def test_ellipse_model_residuals():
model = EllipseModel()
# vertical line through origin
model.params = (0, 0, 10, 5, 0)
assert_almost_equal(abs(model.residuals(np.array([[10, 0]]))), 0)
assert_almost_equal(abs(model.residuals(np.array([[0, 5]]))), 0)
assert_almost_equal(abs(model.residuals(np.array([[0, 10]]))), 5)
def test_ellipse_model_predict():
model = EllipseModel()
model.params = (0, 0, 5, 10, 0)
t = np.arange(0, 2 * np.pi, np.pi / 2)
xy = np.array(((5, 0), (0, 10), (-5, 0), (0, -10)))
assert_almost_equal(xy, model.predict_xy(t))
def test_ellipse_model_predict():
model = EllipseModel()
model.params = (0, 0, 5, 10, 0)
t = np.arange(0, 2 * np.pi, np.pi / 2)
xy = np.array(((5, 0), (0, 10), (-5, 0), (0, -10)))
assert_almost_equal(xy, model.predict_xy(t))
def test_ellipse_model_residuals():
model = EllipseModel()
# vertical line through origin
model.params = (0, 0, 10, 5, 0)
assert_almost_equal(abs(model.residuals(np.array([[10, 0]]))), 0)
assert_almost_equal(abs(model.residuals(np.array([[0, 5]]))), 0)
assert_almost_equal(abs(model.residuals(np.array([[0, 10]]))), 5)
def bounding_ellipses(ellipses_nuclei, n_neighbour_nuclei=1, radial_slack=0.0):
'''Bla bla
'''
ellipses_bounding = {}
for k_label, ellipse_n in ellipses_nuclei.items():
x0, y0, a0, b0, theta0 = ellipse_n.params
dists = []
for l_label, ellipse_n_test in ellipses_nuclei.items():
xt, yt, at, bt, thetat = ellipse_n_test.params
dist = (((xt - x0) * np.cos(theta0) + (yt - y0) * np.sin(theta0)) / a0) ** 2 + \
(((xt - x0) * np.sin(theta0) - (yt - y0) * np.cos(theta0)) / b0) ** 2
dists.append((l_label, dist))
sort_dist = sorted(dists, key=lambda x: x[1])
scale = np.sqrt(
sort_dist[n_neighbour_nuclei][1]) * (1.0 + radial_slack)
ellipse_bounder = EllipseModel()
ellipse_bounder.params = (round(x0), round(y0), round(a0 * scale),
round(b0 * scale), -theta0)
ellipses_bounding[k_label] = ellipse_bounder
return ellipses_bounding
def test_ellipse_model_estimate():
# generate original data without noise
model0 = EllipseModel()
model0.params = (10, 20, 15, 25, 0)
t = np.linspace(0, 2 * np.pi, 100)
data0 = model0.predict_xy(t)
# add gaussian noise to data
np.random.seed(1234)
data = data0 + np.random.normal(size=data0.shape)
# estimate parameters of noisy data
model_est = EllipseModel()
model_est.estimate(data)
# test whether estimated parameters almost equal original parameters
assert_almost_equal(model0.params, model_est.params, 0)
def test_ellipse_model_estimate():
# generate original data without noise
model0 = EllipseModel()
model0.params = (10, 20, 15, 25, 0)
t = np.linspace(0, 2 * np.pi, 100)
data0 = model0.predict_xy(t)
# add gaussian noise to data
np.random.seed(1234)
data = data0 + np.random.normal(size=data0.shape)
# estimate parameters of noisy data
model_est = EllipseModel()
model_est.estimate(data)
# test whether estimated parameters almost equal original parameters
assert_almost_equal(model0.params, model_est.params, 0)
def test_ellipse_model_estimate():
for angle in range(0, 180, 15):
rad = np.deg2rad(angle)
# generate original data without noise
model0 = EllipseModel()
model0.params = (10, 20, 15, 25, rad)
t = np.linspace(0, 2 * np.pi, 100)
data0 = model0.predict_xy(t)
# add gaussian noise to data
random_state = np.random.RandomState(1234)
data = data0 + random_state.normal(size=data0.shape)
# estimate parameters of noisy data
model_est = EllipseModel()
model_est.estimate(data)
# test whether estimated parameters almost equal original parameters
assert_almost_equal(model0.params[:2], model_est.params[:2], 0)
res = model_est.residuals(data0)
assert_array_less(res, np.ones(res.shape))
def test_ellipse_model_estimate():
for angle in range(0, 180, 15):
rad = np.deg2rad(angle)
# generate original data without noise
model0 = EllipseModel()
model0.params = (10, 20, 15, 25, rad)
t = np.linspace(0, 2 * np.pi, 100)
data0 = model0.predict_xy(t)
# add gaussian noise to data
random_state = np.random.RandomState(1234)
data = data0 + random_state.normal(size=data0.shape)
# estimate parameters of noisy data
model_est = EllipseModel()
model_est.estimate(data)
# test whether estimated parameters almost equal original parameters
assert_almost_equal(model0.params[:2], model_est.params[:2], 0)
res = model_est.residuals(data0)
assert_array_less(res, np.ones(res.shape))
def ellipse_fits(img_labelled, exclusion_list=[0]):
'''Bla bla
'''
ellipses_by_label = {}
for k_label in np.unique(img_labelled):
if k_label in exclusion_list:
continue
rows, cols = np.where(img_labelled == k_label)
coords = np.stack([rows, cols]).T
ellipse = EllipseModel()
estimate_success = ellipse.estimate(coords)
if not estimate_success:
xc, yc = tuple([int(x) for x in np.median(coords, axis=0)])
ellipse.params = xc, yc, 10, 10, 0.0
ellipses_by_label[k_label] = ellipse
return ellipses_by_label
