60 lines
2.1 KiB
Python
60 lines
2.1 KiB
Python
"""rankorder.py - convert an image of any type to an image of ints whose
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pixels have an identical rank order compared to the original image
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Originally part of CellProfiler, code licensed under both GPL and BSD licenses.
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Website: http://www.cellprofiler.org
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Copyright (c) 2003-2009 Massachusetts Institute of Technology
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Copyright (c) 2009-2011 Broad Institute
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All rights reserved.
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Original author: Lee Kamentstky
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"""
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import numpy as np
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def rank_order(image):
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"""Return an image of the same shape where each pixel is the
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index of the pixel value in the ascending order of the unique
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values of ``image``, aka the rank-order value.
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Parameters
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----------
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image : ndarray
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Returns
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-------
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labels : ndarray of type np.uint32, of shape image.shape
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New array where each pixel has the rank-order value of the
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corresponding pixel in ``image``. Pixel values are between 0 and
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n - 1, where n is the number of distinct unique values in
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``image``.
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original_values : 1-D ndarray
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Unique original values of ``image``
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Examples
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--------
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>>> a = np.array([[1, 4, 5], [4, 4, 1], [5, 1, 1]])
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>>> a
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array([[1, 4, 5],
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[4, 4, 1],
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[5, 1, 1]])
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>>> rank_order(a)
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(array([[0, 1, 2],
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[1, 1, 0],
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[2, 0, 0]], dtype=uint32), array([1, 4, 5]))
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>>> b = np.array([-1., 2.5, 3.1, 2.5])
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>>> rank_order(b)
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(array([0, 1, 2, 1], dtype=uint32), array([-1. , 2.5, 3.1]))
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"""
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flat_image = image.ravel()
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sort_order = flat_image.argsort().astype(np.uint32)
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flat_image = flat_image[sort_order]
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sort_rank = np.zeros_like(sort_order)
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is_different = flat_image[:-1] != flat_image[1:]
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np.cumsum(is_different, out=sort_rank[1:])
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original_values = np.zeros((sort_rank[-1] + 1,), image.dtype)
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original_values[0] = flat_image[0]
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original_values[1:] = flat_image[1:][is_different]
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int_image = np.zeros_like(sort_order)
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int_image[sort_order] = sort_rank
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return (int_image.reshape(image.shape), original_values)
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