def seqs_overall_metrics(summary_df, verbose=False):
    """
    Calculate overall metrics for multiple sequences
    """
    add_col = [
        'num_frames', 'num_matches', 'num_switches', 'num_transfer',
        'num_ascend', 'num_migrate', 'num_false_positives', 'num_misses',
        'num_detections', 'num_objects', 'num_predictions',
        'num_unique_objects', 'mostly_tracked', 'partially_tracked',
        'mostly_lost', 'num_fragmentations', 'idfp', 'idfn', 'idtp'
    ]
    calc_col = ['motp', 'mota', 'precision', 'recall', 'idp', 'idr', 'idf1']
    calc_df = summary_df.copy()

    overall_dic = {}
    for col in add_col:
        overall_dic[col] = calc_df[col].sum()

    for col in calc_col:
        overall_dic[col] = getattr(MCMOTMetricOverall, col + '_overall')(
            calc_df, overall_dic)

    overall_df = pd.DataFrame(overall_dic, index=['overall_calc'])
    calc_df = pd.concat([calc_df, overall_df])

    if verbose:
        mh = mm.metrics.create()
        str_calc_df = mm.io.render_summary(
            calc_df, formatters=mh.formatters, namemap=NAME_MAP)
        print(str_calc_df)

    return calc_df

    Calculate overall metrics for multiple sequences
    
ppdet/metrics/mcmot_metrics.py