| import os
|
| import numpy as np
|
| import copy
|
| import motmetrics as mm
|
| mm.lap.default_solver = 'lap'
|
|
|
| from tracking_utils.io import read_results, unzip_objs
|
|
|
|
|
| class Evaluator(object):
|
|
|
| def __init__(self, data_root, seq_name, data_type):
|
| self.data_root = data_root
|
| self.seq_name = seq_name
|
| self.data_type = data_type
|
|
|
| self.load_annotations()
|
| self.reset_accumulator()
|
|
|
| def load_annotations(self):
|
| assert self.data_type == 'mot'
|
|
|
| gt_filename = os.path.join(self.data_root, self.seq_name, 'gt', 'gt.txt')
|
| self.gt_frame_dict = read_results(gt_filename, self.data_type, is_gt=True)
|
| self.gt_ignore_frame_dict = read_results(gt_filename, self.data_type, is_ignore=True)
|
|
|
| def reset_accumulator(self):
|
| self.acc = mm.MOTAccumulator(auto_id=True)
|
|
|
| def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):
|
|
|
| trk_tlwhs = np.copy(trk_tlwhs)
|
| trk_ids = np.copy(trk_ids)
|
|
|
|
|
| gt_objs = self.gt_frame_dict.get(frame_id, [])
|
| gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]
|
|
|
|
|
| ignore_objs = self.gt_ignore_frame_dict.get(frame_id, [])
|
| ignore_tlwhs = unzip_objs(ignore_objs)[0]
|
|
|
|
|
| keep = np.ones(len(trk_tlwhs), dtype=bool)
|
| iou_distance = mm.distances.iou_matrix(ignore_tlwhs, trk_tlwhs, max_iou=0.5)
|
| if len(iou_distance) > 0:
|
| match_is, match_js = mm.lap.linear_sum_assignment(iou_distance)
|
| match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js])
|
| match_ious = iou_distance[match_is, match_js]
|
|
|
| match_js = np.asarray(match_js, dtype=int)
|
| match_js = match_js[np.logical_not(np.isnan(match_ious))]
|
| keep[match_js] = False
|
| trk_tlwhs = trk_tlwhs[keep]
|
| trk_ids = trk_ids[keep]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5)
|
|
|
|
|
| self.acc.update(gt_ids, trk_ids, iou_distance)
|
|
|
| if rtn_events and iou_distance.size > 0 and hasattr(self.acc, 'last_mot_events'):
|
| events = self.acc.last_mot_events
|
| else:
|
| events = None
|
| return events
|
|
|
| def eval_file(self, filename):
|
| self.reset_accumulator()
|
|
|
| result_frame_dict = read_results(filename, self.data_type, is_gt=False)
|
|
|
| frames = sorted(list(set(result_frame_dict.keys())))
|
| for frame_id in frames:
|
| trk_objs = result_frame_dict.get(frame_id, [])
|
| trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2]
|
| self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False)
|
|
|
| return self.acc
|
|
|
| @staticmethod
|
| def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall')):
|
| names = copy.deepcopy(names)
|
| if metrics is None:
|
| metrics = mm.metrics.motchallenge_metrics
|
| metrics = copy.deepcopy(metrics)
|
|
|
| mh = mm.metrics.create()
|
| summary = mh.compute_many(
|
| accs,
|
| metrics=metrics,
|
| names=names,
|
| generate_overall=True
|
| )
|
|
|
| return summary
|
|
|
| @staticmethod
|
| def save_summary(summary, filename):
|
| import pandas as pd
|
| writer = pd.ExcelWriter(filename)
|
| summary.to_excel(writer)
|
| writer.close()
|
|
|
