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_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_estimate():
# generate original data without noise
model0 = LineModel()
model0.params = (0, 1)
x0 = np.arange(-100, 100)
y0 = model0.predict_y(x0)
data0 = np.column_stack([x0, y0])
data = data0 + (np.random.random(data0.shape) - 0.5)
# estimate parameters of noisy data
model_est = LineModel()
model_est.estimate(data)
assert_almost_equal(model_est.residuals(data0), np.zeros(len(data)), 1)
# test whether estimated parameters almost equal original parameters
random_state = np.random.RandomState(1234)
x = random_state.rand(100, 2)
assert_almost_equal(model0.predict_y(x), model_est.predict_y(x), 1)
def RANSAC(image):
np.random.seed(seed=1)
# generate coordinates of line
x = np.arange(-200, 200)
y = 0.2 * x + 20
data = np.column_stack([x, y])
# add faulty data
faulty = np.array(30 * [(180., -100)])
faulty += 5 * np.random.normal(size=faulty.shape)
data[:faulty.shape[0]] = faulty
# add gaussian noise to coordinates
noise = np.random.normal(size=data.shape)
data += 0.5 * noise
data[::2] += 5 * noise[::2]
data[::4] += 20 * noise[::4]
# fit line using all data
model = LineModel()
model.estimate(data)
# robustly fit line only using inlier data with RANSAC algorithm
model_robust, inliers = ransac(data, LineModel, min_samples=2,
residual_threshold=1, max_trials=1000)
outliers = inliers == False
# generate coordinates of estimated models
line_x = np.arange(-250, 250)
line_y = model.predict_y(line_x)
line_y_robust = model_robust.predict_y(line_x)
plt.plot(data[inliers, 0], data[inliers, 1], '.b', alpha=0.6,
label='Inlier data')
plt.plot(data[outliers, 0], data[outliers, 1], '.r', alpha=0.6,
label='Outlier data')
plt.plot(line_x, line_y, '-k', label='Line model from all data')
plt.plot(line_x, line_y_robust, '-b', label='Robust line model')
plt.legend(loc='lower left')
plt.show()
def keep_border_points(cloud_pts, convex_hull_threshold):
''' Return the points that are localized onto the convex hull of the cloud
of input points.
Input and output are (N,2) numpy array '''
convexHulls = convex_hull(cloud_pts.tolist())
convexHulls[:+1][:] = convexHulls[0][:]
nsegments = len(convexHulls)
to_keep = np.zeros(len(cloud_pts), dtype=bool)
for P1, P2 in zip(convexHulls[:-1][:], convexHulls[1:][:]):
data = np.asarray([P1, P2])
model = LineModel()
model.estimate(data)
to_keep = np.logical_or(
to_keep,
abs(model.residuals(cloud_pts)) < convex_hull_threshold)
return cloud_pts[to_keep, :]
data = np.column_stack([x, y])
# add faulty data
faulty = np.array(30 * [(180., -100)])
faulty += 5 * np.random.normal(size=faulty.shape)
data[:faulty.shape[0]] = faulty
# add gaussian noise to coordinates
noise = np.random.normal(size=data.shape)
data += 0.5 * noise
data[::2] += 5 * noise[::2]
data[::4] += 20 * noise[::4]
# fit line using all data
model = LineModel()
model.estimate(data)
# robustly fit line only using inlier data with RANSAC algorithm
model_robust, inliers = ransac(data, LineModel, min_samples=2,
residual_threshold=1, max_trials=1000)
outliers = inliers == False
# generate coordinates of estimated models
line_x = np.arange(-250, 250)
line_y = model.predict_y(line_x)
line_y_robust = model_robust.predict_y(line_x)
fig, ax = plt.subplots()
ax.plot(data[inliers, 0], data[inliers, 1], '.b', alpha=0.6,
label='Inlier data')
ax.plot(data[outliers, 0], data[outliers, 1], '.r', alpha=0.6,
data = np.column_stack([x, y])
# add faulty data
faulty = np.array(30 * [(180, -100)])
faulty += 5 * np.random.normal(size=faulty.shape)
data[:faulty.shape[0]] = faulty
# add gaussian noise to coordinates
noise = np.random.normal(size=data.shape)
data += 0.5 * noise
data[::2] += 5 * noise[::2]
data[::4] += 20 * noise[::4]
# fit line using all data
model = LineModel()
model.estimate(data)
# robustly fit line only using inlier data with RANSAC algorithm
model_robust, inliers = ransac(data, LineModel, min_samples=2,
residual_threshold=1, max_trials=1000)
outliers = inliers == False
# generate coordinates of estimated models
line_x = np.arange(-250, 250)
line_y = model.predict_y(line_x)
line_y_robust = model_robust.predict_y(line_x)
plt.plot(data[inliers, 0], data[inliers, 1], '.b', alpha=0.6,
label='Inlier data')
plt.plot(data[outliers, 0], data[outliers, 1], '.r', alpha=0.6,
label='Outlier data')