import tensorflow as tf


def dense_depth_loss_function(y, y_pred):
    """
    Implementation of the loss from the dense depth paper https://arxiv.org/pdf/1812.11941.pdf
    """
    if len(y.shape) == 3:
        y = tf.expand_dims(y, 3)
    if len(y_pred.shape) == 3:
        y_pred = tf.expand_dims(y_pred, 3)
    # Point-wise L1 loss
    l1_depth = tf.reduce_mean(tf.math.abs(y_pred - y), axis=-1)

    # L1 loss over image gradients
    dy, dx = tf.image.image_gradients(y)
    dy_pred, dx_pred = tf.image.image_gradients(y_pred)
    gradient = tf.reduce_mean(tf.math.abs(dy_pred - dy) +
                              tf.math.abs(dx_pred - dx), axis=-1)

    #  Structural Similarity (SSIM)
    ssim = tf.clip_by_value((1 - tf.image.ssim(y, y_pred, 100)) / 2, 0, 1)

    return 0.1 * tf.reduce_mean(l1_depth) + tf.reduce_mean(gradient) + ssim