Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/skimage/segmentation/_quickshift.py

@@ -0,0 +1,74 @@
+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
+    algorithm.
+
+    Parameters
+    ----------
+    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.
+
+    Returns
+    -------
+    segment_mask : (width, height) ndarray
+        Integer mask indicating segment labels.
+
+    Notes
+    -----
+    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.
+
+    References
+    ----------
+    .. [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