multiple object tracking challenge data file and evaluation

mot.py

import pandas as pd
import errno
import numpy as np
import sys
import warnings


def save_mot(filename, df):
    df.to_csv(filename, header=False, index=False)


def load_mot(filename):
    """
    Load Multiple Object Tacking Challenge trajectories file.

    :param filename: mot filename
    :return: DataFrame, columns frame and id start with 1 (MATLAB indexing)
    """
    return pd.read_csv(filename, names=[u'frame', u'id', u'x', u'y', u'width', u'height', u'confidence'])


def eval_mot(df_gt, df_results, sqdistth=10000):
    """
    Evaluate trajectories by comparing them to a ground truth.

    :param df_gt: ground truth DataFrame, columns <frame>, <id>, <x>, <y>; <frame> and <id> are 1-based; see load_mot
    :param df_results: result trajectories DataFrame, format same as df_gt
    :param sqdistth: square of the distance threshold, only detections and ground truth objects closer than
                     the threshold can be matched
    :return: (summary DataFrame, MOTAccumulator)
    """    
    nan_mask = (df_results.x == -1) | (df_results.x == -1) | df_results.x.isna() | df_results.y.isna()
    if len(df_results[nan_mask]) > 0:
        warnings.warn('stripping nans from the evaluated trajectories')
        df_results = df_results[~nan_mask]
    from motmetrics.utils import compare_to_groundtruth
    import motmetrics as mm
    columns_mapper = {'frame': 'FrameId', 'id': 'Id'}
    acc = compare_to_groundtruth(df_gt.set_index(['frame', 'id']).rename(columns=columns_mapper),
                                 df_results.set_index(['frame', 'id']).rename(columns=columns_mapper),
                                 dist='euc', distfields=['x', 'y'], distth=sqdistth)
    mh = mm.metrics.create()
    return mh.compute(acc), acc

def eval_and_save(gt_file, mot_results_file, out_csv=None):
    """
    Evaluate results and save metrics.
    
    :param gt_file: ground truth filename (MOT format)
    :param mot_results_file: results filename (MOT format)
    :param out_csv: output file with a summary
    """    
    df_gt = load_mot(gt_file)
    df_results = load_mot(mot_results_file)
    print('Evaluating...')
    assert sys.version_info >= (3, 5), 'motmetrics requires Python 3.5'
    summary, acc = eval_mot(df_gt, df_results)
    summary['motp_px'] = np.sqrt(summary['motp'])  # convert from square pixels to pixels
    import motmetrics as mm
    print(mm.io.render_summary(summary))
    if out_csv is not None:
        summary.to_csv(out_csv, index=False)