Merge branch 'roboflow:develop' into develop

.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]

README.md

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

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 }

supervision/dataset/utils.py

@@ -55,7 +55,7 @@ def merge_class_lists(class_lists: List[List[str]]) -> List[str]:
 
     for class_list in class_lists:
         for class_name in class_list:
-            unique_classes.add(class_name.lower())
+            unique_classes.add(class_name)
 
     return sorted(list(unique_classes))
 

supervision/detection/utils.py

@@ -23,10 +23,9 @@ 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))
-
-    cv2.fillPoly(mask, [polygon], color=1)
+    width, height = map(int, resolution_wh)
+    mask = np.zeros((height, width), dtype=np.uint8)
+    cv2.fillPoly(mask, [polygon.astype(np.int32)], color=1)
     return mask
 
 
@@ -163,9 +162,9 @@ 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):

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(

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
+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 (
@@ -57,11 +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:
-            raise NotImplementedError(
-                "Mean Average Precision is not implemented for oriented bounding boxes."
-            )
-
         self._class_agnostic = class_agnostic
 
         self._predictions_list: List[Detections] = []
@@ -189,8 +188,12 @@ 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"
                         )
 
@@ -264,7 +267,6 @@ 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):
@@ -352,8 +354,9 @@ 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):
-                return np.ndarray(obb, 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}")
 
@@ -363,7 +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(

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(