forked from 170010011/fr
71 lines
2.4 KiB
Python
71 lines
2.4 KiB
Python
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import numpy as np
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def _match_cumulative_cdf(source, template):
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"""
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Return modified source array so that the cumulative density function of
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its values matches the cumulative density function of the template.
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"""
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src_values, src_unique_indices, src_counts = np.unique(source.ravel(),
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return_inverse=True,
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return_counts=True)
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tmpl_values, tmpl_counts = np.unique(template.ravel(), return_counts=True)
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# calculate normalized quantiles for each array
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src_quantiles = np.cumsum(src_counts) / source.size
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tmpl_quantiles = np.cumsum(tmpl_counts) / template.size
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interp_a_values = np.interp(src_quantiles, tmpl_quantiles, tmpl_values)
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return interp_a_values[src_unique_indices].reshape(source.shape)
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def match_histograms(image, reference, *, multichannel=False):
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"""Adjust an image so that its cumulative histogram matches that of another.
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The adjustment is applied separately for each channel.
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Parameters
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----------
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image : ndarray
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Input image. Can be gray-scale or in color.
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reference : ndarray
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Image to match histogram of. Must have the same number of channels as
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image.
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multichannel : bool, optional
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Apply the matching separately for each channel.
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Returns
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-------
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matched : ndarray
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Transformed input image.
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Raises
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------
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ValueError
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Thrown when the number of channels in the input image and the reference
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differ.
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References
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----------
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.. [1] http://paulbourke.net/miscellaneous/equalisation/
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"""
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if image.ndim != reference.ndim:
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raise ValueError('Image and reference must have the same number '
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'of channels.')
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if multichannel:
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if image.shape[-1] != reference.shape[-1]:
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raise ValueError('Number of channels in the input image and '
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'reference image must match!')
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matched = np.empty(image.shape, dtype=image.dtype)
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for channel in range(image.shape[-1]):
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matched_channel = _match_cumulative_cdf(image[..., channel],
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reference[..., channel])
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matched[..., channel] = matched_channel
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else:
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matched = _match_cumulative_cdf(image, reference)
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return matched
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