That’s why I keep using “ “ around “concave hull”. One possibility is to use skimage.morphology.convex_hull_image(), but this only supports 2D images, so then i have to call this function slice by slice (in the z-axis), which is slow. neighbors vertex_neighbor_vertices: Neighboring vertices of vertices. Given a set of points that define a shape, how do we find its convex hull? Here’s what the concave hull looks like when applied to the same set of points as in the previous image: Concave Hull. This implementation is called the concave hull. (ndarray of ints, shape (nvertices,)) Indices of points forming the vertices of the convex hull. simplices In python import the module and use the compute method to compute a concave hull. To run it, you first need to transform your cloud of 3D points into a volumetric dataset. I'm not actually sure if a concave hull algorithm could be parallelized, but it feels like it could be split into quadrants to allow multiple cores to tackle different sections at once. To find a "concave hull" around a set of 3D points, I found that using the marching cube algorithm for volumetric data works best. A shape that is not convex is called Non-Convex or Concave. Here is an example using Python. A Convex object is one with no interior angles greater than 180 degrees. Finding Contours using Convex Hull. Whereas the convex hull is a well defined concept, concave hulls are less so, verging on the subjective. Moreover, it contains KDTree implementations for nearest-neighbor point queries, and utilities for distance computations in various metrics. Numpy itself has parallel programming capabilities that you might be able to take advantage of. convex_hull: Vertices of facets forming the convex hull of the point set. Interactive PyQt app: generate alpha shapes (concave hulls) using Matplotlib, shapely, and arcpy inside ArcGIS Pro - concave_hulls_pyqt.py Usage. The concave hull is non-unique, and you decide how much ‘wrap’ you may want your concave hull to have around points by choosing the number of nearest neighbours. points (ndarray of double, shape (npoints, ndim)) Coordinates of input points. simplices (ndarray of ints, shape (nfacet, ndim)) Indices of points forming the simplical facets of the convex hull. Fortunately, there are alternatives to this state of affairs: we can calculate a concave hull. ... As you can see, the list of points is implemented as a NumPy array for performance reasons. The concave hull creation is a separate algorithm, that can be implemented in different ways. For other dimensions, they are in input order. In our case, it was implemented using the graphs. Following the calculation of a convex hull as described a few weeks ago, I’ve worked up a way to approximate a “concave” hull. With the FLANN_DIR variable set run the setup.py file using pip, by running the following command in the concave hull directory: pip install . Here is an example using Python. import numpy as np from ConcaveHull import ConcaveHull ch = ConcaveHull() pts = np.random.uniform(size=(100, 2)) ch.loadpoints(pts) ch.calculatehull() boundary_points = np.vstack(ch.boundary.exterior.coords.xy).T # boundary_points is a subset of pts corresponding to the concave hull ... import numpy as np # Read Input image img = cv2.imread("Hammer.png", 1) This can be useful for point clouds of complicated geometries. Concave Hull. Spatial data structures and algorithms (scipy.spatial)¶scipy.spatial can compute triangulations, Voronoi diagrams, and convex hulls of a set of points, by leveraging the Qhull library.. 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