def ReinhardSample(File, Magnification, Percent, Tile):
"""Samples a whole-slide-image to determine LAB colorspace statistics
(mean, variance) needed to perform global Reinhard color normalization.
Normalizing individual tiles independently creates a significant bias
in the results of segmentation and feature extraction, as the color
statistics of each tile in a whole-slide image can vary significantly.
To remedy this, we sample from the entire whole-slide image in order to
obtain the global mean and variance of the LAB colorspace channels that
can then be used when processing individual tiles for uniformity. This
function can also be used to obtain the global target statistics from
an ideal slide that can serve as the normalization standard.
Parameters
----------
File : str
path and filename of slide.
Magnification : scalar
Desired magnification for sampling. Defaults to native scan
magnification.
Percent : double
Percentage of pixels to sample (range (0, 1]).
Tile : int
Tile size used in sampling high-resolution image.
Returns
-------
TargetMu : array_like
A 3-element array containing the means of the target image channels
in LAB color space.
TargetSigma : array_like
A 3-element list containing the standard deviations of the target image
channels in LAB color space.
Notes
-----
Returns a namedtuple.
See Also
--------
RudermanLABFwd, RudermanLABInv
"""
# generate a sampling of RGB pixels from whole-slide image
RGB = Sample(File, Magnification, Percent, Tile)
# reshape the 3xN pixel array into an image for RudermanLABFwd
RGB = np.reshape(RGB.transpose(), (1, RGB.shape[1], 3))
# perform forward LAB transformation
LAB = RudermanLABFwd(RGB)
# compute statistics of LAB channels
Mu = LAB.sum(axis=0).sum(axis=0) / (LAB.size / 3)
LAB[:, :, 0] = LAB[:, :, 0] - Mu[0]
LAB[:, :, 1] = LAB[:, :, 1] - Mu[1]
LAB[:, :, 2] = LAB[:, :, 2] - Mu[2]
Sigma = ((LAB * LAB).sum(axis=0).sum(axis=0) / (LAB.size / 3 - 1))**0.5
# build named tuple for output
OutTuple = collections.namedtuple('Statistics', ['Mu', 'Sigma'])
Output = OutTuple(Mu, Sigma)
return Output
