def directInvokeAlgorithm(self,vals):
print('oooo invoking: nonLinearRegistration', (vals));
target = self.inputs['target'];
reference = self.inputs['reference'];
transform = self.inputs['initial'];
linearmode = bis_baseutils.getLinearModeCode(vals['linearmode']);
initial=transform;
if (linearmode>=0):
try:
mat=libbis.runLinearRegistrationWASM(reference, target, transform, {
'intscale' : vals['intscale'],
'numbins' : vals['numbins'],
'levels' : vals['levels'],
'smoothing' : vals['extrasmoothing'],
'optimization' : bis_baseutils.getOptimizationCode(vals['optimization']),
'stepsize' : vals['stepsize'],
'metric' : bis_baseutils.getMetricCode(vals['metric']),
'steps' : vals['steps'],
'iterations' : vals['iterations'],
'normalize' : self.parseBoolean(vals['norm']),
'debug' : self.parseBoolean(vals['debug']),
'mode' : linearmode,
'resolution' : vals['resolution'],
'return_vector' : False}, self.parseBoolean(vals['debug']));
initial=bis_objects.bisLinearTransformation();
initial.create(mat);
except:
print('Failed to invoke linear registration algorithm.');
return False;
try:
self.outputs['output']=libbis.runNonLinearRegistrationWASM(reference, target, initial, {
'cps' : vals['cps'],
'lambda' : vals['lambda'],
'intscale' : vals['intscale'],
'numbins' : vals['numbins'],
'levels' : vals['levels'],
'smoothing' : vals['extrasmoothing'],
'optimization' : bis_baseutils.getOptimizationCode(vals['optimization']),
'stepsize' : vals['stepsize'],
'debug' : self.parseBoolean(vals['debug']),
'normalize' : self.parseBoolean(vals['norm']),
'metric' : bis_baseutils.getMetricCode(vals['metric']),
'steps' : vals['steps'],
'iterations' : vals['iterations'],
'resolution' : vals['resolution']}, self.parseBoolean(vals['debug']));
if (self.parseBoolean(vals['doreslice'])==True):
self.outputs['resliced']=bis_baseutils.resliceRegistrationOutput(libbis,reference,
target,self.outputs['output']);
except:
print('---- Failed to invoke nonlinear registration algorithm.');
return False
return True
def directInvokeAlgorithm(self, vals):
print('oooo invoking: approximateField', (vals))
inputImg = self.inputs['input']
try:
self.outputs['output'] = libbis.approximateDisplacementFieldWASM2(
inputImg, {
"spacing": (vals['spacing']),
"steps": (vals['steps']),
"stepsize": (vals['stepsize']),
"lambda": (vals['lambda']),
"iterations": (vals['iterations']),
"tolerance": (vals['tolerance']),
'optimization':
bis_baseutils.getOptimizationCode(vals['optimization']),
"windowsize": (vals['windowsize']),
"levels": (vals['levels']),
"resolution": (vals['resolution']),
"inverse":
self.parseBoolean(vals['inverse']),
}, self.parseBoolean(vals['debug']))
except:
print(
'---- Failed to invoke algorithm nonlinear component of module'
)
return False
return True
def directInvokeAlgorithm(self, vals):
print('oooo invoking: linearRegistration', (vals))
target = self.inputs['target']
reference = self.inputs['reference']
transform = self.inputs['initial']
if (reference.hasSameOrientation(target, 'Reference Image',
'Target Image', True) == False):
return False
try:
out = libbis.runLinearRegistrationWASM(
reference, target, transform, {
'intscale':
vals['intscale'],
'numbins':
vals['numbins'],
'levels':
vals['levels'],
'smoothing':
vals['imagesmoothing'],
'optimization':
bis_baseutils.getOptimizationCode(vals['optimization']),
'stepsize':
vals['stepsize'],
'metric':
bis_baseutils.getMetricCode(vals['metric']),
'steps':
vals['steps'],
'normalize':
self.parseBoolean(vals['norm']),
'debug':
self.parseBoolean(vals['debug']),
'iterations':
vals['iterations'],
'mode':
bis_baseutils.getLinearModeCode(vals['mode']),
'resolution':
vals['resolution'],
'return_vector':
False
}, self.parseBoolean(vals['debug']))
self.outputs['output'] = bis_objects.bisLinearTransformation()
self.outputs['output'].create(out)
if (self.parseBoolean(vals['doreslice']) == True):
self.outputs[
'resliced'] = bis_baseutils.resliceRegistrationOutput(
libbis, reference, target, self.outputs['output'])
except:
e = sys.exc_info()[0]
print('---- Failed to invoke algorithm', e)
return False
return True