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Seam Carving

This example demonstrates how images can be resized using seam carving [1]. Resizing to a new aspect ratio distorts image contents. Seam carving attempts to resize without distortion, by removing regions of an image which are less important. In this example we are using the Sobel filter to signify the importance of each pixel.

[1]Shai Avidan and Ariel Shamir “Seam Carving for Content-Aware Image Resizing”
from skimage import data, draw
from skimage import transform, util
import numpy as np
from skimage import filters, color
from matplotlib import pyplot as plt

hl_color = np.array([0, 1, 0])

img = data.rocket()
img = util.img_as_float(img)
eimg = filters.sobel(color.rgb2gray(img))

plt.title('Original Image')
resized = transform.resize(img, (img.shape[0], img.shape[1] - 200),
plt.title('Resized Image')
out = transform.seam_carve(img, eimg, 'vertical', 200)
plt.title('Resized using Seam Carving')

Resizing distorts the rocket and surrounding objects, whereas seam carving removes empty spaces and preserves object proportions.

Object Removal

Seam carving can also be used to remove artifacts from images. This requires weighting the artifact with low values. Recall lower weights are preferentially removed in seam carving. The following code masks the rocket’s region with low weights, indicating it should be removed.

masked_img = img.copy()

poly = [(404, 281), (404, 360), (359, 364), (338, 337), (145, 337), (120, 322),
        (145, 304), (340, 306), (362, 284)]
pr = np.array([p[0] for p in poly])
pc = np.array([p[1] for p in poly])
rr, cc = draw.polygon(pr, pc)

masked_img[rr, cc, :] = masked_img[rr, cc, :]*0.5 + hl_color*.5
plt.title('Object Marked')

eimg[rr, cc] -= 1000

plt.title('Object Removed')
out = transform.seam_carve(img, eimg, 'vertical', 90)
resized = transform.resize(img, out.shape, mode='reflect')

Total running time of the script: ( 0 minutes 1.180 seconds)

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