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import numpy as np
from ._felzenszwalb_cy import _felzenszwalb_cython
def felzenszwalb(image, scale=1, sigma=0.8, min_size=20, multichannel=True):
"""Computes Felsenszwalb's efficient graph based image segmentation.
Produces an oversegmentation of a multichannel (i.e. RGB) image
using a fast, minimum spanning tree based clustering on the image grid.
The parameter ``scale`` sets an observation level. Higher scale means
less and larger segments. ``sigma`` is the diameter of a Gaussian kernel,
used for smoothing the image prior to segmentation.
The number of produced segments as well as their size can only be
controlled indirectly through ``scale``. Segment size within an image can
vary greatly depending on local contrast.
For RGB images, the algorithm uses the euclidean distance between pixels in
color space.
image : (width, height, 3) or (width, height) ndarray
Input image.
scale : float
Free parameter. Higher means larger clusters.
sigma : float
Width (standard deviation) of Gaussian kernel used in preprocessing.
min_size : int
Minimum component size. Enforced using postprocessing.
multichannel : bool, optional (default: True)
Whether the last axis of the image is to be interpreted as multiple
channels. A value of False, for a 3D image, is not currently supported.
segment_mask : (width, height) ndarray
Integer mask indicating segment labels.
.. [1] Efficient graph-based image segmentation, Felzenszwalb, P.F. and
Huttenlocher, D.P. International Journal of Computer Vision, 2004
The `k` parameter used in the original paper renamed to `scale` here.
>>> from skimage.segmentation import felzenszwalb
>>> from import coffee
>>> img = coffee()
>>> segments = felzenszwalb(img, scale=3.0, sigma=0.95, min_size=5)
if not multichannel and image.ndim > 2:
raise ValueError("This algorithm works only on single or "
"multi-channel 2d images. ")
image = np.atleast_3d(image)
return _felzenszwalb_cython(image, scale=scale, sigma=sigma,