defect_handling.py 文件源码

def modified_topology_features(ds, seg_id, use_2d_edges):
    modified_features = ds.topology_features(seg_id, use_2d_edges)
    if not ds.defect_slices:
        return modified_features

    skip_edges   = get_skip_edges(  ds, seg_id)
    skip_ranges  = get_skip_ranges( ds, seg_id)
    skip_starts  = get_skip_starts( ds, seg_id)
    delete_edge_ids = get_delete_edge_ids(ds, seg_id)

    # delete all features corresponding to delete - edges
    modified_features = np.delete(modified_features, delete_edge_ids, axis = 0)

    # get topo features for the new skip edges
    seg = ds.seg(seg_id)
    lower_slices  = np.unique(skip_starts)
    skip_edge_pairs_to_slice = {z : skip_edges[skip_starts == z] for z in lower_slices}
    skip_edge_indices_to_slice = {z : np.where(skip_starts == z)[0] for z in lower_slices}
    skip_edge_ranges_to_slice  = {z : skip_ranges[skip_starts == z] for z in lower_slices}

    n_feats = modified_features.shape[1]
    skip_topo_features = np.zeros( (skip_edges.shape[0], n_feats) )

    for z in lower_slices:
        _get_skip_topo_features_for_slices(
                z,
                seg,
                skip_edge_pairs_to_slice[z],
                skip_edge_ranges_to_slice[z],
                skip_edge_indices_to_slice[z],
                use_2d_edges,
                skip_topo_features)

    skip_topo_features[np.isinf(skip_topo_features)] = 0.
    skip_topo_features[np.isneginf(skip_topo_features)] = 0.
    skip_topo_features = np.nan_to_num(skip_topo_features)
    assert skip_topo_features.shape[1] == modified_features.shape[1]
    return np.concatenate([modified_features, skip_topo_features],axis = 0)


# the last argument is only for caching results with different features correctly