calculate mri nifti segmentation dice score

dice.py

import nibabel as nib

def test_nifti_all_labels_dice_score(seg_path, seg_file, truth_path, truth_file):
    truth_uid = os.listdir(truth_path)
    print(truth_uid)
    dice_score = 0
    for uid in  truth_uid:
        seg_file_path = os.path.join(seg_path, uid, seg_file)
        truth_file_path = os.path.join(truth_path, uid, truth_file)
        seg_nib = nib.load(seg_file_path)
        seg_data = seg_nib.get_data()
        truth_nib= nib.load(truth_file_path)
        truth_data = truth_nib.get_data()
        uid_dice = calculate_nifti_all_labels_dice_score(seg_data, truth_data)
        print(uid_dice)
        dice_score+=uid_dice
    print('dice score:', dice_score/len(truth_uid))

def calculate_nifti_all_labels_dice_score(seg_data, truth_data):
    z_range = range(seg_data.shape[-1])
    z_len = len(z_range)
    dice_sum = 0
    for z in  z_range:
        seg_slice = seg_data[:,:,z]
        truth_slice = truth_data[:,:,z]
        slice_dice = calculate_slice_all_labels_dice_score(seg_slice, truth_slice)
        dice_sum+=slice_dice

    return dice_sum / z_len

def calculate_slice_all_labels_dice_score(segmentation, truth):
    area_sum = np.sum(segmentation) + np.sum(truth)
    if area_sum > 0:
        return np.sum(segmentation[truth>0])*2.0 / area_sum
    else:
        return 1

def calculate_slice_one_label_dice_score(segmentation, truth, k):
    return np.sum(segmentation[truth == k]) * 2.0 / (np.sum(segmentation) + np.sum(truth))

test_nifti_all_labels_dice_score('/mnt/DATA/datasets/tumor-segmentation-pipeline/all-t2/1-3d-unet-preprocessed-nii',
                                 'prediction-mask.nii.gz',
                                 '/mnt/DATA/datasets/tumor-segmentation-pipeline/all-t2/1-3d-unet-preprocessed-nii',
                                 'truth.nii.gz')

should line 36 be 0 instead of 1?