本文整理匯總了Python中skimage.measure.CircleModel._params方法的典型用法代碼示例。如果您正苦於以下問題：Python CircleModel._params方法的具體用法？Python CircleModel._params怎麽用？Python CircleModel._params使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類skimage.measure.CircleModel的用法示例。
在下文中一共展示了CircleModel._params方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_circle_model_residuals
# 需要導入模塊: from skimage.measure import CircleModel [as 別名]
# 或者: from skimage.measure.CircleModel import _params [as 別名]
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)示例2: test_circle_model_predict
# 需要導入模塊: from skimage.measure import CircleModel [as 別名]
# 或者: from skimage.measure.CircleModel import _params [as 別名]
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))示例3: test_circle_model_estimate
# 需要導入模塊: from skimage.measure import CircleModel [as 別名]
# 或者: from skimage.measure.CircleModel import _params [as 別名]
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)示例4: test_ransac_shape
# 需要導入模塊: from skimage.measure import CircleModel [as 別名]
# 或者: from skimage.measure.CircleModel import _params [as 別名]
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