Hfrhyu

I have a fairly easy problem but I can not find any algorithm for it.
I have this kmeans classification of a windspeed pattern from a model output:

What I want now is to count the pixels in each patch of the classification. Basically meaning I want to count the coherent pixels of each instance. It would be furthermore awesome to have an algorithm to describe the shape of the instances.
What I have found so far is a CNN which does an instance segmentation.
OpenCV instance segmentation

Now I wonder if there is something out there which does the job easier than a NN.
Any algorithm/language is welcome!

Here is the dataset which is plotted:
Dropbox Link to .npy file

This solution, using a combination of gdal and python, will count the number of pixels per class (discrete dataset):

import gdal
import numpy as np

file = 'path to input image'

# Open the image and read in the values as an array
dataset = gdal.Open(file)
band = dataset.GetRasterBand(1)
Cols = dataset.RasterXSize
Rows = dataset.RasterYSize
data = band.ReadAsArray(0, 0, Cols, Rows).astype(np.float)

# subset the array to only contain classification values equal to 1
class1 = data[data==1]

# Repeat this for all classification values
class2 = data[data==2]
class3 = data[data==3]

# print the length of class1 (the number of pixels 
with a classification value of 1)

print(len(class1))
print(len(class2))
print(len(class3))

If you have a continuous dataset, you can count the number of pixels within a range instead using:

class1 = np.where((data>0) & (data< 1), 1, 0)
print(np.sum(class1))