def test_li_camera_image():
image = util.img_as_ubyte(camerad)
threshold = threshold_li(image)
ce_actual = _cross_entropy(image, threshold)
assert 78 < threshold_li(image) < 79
assert ce_actual < _cross_entropy(image, threshold + 1)
assert ce_actual < _cross_entropy(image, threshold - 1)
def test_li_arbitrary_start_point():
cell = celld
max_stationary_point = threshold_li(cell)
low_stationary_point = threshold_li(
cell, initial_guess=float(cp.percentile(cell, 5))
)
optimum = threshold_li(cell, initial_guess=float(cp.percentile(cell, 95)))
assert 67 < max_stationary_point < 68
assert 48 < low_stationary_point < 49
assert 111 < optimum < 112
def test_li_coins_image():
image = util.img_as_ubyte(coinsd)
threshold = threshold_li(image)
ce_actual = _cross_entropy(image, threshold)
assert 94 < threshold_li(image) < 95
assert ce_actual < _cross_entropy(image, threshold + 1)
# in the case of the coins image, the minimum cross-entropy is achieved one
# threshold below that found by the iterative method. Not sure why that is
# but `threshold_li` does find the stationary point of the function (ie the
# tolerance can be reduced arbitrarily but the exact same threshold is
# found), so my guess is some kind of histogram binning effect.
assert ce_actual < _cross_entropy(image, threshold - 2)
def test_li_astro_image():
image = util.img_as_ubyte(astronautd)
threshold = threshold_li(image)
ce_actual = _cross_entropy(image, threshold)
assert 64 < threshold < 65
assert ce_actual < _cross_entropy(image, threshold + 1)
assert ce_actual < _cross_entropy(image, threshold - 1)
def test_li_pathological_arrays():
# See https://github.com/scikit-image/scikit-image/issues/4140
a = cp.array([0, 0, 1, 0, 0, 1, 0, 1])
b = cp.array([0, 0, 0.1, 0, 0, 0.1, 0, 0.1])
c = cp.array([0, 0, 0.1, 0, 0, 0.1, 0.01, 0.1])
d = cp.array([0, 0, 1, 0, 0, 1, 0.5, 1])
e = cp.array([1, 1])
f = cp.asarray([1, 2])
arrays = [a, b, c, d, e, f]
with np.errstate(divide='ignore'):
# ignoring "divide by zero encountered in log" error from np.log(0)
thresholds = cp.array([float(threshold_li(arr)) for arr in arrays])
assert cp.all(cp.isfinite(thresholds))
def test_li_constant_image(self):
assert threshold_li(cp.ones((10, 10))) == 1.0
def test_li_float_image(self):
image = self.image.astype(float)
assert 2 < threshold_li(image) < 3
def test_li_negative_int(self):
image = self.image - 2
assert 0 < threshold_li(image) < 1
def test_li(self):
assert 2 < threshold_li(self.image) < 3
def test_li_negative_inital_guess():
with testing.raises(ValueError):
threshold_li(coinsd, initial_guess=-5)
def test_li_constant_image_with_nan():
image = cp.asarray([8, 8, 8, 8, cp.nan])
assert threshold_li(image) == 8
def test_li_inf_minus_inf():
image = cp.array([cp.inf, -cp.inf])
assert threshold_li(image) == 0
def test_li_inf_image():
image = cp.array([cp.inf, cp.nan])
assert threshold_li(image) == cp.inf
def test_li_nan_image():
image = cp.full((5, 5), cp.nan)
assert cp.isnan(threshold_li(image))
def test_li_coins_image_as_float():
coins = util.img_as_float(coinsd)
assert 94 / 255 < threshold_li(coins) < 95 / 255
