762 lines
25 KiB
Python
762 lines
25 KiB
Python
import warnings
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import numpy as np
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import pytest
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from skimage import util
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from skimage import data
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from skimage import exposure
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from skimage.exposure.exposure import intensity_range
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from skimage.color import rgb2gray
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from skimage.util.dtype import dtype_range
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from skimage._shared._warnings import expected_warnings
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from skimage._shared import testing
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from skimage._shared.testing import (assert_array_equal,
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assert_array_almost_equal,
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assert_equal,
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assert_almost_equal)
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# Test integer histograms
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# =======================
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def test_wrong_source_range():
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im = np.array([-1, 100], dtype=np.int8)
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with testing.raises(ValueError):
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frequencies, bin_centers = exposure.histogram(im, source_range='foobar')
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def test_negative_overflow():
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im = np.array([-1, 100], dtype=np.int8)
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frequencies, bin_centers = exposure.histogram(im)
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assert_array_equal(bin_centers, np.arange(-1, 101))
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assert frequencies[0] == 1
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assert frequencies[-1] == 1
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assert_array_equal(frequencies[1:-1], 0)
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def test_all_negative_image():
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im = np.array([-100, -1], dtype=np.int8)
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frequencies, bin_centers = exposure.histogram(im)
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assert_array_equal(bin_centers, np.arange(-100, 0))
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assert frequencies[0] == 1
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assert frequencies[-1] == 1
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assert_array_equal(frequencies[1:-1], 0)
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def test_int_range_image():
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im = np.array([10, 100], dtype=np.int8)
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frequencies, bin_centers = exposure.histogram(im)
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assert_equal(len(bin_centers), len(frequencies))
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assert_equal(bin_centers[0], 10)
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assert_equal(bin_centers[-1], 100)
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def test_peak_uint_range_dtype():
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im = np.array([10, 100], dtype=np.uint8)
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frequencies, bin_centers = exposure.histogram(im, source_range='dtype')
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assert_array_equal(bin_centers, np.arange(0, 256))
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assert_equal(frequencies[10], 1)
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assert_equal(frequencies[100], 1)
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assert_equal(frequencies[101], 0)
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assert_equal(frequencies.shape, (256,))
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def test_peak_int_range_dtype():
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im = np.array([10, 100], dtype=np.int8)
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frequencies, bin_centers = exposure.histogram(im, source_range='dtype')
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assert_array_equal(bin_centers, np.arange(-128, 128))
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assert_equal(frequencies[128+10], 1)
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assert_equal(frequencies[128+100], 1)
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assert_equal(frequencies[128+101], 0)
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assert_equal(frequencies.shape, (256,))
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def test_flat_uint_range_dtype():
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im = np.linspace(0, 255, 256, dtype=np.uint8)
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frequencies, bin_centers = exposure.histogram(im, source_range='dtype')
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assert_array_equal(bin_centers, np.arange(0, 256))
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assert_equal(frequencies.shape, (256,))
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def test_flat_int_range_dtype():
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im = np.linspace(-128, 128, 256, dtype=np.int8)
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frequencies, bin_centers = exposure.histogram(im, source_range='dtype')
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assert_array_equal(bin_centers, np.arange(-128, 128))
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assert_equal(frequencies.shape, (256,))
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def test_peak_float_out_of_range_image():
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im = np.array([10, 100], dtype=np.float16)
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frequencies, bin_centers = exposure.histogram(im, nbins=90)
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# offset values by 0.5 for float...
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assert_array_equal(bin_centers, np.arange(10, 100) + 0.5)
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def test_peak_float_out_of_range_dtype():
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im = np.array([10, 100], dtype=np.float16)
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nbins = 10
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frequencies, bin_centers = exposure.histogram(im, nbins=nbins, source_range='dtype')
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assert_almost_equal(np.min(bin_centers), -0.9, 3)
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assert_almost_equal(np.max(bin_centers), 0.9, 3)
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assert_equal(len(bin_centers), 10)
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def test_normalize():
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im = np.array([0, 255, 255], dtype=np.uint8)
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frequencies, bin_centers = exposure.histogram(im, source_range='dtype',
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normalize=False)
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expected = np.zeros(256)
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expected[0] = 1
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expected[-1] = 2
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assert_equal(frequencies, expected)
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frequencies, bin_centers = exposure.histogram(im, source_range='dtype',
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normalize=True)
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expected /= 3.
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assert_equal(frequencies, expected)
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# Test histogram equalization
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# ===========================
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np.random.seed(0)
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test_img_int = data.camera()
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# squeeze image intensities to lower image contrast
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test_img = util.img_as_float(test_img_int)
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test_img = exposure.rescale_intensity(test_img / 5. + 100)
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def test_equalize_uint8_approx():
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"""Check integer bins used for uint8 images."""
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img_eq0 = exposure.equalize_hist(test_img_int)
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img_eq1 = exposure.equalize_hist(test_img_int, nbins=3)
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np.testing.assert_allclose(img_eq0, img_eq1)
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def test_equalize_ubyte():
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img = util.img_as_ubyte(test_img)
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img_eq = exposure.equalize_hist(img)
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cdf, bin_edges = exposure.cumulative_distribution(img_eq)
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check_cdf_slope(cdf)
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def test_equalize_float():
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img = util.img_as_float(test_img)
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img_eq = exposure.equalize_hist(img)
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cdf, bin_edges = exposure.cumulative_distribution(img_eq)
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check_cdf_slope(cdf)
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def test_equalize_masked():
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img = util.img_as_float(test_img)
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mask = np.zeros(test_img.shape)
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mask[50:150, 50:250] = 1
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img_mask_eq = exposure.equalize_hist(img, mask=mask)
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img_eq = exposure.equalize_hist(img)
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cdf, bin_edges = exposure.cumulative_distribution(img_mask_eq)
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check_cdf_slope(cdf)
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assert not (img_eq == img_mask_eq).all()
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def check_cdf_slope(cdf):
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"""Slope of cdf which should equal 1 for an equalized histogram."""
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norm_intensity = np.linspace(0, 1, len(cdf))
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slope, intercept = np.polyfit(norm_intensity, cdf, 1)
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assert 0.9 < slope < 1.1
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# Test intensity range
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# ====================
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@testing.parametrize("test_input,expected", [
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('image', [0, 1]),
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('dtype', [0, 255]),
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((10, 20), [10, 20])
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])
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def test_intensity_range_uint8(test_input, expected):
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image = np.array([0, 1], dtype=np.uint8)
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out = intensity_range(image, range_values=test_input)
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assert_array_equal(out, expected)
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@testing.parametrize("test_input,expected", [
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('image', [0.1, 0.2]),
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('dtype', [-1, 1]),
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((0.3, 0.4), [0.3, 0.4])
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])
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def test_intensity_range_float(test_input, expected):
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image = np.array([0.1, 0.2], dtype=np.float64)
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out = intensity_range(image, range_values=test_input)
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assert_array_equal(out, expected)
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def test_intensity_range_clipped_float():
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image = np.array([0.1, 0.2], dtype=np.float64)
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out = intensity_range(image, range_values='dtype', clip_negative=True)
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assert_array_equal(out, (0, 1))
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# Test rescale intensity
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# ======================
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uint10_max = 2**10 - 1
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uint12_max = 2**12 - 1
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uint14_max = 2**14 - 1
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uint16_max = 2**16 - 1
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def test_rescale_stretch():
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image = np.array([51, 102, 153], dtype=np.uint8)
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out = exposure.rescale_intensity(image)
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assert out.dtype == np.uint8
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assert_array_almost_equal(out, [0, 127, 255])
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def test_rescale_shrink():
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image = np.array([51., 102., 153.])
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out = exposure.rescale_intensity(image)
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assert_array_almost_equal(out, [0, 0.5, 1])
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def test_rescale_in_range():
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image = np.array([51., 102., 153.])
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out = exposure.rescale_intensity(image, in_range=(0, 255))
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assert_array_almost_equal(out, [0.2, 0.4, 0.6])
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def test_rescale_in_range_clip():
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image = np.array([51., 102., 153.])
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out = exposure.rescale_intensity(image, in_range=(0, 102))
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assert_array_almost_equal(out, [0.5, 1, 1])
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def test_rescale_out_range():
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"""Check that output range is correct.
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.. versionchanged:: 0.17
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This function used to return dtype matching the input dtype. It now
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matches the output.
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"""
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image = np.array([-10, 0, 10], dtype=np.int8)
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out = exposure.rescale_intensity(image, out_range=(0, 127))
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assert out.dtype == np.float_
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assert_array_almost_equal(out, [0, 63.5, 127])
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def test_rescale_named_in_range():
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image = np.array([0, uint10_max, uint10_max + 100], dtype=np.uint16)
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out = exposure.rescale_intensity(image, in_range='uint10')
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assert_array_almost_equal(out, [0, uint16_max, uint16_max])
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def test_rescale_named_out_range():
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image = np.array([0, uint16_max], dtype=np.uint16)
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out = exposure.rescale_intensity(image, out_range='uint10')
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assert_array_almost_equal(out, [0, uint10_max])
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def test_rescale_uint12_limits():
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image = np.array([0, uint16_max], dtype=np.uint16)
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out = exposure.rescale_intensity(image, out_range='uint12')
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assert_array_almost_equal(out, [0, uint12_max])
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def test_rescale_uint14_limits():
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image = np.array([0, uint16_max], dtype=np.uint16)
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out = exposure.rescale_intensity(image, out_range='uint14')
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assert_array_almost_equal(out, [0, uint14_max])
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def test_rescale_all_zeros():
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image = np.zeros((2, 2), dtype=np.uint8)
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out = exposure.rescale_intensity(image)
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assert ~np.isnan(out).all()
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assert_array_almost_equal(out, image)
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def test_rescale_constant():
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image = np.array([130, 130], dtype=np.uint16)
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out = exposure.rescale_intensity(image, out_range=(0, 127))
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assert_array_almost_equal(out, [127, 127])
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def test_rescale_same_values():
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image = np.ones((2, 2))
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out = exposure.rescale_intensity(image)
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assert ~np.isnan(out).all()
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assert_array_almost_equal(out, image)
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@pytest.mark.parametrize(
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"in_range,out_range", [("image", "dtype"),
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("dtype", "image")]
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)
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def test_rescale_nan_warning(in_range, out_range):
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image = np.arange(12, dtype=float).reshape(3, 4)
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image[1, 1] = np.nan
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msg = (
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r"One or more intensity levels are NaN\."
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r" Rescaling will broadcast NaN to the full image\."
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)
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# 2019/11/10 Passing NaN to np.clip raises a DeprecationWarning for
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# versions above 1.17
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# TODO: Remove once NumPy removes this DeprecationWarning
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numpy_warning_1_17_plus = (
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r"Passing `np.nan` to mean no clipping in np.clip "
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r"has always been unreliable|\A\Z"
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)
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# 2019/12/06 Passing NaN to np.min and np.max raises a RuntimeWarning for
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# NumPy < 1.16
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# TODO: Remove once minimal required NumPy version is 1.16
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numpy_warning_smaller_1_16 = r"invalid value encountered in reduce|\A\Z"
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with expected_warnings(
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[msg, numpy_warning_1_17_plus, numpy_warning_smaller_1_16]
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):
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exposure.rescale_intensity(image, in_range, out_range)
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@pytest.mark.parametrize(
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"out_range, out_dtype", [
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('uint8', np.uint8),
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('uint10', np.uint16),
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('uint12', np.uint16),
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('uint16', np.uint16),
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('float', np.float_),
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]
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)
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def test_rescale_output_dtype(out_range, out_dtype):
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image = np.array([-128, 0, 127], dtype=np.int8)
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output_image = exposure.rescale_intensity(image, out_range=out_range)
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assert output_image.dtype == out_dtype
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def test_rescale_no_overflow():
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image = np.array([-128, 0, 127], dtype=np.int8)
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output_image = exposure.rescale_intensity(image, out_range=np.uint8)
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testing.assert_array_equal(output_image, [0, 128, 255])
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assert output_image.dtype == np.uint8
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def test_rescale_float_output():
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image = np.array([-128, 0, 127], dtype=np.int8)
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output_image = exposure.rescale_intensity(image, out_range=(0, 255))
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testing.assert_array_equal(output_image, [0, 128, 255])
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assert output_image.dtype == np.float_
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def test_rescale_raises_on_incorrect_out_range():
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image = np.array([-128, 0, 127], dtype=np.int8)
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with testing.raises(ValueError):
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_ = exposure.rescale_intensity(image, out_range='flat')
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# Test adaptive histogram equalization
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# ====================================
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def test_adapthist_grayscale():
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"""Test a grayscale float image
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"""
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img = util.img_as_float(data.astronaut())
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img = rgb2gray(img)
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img = np.dstack((img, img, img))
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adapted = exposure.equalize_adapthist(img, kernel_size=(57, 51),
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clip_limit=0.01, nbins=128)
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assert img.shape == adapted.shape
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assert_almost_equal(peak_snr(img, adapted), 100.140, 3)
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assert_almost_equal(norm_brightness_err(img, adapted), 0.0529, 3)
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def test_adapthist_color():
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"""Test an RGB color uint16 image
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"""
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img = util.img_as_uint(data.astronaut())
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter('always')
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hist, bin_centers = exposure.histogram(img)
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assert len(w) > 0
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adapted = exposure.equalize_adapthist(img, clip_limit=0.01)
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assert adapted.min() == 0
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assert adapted.max() == 1.0
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assert img.shape == adapted.shape
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full_scale = exposure.rescale_intensity(img)
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assert_almost_equal(peak_snr(full_scale, adapted), 109.393, 1)
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assert_almost_equal(norm_brightness_err(full_scale, adapted), 0.02, 2)
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return data, adapted
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def test_adapthist_alpha():
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"""Test an RGBA color image
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"""
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img = util.img_as_float(data.astronaut())
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alpha = np.ones((img.shape[0], img.shape[1]), dtype=float)
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img = np.dstack((img, alpha))
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adapted = exposure.equalize_adapthist(img)
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assert adapted.shape != img.shape
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img = img[:, :, :3]
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full_scale = exposure.rescale_intensity(img)
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assert img.shape == adapted.shape
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assert_almost_equal(peak_snr(full_scale, adapted), 109.393, 2)
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assert_almost_equal(norm_brightness_err(full_scale, adapted), 0.0248, 3)
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def test_adapthist_grayscale_Nd():
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"""
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Test for n-dimensional consistency with float images
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Note: Currently if img.ndim == 3, img.shape[2] > 4 must hold for the image
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not to be interpreted as a color image by @adapt_rgb
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"""
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# take 2d image, subsample and stack it
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img = util.img_as_float(data.astronaut())
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img = rgb2gray(img)
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a = 15
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img2d = util.img_as_float(img[0:-1:a, 0:-1:a])
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img3d = np.array([img2d] * (img.shape[0] // a))
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# apply CLAHE
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adapted2d = exposure.equalize_adapthist(img2d,
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kernel_size=5,
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clip_limit=0.05)
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adapted3d = exposure.equalize_adapthist(img3d,
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kernel_size=5,
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clip_limit=0.05)
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# check that dimensions of input and output match
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assert img2d.shape == adapted2d.shape
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assert img3d.shape == adapted3d.shape
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# check that the result from the stack of 2d images is similar
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# to the underlying 2d image
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assert np.mean(np.abs(adapted2d
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- adapted3d[adapted3d.shape[0] // 2])) < 0.02
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def test_adapthist_constant():
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"""Test constant image, float and uint
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"""
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img = np.zeros((8, 8))
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img += 2
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img = img.astype(np.uint16)
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adapted = exposure.equalize_adapthist(img, 3)
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assert np.min(adapted) == np.max(adapted)
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img = np.zeros((8, 8))
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img += 0.1
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img = img.astype(np.float64)
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adapted = exposure.equalize_adapthist(img, 3)
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assert np.min(adapted) == np.max(adapted)
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def test_adapthist_borders():
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"""Test border processing
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"""
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img = rgb2gray(util.img_as_float(data.astronaut()))
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# maximize difference between orig and processed img
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img /= 100.
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img[img.shape[0] // 2, img.shape[1] // 2] = 1.
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# check borders are processed for different kernel sizes
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border_index = -1
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for kernel_size in range(51, 71, 2):
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adapted = exposure.equalize_adapthist(img, kernel_size, clip_limit=0.5)
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|
# Check last columns are processed
|
|
assert norm_brightness_err(adapted[:, border_index],
|
|
img[:, border_index]) > 0.1
|
|
# Check last rows are processed
|
|
assert norm_brightness_err(adapted[border_index, :],
|
|
img[border_index, :]) > 0.1
|
|
|
|
|
|
def test_adapthist_clip_limit():
|
|
img_u = data.moon()
|
|
img_f = util.img_as_float(img_u)
|
|
|
|
# uint8 input
|
|
img_clahe = exposure.equalize_adapthist(img_u, clip_limit=1)
|
|
assert_array_equal(img_f, img_clahe)
|
|
|
|
# float64 input
|
|
img_clahe = exposure.equalize_adapthist(img_f, clip_limit=1)
|
|
assert_array_equal(img_f, img_clahe)
|
|
|
|
|
|
def peak_snr(img1, img2):
|
|
"""Peak signal to noise ratio of two images
|
|
|
|
Parameters
|
|
----------
|
|
img1 : array-like
|
|
img2 : array-like
|
|
|
|
Returns
|
|
-------
|
|
peak_snr : float
|
|
Peak signal to noise ratio
|
|
"""
|
|
if img1.ndim == 3:
|
|
img1, img2 = rgb2gray(img1.copy()), rgb2gray(img2.copy())
|
|
img1 = util.img_as_float(img1)
|
|
img2 = util.img_as_float(img2)
|
|
mse = 1. / img1.size * np.square(img1 - img2).sum()
|
|
_, max_ = dtype_range[img1.dtype.type]
|
|
return 20 * np.log(max_ / mse)
|
|
|
|
|
|
def norm_brightness_err(img1, img2):
|
|
"""Normalized Absolute Mean Brightness Error between two images
|
|
|
|
Parameters
|
|
----------
|
|
img1 : array-like
|
|
img2 : array-like
|
|
|
|
Returns
|
|
-------
|
|
norm_brightness_error : float
|
|
Normalized absolute mean brightness error
|
|
"""
|
|
if img1.ndim == 3:
|
|
img1, img2 = rgb2gray(img1), rgb2gray(img2)
|
|
ambe = np.abs(img1.mean() - img2.mean())
|
|
nbe = ambe / dtype_range[img1.dtype.type][1]
|
|
return nbe
|
|
|
|
|
|
# Test Gamma Correction
|
|
# =====================
|
|
|
|
def test_adjust_gamma_1x1_shape():
|
|
"""Check that the shape is maintained"""
|
|
img = np.ones([1,1])
|
|
result = exposure.adjust_gamma(img, 1.5)
|
|
assert img.shape == result.shape
|
|
|
|
|
|
def test_adjust_gamma_one():
|
|
"""Same image should be returned for gamma equal to one"""
|
|
image = np.random.uniform(0, 255, (8, 8))
|
|
result = exposure.adjust_gamma(image, 1)
|
|
assert_array_equal(result, image)
|
|
|
|
|
|
def test_adjust_gamma_zero():
|
|
"""White image should be returned for gamma equal to zero"""
|
|
image = np.random.uniform(0, 255, (8, 8))
|
|
result = exposure.adjust_gamma(image, 0)
|
|
dtype = image.dtype.type
|
|
assert_array_equal(result, dtype_range[dtype][1])
|
|
|
|
|
|
def test_adjust_gamma_less_one():
|
|
"""Verifying the output with expected results for gamma
|
|
correction with gamma equal to half"""
|
|
image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
|
|
expected = np.array([
|
|
[ 0, 31, 45, 55, 63, 71, 78, 84],
|
|
[ 90, 95, 100, 105, 110, 115, 119, 123],
|
|
[127, 131, 135, 139, 142, 146, 149, 153],
|
|
[156, 159, 162, 165, 168, 171, 174, 177],
|
|
[180, 183, 186, 188, 191, 194, 196, 199],
|
|
[201, 204, 206, 209, 211, 214, 216, 218],
|
|
[221, 223, 225, 228, 230, 232, 234, 236],
|
|
[238, 241, 243, 245, 247, 249, 251, 253]], dtype=np.uint8)
|
|
|
|
result = exposure.adjust_gamma(image, 0.5)
|
|
assert_array_equal(result, expected)
|
|
|
|
|
|
def test_adjust_gamma_greater_one():
|
|
"""Verifying the output with expected results for gamma
|
|
correction with gamma equal to two"""
|
|
image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
|
|
expected = np.array([
|
|
[ 0, 0, 0, 0, 1, 1, 2, 3],
|
|
[ 4, 5, 6, 7, 9, 10, 12, 14],
|
|
[ 16, 18, 20, 22, 25, 27, 30, 33],
|
|
[ 36, 39, 42, 45, 49, 52, 56, 60],
|
|
[ 64, 68, 72, 76, 81, 85, 90, 95],
|
|
[100, 105, 110, 116, 121, 127, 132, 138],
|
|
[144, 150, 156, 163, 169, 176, 182, 189],
|
|
[196, 203, 211, 218, 225, 233, 241, 249]], dtype=np.uint8)
|
|
|
|
result = exposure.adjust_gamma(image, 2)
|
|
assert_array_equal(result, expected)
|
|
|
|
|
|
def test_adjust_gamma_neggative():
|
|
image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
|
|
with testing.raises(ValueError):
|
|
exposure.adjust_gamma(image, -1)
|
|
|
|
|
|
# Test Logarithmic Correction
|
|
# ===========================
|
|
|
|
def test_adjust_log_1x1_shape():
|
|
"""Check that the shape is maintained"""
|
|
img = np.ones([1, 1])
|
|
result = exposure.adjust_log(img, 1)
|
|
assert img.shape == result.shape
|
|
|
|
|
|
def test_adjust_log():
|
|
"""Verifying the output with expected results for logarithmic
|
|
correction with multiplier constant multiplier equal to unity"""
|
|
image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
|
|
expected = np.array([
|
|
[ 0, 5, 11, 16, 22, 27, 33, 38],
|
|
[ 43, 48, 53, 58, 63, 68, 73, 77],
|
|
[ 82, 86, 91, 95, 100, 104, 109, 113],
|
|
[117, 121, 125, 129, 133, 137, 141, 145],
|
|
[149, 153, 157, 160, 164, 168, 172, 175],
|
|
[179, 182, 186, 189, 193, 196, 199, 203],
|
|
[206, 209, 213, 216, 219, 222, 225, 228],
|
|
[231, 234, 238, 241, 244, 246, 249, 252]], dtype=np.uint8)
|
|
|
|
result = exposure.adjust_log(image, 1)
|
|
assert_array_equal(result, expected)
|
|
|
|
|
|
def test_adjust_inv_log():
|
|
"""Verifying the output with expected results for inverse logarithmic
|
|
correction with multiplier constant multiplier equal to unity"""
|
|
image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
|
|
expected = np.array([
|
|
[ 0, 2, 5, 8, 11, 14, 17, 20],
|
|
[ 23, 26, 29, 32, 35, 38, 41, 45],
|
|
[ 48, 51, 55, 58, 61, 65, 68, 72],
|
|
[ 76, 79, 83, 87, 90, 94, 98, 102],
|
|
[106, 110, 114, 118, 122, 126, 130, 134],
|
|
[138, 143, 147, 151, 156, 160, 165, 170],
|
|
[174, 179, 184, 188, 193, 198, 203, 208],
|
|
[213, 218, 224, 229, 234, 239, 245, 250]], dtype=np.uint8)
|
|
|
|
result = exposure.adjust_log(image, 1, True)
|
|
assert_array_equal(result, expected)
|
|
|
|
|
|
# Test Sigmoid Correction
|
|
# =======================
|
|
|
|
def test_adjust_sigmoid_1x1_shape():
|
|
"""Check that the shape is maintained"""
|
|
img = np.ones([1, 1])
|
|
result = exposure.adjust_sigmoid(img, 1, 5)
|
|
assert img.shape == result.shape
|
|
|
|
|
|
def test_adjust_sigmoid_cutoff_one():
|
|
"""Verifying the output with expected results for sigmoid correction
|
|
with cutoff equal to one and gain of 5"""
|
|
image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
|
|
expected = np.array([
|
|
[ 1, 1, 1, 2, 2, 2, 2, 2],
|
|
[ 3, 3, 3, 4, 4, 4, 5, 5],
|
|
[ 5, 6, 6, 7, 7, 8, 9, 10],
|
|
[ 10, 11, 12, 13, 14, 15, 16, 18],
|
|
[ 19, 20, 22, 24, 25, 27, 29, 32],
|
|
[ 34, 36, 39, 41, 44, 47, 50, 54],
|
|
[ 57, 61, 64, 68, 72, 76, 80, 85],
|
|
[ 89, 94, 99, 104, 108, 113, 118, 123]], dtype=np.uint8)
|
|
|
|
result = exposure.adjust_sigmoid(image, 1, 5)
|
|
assert_array_equal(result, expected)
|
|
|
|
|
|
def test_adjust_sigmoid_cutoff_zero():
|
|
"""Verifying the output with expected results for sigmoid correction
|
|
with cutoff equal to zero and gain of 10"""
|
|
image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
|
|
expected = np.array([
|
|
[127, 137, 147, 156, 166, 175, 183, 191],
|
|
[198, 205, 211, 216, 221, 225, 229, 232],
|
|
[235, 238, 240, 242, 244, 245, 247, 248],
|
|
[249, 250, 250, 251, 251, 252, 252, 253],
|
|
[253, 253, 253, 253, 254, 254, 254, 254],
|
|
[254, 254, 254, 254, 254, 254, 254, 254],
|
|
[254, 254, 254, 254, 254, 254, 254, 254],
|
|
[254, 254, 254, 254, 254, 254, 254, 254]], dtype=np.uint8)
|
|
|
|
result = exposure.adjust_sigmoid(image, 0, 10)
|
|
assert_array_equal(result, expected)
|
|
|
|
|
|
def test_adjust_sigmoid_cutoff_half():
|
|
"""Verifying the output with expected results for sigmoid correction
|
|
with cutoff equal to half and gain of 10"""
|
|
image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
|
|
expected = np.array([
|
|
[ 1, 1, 2, 2, 3, 3, 4, 5],
|
|
[ 5, 6, 7, 9, 10, 12, 14, 16],
|
|
[ 19, 22, 25, 29, 34, 39, 44, 50],
|
|
[ 57, 64, 72, 80, 89, 99, 108, 118],
|
|
[128, 138, 148, 158, 167, 176, 184, 192],
|
|
[199, 205, 211, 217, 221, 226, 229, 233],
|
|
[236, 238, 240, 242, 244, 246, 247, 248],
|
|
[249, 250, 250, 251, 251, 252, 252, 253]], dtype=np.uint8)
|
|
|
|
result = exposure.adjust_sigmoid(image, 0.5, 10)
|
|
assert_array_equal(result, expected)
|
|
|
|
|
|
def test_adjust_inv_sigmoid_cutoff_half():
|
|
"""Verifying the output with expected results for inverse sigmoid
|
|
correction with cutoff equal to half and gain of 10"""
|
|
image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
|
|
expected = np.array([
|
|
[253, 253, 252, 252, 251, 251, 250, 249],
|
|
[249, 248, 247, 245, 244, 242, 240, 238],
|
|
[235, 232, 229, 225, 220, 215, 210, 204],
|
|
[197, 190, 182, 174, 165, 155, 146, 136],
|
|
[126, 116, 106, 96, 87, 78, 70, 62],
|
|
[ 55, 49, 43, 37, 33, 28, 25, 21],
|
|
[ 18, 16, 14, 12, 10, 8, 7, 6],
|
|
[ 5, 4, 4, 3, 3, 2, 2, 1]], dtype=np.uint8)
|
|
|
|
result = exposure.adjust_sigmoid(image, 0.5, 10, True)
|
|
assert_array_equal(result, expected)
|
|
|
|
|
|
def test_negative():
|
|
image = np.arange(-10, 245, 4).reshape((8, 8)).astype(np.double)
|
|
with testing.raises(ValueError):
|
|
exposure.adjust_gamma(image)
|
|
|
|
|
|
def test_is_low_contrast():
|
|
image = np.linspace(0, 0.04, 100)
|
|
assert exposure.is_low_contrast(image)
|
|
image[-1] = 1
|
|
assert exposure.is_low_contrast(image)
|
|
assert not exposure.is_low_contrast(image, upper_percentile=100)
|
|
|
|
image = (image * 255).astype(np.uint8)
|
|
assert exposure.is_low_contrast(image)
|
|
assert not exposure.is_low_contrast(image, upper_percentile=100)
|
|
|
|
image = (image.astype(np.uint16)) * 2**8
|
|
assert exposure.is_low_contrast(image)
|
|
assert not exposure.is_low_contrast(image, upper_percentile=100)
|
|
|
|
|
|
# Test Dask Compatibility
|
|
# =======================
|
|
|
|
def test_dask_histogram():
|
|
pytest.importorskip('dask', reason="dask python library is not installed")
|
|
import dask.array as da
|
|
dask_array = da.from_array(np.array([[0, 1], [1, 2]]), chunks=(1, 2))
|
|
output_hist, output_bins = exposure.histogram(dask_array)
|
|
expected_bins = [0, 1, 2]
|
|
expected_hist = [1, 2, 1]
|
|
assert np.allclose(expected_bins, output_bins)
|
|
assert np.allclose(expected_hist, output_hist)
|