本文整理汇总了Python中skimage.measure.CircleModel.params方法的典型用法代码示例。如果您正苦于以下问题：Python CircleModel.params方法的具体用法？Python CircleModel.params怎么用？Python CircleModel.params使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类skimage.measure.CircleModel的用法示例。
在下文中一共展示了CircleModel.params方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_circle_model_residuals
# 需要导入模块: from skimage.measure import CircleModel [as 别名]
# 或者: from skimage.measure.CircleModel import params [as 别名]
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
# 需要导入模块: from skimage.measure import CircleModel [as 别名]
# 或者: from skimage.measure.CircleModel import params [as 别名]
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
# 需要导入模块: from skimage.measure import CircleModel [as 别名]
# 或者: from skimage.measure.CircleModel import params [as 别名]
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
random_state = np.random.RandomState(1234)
data = data0 + random_state.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
# 需要导入模块: from skimage.measure import CircleModel [as 别名]
# 或者: from skimage.measure.CircleModel import params [as 别名]
def test_ransac_shape():
# 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,
random_state=1)
# test whether estimated parameters equal original parameters
assert_equal(model0.params, model_est.params)
for outlier in outliers:
assert outlier not in inliers