def test_triangle_float_images():
text = data.text()
int_bins = text.max() - text.min() + 1
# Set nbins to match the uint case and threshold as float.
assert(round(threshold_triangle(
text.astype(np.float), nbins=int_bins)) == 104)
# Check that rescaling image to floats in unit interval is equivalent.
assert(round(threshold_triangle(text / 255., nbins=int_bins) * 255) == 104)
# Repeat for inverted image.
assert(round(threshold_triangle(
np.invert(text).astype(np.float), nbins=int_bins)) == 151)
assert (round(threshold_triangle(
np.invert(text) / 255., nbins=int_bins) * 255) == 151)
def test_triangle_float_images():
text = data.text()
int_bins = text.max() - text.min() + 1
# Set nbins to match the uint case and threshold as float.
assert(round(threshold_triangle(
text.astype(np.float), nbins=int_bins)) == 104)
# Check that rescaling image to floats in unit interval is equivalent.
assert(round(threshold_triangle(text / 255., nbins=int_bins) * 255) == 104)
# Repeat for inverted image.
assert(round(threshold_triangle(
np.invert(text).astype(np.float), nbins=int_bins)) == 151)
assert (round(threshold_triangle(
np.invert(text) / 255., nbins=int_bins) * 255) == 151)
def test_triangle_flip():
# Depending on the skewness, the algorithm flips the histogram.
# We check that the flip doesn't affect too much the result.
img = data.camera()
inv_img = np.invert(img)
t = threshold_triangle(inv_img)
t_inv_img = inv_img > t
t_inv_inv_img = np.invert(t_inv_img)
t = threshold_triangle(img)
t_img = img > t
# Check that most of the pixels are identical
# See numpy #7685 for a future np.testing API
unequal_pos = np.where(t_img.ravel() != t_inv_inv_img.ravel())
assert(len(unequal_pos[0]) / t_img.size < 1e-2)
def test_triangle_flip():
# Depending on the skewness, the algorithm flips the histogram.
# We check that the flip doesn't affect too much the result.
img = data.camera()
inv_img = np.invert(img)
t = threshold_triangle(inv_img)
t_inv_img = inv_img > t
t_inv_inv_img = np.invert(t_inv_img)
t = threshold_triangle(img)
t_img = img > t
# Check that most of the pixels are identical
# See numpy #7685 for a future np.testing API
unequal_pos = np.where(t_img.ravel() != t_inv_inv_img.ravel())
assert (len(unequal_pos[0]) / t_img.size < 1e-2)
def test_triangle_uint_images():
assert(threshold_triangle(np.invert(data.text())) == 151)
assert(threshold_triangle(data.text()) == 104)
assert(threshold_triangle(data.coins()) == 80)
assert(threshold_triangle(np.invert(data.coins())) == 175)
def test_triangle_uint_images():
assert (threshold_triangle(np.invert(data.text())) == 151)
assert (threshold_triangle(data.text()) == 104)
assert (threshold_triangle(data.coins()) == 80)
assert (threshold_triangle(np.invert(data.coins())) == 175)
def test_triangle_images():
assert threshold_triangle(np.invert(data.text())) == 151
assert threshold_triangle(data.text()) == 104
assert threshold_triangle(data.coins()) == 80
assert threshold_triangle(np.invert(data.coins())) == 175
