Merge pull request roboflow#456 from roboflow/revert-436-develop

SkalskiP · web-flow · commit 35142fc7551c · 2023-10-12T01:11:14.000+02:00
Revert "roboflow#418 Make sv.ByteTrack work with segmentation model "
diff --git a/examples/README.md b/examples/README.md
@@ -1,7 +1,7 @@
 ## Supervision Examples
 
-This repository is packed with real-world use-cases, provided through Python scripts or 
-interactive notebooks. Browse through to understand how the Supervision library 
+This repository is packed with real-world use-cases, provided through Python scripts or
+interactive notebooks. Browse through to understand how the Supervision library
 interfaces with diverse applications.
 
 |               **Title**                |             **Contributor**              |
@@ -12,20 +12,20 @@ interfaces with diverse applications.
 
 ## Guidelines for Contributing
 
-We welcome contributions from the community in the form of examples, applications, and 
+We welcome contributions from the community in the form of examples, applications, and
 guides. To contribute, please follow these steps:
 
-1. Create a pull request (PR) with the `[Example]` prefix in the title, adding your 
+1. Create a pull request (PR) with the `[Example]` prefix in the title, adding your
 project folder to the `examples/` directory in the repository.
 2. Confirm your project aligns with the following standards:
    - Incorporates the `supervision` package.
    - Provides a `README.md` file, detailing the instructions to execute the project.
    - Showcases visual results, demonstrating the app's functionality.
    - Avoids adding large assets or dependencies unless absolutely necessary.
-   - The contributor is expected to provide support for issues related to their 
+   - The contributor is expected to provide support for issues related to their
 examples.
-   - In case the presented model has licensing complications, kindly specify them to 
+   - In case the presented model has licensing complications, kindly specify them to
 circumvent potential misunderstandings.
 
-For inquiries or concerns about these prerequisites, feel free to raise a PR. We are 
+For inquiries or concerns about these prerequisites, feel free to raise a PR. We are
 committed to assist and guide you.
diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
@@ -1,4 +1,4 @@
-from typing import List, Optional, Tuple
+from typing import List, Tuple
 
 import numpy as np
 
@@ -11,7 +11,7 @@
 class STrack(BaseTrack):
     shared_kalman = KalmanFilter()
 
-    def __init__(self, tlwh, score, class_ids, mask: Optional[np.array] = None):
+    def __init__(self, tlwh, score, class_ids):
         # wait activate
         self._tlwh = np.asarray(tlwh, dtype=np.float32)
         self.kalman_filter = None
@@ -21,7 +21,6 @@ def __init__(self, tlwh, score, class_ids, mask: Optional[np.array] = None):
         self.score = score
         self.class_ids = class_ids
         self.tracklet_len = 0
-        self.mask = mask
 
     def predict(self):
         mean_state = self.mean.copy()
@@ -75,7 +74,6 @@ def re_activate(self, new_track, frame_id, new_id=False):
         if new_id:
             self.track_id = self.next_id()
         self.score = new_track.score
-        self.mask = new_track.mask
 
     def update(self, new_track, frame_id):
         """
@@ -97,8 +95,6 @@ def update(self, new_track, frame_id):
 
         self.score = new_track.score
 
-        self.mask = new_track.mask
-
     @property
     def tlwh(self):
         """Get current position in bounding box format `(top left x, top left y,
@@ -235,8 +231,9 @@ def update_with_detections(self, detections: Detections) -> Detections:
             ... )
             ```
         """
+
         tracks = self.update_with_tensors(
-            tensors=detections2boxes(detections=detections), masks=detections.mask
+            tensors=detections2boxes(detections=detections)
         )
         detections = Detections.empty()
         if len(tracks) > 0:
@@ -252,22 +249,17 @@ def update_with_detections(self, detections: Detections) -> Detections:
             detections.confidence = np.array(
                 [t.score for t in tracks], dtype=np.float32
             )
-            detections.mask = np.array([t.mask for t in tracks], dtype=bool)
-
         else:
             detections.tracker_id = np.array([], dtype=int)
 
         return detections
 
-    def update_with_tensors(
-        self, tensors: np.ndarray, masks: Optional[np.array] = None
-    ) -> List[STrack]:
+    def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
         """
         Updates the tracker with the provided tensors and returns the updated tracks.
 
         Parameters:
             tensors: The new tensors to update with.
-            masks: The new masks associated to new tensors
 
         Returns:
             List[STrack]: Updated tracks.
@@ -289,12 +281,6 @@ def update_with_tensors(
         inds_second = np.logical_and(inds_low, inds_high)
         dets_second = bboxes[inds_second]
         dets = bboxes[remain_inds]
-        if masks is not None:
-            masks_keep = masks[remain_inds]
-            masks_second = masks[inds_second]
-        else:
-            masks_keep = np.array([None] * len(remain_inds))
-            masks_second = np.array([None] * len(inds_second))
         scores_keep = scores[remain_inds]
         scores_second = scores[inds_second]
 
@@ -304,10 +290,8 @@ def update_with_tensors(
         if len(dets) > 0:
             """Detections"""
             detections = [
-                STrack(STrack.tlbr_to_tlwh(tlbr), s, c, m)
-                for (tlbr, s, c, m) in zip(
-                    dets, scores_keep, class_ids_keep, masks_keep
-                )
+                STrack(STrack.tlbr_to_tlwh(tlbr), s, c)
+                for (tlbr, s, c) in zip(dets, scores_keep, class_ids_keep)
             ]
         else:
             detections = []
@@ -347,10 +331,8 @@ def update_with_tensors(
         if len(dets_second) > 0:
             """Detections"""
             detections_second = [
-                STrack(STrack.tlbr_to_tlwh(tlbr), s, c, m)
-                for (tlbr, s, c, m) in zip(
-                    dets_second, scores_second, class_ids_second, masks_second
-                )
+                STrack(STrack.tlbr_to_tlwh(tlbr), s, c)
+                for (tlbr, s, c) in zip(dets_second, scores_second, class_ids_second)
             ]
         else:
             detections_second = []
