84 lines
2.9 KiB
Python
84 lines
2.9 KiB
Python
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import numpy as np
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from skimage.exposure import histogram_matching
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from skimage import exposure
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from skimage import data
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from skimage._shared.testing import assert_array_almost_equal, \
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assert_almost_equal
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import pytest
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@pytest.mark.parametrize('array, template, expected_array', [
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(np.arange(10), np.arange(100), np.arange(9, 100, 10)),
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(np.random.rand(4), np.ones(3), np.ones(4))
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])
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def test_match_array_values(array, template, expected_array):
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# when
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matched = histogram_matching._match_cumulative_cdf(array, template)
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# then
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assert_array_almost_equal(matched, expected_array)
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class TestMatchHistogram:
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image_rgb = data.chelsea()
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template_rgb = data.astronaut()
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@pytest.mark.parametrize('image, reference, multichannel', [
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(image_rgb, template_rgb, True),
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(image_rgb[:, :, 0], template_rgb[:, :, 0], False)
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])
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def test_match_histograms(self, image, reference, multichannel):
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"""Assert that pdf of matched image is close to the reference's pdf for
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all channels and all values of matched"""
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# when
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matched = exposure.match_histograms(image, reference,
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multichannel=multichannel)
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matched_pdf = self._calculate_image_empirical_pdf(matched)
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reference_pdf = self._calculate_image_empirical_pdf(reference)
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# then
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for channel in range(len(matched_pdf)):
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reference_values, reference_quantiles = reference_pdf[channel]
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matched_values, matched_quantiles = matched_pdf[channel]
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for i, matched_value in enumerate(matched_values):
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closest_id = (
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np.abs(reference_values - matched_value)
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).argmin()
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assert_almost_equal(matched_quantiles[i],
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reference_quantiles[closest_id],
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decimal=1)
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@pytest.mark.parametrize('image, reference', [
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(image_rgb, template_rgb[:, :, 0]),
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(image_rgb[:, :, 0], template_rgb)
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])
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def test_raises_value_error_on_channels_mismatch(self, image, reference):
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with pytest.raises(ValueError):
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exposure.match_histograms(image, reference)
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@classmethod
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def _calculate_image_empirical_pdf(cls, image):
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"""Helper function for calculating empirical probability density
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function of a given image for all channels"""
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if image.ndim > 2:
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image = image.transpose(2, 0, 1)
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channels = np.array(image, copy=False, ndmin=3)
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channels_pdf = []
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for channel in channels:
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channel_values, counts = np.unique(channel, return_counts=True)
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channel_quantiles = np.cumsum(counts).astype(np.float64)
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channel_quantiles /= channel_quantiles[-1]
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channels_pdf.append((channel_values, channel_quantiles))
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return np.asarray(channels_pdf)
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