mirror of
https://github.com/wassname/scikit-image.git
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Vighnesh Birodkar
71 lines
2.7 KiB
Python
71 lines
2.7 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, mode, num, energy_func, extra_args=[],
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extra_kwargs={}, 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 off an image while using the given
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energy function 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 vertical seams are to be removed.
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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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energy_func : callable
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The function used 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. For every iteration `energy_func` is called
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as `energy_func(image, *extra_args, **extra_kwargs)`, where `image`
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is the cropped image during each iteration and is expected to return a
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(M, N) ndarray depicting each pixel's importance.
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extra_args : iterable, optional
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The extra arguments supplied to `energy_func`.
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extra_kwargs : dict, optional
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The extra keyword arguments supplied to `energy_func`.
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border : int, optional
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The number of pixels in the right and left end of the image to be
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excluded from being considered for a seam. This is important as certain
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filters just ignore image boundaries and set them to `0`. By default
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border is set to `1`.
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force_copy : bool, optional
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If set, the image is copied before being used by the method which
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modifies it in place. Set this to `False` if the original image is no
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loner needed after 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)
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if image.ndim == 2:
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image = image[..., np.newaxis]
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if mode == 'vertical':
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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, num, energy_func, extra_args, extra_kwargs, border)
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if mode == 'vertical':
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out = np.transpose(out, (1, 0, 2))
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return np.squeeze(out)
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