890 lines
32 KiB
Python
890 lines
32 KiB
Python
import warnings
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import numpy as np
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import pytest
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from numpy.testing import (assert_allclose,
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assert_almost_equal,
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assert_array_almost_equal,
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assert_array_equal,
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assert_equal)
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from skimage import data
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from skimage import exposure
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from skimage import util
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from skimage.color import rgb2gray
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from skimage.exposure.exposure import intensity_range
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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.utils import _supported_float_type
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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 pytest.raises(ValueError):
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frequencies, bin_centers = exposure.histogram(im,
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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_multichannel_int_range_image():
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im = np.array([[10, 5], [100, 102]], dtype=np.int8)
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frequencies, bin_centers = exposure.histogram(im, channel_axis=-1)
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for ch in range(im.shape[-1]):
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assert_equal(len(frequencies[ch]), len(bin_centers))
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assert_equal(bin_centers[0], 5)
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assert_equal(bin_centers[-1], 102)
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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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@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64])
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def test_peak_float_out_of_range_image(dtype):
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im = np.array([10, 100], dtype=dtype)
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frequencies, bin_centers = exposure.histogram(im, nbins=90)
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assert bin_centers.dtype == dtype
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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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@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64])
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def test_peak_float_out_of_range_dtype(dtype):
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im = np.array([10, 100], dtype=dtype)
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nbins = 10
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frequencies, bin_centers = exposure.histogram(im, nbins=nbins,
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source_range='dtype')
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assert bin_centers.dtype == 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 multichannel histograms
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# ============================
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@pytest.mark.parametrize('source_range', ['dtype', 'image'])
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@pytest.mark.parametrize('dtype', [np.uint8, np.int16, np.float64])
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@pytest.mark.parametrize('channel_axis', [0, 1, -1])
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def test_multichannel_hist_common_bins_uint8(dtype, source_range,
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channel_axis):
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"""Check that all channels use the same binning."""
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# Construct multichannel image with uniform values within each channel,
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# but the full range of values across channels.
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shape = (5, 5)
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channel_size = shape[0] * shape[1]
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imin, imax = dtype_range[dtype]
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im = np.stack(
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(
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np.full(shape, imin, dtype=dtype),
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np.full(shape, imax, dtype=dtype),
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),
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axis=channel_axis
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)
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frequencies, bin_centers = exposure.histogram(
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im, source_range=source_range, channel_axis=channel_axis
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)
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if np.issubdtype(dtype, np.integer):
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assert_array_equal(bin_centers, np.arange(imin, imax + 1))
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assert frequencies[0][0] == channel_size
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assert frequencies[0][-1] == 0
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assert frequencies[1][0] == 0
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assert frequencies[1][-1] == channel_size
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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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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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@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64])
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def test_equalize_float(dtype):
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img = util.img_as_float(test_img).astype(dtype, copy=False)
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img_eq = exposure.equalize_hist(img)
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assert img_eq.dtype == _supported_float_type(dtype)
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cdf, bin_edges = exposure.cumulative_distribution(img_eq)
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check_cdf_slope(cdf)
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assert bin_edges.dtype == _supported_float_type(dtype)
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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[100:400, 100:400] = 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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@pytest.mark.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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@pytest.mark.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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@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64])
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def test_rescale_in_range(dtype):
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image = np.array([51., 102., 153.], dtype=dtype)
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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], decimal=4)
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# with out_range='dtype', the output has the same dtype
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assert out.dtype == image.dtype
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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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@pytest.mark.parametrize('dtype', [np.int8, np.int32, np.float16, np.float32,
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np.float64])
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def test_rescale_out_range(dtype):
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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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.. versionchanged:: 0.19
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float16 and float32 inputs now result in float32 output. Formerly they
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would give float64 outputs.
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"""
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image = np.array([-10, 0, 10], dtype=dtype)
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out = exposure.rescale_intensity(image, out_range=(0, 127))
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assert out.dtype == _supported_float_type(image.dtype)
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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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"Passing `np.nan` to mean no clipping in np.clip"
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)
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if in_range == "image":
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exp_warn = [msg, numpy_warning_1_17_plus]
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else:
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exp_warn = [msg]
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with expected_warnings(exp_warn):
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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', 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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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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assert_array_equal(output_image, [0, 128, 255])
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assert output_image.dtype == 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 pytest.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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@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64])
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def test_adapthist_grayscale(dtype):
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"""Test a grayscale float image
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"""
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img = util.img_as_float(data.astronaut()).astype(dtype, copy=False)
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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 adapted.dtype == _supported_float_type(dtype)
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snr_decimal = 3 if dtype != np.float16 else 2
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assert_almost_equal(peak_snr(img, adapted), 100.140, snr_decimal)
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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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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())
|
|
alpha = np.ones((img.shape[0], img.shape[1]), dtype=float)
|
|
img = np.dstack((img, alpha))
|
|
adapted = exposure.equalize_adapthist(img)
|
|
assert adapted.shape != img.shape
|
|
img = img[:, :, :3]
|
|
full_scale = exposure.rescale_intensity(img)
|
|
assert img.shape == adapted.shape
|
|
assert_almost_equal(peak_snr(full_scale, adapted), 109.393, 2)
|
|
assert_almost_equal(norm_brightness_err(full_scale, adapted), 0.0248, 3)
|
|
|
|
|
|
def test_adapthist_grayscale_Nd():
|
|
"""
|
|
Test for n-dimensional consistency with float images
|
|
Note: Currently if img.ndim == 3, img.shape[2] > 4 must hold for the image
|
|
not to be interpreted as a color image by @adapt_rgb
|
|
"""
|
|
# take 2d image, subsample and stack it
|
|
img = util.img_as_float(data.astronaut())
|
|
img = rgb2gray(img)
|
|
a = 15
|
|
img2d = util.img_as_float(img[0:-1:a, 0:-1:a])
|
|
img3d = np.array([img2d] * (img.shape[0] // a))
|
|
|
|
# apply CLAHE
|
|
adapted2d = exposure.equalize_adapthist(img2d,
|
|
kernel_size=5,
|
|
clip_limit=0.05)
|
|
adapted3d = exposure.equalize_adapthist(img3d,
|
|
kernel_size=5,
|
|
clip_limit=0.05)
|
|
|
|
# check that dimensions of input and output match
|
|
assert img2d.shape == adapted2d.shape
|
|
assert img3d.shape == adapted3d.shape
|
|
|
|
# check that the result from the stack of 2d images is similar
|
|
# to the underlying 2d image
|
|
assert np.mean(np.abs(adapted2d
|
|
- adapted3d[adapted3d.shape[0] // 2])) < 0.02
|
|
|
|
|
|
def test_adapthist_constant():
|
|
"""Test constant image, float and uint
|
|
"""
|
|
img = np.zeros((8, 8))
|
|
img += 2
|
|
img = img.astype(np.uint16)
|
|
adapted = exposure.equalize_adapthist(img, 3)
|
|
assert np.min(adapted) == np.max(adapted)
|
|
|
|
img = np.zeros((8, 8))
|
|
img += 0.1
|
|
img = img.astype(np.float64)
|
|
adapted = exposure.equalize_adapthist(img, 3)
|
|
assert np.min(adapted) == np.max(adapted)
|
|
|
|
|
|
def test_adapthist_borders():
|
|
"""Test border processing
|
|
"""
|
|
img = rgb2gray(util.img_as_float(data.astronaut()))
|
|
|
|
# maximize difference between orig and processed img
|
|
img /= 100.
|
|
img[img.shape[0] // 2, img.shape[1] // 2] = 1.
|
|
|
|
# check borders are processed for different kernel sizes
|
|
border_index = -1
|
|
for kernel_size in range(51, 71, 2):
|
|
adapted = exposure.equalize_adapthist(img, kernel_size, clip_limit=0.5)
|
|
# 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_clahe0 = exposure.equalize_adapthist(img_u, clip_limit=0)
|
|
img_clahe1 = exposure.equalize_adapthist(img_u, clip_limit=1)
|
|
assert_array_equal(img_clahe0, img_clahe1)
|
|
|
|
# float64 input
|
|
img_clahe0 = exposure.equalize_adapthist(img_f, clip_limit=0)
|
|
img_clahe1 = exposure.equalize_adapthist(img_f, clip_limit=1)
|
|
assert_array_equal(img_clahe0, img_clahe1)
|
|
|
|
|
|
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.arange(0, 256, dtype=np.uint8).reshape((16, 16))
|
|
result = exposure.adjust_gamma(image, 1)
|
|
assert_array_equal(result, image)
|
|
|
|
|
|
@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64])
|
|
def test_adjust_gamma_zero(dtype):
|
|
"""White image should be returned for gamma equal to zero"""
|
|
image = np.random.uniform(0, 255, (8, 8)).astype(dtype, copy=False)
|
|
result = exposure.adjust_gamma(image, 0)
|
|
dtype = image.dtype.type
|
|
assert_array_equal(result, dtype_range[dtype][1])
|
|
assert result.dtype == image.dtype
|
|
|
|
|
|
def test_adjust_gamma_less_one():
|
|
"""Verifying the output with expected results for gamma
|
|
correction with gamma equal to half"""
|
|
image = np.arange(0, 256, dtype=np.uint8).reshape((16, 16))
|
|
|
|
expected = np.array([0, 16, 23, 28, 32, 36, 39, 42, 45, 48, 50,
|
|
53, 55, 58, 60, 62, 64, 66, 68, 70, 71, 73,
|
|
75, 77, 78, 80, 81, 83, 84, 86, 87, 89, 90,
|
|
92, 93, 94, 96, 97, 98, 100, 101, 102, 103,
|
|
105, 106, 107, 108, 109, 111, 112, 113, 114,
|
|
115, 116, 117, 118, 119, 121, 122, 123, 124,
|
|
125, 126, 127, 128, 129, 130, 131, 132, 133,
|
|
134, 135, 135, 136, 137, 138, 139, 140, 141,
|
|
142, 143, 144, 145, 145, 146, 147, 148, 149,
|
|
150, 151, 151, 152, 153, 154, 155, 156, 156,
|
|
157, 158, 159, 160, 160, 161, 162, 163, 164,
|
|
164, 165, 166, 167, 167, 168, 169, 170, 170,
|
|
171, 172, 173, 173, 174, 175, 176, 176, 177,
|
|
178, 179, 179, 180, 181, 181, 182, 183, 183,
|
|
184, 185, 186, 186, 187, 188, 188, 189, 190,
|
|
190, 191, 192, 192, 193, 194, 194, 195, 196,
|
|
196, 197, 198, 198, 199, 199, 200, 201, 201,
|
|
202, 203, 203, 204, 204, 205, 206, 206, 207,
|
|
208, 208, 209, 209, 210, 211, 211, 212, 212,
|
|
213, 214, 214, 215, 215, 216, 217, 217, 218,
|
|
218, 219, 220, 220, 221, 221, 222, 222, 223,
|
|
224, 224, 225, 225, 226, 226, 227, 228, 228,
|
|
229, 229, 230, 230, 231, 231, 232, 233, 233,
|
|
234, 234, 235, 235, 236, 236, 237, 237, 238,
|
|
238, 239, 240, 240, 241, 241, 242, 242, 243,
|
|
243, 244, 244, 245, 245, 246, 246, 247, 247,
|
|
248, 248, 249, 249, 250, 250, 251, 251, 252,
|
|
252, 253, 253, 254, 254, 255],
|
|
dtype=np.uint8).reshape((16, 16))
|
|
|
|
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, 256, dtype=np.uint8).reshape((16, 16))
|
|
|
|
expected = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
|
|
1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3,
|
|
4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8,
|
|
8, 8, 9, 9, 9, 10, 10, 11, 11, 11, 12, 12,
|
|
13, 13, 14, 14, 15, 15, 16, 16, 17, 17, 18,
|
|
18, 19, 19, 20, 20, 21, 21, 22, 23, 23, 24,
|
|
24, 25, 26, 26, 27, 28, 28, 29, 30, 30, 31,
|
|
32, 32, 33, 34, 35, 35, 36, 37, 38, 38, 39,
|
|
40, 41, 42, 42, 43, 44, 45, 46, 47, 47, 48,
|
|
49, 50, 51, 52, 53, 54, 55, 56, 56, 57, 58,
|
|
59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
|
|
70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81,
|
|
82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94,
|
|
95, 97, 98, 99, 100, 102, 103, 104, 105,
|
|
107, 108, 109, 111, 112, 113, 115, 116, 117,
|
|
119, 120, 121, 123, 124, 126, 127, 128, 130,
|
|
131, 133, 134, 136, 137, 139, 140, 142, 143,
|
|
145, 146, 148, 149, 151, 152, 154, 155, 157,
|
|
158, 160, 162, 163, 165, 166, 168, 170, 171,
|
|
173, 175, 176, 178, 180, 181, 183, 185, 186,
|
|
188, 190, 192, 193, 195, 197, 199, 200, 202,
|
|
204, 206, 207, 209, 211, 213, 215, 217, 218,
|
|
220, 222, 224, 226, 228, 230, 232, 233, 235,
|
|
237, 239, 241, 243, 245, 247, 249, 251, 253,
|
|
255] , dtype=np.uint8).reshape((16, 16))
|
|
|
|
result = exposure.adjust_gamma(image, 2)
|
|
assert_array_equal(result, expected)
|
|
|
|
|
|
def test_adjust_gamma_negative():
|
|
image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
|
|
with pytest.raises(ValueError):
|
|
exposure.adjust_gamma(image, -1)
|
|
|
|
|
|
def test_adjust_gamma_u8_overflow():
|
|
img = 255 * np.ones((2, 2), dtype=np.uint8)
|
|
|
|
assert np.all(exposure.adjust_gamma(img, gamma=1, gain=1.1) == 255)
|
|
|
|
|
|
# Test Logarithmic Correction
|
|
# ===========================
|
|
|
|
@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64])
|
|
def test_adjust_log_1x1_shape(dtype):
|
|
"""Check that the shape is maintained"""
|
|
img = np.ones([1, 1], dtype=dtype)
|
|
result = exposure.adjust_log(img, 1)
|
|
assert img.shape == result.shape
|
|
assert result.dtype == dtype
|
|
|
|
|
|
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
|
|
# =======================
|
|
|
|
@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64])
|
|
def test_adjust_sigmoid_1x1_shape(dtype):
|
|
"""Check that the shape is maintained"""
|
|
img = np.ones([1, 1], dtype=dtype)
|
|
result = exposure.adjust_sigmoid(img, 1, 5)
|
|
assert img.shape == result.shape
|
|
assert result.dtype == dtype
|
|
|
|
|
|
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_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)
|
|
|
|
|
|
def test_is_low_contrast_boolean():
|
|
image = np.zeros((8, 8), dtype=bool)
|
|
assert exposure.is_low_contrast(image)
|
|
|
|
image[:5] = 1
|
|
assert not exposure.is_low_contrast(image)
|
|
|
|
|
|
# Test negative input
|
|
#####################
|
|
|
|
@pytest.mark.parametrize("exposure_func", [exposure.adjust_gamma,
|
|
exposure.adjust_log,
|
|
exposure.adjust_sigmoid])
|
|
def test_negative_input(exposure_func):
|
|
image = np.arange(-10, 245, 4).reshape((8, 8)).astype(np.float64)
|
|
with pytest.raises(ValueError):
|
|
exposure_func(image)
|
|
|
|
|
|
# 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]
|
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assert np.allclose(expected_bins, output_bins)
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assert np.allclose(expected_hist, output_hist)
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