mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-05 05:22:37 +08:00
Egor Panfilov
57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
"""
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===========
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Inpainting
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===========
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Inpainting [1]_ is the process of reconstructing lost or deteriorated
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parts of images and videos.
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The reconstruction is supposed to be performed in fully automatic way by
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exploiting the information presented in non-damaged regions.
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In this example, we show how the masked pixels get inpainted by
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inpainting algorithm based on 'biharmonic equation'-assumption [2]_ [3]_.
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.. [1] Wikipedia. Inpainting
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https://en.wikipedia.org/wiki/Inpainting
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.. [2] Wikipedia. Biharmonic equation
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https://en.wikipedia.org/wiki/Biharmonic_equation
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.. [3] N.S.Hoang, S.B.Damelin, "On surface completion and image
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inpainting by biharmonic functions: numerical aspects",
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http://www.ima.umn.edu/~damelin/biharmonic
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from skimage import data, color
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from skimage.restoration import inpaint
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image_orig = color.rgb2gray(data.astronaut())
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# Create mask with three defect regions: left, middle, right respectively
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mask = np.zeros_like(image_orig)
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mask[20:60, 0:20] = 1
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mask[200:300, 150:170] = 1
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mask[50:100, 400:430] = 1
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image_defect = image_orig.copy()
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image_defect[np.where(mask)] = 0
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image_result = inpaint.inpaint_biharmonic(image_defect, mask)
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fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, nrows=1, figsize=(10, 6))
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ax1.set_title('Defected image')
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ax1.imshow(image_orig, cmap=plt.cm.gray, interpolation='nearest')
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ax1.set_xticks([]), ax1.set_yticks([])
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ax2.set_title('Defect mask')
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ax2.imshow(mask, cmap=plt.cm.gray, interpolation='nearest')
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ax2.set_xticks([]), ax2.set_yticks([])
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ax3.set_title('Inpainted image')
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ax3.imshow(image_result, cmap=plt.cm.gray, interpolation='nearest')
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ax3.set_xticks([]), ax3.set_yticks([])
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plt.show()
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