.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/filters/plot_inpaint.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_filters_plot_inpaint.py: =============================== Fill in defects with inpainting =============================== Inpainting [1]_ is the process of reconstructing lost or deteriorated parts of images and videos. The reconstruction (restoration) is performed in an automatic way by exploiting the information present in non-damaged regions. In this example, we show how the masked pixels get inpainted using an inpainting algorithm based on the biharmonic equation [2]_ [3]_ [4]_. .. [1] Wikipedia. Inpainting https://en.wikipedia.org/wiki/Inpainting .. [2] Wikipedia. Biharmonic equation https://en.wikipedia.org/wiki/Biharmonic_equation .. [3] S.B.Damelin and N.S.Hoang. "On Surface Completion and Image Inpainting by Biharmonic Functions: Numerical Aspects", International Journal of Mathematics and Mathematical Sciences, Vol. 2018, Article ID 3950312 :DOI:`10.1155/2018/3950312` .. [4] C. K. Chui and H. N. Mhaskar, MRA Contextual-Recovery Extension of Smooth Functions on Manifolds, Appl. and Comp. Harmonic Anal., 28 (2010), 104-113, :DOI:`10.1016/j.acha.2009.04.004` .. GENERATED FROM PYTHON SOURCE LINES 29-88 .. image-sg:: /auto_examples/filters/images/sphx_glr_plot_inpaint_001.png :alt: Original image, Mask, Defected image, Inpainted image :srcset: /auto_examples/filters/images/sphx_glr_plot_inpaint_001.png :class: sphx-glr-single-img .. code-block:: Python import numpy as np import matplotlib.pyplot as plt from skimage import data from skimage.morphology import disk, binary_dilation from skimage.restoration import inpaint image_orig = data.astronaut() # Create mask with six block defect regions mask = np.zeros(image_orig.shape[:-1], dtype=bool) mask[20:60, 0:20] = 1 mask[160:180, 70:155] = 1 mask[30:60, 170:195] = 1 mask[-60:-30, 170:195] = 1 mask[-180:-160, 70:155] = 1 mask[-60:-20, 0:20] = 1 # Add a few long, narrow defects mask[200:205, -200:] = 1 mask[150:255, 20:23] = 1 mask[365:368, 60:130] = 1 # Add randomly positioned small point-like defects rstate = np.random.default_rng(0) for radius in [0, 2, 4]: # larger defects are less common thresh = 3 + 0.25 * radius # make larger defects less common tmp_mask = rstate.standard_normal(image_orig.shape[:-1]) > thresh if radius > 0: tmp_mask = binary_dilation(tmp_mask, disk(radius, dtype=bool)) mask[tmp_mask] = 1 # Apply defect mask to the image over the same region in each color channel image_defect = image_orig * ~mask[..., np.newaxis] image_result = inpaint.inpaint_biharmonic(image_defect, mask, channel_axis=-1) fig, axes = plt.subplots(ncols=2, nrows=2) ax = axes.ravel() ax[0].set_title('Original image') ax[0].imshow(image_orig) ax[1].set_title('Mask') ax[1].imshow(mask, cmap=plt.cm.gray) ax[2].set_title('Defected image') ax[2].imshow(image_defect) ax[3].set_title('Inpainted image') ax[3].imshow(image_result) for a in ax: a.axis('off') fig.tight_layout() plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.445 seconds) .. _sphx_glr_download_auto_examples_filters_plot_inpaint.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/filters/plot_inpaint.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_inpaint.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_inpaint.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_