histogram

Some functions for working with histograms.

mr_utils.utils.histogram.dH(H1, H2, mode='l2')

Histogram metrics.

Parameters:

• H1 (array_like) – 1d histogram.
• H2 (array_like) – 1d histogram with bins matched to H1.
• mode ({'l2', 'l1', 'vcos', 'intersect', 'chi2', 'jsd', 'emd'}, optional) – Metric to use.

Returns:

Distance between H1, H2.

Return type:

float

Notes

Similar bins means the same number and size over the same range.

Modes:

• l2 – Euclidean distance
• l1 – Manhattan distance
• vcos – Vector cosine distance
• intersect – Histogram intersection distance
• chi2 – Chi square distance
• jsd – Jensen-Shannan Divergence
• emd – Earth Mover’s Distance

Issues:

• I’m not completely convinced that intersect is doing the right thing.

The quality of the metric will depend a lot on the qaulity of the histograms themselves. Obviously more samples and well-chosen bins will help out in the comparisons.

mr_utils.utils.histogram.hist_match(source, template)

Histogram matching.

Adjust the pixel values of a grayscale image such that its histogram matches that of a target image

Parameters:

• source (np.ndarray) – Image to transform; the histogram is computed over the flattened array
• template (np.ndarray) – Template image; can have different dimensions to source

Returns:

matched – The transformed output image

Return type:

np.ndarray

Notes

https://stackoverflow.com/questions/32655686/histogram-matching-of-two-images-in-python-2-x