Add test case for ransac algorithm

skimage/measure/tests/test_fit.py

@@ -1,6 +1,6 @@
 import numpy as np
 from numpy.testing import assert_equal, assert_raises, assert_almost_equal
-from skimage.measure import LineModel, CircleModel, EllipseModel
+from skimage.measure import LineModel, CircleModel, EllipseModel, ransac
 
 
 def test_line_model_invalid_input():
@@ -35,7 +35,7 @@ def test_line_model_estimate():
     model_est = LineModel()
     model_est.estimate(data)
 
-    # test whether estimated parameters almost equals original parameters
+    # test whether estimated parameters almost equal original parameters
     assert_almost_equal(model0._params, model_est._params, 1)
 
 
@@ -72,9 +72,66 @@ def test_circle_model_estimate():
     model_est = CircleModel()
     model_est.estimate(data)
 
-    # test whether estimated parameters almost equals original parameters
+    # test whether estimated parameters almost equal original parameters
     assert_almost_equal(model0._params, model_est._params, 1)
 
 
+def test_ellipse_model_invalid_input():
+    assert_raises(ValueError, EllipseModel().estimate, np.empty((5, 3)))
+
+
+def test_ellipse_model_predict():
+    model = EllipseModel()
+    r = 5
+    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_is_degenerate():
+    assert_equal(EllipseModel().is_degenerate(np.empty((1, 2))), True)
+
+
+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_ransac():
+    # generate original data without noise
+    model0 = CircleModel()
+    model0._params = (10, 12, 3)
+    t = np.linspace(0, 2 * np.pi, 1000)
+    data0 = model0.predict_xy(t)
+    outliers = (10, 30, 200)
+    data0[outliers[0], :] = (1000, 1000)
+    data0[outliers[1], :] = (-50, 50)
+    data0[outliers[2], :] = (-100, -10)
+
+    # estimate parameters of corrupted data
+    model_est, inliers = ransac(data0, CircleModel, 3, 5)
+
+    # test whether estimated parameters equal original parameters
+    assert_equal(model0._params, model_est._params)
+    for outlier in outliers:
+        assert outlier not in inliers
+
+
 if __name__ == "__main__":
     np.testing.run_module_suite()