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import cv2
import numpy as np
import lap
from scipy.spatial.distance import cdist
from cython_bbox import bbox_overlaps as bbox_ious
from yolox.motdt_tracker import kalman_filter
def _indices_to_matches(cost_matrix, indices, thresh):
matched_cost = cost_matrix[tuple(zip(*indices))]
matched_mask = (matched_cost <= thresh)
matches = indices[matched_mask]
unmatched_a = tuple(set(range(cost_matrix.shape[0])) - set(matches[:, 0]))
unmatched_b = tuple(set(range(cost_matrix.shape[1])) - set(matches[:, 1]))
return matches, unmatched_a, unmatched_b
def linear_assignment(cost_matrix, thresh):
if cost_matrix.size == 0:
return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))
matches, unmatched_a, unmatched_b = [], [], []
cost, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh)
for ix, mx in enumerate(x):
if mx >= 0:
matches.append([ix, mx])
unmatched_a = np.where(x < 0)[0]
unmatched_b = np.where(y < 0)[0]
matches = np.asarray(matches)
return matches, unmatched_a, unmatched_b
def ious(atlbrs, btlbrs):
"""
Compute cost based on IoU
:type atlbrs: list[tlbr] | np.ndarray
:type atlbrs: list[tlbr] | np.ndarray
:rtype ious np.ndarray
"""
ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)
if ious.size == 0:
return ious
ious = bbox_ious(
np.ascontiguousarray(atlbrs, dtype=np.float),
np.ascontiguousarray(btlbrs, dtype=np.float)
)
return ious
def iou_distance(atracks, btracks):
"""
Compute cost based on IoU
:type atracks: list[STrack]
:type btracks: list[STrack]
:rtype cost_matrix np.ndarray
"""
atlbrs = [track.tlbr for track in atracks]
btlbrs = [track.tlbr for track in btracks]
_ious = ious(atlbrs, btlbrs)
cost_matrix = 1 - _ious
return cost_matrix
def nearest_reid_distance(tracks, detections, metric='cosine'):
"""
Compute cost based on ReID features
:type tracks: list[STrack]
:type detections: list[BaseTrack]
:rtype cost_matrix np.ndarray
"""
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
if cost_matrix.size == 0:
return cost_matrix
det_features = np.asarray([track.curr_feature for track in detections], dtype=np.float32)
for i, track in enumerate(tracks):
cost_matrix[i, :] = np.maximum(0.0, cdist(track.features, det_features, metric).min(axis=0))
return cost_matrix
def mean_reid_distance(tracks, detections, metric='cosine'):
"""
Compute cost based on ReID features
:type tracks: list[STrack]
:type detections: list[BaseTrack]
:type metric: str
:rtype cost_matrix np.ndarray
"""
cost_matrix = np.empty((len(tracks), len(detections)), dtype=np.float)
if cost_matrix.size == 0:
return cost_matrix
track_features = np.asarray([track.curr_feature for track in tracks], dtype=np.float32)
det_features = np.asarray([track.curr_feature for track in detections], dtype=np.float32)
cost_matrix = cdist(track_features, det_features, metric)
return cost_matrix
def gate_cost_matrix(kf, cost_matrix, tracks, detections, only_position=False):
if cost_matrix.size == 0:
return cost_matrix
gating_dim = 2 if only_position else 4
gating_threshold = kalman_filter.chi2inv95[gating_dim]
measurements = np.asarray([det.to_xyah() for det in detections])
for row, track in enumerate(tracks):
gating_distance = kf.gating_distance(
track.mean, track.covariance, measurements, only_position)
cost_matrix[row, gating_distance > gating_threshold] = np.inf
return cost_matrix |