import warnings
import numpy as np
import pytest
from skimage import util
from skimage import data
from skimage import exposure
from skimage.exposure.exposure import intensity_range
from skimage.color import rgb2gray
from skimage.util.dtype import dtype_range
from skimage._shared._warnings import expected_warnings
from skimage._shared import testing
from skimage._shared.testing import (assert_array_equal,
assert_array_almost_equal,
assert_equal,
assert_almost_equal)
# Test integer histograms
# =======================
def test_wrong_source_range():
im = np.array([-1, 100], dtype=np.int8)
with testing.raises(ValueError):
frequencies, bin_centers = exposure.histogram(im, source_range='foobar')
def test_negative_overflow():
im = np.array([-1, 100], dtype=np.int8)
frequencies, bin_centers = exposure.histogram(im)
assert_array_equal(bin_centers, np.arange(-1, 101))
assert frequencies[0] == 1
assert frequencies[-1] == 1
assert_array_equal(frequencies[1:-1], 0)
def test_all_negative_image():
im = np.array([-100, -1], dtype=np.int8)
frequencies, bin_centers = exposure.histogram(im)
assert_array_equal(bin_centers, np.arange(-100, 0))
assert frequencies[0] == 1
assert frequencies[-1] == 1
assert_array_equal(frequencies[1:-1], 0)
def test_int_range_image():
im = np.array([10, 100], dtype=np.int8)
frequencies, bin_centers = exposure.histogram(im)
assert_equal(len(bin_centers), len(frequencies))
assert_equal(bin_centers[0], 10)
assert_equal(bin_centers[-1], 100)
def test_peak_uint_range_dtype():
im = np.array([10, 100], dtype=np.uint8)
frequencies, bin_centers = exposure.histogram(im, source_range='dtype')
assert_array_equal(bin_centers, np.arange(0, 256))
assert_equal(frequencies[10], 1)
assert_equal(frequencies[100], 1)
assert_equal(frequencies[101], 0)
assert_equal(frequencies.shape, (256,))
def test_peak_int_range_dtype():
im = np.array([10, 100], dtype=np.int8)
frequencies, bin_centers = exposure.histogram(im, source_range='dtype')
assert_array_equal(bin_centers, np.arange(-128, 128))
assert_equal(frequencies[128+10], 1)
assert_equal(frequencies[128+100], 1)
assert_equal(frequencies[128+101], 0)
assert_equal(frequencies.shape, (256,))
def test_flat_uint_range_dtype():
im = np.linspace(0, 255, 256, dtype=np.uint8)
frequencies, bin_centers = exposure.histogram(im, source_range='dtype')
assert_array_equal(bin_centers, np.arange(0, 256))
assert_equal(frequencies.shape, (256,))
def test_flat_int_range_dtype():
im = np.linspace(-128, 128, 256, dtype=np.int8)
frequencies, bin_centers = exposure.histogram(im, source_range='dtype')
assert_array_equal(bin_centers, np.arange(-128, 128))
assert_equal(frequencies.shape, (256,))
def test_peak_float_out_of_range_image():
im = np.array([10, 100], dtype=np.float16)
frequencies, bin_centers = exposure.histogram(im, nbins=90)
# offset values by 0.5 for float...
assert_array_equal(bin_centers, np.arange(10, 100) + 0.5)
def test_peak_float_out_of_range_dtype():
im = np.array([10, 100], dtype=np.float16)
nbins = 10
frequencies, bin_centers = exposure.histogram(im, nbins=nbins, source_range='dtype')
assert_almost_equal(np.min(bin_centers), -0.9, 3)
assert_almost_equal(np.max(bin_centers), 0.9, 3)
assert_equal(len(bin_centers), 10)
def test_normalize():
im = np.array([0, 255, 255], dtype=np.uint8)
frequencies, bin_centers = exposure.histogram(im, source_range='dtype',
normalize=False)
expected = np.zeros(256)
expected[0] = 1
expected[-1] = 2
assert_equal(frequencies, expected)
frequencies, bin_centers = exposure.histogram(im, source_range='dtype',
normalize=True)
expected /= 3.
assert_equal(frequencies, expected)
# Test histogram equalization
# ===========================
np.random.seed(0)
test_img_int = data.camera()
# squeeze image intensities to lower image contrast
test_img = util.img_as_float(test_img_int)
test_img = exposure.rescale_intensity(test_img / 5. + 100)
def test_equalize_uint8_approx():
"""Check integer bins used for uint8 images."""
img_eq0 = exposure.equalize_hist(test_img_int)
img_eq1 = exposure.equalize_hist(test_img_int, nbins=3)
np.testing.assert_allclose(img_eq0, img_eq1)
def test_equalize_ubyte():
img = util.img_as_ubyte(test_img)
img_eq = exposure.equalize_hist(img)
cdf, bin_edges = exposure.cumulative_distribution(img_eq)
check_cdf_slope(cdf)
def test_equalize_float():
img = util.img_as_float(test_img)
img_eq = exposure.equalize_hist(img)
cdf, bin_edges = exposure.cumulative_distribution(img_eq)
check_cdf_slope(cdf)
def test_equalize_masked():
img = util.img_as_float(test_img)
mask = np.zeros(test_img.shape)
mask[50:150, 50:250] = 1
img_mask_eq = exposure.equalize_hist(img, mask=mask)
img_eq = exposure.equalize_hist(img)
cdf, bin_edges = exposure.cumulative_distribution(img_mask_eq)
check_cdf_slope(cdf)
assert not (img_eq == img_mask_eq).all()
def check_cdf_slope(cdf):
"""Slope of cdf which should equal 1 for an equalized histogram."""
norm_intensity = np.linspace(0, 1, len(cdf))
slope, intercept = np.polyfit(norm_intensity, cdf, 1)
assert 0.9 < slope < 1.1
# Test intensity range
# ====================
@testing.parametrize("test_input,expected", [
('image', [0, 1]),
('dtype', [0, 255]),
((10, 20), [10, 20])
])
def test_intensity_range_uint8(test_input, expected):
image = np.array([0, 1], dtype=np.uint8)
out = intensity_range(image, range_values=test_input)
assert_array_equal(out, expected)
@testing.parametrize("test_input,expected", [
('image', [0.1, 0.2]),
('dtype', [-1, 1]),
((0.3, 0.4), [0.3, 0.4])
])
def test_intensity_range_float(test_input, expected):
image = np.array([0.1, 0.2], dtype=np.float64)
out = intensity_range(image, range_values=test_input)
assert_array_equal(out, expected)
def test_intensity_range_clipped_float():
image = np.array([0.1, 0.2], dtype=np.float64)
out = intensity_range(image, range_values='dtype', clip_negative=True)
assert_array_equal(out, (0, 1))
# Test rescale intensity
# ======================
uint10_max = 2**10 - 1
uint12_max = 2**12 - 1
uint14_max = 2**14 - 1
uint16_max = 2**16 - 1
def test_rescale_stretch():
image = np.array([51, 102, 153], dtype=np.uint8)
out = exposure.rescale_intensity(image)
assert out.dtype == np.uint8
assert_array_almost_equal(out, [0, 127, 255])
def test_rescale_shrink():
image = np.array([51., 102., 153.])
out = exposure.rescale_intensity(image)
assert_array_almost_equal(out, [0, 0.5, 1])
def test_rescale_in_range():
image = np.array([51., 102., 153.])
out = exposure.rescale_intensity(image, in_range=(0, 255))
assert_array_almost_equal(out, [0.2, 0.4, 0.6])
def test_rescale_in_range_clip():
image = np.array([51., 102., 153.])
out = exposure.rescale_intensity(image, in_range=(0, 102))
assert_array_almost_equal(out, [0.5, 1, 1])
def test_rescale_out_range():
"""Check that output range is correct.
.. versionchanged:: 0.17
This function used to return dtype matching the input dtype. It now
matches the output.
"""
image = np.array([-10, 0, 10], dtype=np.int8)
out = exposure.rescale_intensity(image, out_range=(0, 127))
assert out.dtype == np.float_
assert_array_almost_equal(out, [0, 63.5, 127])
def test_rescale_named_in_range():
image = np.array([0, uint10_max, uint10_max + 100], dtype=np.uint16)
out = exposure.rescale_intensity(image, in_range='uint10')
assert_array_almost_equal(out, [0, uint16_max, uint16_max])
def test_rescale_named_out_range():
image = np.array([0, uint16_max], dtype=np.uint16)
out = exposure.rescale_intensity(image, out_range='uint10')
assert_array_almost_equal(out, [0, uint10_max])
def test_rescale_uint12_limits():
image = np.array([0, uint16_max], dtype=np.uint16)
out = exposure.rescale_intensity(image, out_range='uint12')
assert_array_almost_equal(out, [0, uint12_max])
def test_rescale_uint14_limits():
image = np.array([0, uint16_max], dtype=np.uint16)
out = exposure.rescale_intensity(image, out_range='uint14')
assert_array_almost_equal(out, [0, uint14_max])
def test_rescale_all_zeros():
image = np.zeros((2, 2), dtype=np.uint8)
out = exposure.rescale_intensity(image)
assert ~np.isnan(out).all()
assert_array_almost_equal(out, image)
def test_rescale_constant():
image = np.array([130, 130], dtype=np.uint16)
out = exposure.rescale_intensity(image, out_range=(0, 127))
assert_array_almost_equal(out, [127, 127])
def test_rescale_same_values():
image = np.ones((2, 2))
out = exposure.rescale_intensity(image)
assert ~np.isnan(out).all()
assert_array_almost_equal(out, image)
@pytest.mark.parametrize(
"in_range,out_range", [("image", "dtype"),
("dtype", "image")]
)
def test_rescale_nan_warning(in_range, out_range):
image = np.arange(12, dtype=float).reshape(3, 4)
image[1, 1] = np.nan
msg = (
r"One or more intensity levels are NaN\."
r" Rescaling will broadcast NaN to the full image\."
)
# 2019/11/10 Passing NaN to np.clip raises a DeprecationWarning for
# versions above 1.17
# TODO: Remove once NumPy removes this DeprecationWarning
numpy_warning_1_17_plus = (
r"Passing `np.nan` to mean no clipping in np.clip "
r"has always been unreliable|\A\Z"
)
# 2019/12/06 Passing NaN to np.min and np.max raises a RuntimeWarning for
# NumPy < 1.16
# TODO: Remove once minimal required NumPy version is 1.16
numpy_warning_smaller_1_16 = r"invalid value encountered in reduce|\A\Z"
with expected_warnings(
[msg, numpy_warning_1_17_plus, numpy_warning_smaller_1_16]
):
exposure.rescale_intensity(image, in_range, out_range)
@pytest.mark.parametrize(
"out_range, out_dtype", [
('uint8', np.uint8),
('uint10', np.uint16),
('uint12', np.uint16),
('uint16', np.uint16),
('float', np.float_),
]
)
def test_rescale_output_dtype(out_range, out_dtype):
image = np.array([-128, 0, 127], dtype=np.int8)
output_image = exposure.rescale_intensity(image, out_range=out_range)
assert output_image.dtype == out_dtype
def test_rescale_no_overflow():
image = np.array([-128, 0, 127], dtype=np.int8)
output_image = exposure.rescale_intensity(image, out_range=np.uint8)
testing.assert_array_equal(output_image, [0, 128, 255])
assert output_image.dtype == np.uint8
def test_rescale_float_output():
image = np.array([-128, 0, 127], dtype=np.int8)
output_image = exposure.rescale_intensity(image, out_range=(0, 255))
testing.assert_array_equal(output_image, [0, 128, 255])
assert output_image.dtype == np.float_
def test_rescale_raises_on_incorrect_out_range():
image = np.array([-128, 0, 127], dtype=np.int8)
with testing.raises(ValueError):
_ = exposure.rescale_intensity(image, out_range='flat')
# Test adaptive histogram equalization
# ====================================
def test_adapthist_grayscale():
"""Test a grayscale float image
"""
img = util.img_as_float(data.astronaut())
img = rgb2gray(img)
img = np.dstack((img, img, img))
adapted = exposure.equalize_adapthist(img, kernel_size=(57, 51),
clip_limit=0.01, nbins=128)
assert img.shape == adapted.shape
assert_almost_equal(peak_snr(img, adapted), 100.140, 3)
assert_almost_equal(norm_brightness_err(img, adapted), 0.0529, 3)
def test_adapthist_color():
"""Test an RGB color uint16 image
"""
img = util.img_as_uint(data.astronaut())
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
hist, bin_centers = exposure.histogram(img)
assert len(w) > 0
adapted = exposure.equalize_adapthist(img, clip_limit=0.01)
assert adapted.min() == 0
assert adapted.max() == 1.0
assert img.shape == adapted.shape
full_scale = exposure.rescale_intensity(img)
assert_almost_equal(peak_snr(full_scale, adapted), 109.393, 1)
assert_almost_equal(norm_brightness_err(full_scale, adapted), 0.02, 2)
return data, adapted
def test_adapthist_alpha():
"""Test an RGBA color image
"""
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_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)