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identity.py
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identity.py
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import numpy as np
from scipy.optimize import linear_sum_assignment
from ._base_metric import _BaseMetric
from .. import _timing
from .. import utils
class Identity(_BaseMetric):
"""Class which implements the ID metrics"""
@staticmethod
def get_default_config():
"""Default class config values"""
default_config = {
'THRESHOLD': 0.5, # Similarity score threshold required for a IDTP 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__()
self.integer_fields = ['IDTP', 'IDFN', 'IDFP']
self.float_fields = ['IDF1', 'IDR', 'IDP']
self.fields = self.float_fields + self.integer_fields
self.summary_fields = self.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 ID 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['IDFN'] = data['num_gt_dets']
return res
if data['num_gt_dets'] == 0:
res['IDFP'] = data['num_tracker_dets']
return res
# Variables counting global association
potential_matches_count = np.zeros((data['num_gt_ids'], data['num_tracker_ids']))
gt_id_count = np.zeros(data['num_gt_ids'])
tracker_id_count = np.zeros(data['num_tracker_ids'])
# First loop through each timestep and accumulate global track information.
for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):
# Count the potential matches between ids in each timestep
matches_mask = np.greater_equal(data['similarity_scores'][t], self.threshold)
match_idx_gt, match_idx_tracker = np.nonzero(matches_mask)
potential_matches_count[gt_ids_t[match_idx_gt], tracker_ids_t[match_idx_tracker]] += 1
# Calculate the total number of dets for each gt_id and tracker_id.
gt_id_count[gt_ids_t] += 1
tracker_id_count[tracker_ids_t] += 1
# Calculate optimal assignment cost matrix for ID metrics
num_gt_ids = data['num_gt_ids']
num_tracker_ids = data['num_tracker_ids']
fp_mat = np.zeros((num_gt_ids + num_tracker_ids, num_gt_ids + num_tracker_ids))
fn_mat = np.zeros((num_gt_ids + num_tracker_ids, num_gt_ids + num_tracker_ids))
fp_mat[num_gt_ids:, :num_tracker_ids] = 1e10
fn_mat[:num_gt_ids, num_tracker_ids:] = 1e10
for gt_id in range(num_gt_ids):
fn_mat[gt_id, :num_tracker_ids] = gt_id_count[gt_id]
fn_mat[gt_id, num_tracker_ids + gt_id] = gt_id_count[gt_id]
for tracker_id in range(num_tracker_ids):
fp_mat[:num_gt_ids, tracker_id] = tracker_id_count[tracker_id]
fp_mat[tracker_id + num_gt_ids, tracker_id] = tracker_id_count[tracker_id]
fn_mat[:num_gt_ids, :num_tracker_ids] -= potential_matches_count
fp_mat[:num_gt_ids, :num_tracker_ids] -= potential_matches_count
# Hungarian algorithm
match_rows, match_cols = linear_sum_assignment(fn_mat + fp_mat)
# Accumulate basic statistics
res['IDFN'] = fn_mat[match_rows, match_cols].sum().astype(np.int)
res['IDFP'] = fp_mat[match_rows, match_cols].sum().astype(np.int)
res['IDTP'] = (gt_id_count.sum() - res['IDFN']).astype(np.int)
# Calculate final ID scores
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['IDTP'] + v['IDFN'] + v['IDFP'] > 0 + np.finfo('float').eps},
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['IDTP'] + v['IDFN'] + v['IDFP'] > 0 + np.finfo('float').eps], axis=0)
else:
res[field] = np.mean([v[field] for v in all_res.values()], axis=0)
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.integer_fields:
res[field] = self._combine_sum(all_res, field)
res = self._compute_final_fields(res)
return res
def combine_sequences(self, all_res):
"""Combines metrics across all sequences"""
res = {}
for field in self.integer_fields:
res[field] = self._combine_sum(all_res, field)
res = self._compute_final_fields(res)
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.
"""
res['IDR'] = res['IDTP'] / np.maximum(1.0, res['IDTP'] + res['IDFN'])
res['IDP'] = res['IDTP'] / np.maximum(1.0, res['IDTP'] + res['IDFP'])
res['IDF1'] = res['IDTP'] / np.maximum(1.0, res['IDTP'] + 0.5 * res['IDFP'] + 0.5 * res['IDFN'])
return res