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emmanuelle
844858f01b
Modified comments in some gallery examples for compatibility with sphinx-gallery parsing. Also modified some links in the narrative doc since image file names have changed.
85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
"""
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============
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Seam Carving
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============
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This example demonstrates how images can be resized using seam carving [1]_.
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Resizing to a new aspect ratio distorts image contents. Seam carving attempts
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to resize *without* distortion, by removing regions of an image which are less
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important. In this example we are using the Sobel filter to signify the
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importance of each pixel.
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.. [1] Shai Avidan and Ariel Shamir
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"Seam Carving for Content-Aware Image Resizing"
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http://www.cs.jhu.edu/~misha/ReadingSeminar/Papers/Avidan07.pdf
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"""
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from skimage import data, draw
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from skimage import transform, util
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import numpy as np
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from skimage import filters, color
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from matplotlib import pyplot as plt
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hl_color = np.array([0, 1, 0])
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img = data.rocket()
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img = util.img_as_float(img)
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eimg = filters.sobel(color.rgb2gray(img))
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plt.title('Original Image')
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plt.imshow(img)
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######################################################################
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resized = transform.resize(img, (img.shape[0], img.shape[1] - 200))
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plt.figure()
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plt.title('Resized Image')
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plt.imshow(resized)
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######################################################################
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out = transform.seam_carve(img, eimg, 'vertical', 200)
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plt.figure()
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plt.title('Resized using Seam Carving')
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plt.imshow(out)
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######################################################################
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# Resizing distorts the rocket and surrounding objects, whereas seam carving
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# removes empty spaces and preserves object proportions.
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#
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# Object Removal
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# --------------
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#
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# Seam carving can also be used to remove artifacts from images. This
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# requires weighting the artifact with low values. Recall lower weights are
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# preferentially removed in seam carving. The following code masks the
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# rocket's region with low weights, indicating it should be removed.
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masked_img = img.copy()
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poly = [(404, 281), (404, 360), (359, 364), (338, 337), (145, 337), (120, 322),
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(145, 304), (340, 306), (362, 284)]
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pr = np.array([p[0] for p in poly])
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pc = np.array([p[1] for p in poly])
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rr, cc = draw.polygon(pr, pc)
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masked_img[rr, cc, :] = masked_img[rr, cc, :]*0.5 + hl_color*.5
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plt.figure()
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plt.title('Object Marked')
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plt.imshow(masked_img)
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######################################################################
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eimg[rr, cc] -= 1000
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plt.figure()
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plt.title('Object Removed')
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out = transform.seam_carve(img, eimg, 'vertical', 90)
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resized = transform.resize(img, out.shape)
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plt.imshow(out)
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plt.show()
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######################################################################
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