# ------------------------------------------------------------------------
# Copyright (c) 2022 megvii-research. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
import os
import numpy as np
import copy
import motmetrics as mm
mm.lap.default_solver = 'lap'
import os
from typing import Dict
import numpy as np
import logging
def read_results(filename, data_type: str, is_gt=False, is_ignore=False):
if data_type in ('mot', 'lab'):
read_fun = read_mot_results
else:
raise ValueError('Unknown data type: {}'.format(data_type))
return read_fun(filename, is_gt, is_ignore)
# def read_mot_results(filename, is_gt, is_ignore):
# results_dict = dict()
# if os.path.isfile(filename):
# with open(filename, 'r') as f:
# for line in f.readlines():
# linelist = line.split(',')
# if len(linelist) < 7:
# continue
# fid = int(linelist[0])
# if fid < 1:
# continue
# results_dict.setdefault(fid, list())
# if is_gt:
# mark = int(float(linelist[6]))
# if mark == 0 :
# continue
# score = 1
# elif is_ignore:
# score = 1
# else:
# score = float(linelist[6])
# tlwh = tuple(map(float, linelist[2:6]))
# target_id = int(float(linelist[1]))
# results_dict[fid].append((tlwh, target_id, score))
# return results_dict
def read_mot_results(filename, is_gt, is_ignore):
valid_labels = {1}
ignore_labels = {0, 2, 7, 8, 12}
results_dict = dict()
if os.path.isfile(filename):
with open(filename, 'r') as f:
for line in f.readlines():
linelist = line.split(',')
if len(linelist) < 7:
continue
fid = int(linelist[0])
if fid < 1:
continue
results_dict.setdefault(fid, list())
if is_gt:
if 'MOT16-' in filename or 'MOT17-' in filename:
label = int(float(linelist[7]))
mark = int(float(linelist[6]))
if mark == 0 or label not in valid_labels:
continue
score = 1
elif is_ignore:
if 'MOT16-' in filename or 'MOT17-' in filename:
label = int(float(linelist[7]))
vis_ratio = float(linelist[8])
if label not in ignore_labels and vis_ratio >= 0:
continue
elif 'MOT15' in filename:
label = int(float(linelist[6]))
if label not in ignore_labels:
continue
else:
continue
score = 1
else:
score = float(linelist[6])
tlwh = tuple(map(float, linelist[2:6]))
target_id = int(linelist[1])
results_dict[fid].append((tlwh, target_id, score))
return results_dict
def unzip_objs(objs):
if len(objs) > 0:
tlwhs, ids, scores = zip(*objs)
else:
tlwhs, ids, scores = [], [], []
tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4)
return tlwhs, ids, scores
class Evaluator(object):
def __init__(self, data_root, seq_name, data_type='mot'):
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):
# results
trk_tlwhs = np.copy(trk_tlwhs)
trk_ids = np.copy(trk_ids)
# gts
gt_objs = self.gt_frame_dict.get(frame_id, [])
gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]
# ignore boxes
ignore_objs = self.gt_ignore_frame_dict.get(frame_id, [])
ignore_tlwhs = unzip_objs(ignore_objs)[0]
# remove ignored results
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]
# get distance matrix
iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5)
# acc
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 # only supported by https://github.com/longcw/py-motmetrics
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(self.gt_frame_dict.keys()) | 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.save()