import numpy as np

import tensorflow as tf

from tensorflow.keras import backend as K

def iou(y_true, y_pred):

    def f(y_true, y_pred):

        intersection = (y_true * y_pred).sum()

        union = y_true.sum() + y_pred.sum() - intersection

        x = (intersection + 1e-15) / (union + 1e-15)

        x = x.astype(np.float32)

        return x

    return tf.numpy_function(f, [y_true, y_pred], tf.float32)

smooth = 1e-15

def dice_coef(y_true, y_pred):

    y_true = tf.keras.layers.Flatten()(y_true)

    y_pred = tf.keras.layers.Flatten()(y_pred)

    intersection = tf.reduce_sum(y_true * y_pred)

    return (2.0 * intersection + smooth) / (

        tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) + smooth

    )

def dice_loss(y_true, y_pred):

    return 1.0 - dice_coef(y_true, y_pred)