-
Notifications
You must be signed in to change notification settings - Fork 229
/
clear.py
186 lines (161 loc) · 8.95 KB
/
clear.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
import numpy as np
from scipy.optimize import linear_sum_assignment
from ._base_metric import _BaseMetric
from .. import _timing
from .. import utils
class CLEAR(_BaseMetric):
"""Class which implements the CLEAR metrics"""
@staticmethod
def get_default_config():
"""Default class config values"""
default_config = {
'THRESHOLD': 0.5, # Similarity score threshold required for a TP match. Default 0.5.
'PRINT_CONFIG': True, # Whether to print the config information on init. Default: False.
}
return default_config
def __init__(self, config=None):
super().__init__()
main_integer_fields = ['CLR_TP', 'CLR_FN', 'CLR_FP', 'IDSW', 'MT', 'PT', 'ML', 'Frag']
extra_integer_fields = ['CLR_Frames']
self.integer_fields = main_integer_fields + extra_integer_fields
main_float_fields = ['MOTA', 'MOTP', 'MODA', 'CLR_Re', 'CLR_Pr', 'MTR', 'PTR', 'MLR', 'sMOTA']
extra_float_fields = ['CLR_F1', 'FP_per_frame', 'MOTAL', 'MOTP_sum']
self.float_fields = main_float_fields + extra_float_fields
self.fields = self.float_fields + self.integer_fields
self.summed_fields = self.integer_fields + ['MOTP_sum']
self.summary_fields = main_float_fields + main_integer_fields
# Configuration options:
self.config = utils.init_config(config, self.get_default_config(), self.get_name())
self.threshold = float(self.config['THRESHOLD'])
@_timing.time
def eval_sequence(self, data):
"""Calculates CLEAR metrics for one sequence"""
# Initialise results
res = {}
for field in self.fields:
res[field] = 0
# Return result quickly if tracker or gt sequence is empty
if data['num_tracker_dets'] == 0:
res['CLR_FN'] = data['num_gt_dets']
res['ML'] = data['num_gt_ids']
res['MLR'] = 1.0
return res
if data['num_gt_dets'] == 0:
res['CLR_FP'] = data['num_tracker_dets']
res['MLR'] = 1.0
return res
# Variables counting global association
num_gt_ids = data['num_gt_ids']
gt_id_count = np.zeros(num_gt_ids) # For MT/ML/PT
gt_matched_count = np.zeros(num_gt_ids) # For MT/ML/PT
gt_frag_count = np.zeros(num_gt_ids) # For Frag
# Note that IDSWs are counted based on the last time each gt_id was present (any number of frames previously),
# but are only used in matching to continue current tracks based on the gt_id in the single previous timestep.
prev_tracker_id = np.nan * np.zeros(num_gt_ids) # For scoring IDSW
prev_timestep_tracker_id = np.nan * np.zeros(num_gt_ids) # For matching IDSW
# Calculate scores for each timestep
for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):
# Deal with the case that there are no gt_det/tracker_det in a timestep.
if len(gt_ids_t) == 0:
res['CLR_FP'] += len(tracker_ids_t)
continue
if len(tracker_ids_t) == 0:
res['CLR_FN'] += len(gt_ids_t)
gt_id_count[gt_ids_t] += 1
continue
# Calc score matrix to first minimise IDSWs from previous frame, and then maximise MOTP secondarily
similarity = data['similarity_scores'][t]
score_mat = (tracker_ids_t[np.newaxis, :] == prev_timestep_tracker_id[gt_ids_t[:, np.newaxis]])
score_mat = 1000 * score_mat + similarity
score_mat[similarity < self.threshold - np.finfo('float').eps] = 0
# Hungarian algorithm to find best matches
match_rows, match_cols = linear_sum_assignment(-score_mat)
actually_matched_mask = score_mat[match_rows, match_cols] > 0 + np.finfo('float').eps
match_rows = match_rows[actually_matched_mask]
match_cols = match_cols[actually_matched_mask]
matched_gt_ids = gt_ids_t[match_rows]
matched_tracker_ids = tracker_ids_t[match_cols]
# Calc IDSW for MOTA
prev_matched_tracker_ids = prev_tracker_id[matched_gt_ids]
is_idsw = (np.logical_not(np.isnan(prev_matched_tracker_ids))) & (
np.not_equal(matched_tracker_ids, prev_matched_tracker_ids))
res['IDSW'] += np.sum(is_idsw)
# Update counters for MT/ML/PT/Frag and record for IDSW/Frag for next timestep
gt_id_count[gt_ids_t] += 1
gt_matched_count[matched_gt_ids] += 1
not_previously_tracked = np.isnan(prev_timestep_tracker_id)
prev_tracker_id[matched_gt_ids] = matched_tracker_ids
prev_timestep_tracker_id[:] = np.nan
prev_timestep_tracker_id[matched_gt_ids] = matched_tracker_ids
currently_tracked = np.logical_not(np.isnan(prev_timestep_tracker_id))
gt_frag_count += np.logical_and(not_previously_tracked, currently_tracked)
# Calculate and accumulate basic statistics
num_matches = len(matched_gt_ids)
res['CLR_TP'] += num_matches
res['CLR_FN'] += len(gt_ids_t) - num_matches
res['CLR_FP'] += len(tracker_ids_t) - num_matches
if num_matches > 0:
res['MOTP_sum'] += sum(similarity[match_rows, match_cols])
# Calculate MT/ML/PT/Frag/MOTP
tracked_ratio = gt_matched_count[gt_id_count > 0] / gt_id_count[gt_id_count > 0]
res['MT'] = np.sum(np.greater(tracked_ratio, 0.8))
res['PT'] = np.sum(np.greater_equal(tracked_ratio, 0.2)) - res['MT']
res['ML'] = num_gt_ids - res['MT'] - res['PT']
res['Frag'] = np.sum(np.subtract(gt_frag_count[gt_frag_count > 0], 1))
res['MOTP'] = res['MOTP_sum'] / np.maximum(1.0, res['CLR_TP'])
res['CLR_Frames'] = data['num_timesteps']
# Calculate final CLEAR scores
res = self._compute_final_fields(res)
return res
def combine_sequences(self, all_res):
"""Combines metrics across all sequences"""
res = {}
for field in self.summed_fields:
res[field] = self._combine_sum(all_res, field)
res = self._compute_final_fields(res)
return res
def combine_classes_det_averaged(self, all_res):
"""Combines metrics across all classes by averaging over the detection values"""
res = {}
for field in self.summed_fields:
res[field] = self._combine_sum(all_res, field)
res = self._compute_final_fields(res)
return res
def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):
"""Combines metrics across all classes by averaging over the class values.
If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.
"""
res = {}
for field in self.integer_fields:
if ignore_empty_classes:
res[field] = self._combine_sum(
{k: v for k, v in all_res.items() if v['CLR_TP'] + v['CLR_FN'] + v['CLR_FP'] > 0}, field)
else:
res[field] = self._combine_sum({k: v for k, v in all_res.items()}, field)
for field in self.float_fields:
if ignore_empty_classes:
res[field] = np.mean(
[v[field] for v in all_res.values() if v['CLR_TP'] + v['CLR_FN'] + v['CLR_FP'] > 0], axis=0)
else:
res[field] = np.mean([v[field] for v in all_res.values()], axis=0)
return res
@staticmethod
def _compute_final_fields(res):
"""Calculate sub-metric ('field') values which only depend on other sub-metric values.
This function is used both for both per-sequence calculation, and in combining values across sequences.
"""
num_gt_ids = res['MT'] + res['ML'] + res['PT']
res['MTR'] = res['MT'] / np.maximum(1.0, num_gt_ids)
res['MLR'] = res['ML'] / np.maximum(1.0, num_gt_ids)
res['PTR'] = res['PT'] / np.maximum(1.0, num_gt_ids)
res['CLR_Re'] = res['CLR_TP'] / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])
res['CLR_Pr'] = res['CLR_TP'] / np.maximum(1.0, res['CLR_TP'] + res['CLR_FP'])
res['MODA'] = (res['CLR_TP'] - res['CLR_FP']) / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])
res['MOTA'] = (res['CLR_TP'] - res['CLR_FP'] - res['IDSW']) / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])
res['MOTP'] = res['MOTP_sum'] / np.maximum(1.0, res['CLR_TP'])
res['sMOTA'] = (res['MOTP_sum'] - res['CLR_FP'] - res['IDSW']) / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])
res['CLR_F1'] = res['CLR_TP'] / np.maximum(1.0, res['CLR_TP'] + 0.5*res['CLR_FN'] + 0.5*res['CLR_FP'])
res['FP_per_frame'] = res['CLR_FP'] / np.maximum(1.0, res['CLR_Frames'])
safe_log_idsw = np.log10(res['IDSW']) if res['IDSW'] > 0 else res['IDSW']
res['MOTAL'] = (res['CLR_TP'] - res['CLR_FP'] - safe_log_idsw) / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])
return res