IOU.py

##Calculate Intersection Over Union Score for predicted layer
import numpy as np
import scipy.misc as misc


def GetIOU(Pred, GT, NumClasses, ClassNames=[], DisplyResults=False):
    # Given A ground true and predicted labels per pixel return the intersection over union for each class
    # and the union for each class
    ClassIOU = np.zeros(NumClasses)  # Vector that Contain IOU per class
    ClassWeight = np.zeros(
        NumClasses)  # Vector that Contain Number of pixel per class Predicted U Ground true (Union for this class)
    for i in range(NumClasses):  # Go over all classes
        Intersection = np.float32(np.sum((Pred == GT) * (GT == i)))  # Calculate class intersection
        Union = np.sum(GT == i) + np.sum(Pred == i) - Intersection  # Calculate class Union
        if Union > 0:
            ClassIOU[i] = Intersection / Union  # Calculate intesection over union
            ClassWeight[i] = Union

    # ------------Display results (optional)-------------------------------------------------------------------------------------
    if DisplyResults:
        for i in range(len(ClassNames)):
            print(ClassNames[i] + ") " + str(ClassIOU[i]))
        print("Mean Classes IOU) " + str(np.mean(ClassIOU)))
        print("Image Predicition Accuracy)" + str(np.float32(np.sum(Pred == GT)) / GT.size))
    # -------------------------------------------------------------------------------------------------

    return ClassIOU, ClassWeight