GLCM Texture Features#

This example illustrates texture classification using gray level co-occurrence matrices (GLCMs) [1]. A GLCM is a histogram of co-occurring grayscale values at a given offset over an image.

In this example, samples of two different textures are extracted from an image: grassy areas and sky areas. For each patch, a GLCM with a horizontal offset of 5 (distance=[5] and angles=[0]) is computed. Next, two features of the GLCM matrices are computed: dissimilarity and correlation. These are plotted to illustrate that the classes form clusters in feature space. In a typical classification problem, the final step (not included in this example) would be to train a classifier, such as logistic regression, to label image patches from new images.

Changed in version 0.19: greymatrix was renamed to graymatrix in 0.19.

Changed in version 0.19: greycoprops was renamed to graycoprops in 0.19.


Grey level co-occurrence matrix features
import matplotlib.pyplot as plt

from skimage.feature import graycomatrix, graycoprops
from skimage import data


# open the camera image
image =

# select some patches from grassy areas of the image
grass_locations = [(280, 454), (342, 223), (444, 192), (455, 455)]
grass_patches = []
for loc in grass_locations:
    grass_patches.append(image[loc[0]:loc[0] + PATCH_SIZE,
                               loc[1]:loc[1] + PATCH_SIZE])

# select some patches from sky areas of the image
sky_locations = [(38, 34), (139, 28), (37, 437), (145, 379)]
sky_patches = []
for loc in sky_locations:
    sky_patches.append(image[loc[0]:loc[0] + PATCH_SIZE,
                             loc[1]:loc[1] + PATCH_SIZE])

# compute some GLCM properties each patch
xs = []
ys = []
for patch in (grass_patches + sky_patches):
    glcm = graycomatrix(patch, distances=[5], angles=[0], levels=256,
                        symmetric=True, normed=True)
    xs.append(graycoprops(glcm, 'dissimilarity')[0, 0])
    ys.append(graycoprops(glcm, 'correlation')[0, 0])

# create the figure
fig = plt.figure(figsize=(8, 8))

# display original image with locations of patches
ax = fig.add_subplot(3, 2, 1)
          vmin=0, vmax=255)
for (y, x) in grass_locations:
    ax.plot(x + PATCH_SIZE / 2, y + PATCH_SIZE / 2, 'gs')
for (y, x) in sky_locations:
    ax.plot(x + PATCH_SIZE / 2, y + PATCH_SIZE / 2, 'bs')
ax.set_xlabel('Original Image')

# for each patch, plot (dissimilarity, correlation)
ax = fig.add_subplot(3, 2, 2)
ax.plot(xs[:len(grass_patches)], ys[:len(grass_patches)], 'go',
ax.plot(xs[len(grass_patches):], ys[len(grass_patches):], 'bo',
ax.set_xlabel('GLCM Dissimilarity')
ax.set_ylabel('GLCM Correlation')

# display the image patches
for i, patch in enumerate(grass_patches):
    ax = fig.add_subplot(3, len(grass_patches), len(grass_patches)*1 + i + 1)
              vmin=0, vmax=255)
    ax.set_xlabel(f"Grass {i + 1}")

for i, patch in enumerate(sky_patches):
    ax = fig.add_subplot(3, len(sky_patches), len(sky_patches)*2 + i + 1)
              vmin=0, vmax=255)
    ax.set_xlabel(f"Sky {i + 1}")

# display the patches and plot
fig.suptitle('Grey level co-occurrence matrix features', fontsize=14, y=1.05)

Total running time of the script: (0 minutes 1.032 seconds)

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