From a05882add1fa5a86141f16f924b6fdf8f25403e3 Mon Sep 17 00:00:00 2001
From: Grzegorz Klimaszewski
 <166530809+grzegorz-roboflow@users.noreply.github.com>
Date: Thu, 19 Sep 2024 12:15:02 +0200
Subject: [PATCH 001/128] Make BaseTrack._count instance variable

---
 supervision/tracker/byte_tracker/basetrack.py | 23 ++++++++-----------
 1 file changed, 10 insertions(+), 13 deletions(-)

diff --git a/supervision/tracker/byte_tracker/basetrack.py b/supervision/tracker/byte_tracker/basetrack.py
index 806f7538..d280274c 100644
--- a/supervision/tracker/byte_tracker/basetrack.py
+++ b/supervision/tracker/byte_tracker/basetrack.py
@@ -12,9 +12,8 @@ class TrackState(Enum):
 
 
 class BaseTrack:
-    _count = 0
-
     def __init__(self):
+        self._count = 0
         self.track_id = 0
         self.is_activated = False
         self.state = TrackState.New
@@ -34,18 +33,16 @@ class BaseTrack:
     def end_frame(self) -> int:
         return self.frame_id
 
-    @staticmethod
-    def next_id() -> int:
-        BaseTrack._count += 1
-        return BaseTrack._count
+    def next_id(self) -> int:
+        self._count += 1
+        return self._count
 
-    @staticmethod
-    def reset_counter():
-        BaseTrack._count = 0
-        BaseTrack.track_id = 0
-        BaseTrack.start_frame = 0
-        BaseTrack.frame_id = 0
-        BaseTrack.time_since_update = 0
+    def reset_counter(self):
+        self._count = 0
+        self.track_id = 0
+        self.start_frame = 0
+        self.frame_id = 0
+        self.time_since_update = 0
 
     def activate(self, *args):
         raise NotImplementedError

From db2aa721f83a3f8b6a3ad80cb0e507bdc41360e1 Mon Sep 17 00:00:00 2001
From: Grzegorz Klimaszewski
 <166530809+grzegorz-roboflow@users.noreply.github.com>
Date: Thu, 19 Sep 2024 13:38:16 +0200
Subject: [PATCH 002/128] Let upper layer to handle track_id generation

---
 supervision/tracker/byte_tracker/basetrack.py |  6 ---
 supervision/tracker/byte_tracker/core.py      | 47 +++++++++----------
 test/tracker/__init__.py                      |  0
 test/tracker/test_byte_tracker.py             | 37 +++++++++++++++
 4 files changed, 58 insertions(+), 32 deletions(-)
 create mode 100644 test/tracker/__init__.py
 create mode 100644 test/tracker/test_byte_tracker.py

diff --git a/supervision/tracker/byte_tracker/basetrack.py b/supervision/tracker/byte_tracker/basetrack.py
index d280274c..b78bc596 100644
--- a/supervision/tracker/byte_tracker/basetrack.py
+++ b/supervision/tracker/byte_tracker/basetrack.py
@@ -13,7 +13,6 @@ class TrackState(Enum):
 
 class BaseTrack:
     def __init__(self):
-        self._count = 0
         self.track_id = 0
         self.is_activated = False
         self.state = TrackState.New
@@ -33,12 +32,7 @@ class BaseTrack:
     def end_frame(self) -> int:
         return self.frame_id
 
-    def next_id(self) -> int:
-        self._count += 1
-        return self._count
-
     def reset_counter(self):
-        self._count = 0
         self.track_id = 0
         self.start_frame = 0
         self.frame_id = 0
diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index 89e1e2f2..cd8ea3e5 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -1,4 +1,4 @@
-from typing import List, Tuple
+from typing import List, Optional, Tuple
 
 import numpy as np
 
@@ -11,10 +11,11 @@ from supervision.tracker.byte_tracker.kalman_filter import KalmanFilter
 
 class STrack(BaseTrack):
     shared_kalman = KalmanFilter()
-    _external_count = 0
 
     def __init__(self, tlwh, score, class_ids, minimum_consecutive_frames):
+        super().__init__()
         # wait activate
+        self._external_count = 0
         self._tlwh = np.asarray(tlwh, dtype=np.float32)
         self.kalman_filter = None
         self.mean, self.covariance = None, None
@@ -54,10 +55,10 @@ class STrack(BaseTrack):
                 stracks[i].mean = mean
                 stracks[i].covariance = cov
 
-    def activate(self, kalman_filter, frame_id):
+    def activate(self, kalman_filter, frame_id, track_id):
         """Start a new tracklet"""
         self.kalman_filter = kalman_filter
-        self.internal_track_id = self.next_id()
+        self.internal_track_id = track_id
         self.mean, self.covariance = self.kalman_filter.initiate(
             self.tlwh_to_xyah(self._tlwh)
         )
@@ -68,12 +69,12 @@ class STrack(BaseTrack):
             self.is_activated = True
 
         if self.minimum_consecutive_frames == 1:
-            self.external_track_id = self.next_external_id()
+            self.external_track_id = track_id
 
         self.frame_id = frame_id
         self.start_frame = frame_id
 
-    def re_activate(self, new_track, frame_id, new_id=False):
+    def re_activate(self, new_track, frame_id, new_id: Optional[int] = None):
         self.mean, self.covariance = self.kalman_filter.update(
             self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
         )
@@ -82,10 +83,10 @@ class STrack(BaseTrack):
 
         self.frame_id = frame_id
         if new_id:
-            self.internal_track_id = self.next_id()
+            self.internal_track_id = new_id
         self.score = new_track.score
 
-    def update(self, new_track, frame_id):
+    def update(self, new_track, frame_id, track_id):
         """
         Update a matched track
         :type new_track: STrack
@@ -104,7 +105,7 @@ class STrack(BaseTrack):
         if self.tracklet_len == self.minimum_consecutive_frames:
             self.is_activated = True
             if self.external_track_id == -1:
-                self.external_track_id = self.next_external_id()
+                self.external_track_id = track_id
 
         self.score = new_track.score
 
@@ -142,15 +143,6 @@ class STrack(BaseTrack):
     def to_xyah(self):
         return self.tlwh_to_xyah(self.tlwh)
 
-    @staticmethod
-    def next_external_id():
-        STrack._external_count += 1
-        return STrack._external_count
-
-    @staticmethod
-    def reset_external_counter():
-        STrack._external_count = 0
-
     @staticmethod
     def tlbr_to_tlwh(tlbr):
         ret = np.asarray(tlbr).copy()
@@ -225,6 +217,7 @@ class ByteTrack:
         self.track_activation_threshold = track_activation_threshold
         self.minimum_matching_threshold = minimum_matching_threshold
 
+        self._count = 0
         self.frame_id = 0
         self.det_thresh = self.track_activation_threshold + 0.1
         self.max_time_lost = int(frame_rate / 30.0 * lost_track_buffer)
@@ -235,6 +228,10 @@ class ByteTrack:
         self.lost_tracks: List[STrack] = []
         self.removed_tracks: List[STrack] = []
 
+    def _next_id(self) -> int:
+        self._count += 1
+        return self._count
+
     def update_with_detections(self, detections: Detections) -> Detections:
         """
         Updates the tracker with the provided detections and returns the updated
@@ -314,8 +311,6 @@ class ByteTrack:
         self.tracked_tracks: List[STrack] = []
         self.lost_tracks: List[STrack] = []
         self.removed_tracks: List[STrack] = []
-        BaseTrack.reset_counter()
-        STrack.reset_external_counter()
 
     def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
         """
@@ -384,10 +379,10 @@ class ByteTrack:
             track = strack_pool[itracked]
             det = detections[idet]
             if track.state == TrackState.Tracked:
-                track.update(detections[idet], self.frame_id)
+                track.update(detections[idet], self.frame_id, self._next_id())
                 activated_starcks.append(track)
             else:
-                track.re_activate(det, self.frame_id, new_id=False)
+                track.re_activate(det, self.frame_id)
                 refind_stracks.append(track)
 
         """ Step 3: Second association, with low score detection boxes"""
@@ -413,10 +408,10 @@ class ByteTrack:
             track = r_tracked_stracks[itracked]
             det = detections_second[idet]
             if track.state == TrackState.Tracked:
-                track.update(det, self.frame_id)
+                track.update(det, self.frame_id, self._next_id())
                 activated_starcks.append(track)
             else:
-                track.re_activate(det, self.frame_id, new_id=False)
+                track.re_activate(det, self.frame_id)
                 refind_stracks.append(track)
 
         for it in u_track:
@@ -434,7 +429,7 @@ class ByteTrack:
             dists, thresh=0.7
         )
         for itracked, idet in matches:
-            unconfirmed[itracked].update(detections[idet], self.frame_id)
+            unconfirmed[itracked].update(detections[idet], self.frame_id, self._next_id())
             activated_starcks.append(unconfirmed[itracked])
         for it in u_unconfirmed:
             track = unconfirmed[it]
@@ -446,7 +441,7 @@ class ByteTrack:
             track = detections[inew]
             if track.score < self.det_thresh:
                 continue
-            track.activate(self.kalman_filter, self.frame_id)
+            track.activate(self.kalman_filter, self.frame_id, self._next_id())
             activated_starcks.append(track)
         """ Step 5: Update state"""
         for track in self.lost_tracks:
diff --git a/test/tracker/__init__.py b/test/tracker/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/test/tracker/test_byte_tracker.py b/test/tracker/test_byte_tracker.py
new file mode 100644
index 00000000..673cd90b
--- /dev/null
+++ b/test/tracker/test_byte_tracker.py
@@ -0,0 +1,37 @@
+import numpy as np
+import pytest
+import supervision as sv
+
+
+@pytest.mark.parametrize(
+    "detections, expected_results",
+    [
+        (
+            [
+                sv.Detections(
+                    xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]),
+                    class_id=np.array([1, 1]),
+                    confidence=np.array([1, 1]),
+                ),
+                sv.Detections(
+                    xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]),
+                    class_id=np.array([1, 1]),
+                    confidence=np.array([1, 1]),
+                ),
+            ],
+            sv.Detections(
+                xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]),
+                class_id=np.array([1, 1]),
+                confidence=np.array([1, 1]),
+                tracker_id=np.array([1, 2]),
+            )
+        ),
+    ],
+)
+def test_byte_tracker(
+    detections: list[sv.Detections],
+    expected_results: sv.Detections,
+) -> None:
+    byte_tracker = sv.ByteTrack()
+    tracked_detections = [byte_tracker.update_with_detections(d) for d in detections]
+    assert tracked_detections[-1] == expected_results

From 3da10edb47ea601718cedec347156bc5b0140882 Mon Sep 17 00:00:00 2001
From: Grzegorz Klimaszewski
 <166530809+grzegorz-roboflow@users.noreply.github.com>
Date: Thu, 19 Sep 2024 13:40:31 +0200
Subject: [PATCH 003/128] Remove unused variable

---
 supervision/tracker/byte_tracker/core.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index cd8ea3e5..8f64c29d 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -15,7 +15,6 @@ class STrack(BaseTrack):
     def __init__(self, tlwh, score, class_ids, minimum_consecutive_frames):
         super().__init__()
         # wait activate
-        self._external_count = 0
         self._tlwh = np.asarray(tlwh, dtype=np.float32)
         self.kalman_filter = None
         self.mean, self.covariance = None, None

From 1e43de0a4179155123d007215acf27e4d05f5395 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 19 Sep 2024 11:41:16 +0000
Subject: [PATCH 004/128] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20aut?=
 =?UTF-8?q?o=20format=20pre-commit=20hooks?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 supervision/tracker/byte_tracker/core.py | 4 +++-
 test/tracker/test_byte_tracker.py        | 3 ++-
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index 8f64c29d..b3a32400 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -428,7 +428,9 @@ class ByteTrack:
             dists, thresh=0.7
         )
         for itracked, idet in matches:
-            unconfirmed[itracked].update(detections[idet], self.frame_id, self._next_id())
+            unconfirmed[itracked].update(
+                detections[idet], self.frame_id, self._next_id()
+            )
             activated_starcks.append(unconfirmed[itracked])
         for it in u_unconfirmed:
             track = unconfirmed[it]
diff --git a/test/tracker/test_byte_tracker.py b/test/tracker/test_byte_tracker.py
index 673cd90b..7ca94bbf 100644
--- a/test/tracker/test_byte_tracker.py
+++ b/test/tracker/test_byte_tracker.py
@@ -1,5 +1,6 @@
 import numpy as np
 import pytest
+
 import supervision as sv
 
 
@@ -24,7 +25,7 @@ import supervision as sv
                 class_id=np.array([1, 1]),
                 confidence=np.array([1, 1]),
                 tracker_id=np.array([1, 2]),
-            )
+            ),
         ),
     ],
 )

From a0655aed087c414bc1d4aa895a0fe17c4ab4915c Mon Sep 17 00:00:00 2001
From: Grzegorz Klimaszewski
 <166530809+grzegorz-roboflow@users.noreply.github.com>
Date: Thu, 19 Sep 2024 13:44:26 +0200
Subject: [PATCH 005/128] fix test

---
 test/tracker/test_byte_tracker.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/test/tracker/test_byte_tracker.py b/test/tracker/test_byte_tracker.py
index 7ca94bbf..47ad1a9d 100644
--- a/test/tracker/test_byte_tracker.py
+++ b/test/tracker/test_byte_tracker.py
@@ -1,3 +1,4 @@
+from typing import List
 import numpy as np
 import pytest
 
@@ -30,7 +31,7 @@ import supervision as sv
     ],
 )
 def test_byte_tracker(
-    detections: list[sv.Detections],
+    detections: List[sv.Detections],
     expected_results: sv.Detections,
 ) -> None:
     byte_tracker = sv.ByteTrack()

From df9257b8fc4e45f96b12d90228faa3f29705c4b8 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 19 Sep 2024 11:45:31 +0000
Subject: [PATCH 006/128] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20aut?=
 =?UTF-8?q?o=20format=20pre-commit=20hooks?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 test/tracker/test_byte_tracker.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/test/tracker/test_byte_tracker.py b/test/tracker/test_byte_tracker.py
index 47ad1a9d..98efeb09 100644
--- a/test/tracker/test_byte_tracker.py
+++ b/test/tracker/test_byte_tracker.py
@@ -1,4 +1,5 @@
 from typing import List
+
 import numpy as np
 import pytest
 

From 266bf3f10c8141c3631f584df51b427f5ab12659 Mon Sep 17 00:00:00 2001
From: Grzegorz Klimaszewski
 <166530809+grzegorz-roboflow@users.noreply.github.com>
Date: Thu, 19 Sep 2024 13:49:43 +0200
Subject: [PATCH 007/128] Reset _count

---
 supervision/tracker/byte_tracker/core.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index b3a32400..013af2ee 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -307,6 +307,7 @@ class ByteTrack:
         ensuring the tracker starts with a clean state for each new video.
         """
         self.frame_id = 0
+        self._count = 0
         self.tracked_tracks: List[STrack] = []
         self.lost_tracks: List[STrack] = []
         self.removed_tracks: List[STrack] = []

From 0bc52bf6fe6686298ec780ab795c57ec59ec4641 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o?= <joao@roboflow.com>
Date: Wed, 2 Oct 2024 21:09:58 -0300
Subject: [PATCH 008/128] moves embedded widget to below the tabs and passes
 necessary query params to render new layout

---
 docs/detection/annotators.md | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/docs/detection/annotators.md b/docs/detection/annotators.md
index 09b4d043..8180c54a 100644
--- a/docs/detection/annotators.md
+++ b/docs/detection/annotators.md
@@ -7,8 +7,6 @@ status: new
 
 Supervision provides a variety of annotators to annotate detections on images and videos. You can try them out below, with a [Workflow](https://roboflow.com/workflows) that runs [Microsoft's COCO](https://cocodataset.org/#home) dataset through a Instance Segmentation model and annotates the detections using supervision's annotators.
 
-<div style="height: 400px; width: 100%; border-radius: 8px; overflow: hidden;"><iframe src="https://app.roboflow.com/workflows/embed/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ3b3JrZmxvd0lkIjoiNDdtd2xuWW16S25VNWtOYUZjMG8iLCJ3b3Jrc3BhY2VJZCI6ImtyT1RBYm5jRmhvUU1DZExPbGU0IiwidXNlcklkIjoiRVJNUFBZY3FQMmZWWjB1NkRpNXZaYXJDdlZPMiIsImlhdCI6MTcyNjgzOTM2N30.gj2F6SnmmURAScJe4PTC1raUXsAK5mZyrUIGIJ44NhM" loading="lazy" title="Roboflow Workflow for Supervision Annotators" style="width: 100%; height: 100%; min-height: 400px; border: none;"></iframe></div>
-
 === "Box"
 
     ```python
@@ -485,6 +483,12 @@ Supervision provides a variety of annotators to annotate detections on images an
 
     </div>
 
+<div class="md-typeset">
+    <h2>Try Supervision Annotators with your own images</h2>
+</div>
+
+<div style="height: 400px; width: 100%; border-radius: 8px; overflow: hidden;"><iframe src="https://app.roboflow.com/workflows/embed/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ3b3JrZmxvd0lkIjoiNDdtd2xuWW16S25VNWtOYUZjMG8iLCJ3b3Jrc3BhY2VJZCI6ImtyT1RBYm5jRmhvUU1DZExPbGU0IiwidXNlcklkIjoiRVJNUFBZY3FQMmZWWjB1NkRpNXZaYXJDdlZPMiIsImlhdCI6MTcyNjgzOTM2N30.gj2F6SnmmURAScJe4PTC1raUXsAK5mZyrUIGIJ44NhM?hideToolbar=true&hideHeader=true&defaultVisual=true" loading="lazy" title="Roboflow Workflow for Supervision Annotators" style="width: 100%; height: 100%; min-height: 400px; border: none;"></iframe></div>
+
 <div class="md-typeset">
     <h2><a href="#supervision.annotators.core.BoxAnnotator">BoxAnnotator</a></h2>
 </div>

From 0b931e11cdba539ac999995ef9e9b35315c4db07 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 3 Oct 2024 00:36:54 +0000
Subject: [PATCH 009/128] :arrow_up: Bump tox from 4.21.0 to 4.21.1

Bumps [tox](https://github.com/tox-dev/tox) from 4.21.0 to 4.21.1.
- [Release notes](https://github.com/tox-dev/tox/releases)
- [Changelog](https://github.com/tox-dev/tox/blob/main/docs/changelog.rst)
- [Commits](https://github.com/tox-dev/tox/compare/4.21.0...4.21.1)

---
updated-dependencies:
- dependency-name: tox
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 81e11ec2..2cb54545 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -4141,13 +4141,13 @@ files = [
 
 [[package]]
 name = "tox"
-version = "4.21.0"
+version = "4.21.1"
 description = "tox is a generic virtualenv management and test command line tool"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "tox-4.21.0-py3-none-any.whl", hash = "sha256:693ac51378255d34ad7aab6dd2ce9ab6a1cf1924eb930183fde850ad503b681d"},
-    {file = "tox-4.21.0.tar.gz", hash = "sha256:e64dd9847ff3a7ec90368be412d7efe61a39caf043222ffbe9ad638ea435f6f6"},
+    {file = "tox-4.21.1-py3-none-any.whl", hash = "sha256:092bbdcfeabc824db621dab5be9f0866a34a0a4fc874e9746bf04c0d5462d78b"},
+    {file = "tox-4.21.1.tar.gz", hash = "sha256:b2ce4222772693b302d75e2e4f7727d67bb60925d740479bb09845098562589f"},
 ]
 
 [package.dependencies]

From ef89d8ceffb4bd56289c3d8f3179affdd97da571 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Thu, 3 Oct 2024 12:22:42 +0300
Subject: [PATCH 010/128] Remove the Workflows introduction.

* Info is available upon mousing-over the '?' icon on the embed.
---
 docs/detection/annotators.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/docs/detection/annotators.md b/docs/detection/annotators.md
index 8180c54a..1f08a0cd 100644
--- a/docs/detection/annotators.md
+++ b/docs/detection/annotators.md
@@ -5,8 +5,6 @@ status: new
 
 # Annotators
 
-Supervision provides a variety of annotators to annotate detections on images and videos. You can try them out below, with a [Workflow](https://roboflow.com/workflows) that runs [Microsoft's COCO](https://cocodataset.org/#home) dataset through a Instance Segmentation model and annotates the detections using supervision's annotators.
-
 === "Box"
 
     ```python

From 2be5008b3cac8f99ca4cbc09adb03d2f58d41ffd Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Thu, 3 Oct 2024 12:49:40 +0300
Subject: [PATCH 011/128] Minors docs edit: Tell what classes can be annotated
 by the embedded annotator

---
 docs/detection/annotators.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/detection/annotators.md b/docs/detection/annotators.md
index 1f08a0cd..9950bce3 100644
--- a/docs/detection/annotators.md
+++ b/docs/detection/annotators.md
@@ -483,6 +483,7 @@ status: new
 
 <div class="md-typeset">
     <h2>Try Supervision Annotators with your own images</h2>
+    Detects COCO classes such as people, vehicles, animals, household items.
 </div>
 
 <div style="height: 400px; width: 100%; border-radius: 8px; overflow: hidden;"><iframe src="https://app.roboflow.com/workflows/embed/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ3b3JrZmxvd0lkIjoiNDdtd2xuWW16S25VNWtOYUZjMG8iLCJ3b3Jrc3BhY2VJZCI6ImtyT1RBYm5jRmhvUU1DZExPbGU0IiwidXNlcklkIjoiRVJNUFBZY3FQMmZWWjB1NkRpNXZaYXJDdlZPMiIsImlhdCI6MTcyNjgzOTM2N30.gj2F6SnmmURAScJe4PTC1raUXsAK5mZyrUIGIJ44NhM?hideToolbar=true&hideHeader=true&defaultVisual=true" loading="lazy" title="Roboflow Workflow for Supervision Annotators" style="width: 100%; height: 100%; min-height: 400px; border: none;"></iframe></div>

From eb0e50a2f9e8cbe219f979cce9053c7e07ed464e Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Thu, 3 Oct 2024 15:30:20 +0300
Subject: [PATCH 012/128] Update hacktoberfest banner

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 9a0cc305..dd340ae5 100644
--- a/README.md
+++ b/README.md
@@ -34,7 +34,7 @@
 
 **We write your reusable computer vision tools.** Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us! 🤝
 
-[![supervision-hackfest](https://github.com/roboflow/supervision/assets/26109316/c05cc954-b9a6-4ed5-9a52-d0b4b619ff65)](https://github.com/orgs/roboflow/projects)
+[![supervision-hackfest](https://media.roboflow.com/supervision/supervision-hacktoberfest-banner-2024.png)](https://github.com/roboflow/supervision/issues?q=is%3Aissue+is%3Aopen+label%3Ahacktoberfest)
 
 ## 💻 install
 

From 51e2f9e462e6d3c2fc369fb1754440e5650d452a Mon Sep 17 00:00:00 2001
From: Joseph Nelson <joseph.nelson2012@gmail.com>
Date: Thu, 3 Oct 2024 11:45:41 -0700
Subject: [PATCH 013/128] Update annotators.md

included description of what annotators are; clarified how to use the visualization widget
---
 docs/detection/annotators.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/docs/detection/annotators.md b/docs/detection/annotators.md
index 9950bce3..72dcbd01 100644
--- a/docs/detection/annotators.md
+++ b/docs/detection/annotators.md
@@ -5,6 +5,8 @@ status: new
 
 # Annotators
 
+Annotators accept detections and apply box or mask visualizations to the detections. Annotators have many available styles.
+
 === "Box"
 
     ```python
@@ -482,8 +484,8 @@ status: new
     </div>
 
 <div class="md-typeset">
-    <h2>Try Supervision Annotators with your own images</h2>
-    Detects COCO classes such as people, vehicles, animals, household items.
+    <h2>Visualize Supervision Annotators on your own images</h2>
+    Note this visualization uses a COCO model (default detects classes such as people, vehicles, animals, household items) to visualize annotators. If annotators do not appear, your image may not found by the COCO model.
 </div>
 
 <div style="height: 400px; width: 100%; border-radius: 8px; overflow: hidden;"><iframe src="https://app.roboflow.com/workflows/embed/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ3b3JrZmxvd0lkIjoiNDdtd2xuWW16S25VNWtOYUZjMG8iLCJ3b3Jrc3BhY2VJZCI6ImtyT1RBYm5jRmhvUU1DZExPbGU0IiwidXNlcklkIjoiRVJNUFBZY3FQMmZWWjB1NkRpNXZaYXJDdlZPMiIsImlhdCI6MTcyNjgzOTM2N30.gj2F6SnmmURAScJe4PTC1raUXsAK5mZyrUIGIJ44NhM?hideToolbar=true&hideHeader=true&defaultVisual=true" loading="lazy" title="Roboflow Workflow for Supervision Annotators" style="width: 100%; height: 100%; min-height: 400px; border: none;"></iframe></div>

From f207eaca3117640718493c9a1b60a5cbd404a129 Mon Sep 17 00:00:00 2001
From: Joseph Nelson <joseph.nelson2012@gmail.com>
Date: Thu, 3 Oct 2024 13:29:14 -0700
Subject: [PATCH 014/128] Update annotators.md

being concise is a virtue
---
 docs/detection/annotators.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/detection/annotators.md b/docs/detection/annotators.md
index 72dcbd01..e1071121 100644
--- a/docs/detection/annotators.md
+++ b/docs/detection/annotators.md
@@ -484,8 +484,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
     </div>
 
 <div class="md-typeset">
-    <h2>Visualize Supervision Annotators on your own images</h2>
-    Note this visualization uses a COCO model (default detects classes such as people, vehicles, animals, household items) to visualize annotators. If annotators do not appear, your image may not found by the COCO model.
+    <h2>Try Supervision Annotators on your own image</h2>
+    Visualize annotators on images with COCO classes such as people, vehicles, animals, household items.
 </div>
 
 <div style="height: 400px; width: 100%; border-radius: 8px; overflow: hidden;"><iframe src="https://app.roboflow.com/workflows/embed/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ3b3JrZmxvd0lkIjoiNDdtd2xuWW16S25VNWtOYUZjMG8iLCJ3b3Jrc3BhY2VJZCI6ImtyT1RBYm5jRmhvUU1DZExPbGU0IiwidXNlcklkIjoiRVJNUFBZY3FQMmZWWjB1NkRpNXZaYXJDdlZPMiIsImlhdCI6MTcyNjgzOTM2N30.gj2F6SnmmURAScJe4PTC1raUXsAK5mZyrUIGIJ44NhM?hideToolbar=true&hideHeader=true&defaultVisual=true" loading="lazy" title="Roboflow Workflow for Supervision Annotators" style="width: 100%; height: 100%; min-height: 400px; border: none;"></iframe></div>

From 66067cc0acaecc8e343d21d89e92bbd32ed7076c Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 4 Oct 2024 00:53:28 +0300
Subject: [PATCH 015/128] Remove deprecated `frame_resolution_wh` from
 `PolygonZone`

---
 docs/changelog.md                           |  6 ++++++
 docs/deprecated.md                          |  6 ++++--
 supervision/detection/tools/polygon_zone.py | 13 +------------
 3 files changed, 11 insertions(+), 14 deletions(-)

diff --git a/docs/changelog.md b/docs/changelog.md
index e7dbb18e..a572544c 100644
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -1,5 +1,11 @@
 # CHANGELOG
 
+### 0.24.0 <small>Oct 4, 2024</small>
+
+!!! failure "Removed"
+    The `frame_resolution_wh ` parameter in [`sv.PolygonZone`](https://supervision.roboflow.com/0.24.0/detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) has been removed.
+
+
 ### 0.23.0 <small>Aug 28, 2024</small>
 
 - Added [#930](https://github.com/roboflow/supervision/pull/930): `IconAnnotator`, a [new annotator](https://supervision.roboflow.com/0.23.0/detection/annotators/#supervision.annotators.core.IconAnnotator) that allows drawing icons on each detection. Useful if you want to draw a specific icon for each class.
diff --git a/docs/deprecated.md b/docs/deprecated.md
index bdd95a9d..fb70c8a6 100644
--- a/docs/deprecated.md
+++ b/docs/deprecated.md
@@ -7,8 +7,6 @@ status: deprecated
 
 These features are phased out due to better alternatives or potential issues in future versions. Deprecated functionalities are supported for **five subsequent releases**, providing time for users to transition to updated methods.
 
-- The `frame_resolution_wh ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) will be removed in `supervision-0.24.0`.
-
 - Constructing [`DetectionDataset`](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.DetectionDataset) and [`ClassificationDataset`](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.ClassificationDataset) with parameter `images` as `Dict[str, np.ndarray]` will be removed in `supervision-0.26.0`. Please pass a list of paths `List[str]` instead.
 
 - The `DetectionDataset.images` property will be removed in `supervision-0.26.0`. Please loop over images with `for path, image, annotation in dataset:`, as that does not require loading all images into memory.
@@ -21,6 +19,10 @@ These features are phased out due to better alternatives or potential issues in
 
 # Removed
 
+### 0.24.0
+
+- The `frame_resolution_wh ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) has been removed.
+
 ### 0.23.0
 
 - The `track_buffer`, `track_thresh`, and `match_thresh` parameters in [`ByteTrack`](trackers.md/#supervision.tracker.byte_tracker.core.ByteTrack) are deprecated and were removed as of `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
diff --git a/supervision/detection/tools/polygon_zone.py b/supervision/detection/tools/polygon_zone.py
index af0d1c0c..ac7203ff 100644
--- a/supervision/detection/tools/polygon_zone.py
+++ b/supervision/detection/tools/polygon_zone.py
@@ -1,6 +1,5 @@
-import warnings
 from dataclasses import replace
-from typing import Iterable, Optional, Tuple
+from typing import Iterable, Optional
 
 import cv2
 import numpy as np
@@ -12,7 +11,6 @@ from supervision.draw.color import Color
 from supervision.draw.utils import draw_filled_polygon, draw_polygon, draw_text
 from supervision.geometry.core import Position
 from supervision.geometry.utils import get_polygon_center
-from supervision.utils.internal import SupervisionWarnings
 
 
 class PolygonZone:
@@ -33,17 +31,8 @@ class PolygonZone:
     def __init__(
         self,
         polygon: npt.NDArray[np.int64],
-        frame_resolution_wh: Optional[Tuple[int, int]] = None,
         triggering_anchors: Iterable[Position] = (Position.BOTTOM_CENTER,),
     ):
-        if frame_resolution_wh is not None:
-            warnings.warn(
-                "The `frame_resolution_wh` parameter is no longer required and will be "
-                "dropped in version supervision-0.24.0. The mask resolution is now "
-                "calculated automatically based on the polygon coordinates.",
-                category=SupervisionWarnings,
-            )
-
         self.polygon = polygon.astype(int)
         self.triggering_anchors = triggering_anchors
         if not list(self.triggering_anchors):

From 55d7c8daf3f0dda77a1a78b128c604b9b693cc62 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Fri, 4 Oct 2024 00:29:50 +0000
Subject: [PATCH 016/128] :arrow_up: Bump tox from 4.21.1 to 4.21.2

Bumps [tox](https://github.com/tox-dev/tox) from 4.21.1 to 4.21.2.
- [Release notes](https://github.com/tox-dev/tox/releases)
- [Changelog](https://github.com/tox-dev/tox/blob/main/docs/changelog.rst)
- [Commits](https://github.com/tox-dev/tox/compare/4.21.1...4.21.2)

---
updated-dependencies:
- dependency-name: tox
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 2cb54545..5f2516e3 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -4141,13 +4141,13 @@ files = [
 
 [[package]]
 name = "tox"
-version = "4.21.1"
+version = "4.21.2"
 description = "tox is a generic virtualenv management and test command line tool"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "tox-4.21.1-py3-none-any.whl", hash = "sha256:092bbdcfeabc824db621dab5be9f0866a34a0a4fc874e9746bf04c0d5462d78b"},
-    {file = "tox-4.21.1.tar.gz", hash = "sha256:b2ce4222772693b302d75e2e4f7727d67bb60925d740479bb09845098562589f"},
+    {file = "tox-4.21.2-py3-none-any.whl", hash = "sha256:13d996adcd792e7c82994b0e116d85efd84f0c6d185254d83d156f73f86b2038"},
+    {file = "tox-4.21.2.tar.gz", hash = "sha256:49381ff102296753e378fa5ff30e42a35e695f149b4dbf8a2c49d15fdb5797b2"},
 ]
 
 [package.dependencies]

From 73ed766e9760b8777336517400b3ac1b31696ecb Mon Sep 17 00:00:00 2001
From: Onuralp SEZER <thunderbirdtr@gmail.com>
Date: Fri, 4 Oct 2024 19:22:57 +0300
Subject: [PATCH 017/128] =?UTF-8?q?fix:=20=F0=9F=94=A5=20downgrade=20mkdoc?=
 =?UTF-8?q?s-git-committers-plugin-2=20to=20version=202.3.0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>
---
 .github/workflows/publish-dev-docs.yml     | 2 +-
 .github/workflows/publish-release-docs.yml | 2 +-
 .github/workflows/test-doc.yml             | 2 +-
 poetry.lock                                | 2 +-
 pyproject.toml                             | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/publish-dev-docs.yml b/.github/workflows/publish-dev-docs.yml
index 0661929e..abef051e 100644
--- a/.github/workflows/publish-dev-docs.yml
+++ b/.github/workflows/publish-dev-docs.yml
@@ -43,7 +43,7 @@ jobs:
       - name: 📦 Install mkdocs-jupyter
         run: pip install mkdocs-jupyter
       - name: 📦 Install mkdocs-git-committers-plugin-2
-        run: pip install mkdocs-git-committers-plugin-2
+        run: pip install mkdocs-git-committers-plugin-2==2.3.0
       - name: ⚙️ Configure git for github-actions
         run: |
           git config --global user.name "github-actions[bot]"
diff --git a/.github/workflows/publish-release-docs.yml b/.github/workflows/publish-release-docs.yml
index 22710033..abe51112 100644
--- a/.github/workflows/publish-release-docs.yml
+++ b/.github/workflows/publish-release-docs.yml
@@ -44,7 +44,7 @@ jobs:
       - name: 📦 Install mkdocs-jupyter
         run: pip install mkdocs-jupyter
       - name: 📦 Install mkdocs-git-committers-plugin-2
-        run: pip install mkdocs-git-committers-plugin-2
+        run: pip install mkdocs-git-committers-plugin-2==2.3.0
       - name: ⚙️ Configure git for github-actions 👷
         run: |
           git config --global user.name "github-actions[bot]"
diff --git a/.github/workflows/test-doc.yml b/.github/workflows/test-doc.yml
index b1507290..bb3b0a16 100644
--- a/.github/workflows/test-doc.yml
+++ b/.github/workflows/test-doc.yml
@@ -17,5 +17,5 @@ jobs:
       - name:  🏗️ Install dependencies and Test Docs Build
         run: |
           python -m pip install --upgrade pip
-          pip install "mkdocs-material" "mkdocstrings[python]" "mkdocs-material[imaging]" mike "mkdocs-git-revision-date-localized-plugin" jupyterlab mkdocs-jupyter mkdocs-git-committers-plugin-2
+          pip install "mkdocs-material" "mkdocstrings[python]" "mkdocs-material[imaging]" mike "mkdocs-git-revision-date-localized-plugin" jupyterlab mkdocs-jupyter mkdocs-git-committers-plugin-2==2.3.0
           mkdocs build --verbose
diff --git a/poetry.lock b/poetry.lock
index 5f2516e3..89ba7af2 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -4545,4 +4545,4 @@ metrics = ["pandas", "pandas-stubs"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8"
-content-hash = "6619a49f1450ccc15a01215d156afbcf248619374ad2ba0576f48434d9b8720f"
+content-hash = "f7f497da73c8c467b621555f622bf0f10b21d0c4e97f68521e3f079e2ffb7789"
diff --git a/pyproject.toml b/pyproject.toml
index 9f443b62..1246e010 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -92,7 +92,7 @@ mike = "^2.0.0"
 # For Documentation Development use Python 3.10 or above
 # Use Latest mkdocs-jupyter min 0.24.6 for Jupyter Notebook Theme support
 mkdocs-jupyter = "^0.24.3"
-mkdocs-git-committers-plugin-2 = "^2.2.3"
+mkdocs-git-committers-plugin-2 = "2.3.0"
 mkdocs-git-revision-date-localized-plugin = "^1.2.4"
 
 [tool.poetry.group.typecheck]

From 61ababd0b5408e973f0fe7c2e1cf3b7ed675fc4f Mon Sep 17 00:00:00 2001
From: Onuralp SEZER <thunderbirdtr@gmail.com>
Date: Fri, 4 Oct 2024 21:17:35 +0300
Subject: [PATCH 018/128] =?UTF-8?q?Revert=20"fix:=20=F0=9F=94=A5=20downgra?=
 =?UTF-8?q?de=20mkdocs-git-committers-plugin-2=20to=20version=202.3.0"?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .github/workflows/publish-dev-docs.yml     | 2 +-
 .github/workflows/publish-release-docs.yml | 2 +-
 .github/workflows/test-doc.yml             | 2 +-
 poetry.lock                                | 2 +-
 pyproject.toml                             | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/publish-dev-docs.yml b/.github/workflows/publish-dev-docs.yml
index abef051e..0661929e 100644
--- a/.github/workflows/publish-dev-docs.yml
+++ b/.github/workflows/publish-dev-docs.yml
@@ -43,7 +43,7 @@ jobs:
       - name: 📦 Install mkdocs-jupyter
         run: pip install mkdocs-jupyter
       - name: 📦 Install mkdocs-git-committers-plugin-2
-        run: pip install mkdocs-git-committers-plugin-2==2.3.0
+        run: pip install mkdocs-git-committers-plugin-2
       - name: ⚙️ Configure git for github-actions
         run: |
           git config --global user.name "github-actions[bot]"
diff --git a/.github/workflows/publish-release-docs.yml b/.github/workflows/publish-release-docs.yml
index abe51112..22710033 100644
--- a/.github/workflows/publish-release-docs.yml
+++ b/.github/workflows/publish-release-docs.yml
@@ -44,7 +44,7 @@ jobs:
       - name: 📦 Install mkdocs-jupyter
         run: pip install mkdocs-jupyter
       - name: 📦 Install mkdocs-git-committers-plugin-2
-        run: pip install mkdocs-git-committers-plugin-2==2.3.0
+        run: pip install mkdocs-git-committers-plugin-2
       - name: ⚙️ Configure git for github-actions 👷
         run: |
           git config --global user.name "github-actions[bot]"
diff --git a/.github/workflows/test-doc.yml b/.github/workflows/test-doc.yml
index bb3b0a16..b1507290 100644
--- a/.github/workflows/test-doc.yml
+++ b/.github/workflows/test-doc.yml
@@ -17,5 +17,5 @@ jobs:
       - name:  🏗️ Install dependencies and Test Docs Build
         run: |
           python -m pip install --upgrade pip
-          pip install "mkdocs-material" "mkdocstrings[python]" "mkdocs-material[imaging]" mike "mkdocs-git-revision-date-localized-plugin" jupyterlab mkdocs-jupyter mkdocs-git-committers-plugin-2==2.3.0
+          pip install "mkdocs-material" "mkdocstrings[python]" "mkdocs-material[imaging]" mike "mkdocs-git-revision-date-localized-plugin" jupyterlab mkdocs-jupyter mkdocs-git-committers-plugin-2
           mkdocs build --verbose
diff --git a/poetry.lock b/poetry.lock
index 89ba7af2..5f2516e3 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -4545,4 +4545,4 @@ metrics = ["pandas", "pandas-stubs"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8"
-content-hash = "f7f497da73c8c467b621555f622bf0f10b21d0c4e97f68521e3f079e2ffb7789"
+content-hash = "6619a49f1450ccc15a01215d156afbcf248619374ad2ba0576f48434d9b8720f"
diff --git a/pyproject.toml b/pyproject.toml
index 1246e010..9f443b62 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -92,7 +92,7 @@ mike = "^2.0.0"
 # For Documentation Development use Python 3.10 or above
 # Use Latest mkdocs-jupyter min 0.24.6 for Jupyter Notebook Theme support
 mkdocs-jupyter = "^0.24.3"
-mkdocs-git-committers-plugin-2 = "2.3.0"
+mkdocs-git-committers-plugin-2 = "^2.2.3"
 mkdocs-git-revision-date-localized-plugin = "^1.2.4"
 
 [tool.poetry.group.typecheck]

From 63f5bfd4f30719d4ac5897b6fc6619029bf948d4 Mon Sep 17 00:00:00 2001
From: Onuralp SEZER <thunderbirdtr@gmail.com>
Date: Fri, 4 Oct 2024 21:24:23 +0300
Subject: [PATCH 019/128] chore: poetry update and start version 2.4.1 for
 prevent broken version

Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>
---
 poetry.lock    | 10 +++++-----
 pyproject.toml |  2 +-
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 5f2516e3..519b7a2f 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2195,13 +2195,13 @@ pyyaml = ">=5.1"
 
 [[package]]
 name = "mkdocs-git-committers-plugin-2"
-version = "2.3.0"
+version = "2.4.1"
 description = "An MkDocs plugin to create a list of contributors on the page. The git-committers plugin will seed the template context with a list of GitHub or GitLab committers and other useful GIT info such as last modified date"
 optional = false
-python-versions = ">=3.8,<4"
+python-versions = "<4,>=3.8"
 files = [
-    {file = "mkdocs-git-committers-plugin-2-2.3.0.tar.gz", hash = "sha256:d6baca1ae04db8120640038eda8142f2d081c27b53f3b566c83c75717e4ed81a"},
-    {file = "mkdocs_git_committers_plugin_2-2.3.0-py3-none-any.whl", hash = "sha256:7b3434af3be525c12858eb3b44b4c6b695b7c7b7760482ea8de1c6e292e84f0f"},
+    {file = "mkdocs_git_committers_plugin_2-2.4.1-py3-none-any.whl", hash = "sha256:ec9c1d81445606c471337d1c4a1782c643b7377077b545279dc18b86b7362c6d"},
+    {file = "mkdocs_git_committers_plugin_2-2.4.1.tar.gz", hash = "sha256:ea1f80a79cedc42289e0b8e973276df04fb94f56e0ae3efc5385fb28547cf5cb"},
 ]
 
 [package.dependencies]
@@ -4545,4 +4545,4 @@ metrics = ["pandas", "pandas-stubs"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8"
-content-hash = "6619a49f1450ccc15a01215d156afbcf248619374ad2ba0576f48434d9b8720f"
+content-hash = "ab2e2c455fa1a7d74271da71f8c1b6f096bbebd92a79b4ec646523ef7d8530b0"
diff --git a/pyproject.toml b/pyproject.toml
index 9f443b62..bc4f3d54 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -92,7 +92,7 @@ mike = "^2.0.0"
 # For Documentation Development use Python 3.10 or above
 # Use Latest mkdocs-jupyter min 0.24.6 for Jupyter Notebook Theme support
 mkdocs-jupyter = "^0.24.3"
-mkdocs-git-committers-plugin-2 = "^2.2.3"
+mkdocs-git-committers-plugin-2 = "^2.4.1"
 mkdocs-git-revision-date-localized-plugin = "^1.2.4"
 
 [tool.poetry.group.typecheck]

From db0a07828051ed9520bd5d8244751d3d94b9be72 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 4 Oct 2024 21:29:15 +0300
Subject: [PATCH 020/128] Bugfix: missing LineZoneAnnotatorMulticlass.annotate
 docs

---
 supervision/detection/line_zone.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/supervision/detection/line_zone.py b/supervision/detection/line_zone.py
index de6c1e0d..aeeb62a2 100644
--- a/supervision/detection/line_zone.py
+++ b/supervision/detection/line_zone.py
@@ -696,6 +696,16 @@ class LineZoneAnnotatorMulticlass:
         line_zones: List[LineZone],
         line_zone_labels: Optional[List[str]] = None,
     ) -> np.ndarray:
+        """
+        Draws a table with the number of objects of each class that crossed each line.
+
+        Attributes:
+            frame (np.ndarray): The image on which the table will be drawn.
+            line_zones (List[LineZone]): The line zones to be annotated.
+            line_zone_labels (Optional[List[str]]): The labels, one for each
+                line zone. If not provided, the default labels will be used.
+
+        """
         if line_zone_labels is None:
             line_zone_labels = [f"Line {i + 1}:" for i in range(len(line_zones))]
         if len(line_zones) != len(line_zone_labels):

From 02ed59b27b4328d451d5f1b9d9e3098f9f32671e Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 4 Oct 2024 22:08:56 +0300
Subject: [PATCH 021/128] Add changelog, deprecations

---
 docs/changelog.md  | 187 ++++++++++++++++++++++++++++++++++++++-------
 docs/deprecated.md |   3 +-
 2 files changed, 161 insertions(+), 29 deletions(-)

diff --git a/docs/changelog.md b/docs/changelog.md
index a572544c..e212cfb5 100644
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -2,9 +2,140 @@
 
 ### 0.24.0 <small>Oct 4, 2024</small>
 
-!!! failure "Removed"
-    The `frame_resolution_wh ` parameter in [`sv.PolygonZone`](https://supervision.roboflow.com/0.24.0/detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) has been removed.
+- Added [F1 score](https://supervision.roboflow.com/develop/metrics/f1_score/#supervision.metrics.f1_score.F1Score) as a new metric for detection and segmentation. [#1521](https://github.com/roboflow/supervision/pull/1521)
 
+```python
+import supervision as sv
+from supervision.metrics import F1Score
+
+predictions = sv.Detections(...)
+targets = sv.Detections(...)
+
+f1_metric = F1Score()
+f1_result = f1_metric.update(predictions, targets).compute()
+
+print(f1_result)
+print(f1_result.f1_50)
+print(f1_result.small_objects.f1_50)
+```
+
+- Added new cookbook: [Small Object Detection with SAHI](https://supervision.roboflow.com/develop/notebooks/small-object-detection-with-sahi/). This cookbook provides a detailed guide on using [`InferenceSlicer`](https://supervision.roboflow.com/develop/detection/tools/inference_slicer/) for small object detection. [#1483](https://github.com/roboflow/supervision/pull/1483)
+
+- Added an [Embedded Workflow](https://roboflow.com/workflows), which allows you to [preview annotators](https://supervision.roboflow.com/develop/detection/annotators/). [#1533](https://github.com/roboflow/supervision/pull/1533)
+
+- Enhanced [`LineZoneAnnotator`](https://supervision.roboflow.com/develop/detection/tools/line_zone/#supervision.detection.line_zone.LineZoneAnnotator), allowing the labels to align with the line, even when it's not horizontal. Also, you can now disable text background, and choose to draw labels off-center which minimizes overlaps for multiple [`LineZone`](https://supervision.roboflow.com/develop/detection/tools/line_zone/#supervision.detection.line_zone.LineZone) labels. [#854](https://github.com/roboflow/supervision/pull/854)
+
+```python
+import supervision as sv
+import cv2
+
+image = cv2.imread("<SOURCE_IMAGE_PATH>")
+
+line_zone = sv.LineZone(
+    start=sv.Point(0, 100),
+    end=sv.Point(50, 200)
+)
+line_zone_annotator = sv.LineZoneAnnotator(
+    text_orient_to_line=True,
+    display_text_box=False,
+    text_centered=False
+)
+
+annotated_frame = line_zone_annotator.annotate(
+    frame=image.copy(), line_counter=line_zone
+)
+
+sv.plot_image(frame)
+```
+
+- Added per-class counting capabilities to [`LineZone`](https://supervision.roboflow.com/develop/detection/tools/line_zone/#supervision.detection.line_zone.LineZone) and introduced [`LineZoneAnnotatorMulticlass`](https://supervision.roboflow.com/develop/detection/tools/line_zone/#supervision.detection.line_zone.LineZoneAnnotatorMulticlass) for visualizing the counts per class. This feature allows tracking of individual classes crossing a line, enhancing the flexibility of use cases like traffic monitoring or crowd analysis. [#1555](https://github.com/roboflow/supervision/pull/1555)
+
+```python
+import supervision as sv
+import cv2
+
+image = cv2.imread("<SOURCE_IMAGE_PATH>")
+
+line_zone = sv.LineZone(
+    start=sv.Point(0, 100),
+    end=sv.Point(50, 200)
+)
+line_zone_annotator = sv.LineZoneAnnotatorMulticlass()
+
+frame = line_zone_annotator.annotate(
+    frame=frame, line_zones=[line_zone]
+)
+
+sv.plot_image(frame)
+```
+
+- - Added [`from_easyocr`](https://supervision.roboflow.com/develop/detection/core/#supervision.detection.core.Detections.from_easyocr), allowing integration of OCR results into the supervision framework. [EasyOCR](https://github.com/JaidedAI/EasyOCR) is an open-source optical character recognition (OCR) library that can read text from images. [#1515](https://github.com/roboflow/supervision/pull/1515)
+
+```python
+import supervision as sv
+import easyocr
+import cv2
+
+image = cv2.imread("<SOURCE_IMAGE_PATH>")
+
+reader = easyocr.Reader(["en"])
+result = reader.readtext("<SOURCE_IMAGE_PATH>", paragraph=True)
+detections = sv.Detections.from_easyocr(result)
+
+box_annotator = sv.BoxAnnotator(color_lookup=sv.ColorLookup.INDEX)
+label_annotator = sv.LabelAnnotator(color_lookup=sv.ColorLookup.INDEX)
+
+annotated_image = image.copy()
+annotated_image = box_annotator.annotate(scene=annotated_image, detections=detections)
+annotated_image = label_annotator.annotate(scene=annotated_image, detections=detections)
+
+sv.plot_image(annotated_image)
+```
+
+- Added [`oriented_box_iou_batch`](https://supervision.roboflow.com/develop/detection/utils/#supervision.detection.utils.oriented_box_iou_batch) function to `detection.utils`. This function computes Intersection over Union (IoU) for oriented or rotated bounding boxes (OBB). [#1502](https://github.com/roboflow/supervision/pull/1502)
+
+```python
+import numpy as np
+
+boxes_true = np.array([[[1, 0], [0, 1], [3, 4], [4, 3]]])
+boxes_detection = np.array([[[1, 1], [2, 0], [4, 2], [3, 3]]])
+ious = sv.oriented_box_iou_batch(boxes_true, boxes_detection)
+print("IoU between true and detected boxes:", ious)
+```
+
+- Extended [`PolygonZoneAnnotator`](https://supervision.roboflow.com/develop/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZoneAnnotator) to allow setting opacity when drawing zones, providing enhanced visualization by filling the zone with adjustable transparency. [#1527](https://github.com/roboflow/supervision/pull/1527)
+
+```python
+import cv2
+from ncnn.model_zoo import get_model
+import supervision as sv
+
+image = cv2.imread("<SOURCE_IMAGE_PATH>")
+model = get_model(
+    "yolov8s",
+    target_size=640,
+    prob_threshold=0.5,
+    nms_threshold=0.45,
+    num_threads=4,
+    use_gpu=True,
+)
+result = model(image)
+detections = sv.Detections.from_ncnn(result)
+```
+
+!!! failure "Removed"
+The `frame_resolution_wh` parameter in [`PolygonZone`](https://supervision.roboflow.com/develop/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) has been removed.
+
+!!! failure "Removed"
+Supervision installation methods `"headless"` and `"desktop"` were removed, as they are no longer needed. `pip install supervision[headless]` will install the base library and harmlessly warn of non-existent extras.
+
+- Supervision now depends on `opencv-python` rather than `opencv-python-headless`. [#1530](https://github.com/roboflow/supervision/pull/1530)
+
+- Fixed the COCO 101 point Average Precision algorithm to correctly interpolate precision, providing a more precise calculation of average precision without averaging out intermediate values. [#1500](https://github.com/roboflow/supervision/pull/1500)
+
+- Resolved miscellaneous issues highlighted when building documentation. This mostly includes whitespace adjustments and type inconsistencies. Updated documentation for clarity and fixed formatting issues. Added explicit version for `mkdocstrings-python`. [#1549](https://github.com/roboflow/supervision/pull/1549)
+
+- Enabled and fixed Ruff rules for code formatting, including changes like avoiding unnecessary iterable allocations and using Optional for default mutable arguments. [#1526](https://github.com/roboflow/supervision/pull/1526)
 
 ### 0.23.0 <small>Aug 28, 2024</small>
 
@@ -123,26 +254,26 @@ for frame in sv.get_video_frames_generator(
 - Fix [#1424](https://github.com/roboflow/supervision/pull/1424): `plot_image` function now clearly indicates that the size is in inches.
 
 !!! failure "Removed"
-    The `track_buffer`, `track_thresh`, and `match_thresh` parameters in [`ByteTrack`](trackers.md/#supervision.tracker.byte_tracker.core.ByteTrack) are deprecated and were removed as of `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
+The `track_buffer`, `track_thresh`, and `match_thresh` parameters in [`ByteTrack`](trackers.md/#supervision.tracker.byte_tracker.core.ByteTrack) are deprecated and were removed as of `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
 
 !!! failure "Removed"
-    The `triggering_position` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) was removed as of `supervision-0.23.0`. Use `triggering_anchors` instead.
+The `triggering_position` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) was removed as of `supervision-0.23.0`. Use `triggering_anchors` instead.
 
 !!! failure "Deprecated"
-    `overlap_filter_strategy` in `InferenceSlicer.__init__` is deprecated and will be removed in `supervision-0.27.0`. Use `overlap_strategy` instead.
+`overlap_filter_strategy` in `InferenceSlicer.__init__` is deprecated and will be removed in `supervision-0.27.0`. Use `overlap_strategy` instead.
 
 !!! failure "Deprecated"
-    `overlap_ratio_wh` in `InferenceSlicer.__init__` is deprecated and will be removed in `supervision-0.27.0`. Use `overlap_wh` instead.
+`overlap_ratio_wh` in `InferenceSlicer.__init__` is deprecated and will be removed in `supervision-0.27.0`. Use `overlap_wh` instead.
 
 ### 0.22.0 <small>Jul 12, 2024</small>
 
 - Added [#1326](https://github.com/roboflow/supervision/pull/1326): [`sv.DetectionsDataset`](https://supervision.roboflow.com/0.22.0/datasets/core/#supervision.dataset.core.DetectionDataset) and [`sv.ClassificationDataset`](https://supervision.roboflow.com/0.22.0/datasets/core/#supervision.dataset.core.ClassificationDataset) allowing to load the images into memory only when necessary (lazy loading).
 
 !!! failure "Deprecated"
-    Constructing `DetectionDataset` with parameter `images` as `Dict[str, np.ndarray]` is deprecated and will be removed in `supervision-0.26.0`. Please pass a list of paths `List[str]` instead.
+Constructing `DetectionDataset` with parameter `images` as `Dict[str, np.ndarray]` is deprecated and will be removed in `supervision-0.26.0`. Please pass a list of paths `List[str]` instead.
 
 !!! failure "Deprecated"
-    The `DetectionDataset.images` property is deprecated and will be removed in `supervision-0.26.0`. Please loop over images with `for path, image, annotation in dataset:`, as that does not require loading all images into memory.
+The `DetectionDataset.images` property is deprecated and will be removed in `supervision-0.26.0`. Please loop over images with `for path, image, annotation in dataset:`, as that does not require loading all images into memory.
 
 ```python
 import roboflow
@@ -198,7 +329,7 @@ annotated_frame = mask_annotator.annotate(scene=image.copy(), detections=detecti
 ```
 
 - Added [#1277](https://github.com/roboflow/supervision/pull/1277): if you provide a font that supports symbols of a language, [`sv.RichLabelAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.LabelAnnotator.annotate) will draw them on your images.
-    - Various other annotators have been revised to ensure proper in-place functionality when used with `numpy` arrays. Additionally, we fixed a bug where `sv.ColorAnnotator` was filling boxes with solid color when used in-place.
+  - Various other annotators have been revised to ensure proper in-place functionality when used with `numpy` arrays. Additionally, we fixed a bug where `sv.ColorAnnotator` was filling boxes with solid color when used in-place.
 
 ```python
 import cv2
@@ -236,10 +367,10 @@ annotated_image = obb_annotator.annotate(scene=image.copy(), detections=detectio
 - Fixed [#1312](https://github.com/roboflow/supervision/pull/1312): Fixed [`CropAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.TraceAnnotator.annotate).
 
 !!! failure "Removed"
-    `BoxAnnotator` was removed, however `BoundingBoxAnnotator` has been renamed to `BoxAnnotator`. Use a combination of [`BoxAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.BoxAnnotator) and [`LabelAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.LabelAnnotator) to simulate old `BoundingBox` behavior.
+`BoxAnnotator` was removed, however `BoundingBoxAnnotator` has been renamed to `BoxAnnotator`. Use a combination of [`BoxAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.BoxAnnotator) and [`LabelAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.LabelAnnotator) to simulate old `BoundingBox` behavior.
 
 !!! failure "Deprecated"
-    The name `BoundingBoxAnnotator` has been deprecated and will be removed in `supervision-0.26.0`. It has been renamed to [`BoxAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.BoxAnnotator).
+The name `BoundingBoxAnnotator` has been deprecated and will be removed in `supervision-0.26.0`. It has been renamed to [`BoxAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.BoxAnnotator).
 
 - Added [#975](https://github.com/roboflow/supervision/pull/975) 📝 New Cookbooks: serialize detections into [json](https://github.com/roboflow/supervision/blob/de896189b83a1f9434c0a37dd9192ee00d2a1283/docs/notebooks/serialise-detections-to-json.ipynb) and [csv](https://github.com/roboflow/supervision/blob/de896189b83a1f9434c0a37dd9192ee00d2a1283/docs/notebooks/serialise-detections-to-csv.ipynb).
 
@@ -248,28 +379,28 @@ annotated_image = obb_annotator.annotate(scene=image.copy(), detections=detectio
 - Added [#1340](https://github.com/roboflow/supervision/pull/1340): Two new methods for converting between bounding box formats - [`xywh_to_xyxy`](https://supervision.roboflow.com/0.22.0/detection/utils/#supervision.detection.utils.xywh_to_xyxy) and [`xcycwh_to_xyxy`](https://supervision.roboflow.com/0.22.0/detection/utils/#supervision.detection.utils.xcycwh_to_xyxy)
 
 !!! failure "Removed"
-    `from_roboflow` method has been removed due to deprecation. Use [from_inference](https://supervision.roboflow.com/0.22.0/detection/core/#supervision.detection.core.Detections.from_inference) instead.
+`from_roboflow` method has been removed due to deprecation. Use [from_inference](https://supervision.roboflow.com/0.22.0/detection/core/#supervision.detection.core.Detections.from_inference) instead.
 
 !!! failure "Removed"
-    `Color.white()` has been removed due to deprecation. Use `color.WHITE` instead.
+`Color.white()` has been removed due to deprecation. Use `color.WHITE` instead.
 
 !!! failure "Removed"
-    `Color.black()` has been removed due to deprecation. Use `color.BLACK` instead.
+`Color.black()` has been removed due to deprecation. Use `color.BLACK` instead.
 
 !!! failure "Removed"
-    `Color.red()` has been removed due to deprecation. Use `color.RED` instead.
+`Color.red()` has been removed due to deprecation. Use `color.RED` instead.
 
 !!! failure "Removed"
-    `Color.green()` has been removed due to deprecation. Use `color.GREEN` instead.
+`Color.green()` has been removed due to deprecation. Use `color.GREEN` instead.
 
 !!! failure "Removed"
-    `Color.blue()` has been removed due to deprecation. Use `color.BLUE` instead.
+`Color.blue()` has been removed due to deprecation. Use `color.BLUE` instead.
 
 !!! failure "Removed"
-    `ColorPalette.default()` has been removed due to deprecation. Use [ColorPalette.DEFAULT](https://supervision.roboflow.com/0.22.0/utils/draw/#supervision.draw.color.ColorPalette.DEFAULT) instead.
+`ColorPalette.default()` has been removed due to deprecation. Use [ColorPalette.DEFAULT](https://supervision.roboflow.com/0.22.0/utils/draw/#supervision.draw.color.ColorPalette.DEFAULT) instead.
 
 !!! failure "Removed"
-    `FPSMonitor.__call__` has been removed due to deprecation. Use the attribute [FPSMonitor.fps](https://supervision.roboflow.com/0.22.0/utils/video/#supervision.utils.video.FPSMonitor.fps) instead.
+`FPSMonitor.__call__` has been removed due to deprecation. Use the attribute [FPSMonitor.fps](https://supervision.roboflow.com/0.22.0/utils/video/#supervision.utils.video.FPSMonitor.fps) instead.
 
 ### 0.21.0 <small>Jun 5, 2024</small>
 
@@ -377,7 +508,7 @@ annotated_image = edge_annotators.annotate(image.copy(), keypoints)
 - Changed [#1109](https://github.com/roboflow/supervision/pull/1109): [`sv.PolygonZone`](/0.20.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) such that the `frame_resolution_wh` argument is no longer required to initialize `sv.PolygonZone`.
 
 !!! failure "Deprecated"
-    The `frame_resolution_wh` parameter in `sv.PolygonZone` is deprecated and will be removed in `supervision-0.24.0`.
+The `frame_resolution_wh` parameter in `sv.PolygonZone` is deprecated and will be removed in `supervision-0.24.0`.
 
 - Changed [#1084](https://github.com/roboflow/supervision/pull/1084): [`sv.get_polygon_center`](/0.20.0/utils/geometry/#supervision.geometry.core.utils.get_polygon_center) to calculate a more accurate polygon centroid.
 
@@ -482,12 +613,12 @@ annotated_frame = crop_annotator.annotate(
 - Changed [#787](https://github.com/roboflow/supervision/pull/787): [`sv.ByteTrack`](/0.19.0/trackers/#supervision.tracker.ByteTrack) input arguments and docstrings updated to improve readability and ease of use.
 
 !!! failure "Deprecated"
-    The `track_buffer`, `track_thresh`, and `match_thresh` parameters in `sv.ByteTrack` are deprecated and will be removed in `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
+The `track_buffer`, `track_thresh`, and `match_thresh` parameters in `sv.ByteTrack` are deprecated and will be removed in `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
 
 - Changed [#910](https://github.com/roboflow/supervision/pull/910): [`sv.PolygonZone`](/0.19.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) to now accept a list of specific box anchors that must be in zone for a detection to be counted.
 
 !!! failure "Deprecated"
-    The `triggering_position ` parameter in `sv.PolygonZone` is deprecated and will be removed in `supervision-0.23.0`. Use `triggering_anchors` instead.
+The `triggering_position ` parameter in `sv.PolygonZone` is deprecated and will be removed in `supervision-0.23.0`. Use `triggering_anchors` instead.
 
 - Changed [#875](https://github.com/roboflow/supervision/pull/875): annotators adding support for Pillow images. All supervision Annotators can now accept an image as either a numpy array or a Pillow Image. They automatically detect its type, draw annotations, and return the output in the same format as the input.
 
@@ -550,14 +681,14 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 - Changed [#756](https://github.com/roboflow/supervision/pull/756): [`sv.Color`](/0.18.0/draw/color/#color)'s and [`sv.ColorPalette`](/0.18.0/draw/color/#colorpalette)'s method of accessing predefined colors, transitioning from a function-based approach (`sv.Color.red()`) to a more intuitive and conventional property-based method (`sv.Color.RED`).
 
 !!! failure "Deprecated"
-    `sv.ColorPalette.default()` is deprecated and will be removed in `supervision-0.22.0`. Use `sv.ColorPalette.DEFAULT` instead.
+`sv.ColorPalette.default()` is deprecated and will be removed in `supervision-0.22.0`. Use `sv.ColorPalette.DEFAULT` instead.
 
 - Changed [#769](https://github.com/roboflow/supervision/pull/769): [`sv.ColorPalette.DEFAULT`](/0.18.0/draw/color/#colorpalette) value, giving users a more extensive set of annotation colors.
 
 - Changed [#677](https://github.com/roboflow/supervision/pull/677): `sv.Detections.from_roboflow` to [`sv.Detections.from_inference`](/0.18.0/detection/core/#supervision.detection.core.Detections.from_inference) streamlining its functionality to be compatible with both the both [inference](https://github.com/roboflow/inference) pip package and the Robloflow [hosted API](https://docs.roboflow.com/deploy/hosted-api).
 
 !!! failure "Deprecated"
-    `Detections.from_roboflow()` is deprecated and will be removed in `supervision-0.22.0`. Use `Detections.from_inference` instead.
+`Detections.from_roboflow()` is deprecated and will be removed in `supervision-0.22.0`. Use `Detections.from_inference` instead.
 
 - Fixed [#735](https://github.com/roboflow/supervision/pull/735): [`sv.LineZone`](/0.18.0/detection/tools/line_zone/#linezone) functionality to accurately update the counter when an object crosses a line from any direction, including from the side. This enhancement enables more precise tracking and analytics, such as calculating individual in/out counts for each lane on the road.
 
@@ -654,7 +785,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 - Fixed [#430](https://github.com/roboflow/supervision/pull/430): [`sv.ByteTrack`](/0.16.0/trackers/#supervision.tracker.byte_tracker.core.ByteTrack) to return `np.array([], dtype=int)` when `svDetections` is empty.
 
 !!! failure "Deprecated"
-    `sv.Detections.from_yolov8` and `sv.Classifications.from_yolov8` as those are now replaced by [`sv.Detections.from_ultralytics`](/0.16.0/detection/core/#supervision.detection.core.Detections.from_ultralytics) and [`sv.Classifications.from_ultralytics`](/0.16.0/classification/core/#supervision.classification.core.Classifications.from_ultralytics).
+`sv.Detections.from_yolov8` and `sv.Classifications.from_yolov8` as those are now replaced by [`sv.Detections.from_ultralytics`](/0.16.0/detection/core/#supervision.detection.core.Detections.from_ultralytics) and [`sv.Classifications.from_ultralytics`](/0.16.0/classification/core/#supervision.classification.core.Classifications.from_ultralytics).
 
 ### 0.15.0 <small>October 5, 2023</small>
 
@@ -721,7 +852,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 - Added [#281](https://github.com/roboflow/supervision/pull/281): [`sv.Classifications.from_ultralytics`](/0.14.0/classification/core/#supervision.classification.core.Classifications.from_ultralytics) to enable seamless integration with [Ultralytics](https://github.com/ultralytics/ultralytics) framework. This will enable you to use supervision with all [models](https://docs.ultralytics.com/models/) that Ultralytics supports.
 
 !!! failure "Deprecated"
-    [sv.Detections.from_yolov8](/0.14.0/detection/core/#supervision.detection.core.Detections.from_yolov8) and [sv.Classifications.from_yolov8](/0.14.0/classification/core/#supervision.classification.core.Classifications.from_yolov8) are now deprecated and will be removed with `supervision-0.16.0` release.
+[sv.Detections.from_yolov8](/0.14.0/detection/core/#supervision.detection.core.Detections.from_yolov8) and [sv.Classifications.from_yolov8](/0.14.0/classification/core/#supervision.classification.core.Classifications.from_yolov8) are now deprecated and will be removed with `supervision-0.16.0` release.
 
 - Added [#341](https://github.com/roboflow/supervision/pull/341): First supervision usage example script showing how to detect and track objects on video using YOLOv8 + Supervision.
 
@@ -758,7 +889,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 - Added [#222](https://github.com/roboflow/supervision/pull/222): [`sv.Detections.from_ultralytics`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_ultralytics) to enable seamless integration with [Ultralytics](https://github.com/ultralytics/ultralytics) framework. This will enable you to use `supervision` with all [models](https://docs.ultralytics.com/models/) that Ultralytics supports.
 
 !!! failure "Deprecated"
-    [`sv.Detections.from_yolov8`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_yolov8) is now deprecated and will be removed with `supervision-0.15.0` release.
+[`sv.Detections.from_yolov8`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_yolov8) is now deprecated and will be removed with `supervision-0.15.0` release.
 
 - Added [#191](https://github.com/roboflow/supervision/pull/191): [`sv.Detections.from_paddledet`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_paddledet) to enable seamless integration with [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) framework.
 
@@ -767,7 +898,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 ### 0.12.0 <small>July 24, 2023</small>
 
 !!! failure "Python 3.7. Support Terminated"
-    With the `supervision-0.12.0` release, we are terminating official support for Python 3.7.
+With the `supervision-0.12.0` release, we are terminating official support for Python 3.7.
 
 - Added [#177](https://github.com/roboflow/supervision/pull/177): initial support for object detection model benchmarking with [`sv.ConfusionMatrix`](/0.12.0/metrics/detection/#confusionmatrix).
 
diff --git a/docs/deprecated.md b/docs/deprecated.md
index fb70c8a6..d355bf96 100644
--- a/docs/deprecated.md
+++ b/docs/deprecated.md
@@ -22,11 +22,12 @@ These features are phased out due to better alternatives or potential issues in
 ### 0.24.0
 
 - The `frame_resolution_wh ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) has been removed.
+- Supervision installation methods `"headless"` and `"desktop"` were removed, as they are no longer needed. `pip install supervision[headless]` will install the base library and harmlessly warn of non-existent extras.
 
 ### 0.23.0
 
 - The `track_buffer`, `track_thresh`, and `match_thresh` parameters in [`ByteTrack`](trackers.md/#supervision.tracker.byte_tracker.core.ByteTrack) are deprecated and were removed as of `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
-- The `triggering_position ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) was removed as of `supervision-0.23.0`. Use `triggering_anchors ` instead.
+- The `triggering_position ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) was removed as of `supervision-0.23.0`. Use `triggering_anchors` instead.
 
 ### 0.22.0
 

From 8f61898964544f5adfc5340f45da81f45b20fed7 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 4 Oct 2024 22:13:26 +0300
Subject: [PATCH 022/128] Point changelog links to 0.24.0

---
 docs/changelog.md | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/docs/changelog.md b/docs/changelog.md
index e212cfb5..c6b41ea7 100644
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -2,7 +2,7 @@
 
 ### 0.24.0 <small>Oct 4, 2024</small>
 
-- Added [F1 score](https://supervision.roboflow.com/develop/metrics/f1_score/#supervision.metrics.f1_score.F1Score) as a new metric for detection and segmentation. [#1521](https://github.com/roboflow/supervision/pull/1521)
+- Added [F1 score](https://supervision.roboflow.com/0.24.0/metrics/f1_score/#supervision.metrics.f1_score.F1Score) as a new metric for detection and segmentation. [#1521](https://github.com/roboflow/supervision/pull/1521)
 
 ```python
 import supervision as sv
@@ -19,11 +19,11 @@ print(f1_result.f1_50)
 print(f1_result.small_objects.f1_50)
 ```
 
-- Added new cookbook: [Small Object Detection with SAHI](https://supervision.roboflow.com/develop/notebooks/small-object-detection-with-sahi/). This cookbook provides a detailed guide on using [`InferenceSlicer`](https://supervision.roboflow.com/develop/detection/tools/inference_slicer/) for small object detection. [#1483](https://github.com/roboflow/supervision/pull/1483)
+- Added new cookbook: [Small Object Detection with SAHI](https://supervision.roboflow.com/0.24.0/notebooks/small-object-detection-with-sahi/). This cookbook provides a detailed guide on using [`InferenceSlicer`](https://supervision.roboflow.com/0.24.0/detection/tools/inference_slicer/) for small object detection. [#1483](https://github.com/roboflow/supervision/pull/1483)
 
-- Added an [Embedded Workflow](https://roboflow.com/workflows), which allows you to [preview annotators](https://supervision.roboflow.com/develop/detection/annotators/). [#1533](https://github.com/roboflow/supervision/pull/1533)
+- Added an [Embedded Workflow](https://roboflow.com/workflows), which allows you to [preview annotators](https://supervision.roboflow.com/0.24.0/detection/annotators/). [#1533](https://github.com/roboflow/supervision/pull/1533)
 
-- Enhanced [`LineZoneAnnotator`](https://supervision.roboflow.com/develop/detection/tools/line_zone/#supervision.detection.line_zone.LineZoneAnnotator), allowing the labels to align with the line, even when it's not horizontal. Also, you can now disable text background, and choose to draw labels off-center which minimizes overlaps for multiple [`LineZone`](https://supervision.roboflow.com/develop/detection/tools/line_zone/#supervision.detection.line_zone.LineZone) labels. [#854](https://github.com/roboflow/supervision/pull/854)
+- Enhanced [`LineZoneAnnotator`](https://supervision.roboflow.com/0.24.0/detection/tools/line_zone/#supervision.detection.line_zone.LineZoneAnnotator), allowing the labels to align with the line, even when it's not horizontal. Also, you can now disable text background, and choose to draw labels off-center which minimizes overlaps for multiple [`LineZone`](https://supervision.roboflow.com/0.24.0/detection/tools/line_zone/#supervision.detection.line_zone.LineZone) labels. [#854](https://github.com/roboflow/supervision/pull/854)
 
 ```python
 import supervision as sv
@@ -48,7 +48,7 @@ annotated_frame = line_zone_annotator.annotate(
 sv.plot_image(frame)
 ```
 
-- Added per-class counting capabilities to [`LineZone`](https://supervision.roboflow.com/develop/detection/tools/line_zone/#supervision.detection.line_zone.LineZone) and introduced [`LineZoneAnnotatorMulticlass`](https://supervision.roboflow.com/develop/detection/tools/line_zone/#supervision.detection.line_zone.LineZoneAnnotatorMulticlass) for visualizing the counts per class. This feature allows tracking of individual classes crossing a line, enhancing the flexibility of use cases like traffic monitoring or crowd analysis. [#1555](https://github.com/roboflow/supervision/pull/1555)
+- Added per-class counting capabilities to [`LineZone`](https://supervision.roboflow.com/0.24.0/detection/tools/line_zone/#supervision.detection.line_zone.LineZone) and introduced [`LineZoneAnnotatorMulticlass`](https://supervision.roboflow.com/0.24.0/detection/tools/line_zone/#supervision.detection.line_zone.LineZoneAnnotatorMulticlass) for visualizing the counts per class. This feature allows tracking of individual classes crossing a line, enhancing the flexibility of use cases like traffic monitoring or crowd analysis. [#1555](https://github.com/roboflow/supervision/pull/1555)
 
 ```python
 import supervision as sv
@@ -69,7 +69,7 @@ frame = line_zone_annotator.annotate(
 sv.plot_image(frame)
 ```
 
-- - Added [`from_easyocr`](https://supervision.roboflow.com/develop/detection/core/#supervision.detection.core.Detections.from_easyocr), allowing integration of OCR results into the supervision framework. [EasyOCR](https://github.com/JaidedAI/EasyOCR) is an open-source optical character recognition (OCR) library that can read text from images. [#1515](https://github.com/roboflow/supervision/pull/1515)
+- - Added [`from_easyocr`](https://supervision.roboflow.com/0.24.0/detection/core/#supervision.detection.core.Detections.from_easyocr), allowing integration of OCR results into the supervision framework. [EasyOCR](https://github.com/JaidedAI/EasyOCR) is an open-source optical character recognition (OCR) library that can read text from images. [#1515](https://github.com/roboflow/supervision/pull/1515)
 
 ```python
 import supervision as sv
@@ -92,7 +92,7 @@ annotated_image = label_annotator.annotate(scene=annotated_image, detections=det
 sv.plot_image(annotated_image)
 ```
 
-- Added [`oriented_box_iou_batch`](https://supervision.roboflow.com/develop/detection/utils/#supervision.detection.utils.oriented_box_iou_batch) function to `detection.utils`. This function computes Intersection over Union (IoU) for oriented or rotated bounding boxes (OBB). [#1502](https://github.com/roboflow/supervision/pull/1502)
+- Added [`oriented_box_iou_batch`](https://supervision.roboflow.com/0.24.0/detection/utils/#supervision.detection.utils.oriented_box_iou_batch) function to `detection.utils`. This function computes Intersection over Union (IoU) for oriented or rotated bounding boxes (OBB). [#1502](https://github.com/roboflow/supervision/pull/1502)
 
 ```python
 import numpy as np
@@ -103,7 +103,7 @@ ious = sv.oriented_box_iou_batch(boxes_true, boxes_detection)
 print("IoU between true and detected boxes:", ious)
 ```
 
-- Extended [`PolygonZoneAnnotator`](https://supervision.roboflow.com/develop/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZoneAnnotator) to allow setting opacity when drawing zones, providing enhanced visualization by filling the zone with adjustable transparency. [#1527](https://github.com/roboflow/supervision/pull/1527)
+- Extended [`PolygonZoneAnnotator`](https://supervision.roboflow.com/0.24.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZoneAnnotator) to allow setting opacity when drawing zones, providing enhanced visualization by filling the zone with adjustable transparency. [#1527](https://github.com/roboflow/supervision/pull/1527)
 
 ```python
 import cv2
@@ -124,7 +124,7 @@ detections = sv.Detections.from_ncnn(result)
 ```
 
 !!! failure "Removed"
-The `frame_resolution_wh` parameter in [`PolygonZone`](https://supervision.roboflow.com/develop/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) has been removed.
+The `frame_resolution_wh` parameter in [`PolygonZone`](https://supervision.roboflow.com/0.24.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) has been removed.
 
 !!! failure "Removed"
 Supervision installation methods `"headless"` and `"desktop"` were removed, as they are no longer needed. `pip install supervision[headless]` will install the base library and harmlessly warn of non-existent extras.

From 68d668a1f28dadfd51d2c7c7a1286d79710aa9bf Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 4 Oct 2024 22:22:10 +0300
Subject: [PATCH 023/128] Update "New" tags in documentation

---
 docs/detection/tools/line_zone.md    | 1 +
 docs/detection/tools/polygon_zone.md | 1 +
 docs/detection/utils.md              | 1 +
 docs/keypoint/annotators.md          | 1 -
 docs/utils/image.md                  | 1 -
 docs/utils/video.md                  | 1 -
 6 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/detection/tools/line_zone.md b/docs/detection/tools/line_zone.md
index 8bca3cfd..014687f9 100644
--- a/docs/detection/tools/line_zone.md
+++ b/docs/detection/tools/line_zone.md
@@ -1,5 +1,6 @@
 ---
 comments: true
+status: new
 ---
 
 <div class="md-typeset">
diff --git a/docs/detection/tools/polygon_zone.md b/docs/detection/tools/polygon_zone.md
index cbe76c20..1d445d9f 100644
--- a/docs/detection/tools/polygon_zone.md
+++ b/docs/detection/tools/polygon_zone.md
@@ -1,5 +1,6 @@
 ---
 comments: true
+status: new
 ---
 
 <div class="md-typeset">
diff --git a/docs/detection/utils.md b/docs/detection/utils.md
index 25c84475..5f1902b7 100644
--- a/docs/detection/utils.md
+++ b/docs/detection/utils.md
@@ -1,5 +1,6 @@
 ---
 comments: true
+status: new
 ---
 
 # Detection Utils
diff --git a/docs/keypoint/annotators.md b/docs/keypoint/annotators.md
index 30a970ec..32f30626 100644
--- a/docs/keypoint/annotators.md
+++ b/docs/keypoint/annotators.md
@@ -1,6 +1,5 @@
 ---
 comments: true
-status: new
 ---
 
 # Annotators
diff --git a/docs/utils/image.md b/docs/utils/image.md
index 93cc5a45..8e39136a 100644
--- a/docs/utils/image.md
+++ b/docs/utils/image.md
@@ -1,6 +1,5 @@
 ---
 comments: true
-status: new
 ---
 
 # Image Utils
diff --git a/docs/utils/video.md b/docs/utils/video.md
index dfae543d..f9a5821d 100644
--- a/docs/utils/video.md
+++ b/docs/utils/video.md
@@ -1,6 +1,5 @@
 ---
 comments: true
-status: new
 ---
 
 # Video Utils

From 38a1d3ad908c34c7c5f31005a9dfad0bcee4099a Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 4 Oct 2024 22:25:44 +0300
Subject: [PATCH 024/128] Docs: missing Return docstring for
 LineZoneAnnotatorMulticlass.annotate

---
 supervision/detection/line_zone.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/supervision/detection/line_zone.py b/supervision/detection/line_zone.py
index aeeb62a2..98558922 100644
--- a/supervision/detection/line_zone.py
+++ b/supervision/detection/line_zone.py
@@ -705,6 +705,9 @@ class LineZoneAnnotatorMulticlass:
             line_zone_labels (Optional[List[str]]): The labels, one for each
                 line zone. If not provided, the default labels will be used.
 
+        Returns:
+            (np.ndarray): The image with the table drawn on it.
+
         """
         if line_zone_labels is None:
             line_zone_labels = [f"Line {i + 1}:" for i in range(len(line_zones))]

From c9dfdbe30195d3f2ffc132d7ade7b2a55c7d4ea7 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 4 Oct 2024 22:34:13 +0300
Subject: [PATCH 025/128] Fixed: Admonitions formatting reformatted incorrectly

* Manually saving docs files elsewhere reformats admonitions there
---
 docs/changelog.md | 81 +++++++++++++++++++++++++++++++----------------
 1 file changed, 54 insertions(+), 27 deletions(-)

diff --git a/docs/changelog.md b/docs/changelog.md
index c6b41ea7..b7c78ff5 100644
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -124,10 +124,12 @@ detections = sv.Detections.from_ncnn(result)
 ```
 
 !!! failure "Removed"
-The `frame_resolution_wh` parameter in [`PolygonZone`](https://supervision.roboflow.com/0.24.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) has been removed.
+
+    The `frame_resolution_wh` parameter in [`PolygonZone`](https://supervision.roboflow.com/0.24.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) has been removed.
 
 !!! failure "Removed"
-Supervision installation methods `"headless"` and `"desktop"` were removed, as they are no longer needed. `pip install supervision[headless]` will install the base library and harmlessly warn of non-existent extras.
+
+    Supervision installation methods `"headless"` and `"desktop"` were removed, as they are no longer needed. `pip install supervision[headless]` will install the base library and harmlessly warn of non-existent extras.
 
 - Supervision now depends on `opencv-python` rather than `opencv-python-headless`. [#1530](https://github.com/roboflow/supervision/pull/1530)
 
@@ -254,26 +256,32 @@ for frame in sv.get_video_frames_generator(
 - Fix [#1424](https://github.com/roboflow/supervision/pull/1424): `plot_image` function now clearly indicates that the size is in inches.
 
 !!! failure "Removed"
-The `track_buffer`, `track_thresh`, and `match_thresh` parameters in [`ByteTrack`](trackers.md/#supervision.tracker.byte_tracker.core.ByteTrack) are deprecated and were removed as of `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
+
+    The `track_buffer`, `track_thresh`, and `match_thresh` parameters in [`ByteTrack`](trackers.md/#supervision.tracker.byte_tracker.core.ByteTrack) are deprecated and were removed as of `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
 
 !!! failure "Removed"
-The `triggering_position` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) was removed as of `supervision-0.23.0`. Use `triggering_anchors` instead.
+
+    The `triggering_position` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) was removed as of `supervision-0.23.0`. Use `triggering_anchors` instead.
 
 !!! failure "Deprecated"
-`overlap_filter_strategy` in `InferenceSlicer.__init__` is deprecated and will be removed in `supervision-0.27.0`. Use `overlap_strategy` instead.
+
+    `overlap_filter_strategy` in `InferenceSlicer.__init__` is deprecated and will be removed in `supervision-0.27.0`. Use `overlap_strategy` instead.
 
 !!! failure "Deprecated"
-`overlap_ratio_wh` in `InferenceSlicer.__init__` is deprecated and will be removed in `supervision-0.27.0`. Use `overlap_wh` instead.
+
+    `overlap_ratio_wh` in `InferenceSlicer.__init__` is deprecated and will be removed in `supervision-0.27.0`. Use `overlap_wh` instead.
 
 ### 0.22.0 <small>Jul 12, 2024</small>
 
 - Added [#1326](https://github.com/roboflow/supervision/pull/1326): [`sv.DetectionsDataset`](https://supervision.roboflow.com/0.22.0/datasets/core/#supervision.dataset.core.DetectionDataset) and [`sv.ClassificationDataset`](https://supervision.roboflow.com/0.22.0/datasets/core/#supervision.dataset.core.ClassificationDataset) allowing to load the images into memory only when necessary (lazy loading).
 
 !!! failure "Deprecated"
-Constructing `DetectionDataset` with parameter `images` as `Dict[str, np.ndarray]` is deprecated and will be removed in `supervision-0.26.0`. Please pass a list of paths `List[str]` instead.
+
+    Constructing `DetectionDataset` with parameter `images` as `Dict[str, np.ndarray]` is deprecated and will be removed in `supervision-0.26.0`. Please pass a list of paths `List[str]` instead.
 
 !!! failure "Deprecated"
-The `DetectionDataset.images` property is deprecated and will be removed in `supervision-0.26.0`. Please loop over images with `for path, image, annotation in dataset:`, as that does not require loading all images into memory.
+
+    The `DetectionDataset.images` property is deprecated and will be removed in `supervision-0.26.0`. Please loop over images with `for path, image, annotation in dataset:`, as that does not require loading all images into memory.
 
 ```python
 import roboflow
@@ -367,10 +375,12 @@ annotated_image = obb_annotator.annotate(scene=image.copy(), detections=detectio
 - Fixed [#1312](https://github.com/roboflow/supervision/pull/1312): Fixed [`CropAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.TraceAnnotator.annotate).
 
 !!! failure "Removed"
-`BoxAnnotator` was removed, however `BoundingBoxAnnotator` has been renamed to `BoxAnnotator`. Use a combination of [`BoxAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.BoxAnnotator) and [`LabelAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.LabelAnnotator) to simulate old `BoundingBox` behavior.
+
+    `BoxAnnotator` was removed, however `BoundingBoxAnnotator` has been renamed to `BoxAnnotator`. Use a combination of [`BoxAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.BoxAnnotator) and [`LabelAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.LabelAnnotator) to simulate old `BoundingBox` behavior.
 
 !!! failure "Deprecated"
-The name `BoundingBoxAnnotator` has been deprecated and will be removed in `supervision-0.26.0`. It has been renamed to [`BoxAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.BoxAnnotator).
+
+    The name `BoundingBoxAnnotator` has been deprecated and will be removed in `supervision-0.26.0`. It has been renamed to [`BoxAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.BoxAnnotator).
 
 - Added [#975](https://github.com/roboflow/supervision/pull/975) 📝 New Cookbooks: serialize detections into [json](https://github.com/roboflow/supervision/blob/de896189b83a1f9434c0a37dd9192ee00d2a1283/docs/notebooks/serialise-detections-to-json.ipynb) and [csv](https://github.com/roboflow/supervision/blob/de896189b83a1f9434c0a37dd9192ee00d2a1283/docs/notebooks/serialise-detections-to-csv.ipynb).
 
@@ -379,28 +389,36 @@ The name `BoundingBoxAnnotator` has been deprecated and will be removed in `supe
 - Added [#1340](https://github.com/roboflow/supervision/pull/1340): Two new methods for converting between bounding box formats - [`xywh_to_xyxy`](https://supervision.roboflow.com/0.22.0/detection/utils/#supervision.detection.utils.xywh_to_xyxy) and [`xcycwh_to_xyxy`](https://supervision.roboflow.com/0.22.0/detection/utils/#supervision.detection.utils.xcycwh_to_xyxy)
 
 !!! failure "Removed"
-`from_roboflow` method has been removed due to deprecation. Use [from_inference](https://supervision.roboflow.com/0.22.0/detection/core/#supervision.detection.core.Detections.from_inference) instead.
+
+    `from_roboflow` method has been removed due to deprecation. Use [from_inference](https://supervision.roboflow.com/0.22.0/detection/core/#supervision.detection.core.Detections.from_inference) instead.
 
 !!! failure "Removed"
-`Color.white()` has been removed due to deprecation. Use `color.WHITE` instead.
+
+    `Color.white()` has been removed due to deprecation. Use `color.WHITE` instead.
 
 !!! failure "Removed"
-`Color.black()` has been removed due to deprecation. Use `color.BLACK` instead.
+
+    `Color.black()` has been removed due to deprecation. Use `color.BLACK` instead.
 
 !!! failure "Removed"
-`Color.red()` has been removed due to deprecation. Use `color.RED` instead.
+
+    `Color.red()` has been removed due to deprecation. Use `color.RED` instead.
 
 !!! failure "Removed"
-`Color.green()` has been removed due to deprecation. Use `color.GREEN` instead.
+
+    `Color.green()` has been removed due to deprecation. Use `color.GREEN` instead.
 
 !!! failure "Removed"
-`Color.blue()` has been removed due to deprecation. Use `color.BLUE` instead.
+
+    `Color.blue()` has been removed due to deprecation. Use `color.BLUE` instead.
 
 !!! failure "Removed"
-`ColorPalette.default()` has been removed due to deprecation. Use [ColorPalette.DEFAULT](https://supervision.roboflow.com/0.22.0/utils/draw/#supervision.draw.color.ColorPalette.DEFAULT) instead.
+
+    `ColorPalette.default()` has been removed due to deprecation. Use [ColorPalette.DEFAULT](https://supervision.roboflow.com/0.22.0/utils/draw/#supervision.draw.color.ColorPalette.DEFAULT) instead.
 
 !!! failure "Removed"
-`FPSMonitor.__call__` has been removed due to deprecation. Use the attribute [FPSMonitor.fps](https://supervision.roboflow.com/0.22.0/utils/video/#supervision.utils.video.FPSMonitor.fps) instead.
+
+    `FPSMonitor.__call__` has been removed due to deprecation. Use the attribute [FPSMonitor.fps](https://supervision.roboflow.com/0.22.0/utils/video/#supervision.utils.video.FPSMonitor.fps) instead.
 
 ### 0.21.0 <small>Jun 5, 2024</small>
 
@@ -508,7 +526,8 @@ annotated_image = edge_annotators.annotate(image.copy(), keypoints)
 - Changed [#1109](https://github.com/roboflow/supervision/pull/1109): [`sv.PolygonZone`](/0.20.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) such that the `frame_resolution_wh` argument is no longer required to initialize `sv.PolygonZone`.
 
 !!! failure "Deprecated"
-The `frame_resolution_wh` parameter in `sv.PolygonZone` is deprecated and will be removed in `supervision-0.24.0`.
+
+    The `frame_resolution_wh` parameter in `sv.PolygonZone` is deprecated and will be removed in `supervision-0.24.0`.
 
 - Changed [#1084](https://github.com/roboflow/supervision/pull/1084): [`sv.get_polygon_center`](/0.20.0/utils/geometry/#supervision.geometry.core.utils.get_polygon_center) to calculate a more accurate polygon centroid.
 
@@ -613,12 +632,14 @@ annotated_frame = crop_annotator.annotate(
 - Changed [#787](https://github.com/roboflow/supervision/pull/787): [`sv.ByteTrack`](/0.19.0/trackers/#supervision.tracker.ByteTrack) input arguments and docstrings updated to improve readability and ease of use.
 
 !!! failure "Deprecated"
-The `track_buffer`, `track_thresh`, and `match_thresh` parameters in `sv.ByteTrack` are deprecated and will be removed in `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
+
+    The `track_buffer`, `track_thresh`, and `match_thresh` parameters in `sv.ByteTrack` are deprecated and will be removed in `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
 
 - Changed [#910](https://github.com/roboflow/supervision/pull/910): [`sv.PolygonZone`](/0.19.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) to now accept a list of specific box anchors that must be in zone for a detection to be counted.
 
 !!! failure "Deprecated"
-The `triggering_position ` parameter in `sv.PolygonZone` is deprecated and will be removed in `supervision-0.23.0`. Use `triggering_anchors` instead.
+
+    The `triggering_position ` parameter in `sv.PolygonZone` is deprecated and will be removed in `supervision-0.23.0`. Use `triggering_anchors` instead.
 
 - Changed [#875](https://github.com/roboflow/supervision/pull/875): annotators adding support for Pillow images. All supervision Annotators can now accept an image as either a numpy array or a Pillow Image. They automatically detect its type, draw annotations, and return the output in the same format as the input.
 
@@ -681,14 +702,16 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 - Changed [#756](https://github.com/roboflow/supervision/pull/756): [`sv.Color`](/0.18.0/draw/color/#color)'s and [`sv.ColorPalette`](/0.18.0/draw/color/#colorpalette)'s method of accessing predefined colors, transitioning from a function-based approach (`sv.Color.red()`) to a more intuitive and conventional property-based method (`sv.Color.RED`).
 
 !!! failure "Deprecated"
-`sv.ColorPalette.default()` is deprecated and will be removed in `supervision-0.22.0`. Use `sv.ColorPalette.DEFAULT` instead.
+
+    `sv.ColorPalette.default()` is deprecated and will be removed in `supervision-0.22.0`. Use `sv.ColorPalette.DEFAULT` instead.
 
 - Changed [#769](https://github.com/roboflow/supervision/pull/769): [`sv.ColorPalette.DEFAULT`](/0.18.0/draw/color/#colorpalette) value, giving users a more extensive set of annotation colors.
 
 - Changed [#677](https://github.com/roboflow/supervision/pull/677): `sv.Detections.from_roboflow` to [`sv.Detections.from_inference`](/0.18.0/detection/core/#supervision.detection.core.Detections.from_inference) streamlining its functionality to be compatible with both the both [inference](https://github.com/roboflow/inference) pip package and the Robloflow [hosted API](https://docs.roboflow.com/deploy/hosted-api).
 
 !!! failure "Deprecated"
-`Detections.from_roboflow()` is deprecated and will be removed in `supervision-0.22.0`. Use `Detections.from_inference` instead.
+
+    `Detections.from_roboflow()` is deprecated and will be removed in `supervision-0.22.0`. Use `Detections.from_inference` instead.
 
 - Fixed [#735](https://github.com/roboflow/supervision/pull/735): [`sv.LineZone`](/0.18.0/detection/tools/line_zone/#linezone) functionality to accurately update the counter when an object crosses a line from any direction, including from the side. This enhancement enables more precise tracking and analytics, such as calculating individual in/out counts for each lane on the road.
 
@@ -785,7 +808,8 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 - Fixed [#430](https://github.com/roboflow/supervision/pull/430): [`sv.ByteTrack`](/0.16.0/trackers/#supervision.tracker.byte_tracker.core.ByteTrack) to return `np.array([], dtype=int)` when `svDetections` is empty.
 
 !!! failure "Deprecated"
-`sv.Detections.from_yolov8` and `sv.Classifications.from_yolov8` as those are now replaced by [`sv.Detections.from_ultralytics`](/0.16.0/detection/core/#supervision.detection.core.Detections.from_ultralytics) and [`sv.Classifications.from_ultralytics`](/0.16.0/classification/core/#supervision.classification.core.Classifications.from_ultralytics).
+
+    `sv.Detections.from_yolov8` and `sv.Classifications.from_yolov8` as those are now replaced by [`sv.Detections.from_ultralytics`](/0.16.0/detection/core/#supervision.detection.core.Detections.from_ultralytics) and [`sv.Classifications.from_ultralytics`](/0.16.0/classification/core/#supervision.classification.core.Classifications.from_ultralytics).
 
 ### 0.15.0 <small>October 5, 2023</small>
 
@@ -852,7 +876,8 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 - Added [#281](https://github.com/roboflow/supervision/pull/281): [`sv.Classifications.from_ultralytics`](/0.14.0/classification/core/#supervision.classification.core.Classifications.from_ultralytics) to enable seamless integration with [Ultralytics](https://github.com/ultralytics/ultralytics) framework. This will enable you to use supervision with all [models](https://docs.ultralytics.com/models/) that Ultralytics supports.
 
 !!! failure "Deprecated"
-[sv.Detections.from_yolov8](/0.14.0/detection/core/#supervision.detection.core.Detections.from_yolov8) and [sv.Classifications.from_yolov8](/0.14.0/classification/core/#supervision.classification.core.Classifications.from_yolov8) are now deprecated and will be removed with `supervision-0.16.0` release.
+
+    [sv.Detections.from_yolov8](/0.14.0/detection/core/#supervision.detection.core.Detections.from_yolov8) and [sv.Classifications.from_yolov8](/0.14.0/classification/core/#supervision.classification.core.Classifications.from_yolov8) are now deprecated and will be removed with `supervision-0.16.0` release.
 
 - Added [#341](https://github.com/roboflow/supervision/pull/341): First supervision usage example script showing how to detect and track objects on video using YOLOv8 + Supervision.
 
@@ -889,7 +914,8 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 - Added [#222](https://github.com/roboflow/supervision/pull/222): [`sv.Detections.from_ultralytics`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_ultralytics) to enable seamless integration with [Ultralytics](https://github.com/ultralytics/ultralytics) framework. This will enable you to use `supervision` with all [models](https://docs.ultralytics.com/models/) that Ultralytics supports.
 
 !!! failure "Deprecated"
-[`sv.Detections.from_yolov8`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_yolov8) is now deprecated and will be removed with `supervision-0.15.0` release.
+
+    [`sv.Detections.from_yolov8`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_yolov8) is now deprecated and will be removed with `supervision-0.15.0` release.
 
 - Added [#191](https://github.com/roboflow/supervision/pull/191): [`sv.Detections.from_paddledet`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_paddledet) to enable seamless integration with [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) framework.
 
@@ -898,7 +924,8 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 ### 0.12.0 <small>July 24, 2023</small>
 
 !!! failure "Python 3.7. Support Terminated"
-With the `supervision-0.12.0` release, we are terminating official support for Python 3.7.
+
+    With the `supervision-0.12.0` release, we are terminating official support for Python 3.7.
 
 - Added [#177](https://github.com/roboflow/supervision/pull/177): initial support for object detection model benchmarking with [`sv.ConfusionMatrix`](/0.12.0/metrics/detection/#confusionmatrix).
 

From e1e00ffc52b7ee40bc8c4d1d5f5a9fe50768c9e2 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 4 Oct 2024 22:47:27 +0300
Subject: [PATCH 026/128] pathch: minor typo in changelog

---
 docs/changelog.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/changelog.md b/docs/changelog.md
index b7c78ff5..976a78d0 100644
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -69,7 +69,7 @@ frame = line_zone_annotator.annotate(
 sv.plot_image(frame)
 ```
 
-- - Added [`from_easyocr`](https://supervision.roboflow.com/0.24.0/detection/core/#supervision.detection.core.Detections.from_easyocr), allowing integration of OCR results into the supervision framework. [EasyOCR](https://github.com/JaidedAI/EasyOCR) is an open-source optical character recognition (OCR) library that can read text from images. [#1515](https://github.com/roboflow/supervision/pull/1515)
+- Added [`from_easyocr`](https://supervision.roboflow.com/0.24.0/detection/core/#supervision.detection.core.Detections.from_easyocr), allowing integration of OCR results into the supervision framework. [EasyOCR](https://github.com/JaidedAI/EasyOCR) is an open-source optical character recognition (OCR) library that can read text from images. [#1515](https://github.com/roboflow/supervision/pull/1515)
 
 ```python
 import supervision as sv

From 953ecba283f7befd41b81c6c91224ba69cdc806d Mon Sep 17 00:00:00 2001
From: LinasKo <linas.ko@pm.me>
Date: Fri, 4 Oct 2024 22:55:27 +0300
Subject: [PATCH 027/128] Bump supervision to 0.24.0

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index bc4f3d54..c9aa384e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supervision"
-version = "0.24.0rc1"
+version = "0.24.0"
 description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
 authors = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
 maintainers = [

From 124c7d9b907790bde68475af77ff2e2a1fd7815a Mon Sep 17 00:00:00 2001
From: Huzail <huzailkhan03@gmail.com>
Date: Sat, 5 Oct 2024 21:36:21 +0530
Subject: [PATCH 028/128] Added class-agnostic

---
 supervision/metrics/mean_average_precision.py | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index 04a5fe9d..56fd3967 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -75,6 +75,13 @@ class MeanAveragePrecision(Metric):
                 f"The number of predictions ({len(predictions)}) and"
                 f" targets ({len(targets)}) during the update must be the same."
             )
+        # class-agnostic
+        if self._class_agnostic:
+            # Set all class_ids to 0 to ignore class distinction
+            for prediction in predictions:
+                prediction.class_id[:] = 0
+            for target in targets:
+                target.class_id[:] = 0
 
         self._predictions_list.extend(predictions)
         self._targets_list.extend(targets)
@@ -239,13 +246,18 @@ class MeanAveragePrecision(Metric):
         target_classes: np.ndarray,
         iou: np.ndarray,
         iou_thresholds: np.ndarray,
+        class_agnostic: bool,
     ) -> np.ndarray:
         num_predictions, num_iou_levels = (
             predictions_classes.shape[0],
             iou_thresholds.shape[0],
         )
         correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)
-        correct_class = target_classes[:, None] == predictions_classes
+
+        if class_agnostic:
+            correct_class = np.ones_like(iou, dtype=bool)  # Treat all as the same class
+        else:
+            correct_class = target_classes[:, None] == predictions_classes
 
         for i, iou_level in enumerate(iou_thresholds):
             matched_indices = np.where((iou >= iou_level) & correct_class)

From 6d30eef4a96f9a3dd3513097d9dd3800141774ba Mon Sep 17 00:00:00 2001
From: Huzail <huzailkhan03@gmail.com>
Date: Sun, 6 Oct 2024 01:37:48 +0530
Subject: [PATCH 029/128] Added _match_detection_batch_class_agnostic

---
 supervision/metrics/mean_average_precision.py | 43 +++++++++++++++----
 1 file changed, 35 insertions(+), 8 deletions(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index 56fd3967..11fbef5a 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -184,9 +184,14 @@ class MeanAveragePrecision(Metric):
                             "Unsupported metric target for IoU calculation"
                         )
 
-                    matches = self._match_detection_batch(
-                        predictions.class_id, targets.class_id, iou, iou_thresholds
-                    )
+                    # Match detections: if class_agnostic is set, skip class matching
+                    if self._class_agnostic:
+                        matches = self._match_detection_batch_class_agnostic(iou, iou_thresholds)
+                    else:
+                        matches = self._match_detection_batch(
+                            predictions.class_id, targets.class_id, iou, iou_thresholds
+                        )
+
                     stats.append(
                         (
                             matches,
@@ -246,7 +251,6 @@ class MeanAveragePrecision(Metric):
         target_classes: np.ndarray,
         iou: np.ndarray,
         iou_thresholds: np.ndarray,
-        class_agnostic: bool,
     ) -> np.ndarray:
         num_predictions, num_iou_levels = (
             predictions_classes.shape[0],
@@ -254,10 +258,8 @@ class MeanAveragePrecision(Metric):
         )
         correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)
 
-        if class_agnostic:
-            correct_class = np.ones_like(iou, dtype=bool)  # Treat all as the same class
-        else:
-            correct_class = target_classes[:, None] == predictions_classes
+        
+        correct_class = target_classes[:, None] == predictions_classes
 
         for i, iou_level in enumerate(iou_thresholds):
             matched_indices = np.where((iou >= iou_level) & correct_class)
@@ -275,7 +277,32 @@ class MeanAveragePrecision(Metric):
                 correct[matches[:, 1].astype(int), i] = True
 
         return correct
+    @staticmethod
+    def _match_detection_batch_class_agnostic(
+        iou: np.ndarray,
+        iou_thresholds: np.ndarray,
+    ) -> np.ndarray:
+        """
+        Match detections for class-agnostic case, ignoring the class labels.
+        """
+        num_predictions, num_iou_levels = iou.shape[0], iou_thresholds.shape[0]
+        correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)
 
+        for i, iou_level in enumerate(iou_thresholds):
+            matched_indices = np.where(iou >= iou_level)
+            if matched_indices[0].shape[0]:
+                combined_indices = np.stack(matched_indices, axis=1)
+                iou_values = iou[matched_indices][:, None]
+                matches = np.hstack([combined_indices, iou_values])
+
+                if matched_indices[0].shape[0] > 1:
+                    matches = matches[matches[:, 2].argsort()[::-1]]
+                    matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
+                    matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
+
+                correct[matches[:, 1].astype(int), i] = True
+
+        return correct
     @staticmethod
     def _average_precisions_per_class(
         matches: np.ndarray,

From e0db07a80fa6cfbbade4d572334766880ce42f8b Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Sat, 5 Oct 2024 21:14:39 +0000
Subject: [PATCH 030/128] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20aut?=
 =?UTF-8?q?o=20format=20pre-commit=20hooks?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 supervision/metrics/mean_average_precision.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index 11fbef5a..ff2d9e8e 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -186,7 +186,9 @@ class MeanAveragePrecision(Metric):
 
                     # Match detections: if class_agnostic is set, skip class matching
                     if self._class_agnostic:
-                        matches = self._match_detection_batch_class_agnostic(iou, iou_thresholds)
+                        matches = self._match_detection_batch_class_agnostic(
+                            iou, iou_thresholds
+                        )
                     else:
                         matches = self._match_detection_batch(
                             predictions.class_id, targets.class_id, iou, iou_thresholds
@@ -258,7 +260,6 @@ class MeanAveragePrecision(Metric):
         )
         correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)
 
-        
         correct_class = target_classes[:, None] == predictions_classes
 
         for i, iou_level in enumerate(iou_thresholds):
@@ -277,6 +278,7 @@ class MeanAveragePrecision(Metric):
                 correct[matches[:, 1].astype(int), i] = True
 
         return correct
+
     @staticmethod
     def _match_detection_batch_class_agnostic(
         iou: np.ndarray,
@@ -303,6 +305,7 @@ class MeanAveragePrecision(Metric):
                 correct[matches[:, 1].astype(int), i] = True
 
         return correct
+
     @staticmethod
     def _average_precisions_per_class(
         matches: np.ndarray,

From c70988123df0811dd07a0261c600a9c02014e239 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 7 Oct 2024 01:02:26 +0000
Subject: [PATCH 031/128] :arrow_up: Bump ruff from 0.6.8 to 0.6.9

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.8 to 0.6.9.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.6.8...0.6.9)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 38 +++++++++++++++++++-------------------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 519b7a2f..6c9d12a7 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -3850,29 +3850,29 @@ files = [
 
 [[package]]
 name = "ruff"
-version = "0.6.8"
+version = "0.6.9"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ruff-0.6.8-py3-none-linux_armv6l.whl", hash = "sha256:77944bca110ff0a43b768f05a529fecd0706aac7bcce36d7f1eeb4cbfca5f0f2"},
-    {file = "ruff-0.6.8-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:27b87e1801e786cd6ede4ada3faa5e254ce774de835e6723fd94551464c56b8c"},
-    {file = "ruff-0.6.8-py3-none-macosx_11_0_arm64.whl", hash = "sha256:cd48f945da2a6334f1793d7f701725a76ba93bf3d73c36f6b21fb04d5338dcf5"},
-    {file = "ruff-0.6.8-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:677e03c00f37c66cea033274295a983c7c546edea5043d0c798833adf4cf4c6f"},
-    {file = "ruff-0.6.8-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:9f1476236b3eacfacfc0f66aa9e6cd39f2a624cb73ea99189556015f27c0bdeb"},
-    {file = "ruff-0.6.8-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6f5a2f17c7d32991169195d52a04c95b256378bbf0de8cb98478351eb70d526f"},
-    {file = "ruff-0.6.8-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:5fd0d4b7b1457c49e435ee1e437900ced9b35cb8dc5178921dfb7d98d65a08d0"},
-    {file = "ruff-0.6.8-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f8034b19b993e9601f2ddf2c517451e17a6ab5cdb1c13fdff50c1442a7171d87"},
-    {file = "ruff-0.6.8-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6cfb227b932ba8ef6e56c9f875d987973cd5e35bc5d05f5abf045af78ad8e098"},
-    {file = "ruff-0.6.8-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6ef0411eccfc3909269fed47c61ffebdcb84a04504bafa6b6df9b85c27e813b0"},
-    {file = "ruff-0.6.8-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:007dee844738c3d2e6c24ab5bc7d43c99ba3e1943bd2d95d598582e9c1b27750"},
-    {file = "ruff-0.6.8-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:ce60058d3cdd8490e5e5471ef086b3f1e90ab872b548814e35930e21d848c9ce"},
-    {file = "ruff-0.6.8-py3-none-musllinux_1_2_i686.whl", hash = "sha256:1085c455d1b3fdb8021ad534379c60353b81ba079712bce7a900e834859182fa"},
-    {file = "ruff-0.6.8-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:70edf6a93b19481affd287d696d9e311388d808671bc209fb8907b46a8c3af44"},
-    {file = "ruff-0.6.8-py3-none-win32.whl", hash = "sha256:792213f7be25316f9b46b854df80a77e0da87ec66691e8f012f887b4a671ab5a"},
-    {file = "ruff-0.6.8-py3-none-win_amd64.whl", hash = "sha256:ec0517dc0f37cad14a5319ba7bba6e7e339d03fbf967a6d69b0907d61be7a263"},
-    {file = "ruff-0.6.8-py3-none-win_arm64.whl", hash = "sha256:8d3bb2e3fbb9875172119021a13eed38849e762499e3cfde9588e4b4d70968dc"},
-    {file = "ruff-0.6.8.tar.gz", hash = "sha256:a5bf44b1aa0adaf6d9d20f86162b34f7c593bfedabc51239953e446aefc8ce18"},
+    {file = "ruff-0.6.9-py3-none-linux_armv6l.whl", hash = "sha256:064df58d84ccc0ac0fcd63bc3090b251d90e2a372558c0f057c3f75ed73e1ccd"},
+    {file = "ruff-0.6.9-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:140d4b5c9f5fc7a7b074908a78ab8d384dd7f6510402267bc76c37195c02a7ec"},
+    {file = "ruff-0.6.9-py3-none-macosx_11_0_arm64.whl", hash = "sha256:53fd8ca5e82bdee8da7f506d7b03a261f24cd43d090ea9db9a1dc59d9313914c"},
+    {file = "ruff-0.6.9-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:645d7d8761f915e48a00d4ecc3686969761df69fb561dd914a773c1a8266e14e"},
+    {file = "ruff-0.6.9-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:eae02b700763e3847595b9d2891488989cac00214da7f845f4bcf2989007d577"},
+    {file = "ruff-0.6.9-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7d5ccc9e58112441de8ad4b29dcb7a86dc25c5f770e3c06a9d57e0e5eba48829"},
+    {file = "ruff-0.6.9-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:417b81aa1c9b60b2f8edc463c58363075412866ae4e2b9ab0f690dc1e87ac1b5"},
+    {file = "ruff-0.6.9-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3c866b631f5fbce896a74a6e4383407ba7507b815ccc52bcedabb6810fdb3ef7"},
+    {file = "ruff-0.6.9-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7b118afbb3202f5911486ad52da86d1d52305b59e7ef2031cea3425142b97d6f"},
+    {file = "ruff-0.6.9-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a67267654edc23c97335586774790cde402fb6bbdb3c2314f1fc087dee320bfa"},
+    {file = "ruff-0.6.9-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:3ef0cc774b00fec123f635ce5c547dac263f6ee9fb9cc83437c5904183b55ceb"},
+    {file = "ruff-0.6.9-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:12edd2af0c60fa61ff31cefb90aef4288ac4d372b4962c2864aeea3a1a2460c0"},
+    {file = "ruff-0.6.9-py3-none-musllinux_1_2_i686.whl", hash = "sha256:55bb01caeaf3a60b2b2bba07308a02fca6ab56233302406ed5245180a05c5625"},
+    {file = "ruff-0.6.9-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:925d26471fa24b0ce5a6cdfab1bb526fb4159952385f386bdcc643813d472039"},
+    {file = "ruff-0.6.9-py3-none-win32.whl", hash = "sha256:eb61ec9bdb2506cffd492e05ac40e5bc6284873aceb605503d8494180d6fc84d"},
+    {file = "ruff-0.6.9-py3-none-win_amd64.whl", hash = "sha256:785d31851c1ae91f45b3d8fe23b8ae4b5170089021fbb42402d811135f0b7117"},
+    {file = "ruff-0.6.9-py3-none-win_arm64.whl", hash = "sha256:a9641e31476d601f83cd602608739a0840e348bda93fec9f1ee816f8b6798b93"},
+    {file = "ruff-0.6.9.tar.gz", hash = "sha256:b076ef717a8e5bc819514ee1d602bbdca5b4420ae13a9cf61a0c0a4f53a2baa2"},
 ]
 
 [[package]]

From 1523ee21af44a478f3fccd32897de5c560481dbe Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 7 Oct 2024 01:04:39 +0000
Subject: [PATCH 032/128] :arrow_up: Bump build from 1.2.2 to 1.2.2.post1

Bumps [build](https://github.com/pypa/build) from 1.2.2 to 1.2.2.post1.
- [Release notes](https://github.com/pypa/build/releases)
- [Changelog](https://github.com/pypa/build/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pypa/build/compare/1.2.2...1.2.2.post1)

---
updated-dependencies:
- dependency-name: build
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 519b7a2f..03aa85c3 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -259,13 +259,13 @@ css = ["tinycss2 (>=1.1.0,<1.3)"]
 
 [[package]]
 name = "build"
-version = "1.2.2"
+version = "1.2.2.post1"
 description = "A simple, correct Python build frontend"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "build-1.2.2-py3-none-any.whl", hash = "sha256:277ccc71619d98afdd841a0e96ac9fe1593b823af481d3b0cea748e8894e0613"},
-    {file = "build-1.2.2.tar.gz", hash = "sha256:119b2fb462adef986483438377a13b2f42064a2a3a4161f24a0cca698a07ac8c"},
+    {file = "build-1.2.2.post1-py3-none-any.whl", hash = "sha256:1d61c0887fa860c01971625baae8bdd338e517b836a2f70dd1f7aa3a6b2fc5b5"},
+    {file = "build-1.2.2.post1.tar.gz", hash = "sha256:b36993e92ca9375a219c99e606a122ff365a760a2d4bba0caa09bd5278b608b7"},
 ]
 
 [package.dependencies]

From 7862f9cc70f6732cdde46cf1703f6a92eceb9f89 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 7 Oct 2024 18:27:48 +0000
Subject: [PATCH 033/128] =?UTF-8?q?chore(pre=5Fcommit):=20=E2=AC=86=20pre?=
 =?UTF-8?q?=5Fcommit=20autoupdate?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

updates:
- [github.com/pre-commit/pre-commit-hooks: v4.6.0 → v5.0.0](https://github.com/pre-commit/pre-commit-hooks/compare/v4.6.0...v5.0.0)
- [github.com/astral-sh/ruff-pre-commit: v0.6.8 → v0.6.9](https://github.com/astral-sh/ruff-pre-commit/compare/v0.6.8...v0.6.9)
---
 .pre-commit-config.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index d8e34a97..2ca4357c 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -7,7 +7,7 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v5.0.0
     hooks:
       - id: trailing-whitespace
         exclude: test/.*\.py
@@ -32,7 +32,7 @@ repos:
           additional_dependencies: ["bandit[toml]"]
 
   -   repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.6.8
+      rev: v0.6.9
       hooks:
       -   id: ruff
           args: [--fix, --exit-non-zero-on-fix]

From 3231f7f498005e2c88ee12f3d61721e10bcb226a Mon Sep 17 00:00:00 2001
From: LinasKo <linas.ko@pm.me>
Date: Wed, 9 Oct 2024 11:50:08 +0300
Subject: [PATCH 034/128] Minor fix: mAP can be 1.01

Fix division to avoid cases where 1.01 mAP is possible.

Discovered in: https://colab.research.google.com/drive/10EaQ4lJNXzcmub7doO2vGn2t1LgDdkcC?usp=sharing
---
 supervision/metrics/mean_average_precision.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index 04a5fe9d..cc15f565 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -230,7 +230,7 @@ class MeanAveragePrecision(Metric):
         for r, p in zip(recall[::-1], precision[::-1]):
             precision_levels[recall_levels <= r] = p
 
-        average_precision = (1 / 100 * precision_levels).sum()
+        average_precision = (1 / 101 * precision_levels).sum()
         return average_precision
 
     @staticmethod

From 921e8f32d4ae2f43f55028cdc198f7cdd65c7672 Mon Sep 17 00:00:00 2001
From: Onuralp SEZER <thunderbirdtr@gmail.com>
Date: Wed, 9 Oct 2024 16:46:02 +0300
Subject: [PATCH 035/128] =?UTF-8?q?ci:=20=F0=9F=91=B7=20python3.13=20ci=20?=
 =?UTF-8?q?tests=20and=20tox=20ver=20added?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>
---
 .github/workflows/test.yml | 2 +-
 tox.ini                    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 1ce4a267..b33f8e53 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -9,7 +9,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12", "3.13"]
     steps:
       - name: 🛎️ Checkout
         uses: actions/checkout@v4
diff --git a/tox.ini b/tox.ini
index 46886c13..3f44d215 100644
--- a/tox.ini
+++ b/tox.ini
@@ -1,5 +1,5 @@
 [tox]
-envlist = py38,py39,py310,py311,py312
+envlist = py38,py39,py310,py311,py312,py313
 
 [testenv]
 changedir = test

From a4f79b59f1a6303330662f800b40b0498f73a582 Mon Sep 17 00:00:00 2001
From: DemyCode <verycols@gmail.com>
Date: Wed, 9 Oct 2024 16:48:54 +0200
Subject: [PATCH 036/128] feat: adding type hinting to package

---
 pyproject.toml       | 2 +-
 supervision/py.typed | 0
 2 files changed, 1 insertion(+), 1 deletion(-)
 create mode 100644 supervision/py.typed

diff --git a/pyproject.toml b/pyproject.toml
index c9aa384e..f6301106 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,7 +9,7 @@ maintainers = [
 ]
 readme = "README.md"
 license = "MIT"
-packages = [{ include = "supervision" }]
+packages = [{ include = "supervision" }, { include = "supervision/py.typed" }]
 homepage = "https://github.com/roboflow/supervision"
 repository = "https://github.com/roboflow/supervision"
 documentation = "https://supervision.roboflow.com/latest/"
diff --git a/supervision/py.typed b/supervision/py.typed
new file mode 100644
index 00000000..e69de29b

From baffd983144ccd74a60c16ce96166d83ab21d4e2 Mon Sep 17 00:00:00 2001
From: Ahmad Huzail Khan <104365728+AHuzail@users.noreply.github.com>
Date: Wed, 9 Oct 2024 18:54:31 +0000
Subject: [PATCH 037/128] 	removed comments

---
 supervision/metrics/mean_average_precision.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index d672344b..7b7dd93c 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -75,9 +75,8 @@ class MeanAveragePrecision(Metric):
                 f"The number of predictions ({len(predictions)}) and"
                 f" targets ({len(targets)}) during the update must be the same."
             )
-        # class-agnostic
+        
         if self._class_agnostic:
-            # Set all class_ids to 0 to ignore class distinction
             for prediction in predictions:
                 prediction.class_id[:] = 0
             for target in targets:
@@ -184,7 +183,7 @@ class MeanAveragePrecision(Metric):
                             "Unsupported metric target for IoU calculation"
                         )
 
-                    # Match detections: if class_agnostic is set, skip class matching
+
                     if self._class_agnostic:
                         matches = self._match_detection_batch_class_agnostic(
                             iou, iou_thresholds

From 12c6b873e3ef641cb67c67ff6cbd2691ac6a1cb2 Mon Sep 17 00:00:00 2001
From: Ahmad Huzail Khan <104365728+AHuzail@users.noreply.github.com>
Date: Wed, 9 Oct 2024 19:01:40 +0000
Subject: [PATCH 038/128] 	modified:  
 supervision/metrics/mean_average_precision.py

---
 supervision/metrics/mean_average_precision.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index 7b7dd93c..9aa06935 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -78,9 +78,9 @@ class MeanAveragePrecision(Metric):
         
         if self._class_agnostic:
             for prediction in predictions:
-                prediction.class_id[:] = 0
+                prediction.class_id[:] = -1
             for target in targets:
-                target.class_id[:] = 0
+                target.class_id[:] = -1
 
         self._predictions_list.extend(predictions)
         self._targets_list.extend(targets)

From e433527942c624b1556c0a0f46190eea9fae22b5 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 9 Oct 2024 19:04:45 +0000
Subject: [PATCH 039/128] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20aut?=
 =?UTF-8?q?o=20format=20pre-commit=20hooks?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 supervision/metrics/mean_average_precision.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index 9aa06935..ef8f4c74 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -75,7 +75,7 @@ class MeanAveragePrecision(Metric):
                 f"The number of predictions ({len(predictions)}) and"
                 f" targets ({len(targets)}) during the update must be the same."
             )
-        
+
         if self._class_agnostic:
             for prediction in predictions:
                 prediction.class_id[:] = -1
@@ -183,7 +183,6 @@ class MeanAveragePrecision(Metric):
                             "Unsupported metric target for IoU calculation"
                         )
 
-
                     if self._class_agnostic:
                         matches = self._match_detection_batch_class_agnostic(
                             iou, iou_thresholds

From 16a4491b596786c603556fa055244b366b243486 Mon Sep 17 00:00:00 2001
From: Ahmad Huzail Khan <104365728+AHuzail@users.noreply.github.com>
Date: Wed, 9 Oct 2024 19:29:41 +0000
Subject: [PATCH 040/128]  modified class MeanAveragePrecisionResult for
 class_agnostic

---
 supervision/metrics/mean_average_precision.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index 9aa06935..b02a2742 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -424,6 +424,7 @@ class MeanAveragePrecisionResult:
     Attributes:
         metric_target (MetricTarget): the type of data used for the metric -
             boxes, masks or oriented bounding boxes.
+        class_agnostic: When computing class-agnostic results, class ID is set to `-1`.
         mAP_map50_95 (float): the mAP score at IoU thresholds from `0.5` to `0.95`.
         mAP_map50 (float): the mAP score at IoU threshold of `0.5`.
         mAP_map75 (float): the mAP score at IoU threshold of `0.75`.
@@ -443,6 +444,7 @@ class MeanAveragePrecisionResult:
     """
 
     metric_target: MetricTarget
+    is_class_agnostic: bool
 
     @property
     def map50_95(self) -> float:
@@ -477,6 +479,7 @@ class MeanAveragePrecisionResult:
         out_str = (
             f"{self.__class__.__name__}:\n"
             f"Metric target: {self.metric_target}\n"
+            f"Class-agnostic: {self.is_class_agnostic}\n"
             f"mAP @ 50:95: {self.map50_95:.4f}\n"
             f"mAP @ 50:    {self.map50:.4f}\n"
             f"mAP @ 75:    {self.map75:.4f}\n"

From 62a3cfe480f749c27affbaace1f6463f10ace180 Mon Sep 17 00:00:00 2001
From: Ahmad Huzail Khan <104365728+AHuzail@users.noreply.github.com>
Date: Wed, 9 Oct 2024 19:33:56 +0000
Subject: [PATCH 041/128] 	modified:  
 supervision/metrics/mean_average_precision.py

---
 supervision/metrics/mean_average_precision.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index ec9d0fd4..b496ffaa 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -478,7 +478,7 @@ class MeanAveragePrecisionResult:
         out_str = (
             f"{self.__class__.__name__}:\n"
             f"Metric target: {self.metric_target}\n"
-            f"Class-agnostic: {self.is_class_agnostic}\n"
+            f"Class agnostic: {self.is_class_agnostic}\n"
             f"mAP @ 50:95: {self.map50_95:.4f}\n"
             f"mAP @ 50:    {self.map50:.4f}\n"
             f"mAP @ 75:    {self.map75:.4f}\n"

From 0f42a87ee8d55370c52d0709da6d484d24b47107 Mon Sep 17 00:00:00 2001
From: Ahmad Huzail Khan <104365728+AHuzail@users.noreply.github.com>
Date: Wed, 9 Oct 2024 19:44:50 +0000
Subject: [PATCH 042/128] 	modified:  
 supervision/metrics/mean_average_precision.py

---
 supervision/metrics/mean_average_precision.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index b496ffaa..3d3f10f4 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -215,6 +215,7 @@ class MeanAveragePrecision(Metric):
 
         return MeanAveragePrecisionResult(
             metric_target=self._metric_target,
+            is_class_agnostic= self._class_agnostic,
             mAP_scores=mAP_scores,
             iou_thresholds=iou_thresholds,
             matched_classes=unique_classes,
@@ -443,7 +444,7 @@ class MeanAveragePrecisionResult:
     """
 
     metric_target: MetricTarget
-    is_class_agnostic: bool
+    is_class_agnostic: 
 
     @property
     def map50_95(self) -> float:

From 201a186539528b712b78e572deda7ed0ec7e3588 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 9 Oct 2024 19:45:22 +0000
Subject: [PATCH 043/128] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20aut?=
 =?UTF-8?q?o=20format=20pre-commit=20hooks?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 supervision/metrics/mean_average_precision.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index 3d3f10f4..3c7ae7ae 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -444,7 +444,7 @@ class MeanAveragePrecisionResult:
     """
 
     metric_target: MetricTarget
-    is_class_agnostic: 
+    is_class_agnostic:
 
     @property
     def map50_95(self) -> float:

From 53b26d0c05edadb6db80c1e7f97fde131bc1544c Mon Sep 17 00:00:00 2001
From: Ahmad Huzail Khan <104365728+AHuzail@users.noreply.github.com>
Date: Wed, 9 Oct 2024 19:48:05 +0000
Subject: [PATCH 044/128] 	modified:  
 supervision/metrics/mean_average_precision.py

---
 supervision/metrics/mean_average_precision.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index 3d3f10f4..05885784 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -444,7 +444,7 @@ class MeanAveragePrecisionResult:
     """
 
     metric_target: MetricTarget
-    is_class_agnostic: 
+    is_class_agnostic: bool
 
     @property
     def map50_95(self) -> float:

From 75b6384a9aacaf0a90a942bc9db6cf76d02eebfd Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 9 Oct 2024 19:52:56 +0000
Subject: [PATCH 045/128] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20aut?=
 =?UTF-8?q?o=20format=20pre-commit=20hooks?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 supervision/metrics/mean_average_precision.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index aeff48b3..c5338a6d 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -215,7 +215,7 @@ class MeanAveragePrecision(Metric):
 
         return MeanAveragePrecisionResult(
             metric_target=self._metric_target,
-            is_class_agnostic= self._class_agnostic,
+            is_class_agnostic=self._class_agnostic,
             mAP_scores=mAP_scores,
             iou_thresholds=iou_thresholds,
             matched_classes=unique_classes,
@@ -443,7 +443,6 @@ class MeanAveragePrecisionResult:
             for large objects.
     """
 
-
     metric_target: MetricTarget
     is_class_agnostic: bool
 

From f1e32e7c079d17b9444863983b24f8bdbe01afdf Mon Sep 17 00:00:00 2001
From: Ahmad Huzail Khan <104365728+AHuzail@users.noreply.github.com>
Date: Wed, 9 Oct 2024 20:16:25 +0000
Subject: [PATCH 046/128] Added deepcopy

---
 supervision/metrics/mean_average_precision.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index c5338a6d..b05d6009 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -77,6 +77,9 @@ class MeanAveragePrecision(Metric):
             )
 
         if self._class_agnostic:
+            predictions = deepcopy(predictions)
+            targets = deepcopy(targets)
+
             for prediction in predictions:
                 prediction.class_id[:] = -1
             for target in targets:

From 5fc7c3aa53727dd4310330e7b80fcb9a12348d54 Mon Sep 17 00:00:00 2001
From: Ahmad Huzail Khan <104365728+AHuzail@users.noreply.github.com>
Date: Wed, 9 Oct 2024 20:19:26 +0000
Subject: [PATCH 047/128] only using _match_detection_batch

---
 supervision/metrics/mean_average_precision.py | 13 +++++--------
 1 file changed, 5 insertions(+), 8 deletions(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index b05d6009..4e3aa26c 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -186,14 +186,11 @@ class MeanAveragePrecision(Metric):
                             "Unsupported metric target for IoU calculation"
                         )
 
-                    if self._class_agnostic:
-                        matches = self._match_detection_batch_class_agnostic(
-                            iou, iou_thresholds
-                        )
-                    else:
-                        matches = self._match_detection_batch(
-                            predictions.class_id, targets.class_id, iou, iou_thresholds
-                        )
+                    
+                    
+                    matches = self._match_detection_batch(
+                        predictions.class_id, targets.class_id, iou, iou_thresholds
+                    )
 
                     stats.append(
                         (

From 9659f7b2ebd161c308cd33957f84c3a21cb3dfe2 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 9 Oct 2024 20:21:00 +0000
Subject: [PATCH 048/128] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20aut?=
 =?UTF-8?q?o=20format=20pre-commit=20hooks?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 supervision/metrics/mean_average_precision.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index 4e3aa26c..ff606298 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -186,8 +186,6 @@ class MeanAveragePrecision(Metric):
                             "Unsupported metric target for IoU calculation"
                         )
 
-                    
-                    
                     matches = self._match_detection_batch(
                         predictions.class_id, targets.class_id, iou, iou_thresholds
                     )

From 05b17c483af5eab63ee46d73c4c3554f9e13247f Mon Sep 17 00:00:00 2001
From: Ahmad Huzail Khan <104365728+AHuzail@users.noreply.github.com>
Date: Wed, 9 Oct 2024 20:25:10 +0000
Subject: [PATCH 049/128] Deleted _match_detection_batch_class_agnostic

---
 supervision/metrics/mean_average_precision.py | 27 -------------------
 1 file changed, 27 deletions(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index ff606298..ed556719 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -276,33 +276,6 @@ class MeanAveragePrecision(Metric):
 
         return correct
 
-    @staticmethod
-    def _match_detection_batch_class_agnostic(
-        iou: np.ndarray,
-        iou_thresholds: np.ndarray,
-    ) -> np.ndarray:
-        """
-        Match detections for class-agnostic case, ignoring the class labels.
-        """
-        num_predictions, num_iou_levels = iou.shape[0], iou_thresholds.shape[0]
-        correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)
-
-        for i, iou_level in enumerate(iou_thresholds):
-            matched_indices = np.where(iou >= iou_level)
-            if matched_indices[0].shape[0]:
-                combined_indices = np.stack(matched_indices, axis=1)
-                iou_values = iou[matched_indices][:, None]
-                matches = np.hstack([combined_indices, iou_values])
-
-                if matched_indices[0].shape[0] > 1:
-                    matches = matches[matches[:, 2].argsort()[::-1]]
-                    matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
-                    matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
-
-                correct[matches[:, 1].astype(int), i] = True
-
-        return correct
-
     @staticmethod
     def _average_precisions_per_class(
         matches: np.ndarray,

From 4c928eda2e7592b00bc6d202ccd79d62a887adad Mon Sep 17 00:00:00 2001
From: Soham <kalburgisoham@gmail.com>
Date: Thu, 10 Oct 2024 15:22:55 +0530
Subject: [PATCH 050/128] feat: add detection metadata and update related
 methods

---
 supervision/detection/core.py  | 20 +++++++++++++++++++-
 supervision/detection/utils.py | 25 ++++++++++++++++++++++++-
 2 files changed, 43 insertions(+), 2 deletions(-)

diff --git a/supervision/detection/core.py b/supervision/detection/core.py
index 113948fc..a970cc2d 100644
--- a/supervision/detection/core.py
+++ b/supervision/detection/core.py
@@ -34,6 +34,7 @@ from supervision.detection.utils import (
     is_data_equal,
     mask_to_xyxy,
     merge_data,
+    merge_metadata,
     process_roboflow_result,
     xywh_to_xyxy,
 )
@@ -125,6 +126,9 @@ class Detections:
         data (Dict[str, Union[np.ndarray, List]]): A dictionary containing additional
             data where each key is a string representing the data type, and the value
             is either a NumPy array or a list of corresponding data.
+        metadata (Dict[str, Any]): A dictionary containing collection-level metadata
+            that applies to the entire set of detections. This may include information such
+            as the video name, camera parameters, timestamp, or other global metadata.
     """  # noqa: E501 // docs
 
     xyxy: np.ndarray
@@ -133,6 +137,7 @@ class Detections:
     class_id: Optional[np.ndarray] = None
     tracker_id: Optional[np.ndarray] = None
     data: Dict[str, Union[np.ndarray, List]] = field(default_factory=dict)
+    metadata: Dict[str, Any] = field(default_factory=dict)
 
     def __post_init__(self):
         validate_detections_fields(
@@ -185,6 +190,7 @@ class Detections:
                 np.array_equal(self.confidence, other.confidence),
                 np.array_equal(self.tracker_id, other.tracker_id),
                 is_data_equal(self.data, other.data),
+                self.metadata == other.metadata
             ]
         )
 
@@ -985,6 +991,7 @@ class Detections:
         """
         empty_detections = Detections.empty()
         empty_detections.data = self.data
+        empty_detections.metadata = self.metadata
         return self == empty_detections
 
     @classmethod
@@ -1041,12 +1048,18 @@ class Detections:
             array([0.1, 0.2, 0.3])
             ```
         """
+        metadata_list = [
+            detections.metadata for detections in detections_list
+        ]
+        
         detections_list = [
             detections for detections in detections_list if not detections.is_empty()
         ]
+        
+        metadata = merge_metadata(metadata_list)
 
         if len(detections_list) == 0:
-            return Detections.empty()
+            return Detections.empty(metadata=metadata)
 
         for detections in detections_list:
             validate_detections_fields(
@@ -1085,6 +1098,7 @@ class Detections:
             class_id=class_id,
             tracker_id=tracker_id,
             data=data,
+            metadata=metadata
         )
 
     def get_anchors_coordinates(self, anchor: Position) -> np.ndarray:
@@ -1198,6 +1212,7 @@ class Detections:
             class_id=self.class_id[index] if self.class_id is not None else None,
             tracker_id=self.tracker_id[index] if self.tracker_id is not None else None,
             data=get_data_item(self.data, index),
+            metadata=self.metadata
         )
 
     def __setitem__(self, key: str, value: Union[np.ndarray, List]):
@@ -1458,6 +1473,8 @@ def merge_inner_detection_object_pair(
         winning_detection = detections_1
     else:
         winning_detection = detections_2
+        
+    metadata = merge_metadata([detections_1.metadata, detections_2.metadata])
 
     return Detections(
         xyxy=merged_xyxy,
@@ -1466,6 +1483,7 @@ def merge_inner_detection_object_pair(
         class_id=winning_detection.class_id,
         tracker_id=winning_detection.tracker_id,
         data=winning_detection.data,
+        metadata=metadata
     )
 
 
diff --git a/supervision/detection/utils.py b/supervision/detection/utils.py
index 43fcec5a..efc98099 100644
--- a/supervision/detection/utils.py
+++ b/supervision/detection/utils.py
@@ -1,5 +1,5 @@
 from itertools import chain
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Dict, List, Optional, Tuple, Union, Any
 
 import cv2
 import numpy as np
@@ -865,6 +865,29 @@ def merge_data(
 
     return merged_data
 
+def merge_metadata(metadata_list: List[Dict[str, Any]]) -> Dict[str, Any]:
+    """
+    Merge metadata from a list of metadata dictionaries.
+
+    This function combines the metadata dictionaries. If a key appears in more than one
+    dictionary, the values must be identical for the merge to succeed.
+
+    Args:
+        metadata_list (List[Dict[str, Any]]): A list of metadata dictionaries to merge.
+
+    Returns:
+        Dict[str, Any]: A single merged metadata dictionary.
+    """
+    merged_metadata = {}
+    for metadata in metadata_list:
+        for key, value in metadata.items():
+            if key in merged_metadata:
+                if merged_metadata[key] != value:
+                    raise ValueError(f"Conflicting metadata for key: {key}")
+            else:
+                merged_metadata[key] = value
+    return merged_metadata
+
 
 def get_data_item(
     data: Dict[str, Union[np.ndarray, List]],

From 2530c41ffa9de5b62e39ffabe66410191ce02dd2 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Thu, 10 Oct 2024 13:00:07 +0300
Subject: [PATCH 051/128] docs: fix datatype of class_agnostic in
 MeanAveragePrecisionResult

---
 supervision/metrics/mean_average_precision.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index ed556719..dbd60b2e 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -395,7 +395,8 @@ class MeanAveragePrecisionResult:
     Attributes:
         metric_target (MetricTarget): the type of data used for the metric -
             boxes, masks or oriented bounding boxes.
-        class_agnostic: When computing class-agnostic results, class ID is set to `-1`.
+        class_agnostic (bool): When computing class-agnostic results, class ID
+            is set to `-1`.
         mAP_map50_95 (float): the mAP score at IoU thresholds from `0.5` to `0.95`.
         mAP_map50 (float): the mAP score at IoU threshold of `0.5`.
         mAP_map75 (float): the mAP score at IoU threshold of `0.75`.

From c5dfb47d5c94714e16bd688226cafaab64d7c8f0 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Thu, 10 Oct 2024 13:02:07 +0300
Subject: [PATCH 052/128] Remove old unused code: CLASS_ID_NONE

---
 supervision/metrics/__init__.py | 1 -
 supervision/metrics/core.py     | 3 ---
 2 files changed, 4 deletions(-)

diff --git a/supervision/metrics/__init__.py b/supervision/metrics/__init__.py
index 17a6cd48..8ae33e63 100644
--- a/supervision/metrics/__init__.py
+++ b/supervision/metrics/__init__.py
@@ -1,5 +1,4 @@
 from supervision.metrics.core import (
-    CLASS_ID_NONE,
     AveragingMethod,
     Metric,
     MetricTarget,
diff --git a/supervision/metrics/core.py b/supervision/metrics/core.py
index 1440fd43..d1818441 100644
--- a/supervision/metrics/core.py
+++ b/supervision/metrics/core.py
@@ -4,9 +4,6 @@ from abc import ABC, abstractmethod
 from enum import Enum
 from typing import Any
 
-CLASS_ID_NONE = -1
-"""Used by metrics module as class ID, when none is present"""
-
 
 class Metric(ABC):
     """

From b59f867bcb70776aac60dbfcd908b3d906523861 Mon Sep 17 00:00:00 2001
From: Soham <kalburgisoham@gmail.com>
Date: Thu, 10 Oct 2024 16:02:20 +0530
Subject: [PATCH 053/128] format with precommit

---
 supervision/detection/core.py  | 18 ++++++++----------
 supervision/detection/utils.py |  3 ++-
 2 files changed, 10 insertions(+), 11 deletions(-)

diff --git a/supervision/detection/core.py b/supervision/detection/core.py
index a970cc2d..8216d054 100644
--- a/supervision/detection/core.py
+++ b/supervision/detection/core.py
@@ -190,7 +190,7 @@ class Detections:
                 np.array_equal(self.confidence, other.confidence),
                 np.array_equal(self.tracker_id, other.tracker_id),
                 is_data_equal(self.data, other.data),
-                self.metadata == other.metadata
+                self.metadata == other.metadata,
             ]
         )
 
@@ -1048,14 +1048,12 @@ class Detections:
             array([0.1, 0.2, 0.3])
             ```
         """
-        metadata_list = [
-            detections.metadata for detections in detections_list
-        ]
-        
+        metadata_list = [detections.metadata for detections in detections_list]
+
         detections_list = [
             detections for detections in detections_list if not detections.is_empty()
         ]
-        
+
         metadata = merge_metadata(metadata_list)
 
         if len(detections_list) == 0:
@@ -1098,7 +1096,7 @@ class Detections:
             class_id=class_id,
             tracker_id=tracker_id,
             data=data,
-            metadata=metadata
+            metadata=metadata,
         )
 
     def get_anchors_coordinates(self, anchor: Position) -> np.ndarray:
@@ -1212,7 +1210,7 @@ class Detections:
             class_id=self.class_id[index] if self.class_id is not None else None,
             tracker_id=self.tracker_id[index] if self.tracker_id is not None else None,
             data=get_data_item(self.data, index),
-            metadata=self.metadata
+            metadata=self.metadata,
         )
 
     def __setitem__(self, key: str, value: Union[np.ndarray, List]):
@@ -1473,7 +1471,7 @@ def merge_inner_detection_object_pair(
         winning_detection = detections_1
     else:
         winning_detection = detections_2
-        
+
     metadata = merge_metadata([detections_1.metadata, detections_2.metadata])
 
     return Detections(
@@ -1483,7 +1481,7 @@ def merge_inner_detection_object_pair(
         class_id=winning_detection.class_id,
         tracker_id=winning_detection.tracker_id,
         data=winning_detection.data,
-        metadata=metadata
+        metadata=metadata,
     )
 
 
diff --git a/supervision/detection/utils.py b/supervision/detection/utils.py
index efc98099..e9060edb 100644
--- a/supervision/detection/utils.py
+++ b/supervision/detection/utils.py
@@ -1,5 +1,5 @@
 from itertools import chain
-from typing import Dict, List, Optional, Tuple, Union, Any
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import cv2
 import numpy as np
@@ -865,6 +865,7 @@ def merge_data(
 
     return merged_data
 
+
 def merge_metadata(metadata_list: List[Dict[str, Any]]) -> Dict[str, Any]:
     """
     Merge metadata from a list of metadata dictionaries.

From a988b7282256e3867498d18d302eca7b3014c375 Mon Sep 17 00:00:00 2001
From: Soham <kalburgisoham@gmail.com>
Date: Thu, 10 Oct 2024 17:44:52 +0530
Subject: [PATCH 054/128] fix: merge_metadata function

---
 supervision/detection/utils.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/supervision/detection/utils.py b/supervision/detection/utils.py
index e9060edb..074f409c 100644
--- a/supervision/detection/utils.py
+++ b/supervision/detection/utils.py
@@ -884,9 +884,13 @@ def merge_metadata(metadata_list: List[Dict[str, Any]]) -> Dict[str, Any]:
         for key, value in metadata.items():
             if key in merged_metadata:
                 if merged_metadata[key] != value:
-                    raise ValueError(f"Conflicting metadata for key: {key}")
+                    if not isinstance(merged_metadata[key], list):
+                        merged_metadata[key] = [merged_metadata[key]]
+                    if value not in merged_metadata[key]:
+                        merged_metadata[key].append(value)
             else:
                 merged_metadata[key] = value
+
     return merged_metadata
 
 

From 6b3735001b95fe56a216fdacdca5926e06f6704b Mon Sep 17 00:00:00 2001
From: Soham <kalburgisoham@gmail.com>
Date: Thu, 10 Oct 2024 18:59:50 +0530
Subject: [PATCH 055/128] fix: merge_metadata for conflicting keys

---
 supervision/detection/utils.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/supervision/detection/utils.py b/supervision/detection/utils.py
index 074f409c..71e67c49 100644
--- a/supervision/detection/utils.py
+++ b/supervision/detection/utils.py
@@ -878,16 +878,17 @@ def merge_metadata(metadata_list: List[Dict[str, Any]]) -> Dict[str, Any]:
 
     Returns:
         Dict[str, Any]: A single merged metadata dictionary.
+
+    Raises:
+        ValueError: If there are conflicting values for the same key.
     """
     merged_metadata = {}
+
     for metadata in metadata_list:
         for key, value in metadata.items():
             if key in merged_metadata:
                 if merged_metadata[key] != value:
-                    if not isinstance(merged_metadata[key], list):
-                        merged_metadata[key] = [merged_metadata[key]]
-                    if value not in merged_metadata[key]:
-                        merged_metadata[key].append(value)
+                    raise ValueError(f"Conflicting metadata for key: {key}.")
             else:
                 merged_metadata[key] = value
 

From dcdeea85d1b475efc93d180df3e1b355157d10c1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o?= <joao@roboflow.com>
Date: Thu, 10 Oct 2024 14:58:57 -0300
Subject: [PATCH 056/128] replaces github hosted to roboflow cdn images links

---
 .../small-object-detection-with-sahi.ipynb    | 36 +++++++++----------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/docs/notebooks/small-object-detection-with-sahi.ipynb b/docs/notebooks/small-object-detection-with-sahi.ipynb
index 1654ff3c..986849de 100644
--- a/docs/notebooks/small-object-detection-with-sahi.ipynb
+++ b/docs/notebooks/small-object-detection-with-sahi.ipynb
@@ -15,7 +15,7 @@
                 "\n",
                 "This cookbook shows how to use [Slicing Aided Hyper Inference (SAHI) ](https://arxiv.org/abs/2202.06934) for small object detection with `supervision`.\n",
                 "\n",
-                "![\"Small Object Detection\"](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/animation.gif \"Small Object Detection\")\n",
+                "![\"Small Object Detection\"](https://media.roboflow.com/supervision/cookbooks/sahi/animation.gif \"Small Object Detection\")\n",
                 "\n",
                 "Click the Open in Colab button to run the cookbook on Google Colab.\n",
                 "\n",
@@ -64,13 +64,13 @@
             "source": [
                 "## Crowd counting with Computer Vision\n",
                 "\n",
-                "How would you go about solving the problem of counting people in crowds? After some tests, I found that the best approach is to detect people\u2019s heads. Other body parts are likely occluded by other people, but heads are usually exposed, especially in aerial or high-level shots.\n",
+                "How would you go about solving the problem of counting people in crowds? After some tests, I found that the best approach is to detect people’s heads. Other body parts are likely occluded by other people, but heads are usually exposed, especially in aerial or high-level shots.\n",
                 "\n",
                 "### Using an Open-Source Public Model for People Detection\n",
                 "\n",
-                "Detecting people (or their heads) is a common problem that has been addressed by many researchers in the past. In this project, we\u2019ll use an open-source public dataset and a fine-tuned model to perform inference on images.\n",
+                "Detecting people (or their heads) is a common problem that has been addressed by many researchers in the past. In this project, we’ll use an open-source public dataset and a fine-tuned model to perform inference on images.\n",
                 "\n",
-                "![Roboflow Universe](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/roboflow_universe.png \"Open source model for counting people's heads\")\n",
+                "![Roboflow Universe](https://media.roboflow.com/supervision/cookbooks/sahi/roboflow_universe.png \"Open source model for counting people's heads\")\n",
                 "\n",
                 "Some details about the project [\"people_counterv0 Computer Vision Project\"](https://universe.roboflow.com/sit-cx0ng/people_counterv0):\n",
                 "\n",
@@ -179,9 +179,9 @@
                 "\n",
                 "## Let's try our model's performance\n",
                 "\n",
-                "Before we dive into the SAHI technique for small object detection, it\u2019s useful to see how a fine-tuned model performs with the image as is\u2014without any pre-processing or slicing. The goal is to understand when the model starts to fail so that we can progressively move towards an efficient slicing strategy.\n",
+                "Before we dive into the SAHI technique for small object detection, it’s useful to see how a fine-tuned model performs with the image as is—without any pre-processing or slicing. The goal is to understand when the model starts to fail so that we can progressively move towards an efficient slicing strategy.\n",
                 "\n",
-                "Let\u2019s run the model!"
+                "Let’s run the model!"
             ]
         },
         {
@@ -324,7 +324,7 @@
                 "id": "AutFkxbuxkPa"
             },
             "source": [
-                "The model shows strong performance in detecting people in the lower half of the image, but it struggles to accurately predict boxes in the upper half. This suggests two key insights: first, the model is proficient at identifying people\u2019s heads from various angles, and second, using SAHI could effectively address the detection challenges in the upper portion of the image. Now, it\u2019s time to try SAHI!"
+                "The model shows strong performance in detecting people in the lower half of the image, but it struggles to accurately predict boxes in the upper half. This suggests two key insights: first, the model is proficient at identifying people’s heads from various angles, and second, using SAHI could effectively address the detection challenges in the upper portion of the image. Now, it’s time to try SAHI!"
             ]
         },
         {
@@ -376,7 +376,7 @@
                 "\n",
                 "## Slicing our image with `supervision`\n",
                 "\n",
-                "Let\u2019s begin by visualizing how these tiles would appear on our image. Let's start with a small set of 2x2 tiles, with a zero overlap both vertically (height) and horizontally (width) between the tiles. The final values of these parameters will ultimately depend on your use case, so trial and error is encouraged!\n",
+                "Let’s begin by visualizing how these tiles would appear on our image. Let's start with a small set of 2x2 tiles, with a zero overlap both vertically (height) and horizontally (width) between the tiles. The final values of these parameters will ultimately depend on your use case, so trial and error is encouraged!\n",
                 "\n",
                 "Some of the methods below are for visualizing the tiles and overlapping. You'll only need the `calculate_tile_size` method in your application to calculate the size of the tiles.\n",
                 "\n",
@@ -694,13 +694,13 @@
                 "id": "W6TvNnXpewwc"
             },
             "source": [
-                "Great! We\u2019ve detected 726 people, up from the 185 we initially detected without image slicing. The model is still detecting people from different angles, but it continues to struggle with detecting people located in the farther parts of the plaza. It\u2019s time to increase the number of tiles\u2014in other words, zoom in so the model can capture more details of the small heads of people.\n",
+                "Great! We’ve detected 726 people, up from the 185 we initially detected without image slicing. The model is still detecting people from different angles, but it continues to struggle with detecting people located in the farther parts of the plaza. It’s time to increase the number of tiles—in other words, zoom in so the model can capture more details of the small heads of people.\n",
                 "\n",
                 "![Missing detections](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/detections.png)\n",
                 "\n",
                 "### Increasing Tile Density: Moving to a 5x5 Grid\n",
                 "\n",
-                "Now that we\u2019ve seen improvements with a 2x2 grid, it\u2019s time to push the model further. By increasing the number of tiles to a 5x5 grid, we effectively zoom in on the image, allowing the model to capture finer details, such as smaller and more distant features that might have been missed before. This approach will help us understand how well the model performs with even more zoomed-in images. Let\u2019s explore how this change affects our detection accuracy and overall performance."
+                "Now that we’ve seen improvements with a 2x2 grid, it’s time to push the model further. By increasing the number of tiles to a 5x5 grid, we effectively zoom in on the image, allowing the model to capture finer details, such as smaller and more distant features that might have been missed before. This approach will help us understand how well the model performs with even more zoomed-in images. Let’s explore how this change affects our detection accuracy and overall performance."
             ]
         },
         {
@@ -778,19 +778,19 @@
                 "id": "eFQasUU3xkPb"
             },
             "source": [
-                "We\u2019ve just detected 1,494 people using a 25-tile grid (5 rows x 5 columns), a significant increase from the 726 people detected with the 4-tile (2x2) grid. However, as we increase the number of tiles, a new challenge arises: duplicate detections or missed detections along the edges of the tiles. This issue becomes evident in these examples, where overlapping or gaps between tiles lead to inaccuracies in our model\u2019s detection.\n",
+                "We’ve just detected 1,494 people using a 25-tile grid (5 rows x 5 columns), a significant increase from the 726 people detected with the 4-tile (2x2) grid. However, as we increase the number of tiles, a new challenge arises: duplicate detections or missed detections along the edges of the tiles. This issue becomes evident in these examples, where overlapping or gaps between tiles lead to inaccuracies in our model’s detection.\n",
                 "\n",
                 "| Example| Observations   |\n",
                 "|----|----|\n",
-                "| ![Overlapping](https://github.com/ediardo/notebooks/blob/main/sahi/overlapping_1.png?raw=true \"Overlapping\") | False Negative, Incomplete bbox  |\n",
-                "| ![Overlapping](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/overlapping_2.png \"Overlapping\")| Double detection, Incomplete bbox|\n",
-                "| ![Overlapping](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/overlapping_3.png \"Overlapping\")| Incomplete bounding box|\n",
+                "| ![Overlapping](https://media.roboflow.com/supervision/cookbooks/sahi/overlapping_1.png \"Overlapping\") | False Negative, Incomplete bbox  |\n",
+                "| ![Overlapping](https://media.roboflow.com/supervision/cookbooks/sahi/overlapping_2.png \"Overlapping\")| Double detection, Incomplete bbox|\n",
+                "| ![Overlapping](https://media.roboflow.com/supervision/cookbooks/sahi/overlapping_3.png \"Overlapping\")| Incomplete bounding box|\n",
                 "\n",
                 "## Improving Object Detection Near Boundaries with Overlapping\n",
                 "\n",
                 "When objects, like people, appear at the edges of tiles, they might be detected twice or missed entirely if they span across two tiles. This can lead to inaccurate detection results. To solve this, we use overlapping tiles, allowing the model to see parts of adjacent tiles simultaneously. This overlap helps ensure that objects near the boundaries are fully captured, reducing duplicates and improving accuracy.\n",
                 "\n",
-                "We\u2019ll set the overlap ratio to `(0.2, 0.2)` on the tile\u2019s width and height. This overlap helps ensure that objects near the boundaries are fully captured, reducing duplicates and improving accuracy."
+                "We’ll set the overlap ratio to `(0.2, 0.2)` on the tile’s width and height. This overlap helps ensure that objects near the boundaries are fully captured, reducing duplicates and improving accuracy."
             ]
         },
         {
@@ -869,14 +869,14 @@
             "source": [
                 "## Non-Max Supression vs Non-Max Merge\n",
                 "\n",
-                "When dealing with overlapping detections, it\u2019s essential to determine which detections represent the same object and which are unique. Non-Maximum Suppression (NMS) and Non-Maximum Merging (NMM) are two techniques commonly used to address this challenge. NMS works by eliminating redundant detections based on confidence scores, while NMM combines overlapping detections to enhance the representation of objects spanning multiple tiles. Understanding the difference between these methods helps optimize object detection, particularly near tile boundaries.\n",
+                "When dealing with overlapping detections, it’s essential to determine which detections represent the same object and which are unique. Non-Maximum Suppression (NMS) and Non-Maximum Merging (NMM) are two techniques commonly used to address this challenge. NMS works by eliminating redundant detections based on confidence scores, while NMM combines overlapping detections to enhance the representation of objects spanning multiple tiles. Understanding the difference between these methods helps optimize object detection, particularly near tile boundaries.\n",
                 "\n",
                 "In `supervision`, the `overlap_filter` parameter allows us to specify the strategy for handling overlapping detections in slices. This parameter can take on two values:\n",
                 "\n",
                 "- `sv.OverlapFilter.NON_MAX_SUPRESSION` (default): Eliminates redundant detections by keeping the one with the highest confidence score.\n",
                 "- `sv.OverlapFilter.NON_MAX_MERGE`: Combines overlapping detections to create a more comprehensive representation of objects spanning multiple tiles.\n",
                 "\n",
-                "It\u2019s important to note that this method is not perfect and may require further testing and fine-tuning to achieve optimal results in various use cases. You should validate the outputs and adjust parameters as needed to handle specific scenarios effectively."
+                "It’s important to note that this method is not perfect and may require further testing and fine-tuning to achieve optimal results in various use cases. You should validate the outputs and adjust parameters as needed to handle specific scenarios effectively."
             ]
         },
         {
@@ -1023,7 +1023,7 @@
             "source": [
                 "## Conclusion\n",
                 "\n",
-                "In this cookbook, we\u2019ve explored the advantages of using the SAHI technique for enhancing small object detection and the importance of experimenting with various tiling strategies to effectively zoom into images. By combining these approaches, we can improve the accuracy and reliability of object detection models, particularly in challenging scenarios where objects are small or located near the boundaries of tiles. These methods offer practical solutions to common challenges in computer vision, empowering developers to build more robust and precise detection systems.\n",
+                "In this cookbook, we’ve explored the advantages of using the SAHI technique for enhancing small object detection and the importance of experimenting with various tiling strategies to effectively zoom into images. By combining these approaches, we can improve the accuracy and reliability of object detection models, particularly in challenging scenarios where objects are small or located near the boundaries of tiles. These methods offer practical solutions to common challenges in computer vision, empowering developers to build more robust and precise detection systems.\n",
                 "\n",
                 "![\"Crowd Detection\"](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/5x5_nms.png \"Crowd Detection\")\n"
             ]

From bfe4f0f82902cf60af70ca406282fb1ad6872d85 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 10 Oct 2024 19:06:36 +0000
Subject: [PATCH 057/128] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20aut?=
 =?UTF-8?q?o=20format=20pre-commit=20hooks?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../small-object-detection-with-sahi.ipynb    | 26 +++++++++----------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/docs/notebooks/small-object-detection-with-sahi.ipynb b/docs/notebooks/small-object-detection-with-sahi.ipynb
index 986849de..db69b085 100644
--- a/docs/notebooks/small-object-detection-with-sahi.ipynb
+++ b/docs/notebooks/small-object-detection-with-sahi.ipynb
@@ -64,11 +64,11 @@
             "source": [
                 "## Crowd counting with Computer Vision\n",
                 "\n",
-                "How would you go about solving the problem of counting people in crowds? After some tests, I found that the best approach is to detect people’s heads. Other body parts are likely occluded by other people, but heads are usually exposed, especially in aerial or high-level shots.\n",
+                "How would you go about solving the problem of counting people in crowds? After some tests, I found that the best approach is to detect people\u2019s heads. Other body parts are likely occluded by other people, but heads are usually exposed, especially in aerial or high-level shots.\n",
                 "\n",
                 "### Using an Open-Source Public Model for People Detection\n",
                 "\n",
-                "Detecting people (or their heads) is a common problem that has been addressed by many researchers in the past. In this project, we’ll use an open-source public dataset and a fine-tuned model to perform inference on images.\n",
+                "Detecting people (or their heads) is a common problem that has been addressed by many researchers in the past. In this project, we\u2019ll use an open-source public dataset and a fine-tuned model to perform inference on images.\n",
                 "\n",
                 "![Roboflow Universe](https://media.roboflow.com/supervision/cookbooks/sahi/roboflow_universe.png \"Open source model for counting people's heads\")\n",
                 "\n",
@@ -179,9 +179,9 @@
                 "\n",
                 "## Let's try our model's performance\n",
                 "\n",
-                "Before we dive into the SAHI technique for small object detection, it’s useful to see how a fine-tuned model performs with the image as is—without any pre-processing or slicing. The goal is to understand when the model starts to fail so that we can progressively move towards an efficient slicing strategy.\n",
+                "Before we dive into the SAHI technique for small object detection, it\u2019s useful to see how a fine-tuned model performs with the image as is\u2014without any pre-processing or slicing. The goal is to understand when the model starts to fail so that we can progressively move towards an efficient slicing strategy.\n",
                 "\n",
-                "Let’s run the model!"
+                "Let\u2019s run the model!"
             ]
         },
         {
@@ -324,7 +324,7 @@
                 "id": "AutFkxbuxkPa"
             },
             "source": [
-                "The model shows strong performance in detecting people in the lower half of the image, but it struggles to accurately predict boxes in the upper half. This suggests two key insights: first, the model is proficient at identifying people’s heads from various angles, and second, using SAHI could effectively address the detection challenges in the upper portion of the image. Now, it’s time to try SAHI!"
+                "The model shows strong performance in detecting people in the lower half of the image, but it struggles to accurately predict boxes in the upper half. This suggests two key insights: first, the model is proficient at identifying people\u2019s heads from various angles, and second, using SAHI could effectively address the detection challenges in the upper portion of the image. Now, it\u2019s time to try SAHI!"
             ]
         },
         {
@@ -376,7 +376,7 @@
                 "\n",
                 "## Slicing our image with `supervision`\n",
                 "\n",
-                "Let’s begin by visualizing how these tiles would appear on our image. Let's start with a small set of 2x2 tiles, with a zero overlap both vertically (height) and horizontally (width) between the tiles. The final values of these parameters will ultimately depend on your use case, so trial and error is encouraged!\n",
+                "Let\u2019s begin by visualizing how these tiles would appear on our image. Let's start with a small set of 2x2 tiles, with a zero overlap both vertically (height) and horizontally (width) between the tiles. The final values of these parameters will ultimately depend on your use case, so trial and error is encouraged!\n",
                 "\n",
                 "Some of the methods below are for visualizing the tiles and overlapping. You'll only need the `calculate_tile_size` method in your application to calculate the size of the tiles.\n",
                 "\n",
@@ -694,13 +694,13 @@
                 "id": "W6TvNnXpewwc"
             },
             "source": [
-                "Great! We’ve detected 726 people, up from the 185 we initially detected without image slicing. The model is still detecting people from different angles, but it continues to struggle with detecting people located in the farther parts of the plaza. It’s time to increase the number of tiles—in other words, zoom in so the model can capture more details of the small heads of people.\n",
+                "Great! We\u2019ve detected 726 people, up from the 185 we initially detected without image slicing. The model is still detecting people from different angles, but it continues to struggle with detecting people located in the farther parts of the plaza. It\u2019s time to increase the number of tiles\u2014in other words, zoom in so the model can capture more details of the small heads of people.\n",
                 "\n",
                 "![Missing detections](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/detections.png)\n",
                 "\n",
                 "### Increasing Tile Density: Moving to a 5x5 Grid\n",
                 "\n",
-                "Now that we’ve seen improvements with a 2x2 grid, it’s time to push the model further. By increasing the number of tiles to a 5x5 grid, we effectively zoom in on the image, allowing the model to capture finer details, such as smaller and more distant features that might have been missed before. This approach will help us understand how well the model performs with even more zoomed-in images. Let’s explore how this change affects our detection accuracy and overall performance."
+                "Now that we\u2019ve seen improvements with a 2x2 grid, it\u2019s time to push the model further. By increasing the number of tiles to a 5x5 grid, we effectively zoom in on the image, allowing the model to capture finer details, such as smaller and more distant features that might have been missed before. This approach will help us understand how well the model performs with even more zoomed-in images. Let\u2019s explore how this change affects our detection accuracy and overall performance."
             ]
         },
         {
@@ -778,7 +778,7 @@
                 "id": "eFQasUU3xkPb"
             },
             "source": [
-                "We’ve just detected 1,494 people using a 25-tile grid (5 rows x 5 columns), a significant increase from the 726 people detected with the 4-tile (2x2) grid. However, as we increase the number of tiles, a new challenge arises: duplicate detections or missed detections along the edges of the tiles. This issue becomes evident in these examples, where overlapping or gaps between tiles lead to inaccuracies in our model’s detection.\n",
+                "We\u2019ve just detected 1,494 people using a 25-tile grid (5 rows x 5 columns), a significant increase from the 726 people detected with the 4-tile (2x2) grid. However, as we increase the number of tiles, a new challenge arises: duplicate detections or missed detections along the edges of the tiles. This issue becomes evident in these examples, where overlapping or gaps between tiles lead to inaccuracies in our model\u2019s detection.\n",
                 "\n",
                 "| Example| Observations   |\n",
                 "|----|----|\n",
@@ -790,7 +790,7 @@
                 "\n",
                 "When objects, like people, appear at the edges of tiles, they might be detected twice or missed entirely if they span across two tiles. This can lead to inaccurate detection results. To solve this, we use overlapping tiles, allowing the model to see parts of adjacent tiles simultaneously. This overlap helps ensure that objects near the boundaries are fully captured, reducing duplicates and improving accuracy.\n",
                 "\n",
-                "We’ll set the overlap ratio to `(0.2, 0.2)` on the tile’s width and height. This overlap helps ensure that objects near the boundaries are fully captured, reducing duplicates and improving accuracy."
+                "We\u2019ll set the overlap ratio to `(0.2, 0.2)` on the tile\u2019s width and height. This overlap helps ensure that objects near the boundaries are fully captured, reducing duplicates and improving accuracy."
             ]
         },
         {
@@ -869,14 +869,14 @@
             "source": [
                 "## Non-Max Supression vs Non-Max Merge\n",
                 "\n",
-                "When dealing with overlapping detections, it’s essential to determine which detections represent the same object and which are unique. Non-Maximum Suppression (NMS) and Non-Maximum Merging (NMM) are two techniques commonly used to address this challenge. NMS works by eliminating redundant detections based on confidence scores, while NMM combines overlapping detections to enhance the representation of objects spanning multiple tiles. Understanding the difference between these methods helps optimize object detection, particularly near tile boundaries.\n",
+                "When dealing with overlapping detections, it\u2019s essential to determine which detections represent the same object and which are unique. Non-Maximum Suppression (NMS) and Non-Maximum Merging (NMM) are two techniques commonly used to address this challenge. NMS works by eliminating redundant detections based on confidence scores, while NMM combines overlapping detections to enhance the representation of objects spanning multiple tiles. Understanding the difference between these methods helps optimize object detection, particularly near tile boundaries.\n",
                 "\n",
                 "In `supervision`, the `overlap_filter` parameter allows us to specify the strategy for handling overlapping detections in slices. This parameter can take on two values:\n",
                 "\n",
                 "- `sv.OverlapFilter.NON_MAX_SUPRESSION` (default): Eliminates redundant detections by keeping the one with the highest confidence score.\n",
                 "- `sv.OverlapFilter.NON_MAX_MERGE`: Combines overlapping detections to create a more comprehensive representation of objects spanning multiple tiles.\n",
                 "\n",
-                "It’s important to note that this method is not perfect and may require further testing and fine-tuning to achieve optimal results in various use cases. You should validate the outputs and adjust parameters as needed to handle specific scenarios effectively."
+                "It\u2019s important to note that this method is not perfect and may require further testing and fine-tuning to achieve optimal results in various use cases. You should validate the outputs and adjust parameters as needed to handle specific scenarios effectively."
             ]
         },
         {
@@ -1023,7 +1023,7 @@
             "source": [
                 "## Conclusion\n",
                 "\n",
-                "In this cookbook, we’ve explored the advantages of using the SAHI technique for enhancing small object detection and the importance of experimenting with various tiling strategies to effectively zoom into images. By combining these approaches, we can improve the accuracy and reliability of object detection models, particularly in challenging scenarios where objects are small or located near the boundaries of tiles. These methods offer practical solutions to common challenges in computer vision, empowering developers to build more robust and precise detection systems.\n",
+                "In this cookbook, we\u2019ve explored the advantages of using the SAHI technique for enhancing small object detection and the importance of experimenting with various tiling strategies to effectively zoom into images. By combining these approaches, we can improve the accuracy and reliability of object detection models, particularly in challenging scenarios where objects are small or located near the boundaries of tiles. These methods offer practical solutions to common challenges in computer vision, empowering developers to build more robust and precise detection systems.\n",
                 "\n",
                 "![\"Crowd Detection\"](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/5x5_nms.png \"Crowd Detection\")\n"
             ]

From 68a1052cfc01307d524ab9fc61bf9942c28a00dc Mon Sep 17 00:00:00 2001
From: Soham <kalburgisoham@gmail.com>
Date: Fri, 11 Oct 2024 01:14:00 +0530
Subject: [PATCH 058/128] fix: is_empty no longer compares metadata directly

---
 supervision/detection/core.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/supervision/detection/core.py b/supervision/detection/core.py
index 8216d054..8b470d60 100644
--- a/supervision/detection/core.py
+++ b/supervision/detection/core.py
@@ -991,7 +991,6 @@ class Detections:
         """
         empty_detections = Detections.empty()
         empty_detections.data = self.data
-        empty_detections.metadata = self.metadata
         return self == empty_detections
 
     @classmethod

From c7611e384bd8e1d12faa641bf5227bfe449c0405 Mon Sep 17 00:00:00 2001
From: Soham <kalburgisoham@gmail.com>
Date: Fri, 11 Oct 2024 01:44:47 +0530
Subject: [PATCH 059/128] fix: update empty and is_empty methods

---
 supervision/detection/core.py | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/supervision/detection/core.py b/supervision/detection/core.py
index 8b470d60..6ef52a03 100644
--- a/supervision/detection/core.py
+++ b/supervision/detection/core.py
@@ -964,7 +964,7 @@ class Detections:
         )
 
     @classmethod
-    def empty(cls) -> Detections:
+    def empty(cls, metadata: Optional[Dict[str, Any]] = None) -> Detections:
         """
         Create an empty Detections object with no bounding boxes,
             confidences, or class IDs.
@@ -983,15 +983,21 @@ class Detections:
             xyxy=np.empty((0, 4), dtype=np.float32),
             confidence=np.array([], dtype=np.float32),
             class_id=np.array([], dtype=int),
+            metadata=metadata if metadata is not None else {},
         )
 
     def is_empty(self) -> bool:
         """
         Returns `True` if the `Detections` object is considered empty.
         """
-        empty_detections = Detections.empty()
-        empty_detections.data = self.data
-        return self == empty_detections
+        return (
+            len(self.xyxy) == 0
+            and (self.mask is None or len(self.mask) == 0)
+            and (self.class_id is None or len(self.class_id) == 0)
+            and (self.confidence is None or len(self.confidence) == 0)
+            and (self.tracker_id is None or len(self.tracker_id) == 0)
+            and not self.data
+        )
 
     @classmethod
     def merge(cls, detections_list: List[Detections]) -> Detections:

From 892185e4c60ca14913e53bf15adc1f913dd9b7fa Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Fri, 11 Oct 2024 00:35:27 +0000
Subject: [PATCH 060/128] :arrow_up: Bump mkdocs-material from 9.5.39 to 9.5.40

Bumps [mkdocs-material](https://github.com/squidfunk/mkdocs-material) from 9.5.39 to 9.5.40.
- [Release notes](https://github.com/squidfunk/mkdocs-material/releases)
- [Changelog](https://github.com/squidfunk/mkdocs-material/blob/master/CHANGELOG)
- [Commits](https://github.com/squidfunk/mkdocs-material/compare/9.5.39...9.5.40)

---
updated-dependencies:
- dependency-name: mkdocs-material
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 5d47a083..aebc04c7 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2252,13 +2252,13 @@ pygments = ">2.12.0"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.39"
+version = "9.5.40"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.5.39-py3-none-any.whl", hash = "sha256:0f2f68c8db89523cb4a59705cd01b4acd62b2f71218ccb67e1e004e560410d2b"},
-    {file = "mkdocs_material-9.5.39.tar.gz", hash = "sha256:25faa06142afa38549d2b781d475a86fb61de93189f532b88e69bf11e5e5c3be"},
+    {file = "mkdocs_material-9.5.40-py3-none-any.whl", hash = "sha256:8e7a16ada34e79a7b6459ff2602584222f522c738b6a023d1bea853d5049da6f"},
+    {file = "mkdocs_material-9.5.40.tar.gz", hash = "sha256:b69d70e667ec51fc41f65e006a3184dd00d95b2439d982cb1586e4c018943156"},
 ]
 
 [package.dependencies]

From b86b660634d6037542472307c17e6594e6f6aec5 Mon Sep 17 00:00:00 2001
From: DivyaVijay1234 <divvij2004@gmail.com>
Date: Fri, 11 Oct 2024 16:56:22 +0530
Subject: [PATCH 061/128] feat-Added Default color

---
 supervision/draw/utils.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/supervision/draw/utils.py b/supervision/draw/utils.py
index 19ce4a25..50c911d4 100644
--- a/supervision/draw/utils.py
+++ b/supervision/draw/utils.py
@@ -9,7 +9,7 @@ from supervision.geometry.core import Point, Rect
 
 
 def draw_line(
-    scene: np.ndarray, start: Point, end: Point, color: Color, thickness: int = 2
+    scene: np.ndarray, start: Point, end: Point, color: Color=Color(163, 81, 251), thickness: int = 2
 ) -> np.ndarray:
     """
     Draws a line on a given scene.
@@ -35,7 +35,7 @@ def draw_line(
 
 
 def draw_rectangle(
-    scene: np.ndarray, rect: Rect, color: Color, thickness: int = 2
+    scene: np.ndarray, rect: Rect, color: Color=Color(163, 81, 251), thickness: int = 2
 ) -> np.ndarray:
     """
     Draws a rectangle on an image.
@@ -60,7 +60,7 @@ def draw_rectangle(
 
 
 def draw_filled_rectangle(
-    scene: np.ndarray, rect: Rect, color: Color, opacity: float = 1
+    scene: np.ndarray, rect: Rect, color: Color=Color(163, 81, 251), opacity: float = 1
 ) -> np.ndarray:
     """
     Draws a filled rectangle on an image.
@@ -151,7 +151,7 @@ def draw_rounded_rectangle(
 
 
 def draw_polygon(
-    scene: np.ndarray, polygon: np.ndarray, color: Color, thickness: int = 2
+    scene: np.ndarray, polygon: np.ndarray, color: Color=Color(163, 81, 251), thickness: int = 2
 ) -> np.ndarray:
     """Draw a polygon on a scene.
 
@@ -171,7 +171,7 @@ def draw_polygon(
 
 
 def draw_filled_polygon(
-    scene: np.ndarray, polygon: np.ndarray, color: Color, opacity: float = 1
+    scene: np.ndarray, polygon: np.ndarray, color: Color=Color(163, 81, 251), opacity: float = 1
 ) -> np.ndarray:
     """Draw a filled polygon on a scene.
 

From 5242614b01969b261f86982ee84db7a797dacac3 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 11 Oct 2024 11:38:20 +0000
Subject: [PATCH 062/128] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20aut?=
 =?UTF-8?q?o=20format=20pre-commit=20hooks?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 supervision/draw/utils.py | 26 +++++++++++++++++++++-----
 1 file changed, 21 insertions(+), 5 deletions(-)

diff --git a/supervision/draw/utils.py b/supervision/draw/utils.py
index 50c911d4..beec3f31 100644
--- a/supervision/draw/utils.py
+++ b/supervision/draw/utils.py
@@ -9,7 +9,11 @@ from supervision.geometry.core import Point, Rect
 
 
 def draw_line(
-    scene: np.ndarray, start: Point, end: Point, color: Color=Color(163, 81, 251), thickness: int = 2
+    scene: np.ndarray,
+    start: Point,
+    end: Point,
+    color: Color = Color(163, 81, 251),
+    thickness: int = 2,
 ) -> np.ndarray:
     """
     Draws a line on a given scene.
@@ -35,7 +39,10 @@ def draw_line(
 
 
 def draw_rectangle(
-    scene: np.ndarray, rect: Rect, color: Color=Color(163, 81, 251), thickness: int = 2
+    scene: np.ndarray,
+    rect: Rect,
+    color: Color = Color(163, 81, 251),
+    thickness: int = 2,
 ) -> np.ndarray:
     """
     Draws a rectangle on an image.
@@ -60,7 +67,10 @@ def draw_rectangle(
 
 
 def draw_filled_rectangle(
-    scene: np.ndarray, rect: Rect, color: Color=Color(163, 81, 251), opacity: float = 1
+    scene: np.ndarray,
+    rect: Rect,
+    color: Color = Color(163, 81, 251),
+    opacity: float = 1,
 ) -> np.ndarray:
     """
     Draws a filled rectangle on an image.
@@ -151,7 +161,10 @@ def draw_rounded_rectangle(
 
 
 def draw_polygon(
-    scene: np.ndarray, polygon: np.ndarray, color: Color=Color(163, 81, 251), thickness: int = 2
+    scene: np.ndarray,
+    polygon: np.ndarray,
+    color: Color = Color(163, 81, 251),
+    thickness: int = 2,
 ) -> np.ndarray:
     """Draw a polygon on a scene.
 
@@ -171,7 +184,10 @@ def draw_polygon(
 
 
 def draw_filled_polygon(
-    scene: np.ndarray, polygon: np.ndarray, color: Color=Color(163, 81, 251), opacity: float = 1
+    scene: np.ndarray,
+    polygon: np.ndarray,
+    color: Color = Color(163, 81, 251),
+    opacity: float = 1,
 ) -> np.ndarray:
     """Draw a filled polygon on a scene.
 

From a6897d4117447c28c2a2629d732177098cfc4fc0 Mon Sep 17 00:00:00 2001
From: DivyaVijay1234 <divvij2004@gmail.com>
Date: Fri, 11 Oct 2024 20:01:42 +0530
Subject: [PATCH 063/128] feat: add default color

---
 supervision/draw/utils.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/supervision/draw/utils.py b/supervision/draw/utils.py
index 50c911d4..1c207b5d 100644
--- a/supervision/draw/utils.py
+++ b/supervision/draw/utils.py
@@ -9,7 +9,7 @@ from supervision.geometry.core import Point, Rect
 
 
 def draw_line(
-    scene: np.ndarray, start: Point, end: Point, color: Color=Color(163, 81, 251), thickness: int = 2
+    scene: np.ndarray, start: Point, end: Point, color: Color=Color.ROBOFLOW, thickness: int = 2
 ) -> np.ndarray:
     """
     Draws a line on a given scene.
@@ -35,7 +35,7 @@ def draw_line(
 
 
 def draw_rectangle(
-    scene: np.ndarray, rect: Rect, color: Color=Color(163, 81, 251), thickness: int = 2
+    scene: np.ndarray, rect: Rect, color: Color=Color.ROBOFLOW, thickness: int = 2
 ) -> np.ndarray:
     """
     Draws a rectangle on an image.
@@ -60,7 +60,7 @@ def draw_rectangle(
 
 
 def draw_filled_rectangle(
-    scene: np.ndarray, rect: Rect, color: Color=Color(163, 81, 251), opacity: float = 1
+    scene: np.ndarray, rect: Rect, color: Color=Color.ROBOFLOW, opacity: float = 1
 ) -> np.ndarray:
     """
     Draws a filled rectangle on an image.
@@ -151,7 +151,7 @@ def draw_rounded_rectangle(
 
 
 def draw_polygon(
-    scene: np.ndarray, polygon: np.ndarray, color: Color=Color(163, 81, 251), thickness: int = 2
+    scene: np.ndarray, polygon: np.ndarray, color: Color=Color.ROBOFLOW, thickness: int = 2
 ) -> np.ndarray:
     """Draw a polygon on a scene.
 
@@ -171,7 +171,7 @@ def draw_polygon(
 
 
 def draw_filled_polygon(
-    scene: np.ndarray, polygon: np.ndarray, color: Color=Color(163, 81, 251), opacity: float = 1
+    scene: np.ndarray, polygon: np.ndarray, color: Color=Color.ROBOFLOW, opacity: float = 1
 ) -> np.ndarray:
     """Draw a filled polygon on a scene.
 

From 7e7eb8bd5ae5e264ee5e57a0bc7f9220c447d8f7 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 11 Oct 2024 14:35:04 +0000
Subject: [PATCH 064/128] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20aut?=
 =?UTF-8?q?o=20format=20pre-commit=20hooks?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 supervision/draw/utils.py | 20 +++++++++++++++-----
 1 file changed, 15 insertions(+), 5 deletions(-)

diff --git a/supervision/draw/utils.py b/supervision/draw/utils.py
index 1c207b5d..64397028 100644
--- a/supervision/draw/utils.py
+++ b/supervision/draw/utils.py
@@ -9,7 +9,11 @@ from supervision.geometry.core import Point, Rect
 
 
 def draw_line(
-    scene: np.ndarray, start: Point, end: Point, color: Color=Color.ROBOFLOW, thickness: int = 2
+    scene: np.ndarray,
+    start: Point,
+    end: Point,
+    color: Color = Color.ROBOFLOW,
+    thickness: int = 2,
 ) -> np.ndarray:
     """
     Draws a line on a given scene.
@@ -35,7 +39,7 @@ def draw_line(
 
 
 def draw_rectangle(
-    scene: np.ndarray, rect: Rect, color: Color=Color.ROBOFLOW, thickness: int = 2
+    scene: np.ndarray, rect: Rect, color: Color = Color.ROBOFLOW, thickness: int = 2
 ) -> np.ndarray:
     """
     Draws a rectangle on an image.
@@ -60,7 +64,7 @@ def draw_rectangle(
 
 
 def draw_filled_rectangle(
-    scene: np.ndarray, rect: Rect, color: Color=Color.ROBOFLOW, opacity: float = 1
+    scene: np.ndarray, rect: Rect, color: Color = Color.ROBOFLOW, opacity: float = 1
 ) -> np.ndarray:
     """
     Draws a filled rectangle on an image.
@@ -151,7 +155,10 @@ def draw_rounded_rectangle(
 
 
 def draw_polygon(
-    scene: np.ndarray, polygon: np.ndarray, color: Color=Color.ROBOFLOW, thickness: int = 2
+    scene: np.ndarray,
+    polygon: np.ndarray,
+    color: Color = Color.ROBOFLOW,
+    thickness: int = 2,
 ) -> np.ndarray:
     """Draw a polygon on a scene.
 
@@ -171,7 +178,10 @@ def draw_polygon(
 
 
 def draw_filled_polygon(
-    scene: np.ndarray, polygon: np.ndarray, color: Color=Color.ROBOFLOW, opacity: float = 1
+    scene: np.ndarray,
+    polygon: np.ndarray,
+    color: Color = Color.ROBOFLOW,
+    opacity: float = 1,
 ) -> np.ndarray:
     """Draw a filled polygon on a scene.
 

From c339315b318106e2a7bd760a4d0f20a1a39e1187 Mon Sep 17 00:00:00 2001
From: Onuralp SEZER <thunderbirdtr@gmail.com>
Date: Sat, 12 Oct 2024 18:58:45 +0300
Subject: [PATCH 065/128] =?UTF-8?q?feat:=20=E2=9C=A8=20set=20default=20col?=
 =?UTF-8?q?ors=20for=20polygon=20drawing=20functions=20and=20add=20documen?=
 =?UTF-8?q?tations?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 supervision/detection/tools/polygon_zone.py | 4 ++--
 supervision/draw/utils.py                   | 6 +++---
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/supervision/detection/tools/polygon_zone.py b/supervision/detection/tools/polygon_zone.py
index ac7203ff..b8b728fc 100644
--- a/supervision/detection/tools/polygon_zone.py
+++ b/supervision/detection/tools/polygon_zone.py
@@ -88,7 +88,7 @@ class PolygonZoneAnnotator:
 
     Attributes:
         zone (PolygonZone): The polygon zone to be annotated
-        color (Color): The color to draw the polygon lines
+        color (Color): The color to draw the polygon lines, default is white
         thickness (int): The thickness of the polygon lines, default is 2
         text_color (Color): The color of the text on the polygon, default is black
         text_scale (float): The scale of the text on the polygon, default is 0.5
@@ -104,7 +104,7 @@ class PolygonZoneAnnotator:
     def __init__(
         self,
         zone: PolygonZone,
-        color: Color,
+        color: Color = Color.WHITE,
         thickness: int = 2,
         text_color: Color = Color.BLACK,
         text_scale: float = 0.5,
diff --git a/supervision/draw/utils.py b/supervision/draw/utils.py
index 64397028..0c3767ff 100644
--- a/supervision/draw/utils.py
+++ b/supervision/draw/utils.py
@@ -22,7 +22,7 @@ def draw_line(
         scene (np.ndarray): The scene on which the line will be drawn
         start (Point): The starting point of the line
         end (Point): The end point of the line
-        color (Color): The color of the line
+        color (Color): The color of the line, defaults to Color.ROBOFLOW
         thickness (int): The thickness of the line
 
     Returns:
@@ -165,7 +165,7 @@ def draw_polygon(
     Parameters:
         scene (np.ndarray): The scene to draw the polygon on.
         polygon (np.ndarray): The polygon to be drawn, given as a list of vertices.
-        color (Color): The color of the polygon.
+        color (Color): The color of the polygon. Defaults to Color.ROBOFLOW.
         thickness (int): The thickness of the polygon lines, by default 2.
 
     Returns:
@@ -188,7 +188,7 @@ def draw_filled_polygon(
     Parameters:
         scene (np.ndarray): The scene to draw the polygon on.
         polygon (np.ndarray): The polygon to be drawn, given as a list of vertices.
-        color (Color): The color of the polygon.
+        color (Color): The color of the polygon. Defaults to Color.ROBOFLOW.
         opacity (float): The opacity of polygon when drawn on the scene.
 
     Returns:

From 1d3f61af85ba037ba9dc35f1f7cc2581a55e8e41 Mon Sep 17 00:00:00 2001
From: prakharjain <prakharjain2004@gmail.com>
Date: Sun, 13 Oct 2024 13:10:24 +0530
Subject: [PATCH 066/128] Added support by using oriented_box_iou_batch

---
 supervision/metrics/mean_average_precision.py | 17 +++++------------
 1 file changed, 5 insertions(+), 12 deletions(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index dbd60b2e..2429c189 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -9,7 +9,7 @@ from matplotlib import pyplot as plt
 
 from supervision.config import ORIENTED_BOX_COORDINATES
 from supervision.detection.core import Detections
-from supervision.detection.utils import box_iou_batch, mask_iou_batch
+from supervision.detection.utils import box_iou_batch, mask_iou_batch, oriented_box_iou_batch
 from supervision.draw.color import LEGACY_COLOR_PALETTE
 from supervision.metrics.core import Metric, MetricTarget
 from supervision.metrics.utils.object_size import (
@@ -36,11 +36,6 @@ class MeanAveragePrecision(Metric):
             class_agnostic (bool): Whether to treat all data as a single class.
         """
         self._metric_target = metric_target
-        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
-            raise NotImplementedError(
-                "Mean Average Precision is not implemented for oriented bounding boxes."
-            )
-
         self._class_agnostic = class_agnostic
 
         self._predictions_list: List[Detections] = []
@@ -181,15 +176,14 @@ class MeanAveragePrecision(Metric):
                         iou = box_iou_batch(target_contents, prediction_contents)
                     elif self._metric_target == MetricTarget.MASKS:
                         iou = mask_iou_batch(target_contents, prediction_contents)
+                    elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+                        iou = oriented_box_iou_batch(target_contents, prediction_contents)
                     else:
-                        raise NotImplementedError(
-                            "Unsupported metric target for IoU calculation"
-                        )
+                        raise ValueError(f"Unsupported metric target: {self._metric_target}")
 
                     matches = self._match_detection_batch(
                         predictions.class_id, targets.class_id, iou, iou_thresholds
                     )
-
                     stats.append(
                         (
                             matches,
@@ -213,7 +207,6 @@ class MeanAveragePrecision(Metric):
 
         return MeanAveragePrecisionResult(
             metric_target=self._metric_target,
-            is_class_agnostic=self._class_agnostic,
             mAP_scores=mAP_scores,
             iou_thresholds=iou_thresholds,
             matched_classes=unique_classes,
@@ -345,7 +338,7 @@ class MeanAveragePrecision(Metric):
             )
         if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
             if obb := detections.data.get(ORIENTED_BOX_COORDINATES):
-                return np.ndarray(obb, dtype=np.float32)
+                return np.array(obb, dtype=np.float32)
             return self._make_empty_content()
         raise ValueError(f"Invalid metric target: {self._metric_target}")
 

From 9822406cde35e34e9f3006787ef09da38efcc28e Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Sun, 13 Oct 2024 07:45:02 +0000
Subject: [PATCH 067/128] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20aut?=
 =?UTF-8?q?o=20format=20pre-commit=20hooks?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 supervision/metrics/mean_average_precision.py | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index 2429c189..58d4764c 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -9,7 +9,11 @@ from matplotlib import pyplot as plt
 
 from supervision.config import ORIENTED_BOX_COORDINATES
 from supervision.detection.core import Detections
-from supervision.detection.utils import box_iou_batch, mask_iou_batch, oriented_box_iou_batch
+from supervision.detection.utils import (
+    box_iou_batch,
+    mask_iou_batch,
+    oriented_box_iou_batch,
+)
 from supervision.draw.color import LEGACY_COLOR_PALETTE
 from supervision.metrics.core import Metric, MetricTarget
 from supervision.metrics.utils.object_size import (
@@ -177,9 +181,13 @@ class MeanAveragePrecision(Metric):
                     elif self._metric_target == MetricTarget.MASKS:
                         iou = mask_iou_batch(target_contents, prediction_contents)
                     elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
-                        iou = oriented_box_iou_batch(target_contents, prediction_contents)
+                        iou = oriented_box_iou_batch(
+                            target_contents, prediction_contents
+                        )
                     else:
-                        raise ValueError(f"Unsupported metric target: {self._metric_target}")
+                        raise ValueError(
+                            f"Unsupported metric target: {self._metric_target}"
+                        )
 
                     matches = self._match_detection_batch(
                         predictions.class_id, targets.class_id, iou, iou_thresholds

From 302041d8aadb823b895f273bc4731df5dd00c163 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Mon, 14 Oct 2024 13:56:52 +0300
Subject: [PATCH 068/128] Removed welcome workflow

* It doesn't work, and trip new contributors up
---
 .github/workflows/welcome.yml | 18 ------------------
 1 file changed, 18 deletions(-)
 delete mode 100644 .github/workflows/welcome.yml

diff --git a/.github/workflows/welcome.yml b/.github/workflows/welcome.yml
deleted file mode 100644
index 4cb70b93..00000000
--- a/.github/workflows/welcome.yml
+++ /dev/null
@@ -1,18 +0,0 @@
-name: Welcome WorkFlow
-
-on:
-  issues:
-    types: [opened]
-  pull_request_target:
-    types: [opened]
-
-jobs:
-  build:
-    name: 👋 Welcome
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/first-interaction@v1.3.0
-        with:
-          repo-token: ${{ secrets.GITHUB_TOKEN }}
-          issue-message: "Hello there, thank you for opening an Issue ! 🙏🏻 The team was notified and they will get back to you asap."
-          pr-message:  "Hello there, thank you for opening an PR ! 🙏🏻 The team was notified and they will get back to you asap."

From 62a4d45c6c98acfb12be48c93f4f3880941edcd6 Mon Sep 17 00:00:00 2001
From: Onuralp SEZER <thunderbirdtr@gmail.com>
Date: Mon, 14 Oct 2024 13:47:49 +0300
Subject: [PATCH 069/128] =?UTF-8?q?feat:=20=F0=9F=9A=80=20python3.13=20sup?=
 =?UTF-8?q?port=20for=20poetry=20and=20library?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>
---
 .github/workflows/poetry-test.yml |  37 ++
 poetry.lock                       | 661 ++++++++++++++++++++++--------
 pyproject.toml                    |  15 +
 3 files changed, 541 insertions(+), 172 deletions(-)
 create mode 100644 .github/workflows/poetry-test.yml

diff --git a/.github/workflows/poetry-test.yml b/.github/workflows/poetry-test.yml
new file mode 100644
index 00000000..95e2eb7f
--- /dev/null
+++ b/.github/workflows/poetry-test.yml
@@ -0,0 +1,37 @@
+name: 🔧 Poetry Check and Installation Test Workflow
+on:
+  push:
+    paths:
+      - 'poetry.lock'
+      - 'pyproject.toml'
+  pull_request:
+    paths:
+      - 'poetry.lock'
+      - 'pyproject.toml'
+  workflow_dispatch:
+
+jobs:
+  poetry-tests:
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, windows-latest, macos-latest]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12", "3.13"]
+    runs-on: ${{ matrix.os }}
+    steps:
+      - name: 📥 Checkout the repository
+        uses: actions/checkout@v4
+
+      - name: 🐍 Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: 📦 Install the base dependencies
+        run: python -m pip install --upgrade poetry
+
+      - name: 🔍 Check the correctness of the project config
+        run: poetry check
+
+      - name: 🚀 Do Install the package Test
+        run: poetry install
diff --git a/poetry.lock b/poetry.lock
index aebc04c7..1994447d 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,13 +2,13 @@
 
 [[package]]
 name = "anyio"
-version = "4.5.0"
+version = "4.6.2"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "anyio-4.5.0-py3-none-any.whl", hash = "sha256:fdeb095b7cc5a5563175eedd926ec4ae55413bb4be5770c424af0ba46ccb4a78"},
-    {file = "anyio-4.5.0.tar.gz", hash = "sha256:c5a275fe5ca0afd788001f58fca1e69e29ce706d746e317d660e21f70c530ef9"},
+    {file = "anyio-4.6.2-py3-none-any.whl", hash = "sha256:6caec6b1391f6f6d7b2ef2258d2902d36753149f67478f7df4be8e54d03a8f54"},
+    {file = "anyio-4.6.2.tar.gz", hash = "sha256:f72a7bb3dd0752b3bd8b17a844a019d7fbf6ae218c588f4f9ba1b2f600b12347"},
 ]
 
 [package.dependencies]
@@ -19,7 +19,7 @@ typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.21.0b1)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -448,101 +448,116 @@ files = [
 
 [[package]]
 name = "charset-normalizer"
-version = "3.3.2"
+version = "3.4.0"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
 files = [
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 test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
 test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
 
+[[package]]
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+optional = false
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+
+[package.extras]
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+docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"]
+mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.11.1)", "types-Pillow"]
+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
+test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "wurlitzer"]
+
 [[package]]
 name = "cryptography"
 version = "43.0.1"
@@ -746,33 +845,37 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
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 [[package]]
@@ -799,13 +902,13 @@ files = [
 
 [[package]]
 name = "distlib"
-version = "0.3.8"
+version = "0.3.9"
 description = "Distribution utilities"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
@@ -1021,13 +1124,13 @@ test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit",
 
 [[package]]
 name = "griffe"
-version = "1.3.1"
+version = "1.4.0"
 description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1047,13 +1150,13 @@ files = [
 
 [[package]]
 name = "httpcore"
-version = "1.0.5"
+version = "1.0.6"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1064,7 +1167,7 @@ h11 = ">=0.13,<0.15"
 asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
-trio = ["trio (>=0.22.0,<0.26.0)"]
+trio = ["trio (>=0.22.0,<1.0)"]
 
 [[package]]
 name = "httpx"
@@ -1320,21 +1423,25 @@ test = ["portend", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-c
 
 [[package]]
 name = "jaraco-functools"
-version = "4.0.2"
+version = "4.1.0"
 description = "Functools like those found in stdlib"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 more-itertools = "*"
 
 [package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
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+enabler = ["pytest-enabler (>=2.2)"]
+test = ["jaraco.classes", "pytest (>=6,!=8.1.*)"]
+type = ["pytest-mypy"]
 
 [[package]]
 name = "jedi"
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 ]
 
+[[package]]
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+version = "3.7.2"
+description = "Python plotting package"
+optional = false
+python-versions = ">=3.8"
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+
 [[package]]
 name = "opencv-python"
 version = "4.10.0.84"
@@ -2800,6 +3031,24 @@ files = [
 [package.dependencies]
 types-pytz = ">=2022.1.1"
 
+[[package]]
+name = "pandas-stubs"
+version = "2.0.3.230814"
+description = "Type annotations for pandas"
+optional = true
+python-versions = ">=3.8"
+files = [
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+[package.dependencies]
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+    {version = ">=1.25.0", markers = "python_version >= \"3.9\""},
+]
+types-pytz = ">=2022.1.1"
+
 [[package]]
 name = "pandocfilters"
 version = "1.5.1"
@@ -3142,13 +3391,13 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pymdown-extensions"
-version = "10.10.2"
+version = "10.11.2"
 description = "Extension pack for Python Markdown."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3158,6 +3407,20 @@ pyyaml = "*"
 [package.extras]
 extra = ["pygments (>=2.12)"]
 
+[[package]]
+name = "pyparsing"
+version = "3.0.9"
+description = "pyparsing module - Classes and methods to define and execute parsing grammars"
+optional = false
+python-versions = ">=3.6.8"
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+
+[package.extras]
+diagrams = ["jinja2", "railroad-diagrams"]
+
 [[package]]
 name = "pyparsing"
 version = "3.1.4"
@@ -3193,13 +3456,13 @@ testing = ["covdefaults (>=2.3)", "pytest (>=8.3.3)", "pytest-cov (>=5)", "pytes
 
 [[package]]
 name = "pyproject-hooks"
-version = "1.1.0"
+version = "1.2.0"
 description = "Wrappers to call pyproject.toml-based build backend hooks."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
@@ -3262,25 +3525,29 @@ files = [
 
 [[package]]
 name = "pywin32"
-version = "306"
+version = "308"
 description = "Python for Window Extensions"
 optional = false
 python-versions = "*"
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 [[package]]
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+version = "13.9.2"
 description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
 optional = false
-python-versions = ">=3.7.0"
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 [package.extras]
 jupyter = ["ipywidgets (>=7.5.1,<9)"]
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+[package.extras]
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+
 [[package]]
 name = "secretstorage"
 version = "3.3.3"
@@ -4110,13 +4427,13 @@ test = ["pytest", "ruff"]
 
 [[package]]
 name = "tomli"
-version = "2.0.1"
+version = "2.0.2"
 description = "A lil' TOML parser"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -4240,24 +4557,24 @@ types-setuptools = "*"
 
 [[package]]
 name = "types-python-dateutil"
-version = "2.9.0.20240906"
+version = "2.9.0.20241003"
 description = "Typing stubs for python-dateutil"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
 name = "types-pytz"
-version = "2024.2.0.20240913"
+version = "2024.2.0.20241003"
 description = "Typing stubs for pytz"
 optional = true
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -4287,13 +4604,13 @@ urllib3 = ">=2"
 
 [[package]]
 name = "types-setuptools"
-version = "75.1.0.20240917"
+version = "75.1.0.20241014"
 description = "Typing stubs for setuptools"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -4545,4 +4862,4 @@ metrics = ["pandas", "pandas-stubs"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8"
-content-hash = "ab2e2c455fa1a7d74271da71f8c1b6f096bbebd92a79b4ec646523ef7d8530b0"
+content-hash = "fe9efd8caf098dc0301e1d7007ac3b00647b48cff6b060cc0eff57656d082099"
diff --git a/pyproject.toml b/pyproject.toml
index f6301106..6464346b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -46,11 +46,26 @@ python = "^3.8"
 numpy = [
     { version = ">=1.21.2,<1.23.3", python = "<=3.10" },
     { version = ">=1.23.3", python = ">3.10" },
+    { version = ">=2.1.0", python = ">=3.13" },
 ]
 scipy = [
     { version = "1.10.0", python = "<3.9" },
     { version = "^1.10.0", python = ">=3.9" },
+    { version = ">=1.14.1", python = ">=3.13" },
+
 ]
+
+# Matplotlib sub-dependency
+# The 'contourpy' package is required by Matplotlib for contour plotting.
+# We need to ensure compatibility with both Python 3.8 and Python 3.13.
+#
+# For Python 3.8 and above, we use version 1.0.7 or higher, as it is the lowest major version that supports Python 3.8.
+# For Python 3.13 and above, we use version 1.3.0 or higher, as it is the first version that explicitly supports Python 3.13.
+contourpy = [
+    { version = ">=1.0.7", python = ">=3.8" },
+    { version = ">=1.3.0", python = ">=3.13" },
+]
+
 matplotlib = ">=3.6.0"
 pyyaml = ">=5.3"
 defusedxml = "^0.7.1"

From a879c1d39ac716ab38ecbf28ca297ee4b375316c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 15 Oct 2024 00:26:36 +0000
Subject: [PATCH 070/128] :arrow_up: Bump mypy from 1.11.2 to 1.12.0

Bumps [mypy](https://github.com/python/mypy) from 1.11.2 to 1.12.0.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.11.2...v1.12.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 277 +++++++++++++++++++---------------------------------
 1 file changed, 98 insertions(+), 179 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 1994447d..45db2b43 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -602,6 +602,68 @@ traitlets = ">=4"
 [package.extras]
 test = ["pytest"]
 
+[[package]]
+name = "contourpy"
+version = "1.1.0"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "contourpy-1.1.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:084eaa568400cfaf7179b847ac871582199b1b44d5699198e9602ecbbb5f6104"},
+    {file = "contourpy-1.1.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:911ff4fd53e26b019f898f32db0d4956c9d227d51338fb3b03ec72ff0084ee5f"},
+    {file = "contourpy-1.1.0.tar.gz", hash = "sha256:e53046c3863828d21d531cc3b53786e6580eb1ba02477e8681009b6aa0870b21"},
+]
+
+[package.dependencies]
+numpy = ">=1.16"
+
+[package.extras]
+bokeh = ["bokeh", "selenium"]
+docs = ["furo", "sphinx-copybutton"]
+mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.2.0)", "types-Pillow"]
+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
+test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
+
 [[package]]
 name = "contourpy"
 version = "1.1.1"
@@ -664,10 +726,7 @@ files = [
 ]
 
 [package.dependencies]
-numpy = [
-    {version = ">=1.16,<2.0", markers = "python_version <= \"3.11\""},
-    {version = ">=1.26.0rc1,<2.0", markers = "python_version >= \"3.12\""},
-]
+numpy = {version = ">=1.16,<2.0", markers = "python_version <= \"3.11\""}
 
 [package.extras]
 bokeh = ["bokeh", "selenium"]
@@ -2139,74 +2198,6 @@ pillow = ">=6.2.0"
 pyparsing = ">=2.3.1,<3.1"
 python-dateutil = ">=2.7"
 
-[[package]]
-name = "matplotlib"
-version = "3.7.5"
-description = "Python plotting package"
-optional = false
-python-versions = ">=3.8"
-files = [
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-    {file = "numpy-1.26.4-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:b3ce300f3644fb06443ee2222c2201dd3a89ea6040541412b8fa189341847218"},
-    {file = "numpy-1.26.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:03a8c78d01d9781b28a6989f6fa1bb2c4f2d51201cf99d3dd875df6fbd96b23b"},
-    {file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9fad7dcb1aac3c7f0584a5a8133e3a43eeb2fe127f47e3632d43d677c66c102b"},
-    {file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:675d61ffbfa78604709862923189bad94014bef562cc35cf61d3a07bba02a7ed"},
-    {file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:ab47dbe5cc8210f55aa58e4805fe224dac469cde56b9f731a4c098b91917159a"},
-    {file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:1dda2e7b4ec9dd512f84935c5f126c8bd8b9f2fc001e9f54af255e8c5f16b0e0"},
-    {file = "numpy-1.26.4-cp312-cp312-win32.whl", hash = "sha256:50193e430acfc1346175fcbdaa28ffec49947a06918b7b92130744e81e640110"},
-    {file = "numpy-1.26.4-cp312-cp312-win_amd64.whl", hash = "sha256:08beddf13648eb95f8d867350f6a018a4be2e5ad54c8d8caed89ebca558b2818"},
-    {file = "numpy-1.26.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7349ab0fa0c429c82442a27a9673fc802ffdb7c7775fad780226cb234965e53c"},
-    {file = "numpy-1.26.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:52b8b60467cd7dd1e9ed082188b4e6bb35aa5cdd01777621a1658910745b90be"},
-    {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d5241e0a80d808d70546c697135da2c613f30e28251ff8307eb72ba696945764"},
-    {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f870204a840a60da0b12273ef34f7051e98c3b5961b61b0c2c1be6dfd64fbcd3"},
-    {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:679b0076f67ecc0138fd2ede3a8fd196dddc2ad3254069bcb9faf9a79b1cebcd"},
-    {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:47711010ad8555514b434df65f7d7b076bb8261df1ca9bb78f53d3b2db02e95c"},
-    {file = "numpy-1.26.4-cp39-cp39-win32.whl", hash = "sha256:a354325ee03388678242a4d7ebcd08b5c727033fcff3b2f536aea978e15ee9e6"},
-    {file = "numpy-1.26.4-cp39-cp39-win_amd64.whl", hash = "sha256:3373d5d70a5fe74a2c1bb6d2cfd9609ecf686d47a2d7b1d37a8f3b6bf6003aea"},
-    {file = "numpy-1.26.4-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:afedb719a9dcfc7eaf2287b839d8198e06dcd4cb5d276a3df279231138e83d30"},
-    {file = "numpy-1.26.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95a7476c59002f2f6c590b9b7b998306fba6a5aa646b1e22ddfeaf8f78c3a29c"},
-    {file = "numpy-1.26.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:7e50d0a0cc3189f9cb0aeb3a6a6af18c16f59f004b866cd2be1c14b36134a4a0"},
-    {file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"},
-]
-
 [[package]]
 name = "numpy"
 version = "2.1.2"
@@ -2904,12 +2855,12 @@ files = [
 
 [package.dependencies]
 numpy = [
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
     {version = ">=1.21.0", markers = "python_version <= \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\" and python_version >= \"3.8\""},
     {version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
     {version = ">=1.17.3", markers = "(platform_system != \"Darwin\" and platform_system != \"Linux\") and python_version >= \"3.8\" and python_version < \"3.9\" or platform_system != \"Darwin\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_machine != \"aarch64\" or platform_machine != \"arm64\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_system != \"Linux\" or (platform_machine != \"arm64\" and platform_machine != \"aarch64\") and python_version >= \"3.8\" and python_version < \"3.9\""},
     {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
     {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
-    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
     {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
 ]
 
@@ -2986,9 +2937,9 @@ files = [
 
 [package.dependencies]
 numpy = [
+    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
     {version = ">=1.20.3", markers = "python_version < \"3.10\""},
     {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
-    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
 ]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
@@ -3031,24 +2982,6 @@ files = [
 [package.dependencies]
 types-pytz = ">=2022.1.1"
 
-[[package]]
-name = "pandas-stubs"
-version = "2.0.3.230814"
-description = "Type annotations for pandas"
-optional = true
-python-versions = ">=3.8"
-files = [
-    {file = "pandas_stubs-2.0.3.230814-py3-none-any.whl", hash = "sha256:4b3dfc027d49779176b7daa031a3405f7b839bcb6e312f4b9f29fea5feec5b4f"},
-    {file = "pandas_stubs-2.0.3.230814.tar.gz", hash = "sha256:1d5cc09e36e3d9f9a1ed9dceae4e03eeb26d1b898dd769996925f784365c8769"},
-]
-
-[package.dependencies]
-numpy = [
-    {version = "<=1.24.3", markers = "python_full_version <= \"3.8.0\""},
-    {version = ">=1.25.0", markers = "python_version >= \"3.9\""},
-]
-types-pytz = ">=2022.1.1"
-
 [[package]]
 name = "pandocfilters"
 version = "1.5.1"
@@ -3421,20 +3354,6 @@ files = [
 [package.extras]
 diagrams = ["jinja2", "railroad-diagrams"]
 
-[[package]]
-name = "pyparsing"
-version = "3.1.4"
-description = "pyparsing module - Classes and methods to define and execute parsing grammars"
-optional = false
-python-versions = ">=3.6.8"
-files = [
-    {file = "pyparsing-3.1.4-py3-none-any.whl", hash = "sha256:a6a7ee4235a3f944aa1fa2249307708f893fe5717dc603503c6c7969c070fb7c"},
-    {file = "pyparsing-3.1.4.tar.gz", hash = "sha256:f86ec8d1a83f11977c9a6ea7598e8c27fc5cddfa5b07ea2241edbbde1d7bc032"},
-]
-
-[package.extras]
-diagrams = ["jinja2", "railroad-diagrams"]
-
 [[package]]
 name = "pyproject-api"
 version = "1.8.0"

From e9d5f861a28eab7b6bc819d3e620e01576fedefc Mon Sep 17 00:00:00 2001
From: prakharjain <prakharjain2004@gmail.com>
Date: Tue, 15 Oct 2024 22:44:01 +0530
Subject: [PATCH 071/128] Added support by using oriented_box_iou_batch Final

---
 supervision/detection/utils.py                |  8 ++--
 supervision/metrics/mean_average_precision.py | 38 +++++--------------
 2 files changed, 14 insertions(+), 32 deletions(-)

diff --git a/supervision/detection/utils.py b/supervision/detection/utils.py
index 43fcec5a..a0a4d3e2 100644
--- a/supervision/detection/utils.py
+++ b/supervision/detection/utils.py
@@ -23,8 +23,8 @@ def polygon_to_mask(polygon: np.ndarray, resolution_wh: Tuple[int, int]) -> np.n
         np.ndarray: The generated 2D mask, where the polygon is marked with
             `1`'s and the rest is filled with `0`'s.
     """
-    width, height = resolution_wh
-    mask = np.zeros((height, width))
+    width, height = map(int, resolution_wh)
+    mask = np.zeros((height, width), dtype=np.uint8)
 
     cv2.fillPoly(mask, [polygon], color=1)
     return mask
@@ -163,9 +163,9 @@ def oriented_box_iou_batch(
     boxes_true = boxes_true.reshape(-1, 4, 2)
     boxes_detection = boxes_detection.reshape(-1, 4, 2)
 
-    max_height = max(boxes_true[:, :, 0].max(), boxes_detection[:, :, 0].max()) + 1
+    max_height = int(max(boxes_true[:, :, 0].max(), boxes_detection[:, :, 0].max()) + 1)
     # adding 1 because we are 0-indexed
-    max_width = max(boxes_true[:, :, 1].max(), boxes_detection[:, :, 1].max()) + 1
+    max_width = int(max(boxes_true[:, :, 1].max(), boxes_detection[:, :, 1].max()) + 1)
 
     mask_true = np.zeros((boxes_true.shape[0], max_height, max_width))
     for i, box_true in enumerate(boxes_true):
diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index 58d4764c..f0613f2b 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -9,11 +9,7 @@ from matplotlib import pyplot as plt
 
 from supervision.config import ORIENTED_BOX_COORDINATES
 from supervision.detection.core import Detections
-from supervision.detection.utils import (
-    box_iou_batch,
-    mask_iou_batch,
-    oriented_box_iou_batch,
-)
+from supervision.detection.utils import box_iou_batch, mask_iou_batch, oriented_box_iou_batch
 from supervision.draw.color import LEGACY_COLOR_PALETTE
 from supervision.metrics.core import Metric, MetricTarget
 from supervision.metrics.utils.object_size import (
@@ -40,6 +36,9 @@ class MeanAveragePrecision(Metric):
             class_agnostic (bool): Whether to treat all data as a single class.
         """
         self._metric_target = metric_target
+        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+            pass
+
         self._class_agnostic = class_agnostic
 
         self._predictions_list: List[Detections] = []
@@ -75,15 +74,6 @@ class MeanAveragePrecision(Metric):
                 f" targets ({len(targets)}) during the update must be the same."
             )
 
-        if self._class_agnostic:
-            predictions = deepcopy(predictions)
-            targets = deepcopy(targets)
-
-            for prediction in predictions:
-                prediction.class_id[:] = -1
-            for target in targets:
-                target.class_id[:] = -1
-
         self._predictions_list.extend(predictions)
         self._targets_list.extend(targets)
 
@@ -181,13 +171,9 @@ class MeanAveragePrecision(Metric):
                     elif self._metric_target == MetricTarget.MASKS:
                         iou = mask_iou_batch(target_contents, prediction_contents)
                     elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
-                        iou = oriented_box_iou_batch(
-                            target_contents, prediction_contents
-                        )
+                        iou = oriented_box_iou_batch(target_contents, prediction_contents)
                     else:
-                        raise ValueError(
-                            f"Unsupported metric target: {self._metric_target}"
-                        )
+                        raise NotImplementedError("Unsupported metric target for IoU calculation")
 
                     matches = self._match_detection_batch(
                         predictions.class_id, targets.class_id, iou, iou_thresholds
@@ -242,7 +228,7 @@ class MeanAveragePrecision(Metric):
         for r, p in zip(recall[::-1], precision[::-1]):
             precision_levels[recall_levels <= r] = p
 
-        average_precision = (1 / 101 * precision_levels).sum()
+        average_precision = (1 / 100 * precision_levels).sum()
         return average_precision
 
     @staticmethod
@@ -257,7 +243,6 @@ class MeanAveragePrecision(Metric):
             iou_thresholds.shape[0],
         )
         correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)
-
         correct_class = target_classes[:, None] == predictions_classes
 
         for i, iou_level in enumerate(iou_thresholds):
@@ -345,7 +330,8 @@ class MeanAveragePrecision(Metric):
                 else self._make_empty_content()
             )
         if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
-            if obb := detections.data.get(ORIENTED_BOX_COORDINATES):
+            obb = detections.data.get(ORIENTED_BOX_COORDINATES)
+            if obb is not None and len(obb) > 0:
                 return np.array(obb, dtype=np.float32)
             return self._make_empty_content()
         raise ValueError(f"Invalid metric target: {self._metric_target}")
@@ -396,8 +382,6 @@ class MeanAveragePrecisionResult:
     Attributes:
         metric_target (MetricTarget): the type of data used for the metric -
             boxes, masks or oriented bounding boxes.
-        class_agnostic (bool): When computing class-agnostic results, class ID
-            is set to `-1`.
         mAP_map50_95 (float): the mAP score at IoU thresholds from `0.5` to `0.95`.
         mAP_map50 (float): the mAP score at IoU threshold of `0.5`.
         mAP_map75 (float): the mAP score at IoU threshold of `0.75`.
@@ -417,7 +401,6 @@ class MeanAveragePrecisionResult:
     """
 
     metric_target: MetricTarget
-    is_class_agnostic: bool
 
     @property
     def map50_95(self) -> float:
@@ -452,7 +435,6 @@ class MeanAveragePrecisionResult:
         out_str = (
             f"{self.__class__.__name__}:\n"
             f"Metric target: {self.metric_target}\n"
-            f"Class agnostic: {self.is_class_agnostic}\n"
             f"mAP @ 50:95: {self.map50_95:.4f}\n"
             f"mAP @ 50:    {self.map50:.4f}\n"
             f"mAP @ 75:    {self.map75:.4f}\n"
@@ -576,4 +558,4 @@ class MeanAveragePrecisionResult:
         plt.rcParams["font.family"] = "sans-serif"
 
         plt.tight_layout()
-        plt.show()
+        plt.show()
\ No newline at end of file

From 865c4e42317f75e822dbe3b3abfd67eea3164b73 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 15 Oct 2024 17:15:13 +0000
Subject: [PATCH 072/128] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20aut?=
 =?UTF-8?q?o=20format=20pre-commit=20hooks?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 supervision/metrics/mean_average_precision.py | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index f0613f2b..0ebffd51 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -9,7 +9,11 @@ from matplotlib import pyplot as plt
 
 from supervision.config import ORIENTED_BOX_COORDINATES
 from supervision.detection.core import Detections
-from supervision.detection.utils import box_iou_batch, mask_iou_batch, oriented_box_iou_batch
+from supervision.detection.utils import (
+    box_iou_batch,
+    mask_iou_batch,
+    oriented_box_iou_batch,
+)
 from supervision.draw.color import LEGACY_COLOR_PALETTE
 from supervision.metrics.core import Metric, MetricTarget
 from supervision.metrics.utils.object_size import (
@@ -171,9 +175,13 @@ class MeanAveragePrecision(Metric):
                     elif self._metric_target == MetricTarget.MASKS:
                         iou = mask_iou_batch(target_contents, prediction_contents)
                     elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
-                        iou = oriented_box_iou_batch(target_contents, prediction_contents)
+                        iou = oriented_box_iou_batch(
+                            target_contents, prediction_contents
+                        )
                     else:
-                        raise NotImplementedError("Unsupported metric target for IoU calculation")
+                        raise NotImplementedError(
+                            "Unsupported metric target for IoU calculation"
+                        )
 
                     matches = self._match_detection_batch(
                         predictions.class_id, targets.class_id, iou, iou_thresholds
@@ -558,4 +566,4 @@ class MeanAveragePrecisionResult:
         plt.rcParams["font.family"] = "sans-serif"
 
         plt.tight_layout()
-        plt.show()
\ No newline at end of file
+        plt.show()

From 85f400c1625867bea5ac12af9296a5ef65ec47d0 Mon Sep 17 00:00:00 2001
From: prakharjain <prakharjain2004@gmail.com>
Date: Tue, 15 Oct 2024 22:55:41 +0530
Subject: [PATCH 073/128] Added support by using oriented_box_iou_batch

---
 supervision/metrics/mean_average_precision.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index 0ebffd51..2a4f121d 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -78,6 +78,15 @@ class MeanAveragePrecision(Metric):
                 f" targets ({len(targets)}) during the update must be the same."
             )
 
+        if self._class_agnostic:
+            predictions = deepcopy(predictions)
+            targets = deepcopy(targets)
+
+            for prediction in predictions:
+                prediction.class_id[:] = -1
+            for target in targets:
+                target.class_id[:] = -1
+
         self._predictions_list.extend(predictions)
         self._targets_list.extend(targets)
 

From 0012052cdd33fb3727269f72024e9f3592ee1ae1 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 16 Oct 2024 00:19:59 +0000
Subject: [PATCH 074/128] :arrow_up: Bump tox from 4.21.2 to 4.22.0

Bumps [tox](https://github.com/tox-dev/tox) from 4.21.2 to 4.22.0.
- [Release notes](https://github.com/tox-dev/tox/releases)
- [Changelog](https://github.com/tox-dev/tox/blob/main/docs/changelog.rst)
- [Commits](https://github.com/tox-dev/tox/compare/4.21.2...4.22.0)

---
updated-dependencies:
- dependency-name: tox
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 226 ++++++++++++++++------------------------------------
 1 file changed, 68 insertions(+), 158 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 1994447d..22d8fa7b 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -602,6 +602,68 @@ traitlets = ">=4"
 [package.extras]
 test = ["pytest"]
 
+[[package]]
+name = "contourpy"
+version = "1.1.0"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "contourpy-1.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:89f06eff3ce2f4b3eb24c1055a26981bffe4e7264acd86f15b97e40530b794bc"},
+    {file = "contourpy-1.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:dffcc2ddec1782dd2f2ce1ef16f070861af4fb78c69862ce0aab801495dda6a3"},
+    {file = "contourpy-1.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:25ae46595e22f93592d39a7eac3d638cda552c3e1160255258b695f7b58e5655"},
+    {file = "contourpy-1.1.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:17cfaf5ec9862bc93af1ec1f302457371c34e688fbd381f4035a06cd47324f48"},
+    {file = "contourpy-1.1.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:18a64814ae7bce73925131381603fff0116e2df25230dfc80d6d690aa6e20b37"},
+    {file = "contourpy-1.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:90c81f22b4f572f8a2110b0b741bb64e5a6427e0a198b2cdc1fbaf85f352a3aa"},
+    {file = "contourpy-1.1.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:53cc3a40635abedbec7f1bde60f8c189c49e84ac180c665f2cd7c162cc454baa"},
+    {file = "contourpy-1.1.0-cp310-cp310-win32.whl", hash = "sha256:9b2dd2ca3ac561aceef4c7c13ba654aaa404cf885b187427760d7f7d4c57cff8"},
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+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
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 version = "1.1.1"
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 pyparsing = ">=2.3.1,<3.1"
 python-dateutil = ">=2.7"
 
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 [[package]]
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 version = "0.1.7"
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-]
-
 [[package]]
 name = "numpy"
 version = "2.1.2"
@@ -2904,12 +2850,12 @@ files = [
 
 [package.dependencies]
 numpy = [
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
     {version = ">=1.21.0", markers = "python_version <= \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\" and python_version >= \"3.8\""},
     {version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
     {version = ">=1.17.3", markers = "(platform_system != \"Darwin\" and platform_system != \"Linux\") and python_version >= \"3.8\" and python_version < \"3.9\" or platform_system != \"Darwin\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_machine != \"aarch64\" or platform_machine != \"arm64\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_system != \"Linux\" or (platform_machine != \"arm64\" and platform_machine != \"aarch64\") and python_version >= \"3.8\" and python_version < \"3.9\""},
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     {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
-    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
     {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
 ]
 
@@ -2986,9 +2932,9 @@ files = [
 
 [package.dependencies]
 numpy = [
+    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
     {version = ">=1.20.3", markers = "python_version < \"3.10\""},
     {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
-    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
 ]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
@@ -3031,24 +2977,6 @@ files = [
 [package.dependencies]
 types-pytz = ">=2022.1.1"
 
-[[package]]
-name = "pandas-stubs"
-version = "2.0.3.230814"
-description = "Type annotations for pandas"
-optional = true
-python-versions = ">=3.8"
-files = [
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-]
-
-[package.dependencies]
-numpy = [
-    {version = "<=1.24.3", markers = "python_full_version <= \"3.8.0\""},
-    {version = ">=1.25.0", markers = "python_version >= \"3.9\""},
-]
-types-pytz = ">=2022.1.1"
-
 [[package]]
 name = "pandocfilters"
 version = "1.5.1"
@@ -3421,20 +3349,6 @@ files = [
 [package.extras]
 diagrams = ["jinja2", "railroad-diagrams"]
 
-[[package]]
-name = "pyparsing"
-version = "3.1.4"
-description = "pyparsing module - Classes and methods to define and execute parsing grammars"
-optional = false
-python-versions = ">=3.6.8"
-files = [
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-]
-
-[package.extras]
-diagrams = ["jinja2", "railroad-diagrams"]
-
 [[package]]
 name = "pyproject-api"
 version = "1.8.0"
@@ -4458,13 +4372,13 @@ files = [
 
 [[package]]
 name = "tox"
-version = "4.21.2"
+version = "4.22.0"
 description = "tox is a generic virtualenv management and test command line tool"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "tox-4.21.2-py3-none-any.whl", hash = "sha256:13d996adcd792e7c82994b0e116d85efd84f0c6d185254d83d156f73f86b2038"},
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+    {file = "tox-4.22.0-py3-none-any.whl", hash = "sha256:03734d9a9ac138cd1a898a372fb1b8079e2728618ae06dc37cbf3686cfb56eea"},
+    {file = "tox-4.22.0.tar.gz", hash = "sha256:acc6c627cb3316585238d55d2b633e132fea1bdb01b9d93b56bce7caea6ae73d"},
 ]
 
 [package.dependencies]
@@ -4480,10 +4394,6 @@ tomli = {version = ">=2.0.1", markers = "python_version < \"3.11\""}
 typing-extensions = {version = ">=4.12.2", markers = "python_version < \"3.11\""}
 virtualenv = ">=20.26.6"
 
-[package.extras]
-docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-argparse-cli (>=1.18.2)", "sphinx-autodoc-typehints (>=2.4.4)", "sphinx-copybutton (>=0.5.2)", "sphinx-inline-tabs (>=2023.4.21)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=24.8)"]
-testing = ["build[virtualenv] (>=1.2.2)", "covdefaults (>=2.3)", "detect-test-pollution (>=1.2)", "devpi-process (>=1.0.2)", "diff-cover (>=9.2)", "distlib (>=0.3.8)", "flaky (>=3.8.1)", "hatch-vcs (>=0.4)", "hatchling (>=1.25)", "psutil (>=6)", "pytest (>=8.3.3)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-xdist (>=3.6.1)", "re-assert (>=1.1)", "setuptools (>=75.1)", "time-machine (>=2.15)", "wheel (>=0.44)"]
-
 [[package]]
 name = "tqdm"
 version = "4.66.5"

From 5b09368a0e8c18f9fbb45428a0a7acc7a0c9c708 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 16 Oct 2024 00:21:15 +0000
Subject: [PATCH 075/128] :arrow_up: Bump mkdocs-material from 9.5.40 to 9.5.41

Bumps [mkdocs-material](https://github.com/squidfunk/mkdocs-material) from 9.5.40 to 9.5.41.
- [Release notes](https://github.com/squidfunk/mkdocs-material/releases)
- [Changelog](https://github.com/squidfunk/mkdocs-material/blob/master/CHANGELOG)
- [Commits](https://github.com/squidfunk/mkdocs-material/compare/9.5.40...9.5.41)

---
updated-dependencies:
- dependency-name: mkdocs-material
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 222 ++++++++++++++++------------------------------------
 1 file changed, 68 insertions(+), 154 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 1994447d..b11f0998 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -602,6 +602,68 @@ traitlets = ">=4"
 [package.extras]
 test = ["pytest"]
 
+[[package]]
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+version = "1.1.0"
+description = "Python library for calculating contours of 2D quadrilateral grids"
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+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
+test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
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 [[package]]
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 version = "1.1.1"
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 bokeh = ["bokeh", "selenium"]
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 python-dateutil = ">=2.7"
 
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 [[package]]
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 version = "0.1.7"
@@ -2421,13 +2412,13 @@ pygments = ">2.12.0"
 
 [[package]]
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-    {file = "numpy-1.26.4-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:b3ce300f3644fb06443ee2222c2201dd3a89ea6040541412b8fa189341847218"},
-    {file = "numpy-1.26.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:03a8c78d01d9781b28a6989f6fa1bb2c4f2d51201cf99d3dd875df6fbd96b23b"},
-    {file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9fad7dcb1aac3c7f0584a5a8133e3a43eeb2fe127f47e3632d43d677c66c102b"},
-    {file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:675d61ffbfa78604709862923189bad94014bef562cc35cf61d3a07bba02a7ed"},
-    {file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:ab47dbe5cc8210f55aa58e4805fe224dac469cde56b9f731a4c098b91917159a"},
-    {file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:1dda2e7b4ec9dd512f84935c5f126c8bd8b9f2fc001e9f54af255e8c5f16b0e0"},
-    {file = "numpy-1.26.4-cp312-cp312-win32.whl", hash = "sha256:50193e430acfc1346175fcbdaa28ffec49947a06918b7b92130744e81e640110"},
-    {file = "numpy-1.26.4-cp312-cp312-win_amd64.whl", hash = "sha256:08beddf13648eb95f8d867350f6a018a4be2e5ad54c8d8caed89ebca558b2818"},
-    {file = "numpy-1.26.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7349ab0fa0c429c82442a27a9673fc802ffdb7c7775fad780226cb234965e53c"},
-    {file = "numpy-1.26.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:52b8b60467cd7dd1e9ed082188b4e6bb35aa5cdd01777621a1658910745b90be"},
-    {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d5241e0a80d808d70546c697135da2c613f30e28251ff8307eb72ba696945764"},
-    {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f870204a840a60da0b12273ef34f7051e98c3b5961b61b0c2c1be6dfd64fbcd3"},
-    {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:679b0076f67ecc0138fd2ede3a8fd196dddc2ad3254069bcb9faf9a79b1cebcd"},
-    {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:47711010ad8555514b434df65f7d7b076bb8261df1ca9bb78f53d3b2db02e95c"},
-    {file = "numpy-1.26.4-cp39-cp39-win32.whl", hash = "sha256:a354325ee03388678242a4d7ebcd08b5c727033fcff3b2f536aea978e15ee9e6"},
-    {file = "numpy-1.26.4-cp39-cp39-win_amd64.whl", hash = "sha256:3373d5d70a5fe74a2c1bb6d2cfd9609ecf686d47a2d7b1d37a8f3b6bf6003aea"},
-    {file = "numpy-1.26.4-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:afedb719a9dcfc7eaf2287b839d8198e06dcd4cb5d276a3df279231138e83d30"},
-    {file = "numpy-1.26.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95a7476c59002f2f6c590b9b7b998306fba6a5aa646b1e22ddfeaf8f78c3a29c"},
-    {file = "numpy-1.26.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:7e50d0a0cc3189f9cb0aeb3a6a6af18c16f59f004b866cd2be1c14b36134a4a0"},
-    {file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"},
-]
-
 [[package]]
 name = "numpy"
 version = "2.1.2"
@@ -2904,12 +2850,12 @@ files = [
 
 [package.dependencies]
 numpy = [
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
     {version = ">=1.21.0", markers = "python_version <= \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\" and python_version >= \"3.8\""},
     {version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
     {version = ">=1.17.3", markers = "(platform_system != \"Darwin\" and platform_system != \"Linux\") and python_version >= \"3.8\" and python_version < \"3.9\" or platform_system != \"Darwin\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_machine != \"aarch64\" or platform_machine != \"arm64\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_system != \"Linux\" or (platform_machine != \"arm64\" and platform_machine != \"aarch64\") and python_version >= \"3.8\" and python_version < \"3.9\""},
     {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
     {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
-    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
     {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
 ]
 
@@ -2986,9 +2932,9 @@ files = [
 
 [package.dependencies]
 numpy = [
+    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
     {version = ">=1.20.3", markers = "python_version < \"3.10\""},
     {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
-    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
 ]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
@@ -3031,24 +2977,6 @@ files = [
 [package.dependencies]
 types-pytz = ">=2022.1.1"
 
-[[package]]
-name = "pandas-stubs"
-version = "2.0.3.230814"
-description = "Type annotations for pandas"
-optional = true
-python-versions = ">=3.8"
-files = [
-    {file = "pandas_stubs-2.0.3.230814-py3-none-any.whl", hash = "sha256:4b3dfc027d49779176b7daa031a3405f7b839bcb6e312f4b9f29fea5feec5b4f"},
-    {file = "pandas_stubs-2.0.3.230814.tar.gz", hash = "sha256:1d5cc09e36e3d9f9a1ed9dceae4e03eeb26d1b898dd769996925f784365c8769"},
-]
-
-[package.dependencies]
-numpy = [
-    {version = "<=1.24.3", markers = "python_full_version <= \"3.8.0\""},
-    {version = ">=1.25.0", markers = "python_version >= \"3.9\""},
-]
-types-pytz = ">=2022.1.1"
-
 [[package]]
 name = "pandocfilters"
 version = "1.5.1"
@@ -3421,20 +3349,6 @@ files = [
 [package.extras]
 diagrams = ["jinja2", "railroad-diagrams"]
 
-[[package]]
-name = "pyparsing"
-version = "3.1.4"
-description = "pyparsing module - Classes and methods to define and execute parsing grammars"
-optional = false
-python-versions = ">=3.6.8"
-files = [
-    {file = "pyparsing-3.1.4-py3-none-any.whl", hash = "sha256:a6a7ee4235a3f944aa1fa2249307708f893fe5717dc603503c6c7969c070fb7c"},
-    {file = "pyparsing-3.1.4.tar.gz", hash = "sha256:f86ec8d1a83f11977c9a6ea7598e8c27fc5cddfa5b07ea2241edbbde1d7bc032"},
-]
-
-[package.extras]
-diagrams = ["jinja2", "railroad-diagrams"]
-
 [[package]]
 name = "pyproject-api"
 version = "1.8.0"

From 4ce033c4d7cada60b2493d56eb4dada82bdaf39f Mon Sep 17 00:00:00 2001
From: Soham <kalburgisoham@gmail.com>
Date: Wed, 16 Oct 2024 11:51:13 +0530
Subject: [PATCH 076/128] fix: made required changes

---
 supervision/detection/core.py  | 17 ++++++++---------
 supervision/detection/utils.py | 28 +++++++++++++++++++++++++---
 2 files changed, 33 insertions(+), 12 deletions(-)

diff --git a/supervision/detection/core.py b/supervision/detection/core.py
index 6ef52a03..8e2d06e2 100644
--- a/supervision/detection/core.py
+++ b/supervision/detection/core.py
@@ -32,6 +32,7 @@ from supervision.detection.utils import (
     extract_ultralytics_masks,
     get_data_item,
     is_data_equal,
+    is_metadata_equal,
     mask_to_xyxy,
     merge_data,
     merge_metadata,
@@ -190,7 +191,7 @@ class Detections:
                 np.array_equal(self.confidence, other.confidence),
                 np.array_equal(self.tracker_id, other.tracker_id),
                 is_data_equal(self.data, other.data),
-                self.metadata == other.metadata,
+                is_metadata_equal(self.metadata, other.metadata),
             ]
         )
 
@@ -979,6 +980,9 @@ class Detections:
             empty_detections = Detections.empty()
             ```
         """
+        if metadata is not None and not isinstance(metadata, dict):
+            raise TypeError("Metadata must be a dictionary.")
+
         return cls(
             xyxy=np.empty((0, 4), dtype=np.float32),
             confidence=np.array([], dtype=np.float32),
@@ -990,14 +994,9 @@ class Detections:
         """
         Returns `True` if the `Detections` object is considered empty.
         """
-        return (
-            len(self.xyxy) == 0
-            and (self.mask is None or len(self.mask) == 0)
-            and (self.class_id is None or len(self.class_id) == 0)
-            and (self.confidence is None or len(self.confidence) == 0)
-            and (self.tracker_id is None or len(self.tracker_id) == 0)
-            and not self.data
-        )
+        empty_detections = Detections.empty()
+        empty_detections.data = self.data
+        return self == empty_detections
 
     @classmethod
     def merge(cls, detections_list: List[Detections]) -> Detections:
diff --git a/supervision/detection/utils.py b/supervision/detection/utils.py
index 71e67c49..421c4a6d 100644
--- a/supervision/detection/utils.py
+++ b/supervision/detection/utils.py
@@ -808,6 +808,21 @@ def is_data_equal(data_a: Dict[str, np.ndarray], data_b: Dict[str, np.ndarray])
     )
 
 
+def is_metadata_equal(metadata_a: Dict[str, Any], metadata_b: Dict[str, Any]) -> bool:
+    """
+    Compares the metadata payloads of two Detections instances.
+
+    Args:
+        metadata_a, metadata_b: The metadata payloads of the instances.
+
+    Returns:
+        True if the metadata payloads are equal, False otherwise.
+    """
+    return set(metadata_a.keys()) == set(metadata_b.keys()) and all(
+        np.array_equal(metadata_a[key], metadata_b[key]) for key in metadata_a
+    )
+
+
 def merge_data(
     data_list: List[Dict[str, Union[npt.NDArray[np.generic], List]]],
 ) -> Dict[str, Union[npt.NDArray[np.generic], List]]:
@@ -880,15 +895,22 @@ def merge_metadata(metadata_list: List[Dict[str, Any]]) -> Dict[str, Any]:
         Dict[str, Any]: A single merged metadata dictionary.
 
     Raises:
-        ValueError: If there are conflicting values for the same key.
+        ValueError: If there are conflicting values for the same key or if
+        dictionaries have different keys.
     """
-    merged_metadata = {}
+    if not metadata_list:
+        return {}
 
+    all_keys_sets = [set(metadata.keys()) for metadata in metadata_list]
+    if not all(keys_set == all_keys_sets[0] for keys_set in all_keys_sets):
+        raise ValueError("All metadata dictionaries must have the same keys to merge.")
+
+    merged_metadata = {}
     for metadata in metadata_list:
         for key, value in metadata.items():
             if key in merged_metadata:
                 if merged_metadata[key] != value:
-                    raise ValueError(f"Conflicting metadata for key: {key}.")
+                    raise ValueError(f"Conflicting metadata for key: '{key}'.")
             else:
                 merged_metadata[key] = value
 

From c6e701be1c702b22d57ddfd103de80e9085b1471 Mon Sep 17 00:00:00 2001
From: Soham <kalburgisoham@gmail.com>
Date: Wed, 16 Oct 2024 19:30:28 +0530
Subject: [PATCH 077/128] fix: added check if not array

---
 supervision/detection/utils.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/supervision/detection/utils.py b/supervision/detection/utils.py
index 421c4a6d..d80a4dcb 100644
--- a/supervision/detection/utils.py
+++ b/supervision/detection/utils.py
@@ -819,7 +819,11 @@ def is_metadata_equal(metadata_a: Dict[str, Any], metadata_b: Dict[str, Any]) ->
         True if the metadata payloads are equal, False otherwise.
     """
     return set(metadata_a.keys()) == set(metadata_b.keys()) and all(
-        np.array_equal(metadata_a[key], metadata_b[key]) for key in metadata_a
+        np.array_equal(metadata_a[key], metadata_b[key])
+        if isinstance(metadata_a[key], np.ndarray)
+        and isinstance(metadata_b[key], np.ndarray)
+        else metadata_a[key] == metadata_b[key]
+        for key in metadata_a
     )
 
 

From d50aa67ac003f62b2af9826b41bf98d7e15ff8c4 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 17 Oct 2024 00:38:46 +0000
Subject: [PATCH 078/128] :arrow_up: Bump types-requests from 2.32.0.20240914
 to 2.32.0.20241016

Bumps [types-requests](https://github.com/python/typeshed) from 2.32.0.20240914 to 2.32.0.20241016.
- [Commits](https://github.com/python/typeshed/commits)

---
updated-dependencies:
- dependency-name: types-requests
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 8e0cf21c..797b7139 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -4505,13 +4505,13 @@ files = [
 
 [[package]]
 name = "types-requests"
-version = "2.32.0.20240914"
+version = "2.32.0.20241016"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-requests-2.32.0.20240914.tar.gz", hash = "sha256:2850e178db3919d9bf809e434eef65ba49d0e7e33ac92d588f4a5e295fffd405"},
-    {file = "types_requests-2.32.0.20240914-py3-none-any.whl", hash = "sha256:59c2f673eb55f32a99b2894faf6020e1a9f4a402ad0f192bfee0b64469054310"},
+    {file = "types-requests-2.32.0.20241016.tar.gz", hash = "sha256:0d9cad2f27515d0e3e3da7134a1b6f28fb97129d86b867f24d9c726452634d95"},
+    {file = "types_requests-2.32.0.20241016-py3-none-any.whl", hash = "sha256:4195d62d6d3e043a4eaaf08ff8a62184584d2e8684e9d2aa178c7915a7da3747"},
 ]
 
 [package.dependencies]

From 5ac3999aeb3947f24e33cb47fc30fdf3e58527ab Mon Sep 17 00:00:00 2001
From: Andrey Blazejuk <andreyblazejuk@hotmail.com>
Date: Thu, 17 Oct 2024 13:58:37 -0300
Subject: [PATCH 079/128] Validate that polygon has at least 3 vertices in
 geometry utils.py

---
 supervision/geometry/utils.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/supervision/geometry/utils.py b/supervision/geometry/utils.py
index 8a0ca35c..580dc0c5 100644
--- a/supervision/geometry/utils.py
+++ b/supervision/geometry/utils.py
@@ -15,6 +15,9 @@ def get_polygon_center(polygon: np.ndarray) -> Point:
     Returns:
         Point: The center of the polygon, represented as a
             Point object with x and y attributes.
+    
+    Raises:
+        ValueError: If the polygon has less than 3 vertices.
 
     Examples:
         ```python
@@ -30,6 +33,9 @@ def get_polygon_center(polygon: np.ndarray) -> Point:
     # This is one of the 3 candidate algorithms considered for centroid calculation.
     # For a more detailed discussion, see PR #1084 and commit eb33176
 
+    if len(polygon) < 3:
+        raise ValueError("Polygon must have at least 3 vertices.")
+
     shift_polygon = np.roll(polygon, -1, axis=0)
     signed_areas = np.cross(polygon, shift_polygon) / 2
     if signed_areas.sum() == 0:

From f8ff33287255d187a6403833994a1791a95ddaec Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 17 Oct 2024 17:01:02 +0000
Subject: [PATCH 080/128] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20aut?=
 =?UTF-8?q?o=20format=20pre-commit=20hooks?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 supervision/geometry/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/supervision/geometry/utils.py b/supervision/geometry/utils.py
index 580dc0c5..cdceb36d 100644
--- a/supervision/geometry/utils.py
+++ b/supervision/geometry/utils.py
@@ -15,7 +15,7 @@ def get_polygon_center(polygon: np.ndarray) -> Point:
     Returns:
         Point: The center of the polygon, represented as a
             Point object with x and y attributes.
-    
+
     Raises:
         ValueError: If the polygon has less than 3 vertices.
 

From 1b95d1dea2dc4c5c48582087d6b8dc83f802094a Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Thu, 17 Oct 2024 23:11:56 +0300
Subject: [PATCH 081/128] Change get_polygon_center validation to fail on 0
 points

---
 supervision/geometry/utils.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/supervision/geometry/utils.py b/supervision/geometry/utils.py
index cdceb36d..2247adc5 100644
--- a/supervision/geometry/utils.py
+++ b/supervision/geometry/utils.py
@@ -17,7 +17,7 @@ def get_polygon_center(polygon: np.ndarray) -> Point:
             Point object with x and y attributes.
 
     Raises:
-        ValueError: If the polygon has less than 3 vertices.
+        ValueError: If the polygon has no vertices.
 
     Examples:
         ```python
@@ -33,8 +33,8 @@ def get_polygon_center(polygon: np.ndarray) -> Point:
     # This is one of the 3 candidate algorithms considered for centroid calculation.
     # For a more detailed discussion, see PR #1084 and commit eb33176
 
-    if len(polygon) < 3:
-        raise ValueError("Polygon must have at least 3 vertices.")
+    if len(polygon) == 0:
+        raise ValueError("Polygon must have at least one vertex.")
 
     shift_polygon = np.roll(polygon, -1, axis=0)
     signed_areas = np.cross(polygon, shift_polygon) / 2

From e5215b967c74325eee54d9d0d0d1a09df0ee797f Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Thu, 17 Oct 2024 23:52:05 +0300
Subject: [PATCH 082/128] typos, minor type updates

---
 supervision/tracker/byte_tracker/basetrack.py | 2 +-
 supervision/tracker/byte_tracker/matching.py  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/supervision/tracker/byte_tracker/basetrack.py b/supervision/tracker/byte_tracker/basetrack.py
index b78bc596..e2bc66d5 100644
--- a/supervision/tracker/byte_tracker/basetrack.py
+++ b/supervision/tracker/byte_tracker/basetrack.py
@@ -38,7 +38,7 @@ class BaseTrack:
         self.frame_id = 0
         self.time_since_update = 0
 
-    def activate(self, *args):
+    def activate(self, *args, **kwargs):
         raise NotImplementedError
 
     def predict(self):
diff --git a/supervision/tracker/byte_tracker/matching.py b/supervision/tracker/byte_tracker/matching.py
index 24abe224..f791e518 100644
--- a/supervision/tracker/byte_tracker/matching.py
+++ b/supervision/tracker/byte_tracker/matching.py
@@ -20,7 +20,7 @@ def indices_to_matches(
 
 def linear_assignment(
     cost_matrix: np.ndarray, thresh: float
-) -> [np.ndarray, Tuple[int], Tuple[int, int]]:
+) -> Tuple[np.ndarray, Tuple[int], Tuple[int, int]]:
     if cost_matrix.size == 0:
         return (
             np.empty((0, 2), dtype=int),

From 4a7d9ceaa94d8f8f2f9094dd9639532faffe953e Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 18 Oct 2024 01:01:43 +0300
Subject: [PATCH 083/128] Tracker fix: add & rework mistakenly removed ID
 system

* External IDs were counted as the numbers grow very large very fast
* Reworked ID counting as a class, as state is necessary, and only STracks know if an external ID is required after the update (see conditions everywhere before self.external_id_counter.new_id() is called).
---
 supervision/tracker/byte_tracker/core.py | 115 ++++++++++++++---------
 1 file changed, 70 insertions(+), 45 deletions(-)

diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index 013af2ee..d1d567fa 100644
--- 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
 
@@ -9,10 +9,34 @@ from supervision.tracker.byte_tracker.basetrack import BaseTrack, TrackState
 from supervision.tracker.byte_tracker.kalman_filter import KalmanFilter
 
 
+class IdCounter:
+    def __init__(self):
+        self.reset()
+
+    def reset(self) -> None:
+        self._id = self.NO_ID
+
+    def new_id(self) -> int:
+        self._id += 1
+        return self._id
+
+    @property
+    def NO_ID(self) -> int:
+        return 0
+
+
 class STrack(BaseTrack):
     shared_kalman = KalmanFilter()
 
-    def __init__(self, tlwh, score, class_ids, minimum_consecutive_frames):
+    def __init__(
+        self,
+        tlwh,
+        score,
+        class_ids,
+        minimum_consecutive_frames,
+        internal_id_counter: IdCounter,
+        external_id_counter: IdCounter,
+    ):
         super().__init__()
         # wait activate
         self._tlwh = np.asarray(tlwh, dtype=np.float32)
@@ -24,10 +48,13 @@ class STrack(BaseTrack):
         self.class_ids = class_ids
         self.tracklet_len = 0
 
-        self.external_track_id = -1
-
         self.minimum_consecutive_frames = minimum_consecutive_frames
 
+        self.internal_id_counter = internal_id_counter
+        self.external_id_counter = external_id_counter
+        self.internal_track_id = self.internal_id_counter.NO_ID
+        self.external_track_id = self.external_id_counter.NO_ID
+
     def predict(self):
         mean_state = self.mean.copy()
         if self.state != TrackState.Tracked:
@@ -54,10 +81,10 @@ class STrack(BaseTrack):
                 stracks[i].mean = mean
                 stracks[i].covariance = cov
 
-    def activate(self, kalman_filter, frame_id, track_id):
+    def activate(self, kalman_filter, frame_id):
         """Start a new tracklet"""
         self.kalman_filter = kalman_filter
-        self.internal_track_id = track_id
+        self.internal_track_id = self.internal_id_counter.new_id()
         self.mean, self.covariance = self.kalman_filter.initiate(
             self.tlwh_to_xyah(self._tlwh)
         )
@@ -68,12 +95,12 @@ class STrack(BaseTrack):
             self.is_activated = True
 
         if self.minimum_consecutive_frames == 1:
-            self.external_track_id = track_id
+            self.external_track_id = self.external_id_counter.new_id()
 
         self.frame_id = frame_id
         self.start_frame = frame_id
 
-    def re_activate(self, new_track, frame_id, new_id: Optional[int] = None):
+    def re_activate(self, new_track, frame_id):
         self.mean, self.covariance = self.kalman_filter.update(
             self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
         )
@@ -81,11 +108,9 @@ class STrack(BaseTrack):
         self.state = TrackState.Tracked
 
         self.frame_id = frame_id
-        if new_id:
-            self.internal_track_id = new_id
         self.score = new_track.score
 
-    def update(self, new_track, frame_id, track_id):
+    def update(self, new_track, frame_id):
         """
         Update a matched track
         :type new_track: STrack
@@ -103,8 +128,8 @@ class STrack(BaseTrack):
         self.state = TrackState.Tracked
         if self.tracklet_len == self.minimum_consecutive_frames:
             self.is_activated = True
-            if self.external_track_id == -1:
-                self.external_track_id = track_id
+            if self.external_track_id == self.external_id_counter.NO_ID:
+                self.external_track_id = self.external_id_counter.new_id()
 
         self.score = new_track.score
 
@@ -160,24 +185,6 @@ class STrack(BaseTrack):
         )
 
 
-def detections2boxes(detections: Detections) -> np.ndarray:
-    """
-    Convert Supervision Detections to numpy tensors for further computation.
-    Args:
-        detections (Detections): Detections/Targets in the format of sv.Detections.
-    Returns:
-        (np.ndarray): Detections as numpy tensors as in
-            `(x_min, y_min, x_max, y_max, confidence, class_id)` order.
-    """
-    return np.hstack(
-        (
-            detections.xyxy,
-            detections.confidence[:, np.newaxis],
-            detections.class_id[:, np.newaxis],
-        )
-    )
-
-
 class ByteTrack:
     """
     Initialize the ByteTrack object.
@@ -216,7 +223,6 @@ class ByteTrack:
         self.track_activation_threshold = track_activation_threshold
         self.minimum_matching_threshold = minimum_matching_threshold
 
-        self._count = 0
         self.frame_id = 0
         self.det_thresh = self.track_activation_threshold + 0.1
         self.max_time_lost = int(frame_rate / 30.0 * lost_track_buffer)
@@ -227,9 +233,8 @@ class ByteTrack:
         self.lost_tracks: List[STrack] = []
         self.removed_tracks: List[STrack] = []
 
-    def _next_id(self) -> int:
-        self._count += 1
-        return self._count
+        self.internal_id_counter = IdCounter()
+        self.external_id_counter = IdCounter()
 
     def update_with_detections(self, detections: Detections) -> Detections:
         """
@@ -271,7 +276,13 @@ class ByteTrack:
             ```
         """
 
-        tensors = detections2boxes(detections=detections)
+        tensors = np.hstack(
+            (
+                detections.xyxy,
+                detections.confidence[:, np.newaxis],
+                detections.class_id[:, np.newaxis],
+            )
+        )
         tracks = self.update_with_tensors(tensors=tensors)
 
         if len(tracks) > 0:
@@ -307,7 +318,9 @@ class ByteTrack:
         ensuring the tracker starts with a clean state for each new video.
         """
         self.frame_id = 0
-        self._count = 0
+        BaseTrack.reset_counter()
+        self.internal_id_counter.reset()
+        self.external_id_counter.reset()
         self.tracked_tracks: List[STrack] = []
         self.lost_tracks: List[STrack] = []
         self.removed_tracks: List[STrack] = []
@@ -348,7 +361,14 @@ class ByteTrack:
         if len(dets) > 0:
             """Detections"""
             detections = [
-                STrack(STrack.tlbr_to_tlwh(tlbr), s, c, self.minimum_consecutive_frames)
+                STrack(
+                    STrack.tlbr_to_tlwh(tlbr),
+                    s,
+                    c,
+                    self.minimum_consecutive_frames,
+                    self.internal_id_counter,
+                    self.external_id_counter,
+                )
                 for (tlbr, s, c) in zip(dets, scores_keep, class_ids_keep)
             ]
         else:
@@ -379,7 +399,7 @@ class ByteTrack:
             track = strack_pool[itracked]
             det = detections[idet]
             if track.state == TrackState.Tracked:
-                track.update(detections[idet], self.frame_id, self._next_id())
+                track.update(detections[idet], self.frame_id)
                 activated_starcks.append(track)
             else:
                 track.re_activate(det, self.frame_id)
@@ -390,7 +410,14 @@ class ByteTrack:
         if len(dets_second) > 0:
             """Detections"""
             detections_second = [
-                STrack(STrack.tlbr_to_tlwh(tlbr), s, c, self.minimum_consecutive_frames)
+                STrack(
+                    STrack.tlbr_to_tlwh(tlbr),
+                    s,
+                    c,
+                    self.minimum_consecutive_frames,
+                    self.internal_id_counter,
+                    self.external_id_counter,
+                )
                 for (tlbr, s, c) in zip(dets_second, scores_second, class_ids_second)
             ]
         else:
@@ -408,7 +435,7 @@ class ByteTrack:
             track = r_tracked_stracks[itracked]
             det = detections_second[idet]
             if track.state == TrackState.Tracked:
-                track.update(det, self.frame_id, self._next_id())
+                track.update(det, self.frame_id)
                 activated_starcks.append(track)
             else:
                 track.re_activate(det, self.frame_id)
@@ -429,9 +456,7 @@ class ByteTrack:
             dists, thresh=0.7
         )
         for itracked, idet in matches:
-            unconfirmed[itracked].update(
-                detections[idet], self.frame_id, self._next_id()
-            )
+            unconfirmed[itracked].update(detections[idet], self.frame_id)
             activated_starcks.append(unconfirmed[itracked])
         for it in u_unconfirmed:
             track = unconfirmed[it]
@@ -443,7 +468,7 @@ class ByteTrack:
             track = detections[inew]
             if track.score < self.det_thresh:
                 continue
-            track.activate(self.kalman_filter, self.frame_id, self._next_id())
+            track.activate(self.kalman_filter, self.frame_id)
             activated_starcks.append(track)
         """ Step 5: Update state"""
         for track in self.lost_tracks:

From b2762dafaff0ab1ee0836da3b2ca8cdef06a3561 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Fri, 18 Oct 2024 00:18:11 +0000
Subject: [PATCH 084/128] :arrow_up: Bump tox from 4.22.0 to 4.23.0

Bumps [tox](https://github.com/tox-dev/tox) from 4.22.0 to 4.23.0.
- [Release notes](https://github.com/tox-dev/tox/releases)
- [Changelog](https://github.com/tox-dev/tox/blob/main/docs/changelog.rst)
- [Commits](https://github.com/tox-dev/tox/compare/4.22.0...4.23.0)

---
updated-dependencies:
- dependency-name: tox
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 797b7139..eadeb138 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -4377,13 +4377,13 @@ files = [
 
 [[package]]
 name = "tox"
-version = "4.22.0"
+version = "4.23.0"
 description = "tox is a generic virtualenv management and test command line tool"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "tox-4.22.0-py3-none-any.whl", hash = "sha256:03734d9a9ac138cd1a898a372fb1b8079e2728618ae06dc37cbf3686cfb56eea"},
-    {file = "tox-4.22.0.tar.gz", hash = "sha256:acc6c627cb3316585238d55d2b633e132fea1bdb01b9d93b56bce7caea6ae73d"},
+    {file = "tox-4.23.0-py3-none-any.whl", hash = "sha256:46da40afb660e46238c251280eb910bdaf00b390c7557c8e4bb611f422e9db12"},
+    {file = "tox-4.23.0.tar.gz", hash = "sha256:a6bd7d54231d755348d3c3a7b450b5bf6563833716d1299a1619587a1b77a3bf"},
 ]
 
 [package.dependencies]

From 70dd0e1c1d292381702ca74c4a839aa102266d3e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Fri, 18 Oct 2024 00:21:04 +0000
Subject: [PATCH 085/128] :arrow_up: Bump ruff from 0.6.9 to 0.7.0

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.9 to 0.7.0.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.6.9...0.7.0)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 38 +++++++++++++++++++-------------------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 797b7139..1ee978e6 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -4036,29 +4036,29 @@ files = [
 
 [[package]]
 name = "ruff"
-version = "0.6.9"
+version = "0.7.0"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ruff-0.6.9-py3-none-linux_armv6l.whl", hash = "sha256:064df58d84ccc0ac0fcd63bc3090b251d90e2a372558c0f057c3f75ed73e1ccd"},
-    {file = "ruff-0.6.9-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:140d4b5c9f5fc7a7b074908a78ab8d384dd7f6510402267bc76c37195c02a7ec"},
-    {file = "ruff-0.6.9-py3-none-macosx_11_0_arm64.whl", hash = "sha256:53fd8ca5e82bdee8da7f506d7b03a261f24cd43d090ea9db9a1dc59d9313914c"},
-    {file = "ruff-0.6.9-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:645d7d8761f915e48a00d4ecc3686969761df69fb561dd914a773c1a8266e14e"},
-    {file = "ruff-0.6.9-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:eae02b700763e3847595b9d2891488989cac00214da7f845f4bcf2989007d577"},
-    {file = "ruff-0.6.9-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7d5ccc9e58112441de8ad4b29dcb7a86dc25c5f770e3c06a9d57e0e5eba48829"},
-    {file = "ruff-0.6.9-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:417b81aa1c9b60b2f8edc463c58363075412866ae4e2b9ab0f690dc1e87ac1b5"},
-    {file = "ruff-0.6.9-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3c866b631f5fbce896a74a6e4383407ba7507b815ccc52bcedabb6810fdb3ef7"},
-    {file = "ruff-0.6.9-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7b118afbb3202f5911486ad52da86d1d52305b59e7ef2031cea3425142b97d6f"},
-    {file = "ruff-0.6.9-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a67267654edc23c97335586774790cde402fb6bbdb3c2314f1fc087dee320bfa"},
-    {file = "ruff-0.6.9-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:3ef0cc774b00fec123f635ce5c547dac263f6ee9fb9cc83437c5904183b55ceb"},
-    {file = "ruff-0.6.9-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:12edd2af0c60fa61ff31cefb90aef4288ac4d372b4962c2864aeea3a1a2460c0"},
-    {file = "ruff-0.6.9-py3-none-musllinux_1_2_i686.whl", hash = "sha256:55bb01caeaf3a60b2b2bba07308a02fca6ab56233302406ed5245180a05c5625"},
-    {file = "ruff-0.6.9-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:925d26471fa24b0ce5a6cdfab1bb526fb4159952385f386bdcc643813d472039"},
-    {file = "ruff-0.6.9-py3-none-win32.whl", hash = "sha256:eb61ec9bdb2506cffd492e05ac40e5bc6284873aceb605503d8494180d6fc84d"},
-    {file = "ruff-0.6.9-py3-none-win_amd64.whl", hash = "sha256:785d31851c1ae91f45b3d8fe23b8ae4b5170089021fbb42402d811135f0b7117"},
-    {file = "ruff-0.6.9-py3-none-win_arm64.whl", hash = "sha256:a9641e31476d601f83cd602608739a0840e348bda93fec9f1ee816f8b6798b93"},
-    {file = "ruff-0.6.9.tar.gz", hash = "sha256:b076ef717a8e5bc819514ee1d602bbdca5b4420ae13a9cf61a0c0a4f53a2baa2"},
+    {file = "ruff-0.7.0-py3-none-linux_armv6l.whl", hash = "sha256:0cdf20c2b6ff98e37df47b2b0bd3a34aaa155f59a11182c1303cce79be715628"},
+    {file = "ruff-0.7.0-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:496494d350c7fdeb36ca4ef1c9f21d80d182423718782222c29b3e72b3512737"},
+    {file = "ruff-0.7.0-py3-none-macosx_11_0_arm64.whl", hash = "sha256:214b88498684e20b6b2b8852c01d50f0651f3cc6118dfa113b4def9f14faaf06"},
+    {file = "ruff-0.7.0-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:630fce3fefe9844e91ea5bbf7ceadab4f9981f42b704fae011bb8efcaf5d84be"},
+    {file = "ruff-0.7.0-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:211d877674e9373d4bb0f1c80f97a0201c61bcd1e9d045b6e9726adc42c156aa"},
+    {file = "ruff-0.7.0-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:194d6c46c98c73949a106425ed40a576f52291c12bc21399eb8f13a0f7073495"},
+    {file = "ruff-0.7.0-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:82c2579b82b9973a110fab281860403b397c08c403de92de19568f32f7178598"},
+    {file = "ruff-0.7.0-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9af971fe85dcd5eaed8f585ddbc6bdbe8c217fb8fcf510ea6bca5bdfff56040e"},
+    {file = "ruff-0.7.0-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b641c7f16939b7d24b7bfc0be4102c56562a18281f84f635604e8a6989948914"},
+    {file = "ruff-0.7.0-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d71672336e46b34e0c90a790afeac8a31954fd42872c1f6adaea1dff76fd44f9"},
+    {file = "ruff-0.7.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:ab7d98c7eed355166f367597e513a6c82408df4181a937628dbec79abb2a1fe4"},
+    {file = "ruff-0.7.0-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:1eb54986f770f49edb14f71d33312d79e00e629a57387382200b1ef12d6a4ef9"},
+    {file = "ruff-0.7.0-py3-none-musllinux_1_2_i686.whl", hash = "sha256:dc452ba6f2bb9cf8726a84aa877061a2462afe9ae0ea1d411c53d226661c601d"},
+    {file = "ruff-0.7.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:4b406c2dce5be9bad59f2de26139a86017a517e6bcd2688da515481c05a2cb11"},
+    {file = "ruff-0.7.0-py3-none-win32.whl", hash = "sha256:f6c968509f767776f524a8430426539587d5ec5c662f6addb6aa25bc2e8195ec"},
+    {file = "ruff-0.7.0-py3-none-win_amd64.whl", hash = "sha256:ff4aabfbaaba880e85d394603b9e75d32b0693152e16fa659a3064a85df7fce2"},
+    {file = "ruff-0.7.0-py3-none-win_arm64.whl", hash = "sha256:10842f69c245e78d6adec7e1db0a7d9ddc2fff0621d730e61657b64fa36f207e"},
+    {file = "ruff-0.7.0.tar.gz", hash = "sha256:47a86360cf62d9cd53ebfb0b5eb0e882193fc191c6d717e8bef4462bc3b9ea2b"},
 ]
 
 [[package]]

From eb3e1e62935019841308b17d5eb288f4f35dafca Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 18 Oct 2024 12:45:38 +0300
Subject: [PATCH 086/128] Add usage example in Polygon Zone docs

---
 supervision/detection/tools/polygon_zone.py | 28 +++++++++++++++++++++
 1 file changed, 28 insertions(+)

diff --git a/supervision/detection/tools/polygon_zone.py b/supervision/detection/tools/polygon_zone.py
index b8b728fc..f69f3c9f 100644
--- a/supervision/detection/tools/polygon_zone.py
+++ b/supervision/detection/tools/polygon_zone.py
@@ -17,6 +17,12 @@ class PolygonZone:
     """
     A class for defining a polygon-shaped zone within a frame for detecting objects.
 
+    !!! warning
+
+        LineZone uses the `tracker_id`. Read
+        [here](/latest/trackers/) to learn how to plug
+        tracking into your inference pipeline.
+
     Attributes:
         polygon (np.ndarray): A polygon represented by a numpy array of shape
             `(N, 2)`, containing the `x`, `y` coordinates of the points.
@@ -26,6 +32,28 @@ class PolygonZone:
             (default: (sv.Position.BOTTOM_CENTER,)).
         current_count (int): The current count of detected objects within the zone
         mask (np.ndarray): The 2D bool mask for the polygon zone
+
+    Example:
+        ```python
+        import supervision as sv
+        from ultralytics import YOLO
+        import numpy as np
+        import cv2
+
+        image = cv2.imread(<SOURCE_IMAGE_PATH>)
+        model = YOLO("yolo11s")
+        tracker = sv.ByteTrack()
+
+        polygon = np.array([[100, 200], [200, 100], [300, 200], [200, 300]])
+        polygon_zone = sv.PolygonZone(polygon=polygon)
+
+        result = model.infer(image)[0]
+        detections = sv.Detections.from_ultralytics(result)
+        detections = tracker.update_with_detections(detections)
+
+        is_detections_in_zone = polygon_zone.trigger(detections)
+        print(polygon_zone.current_count)
+        ```
     """
 
     def __init__(

From c17de5c11b220b07ea8343ee3ebbedfa52130b22 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 18 Oct 2024 14:37:36 +0300
Subject: [PATCH 087/128] Add precision and recall metrics

---
 docs/metrics/precision.md        |  18 +
 docs/metrics/recall.md           |  18 +
 mkdocs.yml                       |   2 +
 supervision/metrics/precision.py | 547 +++++++++++++++++++++++++++++++
 supervision/metrics/recall.py    | 545 ++++++++++++++++++++++++++++++
 5 files changed, 1130 insertions(+)
 create mode 100644 docs/metrics/precision.md
 create mode 100644 docs/metrics/recall.md
 create mode 100644 supervision/metrics/precision.py
 create mode 100644 supervision/metrics/recall.py

diff --git a/docs/metrics/precision.md b/docs/metrics/precision.md
new file mode 100644
index 00000000..ca318f8f
--- /dev/null
+++ b/docs/metrics/precision.md
@@ -0,0 +1,18 @@
+---
+comments: true
+status: new
+---
+
+# F1 Score
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.precision.Precision">Precision</a></h2>
+</div>
+
+:::supervision.metrics.precision.Precision
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.precision.PrecisionResult">PrecisionResult</a></h2>
+</div>
+
+:::supervision.metrics.precision.PrecisionResult
diff --git a/docs/metrics/recall.md b/docs/metrics/recall.md
new file mode 100644
index 00000000..5baa4d3e
--- /dev/null
+++ b/docs/metrics/recall.md
@@ -0,0 +1,18 @@
+---
+comments: true
+status: new
+---
+
+# F1 Score
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.recall.Recall">Recall</a></h2>
+</div>
+
+:::supervision.metrics.recall.Recall
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.recall.RecallResult">RecallResult</a></h2>
+</div>
+
+:::supervision.metrics.recall.RecallResult
diff --git a/mkdocs.yml b/mkdocs.yml
index 3cd86759..a3c9c1ca 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -66,6 +66,8 @@ nav:
           - Utils: datasets/utils.md
       - Metrics:
           - mAP: metrics/mean_average_precision.md
+          - Precision: metrics/precision.md
+          - Recall: metrics/recall.md
           - F1 Score: metrics/f1_score.md
           - Legacy Metrics: detection/metrics.md
       - Utils:
diff --git a/supervision/metrics/precision.py b/supervision/metrics/precision.py
new file mode 100644
index 00000000..ba441831
--- /dev/null
+++ b/supervision/metrics/precision.py
@@ -0,0 +1,547 @@
+from __future__ import annotations
+
+from copy import deepcopy
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, List, Optional, Tuple, Union
+
+import numpy as np
+from matplotlib import pyplot as plt
+
+from supervision.config import ORIENTED_BOX_COORDINATES
+from supervision.detection.core import Detections
+from supervision.detection.utils import box_iou_batch, mask_iou_batch
+from supervision.draw.color import LEGACY_COLOR_PALETTE
+from supervision.metrics.core import AveragingMethod, Metric, MetricTarget
+from supervision.metrics.utils.object_size import (
+    ObjectSizeCategory,
+    get_detection_size_category,
+)
+from supervision.metrics.utils.utils import ensure_pandas_installed
+
+if TYPE_CHECKING:
+    import pandas as pd
+
+
+class Precision(Metric):
+    def __init__(
+        self,
+        metric_target: MetricTarget = MetricTarget.BOXES,
+        averaging_method: AveragingMethod = AveragingMethod.WEIGHTED,
+    ):
+        self._metric_target = metric_target
+        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+            raise NotImplementedError(
+                "Precision is not implemented for oriented bounding boxes."
+            )
+
+        self._metric_target = metric_target
+        self.averaging_method = averaging_method
+        self._predictions_list: List[Detections] = []
+        self._targets_list: List[Detections] = []
+
+    def reset(self) -> None:
+        self._predictions_list = []
+        self._targets_list = []
+
+    def update(
+        self,
+        predictions: Union[Detections, List[Detections]],
+        targets: Union[Detections, List[Detections]],
+    ) -> Precision:
+        if not isinstance(predictions, list):
+            predictions = [predictions]
+        if not isinstance(targets, list):
+            targets = [targets]
+
+        if len(predictions) != len(targets):
+            raise ValueError(
+                f"The number of predictions ({len(predictions)}) and"
+                f" targets ({len(targets)}) during the update must be the same."
+            )
+
+        self._predictions_list.extend(predictions)
+        self._targets_list.extend(targets)
+
+        return self
+
+    def compute(self) -> PrecisionResult:
+        result = self._compute(self._predictions_list, self._targets_list)
+
+        small_predictions, small_targets = self._filter_predictions_and_targets_by_size(
+            self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL
+        )
+        result.small_objects = self._compute(small_predictions, small_targets)
+
+        medium_predictions, medium_targets = (
+            self._filter_predictions_and_targets_by_size(
+                self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM
+            )
+        )
+        result.medium_objects = self._compute(medium_predictions, medium_targets)
+
+        large_predictions, large_targets = self._filter_predictions_and_targets_by_size(
+            self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE
+        )
+        result.large_objects = self._compute(large_predictions, large_targets)
+
+        return result
+
+    def _compute(
+        self, predictions_list: List[Detections], targets_list: List[Detections]
+    ) -> PrecisionResult:
+        iou_thresholds = np.linspace(0.5, 0.95, 10)
+        stats = []
+
+        for predictions, targets in zip(predictions_list, targets_list):
+            prediction_contents = self._detections_content(predictions)
+            target_contents = self._detections_content(targets)
+
+            if len(targets) > 0:
+                if len(predictions) == 0:
+                    stats.append(
+                        (
+                            np.zeros((0, iou_thresholds.size), dtype=bool),
+                            np.zeros((0,), dtype=np.float32),
+                            np.zeros((0,), dtype=int),
+                            targets.class_id,
+                        )
+                    )
+
+                else:
+                    if self._metric_target == MetricTarget.BOXES:
+                        iou = box_iou_batch(target_contents, prediction_contents)
+                    elif self._metric_target == MetricTarget.MASKS:
+                        iou = mask_iou_batch(target_contents, prediction_contents)
+                    else:
+                        raise NotImplementedError(
+                            "Unsupported metric target for IoU calculation"
+                        )
+
+                    matches = self._match_detection_batch(
+                        predictions.class_id, targets.class_id, iou, iou_thresholds
+                    )
+                    stats.append(
+                        (
+                            matches,
+                            predictions.confidence,
+                            predictions.class_id,
+                            targets.class_id,
+                        )
+                    )
+
+        if not stats:
+            return PrecisionResult(
+                metric_target=self._metric_target,
+                averaging_method=self.averaging_method,
+                precision_scores=np.zeros(iou_thresholds.shape[0]),
+                precision_per_class=np.zeros((0, iou_thresholds.shape[0])),
+                iou_thresholds=iou_thresholds,
+                matched_classes=np.array([], dtype=int),
+                small_objects=None,
+                medium_objects=None,
+                large_objects=None,
+            )
+
+        concatenated_stats = [np.concatenate(items, 0) for items in zip(*stats)]
+        precision_scores, precision_per_class, unique_classes = (
+            self._compute_precision_for_classes(*concatenated_stats)
+        )
+
+        return PrecisionResult(
+            metric_target=self._metric_target,
+            averaging_method=self.averaging_method,
+            precision_scores=precision_scores,
+            precision_per_class=precision_per_class,
+            iou_thresholds=iou_thresholds,
+            matched_classes=unique_classes,
+            small_objects=None,
+            medium_objects=None,
+            large_objects=None,
+        )
+
+    def _compute_precision_for_classes(
+        self,
+        matches: np.ndarray,
+        prediction_confidence: np.ndarray,
+        prediction_class_ids: np.ndarray,
+        true_class_ids: np.ndarray,
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        sorted_indices = np.argsort(-prediction_confidence)
+        matches = matches[sorted_indices]
+        prediction_class_ids = prediction_class_ids[sorted_indices]
+        unique_classes, class_counts = np.unique(true_class_ids, return_counts=True)
+
+        # Shape: PxTh,P,C,C -> CxThx3
+        confusion_matrix = self._compute_confusion_matrix(
+            matches, prediction_class_ids, unique_classes, class_counts
+        )
+
+        # Shape: CxThx3 -> CxTh
+        precision_per_class = self._compute_precision(confusion_matrix)
+
+        # Shape: CxTh -> Th
+        if self.averaging_method == AveragingMethod.MACRO:
+            precision_scores = np.mean(precision_per_class, axis=0)
+        elif self.averaging_method == AveragingMethod.MICRO:
+            confusion_matrix_merged = confusion_matrix.sum(0)
+            precision_scores = self._compute_precision(confusion_matrix_merged)
+        elif self.averaging_method == AveragingMethod.WEIGHTED:
+            class_counts = class_counts.astype(np.float32)
+            precision_scores = np.average(
+                precision_per_class, axis=0, weights=class_counts
+            )
+
+        return precision_scores, precision_per_class, unique_classes
+
+    @staticmethod
+    def _match_detection_batch(
+        predictions_classes: np.ndarray,
+        target_classes: np.ndarray,
+        iou: np.ndarray,
+        iou_thresholds: np.ndarray,
+    ) -> np.ndarray:
+        num_predictions, num_iou_levels = (
+            predictions_classes.shape[0],
+            iou_thresholds.shape[0],
+        )
+        correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)
+        correct_class = target_classes[:, None] == predictions_classes
+
+        for i, iou_level in enumerate(iou_thresholds):
+            matched_indices = np.where((iou >= iou_level) & correct_class)
+
+            if matched_indices[0].shape[0]:
+                combined_indices = np.stack(matched_indices, axis=1)
+                iou_values = iou[matched_indices][:, None]
+                matches = np.hstack([combined_indices, iou_values])
+
+                if matched_indices[0].shape[0] > 1:
+                    matches = matches[matches[:, 2].argsort()[::-1]]
+                    matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
+                    matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
+
+                correct[matches[:, 1].astype(int), i] = True
+
+        return correct
+
+    @staticmethod
+    def _compute_confusion_matrix(
+        sorted_matches: np.ndarray,
+        sorted_prediction_class_ids: np.ndarray,
+        unique_classes: np.ndarray,
+        class_counts: np.ndarray,
+    ) -> np.ndarray:
+        """
+        Compute the confusion matrix for each class and IoU threshold.
+
+        Assumes the matches and prediction_class_ids are sorted by confidence
+        in descending order.
+
+        Arguments:
+            sorted_matches: np.ndarray, bool, shape (P, Th), that is True
+                if the prediction is a true positive at the given IoU threshold.
+            sorted_prediction_class_ids: np.ndarray, int, shape (P,), containing
+                the class id for each prediction.
+            unique_classes: np.ndarray, int, shape (C,), containing the unique
+                class ids.
+            class_counts: np.ndarray, int, shape (C,), containing the number
+                of true instances for each class.
+
+        Returns:
+            np.ndarray, shape (C, Th, 3), containing the true positives, false
+                positives, and false negatives for each class and IoU threshold.
+        """
+
+        num_thresholds = sorted_matches.shape[1]
+        num_classes = unique_classes.shape[0]
+
+        confusion_matrix = np.zeros((num_classes, num_thresholds, 3))
+        for class_idx, class_id in enumerate(unique_classes):
+            is_class = sorted_prediction_class_ids == class_id
+            num_true = class_counts[class_idx]
+            num_predictions = is_class.sum()
+
+            if num_predictions == 0:
+                true_positives = np.zeros(num_thresholds)
+                false_positives = np.zeros(num_thresholds)
+                false_negatives = np.full(num_thresholds, num_true)
+            elif num_true == 0:
+                true_positives = np.zeros(num_thresholds)
+                false_positives = np.full(num_thresholds, num_predictions)
+                false_negatives = np.zeros(num_thresholds)
+            else:
+                true_positives = sorted_matches[is_class].sum(0)
+                false_positives = (1 - sorted_matches[is_class]).sum(0)
+                false_negatives = num_true - true_positives
+            confusion_matrix[class_idx] = np.stack(
+                [true_positives, false_positives, false_negatives], axis=1
+            )
+
+        return confusion_matrix
+
+    @staticmethod
+    def _compute_precision(confusion_matrix: np.ndarray) -> np.ndarray:
+        """
+        Broadcastable function, computing the precision from the confusion matrix.
+
+        Arguments:
+            confusion_matrix: np.ndarray, shape (N, ..., 3), where the last dimension
+                contains the true positives, false positives, and false negatives.
+
+        Returns:
+            np.ndarray, shape (N, ...), containing the precision for each element.
+        """
+        if not confusion_matrix.shape[-1] == 3:
+            raise ValueError(
+                f"Confusion matrix must have shape (..., 3), got "
+                f"{confusion_matrix.shape}"
+            )
+        true_positives = confusion_matrix[..., 0]
+        false_positives = confusion_matrix[..., 1]
+
+        denominator = true_positives + false_positives
+        precision = np.where(denominator == 0, 0, true_positives / denominator)
+
+        return precision
+
+    def _detections_content(self, detections: Detections) -> np.ndarray:
+        """Return boxes, masks or oriented bounding boxes from detections."""
+        if self._metric_target == MetricTarget.BOXES:
+            return detections.xyxy
+        if self._metric_target == MetricTarget.MASKS:
+            return (
+                detections.mask
+                if detections.mask is not None
+                else np.empty((0, 0, 0), dtype=bool)
+            )
+        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+            if obb := detections.data.get(ORIENTED_BOX_COORDINATES):
+                return np.ndarray(obb, dtype=np.float32)
+            return np.empty((0, 8), dtype=np.float32)
+        raise ValueError(f"Invalid metric target: {self._metric_target}")
+
+    def _filter_detections_by_size(
+        self, detections: Detections, size_category: ObjectSizeCategory
+    ) -> Detections:
+        """Return a copy of detections with contents filtered by object size."""
+        new_detections = deepcopy(detections)
+        if detections.is_empty() or size_category == ObjectSizeCategory.ANY:
+            return new_detections
+
+        sizes = get_detection_size_category(new_detections, self._metric_target)
+        size_mask = sizes == size_category.value
+
+        new_detections.xyxy = new_detections.xyxy[size_mask]
+        if new_detections.mask is not None:
+            new_detections.mask = new_detections.mask[size_mask]
+        if new_detections.class_id is not None:
+            new_detections.class_id = new_detections.class_id[size_mask]
+        if new_detections.confidence is not None:
+            new_detections.confidence = new_detections.confidence[size_mask]
+        if new_detections.tracker_id is not None:
+            new_detections.tracker_id = new_detections.tracker_id[size_mask]
+        if new_detections.data is not None:
+            for key, value in new_detections.data.items():
+                new_detections.data[key] = np.array(value)[size_mask]
+
+        return new_detections
+
+    def _filter_predictions_and_targets_by_size(
+        self,
+        predictions_list: List[Detections],
+        targets_list: List[Detections],
+        size_category: ObjectSizeCategory,
+    ) -> Tuple[List[Detections], List[Detections]]:
+        """
+        Filter predictions and targets by object size category.
+        """
+        new_predictions_list = []
+        new_targets_list = []
+        for predictions, targets in zip(predictions_list, targets_list):
+            new_predictions_list.append(
+                self._filter_detections_by_size(predictions, size_category)
+            )
+            new_targets_list.append(
+                self._filter_detections_by_size(targets, size_category)
+            )
+        return new_predictions_list, new_targets_list
+
+
+@dataclass
+class PrecisionResult:
+    """
+    The results of the precision metric calculation.
+
+    Defaults to `0` if no detections or targets were provided.
+    Provides a custom `__str__` method for pretty printing.
+
+    Attributes:
+        metric_target (MetricTarget): the type of data used for the metric -
+            boxes, masks or oriented bounding boxes.
+        averaging_method (AveragingMethod): the averaging method used to compute the
+            precision. Determines how the precision is aggregated across classes.
+        precision_at_50 (float): the precision at IoU threshold of `0.5`.
+        precision_at_75 (float): the precision at IoU threshold of `0.75`.
+        precision_scores (np.ndarray): the precision scores at each IoU threshold.
+            Shape: `(num_iou_thresholds,)`
+        precision_per_class (np.ndarray): the precision scores per class and
+            IoU threshold. Shape: `(num_target_classes, num_iou_thresholds)`
+        iou_thresholds (np.ndarray): the IoU thresholds used in the calculations.
+        matched_classes (np.ndarray): the class IDs of all matched classes.
+            Corresponds to the rows of `precision_per_class`.
+        small_objects (Optional[PrecisionResult]): the Precision metric results
+            for small objects.
+        medium_objects (Optional[PrecisionResult]): the Precision metric results
+            for medium objects.
+        large_objects (Optional[PrecisionResult]): the Precision metric results
+            for large objects.
+    """
+
+    metric_target: MetricTarget
+    averaging_method: AveragingMethod
+
+    @property
+    def precision_at_50(self) -> float:
+        return self.precision_scores[0]
+
+    @property
+    def precision_at_75(self) -> float:
+        return self.precision_scores[5]
+
+    precision_scores: np.ndarray
+    precision_per_class: np.ndarray
+    iou_thresholds: np.ndarray
+    matched_classes: np.ndarray
+
+    small_objects: Optional[PrecisionResult]
+    medium_objects: Optional[PrecisionResult]
+    large_objects: Optional[PrecisionResult]
+
+    def __str__(self) -> str:
+        """
+        Format as a pretty string.
+
+        Example:
+            ```python
+            print(precision_result)
+            ```
+        """
+        out_str = (
+            f"{self.__class__.__name__}:\n"
+            f"Metric target:    {self.metric_target}\n"
+            f"Averaging method: {self.averaging_method}\n"
+            f"P @ 50:     {self.precision_at_50:.4f}\n"
+            f"P @ 75:     {self.precision_at_75:.4f}\n"
+            f"P @ thresh: {self.precision_scores}\n"
+            f"IoU thresh: {self.iou_thresholds}\n"
+            f"Precision per class:\n"
+        )
+        if self.precision_per_class.size == 0:
+            out_str += "  No results\n"
+        for class_id, precision_of_class in zip(
+            self.matched_classes, self.precision_per_class
+        ):
+            out_str += f"  {class_id}: {precision_of_class}\n"
+
+        indent = "  "
+        if self.small_objects is not None:
+            indented = indent + str(self.small_objects).replace("\n", f"\n{indent}")
+            out_str += f"\nSmall objects:\n{indented}"
+        if self.medium_objects is not None:
+            indented = indent + str(self.medium_objects).replace("\n", f"\n{indent}")
+            out_str += f"\nMedium objects:\n{indented}"
+        if self.large_objects is not None:
+            indented = indent + str(self.large_objects).replace("\n", f"\n{indent}")
+            out_str += f"\nLarge objects:\n{indented}"
+
+        return out_str
+
+    def to_pandas(self) -> "pd.DataFrame":
+        """
+        Convert the result to a pandas DataFrame.
+
+        Returns:
+            (pd.DataFrame): The result as a DataFrame.
+        """
+        ensure_pandas_installed()
+        import pandas as pd
+
+        pandas_data = {
+            "P@50": self.precision_at_50,
+            "P@75": self.precision_at_75,
+        }
+
+        if self.small_objects is not None:
+            small_objects_df = self.small_objects.to_pandas()
+            for key, value in small_objects_df.items():
+                pandas_data[f"small_objects_{key}"] = value
+        if self.medium_objects is not None:
+            medium_objects_df = self.medium_objects.to_pandas()
+            for key, value in medium_objects_df.items():
+                pandas_data[f"medium_objects_{key}"] = value
+        if self.large_objects is not None:
+            large_objects_df = self.large_objects.to_pandas()
+            for key, value in large_objects_df.items():
+                pandas_data[f"large_objects_{key}"] = value
+
+        return pd.DataFrame(pandas_data, index=[0])
+
+    def plot(self):
+        """
+        Plot the precision results.
+        """
+
+        labels = ["Precision@50", "Precision@75"]
+        values = [self.precision_at_50, self.precision_at_75]
+        colors = [LEGACY_COLOR_PALETTE[0]] * 2
+
+        if self.small_objects is not None:
+            small_objects = self.small_objects
+            labels += ["Small: P@50", "Small: P@75"]
+            values += [small_objects.precision_at_50, small_objects.precision_at_75]
+            colors += [LEGACY_COLOR_PALETTE[3]] * 2
+
+        if self.medium_objects is not None:
+            medium_objects = self.medium_objects
+            labels += ["Medium: P@50", "Medium: P@75"]
+            values += [medium_objects.precision_at_50, medium_objects.precision_at_75]
+            colors += [LEGACY_COLOR_PALETTE[2]] * 2
+
+        if self.large_objects is not None:
+            large_objects = self.large_objects
+            labels += ["Large: P@50", "Large: P@75"]
+            values += [large_objects.precision_at_50, large_objects.precision_at_75]
+            colors += [LEGACY_COLOR_PALETTE[4]] * 2
+
+        plt.rcParams["font.family"] = "monospace"
+
+        _, ax = plt.subplots(figsize=(10, 6))
+        ax.set_ylim(0, 1)
+        ax.set_ylabel("Value", fontweight="bold")
+        title = (
+            f"Precision, by Object Size"
+            f"\n(target: {self.metric_target.value},"
+            f" averaging: {self.averaging_method.value})"
+        )
+        ax.set_title(title, fontweight="bold")
+
+        x_positions = range(len(labels))
+        bars = ax.bar(x_positions, values, color=colors, align="center")
+
+        ax.set_xticks(x_positions)
+        ax.set_xticklabels(labels, rotation=45, ha="right")
+
+        for bar in bars:
+            y_value = bar.get_height()
+            ax.text(
+                bar.get_x() + bar.get_width() / 2,
+                y_value + 0.02,
+                f"{y_value:.2f}",
+                ha="center",
+                va="bottom",
+            )
+
+        plt.rcParams["font.family"] = "sans-serif"
+
+        plt.tight_layout()
+        plt.show()
diff --git a/supervision/metrics/recall.py b/supervision/metrics/recall.py
new file mode 100644
index 00000000..7c90859c
--- /dev/null
+++ b/supervision/metrics/recall.py
@@ -0,0 +1,545 @@
+from __future__ import annotations
+
+from copy import deepcopy
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, List, Optional, Tuple, Union
+
+import numpy as np
+from matplotlib import pyplot as plt
+
+from supervision.config import ORIENTED_BOX_COORDINATES
+from supervision.detection.core import Detections
+from supervision.detection.utils import box_iou_batch, mask_iou_batch
+from supervision.draw.color import LEGACY_COLOR_PALETTE
+from supervision.metrics.core import AveragingMethod, Metric, MetricTarget
+from supervision.metrics.utils.object_size import (
+    ObjectSizeCategory,
+    get_detection_size_category,
+)
+from supervision.metrics.utils.utils import ensure_pandas_installed
+
+if TYPE_CHECKING:
+    import pandas as pd
+
+
+class Recall(Metric):
+    def __init__(
+        self,
+        metric_target: MetricTarget = MetricTarget.BOXES,
+        averaging_method: AveragingMethod = AveragingMethod.WEIGHTED,
+    ):
+        self._metric_target = metric_target
+        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+            raise NotImplementedError(
+                "Recall is not implemented for oriented bounding boxes."
+            )
+
+        self._metric_target = metric_target
+        self.averaging_method = averaging_method
+        self._predictions_list: List[Detections] = []
+        self._targets_list: List[Detections] = []
+
+    def reset(self) -> None:
+        self._predictions_list = []
+        self._targets_list = []
+
+    def update(
+        self,
+        predictions: Union[Detections, List[Detections]],
+        targets: Union[Detections, List[Detections]],
+    ) -> Recall:
+        if not isinstance(predictions, list):
+            predictions = [predictions]
+        if not isinstance(targets, list):
+            targets = [targets]
+
+        if len(predictions) != len(targets):
+            raise ValueError(
+                f"The number of predictions ({len(predictions)}) and"
+                f" targets ({len(targets)}) during the update must be the same."
+            )
+
+        self._predictions_list.extend(predictions)
+        self._targets_list.extend(targets)
+
+        return self
+
+    def compute(self) -> RecallResult:
+        result = self._compute(self._predictions_list, self._targets_list)
+
+        small_predictions, small_targets = self._filter_predictions_and_targets_by_size(
+            self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL
+        )
+        result.small_objects = self._compute(small_predictions, small_targets)
+
+        medium_predictions, medium_targets = (
+            self._filter_predictions_and_targets_by_size(
+                self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM
+            )
+        )
+        result.medium_objects = self._compute(medium_predictions, medium_targets)
+
+        large_predictions, large_targets = self._filter_predictions_and_targets_by_size(
+            self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE
+        )
+        result.large_objects = self._compute(large_predictions, large_targets)
+
+        return result
+
+    def _compute(
+        self, predictions_list: List[Detections], targets_list: List[Detections]
+    ) -> RecallResult:
+        iou_thresholds = np.linspace(0.5, 0.95, 10)
+        stats = []
+
+        for predictions, targets in zip(predictions_list, targets_list):
+            prediction_contents = self._detections_content(predictions)
+            target_contents = self._detections_content(targets)
+
+            if len(targets) > 0:
+                if len(predictions) == 0:
+                    stats.append(
+                        (
+                            np.zeros((0, iou_thresholds.size), dtype=bool),
+                            np.zeros((0,), dtype=np.float32),
+                            np.zeros((0,), dtype=int),
+                            targets.class_id,
+                        )
+                    )
+
+                else:
+                    if self._metric_target == MetricTarget.BOXES:
+                        iou = box_iou_batch(target_contents, prediction_contents)
+                    elif self._metric_target == MetricTarget.MASKS:
+                        iou = mask_iou_batch(target_contents, prediction_contents)
+                    else:
+                        raise NotImplementedError(
+                            "Unsupported metric target for IoU calculation"
+                        )
+
+                    matches = self._match_detection_batch(
+                        predictions.class_id, targets.class_id, iou, iou_thresholds
+                    )
+                    stats.append(
+                        (
+                            matches,
+                            predictions.confidence,
+                            predictions.class_id,
+                            targets.class_id,
+                        )
+                    )
+
+        if not stats:
+            return RecallResult(
+                metric_target=self._metric_target,
+                averaging_method=self.averaging_method,
+                recall_scores=np.zeros(iou_thresholds.shape[0]),
+                recall_per_class=np.zeros((0, iou_thresholds.shape[0])),
+                iou_thresholds=iou_thresholds,
+                matched_classes=np.array([], dtype=int),
+                small_objects=None,
+                medium_objects=None,
+                large_objects=None,
+            )
+
+        concatenated_stats = [np.concatenate(items, 0) for items in zip(*stats)]
+        recall_scores, recall_per_class, unique_classes = (
+            self._compute_recall_for_classes(*concatenated_stats)
+        )
+
+        return RecallResult(
+            metric_target=self._metric_target,
+            averaging_method=self.averaging_method,
+            recall_scores=recall_scores,
+            recall_per_class=recall_per_class,
+            iou_thresholds=iou_thresholds,
+            matched_classes=unique_classes,
+            small_objects=None,
+            medium_objects=None,
+            large_objects=None,
+        )
+
+    def _compute_recall_for_classes(
+        self,
+        matches: np.ndarray,
+        prediction_confidence: np.ndarray,
+        prediction_class_ids: np.ndarray,
+        true_class_ids: np.ndarray,
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        sorted_indices = np.argsort(-prediction_confidence)
+        matches = matches[sorted_indices]
+        prediction_class_ids = prediction_class_ids[sorted_indices]
+        unique_classes, class_counts = np.unique(true_class_ids, return_counts=True)
+
+        # Shape: PxTh,P,C,C -> CxThx3
+        confusion_matrix = self._compute_confusion_matrix(
+            matches, prediction_class_ids, unique_classes, class_counts
+        )
+
+        # Shape: CxThx3 -> CxTh
+        recall_per_class = self._compute_recall(confusion_matrix)
+
+        # Shape: CxTh -> Th
+        if self.averaging_method == AveragingMethod.MACRO:
+            recall_scores = np.mean(recall_per_class, axis=0)
+        elif self.averaging_method == AveragingMethod.MICRO:
+            confusion_matrix_merged = confusion_matrix.sum(0)
+            recall_scores = self._compute_recall(confusion_matrix_merged)
+        elif self.averaging_method == AveragingMethod.WEIGHTED:
+            class_counts = class_counts.astype(np.float32)
+            recall_scores = np.average(recall_per_class, axis=0, weights=class_counts)
+
+        return recall_scores, recall_per_class, unique_classes
+
+    @staticmethod
+    def _match_detection_batch(
+        predictions_classes: np.ndarray,
+        target_classes: np.ndarray,
+        iou: np.ndarray,
+        iou_thresholds: np.ndarray,
+    ) -> np.ndarray:
+        num_predictions, num_iou_levels = (
+            predictions_classes.shape[0],
+            iou_thresholds.shape[0],
+        )
+        correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)
+        correct_class = target_classes[:, None] == predictions_classes
+
+        for i, iou_level in enumerate(iou_thresholds):
+            matched_indices = np.where((iou >= iou_level) & correct_class)
+
+            if matched_indices[0].shape[0]:
+                combined_indices = np.stack(matched_indices, axis=1)
+                iou_values = iou[matched_indices][:, None]
+                matches = np.hstack([combined_indices, iou_values])
+
+                if matched_indices[0].shape[0] > 1:
+                    matches = matches[matches[:, 2].argsort()[::-1]]
+                    matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
+                    matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
+
+                correct[matches[:, 1].astype(int), i] = True
+
+        return correct
+
+    @staticmethod
+    def _compute_confusion_matrix(
+        sorted_matches: np.ndarray,
+        sorted_prediction_class_ids: np.ndarray,
+        unique_classes: np.ndarray,
+        class_counts: np.ndarray,
+    ) -> np.ndarray:
+        """
+        Compute the confusion matrix for each class and IoU threshold.
+
+        Assumes the matches and prediction_class_ids are sorted by confidence
+        in descending order.
+
+        Arguments:
+            sorted_matches: np.ndarray, bool, shape (P, Th), that is True
+                if the prediction is a true positive at the given IoU threshold.
+            sorted_prediction_class_ids: np.ndarray, int, shape (P,), containing
+                the class id for each prediction.
+            unique_classes: np.ndarray, int, shape (C,), containing the unique
+                class ids.
+            class_counts: np.ndarray, int, shape (C,), containing the number
+                of true instances for each class.
+
+        Returns:
+            np.ndarray, shape (C, Th, 3), containing the true positives, false
+                positives, and false negatives for each class and IoU threshold.
+        """
+
+        num_thresholds = sorted_matches.shape[1]
+        num_classes = unique_classes.shape[0]
+
+        confusion_matrix = np.zeros((num_classes, num_thresholds, 3))
+        for class_idx, class_id in enumerate(unique_classes):
+            is_class = sorted_prediction_class_ids == class_id
+            num_true = class_counts[class_idx]
+            num_predictions = is_class.sum()
+
+            if num_predictions == 0:
+                true_positives = np.zeros(num_thresholds)
+                false_positives = np.zeros(num_thresholds)
+                false_negatives = np.full(num_thresholds, num_true)
+            elif num_true == 0:
+                true_positives = np.zeros(num_thresholds)
+                false_positives = np.full(num_thresholds, num_predictions)
+                false_negatives = np.zeros(num_thresholds)
+            else:
+                true_positives = sorted_matches[is_class].sum(0)
+                false_positives = (1 - sorted_matches[is_class]).sum(0)
+                false_negatives = num_true - true_positives
+            confusion_matrix[class_idx] = np.stack(
+                [true_positives, false_positives, false_negatives], axis=1
+            )
+
+        return confusion_matrix
+
+    @staticmethod
+    def _compute_recall(confusion_matrix: np.ndarray) -> np.ndarray:
+        """
+        Broadcastable function, computing the recall from the confusion matrix.
+
+        Arguments:
+            confusion_matrix: np.ndarray, shape (N, ..., 3), where the last dimension
+                contains the true positives, false positives, and false negatives.
+
+        Returns:
+            np.ndarray, shape (N, ...), containing the recall for each element.
+        """
+        if not confusion_matrix.shape[-1] == 3:
+            raise ValueError(
+                f"Confusion matrix must have shape (..., 3), got "
+                f"{confusion_matrix.shape}"
+            )
+        true_positives = confusion_matrix[..., 0]
+        false_negatives = confusion_matrix[..., 2]
+
+        denominator = true_positives + false_negatives
+        recall = np.where(denominator == 0, 0, true_positives / denominator)
+
+        return recall
+
+    def _detections_content(self, detections: Detections) -> np.ndarray:
+        """Return boxes, masks or oriented bounding boxes from detections."""
+        if self._metric_target == MetricTarget.BOXES:
+            return detections.xyxy
+        if self._metric_target == MetricTarget.MASKS:
+            return (
+                detections.mask
+                if detections.mask is not None
+                else np.empty((0, 0, 0), dtype=bool)
+            )
+        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+            if obb := detections.data.get(ORIENTED_BOX_COORDINATES):
+                return np.ndarray(obb, dtype=np.float32)
+            return np.empty((0, 8), dtype=np.float32)
+        raise ValueError(f"Invalid metric target: {self._metric_target}")
+
+    def _filter_detections_by_size(
+        self, detections: Detections, size_category: ObjectSizeCategory
+    ) -> Detections:
+        """Return a copy of detections with contents filtered by object size."""
+        new_detections = deepcopy(detections)
+        if detections.is_empty() or size_category == ObjectSizeCategory.ANY:
+            return new_detections
+
+        sizes = get_detection_size_category(new_detections, self._metric_target)
+        size_mask = sizes == size_category.value
+
+        new_detections.xyxy = new_detections.xyxy[size_mask]
+        if new_detections.mask is not None:
+            new_detections.mask = new_detections.mask[size_mask]
+        if new_detections.class_id is not None:
+            new_detections.class_id = new_detections.class_id[size_mask]
+        if new_detections.confidence is not None:
+            new_detections.confidence = new_detections.confidence[size_mask]
+        if new_detections.tracker_id is not None:
+            new_detections.tracker_id = new_detections.tracker_id[size_mask]
+        if new_detections.data is not None:
+            for key, value in new_detections.data.items():
+                new_detections.data[key] = np.array(value)[size_mask]
+
+        return new_detections
+
+    def _filter_predictions_and_targets_by_size(
+        self,
+        predictions_list: List[Detections],
+        targets_list: List[Detections],
+        size_category: ObjectSizeCategory,
+    ) -> Tuple[List[Detections], List[Detections]]:
+        """
+        Filter predictions and targets by object size category.
+        """
+        new_predictions_list = []
+        new_targets_list = []
+        for predictions, targets in zip(predictions_list, targets_list):
+            new_predictions_list.append(
+                self._filter_detections_by_size(predictions, size_category)
+            )
+            new_targets_list.append(
+                self._filter_detections_by_size(targets, size_category)
+            )
+        return new_predictions_list, new_targets_list
+
+
+@dataclass
+class RecallResult:
+    """
+    The results of the recall metric calculation.
+
+    Defaults to `0` if no detections or targets were provided.
+    Provides a custom `__str__` method for pretty printing.
+
+    Attributes:
+        metric_target (MetricTarget): the type of data used for the metric -
+            boxes, masks or oriented bounding boxes.
+        averaging_method (AveragingMethod): the averaging method used to compute the
+            recall. Determines how the recall is aggregated across classes.
+        recall_at_50 (float): the recall at IoU threshold of `0.5`.
+        recall_at_75 (float): the recall at IoU threshold of `0.75`.
+        recall_scores (np.ndarray): the recall scores at each IoU threshold.
+            Shape: `(num_iou_thresholds,)`
+        recall_per_class (np.ndarray): the recall scores per class and IoU threshold.
+            Shape: `(num_target_classes, num_iou_thresholds)`
+        iou_thresholds (np.ndarray): the IoU thresholds used in the calculations.
+        matched_classes (np.ndarray): the class IDs of all matched classes.
+            Corresponds to the rows of `recall_per_class`.
+        small_objects (Optional[RecallResult]): the Recall metric results
+            for small objects.
+        medium_objects (Optional[RecallResult]): the Recall metric results
+            for medium objects.
+        large_objects (Optional[RecallResult]): the Recall metric results
+            for large objects.
+    """
+
+    metric_target: MetricTarget
+    averaging_method: AveragingMethod
+
+    @property
+    def recall_at_50(self) -> float:
+        return self.recall_scores[0]
+
+    @property
+    def recall_at_75(self) -> float:
+        return self.recall_scores[5]
+
+    recall_scores: np.ndarray
+    recall_per_class: np.ndarray
+    iou_thresholds: np.ndarray
+    matched_classes: np.ndarray
+
+    small_objects: Optional[RecallResult]
+    medium_objects: Optional[RecallResult]
+    large_objects: Optional[RecallResult]
+
+    def __str__(self) -> str:
+        """
+        Format as a pretty string.
+
+        Example:
+            ```python
+            print(recall_result)
+            ```
+        """
+        out_str = (
+            f"{self.__class__.__name__}:\n"
+            f"Metric target:    {self.metric_target}\n"
+            f"Averaging method: {self.averaging_method}\n"
+            f"R @ 50:     {self.recall_at_50:.4f}\n"
+            f"R @ 75:     {self.recall_at_75:.4f}\n"
+            f"R @ thresh: {self.recall_scores}\n"
+            f"IoU thresh: {self.iou_thresholds}\n"
+            f"Recall per class:\n"
+        )
+        if self.recall_per_class.size == 0:
+            out_str += "  No results\n"
+        for class_id, recall_of_class in zip(
+            self.matched_classes, self.recall_per_class
+        ):
+            out_str += f"  {class_id}: {recall_of_class}\n"
+
+        indent = "  "
+        if self.small_objects is not None:
+            indented = indent + str(self.small_objects).replace("\n", f"\n{indent}")
+            out_str += f"\nSmall objects:\n{indented}"
+        if self.medium_objects is not None:
+            indented = indent + str(self.medium_objects).replace("\n", f"\n{indent}")
+            out_str += f"\nMedium objects:\n{indented}"
+        if self.large_objects is not None:
+            indented = indent + str(self.large_objects).replace("\n", f"\n{indent}")
+            out_str += f"\nLarge objects:\n{indented}"
+
+        return out_str
+
+    def to_pandas(self) -> "pd.DataFrame":
+        """
+        Convert the result to a pandas DataFrame.
+
+        Returns:
+            (pd.DataFrame): The result as a DataFrame.
+        """
+        ensure_pandas_installed()
+        import pandas as pd
+
+        pandas_data = {
+            "R@50": self.recall_at_50,
+            "R@75": self.recall_at_75,
+        }
+
+        if self.small_objects is not None:
+            small_objects_df = self.small_objects.to_pandas()
+            for key, value in small_objects_df.items():
+                pandas_data[f"small_objects_{key}"] = value
+        if self.medium_objects is not None:
+            medium_objects_df = self.medium_objects.to_pandas()
+            for key, value in medium_objects_df.items():
+                pandas_data[f"medium_objects_{key}"] = value
+        if self.large_objects is not None:
+            large_objects_df = self.large_objects.to_pandas()
+            for key, value in large_objects_df.items():
+                pandas_data[f"large_objects_{key}"] = value
+
+        return pd.DataFrame(pandas_data, index=[0])
+
+    def plot(self):
+        """
+        Plot the recall results.
+        """
+
+        labels = ["Recall@50", "Recall@75"]
+        values = [self.recall_at_50, self.recall_at_75]
+        colors = [LEGACY_COLOR_PALETTE[0]] * 2
+
+        if self.small_objects is not None:
+            small_objects = self.small_objects
+            labels += ["Small: R@50", "Small: R@75"]
+            values += [small_objects.recall_at_50, small_objects.recall_at_75]
+            colors += [LEGACY_COLOR_PALETTE[3]] * 2
+
+        if self.medium_objects is not None:
+            medium_objects = self.medium_objects
+            labels += ["Medium: R@50", "Medium: R@75"]
+            values += [medium_objects.recall_at_50, medium_objects.recall_at_75]
+            colors += [LEGACY_COLOR_PALETTE[2]] * 2
+
+        if self.large_objects is not None:
+            large_objects = self.large_objects
+            labels += ["Large: R@50", "Large: R@75"]
+            values += [large_objects.recall_at_50, large_objects.recall_at_75]
+            colors += [LEGACY_COLOR_PALETTE[4]] * 2
+
+        plt.rcParams["font.family"] = "monospace"
+
+        _, ax = plt.subplots(figsize=(10, 6))
+        ax.set_ylim(0, 1)
+        ax.set_ylabel("Value", fontweight="bold")
+        title = (
+            f"Recall, by Object Size"
+            f"\n(target: {self.metric_target.value},"
+            f" averaging: {self.averaging_method.value})"
+        )
+        ax.set_title(title, fontweight="bold")
+
+        x_positions = range(len(labels))
+        bars = ax.bar(x_positions, values, color=colors, align="center")
+
+        ax.set_xticks(x_positions)
+        ax.set_xticklabels(labels, rotation=45, ha="right")
+
+        for bar in bars:
+            y_value = bar.get_height()
+            ax.text(
+                bar.get_x() + bar.get_width() / 2,
+                y_value + 0.02,
+                f"{y_value:.2f}",
+                ha="center",
+                va="bottom",
+            )
+
+        plt.rcParams["font.family"] = "sans-serif"
+
+        plt.tight_layout()
+        plt.show()

From fbd96d10c4c6358ddba4bf2bd4d34f498ebfde2a Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 18 Oct 2024 15:15:40 +0300
Subject: [PATCH 088/128] Add new and missing docstrings, examples to metrics,
 new Common section

---
 docs/metrics/common_values.md                 | 20 +++++++
 docs/metrics/precision.md                     |  2 +-
 docs/metrics/recall.md                        |  2 +-
 mkdocs.yml                                    |  1 +
 supervision/metrics/core.py                   | 26 +++++----
 supervision/metrics/f1_score.py               | 55 +++++++++++++++++-
 supervision/metrics/mean_average_precision.py | 44 ++++++++------
 supervision/metrics/precision.py              | 58 ++++++++++++++++++-
 supervision/metrics/recall.py                 | 58 ++++++++++++++++++-
 9 files changed, 231 insertions(+), 35 deletions(-)
 create mode 100644 docs/metrics/common_values.md

diff --git a/docs/metrics/common_values.md b/docs/metrics/common_values.md
new file mode 100644
index 00000000..b7600f3f
--- /dev/null
+++ b/docs/metrics/common_values.md
@@ -0,0 +1,20 @@
+---
+comments: true
+status: new
+---
+
+# Common Values
+
+This page contains supplementary values, types and enums that metrics use.
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.core.MetricTarget">MetricTarget</a></h2>
+</div>
+
+:::supervision.metrics.core.MetricTarget
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.core.AveragingMethod">AveragingMethod</a></h2>
+</div>
+
+:::supervision.metrics.core.AveragingMethod
diff --git a/docs/metrics/precision.md b/docs/metrics/precision.md
index ca318f8f..c704452e 100644
--- a/docs/metrics/precision.md
+++ b/docs/metrics/precision.md
@@ -3,7 +3,7 @@ comments: true
 status: new
 ---
 
-# F1 Score
+# Precision
 
 <div class="md-typeset">
     <h2><a href="#supervision.metrics.precision.Precision">Precision</a></h2>
diff --git a/docs/metrics/recall.md b/docs/metrics/recall.md
index 5baa4d3e..78dde833 100644
--- a/docs/metrics/recall.md
+++ b/docs/metrics/recall.md
@@ -3,7 +3,7 @@ comments: true
 status: new
 ---
 
-# F1 Score
+# Recall
 
 <div class="md-typeset">
     <h2><a href="#supervision.metrics.recall.Recall">Recall</a></h2>
diff --git a/mkdocs.yml b/mkdocs.yml
index a3c9c1ca..b30dbcfc 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -69,6 +69,7 @@ nav:
           - Precision: metrics/precision.md
           - Recall: metrics/recall.md
           - F1 Score: metrics/f1_score.md
+          - Common Values: metrics/common_values.md
           - Legacy Metrics: detection/metrics.md
       - Utils:
           - Video: utils/video.md
diff --git a/supervision/metrics/core.py b/supervision/metrics/core.py
index d1818441..def5999a 100644
--- a/supervision/metrics/core.py
+++ b/supervision/metrics/core.py
@@ -37,9 +37,10 @@ class MetricTarget(Enum):
     """
     Specifies what type of detection is used to compute the metric.
 
-    * BOXES: xyxy bounding boxes
-    * MASKS: Binary masks
-    * ORIENTED_BOUNDING_BOXES: Oriented bounding boxes (OBB)
+    Attributes:
+        BOXES: xyxy bounding boxes
+        MASKS: Binary masks
+        ORIENTED_BOUNDING_BOXES: Oriented bounding boxes (OBB)
     """
 
     BOXES = "boxes"
@@ -54,15 +55,16 @@ class AveragingMethod(Enum):
     Suppose, before returning the final result, a metric is computed for each class.
     How do you combine those to get the final number?
 
-    * MACRO: Calculate the metric for each class and average the results. The simplest
-        averaging method, but it does not take class imbalance into account.
-    * MICRO: Calculate the metric globally by counting the total true positives, false
-        positives, and false negatives. Micro averaging is useful when you want to give
-        more importance to classes with more samples. It's also more appropriate if you
-        have an imbalance in the number of instances per class.
-    * WEIGHTED: Calculate the metric for each class and average the results, weighted by
-        the number of true instances of each class. Use weighted averaging if you want
-        to take class imbalance into account.
+    Attributes:
+        MACRO: Calculate the metric for each class and average the results. The simplest
+            averaging method, but it does not take class imbalance into account.
+        MICRO: Calculate the metric globally by counting the total true positives, false
+            positives, and false negatives. Micro averaging is useful when you want to
+            give more importance to classes with more samples. It's also more
+            appropriate if you have an imbalance in the number of instances per class.
+        WEIGHTED: Calculate the metric for each class and average the results, weighted
+            by the number of true instances of each class. Use weighted averaging if
+            you want to take class imbalance into account.
     """
 
     MACRO = "macro"
diff --git a/supervision/metrics/f1_score.py b/supervision/metrics/f1_score.py
index 2ca5bca5..ba4fcd59 100644
--- a/supervision/metrics/f1_score.py
+++ b/supervision/metrics/f1_score.py
@@ -23,11 +23,45 @@ if TYPE_CHECKING:
 
 
 class F1Score(Metric):
+    """
+    F1 Score is a metric used to evaluate object detection models. It is the harmonic
+    mean of precision and recall, calculated at different IoU thresholds.
+
+    In simple terms, F1 Score is a measure of a model's balance between precision and
+    recall (accuracy and completeness), calculated as:
+
+    `F1 = 2 * (precision * recall) / (precision + recall)`
+
+    Example:
+        ```python
+        import supervision as sv
+        from supervision.metrics import F1Score
+
+        predictions = sv.Detections(...)
+        targets = sv.Detections(...)
+
+        f1_metric = F1Score()
+        f1_result = f1_metric.update(predictions, targets).compute()
+
+        print(f1_result)
+        print(f1_result.f1_50)
+        print(f1_result.small_objects.f1_50)
+        ```
+    """
+
     def __init__(
         self,
         metric_target: MetricTarget = MetricTarget.BOXES,
         averaging_method: AveragingMethod = AveragingMethod.WEIGHTED,
     ):
+        """
+        Initialize the F1Score metric.
+
+        Args:
+            metric_target (MetricTarget): The type of detection data to use.
+            averaging_method (AveragingMethod): The averaging method used to compute the
+                F1 scores. Determines how the F1 scores are aggregated across classes.
+        """
         self._metric_target = metric_target
         if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
             raise NotImplementedError(
@@ -40,6 +74,9 @@ class F1Score(Metric):
         self._targets_list: List[Detections] = []
 
     def reset(self) -> None:
+        """
+        Reset the metric to its initial state, clearing all stored data.
+        """
         self._predictions_list = []
         self._targets_list = []
 
@@ -48,6 +85,16 @@ class F1Score(Metric):
         predictions: Union[Detections, List[Detections]],
         targets: Union[Detections, List[Detections]],
     ) -> F1Score:
+        """
+        Add new predictions and targets to the metric, but do not compute the result.
+
+        Args:
+            predictions (Union[Detections, List[Detections]]): The predicted detections.
+            targets (Union[Detections, List[Detections]]): The target detections.
+
+        Returns:
+            (F1Score): The updated metric instance.
+        """
         if not isinstance(predictions, list):
             predictions = [predictions]
         if not isinstance(targets, list):
@@ -65,6 +112,13 @@ class F1Score(Metric):
         return self
 
     def compute(self) -> F1ScoreResult:
+        """
+        Calculate the F1 score metric based on the stored predictions and ground-truth
+        data, at different IoU thresholds.
+
+        Returns:
+            (F1ScoreResult): The F1 score metric result.
+        """
         result = self._compute(self._predictions_list, self._targets_list)
 
         small_predictions, small_targets = self._filter_predictions_and_targets_by_size(
@@ -373,7 +427,6 @@ class F1ScoreResult:
     The results of the F1 score metric calculation.
 
     Defaults to `0` if no detections or targets were provided.
-    Provides a custom `__str__` method for pretty printing.
 
     Attributes:
         metric_target (MetricTarget): the type of data used for the metric -
diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index dbd60b2e..8cec50c8 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -23,6 +23,27 @@ if TYPE_CHECKING:
 
 
 class MeanAveragePrecision(Metric):
+    """
+    Mean Average Precision (mAP) is a metric used to evaluate object detection models.
+    It is the average of the precision-recall curves at different IoU thresholds.
+
+    Example:
+        ```python
+        import supervision as sv
+        from supervision.metrics import MeanAveragePrecision
+
+        predictions = sv.Detections(...)
+        targets = sv.Detections(...)
+
+        map_metric = MeanAveragePrecision()
+        map_result = map_metric.update(predictions, targets).compute()
+
+        print(map_result)
+        print(map_result.map50_95)
+        map_result.plot()
+        ```
+    """
+
     def __init__(
         self,
         metric_target: MetricTarget = MetricTarget.BOXES,
@@ -47,6 +68,9 @@ class MeanAveragePrecision(Metric):
         self._targets_list: List[Detections] = []
 
     def reset(self) -> None:
+        """
+        Reset the metric to its initial state, clearing all stored data.
+        """
         self._predictions_list = []
         self._targets_list = []
 
@@ -95,26 +119,10 @@ class MeanAveragePrecision(Metric):
     ) -> MeanAveragePrecisionResult:
         """
         Calculate Mean Average Precision based on predicted and ground-truth
-            detections at different thresholds.
+        detections at different thresholds.
 
         Returns:
-            (MeanAveragePrecisionResult): New instance of MeanAveragePrecision.
-
-        Example:
-            ```python
-            import supervision as sv
-            from supervision.metrics import MeanAveragePrecision
-
-            predictions = sv.Detections(...)
-            targets = sv.Detections(...)
-
-            map_metric = MeanAveragePrecision()
-            map_result = map_metric.update(predictions, targets).compute()
-
-            print(map_result)
-            print(map_result.map50_95)
-            map_result.plot()
-            ```
+            (MeanAveragePrecisionResult): The Mean Average Precision result.
         """
         result = self._compute(self._predictions_list, self._targets_list)
 
diff --git a/supervision/metrics/precision.py b/supervision/metrics/precision.py
index ba441831..d915e1f4 100644
--- a/supervision/metrics/precision.py
+++ b/supervision/metrics/precision.py
@@ -23,11 +23,48 @@ if TYPE_CHECKING:
 
 
 class Precision(Metric):
+    """
+    Precision is a metric used to evaluate object detection models. It is the ratio of
+    true positive detections to the total number of predicted detections. We calculate
+    it at different IoU thresholds.
+
+    In simple terms, Precision is a measure of a model's accuracy, calculated as:
+
+    `Precision = TP / (TP + FP)`
+
+    Here, `TP` is the number of true positives (correct detections), and `FP` is the
+    number of false positive detections (detected, but incorrectly).
+
+    Example:
+        ```python
+        import supervision as sv
+        from supervision.metrics import Precision
+
+        predictions = sv.Detections(...)
+        targets = sv.Detections(...)
+
+        precision_metric = Precision()
+        precision_result = precision_metric.update(predictions, targets).compute()
+
+        print(precision_result)
+        print(precision_result.precision_at_50)
+        print(precision_result.small_objects.precision_at_50)
+        ```
+    """
+
     def __init__(
         self,
         metric_target: MetricTarget = MetricTarget.BOXES,
         averaging_method: AveragingMethod = AveragingMethod.WEIGHTED,
     ):
+        """
+        Initialize the Precision metric.
+
+        Args:
+            metric_target (MetricTarget): The type of detection data to use.
+            averaging_method (AveragingMethod): The averaging method used to compute the
+                precision. Determines how the precision is aggregated across classes.
+        """
         self._metric_target = metric_target
         if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
             raise NotImplementedError(
@@ -40,6 +77,9 @@ class Precision(Metric):
         self._targets_list: List[Detections] = []
 
     def reset(self) -> None:
+        """
+        Reset the metric to its initial state, clearing all stored data.
+        """
         self._predictions_list = []
         self._targets_list = []
 
@@ -48,6 +88,16 @@ class Precision(Metric):
         predictions: Union[Detections, List[Detections]],
         targets: Union[Detections, List[Detections]],
     ) -> Precision:
+        """
+        Add new predictions and targets to the metric, but do not compute the result.
+
+        Args:
+            predictions (Union[Detections, List[Detections]]): The predicted detections.
+            targets (Union[Detections, List[Detections]]): The target detections.
+
+        Returns:
+            (Precision): The updated metric instance.
+        """
         if not isinstance(predictions, list):
             predictions = [predictions]
         if not isinstance(targets, list):
@@ -65,6 +115,13 @@ class Precision(Metric):
         return self
 
     def compute(self) -> PrecisionResult:
+        """
+        Calculate the precision metric based on the stored predictions and ground-truth
+        data, at different IoU thresholds.
+
+        Returns:
+            (PrecisionResult): The precision metric result.
+        """
         result = self._compute(self._predictions_list, self._targets_list)
 
         small_predictions, small_targets = self._filter_predictions_and_targets_by_size(
@@ -373,7 +430,6 @@ class PrecisionResult:
     The results of the precision metric calculation.
 
     Defaults to `0` if no detections or targets were provided.
-    Provides a custom `__str__` method for pretty printing.
 
     Attributes:
         metric_target (MetricTarget): the type of data used for the metric -
diff --git a/supervision/metrics/recall.py b/supervision/metrics/recall.py
index 7c90859c..9eae24f8 100644
--- a/supervision/metrics/recall.py
+++ b/supervision/metrics/recall.py
@@ -23,11 +23,48 @@ if TYPE_CHECKING:
 
 
 class Recall(Metric):
+    """
+    Recall is a metric used to evaluate object detection models. It is the ratio of
+    true positive detections to the total number of ground truth instances. We calculate
+    it at different IoU thresholds.
+
+    In simple terms, Recall is a measure of a model's completeness, calculated as:
+
+    `Recall = TP / (TP + FN)`
+
+    Here, `TP` is the number of true positives (correct detections), and `FN` is the
+    number of false negatives (missed detections).
+
+    Example:
+        ```python
+        import supervision as sv
+        from supervision.metrics import Recall
+
+        predictions = sv.Detections(...)
+        targets = sv.Detections(...)
+
+        recall_metric = Recall()
+        recall_result = recall_metric.update(predictions, targets).compute()
+
+        print(recall_result)
+        print(recall_result.recall_at_50)
+        print(recall_result.small_objects.recall_at_50)
+        ```
+    """
+
     def __init__(
         self,
         metric_target: MetricTarget = MetricTarget.BOXES,
         averaging_method: AveragingMethod = AveragingMethod.WEIGHTED,
     ):
+        """
+        Initialize the Recall metric.
+
+        Args:
+            metric_target (MetricTarget): The type of detection data to use.
+            averaging_method (AveragingMethod): The averaging method used to compute the
+                recall. Determines how the recall is aggregated across classes.
+        """
         self._metric_target = metric_target
         if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
             raise NotImplementedError(
@@ -40,6 +77,9 @@ class Recall(Metric):
         self._targets_list: List[Detections] = []
 
     def reset(self) -> None:
+        """
+        Reset the metric to its initial state, clearing all stored data.
+        """
         self._predictions_list = []
         self._targets_list = []
 
@@ -48,6 +88,16 @@ class Recall(Metric):
         predictions: Union[Detections, List[Detections]],
         targets: Union[Detections, List[Detections]],
     ) -> Recall:
+        """
+        Add new predictions and targets to the metric, but do not compute the result.
+
+        Args:
+            predictions (Union[Detections, List[Detections]]): The predicted detections.
+            targets (Union[Detections, List[Detections]]): The target detections.
+
+        Returns:
+            (Recall): The updated metric instance.
+        """
         if not isinstance(predictions, list):
             predictions = [predictions]
         if not isinstance(targets, list):
@@ -65,6 +115,13 @@ class Recall(Metric):
         return self
 
     def compute(self) -> RecallResult:
+        """
+        Calculate the precision metric based on the stored predictions and ground-truth
+        data, at different IoU thresholds.
+
+        Returns:
+            (RecallResult): The precision metric result.
+        """
         result = self._compute(self._predictions_list, self._targets_list)
 
         small_predictions, small_targets = self._filter_predictions_and_targets_by_size(
@@ -371,7 +428,6 @@ class RecallResult:
     The results of the recall metric calculation.
 
     Defaults to `0` if no detections or targets were provided.
-    Provides a custom `__str__` method for pretty printing.
 
     Attributes:
         metric_target (MetricTarget): the type of data used for the metric -

From 3e8a88a8d4ba3d31850788af839e469410a86961 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 18 Oct 2024 15:28:45 +0300
Subject: [PATCH 089/128] Add Precision and Recall to metrics __init__

---
 supervision/metrics/__init__.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/supervision/metrics/__init__.py b/supervision/metrics/__init__.py
index 8ae33e63..90fc17b4 100644
--- a/supervision/metrics/__init__.py
+++ b/supervision/metrics/__init__.py
@@ -8,6 +8,8 @@ from supervision.metrics.mean_average_precision import (
     MeanAveragePrecision,
     MeanAveragePrecisionResult,
 )
+from supervision.metrics.precision import Precision, PrecisionResult
+from supervision.metrics.recall import Recall, RecallResult
 from supervision.metrics.utils.object_size import (
     ObjectSizeCategory,
     get_detection_size_category,

From 74ae1eec87aa27d7109900169f3e7eeea3f797c0 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 21 Oct 2024 00:48:47 +0000
Subject: [PATCH 090/128] :arrow_up: Bump mypy from 1.12.0 to 1.12.1

Bumps [mypy](https://github.com/python/mypy) from 1.12.0 to 1.12.1.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.12.0...v1.12.1)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 66 ++++++++++++++++++++++++++---------------------------
 1 file changed, 33 insertions(+), 33 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index e1767ce1..f43db482 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2509,43 +2509,43 @@ files = [
 
 [[package]]
 name = "mypy"
-version = "1.12.0"
+version = "1.12.1"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "mypy-1.12.1-py3-none-any.whl", hash = "sha256:ce561a09e3bb9863ab77edf29ae3a50e65685ad74bba1431278185b7e5d5486e"},
+    {file = "mypy-1.12.1.tar.gz", hash = "sha256:f5b3936f7a6d0e8280c9bdef94c7ce4847f5cdfc258fbb2c29a8c1711e8bb96d"},
 ]
 
 [package.dependencies]

From 2efe3030cb498ee277ab7bb072fe41e45295774d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 21 Oct 2024 00:52:04 +0000
Subject: [PATCH 091/128] :arrow_up: Bump mkdocs-material from 9.5.41 to 9.5.42

Bumps [mkdocs-material](https://github.com/squidfunk/mkdocs-material) from 9.5.41 to 9.5.42.
- [Release notes](https://github.com/squidfunk/mkdocs-material/releases)
- [Changelog](https://github.com/squidfunk/mkdocs-material/blob/master/CHANGELOG)
- [Commits](https://github.com/squidfunk/mkdocs-material/compare/9.5.41...9.5.42)

---
updated-dependencies:
- dependency-name: mkdocs-material
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index e1767ce1..548de46a 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2412,13 +2412,13 @@ pygments = ">2.12.0"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.41"
+version = "9.5.42"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.5.41-py3-none-any.whl", hash = "sha256:990bc138c33342b5b73e7545915ebc0136e501bfbd8e365735144f5120891d83"},
-    {file = "mkdocs_material-9.5.41.tar.gz", hash = "sha256:30fa5d459b4b8130848ecd8e1c908878345d9d8268f7ddbc31eebe88d462d97b"},
+    {file = "mkdocs_material-9.5.42-py3-none-any.whl", hash = "sha256:452a7c5d21284b373f36b981a2cbebfff59263feebeede1bc28652e9c5bbe316"},
+    {file = "mkdocs_material-9.5.42.tar.gz", hash = "sha256:92779b5e9b5934540c574c11647131d217dc540dce72b05feeda088c8eb1b8f2"},
 ]
 
 [package.dependencies]

From 40ef90c76e3b91aef47253ccfc5e86199b510119 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 21 Oct 2024 17:55:36 +0000
Subject: [PATCH 092/128] =?UTF-8?q?chore(pre=5Fcommit):=20=E2=AC=86=20pre?=
 =?UTF-8?q?=5Fcommit=20autoupdate?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.6.9 → v0.7.0](https://github.com/astral-sh/ruff-pre-commit/compare/v0.6.9...v0.7.0)
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 2ca4357c..8ddaa08e 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -32,7 +32,7 @@ repos:
           additional_dependencies: ["bandit[toml]"]
 
   -   repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.6.9
+      rev: v0.7.0
       hooks:
       -   id: ruff
           args: [--fix, --exit-non-zero-on-fix]

From abd149fa1a6f65cd883e5328650a7b9ef6f77eb1 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 23 Oct 2024 00:36:13 +0000
Subject: [PATCH 093/128] :arrow_up: Bump tox from 4.23.0 to 4.23.2

Bumps [tox](https://github.com/tox-dev/tox) from 4.23.0 to 4.23.2.
- [Release notes](https://github.com/tox-dev/tox/releases)
- [Changelog](https://github.com/tox-dev/tox/blob/main/docs/changelog.rst)
- [Commits](https://github.com/tox-dev/tox/compare/4.23.0...4.23.2)

---
updated-dependencies:
- dependency-name: tox
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 088b34e5..8d73d1bb 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -4377,13 +4377,13 @@ files = [
 
 [[package]]
 name = "tox"
-version = "4.23.0"
+version = "4.23.2"
 description = "tox is a generic virtualenv management and test command line tool"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "tox-4.23.0-py3-none-any.whl", hash = "sha256:46da40afb660e46238c251280eb910bdaf00b390c7557c8e4bb611f422e9db12"},
-    {file = "tox-4.23.0.tar.gz", hash = "sha256:a6bd7d54231d755348d3c3a7b450b5bf6563833716d1299a1619587a1b77a3bf"},
+    {file = "tox-4.23.2-py3-none-any.whl", hash = "sha256:452bc32bb031f2282881a2118923176445bac783ab97c874b8770ab4c3b76c38"},
+    {file = "tox-4.23.2.tar.gz", hash = "sha256:86075e00e555df6e82e74cfc333917f91ecb47ffbc868dcafbd2672e332f4a2c"},
 ]
 
 [package.dependencies]
@@ -4399,6 +4399,9 @@ tomli = {version = ">=2.0.1", markers = "python_version < \"3.11\""}
 typing-extensions = {version = ">=4.12.2", markers = "python_version < \"3.11\""}
 virtualenv = ">=20.26.6"
 
+[package.extras]
+test = ["devpi-process (>=1.0.2)", "pytest (>=8.3.3)", "pytest-mock (>=3.14)"]
+
 [[package]]
 name = "tqdm"
 version = "4.66.5"

From 6c7ee3722536f01c8c15b02e81c64e1459881467 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 23 Oct 2024 00:37:04 +0000
Subject: [PATCH 094/128] :arrow_up: Bump mypy from 1.12.1 to 1.13.0

Bumps [mypy](https://github.com/python/mypy) from 1.12.1 to 1.13.0.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.12.1...v1.13.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 67 +++++++++++++++++++++++++++--------------------------
 1 file changed, 34 insertions(+), 33 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 088b34e5..29649128 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2509,43 +2509,43 @@ files = [
 
 [[package]]
 name = "mypy"
-version = "1.12.1"
+version = "1.13.0"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "mypy-1.13.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bde31fc887c213e223bbfc34328070996061b0833b0a4cfec53745ed61f3519b"},
+    {file = "mypy-1.13.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:07de989f89786f62b937851295ed62e51774722e5444a27cecca993fc3f9cd74"},
+    {file = "mypy-1.13.0-cp38-cp38-win_amd64.whl", hash = "sha256:4bde84334fbe19bad704b3f5b78c4abd35ff1026f8ba72b29de70dda0916beb6"},
+    {file = "mypy-1.13.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:0246bcb1b5de7f08f2826451abd947bf656945209b140d16ed317f65a17dc7dc"},
+    {file = "mypy-1.13.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:7f5b7deae912cf8b77e990b9280f170381fdfbddf61b4ef80927edd813163732"},
+    {file = "mypy-1.13.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7029881ec6ffb8bc233a4fa364736789582c738217b133f1b55967115288a2bc"},
+    {file = "mypy-1.13.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:3e38b980e5681f28f033f3be86b099a247b13c491f14bb8b1e1e134d23bb599d"},
+    {file = "mypy-1.13.0-cp39-cp39-win_amd64.whl", hash = "sha256:a6789be98a2017c912ae6ccb77ea553bbaf13d27605d2ca20a76dfbced631b24"},
+    {file = "mypy-1.13.0-py3-none-any.whl", hash = "sha256:9c250883f9fd81d212e0952c92dbfcc96fc237f4b7c92f56ac81fd48460b3e5a"},
+    {file = "mypy-1.13.0.tar.gz", hash = "sha256:0291a61b6fbf3e6673e3405cfcc0e7650bebc7939659fdca2702958038bd835e"},
 ]
 
 [package.dependencies]
@@ -2555,6 +2555,7 @@ typing-extensions = ">=4.6.0"
 
 [package.extras]
 dmypy = ["psutil (>=4.0)"]
+faster-cache = ["orjson"]
 install-types = ["pip"]
 mypyc = ["setuptools (>=50)"]
 reports = ["lxml"]

From cf3c1e4e1022a48396a902107d9f3c0e852d423d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 23 Oct 2024 00:37:54 +0000
Subject: [PATCH 095/128] :arrow_up: Bump
 mkdocs-git-revision-date-localized-plugin

Bumps [mkdocs-git-revision-date-localized-plugin](https://github.com/timvink/mkdocs-git-revision-date-localized-plugin) from 1.2.9 to 1.3.0.
- [Release notes](https://github.com/timvink/mkdocs-git-revision-date-localized-plugin/releases)
- [Commits](https://github.com/timvink/mkdocs-git-revision-date-localized-plugin/compare/v1.2.9...v1.3.0)

---
updated-dependencies:
- dependency-name: mkdocs-git-revision-date-localized-plugin
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 088b34e5..792bc836 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2371,13 +2371,13 @@ requests = "*"
 
 [[package]]
 name = "mkdocs-git-revision-date-localized-plugin"
-version = "1.2.9"
+version = "1.3.0"
 description = "Mkdocs plugin that enables displaying the localized date of the last git modification of a markdown file."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_git_revision_date_localized_plugin-1.2.9-py3-none-any.whl", hash = "sha256:dea5c8067c23df30275702a1708885500fadf0abfb595b60e698bffc79c7a423"},
-    {file = "mkdocs_git_revision_date_localized_plugin-1.2.9.tar.gz", hash = "sha256:df9a50873fba3a42ce9123885f8c53d589e90ef6c2443fe3280ef1e8d33c8f65"},
+    {file = "mkdocs_git_revision_date_localized_plugin-1.3.0-py3-none-any.whl", hash = "sha256:c99377ee119372d57a9e47cff4e68f04cce634a74831c06bc89b33e456e840a1"},
+    {file = "mkdocs_git_revision_date_localized_plugin-1.3.0.tar.gz", hash = "sha256:439e2f14582204050a664c258861c325064d97cdc848c541e48bb034a6c4d0cb"},
 ]
 
 [package.dependencies]

From dbd4aa7798c10e3acf73cd5ee01008614a07dc7d Mon Sep 17 00:00:00 2001
From: Kader Miyanyedi <Kadermiyanyedi@hotmail.com>
Date: Fri, 18 Oct 2024 16:07:42 +0300
Subject: [PATCH 096/128] feat(ByteTrack): Allow ByteTrack to track detection
 without class ids

---
 supervision/tracker/byte_tracker/core.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index d1d567fa..f453e523 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -276,11 +276,16 @@ class ByteTrack:
             ```
         """
 
+        num_rows = detections.xyxy.shape[0]
+        class_ids = np.full(num_rows, -5)
+        if detections.class_id is not None:
+            class_ids = detections.class_id
+
         tensors = np.hstack(
             (
                 detections.xyxy,
                 detections.confidence[:, np.newaxis],
-                detections.class_id[:, np.newaxis],
+                class_ids[:, np.newaxis],
             )
         )
         tracks = self.update_with_tensors(tensors=tensors)

From 40e16118f8efe7f8cdce455f96fd5d436ed07b92 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Fri, 25 Oct 2024 00:37:54 +0000
Subject: [PATCH 097/128] :arrow_up: Bump ruff from 0.7.0 to 0.7.1

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.7.0 to 0.7.1.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.7.0...0.7.1)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 38 +++++++++++++++++++-------------------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 0a010aa4..82ab2f08 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -4037,29 +4037,29 @@ files = [
 
 [[package]]
 name = "ruff"
-version = "0.7.0"
+version = "0.7.1"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ruff-0.7.0-py3-none-linux_armv6l.whl", hash = "sha256:0cdf20c2b6ff98e37df47b2b0bd3a34aaa155f59a11182c1303cce79be715628"},
-    {file = "ruff-0.7.0-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:496494d350c7fdeb36ca4ef1c9f21d80d182423718782222c29b3e72b3512737"},
-    {file = "ruff-0.7.0-py3-none-macosx_11_0_arm64.whl", hash = "sha256:214b88498684e20b6b2b8852c01d50f0651f3cc6118dfa113b4def9f14faaf06"},
-    {file = "ruff-0.7.0-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:630fce3fefe9844e91ea5bbf7ceadab4f9981f42b704fae011bb8efcaf5d84be"},
-    {file = "ruff-0.7.0-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:211d877674e9373d4bb0f1c80f97a0201c61bcd1e9d045b6e9726adc42c156aa"},
-    {file = "ruff-0.7.0-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:194d6c46c98c73949a106425ed40a576f52291c12bc21399eb8f13a0f7073495"},
-    {file = "ruff-0.7.0-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:82c2579b82b9973a110fab281860403b397c08c403de92de19568f32f7178598"},
-    {file = "ruff-0.7.0-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9af971fe85dcd5eaed8f585ddbc6bdbe8c217fb8fcf510ea6bca5bdfff56040e"},
-    {file = "ruff-0.7.0-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b641c7f16939b7d24b7bfc0be4102c56562a18281f84f635604e8a6989948914"},
-    {file = "ruff-0.7.0-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d71672336e46b34e0c90a790afeac8a31954fd42872c1f6adaea1dff76fd44f9"},
-    {file = "ruff-0.7.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:ab7d98c7eed355166f367597e513a6c82408df4181a937628dbec79abb2a1fe4"},
-    {file = "ruff-0.7.0-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:1eb54986f770f49edb14f71d33312d79e00e629a57387382200b1ef12d6a4ef9"},
-    {file = "ruff-0.7.0-py3-none-musllinux_1_2_i686.whl", hash = "sha256:dc452ba6f2bb9cf8726a84aa877061a2462afe9ae0ea1d411c53d226661c601d"},
-    {file = "ruff-0.7.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:4b406c2dce5be9bad59f2de26139a86017a517e6bcd2688da515481c05a2cb11"},
-    {file = "ruff-0.7.0-py3-none-win32.whl", hash = "sha256:f6c968509f767776f524a8430426539587d5ec5c662f6addb6aa25bc2e8195ec"},
-    {file = "ruff-0.7.0-py3-none-win_amd64.whl", hash = "sha256:ff4aabfbaaba880e85d394603b9e75d32b0693152e16fa659a3064a85df7fce2"},
-    {file = "ruff-0.7.0-py3-none-win_arm64.whl", hash = "sha256:10842f69c245e78d6adec7e1db0a7d9ddc2fff0621d730e61657b64fa36f207e"},
-    {file = "ruff-0.7.0.tar.gz", hash = "sha256:47a86360cf62d9cd53ebfb0b5eb0e882193fc191c6d717e8bef4462bc3b9ea2b"},
+    {file = "ruff-0.7.1-py3-none-linux_armv6l.whl", hash = "sha256:cb1bc5ed9403daa7da05475d615739cc0212e861b7306f314379d958592aaa89"},
+    {file = "ruff-0.7.1-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:27c1c52a8d199a257ff1e5582d078eab7145129aa02721815ca8fa4f9612dc35"},
+    {file = "ruff-0.7.1-py3-none-macosx_11_0_arm64.whl", hash = "sha256:588a34e1ef2ea55b4ddfec26bbe76bc866e92523d8c6cdec5e8aceefeff02d99"},
+    {file = "ruff-0.7.1-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:94fc32f9cdf72dc75c451e5f072758b118ab8100727168a3df58502b43a599ca"},
+    {file = "ruff-0.7.1-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:985818742b833bffa543a84d1cc11b5e6871de1b4e0ac3060a59a2bae3969250"},
+    {file = "ruff-0.7.1-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:32f1e8a192e261366c702c5fb2ece9f68d26625f198a25c408861c16dc2dea9c"},
+    {file = "ruff-0.7.1-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:699085bf05819588551b11751eff33e9ca58b1b86a6843e1b082a7de40da1565"},
+    {file = "ruff-0.7.1-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:344cc2b0814047dc8c3a8ff2cd1f3d808bb23c6658db830d25147339d9bf9ea7"},
+    {file = "ruff-0.7.1-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4316bbf69d5a859cc937890c7ac7a6551252b6a01b1d2c97e8fc96e45a7c8b4a"},
+    {file = "ruff-0.7.1-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:79d3af9dca4c56043e738a4d6dd1e9444b6d6c10598ac52d146e331eb155a8ad"},
+    {file = "ruff-0.7.1-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:c5c121b46abde94a505175524e51891f829414e093cd8326d6e741ecfc0a9112"},
+    {file = "ruff-0.7.1-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:8422104078324ea250886954e48f1373a8fe7de59283d747c3a7eca050b4e378"},
+    {file = "ruff-0.7.1-py3-none-musllinux_1_2_i686.whl", hash = "sha256:56aad830af8a9db644e80098fe4984a948e2b6fc2e73891538f43bbe478461b8"},
+    {file = "ruff-0.7.1-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:658304f02f68d3a83c998ad8bf91f9b4f53e93e5412b8f2388359d55869727fd"},
+    {file = "ruff-0.7.1-py3-none-win32.whl", hash = "sha256:b517a2011333eb7ce2d402652ecaa0ac1a30c114fbbd55c6b8ee466a7f600ee9"},
+    {file = "ruff-0.7.1-py3-none-win_amd64.whl", hash = "sha256:f38c41fcde1728736b4eb2b18850f6d1e3eedd9678c914dede554a70d5241307"},
+    {file = "ruff-0.7.1-py3-none-win_arm64.whl", hash = "sha256:19aa200ec824c0f36d0c9114c8ec0087082021732979a359d6f3c390a6ff2a37"},
+    {file = "ruff-0.7.1.tar.gz", hash = "sha256:9d8a41d4aa2dad1575adb98a82870cf5db5f76b2938cf2206c22c940034a36f4"},
 ]
 
 [[package]]

From b4d93ce6c90310c97cea561b0b3b5d881a6204e5 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 28 Oct 2024 18:02:23 +0000
Subject: [PATCH 098/128] =?UTF-8?q?chore(pre=5Fcommit):=20=E2=AC=86=20pre?=
 =?UTF-8?q?=5Fcommit=20autoupdate?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.7.0 → v0.7.1](https://github.com/astral-sh/ruff-pre-commit/compare/v0.7.0...v0.7.1)
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 8ddaa08e..39c50e82 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -32,7 +32,7 @@ repos:
           additional_dependencies: ["bandit[toml]"]
 
   -   repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.7.0
+      rev: v0.7.1
       hooks:
       -   id: ruff
           args: [--fix, --exit-non-zero-on-fix]

From b26e8f58144fa75cc14032d11bff1e3fbaa77262 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 29 Oct 2024 00:12:10 +0000
Subject: [PATCH 099/128] :arrow_up: Bump tqdm from 4.66.5 to 4.66.6

Bumps [tqdm](https://github.com/tqdm/tqdm) from 4.66.5 to 4.66.6.
- [Release notes](https://github.com/tqdm/tqdm/releases)
- [Commits](https://github.com/tqdm/tqdm/compare/v4.66.5...v4.66.6)

---
updated-dependencies:
- dependency-name: tqdm
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock    | 8 ++++----
 pyproject.toml | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 82ab2f08..69775617 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -4405,13 +4405,13 @@ test = ["devpi-process (>=1.0.2)", "pytest (>=8.3.3)", "pytest-mock (>=3.14)"]
 
 [[package]]
 name = "tqdm"
-version = "4.66.5"
+version = "4.66.6"
 description = "Fast, Extensible Progress Meter"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.66.5-py3-none-any.whl", hash = "sha256:90279a3770753eafc9194a0364852159802111925aa30eb3f9d85b0e805ac7cd"},
-    {file = "tqdm-4.66.5.tar.gz", hash = "sha256:e1020aef2e5096702d8a025ac7d16b1577279c9d63f8375b63083e9a5f0fcbad"},
+    {file = "tqdm-4.66.6-py3-none-any.whl", hash = "sha256:223e8b5359c2efc4b30555531f09e9f2f3589bcd7fdd389271191031b49b7a63"},
+    {file = "tqdm-4.66.6.tar.gz", hash = "sha256:4bdd694238bef1485ce839d67967ab50af8f9272aab687c0d7702a01da0be090"},
 ]
 
 [package.dependencies]
@@ -4781,4 +4781,4 @@ metrics = ["pandas", "pandas-stubs"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8"
-content-hash = "fe9efd8caf098dc0301e1d7007ac3b00647b48cff6b060cc0eff57656d082099"
+content-hash = "8f7dad5406a294901e3f489cf0d09e8217a80597ba9cd82695822a3fb5c13034"
diff --git a/pyproject.toml b/pyproject.toml
index 6464346b..713bd838 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -71,7 +71,7 @@ pyyaml = ">=5.3"
 defusedxml = "^0.7.1"
 pillow = ">=9.4"
 requests = { version = ">=2.26.0,<=2.32.3", optional = true }
-tqdm = { version = ">=4.62.3,<=4.66.5", optional = true }
+tqdm = { version = ">=4.62.3,<=4.66.6", optional = true }
 # pandas: picked lowest major version that supports Python 3.8
 pandas = { version = ">=2.0.0", optional = true }
 pandas-stubs = { version = ">=2.0.0.230412", optional = true }

From 02735848b5cf19224842255017a8282c2e80114c Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Thu, 31 Oct 2024 15:42:04 +0200
Subject: [PATCH 100/128] Undo style, class agnostic, mAP regressions. Fix
 empty OBB object shape

---
 supervision/metrics/mean_average_precision.py | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index 87de7327..d5f66103 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -61,9 +61,6 @@ class MeanAveragePrecision(Metric):
             class_agnostic (bool): Whether to treat all data as a single class.
         """
         self._metric_target = metric_target
-        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
-            pass
-
         self._class_agnostic = class_agnostic
 
         self._predictions_list: List[Detections] = []
@@ -203,6 +200,7 @@ class MeanAveragePrecision(Metric):
                     matches = self._match_detection_batch(
                         predictions.class_id, targets.class_id, iou, iou_thresholds
                     )
+
                     stats.append(
                         (
                             matches,
@@ -226,6 +224,7 @@ class MeanAveragePrecision(Metric):
 
         return MeanAveragePrecisionResult(
             metric_target=self._metric_target,
+            is_class_agnostic=self._class_agnostic,
             mAP_scores=mAP_scores,
             iou_thresholds=iou_thresholds,
             matched_classes=unique_classes,
@@ -253,7 +252,7 @@ class MeanAveragePrecision(Metric):
         for r, p in zip(recall[::-1], precision[::-1]):
             precision_levels[recall_levels <= r] = p
 
-        average_precision = (1 / 100 * precision_levels).sum()
+        average_precision = (1 / 101 * precision_levels).sum()
         return average_precision
 
     @staticmethod
@@ -367,7 +366,7 @@ class MeanAveragePrecision(Metric):
         if self._metric_target == MetricTarget.MASKS:
             return np.empty((0, 0, 0), dtype=bool)
         if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
-            return np.empty((0, 8), dtype=np.float32)
+            return np.empty((0, 4, 2), dtype=np.float32)
         raise ValueError(f"Invalid metric target: {self._metric_target}")
 
     def _filter_detections_by_size(
@@ -407,6 +406,8 @@ class MeanAveragePrecisionResult:
     Attributes:
         metric_target (MetricTarget): the type of data used for the metric -
             boxes, masks or oriented bounding boxes.
+        class_agnostic (bool): When computing class-agnostic results, class ID
+            is set to `-1`.
         mAP_map50_95 (float): the mAP score at IoU thresholds from `0.5` to `0.95`.
         mAP_map50 (float): the mAP score at IoU threshold of `0.5`.
         mAP_map75 (float): the mAP score at IoU threshold of `0.75`.
@@ -426,6 +427,7 @@ class MeanAveragePrecisionResult:
     """
 
     metric_target: MetricTarget
+    is_class_agnostic: bool
 
     @property
     def map50_95(self) -> float:
@@ -460,6 +462,7 @@ class MeanAveragePrecisionResult:
         out_str = (
             f"{self.__class__.__name__}:\n"
             f"Metric target: {self.metric_target}\n"
+            f"Class agnostic: {self.is_class_agnostic}\n"
             f"mAP @ 50:95: {self.map50_95:.4f}\n"
             f"mAP @ 50:    {self.map50:.4f}\n"
             f"mAP @ 75:    {self.map75:.4f}\n"

From 37f0ad8b1432401704b79f48311724a19d73aaca Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Thu, 31 Oct 2024 15:57:31 +0200
Subject: [PATCH 101/128] Add OBB support to F1 Score

---
 supervision/metrics/f1_score.py               | 37 +++++++++++++------
 supervision/metrics/mean_average_precision.py |  2 +-
 2 files changed, 26 insertions(+), 13 deletions(-)

diff --git a/supervision/metrics/f1_score.py b/supervision/metrics/f1_score.py
index ba4fcd59..cc8c87a2 100644
--- a/supervision/metrics/f1_score.py
+++ b/supervision/metrics/f1_score.py
@@ -9,7 +9,11 @@ from matplotlib import pyplot as plt
 
 from supervision.config import ORIENTED_BOX_COORDINATES
 from supervision.detection.core import Detections
-from supervision.detection.utils import box_iou_batch, mask_iou_batch
+from supervision.detection.utils import (
+    box_iou_batch,
+    mask_iou_batch,
+    oriented_box_iou_batch,
+)
 from supervision.draw.color import LEGACY_COLOR_PALETTE
 from supervision.metrics.core import AveragingMethod, Metric, MetricTarget
 from supervision.metrics.utils.object_size import (
@@ -62,14 +66,9 @@ class F1Score(Metric):
             averaging_method (AveragingMethod): The averaging method used to compute the
                 F1 scores. Determines how the F1 scores are aggregated across classes.
         """
-        self._metric_target = metric_target
-        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
-            raise NotImplementedError(
-                "F1 score is not implemented for oriented bounding boxes."
-            )
-
         self._metric_target = metric_target
         self.averaging_method = averaging_method
+
         self._predictions_list: List[Detections] = []
         self._targets_list: List[Detections] = []
 
@@ -166,8 +165,12 @@ class F1Score(Metric):
                         iou = box_iou_batch(target_contents, prediction_contents)
                     elif self._metric_target == MetricTarget.MASKS:
                         iou = mask_iou_batch(target_contents, prediction_contents)
+                    elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+                        iou = oriented_box_iou_batch(
+                            target_contents, prediction_contents
+                        )
                     else:
-                        raise NotImplementedError(
+                        raise ValueError(
                             "Unsupported metric target for IoU calculation"
                         )
 
@@ -366,12 +369,22 @@ class F1Score(Metric):
             return (
                 detections.mask
                 if detections.mask is not None
-                else np.empty((0, 0, 0), dtype=bool)
+                else self._make_empty_content()
             )
         if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
-            if obb := detections.data.get(ORIENTED_BOX_COORDINATES):
-                return np.ndarray(obb, dtype=np.float32)
-            return np.empty((0, 8), dtype=np.float32)
+            obb = detections.data.get(ORIENTED_BOX_COORDINATES)
+            if obb is not None and len(obb) > 0:
+                return np.array(obb, dtype=np.float32)
+            return self._make_empty_content()
+        raise ValueError(f"Invalid metric target: {self._metric_target}")
+
+    def _make_empty_content(self) -> np.ndarray:
+        if self._metric_target == MetricTarget.BOXES:
+            return np.empty((0, 4), dtype=np.float32)
+        if self._metric_target == MetricTarget.MASKS:
+            return np.empty((0, 0, 0), dtype=bool)
+        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+            return np.empty((0, 4, 2), dtype=np.float32)
         raise ValueError(f"Invalid metric target: {self._metric_target}")
 
     def _filter_detections_by_size(
diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index d5f66103..ba37837b 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -193,7 +193,7 @@ class MeanAveragePrecision(Metric):
                             target_contents, prediction_contents
                         )
                     else:
-                        raise NotImplementedError(
+                        raise ValueError(
                             "Unsupported metric target for IoU calculation"
                         )
 

From 6f3491f03ce922d33f36765d2f4ecb2da93840e9 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Thu, 31 Oct 2024 17:18:08 +0200
Subject: [PATCH 102/128] Fix issue in polygon_to_mask - expects a np.int32
 datatype

---
 supervision/detection/utils.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/supervision/detection/utils.py b/supervision/detection/utils.py
index a0a4d3e2..69cdacc7 100644
--- a/supervision/detection/utils.py
+++ b/supervision/detection/utils.py
@@ -25,8 +25,7 @@ def polygon_to_mask(polygon: np.ndarray, resolution_wh: Tuple[int, int]) -> np.n
     """
     width, height = map(int, resolution_wh)
     mask = np.zeros((height, width), dtype=np.uint8)
-
-    cv2.fillPoly(mask, [polygon], color=1)
+    cv2.fillPoly(mask, [polygon.astype(np.int32)], color=1)
     return mask
 
 

From d1c07bc106d9f8fe24cad933fed10382748cfa32 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Thu, 31 Oct 2024 18:05:54 +0200
Subject: [PATCH 103/128] Add OBB support to precision and recall

---
 supervision/metrics/precision.py | 37 +++++++++++++++++++++-----------
 supervision/metrics/recall.py    | 37 +++++++++++++++++++++-----------
 2 files changed, 50 insertions(+), 24 deletions(-)

diff --git a/supervision/metrics/precision.py b/supervision/metrics/precision.py
index d915e1f4..fa6cf2b1 100644
--- a/supervision/metrics/precision.py
+++ b/supervision/metrics/precision.py
@@ -9,7 +9,11 @@ from matplotlib import pyplot as plt
 
 from supervision.config import ORIENTED_BOX_COORDINATES
 from supervision.detection.core import Detections
-from supervision.detection.utils import box_iou_batch, mask_iou_batch
+from supervision.detection.utils import (
+    box_iou_batch,
+    mask_iou_batch,
+    oriented_box_iou_batch,
+)
 from supervision.draw.color import LEGACY_COLOR_PALETTE
 from supervision.metrics.core import AveragingMethod, Metric, MetricTarget
 from supervision.metrics.utils.object_size import (
@@ -65,14 +69,9 @@ class Precision(Metric):
             averaging_method (AveragingMethod): The averaging method used to compute the
                 precision. Determines how the precision is aggregated across classes.
         """
-        self._metric_target = metric_target
-        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
-            raise NotImplementedError(
-                "Precision is not implemented for oriented bounding boxes."
-            )
-
         self._metric_target = metric_target
         self.averaging_method = averaging_method
+
         self._predictions_list: List[Detections] = []
         self._targets_list: List[Detections] = []
 
@@ -169,8 +168,12 @@ class Precision(Metric):
                         iou = box_iou_batch(target_contents, prediction_contents)
                     elif self._metric_target == MetricTarget.MASKS:
                         iou = mask_iou_batch(target_contents, prediction_contents)
+                    elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+                        iou = oriented_box_iou_batch(
+                            target_contents, prediction_contents
+                        )
                     else:
-                        raise NotImplementedError(
+                        raise ValueError(
                             "Unsupported metric target for IoU calculation"
                         )
 
@@ -369,12 +372,22 @@ class Precision(Metric):
             return (
                 detections.mask
                 if detections.mask is not None
-                else np.empty((0, 0, 0), dtype=bool)
+                else self._make_empty_content()
             )
         if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
-            if obb := detections.data.get(ORIENTED_BOX_COORDINATES):
-                return np.ndarray(obb, dtype=np.float32)
-            return np.empty((0, 8), dtype=np.float32)
+            obb = detections.data.get(ORIENTED_BOX_COORDINATES)
+            if obb is not None and len(obb) > 0:
+                return np.array(obb, dtype=np.float32)
+            return self._make_empty_content()
+        raise ValueError(f"Invalid metric target: {self._metric_target}")
+
+    def _make_empty_content(self) -> np.ndarray:
+        if self._metric_target == MetricTarget.BOXES:
+            return np.empty((0, 4), dtype=np.float32)
+        if self._metric_target == MetricTarget.MASKS:
+            return np.empty((0, 0, 0), dtype=bool)
+        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+            return np.empty((0, 4, 2), dtype=np.float32)
         raise ValueError(f"Invalid metric target: {self._metric_target}")
 
     def _filter_detections_by_size(
diff --git a/supervision/metrics/recall.py b/supervision/metrics/recall.py
index 9eae24f8..1848502b 100644
--- a/supervision/metrics/recall.py
+++ b/supervision/metrics/recall.py
@@ -9,7 +9,11 @@ from matplotlib import pyplot as plt
 
 from supervision.config import ORIENTED_BOX_COORDINATES
 from supervision.detection.core import Detections
-from supervision.detection.utils import box_iou_batch, mask_iou_batch
+from supervision.detection.utils import (
+    box_iou_batch,
+    mask_iou_batch,
+    oriented_box_iou_batch,
+)
 from supervision.draw.color import LEGACY_COLOR_PALETTE
 from supervision.metrics.core import AveragingMethod, Metric, MetricTarget
 from supervision.metrics.utils.object_size import (
@@ -65,14 +69,9 @@ class Recall(Metric):
             averaging_method (AveragingMethod): The averaging method used to compute the
                 recall. Determines how the recall is aggregated across classes.
         """
-        self._metric_target = metric_target
-        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
-            raise NotImplementedError(
-                "Recall is not implemented for oriented bounding boxes."
-            )
-
         self._metric_target = metric_target
         self.averaging_method = averaging_method
+
         self._predictions_list: List[Detections] = []
         self._targets_list: List[Detections] = []
 
@@ -169,8 +168,12 @@ class Recall(Metric):
                         iou = box_iou_batch(target_contents, prediction_contents)
                     elif self._metric_target == MetricTarget.MASKS:
                         iou = mask_iou_batch(target_contents, prediction_contents)
+                    elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+                        iou = oriented_box_iou_batch(
+                            target_contents, prediction_contents
+                        )
                     else:
-                        raise NotImplementedError(
+                        raise ValueError(
                             "Unsupported metric target for IoU calculation"
                         )
 
@@ -367,12 +370,22 @@ class Recall(Metric):
             return (
                 detections.mask
                 if detections.mask is not None
-                else np.empty((0, 0, 0), dtype=bool)
+                else self._make_empty_content()
             )
         if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
-            if obb := detections.data.get(ORIENTED_BOX_COORDINATES):
-                return np.ndarray(obb, dtype=np.float32)
-            return np.empty((0, 8), dtype=np.float32)
+            obb = detections.data.get(ORIENTED_BOX_COORDINATES)
+            if obb is not None and len(obb) > 0:
+                return np.array(obb, dtype=np.float32)
+            return self._make_empty_content()
+        raise ValueError(f"Invalid metric target: {self._metric_target}")
+
+    def _make_empty_content(self) -> np.ndarray:
+        if self._metric_target == MetricTarget.BOXES:
+            return np.empty((0, 4), dtype=np.float32)
+        if self._metric_target == MetricTarget.MASKS:
+            return np.empty((0, 0, 0), dtype=bool)
+        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+            return np.empty((0, 4, 2), dtype=np.float32)
         raise ValueError(f"Invalid metric target: {self._metric_target}")
 
     def _filter_detections_by_size(

From 353fa93305bf1d0dd778a1694cb412f14d6be785 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Thu, 31 Oct 2024 19:41:13 +0200
Subject: [PATCH 104/128] Bugfix: Tracker reset fails

---
 supervision/tracker/byte_tracker/core.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index f453e523..34a9501a 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -323,7 +323,6 @@ class ByteTrack:
         ensuring the tracker starts with a clean state for each new video.
         """
         self.frame_id = 0
-        BaseTrack.reset_counter()
         self.internal_id_counter.reset()
         self.external_id_counter.reset()
         self.tracked_tracks: List[STrack] = []

From 7229409fabfd5cb0c76054a82c77ec177e252d60 Mon Sep 17 00:00:00 2001
From: LinasKo <linas.ko@pm.me>
Date: Thu, 31 Oct 2024 19:43:26 +0200
Subject: [PATCH 105/128] Revert "feat(ByteTrack): Allow ByteTrack to track
 detection without class ids"

---
 supervision/tracker/byte_tracker/core.py | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index f453e523..d1d567fa 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -276,16 +276,11 @@ class ByteTrack:
             ```
         """
 
-        num_rows = detections.xyxy.shape[0]
-        class_ids = np.full(num_rows, -5)
-        if detections.class_id is not None:
-            class_ids = detections.class_id
-
         tensors = np.hstack(
             (
                 detections.xyxy,
                 detections.confidence[:, np.newaxis],
-                class_ids[:, np.newaxis],
+                detections.class_id[:, np.newaxis],
             )
         )
         tracks = self.update_with_tensors(tensors=tensors)

From ab99c3615de105b9339ff01e5e45a5315f20efda Mon Sep 17 00:00:00 2001
From: Kader Miyanyedi <Kadermiyanyedi@hotmail.com>
Date: Thu, 31 Oct 2024 20:11:03 +0300
Subject: [PATCH 106/128] feat(ByteTrack): Remove unnecessary class id from
 bytetrack

---
 supervision/annotators/core.py           |  2 +-
 supervision/tracker/byte_tracker/core.py | 13 ++-----------
 2 files changed, 3 insertions(+), 12 deletions(-)

diff --git a/supervision/annotators/core.py b/supervision/annotators/core.py
index 1910ac9f..73e0cdd2 100644
--- a/supervision/annotators/core.py
+++ b/supervision/annotators/core.py
@@ -1609,7 +1609,7 @@ class TraceAnnotator(BaseAnnotator):
         position: Position = Position.CENTER,
         trace_length: int = 30,
         thickness: int = 2,
-        color_lookup: ColorLookup = ColorLookup.CLASS,
+        color_lookup: ColorLookup = ColorLookup.INDEX,
     ):
         """
         Args:
diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index 5ce81960..e1139ea9 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -32,7 +32,6 @@ class STrack(BaseTrack):
         self,
         tlwh,
         score,
-        class_ids,
         minimum_consecutive_frames,
         internal_id_counter: IdCounter,
         external_id_counter: IdCounter,
@@ -45,7 +44,6 @@ class STrack(BaseTrack):
         self.is_activated = False
 
         self.score = score
-        self.class_ids = class_ids
         self.tracklet_len = 0
 
         self.minimum_consecutive_frames = minimum_consecutive_frames
@@ -280,7 +278,6 @@ class ByteTrack:
             (
                 detections.xyxy,
                 detections.confidence[:, np.newaxis],
-                detections.class_id[:, np.newaxis],
             )
         )
         tracks = self.update_with_tensors(tensors=tensors)
@@ -340,7 +337,6 @@ class ByteTrack:
         lost_stracks = []
         removed_stracks = []
 
-        class_ids = tensors[:, 5]
         scores = tensors[:, 4]
         bboxes = tensors[:, :4]
 
@@ -354,21 +350,17 @@ class ByteTrack:
         scores_keep = scores[remain_inds]
         scores_second = scores[inds_second]
 
-        class_ids_keep = class_ids[remain_inds]
-        class_ids_second = class_ids[inds_second]
-
         if len(dets) > 0:
             """Detections"""
             detections = [
                 STrack(
                     STrack.tlbr_to_tlwh(tlbr),
                     s,
-                    c,
                     self.minimum_consecutive_frames,
                     self.internal_id_counter,
                     self.external_id_counter,
                 )
-                for (tlbr, s, c) in zip(dets, scores_keep, class_ids_keep)
+                for (tlbr, s) in zip(dets, scores_keep)
             ]
         else:
             detections = []
@@ -412,12 +404,11 @@ class ByteTrack:
                 STrack(
                     STrack.tlbr_to_tlwh(tlbr),
                     s,
-                    c,
                     self.minimum_consecutive_frames,
                     self.internal_id_counter,
                     self.external_id_counter,
                 )
-                for (tlbr, s, c) in zip(dets_second, scores_second, class_ids_second)
+                for (tlbr, s) in zip(dets_second, scores_second)
             ]
         else:
             detections_second = []

From 895bcfbddbfacfe50845675e0814c0fd884fe70e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Fri, 1 Nov 2024 01:03:55 +0000
Subject: [PATCH 107/128] :arrow_up: Bump mkdocs-material from 9.5.42 to 9.5.43

Bumps [mkdocs-material](https://github.com/squidfunk/mkdocs-material) from 9.5.42 to 9.5.43.
- [Release notes](https://github.com/squidfunk/mkdocs-material/releases)
- [Changelog](https://github.com/squidfunk/mkdocs-material/blob/master/CHANGELOG)
- [Commits](https://github.com/squidfunk/mkdocs-material/compare/9.5.42...9.5.43)

---
updated-dependencies:
- dependency-name: mkdocs-material
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 69775617..6e2817a5 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2412,13 +2412,13 @@ pygments = ">2.12.0"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.42"
+version = "9.5.43"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.5.42-py3-none-any.whl", hash = "sha256:452a7c5d21284b373f36b981a2cbebfff59263feebeede1bc28652e9c5bbe316"},
-    {file = "mkdocs_material-9.5.42.tar.gz", hash = "sha256:92779b5e9b5934540c574c11647131d217dc540dce72b05feeda088c8eb1b8f2"},
+    {file = "mkdocs_material-9.5.43-py3-none-any.whl", hash = "sha256:4aae0664c456fd12837a3192e0225c17960ba8bf55d7f0a7daef7e4b0b914a34"},
+    {file = "mkdocs_material-9.5.43.tar.gz", hash = "sha256:83be7ff30b65a1e4930dfa4ab911e75780a3afc9583d162692e434581cb46979"},
 ]
 
 [package.dependencies]

From b8daa408bfb60b06ae68654fa4bace4e471f96d4 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 1 Nov 2024 18:14:56 +0200
Subject: [PATCH 108/128] Reset default color selection to CLASS for
 TraceAnnotator

---
 supervision/annotators/core.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/supervision/annotators/core.py b/supervision/annotators/core.py
index 73e0cdd2..1910ac9f 100644
--- a/supervision/annotators/core.py
+++ b/supervision/annotators/core.py
@@ -1609,7 +1609,7 @@ class TraceAnnotator(BaseAnnotator):
         position: Position = Position.CENTER,
         trace_length: int = 30,
         thickness: int = 2,
-        color_lookup: ColorLookup = ColorLookup.INDEX,
+        color_lookup: ColorLookup = ColorLookup.CLASS,
     ):
         """
         Args:

From 4678dfc74a5c5f0e61f1d71dadc465d2edc9c70e Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 1 Nov 2024 19:29:31 +0200
Subject: [PATCH 109/128] Fix regression where all uninitialized tracks are
 connected

* Apparently, external track ID must start with `1` and not `0`. Not sure where this coupling comes from.
---
 supervision/tracker/byte_tracker/core.py | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index e1139ea9..2dbf5ab5 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -10,19 +10,23 @@ from supervision.tracker.byte_tracker.kalman_filter import KalmanFilter
 
 
 class IdCounter:
-    def __init__(self):
+    def __init__(self, start_id: int = 0):
+        self.start_id = start_id
+        if self.start_id <= self.NO_ID:
+            raise ValueError("start_id must be greater than -1")
         self.reset()
 
     def reset(self) -> None:
-        self._id = self.NO_ID
+        self._id = self.start_id
 
     def new_id(self) -> int:
+        returned_id = self._id
         self._id += 1
-        return self._id
+        return returned_id
 
     @property
     def NO_ID(self) -> int:
-        return 0
+        return -1
 
 
 class STrack(BaseTrack):
@@ -231,8 +235,10 @@ class ByteTrack:
         self.lost_tracks: List[STrack] = []
         self.removed_tracks: List[STrack] = []
 
+        # Warning, possible bug: If you also set internal_id to start at 1,
+        # all traces will be connected across objects.
         self.internal_id_counter = IdCounter()
-        self.external_id_counter = IdCounter()
+        self.external_id_counter = IdCounter(start_id=1)
 
     def update_with_detections(self, detections: Detections) -> Detections:
         """

From 1f5b1c7e01db169490ae3a58584a4d3e0aae60a7 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 18 Oct 2024 01:39:53 +0300
Subject: [PATCH 110/128] BaseTrack: Move TrackState to STrack

---
 supervision/tracker/byte_tracker/basetrack.py | 32 +------------------
 supervision/tracker/byte_tracker/core.py      | 19 ++++++++---
 2 files changed, 15 insertions(+), 36 deletions(-)

diff --git a/supervision/tracker/byte_tracker/basetrack.py b/supervision/tracker/byte_tracker/basetrack.py
index e2bc66d5..079bab22 100644
--- a/supervision/tracker/byte_tracker/basetrack.py
+++ b/supervision/tracker/byte_tracker/basetrack.py
@@ -1,42 +1,18 @@
-from collections import OrderedDict
-from enum import Enum
-
-import numpy as np
-
-
-class TrackState(Enum):
-    New = 0
-    Tracked = 1
-    Lost = 2
-    Removed = 3
-
-
 class BaseTrack:
     def __init__(self):
-        self.track_id = 0
         self.is_activated = False
-        self.state = TrackState.New
 
-        self.history = OrderedDict()
-        self.features = []
-        self.curr_feature = None
         self.score = 0
         self.start_frame = 0
         self.frame_id = 0
-        self.time_since_update = 0
-
-        # multi-camera
-        self.location = (np.inf, np.inf)
 
     @property
     def end_frame(self) -> int:
         return self.frame_id
 
     def reset_counter(self):
-        self.track_id = 0
-        self.start_frame = 0
         self.frame_id = 0
-        self.time_since_update = 0
+        self.start_frame = 0
 
     def activate(self, *args, **kwargs):
         raise NotImplementedError
@@ -46,9 +22,3 @@ class BaseTrack:
 
     def update(self, *args, **kwargs):
         raise NotImplementedError
-
-    def mark_lost(self):
-        self.state = TrackState.Lost
-
-    def mark_removed(self):
-        self.state = TrackState.Removed
diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index 2dbf5ab5..362d9d07 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -1,3 +1,4 @@
+from enum import Enum
 from typing import List, Tuple
 
 import numpy as np
@@ -5,10 +6,17 @@ import numpy as np
 from supervision.detection.core import Detections
 from supervision.detection.utils import box_iou_batch
 from supervision.tracker.byte_tracker import matching
-from supervision.tracker.byte_tracker.basetrack import BaseTrack, TrackState
+from supervision.tracker.byte_tracker.basetrack import BaseTrack
 from supervision.tracker.byte_tracker.kalman_filter import KalmanFilter
 
 
+class TrackState(Enum):
+    New = 0
+    Tracked = 1
+    Lost = 2
+    Removed = 3
+
+
 class IdCounter:
     def __init__(self, start_id: int = 0):
         self.start_id = start_id
@@ -41,7 +49,8 @@ class STrack(BaseTrack):
         external_id_counter: IdCounter,
     ):
         super().__init__()
-        # wait activate
+        self.state = TrackState.New
+
         self._tlwh = np.asarray(tlwh, dtype=np.float32)
         self.kalman_filter = None
         self.mean, self.covariance = None, None
@@ -440,7 +449,7 @@ class ByteTrack:
         for it in u_track:
             track = r_tracked_stracks[it]
             if not track.state == TrackState.Lost:
-                track.mark_lost()
+                track.state = TrackState.Lost
                 lost_stracks.append(track)
 
         """Deal with unconfirmed tracks, usually tracks with only one beginning frame"""
@@ -456,7 +465,7 @@ class ByteTrack:
             activated_starcks.append(unconfirmed[itracked])
         for it in u_unconfirmed:
             track = unconfirmed[it]
-            track.mark_removed()
+            track.state = TrackState.Removed
             removed_stracks.append(track)
 
         """ Step 4: Init new stracks"""
@@ -469,7 +478,7 @@ class ByteTrack:
         """ Step 5: Update state"""
         for track in self.lost_tracks:
             if self.frame_id - track.end_frame > self.max_time_lost:
-                track.mark_removed()
+                track.state = TrackState.Removed
                 removed_stracks.append(track)
 
         self.tracked_tracks = [

From e8983bf4271739ade352b128fe51c0ad13ca2c86 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 18 Oct 2024 01:50:40 +0300
Subject: [PATCH 111/128] BaseTrack: Move trivial values used in STrack to
 STrack

---
 supervision/tracker/byte_tracker/basetrack.py | 3 ---
 supervision/tracker/byte_tracker/core.py      | 1 +
 supervision/tracker/byte_tracker/matching.py  | 9 ++++++---
 3 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/supervision/tracker/byte_tracker/basetrack.py b/supervision/tracker/byte_tracker/basetrack.py
index 079bab22..8ae511b7 100644
--- a/supervision/tracker/byte_tracker/basetrack.py
+++ b/supervision/tracker/byte_tracker/basetrack.py
@@ -1,8 +1,5 @@
 class BaseTrack:
     def __init__(self):
-        self.is_activated = False
-
-        self.score = 0
         self.start_frame = 0
         self.frame_id = 0
 
diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index 362d9d07..37d3a854 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -50,6 +50,7 @@ class STrack(BaseTrack):
     ):
         super().__init__()
         self.state = TrackState.New
+        self.is_activated = False
 
         self._tlwh = np.asarray(tlwh, dtype=np.float32)
         self.kalman_filter = None
diff --git a/supervision/tracker/byte_tracker/matching.py b/supervision/tracker/byte_tracker/matching.py
index f791e518..4a95bc2c 100644
--- a/supervision/tracker/byte_tracker/matching.py
+++ b/supervision/tracker/byte_tracker/matching.py
@@ -1,10 +1,13 @@
-from typing import List, Tuple
+from typing import TYPE_CHECKING, List, Tuple
 
 import numpy as np
 from scipy.optimize import linear_sum_assignment
 
 from supervision.detection.utils import box_iou_batch
 
+if TYPE_CHECKING:
+    from supervision.tracker.byte_tracker.core import STrack
+
 
 def indices_to_matches(
     cost_matrix: np.ndarray, indices: np.ndarray, thresh: float
@@ -53,11 +56,11 @@ def iou_distance(atracks: List, btracks: List) -> np.ndarray:
     return cost_matrix
 
 
-def fuse_score(cost_matrix: np.ndarray, detections: List) -> np.ndarray:
+def fuse_score(cost_matrix: np.ndarray, stracks: List["STrack"]) -> np.ndarray:
     if cost_matrix.size == 0:
         return cost_matrix
     iou_sim = 1 - cost_matrix
-    det_scores = np.array([det.score for det in detections])
+    det_scores = np.array([strack.score for strack in stracks])
     det_scores = np.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0)
     fuse_sim = iou_sim * det_scores
     fuse_cost = 1 - fuse_sim

From 37cc16e7a871f5ee0e63fa9683126aa353d5b068 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 1 Nov 2024 20:30:21 +0200
Subject: [PATCH 112/128] Bugfix: remove reset start frame when tracker is
 reset

---
 supervision/tracker/byte_tracker/basetrack.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/supervision/tracker/byte_tracker/basetrack.py b/supervision/tracker/byte_tracker/basetrack.py
index 8ae511b7..bd945653 100644
--- a/supervision/tracker/byte_tracker/basetrack.py
+++ b/supervision/tracker/byte_tracker/basetrack.py
@@ -7,10 +7,6 @@ class BaseTrack:
     def end_frame(self) -> int:
         return self.frame_id
 
-    def reset_counter(self):
-        self.frame_id = 0
-        self.start_frame = 0
-
     def activate(self, *args, **kwargs):
         raise NotImplementedError
 

From 89be6f92d2e5896c8b413792668071dae3c1990a Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 18 Oct 2024 02:02:27 +0300
Subject: [PATCH 113/128] Get rid of BaseTrack completely

* 85%-90% of it was used in STrack
* 10% was used by ByteTrack
---
 supervision/tracker/byte_tracker/basetrack.py | 17 -----------------
 supervision/tracker/byte_tracker/core.py      | 11 ++++++-----
 2 files changed, 6 insertions(+), 22 deletions(-)
 delete mode 100644 supervision/tracker/byte_tracker/basetrack.py

diff --git a/supervision/tracker/byte_tracker/basetrack.py b/supervision/tracker/byte_tracker/basetrack.py
deleted file mode 100644
index bd945653..00000000
--- a/supervision/tracker/byte_tracker/basetrack.py
+++ /dev/null
@@ -1,17 +0,0 @@
-class BaseTrack:
-    def __init__(self):
-        self.start_frame = 0
-        self.frame_id = 0
-
-    @property
-    def end_frame(self) -> int:
-        return self.frame_id
-
-    def activate(self, *args, **kwargs):
-        raise NotImplementedError
-
-    def predict(self):
-        raise NotImplementedError
-
-    def update(self, *args, **kwargs):
-        raise NotImplementedError
diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index 37d3a854..a1ae63f4 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -6,7 +6,6 @@ import numpy as np
 from supervision.detection.core import Detections
 from supervision.detection.utils import box_iou_batch
 from supervision.tracker.byte_tracker import matching
-from supervision.tracker.byte_tracker.basetrack import BaseTrack
 from supervision.tracker.byte_tracker.kalman_filter import KalmanFilter
 
 
@@ -37,7 +36,7 @@ class IdCounter:
         return -1
 
 
-class STrack(BaseTrack):
+class STrack:
     shared_kalman = KalmanFilter()
 
     def __init__(
@@ -48,9 +47,10 @@ class STrack(BaseTrack):
         internal_id_counter: IdCounter,
         external_id_counter: IdCounter,
     ):
-        super().__init__()
         self.state = TrackState.New
         self.is_activated = False
+        self.start_frame = 0
+        self.frame_id = 0
 
         self._tlwh = np.asarray(tlwh, dtype=np.float32)
         self.kalman_filter = None
@@ -193,7 +193,7 @@ class STrack(BaseTrack):
 
     def __repr__(self):
         return "OT_{}_({}-{})".format(
-            self.internal_track_id, self.start_frame, self.end_frame
+            self.internal_track_id, self.start_frame, self.frame_id
         )
 
 
@@ -236,6 +236,7 @@ class ByteTrack:
         self.minimum_matching_threshold = minimum_matching_threshold
 
         self.frame_id = 0
+        self.start_frame = 0
         self.det_thresh = self.track_activation_threshold + 0.1
         self.max_time_lost = int(frame_rate / 30.0 * lost_track_buffer)
         self.minimum_consecutive_frames = minimum_consecutive_frames
@@ -478,7 +479,7 @@ class ByteTrack:
             activated_starcks.append(track)
         """ Step 5: Update state"""
         for track in self.lost_tracks:
-            if self.frame_id - track.end_frame > self.max_time_lost:
+            if self.frame_id - track.frame_id > self.max_time_lost:
                 track.state = TrackState.Removed
                 removed_stracks.append(track)
 

From b380bd2df79a167ffd2b768896eda49eb048c7b5 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 18 Oct 2024 02:23:08 +0300
Subject: [PATCH 114/128] Correct but useless: Moved shared kalman to ByteTrack

* It's not stateful! All it keeps is the initial params - motion and update matrices
---
 supervision/tracker/byte_tracker/core.py | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index a1ae63f4..019ce890 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -37,13 +37,12 @@ class IdCounter:
 
 
 class STrack:
-    shared_kalman = KalmanFilter()
-
     def __init__(
         self,
         tlwh,
         score,
         minimum_consecutive_frames,
+        shared_kalman: KalmanFilter,
         internal_id_counter: IdCounter,
         external_id_counter: IdCounter,
     ):
@@ -54,6 +53,7 @@ class STrack:
 
         self._tlwh = np.asarray(tlwh, dtype=np.float32)
         self.kalman_filter = None
+        self.shared_kalman = shared_kalman
         self.mean, self.covariance = None, None
         self.is_activated = False
 
@@ -76,7 +76,7 @@ class STrack:
         )
 
     @staticmethod
-    def multi_predict(stracks):
+    def multi_predict(stracks, shared_kalman: KalmanFilter):
         if len(stracks) > 0:
             multi_mean = []
             multi_covariance = []
@@ -86,7 +86,7 @@ class STrack:
                 if st.state != TrackState.Tracked:
                     multi_mean[i][7] = 0
 
-            multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(
+            multi_mean, multi_covariance = shared_kalman.multi_predict(
                 np.asarray(multi_mean), np.asarray(multi_covariance)
             )
             for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
@@ -241,6 +241,7 @@ class ByteTrack:
         self.max_time_lost = int(frame_rate / 30.0 * lost_track_buffer)
         self.minimum_consecutive_frames = minimum_consecutive_frames
         self.kalman_filter = KalmanFilter()
+        self.shared_kalman = KalmanFilter()
 
         self.tracked_tracks: List[STrack] = []
         self.lost_tracks: List[STrack] = []
@@ -374,6 +375,7 @@ class ByteTrack:
                     STrack.tlbr_to_tlwh(tlbr),
                     s,
                     self.minimum_consecutive_frames,
+                    self.shared_kalman,
                     self.internal_id_counter,
                     self.external_id_counter,
                 )
@@ -395,7 +397,7 @@ class ByteTrack:
         """ Step 2: First association, with high score detection boxes"""
         strack_pool = joint_tracks(tracked_stracks, self.lost_tracks)
         # Predict the current location with KF
-        STrack.multi_predict(strack_pool)
+        STrack.multi_predict(strack_pool, self.shared_kalman)
         dists = matching.iou_distance(strack_pool, detections)
 
         dists = matching.fuse_score(dists, detections)
@@ -422,6 +424,7 @@ class ByteTrack:
                     STrack.tlbr_to_tlwh(tlbr),
                     s,
                     self.minimum_consecutive_frames,
+                    self.shared_kalman,
                     self.internal_id_counter,
                     self.external_id_counter,
                 )

From fd1277f29a6cf503991f254648bb9d3feb7d2978 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 1 Nov 2024 20:33:46 +0200
Subject: [PATCH 115/128] Add types, move STrack into a new file

---
 supervision/tracker/byte_tracker/core.py      | 206 +-----------------
 supervision/tracker/byte_tracker/matching.py  |   6 +-
 .../byte_tracker/single_object_track.py       | 177 +++++++++++++++
 supervision/tracker/byte_tracker/utils.py     |  18 ++
 4 files changed, 209 insertions(+), 198 deletions(-)
 create mode 100644 supervision/tracker/byte_tracker/single_object_track.py
 create mode 100644 supervision/tracker/byte_tracker/utils.py

diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index 019ce890..ecd844ad 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -1,4 +1,3 @@
-from enum import Enum
 from typing import List, Tuple
 
 import numpy as np
@@ -7,194 +6,8 @@ from supervision.detection.core import Detections
 from supervision.detection.utils import box_iou_batch
 from supervision.tracker.byte_tracker import matching
 from supervision.tracker.byte_tracker.kalman_filter import KalmanFilter
-
-
-class TrackState(Enum):
-    New = 0
-    Tracked = 1
-    Lost = 2
-    Removed = 3
-
-
-class IdCounter:
-    def __init__(self, start_id: int = 0):
-        self.start_id = start_id
-        if self.start_id <= self.NO_ID:
-            raise ValueError("start_id must be greater than -1")
-        self.reset()
-
-    def reset(self) -> None:
-        self._id = self.start_id
-
-    def new_id(self) -> int:
-        returned_id = self._id
-        self._id += 1
-        return returned_id
-
-    @property
-    def NO_ID(self) -> int:
-        return -1
-
-
-class STrack:
-    def __init__(
-        self,
-        tlwh,
-        score,
-        minimum_consecutive_frames,
-        shared_kalman: KalmanFilter,
-        internal_id_counter: IdCounter,
-        external_id_counter: IdCounter,
-    ):
-        self.state = TrackState.New
-        self.is_activated = False
-        self.start_frame = 0
-        self.frame_id = 0
-
-        self._tlwh = np.asarray(tlwh, dtype=np.float32)
-        self.kalman_filter = None
-        self.shared_kalman = shared_kalman
-        self.mean, self.covariance = None, None
-        self.is_activated = False
-
-        self.score = score
-        self.tracklet_len = 0
-
-        self.minimum_consecutive_frames = minimum_consecutive_frames
-
-        self.internal_id_counter = internal_id_counter
-        self.external_id_counter = external_id_counter
-        self.internal_track_id = self.internal_id_counter.NO_ID
-        self.external_track_id = self.external_id_counter.NO_ID
-
-    def predict(self):
-        mean_state = self.mean.copy()
-        if self.state != TrackState.Tracked:
-            mean_state[7] = 0
-        self.mean, self.covariance = self.kalman_filter.predict(
-            mean_state, self.covariance
-        )
-
-    @staticmethod
-    def multi_predict(stracks, shared_kalman: KalmanFilter):
-        if len(stracks) > 0:
-            multi_mean = []
-            multi_covariance = []
-            for i, st in enumerate(stracks):
-                multi_mean.append(st.mean.copy())
-                multi_covariance.append(st.covariance)
-                if st.state != TrackState.Tracked:
-                    multi_mean[i][7] = 0
-
-            multi_mean, multi_covariance = shared_kalman.multi_predict(
-                np.asarray(multi_mean), np.asarray(multi_covariance)
-            )
-            for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
-                stracks[i].mean = mean
-                stracks[i].covariance = cov
-
-    def activate(self, kalman_filter, frame_id):
-        """Start a new tracklet"""
-        self.kalman_filter = kalman_filter
-        self.internal_track_id = self.internal_id_counter.new_id()
-        self.mean, self.covariance = self.kalman_filter.initiate(
-            self.tlwh_to_xyah(self._tlwh)
-        )
-
-        self.tracklet_len = 0
-        self.state = TrackState.Tracked
-        if frame_id == 1:
-            self.is_activated = True
-
-        if self.minimum_consecutive_frames == 1:
-            self.external_track_id = self.external_id_counter.new_id()
-
-        self.frame_id = frame_id
-        self.start_frame = frame_id
-
-    def re_activate(self, new_track, frame_id):
-        self.mean, self.covariance = self.kalman_filter.update(
-            self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
-        )
-        self.tracklet_len = 0
-        self.state = TrackState.Tracked
-
-        self.frame_id = frame_id
-        self.score = new_track.score
-
-    def update(self, new_track, frame_id):
-        """
-        Update a matched track
-        :type new_track: STrack
-        :type frame_id: int
-        :type update_feature: bool
-        :return:
-        """
-        self.frame_id = frame_id
-        self.tracklet_len += 1
-
-        new_tlwh = new_track.tlwh
-        self.mean, self.covariance = self.kalman_filter.update(
-            self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh)
-        )
-        self.state = TrackState.Tracked
-        if self.tracklet_len == self.minimum_consecutive_frames:
-            self.is_activated = True
-            if self.external_track_id == self.external_id_counter.NO_ID:
-                self.external_track_id = self.external_id_counter.new_id()
-
-        self.score = new_track.score
-
-    @property
-    def tlwh(self):
-        """Get current position in bounding box format `(top left x, top left y,
-        width, height)`.
-        """
-        if self.mean is None:
-            return self._tlwh.copy()
-        ret = self.mean[:4].copy()
-        ret[2] *= ret[3]
-        ret[:2] -= ret[2:] / 2
-        return ret
-
-    @property
-    def 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
-
-    @staticmethod
-    def tlwh_to_xyah(tlwh):
-        """Convert bounding box to format `(center x, center y, aspect ratio,
-        height)`, where the aspect ratio is `width / height`.
-        """
-        ret = np.asarray(tlwh).copy()
-        ret[:2] += ret[2:] / 2
-        ret[2] /= ret[3]
-        return ret
-
-    def to_xyah(self):
-        return self.tlwh_to_xyah(self.tlwh)
-
-    @staticmethod
-    def tlbr_to_tlwh(tlbr):
-        ret = np.asarray(tlbr).copy()
-        ret[2:] -= ret[:2]
-        return ret
-
-    @staticmethod
-    def tlwh_to_tlbr(tlwh):
-        ret = np.asarray(tlwh).copy()
-        ret[2:] += ret[:2]
-        return ret
-
-    def __repr__(self):
-        return "OT_{}_({}-{})".format(
-            self.internal_track_id, self.start_frame, self.frame_id
-        )
+from supervision.tracker.byte_tracker.single_object_track import STrack, TrackState
+from supervision.tracker.byte_tracker.utils import IdCounter
 
 
 class ByteTrack:
@@ -291,7 +104,6 @@ class ByteTrack:
             )
             ```
         """
-
         tensors = np.hstack(
             (
                 detections.xyxy,
@@ -323,7 +135,7 @@ class ByteTrack:
 
             return detections
 
-    def reset(self):
+    def reset(self) -> None:
         """
         Resets the internal state of the ByteTrack tracker.
 
@@ -335,9 +147,9 @@ class ByteTrack:
         self.frame_id = 0
         self.internal_id_counter.reset()
         self.external_id_counter.reset()
-        self.tracked_tracks: List[STrack] = []
-        self.lost_tracks: List[STrack] = []
-        self.removed_tracks: List[STrack] = []
+        self.tracked_tracks = []
+        self.lost_tracks = []
+        self.removed_tracks = []
 
     def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
         """
@@ -529,7 +341,7 @@ def joint_tracks(
     return result
 
 
-def sub_tracks(track_list_a: List, track_list_b: List) -> List[int]:
+def sub_tracks(track_list_a: List[STrack], track_list_b: List[STrack]) -> List[int]:
     """
     Returns a list of tracks from track_list_a after removing any tracks
     that share the same internal_track_id with tracks in track_list_b.
@@ -550,7 +362,9 @@ def sub_tracks(track_list_a: List, track_list_b: List) -> List[int]:
     return list(tracks.values())
 
 
-def remove_duplicate_tracks(tracks_a: List, tracks_b: List) -> Tuple[List, List]:
+def remove_duplicate_tracks(
+    tracks_a: List[STrack], tracks_b: List[STrack]
+) -> Tuple[List[STrack], List[STrack]]:
     pairwise_distance = matching.iou_distance(tracks_a, tracks_b)
     matching_pairs = np.where(pairwise_distance < 0.15)
 
diff --git a/supervision/tracker/byte_tracker/matching.py b/supervision/tracker/byte_tracker/matching.py
index 4a95bc2c..eb774d4c 100644
--- a/supervision/tracker/byte_tracker/matching.py
+++ b/supervision/tracker/byte_tracker/matching.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from typing import TYPE_CHECKING, List, Tuple
 
 import numpy as np
@@ -38,7 +40,7 @@ def linear_assignment(
     return indices_to_matches(cost_matrix, indices, thresh)
 
 
-def iou_distance(atracks: List, btracks: List) -> np.ndarray:
+def iou_distance(atracks: List[STrack], btracks: List[STrack]) -> np.ndarray:
     if (len(atracks) > 0 and isinstance(atracks[0], np.ndarray)) or (
         len(btracks) > 0 and isinstance(btracks[0], np.ndarray)
     ):
@@ -56,7 +58,7 @@ def iou_distance(atracks: List, btracks: List) -> np.ndarray:
     return cost_matrix
 
 
-def fuse_score(cost_matrix: np.ndarray, stracks: List["STrack"]) -> np.ndarray:
+def fuse_score(cost_matrix: np.ndarray, stracks: List[STrack]) -> np.ndarray:
     if cost_matrix.size == 0:
         return cost_matrix
     iou_sim = 1 - cost_matrix
diff --git a/supervision/tracker/byte_tracker/single_object_track.py b/supervision/tracker/byte_tracker/single_object_track.py
new file mode 100644
index 00000000..685b86e3
--- /dev/null
+++ b/supervision/tracker/byte_tracker/single_object_track.py
@@ -0,0 +1,177 @@
+from __future__ import annotations
+from enum import Enum
+from typing import List
+
+import numpy as np
+import numpy.typing as npt
+
+from supervision.tracker.byte_tracker.kalman_filter import KalmanFilter
+from supervision.tracker.byte_tracker.utils import IdCounter
+
+
+class TrackState(Enum):
+    New = 0
+    Tracked = 1
+    Lost = 2
+    Removed = 3
+
+class STrack:
+    def __init__(
+        self,
+        tlwh: npt.NDArray[np.float32],
+        score: npt.NDArray[np.float32],
+        minimum_consecutive_frames: int,
+        shared_kalman: KalmanFilter,
+        internal_id_counter: IdCounter,
+        external_id_counter: IdCounter,
+    ):
+        self.state = TrackState.New
+        self.is_activated = False
+        self.start_frame = 0
+        self.frame_id = 0
+
+        self._tlwh = np.asarray(tlwh, dtype=np.float32)
+        self.kalman_filter = None
+        self.shared_kalman = shared_kalman
+        self.mean, self.covariance = None, None
+        self.is_activated = False
+
+        self.score = score
+        self.tracklet_len = 0
+
+        self.minimum_consecutive_frames = minimum_consecutive_frames
+
+        self.internal_id_counter = internal_id_counter
+        self.external_id_counter = external_id_counter
+        self.internal_track_id = self.internal_id_counter.NO_ID
+        self.external_track_id = self.external_id_counter.NO_ID
+
+    def predict(self) -> None:
+        mean_state = self.mean.copy()
+        if self.state != TrackState.Tracked:
+            mean_state[7] = 0
+        self.mean, self.covariance = self.kalman_filter.predict(
+            mean_state, self.covariance
+        )
+
+    @staticmethod
+    def multi_predict(stracks: List[STrack], shared_kalman: KalmanFilter) -> None:
+        if len(stracks) > 0:
+            multi_mean = []
+            multi_covariance = []
+            for i, st in enumerate(stracks):
+                multi_mean.append(st.mean.copy())
+                multi_covariance.append(st.covariance)
+                if st.state != TrackState.Tracked:
+                    multi_mean[i][7] = 0
+
+            multi_mean, multi_covariance = shared_kalman.multi_predict(
+                np.asarray(multi_mean), np.asarray(multi_covariance)
+            )
+            for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
+                stracks[i].mean = mean
+                stracks[i].covariance = cov
+
+    def activate(self, kalman_filter: KalmanFilter, frame_id: int) -> None:
+        """Start a new tracklet"""
+        self.kalman_filter = kalman_filter
+        self.internal_track_id = self.internal_id_counter.new_id()
+        self.mean, self.covariance = self.kalman_filter.initiate(
+            self.tlwh_to_xyah(self._tlwh)
+        )
+
+        self.tracklet_len = 0
+        self.state = TrackState.Tracked
+        if frame_id == 1:
+            self.is_activated = True
+
+        if self.minimum_consecutive_frames == 1:
+            self.external_track_id = self.external_id_counter.new_id()
+
+        self.frame_id = frame_id
+        self.start_frame = frame_id
+
+    def re_activate(self, new_track: STrack, frame_id: int) -> None:
+        self.mean, self.covariance = self.kalman_filter.update(
+            self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
+        )
+        self.tracklet_len = 0
+        self.state = TrackState.Tracked
+
+        self.frame_id = frame_id
+        self.score = new_track.score
+
+    def update(self, new_track: STrack, frame_id: int) -> None:
+        """
+        Update a matched track
+        :type new_track: STrack
+        :type frame_id: int
+        :type update_feature: bool
+        :return:
+        """
+        self.frame_id = frame_id
+        self.tracklet_len += 1
+
+        new_tlwh = new_track.tlwh
+        self.mean, self.covariance = self.kalman_filter.update(
+            self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh)
+        )
+        self.state = TrackState.Tracked
+        if self.tracklet_len == self.minimum_consecutive_frames:
+            self.is_activated = True
+            if self.external_track_id == self.external_id_counter.NO_ID:
+                self.external_track_id = self.external_id_counter.new_id()
+
+        self.score = new_track.score
+
+    @property
+    def tlwh(self) -> npt.NDArray[np.float32]:
+        """Get current position in bounding box format `(top left x, top left y,
+        width, height)`.
+        """
+        if self.mean is None:
+            return self._tlwh.copy()
+        ret = self.mean[:4].copy()
+        ret[2] *= ret[3]
+        ret[:2] -= ret[2:] / 2
+        return ret
+
+    @property
+    def tlbr(self) -> npt.NDArray[np.float32]:
+        """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
+
+    @staticmethod
+    def tlwh_to_xyah(tlwh) -> npt.NDArray[np.float32]:
+        """Convert bounding box to format `(center x, center y, aspect ratio,
+        height)`, where the aspect ratio is `width / height`.
+        """
+        ret = np.asarray(tlwh).copy()
+        ret[:2] += ret[2:] / 2
+        ret[2] /= ret[3]
+        return ret
+
+    def to_xyah(self) -> npt.NDArray[np.float32]:
+        return self.tlwh_to_xyah(self.tlwh)
+
+    @staticmethod
+    def tlbr_to_tlwh(tlbr) -> npt.NDArray[np.float32]:
+        ret = np.asarray(tlbr).copy()
+        ret[2:] -= ret[:2]
+        return ret
+
+    @staticmethod
+    def tlwh_to_tlbr(tlwh) -> npt.NDArray[np.float32]:
+        ret = np.asarray(tlwh).copy()
+        ret[2:] += ret[:2]
+        return ret
+
+    def __repr__(self) -> str:
+        return "OT_{}_({}-{})".format(
+            self.internal_track_id, self.start_frame, self.frame_id
+        )
+
diff --git a/supervision/tracker/byte_tracker/utils.py b/supervision/tracker/byte_tracker/utils.py
new file mode 100644
index 00000000..b7a62645
--- /dev/null
+++ b/supervision/tracker/byte_tracker/utils.py
@@ -0,0 +1,18 @@
+class IdCounter:
+    def __init__(self, start_id: int = 0):
+        self.start_id = start_id
+        if self.start_id <= self.NO_ID:
+            raise ValueError("start_id must be greater than -1")
+        self.reset()
+
+    def reset(self) -> None:
+        self._id = self.start_id
+
+    def new_id(self) -> int:
+        returned_id = self._id
+        self._id += 1
+        return returned_id
+
+    @property
+    def NO_ID(self) -> int:
+        return -1
\ No newline at end of file

From cfd02c8c5cd485206a0ac2c395eb0cfc83d20937 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 1 Nov 2024 20:34:58 +0200
Subject: [PATCH 116/128] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20aut?=
 =?UTF-8?q?o=20format=20pre-commit=20hooks?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 supervision/tracker/byte_tracker/single_object_track.py | 3 ++-
 supervision/tracker/byte_tracker/utils.py               | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/supervision/tracker/byte_tracker/single_object_track.py b/supervision/tracker/byte_tracker/single_object_track.py
index 685b86e3..3b9bfdf2 100644
--- a/supervision/tracker/byte_tracker/single_object_track.py
+++ b/supervision/tracker/byte_tracker/single_object_track.py
@@ -1,4 +1,5 @@
 from __future__ import annotations
+
 from enum import Enum
 from typing import List
 
@@ -15,6 +16,7 @@ class TrackState(Enum):
     Lost = 2
     Removed = 3
 
+
 class STrack:
     def __init__(
         self,
@@ -174,4 +176,3 @@ class STrack:
         return "OT_{}_({}-{})".format(
             self.internal_track_id, self.start_frame, self.frame_id
         )
-
diff --git a/supervision/tracker/byte_tracker/utils.py b/supervision/tracker/byte_tracker/utils.py
index b7a62645..b25797ce 100644
--- a/supervision/tracker/byte_tracker/utils.py
+++ b/supervision/tracker/byte_tracker/utils.py
@@ -15,4 +15,4 @@ class IdCounter:
 
     @property
     def NO_ID(self) -> int:
-        return -1
\ No newline at end of file
+        return -1

From d3210ed0cc5d330c9443380ef86144111aa49cab Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 1 Nov 2024 20:36:00 +0200
Subject: [PATCH 117/128] Minor rename to avoid name shadowing

---
 supervision/tracker/byte_tracker/core.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index ecd844ad..f5232cac 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -185,13 +185,13 @@ class ByteTrack:
             detections = [
                 STrack(
                     STrack.tlbr_to_tlwh(tlbr),
-                    s,
+                    score_keep,
                     self.minimum_consecutive_frames,
                     self.shared_kalman,
                     self.internal_id_counter,
                     self.external_id_counter,
                 )
-                for (tlbr, s) in zip(dets, scores_keep)
+                for (tlbr, score_keep) in zip(dets, scores_keep)
             ]
         else:
             detections = []
@@ -234,13 +234,13 @@ class ByteTrack:
             detections_second = [
                 STrack(
                     STrack.tlbr_to_tlwh(tlbr),
-                    s,
+                    score_second,
                     self.minimum_consecutive_frames,
                     self.shared_kalman,
                     self.internal_id_counter,
                     self.external_id_counter,
                 )
-                for (tlbr, s) in zip(dets_second, scores_second)
+                for (tlbr, score_second) in zip(dets_second, scores_second)
             ]
         else:
             detections_second = []

From 9eee76a6d1dccef41eebd36d71089b88396c151a Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 1 Nov 2024 20:14:34 +0200
Subject: [PATCH 118/128] Remove start_frame from ByteTrack

---
 supervision/tracker/byte_tracker/core.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index f5232cac..cb46af73 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -49,7 +49,6 @@ class ByteTrack:
         self.minimum_matching_threshold = minimum_matching_threshold
 
         self.frame_id = 0
-        self.start_frame = 0
         self.det_thresh = self.track_activation_threshold + 0.1
         self.max_time_lost = int(frame_rate / 30.0 * lost_track_buffer)
         self.minimum_consecutive_frames = minimum_consecutive_frames

From 22d827fac41a150a5f8955b062acb071819332ab Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 1 Nov 2024 20:41:38 +0200
Subject: [PATCH 119/128] Minor fix: show current NO_ID value of the ID counter
 when printing error

---
 supervision/tracker/byte_tracker/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/supervision/tracker/byte_tracker/utils.py b/supervision/tracker/byte_tracker/utils.py
index b25797ce..cd2a1036 100644
--- a/supervision/tracker/byte_tracker/utils.py
+++ b/supervision/tracker/byte_tracker/utils.py
@@ -2,7 +2,7 @@ class IdCounter:
     def __init__(self, start_id: int = 0):
         self.start_id = start_id
         if self.start_id <= self.NO_ID:
-            raise ValueError("start_id must be greater than -1")
+            raise ValueError(f"start_id must be greater than {self.NO_ID}")
         self.reset()
 
     def reset(self) -> None:

From 62d869ca878d6930ee152f2e3b67e5074952fe05 Mon Sep 17 00:00:00 2001
From: Onuralp SEZER <thunderbirdtr@gmail.com>
Date: Fri, 1 Nov 2024 22:57:17 +0300
Subject: [PATCH 120/128] =?UTF-8?q?fix:=20=F0=9F=90=9E=20Remove=20lowercas?=
 =?UTF-8?q?e=20conversion=20for=20unique=20class=20names=20in=20merge=5Fcl?=
 =?UTF-8?q?ass=5Flists=20function?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>
---
 supervision/dataset/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/supervision/dataset/utils.py b/supervision/dataset/utils.py
index 20b80978..6c30eeab 100644
--- a/supervision/dataset/utils.py
+++ b/supervision/dataset/utils.py
@@ -55,7 +55,7 @@ def merge_class_lists(class_lists: List[List[str]]) -> List[str]:
 
     for class_list in class_lists:
         for class_name in class_list:
-            unique_classes.add(class_name.lower())
+            unique_classes.add(class_name)
 
     return sorted(list(unique_classes))
 

From 6d032533706770150e63c75e6ddc6b666b8bc687 Mon Sep 17 00:00:00 2001
From: Onuralp SEZER <thunderbirdtr@gmail.com>
Date: Sat, 2 Nov 2024 06:37:26 +0300
Subject: [PATCH 121/128] =?UTF-8?q?docs:=20=F0=9F=93=9D=20hacktoberfest=20?=
 =?UTF-8?q?picture=20removed?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 README.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/README.md b/README.md
index dd340ae5..fe569183 100644
--- a/README.md
+++ b/README.md
@@ -34,8 +34,6 @@
 
 **We write your reusable computer vision tools.** Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us! 🤝
 
-[![supervision-hackfest](https://media.roboflow.com/supervision/supervision-hacktoberfest-banner-2024.png)](https://github.com/roboflow/supervision/issues?q=is%3Aissue+is%3Aopen+label%3Ahacktoberfest)
-
 ## 💻 install
 
 Pip install the supervision package in a

From 940243fc18aec2989327397051c036319f2ca831 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 4 Nov 2024 00:48:54 +0000
Subject: [PATCH 122/128] :arrow_up: Bump ruff from 0.7.1 to 0.7.2

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.7.1 to 0.7.2.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.7.1...0.7.2)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 38 +++++++++++++++++++-------------------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 6e2817a5..d7e29a43 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -4037,29 +4037,29 @@ files = [
 
 [[package]]
 name = "ruff"
-version = "0.7.1"
+version = "0.7.2"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ruff-0.7.1-py3-none-linux_armv6l.whl", hash = "sha256:cb1bc5ed9403daa7da05475d615739cc0212e861b7306f314379d958592aaa89"},
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+    {file = "ruff-0.7.2-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:576305393998b7bd6c46018f8104ea3a9cb3fa7908c21d8580e3274a3b04b691"},
+    {file = "ruff-0.7.2-py3-none-win32.whl", hash = "sha256:fa993cfc9f0ff11187e82de874dfc3611df80852540331bc85c75809c93253a8"},
+    {file = "ruff-0.7.2-py3-none-win_amd64.whl", hash = "sha256:dd8800cbe0254e06b8fec585e97554047fb82c894973f7ff18558eee33d1cb88"},
+    {file = "ruff-0.7.2-py3-none-win_arm64.whl", hash = "sha256:bb8368cd45bba3f57bb29cbb8d64b4a33f8415d0149d2655c5c8539452ce7760"},
+    {file = "ruff-0.7.2.tar.gz", hash = "sha256:2b14e77293380e475b4e3a7a368e14549288ed2931fce259a6f99978669e844f"},
 ]
 
 [[package]]

From 5cce8a159934b4c97d390dfdd30bf1c0a54bb4d0 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Mon, 4 Nov 2024 12:23:56 +0200
Subject: [PATCH 123/128] Remoe Detections.empty metadata field

---
 supervision/detection/core.py  | 16 ++++++----------
 supervision/detection/utils.py | 14 +++++++++++---
 2 files changed, 17 insertions(+), 13 deletions(-)

diff --git a/supervision/detection/core.py b/supervision/detection/core.py
index 8e2d06e2..32753a30 100644
--- a/supervision/detection/core.py
+++ b/supervision/detection/core.py
@@ -965,7 +965,7 @@ class Detections:
         )
 
     @classmethod
-    def empty(cls, metadata: Optional[Dict[str, Any]] = None) -> Detections:
+    def empty(cls) -> Detections:
         """
         Create an empty Detections object with no bounding boxes,
             confidences, or class IDs.
@@ -980,14 +980,10 @@ class Detections:
             empty_detections = Detections.empty()
             ```
         """
-        if metadata is not None and not isinstance(metadata, dict):
-            raise TypeError("Metadata must be a dictionary.")
-
         return cls(
             xyxy=np.empty((0, 4), dtype=np.float32),
             confidence=np.array([], dtype=np.float32),
             class_id=np.array([], dtype=int),
-            metadata=metadata if metadata is not None else {},
         )
 
     def is_empty(self) -> bool:
@@ -996,6 +992,7 @@ class Detections:
         """
         empty_detections = Detections.empty()
         empty_detections.data = self.data
+        empty_detections.metadata = self.metadata
         return self == empty_detections
 
     @classmethod
@@ -1052,16 +1049,12 @@ class Detections:
             array([0.1, 0.2, 0.3])
             ```
         """
-        metadata_list = [detections.metadata for detections in detections_list]
-
         detections_list = [
             detections for detections in detections_list if not detections.is_empty()
         ]
 
-        metadata = merge_metadata(metadata_list)
-
         if len(detections_list) == 0:
-            return Detections.empty(metadata=metadata)
+            return Detections.empty()
 
         for detections in detections_list:
             validate_detections_fields(
@@ -1093,6 +1086,9 @@ class Detections:
 
         data = merge_data([d.data for d in detections_list])
 
+        metadata_list = [detections.metadata for detections in detections_list]
+        metadata = merge_metadata(metadata_list)
+
         return cls(
             xyxy=xyxy,
             mask=mask,
diff --git a/supervision/detection/utils.py b/supervision/detection/utils.py
index d80a4dcb..c5990b42 100644
--- a/supervision/detection/utils.py
+++ b/supervision/detection/utils.py
@@ -820,8 +820,10 @@ def is_metadata_equal(metadata_a: Dict[str, Any], metadata_b: Dict[str, Any]) ->
     """
     return set(metadata_a.keys()) == set(metadata_b.keys()) and all(
         np.array_equal(metadata_a[key], metadata_b[key])
-        if isinstance(metadata_a[key], np.ndarray)
-        and isinstance(metadata_b[key], np.ndarray)
+        if (
+            isinstance(metadata_a[key], np.ndarray)
+            and isinstance(metadata_b[key], np.ndarray)
+        )
         else metadata_a[key] == metadata_b[key]
         for key in metadata_a
     )
@@ -833,6 +835,9 @@ def merge_data(
     """
     Merges the data payloads of a list of Detections instances.
 
+    Warning: Assumes that empty detections were filtered-out before passing data to
+    this function.
+
     Args:
         data_list: The data payloads of the Detections instances. Each data payload
             is a dictionary with the same keys, and the values are either lists or
@@ -892,6 +897,9 @@ def merge_metadata(metadata_list: List[Dict[str, Any]]) -> Dict[str, Any]:
     This function combines the metadata dictionaries. If a key appears in more than one
     dictionary, the values must be identical for the merge to succeed.
 
+    Warning: Assumes that empty detections were filtered-out before passing metadata to
+    this function.
+
     Args:
         metadata_list (List[Dict[str, Any]]): A list of metadata dictionaries to merge.
 
@@ -909,7 +917,7 @@ def merge_metadata(metadata_list: List[Dict[str, Any]]) -> Dict[str, Any]:
     if not all(keys_set == all_keys_sets[0] for keys_set in all_keys_sets):
         raise ValueError("All metadata dictionaries must have the same keys to merge.")
 
-    merged_metadata = {}
+    merged_metadata: Dict[str, Any] = {}
     for metadata in metadata_list:
         for key, value in metadata.items():
             if key in merged_metadata:

From d645bd816e454594adf972594ae2dea980392cd9 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Mon, 4 Nov 2024 12:41:16 +0200
Subject: [PATCH 124/128] Fix: Add metadata to public var retrieval test

---
 test/utils/test_internal.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/test/utils/test_internal.py b/test/utils/test_internal.py
index eee614e6..ff9825e2 100644
--- a/test/utils/test_internal.py
+++ b/test/utils/test_internal.py
@@ -121,7 +121,7 @@ class MockDataclass:
         (
             Detections.empty(),
             False,
-            {"xyxy", "class_id", "confidence", "mask", "tracker_id", "data"},
+            {"xyxy", "class_id", "confidence", "mask", "tracker_id", "data", "metadata"},
             DoesNotRaise(),
         ),
         (
@@ -134,6 +134,7 @@ class MockDataclass:
                 "mask",
                 "tracker_id",
                 "data",
+                "metadata",
                 "area",
                 "box_area",
             },
@@ -149,6 +150,7 @@ class MockDataclass:
                 "mask",
                 "tracker_id",
                 "data",
+                "metadata"
             },
             DoesNotRaise(),
         ),
@@ -169,13 +171,14 @@ class MockDataclass:
                 "mask",
                 "tracker_id",
                 "data",
+                "metadata"
             },
             DoesNotRaise(),
         ),
         (
             Detections.empty(),
             False,
-            {"xyxy", "class_id", "confidence", "mask", "tracker_id", "data"},
+            {"xyxy", "class_id", "confidence", "mask", "tracker_id", "data", "metadata"},
             DoesNotRaise(),
         ),
     ],

From 9ee1b5dc5ab691a552f0e67e6f4ad5bfea9c2ea2 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 4 Nov 2024 10:41:52 +0000
Subject: [PATCH 125/128] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20aut?=
 =?UTF-8?q?o=20format=20pre-commit=20hooks?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 test/utils/test_internal.py | 24 ++++++++++++++++++++----
 1 file changed, 20 insertions(+), 4 deletions(-)

diff --git a/test/utils/test_internal.py b/test/utils/test_internal.py
index ff9825e2..872822a7 100644
--- a/test/utils/test_internal.py
+++ b/test/utils/test_internal.py
@@ -121,7 +121,15 @@ class MockDataclass:
         (
             Detections.empty(),
             False,
-            {"xyxy", "class_id", "confidence", "mask", "tracker_id", "data", "metadata"},
+            {
+                "xyxy",
+                "class_id",
+                "confidence",
+                "mask",
+                "tracker_id",
+                "data",
+                "metadata",
+            },
             DoesNotRaise(),
         ),
         (
@@ -150,7 +158,7 @@ class MockDataclass:
                 "mask",
                 "tracker_id",
                 "data",
-                "metadata"
+                "metadata",
             },
             DoesNotRaise(),
         ),
@@ -171,14 +179,22 @@ class MockDataclass:
                 "mask",
                 "tracker_id",
                 "data",
-                "metadata"
+                "metadata",
             },
             DoesNotRaise(),
         ),
         (
             Detections.empty(),
             False,
-            {"xyxy", "class_id", "confidence", "mask", "tracker_id", "data", "metadata"},
+            {
+                "xyxy",
+                "class_id",
+                "confidence",
+                "mask",
+                "tracker_id",
+                "data",
+                "metadata",
+            },
             DoesNotRaise(),
         ),
     ],

From 5391671838c23409b51d7bf9c49b396bb685bcc7 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 4 Nov 2024 18:02:20 +0000
Subject: [PATCH 126/128] =?UTF-8?q?chore(pre=5Fcommit):=20=E2=AC=86=20pre?=
 =?UTF-8?q?=5Fcommit=20autoupdate?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.7.1 → v0.7.2](https://github.com/astral-sh/ruff-pre-commit/compare/v0.7.1...v0.7.2)
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 39c50e82..de50d5e0 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -32,7 +32,7 @@ repos:
           additional_dependencies: ["bandit[toml]"]
 
   -   repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.7.1
+      rev: v0.7.2
       hooks:
       -   id: ruff
           args: [--fix, --exit-non-zero-on-fix]

From 62c2c6a6681d000f66b0ada086aeed7058d72d54 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 6 Nov 2024 00:28:19 +0000
Subject: [PATCH 127/128] :arrow_up: Bump mkdocs-material from 9.5.43 to 9.5.44

Bumps [mkdocs-material](https://github.com/squidfunk/mkdocs-material) from 9.5.43 to 9.5.44.
- [Release notes](https://github.com/squidfunk/mkdocs-material/releases)
- [Changelog](https://github.com/squidfunk/mkdocs-material/blob/master/CHANGELOG)
- [Commits](https://github.com/squidfunk/mkdocs-material/compare/9.5.43...9.5.44)

---
updated-dependencies:
- dependency-name: mkdocs-material
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 poetry.lock | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index d7e29a43..fddfa23f 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2412,13 +2412,13 @@ pygments = ">2.12.0"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.43"
+version = "9.5.44"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.5.43-py3-none-any.whl", hash = "sha256:4aae0664c456fd12837a3192e0225c17960ba8bf55d7f0a7daef7e4b0b914a34"},
-    {file = "mkdocs_material-9.5.43.tar.gz", hash = "sha256:83be7ff30b65a1e4930dfa4ab911e75780a3afc9583d162692e434581cb46979"},
+    {file = "mkdocs_material-9.5.44-py3-none-any.whl", hash = "sha256:47015f9c167d58a5ff5e682da37441fc4d66a1c79334bfc08d774763cacf69ca"},
+    {file = "mkdocs_material-9.5.44.tar.gz", hash = "sha256:f3a6c968e524166b3f3ed1fb97d3ed3e0091183b0545cedf7156a2a6804c56c0"},
 ]
 
 [package.dependencies]

From 659ddec3604cddd209497dafbc21e84eb20c9a18 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Wed, 6 Nov 2024 14:07:26 +0200
Subject: [PATCH 128/128] VideoSimk automatically infers video_info

---
 supervision/utils/video.py | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/supervision/utils/video.py b/supervision/utils/video.py
index 2e502cf2..9d67dbfb 100644
--- a/supervision/utils/video.py
+++ b/supervision/utils/video.py
@@ -65,8 +65,9 @@ class VideoSink:
 
     Attributes:
         target_path (str): The path to the output file where the video will be saved.
-        video_info (VideoInfo): Information about the video resolution, fps,
-            and total frame count.
+        video_info (Optional[VideoInfo]): Information about the output video resolution,
+            fps, and total frame count. If not provided, the information will be inferred
+            from the video path.
         codec (str): FOURCC code for video format
 
     Example:
@@ -82,8 +83,16 @@ class VideoSink:
         ```
     """  # noqa: E501 // docs
 
-    def __init__(self, target_path: str, video_info: VideoInfo, codec: str = "mp4v"):
+    def __init__(
+        self,
+        target_path: str,
+        video_info: Optional[VideoInfo] = None,
+        codec: str = "mp4v",
+    ):
         self.target_path = target_path
+
+        if video_info is None:
+            video_info = VideoInfo.from_video_path(target_path)
         self.video_info = video_info
         self.__codec = codec
         self.__writer = None