I have an image which has three values (0,1, and 2) that each of them relates to different objects. I want to dynamically create a probability matrix (H) like this (reads an image and calculate this for each image):
H(i, j) = 0 if i != j
H(i, j) = 1 - f(i) if i == j (with f(i) = the frequency of class i in array)
First I counted pixels with each class:
cl0=np.count_nonzero(im == 0) #0=background class
cl1=np.count_nonzero(im == 1) #obj1
cl2=np.count_nonzero(im == 2) #obj2
I have set of images that some of them do not have one class so the frequency of that value is zero. For example,(65228, 308, 0) for one image.
Once I want to create inverse class weight by this formula (total number of sample)/((number of classes)*(number of sample in class i)) to give more probability those classes with less samples.
w0=round(sum_/(no_classes*cl0),3)
w1=round(sum_/(no_classes*cl1),3)
w2=round(sum_/(no_classes*cl2),3)
I am facing this error:
ZeroDivisionError: division by zero
Could someone please guide me how can I tackle with this classes with zero samples to tackle imbalance classes? How can I normalize the weights?
Thank u