Merge branch 'develop' into feat/supervision-cleanup

.pre-commit-config.yaml

@@ -28,7 +28,7 @@ repos:
 
 
   -   repo: https://github.com/PyCQA/bandit
-      rev: '1.7.8'
+      rev: '1.7.9'
       hooks:
       -   id: bandit
           args: ["-c", "pyproject.toml"]
@@ -45,7 +45,7 @@ repos:
 
 
   -   repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.4.8
+      rev: v0.4.9
       hooks:
       -   id: ruff
           args: [--fix, --exit-non-zero-on-fix]

README.md

@@ -28,7 +28,7 @@
 
 **We write your reusable computer vision tools.** Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us! 🤝
 
-[![supervision-hackfest](https://github.com/roboflow/supervision/assets/26109316/c05cc954-b9a6-4ed5-9a52-d0b4b619ff65)](https://github.com/orgs/roboflow/projects/10)
+[![supervision-hackfest](https://github.com/roboflow/supervision/assets/26109316/c05cc954-b9a6-4ed5-9a52-d0b4b619ff65)](https://github.com/orgs/roboflow/projects)
 
 ## 💻 install
 
@@ -86,7 +86,7 @@ len(detections)
 
 ### annotators
 
-Supervision offers a wide range of highly customizable [annotators](https://supervision.roboflow.com/latest/annotators/), allowing you to compose the perfect visualization for your use case.
+Supervision offers a wide range of highly customizable [annotators](https://supervision.roboflow.com/latest/detection/annotators/), allowing you to compose the perfect visualization for your use case.
 
 ```python
 import cv2
@@ -106,7 +106,7 @@ https://github.com/roboflow/supervision/assets/26109316/691e219c-0565-4403-9218-
 
 ### datasets
 
-Supervision provides a set of [utils](https://supervision.roboflow.com/latest/datasets/) that allow you to load, split, merge, and save datasets in one of the supported formats.
+Supervision provides a set of [utils](https://supervision.roboflow.com/latest/datasets/core/) that allow you to load, split, merge, and save datasets in one of the supported formats.
 
 ```python
 import supervision as sv

docs/theme/cookbooks.html

@@ -19,6 +19,9 @@ <h1>Supervision Cookbooks</h1>
       <p class="card repo-card" data-url="/develop/notebooks/object-tracking" data-name="Object Tracking"  data-labels="TRACKING, ANNOTATOR" data-version="v0.18.0" data-author="nickherrig"></p>
       <p class="card repo-card" data-url="/develop/notebooks/occupancy_analytics" data-name="Analyzing Zone Occupancy"  data-labels="ANNOTATOR,DETECTION,ZONES" data-version="v0.19.0" data-author="stellasphere"></p>
       <p class="card repo-card" data-url="/develop/notebooks/evaluating-alignment-of-text-to-image-diffusion-models" data-name="Evaluating Alignment of Text-to-image Diffusion Models"  data-labels="ANNOTATORS,YOLO WORLD" data-version="v0.19.0rc5" data-author="iamhatesz"></p>
+      <p class="card repo-card" data-url="/develop/notebooks/serialise-detections-to-csv" data-name="Serialise Detections to a CSV File"  data-labels="DETECTIONS,CSV SINK,INFERENCE" data-version="v0.21.0" data-author="onuralpszr"></p>
+      <p class="card repo-card" data-url="/develop/notebooks/serialise-detections-to-json" data-name="Serialise Detections to a JSON File"  data-labels="DETECTIONS,JSON SINK,INFERENCE" data-version="v0.21.0" data-author="onuralpszr"></p>
+
     </div>
   </div>
 </section>

pyproject.toml

@@ -32,7 +32,7 @@ classifiers=[
 
 [tool.poetry.dependencies]
 python = "^3.8"
-numpy = ">=1.21.2"
+numpy = ">=1.21.2,<2.0.0"
 scipy = [
     { version = "1.10.0", python = "<3.9" },
     { version = "^1.10.0", python = ">=3.9" }

supervision/annotators/core.py

@@ -172,7 +172,7 @@ def annotate(
             return scene
 
         for detection_idx in range(len(detections)):
-            bbox = np.int0(detections.data.get(ORIENTED_BOX_COORDINATES)[detection_idx])
+            bbox = np.intp(detections.data.get(ORIENTED_BOX_COORDINATES)[detection_idx])
             color = resolve_color(
                 color=self.color,
                 detections=detections,
@@ -833,6 +833,7 @@ def __init__(
         radius: int = 4,
         position: Position = Position.CENTER,
         color_lookup: ColorLookup = ColorLookup.CLASS,
+        outline_thickness: int = 0,
     ):
         """
         Args:
@@ -842,11 +843,13 @@ def __init__(
             position (Position): The anchor position for placing the dot.
             color_lookup (ColorLookup): Strategy for mapping colors to annotations.
                 Options are `INDEX`, `CLASS`, `TRACK`.
+            outline_thickness (int): Thickness of the outline of the dot.
         """
         self.color: Union[Color, ColorPalette] = color
         self.radius: int = radius
         self.position: Position = position
         self.color_lookup: ColorLookup = color_lookup
+        self.outline_thickness = outline_thickness
 
     @convert_for_annotation_method
     def annotate(
@@ -898,7 +901,12 @@ def annotate(
                 else custom_color_lookup,
             )
             center = (int(xy[detection_idx, 0]), int(xy[detection_idx, 1]))
+
             cv2.circle(scene, center, self.radius, color.as_bgr(), -1)
+            if self.outline_thickness:
+                cv2.circle(
+                    scene, center, self.radius, (0, 0, 0), self.outline_thickness
+                )
         return scene
 
 
@@ -1613,6 +1621,7 @@ def __init__(
         height: int = 10,
         position: Position = Position.TOP_CENTER,
         color_lookup: ColorLookup = ColorLookup.CLASS,
+        outline_thickness: int = 0,
     ):
         """
         Args:
@@ -1623,12 +1632,14 @@ def __init__(
             position (Position): The anchor position for placing the triangle.
             color_lookup (ColorLookup): Strategy for mapping colors to annotations.
                 Options are `INDEX`, `CLASS`, `TRACK`.
+            outline_thickness (int): Thickness of the outline of the triangle.
         """
         self.color: Union[Color, ColorPalette] = color
         self.base: int = base
         self.height: int = height
         self.position: Position = position
         self.color_lookup: ColorLookup = color_lookup
+        self.outline_thickness: int = outline_thickness
 
     @convert_for_annotation_method
     def annotate(
@@ -1690,7 +1701,10 @@ def annotate(
             )
 
             cv2.fillPoly(scene, [vertices], color.as_bgr())
-
+            if self.outline_thickness:
+                cv2.polylines(
+                    scene, [vertices], True, (0, 0, 0), thickness=self.outline_thickness
+                )
         return scene
 
 

supervision/dataset/core.py

@@ -266,6 +266,7 @@ def from_yolo(
         annotations_directory_path: str,
         data_yaml_path: str,
         force_masks: bool = False,
+        is_obb: bool = False,
     ) -> DetectionDataset:
         """
         Creates a Dataset instance from YOLO formatted data.
@@ -280,6 +281,9 @@ def from_yolo(
             force_masks (bool, optional): If True, forces
                 masks to be loaded for all annotations,
                 regardless of whether they are present.
+            is_obb (bool, optional): If True, loads the annotations in OBB format.
+                OBB annotations are defined as `[class_id, x, y, x, y, x, y, x, y]`,
+                where pairs of [x, y] are box corners.
 
         Returns:
             DetectionDataset: A DetectionDataset instance
@@ -312,6 +316,7 @@ def from_yolo(
             annotations_directory_path=annotations_directory_path,
             data_yaml_path=data_yaml_path,
             force_masks=force_masks,
+            is_obb=is_obb,
         )
         return DetectionDataset(classes=classes, images=images, annotations=annotations)
 

supervision/dataset/formats/yolo.py

@@ -5,6 +5,7 @@
 import cv2
 import numpy as np
 
+from supervision.config import ORIENTED_BOX_COORDINATES
 from supervision.dataset.utils import approximate_mask_with_polygons
 from supervision.detection.core import Detections
 from supervision.detection.utils import polygon_to_mask, polygon_to_xyxy
@@ -70,12 +71,15 @@ def _image_name_to_annotation_name(image_name: str) -> str:
 
 
 def yolo_annotations_to_detections(
-    lines: List[str], resolution_wh: Tuple[int, int], with_masks: bool
+    lines: List[str],
+    resolution_wh: Tuple[int, int],
+    with_masks: bool,
+    is_obb: bool = False,
 ) -> Detections:
     if len(lines) == 0:
         return Detections.empty()
 
-    class_id, relative_xyxy, relative_polygon = [], [], []
+    class_id, relative_xyxy, relative_polygon, relative_xyxyxyxy = [], [], [], []
     w, h = resolution_wh
     for line in lines:
         values = line.split()
@@ -88,28 +92,38 @@ def yolo_annotations_to_detections(
         elif len(values) > 5:
             polygon = _parse_polygon(values=values[1:])
             relative_xyxy.append(polygon_to_xyxy(polygon=polygon))
+            if is_obb:
+                relative_xyxyxyxy.append(np.array(values[1:]))
             if with_masks:
                 relative_polygon.append(polygon)
 
     class_id = np.array(class_id, dtype=int)
     relative_xyxy = np.array(relative_xyxy, dtype=np.float32)
     xyxy = relative_xyxy * np.array([w, h, w, h], dtype=np.float32)
+    data = {}
+
+    if is_obb:
+        relative_xyxyxyxy = np.array(relative_xyxyxyxy, dtype=np.float32)
+        xyxyxyxy = relative_xyxyxyxy.reshape(-1, 4, 2)
+        xyxyxyxy *= np.array([w, h], dtype=np.float32)
+        data[ORIENTED_BOX_COORDINATES] = xyxyxyxy
 
     if not with_masks:
-        return Detections(class_id=class_id, xyxy=xyxy)
+        return Detections(class_id=class_id, xyxy=xyxy, data=data)
 
     polygons = [
         (polygon * np.array(resolution_wh)).astype(int) for polygon in relative_polygon
     ]
     mask = _polygons_to_masks(polygons=polygons, resolution_wh=resolution_wh)
-    return Detections(class_id=class_id, xyxy=xyxy, mask=mask)
+    return Detections(class_id=class_id, xyxy=xyxy, data=data, mask=mask)
 
 
 def load_yolo_annotations(
     images_directory_path: str,
     annotations_directory_path: str,
     data_yaml_path: str,
     force_masks: bool = False,
+    is_obb: bool = False,
 ) -> Tuple[List[str], Dict[str, np.ndarray], Dict[str, Detections]]:
     """
     Loads YOLO annotations and returns class names, images,
@@ -123,6 +137,9 @@ def load_yolo_annotations(
             YAML file containing class information.
         force_masks (bool, optional): If True, forces masks to be loaded
             for all annotations, regardless of whether they are present.
+        is_obb (bool, optional): If True, loads the annotations in OBB format.
+            OBB annotations are defined as `[class_id, x, y, x, y, x, y, x, y]`,
+            where pairs of [x, y] are box corners.
 
     Returns:
         Tuple[List[str], Dict[str, np.ndarray], Dict[str, Detections]]:
@@ -156,9 +173,11 @@ def load_yolo_annotations(
         with_masks = _with_mask(lines=lines)
         with_masks = force_masks if force_masks else with_masks
         annotation = yolo_annotations_to_detections(
-            lines=lines, resolution_wh=resolution_wh, with_masks=with_masks
+            lines=lines,
+            resolution_wh=resolution_wh,
+            with_masks=with_masks,
+            is_obb=is_obb,
         )
-
         images[image_path] = image
         annotations[image_path] = annotation
     return classes, images, annotations