import keras.backend as K

# dice loss as defined above for 4 classes
def dice_coef(y_true, y_pred, smooth=1.0):
    class_num = 4
    for i in range(class_num):
        y_true_f = K.flatten(y_true[:,:,:,i])
        y_pred_f = K.flatten(y_pred[:,:,:,i])
        intersection = K.sum(y_true_f * y_pred_f)
        loss = ((2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth))
   #     K.print_tensor(loss, message='loss value for class {} : '.format(SEGMENT_CLASSES[i]))
        if i == 0:
            total_loss = loss
        else:
            total_loss = total_loss + loss
    total_loss = total_loss / class_num
#    K.print_tensor(total_loss, message=' total dice coef: ')
    return total_loss



# define per class evaluation of dice coef
# inspired by https://github.com/keras-team/keras/issues/9395
def dice_coef_necrotic(y_true, y_pred, epsilon=1e-6):
    intersection = K.sum(K.abs(y_true[:,:,:,1] * y_pred[:,:,:,1]))
    return (2. * intersection) / (K.sum(K.square(y_true[:,:,:,1])) + K.sum(K.square(y_pred[:,:,:,1])) + epsilon)

def dice_coef_edema(y_true, y_pred, epsilon=1e-6):
    intersection = K.sum(K.abs(y_true[:,:,:,2] * y_pred[:,:,:,2]))
    return (2. * intersection) / (K.sum(K.square(y_true[:,:,:,2])) + K.sum(K.square(y_pred[:,:,:,2])) + epsilon)

def dice_coef_enhancing(y_true, y_pred, epsilon=1e-6):
    intersection = K.sum(K.abs(y_true[:,:,:,3] * y_pred[:,:,:,3]))
    return (2. * intersection) / (K.sum(K.square(y_true[:,:,:,3])) + K.sum(K.square(y_pred[:,:,:,3])) + epsilon)



# Computing Precision
def precision(y_true, y_pred):
        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
        precision = true_positives / (predicted_positives + K.epsilon())
        return precision


# Computing Sensitivity
def sensitivity(y_true, y_pred):
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
    return true_positives / (possible_positives + K.epsilon())


# Computing Specificity
def specificity(y_true, y_pred):
    true_negatives = K.sum(K.round(K.clip((1-y_true) * (1-y_pred), 0, 1)))
    possible_negatives = K.sum(K.round(K.clip(1-y_true, 0, 1)))
    return true_negatives / (possible_negatives + K.epsilon())