mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 22:37:50 +08:00
Andreas Mueller
77 lines
2.9 KiB
Python
77 lines
2.9 KiB
Python
import warnings
|
|
import numpy as np
|
|
|
|
from ._felzenszwalb_cy import _felzenszwalb_grey
|
|
|
|
|
|
def felzenszwalb(image, scale=1, sigma=0.8, min_size=20):
|
|
"""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 computes a separate segmentation for each
|
|
channel and then combines these. The combined segmentation is the
|
|
intersection of the separate segmentations on the color channels.
|
|
|
|
Parameters
|
|
----------
|
|
image : (width, height, 3) or (width, height) ndarray
|
|
Input image.
|
|
scale : float
|
|
Free parameter. Higher means larger clusters.
|
|
sigma : float
|
|
Width of Gaussian kernel used in preprocessing.
|
|
min_size : int
|
|
Minimum component size. Enforced using postprocessing.
|
|
|
|
Returns
|
|
-------
|
|
segment_mask : (width, height) ndarray
|
|
Integer mask indicating segment labels.
|
|
|
|
References
|
|
----------
|
|
.. [1] Efficient graph-based image segmentation, Felzenszwalb, P.F. and
|
|
Huttenlocher, D.P. International Journal of Computer Vision, 2004
|
|
"""
|
|
|
|
if image.ndim == 2:
|
|
# assume single channel image
|
|
return _felzenszwalb_grey(image, scale=scale, sigma=sigma, min_size=min_size)
|
|
|
|
elif image.ndim != 3:
|
|
raise ValueError("Felzenswalb segmentation can only operate on RGB and"
|
|
" grey images, but input array of ndim %d given."
|
|
% image.ndim)
|
|
|
|
# assume we got 2d image with multiple channels
|
|
n_channels = image.shape[2]
|
|
if n_channels != 3:
|
|
warnings.warn("Got image with %d channels. Is that really what you"
|
|
" wanted?" % image.shape[2])
|
|
segmentations = []
|
|
# compute quickshift for each channel
|
|
for c in range(n_channels):
|
|
channel = np.ascontiguousarray(image[:, :, c])
|
|
s = _felzenszwalb_grey(channel, scale=scale, sigma=sigma,
|
|
min_size=min_size)
|
|
segmentations.append(s)
|
|
|
|
# put pixels in same segment only if in the same segment in all images
|
|
# we do this by combining the channels to one number
|
|
n0 = segmentations[0].max() + 1
|
|
n1 = segmentations[1].max() + 1
|
|
segmentation = (segmentations[0] + segmentations[1] * n0
|
|
+ segmentations[2] * n0 * n1)
|
|
# make segment labels consecutive numbers starting at 0
|
|
labels = np.unique(segmentation, return_inverse=True)[1]
|
|
return labels.reshape(image.shape[:2])
|