def test_mask():
vol = np.zeros((30, 30, 30))
vol[15, 15, 15] = 1
struct = generate_binary_structure(3, 1)
voln = binary_dilation(vol, structure=struct, iterations=4).astype('f4')
initial = np.sum(voln > 0)
mask = voln.copy()
thresh = otsu(mask)
mask = mask > thresh
initial_otsu = np.sum(mask > 0)
assert_equal(initial_otsu, initial)
mins, maxs = bounding_box(mask)
voln_crop = crop(mask, mins, maxs)
initial_crop = np.sum(voln_crop > 0)
assert_equal(initial_crop, initial)
applymask(voln, mask)
final = np.sum(voln > 0)
assert_equal(final, initial)
# Test multi_median.
median_test = np.arange(25).reshape(5, 5)
median_control = median_test.copy()
medianradius = 3
median_test = multi_median(median_test, medianradius, 3)
medarr = np.ones_like(median_control.shape) * ((medianradius * 2) + 1)
median_filter(median_control, medarr, output=median_control)
median_filter(median_control, medarr, output=median_control)
median_filter(median_control, medarr, output=median_control)
assert_equal(median_test, median_control)
def test_mask():
vol = np.zeros((30, 30, 30))
vol[15, 15, 15] = 1
struct = generate_binary_structure(3, 1)
voln = binary_dilation(vol, structure=struct, iterations=4).astype('f4')
initial = np.sum(voln > 0)
mask = voln.copy()
thresh = otsu(mask)
mask = mask > thresh
initial_otsu = np.sum(mask > 0)
assert_equal(initial_otsu, initial)
mins, maxs = bounding_box(mask)
voln_crop = crop(mask, mins, maxs)
initial_crop = np.sum(voln_crop > 0)
assert_equal(initial_crop, initial)
applymask(voln, mask)
final = np.sum(voln > 0)
assert_equal(final, initial)
# Test multi_median.
median_test = np.arange(25).reshape(5, 5)
median_control = median_test.copy()
medianradius = 3
median_test = multi_median(median_test, medianradius, 3)
medarr = np.ones_like(median_control.shape) * ((medianradius * 2) + 1)
median_filter(median_control, medarr, output=median_control)
median_filter(median_control, medarr, output=median_control)
median_filter(median_control, medarr, output=median_control)
assert_equal(median_test, median_control)
def median_otsu(mean_b0, median_radius=4, numpass=2, dilate=1):
mask = multi_median(mean_b0.eval(), median_radius, numpass)
thresh = tf.constant(otsu(mask))
mask = tf.constant(mask) > tf.to_float(thresh)
for _ in range(dilate):
mask = dilation(mask)
return tf.squeeze(mask)
