def compute_gt_annotations(
    anchors,
    annotations,
    negative_overlap=0.4,
    positive_overlap=0.5
):
    """ Obtain indices of gt annotations with the greatest overlap.

    Args
        anchors: np.array of annotations of shape (N, 4) for (x1, y1, x2, y2).
        annotations: np.array of shape (N, 5) for (x1, y1, x2, y2, label).
        negative_overlap: IoU overlap for negative anchors (all anchors with overlap < negative_overlap are negative).
        positive_overlap: IoU overlap or positive anchors (all anchors with overlap > positive_overlap are positive).

    Returns
        positive_indices: indices of positive anchors
        ignore_indices: indices of ignored anchors
        argmax_overlaps_inds: ordered overlaps indices
    """

    overlaps = compute_overlap(anchors.astype(np.float64), annotations.astype(np.float64))
    argmax_overlaps_inds = np.argmax(overlaps, axis=1)
    max_overlaps = overlaps[np.arange(overlaps.shape[0]), argmax_overlaps_inds]

    # assign "dont care" labels
    positive_indices = max_overlaps >= positive_overlap
    ignore_indices = (max_overlaps > negative_overlap) & ~positive_indices

    return positive_indices, ignore_indices, argmax_overlaps_inds
 Obtain indices of gt annotations with the greatest overlap.

    Args
        anchors: np.array of annotations of shape (N, 4) for (x1, y1, x2, y2).
        annotations: np.array of shape (N, 5) for (x1, y1, x2, y2, label).
        negative_overlap: IoU overlap for negative anchors (all anchors with overlap < negative_overlap are negative).
        positive_overlap: IoU overlap or positive anchors (all anchors with overlap > positive_overlap are positive).

    Returns
        positive_indices: indices of positive anchors
        ignore_indices: indices of ignored anchors
        argmax_overlaps_inds: ordered overlaps indices
    
keras_retinanet/utils/anchors.py