using for loop to install conda package

.CondaPkg/env/Lib/site-packages/skimage/segmentation/_felzenszwalb.py

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+import numpy as np
+
+from ._felzenszwalb_cy import _felzenszwalb_cython
+from .._shared import utils
+
+
+@utils.channel_as_last_axis(multichannel_output=False)
+def felzenszwalb(image, scale=1, sigma=0.8, min_size=20, *,
+                 channel_axis=-1):
+    """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.
+
+    Parameters
+    ----------
+    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.
+    channel_axis : int or None, optional
+        If None, the image is assumed to be a grayscale (single channel) image.
+        Otherwise, this parameter indicates which axis of the array corresponds
+        to channels.
+
+        .. versionadded:: 0.19
+           ``channel_axis`` was added in 0.19.
+
+    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
+
+    Notes
+    -----
+        The `k` parameter used in the original paper renamed to `scale` here.
+
+    Examples
+    --------
+    >>> from skimage.segmentation import felzenszwalb
+    >>> from skimage.data import coffee
+    >>> img = coffee()
+    >>> segments = felzenszwalb(img, scale=3.0, sigma=0.95, min_size=5)
+    """
+    if channel_axis is None 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,
+                                min_size=min_size)