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Update matching.py #453

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f7c3b82
Update matching.py
sowrath123 Aug 12, 2025
b7ba64a
Update byte_tracker.py
sowrath123 Aug 22, 2025
376878c
Update matching.py
sowrath123 Aug 22, 2025
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4 changes: 2 additions & 2 deletions yolox/tracker/byte_tracker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,15 +7,15 @@
import torch.nn.functional as F

from .kalman_filter import KalmanFilter
from yolox.tracker import matching
from ByteTrack.yolox.tracker import matching
from .basetrack import BaseTrack, TrackState

class STrack(BaseTrack):
shared_kalman = KalmanFilter()
def __init__(self, tlwh, score):

# wait activate
self._tlwh = np.asarray(tlwh, dtype=np.float)
self._tlwh = np.asarray(tlwh, dtype=float)
self.kalman_filter = None
self.mean, self.covariance = None, None
self.is_activated = False
Expand Down
16 changes: 8 additions & 8 deletions yolox/tracker/matching.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
from scipy.spatial.distance import cdist

from cython_bbox import bbox_overlaps as bbox_ious
from yolox.tracker import kalman_filter
from ByteTrack.yolox.tracker import kalman_filter
import time

def merge_matches(m1, m2, shape):
Expand Down Expand Up @@ -58,13 +58,13 @@ def ious(atlbrs, btlbrs):

:rtype ious np.ndarray
"""
ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)
ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=float)
if ious.size == 0:
return ious

ious = bbox_ious(
np.ascontiguousarray(atlbrs, dtype=np.float),
np.ascontiguousarray(btlbrs, dtype=np.float)
np.ascontiguousarray(atlbrs, dtype=float),
np.ascontiguousarray(btlbrs, dtype=float)
)

return ious
Expand Down Expand Up @@ -118,13 +118,13 @@ def embedding_distance(tracks, detections, metric='cosine'):
:return: cost_matrix np.ndarray
"""

cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=float)
if cost_matrix.size == 0:
return cost_matrix
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
det_features = np.asarray([track.curr_feat for track in detections], dtype=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)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=float)
cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features
return cost_matrix

Expand Down Expand Up @@ -178,4 +178,4 @@ def fuse_score(cost_matrix, 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
return fuse_cost