def to_ometiff(self, channel_clips, sample, json_file):
channel_data = {}
channels = []
images = []
ometif_path = '{}.ome.tif'.format(json_file.name.replace('.json', ''))
s3_target = open('s3_target.txt').read().strip()
for (channel, clip) in channel_clips:
channel_data[channel] = {
'sample':
sample,
'tileSource':
'https://s3.amazonaws.com/{}/linnarsson/'.format(s3_target) +
'linnarsson.tiles/linnarsson.images.{}/'.format(channel) +
'{}.dzi'.format(channel)
}
channels.append(channel)
array = self.scale_sample(channel=channel,
sample=sample,
max_allowed=256,
clip=float(clip)).astype(np.uint8)
images.append(array)
json.dump(channel_data, json_file, indent=2)
image = np.transpose(np.dstack(tuple(images)))
image = np.expand_dims(image, axis=0)
image = np.expand_dims(image, axis=0)
channels = [tup[0] for tup in channel_clips]
omexml = self.get_omexml(pixel_array=image,
channels=channels,
name='linnarsson',
pixel_type='uint8')
io.write_ometiff(ometif_path, image, str(omexml))
import numpy as np
from pathlib import Path
import datetime
def create_omexml(image_array, channel_names):
metadata = omexmlClass.OMEXML()
image = metadata.image()
image.AcquisitionDate = datetime.datetime.now().isoformat()
pixels = image.Pixels
pixels.SizeX = image_array.shape[2]
pixels.SizeY = image_array.shape[1]
pixels.SizeC = image_array.shape[0]
pixels.PixelType = str(image_array.dtype)
pixels.channel_count = len(channel_names)
for i, name in enumerate(channel_names):
pixels.Channel(i).Name = name
return f'<?xml version="1.0" encoding="UTF-8"?>\n{metadata}'
if __name__ == "__main__":
arr = np.arange(3 * 50 * 50).reshape((3, 50, 50))
outfile = "dataset.ome.tif"
channel_names = ["0", "1", "2"]
omexml_string = create_omexml(arr, channel_names)
io.write_ometiff(outfile, arr, omexml_string)
def execute(filepath,
separator=';',
filter_method='none',
filter_size=3,
threshold_method='triangle',
min_objectsize=1000,
max_holesize=100,
saveformat='ome.tiff'):
"""Main function that executed the workflow.
:param filepath: file path of the CZI image
:type filepath: tsr
:param separator: sepeartor for the CSV table, defaults to ';'
:type separator: str, optional
:param filter_method: smoothing filer, defaults to 'none'
:type filter_method: str, optional
:param filter_size: kernel size or radius of filter element, defaults to 3
:type filter_size: int, optional
:param threshold_method: threshold method, defaults to 'triangle'
:type threshold_method: str, optional
:param min_objectsize: minimum object size, defaults to 1000
:type min_objectsize: int, optional
:param max_holesize: maximum object size, defaults to 100
:type max_holesize: int, optional
:param saveformat: format to save the segmented image, defaults to 'ome.tiff'
:type saveformat: str, optional
:return: outputs
:rtype: dict
"""
print('--------------------------------------------------')
print('FilePath : ', filepath)
print(os.getcwd())
print('File exists : ', os.path.exists(filepath))
print('--------------------------------------------------')
# define name for figure to be saved
filename = os.path.basename(filepath)
# get the metadata from the czi file
md, additional_mdczi = imf.get_metadata(filepath)
# to make it more readable extravt values from metadata dictionary
stageX = md['SceneStageCenterX']
stageY = md['SceneStageCenterY']
# define columns names for dataframe
cols = ['S', 'T', 'Z', 'C', 'Number']
objects = pd.DataFrame(columns=cols)
# optional dipslay of "some" results - empty list = no display
show_image = [0]
# scalefactor to read CZI
sf = 1.0
# index for channel - currently only single channel images are supported !
chindex = 0
# define maximum object sizes
max_objectsize = 1000000000
# define save format for mask
adapt_dtype_mask = True
dtype_mask = np.int8
# check if it makes sense
if max_holesize > min_objectsize:
min_objectsize = max_holesize
# read the czi mosaic image
czi = CziFile(filepath)
# get the shape of the data using aicspylibczi
print('Dimensions : ', czi.dims)
print('Size : ', czi.size)
print('Shape : ', czi.dims_shape())
print('IsMosaic : ', czi.is_mosaic())
# read the mosaic pixel data
mosaic = czi.read_mosaic(C=0, scale_factor=sf)
print('Mosaic Shape :', mosaic.shape)
# get the mosaic as NumPy.Array - must fit im memory !!!
image2d = np.squeeze(mosaic, axis=0)
md['SizeX_readmosaic'] = image2d.shape[1]
md['SizeY_readmosaic'] = image2d.shape[0]
# create the savename for the OME-TIFF
if saveformat == 'ome.tiff':
savename_seg = filename.split('.')[0] + '.ome.tiff'
if saveformat == 'tiff':
savename_seg = filename.split('.')[0] + '.tiff'
# initialize empty dataframe
results = pd.DataFrame()
# main loop over all T - Z - C slices
for s in progressbar.progressbar(range(md['SizeS']), redirect_stdout=True):
for t in range(md['SizeT']):
for z in range(md['SizeZ']):
values = {'S': s, 'T': t, 'Z': z, 'C': chindex, 'Number': 0}
# preprocessing - filter the image
if filter_method == 'none' or filter_method == 'None':
image2d_filtered = image2d
if filter_method == 'median':
image2d_filtered = median(image2d, selem=disk(filter_size))
if filter_method == 'gauss':
image2d_filtered = gaussian(image2d,
sigma=filter_size,
mode='reflect')
# threshold image
binary = sgt.autoThresholding(image2d_filtered,
method=threshold_method)
# Remove contiguous holes smaller than the specified size
mask = morphology.remove_small_holes(binary,
area_threshold=max_holesize,
connectivity=1,
in_place=True)
# remove small objects
mask = morphology.remove_small_objects(mask,
min_size=min_objectsize,
in_place=True)
# clear the border
mask = segmentation.clear_border(mask, bgval=0, in_place=True)
# label the objects
mask = measure.label(binary)
# adapt pixel type of mask
if adapt_dtype_mask:
mask = mask.astype(dtype_mask, copy=False)
# measure region properties
to_measure = ('label', 'area', 'centroid', 'bbox')
# measure the specified parameters store in dataframe
props = pd.DataFrame(
measure.regionprops_table(
mask, intensity_image=image2d,
properties=to_measure)).set_index('label')
# filter objects by size
props = props[(props['area'] >= min_objectsize)
& (props['area'] <= max_objectsize)]
# add well information for CZI metadata
try:
props['WellId'] = md['Well_ArrayNames'][s]
props['Well_ColId'] = md['Well_ColId'][s]
props['Well_RowId'] = md['Well_RowId'][s]
except (IndexError, KeyError) as error:
# Output expected ImportErrors.
print('Key not found:', error)
print('Well Information not found. Using S-Index.')
props['WellId'] = s
props['Well_ColId'] = s
props['Well_RowId'] = s
# add plane indices
props['S'] = s
props['T'] = t
props['Z'] = z
props['C'] = chindex
# count the number of objects
values['Number'] = props.shape[0]
# update dataframe containing the number of objects
objects = objects.append(pd.DataFrame(values, index=[0]),
ignore_index=True)
results = results.append(props, ignore_index=True)
# make sure the array as 5D of order (T, Z, C, X, Y) to write an correct OME-TIFF
mask = imf.expand_dims5d(mask, md)
# write the OME-TIFF suing apeer-ometiff-library
io.write_ometiff(savename_seg, mask, omexml_string=None)
# rename columns in pandas datatable
results.rename(columns={
'bbox-0': 'ystart',
'bbox-1': 'xstart',
'bbox-2': 'yend',
'bbox-3': 'xend'
},
inplace=True)
# calculate the bbox width in height in [pixel] and [micron]
results['bbox_width'] = results['xend'] - results['xstart']
results['bbox_height'] = results['yend'] - results['ystart']
results['bbox_width_scaled'] = results['bbox_width'] * md['XScale']
results['bbox_height_scaled'] = results['bbox_height'] * md['XScale']
# calculate the bbox center StageXY
results['bbox_center_stageX'], results[
'bbox_center_stageY'] = bbox2stageXY(
image_stageX=stageX,
image_stageY=stageY,
sizeX=md['SizeX'],
sizeY=md['SizeY'],
scale=md['XScale'],
xstart=results['xstart'],
ystart=results['ystart'],
bbox_width=results['bbox_width'],
bbox_height=results['bbox_height'])
# show results
print(results)
print('Done.')
# write the CSV data table
print('Write to CSV File : ', filename)
csvfile = os.path.splitext(filename)[0] + '_planetable.csv'
results.to_csv(csvfile, sep=separator, index=False)
# set the outputs
outputs = {}
outputs['segmented_image'] = savename_seg
outputs['objects_table'] = csvfile
return outputs
ignore_index=True)
results = results.append(props, ignore_index=True)
image_counter += 1
# optional display of results
if image_counter - 1 in show_image:
print('Well:', props['WellId'].iloc[0], 'Index S-C:', s, chindex,
'Objects:', values['Number'])
ax = vst.plot_segresults(image2d, mask, props, add_bbox=True)
# make sure the array as 5D of order (T, Z, C, X, Y) to write an correct OME-TIFF
mask = imf.expand_dims5d(mask, md)
# write the OME-TIFF suing apeer-ometiff-library
io.write_ometiff(savename, mask, omexml_string=None)
# rename columns in pandas datatable
results.rename(columns={
'bbox-0': 'ystart',
'bbox-1': 'xstart',
'bbox-2': 'yend',
'bbox-3': 'xend'
},
inplace=True)
# calculate the bbox width in height in [pixel] and [micron]
results['bbox_width'] = results['xend'] - results['xstart']
results['bbox_height'] = results['yend'] - results['ystart']
results['bbox_width_scaled'] = results['bbox_width'] * md['XScale']
results['bbox_height_scaled'] = results['bbox_height'] * md['XScale']