def _merge_layers(input_dir, input_files, output_dir, output_file):
zs = [z for z in input_files.keys()] # sorted list of filenames by z-value
zs.sort()
# Initialize the output file
br = BioReader(
str(Path(input_dir).joinpath(input_files[zs[0]][0]).absolute()))
bw = BioWriter(str(Path(output_dir).joinpath(output_file).absolute()),
metadata=br.read_metadata())
bw.num_z(Z=len(zs))
del br
# Load each image and save to the volume file
for z, i in zip(zs, range(len(zs))):
br = BioReader(
str(Path(input_dir).joinpath(input_files[z][0]).absolute()))
bw.write_image(br.read_image(), Z=[i, i + 1])
del br
# Close the output image and delete
bw.close_image()
del bw
def write_ome_tiffs(file_path, out_path):
if Path(file_path).suffix != '.czi':
TypeError("Path must be to a czi file.")
base_name = Path(Path(file_path).name).stem
czi = czifile.CziFile(file_path, detectmosaic=False)
subblocks = [
s for s in czi.filtered_subblock_directory
if s.mosaic_index is not None
]
metadata_str = czi.metadata(True)
metadata = czi.metadata(False)['ImageDocument']['Metadata']
chan_name = _get_channel_names(metadata)
pix_size = _get_physical_dimensions(metadata_str)
ind = {'X': [], 'Y': [], 'Z': [], 'C': [], 'T': [], 'Row': [], 'Col': []}
for s in subblocks:
scene = [
dim.start for dim in s.dimension_entries if dim.dimension == 'S'
]
if scene is not None and scene[0] != 0:
continue
for dim in s.dimension_entries:
if dim.dimension == 'X':
ind['X'].append(dim.start)
elif dim.dimension == 'Y':
ind['Y'].append(dim.start)
elif dim.dimension == 'Z':
ind['Z'].append(dim.start)
elif dim.dimension == 'C':
ind['C'].append(dim.start)
elif dim.dimension == 'T':
ind['T'].append(dim.start)
row_conv = {
y: row
for (y, row) in zip(np.unique(np.sort(ind['Y'])),
range(0, len(np.unique(ind['Y']))))
}
col_conv = {
x: col
for (x, col) in zip(np.unique(np.sort(ind['X'])),
range(0, len(np.unique(ind['X']))))
}
ind['Row'] = [row_conv[y] for y in ind['Y']]
ind['Col'] = [col_conv[x] for x in ind['X']]
for s, i in zip(subblocks, range(0, len(subblocks))):
dims = [
_get_image_dim(s, 'Y'),
_get_image_dim(s, 'X'),
_get_image_dim(s, 'Z'),
_get_image_dim(s, 'C'),
_get_image_dim(s, 'T')
]
data = s.data_segment().data().reshape(dims)
Z = None if len(ind['Z']) == 0 else ind['Z'][i]
C = None if len(ind['C']) == 0 else ind['C'][i]
T = None if len(ind['T']) == 0 else ind['T'][i]
out_file_path = os.path.join(
out_path,
_get_image_name(base_name,
row=ind['Row'][i],
col=ind['Col'][i],
Z=Z,
C=C,
T=T))
bw = BioWriter(out_file_path, data)
bw.channel_names([chan_name[C]])
bw.physical_size_x(pix_size['X'], 'µm')
bw.physical_size_y(pix_size['Y'], 'µm')
if pix_size['Z'] is not None:
bw.physical_size_y(pix_size['Z'], 'µm')
bw.write_image(data)
bw.close_image()
if 't' in variables:
out_dict['t'] = T
if 'c' in variables:
out_dict['c'] = C
base_output = output_name(inp_regex,
[i for i in test.get_matching(R=R, T=T, C=C)],
out_dict)
# Export the flatfield image as a tiled tiff
flatfield_out = base_output.replace('.ome.tif', '_flatfield.ome.tif')
bw = BioWriter(str(output_dir.joinpath(flatfield_out)))
bw.pixel_type('float')
bw.num_x(X)
bw.num_y(Y)
bw.write_image(np.reshape(flatfield, (Y, X, 1, 1, 1)))
bw.close_image()
# Export the darkfield image as a tiled tiff
if new_options['darkfield']:
darkfield_out = base_output.replace('.ome.tif', '_darkfield.ome.tif')
bw = BioWriter(str(output_dir.joinpath(darkfield_out)))
bw.pixel_type('float')
bw.num_x(X)
bw.num_y(Y)
bw.write_image(np.reshape(darkfield, (Y, X, 1, 1, 1)))
bw.close_image()
# Export the photobleaching components as csv
if get_photobleach:
offsets_out = base_output.replace('.ome.tif', '_offsets.csv')
with open(str(metadata_dir.joinpath(offsets_out)), 'w') as fw: