2.6.8.21. Segmentation with Gaussian mixture models

This example performs a Gaussian mixture model analysis of the image histogram to find the right thresholds for separating foreground from background.

import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
from sklearn.mixture import GaussianMixture

rng = np.random.default_rng(27446968)
n = 10
l = 256
im = np.zeros((l, l))
points = l * rng.random((2, n**2))
im[(points[0]).astype(int), (points[1]).astype(int)] = 1
im = sp.ndimage.gaussian_filter(im, sigma=l / (4.0 * n))

mask = (im > im.mean()).astype(float)


img = mask + 0.3 * rng.normal(size=mask.shape)

hist, bin_edges = np.histogram(img, bins=60)
bin_centers = 0.5 * (bin_edges[:-1] + bin_edges[1:])

classif = GaussianMixture(n_components=2)
classif.fit(img.reshape((img.size, 1)))

threshold = np.mean(classif.means_)
binary_img = img > threshold


plt.figure(figsize=(11, 4))

plt.subplot(131)
plt.imshow(img)
plt.axis("off")
plt.subplot(132)
plt.plot(bin_centers, hist, lw=2)
plt.axvline(0.5, color="r", ls="--", lw=2)
plt.text(0.57, 0.8, "histogram", fontsize=20, transform=plt.gca().transAxes)
plt.yticks([])
plt.subplot(133)
plt.imshow(binary_img, cmap=plt.cm.gray, interpolation="nearest")
plt.axis("off")

plt.subplots_adjust(wspace=0.02, hspace=0.3, top=1, bottom=0.1, left=0, right=1)
plt.show()