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(cp.asarray(data.astronaut()))
img = rgb2gray(img)
a = 15
img2d = util.img_as_float(img[0:-1:a, 0:-1:a])
img3d = cp.stack([img2d] * (img.shape[0] // a), axis=0)
# 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 (cp.mean(cp.abs(adapted2d - adapted3d[adapted3d.shape[0] // 2])) <
0.02)
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 test_adapthist_constant():
"""Test constant image, float and uint"""
img = cp.zeros((8, 8))
img += 2
img = img.astype(np.uint16)
adapted = exposure.equalize_adapthist(img, 3)
assert cp.min(adapted) == cp.max(adapted)
img = cp.zeros((8, 8))
img += 0.1
img = img.astype(np.float64)
adapted = exposure.equalize_adapthist(img, 3)
assert cp.min(adapted) == cp.max(adapted)
def test_adapthist_grayscale():
"""Test a grayscale float image"""
img = util.img_as_float(data.astronaut())
img = rgb2gray(img)
img = cp.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(float(peak_snr(img, adapted)), 100.140, 3)
assert_almost_equal(float(norm_brightness_err(img, adapted)), 0.0529, 3)
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_adapthist_borders():
"""Test border processing"""
img = rgb2gray(cp.asarray(util.img_as_float(data.astronaut())))
# maximize difference between orig and processed img
img /= 100.0
img[img.shape[0] // 2, img.shape[1] // 2] = 1.0
# 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)