def test_normalize():
im = cp.asarray([0, 255, 255], dtype=np.uint8)
frequencies, bin_centers = exposure.histogram(im,
source_range="dtype",
normalize=False)
expected = cp.zeros(256)
expected[0] = 1
expected[-1] = 2
assert_array_equal(frequencies, expected)
frequencies, bin_centers = exposure.histogram(im,
source_range="dtype",
normalize=True)
expected /= 3.0
assert_array_equal(frequencies, expected)
def test_all_negative_image():
im = cp.asarray([-100, -1], dtype=np.int8)
frequencies, bin_centers = exposure.histogram(im)
assert_array_equal(bin_centers, cp.arange(-100, 0))
assert frequencies[0] == 1
assert frequencies[-1] == 1
assert_array_equal(frequencies[1:-1], 0)
def test_peak_int_range_dtype():
im = cp.asarray([10, 100], dtype=np.int8)
frequencies, bin_centers = exposure.histogram(im, source_range="dtype")
assert_array_equal(bin_centers, cp.arange(-128, 128))
assert frequencies[128 + 10] == 1
assert frequencies[128 + 100] == 1
assert frequencies[128 + 101] == 0
assert frequencies.shape == (256, )
def test_peak_float_out_of_range_dtype():
im = cp.asarray([10, 100], dtype=np.float16)
nbins = 10
frequencies, bin_centers = exposure.histogram(im,
nbins=nbins,
source_range="dtype")
assert_almost_equal(cp.min(bin_centers).get(), -0.9, 3)
assert_almost_equal(cp.max(bin_centers).get(), 0.9, 3)
assert len(bin_centers) == 10
def test_dask_histogram():
pytest.importorskip("dask", reason="dask python library is not installed")
import dask.array as da
dask_array = da.from_array(cp.asarray([[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 cp.allclose(expected_bins, output_bins)
assert cp.allclose(expected_hist, output_hist)
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_peak_float_out_of_range_image():
im = cp.asarray([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, cp.arange(10, 100) + 0.5)
def test_flat_int_range_dtype():
im = cp.linspace(-128, 128, 256, dtype=np.int8)
frequencies, bin_centers = exposure.histogram(im, source_range="dtype")
assert_array_equal(bin_centers, cp.arange(-128, 128))
assert frequencies.shape == (256, )
def test_int_range_image():
im = cp.asarray([10, 100], dtype=np.int8)
frequencies, bin_centers = exposure.histogram(im)
assert len(bin_centers) == len(frequencies)
assert bin_centers[0] == 10
assert bin_centers[-1] == 100
def test_wrong_source_range():
im = cp.asarray([-1, 100], dtype=np.int8)
with pytest.raises(ValueError):
frequencies, bin_centers = exposure.histogram(im,
source_range="foobar")