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PaddleRS/paddlers/models/ppdet/metrics/mot_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 paddle
import paddle.nn.functional as F
from paddlers.models.ppdet.modeling.bbox_utils import bbox_iou_np_expand
from .map_utils import ap_per_class
from .metrics import Metric
from .munkres import Munkres
from paddlers.models.ppdet.utils.logger import setup_logger
logger = setup_logger(__name__)
__all__ = ['MOTEvaluator', 'MOTMetric', 'JDEDetMetric', 'KITTIMOTMetric']
def read_mot_results(filename, is_gt=False, is_ignore=False):
valid_labels = {1}
ignore_labels = {2, 7, 8, 12} # only in motchallenge datasets like 'MOT16'
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())
box_size = float(linelist[4]) * float(linelist[5])
if is_gt:
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 or 'MOT15-' in filename or 'MOT20-' in filename:
label = int(float(linelist[7]))
vis_ratio = float(linelist[8])
if label not in ignore_labels and vis_ratio >= 0:
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
"""
MOT dataset label list, see in https://motchallenge.net
labels={'ped', ... % 1
'person_on_vhcl', ... % 2
'car', ... % 3
'bicycle', ... % 4
'mbike', ... % 5
'non_mot_vhcl', ... % 6
'static_person', ... % 7
'distractor', ... % 8
'occluder', ... % 9
'occluder_on_grnd', ... % 10
'occluder_full', ... % 11
'reflection', ... % 12
'crowd' ... % 13
};
"""
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 MOTEvaluator(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_mot_results(gt_filename, is_gt=True)
self.gt_ignore_frame_dict = read_mot_results(
gt_filename, is_ignore=True)
def reset_accumulator(self):
import motmetrics as mm
mm.lap.default_solver = 'lap'
self.acc = mm.MOTAccumulator(auto_id=True)
def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):
import motmetrics as mm
mm.lap.default_solver = 'lap'
# 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_mot_results(filename, 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')):
import motmetrics as mm
mm.lap.default_solver = 'lap'
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 MOTMetric(Metric):
def __init__(self, save_summary=False):
self.save_summary = save_summary
self.MOTEvaluator = MOTEvaluator
self.result_root = None
self.reset()
def reset(self):
self.accs = []
self.seqs = []
def update(self, data_root, seq, data_type, result_root, result_filename):
evaluator = self.MOTEvaluator(data_root, seq, data_type)
self.accs.append(evaluator.eval_file(result_filename))
self.seqs.append(seq)
self.result_root = result_root
def accumulate(self):
import motmetrics as mm
import openpyxl
metrics = mm.metrics.motchallenge_metrics
mh = mm.metrics.create()
summary = self.MOTEvaluator.get_summary(self.accs, self.seqs, metrics)
self.strsummary = mm.io.render_summary(
summary,
formatters=mh.formatters,
namemap=mm.io.motchallenge_metric_names)
if self.save_summary:
self.MOTEvaluator.save_summary(
summary, os.path.join(self.result_root, 'summary.xlsx'))
def log(self):
print(self.strsummary)
def get_results(self):
return self.strsummary
class JDEDetMetric(Metric):
# Note this detection AP metric is different from COCOMetric or VOCMetric,
# and the bboxes coordinates are not scaled to the original image
def __init__(self, overlap_thresh=0.5):
self.overlap_thresh = overlap_thresh
self.reset()
def reset(self):
self.AP_accum = np.zeros(1)
self.AP_accum_count = np.zeros(1)
def update(self, inputs, outputs):
bboxes = outputs['bbox'][:, 2:].numpy()
scores = outputs['bbox'][:, 1].numpy()
labels = outputs['bbox'][:, 0].numpy()
bbox_lengths = outputs['bbox_num'].numpy()
if bboxes.shape[0] == 1 and bboxes.sum() == 0.0:
return
gt_boxes = inputs['gt_bbox'].numpy()[0]
gt_labels = inputs['gt_class'].numpy()[0]
if gt_labels.shape[0] == 0:
return
correct = []
detected = []
for i in range(bboxes.shape[0]):
obj_pred = 0
pred_bbox = bboxes[i].reshape(1, 4)
# Compute iou with target boxes
iou = bbox_iou_np_expand(pred_bbox, gt_boxes, x1y1x2y2=True)[0]
# Extract index of largest overlap
best_i = np.argmax(iou)
# If overlap exceeds threshold and classification is correct mark as correct
if iou[best_i] > self.overlap_thresh and obj_pred == gt_labels[
best_i] and best_i not in detected:
correct.append(1)
detected.append(best_i)
else:
correct.append(0)
# Compute Average Precision (AP) per class
target_cls = list(gt_labels.T[0])
AP, AP_class, R, P = ap_per_class(
tp=correct,
conf=scores,
pred_cls=np.zeros_like(scores),
target_cls=target_cls)
self.AP_accum_count += np.bincount(AP_class, minlength=1)
self.AP_accum += np.bincount(AP_class, minlength=1, weights=AP)
def accumulate(self):
logger.info("Accumulating evaluatation results...")
self.map_stat = self.AP_accum[0] / (self.AP_accum_count[0] + 1E-16)
def log(self):
map_stat = 100. * self.map_stat
logger.info("mAP({:.2f}) = {:.2f}%".format(self.overlap_thresh,
map_stat))
def get_results(self):
return self.map_stat
"""
Following code is borrow from https://github.com/xingyizhou/CenterTrack/blob/master/src/tools/eval_kitti_track/evaluate_tracking.py
"""
class tData:
"""
Utility class to load data.
"""
def __init__(self,frame=-1,obj_type="unset",truncation=-1,occlusion=-1,\
obs_angle=-10,x1=-1,y1=-1,x2=-1,y2=-1,w=-1,h=-1,l=-1,\
X=-1000,Y=-1000,Z=-1000,yaw=-10,score=-1000,track_id=-1):
"""
Constructor, initializes the object given the parameters.
"""
self.frame = frame
self.track_id = track_id
self.obj_type = obj_type
self.truncation = truncation
self.occlusion = occlusion
self.obs_angle = obs_angle
self.x1 = x1
self.y1 = y1
self.x2 = x2
self.y2 = y2
self.w = w
self.h = h
self.l = l
self.X = X
self.Y = Y
self.Z = Z
self.yaw = yaw
self.score = score
self.ignored = False
self.valid = False
self.tracker = -1
def __str__(self):
attrs = vars(self)
return '\n'.join("%s: %s" % item for item in attrs.items())
class KITTIEvaluation(object):
""" KITTI tracking statistics (CLEAR MOT, id-switches, fragments, ML/PT/MT, precision/recall)
MOTA - Multi-object tracking accuracy in [0,100]
MOTP - Multi-object tracking precision in [0,100] (3D) / [td,100] (2D)
MOTAL - Multi-object tracking accuracy in [0,100] with log10(id-switches)
id-switches - number of id switches
fragments - number of fragmentations
MT, PT, ML - number of mostly tracked, partially tracked and mostly lost trajectories
recall - recall = percentage of detected targets
precision - precision = percentage of correctly detected targets
FAR - number of false alarms per frame
falsepositives - number of false positives (FP)
missed - number of missed targets (FN)
"""
def __init__(self, result_path, gt_path, min_overlap=0.5, max_truncation = 0,\
min_height = 25, max_occlusion = 2, cls="car",\
n_frames=[], seqs=[], n_sequences=0):
# get number of sequences and
# get number of frames per sequence from test mapping
# (created while extracting the benchmark)
self.gt_path = os.path.join(gt_path, "../labels")
self.n_frames = n_frames
self.sequence_name = seqs
self.n_sequences = n_sequences
self.cls = cls # class to evaluate, i.e. pedestrian or car
self.result_path = result_path
# statistics and numbers for evaluation
self.n_gt = 0 # number of ground truth detections minus ignored false negatives and true positives
self.n_igt = 0 # number of ignored ground truth detections
self.n_gts = [
] # number of ground truth detections minus ignored false negatives and true positives PER SEQUENCE
self.n_igts = [
] # number of ground ignored truth detections PER SEQUENCE
self.n_gt_trajectories = 0
self.n_gt_seq = []
self.n_tr = 0 # number of tracker detections minus ignored tracker detections
self.n_trs = [
] # number of tracker detections minus ignored tracker detections PER SEQUENCE
self.n_itr = 0 # number of ignored tracker detections
self.n_itrs = [] # number of ignored tracker detections PER SEQUENCE
self.n_igttr = 0 # number of ignored ground truth detections where the corresponding associated tracker detection is also ignored
self.n_tr_trajectories = 0
self.n_tr_seq = []
self.MOTA = 0
self.MOTP = 0
self.MOTAL = 0
self.MODA = 0
self.MODP = 0
self.MODP_t = []
self.recall = 0
self.precision = 0
self.F1 = 0
self.FAR = 0
self.total_cost = 0
self.itp = 0 # number of ignored true positives
self.itps = [] # number of ignored true positives PER SEQUENCE
self.tp = 0 # number of true positives including ignored true positives!
self.tps = [
] # number of true positives including ignored true positives PER SEQUENCE
self.fn = 0 # number of false negatives WITHOUT ignored false negatives
self.fns = [
] # number of false negatives WITHOUT ignored false negatives PER SEQUENCE
self.ifn = 0 # number of ignored false negatives
self.ifns = [] # number of ignored false negatives PER SEQUENCE
self.fp = 0 # number of false positives
# a bit tricky, the number of ignored false negatives and ignored true positives
# is subtracted, but if both tracker detection and ground truth detection
# are ignored this number is added again to avoid double counting
self.fps = [] # above PER SEQUENCE
self.mme = 0
self.fragments = 0
self.id_switches = 0
self.MT = 0
self.PT = 0
self.ML = 0
self.min_overlap = min_overlap # minimum bounding box overlap for 3rd party metrics
self.max_truncation = max_truncation # maximum truncation of an object for evaluation
self.max_occlusion = max_occlusion # maximum occlusion of an object for evaluation
self.min_height = min_height # minimum height of an object for evaluation
self.n_sample_points = 500
# this should be enough to hold all groundtruth trajectories
# is expanded if necessary and reduced in any case
self.gt_trajectories = [[] for x in range(self.n_sequences)]
self.ign_trajectories = [[] for x in range(self.n_sequences)]
def loadGroundtruth(self):
try:
self._loadData(
self.gt_path, cls=self.cls, loading_groundtruth=True)
except IOError:
return False
return True
def loadTracker(self):
try:
if not self._loadData(
self.result_path, cls=self.cls, loading_groundtruth=False):
return False
except IOError:
return False
return True
def _loadData(self,
root_dir,
cls,
min_score=-1000,
loading_groundtruth=False):
"""
Generic loader for ground truth and tracking data.
Use loadGroundtruth() or loadTracker() to load this data.
Loads detections in KITTI format from textfiles.
"""
# construct objectDetections object to hold detection data
t_data = tData()
data = []
eval_2d = True
eval_3d = True
seq_data = []
n_trajectories = 0
n_trajectories_seq = []
for seq, s_name in enumerate(self.sequence_name):
i = 0
filename = os.path.join(root_dir, "%s.txt" % s_name)
f = open(filename, "r")
f_data = [
[] for x in range(self.n_frames[seq])
] # current set has only 1059 entries, sufficient length is checked anyway
ids = []
n_in_seq = 0
id_frame_cache = []
for line in f:
# KITTI tracking benchmark data format:
# (frame,tracklet_id,objectType,truncation,occlusion,alpha,x1,y1,x2,y2,h,w,l,X,Y,Z,ry)
line = line.strip()
fields = line.split(" ")
# classes that should be loaded (ignored neighboring classes)
if "car" in cls.lower():
classes = ["car", "van"]
elif "pedestrian" in cls.lower():
classes = ["pedestrian", "person_sitting"]
else:
classes = [cls.lower()]
classes += ["dontcare"]
if not any([s for s in classes if s in fields[2].lower()]):
continue
# get fields from table
t_data.frame = int(float(fields[0])) # frame
t_data.track_id = int(float(fields[1])) # id
t_data.obj_type = fields[
2].lower() # object type [car, pedestrian, cyclist, ...]
t_data.truncation = int(
float(fields[3])) # truncation [-1,0,1,2]
t_data.occlusion = int(
float(fields[4])) # occlusion [-1,0,1,2]
t_data.obs_angle = float(fields[5]) # observation angle [rad]
t_data.x1 = float(fields[6]) # left [px]
t_data.y1 = float(fields[7]) # top [px]
t_data.x2 = float(fields[8]) # right [px]
t_data.y2 = float(fields[9]) # bottom [px]
t_data.h = float(fields[10]) # height [m]
t_data.w = float(fields[11]) # width [m]
t_data.l = float(fields[12]) # length [m]
t_data.X = float(fields[13]) # X [m]
t_data.Y = float(fields[14]) # Y [m]
t_data.Z = float(fields[15]) # Z [m]
t_data.yaw = float(fields[16]) # yaw angle [rad]
if not loading_groundtruth:
if len(fields) == 17:
t_data.score = -1
elif len(fields) == 18:
t_data.score = float(fields[17]) # detection score
else:
logger.info("file is not in KITTI format")
return
# do not consider objects marked as invalid
if t_data.track_id is -1 and t_data.obj_type != "dontcare":
continue
idx = t_data.frame
# check if length for frame data is sufficient
if idx >= len(f_data):
print("extend f_data", idx, len(f_data))
f_data += [[] for x in range(max(500, idx - len(f_data)))]
try:
id_frame = (t_data.frame, t_data.track_id)
if id_frame in id_frame_cache and not loading_groundtruth:
logger.info(
"track ids are not unique for sequence %d: frame %d"
% (seq, t_data.frame))
logger.info(
"track id %d occured at least twice for this frame"
% t_data.track_id)
logger.info("Exiting...")
#continue # this allows to evaluate non-unique result files
return False
id_frame_cache.append(id_frame)
f_data[t_data.frame].append(copy.copy(t_data))
except:
print(len(f_data), idx)
raise
if t_data.track_id not in ids and t_data.obj_type != "dontcare":
ids.append(t_data.track_id)
n_trajectories += 1
n_in_seq += 1
# check if uploaded data provides information for 2D and 3D evaluation
if not loading_groundtruth and eval_2d is True and (
t_data.x1 == -1 or t_data.x2 == -1 or
t_data.y1 == -1 or t_data.y2 == -1):
eval_2d = False
if not loading_groundtruth and eval_3d is True and (
t_data.X == -1000 or t_data.Y == -1000 or
t_data.Z == -1000):
eval_3d = False
# only add existing frames
n_trajectories_seq.append(n_in_seq)
seq_data.append(f_data)
f.close()
if not loading_groundtruth:
self.tracker = seq_data
self.n_tr_trajectories = n_trajectories
self.eval_2d = eval_2d
self.eval_3d = eval_3d
self.n_tr_seq = n_trajectories_seq
if self.n_tr_trajectories == 0:
return False
else:
# split ground truth and DontCare areas
self.dcareas = []
self.groundtruth = []
for seq_idx in range(len(seq_data)):
seq_gt = seq_data[seq_idx]
s_g, s_dc = [], []
for f in range(len(seq_gt)):
all_gt = seq_gt[f]
g, dc = [], []
for gg in all_gt:
if gg.obj_type == "dontcare":
dc.append(gg)
else:
g.append(gg)
s_g.append(g)
s_dc.append(dc)
self.dcareas.append(s_dc)
self.groundtruth.append(s_g)
self.n_gt_seq = n_trajectories_seq
self.n_gt_trajectories = n_trajectories
return True
def boxoverlap(self, a, b, criterion="union"):
"""
boxoverlap computes intersection over union for bbox a and b in KITTI format.
If the criterion is 'union', overlap = (a inter b) / a union b).
If the criterion is 'a', overlap = (a inter b) / a, where b should be a dontcare area.
"""
x1 = max(a.x1, b.x1)
y1 = max(a.y1, b.y1)
x2 = min(a.x2, b.x2)
y2 = min(a.y2, b.y2)
w = x2 - x1
h = y2 - y1
if w <= 0. or h <= 0.:
return 0.
inter = w * h
aarea = (a.x2 - a.x1) * (a.y2 - a.y1)
barea = (b.x2 - b.x1) * (b.y2 - b.y1)
# intersection over union overlap
if criterion.lower() == "union":
o = inter / float(aarea + barea - inter)
elif criterion.lower() == "a":
o = float(inter) / float(aarea)
else:
raise TypeError("Unkown type for criterion")
return o
def compute3rdPartyMetrics(self):
"""
Computes the metrics defined in
- Stiefelhagen 2008: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics
MOTA, MOTAL, MOTP
- Nevatia 2008: Global Data Association for Multi-Object Tracking Using Network Flows
MT/PT/ML
"""
# construct Munkres object for Hungarian Method association
hm = Munkres()
max_cost = 1e9
# go through all frames and associate ground truth and tracker results
# groundtruth and tracker contain lists for every single frame containing lists of KITTI format detections
fr, ids = 0, 0
for seq_idx in range(len(self.groundtruth)):
seq_gt = self.groundtruth[seq_idx]
seq_dc = self.dcareas[seq_idx] # don't care areas
seq_tracker = self.tracker[seq_idx]
seq_trajectories = defaultdict(list)
seq_ignored = defaultdict(list)
# statistics over the current sequence, check the corresponding
# variable comments in __init__ to get their meaning
seqtp = 0
seqitp = 0
seqfn = 0
seqifn = 0
seqfp = 0
seqigt = 0
seqitr = 0
last_ids = [[], []]
n_gts = 0
n_trs = 0
for f in range(len(seq_gt)):
g = seq_gt[f]
dc = seq_dc[f]
t = seq_tracker[f]
# counting total number of ground truth and tracker objects
self.n_gt += len(g)
self.n_tr += len(t)
n_gts += len(g)
n_trs += len(t)
# use hungarian method to associate, using boxoverlap 0..1 as cost
# build cost matrix
cost_matrix = []
this_ids = [[], []]
for gg in g:
# save current ids
this_ids[0].append(gg.track_id)
this_ids[1].append(-1)
gg.tracker = -1
gg.id_switch = 0
gg.fragmentation = 0
cost_row = []
for tt in t:
# overlap == 1 is cost ==0
c = 1 - self.boxoverlap(gg, tt)
# gating for boxoverlap
if c <= self.min_overlap:
cost_row.append(c)
else:
cost_row.append(max_cost) # = 1e9
cost_matrix.append(cost_row)
# all ground truth trajectories are initially not associated
# extend groundtruth trajectories lists (merge lists)
seq_trajectories[gg.track_id].append(-1)
seq_ignored[gg.track_id].append(False)
if len(g) is 0:
cost_matrix = [[]]
# associate
association_matrix = hm.compute(cost_matrix)
# tmp variables for sanity checks and MODP computation
tmptp = 0
tmpfp = 0
tmpfn = 0
tmpc = 0 # this will sum up the overlaps for all true positives
tmpcs = [0] * len(
g) # this will save the overlaps for all true positives
# the reason is that some true positives might be ignored
# later such that the corrsponding overlaps can
# be subtracted from tmpc for MODP computation
# mapping for tracker ids and ground truth ids
for row, col in association_matrix:
# apply gating on boxoverlap
c = cost_matrix[row][col]
if c < max_cost:
g[row].tracker = t[col].track_id
this_ids[1][row] = t[col].track_id
t[col].valid = True
g[row].distance = c
self.total_cost += 1 - c
tmpc += 1 - c
tmpcs[row] = 1 - c
seq_trajectories[g[row].track_id][-1] = t[col].track_id
# true positives are only valid associations
self.tp += 1
tmptp += 1
else:
g[row].tracker = -1
self.fn += 1
tmpfn += 1
# associate tracker and DontCare areas
# ignore tracker in neighboring classes
nignoredtracker = 0 # number of ignored tracker detections
ignoredtrackers = dict() # will associate the track_id with -1
# if it is not ignored and 1 if it is
# ignored;
# this is used to avoid double counting ignored
# cases, see the next loop
for tt in t:
ignoredtrackers[tt.track_id] = -1
# ignore detection if it belongs to a neighboring class or is
# smaller or equal to the minimum height
tt_height = abs(tt.y1 - tt.y2)
if ((self.cls == "car" and tt.obj_type == "van") or
(self.cls == "pedestrian" and
tt.obj_type == "person_sitting") or
tt_height <= self.min_height) and not tt.valid:
nignoredtracker += 1
tt.ignored = True
ignoredtrackers[tt.track_id] = 1
continue
for d in dc:
overlap = self.boxoverlap(tt, d, "a")
if overlap > 0.5 and not tt.valid:
tt.ignored = True
nignoredtracker += 1
ignoredtrackers[tt.track_id] = 1
break
# check for ignored FN/TP (truncation or neighboring object class)
ignoredfn = 0 # the number of ignored false negatives
nignoredtp = 0 # the number of ignored true positives
nignoredpairs = 0 # the number of ignored pairs, i.e. a true positive
# which is ignored but where the associated tracker
# detection has already been ignored
gi = 0
for gg in g:
if gg.tracker < 0:
if gg.occlusion>self.max_occlusion or gg.truncation>self.max_truncation\
or (self.cls=="car" and gg.obj_type=="van") or (self.cls=="pedestrian" and gg.obj_type=="person_sitting"):
seq_ignored[gg.track_id][-1] = True
gg.ignored = True
ignoredfn += 1
elif gg.tracker >= 0:
if gg.occlusion>self.max_occlusion or gg.truncation>self.max_truncation\
or (self.cls=="car" and gg.obj_type=="van") or (self.cls=="pedestrian" and gg.obj_type=="person_sitting"):
seq_ignored[gg.track_id][-1] = True
gg.ignored = True
nignoredtp += 1
# if the associated tracker detection is already ignored,
# we want to avoid double counting ignored detections
if ignoredtrackers[gg.tracker] > 0:
nignoredpairs += 1
# for computing MODP, the overlaps from ignored detections
# are subtracted
tmpc -= tmpcs[gi]
gi += 1
# the below might be confusion, check the comments in __init__
# to see what the individual statistics represent
# correct TP by number of ignored TP due to truncation
# ignored TP are shown as tracked in visualization
tmptp -= nignoredtp
# count the number of ignored true positives
self.itp += nignoredtp
# adjust the number of ground truth objects considered
self.n_gt -= (ignoredfn + nignoredtp)
# count the number of ignored ground truth objects
self.n_igt += ignoredfn + nignoredtp
# count the number of ignored tracker objects
self.n_itr += nignoredtracker
# count the number of ignored pairs, i.e. associated tracker and
# ground truth objects that are both ignored
self.n_igttr += nignoredpairs
# false negatives = associated gt bboxes exceding association threshold + non-associated gt bboxes
tmpfn += len(g) - len(association_matrix) - ignoredfn
self.fn += len(g) - len(association_matrix) - ignoredfn
self.ifn += ignoredfn
# false positives = tracker bboxes - associated tracker bboxes
# mismatches (mme_t)
tmpfp += len(
t) - tmptp - nignoredtracker - nignoredtp + nignoredpairs
self.fp += len(
t) - tmptp - nignoredtracker - nignoredtp + nignoredpairs
# update sequence data
seqtp += tmptp
seqitp += nignoredtp
seqfp += tmpfp
seqfn += tmpfn
seqifn += ignoredfn
seqigt += ignoredfn + nignoredtp
seqitr += nignoredtracker
# sanity checks
# - the number of true positives minues ignored true positives
# should be greater or equal to 0
# - the number of false negatives should be greater or equal to 0
# - the number of false positives needs to be greater or equal to 0
# otherwise ignored detections might be counted double
# - the number of counted true positives (plus ignored ones)
# and the number of counted false negatives (plus ignored ones)
# should match the total number of ground truth objects
# - the number of counted true positives (plus ignored ones)
# and the number of counted false positives
# plus the number of ignored tracker detections should
# match the total number of tracker detections; note that
# nignoredpairs is subtracted here to avoid double counting
# of ignored detection sin nignoredtp and nignoredtracker
if tmptp < 0:
print(tmptp, nignoredtp)
raise NameError("Something went wrong! TP is negative")
if tmpfn < 0:
print(tmpfn,
len(g),
len(association_matrix), ignoredfn, nignoredpairs)
raise NameError("Something went wrong! FN is negative")
if tmpfp < 0:
print(tmpfp,
len(t), tmptp, nignoredtracker, nignoredtp,
nignoredpairs)
raise NameError("Something went wrong! FP is negative")
if tmptp + tmpfn is not len(g) - ignoredfn - nignoredtp:
print("seqidx", seq_idx)
print("frame ", f)
print("TP ", tmptp)
print("FN ", tmpfn)
print("FP ", tmpfp)
print("nGT ", len(g))
print("nAss ", len(association_matrix))
print("ign GT", ignoredfn)
print("ign TP", nignoredtp)
raise NameError(
"Something went wrong! nGroundtruth is not TP+FN")
if tmptp + tmpfp + nignoredtp + nignoredtracker - nignoredpairs is not len(
t):
print(seq_idx, f, len(t), tmptp, tmpfp)
print(len(association_matrix), association_matrix)
raise NameError(
"Something went wrong! nTracker is not TP+FP")
# check for id switches or fragmentations
for i, tt in enumerate(this_ids[0]):
if tt in last_ids[0]:
idx = last_ids[0].index(tt)
tid = this_ids[1][i]
lid = last_ids[1][idx]
if tid != lid and lid != -1 and tid != -1:
if g[i].truncation < self.max_truncation:
g[i].id_switch = 1
ids += 1
if tid != lid and lid != -1:
if g[i].truncation < self.max_truncation:
g[i].fragmentation = 1
fr += 1
# save current index
last_ids = this_ids
# compute MOTP_t
MODP_t = 1
if tmptp != 0:
MODP_t = tmpc / float(tmptp)
self.MODP_t.append(MODP_t)
# remove empty lists for current gt trajectories
self.gt_trajectories[seq_idx] = seq_trajectories
self.ign_trajectories[seq_idx] = seq_ignored
# gather statistics for "per sequence" statistics.
self.n_gts.append(n_gts)
self.n_trs.append(n_trs)
self.tps.append(seqtp)
self.itps.append(seqitp)
self.fps.append(seqfp)
self.fns.append(seqfn)
self.ifns.append(seqifn)
self.n_igts.append(seqigt)
self.n_itrs.append(seqitr)
# compute MT/PT/ML, fragments, idswitches for all groundtruth trajectories
n_ignored_tr_total = 0
for seq_idx, (
seq_trajectories, seq_ignored
) in enumerate(zip(self.gt_trajectories, self.ign_trajectories)):
if len(seq_trajectories) == 0:
continue
tmpMT, tmpML, tmpPT, tmpId_switches, tmpFragments = [0] * 5
n_ignored_tr = 0
for g, ign_g in zip(seq_trajectories.values(),
seq_ignored.values()):
# all frames of this gt trajectory are ignored
if all(ign_g):
n_ignored_tr += 1
n_ignored_tr_total += 1
continue
# all frames of this gt trajectory are not assigned to any detections
if all([this == -1 for this in g]):
tmpML += 1
self.ML += 1
continue
# compute tracked frames in trajectory
last_id = g[0]
# first detection (necessary to be in gt_trajectories) is always tracked
tracked = 1 if g[0] >= 0 else 0
lgt = 0 if ign_g[0] else 1
for f in range(1, len(g)):
if ign_g[f]:
last_id = -1
continue
lgt += 1
if last_id != g[f] and last_id != -1 and g[f] != -1 and g[
f - 1] != -1:
tmpId_switches += 1
self.id_switches += 1
if f < len(g) - 1 and g[f - 1] != g[
f] and last_id != -1 and g[f] != -1 and g[f +
1] != -1:
tmpFragments += 1
self.fragments += 1
if g[f] != -1:
tracked += 1
last_id = g[f]
# handle last frame; tracked state is handled in for loop (g[f]!=-1)
if len(g) > 1 and g[f - 1] != g[f] and last_id != -1 and g[
f] != -1 and not ign_g[f]:
tmpFragments += 1
self.fragments += 1
# compute MT/PT/ML
tracking_ratio = tracked / float(len(g) - sum(ign_g))
if tracking_ratio > 0.8:
tmpMT += 1
self.MT += 1
elif tracking_ratio < 0.2:
tmpML += 1
self.ML += 1
else: # 0.2 <= tracking_ratio <= 0.8
tmpPT += 1
self.PT += 1
if (self.n_gt_trajectories - n_ignored_tr_total) == 0:
self.MT = 0.
self.PT = 0.
self.ML = 0.
else:
self.MT /= float(self.n_gt_trajectories - n_ignored_tr_total)
self.PT /= float(self.n_gt_trajectories - n_ignored_tr_total)
self.ML /= float(self.n_gt_trajectories - n_ignored_tr_total)
# precision/recall etc.
if (self.fp + self.tp) == 0 or (self.tp + self.fn) == 0:
self.recall = 0.
self.precision = 0.
else:
self.recall = self.tp / float(self.tp + self.fn)
self.precision = self.tp / float(self.fp + self.tp)
if (self.recall + self.precision) == 0:
self.F1 = 0.
else:
self.F1 = 2. * (self.precision * self.recall) / (
self.precision + self.recall)
if sum(self.n_frames) == 0:
self.FAR = "n/a"
else:
self.FAR = self.fp / float(sum(self.n_frames))
# compute CLEARMOT
if self.n_gt == 0:
self.MOTA = -float("inf")
self.MODA = -float("inf")
else:
self.MOTA = 1 - (self.fn + self.fp + self.id_switches
) / float(self.n_gt)
self.MODA = 1 - (self.fn + self.fp) / float(self.n_gt)
if self.tp == 0:
self.MOTP = float("inf")
else:
self.MOTP = self.total_cost / float(self.tp)
if self.n_gt != 0:
if self.id_switches == 0:
self.MOTAL = 1 - (self.fn + self.fp + self.id_switches
) / float(self.n_gt)
else:
self.MOTAL = 1 - (self.fn + self.fp +
math.log10(self.id_switches)
) / float(self.n_gt)
else:
self.MOTAL = -float("inf")
if sum(self.n_frames) == 0:
self.MODP = "n/a"
else:
self.MODP = sum(self.MODP_t) / float(sum(self.n_frames))
return True
def createSummary(self):
summary = ""
summary += "tracking evaluation summary".center(80, "=") + "\n"
summary += self.printEntry("Multiple Object Tracking Accuracy (MOTA)",
self.MOTA) + "\n"
summary += self.printEntry("Multiple Object Tracking Precision (MOTP)",
self.MOTP) + "\n"
summary += self.printEntry("Multiple Object Tracking Accuracy (MOTAL)",
self.MOTAL) + "\n"
summary += self.printEntry("Multiple Object Detection Accuracy (MODA)",
self.MODA) + "\n"
summary += self.printEntry(
"Multiple Object Detection Precision (MODP)", self.MODP) + "\n"
summary += "\n"
summary += self.printEntry("Recall", self.recall) + "\n"
summary += self.printEntry("Precision", self.precision) + "\n"
summary += self.printEntry("F1", self.F1) + "\n"
summary += self.printEntry("False Alarm Rate", self.FAR) + "\n"
summary += "\n"
summary += self.printEntry("Mostly Tracked", self.MT) + "\n"
summary += self.printEntry("Partly Tracked", self.PT) + "\n"
summary += self.printEntry("Mostly Lost", self.ML) + "\n"
summary += "\n"
summary += self.printEntry("True Positives", self.tp) + "\n"
#summary += self.printEntry("True Positives per Sequence", self.tps) + "\n"
summary += self.printEntry("Ignored True Positives", self.itp) + "\n"
#summary += self.printEntry("Ignored True Positives per Sequence", self.itps) + "\n"
summary += self.printEntry("False Positives", self.fp) + "\n"
#summary += self.printEntry("False Positives per Sequence", self.fps) + "\n"
summary += self.printEntry("False Negatives", self.fn) + "\n"
#summary += self.printEntry("False Negatives per Sequence", self.fns) + "\n"
summary += self.printEntry("ID-switches", self.id_switches) + "\n"
self.fp = self.fp / self.n_gt
self.fn = self.fn / self.n_gt
self.id_switches = self.id_switches / self.n_gt
summary += self.printEntry("False Positives Ratio", self.fp) + "\n"
#summary += self.printEntry("False Positives per Sequence", self.fps) + "\n"
summary += self.printEntry("False Negatives Ratio", self.fn) + "\n"
#summary += self.printEntry("False Negatives per Sequence", self.fns) + "\n"
summary += self.printEntry("Ignored False Negatives Ratio",
self.ifn) + "\n"
#summary += self.printEntry("Ignored False Negatives per Sequence", self.ifns) + "\n"
summary += self.printEntry("Missed Targets", self.fn) + "\n"
summary += self.printEntry("ID-switches", self.id_switches) + "\n"
summary += self.printEntry("Fragmentations", self.fragments) + "\n"
summary += "\n"
summary += self.printEntry("Ground Truth Objects (Total)", self.n_gt +
self.n_igt) + "\n"
#summary += self.printEntry("Ground Truth Objects (Total) per Sequence", self.n_gts) + "\n"
summary += self.printEntry("Ignored Ground Truth Objects",
self.n_igt) + "\n"
#summary += self.printEntry("Ignored Ground Truth Objects per Sequence", self.n_igts) + "\n"
summary += self.printEntry("Ground Truth Trajectories",
self.n_gt_trajectories) + "\n"
summary += "\n"
summary += self.printEntry("Tracker Objects (Total)", self.n_tr) + "\n"
#summary += self.printEntry("Tracker Objects (Total) per Sequence", self.n_trs) + "\n"
summary += self.printEntry("Ignored Tracker Objects",
self.n_itr) + "\n"
#summary += self.printEntry("Ignored Tracker Objects per Sequence", self.n_itrs) + "\n"
summary += self.printEntry("Tracker Trajectories",
self.n_tr_trajectories) + "\n"
#summary += "\n"
#summary += self.printEntry("Ignored Tracker Objects with Associated Ignored Ground Truth Objects", self.n_igttr) + "\n"
summary += "=" * 80
return summary
def printEntry(self, key, val, width=(70, 10)):
"""
Pretty print an entry in a table fashion.
"""
s_out = key.ljust(width[0])
if type(val) == int:
s = "%%%dd" % width[1]
s_out += s % val
elif type(val) == float:
s = "%%%df" % (width[1])
s_out += s % val
else:
s_out += ("%s" % val).rjust(width[1])
return s_out
def saveToStats(self, save_summary):
"""
Save the statistics in a whitespace separate file.
"""
summary = self.createSummary()
if save_summary:
filename = os.path.join(self.result_path,
"summary_%s.txt" % self.cls)
dump = open(filename, "w+")
dump.write(summary)
dump.close()
return summary
class KITTIMOTMetric(Metric):
def __init__(self, save_summary=True):
self.save_summary = save_summary
self.MOTEvaluator = KITTIEvaluation
self.result_root = None
self.reset()
def reset(self):
self.seqs = []
self.n_sequences = 0
self.n_frames = []
self.strsummary = ''
def update(self, data_root, seq, data_type, result_root, result_filename):
assert data_type == 'kitti', "data_type should 'kitti'"
self.result_root = result_root
self.gt_path = data_root
gt_path = '{}/../labels/{}.txt'.format(data_root, seq)
gt = open(gt_path, "r")
max_frame = 0
for line in gt:
line = line.strip()
line_list = line.split(" ")
if int(line_list[0]) > max_frame:
max_frame = int(line_list[0])
rs = open(result_filename, "r")
for line in rs:
line = line.strip()
line_list = line.split(" ")
if int(line_list[0]) > max_frame:
max_frame = int(line_list[0])
gt.close()
rs.close()
self.n_frames.append(max_frame + 1)
self.seqs.append(seq)
self.n_sequences += 1
def accumulate(self):
logger.info("Processing Result for KITTI Tracking Benchmark")
e = self.MOTEvaluator(result_path=self.result_root, gt_path=self.gt_path,\
n_frames=self.n_frames, seqs=self.seqs, n_sequences=self.n_sequences)
try:
if not e.loadTracker():
return
logger.info("Loading Results - Success")
logger.info("Evaluate Object Class: %s" % c.upper())
except:
logger.info("Caught exception while loading result data.")
if not e.loadGroundtruth():
raise ValueError("Ground truth not found.")
logger.info("Loading Groundtruth - Success")
# sanity checks
if len(e.groundtruth) is not len(e.tracker):
logger.info(
"The uploaded data does not provide results for every sequence."
)
return False
logger.info("Loaded %d Sequences." % len(e.groundtruth))
logger.info("Start Evaluation...")
if e.compute3rdPartyMetrics():
self.strsummary = e.saveToStats(self.save_summary)
else:
logger.info(
"There seem to be no true positives or false positives at all in the submitted data."
)
def log(self):
print(self.strsummary)
def get_results(self):
return self.strsummary