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import numpy as np
import scipy.ndimage as ndi
from ..util import img_as_float
from ..color import rgb2lab
from ._quickshift_cy import _quickshift_cython
def quickshift(image, ratio=1.0, kernel_size=5, max_dist=10,
return_tree=False, sigma=0, convert2lab=True, random_seed=42):
"""Segments image using quickshift clustering in Color-(x,y) space.
Produces an oversegmentation of the image using the quickshift mode-seeking
image : (width, height, channels) ndarray
Input image.
ratio : float, optional, between 0 and 1
Balances color-space proximity and image-space proximity.
Higher values give more weight to color-space.
kernel_size : float, optional
Width of Gaussian kernel used in smoothing the
sample density. Higher means fewer clusters.
max_dist : float, optional
Cut-off point for data distances.
Higher means fewer clusters.
return_tree : bool, optional
Whether to return the full segmentation hierarchy tree and distances.
sigma : float, optional
Width for Gaussian smoothing as preprocessing. Zero means no smoothing.
convert2lab : bool, optional
Whether the input should be converted to Lab colorspace prior to
segmentation. For this purpose, the input is assumed to be RGB.
random_seed : int, optional
Random seed used for breaking ties.
segment_mask : (width, height) ndarray
Integer mask indicating segment labels.
The authors advocate to convert the image to Lab color space prior to
segmentation, though this is not strictly necessary. For this to work, the
image must be given in RGB format.
.. [1] Quick shift and kernel methods for mode seeking,
Vedaldi, A. and Soatto, S.
European Conference on Computer Vision, 2008
image = img_as_float(np.atleast_3d(image))
if convert2lab:
if image.shape[2] != 3:
ValueError("Only RGB images can be converted to Lab space.")
image = rgb2lab(image)
if kernel_size < 1:
raise ValueError("`kernel_size` should be >= 1.")
image = ndi.gaussian_filter(image, [sigma, sigma, 0])
image = np.ascontiguousarray(image * ratio)
segment_mask = _quickshift_cython(
image, kernel_size=kernel_size, max_dist=max_dist,
return_tree=return_tree, random_seed=random_seed)
return segment_mask
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