tutorial72-GMM_image_segmentation.py

# https://youtu.be/__UcukytHuc
"""
@author: Sreenivas Bhattiprolu

NOTE:
Both BIC and AIC are included as built in methods as part of Scikit-Learn's  GaussianMixture. 
Therefore we do not need to import any other libraries to compute these. 
The way you compute them (for example BIC) is by fitting a GMM model and then calling the method BIC. 


"""

import numpy as np
import cv2
from matplotlib import pyplot as plt

#Use plant cells to demo the GMM on 2 components
#Use BSE_Image to demo it on 4 components
#USe alloy.jpg to demonstrate bic and how 2 is optimal for alloy

img = cv2.imread("images/BSE.tif")
plt.imshow(img)
# Convert MxNx3 image into Kx3 where K=MxN
img2 = img.reshape((-1,3))  #-1 reshape means, in this case MxN

from sklearn.mixture import GaussianMixture as GMM

#covariance choices, full, tied, diag, spherical
gmm_model = GMM(n_components=4, covariance_type='tied').fit(img2)  #tied works better than full
gmm_labels = gmm_model.predict(img2)

#Put numbers back to original shape so we can reconstruct segmented image
original_shape = img.shape
segmented = gmm_labels.reshape(original_shape[0], original_shape[1])
plt.imshow(segmented)
#cv2.imwrite("images/segmented.jpg", segmented)
##############################################################
#How to know the best number of components?
#Using Bayesian information criterion (BIC) to find the best number of components
import numpy as np
import cv2

img = cv2.imread("images/BSE.tif")
img2 = img.reshape((-1,3))

from sklearn.mixture import GaussianMixture as GMM

n = 4
gmm_model = GMM(n, covariance_type='tied').fit(img2)
#The above line generates GMM model for n=2
#Now let us call the bic method (or aic if you want).

bic_value = gmm_model.bic(img2)  #Remember to call the same model name from above)
print(bic_value)  #You should see bic for GMM model generated using n=2.
#Do this exercise for different n values and plot them to find the minimum.


#Now, to explain m.bic, here are the lines I used in the video. 
n_components = np.arange(1,10)
gmm_models = [GMM(n, covariance_type='tied').fit(img2) for n in n_components]
plt.plot(n_components, [m.bic(img2) for m in gmm_models], label='BIC')