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PaddleRS/paddlers/models/ppdet/metrics/mcmot_metrics.py

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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import copy
import sys
import math
from collections import defaultdict
import numpy as np
import pandas as pd
from .metrics import Metric
try:
import motmetrics as mm
from motmetrics.math_util import quiet_divide
metrics = mm.metrics.motchallenge_metrics
mh = mm.metrics.create()
except:
print(
'Warning: Unable to use MCMOT metric, please install motmetrics, for example: `pip install motmetrics`, see https://github.com/longcw/py-motmetrics'
)
pass
from paddlers.models.ppdet.utils.logger import setup_logger
logger = setup_logger(__name__)
__all__ = ['MCMOTEvaluator', 'MCMOTMetric']
METRICS_LIST = [
'num_frames', 'num_matches', 'num_switches', 'num_transfer', 'num_ascend',
'num_migrate', 'num_false_positives', 'num_misses', 'num_detections',
'num_objects', 'num_predictions', 'num_unique_objects', 'mostly_tracked',
'partially_tracked', 'mostly_lost', 'num_fragmentations', 'motp', 'mota',
'precision', 'recall', 'idfp', 'idfn', 'idtp', 'idp', 'idr', 'idf1'
]
NAME_MAP = {
'num_frames': 'num_frames',
'num_matches': 'num_matches',
'num_switches': 'IDs',
'num_transfer': 'IDt',
'num_ascend': 'IDa',
'num_migrate': 'IDm',
'num_false_positives': 'FP',
'num_misses': 'FN',
'num_detections': 'num_detections',
'num_objects': 'num_objects',
'num_predictions': 'num_predictions',
'num_unique_objects': 'GT',
'mostly_tracked': 'MT',
'partially_tracked': 'partially_tracked',
'mostly_lost': 'ML',
'num_fragmentations': 'FM',
'motp': 'MOTP',
'mota': 'MOTA',
'precision': 'Prcn',
'recall': 'Rcll',
'idfp': 'idfp',
'idfn': 'idfn',
'idtp': 'idtp',
'idp': 'IDP',
'idr': 'IDR',
'idf1': 'IDF1'
}
def parse_accs_metrics(seq_acc, index_name, verbose=False):
"""
Parse the evaluation indicators of multiple MOTAccumulator
"""
mh = mm.metrics.create()
summary = MCMOTEvaluator.get_summary(seq_acc, index_name, METRICS_LIST)
summary.loc['OVERALL', 'motp'] = (summary['motp'] * summary['num_detections']).sum() / \
summary.loc['OVERALL', 'num_detections']
if verbose:
strsummary = mm.io.render_summary(
summary, formatters=mh.formatters, namemap=NAME_MAP)
print(strsummary)
return summary
def seqs_overall_metrics(summary_df, verbose=False):
"""
Calculate overall metrics for multiple sequences
"""
add_col = [
'num_frames', 'num_matches', 'num_switches', 'num_transfer',
'num_ascend', 'num_migrate', 'num_false_positives', 'num_misses',
'num_detections', 'num_objects', 'num_predictions',
'num_unique_objects', 'mostly_tracked', 'partially_tracked',
'mostly_lost', 'num_fragmentations', 'idfp', 'idfn', 'idtp'
]
calc_col = ['motp', 'mota', 'precision', 'recall', 'idp', 'idr', 'idf1']
calc_df = summary_df.copy()
overall_dic = {}
for col in add_col:
overall_dic[col] = calc_df[col].sum()
for col in calc_col:
overall_dic[col] = getattr(MCMOTMetricOverall, col + '_overall')(
calc_df, overall_dic)
overall_df = pd.DataFrame(overall_dic, index=['overall_calc'])
calc_df = pd.concat([calc_df, overall_df])
if verbose:
mh = mm.metrics.create()
str_calc_df = mm.io.render_summary(
calc_df, formatters=mh.formatters, namemap=NAME_MAP)
print(str_calc_df)
return calc_df
class MCMOTMetricOverall(object):
def motp_overall(summary_df, overall_dic):
motp = quiet_divide((summary_df['motp'] *
summary_df['num_detections']).sum(),
overall_dic['num_detections'])
return motp
def mota_overall(summary_df, overall_dic):
del summary_df
mota = 1. - quiet_divide(
(overall_dic['num_misses'] + overall_dic['num_switches'] +
overall_dic['num_false_positives']), overall_dic['num_objects'])
return mota
def precision_overall(summary_df, overall_dic):
del summary_df
precision = quiet_divide(overall_dic['num_detections'], (
overall_dic['num_false_positives'] + overall_dic['num_detections']))
return precision
def recall_overall(summary_df, overall_dic):
del summary_df
recall = quiet_divide(overall_dic['num_detections'],
overall_dic['num_objects'])
return recall
def idp_overall(summary_df, overall_dic):
del summary_df
idp = quiet_divide(overall_dic['idtp'],
(overall_dic['idtp'] + overall_dic['idfp']))
return idp
def idr_overall(summary_df, overall_dic):
del summary_df
idr = quiet_divide(overall_dic['idtp'],
(overall_dic['idtp'] + overall_dic['idfn']))
return idr
def idf1_overall(summary_df, overall_dic):
del summary_df
idf1 = quiet_divide(2. * overall_dic['idtp'], (
overall_dic['num_objects'] + overall_dic['num_predictions']))
return idf1
def read_mcmot_results_union(filename, is_gt, is_ignore):
results_dict = dict()
if os.path.isfile(filename):
all_result = np.loadtxt(filename, delimiter=',')
if all_result.shape[0] == 0 or all_result.shape[1] < 7:
return results_dict
if is_ignore:
return results_dict
if is_gt:
# only for test use
all_result = all_result[all_result[:, 7] != 0]
all_result[:, 7] = all_result[:, 7] - 1
if all_result.shape[0] == 0:
return results_dict
class_unique = np.unique(all_result[:, 7])
last_max_id = 0
result_cls_list = []
for cls in class_unique:
result_cls_split = all_result[all_result[:, 7] == cls]
result_cls_split[:, 1] = result_cls_split[:, 1] + last_max_id
# make sure track id different between every category
last_max_id = max(np.unique(result_cls_split[:, 1])) + 1
result_cls_list.append(result_cls_split)
results_con = np.concatenate(result_cls_list)
for line in range(len(results_con)):
linelist = results_con[line]
fid = int(linelist[0])
if fid < 1:
continue
results_dict.setdefault(fid, list())
if is_gt:
score = 1
else:
score = float(linelist[6])
tlwh = tuple(map(float, linelist[2:6]))
target_id = int(linelist[1])
cls = int(linelist[7])
results_dict[fid].append((tlwh, target_id, cls, score))
return results_dict
def read_mcmot_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.strip().split(',')
if len(linelist) < 7:
continue
fid = int(linelist[0])
if fid < 1:
continue
cid = int(linelist[7])
if is_gt:
score = 1
# only for test use
cid -= 1
else:
score = float(linelist[6])
cls_result_dict = results_dict.setdefault(cid, dict())
cls_result_dict.setdefault(fid, list())
tlwh = tuple(map(float, linelist[2:6]))
target_id = int(linelist[1])
cls_result_dict[fid].append((tlwh, target_id, score))
return results_dict
def read_results(filename,
data_type,
is_gt=False,
is_ignore=False,
multi_class=False,
union=False):
if data_type in ['mcmot', 'lab']:
if multi_class:
if union:
# The results are evaluated by union all the categories.
# Track IDs between different categories cannot be duplicate.
read_fun = read_mcmot_results_union
else:
# The results are evaluated separately by category.
read_fun = read_mcmot_results
else:
raise ValueError('multi_class: {}, MCMOT should have cls_id.'.
format(multi_class))
else:
raise ValueError('Unknown data type: {}'.format(data_type))
return read_fun(filename, is_gt, is_ignore)
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
def unzip_objs_cls(objs):
if len(objs) > 0:
tlwhs, ids, cls, scores = zip(*objs)
else:
tlwhs, ids, cls, scores = [], [], [], []
tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4)
ids = np.array(ids)
cls = np.array(cls)
scores = np.array(scores)
return tlwhs, ids, cls, scores
class MCMOTEvaluator(object):
def __init__(self, data_root, seq_name, data_type, num_classes):
self.data_root = data_root
self.seq_name = seq_name
self.data_type = data_type
self.num_classes = num_classes
self.load_annotations()
try:
import motmetrics as mm
mm.lap.default_solver = 'lap'
except Exception as e:
raise RuntimeError(
'Unable to use MCMOT metric, please install motmetrics, for example: `pip install motmetrics`, see https://github.com/longcw/py-motmetrics'
)
self.reset_accumulator()
self.class_accs = []
def load_annotations(self):
assert self.data_type == 'mcmot'
self.gt_filename = os.path.join(self.data_root, '../', 'sequences',
'{}.txt'.format(self.seq_name))
if not os.path.exists(self.gt_filename):
logger.warning(
"gt_filename '{}' of MCMOTEvaluator is not exist, so the MOTA will be -INF."
)
def reset_accumulator(self):
self.acc = mm.MOTAccumulator(auto_id=True)
def eval_frame_dict(self, trk_objs, gt_objs, rtn_events=False, union=False):
if union:
trk_tlwhs, trk_ids, trk_cls = unzip_objs_cls(trk_objs)[:3]
gt_tlwhs, gt_ids, gt_cls = unzip_objs_cls(gt_objs)[:3]
# get distance matrix
iou_distance = mm.distances.iou_matrix(
gt_tlwhs, trk_tlwhs, max_iou=0.5)
# Set the distance between objects of different categories to nan
gt_cls_len = len(gt_cls)
trk_cls_len = len(trk_cls)
# When the number of GT or Trk is 0, iou_distance dimension is (0,0)
if gt_cls_len != 0 and trk_cls_len != 0:
gt_cls = gt_cls.reshape(gt_cls_len, 1)
gt_cls = np.repeat(gt_cls, trk_cls_len, axis=1)
trk_cls = trk_cls.reshape(1, trk_cls_len)
trk_cls = np.repeat(trk_cls, gt_cls_len, axis=0)
iou_distance = np.where(gt_cls == trk_cls, iou_distance, np.nan)
else:
trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2]
gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]
# get distance matrix
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,
'mot_events'):
events = self.acc.mot_events # only supported by https://github.com/longcw/py-motmetrics
else:
events = None
return events
def eval_file(self, result_filename):
# evaluation of each category
gt_frame_dict = read_results(
self.gt_filename,
self.data_type,
is_gt=True,
multi_class=True,
union=False)
result_frame_dict = read_results(
result_filename,
self.data_type,
is_gt=False,
multi_class=True,
union=False)
for cid in range(self.num_classes):
self.reset_accumulator()
cls_result_frame_dict = result_frame_dict.setdefault(cid, dict())
cls_gt_frame_dict = gt_frame_dict.setdefault(cid, dict())
# only labeled frames will be evaluated
frames = sorted(list(set(cls_gt_frame_dict.keys())))
for frame_id in frames:
trk_objs = cls_result_frame_dict.get(frame_id, [])
gt_objs = cls_gt_frame_dict.get(frame_id, [])
self.eval_frame_dict(trk_objs, gt_objs, rtn_events=False)
self.class_accs.append(self.acc)
return self.class_accs
@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()
class MCMOTMetric(Metric):
def __init__(self, num_classes, save_summary=False):
self.num_classes = num_classes
self.save_summary = save_summary
self.MCMOTEvaluator = MCMOTEvaluator
self.result_root = None
self.reset()
self.seqs_overall = defaultdict(list)
def reset(self):
self.accs = []
self.seqs = []
def update(self, data_root, seq, data_type, result_root, result_filename):
evaluator = self.MCMOTEvaluator(data_root, seq, data_type,
self.num_classes)
seq_acc = evaluator.eval_file(result_filename)
self.accs.append(seq_acc)
self.seqs.append(seq)
self.result_root = result_root
cls_index_name = [
'{}_{}'.format(seq, i) for i in range(self.num_classes)
]
summary = parse_accs_metrics(seq_acc, cls_index_name)
summary.rename(
index={'OVERALL': '{}_OVERALL'.format(seq)}, inplace=True)
for row in range(len(summary)):
self.seqs_overall[row].append(summary.iloc[row:row + 1])
def accumulate(self):
self.cls_summary_list = []
for row in range(self.num_classes):
seqs_cls_df = pd.concat(self.seqs_overall[row])
seqs_cls_summary = seqs_overall_metrics(seqs_cls_df)
cls_summary_overall = seqs_cls_summary.iloc[-1:].copy()
cls_summary_overall.rename(
index={'overall_calc': 'overall_calc_{}'.format(row)},
inplace=True)
self.cls_summary_list.append(cls_summary_overall)
def log(self):
seqs_summary = seqs_overall_metrics(
pd.concat(self.seqs_overall[self.num_classes]), verbose=True)
class_summary = seqs_overall_metrics(
pd.concat(self.cls_summary_list), verbose=True)
def get_results(self):
return 1