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_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 get_ellipse_fit_cost(region):
pts = np.where(region.image)
pts = np.array([pts[1], pts[0]]).T
e = EllipseModel()
e.estimate(pts)
cost = np.mean(e.residuals(pts)**2)
return (cost)
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))
