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77 lines
2.8 KiB
Python
77 lines
2.8 KiB
Python
import warnings
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import numpy as np
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from .felzenszwalb_cy import _felzenszwalb_grey
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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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Calls the algorithm on each channel separately, then combines
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using "and", i.e. two pixels are in the same segment if they are
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in the same segment for each channel.
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Parameters
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----------
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image: (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: ndarray, [width, height]
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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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"""
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#image = img_as_float(image)
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if image.ndim == 2:
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# assume single channel image
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return _felzenszwalb_grey(image, scale=scale, sigma=sigma)
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elif image.ndim != 3:
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raise ValueError("Got image with ndim=%d, don't know"
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" what to do." % image.ndim)
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# assume we got 2d image with multiple channels
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n_channels = image.shape[2]
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if n_channels != 3:
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warnings.warn("Got image with %d channels. Is that really what you"
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" wanted?" % image.shape[2])
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segmentations = []
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# compute quickshift for each channel
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for c in xrange(n_channels):
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channel = np.ascontiguousarray(image[:, :, c])
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s = _felzenszwalb_grey(channel, scale=scale, sigma=sigma,
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min_size=min_size)
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segmentations.append(s)
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# put pixels in same segment only if in the same segment in all images
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# we do this by combining the channels to one number
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n0 = segmentations[0].max() + 1
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n1 = segmentations[1].max() + 1
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segmentation = (segmentations[0] + segmentations[1] * n0
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+ segmentations[2] * n0 * n1)
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# make segment labels consecutive numbers starting at 0
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labels = np.unique(segmentation, return_inverse=True)[1]
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return labels.reshape(image.shape[:2])
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