def get_dimension_only(imagefile):
# get OME-XML and change the encoding to UTF-8
omexml = bioformats.get_omexml_metadata(imagefile)
new_omexml = omexml.encode('utf-8')
rdr = bioformats.get_image_reader(None, path=imagefile)
# read total number of image series
totalseries = rdr.rdr.getSeriesCount()
# get dimensions for CTZXY
md = bioformats.OMEXML(new_omexml)
pixels = md.image(IMAGEID).Pixels
SizeC = pixels.SizeC
SizeT = pixels.SizeT
SizeZ = pixels.SizeZ
SizeX = pixels.SizeX
SizeY = pixels.SizeY
print 'Series: ', totalseries, 'T: ', SizeT, 'Z: ', SizeZ, 'C: ', SizeC, 'X: ', SizeX, 'Y: ', SizeY
# usually the x-axis of an image is from left --> right and y from top --> bottom
# in order to be compatible with numpy arrays XY are switched
# for numpy arrays the 2st axis are columns (top --> down) = Y-Axis for an image
sizes = [totalseries, SizeT, SizeZ, SizeC, SizeY, SizeX]
return sizes
def get_czi_metadata(infile):
"""
This parses the CZI file with the bioformats tool: showinf.
:param infile: file location of the CZI file
:return: dictionary of meta information
"""
command = [
'/usr/local/share/bftools/showinf', '-nopix', '-omexml-only', infile
]
metadata = subprocess.check_output(command).decode('utf-8')
metadata = bioformats.OMEXML(metadata)
# Series #0 should be the first tissue sample at full resolution.
# Series #1 tends to be this same tissue sample at half the resolution.
# This continues halving resolution 5-6 times in succession. We only
# want the full resolution tissue series so we ignore those with dimensions
# that are much smaller than expected. Valid series are checked in get_fullres_series_indices
metadata_dict = {}
for i in range(metadata.image_count):
image = metadata.image(i)
metadata_dict[i] = {}
metadata_dict[i]['width'] = image.Pixels.SizeX
metadata_dict[i]['height'] = image.Pixels.SizeY
metadata_dict[i]['channels'] = image.Pixels.channel_count
image = metadata.image(0)
for i in range(image.Pixels.channel_count):
metadata_dict[f'channel_{i}_name'] = image.Pixels.channel(i).Name
return metadata_dict
def readZPositions(stk_files):
# the only entry parameter for this function is a list with
# the stk files
print('\n\t\treading z-values... ')
javabridge.start_vm(class_path=bioformats.JARS)
zs = [] # array with z-positions
ts = [] # array with time point (got from data files)
print('starting loop...')
for file_path in stk_files:
print('reading file: {}'.format(file_path))
md = bioformats.get_omexml_metadata(file_path)
ome = bioformats.OMEXML(md)
# create an instance of an image to read z-position
zp = ome.image().Pixels.Plane().get_PositionZ()
time = int(file_path[:-4].split('t')[-1])
zs.append(zp)
ts.append(time)
z_offsets = np.array([item - min(np.array(zs)) for item in zs])
javabridge.kill_vm()
return np.array(zs), np.array(ts), z_offsets
def save_images(self):
"""Saves the individual images as a npy file
2D might have more acquisitions +/- focal plane, (usually 3 images).
focal_plane_idx corresponds to the plane to consider. Mid-plane is the
one in focus and the +/- on either side would be blurred. For 2D
acquisitions, this is stored along the Z dimension. How is this handled
for 3D acquisitions?
"""
if not os.path.exists(self.lif_fname):
raise FileNotFoundError("LIF file doesn't exist at:",
self.lif_fname)
os.makedirs(self.split_dir, exist_ok=True)
jv.start_vm(class_path=bf.JARS, max_heap_size='8G')
metadata = bf.get_omexml_metadata(self.lif_fname)
omexml_object = bf.OMEXML(metadata)
num_channels = omexml_object.image().Pixels.channel_count
num_samples = omexml_object.get_image_count()
num_timepoints = omexml_object.image().Pixels.SizeT
num_pix_z = omexml_object.image().Pixels.SizeZ
size_x_um = omexml_object.image().Pixels.PhysicalSizeX
size_y_um = omexml_object.image().Pixels.PhysicalSizeY
size_z_um = omexml_object.image().Pixels.PhysicalSizeZ
reader = bf.ImageReader(self.lif_fname, perform_init=True)
records = []
for timepoint_idx in range(num_timepoints):
timepoint_dir = os.path.join(self.split_dir,
'timepoint_{}'.format(timepoint_idx))
os.makedirs(timepoint_dir, exist_ok=True)
for channel_idx in range(num_channels):
channel_dir = os.path.join(timepoint_dir,
'channel_{}'.format(channel_idx))
os.makedirs(channel_dir, exist_ok=True)
for sample_idx in range(15, 412): # num_samples
cur_records = self.save_each_image(reader, num_pix_z,
channel_dir,
timepoint_idx,
channel_idx, sample_idx,
size_x_um, size_y_um,
size_z_um)
records.extend(cur_records)
msg = 'Wrote files for tp:{}, channel:{}'.format(
timepoint_idx, channel_idx)
self._log_info(msg)
df = pd.DataFrame.from_records(records,
columns=[
'timepoint', 'channel_num',
'sample_num', 'slice_num', 'fname',
'size_x_microns', 'size_y_microns',
'size_z_microns'
])
metadata_fname = os.path.join(self.split_dir, 'split_images_info.csv')
df.to_csv(metadata_fname, sep=',')
jv.kill_vm()
def extract_meta_bioformats(filepath, metadata=dict()):
omexmlstr = bioformats.get_omexml_metadata(filepath)
o = bioformats.OMEXML(omexmlstr)
x = o.image().Pixels
metadata['size_Z'] = x.SizeZ
metadata['size_T'] = x.SizeT
metadata['scale'] = x.PhysicalSizeX
return metadata
def getImageInfo(filename):
md = bf.get_omexml_metadata(filename)
ome = bf.OMEXML(md)
pixels = ome.image().Pixels
pT = pixels.SizeT
pX = pixels.SizeX
pY = pixels.SizeY
return pT, pX, pY
def getImageShape(path):
xmlimage=bf.get_omexml_metadata(path=path)
metadata= bf.OMEXML(xmlimage)
NX= metadata.image().Pixels.SizeX
NY= metadata.image().Pixels.SizeY
NZ= metadata.image().Pixels.SizeZ
NC= metadata.image().Pixels.SizeC
NT= metadata.image().Pixels.SizeT
return (NX,NY,NZ,NC,NT)
def get_editable_omexml(path):
"""
Parse OMEXML header data from a Bio-Formats-compatible file.
Used to parse metadata from .nd2 file and pass on to .ome.tiff file.
"""
o = bioformats.get_omexml_metadata(path)
new_omexml = bioformats.OMEXML(o)
return new_omexml
def get_movie_shape(path):
xml_image = bf.get_omexml_metadata(path=path)
metadata = bf.OMEXML(xml_image)
path = path
NX = metadata.image().Pixels.SizeX
NY = metadata.image().Pixels.SizeY
NZ = metadata.image().Pixels.SizeZ
NC = metadata.image().Pixels.SizeC
NT = metadata.image().Pixels.SizeT
return NX,NY,NZ,NC,NT
def get_metadata_store(imagefile):
if not VM_STARTED:
start_jvm()
if VM_KILLED:
jvm_error()
omexml = bioformats.get_omexml_metadata(imagefile)
new_omexml = omexml.encode('utf-8')
metadatastore = bioformats.OMEXML(new_omexml)
return metadatastore
def get_metadata_store(imagefile):
if not VM_STARTED:
start_jvm()
if VM_KILLED:
jvm_error()
# get OME-XML and change the encoding to UTF-8
omexml = get_OMEXML(imagefile)
# get the metadata from the OME-XML
omexmlmetadata = bioformats.OMEXML(omexml)
return omexmlmetadata
def showGrahp(filename, num):
md = bf.get_omexml_metadata(filename)
#rdr = bf.ImageReader(filename, perform_init=True)
ome = bf.OMEXML(md)
pixels = ome.image().Pixels
print('pixels in Z: ' + str(pixels.SizeZ))
print('pixels in C: ' + str(pixels.SizeC))
print('pixels in T: ' + str(pixels.SizeT))
print('pixels in X: ' + str(pixels.SizeX))
print('pixels in Y: ' + str(pixels.SizeY))
with bf.ImageReader(filename) as reader:
img = reader.read(t=num)
plt.imshow(img, cmap=plt.cm.binary)
plt.show()
reader.close()
def readfile(filename):
# read metadata
metadata = bioformats.get_omexml_metadata(filename)
xml = bioformats.OMEXML(metadata)
Pixels = xml.image().Pixels
nx, ny, nz, nt = Pixels.SizeX, Pixels.SizeY, Pixels.SizeZ, Pixels.SizeT
# read image data
image4d = np.zeros(shape=(nx, ny, nz, nt))
reader = bioformats.ImageReader(filename)
for t in range(nt):
for z in range(nz):
image4d[:, :, z, t] = reader.read(z=z, t=t, rescale=False)
return image4d
def read(self, microscope_metadata_files, microscope_image_files):
'''Provides an empty OMEXML.
Parameters
----------
microscope_metadata_files: List[str]
absolute path to the microscope metadata files
microscope_image_files: List[str]
absolute path to the microscope image files
Returns
-------
bioformats.omexml.OMEXML
OMEXML image metadata
'''
return bioformats.OMEXML(XML_DECLARATION)
def read_metadata(self):
omexmlstr = bioformats.get_omexml_metadata(self.path)
omexml = bioformats.OMEXML(omexmlstr)
# orgmetadata = bioformats.OMEXML.OriginalMetadata(omexml)
root = ET.fromstring(omexmlstr)
print(omexml)
tree = ET.ElementTree(root)
self.channel_count = omexml.image().Pixels.channel_count
self.channel_names = []
# populate channel_names
for i in np.arange(0, self.channel_count, 1):
self.channel_names.append(omexml.image().Pixels.Channel(i).Name)
# print('image_count:',omexml.get_image_count())
# print('image physical sizex unit',omexml.image().Pixels.get_PhysicalSizeXUnit())
# print('image physical sizex',omexml.image().Pixels.get_PhysicalSizeX())
# print('image sizex',omexml.image().Pixels.get_SizeX())
# print('image physical sizey unit',omexml.image().Pixels.get_PhysicalSizeYUnit())
# print('image physical sizey',omexml.image().Pixels.get_PhysicalSizeY())
# print('image sizey',omexml.image().Pixels.get_SizeY())
# print('image sizez',omexml.image().Pixels.get_SizeZ())
# print('image sixec',omexml.image().Pixels.get_SizeC())
# print('image sizet',omexml.image().Pixels.get_SizeT())
# print('image dimensions',omexml.image().Pixels.get_DimensionOrder())
tagprefix = '{http://www.openmicroscopy.org/Schemas/OME/2016-06}'
for child1 in root:
print('Child1: ', child1.tag, '\t', child1.attrib)
for child2 in child1:
print('Child2: ', child2.tag, '\t', child2.attrib)
count = 0
for child3 in child2:
print('Child3: ', child3.tag, child3.attrib)
if (count >= 10):
break
if (child3.tag == tagprefix + 'Plane'):
count = count + 1
for child4 in child3:
print('Child4: ', child4.tag, child4.attrib)
for child5 in child4:
print('Child5: ', child5.tag, child5.attrib)
for child6 in child5:
print('Child6: ', child6.tag, child6.attrib)
print('-----------------------------------')
print('====================================')
def load_slide_scanner_image(self, z=0, serie=4):
self.downfactor = 1
self._img_vsi = bf.load_image(self.path.as_posix(),
t=0,
series=serie,
z=z)
metadata = bf.get_omexml_metadata(self.path.as_posix())
o = bf.OMEXML(metadata)
n_zslices = o.image_count
self.n_slices_known.emit(n_zslices, self._img_vsi.shape[-1], 4, 1)
self.slice_slide_scanner()
def __init__(self, filename):
self.reader: bioformats.ImageReader
self.indexes = []
self.sizes = []
self.mags = []
xml = bioformats.get_omexml_metadata(filename)
self.meta = bioformats.OMEXML(xml)
# get magnification
#meta.Objective()...
for i in range(self.meta.get_image_count()):
imeta = self.meta.image(i)
pmeta = imeta.Pixels
name = imeta.Name
if pmeta.PhysicalSizeX is not None and name.endswith('x'):
self.indexes.append(i)
self.sizes.append((pmeta.SizeX, pmeta.SizeY))
self.mags.append(int(name[0:-1]))
self.reader = bioformats.ImageReader(filename)
def get_metadata(self, save=True):
# Retrieve metadata info, put it in data frame and save it.
print('Getting and saving metadata for ' + self.fullpath + '...')
ome = bf.OMEXML(bf.get_omexml_metadata(self.fullpath))
md = []
for ind in range(ome.image_count):
iome = ome.image(ind)
md.append([
iome.get_ID(),
iome.get_Name(),
iome.Pixels.get_SizeC(),
iome.Pixels.get_SizeX(),
iome.Pixels.get_PhysicalSizeX(),
iome.Pixels.get_PhysicalSizeXUnit(),
iome.Pixels.get_SizeY(),
iome.Pixels.get_PhysicalSizeY(),
iome.Pixels.get_PhysicalSizeYUnit(),
iome.Pixels.get_SizeZ(),
iome.Pixels.get_PhysicalSizeZ(),
iome.Pixels.get_PhysicalSizeZUnit(),
iome.Pixels.get_PixelType()
])
self.md = pd.DataFrame(md)
self.md.columns = [
'ID', 'Name', 'Nchan', 'SizeX', 'PhysicalSizeX',
'PhysicalSizeXUnit', 'SizeY', 'PhysicalSizeY', 'PhysicalSizeYUnit',
'SizeZ', 'PhysicalSizeZ', 'PhysicalSizeZUnit', 'PixelType'
]
if save:
print('Saving metadata...')
fname = os.path.join(self.dpath, self.date, self.project,
'parsed_metadata')
with open(fname + '.pickle', 'wb') as f:
pickle.dump(self.md, f)
self.md.to_csv(fname + '.csv', index=False)
# Create one folder per series
project_path = os.path.join(self.dpath, self.date, self.project)
for i in range(ome.image_count):
folder_name = 'S{:0>2d}'.format(i + 1) + '_' + self.md.Name[i]
# Useful ref: https://mkaz.blog/code/python-string-format-cookbook/
series_folder = os.path.join(project_path, folder_name)
if not os.path.exists(series_folder):
print('Creating subfolder ' + series_folder + '...')
os.makedirs(series_folder)
def read_metadata(self,update=False):
if self._metadata and not update:
return self._metadata
# For some reason, tif files need to use the generic ImageReader while everything else
# can use the OMETiffReader.
if self._file_path.endswith('.ome.tif'):
rdr = jutil.JClassWrapper('loci.formats.in.OMETiffReader')()
else:
rdr = jutil.JClassWrapper('loci.formats.ImageReader')()
rdr.setOriginalMetadataPopulated(True)
clsOMEXMLService = jutil.JClassWrapper('loci.formats.services.OMEXMLService')
serviceFactory = jutil.JClassWrapper('loci.common.services.ServiceFactory')()
service = serviceFactory.getInstance(clsOMEXMLService.klass)
omexml = service.createOMEXMLMetadata()
rdr.setMetadataStore(omexml)
rdr.setId(self._file_path)
self._metadata = bioformats.OMEXML(omexml.dumpXML())
return self._metadata
def read_stack(file_path, axis='T'):
xml = bf.OMEXML(bf.get_omexml_metadata(file_path))
sizeZ = xml.image().Pixels.SizeZ
sizeC = xml.image().Pixels.SizeC
sizeT = xml.image().Pixels.SizeT
sizeX = xml.image().Pixels.SizeX
sizeY = xml.image().Pixels.SizeY
axes = {'z': sizeZ, 'c': sizeC, 't': sizeT}
ax3 = axes[axis.lower()]
out_arr = np.zeros((ax3, sizeX, sizeY), dtype='uint16')
with bf.ImageReader(file_path) as r:
for i in range(ax3):
kwargs = {axis.lower(): i}
im = r.read(rescale=False, **kwargs)
out_arr[i] = im
return out_arr
def getMax(filename):
md = bf.get_omexml_metadata(filename)
#rdr = bf.ImageReader(filename, perform_init=True)
ome = bf.OMEXML(md)
pixels = ome.image().Pixels
count = pixels.SizeT
xSum = []
with bf.ImageReader(filename) as reader:
for i in range(count):
img = reader.read(t=i)
mm = np.max(img)
v2 = img >= mm / 15
im2 = img * v2
ssum = sum(sum(im2)) / sum(sum(v2))
xSum.append(ssum)
reader.close()
mP = max(xSum)
idx = xSum.index(mP)
return mP, idx
def im_compile(filename, path=None, rescale=False):
"""
Converts image stack to a numpy nd array using python-bioformats.
Parses the metadata of an image file and constructs a numpy ndarray of the
images based on the number of channels, z-planes, and timepoints.
Args:
filename: Filename of the image to parse.
path: The path to the image file.
Returns:
An ndarray of the images in the order YXCZT.
"""
import bioformats as bf
import numpy as np
import os
if path is None:
fullfile = filename
elif isinstance(path, str):
fullfile = os.path.join(path, filename)
metadata = bf.get_omexml_metadata(fullfile)
ome_data = bf.OMEXML(xml=metadata)
mat = np.zeros([
ome_data.image().Pixels.SizeY,
ome_data.image().Pixels.SizeX,
ome_data.image().Pixels.channel_count,
ome_data.image().Pixels.SizeZ,
ome_data.image().Pixels.SizeT
])
with bf.ImageReader(fullfile) as rdr:
for c_cnt in range(ome_data.image().Pixels.channel_count):
for t_cnt in range(ome_data.image().Pixels.SizeT):
for z_cnt in range(ome_data.image().Pixels.SizeZ):
image = rdr.read(c=c_cnt,
t=t_cnt,
z=z_cnt,
rescale=rescale)
mat[:, :, c_cnt, z_cnt, t_cnt] = image
return mat.astype(ome_data.image().Pixels.PixelType)
def czi2tif(nameczi, nametif, slice):
image_info = bioformats.get_omexml_metadata(nameczi)
omxml_data = bioformats.OMEXML(image_info)
pixels = omxml_data.image(slice).Pixels
sx = pixels.SizeX
sy = pixels.SizeY
dst_ds = gdal.GetDriverByName('GTiff').Create(
nametif, sx, sy, 3, gdal.GDT_Byte,
['COMPRESS=LZW', 'BIGTIFF=YES', 'TILED=YES'])
r1 = dst_ds.GetRasterBand(1)
r2 = dst_ds.GetRasterBand(2)
r3 = dst_ds.GetRasterBand(3)
step = int(np.round(sy / 4))
with bioformats.formatreader.ImageReader(nameczi) as reader:
for k in range(0, sy, step):
# print(k)
if k + step >= sy:
step = step - (k + step - sy) - 1
if step > 0:
data = reader.read(nameczi,
z=0,
t=0,
series=slice,
rescale=False,
XYWH=(0, k, sx, step))
data = np.array(np.reshape(data, (step, -1, 3)))
r1.WriteArray(data[:, :, 0], 0, k)
r2.WriteArray(data[:, :, 1], 0, k)
r3.WriteArray(data[:, :, 2], 0, k)
dst_ds.BuildOverviews("NEAREST", [2, 4, 8, 16, 32, 64])
dst_ds.FlushCache() # write to disk
dst_ds = None
def _read_metadata(self):
## Read metadata from ONEXML
metadata = bioformats.get_omexml_metadata(str(self.file_path))
o = bioformats.OMEXML(metadata)
dom = o.dom
self._root = dom.getroot()
for ch in self._root:
tag = ch.tag[ch.tag.find('}') + 1:]
if 'Image' in tag:
attrib = ch.attrib
if 'Primary' in attrib['Name']:
for ch1 in ch:
tag1 = ch1.tag[ch1.tag.find('}') + 1:]
attrib1 = ch1.attrib
if 'Pixels' in tag1:
pixels_info = attrib1
self._num_channels = int(pixels_info['SizeC'])
self._num_frames = int(pixels_info['SizeT'])
self._size_x = int(pixels_info['SizeX'])
self._size_y = int(pixels_info['SizeY'])
self._size_z = int(pixels_info['SizeZ'])
self._dtype = pixels_info['Type']
self._pixelinfo = pixels_info
# TODO retrieve channel names
self._channel_names = [f'channel_{i}' for i in range(self._num_channels)]
# TODO find better approach
get_next = False
for it in self._root.iter():
if it.text is not None:
if get_next:
self._sampling_frequency = float(it.text)
get_next = False
if 'frame' in it.text:
get_next = True
def readBioFormatsMeta(fn):
"""Reads meta data out of bioformats format.
.. note:: Changes system default encoding to UTF8.
Args:
fn (str): Path to file.
Returns:
OMEXML: meta data of all data.
"""
#Change system encoding to UTF 8
reload(sys)
sys.setdefaultencoding('UTF8')
#Load and convert to utf8
meta = bioformats.get_omexml_metadata(path=fn)
meta = meta.decode().encode('utf-8')
meta2 = bioformats.OMEXML(meta)
return meta2
def genGraphSingle(filename):
md = bf.get_omexml_metadata(filename)
#rdr = bf.ImageReader(filename, perform_init=True)
ome = bf.OMEXML(md)
pixels = ome.image().Pixels
count = pixels.SizeT
vPZ = []
pZ = 250
for i in range(count):
vPZ.append(i * pZ / 1000)
xSum = []
with bf.ImageReader(filename) as reader:
for i in range(count):
#print(i)
img = reader.read(t=i)
#img = ndimage.gaussian_filter(img, sigma=(200, 200))
mm = np.max(img)
v2 = img >= mm / 15
im2 = img * v2
ssum = sum(sum(im2)) / sum(sum(v2))
xSum.append(ssum)
reader.close()
plt.plot(vPZ, xSum)
plt.show()
import javabridge
import bioformats
import numpy as np
import nrrd
import matplotlib.pyplot as plt
javabridge.start_vm(class_path=bioformats.JARS)
path_to_data = 'test_data/Nucleisegmentedfill.tif'
depth = 't' # z or t
# Get XML metadata of complete file
xml_string = bioformats.get_omexml_metadata(path_to_data)
ome = bioformats.OMEXML(xml_string)
# Print the number of images that are in the tif
print(ome.image_count)
# Read the metadata from the first image -> series 0
iome = ome.image(0)
series_count = ome.get_image_count()
image_name = iome.get_Name()
image_id = iome.get_ID()
image_acquisition = iome.get_AcquisitionDate()
print('Series count: ', series_count)
print('Name: ', image_name)
print('ID: ', image_id)
print('Acquisition Date: ', image_acquisition)
# Geth the pixel meta data from the image
print('User Selected: %s' %filename)
# ##### Show metadata
# I am using Python-bioformats version 1.1.0 and, as of 4/26/2016, there is a bug in omexml.py. To fix this bug,
# - Go to your path that contains omexml.py (/anaconda/envs/env2_bioformats/lib/python2.7/site-packages/bioformats/ in my case)
# - Delete omexml.pyc
# - Then open omexml.py and change line 318 from 'isinstance(xml, str):' to 'isinstance(xml, basestring)'
#
# If you don't do this, you may get an error that says "ascii codec can't encode character"
# In[60]:
md = bf.get_omexml_metadata(file_full_path)
ome = bf.OMEXML(md)
iome = ome.image(0) # e.g. first image
#print(ome.image_count)
print('Image Name: %s' %iome.get_Name())
print('Image ID: %s' %iome.get_ID()) #what is image ID?
print('Acquisition Date: %s' %iome.AcquisitionDate)
print('')
print('Bit Depth: %s' %iome.Pixels.get_PixelType())
print('XYZ Dimensions: %s x %s x %s pixels' %(iome.Pixels.get_SizeX(),iome.Pixels.get_SizeY(),iome.Pixels.get_SizeZ()))
print('Time Points: %s' %iome.Pixels.get_SizeT())
print('DimensionOrder: %s' %iome.Pixels.DimensionOrder)
#print('get_DimensionOrder: %s' %iome.Pixels.get_DimensionOrder()) #what is the difference between get_DimensionOrder() and DimensionOrder?
print('Channels: %s' %iome.Pixels.get_SizeC())
print('Ch1: %s' %iome.Pixels.Channel(0).Name)
def _combine_omexml_elements(self, omexml_images, omexml_metadata):
logger.info('combine OMEXML elements')
# We assume here that each image files contains the same number images.
n_images = omexml_images.values()[0].image_count * len(omexml_images)
if omexml_metadata is not None:
extra_omexml_available = True
if not isinstance(omexml_metadata, bioformats.omexml.OMEXML):
raise TypeError('Argument "omexml_metadata" must have type '
'bioformats.omexml.OMEXML.')
if omexml_metadata.image_count != n_images:
raise MetadataError(
'Number of images in "omexml_metadata" must match '
'the total number of Image elements in "omexml_images".')
else:
extra_omexml_available = False
omexml_metadata = bioformats.OMEXML(XML_DECLARATION)
omexml_metadata.image_count = n_images
image_element_attributes = {'AcquisitionDate', 'Name'}
channel_element_attributes = {'Name'}
pixel_element_attributes = {
'PixelType', 'SizeC', 'SizeT', 'SizeX', 'SizeY', 'SizeZ'
}
plane_element_attributes = {
'PositionX', 'PositionY', 'PositionZ', 'TheC', 'TheT', 'TheZ'
}
filenames = natsorted(omexml_images)
count = 0
for i, f in enumerate(filenames):
omexml_img = omexml_images[f]
n_series = omexml_img.image_count
for s in xrange(n_series):
extracted_image = omexml_img.image(s)
md_image = omexml_metadata.image(count)
for attr in image_element_attributes:
extracted_value = getattr(extracted_image, attr)
if extracted_value is not None:
setattr(md_image, attr, extracted_value)
extracted_pixels = extracted_image.Pixels
n_planes = extracted_pixels.plane_count
if n_planes == 0:
# Sometimes an image doesn't have any plane elements.
# Let's create them for consistency.
extracted_pixels = self._create_channel_planes(
extracted_pixels)
n_planes = extracted_pixels.plane_count
md_pixels = md_image.Pixels
md_pixels.plane_count = n_planes
if extra_omexml_available and (md_pixels.plane_count !=
n_planes):
raise MetadataError(
'Image element #%d in OMEXML obtained from additional '
'metdata files must have the same number of Plane '
'elements as the corresponding Image elements in the '
'OMEXML element obtained from image file "%s".' %
(i, f))
for attr in pixel_element_attributes:
extracted_value = getattr(extracted_pixels, attr)
if extracted_value is not None:
# This is python-bioformats being stupid by setting
# random default values.
setattr(md_pixels, attr, extracted_value)
for p in xrange(n_planes):
extracted_plane = extracted_pixels.Plane(p)
md_plane = md_pixels.Plane(p)
for attr in plane_element_attributes:
extracted_value = getattr(extracted_plane, attr)
md_value = getattr(md_plane, attr)
if md_value is None and extracted_value is not None:
setattr(md_plane, attr, extracted_value)
fm = ImageFileMapping()
fm.ref_index = count + p
fm.files = [f]
fm.series = [s]
fm.planes = [p]
self._file_mapper_list.append(fm)
self._file_mapper_lut[f].append(fm)
n_channels = extracted_pixels.channel_count
md_image.channel_count = n_channels
for c in xrange(n_channels):
extracted_channel = extracted_pixels.Channel(c)
md_channel = md_pixels.Channel(c)
for attr in channel_element_attributes:
extracted_value = getattr(extracted_channel, attr)
if extracted_value is not None:
setattr(md_channel, attr, extracted_value)
count += 1
return omexml_metadata
def run_job(self, batch, assume_clean_state=False):
'''Configures OMEXML metadata extracted from microscope image files and
complements it with metadata retrieved from additional microscope
metadata files and/or user input.
The actual processing is delegated to a format-specific implementation of
:class:`MetadataHandler <tmlib.workflow.metaconfig.base.MetadataHandler>`.
Parameters
----------
batch: dict
job description
assume_clean_state: bool, optional
assume that output of previous runs has already been cleaned up
See also
--------
:mod:`tmlib.workflow.metaconfig.cellvoyager`
'''
regexp = batch.get('regex', '')
if not regexp:
regexp = get_microscope_type_regex(
batch['microscope_type'], as_string=True
)[0]
with tm.utils.ExperimentSession(self.experiment_id) as session:
experiment = session.query(tm.Experiment).one()
plate_dimensions = experiment.plates[0].dimensions
acquisition = session.query(tm.Acquisition).\
get(batch['acquisition_id'])
metadata_files = session.query(tm.MicroscopeMetadataFile.location).\
filter_by(acquisition_id=batch['acquisition_id']).\
all()
metadata_filenames = [f.location for f in metadata_files]
image_files = session.query(
tm.MicroscopeImageFile.name, tm.MicroscopeImageFile.omexml
).\
filter_by(acquisition_id=batch['acquisition_id']).\
all()
omexml_images = {
f.name: bioformats.OMEXML(f.omexml) for f in image_files
}
MetadataReader = metadata_reader_factory(batch['microscope_type'])
if MetadataReader is not None:
with MetadataReader() as mdreader:
omexml_metadata = mdreader.read(
metadata_filenames, omexml_images.keys()
)
else:
omexml_metadata = None
MetadataHandler = metadata_handler_factory(batch['microscope_type'])
mdhandler = MetadataHandler(omexml_images, omexml_metadata)
mdhandler.configure_from_omexml()
missing = mdhandler.determine_missing_metadata()
if missing:
logger.warning(
'required metadata information is missing: "%s"',
'", "'.join(missing)
)
logger.info(
'try to retrieve missing metadata from filenames '
'using regular expression'
)
if regexp is None:
logger.warn('no regular expression provided')
mdhandler.configure_from_filenames(
plate_dimensions=plate_dimensions, regex=regexp
)
missing = mdhandler.determine_missing_metadata()
if missing:
raise MetadataError(
'The following metadata information is missing:\n"%s"\n'
% '", "'.join(missing)
)
# Once we have collected basic metadata such as information about
# channels and focal planes, we try to determine the relative position
# of images within the acquisition grid
try:
logger.info(
'try to determine grid coordinates from microscope '
'stage positions'
)
mdhandler.determine_grid_coordinates_from_stage_positions()
except MetadataError as error:
logger.warning(
'microscope stage positions are not available: "%s"'
% str(error)
)
logger.info(
'try to determine grid coordinates from provided stitch layout'
)
# In general, the values of these arguments can be ``None``, because
# they are not required and may not be used.
# However, in case the grid coordinates should be determined based
# on user interput, these arguments are required.
if not isinstance(batch['n_vertical'], int):
raise TypeError(
'Value of argument "n_vertical" must be an integer.'
)
if not isinstance(batch['n_horizontal'], int):
raise TypeError(
'Value of argument "n_horizontal" must be an integer.'
)
mdhandler.determine_grid_coordinates_from_layout(
stitch_layout=batch['stitch_layout'],
stitch_dimensions=(batch['n_vertical'], batch['n_horizontal'])
)
if batch['perform_mip']:
mdhandler.group_metadata_per_zstack()
# Create consistent zero-based ids
mdhandler.update_indices()
mdhandler.assign_acquisition_site_indices()
md = mdhandler.remove_redundant_columns()
fmaps = mdhandler.create_image_file_mappings()
logger.info('create database entries')
with tm.utils.ExperimentSession(self.experiment_id) as session:
channels = dict()
bit_depth = md['bit_depth'][0]
for ch_name in np.unique(md['channel_name']):
logger.info('create channel "%s"', ch_name)
ch = session.get_or_create(
tm.Channel, experiment_id=self.experiment_id,
name=ch_name, wavelength=ch_name, bit_depth=bit_depth,
)
channels[ch_name] = ch.id
for w in np.unique(md.well_name):
with tm.utils.ExperimentSession(self.experiment_id) as session:
acquisition = session.query(tm.Acquisition).\
get(batch['acquisition_id'])
logger.info('create well "%s"', w)
w_index = (md.well_name == w)
well = session.get_or_create(
tm.Well, plate_id=acquisition.plate.id, name=w
)
channel_image_files = []
for s in np.unique(md.loc[w_index, 'site']):
logger.debug('create site #%d', s)
s_index = (md.site == s)
y = md.loc[s_index, 'well_position_y'].values[0]
x = md.loc[s_index, 'well_position_x'].values[0]
height = md.loc[s_index, 'height'].values[0]
width = md.loc[s_index, 'width'].values[0]
site = session.get_or_create(
tm.Site, y=y, x=x, height=height, width=width,
well_id=well.id
)
for index, i in md.ix[s_index].iterrows():
channel_image_files.append(
tm.ChannelImageFile(
tpoint=i.tpoint, zplane=i.zplane,
channel_id=channels[i.channel_name],
site_id=site.id, acquisition_id=acquisition.id,
file_map=fmaps[index],
)
)
session.bulk_save_objects(channel_image_files)
