.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/edges/plot_edge_filter.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_edges_plot_edge_filter.py: ============== Edge operators ============== Edge operators are used in image processing within edge detection algorithms. They are discrete differentiation operators, computing an approximation of the gradient of the image intensity function. .. GENERATED FROM PYTHON SOURCE LINES 10-37 .. code-block:: Python import numpy as np import matplotlib.pyplot as plt from skimage import filters from skimage.data import camera from skimage.util import compare_images image = camera() edge_roberts = filters.roberts(image) edge_sobel = filters.sobel(image) fig, axes = plt.subplots(ncols=2, sharex=True, sharey=True, figsize=(8, 4)) axes[0].imshow(edge_roberts, cmap=plt.cm.gray) axes[0].set_title('Roberts Edge Detection') axes[1].imshow(edge_sobel, cmap=plt.cm.gray) axes[1].set_title('Sobel Edge Detection') for ax in axes: ax.axis('off') plt.tight_layout() plt.show() .. image-sg:: /auto_examples/edges/images/sphx_glr_plot_edge_filter_001.png :alt: Roberts Edge Detection, Sobel Edge Detection :srcset: /auto_examples/edges/images/sphx_glr_plot_edge_filter_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 38-57 Different operators compute different finite-difference approximations of the gradient. For example, the Scharr filter results in a less rotational variance than the Sobel filter that is in turn better than the Prewitt filter [1]_ [2]_ [3]_. The difference between the Prewitt and Sobel filters and the Scharr filter is illustrated below with an image that is the discretization of a rotation- invariant continuous function. The discrepancy between the Prewitt and Sobel filters, and the Scharr filter is stronger for regions of the image where the direction of the gradient is close to diagonal, and for regions with high spatial frequencies. For the example image the differences between the filter results are very small and the filter results are visually almost indistinguishable. .. [1] https://en.wikipedia.org/wiki/Sobel_operator#Alternative_operators .. [2] B. Jaehne, H. Scharr, and S. Koerkel. Principles of filter design. In Handbook of Computer Vision and Applications. Academic Press, 1999. .. [3] https://en.wikipedia.org/wiki/Prewitt_operator .. GENERATED FROM PYTHON SOURCE LINES 57-92 .. code-block:: Python x, y = np.ogrid[:100, :100] # Creating a rotation-invariant image with different spatial frequencies. image_rot = np.exp(1j * np.hypot(x, y) ** 1.3 / 20.0).real edge_sobel = filters.sobel(image_rot) edge_scharr = filters.scharr(image_rot) edge_prewitt = filters.prewitt(image_rot) diff_scharr_prewitt = compare_images(edge_scharr, edge_prewitt) diff_scharr_sobel = compare_images(edge_scharr, edge_sobel) max_diff = np.max(np.maximum(diff_scharr_prewitt, diff_scharr_sobel)) fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True, figsize=(8, 8)) axes = axes.ravel() axes[0].imshow(image_rot, cmap=plt.cm.gray) axes[0].set_title('Original image') axes[1].imshow(edge_scharr, cmap=plt.cm.gray) axes[1].set_title('Scharr Edge Detection') axes[2].imshow(diff_scharr_prewitt, cmap=plt.cm.gray, vmax=max_diff) axes[2].set_title('Scharr - Prewitt') axes[3].imshow(diff_scharr_sobel, cmap=plt.cm.gray, vmax=max_diff) axes[3].set_title('Scharr - Sobel') for ax in axes: ax.axis('off') plt.tight_layout() plt.show() .. image-sg:: /auto_examples/edges/images/sphx_glr_plot_edge_filter_002.png :alt: Original image, Scharr Edge Detection, Scharr - Prewitt, Scharr - Sobel :srcset: /auto_examples/edges/images/sphx_glr_plot_edge_filter_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 93-109 As in the previous example, here we illustrate the rotational invariance of the filters. The top row shows a rotationally invariant image along with the angle of its analytical gradient. The other two rows contain the difference between the different gradient approximations (Sobel, Prewitt, Scharr & Farid) and analytical gradient. The Farid & Simoncelli derivative filters [4]_, [5]_ are the most rotationally invariant, but require a 5x5 kernel, which is computationally more intensive than a 3x3 kernel. .. [4] Farid, H. and Simoncelli, E. P., "Differentiation of discrete multidimensional signals", IEEE Transactions on Image Processing 13(4): 496-508, 2004. :DOI:`10.1109/TIP.2004.823819` .. [5] Wikipedia, "Farid and Simoncelli Derivatives." Available at: .. GENERATED FROM PYTHON SOURCE LINES 109-169 .. code-block:: Python x, y = np.mgrid[-10:10:255j, -10:10:255j] image_rotinv = np.sin(x**2 + y**2) image_x = 2 * x * np.cos(x**2 + y**2) image_y = 2 * y * np.cos(x**2 + y**2) def angle(dx, dy): """Calculate the angles between horizontal and vertical operators.""" return np.mod(np.arctan2(dy, dx), np.pi) true_angle = angle(image_x, image_y) angle_farid = angle(filters.farid_h(image_rotinv), filters.farid_v(image_rotinv)) angle_sobel = angle(filters.sobel_h(image_rotinv), filters.sobel_v(image_rotinv)) angle_scharr = angle(filters.scharr_h(image_rotinv), filters.scharr_v(image_rotinv)) angle_prewitt = angle(filters.prewitt_h(image_rotinv), filters.prewitt_v(image_rotinv)) def diff_angle(angle_1, angle_2): """Calculate the differences between two angles.""" return np.minimum(np.pi - np.abs(angle_1 - angle_2), np.abs(angle_1 - angle_2)) diff_farid = diff_angle(true_angle, angle_farid) diff_sobel = diff_angle(true_angle, angle_sobel) diff_scharr = diff_angle(true_angle, angle_scharr) diff_prewitt = diff_angle(true_angle, angle_prewitt) fig, axes = plt.subplots(nrows=3, ncols=2, sharex=True, sharey=True, figsize=(8, 8)) axes = axes.ravel() axes[0].imshow(image_rotinv, cmap=plt.cm.gray) axes[0].set_title('Original image') axes[1].imshow(true_angle, cmap=plt.cm.hsv) axes[1].set_title('Analytical gradient angle') axes[2].imshow(diff_sobel, cmap=plt.cm.inferno, vmin=0, vmax=0.02) axes[2].set_title('Sobel error') axes[3].imshow(diff_prewitt, cmap=plt.cm.inferno, vmin=0, vmax=0.02) axes[3].set_title('Prewitt error') axes[4].imshow(diff_scharr, cmap=plt.cm.inferno, vmin=0, vmax=0.02) axes[4].set_title('Scharr error') color_ax = axes[5].imshow(diff_farid, cmap=plt.cm.inferno, vmin=0, vmax=0.02) axes[5].set_title('Farid error') fig.subplots_adjust(right=0.8) colorbar_ax = fig.add_axes([0.90, 0.10, 0.02, 0.50]) fig.colorbar(color_ax, cax=colorbar_ax, ticks=[0, 0.01, 0.02]) for ax in axes: ax.axis('off') plt.show() .. image-sg:: /auto_examples/edges/images/sphx_glr_plot_edge_filter_003.png :alt: Original image, Analytical gradient angle, Sobel error, Prewitt error, Scharr error, Farid error :srcset: /auto_examples/edges/images/sphx_glr_plot_edge_filter_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.389 seconds) .. _sphx_glr_download_auto_examples_edges_plot_edge_filter.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/edges/plot_edge_filter.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_edge_filter.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_edge_filter.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_