_coordinates_y.append(pos_y)
stage_coordinates_z.append(pos_z)
# calculate the store the relative stage movements in px (for the grid/collection stitcher)
relative_coordinates_x_px = []
relative_coordinates_y_px = []
relative_coordinates_z_px = []
for i in range(len(stage_coordinates_x)):
rel_pos_x = (stage_coordinates_x[i] -
stage_coordinates_x[0]) / physSizeX.value()
rel_pos_y = (stage_coordinates_y[i] -
stage_coordinates_y[0]) / physSizeY.value()
rel_pos_z = (stage_coordinates_z[i] -
stage_coordinates_z[0]) / z_interval
relative_coordinates_x_px.append(rel_pos_x)
relative_coordinates_y_px.append(rel_pos_y)
relative_coordinates_z_px.append(rel_pos_z)
return (dimensions, stage_coordinates_x, stage_coordinates_y,
stage_coordinates_z, relative_coordinates_x_px,
relative_coordinates_y_px, relative_coordinates_z_px,
image_calibration, calibration_unit, image_dimensions_czt)
def run():
t_start = datetime.now()
image_paths = glob(os.path.join(str(import_dir.getPath()), '*tif'))
print '\tread image metadata'
reader = ImageReader()
in_meta = MetadataTools.createOMEXMLMetadata()
reader.setMetadataStore(in_meta)
x_dims = []
y_dims = []
z_dims = []
c_dims = []
t_dims = []
eff = []
spp = []
for image_path in image_paths:
print '\t parse %s' % (image_path)
reader.setId(image_path)
x_dims.append(reader.getSizeX())
y_dims.append(reader.getSizeY())
z_dims.append(reader.getSizeZ())
c_dims.append(reader.getSizeC())
t_dims.append(reader.getSizeT())
eff.append(reader.imageCount / z_dims[-1] / t_dims[-1])
spp.append(reader.getSizeC() / eff[-1])
format = FormatTools.getPixelTypeString(reader.getPixelType())
series = reader.getSeries()
big_endian = Boolean.FALSE
order = reader.getDimensionOrder()
reader.close()
# Compute the dimensions of the output file
x_dim = max(x_dims)
y_dim = max(y_dims)
z_dim = max(z_dims)
c_dim = max(c_dims)
t_dim = max(t_dims)
print '\t series: %i' % series
print '\t format: %s' % format
print '\t dimension order: %s' % order
print '\t x: %s -> %i' % (x_dims, x_dim)
print '\t y: %s -> %i' % (y_dims, y_dim)
print '\t z: %s -> %i' % (z_dims, z_dim)
print '\t c: %s -> %i' % (c_dims, c_dim)
print '\t t: %s -> %i' % (t_dims, t_dim)
print '\t effective size c: %s' % eff
print '\t samples per pixel: %s' % spp
# Get the time dimension from the number of input files
t_dim = len(image_paths)
# TODO: Tried to work out the order with Axes class, got something weird though.
dimensions = [Short(x_dim), Short(y_dim), Short(c_dim), Short(z_dim)]
pixels_per_plane = x_dim * y_dim
# Assemble the metadata for the output file
out_meta = MetadataTools.createOMEXMLMetadata()
out_meta.setImageID(MetadataTools.createLSID('Image', series), series)
out_meta.setPixelsID(MetadataTools.createLSID('Pixels', series), series)
out_meta.setPixelsBinDataBigEndian(Boolean.TRUE, 0, 0)
out_meta.setPixelsDimensionOrder(DimensionOrder.fromString(order), series)
out_meta.setPixelsType(PixelType.fromString(format), series)
out_meta.setPixelsSizeX(PositiveInteger(x_dim), series)
out_meta.setPixelsSizeY(PositiveInteger(y_dim), series)
out_meta.setPixelsSizeZ(PositiveInteger(z_dim), series)
out_meta.setPixelsSizeC(PositiveInteger(c_dim), series)
out_meta.setPixelsSizeT(PositiveInteger(t_dim), series)
for c in range(c_dim):
out_meta.setChannelID(MetadataTools.createLSID('Channel', series, c),
series, c)
out_meta.setChannelSamplesPerPixel(PositiveInteger(1), series, c)
# Initialize the BF writer
result_path = os.path.join(result_dir.getPath(), result_name)
writer = ImageWriter()
writer.setMetadataRetrieve(out_meta)
writer.setId(result_path)
print '\tcreated to %s' % (result_path)
# Write the stacks into the output file
N = len(image_paths)
for i, image_path in enumerate(image_paths):
status.showStatus(i, N, "catenating %i of %i time-points" % (i, N))
print '\t processing %s' % (image_path)
ds = io.open(image_path)
xi = ds.dimensionIndex(Axes.X)
xv = ds.dimension(xi)
yi = ds.dimensionIndex(Axes.Y)
yv = ds.dimension(yi)
zi = ds.dimensionIndex(Axes.Z)
zv = ds.dimension(zi)
ti = ds.dimensionIndex(Axes.TIME)
tv = ds.dimension(ti)
ci = ds.dimensionIndex(Axes.CHANNEL)
cv = ds.dimension(ci)
dx = float(x_dim - xv) / 2.0
dy = float(y_dim - yv) / 2.0
dz = float(z_dim - zv) / 2.0
print '\t translation vector (dx, dy, dz) = (%f, %f, %f)' % (
dx, dy, dz)
if (dx != 0) or (dy != 0) or (dz != 0):
stk = Views.translate(ds, long(dx), long(dy), long(0), long(dz))
stk = Views.extendZero(stk)
else:
stk = Views.extendZero(ds.getImgPlus().getImg())
print '\t writing planes ',
n = 0
plane = 1
byte_array = []
interval_view = Views.interval(stk, \
[Long(0), Long(0), Long(0), Long(0)], \
[Long(x_dim - 1), Long(y_dim - 1), Long(c_dim - 1), Long(z_dim - 1)])
cursor = interval_view.cursor()
while cursor.hasNext():
n += 1
cursor.fwd()
value = cursor.get().getInteger()
bytes = DataTools.shortToBytes(value, big_endian)
byte_array.extend(bytes)
if n == pixels_per_plane:
writer.saveBytes(plane - 1, byte_array)
print '.',
if ((plane) % 10) == 0:
print '\n\t ',
byte_array = []
plane += 1
n = 0
print ' '
writer.close()
t = datetime.now() - t_start
print '\twrote %i planes to %s in %i sec.' % (plane - 1, result_path,
t.total_seconds())
print '... done.'
def readZeissHeader(self, infoStr):
# This is incredibly difficult to get working as (date, time, voxels) are in different obscure places in lsm and czi
# Furthermore, just trying to read the raw ome xls is futile
#
# parsing ome xml as a string and searching it with regular expression(re) does not work
# it is beyond the scope of my work to figure this out
# the fact that it does not work and there is little documentaiton is a pretty big waste of time
#
# get and parse xml to find date/time
#fi = self.imp.getOriginalFileInfo(); # returns a FileInfo object
#omexml = fi.description #omexml is a string
#omexml = omexml.encode('utf-8')
#omexml = omexml.replaceAll("[^\\x20-\\x7e]", "") # see: https://stackoverflow.com/questions/2599919/java-parsing-xml-document-gives-content-not-allowed-in-prolog-error
# (1) try and search the ome xml like a string, this gives errors
#docsPattern = '<AcquisitionDate>.*</AcquisitionDate>'
#searchresult = re.search(docsPattern, omexml)
#print 'searchresult:', searchresult.group(0)
# 2) treat the ome xml like any other xml (because it's xml, right?)
# well this raises errors too
#omexml has <AcquisitionDate>2016-08-17T15:21:50</AcquisitionDate>
#import xml.etree.ElementTree
#e = xml.etree.ElementTree.fromstring(omexml).getroot() #print omexml
#for atype in e.findall('AcquisitionDate'):
# print 'AcquisitionDate:', atype #.get('foobar')
#
#
if self.islsm:
# lsm have date hidden in omeMeta.getImageAcquisitionDate(0)
# this is copied from code at: https://gist.github.com/ctrueden/6282856
reader = ImageReader()
omeMeta = MetadataTools.createOMEXMLMetadata() #omeMeta.getImageAcquisitionDate(0)
reader.setMetadataStore(omeMeta)
reader.setId(self.filepath)
#seriesCount = reader.getSeriesCount()
dateTimeStr = omeMeta.getImageAcquisitionDate(0) #2016-08-17T16:36:26
reader.close()
if dateTimeStr:
self.dateStr, self.timeStr = dateTimeStr.toString().split('T')
self.dateStr = bFixDate(self.dateStr)
self.timeStr = bFixTime(self.timeStr)
#bPrintLog('LSM date/time is: ' + self.dateStr + ' ' + self.timeStr, 3)
else:
bPrintLog('WARNING: did not get Zeiss date/time string')
# lsm have voxels in infoStr
for line in infoStr.split('\n'):
#print line
if line.find('VoxelSizeX') != -1:
self.voxelx = float(line.split('=')[1])
if line.find('VoxelSizeY') != -1:
self.voxely = float(line.split('=')[1])
if line.find('VoxelSizeZ') != -1:
self.voxelz = float(line.split('=')[1])
if line.find('SizeC') != -1:
self.numChannels = int(line.split('=')[1])
#if line.find('BitsPerPixel') and not line.startswith('Experiment') != -1: # 20170811, startswith is for czi
# self.bitsPerPixel = int(line.split('=')[1])
if line.find('RecordingZoomX#1') != -1:
self.zoom = int(line.split('=')[1])
if self.isczi:
# czi has date/time in infoStr (lsm does not)
for line in infoStr.split('\n'):
if line.find('CreationDate #1') != -1: # w.t.f. is #1 referring to?
lhs, rhs = line.split('=')
rhs = rhs.replace(' ', ' ')
if rhs.startswith(' '):
rhs = rhs[1:-1]
#print "lhs: '" + lhs + "'" + "rhs: '" + rhs + "'"
if rhs.find('T') != -1:
self.dateStr, self.timeStr = rhs.split('T')
else:
self.dateStr, self.timeStr = rhs.split(' ')
self.dateStr = bFixDate(self.dateStr)
self.timeStr = bFixTime(self.timeStr)
#bPrintLog('CZI date/time is: ' + self.dateStr + ' ' + self.timeStr, 3)
# .czi
# <Pixels BigEndian="false" DimensionOrder="XYCZT" ID="Pixels:0" Interleaved="false" PhysicalSizeX="0.20756645602494875" PhysicalSizeXUnit="µm" PhysicalSizeY="0.20756645602494875" PhysicalSizeYUnit="µm" PhysicalSizeZ="0.75" PhysicalSizeZUnit="µm" SignificantBits="8" SizeC="1" SizeT="1" SizeX="1024" SizeY="1024" SizeZ="50" Type="uint8">
# czi have voxel in calibration
self.voxelx = self.imp.getCalibration().pixelWidth;
self.voxely = self.imp.getCalibration().pixelHeight;
self.voxelz = self.imp.getCalibration().pixelDepth;
#bPrintLog('readZeissHeader() read czi scale as: ' + str(self.voxelx) + ' ' + str(self.voxely) + ' ' + str(self.voxelz), 3)
# CLEARING self.infoStr for CZI ... it was WAY to big to parse in Map Manager
self.infoStr = ''
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:
