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import numpy as np
from scipy.optimize import linear_sum_assignment
from ._base_metric import _BaseMetric
from trackeval import _timing
from trackeval import utils
class CLEAR(_BaseMetric):
"""
Class which implements the CLEAR metrics
:param Dict config: configuration for the app
::
identity = trackeval.metrics.CLEAR(config)
"""
@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
:param Dict[str, float] data: dictionary containing the data for the sequence
:return: dictionary containing the calculated count metrics
:rtype: Dict[str, float]
"""
# 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
:param Dict[str, float] all_res: dictionary containing the metrics for each sequence
:return: dictionary containing the combined metrics across sequences
:rtype: Dict[str, float]
"""
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
:param Dict[str, float] all_res: dictionary containing the metrics for each class
:return: dictionary containing the combined metrics averaged over detections
:rtype: Dict[str, float]
"""
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.
:param Dict[str, float] all_res: dictionary containing the ID metrics for each class
:param bool ignore_empty_classes: Flag to ignore empty classes, defaults to False
:return: dictionary containing the combined metrics averaged over classes
:rtype: Dict[str, float]
"""
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.
:param Dict[str, float] res: dictionary containing the sub-metric values
:return: dictionary containing the updated sub-metric values
:rtype: Dict[str, float]
"""
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 |