def module_main(ctx):
try:
a3.outputs['ChA Image'] = a3.inputs['ChA Image']
a3.outputs['ChB Image'] = a3.inputs['ChB Image']
a3.outputs['ChA Thresholded'] = ImageClass(
a3.inputs['ChA Thresholded'].image,
a3.inputs['ChA Thresholded'].metadata).to_multidimimage()
a3.outputs['ChB Thresholded'] = ImageClass(
a3.inputs['ChB Thresholded'].image,
a3.inputs['ChB Thresholded'].metadata).to_multidimimage()
except Exception as e:
error("Error occured while executing '" + str(ctx.type()) +
"' module '" + str(ctx.name()) + "' !",
exception=e)
def module_main(ctx):
try:
filename = a3.inputs['FileName'].path
#Inizialization
tstart = time.clock()
print(SEPARATOR)
print('Loading the following image: ', filename)
#Load and reshape image
img = ImageClass.load(filename)
#Print important image parameters
print_line_by_line(str(img))
#Create Output 1
ch_1_Nb = a3.inputs['Channel'] - 1
ch_1 = img.get_dimension(ch_1_Nb, 'C')
ch_1.metadata['Path'] = filename
ch_1.reorder('ZYXCT')
a3.outputs['Channel'] = ch_1
#Finalization
tstop = time.clock()
print('Processing finished in ' + str((tstop - tstart)) + ' seconds! ')
print('Image loaded 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):
try:
#Inizialization
tstart = time.clock()
print(SEPARATOR)
print('Object analysis started!')
#Read Parameters
print('Reading input parameters!')
params = read_params()
output=analyze_image(params['Source'],
params['Mask'],
params['Settings'],
params['removeFiltered'])
#Change Name in metadata
#output.metadata['Name']=params['Mask'].metadata['Name']+'_tagged'
#Create Output
a3.outputs['Analyzed Image'] = output
a3.outputs['Analyzed Binary'] = ImageClass(output.image>0,output.metadata)
a3.outputs['Analyzed Database']=output.database
#Finalization
tstop = time.clock()
print('Processing finished in ' + str((tstop - tstart)) + ' seconds! ')
print('Object analysis was run successfully!')
print(SEPARATOR)
except Exception as e:
raise error("Error occured while executing '"+str(ctx.type())+"' module '"+str(ctx.name())+"' !",exception=e)
def tagImage(image):
'''
Function that runs ITK connected components on input image
:param image: nd Array
:param outputImage: nd Array
'''
# Start timing
tstart = time.process_time()
# Creatre LogText and start logging
logText = '\nRunning connected components on : ' + str(
image.metadata['Name'])
#Tag image
output_array = tag_image(image.get_3d_array())
#Create metadata ditionary and set type to match tagged image
output_metadata = image.metadata
image.metadata['Type'] = str(output_array.dtype)
# Finish timing and add to logText
tstop = time.process_time()
logText += '\n\tProcessing finished in ' + str(
(tstop - tstart)) + ' seconds! '
return ImageClass(output_array, output_metadata), logText
def module_main(ctx):
try:
#Inizialization
tstart = time.process_time()
print(SEPARATOR)
print('Colocalization analysis started!')
#Read Parameters
print('Reading input parameters!')
params = read_params()
output = colocalize(params['ChA Image'],
params['ChB Image'],
params['Ovl'],
params['Path'],
to_text=params['to_text'],
remove_filtered=params['remove_filtered'])
a3.outputs['Overlapping Image'] = output[0]
a3.outputs['Overlapping Binary'] = ImageClass(output[0].image > 0,
output[0].metadata)
a3.outputs['Overlapping DataBase'] = output[0].database
path = a3.Url()
path.path = output[3]
a3.outputs['Overlapping Path'] = path
#Finalization
tstop = time.process_time()
print('Processing finished in ' + str((tstop - tstart)) + ' seconds! ')
print('Object analysis was run successfully!')
print(SEPARATOR)
quote(verbose=True)
print(SEPARATOR)
except IOError as e:
print("Warning: Failed to write to file!!", file=sys.stderr)
print(str(e), file=sys.stderr)
except Exception as e:
error("Error occured while executing '" + str(ctx.type()) +
"' module '" + str(ctx.name()) + "' !",
exception=e)
def module_main(ctx):
try:
#Inizialization
tstart = time.clock()
print(SEPARATOR)
print('Thresholding started!')
#Create Image object
img = ImageClass.from_multidimimage(a3.inputs['Input Image'])
#img.image=img.image.astype('float')
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'
#Run thresholding
output_img = module_threshold(img, method, kwargs)
#Set output
a3.outputs['Thresholded Image'] = output_img
a3.outputs['Image'] = img
#Finalization
tstop = time.clock()
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):
try:
#Inizialization
tstart = time.process_time()
print(SEPARATOR)
print('Thresholding started!')
#Create Image object
img = ImageClass.from_multidimimage(a3.inputs['Input Image'])
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)
print(
'#########################################################################'
)
print(log_text)
threshold_value = str(log_text.split('\n')[-1].split(':')[-1])
print(threshold_value)
print(log_text)
print(
'#########################################################################'
)
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 read_params(filters=FILTERS):
out_dict = {}
#Get Path. If "Output Path" is not set or does not exist use "File Path".
if os.path.isdir(a3.inputs['Output Path'].path):
out_dict['Path'] = a3.inputs['Output Path'].path
else:
out_dict['Path'] = os.path.dirname(a3.inputs['File Path'].path)
out_dict['ChA Image'] = ImageClass(a3.inputs['ChA Image'].image,
a3.inputs['ChA Image'].metadata,
a3.inputs['ChA DataBase'])
out_dict['ChB Image'] = ImageClass(a3.inputs['ChB Image'].image,
a3.inputs['ChB Image'].metadata,
a3.inputs['ChB DataBase'])
out_dict['to_text'] = a3.inputs['Save to xlsx/text']
out_dict['remove_filtered'] = a3.inputs['Keep/Remove filtered objects']
#Load parameters
settings = {}
for f in [TRANSLATE[key] for key in FILTERS]:
settings[value_to_key(TRANSLATE, f)] = {}
for m in ['min', 'max']:
settings[value_to_key(TRANSLATE,
f)][m] = a3.inputs['{} {}'.format(f, m)]
#Generate overlapping settings dictionary
ovl_settings = {}
for key in OVLFILTERS:
if key in settings:
prefix = key.split(' ', 1)[0]
filter_key = key.split(' ', 1)[-1]
if prefix == 'ChA':
ovl_settings[filter_key + ' in ' + str(
a3.inputs['ChA Image'].metadata['Name'])] = settings[key]
if prefix == 'ChB':
ovl_settings[filter_key + ' in ' + str(
a3.inputs['ChB Image'].metadata['Name'])] = settings[key]
else:
ovl_settings[key] = settings[key]
if a3.inputs['Volume in pixels/um\u00B3'] and ('volume'
in settings.keys()):
#Check if unit metadata is available, default Unit is um!!!!!!!!
unit_list = [
'PhysicalSizeX', 'PhysicalSizeY', 'PhysicalSizeZ',
'PhysicalSizeZUnit', 'PhysicalSizeZUnit', 'PhysicalSizeZUnit'
]
missing_unit_A = [
u for u in unit_list
if u not in out_dict['ChA Image'].metadata.keys()
]
if len(missing_unit_A) != 0:
raise Exception('ChA Image is missing the following unit :' +
str(missing_unit_A))
missing_unit_B = [
u for u in unit_list
if u not in out_dict['ChB Image'].metadata.keys()
]
if len(missing_unit_B) != 0:
raise Exception('ChB Image is missing the following unit :' +
str(missing_unit_B))
print('Physical voxel volume is : ' + str(
float(out_dict['ChA Image'].metadata['PhysicalSizeX']) *
float(out_dict['ChA Image'].metadata['PhysicalSizeY']) *
float(out_dict['ChA Image'].metadata['PhysicalSizeZ'])) + ' ' +
out_dict['ChA Image'].metadata['PhysicalSizeXUnit'] + '*' +
out_dict['ChA Image'].metadata['PhysicalSizeYUnit'] + '*' +
out_dict['ChA Image'].metadata['PhysicalSizeZUnit'])
ovl_settings['volume'] = settings.pop('volume')
else:
ovl_settings['voxelCount'] = settings.pop('volume')
out_dict['Ovl'] = ovl_settings
#out_dict['FileName']=a3.inputs['FileName']
return out_dict
def module_main(ctx):
try:
filename = a3.inputs['FileName'].path
#Inizialization
tstart = time.clock()
print(SEPARATOR)
print('Loading the following image: ', filename)
#Process Files
directory = os.path.dirname(filename)
extension = os.path.splitext(filename)[1]
file_list = [
f for f in os.listdir(directory)
if (os.path.isfile(os.path.join(directory, f))
and os.path.splitext(f)[1] == extension)
]
#Thresholding Methods
METHODS = [
'Huang', 'IsoData', 'IsoData_skimage', 'KittlerIllingworth', 'Li',
'Li_skimage', 'MaxEntropy', 'MaxEntropy_skimage', 'Moments',
'Otsu', 'Otsu_skimage', 'RenyiEntropy', 'Shanbhag', 'Triangle',
'Triangle_skimage', 'Yen', 'Yen_skimage'
]
print('Filename ' + 'meanIntensity sumIntensity ' +
str(METHODS).replace('[', '').replace(']', '').replace(',', ''))
for name in file_list:
#Load and reshape image
img = ImageClass.load(os.path.join(directory, name))
#Print important image parameters
#print_line_by_line(str(img))
ch_1_Nb = a3.inputs['Channel A'] - 1
ch_1 = img.get_dimension(ch_1_Nb, 'C')
ch_1.metadata['Path'] = filename
ch_1.reorder('ZYXCT')
#Run thresholding
res = name
#Analyze raw image parameters
raw_data = core.analyze_raw(ch_1)
res += ' ' + str(raw_data['meanIntensity']) + ' ' + str(
raw_data['sumIntensity'])
for meth in METHODS:
ch_1_thr, thr_value = core.threshold(ch_1, meth, mode='Stack')
res += ' ' + str(thr_value)
print(res)
#Output
a3.outputs['Raw Image'] = ch_1.to_multidimimage()
a3.outputs['Thresholded Image'] = ch_1_thr.to_multidimimage()
#Finalization
tstop = time.clock()
print('Processing finished in ' + str((tstop - tstart)) + ' seconds! ')
print('Image loaded successfully!')
print(SEPARATOR)
except Exception as e:
raise error("Error occured while executing '" + str(ctx.type()) +
"' module '" + str(ctx.name()) + "' !",
exception=e)