def module_main(ctx):
try:
#Inizialization
tstart = time.process_time()
print(SEPARATOR)
print('Thresholding started!')
#Create Image object
img = a3.inputs['Input Image']
print('Thresholding: ' + img.metadata['Name'])
#Get method and mode. Get kwargs if method is manual
method = METHODS[a3.inputs['Method'][-1]]
if method == 'Manual':
kwargs = {'lower': 0, 'upper': a3.inputs['Manual threshold value']}
elif method == 'None':
method = 'Manual'
kwargs = {'lower': 0, 'upper': 0}
else:
kwargs = {}
if a3.inputs['Slice/Stack histogram']:
kwargs['mode'] = 'Stack'
else:
kwargs['mode'] = 'Slice'
print('Mode: ' + kwargs['mode'])
print('Method: ' + method)
#Run thresholding
output_img, log_text = module_threshold(img, method, kwargs)
threshold_value = str(log_text.split('\n')[-1].split(':')[-1])
print('Threshold value(s): ' + threshold_value)
#Change Name in metadata
output_img.metadata['Name'] = img.metadata['Name'] + '_auto_thr'
if (a3.inputs['Save Threshold(s)'] or a3.inputs['Save Image']):
#Set path and filename
#Generate base directory
if os.path.isdir(a3.inputs['Output Path'].path):
output_path = a3.inputs['Output Path'].path
else:
output_path = os.path.dirname(a3.inputs['File Path'].path)
#Create otput directory
output_path = os.path.join(output_path, 'Thresholding')
if not os.path.exists(output_path):
os.makedirs(output_path)
#Save threshold in file
if a3.inputs['Save Threshold(s)']:
print('Saving Threshold Values to file!')
channel_name = img.metadata['Name']
filename = a3.inputs['File Path'].path
print(
'#########################################################################'
)
print(threshold_value)
threshold_to_text(output_path, filename, channel_name, method,
threshold_value)
#Save images
#Generate output filename base for images
filename_img = os.path.basename(output_img.metadata['Path'])
basename_img = os.path.splitext(filename_img)[0]
if a3.inputs['Save Image']:
print('Saving output images!')
#image_list=[ch1_img, ch2_img, ovl_img]
name_img = basename_img + '_{}_{}.ome.tiff'.format(
str(method), str(kwargs['mode']))
save_image(output_img, output_path, name_img)
#Set output
a3.outputs['Thresholded Image'] = output_img
#Finalization
tstop = time.process_time()
print('Processing finished in ' + str((tstop - tstart)) + ' seconds!')
print('Autothresholding was successfully!')
print(SEPARATOR)
except Exception as e:
raise error("Error occured while executing '" + str(ctx.type()) +
"' module '" + str(ctx.name()) + "' !",
exception=e)
def module_main(ctx, methods=meth_list):
try:
#Get file name and output path. If "Output Path" is not set or does not exist use "File Path".
file_name = a3.inputs['File Path'].path
if os.path.isdir(a3.inputs['Output Path'].path):
out_path = a3.inputs['Output Path'].path
else:
out_path = os.path.join(os.path.dirname(file_name), 'Output')
out_file_path = os.path.join(out_path, 'Thresholder_results.xlsx')
#Create output directory if does not exist
if not os.path.exists(out_path):
os.makedirs(out_path)
#If excel file with results from previos run or previous batch steps
#load as dataframe
if not os.path.exists(out_file_path) or not os.path.isfile(
out_file_path):
results = pd.DataFrame()
else:
results = pd.read_excel(out_file_path, 0)
#Run threshold
#Get method and mode. Get kwargs if method is manual
method = methods[a3.inputs['Method'][-1]]
if method == 'Manual':
kwargs = {'lower': 0, 'upper': a3.inputs['Manual Threshold Value']}
elif method == 'None':
method = 'Manual'
kwargs = {'lower': 0, 'upper': 0}
else:
kwargs = {}
if a3.inputs['Slice/Stack Histogram']:
kwargs['mode'] = 'Stack'
else:
kwargs['mode'] = 'Slice'
results_dict = {}
if method == 'All':
methods.remove('All')
else:
methods = [method]
#Analyze raw image parameters
raw_data = core.analyze_raw(a3.inputs['Image'])
#Create ordered dict for results and run analysis
results_dict = OrderedDict()
results_dict['File'] = os.path.basename(file_name)
for keys in raw_data.keys():
results_dict[keys] = raw_data[keys]
for meth in methods:
image_thresholded, thr_value = core.threshold(
a3.inputs['Image'], meth, **kwargs)
if a3.inputs['Save Images']:
save_image(
[image_thresholded], out_path,
str(
os.path.splitext(os.path.basename(file_name))[0] +
'_' + str(meth) + '.tif'))
if kwargs['mode'] == 'Slice':
print('Thresholds using ' + str(meth) + ':')
print('Slice thresholds: ', thr_value)
thr_value = np.mean(thr_value)
print('Mean threshold: ', thr_value)
if kwargs['mode'] == 'Stack':
print('Threshold using ' + str(meth) + ': ', thr_value)
print('')
results_dict[meth] = thr_value
#Append results_dict to dataframe and save
results = results.append(pd.DataFrame(results_dict, index=[0]))
writer = pd.ExcelWriter(out_file_path, engine='xlsxwriter')
results.to_excel(writer, index=False, sheet_name='Thresholds')
writer.save()
except Exception as e:
raise error("Error occured while executing '" + str(ctx.type()) +
"' module '" + str(ctx.name()) + "' !",
exception=e)
def colocalize(ch1_img,
ch2_img,
ovl_settings,
path,
show=True,
to_text=False,
remove_filtered=False):
tagged_img_list = [ch1_img, ch2_img]
#Gennerate list of names without duplicates and change 'Name' field in image metadata
name_list = rename_duplicates(
[x.metadata['Name'] for x in tagged_img_list])
for idx, value in enumerate(name_list):
tagged_img_list[idx].metadata['Name'] = name_list[idx]
print('Processing the following channels: ' +
str([img.metadata['Name'] for img in tagged_img_list]))
print('Filter settings: ' + str(ovl_settings))
#Run colocaliyation analysis
ovl_img, _ = colocalization(tagged_img_list,
overlapping_filter=ovl_settings,
remove_filtered=remove_filtered)
#Run filtering steps
#ch1_img, _ =apply_filter(ch1_img, ch1_settings, overwrite=False, remove_filtered=False)
#ch2_img, _ =apply_filter(ch2_img, ch2_settings, overwrite=False, remove_filtered=False)
#Print number of objects to logText
print('Number of Overlapping Objects: ' +
str(len(ovl_img.database['tag'])))
#Set path and filename
output_path = os.path.join(path, 'Output')
if not os.path.exists(output_path):
os.makedirs(output_path)
#Generate output filename
filename_1 = os.path.basename(ch1_img.metadata['Path'])
filename_2 = os.path.basename(ch2_img.metadata['Path'])
if filename_1 != filename_2:
basename = os.path.splitext(filename_1)[0] + '_' + os.path.splitext(
filename_2)[0]
else:
basename = os.path.splitext(filename_1)[0]
#Save data and give output path
print('Saving object dataBases!')
data_basename = basename + '_' + ch1_img.metadata[
'Name'] + '_' + ch2_img.metadata['Name']
#Get extension
if to_text == True:
extension = ".txt"
else:
extension = ".xlsx"
#If filename exists generate a neme that is not used
file_name = get_next_filename(output_path, data_basename + extension)
#If filename exists issue warning
if file_name != data_basename + extension:
warning('Warning: Filename already exists!! Data will be saved to ' +
file_name)
#Save to file
save_data([ch1_img, ch2_img, ovl_img],
path=output_path,
file_name=os.path.splitext(file_name)[0],
to_text=to_text)
#Save images
print('Saving output images!')
image_list = [ch1_img, ch2_img, ovl_img]
name_img = basename + '_{}_{}.ome.tiff'.format(ch1_img.metadata['Name'],
ch2_img.metadata['Name'])
save_image(image_list, output_path, name_img)
return ovl_img, ch1_img, ch2_img, os.path.join(output_path, file_name)