def test_empty_selem():
# check that min, max and mean returns zeros if structuring element is
# empty
image = np.zeros((5, 5), dtype=np.uint16)
out = np.zeros_like(image)
mask = np.ones_like(image, dtype=np.uint8)
res = np.zeros_like(image)
image[2, 2] = 255
image[2, 3] = 128
image[1, 2] = 16
elem = np.array([[0, 0, 0], [0, 0, 0]], dtype=np.uint8)
rank.mean(image=image, selem=elem, out=out, mask=mask,
shift_x=0, shift_y=0)
assert_equal(res, out)
rank.geometric_mean(image=image, selem=elem, out=out, mask=mask,
shift_x=0, shift_y=0)
assert_equal(res, out)
rank.minimum(image=image, selem=elem, out=out, mask=mask,
shift_x=0, shift_y=0)
assert_equal(res, out)
rank.maximum(image=image, selem=elem, out=out, mask=mask,
shift_x=0, shift_y=0)
assert_equal(res, out)
def test_empty_selem(self):
# check that min, max and mean returns zeros if structuring element is
# empty
image = np.zeros((5, 5), dtype=np.uint16)
out = np.zeros_like(image)
mask = np.ones_like(image, dtype=np.uint8)
res = np.zeros_like(image)
image[2, 2] = 255
image[2, 3] = 128
image[1, 2] = 16
elem = np.array([[0, 0, 0], [0, 0, 0]], dtype=np.uint8)
rank.mean(image=image, selem=elem, out=out, mask=mask,
shift_x=0, shift_y=0)
assert_equal(res, out)
rank.geometric_mean(image=image, selem=elem, out=out, mask=mask,
shift_x=0, shift_y=0)
assert_equal(res, out)
rank.minimum(image=image, selem=elem, out=out, mask=mask,
shift_x=0, shift_y=0)
assert_equal(res, out)
rank.maximum(image=image, selem=elem, out=out, mask=mask,
shift_x=0, shift_y=0)
assert_equal(res, out)
def test_selem_dtypes(self):
image = np.zeros((5, 5), dtype=np.uint8)
out = np.zeros_like(image)
mask = np.ones_like(image, dtype=np.uint8)
image[2, 2] = 255
image[2, 3] = 128
image[1, 2] = 16
for dtype in (bool, np.uint8, np.uint16, np.int32, np.int64,
np.float32, np.float64):
elem = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]], dtype=dtype)
rank.mean(image=image,
selem=elem,
out=out,
mask=mask,
shift_x=0,
shift_y=0)
assert_equal(image, out)
rank.geometric_mean(image=image,
selem=elem,
out=out,
mask=mask,
shift_x=0,
shift_y=0)
assert_equal(image, out)
rank.mean_percentile(image=image,
selem=elem,
out=out,
mask=mask,
shift_x=0,
shift_y=0)
assert_equal(image, out)
def check_all():
selem = morphology.disk(1)
refs = np.load(
os.path.join(skimage.data_dir, "rank_filter_tests.npz"))
assert_equal(refs["autolevel"], rank.autolevel(self.image, selem))
assert_equal(refs["autolevel_percentile"],
rank.autolevel_percentile(self.image, selem))
assert_equal(refs["bottomhat"], rank.bottomhat(self.image, selem))
assert_equal(refs["equalize"], rank.equalize(self.image, selem))
assert_equal(refs["gradient"], rank.gradient(self.image, selem))
assert_equal(refs["gradient_percentile"],
rank.gradient_percentile(self.image, selem))
assert_equal(refs["maximum"], rank.maximum(self.image, selem))
assert_equal(refs["mean"], rank.mean(self.image, selem))
assert_equal(refs["geometric_mean"],
rank.geometric_mean(self.image, selem)),
assert_equal(refs["mean_percentile"],
rank.mean_percentile(self.image, selem))
assert_equal(refs["mean_bilateral"],
rank.mean_bilateral(self.image, selem))
assert_equal(refs["subtract_mean"],
rank.subtract_mean(self.image, selem))
assert_equal(refs["subtract_mean_percentile"],
rank.subtract_mean_percentile(self.image, selem))
assert_equal(refs["median"], rank.median(self.image, selem))
assert_equal(refs["minimum"], rank.minimum(self.image, selem))
assert_equal(refs["modal"], rank.modal(self.image, selem))
assert_equal(refs["enhance_contrast"],
rank.enhance_contrast(self.image, selem))
assert_equal(refs["enhance_contrast_percentile"],
rank.enhance_contrast_percentile(self.image, selem))
assert_equal(refs["pop"], rank.pop(self.image, selem))
assert_equal(refs["pop_percentile"],
rank.pop_percentile(self.image, selem))
assert_equal(refs["pop_bilateral"],
rank.pop_bilateral(self.image, selem))
assert_equal(refs["sum"], rank.sum(self.image, selem))
assert_equal(refs["sum_bilateral"],
rank.sum_bilateral(self.image, selem))
assert_equal(refs["sum_percentile"],
rank.sum_percentile(self.image, selem))
assert_equal(refs["threshold"], rank.threshold(self.image, selem))
assert_equal(refs["threshold_percentile"],
rank.threshold_percentile(self.image, selem))
assert_equal(refs["tophat"], rank.tophat(self.image, selem))
assert_equal(refs["noise_filter"],
rank.noise_filter(self.image, selem))
assert_equal(refs["entropy"], rank.entropy(self.image, selem))
assert_equal(refs["otsu"], rank.otsu(self.image, selem))
assert_equal(refs["percentile"],
rank.percentile(self.image, selem))
assert_equal(refs["windowed_histogram"],
rank.windowed_histogram(self.image, selem))
def test_selem_dtypes():
image = np.zeros((5, 5), dtype=np.uint8)
out = np.zeros_like(image)
mask = np.ones_like(image, dtype=np.uint8)
image[2, 2] = 255
image[2, 3] = 128
image[1, 2] = 16
for dtype in (np.uint8, np.uint16, np.int32, np.int64,
np.float32, np.float64):
elem = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]], dtype=dtype)
rank.mean(image=image, selem=elem, out=out, mask=mask,
shift_x=0, shift_y=0)
assert_equal(image, out)
rank.geometric_mean(image=image, selem=elem, out=out, mask=mask,
shift_x=0, shift_y=0)
assert_equal(image, out)
rank.mean_percentile(image=image, selem=elem, out=out, mask=mask,
shift_x=0, shift_y=0)
assert_equal(image, out)
def test_smallest_selem16(self):
# check that min, max and mean returns identity if structuring element
# contains only central pixel
image = np.zeros((5, 5), dtype=np.uint16)
out = np.zeros_like(image)
mask = np.ones_like(image, dtype=np.uint8)
image[2, 2] = 255
image[2, 3] = 128
image[1, 2] = 16
elem = np.array([[1]], dtype=np.uint8)
rank.mean(image=image,
selem=elem,
out=out,
mask=mask,
shift_x=0,
shift_y=0)
assert_equal(image, out)
rank.geometric_mean(image=image,
selem=elem,
out=out,
mask=mask,
shift_x=0,
shift_y=0)
assert_equal(image, out)
rank.minimum(image=image,
selem=elem,
out=out,
mask=mask,
shift_x=0,
shift_y=0)
assert_equal(image, out)
rank.maximum(image=image,
selem=elem,
out=out,
mask=mask,
shift_x=0,
shift_y=0)
assert_equal(image, out)
def test_trivial_footprint8(self):
# check that min, max and mean returns identity if footprint
# contains only central pixel
image = np.zeros((5, 5), dtype=np.uint8)
out = np.zeros_like(image)
mask = np.ones_like(image, dtype=np.uint8)
image[2, 2] = 255
image[2, 3] = 128
image[1, 2] = 16
elem = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]], dtype=np.uint8)
rank.mean(image=image,
footprint=elem,
out=out,
mask=mask,
shift_x=0,
shift_y=0)
assert_equal(image, out)
rank.geometric_mean(image=image,
footprint=elem,
out=out,
mask=mask,
shift_x=0,
shift_y=0)
assert_equal(image, out)
rank.minimum(image=image,
footprint=elem,
out=out,
mask=mask,
shift_x=0,
shift_y=0)
assert_equal(image, out)
rank.maximum(image=image,
footprint=elem,
out=out,
mask=mask,
shift_x=0,
shift_y=0)
assert_equal(image, out)
def test_smallest_selem16():
# check that min, max and mean returns identity if structuring element
# contains only central pixel
image = np.zeros((5, 5), dtype=np.uint16)
out = np.zeros_like(image)
mask = np.ones_like(image, dtype=np.uint8)
image[2, 2] = 255
image[2, 3] = 128
image[1, 2] = 16
elem = np.array([[1]], dtype=np.uint8)
rank.mean(image=image, selem=elem, out=out, mask=mask,
shift_x=0, shift_y=0)
assert_equal(image, out)
rank.geometric_mean(image=image, selem=elem, out=out, mask=mask,
shift_x=0, shift_y=0)
assert_equal(image, out)
rank.minimum(image=image, selem=elem, out=out, mask=mask,
shift_x=0, shift_y=0)
assert_equal(image, out)
rank.maximum(image=image, selem=elem, out=out, mask=mask,
shift_x=0, shift_y=0)
assert_equal(image, out)
def test_random_sizes():
# make sure the size is not a problem
elem = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]], dtype=np.uint8)
for m, n in np.random.random_integers(1, 100, size=(10, 2)):
mask = np.ones((m, n), dtype=np.uint8)
image8 = np.ones((m, n), dtype=np.uint8)
out8 = np.empty_like(image8)
rank.mean(image=image8, selem=elem, mask=mask, out=out8,
shift_x=0, shift_y=0)
assert_equal(image8.shape, out8.shape)
rank.mean(image=image8, selem=elem, mask=mask, out=out8,
shift_x=+1, shift_y=+1)
assert_equal(image8.shape, out8.shape)
rank.geometric_mean(image=image8, selem=elem, mask=mask, out=out8,
shift_x=0, shift_y=0)
assert_equal(image8.shape, out8.shape)
rank.geometric_mean(image=image8, selem=elem, mask=mask, out=out8,
shift_x=+1, shift_y=+1)
assert_equal(image8.shape, out8.shape)
image16 = np.ones((m, n), dtype=np.uint16)
out16 = np.empty_like(image8, dtype=np.uint16)
rank.mean(image=image16, selem=elem, mask=mask, out=out16,
shift_x=0, shift_y=0)
assert_equal(image16.shape, out16.shape)
rank.mean(image=image16, selem=elem, mask=mask, out=out16,
shift_x=+1, shift_y=+1)
assert_equal(image16.shape, out16.shape)
rank.geometric_mean(image=image16, selem=elem, mask=mask, out=out16,
shift_x=0, shift_y=0)
assert_equal(image16.shape, out16.shape)
rank.geometric_mean(image=image16, selem=elem, mask=mask, out=out16,
shift_x=+1, shift_y=+1)
assert_equal(image16.shape, out16.shape)
rank.mean_percentile(image=image16, mask=mask, out=out16,
selem=elem, shift_x=0, shift_y=0, p0=.1, p1=.9)
assert_equal(image16.shape, out16.shape)
rank.mean_percentile(image=image16, mask=mask, out=out16,
selem=elem, shift_x=+1, shift_y=+1, p0=.1, p1=.9)
assert_equal(image16.shape, out16.shape)
def test_random_sizes(self):
# make sure the size is not a problem
elem = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]], dtype=np.uint8)
for m, n in np.random.randint(1, 101, size=(10, 2)):
mask = np.ones((m, n), dtype=np.uint8)
image8 = np.ones((m, n), dtype=np.uint8)
out8 = np.empty_like(image8)
rank.mean(image=image8, selem=elem, mask=mask, out=out8,
shift_x=0, shift_y=0)
assert_equal(image8.shape, out8.shape)
rank.mean(image=image8, selem=elem, mask=mask, out=out8,
shift_x=+1, shift_y=+1)
assert_equal(image8.shape, out8.shape)
rank.geometric_mean(image=image8, selem=elem, mask=mask, out=out8,
shift_x=0, shift_y=0)
assert_equal(image8.shape, out8.shape)
rank.geometric_mean(image=image8, selem=elem, mask=mask, out=out8,
shift_x=+1, shift_y=+1)
assert_equal(image8.shape, out8.shape)
image16 = np.ones((m, n), dtype=np.uint16)
out16 = np.empty_like(image8, dtype=np.uint16)
rank.mean(image=image16, selem=elem, mask=mask, out=out16,
shift_x=0, shift_y=0)
assert_equal(image16.shape, out16.shape)
rank.mean(image=image16, selem=elem, mask=mask, out=out16,
shift_x=+1, shift_y=+1)
assert_equal(image16.shape, out16.shape)
rank.geometric_mean(image=image16, selem=elem, mask=mask, out=out16,
shift_x=0, shift_y=0)
assert_equal(image16.shape, out16.shape)
rank.geometric_mean(image=image16, selem=elem, mask=mask, out=out16,
shift_x=+1, shift_y=+1)
assert_equal(image16.shape, out16.shape)
rank.mean_percentile(image=image16, mask=mask, out=out16,
selem=elem, shift_x=0, shift_y=0, p0=.1, p1=.9)
assert_equal(image16.shape, out16.shape)
rank.mean_percentile(image=image16, mask=mask, out=out16,
selem=elem, shift_x=+1, shift_y=+1, p0=.1, p1=.9)
assert_equal(image16.shape, out16.shape)
def calculate_features(imfile):
#these features are based on validation results that showed these four
#features were the most important out of a set of ~30 features
#for the classification of "good" and "bad" patches. although
#restricting ourselves to only these 4 features may result in
#slightly lower prediction accuracy, the time it takes to calculate
#30 features on ~1 million images is excessive
#call .compute() for dask array element
image = io.imread(imfile.compute())
features = []
#first lbp stdev
features.append(local_binary_pattern(image, 8, 8).std())
#second median of geo. mean
features.append(np.median(geometric_mean(image, square(11))))
#third stdev of entropy
features.append(entropy(image, square(7)).std())
#fourth mean of the canny filter
features.append(canny(image, sigma=1).mean())
return features
def check_all():
np.random.seed(0)
image = np.random.rand(25, 25)
selem = morphology.disk(1)
refs = np.load(os.path.join(skimage.data_dir, "rank_filter_tests.npz"))
assert_equal(refs["autolevel"],
rank.autolevel(image, selem))
assert_equal(refs["autolevel_percentile"],
rank.autolevel_percentile(image, selem))
assert_equal(refs["bottomhat"],
rank.bottomhat(image, selem))
assert_equal(refs["equalize"],
rank.equalize(image, selem))
assert_equal(refs["gradient"],
rank.gradient(image, selem))
assert_equal(refs["gradient_percentile"],
rank.gradient_percentile(image, selem))
assert_equal(refs["maximum"],
rank.maximum(image, selem))
assert_equal(refs["mean"],
rank.mean(image, selem))
assert_equal(refs["geometric_mean"],
rank.geometric_mean(image, selem)),
assert_equal(refs["mean_percentile"],
rank.mean_percentile(image, selem))
assert_equal(refs["mean_bilateral"],
rank.mean_bilateral(image, selem))
assert_equal(refs["subtract_mean"],
rank.subtract_mean(image, selem))
assert_equal(refs["subtract_mean_percentile"],
rank.subtract_mean_percentile(image, selem))
assert_equal(refs["median"],
rank.median(image, selem))
assert_equal(refs["minimum"],
rank.minimum(image, selem))
assert_equal(refs["modal"],
rank.modal(image, selem))
assert_equal(refs["enhance_contrast"],
rank.enhance_contrast(image, selem))
assert_equal(refs["enhance_contrast_percentile"],
rank.enhance_contrast_percentile(image, selem))
assert_equal(refs["pop"],
rank.pop(image, selem))
assert_equal(refs["pop_percentile"],
rank.pop_percentile(image, selem))
assert_equal(refs["pop_bilateral"],
rank.pop_bilateral(image, selem))
assert_equal(refs["sum"],
rank.sum(image, selem))
assert_equal(refs["sum_bilateral"],
rank.sum_bilateral(image, selem))
assert_equal(refs["sum_percentile"],
rank.sum_percentile(image, selem))
assert_equal(refs["threshold"],
rank.threshold(image, selem))
assert_equal(refs["threshold_percentile"],
rank.threshold_percentile(image, selem))
assert_equal(refs["tophat"],
rank.tophat(image, selem))
assert_equal(refs["noise_filter"],
rank.noise_filter(image, selem))
assert_equal(refs["entropy"],
rank.entropy(image, selem))
assert_equal(refs["otsu"],
rank.otsu(image, selem))
assert_equal(refs["percentile"],
rank.percentile(image, selem))
assert_equal(refs["windowed_histogram"],
rank.windowed_histogram(image, selem))
