setconcat=setconcat,
openallseries=openallseries,
showomexml=showomexml,
attach=attach,
autoscale=autoscale)
if verbose:
for k, v in MetaInfo.items():
log.info(str(k) + ' : ' + str(v))
# do the processing
log.info('Start Processing ...')
if extract_channel:
imp = MiscTools.splitchannel(imp, channelindex)
# correct background
if correct_background:
log.info('Removing Background using Rolling Ball ...')
imp = FilterTools.apply_rollingball(imp,
radius=rb_radius,
createBackground=create_background,
lightBackground=lightbackground,
useParaboloid=useparaboloid,
doPresmooth=dopresmooth,
correctCorners=correct_corners)
# filter image
if filtertype != 'NONE':
background_threshold = 'dark' #imagefile = r"/datadisk1/tuxedo/Github/Tools_and_Scripts/Fiji/scripts/3d_nuclei_image_holes_T.ome.tiff" #imagefile = r"/datadisk1/tuxedo/Github/Tools_and_Scripts/Fiji/scripts/3d_nuclei_image_holes.ome.tiff" imagefile = r"/datadisk1/tuxedo/Github/Tools_and_Scripts/Fiji/scripts/Osteosarcoma_01.ome.tiff" imagefile = r'"C:\Temp\input\Osteosarcoma_01.ome.tiff" corrf = 1.0 if stackopt: thcmd = method + ' ' + background_threshold + ' stack' if not stackopt: thcmd = method + ' ' + background_threshold # open image stack imp, MetaInfo = ImportTools.openfile(imagefile) imp = MiscTools.splitchannel(imp, 3) imp = ThresholdTools.apply_threshold(imp, method=method, background_threshold='dark', stackopt=stackopt, corrf=corrf) imp.show()
def run(imagefile, verbose=False):
# open image file and get a specific series
log.log(LogLevel.INFO, 'Opening Image: ' + imagefile)
imp, MetaInfo = ImportTools.openfile(imagefile)
# output of image metadata in log window
if verbose:
for k, v in MetaInfo.items():
log.log(LogLevel.INFO, str(k) + ' : ' + str(v))
log.log(LogLevel.INFO, 'File Extension : ' + MetaInfo['Extension'])
if 'ResolutionCount' in MetaInfo:
log.log(LogLevel.INFO,
'Resolution Count : ' + str(MetaInfo['ResolutionCount']))
if 'SeriesCount' in MetaInfo:
log.log(LogLevel.INFO,
'SeriesCount : ' + str(MetaInfo['SeriesCount']))
if 'SizeC' in MetaInfo:
log.log(LogLevel.INFO, 'Channel Count : ' + str(MetaInfo['SizeC']))
# do the processing
log.log(LogLevel.INFO, 'Start Processing ...')
# create empty image list
imglist = []
numch = imp.getNChannels()
# do the processing
for ch in range(numch):
# get a channel
imp_c = MiscTools.splitchannel(imp, ch)
name = os.path.basename(imagefile) + '_CH=' + str(ch)
sp, fv, fv_max, fv_max_index = calc_focus(imp_c, name)
log.log(LogLevel.INFO, 'Processing Channel : ' + str(ch))
log.log(LogLevel.INFO, 'Max. Value : ' + str(fv_max))
log.log(LogLevel.INFO, 'Max. Value Slice : ' + str(fv_max_index))
# set correct image properties
sp = MiscTools.setproperties(sp,
scaleX=MetaInfo['ScaleX'],
scaleY=MetaInfo['ScaleY'],
scaleZ=MetaInfo['ScaleZ'],
unit='micron',
sizeC=1,
sizeZ=1,
sizeT=1)
imglist.append(sp)
# in case of more than one channel use an jarray
if numch > 1:
# create an array
imgarray = jarray.array(imglist, ImagePlus)
# create an ImageStack from the array
imgstack = ImageStack.create(imgarray)
# create an ImagePlus object from the jarray
new_name = os.path.splitext(os.path.basename(imagefile))[0]
imp_sp = ImagePlus(new_name + '_SHARPEST', imgstack)
# ic case of exactly one channel directly use the ImgPlus
if numch == 1:
imp_sp = imglist[0]
# set correct image properties for the final image
imp_sp = MiscTools.setproperties(imp_sp,
scaleX=MetaInfo['ScaleX'],
scaleY=MetaInfo['ScaleY'],
scaleZ=MetaInfo['ScaleZ'],
unit='micron',
sizeC=numch,
sizeZ=1,
sizeT=1)
return imp_sp
def run(imagefile,
outputpath_orig,
convert_orig2copy=False,
sft='ome.tiff',
fillholes=True,
watershed=True,
watershed_connectivity=6,
verbose=True):
print fillholes, watershed, watershed_connectivity
# Opening the image
log.info('Opening Image: ' + imagefile)
# stitch togehter tiles
stitchtiles = True
# when set to True the number of pyramid levels can be read
setflatres = True
# select the desired pyramid level - level=0 for full resolution
readpylevel = 0
setconcat = True
openallseries = True
showomexml = False
attach = False
autoscale = True
# read the image
imp, MetaInfo = ImportTools.openfile(imagefile,
stitchtiles=stitchtiles,
setflatres=setflatres,
readpylevel=readpylevel,
setconcat=setconcat,
openallseries=openallseries,
showomexml=showomexml,
attach=attach,
autoscale=autoscale)
if verbose:
for k, v in MetaInfo.items():
log.info(str(k) + ' : ' + str(v))
if EXTRACT_CHANNEL:
imp = MiscTools.splitchannel(imp, CHINDEX)
# optional filtering
if FILTERTYPE != 'NONE':
log.info('Apply Filter : ' + FILTERTYPE)
imp = FilterTools.apply_filter(imp,
radius=FILTER_RADIUS,
filtertype=FILTERTYPE)
# apply threshold
log.info('Apply Threshold ...')
imp = ThresholdTools.apply_threshold(imp,
method=THRESHOLD_METHOD,
background_threshold=THRESHOLD_BGRD,
stackopt=THRESHOLD_STACKOPTION)
print "3D: ", MetaInfo['is3d']
# fill holes
if fillholes:
imp = BinaryTools.fill_holes(imp, is3d=MetaInfo['is3d'])
if watershed:
imp = WaterShedTools.run_watershed(
imp,
mj_normalize=True,
mj_dynamic=1,
mj_connectivity=watershed_connectivity,
is3d=MetaInfo['is3d'])
return imp
def run(imagefile, outputpath_orig, convert_orig2copy=False,
sft='ome.tiff',
verbose=False):
# Opening the image
log.info('Opening Image: ' + imagefile)
# stitch togehter tiles
stitchtiles = True
# when set to True the number of pyramid levels can be read
setflatres = True
# select the desired pyramid level - level=0 for full resolution
readpylevel = 0
setconcat = True
openallseries = True
showomexml = False
attach = False
autoscale = True
# read the image
imp, MetaInfo = ImportTools.openfile(imagefile,
stitchtiles=stitchtiles,
setflatres=setflatres,
readpylevel=readpylevel,
setconcat=setconcat,
openallseries=openallseries,
showomexml=showomexml,
attach=attach,
autoscale=autoscale)
if convert_orig2copy:
log.info('Detected Extension : ' + MetaInfo['Extension'])
# check if it was already an OME-TIFF based on the file extension
ft = ['.ome.tif', '.ome.tiff']
if MetaInfo['Extension'] in ft:
"""
# just copy the original file
new_path = '/output/' + os.path.basename(imagefile)
command = 'cp ' + imagefile + ' ' + new_path
log.info('Copy Original file without conversion to output folder')
os.popen(command)
"""
# just copy the original file
new_path = '/output/' + os.path.basename(IMAGEPATH)
copy_ok = copyorig(IMAGEPATH, new_path, force_overwrite=True)
log.info(copy_ok)
if MetaInfo['Extension'] not in ft:
log.info('Converting original to OME-TIFF and save to output folder.')
savepath_orig = ExportTools.savedata(imp,
outputpath_orig,
extension=sft,
replace=True)
log.info('Saving converted original image : ' + savepath_orig)
if verbose:
for k, v in MetaInfo.items():
log.info(str(k) + ' : ' + str(v))
# do the processing
log.info('Start Processing ...')
if EXTRACT_CHANNEL:
imp = MiscTools.splitchannel(imp, CHINDEX)
# correct background
if CORRECT_BACKGROUND:
log.info('Removing Background using Rolling Ball ...')
imp = FilterTools.apply_rollingball(imp,
radius=RB_RADIUS,
createBackground=CREATEBACKGROUND,
lightBackground=LIGHTBACKGROUND,
useParaboloid=USEPARABOLOID,
doPresmooth=DOPRESMOOTH,
correctCorners=CORRECTCORNERS)
# optional filtering
if FILTERTYPE != 'NONE':
log.info('Apply Filter : ' + FILTERTYPE)
imp = FilterTools.apply_filter(imp,
radius=FILTER_RADIUS,
filtertype=FILTERTYPE)
# apply threshold
log.info('Apply Threshold ...')
imp = ThresholdTools.apply_threshold(imp,
method=THRESHOLD_METHOD,
background_threshold=THRESHOLD_BGRD,
stackopt=THRESHOLD_STACKOPTION)
#ip = imp.getProcessor()
#ip = ip.convertToByte(False)
#imp.setProcessor(ip)
#ImageConverter.setDoScaling(False)
#ImageConverter(imp).convertToGray8()
#imp.show()
#ImageConverter.setDoScaling(True);
#IJ.run(imp, "8-bit", "");
#IJ.run(imp, "Enhance Contrast", "saturated=0.35");
"""
# run watershed on stack
weights = ChamferWeights3D.BORGEFORS.getFloatWeights()
normalize = True
dynamic = 2
connectivity = 26
ImageConverter(imp).convertToGray8()
try:
dist = BinaryImages.distanceMap(imp, weights, normalize)
except:
dist = BinaryImages.distanceMap(imp.getImageStack(), weights, normalize)
Images3D.invert(dist)
#imp = ExtendedMinimaWatershed.extendedMinimaWatershed(dist, imp.getImageStack(), dynamic, connectivity, 32, False )
try:
imp = ExtendedMinimaWatershed.extendedMinimaWatershed(dist, imp, dynamic, connectivity, 32, False)
except:
imp = ExtendedMinimaWatershed.extendedMinimaWatershed(dist, imp.getImageStack(), dynamic, connectivity, 32, False)
"""
"""
pastack, results = AnalyzeTools.analyzeParticles(imp, MINSIZE, MAXSIZE, MINCIRC, MAXCIRC,
filename=imagefile,
addROIManager=ROIS)
# apply suitable LUT to visualize particles
pastack = ImagePlus('Particles', pastack)
IJ.run(pastack, 'glasbey_inverted', '')
IJ.run(pastack, 'Enhance Contrast', 'saturated=0.35')
return imp, pastack, results
"""
return imp