def test_03_06_edges_of_objects(self):
r = np.random.RandomState()
r.seed(36)
i, j = np.mgrid[-20:21, -20:21]
labels = ((i > -19) & (i < 19) & (j > -19) & (j < 19)).astype(int)
centers = np.zeros(labels.shape, int)
centers[(i > -5) * (i < 5) & (j > -5) & (j < 5)] = 1
image = r.uniform(size=labels.shape)
m = self.run_module(image, labels,
center_labels=centers,
center_choice = M.C_EDGES_OF_OTHER,
bin_count = 4,
maximum_radius = 8,
wants_scaled=False)
_, d_from_center = M.propagate(np.zeros(labels.shape),
centers,
(labels > 0), 1)
good_mask = (labels > 0) & (centers == 0)
d_from_center = d_from_center[good_mask]
bins = (d_from_center / 2).astype(int)
bins[bins > 4] = 4
bin_counts = np.bincount(bins)
image_sums = np.bincount(bins, image[good_mask])
frac_at_d = image_sums / np.sum(image_sums)
for i in range(1,6):
data = m.get_current_measurement(OBJECT_NAME,
feature_frac_at_d(i, 4))
self.assertEqual(len(data), 1)
self.assertAlmostEqual(data[0], frac_at_d[i-1])
def test_03_06_edges_of_objects(self):
r = np.random.RandomState()
r.seed(36)
i, j = np.mgrid[-20:21, -20:21]
labels = ((i > -19) & (i < 19) & (j > -19) & (j < 19)).astype(int)
centers = np.zeros(labels.shape, int)
centers[(i > -5) * (i < 5) & (j > -5) & (j < 5)] = 1
image = r.uniform(size=labels.shape)
m = self.run_module(image,
labels,
center_labels=centers,
center_choice=M.C_EDGES_OF_OTHER,
bin_count=4,
maximum_radius=8,
wants_scaled=False)
_, d_from_center = M.propagate(np.zeros(labels.shape), centers,
(labels > 0), 1)
good_mask = (labels > 0) & (centers == 0)
d_from_center = d_from_center[good_mask]
bins = (d_from_center / 2).astype(int)
bins[bins > 4] = 4
bin_counts = np.bincount(bins)
image_sums = np.bincount(bins, image[good_mask])
frac_at_d = image_sums / np.sum(image_sums)
for i in range(1, 6):
data = m.get_current_measurement(OBJECT_NAME,
feature_frac_at_d(i, 4))
self.assertEqual(len(data), 1)
self.assertAlmostEqual(data[0], frac_at_d[i - 1])