def main():
args = getArguments(getParser())
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# loading input images (as image, header pairs)
images = []
headers = []
for image_name in args.images:
i, h = load(image_name)
images.append(i)
headers.append(h)
# loading binary foreground masks if supplied, else create masks from threshold value
if args.masks:
masks = [load(mask_name)[0].astype(numpy.bool) for mask_name in args.masks]
else:
masks = [i > args.threshold for i in images]
# if in application mode, load the supplied model and apply it to the images
if args.lmodel:
logger.info('Loading the model and transforming images...')
with open(args.lmodel, 'r') as f:
trained_model = pickle.load(f)
if not isinstance(trained_model, IntensityRangeStandardization):
raise ArgumentError('{} does not seem to be a valid pickled instance of an IntensityRangeStandardization object'.format(args.lmodel))
transformed_images = [trained_model.transform(i[m], surpress_mapping_check = args.ignore) for i, m in zip(images, masks)]
# in in training mode, train the model, apply it to the images and save it
else:
logger.info('Training the average intensity model...')
irs = IntensityRangeStandardization()
trained_model, transformed_images = irs.train_transform([i[m] for i, m in zip(images, masks)], surpress_mapping_check = args.ignore)
logger.info('Saving the trained model as {}...'.format(args.smodel))
with open(args.smodel, 'wb') as f:
pickle.dump(trained_model, f)
# save the transformed images
if args.simages:
logger.info('Saving intensity transformed images to {}...'.format(args.simages))
for ti, i, m, h, image_name in zip(transformed_images, images, masks, headers, args.images):
i[m] = ti
save(i, '{}/{}'.format(args.simages, image_name.split('/')[-1]), h, args.force)
logger.info('Terminated.')
def test_Method(self):
"""Test the normal functioning of the method."""
# test training with good and bad images
irs = IntensityRangeStandardization()
irs.train(TestIntensityRangeStandardization.good_trainingset + [TestIntensityRangeStandardization.bad_image])
irs.transform(TestIntensityRangeStandardization.bad_image)
# test equal methods
irs = IntensityRangeStandardization()
irs_ = irs.train(TestIntensityRangeStandardization.good_trainingset)
self.assertEqual(irs, irs_)
irs = IntensityRangeStandardization()
irs.train(TestIntensityRangeStandardization.good_trainingset)
timages = []
for i in TestIntensityRangeStandardization.good_trainingset:
timages.append(irs.transform(i))
irs = IntensityRangeStandardization()
irs_, timages_ = irs.train_transform(TestIntensityRangeStandardization.good_trainingset)
self.assertEqual(irs, irs_, 'instance returned by transform() method is not the same as the once initialized')
for ti, ti_ in zip(timages, timages_):
numpy.testing.assert_allclose(ti, ti_, err_msg = 'train_transform() failed to produce the same results as transform()')
# test pickling
irs = IntensityRangeStandardization()
irs_ = irs.train(TestIntensityRangeStandardization.good_trainingset)
timages = []
for i in TestIntensityRangeStandardization.good_trainingset:
timages.append(irs.transform(i))
with tempfile.TemporaryFile() as f:
pickle.dump(irs, f)
f.seek(0, 0)
irs_ = pickle.load(f)
timages_ = []
for i in TestIntensityRangeStandardization.good_trainingset:
timages_.append(irs_.transform(i))
for ti, ti_ in zip(timages, timages_):
numpy.testing.assert_allclose(ti, ti_, err_msg = 'pickling failed to preserve the instances model')
