def find_normalization_params(pat_dce, pat_gt, label, pat_model):
# Create the normalization object and load the model
dce_norm = StandardTimeNormalization(DCEModality())
dce_norm.load_model(pat_model)
# Read the DCE
dce_mod = DCEModality()
dce_mod.read_data_from_path(pat_dce)
# Read the GT
gt_mod = GTModality()
gt_mod.read_data_from_path(label, pat_gt)
# Find the normalization parameters
dce_norm.fit(dce_mod, ground_truth=gt_mod, cat=label[0])
return dce_norm
def find_normalization_params(pat_dce, pat_gt, label, pat_model):
# Create the normalization object and load the model
dce_norm = StandardTimeNormalization(DCEModality())
dce_norm.load_model(pat_model)
# Read the DCE
dce_mod = DCEModality()
dce_mod.read_data_from_path(pat_dce)
# Read the GT
gt_mod = GTModality()
gt_mod.read_data_from_path(label, pat_gt)
# Find the normalization parameters
dce_norm.fit(dce_mod, ground_truth=gt_mod, cat=label[0])
return dce_norm
def test_save_load_model():
"""Test the routine to store and load the model."""
# Load the data with only a single serie
currdir = os.path.dirname(os.path.abspath(__file__))
path_data = os.path.join(currdir, 'data', 'full_dce')
# Create an object to handle the data
dce_mod = DCEModality()
# Read the data
dce_mod.read_data_from_path(path_data)
# Load the GT data
path_gt = [os.path.join(currdir, 'data', 'full_gt', 'prostate')]
label_gt = ['prostate']
gt_mod = GTModality()
gt_mod.read_data_from_path(label_gt, path_gt)
# Create the object to make the normalization
stn = StandardTimeNormalization(dce_mod)
stn.partial_fit_model(dce_mod, gt_mod, label_gt[0])
# Store the file
filename = os.path.join(currdir, 'data', 'model.npy')
stn.save_model(filename)
# Load the model in another object
stn2 = StandardTimeNormalization(dce_mod)
stn2.load_model(filename)
# Check the different variable
model_gt = np.array([22.26479174, 22.51070962, 24.66027277, 23.43488237,
23.75601817, 22.56173871, 26.86244505, 45.06227804,
62.34273874, 71.35327656])
assert_array_almost_equal(stn.model_, model_gt, decimal=PRECISION_DECIMAL)
assert_true(stn.is_model_fitted_)
