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ultralytics 8.0.81 single-line docstring updates (#2061)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-04-17 00:45:36 +02:00
committed by GitHub
parent 5bce1c3021
commit a38f227672
64 changed files with 620 additions and 58 deletions
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12
ultralytics/tracker/trackers/basetrack.py
View File

@ -6,6 +6,8 @@ import numpy as np
class TrackState:
"""Enumeration of possible object tracking states."""
New = 0
Tracked = 1
Lost = 2
@ -13,6 +15,8 @@ class TrackState:
class BaseTrack:
"""Base class for object tracking, handling basic track attributes and operations."""
_count = 0
track_id = 0
@ -32,28 +36,36 @@ class BaseTrack:
@property
def end_frame(self):
"""Return the last frame ID of the track."""
return self.frame_id
@staticmethod
def next_id():
"""Increment and return the global track ID counter."""
BaseTrack._count += 1
return BaseTrack._count
def activate(self, *args):
"""Activate the track with the provided arguments."""
raise NotImplementedError
def predict(self):
"""Predict the next state of the track."""
raise NotImplementedError
def update(self, *args, **kwargs):
"""Update the track with new observations."""
raise NotImplementedError
def mark_lost(self):
"""Mark the track as lost."""
self.state = TrackState.Lost
def mark_removed(self):
"""Mark the track as removed."""
self.state = TrackState.Removed
@staticmethod
def reset_id():
"""Reset the global track ID counter."""
BaseTrack._count = 0

12
ultralytics/tracker/trackers/bot_sort.py
View File

@ -15,6 +15,7 @@ class BOTrack(STrack):
shared_kalman = KalmanFilterXYWH()
def __init__(self, tlwh, score, cls, feat=None, feat_history=50):
"""Initialize YOLOv8 object with temporal parameters, such as feature history, alpha and current features."""
super().__init__(tlwh, score, cls)
self.smooth_feat = None
@ -25,6 +26,7 @@ class BOTrack(STrack):
self.alpha = 0.9
def update_features(self, feat):
"""Update features vector and smooth it using exponential moving average."""
feat /= np.linalg.norm(feat)
self.curr_feat = feat
if self.smooth_feat is None:
@ -35,6 +37,7 @@ class BOTrack(STrack):
self.smooth_feat /= np.linalg.norm(self.smooth_feat)
def predict(self):
"""Predicts the mean and covariance using Kalman filter."""
mean_state = self.mean.copy()
if self.state != TrackState.Tracked:
mean_state[6] = 0
@ -43,11 +46,13 @@ class BOTrack(STrack):
self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)
def re_activate(self, new_track, frame_id, new_id=False):
"""Reactivates a track with updated features and optionally assigns a new ID."""
if new_track.curr_feat is not None:
self.update_features(new_track.curr_feat)
super().re_activate(new_track, frame_id, new_id)
def update(self, new_track, frame_id):
"""Update the YOLOv8 instance with new track and frame ID."""
if new_track.curr_feat is not None:
self.update_features(new_track.curr_feat)
super().update(new_track, frame_id)
@ -65,6 +70,7 @@ class BOTrack(STrack):
@staticmethod
def multi_predict(stracks):
"""Predicts the mean and covariance of multiple object tracks using shared Kalman filter."""
if len(stracks) <= 0:
return
multi_mean = np.asarray([st.mean.copy() for st in stracks])
@ -79,6 +85,7 @@ class BOTrack(STrack):
stracks[i].covariance = cov
def convert_coords(self, tlwh):
"""Converts Top-Left-Width-Height bounding box coordinates to X-Y-Width-Height format."""
return self.tlwh_to_xywh(tlwh)
@staticmethod
@ -94,6 +101,7 @@ class BOTrack(STrack):
class BOTSORT(BYTETracker):
def __init__(self, args, frame_rate=30):
"""Initialize YOLOv8 object with ReID module and GMC algorithm."""
super().__init__(args, frame_rate)
# ReID module
self.proximity_thresh = args.proximity_thresh
@ -106,9 +114,11 @@ class BOTSORT(BYTETracker):
self.gmc = GMC(method=args.cmc_method)
def get_kalmanfilter(self):
"""Returns an instance of KalmanFilterXYWH for object tracking."""
return KalmanFilterXYWH()
def init_track(self, dets, scores, cls, img=None):
"""Initialize track with detections, scores, and classes."""
if len(dets) == 0:
return []
if self.args.with_reid and self.encoder is not None:
@ -118,6 +128,7 @@ class BOTSORT(BYTETracker):
return [BOTrack(xyxy, s, c) for (xyxy, s, c) in zip(dets, scores, cls)] # detections
def get_dists(self, tracks, detections):
"""Get distances between tracks and detections using IoU and (optionally) ReID embeddings."""
dists = matching.iou_distance(tracks, detections)
dists_mask = (dists > self.proximity_thresh)
@ -133,4 +144,5 @@ class BOTSORT(BYTETracker):
return dists
def multi_predict(self, tracks):
"""Predict and track multiple objects with YOLOv8 model."""
BOTrack.multi_predict(tracks)

17
ultralytics/tracker/trackers/byte_tracker.py
View File

@ -23,6 +23,7 @@ class STrack(BaseTrack):
self.idx = tlwh[-1]
def predict(self):
"""Predicts mean and covariance using Kalman filter."""
mean_state = self.mean.copy()
if self.state != TrackState.Tracked:
mean_state[7] = 0
@ -30,6 +31,7 @@ class STrack(BaseTrack):
@staticmethod
def multi_predict(stracks):
"""Perform multi-object predictive tracking using Kalman filter for given stracks."""
if len(stracks) <= 0:
return
multi_mean = np.asarray([st.mean.copy() for st in stracks])
@ -44,6 +46,7 @@ class STrack(BaseTrack):
@staticmethod
def multi_gmc(stracks, H=np.eye(2, 3)):
"""Update state tracks positions and covariances using a homography matrix."""
if len(stracks) > 0:
multi_mean = np.asarray([st.mean.copy() for st in stracks])
multi_covariance = np.asarray([st.covariance for st in stracks])
@ -74,6 +77,7 @@ class STrack(BaseTrack):
self.start_frame = frame_id
def re_activate(self, new_track, frame_id, new_id=False):
"""Reactivates a previously lost track with a new detection."""
self.mean, self.covariance = self.kalman_filter.update(self.mean, self.covariance,
self.convert_coords(new_track.tlwh))
self.tracklet_len = 0
@ -107,6 +111,7 @@ class STrack(BaseTrack):
self.idx = new_track.idx
def convert_coords(self, tlwh):
"""Convert a bounding box's top-left-width-height format to its x-y-angle-height equivalent."""
return self.tlwh_to_xyah(tlwh)
@property
@ -142,23 +147,27 @@ class STrack(BaseTrack):
@staticmethod
def tlbr_to_tlwh(tlbr):
"""Converts top-left bottom-right format to top-left width height format."""
ret = np.asarray(tlbr).copy()
ret[2:] -= ret[:2]
return ret
@staticmethod
def tlwh_to_tlbr(tlwh):
"""Converts tlwh bounding box format to tlbr format."""
ret = np.asarray(tlwh).copy()
ret[2:] += ret[:2]
return ret
def __repr__(self):
"""Return a string representation of the BYTETracker object with start and end frames and track ID."""
return f'OT_{self.track_id}_({self.start_frame}-{self.end_frame})'
class BYTETracker:
def __init__(self, args, frame_rate=30):
"""Initialize a YOLOv8 object to track objects with given arguments and frame rate."""
self.tracked_stracks = [] # type: list[STrack]
self.lost_stracks = [] # type: list[STrack]
self.removed_stracks = [] # type: list[STrack]
@ -170,6 +179,7 @@ class BYTETracker:
self.reset_id()
def update(self, results, img=None):
"""Updates object tracker with new detections and returns tracked object bounding boxes."""
self.frame_id += 1
activated_starcks = []
refind_stracks = []
@ -285,12 +295,15 @@ class BYTETracker:
dtype=np.float32)
def get_kalmanfilter(self):
"""Returns a Kalman filter object for tracking bounding boxes."""
return KalmanFilterXYAH()
def init_track(self, dets, scores, cls, img=None):
"""Initialize object tracking with detections and scores using STrack algorithm."""
return [STrack(xyxy, s, c) for (xyxy, s, c) in zip(dets, scores, cls)] if len(dets) else [] # detections
def get_dists(self, tracks, detections):
"""Calculates the distance between tracks and detections using IOU and fuses scores."""
dists = matching.iou_distance(tracks, detections)
# TODO: mot20
# if not self.args.mot20:
@ -298,13 +311,16 @@ class BYTETracker:
return dists
def multi_predict(self, tracks):
"""Returns the predicted tracks using the YOLOv8 network."""
STrack.multi_predict(tracks)
def reset_id(self):
"""Resets the ID counter of STrack."""
STrack.reset_id()
@staticmethod
def joint_stracks(tlista, tlistb):
"""Combine two lists of stracks into a single one."""
exists = {}
res = []
for t in tlista:
@ -332,6 +348,7 @@ class BYTETracker:
@staticmethod
def remove_duplicate_stracks(stracksa, stracksb):
"""Remove duplicate stracks with non-maximum IOU distance."""
pdist = matching.iou_distance(stracksa, stracksb)
pairs = np.where(pdist < 0.15)
dupa, dupb = [], []