mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-04 04:10:34 +08:00
Jeremy Metz
e9a56655c6
Corrected wrap-80 Switched `ndim` check to `atleast_3d` Removed `elif` from ndim check. Updated felzenszwalb rgb test to reflect min_size fix Update docstring multi to single & multi channel Also Added spaces between `,` and `:` in distance function Updated indentations, array shape specification Updated function name _rgb > _cython Added missing spaces in ValueError message Updated docs
55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
import numpy as np
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from .._shared.utils import warn
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from ._felzenszwalb_cy import _felzenszwalb_cython
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def felzenszwalb(image, scale=1, sigma=0.8, min_size=20):
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"""Computes Felsenszwalb's efficient graph based image segmentation.
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Produces an oversegmentation of a multichannel (i.e. RGB) image
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using a fast, minimum spanning tree based clustering on the image grid.
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The parameter ``scale`` sets an observation level. Higher scale means
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less and larger segments. ``sigma`` is the diameter of a Gaussian kernel,
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used for smoothing the image prior to segmentation.
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The number of produced segments as well as their size can only be
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controlled indirectly through ``scale``. Segment size within an image can
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vary greatly depending on local contrast.
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For RGB images, the algorithm uses the euclidean distance between pixels in
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color space.
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Parameters
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----------
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image : (width, height, 3) or (width, height) ndarray
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Input image.
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scale : float
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Free parameter. Higher means larger clusters.
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sigma : float
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Width of Gaussian kernel used in preprocessing.
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min_size : int
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Minimum component size. Enforced using postprocessing.
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Returns
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-------
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segment_mask : (width, height) ndarray
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Integer mask indicating segment labels.
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References
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----------
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.. [1] Efficient graph-based image segmentation, Felzenszwalb, P.F. and
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Huttenlocher, D.P. International Journal of Computer Vision, 2004
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Examples
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--------
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>>> from skimage.segmentation import felzenszwalb
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>>> from skimage.data import coffee
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>>> img = coffee()
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>>> segments = felzenszwalb(img, scale=3.0, sigma=0.95, min_size=5)
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"""
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image = np.atleast_3d(image)
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return _felzenszwalb_cython(image, scale=scale, sigma=sigma,
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min_size=min_size)
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