Created
October 20, 2017 10:14
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histogram_matching in slice-wise manner.
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def hist_match(im_src, im_tar, n_bins): | |
# normalize both images | |
src_max = im_src.max() | |
im_src_n = min_max_norm(im_src) | |
im_tar_n = min_max_norm(im_tar) | |
# Get histogram | |
hist_src, bins = np.histogram(im_src_n.flatten(), n_bins) | |
hist_tar, bins = np.histogram(im_tar_n.flatten(), n_bins) | |
# Normalize cumulative density function | |
cdf_src = hist_src.cumsum() | |
cdf_tar = hist_tar.cumsum() | |
# cdf_src = cdf_src / cdf_src[-1] | |
cdf_tar = cdf_tar / cdf_tar[-1] | |
# Interpolation & reshape | |
im_match = np.interp(im_src_n.flatten(), cdf_tar, bins[:-1]).astype(np.float32) | |
im_match = im_match.reshape((im_src.shape[0], im_src.shape[1])) | |
# TODO : Should I multiply src_max? | |
im_match_n = min_max_norm(im_match) | |
im_match_n = im_match_n * src_max | |
return im_match_n |
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