def test_rescale_nan_warning(in_range, out_range):
image = cp.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\.")
with expected_warnings([msg]):
exposure.rescale_intensity(image, in_range, out_range)
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 = cp.asarray([-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_adapthist_alpha():
"""Test an RGBA color image"""
img = util.img_as_float(data.astronaut())
alpha = cp.ones((img.shape[0], img.shape[1]), dtype=float)
img = cp.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(float(peak_snr(full_scale, adapted)), 109.393, 2)
assert_almost_equal(float(norm_brightness_err(full_scale, adapted)),
0.0248, 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(float(peak_snr(full_scale, adapted)), 109.393, 1)
assert_almost_equal(float(norm_brightness_err(full_scale, adapted)), 0.02,
2)
return data, adapted
def test_rescale_output_dtype(out_range, out_dtype):
image = cp.asarray([-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_same_values():
image = cp.ones((2, 2))
out = exposure.rescale_intensity(image)
assert ~cp.isnan(out).all()
assert_array_almost_equal(out, image)
def test_rescale_constant():
image = cp.asarray([130, 130], dtype=np.uint16)
out = exposure.rescale_intensity(image, out_range=(0, 127))
assert_array_almost_equal(out, [127, 127])
def test_rescale_all_zeros():
image = cp.zeros((2, 2), dtype=np.uint8)
out = exposure.rescale_intensity(image)
assert ~cp.isnan(out).all()
assert_array_almost_equal(out, image)
def test_rescale_uint14_limits():
image = cp.asarray([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_in_range_clip():
image = cp.asarray([51.0, 102.0, 153.0])
out = exposure.rescale_intensity(image, in_range=(0, 102))
assert_array_almost_equal(out, [0.5, 1, 1])
def test_rescale_in_range():
image = cp.asarray([51.0, 102.0, 153.0])
out = exposure.rescale_intensity(image, in_range=(0, 255))
assert_array_almost_equal(out, [0.2, 0.4, 0.6])
def test_rescale_shrink():
image = cp.asarray([51.0, 102.0, 153.0])
out = exposure.rescale_intensity(image)
assert_array_almost_equal(out, [0, 0.5, 1])
def test_rescale_stretch():
image = cp.asarray([51, 102, 153], dtype=np.uint8)
out = exposure.rescale_intensity(image)
assert out.dtype == np.uint8
assert_array_almost_equal(out, [0, 127, 255])
source_range="dtype",
normalize=True)
expected /= 3.0
assert_array_equal(frequencies, expected)
# Test histogram equalization
# ===========================
np.random.seed(0)
test_img_int = data.camera()
test_img_int = cp.asarray(test_img_int)
# squeeze image intensities to lower image contrast
test_img = util.img_as_float(test_img_int)
test_img = exposure.rescale_intensity(test_img / 5.0 + 100)
test_img = cp.asarray(test_img)
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)
cp.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)
def test_rescale_float_output():
image = cp.asarray([-128, 0, 127], dtype=np.int8)
output_image = exposure.rescale_intensity(image, out_range=(0, 255))
cp.testing.assert_array_equal(output_image, [0, 128, 255])
assert output_image.dtype == np.float_
def test_rescale_raises_on_incorrect_out_range():
image = cp.asarray([-128, 0, 127], dtype=np.int8)
with pytest.raises(ValueError):
_ = exposure.rescale_intensity(image, out_range="flat")
def test_rescale_named_in_range():
image = cp.asarray([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])
