Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/skimage/exposure/histogram_matching.py

@@ -0,0 +1,70 @@
+import numpy as np
+
+
+def _match_cumulative_cdf(source, template):
+    """
+    Return modified source array so that the cumulative density function of
+    its values matches the cumulative density function of the template.
+    """
+    src_values, src_unique_indices, src_counts = np.unique(source.ravel(),
+                                                           return_inverse=True,
+                                                           return_counts=True)
+    tmpl_values, tmpl_counts = np.unique(template.ravel(), return_counts=True)
+
+    # calculate normalized quantiles for each array
+    src_quantiles = np.cumsum(src_counts) / source.size
+    tmpl_quantiles = np.cumsum(tmpl_counts) / template.size
+
+    interp_a_values = np.interp(src_quantiles, tmpl_quantiles, tmpl_values)
+    return interp_a_values[src_unique_indices].reshape(source.shape)
+
+
+def match_histograms(image, reference, *, multichannel=False):
+    """Adjust an image so that its cumulative histogram matches that of another.
+
+    The adjustment is applied separately for each channel.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image. Can be gray-scale or in color.
+    reference : ndarray
+        Image to match histogram of. Must have the same number of channels as
+        image.
+    multichannel : bool, optional
+        Apply the matching separately for each channel.
+
+    Returns
+    -------
+    matched : ndarray
+        Transformed input image.
+
+    Raises
+    ------
+    ValueError
+        Thrown when the number of channels in the input image and the reference
+        differ.
+
+    References
+    ----------
+    .. [1] http://paulbourke.net/miscellaneous/equalisation/
+
+    """
+    if image.ndim != reference.ndim:
+        raise ValueError('Image and reference must have the same number '
+                         'of channels.')
+
+    if multichannel:
+        if image.shape[-1] != reference.shape[-1]:
+            raise ValueError('Number of channels in the input image and '
+                             'reference image must match!')
+
+        matched = np.empty(image.shape, dtype=image.dtype)
+        for channel in range(image.shape[-1]):
+            matched_channel = _match_cumulative_cdf(image[..., channel],
+                                                    reference[..., channel])
+            matched[..., channel] = matched_channel
+    else:
+        matched = _match_cumulative_cdf(image, reference)
+
+    return matched