Merge pull request #1054 from mikel-brostrom/fix-strong-sort

Clean up strong sort

boxmot/appearance/reid_multibackend.py

@@ -284,9 +284,7 @@ def from_numpy(self, x):
         return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x
 
     def warmup(self, imgsz=[(256, 128, 3)]):
-        # Warmup model by running inference once
-        warmup_types = self.pt, self.jit, self.onnx, self.engine, self.tflite
-        if any(warmup_types) and self.device.type != "cpu":
+        # warmup model by running inference once
+        if self.device.type != "cpu":
             im = [np.empty(*imgsz).astype(np.uint8)]  # input
-            for _ in range(2 if self.jit else 1):  #
-                self.forward(im)  # warmup
+            self.forward(im)  # warmup

boxmot/configs/deepocsort.yaml

@@ -1,7 +1,7 @@
 # Trial number:      0
 # HOTA, MOTA, IDF1:  [27.884, 11.425, 21.545]
 alpha_fixed_emb: 0.95
-asso_func: iou
+asso_func: giou
 aw_off: false
 aw_param: 0.5
 cmc_off: false
@@ -12,6 +12,6 @@ embedding_off: false
 inertia: 0.2
 iou_thresh: 0.3
 max_age: 30
-min_hits: 3
+min_hits: 1
 new_kf_off: false
 w_association_emb: 0.75

boxmot/configs/ocsort.yaml

@@ -2,10 +2,10 @@
 # HOTA, MOTA, IDF1:  [55.567]
 asso_func: giou
 conf_thres: 0.5122620708221085
-delta_t: 1
+delta_t: 3
 det_thresh: 0
-inertia: 0.3941737016672115
-iou_thresh: 0.22136877277096445
-max_age: 50
+inertia: 0.2
+iou_thresh: 0.3
+max_age: 30
 min_hits: 1
 use_byte: false

boxmot/configs/strongsort.yaml

@@ -1,10 +1,9 @@
 ecc: true
-ema_alpha: 0.8962157769329083
-max_age: 40
-max_dist: 0.1594374041012136
-max_iou_dist: 0.5431835667667874
-max_unmatched_preds: 0
+ema_alpha: 0.8
+max_age: 30
+max_dist: 0.2
+max_iou_dist: 0.7
 mc_lambda: 0.995
-n_init: 3
+n_init: 1
 nn_budget: 100
 conf_thres: 0.5122620708221085

boxmot/tracker_zoo.py

@@ -30,7 +30,6 @@ def create_tracker(tracker_type, tracker_config, reid_weights, device, half, per
             max_dist=cfg.max_dist,
             max_iou_dist=cfg.max_iou_dist,
             max_age=cfg.max_age,
-            max_unmatched_preds=cfg.max_unmatched_preds,
             n_init=cfg.n_init,
             nn_budget=cfg.nn_budget,
             mc_lambda=cfg.mc_lambda,

boxmot/trackers/deepocsort/deep_ocsort.py

@@ -341,7 +341,7 @@ def __init__(
         self.per_class = per_class
         KalmanBoxTracker.count = 0
 
-        self.embedder = ReIDDetectMultiBackend(weights=model_weights, device=device, fp16=fp16)
+        self.model = ReIDDetectMultiBackend(weights=model_weights, device=device, fp16=fp16)
         # "similarity transforms using feature point extraction, optical flow, and RANSAC"
         self.cmc = get_cmc_method('sof')()
         self.embedding_off = embedding_off
@@ -528,7 +528,7 @@ def _get_features(self, bbox_xyxy, ori_img):
             im = ori_img[y1:y2, x1:x2]
             im_crops.append(im)
         if im_crops:
-            features = self.embedder(im_crops).cpu()
+            features = self.model(im_crops).cpu()
         else:
             features = np.array([])
 

boxmot/trackers/strongsort/sort/detection.py

@@ -32,14 +32,6 @@ def __init__(self, tlwh, confidence, feature):
         self.confidence = float(confidence)
         self.feature = np.asarray(feature.cpu(), dtype=np.float32)
 
-    def to_tlbr(self):
-        """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
-        `(top left, bottom right)`.
-        """
-        ret = self.tlwh.copy()
-        ret[2:] += ret[:2]
-        return ret
-
     def to_xyah(self):
         """Convert bounding box to format `(center x, center y, aspect ratio,
         height)`, where the aspect ratio is `width / height`.
@@ -48,13 +40,3 @@ def to_xyah(self):
         ret[:2] += ret[2:] / 2
         ret[2] /= ret[3]
         return ret
-
-
-def to_xyah_ext(bbox):
-    """Convert bounding box to format `(center x, center y, aspect ratio,
-    height)`, where the aspect ratio is `width / height`.
-    """
-    ret = bbox.copy()
-    ret[:2] += ret[2:] / 2
-    ret[2] /= ret[3]
-    return ret

boxmot/trackers/strongsort/sort/linear_assignment.py

@@ -127,11 +127,7 @@ def matching_cascade(
 
     unmatched_detections = detection_indices
     matches = []
-    track_indices_l = [
-        k
-        for k in track_indices
-        # if tracks[k].time_since_update == 1 + level
-    ]
+    track_indices_l = [k for k in track_indices]
     matches_l, _, unmatched_detections = min_cost_matching(
         distance_metric,
         max_distance,
@@ -186,8 +182,7 @@ def gate_cost_matrix(
     ndarray
         Returns the modified cost matrix.
     """
-    gating_dim = 2 if only_position else 4
-    gating_threshold = chi2inv95[gating_dim]
+    gating_threshold = chi2inv95[4]
     measurements = np.asarray([detections[i].to_xyah() for i in detection_indices])
     for row, track_idx in enumerate(track_indices):
         track = tracks[track_idx]

boxmot/trackers/strongsort/sort/track.py

@@ -4,7 +4,7 @@
 
 import numpy as np
 
-from ....motion.kalman_filters.adapters import StrongSortKalmanFilterAdapter
+from boxmot.motion.kalman_filters.adapters import StrongSortKalmanFilterAdapter
 
 
 class TrackState:
@@ -85,8 +85,6 @@ def __init__(
         self.hits = 1
         self.age = 1
         self.time_since_update = 0
-        self.max_num_updates_wo_assignment = 7
-        self.updates_wo_assignment = 0
         self.ema_alpha = ema_alpha
 
         self.state = TrackState.Tentative
@@ -134,25 +132,13 @@ def to_tlbr(self):
         ret[2:] = ret[:2] + ret[2:]
         return ret
 
-    def get_matrix(self, matrix):
-        eye = np.eye(3)
-        dist = np.linalg.norm(eye - matrix)
-        if dist < 100:
-            return matrix
-        else:
-            return eye
-
     def camera_update(self, warp_matrix):
-        if warp_matrix is None:
-            return
         [a, b] = warp_matrix
         warp_matrix = np.array([a, b, [0, 0, 1]])
         warp_matrix = warp_matrix.tolist()
-        matrix = self.get_matrix(warp_matrix)
-
         x1, y1, x2, y2 = self.to_tlbr()
-        x1_, y1_, _ = matrix @ np.array([x1, y1, 1]).T
-        x2_, y2_, _ = matrix @ np.array([x2, y2, 1]).T
+        x1_, y1_, _ = warp_matrix @ np.array([x1, y1, 1]).T
+        x2_, y2_, _ = warp_matrix @ np.array([x2, y2, 1]).T
         w, h = x2_ - x1_, y2_ - y1_
         cx, cy = x1_ + w / 2, y1_ + h / 2
         self.mean[:4] = [cx, cy, w / h, h]
@@ -169,16 +155,6 @@ def predict(self):
         self.age += 1
         self.time_since_update += 1
 
-    def update_kf(self, bbox, confidence=0.5):
-        self.updates_wo_assignment = self.updates_wo_assignment + 1
-        self.mean, self.covariance = self.kf.update(
-            self.mean, self.covariance, bbox, confidence
-        )
-        tlbr = self.to_tlbr()
-        x_c = int((tlbr[0] + tlbr[2]) / 2)
-        y_c = int((tlbr[1] + tlbr[3]) / 2)
-        self.q.append(("predupdate", (x_c, y_c)))
-
     def update(self, detection, class_id, conf):
         """Perform Kalman filter measurement update step and update the feature
         cache.
@@ -188,7 +164,7 @@ def update(self, detection, class_id, conf):
             The associated detection.
         """
         self.conf = conf
-        self.class_id = class_id.astype("int64")
+        self.class_id = class_id.astype("int")
         self.mean, self.covariance = self.kf.update(
             self.mean, self.covariance, detection.to_xyah(), detection.confidence
         )
@@ -206,11 +182,6 @@ def update(self, detection, class_id, conf):
         if self.state == TrackState.Tentative and self.hits >= self._n_init:
             self.state = TrackState.Confirmed
 
-        tlbr = self.to_tlbr()
-        x_c = int((tlbr[0] + tlbr[2]) / 2)
-        y_c = int((tlbr[1] + tlbr[3]) / 2)
-        self.q.append(("observationupdate", (x_c, y_c)))
-
     def mark_missed(self):
         """Mark this track as missed (no association at the current time step)."""
         if self.state == TrackState.Tentative:

boxmot/trackers/strongsort/sort/tracker.py

@@ -4,11 +4,10 @@
 
 import numpy as np
 
-from boxmot.motion.cmc.ecc import ECC
-
-from ....utils.matching import chi2inv95
-from . import detection, iou_matching, linear_assignment
-from .track import Track
+from boxmot.motion.cmc import get_cmc_method
+from boxmot.trackers.strongsort.sort import iou_matching, linear_assignment
+from boxmot.trackers.strongsort.sort.track import Track
+from boxmot.utils.matching import chi2inv95
 
 
 class Tracker:
@@ -43,7 +42,6 @@ def __init__(
         metric,
         max_iou_dist=0.9,
         max_age=30,
-        max_unmatched_preds=7,
         n_init=3,
         _lambda=0,
         ema_alpha=0.9,
@@ -56,11 +54,10 @@ def __init__(
         self._lambda = _lambda
         self.ema_alpha = ema_alpha
         self.mc_lambda = mc_lambda
-        self.max_unmatched_preds = max_unmatched_preds
 
         self.tracks = []
         self._next_id = 1
-        self.ecc = ECC()
+        self.cmc = get_cmc_method('ecc')()
 
     def predict(self):
         """Propagate track state distributions one time step forward.
@@ -75,23 +72,6 @@ def increment_ages(self):
             track.increment_age()
             track.mark_missed()
 
-    def camera_update(self, curr_img):
-        if len(self.tracks) > 0:
-            warp_matrix = self.ecc.apply(curr_img=curr_img, dets=None)
-        for track in self.tracks:
-            track.camera_update(warp_matrix)
-
-    def pred_n_update_all_tracks(self):
-        """Perform predictions and updates for all tracks by its own predicted state."""
-        self.predict()
-        for t in self.tracks:
-            if (
-                self.max_unmatched_preds != 0 and
-                t.updates_wo_assignment < t.max_num_updates_wo_assignment
-            ):
-                bbox = t.to_tlwh()
-                t.update_kf(detection.to_xyah_ext(bbox))
-
     def update(self, detections, classes, confidences):
         """Perform measurement update and track management.
 
@@ -113,13 +93,6 @@ def update(self, detections, classes, confidences):
             )
         for track_idx in unmatched_tracks:
             self.tracks[track_idx].mark_missed()
-            if (
-                self.max_unmatched_preds != 0 and
-                self.tracks[track_idx].updates_wo_assignment <
-                self.tracks[track_idx].max_num_updates_wo_assignment
-            ):
-                bbox = self.tracks[track_idx].to_tlwh()
-                self.tracks[track_idx].update_kf(detection.to_xyah_ext(bbox))
         for detection_idx in unmatched_detections:
             self._initiate_track(
                 detections[detection_idx],
@@ -140,39 +113,6 @@ def update(self, detections, classes, confidences):
             np.asarray(features), np.asarray(targets), active_targets
         )
 
-    def _full_cost_metric(self, tracks, dets, track_indices, detection_indices):
-        """
-        This implements the full lambda-based cost-metric. However, in doing so, it disregards
-        the possibility to gate the position only which is provided by
-        linear_assignment.gate_cost_matrix(). Instead, I gate by everything.
-        Note that the Mahalanobis distance is itself an unnormalised metric. Given the cosine
-        distance being normalised, we employ a quick and dirty normalisation based on the
-        threshold: that is, we divide the positional-cost by the gating threshold, thus ensuring
-        that the valid values range 0-1.
-        Note also that the authors work with the squared distance. I also sqrt this, so that it
-        is more intuitive in terms of values.
-        """
-        # Compute First the Position-based Cost Matrix
-        pos_cost = np.empty([len(track_indices), len(detection_indices)])
-        msrs = np.asarray([dets[i].to_xyah() for i in detection_indices])
-        for row, track_idx in enumerate(track_indices):
-            pos_cost[row, :] = (
-                np.sqrt(tracks[track_idx].kf.gating_distance(msrs)) /
-                self.GATING_THRESHOLD
-            )
-        pos_gate = pos_cost > 1.0
-        # Now Compute the Appearance-based Cost Matrix
-        app_cost = self.metric.distance(
-            np.array([dets[i].feature for i in detection_indices]),
-            np.array([tracks[i].track_id for i in track_indices]),
-        )
-        app_gate = app_cost > self.metric.matching_threshold
-        # Now combine and threshold
-        cost_matrix = self._lambda * pos_cost + (1 - self._lambda) * app_cost
-        cost_matrix[np.logical_or(pos_gate, app_gate)] = linear_assignment.INFTY_COST
-        # Return Matrix
-        return cost_matrix
-
     def _match(self, detections):
         def gated_metric(tracks, dets, track_indices, detection_indices):
             features = np.array([dets[i].feature for i in detection_indices])
@@ -191,16 +131,10 @@ def gated_metric(tracks, dets, track_indices, detection_indices):
 
         # Split track set into confirmed and unconfirmed tracks.
         confirmed_tracks = [i for i, t in enumerate(self.tracks) if t.is_confirmed()]
-        unconfirmed_tracks = [
-            i for i, t in enumerate(self.tracks) if not t.is_confirmed()
-        ]
+        unconfirmed_tracks = [i for i, t in enumerate(self.tracks) if not t.is_confirmed()]
 
         # Associate confirmed tracks using appearance features.
-        (
-            matches_a,
-            unmatched_tracks_a,
-            unmatched_detections,
-        ) = linear_assignment.matching_cascade(
+        matches_a, unmatched_tracks_a, unmatched_detections = linear_assignment.matching_cascade(
             gated_metric,
             self.metric.matching_threshold,
             self.max_age,
@@ -216,11 +150,8 @@ def gated_metric(tracks, dets, track_indices, detection_indices):
         unmatched_tracks_a = [
             k for k in unmatched_tracks_a if self.tracks[k].time_since_update != 1
         ]
-        (
-            matches_b,
-            unmatched_tracks_b,
-            unmatched_detections,
-        ) = linear_assignment.min_cost_matching(
+
+        matches_b, unmatched_tracks_b, unmatched_detections = linear_assignment.min_cost_matching(
             iou_matching.iou_cost,
             self.max_iou_dist,
             self.tracks,