.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/features_detection/plot_gabors_from_astronaut.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_gabors_from_astronaut.py: ============================================================ Gabors / Primary Visual Cortex "Simple Cells" from an Image ============================================================ How to build a (bio-plausible) *sparse* dictionary (or 'codebook', or 'filterbank') for e.g. image classification without any fancy math and with just standard python scientific libraries? Please find below a short answer ;-) This simple example shows how to get Gabor-like filters [1]_ using just a simple image. In our example, we use a photograph of the astronaut Eileen Collins. Gabor filters are good approximations of the "Simple Cells" [2]_ receptive fields [3]_ found in the mammalian primary visual cortex (V1) (for details, see e.g. the Nobel-prize winning work of Hubel & Wiesel done in the 60s [4]_ [5]_). Here we use McQueen's 'kmeans' algorithm [6]_, as a simple biologically plausible hebbian-like learning rule and we apply it (a) to patches of the original image (retinal projection), and (b) to patches of an LGN-like [7]_ image using a simple difference of gaussians (DoG) approximation. Enjoy ;-) And keep in mind that getting Gabors on natural image patches is not rocket science. .. [1] https://en.wikipedia.org/wiki/Gabor_filter .. [2] https://en.wikipedia.org/wiki/Simple_cell .. [3] https://en.wikipedia.org/wiki/Receptive_field .. [4] D. H. Hubel and T. N., Wiesel Receptive Fields of Single Neurones in the Cat's Striate Cortex, J. Physiol. pp. 574-591 (148) 1959 .. [5] D. H. Hubel and T. N., Wiesel Receptive Fields, Binocular Interaction, and Functional Architecture in the Cat's Visual Cortex, J. Physiol. 160 pp. 106-154 1962 .. [6] https://en.wikipedia.org/wiki/K-means_clustering .. [7] https://en.wikipedia.org/wiki/Lateral_geniculate_nucleus .. GENERATED FROM PYTHON SOURCE LINES 39-90 .. image-sg:: /auto_examples/features_detection/images/sphx_glr_plot_gabors_from_astronaut_001.png :alt: Image (original), K-means filterbank (codebook) on original image, Image (LGN-like DoG), K-means filterbank (codebook) on LGN-like DoG image :srcset: /auto_examples/features_detection/images/sphx_glr_plot_gabors_from_astronaut_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none /Users/cw/baidu/code/fin_tool/github/scikit-image/doc/examples/features_detection/plot_gabors_from_astronaut.py:57: UserWarning: One of the clusters is empty. Re-run kmeans with a different initialization. /Users/cw/baidu/code/fin_tool/github/scikit-image/doc/examples/features_detection/plot_gabors_from_astronaut.py:65: UserWarning: One of the clusters is empty. Re-run kmeans with a different initialization. | .. code-block:: Python from scipy.cluster.vq import kmeans2 from scipy import ndimage as ndi import matplotlib.pyplot as plt from skimage import data from skimage import color from skimage.util.shape import view_as_windows from skimage.util import montage patch_shape = 8, 8 n_filters = 49 astro = color.rgb2gray(data.astronaut()) # -- filterbank1 on original image patches1 = view_as_windows(astro, patch_shape) patches1 = patches1.reshape(-1, patch_shape[0] * patch_shape[1])[::8] fb1, _ = kmeans2(patches1, n_filters, minit='points') fb1 = fb1.reshape((-1,) + patch_shape) fb1_montage = montage(fb1, rescale_intensity=True) # -- filterbank2 LGN-like image astro_dog = ndi.gaussian_filter(astro, 0.5) - ndi.gaussian_filter(astro, 1) patches2 = view_as_windows(astro_dog, patch_shape) patches2 = patches2.reshape(-1, patch_shape[0] * patch_shape[1])[::8] fb2, _ = kmeans2(patches2, n_filters, minit='points') fb2 = fb2.reshape((-1,) + patch_shape) fb2_montage = montage(fb2, rescale_intensity=True) # -- plotting fig, axes = plt.subplots(2, 2, figsize=(7, 6)) ax = axes.ravel() ax[0].imshow(astro, cmap=plt.cm.gray) ax[0].set_title("Image (original)") ax[1].imshow(fb1_montage, cmap=plt.cm.gray) ax[1].set_title("K-means filterbank (codebook)\non original image") ax[2].imshow(astro_dog, cmap=plt.cm.gray) ax[2].set_title("Image (LGN-like DoG)") ax[3].imshow(fb2_montage, cmap=plt.cm.gray) ax[3].set_title("K-means filterbank (codebook)\non LGN-like DoG image") for a in ax.ravel(): a.axis('off') fig.tight_layout() plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.467 seconds) .. _sphx_glr_download_auto_examples_features_detection_plot_gabors_from_astronaut.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/v0.24.0?filepath=notebooks/auto_examples/features_detection/plot_gabors_from_astronaut.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_gabors_from_astronaut.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_gabors_from_astronaut.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_gabors_from_astronaut.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_