def __init__(self, userDetails, inpath, outpath, compression, section_id, total_sections, input_data, pixelregion, outChan, scalefact, rotation):
OMEBase.__init__(self)
self.outputtif_path = outpath
self.ID = section_id
self.Total = total_sections
self.InputPath = inpath
self.InputFilename = self.InputPath.split("\\")[-1]
self.imarray = input_data
self.outChan = outChan
self.rotation = rotation
self.roi = pixelregion
self.meta = SlideImage(self.InputPath)
self.mode = self.meta.micro_mode()
self.scalefact = scalefact
self.tile_width = 1024
self.tile_height = 1024
self.compression = compression
if not userDetails:
current_user = '******'
current_group = 'QBIgroup'
else:
current_user = userDetails[0]
current_group = userDetails[1]
if current_user not in people:
people[current_user] = dict(first_name = userDetails[2], last_name = userDetails[3],
email = userDetails[0]+'@uq.edu.au', user_name = userDetails[0],
institution='Queensland Brain Institute', groups = current_group)
if current_group not in groups:
groups[current_group] = dict(name = userDetails[1], contact_id = userDetails[0])
self.current_user = current_user
self.current_group = current_group
def __init__(self, userDetails, inpath, outpath, compression, section_id,
total_sections, input_data, pixelregion, outChan, scalefact,
rotation):
OMEBase.__init__(self)
self.outputtif_path = outpath
self.ID = section_id
self.Total = total_sections
self.InputPath = inpath
self.InputFilename = self.InputPath.split("\\")[-1]
self.imarray = input_data
self.outChan = outChan
self.rotation = rotation
self.roi = pixelregion
self.meta = SlideImage(self.InputPath)
self.mode = self.meta.micro_mode()
self.scalefact = scalefact
self.tile_width = 1024
self.tile_height = 1024
self.compression = compression
if not userDetails:
current_user = '******'
current_group = 'QBIgroup'
else:
current_user = userDetails[0]
current_group = userDetails[1]
if current_user not in people:
people[current_user] = dict(
first_name=userDetails[2],
last_name=userDetails[3],
email=userDetails[0] + '@uq.edu.au',
user_name=userDetails[0],
institution='Queensland Brain Institute',
groups=current_group)
if current_group not in groups:
groups[current_group] = dict(name=userDetails[1],
contact_id=userDetails[0])
self.current_user = current_user
self.current_group = current_group
class OMETIFF(OMEBase):
prefix = 'ome'
def __init__(self, userDetails, inpath, outpath, compression, section_id,
total_sections, input_data, pixelregion, outChan, scalefact,
rotation):
OMEBase.__init__(self)
self.outputtif_path = outpath
self.ID = section_id
self.Total = total_sections
self.InputPath = inpath
self.InputFilename = self.InputPath.split("\\")[-1]
self.imarray = input_data
self.outChan = outChan
self.rotation = rotation
self.roi = pixelregion
self.meta = SlideImage(self.InputPath)
self.mode = self.meta.micro_mode()
self.scalefact = scalefact
self.tile_width = 1024
self.tile_height = 1024
self.compression = compression
if not userDetails:
current_user = '******'
current_group = 'QBIgroup'
else:
current_user = userDetails[0]
current_group = userDetails[1]
if current_user not in people:
people[current_user] = dict(
first_name=userDetails[2],
last_name=userDetails[3],
email=userDetails[0] + '@uq.edu.au',
user_name=userDetails[0],
institution='Queensland Brain Institute',
groups=current_group)
if current_group not in groups:
groups[current_group] = dict(name=userDetails[1],
contact_id=userDetails[0])
self.current_user = current_user
self.current_group = current_group
def iter_Image(self, func):
# get the meta data dictionaries
meta = self.meta
if self.mode == 'MetaCyte TL':
self.instrument = 'Bright-field'
elif self.mode == 'MetaCyte FL':
self.instrument = 'Fluorescence'
channelData = meta.datasetinfo_channels()
ImageData = meta.datasetinfo_image()
self.sizeArray = meta.image_size()
fullressize = self.sizeArray[0]
cropressize = self.sizeArray[self.scalefact]
scaleX = float(fullressize[0]) / float(cropressize[0])
scaleY = float(fullressize[1]) / float(cropressize[1])
classifier = ImageData['Metafer Classifer']
if ' ' in str(self.InputFilename):
DatasetID = str(self.InputFilename).replace(' ', '_')
else:
DatasetID = str(self.InputFilename)
if self.rotation == 0:
self.sizeX = self.roi[3] - self.roi[2]
self.sizeY = self.roi[1] - self.roi[0]
else:
self.sizeX = self.roi[1] - self.roi[0]
self.sizeY = self.roi[3] - self.roi[2]
self.sizeZ = int(ImageData['Z'])
writeChans = self.outChan
self.sizeC = len(writeChans)
self.sizeT = meta.time_points()
if self.sizeT == 1:
TimeIncrement = '0.0'
else:
TimeIncrement = str(self.sizeT)
dtype = np.dtype('uint8')
ExtminX = float(ImageData['ExtMin0'])
ExtmaxX = float(ImageData['ExtMax0'])
ExtminY = float(ImageData['ExtMin1'])
ExtmaxY = float(ImageData['ExtMax1'])
ExtminZ = float(ImageData['ExtMin2'])
ExtmaxZ = float(ImageData['ExtMax2'])
Width = m.fabs(ExtminX) + m.fabs(ExtmaxX)
Height = m.fabs(ExtminY) + m.fabs(ExtmaxY)
Depth = 0.32 #m.fabs(ExtminZ) + m.fabs(ExtmaxZ)
Xres = ((Width / fullressize[0]) * scaleX)
Yres = ((Height / fullressize[1]) * scaleY)
Zres = Depth
pixels_d = {}
pixels_d['PhysicalSizeX'] = str(Xres)
pixels_d['PhysicalSizeY'] = str(Yres)
pixels_d['PhysicalSizeZ'] = str(Zres)
pixels_d['TimeIncrement'] = TimeIncrement
self.PhysSize = (1 / Xres, 1 / Yres, 1 / Zres)
order = 'XYZCT'
channel_d = dict(SamplesPerPixel='1')
channel_d['AcquisitionMode'] = 'WideField'
lpath_l = []
self.tif_uuid = self._mk_uuid()
self.tif_filename = self.outputtif_path
# self.tif_images[self.instrument,self.tif_filename,self.tif_uuid,self.PhysSize] = tif_data
pixels = ome.Pixels(DimensionOrder=order,
ID='Pixels:%s' % (self.instrument),
SizeX=str(self.sizeX),
SizeY=str(self.sizeY),
SizeZ=str(self.sizeZ),
SizeT=str(self.sizeT),
SizeC=str(self.sizeC),
Type=self.dtype2PixelIType(dtype),
**pixels_d)
if (len(writeChans) > 1):
for chan in writeChans:
chan_dict = channelData[chan]
channel_d['Name'] = chan_dict['Name']
chan_color = chan_dict['Color']
channel = ome.Channel(ID='Channel:0:%s' % (chan), **channel_d)
lpath = ome.LightPath(*lpath_l)
channel.append(lpath)
pixels.append(channel)
plane_l = []
for idx_chan in range(len(writeChans)):
d = dict(IFD=str(idx_chan),
FirstC=str(idx_chan),
FirstZ='0',
FirstT='0',
PlaneCount='1')
plane_l.append(d)
tiffdata = ome.TiffData(
ome.UUID(self.tif_uuid,
FileName=self.tif_filename.split("\\")[-1]), **d)
pixels.append(tiffdata)
elif len(writeChans) == 1:
chan_dict = channelData[writeChans[0]]
channel_d['Name'] = chan_dict['Name']
channel_d['Name'] = chan_dict['Name']
chan_color = chan_dict['Color']
print(chan_color)
channel = ome.Channel(ID='Channel:0:%s' % (0), **channel_d)
lpath = ome.LightPath(*lpath_l)
channel.append(lpath)
pixels.append(channel)
plane_l = []
d = dict(IFD='0',
FirstC='0',
FirstZ='0',
FirstT='0',
PlaneCount='1')
plane_l.append(d)
tiffdata = ome.TiffData(
ome.UUID(self.tif_uuid,
FileName=self.tif_filename.split("\\")[-1]), **d)
pixels.append(tiffdata)
date = ImageData['RecordingDate']
date = date.replace(' ', 'T')
description = 'This is section number ' + str(self.ID) + ' of ' + str(self.Total) + \
' on ' + self.InputFilename + ' acquired using classifier: ' + classifier
image = ome.Image(
ome.AcquiredDate(date),
#ome.ExperimenterRef(ID='Experimenter:%s' % (self.current_user)),
ome.Description('Description:%s' % (description)),
#ome.GroupRef(ID='Group:%s' %(self.current_group)),
ome.DatasetRef(ID='Dataset:%s' % (DatasetID)),
pixels,
ID='Image:%s' % (self.instrument))
yield image
return
def iter_Dataset(self, func):
if ' ' in str(self.InputFilename):
DatasetID = str(self.InputFilename).replace(' ', '_')
else:
DatasetID = str(self.InputFilename)
e = func(ID='Dataset:%s' % (DatasetID))
yield e
def iter_Experimenter(self, func):
expid = self.current_user
d = people[expid]
d1 = dict(Email=d['email'],
Institution=d['institution'],
UserName=expid)
if 'first_name' in d:
d1['FirstName'] = d['first_name']
d1['LastName'] = d['last_name']
d1['DisplayName'] = '%(first_name)s %(last_name)s' % d
else:
d1['DisplayName'] = expid
e = func(ID='Experimenter:%s' % (expid), **d1)
g = d['groups']
e.append(ome.GroupRef(ID='Group:%s' % g))
yield e
def iter_Group(self, func):
groupid = self.current_group
d = groups[groupid]
e = func(ID='Group:%s' % groupid, Name=d['name'])
if 'descr' in d:
e.append(ome.Description(d['descr']))
if 'contact_id' in d:
e.append(ome.Contact(ID='Experimenter:%s' % (d['contact_id'])))
yield e
def iter_Instrument(self, func):
yield self.get_Instrument(func)
def get_Instrument(self, func):
if self.mode == 'MetaCyte TL':
self.instrument = 'Bright-field Slide Scanner'
scope = 'MetaCyteTL'
elif self.mode == 'MetaCyte FL':
self.instrument = 'Fluorescence Slide Scanner'
scope = 'MetaCyteFL'
ImarisInfo = self.meta.datasetinfo_imaris()
e = func(ID='Instrument:0')
e.append(
ome.Microscope(Manufacturer='%s' % ImarisInfo['ManufactorString'],
Model=self.instrument,
Type='Upright'))
return e
class OMETIFF(OMEBase):
prefix = 'ome'
def __init__(self, userDetails, inpath, outpath, compression, section_id, total_sections, input_data, pixelregion, outChan, scalefact, rotation):
OMEBase.__init__(self)
self.outputtif_path = outpath
self.ID = section_id
self.Total = total_sections
self.InputPath = inpath
self.InputFilename = self.InputPath.split("\\")[-1]
self.imarray = input_data
self.outChan = outChan
self.rotation = rotation
self.roi = pixelregion
self.meta = SlideImage(self.InputPath)
self.mode = self.meta.micro_mode()
self.scalefact = scalefact
self.tile_width = 1024
self.tile_height = 1024
self.compression = compression
if not userDetails:
current_user = '******'
current_group = 'QBIgroup'
else:
current_user = userDetails[0]
current_group = userDetails[1]
if current_user not in people:
people[current_user] = dict(first_name = userDetails[2], last_name = userDetails[3],
email = userDetails[0]+'@uq.edu.au', user_name = userDetails[0],
institution='Queensland Brain Institute', groups = current_group)
if current_group not in groups:
groups[current_group] = dict(name = userDetails[1], contact_id = userDetails[0])
self.current_user = current_user
self.current_group = current_group
def iter_Image(self, func):
# get the meta data dictionaries
meta = self.meta
if self.mode == 'MetaCyte TL':
self.instrument = 'Bright-field'
elif self.mode == 'MetaCyte FL':
self.instrument = 'Fluorescence'
channelData = meta.datasetinfo_channels()
ImageData = meta.datasetinfo_image()
self.sizeArray = meta.image_size()
fullressize = self.sizeArray[0]
cropressize = self.sizeArray[self.scalefact]
scaleX = float(fullressize[0]) / float(cropressize[0])
scaleY = float(fullressize[1]) / float(cropressize[1])
classifier = ImageData['Metafer Classifer']
if ' ' in str(self.InputFilename):
DatasetID = str(self.InputFilename).replace(' ','_')
else:
DatasetID = str(self.InputFilename)
if self.rotation == 0:
self.sizeX = self.roi[3] - self.roi[2]
self.sizeY = self.roi[1] - self.roi[0]
else:
self.sizeX = self.roi[1] - self.roi[0]
self.sizeY = self.roi[3] - self.roi[2]
self.sizeZ = int(ImageData['Z'])
writeChans = self.outChan
self.sizeC = len(writeChans)
self.sizeT = meta.time_points()
if self.sizeT == 1:
TimeIncrement = '0.0'
else:
TimeIncrement = str(self.sizeT)
dtype = np.dtype('uint8')
ExtminX = float(ImageData['ExtMin0'])
ExtmaxX = float(ImageData['ExtMax0'])
ExtminY = float(ImageData['ExtMin1'])
ExtmaxY = float(ImageData['ExtMax1'])
ExtminZ = float(ImageData['ExtMin2'])
ExtmaxZ = float(ImageData['ExtMax2'])
Width = m.fabs(ExtminX) + m.fabs(ExtmaxX)
Height = m.fabs(ExtminY) + m.fabs(ExtmaxY)
Depth = 0.32 #m.fabs(ExtminZ) + m.fabs(ExtmaxZ)
Xres = ((Width / fullressize[0]) * scaleX)
Yres = ((Height / fullressize[1]) * scaleY)
Zres = Depth
pixels_d = {}
pixels_d['PhysicalSizeX'] = str(Xres)
pixels_d['PhysicalSizeY'] = str(Yres)
pixels_d['PhysicalSizeZ'] = str(Zres)
pixels_d['TimeIncrement'] = TimeIncrement
self.PhysSize = (1/Xres,1/Yres,1/Zres)
order = 'XYZCT'
channel_d = dict(SamplesPerPixel='1')
channel_d['AcquisitionMode'] = 'WideField'
lpath_l = []
self.tif_uuid = self._mk_uuid()
self.tif_filename = self.outputtif_path
# self.tif_images[self.instrument,self.tif_filename,self.tif_uuid,self.PhysSize] = tif_data
pixels = ome.Pixels(
DimensionOrder=order, ID='Pixels:%s' % (self.instrument),
SizeX = str(self.sizeX), SizeY = str(self.sizeY), SizeZ = str(self.sizeZ), SizeT=str(self.sizeT), SizeC = str(self.sizeC),
Type = self.dtype2PixelIType (dtype), **pixels_d
)
if (len(writeChans) > 1):
for chan in writeChans:
chan_dict = channelData[chan]
channel_d['Name'] = chan_dict['Name']
chan_color = chan_dict['Color']
channel = ome.Channel(ID='Channel:0:%s' %(chan), **channel_d)
lpath = ome.LightPath(*lpath_l)
channel.append(lpath)
pixels.append(channel)
plane_l = []
for idx_chan in range(len(writeChans)):
d = dict(IFD=str(idx_chan),FirstC=str(idx_chan), FirstZ='0',FirstT='0', PlaneCount='1')
plane_l.append(d)
tiffdata = ome.TiffData(ome.UUID (self.tif_uuid, FileName=self.tif_filename.split("\\")[-1]), **d)
pixels.append(tiffdata)
elif len(writeChans) == 1:
chan_dict = channelData[writeChans[0]]
channel_d['Name'] = chan_dict['Name']
channel_d['Name'] = chan_dict['Name']
chan_color = chan_dict['Color']
print(chan_color)
channel = ome.Channel(ID='Channel:0:%s' %(0), **channel_d)
lpath = ome.LightPath(*lpath_l)
channel.append(lpath)
pixels.append(channel)
plane_l = []
d = dict(IFD='0',FirstC='0', FirstZ='0',FirstT='0', PlaneCount='1')
plane_l.append(d)
tiffdata = ome.TiffData(ome.UUID (self.tif_uuid, FileName=self.tif_filename.split("\\")[-1]), **d)
pixels.append(tiffdata)
date = ImageData['RecordingDate']
date = date.replace(' ','T')
description = 'This is section number ' + str(self.ID) + ' of ' + str(self.Total) + \
' on ' + self.InputFilename + ' acquired using classifier: ' + classifier
image = ome.Image (ome.AcquiredDate (date),
#ome.ExperimenterRef(ID='Experimenter:%s' % (self.current_user)),
ome.Description('Description:%s' %(description)),
#ome.GroupRef(ID='Group:%s' %(self.current_group)),
ome.DatasetRef(ID='Dataset:%s' % (DatasetID)),
pixels, ID='Image:%s' % (self.instrument))
yield image
return
def iter_Dataset(self, func):
if ' ' in str(self.InputFilename):
DatasetID = str(self.InputFilename).replace(' ','_')
else:
DatasetID = str(self.InputFilename)
e = func (ID='Dataset:%s' % (DatasetID))
yield e
def iter_Experimenter(self, func):
expid = self.current_user
d = people[expid]
d1 = dict (Email=d['email'], Institution=d['institution'], UserName=expid)
if 'first_name' in d:
d1['FirstName'] = d['first_name']
d1['LastName'] = d['last_name']
d1['DisplayName'] = '%(first_name)s %(last_name)s' % d
else:
d1['DisplayName'] = expid
e = func (ID='Experimenter:%s' % (expid), **d1)
g = d['groups']
e.append (ome.GroupRef(ID='Group:%s' % g))
yield e
def iter_Group(self, func):
groupid = self.current_group
d = groups[groupid]
e = func(ID='Group:%s' % groupid, Name=d['name'])
if 'descr' in d:
e.append(ome.Description (d['descr']))
if 'contact_id' in d:
e.append (ome.Contact(ID='Experimenter:%s' % (d['contact_id'])))
yield e
def iter_Instrument(self,func):
yield self.get_Instrument(func)
def get_Instrument(self,func):
if self.mode == 'MetaCyte TL':
self.instrument = 'Bright-field Slide Scanner'
scope = 'MetaCyteTL'
elif self.mode == 'MetaCyte FL':
self.instrument = 'Fluorescence Slide Scanner'
scope = 'MetaCyteFL'
ImarisInfo = self.meta.datasetinfo_imaris()
e = func(ID='Instrument:0')
e.append(ome.Microscope(Manufacturer='%s' %ImarisInfo['ManufactorString'],
Model=self.instrument,Type='Upright'))
return e
