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import numpy as np
from .ocsort import (
KalmanBoxTracker,
associate,
ciou_batch,
ct_dist,
diou_batch,
giou_batch,
iou_batch,
linear_assignment,
)
from .onnx_models import HandClassification, HandDetection
from .utils import Deque, Drawer, Hand
from .config import is_monitoring_enabled
# Configure logfire monitoring if available
logfire = None
if is_monitoring_enabled():
try:
import logfire
except ImportError:
logfire = None
ASSO_FUNCS = {"iou": iou_batch, "giou": giou_batch, "ciou": ciou_batch, "diou": diou_batch, "ct_dist": ct_dist}
def k_previous_obs(observations, cur_age, k):
if len(observations) == 0:
return [-1, -1, -1, -1, -1]
for i in range(k):
dt = k - i
if cur_age - dt in observations:
return observations[cur_age - dt]
max_age = max(observations.keys())
return observations[max_age]
class MainController:
"""
Main tracking function.
Class contains a list of tracks, each track contains a KalmanBoxTracker object and a Deque object with Hand objects.
"""
def __init__(
self, detection_model, classification_model, max_age=30, min_hits=3, iou_threshold=0.3, maxlen=30, min_frames=20
):
"""
Parameters
----------
detection_model : str
Path to detection model.
classification_model : str
Path to classification model.
max_age : int
Maximum age of track.
min_hits : int
Minimum number of hits to confirm track.
iou_threshold : float
IOU threshold for track association.
maxlen : int
Maximum length of deque in track.
min_frames : int
Minimum number of frames to confirm track.
"""
self.maxlen = maxlen
self.min_frames = min_frames
self.max_age = max_age
self.min_hits = min_hits
self.delta_t = 3
self.iou_threshold = iou_threshold
self.inertia = 0.2
self.asso_func = ASSO_FUNCS["giou"]
self.tracks = []
self.frame_count = 0
self.detection_model = HandDetection(detection_model)
self.classification_model = HandClassification(classification_model)
self.drawer = Drawer()
def update(self, dets=np.empty((0, 5)), labels=None):
"""
Parameters
----------
dets : np.array
Bounding boxes with shape [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...] .
Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).
labels : np.array
Labels with shape (N, 1) where N is number of bounding boxes.
Returns
-------
np.array
Returns the similar array, where the last column is the object ID.
Notes
-----
The number of objects returned may differ from the number of detections provided.
"""
# Advance frame count on every call to keep tracker state in sync with real time.
# This method is required to be called once per frame (even if there are no detections),
# so we must advance the internal Kalman filters and aging logic on empty frames as well.
self.frame_count += 1
# Get predicted locations from existing trackers for this frame.
# This advances age/time_since_update and is required also when there are no detections,
# ensuring tracks can age out (max_age) and do not persist indefinitely across gaps.
trks = np.zeros((len(self.tracks), 5))
to_del = []
ret = []
lbs = []
for t, trk in enumerate(trks):
pos = self.tracks[t]["tracker"].predict()[0]
trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
if np.any(np.isnan(pos)):
to_del.append(t)
trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
for t in reversed(to_del):
self.tracks.pop(t)
velocities = np.array(
[
trk["tracker"].velocity if trk["tracker"].velocity is not None else np.array((0, 0))
for trk in self.tracks
]
)
last_boxes = np.array([trk["tracker"].last_observation for trk in self.tracks])
k_observations = np.array(
[k_previous_obs(trk["tracker"].observations, trk["tracker"].age, self.delta_t) for trk in self.tracks]
)
"""
First round of association
"""
matched, unmatched_dets, unmatched_trks = associate(
dets, trks, self.iou_threshold, velocities, k_observations, self.inertia
)
for m in matched:
self.tracks[m[1]]["tracker"].update(dets[m[0], :])
self.tracks[m[1]]["hands"].append(Hand(bbox=dets[m[0], :4], gesture=labels[m[0]]))
"""
Second round of associaton by OCR
"""
if unmatched_dets.shape[0] > 0 and unmatched_trks.shape[0] > 0:
left_dets = dets[unmatched_dets]
left_trks = last_boxes[unmatched_trks]
iou_left = self.asso_func(left_dets, left_trks)
iou_left = np.array(iou_left)
if iou_left.max() > self.iou_threshold:
"""
NOTE: by using a lower threshold, e.g., self.iou_threshold - 0.1, you may
get a higher performance especially on MOT17/MOT20 datasets. But we keep it
uniform here for simplicity
"""
rematched_indices = linear_assignment(-iou_left)
to_remove_det_indices = []
to_remove_trk_indices = []
for m in rematched_indices:
det_ind, trk_ind = unmatched_dets[m[0]], unmatched_trks[m[1]]
if iou_left[m[0], m[1]] < self.iou_threshold:
continue
self.tracks[trk_ind]["tracker"].update(dets[det_ind, :])
self.tracks[trk_ind]["hands"].append(Hand(bbox=dets[det_ind, :4], gesture=labels[det_ind]))
to_remove_det_indices.append(det_ind)
to_remove_trk_indices.append(trk_ind)
unmatched_dets = np.setdiff1d(unmatched_dets, np.array(to_remove_det_indices))
unmatched_trks = np.setdiff1d(unmatched_trks, np.array(to_remove_trk_indices))
# For unmatched trackers (including the case with no detections),
# update with None to keep the filter consistent and append a dummy Hand.
for m in unmatched_trks:
self.tracks[m]["tracker"].update(None)
self.tracks[m]["hands"].append(Hand(bbox=None, gesture=None))
# create and initialise new trackers for unmatched detections
for i in unmatched_dets:
self.tracks.append(
{
"hands": Deque(self.maxlen, self.min_frames),
"tracker": KalmanBoxTracker(dets[i, :], delta_t=self.delta_t),
}
)
i = len(self.tracks)
for trk in reversed(self.tracks):
if trk["tracker"].last_observation.sum() < 0:
d = trk["tracker"].get_state()[0]
else:
"""
this is optional to use the recent observation or the kalman filter prediction,
we didn't notice significant difference here
"""
d = trk["tracker"].last_observation[:4]
if (trk["tracker"].time_since_update < 1) and (
trk["tracker"].hit_streak >= self.min_hits or self.frame_count <= self.min_hits
):
# +1 as MOT benchmark requires positive
ret.append(np.concatenate((d, [trk["tracker"].id + 1])).reshape(1, -1))
if len(trk["hands"]) > 0:
lbs.append(trk["hands"][-1].gesture)
else:
lbs.append(None)
i -= 1
# remove dead tracklet
if trk["tracker"].time_since_update > self.max_age:
self.tracks.pop(i)
if len(ret) > 0:
return np.concatenate(ret), lbs
return np.empty((0, 5)), np.empty((0, 1))
def __call__(self, frame):
"""
Parameters
----------
frame : np.array
Image frame with shape (H, W, 3).
Returns
-------
list of np.array
"""
# Log frame processing if monitoring is enabled
if logfire is not None:
with logfire.span('frame_processing', frame_shape=frame.shape):
bboxes, probs = self.detection_model(frame)
if len(bboxes):
detection_scores = np.asarray(probs).tolist()
logfire.debug(
'Hand detections found',
num_detections=len(bboxes),
detection_scores=detection_scores,
)
labels = self.classification_model(frame, bboxes)
bboxes = np.concatenate((bboxes, np.expand_dims(probs, axis=1)), axis=1)
new_bboxes, labels = self.update(dets=bboxes, labels=labels)
# Log classification results
if labels is not None and len(labels) > 0:
labels_list = np.asarray(labels).tolist()
gesture_names = [
f"gesture_{label}" if label is not None else "none"
for label in labels_list
]
logfire.debug(
'Gesture classifications',
labels=labels_list,
gesture_names=gesture_names,
)
return new_bboxes[:, :-1], new_bboxes[:, -1], labels
else:
logfire.debug('No hand detections in frame')
self.update(np.empty((0, 5)), None)
return None, None, None
else:
# Original logic without monitoring
bboxes, probs = self.detection_model(frame)
if len(bboxes):
labels = self.classification_model(frame, bboxes)
bboxes = np.concatenate((bboxes, np.expand_dims(probs, axis=1)), axis=1)
new_bboxes, labels = self.update(dets=bboxes, labels=labels)
return new_bboxes[:, :-1], new_bboxes[:, -1], labels
else:
self.update(np.empty((0, 5)), None)
return None, None, None
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