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# python3 scripts\run_rob_mots.py --ROBMOTS_SPLIT val --TRACKERS_TO_EVAL tracker_name (e.g. STP) --USE_PARALLEL True --NUM_PARALLEL_CORES 4
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': ['kitti_mots', 'davis_unsupervised', 'youtube_vis', 'ovis', 'tao'], # 'bdd_mots' coming soon
'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 config['ROBMOTS_SPLIT'] == 'val':
config['BENCHMARKS'] = ['kitti_mots', 'bdd_mots', 'davis_unsupervised', 'youtube_vis', 'ovis',
'tao', 'mots_challenge']
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', 'ovis', 'tao']
config['SPLIT_TO_EVAL'] = 'test'
elif config['ROBMOTS_SPLIT'] == 'test_post':
config['BENCHMARKS'] = ['mots_challenge', 'waymo']
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' coming soon
config['SPLIT_TO_EVAL'] = 'train'
metrics_config = {'METRICS': ['HOTA']}
# metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity']}
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
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]:
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)
# 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(script_config['TRACKERS_FOLDER'], script_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]))
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