def load_nii_and_gen_label(patients_path, hc_path, mask):
# train data
data1 = main(patients_path)
data1 = np.squeeze(
np.array([np.array(data1).reshape(1, -1) for data1 in data1]))
data2 = main(hc_path)
data2 = np.squeeze(
np.array([np.array(data2).reshape(1, -1) for data2 in data2]))
data = np.vstack([data1, data2])
# validation data
data_val = main(validation_path)
data_val = np.squeeze(
np.array(
[np.array(data_val).reshape(1, -1) for data_val in data_val]))
# data in mask
data_tr = data[:, mask]
data_val = data_val[:, mask]
# label_tr
label_tr = np.hstack(
[np.ones([
len(data1),
]) - 1, np.ones([
len(data2),
])])
return label_tr, data_tr, data_val
def load_nii_and_gen_label(BD_path, MDD_path, HC_path, suffix='.nii'):
# data
data1, _ = main(BD_path, suffix)
data1 = np.squeeze(
np.array([np.array(data1).reshape(1, -1) for data1 in data1]))
data2, _ = main(MDD_path, suffix)
data2 = np.squeeze(
np.array([np.array(data2).reshape(1, -1) for data2 in data2]))
data3, _ = main(HC_path, suffix)
data3 = np.squeeze(
np.array([np.array(data3).reshape(1, -1) for data3 in data3]))
data = np.vstack([data1, data2, data3])
# data in mask
# data_in_mask=data[:,mask]
# label
label = np.hstack([
np.ones([
len(data1),
]) - 1,
np.ones([
len(data2),
]),
np.ones([
len(data2),
]) + 1
])
return data, label
def load_nii_and_gen_label(folder_p, folder_hc, mask):
# data
data_p = main(folder_p)
data_p = np.squeeze(
np.array([np.array(data_p).reshape(1, -1) for data_p in data_p]))
data_hc = main(folder_hc)
data_hc = np.squeeze(
np.array([np.array(data_hc).reshape(1, -1) for data_hc in data_hc]))
data = np.vstack([data_p, data_hc])
# data in mask
# mask=np.sum(data==0,0)<=0
data_in_mask = data[:, mask]
# label
label = np.hstack(
[np.ones([
len(data_p),
]), np.ones([
len(data_hc),
]) - 2])
return data, data_in_mask, label
def load_nii_and_gen_label(patients_path,controls_path,mask):
# data
data_p=main(patients_path)
data_p=np.squeeze(np.array([np.array(data_p).reshape(1,-1) for data_p in data_p]))
data_hc=main(controls_path)
data_hc=np.squeeze(np.array([np.array(data_hc).reshape(1,-1) for data_hc in data_hc]))
data=np.vstack([data_p,data_hc])
# data in mask
data_in_mask=data[:,mask]
# label
label=np.hstack([np.ones([len(data_p),]),np.ones([len(data_hc),])-2])
return data,data_in_mask,label
def load_nii_and_gen_label(patients_path, hc_path, mask):
# data
data1, _ = main(patients_path, '.img')
data1 = np.squeeze(
np.array([np.array(data1).reshape(1, -1) for data1 in data1]))
data2, _ = main(hc_path, '.img')
data2 = np.squeeze(
np.array([np.array(data2).reshape(1, -1) for data2 in data2]))
data = np.vstack([data1, data2])
# data in mask
data_in_mask = data[:, mask]
# label
label = np.hstack([np.ones([
len(data1),
]) - 1, np.ones([
len(data2),
])])
return data, data_in_mask, label