evaluation_metrics.py

from tensorflow.keras import backend as K
from sklearn.metrics import jaccard_score,confusion_matrix


def IoU_coef(y_true, y_pred):
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum(y_true_f * y_pred_f)
    return (intersection + 1.0) / (K.sum(y_true_f) + K.sum(y_pred_f) - intersection + 1.0)

def IoU_loss(y_true, y_pred):
    return -IoU_coef(y_true, y_pred)
 
def dice_coef(y_true, y_pred):
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum(y_true_f * y_pred_f)
    return (2.0 * intersection + 1.0) / (K.sum(y_true_f) + K.sum(y_pred_f) + 1.0)
 
def dice_coef_loss(y_true, y_pred):
    return -dice_coef(y_true, y_pred)
  
def accuracy(y_true, y_pred):
    cm = confusion_matrix(y_true.flatten(),y_pred.flatten(), labels=[0, 1])
    acc = (cm[0,0]+cm[1,1])/(cm[0,0]+cm[0,1]+cm[1,0]+cm[1,1])
    return acc
  
def IoU(y_true, y_pred, labels = [0, 1]):
   IoU = []
   for label in labels:
      jaccard = jaccard_score(y_pred.flatten(),y_true.flatten(), pos_label=label, average='weighted')
      IoU.append(jaccard)     
   return np.mean(IoU)