# python3 scripts/run_rob_mots.py --ROBMOTS_SPLIT train --TRACKERS_TO_EVAL STP --USE_PARALLEL True --NUM_PARALLEL_CORES 8 import sys import os import csv import numpy as np from multiprocessing import freeze_support sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import trackeval # noqa: E402 from trackeval import utils code_path = utils.get_code_path() if __name__ == '__main__': freeze_support() script_config = { 'ROBMOTS_SPLIT': 'train', # 'train', # valid: 'train', 'val', 'test', 'test_live', 'test_post', 'test_all' 'BENCHMARKS': None, # If None, use all for each split. 'GT_FOLDER': os.path.join(code_path, 'data/gt/rob_mots'), 'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/rob_mots'), } default_eval_config = trackeval.Evaluator.get_default_eval_config() default_eval_config['PRINT_ONLY_COMBINED'] = True default_eval_config['DISPLAY_LESS_PROGRESS'] = True default_dataset_config = trackeval.datasets.RobMOTS.get_default_dataset_config() config = {**default_eval_config, **default_dataset_config, **script_config} # Command line interface: config = utils.update_config(config) if not config['BENCHMARKS']: if config['ROBMOTS_SPLIT'] == 'val': config['BENCHMARKS'] = ['kitti_mots', 'bdd_mots', 'davis_unsupervised', 'youtube_vis', 'ovis', 'tao', 'mots_challenge', 'waymo'] config['SPLIT_TO_EVAL'] = 'val' elif config['ROBMOTS_SPLIT'] == 'test' or config['SPLIT_TO_EVAL'] == 'test_live': config['BENCHMARKS'] = ['kitti_mots', 'bdd_mots', 'davis_unsupervised', 'youtube_vis', 'tao'] config['SPLIT_TO_EVAL'] = 'test' elif config['ROBMOTS_SPLIT'] == 'test_post': config['BENCHMARKS'] = ['mots_challenge', 'waymo', 'ovis'] config['SPLIT_TO_EVAL'] = 'test' elif config['ROBMOTS_SPLIT'] == 'test_all': config['BENCHMARKS'] = ['kitti_mots', 'bdd_mots', 'davis_unsupervised', 'youtube_vis', 'ovis', 'tao', 'mots_challenge', 'waymo'] config['SPLIT_TO_EVAL'] = 'test' elif config['ROBMOTS_SPLIT'] == 'train': config['BENCHMARKS'] = ['kitti_mots', 'davis_unsupervised', 'youtube_vis', 'ovis', 'tao', 'bdd_mots'] config['SPLIT_TO_EVAL'] = 'train' else: config['SPLIT_TO_EVAL'] = config['ROBMOTS_SPLIT'] metrics_config = {'METRICS': ['HOTA']} eval_config = {k: v for k, v in config.items() if k in config.keys()} dataset_config = {k: v for k, v in config.items() if k in config.keys()} # Run code try: dataset_list = [] for bench in config['BENCHMARKS']: dataset_config['SUB_BENCHMARK'] = bench dataset_list.append(trackeval.datasets.RobMOTS(dataset_config)) evaluator = trackeval.Evaluator(eval_config) metrics_list = [] for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity, trackeval.metrics.VACE, trackeval.metrics.JAndF]: if metric.get_name() in metrics_config['METRICS']: metrics_list.append(metric()) if len(metrics_list) == 0: raise Exception('No metrics selected for evaluation') output_res, output_msg = evaluator.evaluate(dataset_list, metrics_list) output = list(list(output_msg.values())[0].values())[0] except Exception as err: if type(err) == trackeval.utils.TrackEvalException: output = str(err) else: output = 'Unknown error occurred.' success = output == 'Success' if not success: output = 'ERROR, evaluation failed. \n\nError message: ' + output print(output) if config['TRACKERS_TO_EVAL']: msg = "Thanks you for participating in the RobMOTS benchmark.\n\n" msg += "The status of your evaluation is: \n" + output + '\n\n' msg += "If your tracking results evaluated successfully on the evaluation server you can see your results here: \n" msg += "https://eval.vision.rwth-aachen.de/vision/" status_file = os.path.join(config['TRACKERS_FOLDER'], config['ROBMOTS_SPLIT'], config['TRACKERS_TO_EVAL'][0], 'status.txt') with open(status_file, 'w', newline='') as f: f.write(msg) if success: # For each benchmark, combine the 'all' score with the 'cls_averaged' using geometric mean. metrics_to_calc = ['HOTA', 'DetA', 'AssA', 'DetRe', 'DetPr', 'AssRe', 'AssPr', 'LocA'] trackers = list(output_res['RobMOTS.' + config['BENCHMARKS'][0]].keys()) for tracker in trackers: # final_results[benchmark][result_type][metric] final_results = {} res = {bench: output_res['RobMOTS.' + bench][tracker]['COMBINED_SEQ'] for bench in config['BENCHMARKS']} for bench in config['BENCHMARKS']: final_results[bench] = {'cls_av': {}, 'det_av': {}, 'final': {}} for metric in metrics_to_calc: final_results[bench]['cls_av'][metric] = np.mean(res[bench]['cls_comb_cls_av']['HOTA'][metric]) final_results[bench]['det_av'][metric] = np.mean(res[bench]['all']['HOTA'][metric]) final_results[bench]['final'][metric] = \ np.sqrt(final_results[bench]['cls_av'][metric] * final_results[bench]['det_av'][metric]) # Take the arithmetic mean over all the benchmarks final_results['overall'] = {'cls_av': {}, 'det_av': {}, 'final': {}} for metric in metrics_to_calc: final_results['overall']['cls_av'][metric] = \ np.mean([final_results[bench]['cls_av'][metric] for bench in config['BENCHMARKS']]) final_results['overall']['det_av'][metric] = \ np.mean([final_results[bench]['det_av'][metric] for bench in config['BENCHMARKS']]) final_results['overall']['final'][metric] = \ np.mean([final_results[bench]['final'][metric] for bench in config['BENCHMARKS']]) # Save out result headers = [config['SPLIT_TO_EVAL']] + [x + '___' + metric for x in ['f', 'c', 'd'] for metric in metrics_to_calc] def rowify(d): return [d[x][metric] for x in ['final', 'cls_av', 'det_av'] for metric in metrics_to_calc] out_file = os.path.join(config['TRACKERS_FOLDER'], config['ROBMOTS_SPLIT'], tracker, 'final_results.csv') with open(out_file, 'w', newline='') as f: writer = csv.writer(f, delimiter=',') writer.writerow(headers) writer.writerow(['overall'] + rowify(final_results['overall'])) for bench in config['BENCHMARKS']: if bench == 'overall': continue writer.writerow([bench] + rowify(final_results[bench]))