loss.py

import tensorflow.keras.backend as K
import tensorflow as tf

# def depth_loss_function(y_true, y_pred, theta=0.1, maxDepthVal=1000.0/10.0):
    
#     # Point-wise depth
#     l_depth = K.mean(K.abs(y_pred - y_true), axis=-1)

#     # Edges
#     dy_true, dx_true = tf.image.image_gradients(y_true)
#     dy_pred, dx_pred = tf.image.image_gradients(y_pred)
#     l_edges = K.mean(K.abs(dy_pred - dy_true) + K.abs(dx_pred - dx_true), axis=-1)

#     # Structural similarity (SSIM) index
#     l_ssim = K.clip((1 - tf.image.ssim(y_true, y_pred, maxDepthVal)) * 0.5, 0, 1)

#     # Weights
#     w1 = 1.0
#     w2 = 1.0
#     w3 = theta

#     return (w1 * l_ssim) + (w2 * K.mean(l_edges)) + (w3 * K.mean(l_depth))

# import keras.backend as K
import tensorflow as tf

def depth_loss_function(y_true, y_pred, theta=0.1, maxDepthVal=1000.0/10.0):
    l_depth = K.mean(K.abs(y_pred - y_true), axis=-1)
    dy_true, dx_true = tf.image.image_gradients(y_true)
    dy_pred, dx_pred = tf.image.image_gradients(y_pred)
    l_edges = K.mean(K.abs(dy_pred - dy_true) + K.abs(dx_pred - dx_true), axis=-1)
    l_ssim = K.clip((1 - tf.image.ssim_multiscale(y_true, y_pred, maxDepthVal)) * 0.5, 0, 1)
    w1 = 1.0
    w2 = 1.0
    w3 = theta
    return (w1 * l_ssim) + (w2 * K.mean(l_edges)) + (w3 * K.mean(l_depth))