.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/features_detection/plot_gabor.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_features_detection_plot_gabor.py: ============================================= Gabor filter banks for texture classification ============================================= In this example, we will see how to classify textures based on Gabor filter banks. Frequency and orientation representations of the Gabor filter are similar to those of the human visual system. The images are filtered using the real parts of various different Gabor filter kernels. The mean and variance of the filtered images are then used as features for classification, which is based on the least squared error for simplicity. .. GENERATED FROM PYTHON SOURCE LINES 15-135 .. image-sg:: /auto_examples/features_detection/images/sphx_glr_plot_gabor_001.png :alt: Image responses for Gabor filter kernels, brick, grass, gravel :srcset: /auto_examples/features_detection/images/sphx_glr_plot_gabor_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Rotated images matched against references using Gabor filter banks: original: brick, rotated: 30deg, match result: brick original: brick, rotated: 70deg, match result: brick original: grass, rotated: 145deg, match result: brick | .. code-block:: Python import matplotlib.pyplot as plt import numpy as np from scipy import ndimage as ndi from skimage import data from skimage.util import img_as_float from skimage.filters import gabor_kernel def compute_feats(image, kernels): feats = np.zeros((len(kernels), 2), dtype=np.double) for k, kernel in enumerate(kernels): filtered = ndi.convolve(image, kernel, mode='wrap') feats[k, 0] = filtered.mean() feats[k, 1] = filtered.var() return feats def match(feats, ref_feats): min_error = np.inf min_i = None for i in range(ref_feats.shape[0]): error = np.sum((feats - ref_feats[i, :]) ** 2) if error < min_error: min_error = error min_i = i return min_i # prepare filter bank kernels kernels = [] for theta in range(4): theta = theta / 4.0 * np.pi for sigma in (1, 3): for frequency in (0.05, 0.25): kernel = np.real( gabor_kernel(frequency, theta=theta, sigma_x=sigma, sigma_y=sigma) ) kernels.append(kernel) shrink = (slice(0, None, 3), slice(0, None, 3)) brick = img_as_float(data.brick())[shrink] grass = img_as_float(data.grass())[shrink] gravel = img_as_float(data.gravel())[shrink] image_names = ('brick', 'grass', 'gravel') images = (brick, grass, gravel) # prepare reference features ref_feats = np.zeros((3, len(kernels), 2), dtype=np.double) ref_feats[0, :, :] = compute_feats(brick, kernels) ref_feats[1, :, :] = compute_feats(grass, kernels) ref_feats[2, :, :] = compute_feats(gravel, kernels) print('Rotated images matched against references using Gabor filter banks:') print('original: brick, rotated: 30deg, match result: ', end='') feats = compute_feats(ndi.rotate(brick, angle=190, reshape=False), kernels) print(image_names[match(feats, ref_feats)]) print('original: brick, rotated: 70deg, match result: ', end='') feats = compute_feats(ndi.rotate(brick, angle=70, reshape=False), kernels) print(image_names[match(feats, ref_feats)]) print('original: grass, rotated: 145deg, match result: ', end='') feats = compute_feats(ndi.rotate(grass, angle=145, reshape=False), kernels) print(image_names[match(feats, ref_feats)]) def power(image, kernel): # Normalize images for better comparison. image = (image - image.mean()) / image.std() return np.sqrt( ndi.convolve(image, np.real(kernel), mode='wrap') ** 2 + ndi.convolve(image, np.imag(kernel), mode='wrap') ** 2 ) # Plot a selection of the filter bank kernels and their responses. results = [] kernel_params = [] for theta in (0, 1): theta = theta / 4.0 * np.pi for frequency in (0.1, 0.4): kernel = gabor_kernel(frequency, theta=theta) params = f"theta={theta * 180 / np.pi},\nfrequency={frequency:.2f}" kernel_params.append(params) # Save kernel and the power image for each image results.append((kernel, [power(img, kernel) for img in images])) fig, axes = plt.subplots(nrows=5, ncols=4, figsize=(5, 6)) plt.gray() fig.suptitle('Image responses for Gabor filter kernels', fontsize=12) axes[0][0].axis('off') # Plot original images for label, img, ax in zip(image_names, images, axes[0][1:]): ax.imshow(img) ax.set_title(label, fontsize=9) ax.axis('off') for label, (kernel, powers), ax_row in zip(kernel_params, results, axes[1:]): # Plot Gabor kernel ax = ax_row[0] ax.imshow(np.real(kernel)) ax.set_ylabel(label, fontsize=7) ax.set_xticks([]) ax.set_yticks([]) # Plot Gabor responses with the contrast normalized for each filter vmin = np.min(powers) vmax = np.max(powers) for patch, ax in zip(powers, ax_row[1:]): ax.imshow(patch, vmin=vmin, vmax=vmax) ax.axis('off') plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.158 seconds) .. _sphx_glr_download_auto_examples_features_detection_plot_gabor.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-image/scikit-image/v0.23.2?filepath=notebooks/auto_examples/features_detection/plot_gabor.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_gabor.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_gabor.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_