def module_main(ctx):
try:
#Inizialization
print(SEPARATOR)
print('Saving Database!')
#Read Parameters
params = read_params()
save_database(params['Database'],
params['Path'],
params['Filename'],
to_text=params['Save to text'])
#Finalization
print('Object analysis was run successfully!')
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('Colocalization analysis started!')
#Read Parameters
print('Reading input parameters!')
params = read_params()
output = colocalize(params['ChA Image'],
params['ChB Image'],
params['ChA'],
params['ChB'],
params['Ovl'],
params['Path'],
to_text=params['to_text'],
remove_filtered=params['remove_filtered'])
a3.outputs['Overlapping Image'] = output[0].to_multidimimage()
a3.outputs['Overlapping Binary'] = VividImage(
output[0].image > 0, output[0].metadata).to_multidimimage()
a3.outputs['Overlapping DataBase'] = output[0].database
#a3.outputs['Channel A Image']=VividImage(output[1].image>0,output[1].metadata).to_multidimimage()
#a3.outputs['Channel B Image']=VividImage(output[2].image>0,output[2].metadata).to_multidimimage()
path = a3.Url()
path.path = output[3]
a3.outputs['Overlapping Path'] = path
#Finalization
tstop = time.clock()
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('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.to_multidimimage()
a3.outputs['Analyzed Binary'] = VividImage(
output.image > 0, output.metadata).to_multidimimage()
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 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 = VividImage.load(filename, file_type='ome')
img.reorder('XYZCT')
#Print important image parameters
print_line_by_line(str(img))
#Create Output
a3.outputs['Array'] = img.image
a3.outputs['MetaData']=img.metadata
#Add path and filename to metadata
a3.outputs['MetaData']['Path']=os.path.dirname(filename)
a3.outputs['MetaData']['FileName']=os.path.basename(filename)
#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('Adaptive thresholding started!')
#Create Image object
img = VividImage.from_multidimimage(a3.inputs['Input Image'])
#Get method and mode
method = METHODS[a3.inputs['Method'][-1]]
#Run thresholding
output_img = adaptive_threshold(img,
method,
blocksize=a3.inputs['BlockSize'],
offset=a3.inputs['Offset'])
#Change Name in metadata
#output_img.metadata['Name']=img.metadata['Name']+'_adaptive_thr'
#Set output
a3.outputs['Output Image'] = output_img.to_multidimimage()
#Finalization
tstop = time.clock()
print('Processing finished in ' + str((tstop - tstart)) + ' seconds!')
print('Adaptive thresholding 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.clock()
print(SEPARATOR)
print('Thresholding started!')
#Create Image object
img = VividImage.from_multidimimage(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 = module_threshold(img, method, kwargs)
#Change Name in metadata
#output_img.metadata['Name']=img.metadata['Name']+'_auto_thr'
#Set output
a3.outputs['Thresholded Image'] = output_img.to_multidimimage()
#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.clock()
print(SEPARATOR)
#Load and reshape image
##TempTempTemp##
img = VividImage(a3.inputs['Image'],
copy.deepcopy(a3.inputs['MetaData']))
#img = Image(a3.inputs['Image'], a3.inputs['MetaData'])
img.reorder('XYZCT')
#Get channel from image.
ch = a3.inputs['Channel']
print('Loading the following channel: ', img.metadata['Name'][ch])
if ch >= img.metadata['SizeC']:
raise Exception('Image has %s channels! Invalid channel %s' %
(str(img.metadata['SizeC']), str(ch)))
#Check if image is time series
if img.metadata['SizeT'] > 1:
warning(
"Image is a time series! Only the first time step will be extracted!",
file=sys.stderr)
#Modify metadata
img.metadata['SamplesPerPixel'] = img.metadata['SamplesPerPixel'][ch]
img.metadata['Name'] = img.metadata['Name'][ch]
#img.metadata['Path']=filename
#Create Output
#Extract channel from image array
a3.outputs['Channel 1'] = img.get_dimension(a3.inputs['Channel'],
'C').to_multidimimage()
#to_multidimimage(Image(array.astype(np.float),copy.deepcopy(img.metadata)))
#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)
#Create Image object
img = VividImage.from_multidimimage(a3.inputs['Input Image'])
# Creatre LogText and start logging
print('Thresholding: ' + img.metadata['Name'])
print('Method: Manual')
#Set upper and lower threshold
upper = np.amax([a3.inputs['Upper'], a3.inputs['Lower']])
lower = np.amin([a3.inputs['Upper'], a3.inputs['Lower']])
print('Settings: \n\t\tUpper:%s \n\t\tLower:%s' %
(str(upper), str(lower)))
#Run thresholding
print('Autothresholding started!')
output_img = VividImage(threshold_manual(img.array, upper, lower),
img.metadata)
#Set output
a3.outputs['Output Image'] = output_img.to_multidimimage()
#Finalization
tstop = time.clock()
print('Processing finished in ' + str((tstop - tstart)) + ' seconds!')
print('Manual thresholding was successfully!')
print(SEPARATOR)
except Exception as e:
raise error("Error occured while executing '" + str(ctx.type()) +
"' module '" + str(ctx.name()) + "' !",
exception=e)