import os
import numpy as np
from protoclass.preprocessing import StandardTimeNormalization
path_root = '/data/prostate/pre-processing/lemaitre-2016-nov/norm-objects'
shift_patient = []
# We have to open each npy file
for root, dirs, files in os.walk(path_root):
# Create the string for the file to read
for f in files:
filename = os.path.join(root, f)
# Load the normalization object
dce_norm = StandardTimeNormalization.load_from_pickles(filename)
shift_patient.append(dce_norm.fit_params_['shift-int'])
# Stack the different array vetically
shift_patient = np.vstack(shift_patient)
shift_patient = np.max(shift_patient, axis=0)
# Load the testing data that correspond to the index of the LOPO
# Create the object for the DCE
dce_mod = DCEModality()
dce_mod.read_data_from_path(path_patients_list_dce[idx_pat])
print 'Read the DCE data for the current patient ...'
# Create the corresponding ground-truth
gt_mod = GTModality()
gt_mod.read_data_from_path(label_gt,
path_patients_list_gt[idx_pat])
print 'Read the GT data for the current patient ...'
# Load the approproate normalization object
filename_norm = (id_patient_list[idx_pat].lower().replace(' ', '_') +
'_norm.p')
dce_norm = StandardTimeNormalization.load_from_pickles(
os.path.join(path_norm, filename_norm))
dce_mod = dce_norm.normalize(dce_mod)
# Create the object to extrac data
dce_ese = EnhancementSignalExtraction(DCEModality())
# Concatenate the training data
data = dce_ese.transform(dce_mod, gt_mod, label_gt[0])
# Check that the path is existing
if not os.path.exists(path_store):
os.makedirs(path_store)
pat_chg = (id_patient_list[idx_pat].lower().replace(' ', '_') +
'_ese_' + '_dce.npy')
filename = os.path.join(path_store, pat_chg)
np.save(filename, data)
brix_ext = BrixQuantificationExtraction(DCEModality())
# Read the DCE
print 'Read DCE images'
dce_mod = DCEModality()
dce_mod.read_data_from_path(p_dce)
# Read the GT
print 'Read GT images'
gt_mod = GTModality()
gt_mod.read_data_from_path(label_gt, p_gt)
# Load the approproate normalization object
filename_norm = (pat.lower().replace(' ', '_') +
'_norm.p')
dce_norm = StandardTimeNormalization.load_from_pickles(
os.path.join(path_norm, filename_norm))
dce_mod = dce_norm.normalize(dce_mod)
for idx in range(dce_mod.data_.shape[0]):
dce_mod.data_[idx, :] += shift[idx]
dce_mod.update_histogram()
# Fit the parameters for Brix
print 'Extract Brix'
brix_ext.fit(dce_mod, ground_truth=gt_mod, cat=label_gt[0])
# Extract the matrix
print 'Extract the feature matrix'
data = brix_ext.transform(dce_mod, ground_truth=gt_mod, cat=label_gt[0])
import os
import numpy as np
from protoclass.preprocessing import StandardTimeNormalization
path_root = '/data/prostate/pre-processing/lemaitre-2016-nov/norm-objects'
shift_patient = []
# We have to open each npy file
for root, dirs, files in os.walk(path_root):
# Create the string for the file to read
for f in files:
filename = os.path.join(root, f)
# Load the normalization object
dce_norm = StandardTimeNormalization.load_from_pickles(filename)
shift_patient.append(dce_norm.fit_params_['shift-int'])
# Stack the different array vetically
shift_patient = np.vstack(shift_patient)
shift_patient = np.max(shift_patient, axis=0)
