Ejemplo n.º 1
0
def get_largest_dbscan_clusters(pointcloud,
                                min_return_fragment=0.7,
                                epsilon=0.1,
                                minpoints=250,
                                rgb_weight=0):
    '''
    Finds the largest clusters containing together at least min_return_fragment
    of the complete point cloud. In case less points belong to clusters, all
    clustered points are returned.

    Parameters
    ----------
    pointcloud : pcl.PointCloud
        Input pointcloud.
    min_return_fragment : float
        Minimum desired fragment of pointcloud to be returned
    epsilon : float
        Neighborhood radius for DBSCAN.
    minpoints : integer
        Minimum neighborhood density for DBSCAN.
    rgb_weight : float, optional
        If non-zero, cluster on color information as well as location;
        specifies the relative weight of the RGB components to spatial
        coordinates in distance computations.
        (RGB values have wildly different scales than spatial coordinates.)

    Returns
    -------
    cluster : pcl.PointCloud
        Registered pointcloud of the largest cluster found by dbscan.
    '''
    labels = dbscan_labels(pointcloud,
                           epsilon,
                           minpoints,
                           rgb_weight=rgb_weight).astype(np.int64)
    selection, selected_count = _get_top_labels(labels, min_return_fragment)

    # No clusters were found
    if selected_count < min_return_fragment * len(labels):
        return extract_mask(pointcloud, np.ones(len(pointcloud), dtype=bool))
    else:
        mask = [label in selection for label in labels]
        return extract_mask(pointcloud, mask)
Ejemplo n.º 2
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def get_largest_dbscan_clusters(pointcloud, min_return_fragment=0.7, epsilon=0.1, minpoints=250, rgb_weight=0):
    """
    Finds the largest clusters containing together at least min_return_fragment
    of the complete point cloud. In case less points belong to clusters, all
    clustered points are returned.

    Parameters
    ----------
    pointcloud : pcl.PointCloud
        Input pointcloud.
    min_return_fragment : float
        Minimum desired fragment of pointcloud to be returned
    epsilon : float
        Neighborhood radius for DBSCAN.
    minpoints : integer
        Minimum neighborhood density for DBSCAN.
    rgb_weight : float, optional
        If non-zero, cluster on color information as well as location;
        specifies the relative weight of the RGB components to spatial
        coordinates in distance computations.
        (RGB values have wildly different scales than spatial coordinates.)

    Returns
    -------
    cluster : pcl.PointCloud
        Registered pointcloud of the largest cluster found by dbscan.
    """
    labels = dbscan_labels(pointcloud, epsilon, minpoints, rgb_weight=rgb_weight).astype(np.int64)
    selection, selected_count = _get_top_labels(labels, min_return_fragment)

    # No clusters were found
    if selected_count < min_return_fragment * len(labels):
        return extract_mask(pointcloud, np.ones(len(pointcloud), dtype=bool))
    else:
        mask = [label in selection for label in labels]
        return extract_mask(pointcloud, mask)
Ejemplo n.º 3
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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
Ejemplo n.º 4
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def segment_dbscan(pointcloud, epsilon, minpoints, **kwargs):
    """Run the DBSCAN clustering+outlier detection algorithm on pointcloud.

    Parameters
    ----------
    pointcloud : pcl.PointCloud
        Input pointcloud.
    epsilon : float
        Neighborhood radius for DBSCAN.
    minpoints : integer
        Minimum neighborhood density for DBSCAN.
    **kwargs : keyword arguments, optional
        arguments passed to _dbscan_labels

    Returns
    -------
    clusters : iterable over registered PointCloud
    """
    labels = dbscan_labels(pointcloud, epsilon, minpoints, **kwargs)

    return (extract_mask(pointcloud, labels == label) for label in np.unique(labels[labels != -1]))
Ejemplo n.º 5
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def segment_dbscan(pointcloud, epsilon, minpoints, **kwargs):
    """Run the DBSCAN clustering+outlier detection algorithm on pointcloud.

    Parameters
    ----------
    pointcloud : pcl.PointCloud
        Input pointcloud.
    epsilon : float
        Neighborhood radius for DBSCAN.
    minpoints : integer
        Minimum neighborhood density for DBSCAN.
    **kwargs : keyword arguments, optional
        arguments passed to _dbscan_labels

    Returns
    -------
    clusters : iterable over registered PointCloud
    """
    labels = dbscan_labels(pointcloud, epsilon, minpoints, **kwargs)

    return (extract_mask(pointcloud, labels == label)
            for label in np.unique(labels[labels != -1]))
Ejemplo n.º 6
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#!/usr/bin/env python
"""Segment points by colour from a pointcloud file and saves all reddish points
target pointcloud file. Autodectects ply, pcd and las files.

Usage: redstickdetection.py  [-h] <infile> <outfile>
"""

from docopt import docopt
from patty.segmentation.segRedStick import get_red_mask
from patty.utils import extract_mask, load, save

if __name__ == '__main__':
    args = docopt(__doc__)

    pc = load(args['<infile>'])
    red_pc = extract_mask(pc, get_red_mask(pc))
    save(red_pc, args['<outfile>'])