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 01/80] Make BaseTrack._count instance variable

---
 supervision/tracker/byte_tracker/basetrack.py | 25 ++++++++-----------
 1 file changed, 11 insertions(+), 14 deletions(-)

diff --git a/supervision/tracker/byte_tracker/basetrack.py b/supervision/tracker/byte_tracker/basetrack.py
index 806f75384..d280274cd 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 @@ def __init__(self):
     def end_frame(self) -> int:
         return self.frame_id
 
-    @staticmethod
-    def next_id() -> int:
-        BaseTrack._count += 1
-        return BaseTrack._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 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
+        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 02/80] 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 d280274cd..b78bc5961 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 @@ def __init__(self):
     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 89e1e2f2c..cd8ea3e57 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 @@
 
 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 @@ def multi_predict(stracks):
                 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 @@ def activate(self, kalman_filter, frame_id):
             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 @@ def re_activate(self, new_track, frame_id, new_id=False):
 
         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 @@ def update(self, new_track, frame_id):
         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 @@ def tlwh_to_xyah(tlwh):
     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 @@ def __init__(
         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 @@ def __init__(
         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 @@ def reset(self):
         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 @@ def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
             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 @@ def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
             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 @@ def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
             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 @@ def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
             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 000000000..e69de29bb
diff --git a/test/tracker/test_byte_tracker.py b/test/tracker/test_byte_tracker.py
new file mode 100644
index 000000000..673cd90b9
--- /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 03/80] 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 cd8ea3e57..8f64c29da 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 04/80] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?=
 =?UTF-8?q?=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 8f64c29da..b3a324005 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -428,7 +428,9 @@ def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
             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 673cd90b9..7ca94bbfd 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 @@
                 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 05/80] 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 7ca94bbfd..47ad1a9d0 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 @@
     ],
 )
 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 06/80] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?=
 =?UTF-8?q?=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 47ad1a9d0..98efeb093 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 07/80] 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 b3a324005..013af2ee0 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -307,6 +307,7 @@ def reset(self):
         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 124c7d9b907790bde68475af77ff2e2a1fd7815a Mon Sep 17 00:00:00 2001
From: Huzail <huzailkhan03@gmail.com>
Date: Sat, 5 Oct 2024 21:36:21 +0530
Subject: [PATCH 08/80] 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 04a5fe9dd..56fd3967b 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -75,6 +75,13 @@ def update(
                 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 @@ def _match_detection_batch(
         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 09/80] 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 56fd3967b..11fbef5ac 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -184,9 +184,14 @@ def _compute(
                             "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 @@ def _match_detection_batch(
         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 @@ def _match_detection_batch(
         )
         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 @@ def _match_detection_batch(
                 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 10/80] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?=
 =?UTF-8?q?=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 11fbef5ac..ff2d9e8e4 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -186,7 +186,9 @@ def _compute(
 
                     # 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 @@ def _match_detection_batch(
         )
         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 @@ def _match_detection_batch(
                 correct[matches[:, 1].astype(int), i] = True
 
         return correct
+
     @staticmethod
     def _match_detection_batch_class_agnostic(
         iou: np.ndarray,
@@ -303,6 +305,7 @@ def _match_detection_batch_class_agnostic(
                 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 11/80] :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 519b7a2f8..6c9d12a76 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 12/80] :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 519b7a2f8..03aa85c39 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 13/80] =?UTF-8?q?chore(pre=5Fcommit):=20=E2=AC=86=20pre=5F?=
 =?UTF-8?q?commit=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 d8e34a975..2ca4357cb 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 14/80] 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 04a5fe9dd..cc15f5653 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -230,7 +230,7 @@ def _compute_average_precision(recall: np.ndarray, precision: np.ndarray) -> flo
         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 15/80] =?UTF-8?q?ci:=20=F0=9F=91=B7=20python3.13=20ci=20te?=
 =?UTF-8?q?sts=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 1ce4a267f..b33f8e537 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 46886c13c..3f44d215e 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 16/80] 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 c9aa384e7..f63011062 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 000000000..e69de29bb

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 17/80] 	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 d672344bf..7b7dd93c2 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -75,9 +75,8 @@ def update(
                 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 @@ def _compute(
                             "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 18/80] 	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 7b7dd93c2..9aa069354 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -78,9 +78,9 @@ def update(
         
         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 19/80] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?=
 =?UTF-8?q?=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 9aa069354..ef8f4c74a 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -75,7 +75,7 @@ def update(
                 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 @@ def _compute(
                             "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 20/80]  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 9aa069354..b02a2742f 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 and IoU threshold. Shape: `(num_target_classes, num_iou_thresholds)`
     """
 
     metric_target: MetricTarget
+    is_class_agnostic: bool
 
     @property
     def map50_95(self) -> float:
@@ -477,6 +479,7 @@ def __str__(self) -> str:
         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 21/80] 	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 ec9d0fd4a..b496ffaa3 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -478,7 +478,7 @@ def __str__(self) -> str:
         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 22/80] 	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 b496ffaa3..3d3f10f4e 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -215,6 +215,7 @@ def _compute(
 
         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 and IoU threshold. Shape: `(num_target_classes, num_iou_thresholds)`
     """
 
     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 23/80] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?=
 =?UTF-8?q?=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 3d3f10f4e..3c7ae7ae3 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -444,7 +444,7 @@ class and IoU threshold. Shape: `(num_target_classes, num_iou_thresholds)`
     """
 
     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 24/80] 	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 3d3f10f4e..05885784c 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -444,7 +444,7 @@ class and IoU threshold. Shape: `(num_target_classes, num_iou_thresholds)`
     """
 
     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 25/80] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?=
 =?UTF-8?q?=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 aeff48b38..c5338a6dc 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -215,7 +215,7 @@ def _compute(
 
         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 and IoU threshold. Shape: `(num_target_classes, num_iou_thresholds)`
             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 26/80] 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 c5338a6dc..b05d60093 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -77,6 +77,9 @@ def update(
             )
 
         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 27/80] 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 b05d60093..4e3aa26ca 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -186,14 +186,11 @@ def _compute(
                             "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 28/80] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?=
 =?UTF-8?q?=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 4e3aa26ca..ff6062982 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -186,8 +186,6 @@ def _compute(
                             "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 29/80] 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 ff6062982..ed5567192 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -276,33 +276,6 @@ def _match_detection_batch(
 
         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 2530c41ffa9de5b62e39ffabe66410191ce02dd2 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Thu, 10 Oct 2024 13:00:07 +0300
Subject: [PATCH 30/80] 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 ed5567192..dbd60b2e7 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 31/80] 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 17a6cd485..8ae33e639 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 1440fd435..d1818441e 100644
--- a/supervision/metrics/core.py
+++ b/supervision/metrics/core.py
@@ -4,9 +4,6 @@
 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 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 32/80] 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 1654ff3c0..986849de3 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 33/80] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?=
 =?UTF-8?q?=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 986849de3..db69b0852 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 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 34/80] :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 5d47a083a..aebc04c7d 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 35/80] 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 19ce4a258..50c911d4c 100644
--- a/supervision/draw/utils.py
+++ b/supervision/draw/utils.py
@@ -9,7 +9,7 @@
 
 
 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 36/80] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?=
 =?UTF-8?q?=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 50c911d4c..beec3f31c 100644
--- a/supervision/draw/utils.py
+++ b/supervision/draw/utils.py
@@ -9,7 +9,11 @@
 
 
 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 37/80] 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 50c911d4c..1c207b5df 100644
--- a/supervision/draw/utils.py
+++ b/supervision/draw/utils.py
@@ -9,7 +9,7 @@
 
 
 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 38/80] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?=
 =?UTF-8?q?=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 1c207b5df..643970280 100644
--- a/supervision/draw/utils.py
+++ b/supervision/draw/utils.py
@@ -9,7 +9,11 @@
 
 
 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 39/80] =?UTF-8?q?feat:=20=E2=9C=A8=20set=20default=20color?=
 =?UTF-8?q?s=20for=20polygon=20drawing=20functions=20and=20add=20documenta?=
 =?UTF-8?q?tions?=
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 ac7203ff8..b8b728fc5 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 643970280..0c3767ff7 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 40/80] 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 dbd60b2e7..2429c1898 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -9,7 +9,7 @@
 
 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 @@ def __init__(
             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 @@ def _compute(
                         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 @@ def _compute(
 
         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 @@ def _detections_content(self, detections: Detections) -> np.ndarray:
             )
         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 41/80] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?=
 =?UTF-8?q?=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 2429c1898..58d4764c1 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -9,7 +9,11 @@
 
 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 @@ def _compute(
                     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 42/80] 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 4cb70b93f..000000000
--- 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 43/80] =?UTF-8?q?feat:=20=F0=9F=9A=80=20python3.13=20suppo?=
 =?UTF-8?q?rt=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 000000000..95e2eb7fe
--- /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 aebc04c7d..1994447dd 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 = [
-    {file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9063e24fdb1e498ab71cb7419e24622516c4a04476b17a2dab57e8baa30d6e03"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6897af51655e3691ff853668779c7bad41579facacf5fd7253b0133308cf000d"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cd70574b12bb8a4d2aaa0094515df2463cb429d8536cfb6c7ce983246983e5a6"},
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-    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a9a8e9031d613fd2009c182b69c7b2c1ef8239a0efb1df3f7c8da66d5dd3d537"},
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-    {file = "charset_normalizer-3.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09"},
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 [[package]]
@@ -661,6 +676,90 @@ mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.4.1)", "types-Pill
 test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
 test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
 
+[[package]]
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 [[package]]
 name = "cryptography"
 version = "43.0.1"
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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"
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 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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 ]
 
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+optional = false
+python-versions = ">=3.8"
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+importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
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+pillow = ">=6.2.0"
+pyparsing = ">=2.3.1,<3.1"
+python-dateutil = ">=2.7"
+
 [[package]]
 name = "matplotlib"
 version = "3.7.5"
@@ -2655,6 +2824,68 @@ files = [
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 ]
 
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+optional = false
+python-versions = ">=3.10"
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 types-pytz = ">=2022.1.1"
 
+[[package]]
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+optional = true
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+
 [[package]]
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@@ -3142,13 +3391,13 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
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 [package.extras]
 extra = ["pygments (>=2.12)"]
 
+[[package]]
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 [[package]]
@@ -3719,19 +3986,19 @@ files = [
 
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 [package.extras]
 jupyter = ["ipywidgets (>=7.5.1,<9)"]
@@ -3955,6 +4222,56 @@ dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pyde
 doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.12.0)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0)", "sphinx-design (>=0.4.0)"]
 test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
+[[package]]
+name = "scipy"
+version = "1.14.1"
+description = "Fundamental algorithms for scientific computing in Python"
+optional = false
+python-versions = ">=3.10"
+files = [
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+    {file = "scipy-1.14.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5149e3fd2d686e42144a093b206aef01932a0059c2a33ddfa67f5f035bdfe13e"},
+    {file = "scipy-1.14.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e4f5a7c49323533f9103d4dacf4e4f07078f360743dec7f7596949149efeec06"},
+    {file = "scipy-1.14.1-cp313-cp313-win_amd64.whl", hash = "sha256:baff393942b550823bfce952bb62270ee17504d02a1801d7fd0719534dfb9c84"},
+    {file = "scipy-1.14.1.tar.gz", hash = "sha256:5a275584e726026a5699459aa72f828a610821006228e841b94275c4a7c08417"},
+]
+
+[package.dependencies]
+numpy = ">=1.23.5,<2.3"
+
+[package.extras]
+dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"]
+doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.13.1)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<=7.3.7)", "sphinx-design (>=0.4.0)"]
+test = ["Cython", "array-api-strict (>=2.0)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+
 [[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 = [
-    {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
-    {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
+    {file = "tomli-2.0.2-py3-none-any.whl", hash = "sha256:2ebe24485c53d303f690b0ec092806a085f07af5a5aa1464f3931eec36caaa38"},
+    {file = "tomli-2.0.2.tar.gz", hash = "sha256:d46d457a85337051c36524bc5349dd91b1877838e2979ac5ced3e710ed8a60ed"},
 ]
 
 [[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 = [
-    {file = "types-python-dateutil-2.9.0.20240906.tar.gz", hash = "sha256:9706c3b68284c25adffc47319ecc7947e5bb86b3773f843c73906fd598bc176e"},
-    {file = "types_python_dateutil-2.9.0.20240906-py3-none-any.whl", hash = "sha256:27c8cc2d058ccb14946eebcaaa503088f4f6dbc4fb6093d3d456a49aef2753f6"},
+    {file = "types-python-dateutil-2.9.0.20241003.tar.gz", hash = "sha256:58cb85449b2a56d6684e41aeefb4c4280631246a0da1a719bdbe6f3fb0317446"},
+    {file = "types_python_dateutil-2.9.0.20241003-py3-none-any.whl", hash = "sha256:250e1d8e80e7bbc3a6c99b907762711d1a1cdd00e978ad39cb5940f6f0a87f3d"},
 ]
 
 [[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 = [
-    {file = "types-pytz-2024.2.0.20240913.tar.gz", hash = "sha256:4433b5df4a6fc587bbed41716d86a5ba5d832b4378e506f40d34bc9c81df2c24"},
-    {file = "types_pytz-2024.2.0.20240913-py3-none-any.whl", hash = "sha256:a1eebf57ebc6e127a99d2fa2ba0a88d2b173784ef9b3defcc2004ab6855a44df"},
+    {file = "types-pytz-2024.2.0.20241003.tar.gz", hash = "sha256:575dc38f385a922a212bac00a7d6d2e16e141132a3c955078f4a4fd13ed6cb44"},
+    {file = "types_pytz-2024.2.0.20241003-py3-none-any.whl", hash = "sha256:3e22df1336c0c6ad1d29163c8fda82736909eb977281cb823c57f8bae07118b7"},
 ]
 
 [[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 = [
-    {file = "types-setuptools-75.1.0.20240917.tar.gz", hash = "sha256:12f12a165e7ed383f31def705e5c0fa1c26215dd466b0af34bd042f7d5331f55"},
-    {file = "types_setuptools-75.1.0.20240917-py3-none-any.whl", hash = "sha256:06f78307e68d1bbde6938072c57b81cf8a99bc84bd6dc7e4c5014730b097dc0c"},
+    {file = "types-setuptools-75.1.0.20241014.tar.gz", hash = "sha256:29b0560a8d4b4a91174be085847002c69abfcb048e20b33fc663005aedf56804"},
+    {file = "types_setuptools-75.1.0.20241014-py3-none-any.whl", hash = "sha256:caab58366741fb99673d0138b6e2d760717f154cfb981b74fea5e8de40f0b703"},
 ]
 
 [[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 f63011062..6464346b7 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 44/80] :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 1994447dd..45db2b43f 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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-    {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 45/80] 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 43fcec5a0..a0a4d3e21 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 58d4764c1..f0613f2b9 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -9,11 +9,7 @@
 
 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 @@ def __init__(
             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 @@ def update(
                 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 @@ def _compute(
                     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 @@ def _compute_average_precision(recall: np.ndarray, precision: np.ndarray) -> flo
         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 @@ def _match_detection_batch(
             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 @@ def _detections_content(self, detections: Detections) -> np.ndarray:
                 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 and IoU threshold. Shape: `(num_target_classes, num_iou_thresholds)`
     """
 
     metric_target: MetricTarget
-    is_class_agnostic: bool
 
     @property
     def map50_95(self) -> float:
@@ -452,7 +435,6 @@ def __str__(self) -> str:
         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 @@ def plot(self):
         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 46/80] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?=
 =?UTF-8?q?=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 f0613f2b9..0ebffd51b 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -9,7 +9,11 @@
 
 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 @@ def _compute(
                     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 @@ def plot(self):
         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 47/80] 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 0ebffd51b..2a4f121d4 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -78,6 +78,15 @@ def update(
                 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 48/80] :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 1994447dd..22d8fa7b4 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"},
+    {file = "contourpy-1.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:1f795597073b09d631782e7245016a4323cf1cf0b4e06eef7ea6627e06a37ff2"},
+    {file = "contourpy-1.1.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0b7b04ed0961647691cfe5d82115dd072af7ce8846d31a5fac6c142dcce8b882"},
+    {file = "contourpy-1.1.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:27bc79200c742f9746d7dd51a734ee326a292d77e7d94c8af6e08d1e6c15d545"},
+    {file = "contourpy-1.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:052cc634bf903c604ef1a00a5aa093c54f81a2612faedaa43295809ffdde885e"},
+    {file = "contourpy-1.1.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9382a1c0bc46230fb881c36229bfa23d8c303b889b788b939365578d762b5c18"},
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+    {file = "contourpy-1.1.0.tar.gz", hash = "sha256:e53046c3863828d21d531cc3b53786e6580eb1ba02477e8681009b6aa0870b21"},
+]
+
+[package.dependencies]
+numpy = ">=1.16"
+
+[package.extras]
+bokeh = ["bokeh", "selenium"]
+docs = ["furo", "sphinx-copybutton"]
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+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
+test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
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 version = "1.1.1"
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 [[package]]
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 version = "0.1.7"
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     {version = ">=1.21.0", markers = "python_version <= \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\" and python_version >= \"3.8\""},
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     {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"
@@ -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"},
-    {file = "tox-4.21.2.tar.gz", hash = "sha256:49381ff102296753e378fa5ff30e42a35e695f149b4dbf8a2c49d15fdb5797b2"},
+    {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 49/80] :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 1994447dd..b11f09989 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 = [
-    {file = "matplotlib-3.7.5-cp310-cp310-macosx_10_12_universal2.whl", hash = "sha256:4a87b69cb1cb20943010f63feb0b2901c17a3b435f75349fd9865713bfa63925"},
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 [[package]]
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@@ -2421,13 +2412,13 @@ pygments = ">2.12.0"
 
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-
 [[package]]
 name = "numpy"
 version = "2.1.2"
@@ -2904,12 +2850,12 @@ files = [
 
 [package.dependencies]
 numpy = [
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 ]
 
@@ -2986,9 +2932,9 @@ files = [
 
 [package.dependencies]
 numpy = [
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 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
@@ -3031,24 +2977,6 @@ files = [
 [package.dependencies]
 types-pytz = ">=2022.1.1"
 
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-
 [[package]]
 name = "pandocfilters"
 version = "1.5.1"
@@ -3421,20 +3349,6 @@ files = [
 [package.extras]
 diagrams = ["jinja2", "railroad-diagrams"]
 
-[[package]]
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-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 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 50/80] :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 8e0cf21ca..797b71397 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 51/80] 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 8a0ca35c5..580dc0c5a 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 52/80] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?=
 =?UTF-8?q?=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 580dc0c5a..cdceb36dc 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 53/80] 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 cdceb36dc..2247adc50 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 54/80] 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 b78bc5961..e2bc66d57 100644
--- a/supervision/tracker/byte_tracker/basetrack.py
+++ b/supervision/tracker/byte_tracker/basetrack.py
@@ -38,7 +38,7 @@ def reset_counter(self):
         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 24abe224c..f791e518f 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 55/80] 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 013af2ee0..d1d567fab 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.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 @@ def __init__(self, tlwh, score, class_ids, minimum_consecutive_frames):
         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 @@ def multi_predict(stracks):
                 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 @@ def activate(self, kalman_filter, frame_id, track_id):
             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 @@ def re_activate(self, new_track, frame_id, new_id: Optional[int] = None):
         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 @@ def update(self, new_track, frame_id, track_id):
         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 @@ def __repr__(self):
         )
 
 
-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 @@ def __init__(
         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 @@ def __init__(
         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 @@ def callback(frame: np.ndarray, index: int) -> np.ndarray:
             ```
         """
 
-        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 @@ def reset(self):
         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 @@ def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
         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 @@ def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
             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 @@ def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
         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 @@ def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
             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 @@ def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
             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 @@ def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
             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 56/80] :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 797b71397..eadeb1380 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 57/80] :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 797b71397..1ee978e64 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"},
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 ]
 
 [[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 58/80] 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 b8b728fc5..f69f3c9fe 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 59/80] 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 000000000..ca318f8fb
--- /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 000000000..5baa4d3ee
--- /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 3cd867590..a3c9c1caa 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 000000000..ba441831a
--- /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 000000000..7c90859cc
--- /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 60/80] 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 000000000..b7600f3f1
--- /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 ca318f8fb..c704452ee 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 5baa4d3ee..78dde8334 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 a3c9c1caa..b30dbcfcc 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 d1818441e..def5999a0 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 2ca5bca5c..ba4fcd59a 100644
--- a/supervision/metrics/f1_score.py
+++ b/supervision/metrics/f1_score.py
@@ -23,11 +23,45 @@
 
 
 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 @@ def __init__(
         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 @@ def update(
         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 @@ def update(
         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 dbd60b2e7..8cec50c85 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -23,6 +23,27 @@
 
 
 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 @@ def __init__(
         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 @@ def compute(
     ) -> 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 ba441831a..d915e1f49 100644
--- a/supervision/metrics/precision.py
+++ b/supervision/metrics/precision.py
@@ -23,11 +23,48 @@
 
 
 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 @@ def __init__(
         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 @@ def update(
         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 @@ def update(
         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 7c90859cc..9eae24f8e 100644
--- a/supervision/metrics/recall.py
+++ b/supervision/metrics/recall.py
@@ -23,11 +23,48 @@
 
 
 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 @@ def __init__(
         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 @@ def update(
         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 @@ def update(
         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 61/80] 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 8ae33e639..90fc17b47 100644
--- a/supervision/metrics/__init__.py
+++ b/supervision/metrics/__init__.py
@@ -8,6 +8,8 @@
     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 62/80] :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 e1767ce1e..f43db4828 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-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:8135ffec02121a75f75dc97c81af7c14aa4ae0dda277132cfcd6abcd21551bfd"},
+    {file = "mypy-1.12.1-cp39-cp39-win_amd64.whl", hash = "sha256:a7b76fa83260824300cc4834a3ab93180db19876bce59af921467fd03e692810"},
+    {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 63/80] :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 e1767ce1e..548de46a9 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 64/80] =?UTF-8?q?chore(pre=5Fcommit):=20=E2=AC=86=20pre=5F?=
 =?UTF-8?q?commit=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 2ca4357cb..8ddaa08e9 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 65/80] :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 088b34e51..8d73d1bb6 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 66/80] :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 088b34e51..29649128a 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-macosx_11_0_arm64.whl", hash = "sha256:7bcb0bb7f42a978bb323a7c88f1081d1b5dee77ca86f4100735a6f541299d8fb"},
+    {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 67/80] :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 088b34e51..792bc8360 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 68/80] 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 d1d567fab..f453e5237 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -276,11 +276,16 @@ def callback(frame: np.ndarray, index: int) -> np.ndarray:
             ```
         """
 
+        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 69/80] :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 0a010aa4d..82ab2f08b 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"},
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-    {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 70/80] =?UTF-8?q?chore(pre=5Fcommit):=20=E2=AC=86=20pre=5F?=
 =?UTF-8?q?commit=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 8ddaa08e9..39c50e823 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 71/80] :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 82ab2f08b..69775617e 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 6464346b7..713bd838e 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 72/80] 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 87de73271..d5f66103e 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -61,9 +61,6 @@ def __init__(
             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 @@ def _compute(
                     matches = self._match_detection_batch(
                         predictions.class_id, targets.class_id, iou, iou_thresholds
                     )
+
                     stats.append(
                         (
                             matches,
@@ -226,6 +224,7 @@ def _compute(
 
         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 @@ def _compute_average_precision(recall: np.ndarray, precision: np.ndarray) -> flo
         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 @@ def _make_empty_content(self) -> np.ndarray:
         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 and IoU threshold. Shape: `(num_target_classes, num_iou_thresholds)`
     """
 
     metric_target: MetricTarget
+    is_class_agnostic: bool
 
     @property
     def map50_95(self) -> float:
@@ -460,6 +462,7 @@ def __str__(self) -> str:
         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 73/80] 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 ba4fcd59a..cc8c87a2c 100644
--- a/supervision/metrics/f1_score.py
+++ b/supervision/metrics/f1_score.py
@@ -9,7 +9,11 @@
 
 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 @@ def __init__(
             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 @@ def _compute(
                         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 @@ def _detections_content(self, detections: Detections) -> np.ndarray:
             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 d5f66103e..ba37837b3 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -193,7 +193,7 @@ def _compute(
                             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 74/80] 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 a0a4d3e21..69cdacc7e 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 75/80] 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 d915e1f49..fa6cf2b1a 100644
--- a/supervision/metrics/precision.py
+++ b/supervision/metrics/precision.py
@@ -9,7 +9,11 @@
 
 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 @@ def __init__(
             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 @@ def _compute(
                         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 @@ def _detections_content(self, detections: Detections) -> np.ndarray:
             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 9eae24f8e..1848502b7 100644
--- a/supervision/metrics/recall.py
+++ b/supervision/metrics/recall.py
@@ -9,7 +9,11 @@
 
 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 @@ def __init__(
             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 @@ def _compute(
                         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 @@ def _detections_content(self, detections: Detections) -> np.ndarray:
             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 76/80] 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 f453e5237..34a9501ad 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -323,7 +323,6 @@ def reset(self):
         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 77/80] 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 f453e5237..d1d567fab 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -276,16 +276,11 @@ def callback(frame: np.ndarray, index: int) -> np.ndarray:
             ```
         """
 
-        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 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 78/80] :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 69775617e..6e2817a5d 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 3ea49a08204f0629e7df9fb80a13100f75a7dfc5 Mon Sep 17 00:00:00 2001
From: Archit H Barve <architbarve102@gmail.com>
Date: Sun, 3 Nov 2024 23:18:35 +0530
Subject: [PATCH 79/80] feat/estimate-polygonzone-occupation

---
 supervision/detection/utils.py | 45 ++++++++++++++++++++++++++++++++++
 1 file changed, 45 insertions(+)

diff --git a/supervision/detection/utils.py b/supervision/detection/utils.py
index 69cdacc7e..cf53fefe0 100644
--- a/supervision/detection/utils.py
+++ b/supervision/detection/utils.py
@@ -1038,3 +1038,48 @@ def cross_product(anchors: np.ndarray, vector: Vector) -> np.ndarray:
     )
     vector_start = np.array([vector.start.x, vector.start.y])
     return np.cross(vector_at_zero, anchors - vector_start)
+
+
+#Overlap estimation 
+def generate_mask_from_detections(img_shape: tuple[int, int], detection)-> np.ndarray:
+    """convert bounding polygon coord to binary mask"""
+    # Create binary mask
+    mask = np.zeros(img_shape[:2], np.uint8)
+
+    #Segmentation case 
+    if 'mask' in detection: 
+        mask = detection['mask']
+
+    #Object detection case 
+    elif 'xyxy' in detection: 
+        x1,y1,x2,y2 = map(int, detection['xyxy'])
+        cv2.rectangle(mask, (x1,y1), (x2,y2), color=(1,), thickness=cv2.FILLED)
+
+    #Oriented Bounding box detection case 
+    elif 'polygon' in detection:
+        contour = np.array(detection['polygon'], dtype=np.int32).reshape(-1, 1, 2)
+        mask = cv2.drawContours(mask, [contour], -1,  color=(1,), thickness=cv2.FILLED)
+    
+    return mask
+
+def calculate_overlap_area(zone_mask: np.ndarray, masks: List[np.ndarray]) -> float:
+    """calculate how much % of the zone is occupied"""
+    #create one mask from the object masks as the union
+
+    union_mask = np.bitwise_or.reduce(np.stack(masks), axis=0)
+
+    # Calculate the overlap mask between union_mask & zone_mask
+    overlap_mask = np.bitwise_and(union_mask, zone_mask)
+
+    # Calculate sizes
+    overlap_size = np.sum(overlap_mask)
+    zone_size = np.sum(zone_mask)
+
+    return 100 * overlap_size / zone_size
+
+def calculate_overlap_with_zone(zone_mask: np.ndarray, detections, img_shape: tuple[int, int], show_plot: bool = True) -> float:
+    masks = [generate_mask_from_detections(img_shape, det) for det in detections]
+    return calculate_overlap_area(zone_mask, masks)
+
+
+

From 6157d90bf04f7a9582398d36ca733c512f09be8a Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Sun, 3 Nov 2024 17:57:06 +0000
Subject: [PATCH 80/80] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?=
 =?UTF-8?q?=20format=20pre-commit=20hooks?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 supervision/detection/utils.py | 42 +++++++++++++++++++---------------
 1 file changed, 23 insertions(+), 19 deletions(-)

diff --git a/supervision/detection/utils.py b/supervision/detection/utils.py
index cf53fefe0..6a3da0cd3 100644
--- a/supervision/detection/utils.py
+++ b/supervision/detection/utils.py
@@ -1040,31 +1040,32 @@ def cross_product(anchors: np.ndarray, vector: Vector) -> np.ndarray:
     return np.cross(vector_at_zero, anchors - vector_start)
 
 
-#Overlap estimation 
-def generate_mask_from_detections(img_shape: tuple[int, int], detection)-> np.ndarray:
+# Overlap estimation
+def generate_mask_from_detections(img_shape: tuple[int, int], detection) -> np.ndarray:
     """convert bounding polygon coord to binary mask"""
     # Create binary mask
     mask = np.zeros(img_shape[:2], np.uint8)
 
-    #Segmentation case 
-    if 'mask' in detection: 
-        mask = detection['mask']
+    # Segmentation case
+    if "mask" in detection:
+        mask = detection["mask"]
 
-    #Object detection case 
-    elif 'xyxy' in detection: 
-        x1,y1,x2,y2 = map(int, detection['xyxy'])
-        cv2.rectangle(mask, (x1,y1), (x2,y2), color=(1,), thickness=cv2.FILLED)
+    # Object detection case
+    elif "xyxy" in detection:
+        x1, y1, x2, y2 = map(int, detection["xyxy"])
+        cv2.rectangle(mask, (x1, y1), (x2, y2), color=(1,), thickness=cv2.FILLED)
+
+    # Oriented Bounding box detection case
+    elif "polygon" in detection:
+        contour = np.array(detection["polygon"], dtype=np.int32).reshape(-1, 1, 2)
+        mask = cv2.drawContours(mask, [contour], -1, color=(1,), thickness=cv2.FILLED)
 
-    #Oriented Bounding box detection case 
-    elif 'polygon' in detection:
-        contour = np.array(detection['polygon'], dtype=np.int32).reshape(-1, 1, 2)
-        mask = cv2.drawContours(mask, [contour], -1,  color=(1,), thickness=cv2.FILLED)
-    
     return mask
 
+
 def calculate_overlap_area(zone_mask: np.ndarray, masks: List[np.ndarray]) -> float:
     """calculate how much % of the zone is occupied"""
-    #create one mask from the object masks as the union
+    # create one mask from the object masks as the union
 
     union_mask = np.bitwise_or.reduce(np.stack(masks), axis=0)
 
@@ -1077,9 +1078,12 @@ def calculate_overlap_area(zone_mask: np.ndarray, masks: List[np.ndarray]) -> fl
 
     return 100 * overlap_size / zone_size
 
-def calculate_overlap_with_zone(zone_mask: np.ndarray, detections, img_shape: tuple[int, int], show_plot: bool = True) -> float:
+
+def calculate_overlap_with_zone(
+    zone_mask: np.ndarray,
+    detections,
+    img_shape: tuple[int, int],
+    show_plot: bool = True,
+) -> float:
     masks = [generate_mask_from_detections(img_shape, det) for det in detections]
     return calculate_overlap_area(zone_mask, masks)
-
-
-