mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 17:37:20 +08:00
Vighnesh Birodkar
67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
from ._seam_carving import _seam_carve_v
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from .. import util
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from .._shared import utils
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import numpy as np
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def seam_carve(img, energy_map, mode, num, border=1, force_copy=True):
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""" Carve vertical or horizontal seams off an image.
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Carves out vertical/horizontal seams from an image while using the given
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energy map to decide the importance of each pixel.
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Parameters
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----------
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image : (M, N) or (M, N, 3) ndarray
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Input image whose seams are to be removed.
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energy_map : (M, N) ndarray
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The array to decide the importance of each pixel. The higher
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the value corresponding to a pixel, the more the algorithm will try
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to keep it in the image.
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mode : str {'horizontal', 'vertical'}
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Indicates whether seams are to be removed vertically or horizontally.
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Removing seams horizontally will decrease the height whereas removing
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vertically will decrease the width.
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num : int
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Number of seams are to be removed.
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border : int, optional
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The number of pixels in the right, left and bottom end of the image
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to be excluded from being considered for a seam. This is important as
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certain filters just ignore image boundaries and set them to `0`.
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By default border is set to `1`.
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force_copy : bool, optional
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If set, the `image` and `energy_map` are copied before being used by
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the method which modifies it in place. Set this to `False` if the
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original image and the energy map are no longer needed after
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this opetration.
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Returns
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-------
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out : ndarray
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The cropped image with the seams removed.
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References
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----------
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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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utils.assert_nD(img, (2, 3))
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image = util.img_as_float(img, force_copy)
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energy_map = util.img_as_float(energy_map, force_copy)
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if image.ndim == 2:
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image = image[..., np.newaxis]
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if mode == 'horizontal':
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image = np.transpose(image, (1, 0, 2))
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image = np.ascontiguousarray(image)
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out = _seam_carve_v(image, energy_map, num, border)
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if mode == 'horizontal':
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out = np.transpose(out, (1, 0, 2))
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return np.squeeze(out)
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