calculate_mean.py 文件源码

def get_cut_size(data_path):
    list_D_s, list_D_e, list_H_s, list_H_e, list_W_s, list_W_e = [], [], [], [], [], []

    for i in range(1, 10+1):
        subject_name = 'subject-%d-' % i
        f = os.path.join(data_path, subject_name+'T1.hdr')
        img = nib.load(f)
        inputs_tmp = img.get_data()
        D_s, D_e, H_s, H_e, W_s, W_e = cut_edge(inputs_tmp, margin) # set how many zero margins to keep
        list_D_s.append(D_s)
        list_D_e.append(D_e)
        list_H_s.append(H_s)
        list_H_e.append(H_e)
        list_W_s.append(W_s)
        list_W_e.append(W_e)

    min_D_s, max_D_e, min_H_s, max_H_e, min_W_s, max_W_e = \
    min(list_D_s), max(list_D_e), min(list_H_s), max(list_H_e), min(list_W_s), max(list_W_e)

    print("new size: ", min_D_s, max_D_e, min_H_s, max_H_e, min_W_s, max_W_e)
    return min_D_s, max_D_e, min_H_s, max_H_e, min_W_s, max_W_e