Python Seam Carving module
seam_carver is a small tool for retargetting images to any dimension greater or smaller. It uses the process of seam carving as originally described by Shai Avidan & Ariel Shamir in http://perso.crans.org/frenoy/matlab2012/seamcarving.pdf
A combination of the gradient energy (determined with the sobel filter) and a simple color energy is used to determine the least important seam. Additon of seams occurs by the same mechanism as described in the paper.
pip install seam_carver
from scipy import misc
import numpy as np
from seam_carver import intelligent_resize
rgb_weights = [-3, 1, -3]
mask_weight = 10
cat_img = misc.imread('./demo/cat.png')
mask = np.zeros(cat_img.shape)
resized_img = intelligent_resize(cat_img, 0, -20, rgb_weights, mask, mask_weight)
misc.imsave('./demo/cat_shrunk.png', resized_img)def intelligent_resize(img, d_rows, d_columns, rgb_weights, mask, mask_weight):
"""
Changes the size of the provided image in either the vertical or horizontal direction,
by increasing or decreasing or some combination of the two.
Args:
img (n,m,3 numpy matrix): RGB image to be resized.
d_rows (int): The change (delta) in rows. Positive number indicated insertions, negative is removal.
d_columns (int): The change (delta) in columns. Positive number indicated insertions, negative is removal.
rgb_weights (1,3 numpy matrix): Additional weight paramater to be applied to pixels.
mask (n,m,3 numpy matrix): Mask matrix indicating areas to make more or less likely for removal.
mask_weight (int): Scalar multiple to be applied to mask.
Returns:
n,m,3 numpy matrix: Resized RGB image.
"""For more examples and details see the demo jupyter notebook
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