ultralytics 8.0.80 single-line docstring fixes (#2060)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
@ -27,7 +27,7 @@ class BaseTrack:
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frame_id = 0
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time_since_update = 0
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# multi-camera
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# Multi-camera
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location = (np.inf, np.inf)
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@property
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@ -100,7 +100,7 @@ class BOTSORT(BYTETracker):
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self.appearance_thresh = args.appearance_thresh
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if args.with_reid:
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# haven't supported BoT-SORT(reid) yet
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# Haven't supported BoT-SORT(reid) yet
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self.encoder = None
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# self.gmc = GMC(method=args.cmc_method, verbose=[args.name, args.ablation])
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self.gmc = GMC(method=args.cmc_method)
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@ -11,8 +11,7 @@ class STrack(BaseTrack):
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shared_kalman = KalmanFilterXYAH()
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def __init__(self, tlwh, score, cls):
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# wait activate
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"""wait activate."""
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self._tlwh = np.asarray(self.tlbr_to_tlwh(tlwh[:-1]), dtype=np.float32)
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self.kalman_filter = None
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self.mean, self.covariance = None, None
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@ -62,7 +61,7 @@ class STrack(BaseTrack):
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stracks[i].covariance = cov
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def activate(self, kalman_filter, frame_id):
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"""Start a new tracklet"""
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"""Start a new tracklet."""
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self.kalman_filter = kalman_filter
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self.track_id = self.next_id()
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self.mean, self.covariance = self.kalman_filter.initiate(self.convert_coords(self._tlwh))
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@ -179,7 +178,7 @@ class BYTETracker:
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scores = results.conf
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bboxes = results.xyxy
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# add index
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# Add index
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bboxes = np.concatenate([bboxes, np.arange(len(bboxes)).reshape(-1, 1)], axis=-1)
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cls = results.cls
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@ -196,7 +195,7 @@ class BYTETracker:
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cls_second = cls[inds_second]
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detections = self.init_track(dets, scores_keep, cls_keep, img)
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""" Add newly detected tracklets to tracked_stracks"""
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# Add newly detected tracklets to tracked_stracks
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unconfirmed = []
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tracked_stracks = [] # type: list[STrack]
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for track in self.tracked_stracks:
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@ -204,7 +203,7 @@ class BYTETracker:
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unconfirmed.append(track)
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else:
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tracked_stracks.append(track)
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""" Step 2: First association, with high score detection boxes"""
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# Step 2: First association, with high score detection boxes
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strack_pool = self.joint_stracks(tracked_stracks, self.lost_stracks)
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# Predict the current location with KF
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self.multi_predict(strack_pool)
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@ -225,7 +224,7 @@ class BYTETracker:
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else:
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track.re_activate(det, self.frame_id, new_id=False)
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refind_stracks.append(track)
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""" Step 3: Second association, with low score detection boxes"""
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# Step 3: Second association, with low score detection boxes
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# association the untrack to the low score detections
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detections_second = self.init_track(dets_second, scores_second, cls_second, img)
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r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked]
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@ -247,7 +246,7 @@ class BYTETracker:
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if track.state != TrackState.Lost:
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track.mark_lost()
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lost_stracks.append(track)
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"""Deal with unconfirmed tracks, usually tracks with only one beginning frame"""
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# Deal with unconfirmed tracks, usually tracks with only one beginning frame
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detections = [detections[i] for i in u_detection]
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dists = self.get_dists(unconfirmed, detections)
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matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)
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@ -258,14 +257,14 @@ class BYTETracker:
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track = unconfirmed[it]
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track.mark_removed()
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removed_stracks.append(track)
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""" Step 4: Init new stracks"""
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# Step 4: Init new stracks
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for inew in u_detection:
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track = detections[inew]
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if track.score < self.args.new_track_thresh:
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continue
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track.activate(self.kalman_filter, self.frame_id)
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activated_starcks.append(track)
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""" Step 5: Update state"""
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# Step 5: Update state
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for track in self.lost_stracks:
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if self.frame_id - track.end_frame > self.max_time_lost:
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track.mark_removed()
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@ -320,7 +319,7 @@ class BYTETracker:
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@staticmethod
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def sub_stracks(tlista, tlistb):
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""" DEPRECATED CODE in https://github.com/ultralytics/ultralytics/pull/1890/
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"""DEPRECATED CODE in https://github.com/ultralytics/ultralytics/pull/1890/
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stracks = {t.track_id: t for t in tlista}
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for t in tlistb:
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tid = t.track_id
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@ -83,8 +83,7 @@ class GMC:
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return np.eye(2, 3)
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def applyEcc(self, raw_frame, detections=None):
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# Initialize
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"""Initialize."""
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height, width, _ = raw_frame.shape
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frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
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H = np.eye(2, 3, dtype=np.float32)
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@ -116,8 +115,7 @@ class GMC:
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return H
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def applyFeatures(self, raw_frame, detections=None):
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# Initialize
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"""Initialize."""
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height, width, _ = raw_frame.shape
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frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
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H = np.eye(2, 3)
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@ -129,7 +127,7 @@ class GMC:
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width = width // self.downscale
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height = height // self.downscale
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# find the keypoints
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# Find the keypoints
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mask = np.zeros_like(frame)
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# mask[int(0.05 * height): int(0.95 * height), int(0.05 * width): int(0.95 * width)] = 255
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mask[int(0.02 * height):int(0.98 * height), int(0.02 * width):int(0.98 * width)] = 255
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@ -140,7 +138,7 @@ class GMC:
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keypoints = self.detector.detect(frame, mask)
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# compute the descriptors
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# Compute the descriptors
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keypoints, descriptors = self.extractor.compute(frame, keypoints)
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# Handle first frame
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@ -243,7 +241,7 @@ class GMC:
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return H
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def applySparseOptFlow(self, raw_frame, detections=None):
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# Initialize
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"""Initialize."""
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# t0 = time.time()
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height, width, _ = raw_frame.shape
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frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
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@ -254,7 +252,7 @@ class GMC:
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# frame = cv2.GaussianBlur(frame, (3, 3), 1.5)
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frame = cv2.resize(frame, (width // self.downscale, height // self.downscale))
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# find the keypoints
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# Find the keypoints
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keypoints = cv2.goodFeaturesToTrack(frame, mask=None, **self.feature_params)
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# Handle first frame
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@ -268,10 +266,10 @@ class GMC:
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return H
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# find correspondences
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# Find correspondences
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matchedKeypoints, status, err = cv2.calcOpticalFlowPyrLK(self.prevFrame, frame, self.prevKeyPoints, None)
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# leave good correspondences only
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# Leave good correspondences only
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prevPoints = []
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currPoints = []
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@ -8,6 +8,7 @@ from .kalman_filter import chi2inv95
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try:
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import lap # for linear_assignment
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assert lap.__version__ # verify package is not directory
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except (ImportError, AssertionError, AttributeError):
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from ultralytics.yolo.utils.checks import check_requirements
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@ -45,7 +46,7 @@ def _indices_to_matches(cost_matrix, indices, thresh):
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def linear_assignment(cost_matrix, thresh, use_lap=True):
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# Linear assignment implementations with scipy and lap.lapjv
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"""Linear assignment implementations with scipy and lap.lapjv."""
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if cost_matrix.size == 0:
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return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))
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