import numpy as np from numpy.testing import assert_equal, assert_raises, assert_almost_equal from skimage.measure import LineModel, CircleModel, EllipseModel, ransac from skimage.transform import AffineTransform def test_line_model_invalid_input(): assert_raises(ValueError, LineModel().estimate, np.empty((5, 3))) def test_line_model_predict(): model = LineModel() model.params = (10, 1) x = np.arange(-10, 10) y = model.predict_y(x) assert_almost_equal(x, model.predict_x(y)) def test_line_model_estimate(): # generate original data without noise model0 = LineModel() model0.params = (10, 1) x0 = np.arange(-100, 100) y0 = model0.predict_y(x0) data0 = np.column_stack([x0, y0]) # add gaussian noise to data np.random.seed(1234) data = data0 + np.random.normal(size=data0.shape) # estimate parameters of noisy data model_est = LineModel() model_est.estimate(data) # test whether estimated parameters almost equal original parameters assert_almost_equal(model0.params, model_est.params, 1) def test_line_model_residuals(): model = LineModel() model.params = (0, 0) assert_equal(abs(model.residuals(np.array([[0, 0]]))), 0) assert_equal(abs(model.residuals(np.array([[0, 10]]))), 0) assert_equal(abs(model.residuals(np.array([[10, 0]]))), 10) model.params = (5, np.pi / 4) assert_equal(abs(model.residuals(np.array([[0, 0]]))), 5) assert_almost_equal(abs(model.residuals(np.array([[np.sqrt(50), 0]]))), 0) def test_line_model_under_determined(): data = np.empty((1, 2)) assert_raises(ValueError, LineModel().estimate, data) def test_circle_model_invalid_input(): assert_raises(ValueError, CircleModel().estimate, np.empty((5, 3))) def test_circle_model_predict(): model = CircleModel() r = 5 model.params = (0, 0, r) t = np.arange(0, 2 * np.pi, np.pi / 2) xy = np.array(((5, 0), (0, 5), (-5, 0), (0, -5))) assert_almost_equal(xy, model.predict_xy(t)) def test_circle_model_estimate(): # 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) # add gaussian noise to data np.random.seed(1234) data = data0 + np.random.normal(size=data0.shape) # estimate parameters of noisy data model_est = CircleModel() model_est.estimate(data) # test whether estimated parameters almost equal original parameters assert_almost_equal(model0.params, model_est.params, 1) def test_circle_model_residuals(): model = CircleModel() model.params = (0, 0, 5) assert_almost_equal(abs(model.residuals(np.array([[5, 0]]))), 0) assert_almost_equal(abs(model.residuals(np.array([[6, 6]]))), np.sqrt(2 * 6**2) - 5) assert_almost_equal(abs(model.residuals(np.array([[10, 0]]))), 5) 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_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_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_ransac_shape(): np.random.seed(1) # 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) # add some faulty data 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 def test_ransac_geometric(): np.random.seed(1) # generate original data without noise src = 100 * np.random.random((50, 2)) model0 = AffineTransform(scale=(0.5, 0.3), rotation=1, translation=(10, 20)) dst = model0(src) # add some faulty data outliers = (0, 5, 20) dst[outliers[0]] = (10000, 10000) dst[outliers[1]] = (-100, 100) dst[outliers[2]] = (50, 50) # estimate parameters of corrupted data model_est, inliers = ransac((src, dst), AffineTransform, 2, 20) # test whether estimated parameters equal original parameters assert_almost_equal(model0._matrix, model_est._matrix) assert np.all(np.nonzero(inliers == False)[0] == outliers) def test_ransac_is_data_valid(): np.random.seed(1) is_data_valid = lambda data: data.shape[0] > 2 model, inliers = ransac(np.empty((10, 2)), LineModel, 2, np.inf, is_data_valid=is_data_valid) assert_equal(model, None) assert_equal(inliers, None) def test_ransac_is_model_valid(): np.random.seed(1) def is_model_valid(model, data): return False model, inliers = ransac(np.empty((10, 2)), LineModel, 2, np.inf, is_model_valid=is_model_valid) assert_equal(model, None) assert_equal(inliers, None) def test_deprecated_params_attribute(): model = LineModel() model.params = (10, 1) x = np.arange(-10, 10) y = model.predict_y(x) assert_equal(model.params, model._params) if __name__ == "__main__": np.testing.run_module_suite()