malib.py 文件源码

def get_edges(a, convex=False):
    a = checkma(a)
    #Need to deal with RGB images here
    #Need to be careful, probably want to take minimum value from masks
    if a.ndim == 3:
        #Assume that the same mask applies to each band
        #Only create edges for one band
        b = a[:,:,0]
        #Could convert to HSL and do edges for L channel
        #b = a[:,:,0].mask + a[:,:,1].mask + a[:,:,2].mask
    else:
        b = a

    #Compute edges along both axes, need both to handle undercuts
    #These are inclusive, indices indicate position of unmasked data
    edges0 = np.ma.notmasked_edges(b, axis=0)
    edges1 = np.ma.notmasked_edges(b, axis=1)
    edges = np.array([np.concatenate([edges0[0][0], edges0[1][0], edges1[1][0], edges1[0][0]]), np.concatenate([edges0[0][1], edges0[1][1], edges1[1][1], edges1[0][1]])])

    #This is a rough outline - needs testing
    if convex:
        from scipy.spatial import ConvexHull
        #hull = ConvexHull(edges.T)
        #edges = edges.T[hull.simplices]
        #This is in scipy v0.14
        #edges0 = edges1 = hull.vertices

    return edges0, edges1, edges