NeighborPts = np.hstack((NeighborPts, Setdiff1d)) # 否则,说明为边界点,什么也不需要做 # NeighborPts = NeighborPts i += 1 # 将点p密度可达各点归入p所在簇 pre_target[NeighborPts] = pre_target[p] labels[NeighborPts] = -1 return labels, pre_target def DBSCAN(n, k ,dis, Eps, MinPts...
函数cluster_dbscan,eps定义到群集中邻居的距离,min_points定义形成群集所需的最小点数。该函数返回labels,其中labels=-1表示噪音。 pcd = o3d.io.read_point_cloud("../../TestData/fragment.ply") with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug) as cm: labels = np.array(pcd...
io.read_point_cloud("../models/fragment.ply") # DBSCAN two parameters: # - eps defines the distance to neighbors in a cluster # - and min_points defines the minimum number of points required to form a cluster. # The function returns labels, where the label -1 indicates noise. with o...