def test_single_point(): '''Test measure_length function from patty.utils''' with np.errstate(all='raise'): line = np.array([[0, -1., 0]]) expected = 0 length = measure_length(line) assert_almost_equal(expected, length)
def get_stick_scale(pointcloud, eps=0.1, min_samples=20): """Takes a point cloud, as a numpy array, looks for red segments of scale sticks and returns the scale estimation with most support. Method: pointcloud --dbscan--> clusters --lengthEstimation--> lengths --ransac--> best length Arguments: pointcloud Point cloud containing only measuring stick segments (only the red, or only the white parts) eps DBSCAN parameter: Maximum distance between two samples for them to be considered as in the same neighborhood. min_samples DBSCAN parameter: The number of samples in a neighborhood for a point to be considered as a core point. Returns: scale Estimate of the size of one actual meter in expressed in units of the pointcloud's coordinates. confidence A number expressing the reliability of the estimated scale. Confidence is in [0, 1]. With a confidence greater than .5, the estimate can be considered useable for further calculations. """ # quickly return for trivial case if pointcloud.size == 0: return 1, 0 # find the red segments to measure pc_reds = extract_mask(pointcloud, get_red_mask(pointcloud)) if len(pc_reds) == 0: # unit scale, zero confidence (ie. any other estimation is better) return 1.0, 0.0 cluster_generator = segment_dbscan( pc_reds, eps, min_samples, algorithm='kd_tree') sizes = [{'len': len(cluster), 'meter': measure_length(cluster) * SEGMENTS_PER_METER} for cluster in cluster_generator] if len(sizes) == 0: return 1.0, 0.0 scale, votes, n_clusters = ransac(sizes) confidence = get_confidence_level(votes, n_clusters) return scale, confidence
def test_centered_line_on_x_axis(): '''Test measure_length function from patty.utils''' line = np.array([[-5., 0, 0], [0, 0, 0], [5., 0, 0]]) expected = 10 length = measure_length(line) assert_almost_equal(expected, length)
def test_no_points(): '''Test measure_length function from patty.utils''' line = np.array([]) expected = 0 length = measure_length(line) assert_almost_equal(expected, length)
def test_rectangle_in_y_z_plane(): '''Test measure_length function from patty.utils''' line = np.array([[0, -1., 0], [0, 9., 0], [0, -1., 4], [0, 9., 4]]) expected = 10 length = measure_length(line) assert_almost_equal(expected, length)