nx.py 文件源码

def summarise(skelimage):
    ndim = skelimage.ndim
    g, counts, skelimage_labeled = skeleton_to_nx(skelimage)
    coords = np.nonzero(skelimage)
    ids = skelimage_labeled[coords]
    sorted_coords = np.transpose(coords)[np.argsort(ids)]
    tables = []
    for i, cc in enumerate(nx.connected_component_subgraphs(g)):
        stats = branch_statistics(cc)
        if stats.size == 0:
            continue
        coords0 = sorted_coords[stats[:, 0].astype(int) - 1]
        coords1 = sorted_coords[stats[:, 1].astype(int) - 1]
        distances = np.sqrt(np.sum((coords0 - coords1)**2, axis=1))
        skeleton_id = np.full(distances.shape, i, dtype=float)
        tables.append(np.column_stack((skeleton_id, stats,
                                       coords0, coords1, distances)))
    columns = (['skeleton-id', 'node-id-0', 'node-id-1', 'branch-distance',
                'branch-type'] +
               ['coord-0-%i' % i for i in range(ndim)] +
               ['coord-1-%i' % i for i in range(ndim)] +
               ['euclidean-distance'])
    column_types = [int, int, int, float, int] + 2*ndim*[int] + [float]
    arr = np.row_stack(tables).T
    data_dict = {col: dat.astype(dtype)
                 for col, dat, dtype in zip(columns, arr, column_types)}
    df = pd.DataFrame(data_dict)
    return df