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""" |
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Point cloud segmentation using the DBSCAN clustering algorithm. |
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DBSCAN - Density-Based Spatial Clustering of Applications with Noise. |
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Finds core samples of high density and expands clusters from them. |
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Good for data which contains clusters of similar density. |
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See the scikit-learn documentation for reference: |
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http://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html. |
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""" |
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import numpy as np |
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from sklearn.cluster import dbscan |
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from patty.utils import extract_mask |
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def dbscan_labels(pointcloud, epsilon, minpoints, rgb_weight=0, |
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algorithm='ball_tree'): |
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''' |
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Find an array of point-labels of clusters found by the DBSCAN algorithm. |
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Parameters |
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---------- |
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pointcloud : pcl.PointCloud |
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Input pointcloud. |
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epsilon : float |
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Neighborhood radius for DBSCAN. |
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minpoints : integer |
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Minimum neighborhood density for DBSCAN. |
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rgb_weight : float, optional |
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If non-zero, cluster on color information as well as location; |
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specifies the relative weight of the RGB components to spatial |
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coordinates in distance computations. |
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(RGB values have wildly different scales than spatial coordinates.) |
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Returns |
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------- |
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labels : Sequence |
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A sequence of labels per point. Label -1 indicates a point does not |
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belong to any cluster, other labels indicate the cluster number a |
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point belongs to. |
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''' |
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if rgb_weight > 0: |
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X = pointcloud.to_array() |
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X[:, 3:] *= rgb_weight |
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else: |
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X = pointcloud |
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_, labels = dbscan(X, eps=epsilon, min_samples=minpoints, |
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algorithm=algorithm) |
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return np.asarray(labels) |
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def segment_dbscan(pointcloud, epsilon, minpoints, **kwargs): |
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"""Run the DBSCAN clustering+outlier detection algorithm on pointcloud. |
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Parameters |
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---------- |
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pointcloud : pcl.PointCloud |
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Input pointcloud. |
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epsilon : float |
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Neighborhood radius for DBSCAN. |
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minpoints : integer |
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Minimum neighborhood density for DBSCAN. |
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**kwargs : keyword arguments, optional |
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arguments passed to _dbscan_labels |
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Returns |
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------- |
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clusters : iterable over registered PointCloud |
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""" |
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labels = dbscan_labels(pointcloud, epsilon, minpoints, **kwargs) |
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return (extract_mask(pointcloud, labels == label) |
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for label in np.unique(labels[labels != -1])) |
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def get_largest_dbscan_clusters(pointcloud, min_return_fragment=0.7, |
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epsilon=0.1, minpoints=250, rgb_weight=0): |
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''' |
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Finds the largest clusters containing together at least min_return_fragment |
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of the complete point cloud. In case less points belong to clusters, all |
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clustered points are returned. |
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Parameters |
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---------- |
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pointcloud : pcl.PointCloud |
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Input pointcloud. |
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min_return_fragment : float |
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Minimum desired fragment of pointcloud to be returned |
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epsilon : float |
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Neighborhood radius for DBSCAN. |
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minpoints : integer |
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Minimum neighborhood density for DBSCAN. |
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rgb_weight : float, optional |
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If non-zero, cluster on color information as well as location; |
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specifies the relative weight of the RGB components to spatial |
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coordinates in distance computations. |
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(RGB values have wildly different scales than spatial coordinates.) |
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Returns |
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------- |
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cluster : pcl.PointCloud |
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Registered pointcloud of the largest cluster found by dbscan. |
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''' |
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labels = dbscan_labels(pointcloud, epsilon, minpoints, |
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rgb_weight=rgb_weight).astype(np.int64) |
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selection, selected_count = _get_top_labels(labels, min_return_fragment) |
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110
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# No clusters were found |
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if selected_count < min_return_fragment * len(labels): |
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return extract_mask(pointcloud, np.ones(len(pointcloud), dtype=bool)) |
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else: |
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mask = [label in selection for label in labels] |
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return extract_mask(pointcloud, mask) |
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118
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1 |
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def _get_top_labels(labels, min_return_fragment): |
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"""Return labels of the smallest set of clusters that contain at least |
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min_return_fragment of the points (or everything).""" |
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121
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122
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# +1 to make bincount happy, [1:] to get rid of outliers. |
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bins = np.bincount(labels + 1)[1:] |
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labelbinpairs = sorted(enumerate(bins), key=lambda x: x[1]) |
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total = len(labels) |
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minimum = min_return_fragment * total |
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selected = [] |
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selected_count = 0 |
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while selected_count < minimum and len(labelbinpairs) > 0: |
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label, count = labelbinpairs.pop() |
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selected.append(label) |
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selected_count += count |
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return selected, selected_count |
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NLeSC /
PattyAnalytics
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