2.6.8.21. Segmentation with Gaussian mixture modelsΒΆ

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

plot GMM
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()

Total running time of the script: (0 minutes 0.581 seconds)

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