Spaces:
Runtime error
Runtime error
File size: 6,200 Bytes
7734d5b |
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 |
import cv2
import numpy as np
import scipy
import lap
from scipy.spatial.distance import cdist
from cython_bbox import bbox_overlaps as bbox_ious
from yolox.tracker import kalman_filter
import time
def merge_matches(m1, m2, shape):
O,P,Q = shape
m1 = np.asarray(m1)
m2 = np.asarray(m2)
M1 = scipy.sparse.coo_matrix((np.ones(len(m1)), (m1[:, 0], m1[:, 1])), shape=(O, P))
M2 = scipy.sparse.coo_matrix((np.ones(len(m2)), (m2[:, 0], m2[:, 1])), shape=(P, Q))
mask = M1*M2
match = mask.nonzero()
match = list(zip(match[0], match[1]))
unmatched_O = tuple(set(range(O)) - set([i for i, j in match]))
unmatched_Q = tuple(set(range(Q)) - set([j for i, j in match]))
return match, unmatched_O, unmatched_Q
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
"""
if (len(atracks)>0 and isinstance(atracks[0], np.ndarray)) or (len(btracks) > 0 and isinstance(btracks[0], np.ndarray)):
atlbrs = atracks
btlbrs = btracks
else:
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 v_iou_distance(atracks, btracks):
"""
Compute cost based on IoU
:type atracks: list[STrack]
:type btracks: list[STrack]
:rtype cost_matrix np.ndarray
"""
if (len(atracks)>0 and isinstance(atracks[0], np.ndarray)) or (len(btracks) > 0 and isinstance(btracks[0], np.ndarray)):
atlbrs = atracks
btlbrs = btracks
else:
atlbrs = [track.tlwh_to_tlbr(track.pred_bbox) for track in atracks]
btlbrs = [track.tlwh_to_tlbr(track.pred_bbox) for track in btracks]
_ious = ious(atlbrs, btlbrs)
cost_matrix = 1 - _ious
return cost_matrix
def embedding_distance(tracks, detections, metric='cosine'):
"""
:param tracks: list[STrack]
:param detections: list[BaseTrack]
:param metric:
:return: 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_feat for track in detections], dtype=np.float)
#for i, track in enumerate(tracks):
#cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric))
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float)
cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features
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
def fuse_motion(kf, cost_matrix, tracks, detections, only_position=False, lambda_=0.98):
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, metric='maha')
cost_matrix[row, gating_distance > gating_threshold] = np.inf
cost_matrix[row] = lambda_ * cost_matrix[row] + (1 - lambda_) * gating_distance
return cost_matrix
def fuse_iou(cost_matrix, tracks, detections):
if cost_matrix.size == 0:
return cost_matrix
reid_sim = 1 - cost_matrix
iou_dist = iou_distance(tracks, detections)
iou_sim = 1 - iou_dist
fuse_sim = reid_sim * (1 + iou_sim) / 2
det_scores = np.array([det.score for det in detections])
det_scores = np.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0)
#fuse_sim = fuse_sim * (1 + det_scores) / 2
fuse_cost = 1 - fuse_sim
return fuse_cost
def fuse_score(cost_matrix, detections):
if cost_matrix.size == 0:
return cost_matrix
iou_sim = 1 - cost_matrix
det_scores = np.array([det.score for det in detections])
det_scores = np.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0)
fuse_sim = iou_sim * det_scores
fuse_cost = 1 - fuse_sim
return fuse_cost |