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