update

.CondaPkg/env/Lib/site-packages/skimage/measure/_blur_effect.py

@@ -1,81 +0,0 @@
-import numpy as np
-import scipy.ndimage as ndi
-
-from ..color import rgb2gray
-from ..util import img_as_float
-
-
-__all__ = ['blur_effect']
-
-
-def blur_effect(image, h_size=11, channel_axis=None, reduce_func=np.max):
-    """Compute a metric that indicates the strength of blur in an image
-    (0 for no blur, 1 for maximal blur).
-
-    Parameters
-    ----------
-    image : ndarray
-        RGB or grayscale nD image. The input image is converted to grayscale
-        before computing the blur metric.
-    h_size : int, optional
-        Size of the re-blurring filter.
-    channel_axis : int or None, optional
-        If None, the image is assumed to be grayscale (single-channel).
-        Otherwise, this parameter indicates which axis of the array
-        corresponds to color channels.
-    reduce_func : callable, optional
-        Function used to calculate the aggregation of blur metrics along all
-        axes. If set to None, the entire list is returned, where the i-th
-        element is the blur metric along the i-th axis.
-
-    Returns
-    -------
-    blur : float (0 to 1) or list of floats
-        Blur metric: by default, the maximum of blur metrics along all axes.
-
-    Notes
-    -----
-    `h_size` must keep the same value in order to compare results between
-    images. Most of the time, the default size (11) is enough. This means that
-    the metric can clearly discriminate blur up to an average 11x11 filter; if
-    blur is higher, the metric still gives good results but its values tend
-    towards an asymptote.
-
-    References
-    ----------
-    .. [1] Frederique Crete, Thierry Dolmiere, Patricia Ladret, and Marina
-       Nicolas "The blur effect: perception and estimation with a new
-       no-reference perceptual blur metric" Proc. SPIE 6492, Human Vision and
-       Electronic Imaging XII, 64920I (2007)
-       https://hal.archives-ouvertes.fr/hal-00232709
-       :DOI:`10.1117/12.702790`
-    """
-
-    if channel_axis is not None:
-        try:
-            # ensure color channels are in the final dimension
-            image = np.moveaxis(image, channel_axis, -1)
-        except np.AxisError:
-            print('channel_axis must be one of the image array dimensions')
-            raise
-        except TypeError:
-            print('channel_axis must be an integer')
-            raise
-        image = rgb2gray(image)
-    n_axes = image.ndim
-    image = img_as_float(image)
-    shape = image.shape
-    B = []
-
-    from ..filters import sobel
-    slices = tuple([slice(2, s - 1) for s in shape])
-    for ax in range(n_axes):
-        filt_im = ndi.uniform_filter1d(image, h_size, axis=ax)
-        im_sharp = np.abs(sobel(image, axis=ax))
-        im_blur = np.abs(sobel(filt_im, axis=ax))
-        T = np.maximum(0, im_sharp - im_blur)
-        M1 = np.sum(im_sharp[slices])
-        M2 = np.sum(T[slices])
-        B.append(np.abs(M1 - M2) / M1)
-
-    return B if reduce_func is None else reduce_func(B)