mirror of
https://github.com/wassname/scikit-image.git
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Vighnesh Birodkar
95 lines
2.3 KiB
Python
95 lines
2.3 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 often distorts contents in the image. Seam carving tries to resize
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images while trying to keep important content intact. In this example we are
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using the Sobel filter to signify the 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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.. image:: PLOT2RST.current_figure
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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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.. image:: PLOT2RST.current_figure
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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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.. image:: PLOT2RST.current_figure
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As you can see, resizing as distorted the rocket and the objects around,
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whereas seam carving has reszied by removing the empty spaces in between.
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Object Removal
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--------------
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Seam Carving can also be used to remove atrifacts from images. To do that, we
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have to ensure that pixels to be removes get less importance. In the following
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code I approximately mark the rocket with a mask, and then decrease the
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importance of those pixels
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"""
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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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.. image:: PLOT2RST.current_figure
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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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.. image:: PLOT2RST.current_figure
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"""
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