`ultralytics 8.1.5` add OBB Tracking support (#7731)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Hassaan Farooq <103611273+hassaanfarooq01@users.noreply.github.com>

docs/en/reference/utils/ops.md

@@ -119,6 +119,10 @@ keywords: Ultralytics YOLO, Utility Operations, segment2box, make_divisible, cli
 
 <br><br>
 
+## ::: ultralytics.utils.ops.regularize_rboxes
+
+<br><br>
+
 ## ::: ultralytics.utils.ops.masks2segments
 
 <br><br>

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.1.4"
+__version__ = "8.1.5"
 
 from ultralytics.data.explorer.explorer import Explorer
 from ultralytics.models import RTDETR, SAM, YOLO

ultralytics/engine/results.py

@@ -115,7 +115,7 @@ class Results(SimpleClass):
             if v is not None:
                 return len(v)
 
-    def update(self, boxes=None, masks=None, probs=None):
+    def update(self, boxes=None, masks=None, probs=None, obb=None):
         """Update the boxes, masks, and probs attributes of the Results object."""
         if boxes is not None:
             self.boxes = Boxes(ops.clip_boxes(boxes, self.orig_shape), self.orig_shape)
@@ -123,6 +123,8 @@ class Results(SimpleClass):
             self.masks = Masks(masks, self.orig_shape)
         if probs is not None:
             self.probs = probs
+        if obb is not None:
+            self.obb = OBB(obb, self.orig_shape)
 
     def _apply(self, fn, *args, **kwargs):
         """

ultralytics/hub/session.py

@@ -225,14 +225,14 @@ class HUBTrainingSession:
                     break  # Timeout reached, exit loop
 
                 response = request_func(*args, **kwargs)
-                if progress_total:
-                    self._show_upload_progress(progress_total, response)
-
                 if response is None:
                     LOGGER.warning(f"{PREFIX}Received no response from the request. {HELP_MSG}")
                     time.sleep(2**i)  # Exponential backoff before retrying
                     continue  # Skip further processing and retry
 
+                if progress_total:
+                    self._show_upload_progress(progress_total, response)
+
                 if HTTPStatus.OK <= response.status_code < HTTPStatus.MULTIPLE_CHOICES:
                     return response  # Success, no need to retry
 

ultralytics/models/yolo/obb/predict.py

@@ -45,8 +45,9 @@ class OBBPredictor(DetectionPredictor):
 
         results = []
         for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]):
-            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape, xywh=True)
+            rboxes = ops.regularize_rboxes(torch.cat([pred[:, :4], pred[:, -1:]], dim=-1))
+            rboxes[:, :4] = ops.scale_boxes(img.shape[2:], rboxes[:, :4], orig_img.shape, xywh=True)
             # xywh, r, conf, cls
-            obb = torch.cat([pred[:, :4], pred[:, -1:], pred[:, 4:6]], dim=-1)
+            obb = torch.cat([rboxes, pred[:, 4:6]], dim=-1)
             results.append(Results(orig_img, path=img_path, names=self.model.names, obb=obb))
         return results

ultralytics/trackers/byte_tracker.py

@@ -5,6 +5,8 @@ import numpy as np
 from .basetrack import BaseTrack, TrackState
 from .utils import matching
 from .utils.kalman_filter import KalmanFilterXYAH
+from ..utils.ops import xywh2ltwh
+from ..utils import LOGGER
 
 
 class STrack(BaseTrack):
@@ -35,18 +37,18 @@ class STrack(BaseTrack):
         activate(kalman_filter, frame_id): Activate a new tracklet.
         re_activate(new_track, frame_id, new_id): Reactivate a previously lost tracklet.
         update(new_track, frame_id): Update the state of a matched track.
-        convert_coords(tlwh): Convert bounding box to x-y-angle-height format.
+        convert_coords(tlwh): Convert bounding box to x-y-aspect-height format.
         tlwh_to_xyah(tlwh): Convert tlwh bounding box to xyah format.
-        tlbr_to_tlwh(tlbr): Convert tlbr bounding box to tlwh format.
-        tlwh_to_tlbr(tlwh): Convert tlwh bounding box to tlbr format.
     """
 
     shared_kalman = KalmanFilterXYAH()
 
-    def __init__(self, tlwh, score, cls):
+    def __init__(self, xywh, score, cls):
         """Initialize new STrack instance."""
         super().__init__()
-        self._tlwh = np.asarray(self.tlbr_to_tlwh(tlwh[:-1]), dtype=np.float32)
+        # xywh+idx or xywha+idx
+        assert len(xywh) in [5, 6], f"expected 5 or 6 values but got {len(xywh)}"
+        self._tlwh = np.asarray(xywh2ltwh(xywh[:4]), dtype=np.float32)
         self.kalman_filter = None
         self.mean, self.covariance = None, None
         self.is_activated = False
@@ -54,7 +56,8 @@ class STrack(BaseTrack):
         self.score = score
         self.tracklet_len = 0
         self.cls = cls
-        self.idx = tlwh[-1]
+        self.idx = xywh[-1]
+        self.angle = xywh[4] if len(xywh) == 6 else None
 
     def predict(self):
         """Predicts mean and covariance using Kalman filter."""
@@ -123,6 +126,7 @@ class STrack(BaseTrack):
             self.track_id = self.next_id()
         self.score = new_track.score
         self.cls = new_track.cls
+        self.angle = new_track.angle
         self.idx = new_track.idx
 
     def update(self, new_track, frame_id):
@@ -145,10 +149,11 @@ class STrack(BaseTrack):
 
         self.score = new_track.score
         self.cls = new_track.cls
+        self.angle = new_track.angle
         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."""
+        """Convert a bounding box's top-left-width-height format to its x-y-aspect-height equivalent."""
         return self.tlwh_to_xyah(tlwh)
 
     @property
@@ -162,7 +167,7 @@ class STrack(BaseTrack):
         return ret
 
     @property
-    def tlbr(self):
+    def xyxy(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]
@@ -178,19 +183,26 @@ class STrack(BaseTrack):
         ret[2] /= ret[3]
         return ret
 
-    @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]
+    @property
+    def xywh(self):
+        """Get current position in bounding box format (center x, center y, width, height)."""
+        ret = np.asarray(self.tlwh).copy()
+        ret[:2] += ret[2:] / 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
+    @property
+    def xywha(self):
+        """Get current position in bounding box format (center x, center y, width, height, angle)."""
+        if self.angle is None:
+            LOGGER.warning("WARNING ⚠️ `angle` attr not found, returning `xywh` instead.")
+            return self.xywh
+        return np.concatenate([self.xywh, self.angle[None]])
+
+    @property
+    def result(self):
+        """Get current tracking results."""
+        coords = self.xyxy if self.angle is None else self.xywha
+        return coords.tolist() + [self.track_id, self.score, self.cls, self.idx]
 
     def __repr__(self):
         """Return a string representation of the BYTETracker object with start and end frames and track ID."""
@@ -247,7 +259,7 @@ class BYTETracker:
         removed_stracks = []
 
         scores = results.conf
-        bboxes = results.xyxy
+        bboxes = results.xywhr if hasattr(results, "xywhr") else results.xywh
         # Add index
         bboxes = np.concatenate([bboxes, np.arange(len(bboxes)).reshape(-1, 1)], axis=-1)
         cls = results.cls
@@ -349,10 +361,8 @@ class BYTETracker:
         self.removed_stracks.extend(removed_stracks)
         if len(self.removed_stracks) > 1000:
             self.removed_stracks = self.removed_stracks[-999:]  # clip remove stracks to 1000 maximum
-        return np.asarray(
-            [x.tlbr.tolist() + [x.track_id, x.score, x.cls, x.idx] for x in self.tracked_stracks if x.is_activated],
-            dtype=np.float32,
-        )
+
+        return np.asarray([x.result for x in self.tracked_stracks if x.is_activated], dtype=np.float32)
 
     def get_kalmanfilter(self):
         """Returns a Kalman filter object for tracking bounding boxes."""

ultralytics/trackers/track.py

@@ -25,8 +25,6 @@ def on_predict_start(predictor: object, persist: bool = False) -> None:
     Raises:
         AssertionError: If the tracker_type is not 'bytetrack' or 'botsort'.
     """
-    if predictor.args.task == "obb":
-        raise NotImplementedError("ERROR ❌ OBB task does not support track mode!")
     if hasattr(predictor, "trackers") and persist:
         return
 
@@ -54,11 +52,12 @@ def on_predict_postprocess_end(predictor: object, persist: bool = False) -> None
     bs = predictor.dataset.bs
     path, im0s = predictor.batch[:2]
 
+    is_obb = predictor.args.task == "obb"
     for i in range(bs):
         if not persist and predictor.vid_path[i] != str(predictor.save_dir / Path(path[i]).name):  # new video
             predictor.trackers[i].reset()
 
-        det = predictor.results[i].boxes.cpu().numpy()
+        det = (predictor.results[i].obb if is_obb else predictor.results[i].boxes).cpu().numpy()
         if len(det) == 0:
             continue
         tracks = predictor.trackers[i].update(det, im0s[i])
@@ -66,7 +65,10 @@ def on_predict_postprocess_end(predictor: object, persist: bool = False) -> None
             continue
         idx = tracks[:, -1].astype(int)
         predictor.results[i] = predictor.results[i][idx]
-        predictor.results[i].update(boxes=torch.as_tensor(tracks[:, :-1]))
+
+        update_args = dict()
+        update_args["obb" if is_obb else "boxes"] = torch.as_tensor(tracks[:, :-1])
+        predictor.results[i].update(**update_args)
 
 
 def register_tracker(model: object, persist: bool) -> None:

ultralytics/trackers/utils/matching.py

@@ -4,7 +4,7 @@ import numpy as np
 import scipy
 from scipy.spatial.distance import cdist
 
-from ultralytics.utils.metrics import bbox_ioa
+from ultralytics.utils.metrics import bbox_ioa, batch_probiou
 
 try:
     import lap  # for linear_assignment
@@ -74,14 +74,22 @@ def iou_distance(atracks: list, btracks: list) -> np.ndarray:
         atlbrs = atracks
         btlbrs = btracks
     else:
-        atlbrs = [track.tlbr for track in atracks]
-        btlbrs = [track.tlbr for track in btracks]
+        atlbrs = [track.xywha if track.angle is not None else track.xyxy for track in atracks]
+        btlbrs = [track.xywha if track.angle is not None else track.xyxy for track in btracks]
 
     ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float32)
     if len(atlbrs) and len(btlbrs):
-        ious = bbox_ioa(
-            np.ascontiguousarray(atlbrs, dtype=np.float32), np.ascontiguousarray(btlbrs, dtype=np.float32), iou=True
-        )
+        if len(atlbrs[0]) == 5 and len(btlbrs[0]) == 5:
+            ious = batch_probiou(
+                np.ascontiguousarray(atlbrs, dtype=np.float32),
+                np.ascontiguousarray(btlbrs, dtype=np.float32),
+            ).numpy()
+        else:
+            ious = bbox_ioa(
+                np.ascontiguousarray(atlbrs, dtype=np.float32),
+                np.ascontiguousarray(btlbrs, dtype=np.float32),
+                iou=True,
+            )
     return 1 - ious  # cost matrix
 
 

ultralytics/utils/callbacks/hub.py

@@ -46,7 +46,7 @@ def on_model_save(trainer):
         # Upload checkpoints with rate limiting
         is_best = trainer.best_fitness == trainer.fitness
         if time() - session.timers["ckpt"] > session.rate_limits["ckpt"]:
-            LOGGER.info(f"{PREFIX}Uploading checkpoint {HUB_WEB_ROOT}/models/{session.model_file}")
+            LOGGER.info(f"{PREFIX}Uploading checkpoint {HUB_WEB_ROOT}/models/{session.model_id}")
             session.upload_model(trainer.epoch, trainer.last, is_best)
             session.timers["ckpt"] = time()  # reset timer
 

ultralytics/utils/metrics.py

@@ -239,13 +239,16 @@ def batch_probiou(obb1, obb2, eps=1e-7):
     Calculate the prob iou between oriented bounding boxes, https://arxiv.org/pdf/2106.06072v1.pdf.
 
     Args:
-        obb1 (torch.Tensor): A tensor of shape (N, 5) representing ground truth obbs, with xywhr format.
-        obb2 (torch.Tensor): A tensor of shape (M, 5) representing predicted obbs, with xywhr format.
+        obb1 (torch.Tensor | np.ndarray): A tensor of shape (N, 5) representing ground truth obbs, with xywhr format.
+        obb2 (torch.Tensor | np.ndarray): A tensor of shape (M, 5) representing predicted obbs, with xywhr format.
         eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.
 
     Returns:
         (torch.Tensor): A tensor of shape (N, M) representing obb similarities.
     """
+    obb1 = torch.from_numpy(obb1) if isinstance(obb1, np.ndarray) else obb1
+    obb2 = torch.from_numpy(obb2) if isinstance(obb2, np.ndarray) else obb2
+
     x1, y1 = obb1[..., :2].split(1, dim=-1)
     x2, y2 = (x.squeeze(-1)[None] for x in obb2[..., :2].split(1, dim=-1))
     a1, b1, c1 = _get_covariance_matrix(obb1)

ultralytics/utils/ops.py

@@ -774,6 +774,24 @@ def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None, normalize=False
     return coords
 
 
+def regularize_rboxes(rboxes):
+    """
+    Regularize rotated boxes in range [0, pi/2].
+
+    Args:
+        rboxes (torch.Tensor): (N, 5), xywhr.
+
+    Returns:
+        (torch.Tensor): The regularized boxes.
+    """
+    x, y, w, h, t = rboxes.unbind(dim=-1)
+    # Swap edge and angle if h >= w
+    w_ = torch.where(w > h, w, h)
+    h_ = torch.where(w > h, h, w)
+    t = torch.where(w > h, t, t + math.pi / 2) % math.pi
+    return torch.stack([x, y, w_, h_, t], dim=-1)  # regularized boxes
+
+
 def masks2segments(masks, strategy="largest"):
     """
     It takes a list of masks(n,h,w) and returns a list of segments(n,xy)