👊 bump develop version to 0.15.0rc2

Merge pull request #170 from roboflow/feature/redesigned_annotators
redesigned annotators
2023-09-29 13:46:39 +02:00 · 2023-09-29 14:36:43 +03:00 · 2023-09-29 13:25:26 +03:00 · 2023-09-28 21:55:12 +00:00 · 2023-09-28 23:54:34 +02:00 · 2023-09-28 21:24:53 +00:00
21 changed files with 894 additions and 137 deletions
@@ -22,8 +22,5 @@ jobs:
          python -m pip install --upgrade pip
          pip install -e .
          pip install pytest
-          pip install isort
-          pip install flake8
-          pip install "black==22.3.0"
      - name: 🧪 Test
        run: "python -m pytest ./test"
@@ -69,12 +69,12 @@ repos:


  -   repo: https://github.com/psf/black
-      rev: 23.7.0
+      rev: 23.9.1
      hooks:
      -   id: black

  -   repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.0.286
+      rev: v0.0.291
      hooks:
      -   id: ruff
          args: [--fix, --exit-non-zero-on-fix]
@@ -273,19 +273,13 @@ array([

 </details>

-## 🛠️ built with supervision
-
-Did you build something cool using supervision? [Let us know!](https://github.com/roboflow/supervision/discussions/categories/built-with-supervision)
-
-https://user-images.githubusercontent.com/26109316/207858600-ee862b22-0353-440b-ad85-caa0c4777904.mp4
-
 ## 🎬 tutorials

 <p align="left">
-<a href="https://youtu.be/oEQYStnF2l8" title="Accelerate Image Annotation with SAM and Grounding DINO"><img src="https://github.com/SkalskiP/SkalskiP/assets/26109316/ae1ca38e-40b7-4b35-8582-e8ea5de3806e" alt="Accelerate Image Annotation with SAM and Grounding DINO" width="300px" align="left" /></a>
-<a href="https://youtu.be/oEQYStnF2l8" title="Accelerate Image Annotation with SAM and Grounding DINO"><strong>Accelerate Image Annotation with SAM and Grounding DINO</strong></a>
-<div><strong>Created: 20 Apr 2023</strong> | <strong>Updated: 20 Apr 2023</strong></div>
-<br/> Discover how to speed up your image annotation process using Grounding DINO and Segment Anything Model (SAM). Learn how to convert object detection datasets into instance segmentation datasets, and see the potential of using these models to automatically annotate your datasets for real-time detectors like YOLOv8... </p>
+<a href="https://youtu.be/4Q3ut7vqD5o" title="Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking"><img src="https://github.com/roboflow/supervision/assets/26109316/54afdf1c-218c-4451-8f12-627fb85f1682" alt="Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking" width="300px" align="left" /></a>
+<a href="https://youtu.be/4Q3ut7vqD5o" title="Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking"><strong>Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking</strong></a>
+<div><strong>Created: 6 Sep 2023</strong> | <strong>Updated: 6 Sep 2023</strong></div>
+<br/> In this video, we explore real-time traffic analysis using YOLOv8 and ByteTrack to detect and track vehicles on aerial images. Harnessing the power of Python and Supervision, we delve deep into assigning cars to specific entry zones and understanding their direction of movement. By visualizing their paths, we gain insights into traffic flow across bustling roundabouts... </p>

 <br/>

@@ -295,6 +289,14 @@ https://user-images.githubusercontent.com/26109316/207858600-ee862b22-0353-440b-
 <div><strong>Created: 11 Apr 2023</strong> | <strong>Updated: 11 Apr 2023</strong></div>
 <br/> Discover the incredible potential of Meta AI's Segment Anything Model (SAM)! We dive into SAM, an efficient and promptable model for image segmentation, which has revolutionized computer vision tasks. With over 1 billion masks on 11M licensed and privacy-respecting images, SAM's zero-shot performance is often competitive with or even superior to prior fully supervised results... </p>

+## 💜 built with supervision
+
+Did you build something cool using supervision? [Let us know!](https://github.com/roboflow/supervision/discussions/categories/built-with-supervision)
+
+https://user-images.githubusercontent.com/26109316/207858600-ee862b22-0353-440b-ad85-caa0c4777904.mp4
+
+https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c-60e9c81b3900
+
 ## 📚 documentation

 Visit our [documentation](https://roboflow.github.io/supervision) page to learn how supervision can help you build computer vision applications faster and more reliably.
@@ -1,11 +1,19 @@
-## BoxAnnotator
+## BoundingBoxAnnotator

-:::supervision.detection.annotate.BoxAnnotator
+:::supervision.annotators.core.BoundingBoxAnnotator

 ## MaskAnnotator

-:::supervision.detection.annotate.MaskAnnotator
+:::supervision.annotators.core.MaskAnnotator

-## TraceAnnotator
+## EllipseAnnotator

-:::supervision.detection.annotate.TraceAnnotator
+:::supervision.annotators.core.EllipseAnnotator
+
+## BoxCornerAnnotator
+
+:::supervision.annotators.core.BoxCornerAnnotator
+
+## LabelAnnotator
+
+:::supervision.annotators.core.LabelAnnotator
@@ -0,0 +1,3 @@
+## Position
+
+:::supervision.geometry.core.Position
@@ -16,7 +16,8 @@ def process_video(
    model = YOLO(source_weights_path)

    tracker = sv.ByteTrack()
-    box_annotator = sv.BoxAnnotator()
+    box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator(classes=model.names)
    frame_generator = sv.get_video_frames_generator(source_path=source_video_path)
    video_info = sv.VideoInfo.from_video_path(video_path=source_video_path)

@@ -28,16 +29,15 @@ def process_video(
            detections = sv.Detections.from_ultralytics(results)
            detections = tracker.update_with_detections(detections)

-            labels = [
-                f"#{tracker_id} {model.model.names[class_id]}"
-                for _, _, _, class_id, tracker_id in detections
-            ]
-
            annotated_frame = box_annotator.annotate(
-                scene=frame.copy(), detections=detections, labels=labels
+                scene=frame.copy(), detections=detections
            )

-            sink.write_frame(frame=annotated_frame)
+            annotated_labeled_frame = label_annotator.annotate(
+                scene=annotated_frame, detections=detections
+            )
+
+            sink.write_frame(frame=annotated_labeled_frame)


 if __name__ == "__main__":
@@ -1,4 +1,4 @@
-supervision
+supervision>=0.15.0rc1
 tqdm
 ultralytics
 gdown
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@@ -539,34 +543,34 @@ test-no-images = ["pytest", "pytest-cov", "wurlitzer"]

 [[package]]
 name = "cryptography"
-version = "41.0.3"
+version = "41.0.4"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "cryptography-41.0.4-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:0d9409894f495d465fe6fda92cb70e8323e9648af912d5b9141d616df40a87b8"},
+    {file = "cryptography-41.0.4-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:8ac4f9ead4bbd0bc8ab2d318f97d85147167a488be0e08814a37eb2f439d5cf6"},
+    {file = "cryptography-41.0.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:047c4603aeb4bbd8db2756e38f5b8bd7e94318c047cfe4efeb5d715e08b49311"},
+    {file = "cryptography-41.0.4.tar.gz", hash = "sha256:7febc3094125fc126a7f6fb1f420d0da639f3f32cb15c8ff0dc3997c4549f51a"},
 ]

 [package.dependencies]
@@ -1451,6 +1455,16 @@ files = [
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    {file = "MarkupSafe-2.1.3-cp311-cp311-win32.whl", hash = "sha256:dd15ff04ffd7e05ffcb7fe79f1b98041b8ea30ae9234aed2a9168b5797c3effb"},
    {file = "MarkupSafe-2.1.3-cp311-cp311-win_amd64.whl", hash = "sha256:134da1eca9ec0ae528110ccc9e48041e0828d79f24121a1a146161103c76e686"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:f698de3fd0c4e6972b92290a45bd9b1536bffe8c6759c62471efaa8acb4c37bc"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:aa57bd9cf8ae831a362185ee444e15a93ecb2e344c8e52e4d721ea3ab6ef1823"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ffcc3f7c66b5f5b7931a5aa68fc9cecc51e685ef90282f4a82f0f5e9b704ad11"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:47d4f1c5f80fc62fdd7777d0d40a2e9dda0a05883ab11374334f6c4de38adffd"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1f67c7038d560d92149c060157d623c542173016c4babc0c1913cca0564b9939"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:9aad3c1755095ce347e26488214ef77e0485a3c34a50c5a5e2471dff60b9dd9c"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:14ff806850827afd6b07a5f32bd917fb7f45b046ba40c57abdb636674a8b559c"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8f9293864fe09b8149f0cc42ce56e3f0e54de883a9de90cd427f191c346eb2e1"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-win32.whl", hash = "sha256:715d3562f79d540f251b99ebd6d8baa547118974341db04f5ad06d5ea3eb8007"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-win_amd64.whl", hash = "sha256:1b8dd8c3fd14349433c79fa8abeb573a55fc0fdd769133baac1f5e07abf54aeb"},
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@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supervision"
-version = "0.15.0rc1"
+version = "0.15.0rc2"
 description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
 authors = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
 maintainers = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
@@ -6,14 +6,20 @@ try:
 except importlib_metadata.PackageNotFoundError:
    __version__ = "development"

-
+from supervision.annotators.core import (
+    BoundingBoxAnnotator,
+    BoxCornerAnnotator,
+    EllipseAnnotator,
+    LabelAnnotator,
+    MaskAnnotator,
+)
 from supervision.classification.core import Classifications
 from supervision.dataset.core import (
    BaseDataset,
    ClassificationDataset,
    DetectionDataset,
 )
-from supervision.detection.annotate import BoxAnnotator, MaskAnnotator, TraceAnnotator
+from supervision.detection.annotate import BoxAnnotator, TraceAnnotator
 from supervision.detection.core import Detections
 from supervision.detection.line_counter import LineZone, LineZoneAnnotator
 from supervision.detection.tools.inference_slicer import InferenceSlicer
@@ -0,0 +1,58 @@
+from abc import ABC, abstractmethod
+from enum import Enum
+from typing import Union
+
+import numpy as np
+
+from supervision.detection.core import Detections
+from supervision.draw.color import Color, ColorPalette
+
+
+class ColorMap(Enum):
+    """
+    Enum for annotator color mapping.
+    """
+
+    INDEX = "index"
+    CLASS = "class"
+    TRACK = "track"
+
+
+def resolve_color_idx(
+    detections: Detections, detection_idx: int, color_map: ColorMap = ColorMap.CLASS
+) -> int:
+    if detection_idx >= len(detections):
+        raise ValueError(
+            f"Detection index {detection_idx}"
+            f"is out of bounds for detections of length {len(detections)}"
+        )
+
+    if color_map == ColorMap.INDEX:
+        return detection_idx
+    elif color_map == ColorMap.CLASS:
+        if detections.class_id is None:
+            raise ValueError(
+                "Could not resolve color by class because"
+                "Detections do not have class_id"
+            )
+        return detections.class_id[detection_idx]
+    elif color_map == ColorMap.TRACK:
+        if detections.tracker_id is None:
+            raise ValueError(
+                "Could not resolve color by track because"
+                "Detections do not have tracker_id"
+            )
+        return detections.tracker_id[detection_idx]
+
+
+def resolve_color(color: Union[Color, ColorPalette], idx: int) -> Color:
+    if isinstance(color, ColorPalette):
+        return color.by_idx(idx)
+    else:
+        return color
+
+
+class BaseAnnotator(ABC):
+    @abstractmethod
+    def annotate(self, scene: np.ndarray, detections: Detections) -> np.ndarray:
+        pass
@@ -0,0 +1,478 @@
+from typing import List, Tuple, Union
+
+import cv2
+import numpy as np
+
+from supervision.annotators.base import (
+    BaseAnnotator,
+    ColorMap,
+    resolve_color,
+    resolve_color_idx,
+)
+from supervision.detection.core import Detections
+from supervision.draw.color import Color, ColorPalette
+from supervision.geometry.core import Position
+
+
+class BoundingBoxAnnotator(BaseAnnotator):
+    """
+    A class for drawing bounding boxes on an image using provided detections.
+    """
+
+    def __init__(
+        self,
+        color: Union[Color, ColorPalette] = ColorPalette.default(),
+        thickness: int = 2,
+        color_map: str = "class",
+    ):
+        """
+        Args:
+            color (Union[Color, ColorPalette]): The color or color palette to use for
+                annotating detections.
+            thickness (int): Thickness of the bounding box lines.
+            color_map (str): Strategy for mapping colors to annotations.
+                Options are `index`, `class`, or `track`.
+        """
+        self.color: Union[Color, ColorPalette] = color
+        self.thickness: int = thickness
+        self.color_map: ColorMap = ColorMap(color_map)
+
+    def annotate(self, scene: np.ndarray, detections: Detections) -> np.ndarray:
+        """
+        Annotates the given scene with bounding boxes based on the provided detections.
+
+        Args:
+            scene (np.ndarray): The image where bounding boxes will be drawn.
+            detections (Detections): Object detections to annotate.
+
+        Returns:
+            np.ndarray: The annotated image.
+
+        Example:
+            ```python
+            >>> import supervision as sv
+
+            >>> image = ...
+            >>> detections = sv.Detections(...)
+
+            >>> bounding_box_annotator = sv.BoundingBoxAnnotator()
+            >>> annotated_frame = bounding_box_annotator.annotate(
+            ...     scene=image.copy(),
+            ...     detections=detections
+            ... )
+            ```
+
+        ![bounding-box-annotator-example](https://media.roboflow.com/
+        supervision-annotator-examples/bounding-box-annotator-example.png)
+        """
+        for detection_idx in range(len(detections)):
+            x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
+            idx = resolve_color_idx(
+                detections=detections,
+                detection_idx=detection_idx,
+                color_map=self.color_map,
+            )
+            color = resolve_color(color=self.color, idx=idx)
+            cv2.rectangle(
+                img=scene,
+                pt1=(x1, y1),
+                pt2=(x2, y2),
+                color=color.as_bgr(),
+                thickness=self.thickness,
+            )
+        return scene
+
+
+class MaskAnnotator(BaseAnnotator):
+    """
+    A class for drawing masks on an image using provided detections.
+    """
+
+    def __init__(
+        self,
+        color: Union[Color, ColorPalette] = ColorPalette.default(),
+        opacity: float = 0.5,
+        color_map: str = "class",
+    ):
+        """
+        Args:
+            color (Union[Color, ColorPalette]): The color or color palette to use for
+                annotating detections.
+            opacity (float): Opacity of the overlay mask. Must be between `0` and `1`.
+            color_map (str): Strategy for mapping colors to annotations.
+                Options are `index`, `class`, or `track`.
+        """
+        self.color: Union[Color, ColorPalette] = color
+        self.opacity = opacity
+        self.color_map: ColorMap = ColorMap(color_map)
+
+    def annotate(self, scene: np.ndarray, detections: Detections) -> np.ndarray:
+        """
+        Annotates the given scene with masks based on the provided detections.
+
+        Args:
+            scene (np.ndarray): The image where masks will be drawn.
+            detections (Detections): Object detections to annotate.
+
+        Returns:
+            np.ndarray: The annotated image.
+
+        Example:
+            ```python
+            >>> import supervision as sv
+
+            >>> image = ...
+            >>> detections = sv.Detections(...)
+
+            >>> mask_annotator = sv.MaskAnnotator()
+            >>> annotated_frame = mask_annotator.annotate(
+            ...     scene=image.copy(),
+            ...     detections=detections
+            ... )
+            ```
+
+        ![mask-annotator-example](https://media.roboflow.com/
+        supervision-annotator-examples/mask-annotator-example.png)
+        """
+        if detections.mask is None:
+            return scene
+
+        for detection_idx in np.flip(np.argsort(detections.area)):
+            idx = resolve_color_idx(
+                detections=detections,
+                detection_idx=detection_idx,
+                color_map=self.color_map,
+            )
+            color = resolve_color(color=self.color, idx=idx)
+            mask = detections.mask[detection_idx]
+            colored_mask = np.zeros_like(scene, dtype=np.uint8)
+            colored_mask[:] = color.as_bgr()
+
+            scene = np.where(
+                np.expand_dims(mask, axis=-1),
+                np.uint8(self.opacity * colored_mask + (1 - self.opacity) * scene),
+                scene,
+            )
+        return scene
+
+
+class EllipseAnnotator(BaseAnnotator):
+    """
+    A class for drawing ellipses on an image using provided detections.
+    """
+
+    def __init__(
+        self,
+        color: Union[Color, ColorPalette] = ColorPalette.default(),
+        thickness: int = 2,
+        start_angle: int = -45,
+        end_angle: int = 235,
+        color_map: str = "class",
+    ):
+        """
+        Args:
+            color (Union[Color, ColorPalette]): The color or color palette to use for
+                annotating detections.
+            thickness (int): Thickness of the ellipse lines.
+            start_angle (int): Starting angle of the ellipse.
+            end_angle (int): Ending angle of the ellipse.
+            color_map (str): Strategy for mapping colors to annotations.
+                Options are `index`, `class`, or `track`.
+        """
+        self.color: Union[Color, ColorPalette] = color
+        self.thickness: int = thickness
+        self.start_angle: int = start_angle
+        self.end_angle: int = end_angle
+        self.color_map: ColorMap = ColorMap(color_map)
+
+    def annotate(self, scene: np.ndarray, detections: Detections) -> np.ndarray:
+        """
+        Annotates the given scene with ellipses based on the provided detections.
+
+        Args:
+            scene (np.ndarray): The image where ellipses will be drawn.
+            detections (Detections): Object detections to annotate.
+
+        Returns:
+            np.ndarray: The annotated image.
+
+        Example:
+            ```python
+            >>> import supervision as sv
+
+            >>> image = ...
+            >>> detections = sv.Detections(...)
+
+            >>> ellipse_annotator = sv.EllipseAnnotator()
+            >>> annotated_frame = ellipse_annotator.annotate(
+            ...     scene=image.copy(),
+            ...     detections=detections
+            ... )
+            ```
+
+        ![ellipse-annotator-example](https://media.roboflow.com/
+        supervision-annotator-examples/ellipse-annotator-example.png)
+        """
+        for detection_idx in range(len(detections)):
+            x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
+            idx = resolve_color_idx(
+                detections=detections,
+                detection_idx=detection_idx,
+                color_map=self.color_map,
+            )
+            color = resolve_color(color=self.color, idx=idx)
+
+            center = (int((x1 + x2) / 2), y2)
+            width = x2 - x1
+            cv2.ellipse(
+                scene,
+                center=center,
+                axes=(int(width), int(0.35 * width)),
+                angle=0.0,
+                startAngle=self.start_angle,
+                endAngle=self.end_angle,
+                color=color.as_bgr(),
+                thickness=self.thickness,
+                lineType=cv2.LINE_4,
+            )
+        return scene
+
+
+class BoxCornerAnnotator(BaseAnnotator):
+    """
+    A class for drawing box corners on an image using provided detections.
+    """
+
+    def __init__(
+        self,
+        color: Union[Color, ColorPalette] = ColorPalette.default(),
+        thickness: int = 4,
+        corner_length: int = 25,
+        color_map: str = "class",
+    ):
+        """
+        Args:
+            color (Union[Color, ColorPalette]): The color or color palette to use for
+                annotating detections.
+            thickness (int): Thickness of the corner lines.
+            corner_length (int): Length of each corner line.
+            color_map (str): Strategy for mapping colors to annotations.
+                Options are `index`, `class`, or `track`.
+        """
+        self.color: Union[Color, ColorPalette] = color
+        self.thickness: int = thickness
+        self.corner_length: int = corner_length
+        self.color_map: ColorMap = ColorMap(color_map)
+
+    def annotate(self, scene: np.ndarray, detections: Detections) -> np.ndarray:
+        """
+        Annotates the given scene with box corners based on the provided detections.
+
+        Args:
+            scene (np.ndarray): The image where box corners will be drawn.
+            detections (Detections): Object detections to annotate.
+
+        Returns:
+            np.ndarray: The annotated image.
+
+        Example:
+            ```python
+            >>> import supervision as sv
+
+            >>> image = ...
+            >>> detections = sv.Detections(...)
+
+            >>> corner_annotator = sv.BoxCornerAnnotator()
+            >>> annotated_frame = corner_annotator.annotate(
+            ...     scene=image.copy(),
+            ...     detections=detections
+            ... )
+            ```
+
+        ![box-corner-annotator-example](https://media.roboflow.com/
+        supervision-annotator-examples/box-corner-annotator-example.png)
+        """
+        for detection_idx in range(len(detections)):
+            x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
+            idx = resolve_color_idx(
+                detections=detections,
+                detection_idx=detection_idx,
+                color_map=self.color_map,
+            )
+            color = resolve_color(color=self.color, idx=idx)
+            corners = [(x1, y1), (x2, y1), (x1, y2), (x2, y2)]
+
+            for x, y in corners:
+                x_end = x + self.corner_length if x == x1 else x - self.corner_length
+                cv2.line(
+                    scene, (x, y), (x_end, y), color.as_bgr(), thickness=self.thickness
+                )
+
+                y_end = y + self.corner_length if y == y1 else y - self.corner_length
+                cv2.line(
+                    scene, (x, y), (x, y_end), color.as_bgr(), thickness=self.thickness
+                )
+        return scene
+
+
+class LabelAnnotator:
+    """
+    A class for annotating labels on an image using provided detections.
+    """
+
+    def __init__(
+        self,
+        color: Union[Color, ColorPalette] = ColorPalette.default(),
+        text_color: Color = Color.black(),
+        text_scale: float = 0.5,
+        text_thickness: int = 1,
+        text_padding: int = 10,
+        text_position: Position = Position.TOP_LEFT,
+        color_map: str = "class",
+    ):
+        """
+        Args:
+            color (Union[Color, ColorPalette]): The color or color palette to use for
+                annotating the text background.
+            text_color (Color): The color to use for the text.
+            text_scale (float): Font scale for the text.
+            text_thickness (int): Thickness of the text characters.
+            text_padding (int): Padding around the text within its background box.
+            text_position (Position): Position of the text relative to the detection.
+                Possible values are defined in the `Position` enum.
+            color_map (str): Strategy for mapping colors to annotations.
+                Options are `index`, `class`, or `track`.
+        """
+        self.color: Union[Color, ColorPalette] = color
+        self.text_color: Color = text_color
+        self.text_scale: float = text_scale
+        self.text_thickness: int = text_thickness
+        self.text_padding: int = text_padding
+        self.text_position: Position = text_position
+        self.color_map: ColorMap = ColorMap(color_map)
+
+    @staticmethod
+    def resolve_text_background_xyxy(
+        detection_xyxy: Tuple[int, int, int, int],
+        text_wh: Tuple[int, int],
+        text_padding: int,
+        position: Position,
+    ) -> Tuple[int, int, int, int]:
+        padded_text_wh = (text_wh[0] + 2 * text_padding, text_wh[1] + 2 * text_padding)
+        x1, y1, x2, y2 = detection_xyxy
+        center_x = (x1 + x2) // 2
+        center_y = (y1 + y2) // 2
+
+        if position == Position.TOP_LEFT:
+            return x1, y1 - padded_text_wh[1], x1 + padded_text_wh[0], y1
+        elif position == Position.TOP_RIGHT:
+            return x2 - padded_text_wh[0], y1 - padded_text_wh[1], x2, y1
+        elif position == Position.TOP_CENTER:
+            return (
+                center_x - padded_text_wh[0] // 2,
+                y1 - padded_text_wh[1],
+                center_x + padded_text_wh[0] // 2,
+                y1,
+            )
+        elif position == Position.CENTER:
+            return (
+                center_x - padded_text_wh[0] // 2,
+                center_y - padded_text_wh[1] // 2,
+                center_x + padded_text_wh[0] // 2,
+                center_y + padded_text_wh[1] // 2,
+            )
+        elif position == Position.BOTTOM_LEFT:
+            return x1, y2, x1 + padded_text_wh[0], y2 + padded_text_wh[1]
+        elif position == Position.BOTTOM_RIGHT:
+            return x2 - padded_text_wh[0], y2, x2, y2 + padded_text_wh[1]
+        elif position == Position.BOTTOM_CENTER:
+            return (
+                center_x - padded_text_wh[0] // 2,
+                y2,
+                center_x + padded_text_wh[0] // 2,
+                y2 + padded_text_wh[1],
+            )
+
+    def annotate(
+        self,
+        scene: np.ndarray,
+        detections: Detections,
+        labels: List[str] = None,
+    ) -> np.ndarray:
+        """
+        Annotates the given scene with labels based on the provided detections.
+
+        Args:
+            scene (np.ndarray): The image where labels will be drawn.
+            detections (Detections): Object detections to annotate.
+            labels (List[str]): Optional. Custom labels for each detection.
+
+        Returns:
+            np.ndarray: The annotated image.
+
+        Example:
+            ```python
+            >>> import supervision as sv
+
+            >>> image = ...
+            >>> detections = sv.Detections(...)
+
+            >>> label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
+            >>> annotated_frame = label_annotator.annotate(
+            ...     scene=image.copy(),
+            ...     detections=detections
+            ... )
+            ```
+
+        ![label-annotator-example](https://media.roboflow.com/
+        supervision-annotator-examples/label-annotator-example-2.png)
+        """
+        font = cv2.FONT_HERSHEY_SIMPLEX
+        for detection_idx in range(len(detections)):
+            detection_xyxy = detections.xyxy[detection_idx].astype(int)
+            idx = resolve_color_idx(
+                detections=detections,
+                detection_idx=detection_idx,
+                color_map=self.color_map,
+            )
+            color = resolve_color(color=self.color, idx=idx)
+            text = (
+                f"{detections.class_id[detection_idx]}"
+                if (labels is None or len(detections) != len(labels))
+                else labels[detection_idx]
+            )
+            text_wh = cv2.getTextSize(
+                text=text,
+                fontFace=font,
+                fontScale=self.text_scale,
+                thickness=self.text_thickness,
+            )[0]
+
+            text_background_xyxy = self.resolve_text_background_xyxy(
+                detection_xyxy=detection_xyxy,
+                text_wh=text_wh,
+                text_padding=self.text_padding,
+                position=self.text_position,
+            )
+
+            text_x = text_background_xyxy[0] + self.text_padding
+            text_y = text_background_xyxy[1] + self.text_padding + text_wh[1]
+
+            cv2.rectangle(
+                img=scene,
+                pt1=(text_background_xyxy[0], text_background_xyxy[1]),
+                pt2=(text_background_xyxy[2], text_background_xyxy[3]),
+                color=color.as_bgr(),
+                thickness=cv2.FILLED,
+            )
+            cv2.putText(
+                img=scene,
+                text=text,
+                org=(text_x, text_y),
+                fontFace=font,
+                fontScale=self.text_scale,
+                color=self.text_color.as_rgb(),
+                thickness=self.text_thickness,
+                lineType=cv2.LINE_AA,
+            )
+        return scene
@@ -147,64 +147,6 @@ class BoxAnnotator:
        return scene


-class MaskAnnotator:
-    """
-    A class for overlaying masks on an image using detections provided.
-
-    Attributes:
-        color (Union[Color, ColorPalette]): The color to fill the mask,
-            can be a single color or a color palette
-    """
-
-    def __init__(
-        self,
-        color: Union[Color, ColorPalette] = ColorPalette.default(),
-    ):
-        self.color: Union[Color, ColorPalette] = color
-
-    def annotate(
-        self, scene: np.ndarray, detections: Detections, opacity: float = 0.5
-    ) -> np.ndarray:
-        """
-        Overlays the masks on the given image based on the provided detections,
-            with a specified opacity.
-
-        Args:
-            scene (np.ndarray): The image on which the masks will be overlaid
-            detections (Detections): The detections for which the
-                masks will be overlaid
-            opacity (float): The opacity of the masks, between 0 and 1, default is 0.5
-
-        Returns:
-            np.ndarray: The image with the masks overlaid
-        """
-        if detections.mask is None:
-            return scene
-
-        for i in np.flip(np.argsort(detections.area)):
-            class_id = (
-                detections.class_id[i] if detections.class_id is not None else None
-            )
-            idx = class_id if class_id is not None else i
-            color = (
-                self.color.by_idx(idx)
-                if isinstance(self.color, ColorPalette)
-                else self.color
-            )
-
-            mask = detections.mask[i]
-            colored_mask = np.zeros_like(scene, dtype=np.uint8)
-            colored_mask[:] = color.as_bgr()
-
-            scene = np.where(
-                np.expand_dims(mask, axis=-1),
-                np.uint8(opacity * colored_mask + (1 - opacity) * scene),
-                scene,
-            )
-
-        return scene
-
-
 class Trace:
    def __init__(
        self,
@@ -178,6 +178,7 @@ class Detections:
            ```
        """
        yolov5_detections_predictions = yolov5_results.pred[0].cpu().cpu().numpy()
+
        return cls(
            xyxy=yolov5_detections_predictions[:, :4],
            confidence=yolov5_detections_predictions[:, 4],
@@ -216,6 +217,7 @@ class Detections:
            >>> detections = sv.Detections.from_yolov8(result)
            ```
        """
+
        return cls(
            xyxy=yolov8_results.boxes.xyxy.cpu().numpy(),
            confidence=yolov8_results.boxes.conf.cpu().numpy(),
@@ -255,6 +257,7 @@ class Detections:
            >>> detections = sv.Detections.from_ultralytics(result)
            ```
        """
+
        return cls(
            xyxy=ultralytics_results.boxes.xyxy.cpu().numpy(),
            confidence=ultralytics_results.boxes.conf.cpu().numpy(),
@@ -293,6 +296,9 @@ class Detections:
            >>> detections = sv.Detections.from_yolo_nas(result)
            ```
        """
+        if np.asarray(yolo_nas_results.bboxes_xyxy).shape[0] == 0:
+            return cls.empty()
+
        return cls(
            xyxy=yolo_nas_results.prediction.bboxes_xyxy,
            confidence=yolo_nas_results.prediction.confidence,
@@ -320,7 +326,7 @@ class Detections:

            >>> yolo_pipeline = Pipeline.create(
            ...     task="yolo",
-            ...     model_stub = "zoo:cv/detection/yolov5-l/pytorch/" \
+            ...     model_path = "zoo:cv/detection/yolov5-l/pytorch/" \
            ...                  "ultralytics/coco/pruned80_quant-none"
            >>> pipeline_outputs = yolo_pipeline(SOURCE_IMAGE_PATH,
            ...                         iou_thres=0.6, conf_thres=0.001)
@@ -329,14 +335,12 @@ class Detections:
        """
        if np.asarray(deepsparse_results.boxes[0]).shape[0] == 0:
            return cls.empty()
-        else:
-            return cls(
-                xyxy=np.array(deepsparse_results.boxes[0]),
-                confidence=np.array(deepsparse_results.scores[0]),
-                class_id=np.array(deepsparse_results.labels[0])
-                .astype(float)
-                .astype(int),
-            )
+
+        return cls(
+            xyxy=np.array(deepsparse_results.boxes[0]),
+            confidence=np.array(deepsparse_results.scores[0]),
+            class_id=np.array(deepsparse_results.labels[0]).astype(float).astype(int),
+        )

    @classmethod
    def from_mmdetection(cls, mmdet_results) -> Detections:
@@ -364,6 +368,7 @@ class Detections:
            >>> detections = sv.Detections.from_mmdet(mmdet_result)
            ```
        """
+
        return cls(
            xyxy=mmdet_results.pred_instances.bboxes.cpu().numpy(),
            confidence=mmdet_results.pred_instances.scores.cpu().numpy(),
@@ -379,6 +384,7 @@ class Detections:
        Returns:
            Detections: A new Detections object.
        """
+
        return cls(
            xyxy=transformers_results["boxes"].cpu().numpy(),
            confidence=transformers_results["scores"].cpu().numpy(),
@@ -415,6 +421,7 @@ class Detections:
            >>> detections = sv.Detections.from_detectron2(result)
            ```
        """
+
        return cls(
            xyxy=detectron2_results["instances"].pred_boxes.tensor.cpu().numpy(),
            confidence=detectron2_results["instances"].scores.cpu().numpy(),
@@ -465,7 +472,11 @@ class Detections:
        xyxy, confidence, class_id, masks = process_roboflow_result(
            roboflow_result=roboflow_result, class_list=class_list
        )
-        return Detections(
+
+        if np.asarray(xyxy).shape[0] == 0:
+            return cls.empty()
+
+        return cls(
            xyxy=xyxy,
            confidence=confidence,
            class_id=class_id,
@@ -508,7 +519,11 @@ class Detections:
        xywh = np.array([mask["bbox"] for mask in sorted_generated_masks])
        mask = np.array([mask["segmentation"] for mask in sorted_generated_masks])

-        return Detections(xyxy=xywh_to_xyxy(boxes_xywh=xywh), mask=mask)
+        if np.asarray(xywh).shape[0] == 0:
+            return cls.empty()
+
+        xyxy = xywh_to_xyxy(boxes_xywh=xywh)
+        return cls(xyxy=xyxy, mask=mask)

    @classmethod
    def from_paddledet(cls, paddledet_result) -> Detections:
@@ -542,6 +557,10 @@ class Detections:
            >>> detections = sv.Detections.from_paddledet(paddledet_result)
            ```
        """
+
+        if np.asarray(paddledet_result["bbox"][:, 2:6]).shape[0] == 0:
+            return cls.empty()
+
        return cls(
            xyxy=paddledet_result["bbox"][:, 2:6],
            confidence=paddledet_result["bbox"][:, 1],
@@ -625,15 +644,23 @@ class Detections:

    def get_anchor_coordinates(self, anchor: Position) -> np.ndarray:
        """
-        Returns the bounding box coordinates for a specific anchor.
+        Calculates and returns the coordinates of a specific anchor point
+        within the bounding boxes defined by the `xyxy` attribute. The anchor
+        point can be any of the predefined positions in the `Position` enum,
+        such as `CENTER`, `CENTER_LEFT`, `BOTTOM_RIGHT`, etc.

        Args:
-            anchor (Position): Position of bounding box anchor
-                for which to return the coordinates.
+            anchor (Position): An enum specifying the position of the anchor point
+                within the bounding box. Supported positions are defined in the
+                `Position` enum.

        Returns:
-            np.ndarray: An array of shape `(n, 2)` containing the bounding
-                box anchor coordinates in format `[x, y]`.
+            np.ndarray: An array of shape `(n, 2)`, where `n` is the number of bounding
+                boxes. Each row contains the `[x, y]` coordinates of the specified
+                anchor point for the corresponding bounding box.
+
+        Raises:
+            ValueError: If the provided `anchor` is not supported.
        """
        if anchor == Position.CENTER:
            return np.array(
@@ -642,10 +669,36 @@ class Detections:
                    (self.xyxy[:, 1] + self.xyxy[:, 3]) / 2,
                ]
            ).transpose()
+        elif anchor == Position.CENTER_LEFT:
+            return np.array(
+                [
+                    self.xyxy[:, 0],
+                    (self.xyxy[:, 1] + self.xyxy[:, 3]) / 2,
+                ]
+            ).transpose()
+        elif anchor == Position.CENTER_RIGHT:
+            return np.array(
+                [
+                    self.xyxy[:, 2],
+                    (self.xyxy[:, 1] + self.xyxy[:, 3]) / 2,
+                ]
+            ).transpose()
        elif anchor == Position.BOTTOM_CENTER:
            return np.array(
                [(self.xyxy[:, 0] + self.xyxy[:, 2]) / 2, self.xyxy[:, 3]]
            ).transpose()
+        elif anchor == Position.BOTTOM_LEFT:
+            return np.array([self.xyxy[:, 0], self.xyxy[:, 3]]).transpose()
+        elif anchor == Position.BOTTOM_RIGHT:
+            return np.array([self.xyxy[:, 2], self.xyxy[:, 3]]).transpose()
+        elif anchor == Position.TOP_CENTER:
+            return np.array(
+                [(self.xyxy[:, 0] + self.xyxy[:, 2]) / 2, self.xyxy[:, 1]]
+            ).transpose()
+        elif anchor == Position.TOP_LEFT:
+            return np.array([self.xyxy[:, 0], self.xyxy[:, 1]]).transpose()
+        elif anchor == Position.TOP_RIGHT:
+            return np.array([self.xyxy[:, 2], self.xyxy[:, 1]]).transpose()

        raise ValueError(f"{anchor} is not supported.")

@@ -23,9 +23,10 @@ def move_detections(detections: Detections, offset: np.array) -> Detections:
 class InferenceSlicer:
    """
    InferenceSlicer performs slicing-based inference for small target detection. This
-    method, often referred to as Slicing Adaptive Inference (SAHI), involves dividing a
-    larger image into smaller slices, performing inference on each slice, and then
-    merging the detections.
+    method, often referred to as
+    [Slicing Adaptive Inference (SAHI)](https://ieeexplore.ieee.org/document/9897990),
+    involves dividing a larger image into smaller slices, performing inference on each
+    slice, and then merging the detections.

    Attributes:
        slice_wh (Tuple[int, int]): Dimensions of each slice in the format
@@ -6,8 +6,19 @@ from typing import Tuple


 class Position(Enum):
+    """
+    Enum representing the position of an anchor point.
+    """
+
    CENTER = "CENTER"
+    CENTER_LEFT = "CENTER_LEFT"
+    CENTER_RIGHT = "CENTER_RIGHT"
+    TOP_CENTER = "TOP_CENTER"
+    TOP_LEFT = "TOP_LEFT"
+    TOP_RIGHT = "TOP_RIGHT"
+    BOTTOM_LEFT = "BOTTOM_LEFT"
    BOTTOM_CENTER = "BOTTOM_CENTER"
+    BOTTOM_RIGHT = "BOTTOM_RIGHT"

    @classmethod
    def list(cls):
@@ -65,6 +65,7 @@ class VideoSink:
        target_path (str): The path to the output file where the video will be saved.
        video_info (VideoInfo): Information about the video resolution, fps,
            and total frame count.
+        codec (str): FOURCC code for video format

    Example:
        ```python
@@ -73,20 +74,26 @@ class VideoSink:
        >>> video_info = sv.VideoInfo.from_video_path(video_path='source_video.mp4')

        >>> with sv.VideoSink(target_path='target_video.mp4',
-        ...                   video_info=video_info) as sink:
+        ...                   video_info=video_info,
+                              codec='H264') as sink:
        ...     for frame in get_video_frames_generator(source_path='source_video.mp4',
        ...                                             stride=2):
        ...         sink.write_frame(frame=frame)
        ```
    """

-    def __init__(self, target_path: str, video_info: VideoInfo):
+    def __init__(self, target_path: str, video_info: VideoInfo, codec: str = "mp4v"):
        self.target_path = target_path
        self.video_info = video_info
-        self.__fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+        self.__codec = codec
        self.__writer = None

    def __enter__(self):
+        try:
+            self.__fourcc = cv2.VideoWriter_fourcc(*self.__codec)
+        except TypeError as e:
+            print(str(e) + ". Defaulting to mp4v...")
+            self.__fourcc = cv2.VideoWriter_fourcc(*"mp4v")
        self.__writer = cv2.VideoWriter(
            self.target_path,
            self.__fourcc,
@@ -0,0 +1,94 @@
+from contextlib import ExitStack as DoesNotRaise
+from test.utils import mock_detections
+from typing import Optional
+
+import pytest
+
+from supervision.annotators.core import ColorMap, resolve_color_idx
+from supervision.detection.core import Detections
+
+
+@pytest.mark.parametrize(
+    "detections, detection_idx, color_map, expected_result, exception",
+    [
+        (
+            mock_detections(
+                xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]],
+                class_id=[5, 3],
+                tracker_id=[2, 6],
+            ),
+            0,
+            ColorMap.INDEX,
+            0,
+            DoesNotRaise(),
+        ),  # multiple detections; index mapping
+        (
+            mock_detections(
+                xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]],
+                class_id=[5, 3],
+                tracker_id=[2, 6],
+            ),
+            0,
+            ColorMap.CLASS,
+            5,
+            DoesNotRaise(),
+        ),  # multiple detections; class mapping
+        (
+            mock_detections(
+                xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]],
+                class_id=[5, 3],
+                tracker_id=[2, 6],
+            ),
+            0,
+            ColorMap.TRACK,
+            2,
+            DoesNotRaise(),
+        ),  # multiple detections; track mapping
+        (
+            Detections.empty(),
+            0,
+            ColorMap.INDEX,
+            None,
+            pytest.raises(ValueError),
+        ),  # no detections; index mapping; out of bounds
+        (
+            mock_detections(
+                xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]],
+                class_id=[5, 3],
+                tracker_id=[2, 6],
+            ),
+            2,
+            ColorMap.INDEX,
+            None,
+            pytest.raises(ValueError),
+        ),  # multiple detections; index mapping; out of bounds
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
+            0,
+            ColorMap.CLASS,
+            None,
+            pytest.raises(ValueError),
+        ),  # multiple detections; class mapping; no class_id
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
+            0,
+            ColorMap.TRACK,
+            None,
+            pytest.raises(ValueError),
+        ),  # multiple detections; class mapping; no track_id
+    ],
+)
+def test_resolve_color_idx(
+    detections: Detections,
+    detection_idx: int,
+    color_map: ColorMap,
+    expected_result: Optional[int],
+    exception: Exception,
+) -> None:
+    with exception:
+        result = resolve_color_idx(
+            detections=detections,
+            detection_idx=detection_idx,
+            color_map=color_map,
+        )
+        assert result == expected_result
@@ -5,7 +5,8 @@ from typing import List, Optional, Union
 import numpy as np
 import pytest

-from supervision import Detections
+from supervision.detection.core import Detections
+from supervision.geometry.core import Position

 PREDICTIONS = np.array(
    [
@@ -190,3 +191,85 @@ def test_merge(
    with exception:
        result = Detections.merge(detections_list=detections_list)
        assert result == expected_result
+
+
+@pytest.mark.parametrize(
+    "detections, anchor, expected_result, exception",
+    [
+        (
+            Detections.empty(),
+            Position.CENTER,
+            np.empty((0, 2), dtype=np.float32),
+            DoesNotRaise(),
+        ),  # empty detections
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20]]),
+            Position.CENTER,
+            np.array([[15, 15]], dtype=np.float32),
+            DoesNotRaise(),
+        ),  # single detection; center anchor
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
+            Position.CENTER,
+            np.array([[15, 15], [25, 25]], dtype=np.float32),
+            DoesNotRaise(),
+        ),  # two detections; center anchor
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
+            Position.CENTER_LEFT,
+            np.array([[10, 15], [20, 25]], dtype=np.float32),
+            DoesNotRaise(),
+        ),  # two detections; center left anchor
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
+            Position.CENTER_RIGHT,
+            np.array([[20, 15], [30, 25]], dtype=np.float32),
+            DoesNotRaise(),
+        ),  # two detections; center right anchor
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
+            Position.TOP_CENTER,
+            np.array([[15, 10], [25, 20]], dtype=np.float32),
+            DoesNotRaise(),
+        ),  # two detections; top center anchor
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
+            Position.TOP_LEFT,
+            np.array([[10, 10], [20, 20]], dtype=np.float32),
+            DoesNotRaise(),
+        ),  # two detections; top left anchor
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
+            Position.TOP_RIGHT,
+            np.array([[20, 10], [30, 20]], dtype=np.float32),
+            DoesNotRaise(),
+        ),  # two detections; top right anchor
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
+            Position.BOTTOM_CENTER,
+            np.array([[15, 20], [25, 30]], dtype=np.float32),
+            DoesNotRaise(),
+        ),  # two detections; bottom center anchor
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
+            Position.BOTTOM_LEFT,
+            np.array([[10, 20], [20, 30]], dtype=np.float32),
+            DoesNotRaise(),
+        ),  # two detections; bottom left anchor
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
+            Position.BOTTOM_RIGHT,
+            np.array([[20, 20], [30, 30]], dtype=np.float32),
+            DoesNotRaise(),
+        ),  # two detections; bottom right anchor
+    ],
+)
+def test_get_anchor_coordinates(
+    detections: Detections,
+    anchor: Position,
+    expected_result: np.ndarray,
+    exception: Exception,
+) -> None:
+    result = detections.get_anchor_coordinates(anchor)
+    with exception:
+        assert np.array_equal(result, expected_result)
Author	SHA1	Message	Date
SkalskiP	1b449294c4	👊 bump `develop` version to `0.15.0rc2`	2023-09-29 13:46:39 +02:00
Onuralp SEZER	36f95d060d	Merge pull request #170 from roboflow/feature/redesigned_annotators redesigned annotators	2023-09-29 14:36:43 +03:00
Onuralp SEZER	07c344e050	fix: 🐛 add missing TraceAnnotator (traffic_analysis fix) and sv version adjusted Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2023-09-29 13:25:26 +03:00
pre-commit-ci[bot]	f3bdb6efa2	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-09-28 21:55:12 +00:00
SkalskiP	f4f9e7ddc7	ready for review	2023-09-28 23:54:34 +02:00
pre-commit-ci[bot]	53f2b4fd45	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-09-28 21:24:53 +00:00
SkalskiP	c7582f7c07	Merge remote-tracking branch 'origin/feature/redesigned_annotators' into feature/redesigned_annotators	2023-09-28 23:24:39 +02:00
SkalskiP	3e8906d830	📘 docs updates for `LabelAnnotator`	2023-09-28 23:24:22 +02:00
pre-commit-ci[bot]	6521f9f4ed	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-09-28 19:47:18 +00:00
SkalskiP	560a35b75f	🧪 test `LabelAnnotator` supporting more `Position` values	2023-09-28 21:46:02 +02:00
pre-commit-ci[bot]	405a4c73f9	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-09-28 16:16:11 +00:00
SkalskiP	87fd10c366	Merge remote-tracking branch 'origin/feature/redesigned_annotators' into feature/redesigned_annotators	2023-09-28 18:15:57 +02:00
SkalskiP	8141cb357f	Merge remote-tracking branch 'origin/feature/redesigned_annotators' into feature/redesigned_annotators	2023-09-28 18:15:45 +02:00
SkalskiP	87fb336eb6	Merge remote-tracking branch 'origin/feature/redesigned_annotators' into feature/redesigned_annotators	2023-09-28 18:13:32 +02:00
SkalskiP	28d279bec3	Merge remote-tracking branch 'origin/feature/redesigned_annotators' into feature/redesigned_annotators	2023-09-28 18:12:43 +02:00
pre-commit-ci[bot]	1b0bcea5a6	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-09-28 16:10:18 +00:00
SkalskiP	507e000bca	Merge remote-tracking branch 'origin/feature/redesigned_annotators' into feature/redesigned_annotators	2023-09-28 18:08:45 +02:00
SkalskiP	f7bf766a60	🧪 Test of `LabelAnnotator` alternative `annotate` method implementation.	2023-09-28 18:08:01 +02:00
pre-commit-ci[bot]	1e0f18f792	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-09-28 15:11:28 +00:00
SkalskiP	be5c1b1aa1	Merge remote-tracking branch 'origin/feature/redesigned_annotators' into feature/redesigned_annotators	2023-09-28 17:11:14 +02:00
SkalskiP	c35a7714d2	🧪 Test of `BoxCornerAnnotator` alternative `annotate` method implementation.	2023-09-28 17:11:06 +02:00
pre-commit-ci[bot]	a8cc5a68bb	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-09-28 14:48:53 +00:00
SkalskiP	de4056192e	🛠️ Refactor of `BoxCornerAnnotator` docs and `annotate` method implementation.	2023-09-28 16:48:03 +02:00
SkalskiP	d337a03d28	Merge remote-tracking branch 'origin/feature/redesigned_annotators' into feature/redesigned_annotators	2023-09-28 16:27:07 +02:00
SkalskiP	00b67588d8	🛠️ `EllipseAnnotator.end_angle` fix	2023-09-28 16:25:35 +02:00
pre-commit-ci[bot]	e10e0f21df	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-09-28 14:14:41 +00:00
SkalskiP	4fb6760d2c	Merge remote-tracking branch 'origin/feature/redesigned_annotators' into feature/redesigned_annotators	2023-09-28 16:14:28 +02:00
SkalskiP	de7ed5e115	🔨 `EllipseAnnotator` refactor. Changes in `annotate` method + new docstrings.	2023-09-28 16:14:17 +02:00
pre-commit-ci[bot]	d929b86c9f	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-09-28 13:52:31 +00:00
SkalskiP	0b1627f835	Merge remote-tracking branch 'origin/feature/redesigned_annotators' into feature/redesigned_annotators	2023-09-28 15:51:06 +02:00
SkalskiP	20b1b21774	🧹 Removed old `MaskAnnotator` implementation as it is no longer used. Moved `ColorMap`, `resolve_color_idx`, `resolve_color` and `BaseAnnotator` to `supervision/annotators/base`. Removed `BoxMaskAnnotator` as it can be achieved by `BoundingBoxAnnotator` and `MaskAnnotator` composition.	2023-09-28 15:50:55 +02:00
pre-commit-ci[bot]	e5eaf26137	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-09-28 13:37:05 +00:00
SkalskiP	be3e67949c	Merge remote-tracking branch 'origin/feature/redesigned_annotators' into feature/redesigned_annotators	2023-09-28 15:36:28 +02:00
SkalskiP	2587fdfefd	☝️ updating MaskAnnotator docs and code to be consistent with BoundingBoxAnnotator	2023-09-28 15:36:18 +02:00
pre-commit-ci[bot]	08a88a8c0c	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-09-28 11:47:28 +00:00
SkalskiP	181c0b833b	🔨 small names refactor	2023-09-28 13:46:52 +02:00
pre-commit-ci[bot]	11601e3f2d	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-09-28 10:14:38 +00:00
SkalskiP	1b8ff617c3	📘 docs updates for `Position` and `get_anchor_coordinates`	2023-09-28 12:13:52 +02:00
SkalskiP	5bc83bf6cc	Merge branch 'develop' into feature/redesigned_annotators	2023-09-27 12:08:07 +02:00
fatihandpre-commit-ci[bot]	dd80753b2b	add sahi paper url into docstring (#382 ) * add sahi paper url into docstring * fix(pre_commit): 🎨 auto format pre-commit hooks --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>	2023-09-27 00:59:57 +03:00
Piotr Skalski	b2bb69bebb	Merge pull request #381 from Killua7362/utils-fix Adding Fourcc codecs option to VideoSink	2023-09-26 14:32:44 +02:00
pre-commit-ci[bot]	9c081b0e11	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-09-26 05:40:22 +00:00
Akshay	7f613c6d27	Merge branch 'utils-fix' of https://github.com/Killua7362/supervision into utils-fix	2023-09-26 08:40:01 +03:00
Akshay	9736eba9e6	FIX: Added type error exception in videosink	2023-09-26 08:38:46 +03:00
pre-commit-ci[bot]	53dfeac19b	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-09-26 05:25:00 +00:00
Akshay	5c25e58e39	Added docstrings for codec	2023-09-26 08:22:07 +03:00
Akshay	52949f2fd1	Support for all the codes	2023-09-26 08:12:18 +03:00
pre-commit-ci[bot]	56c511b5d8	chore(pre_commit): ⬆ pre_commit autoupdate (#379 ) updates: - [github.com/astral-sh/ruff-pre-commit: v0.0.290 → v0.0.291](https://github.com/astral-sh/ruff-pre-commit/compare/v0.0.290...v0.0.291) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>	2023-09-26 00:20:59 +03:00
Piotr Skalski	62014186a2	Update `README.md` with latest supervision YouTube tutorial	2023-09-22 13:43:56 +02:00
dependabot[bot]	6cd7cf0209	Bump cryptography from 41.0.3 to 41.0.4 (#375 ) Bumps [cryptography](https://github.com/pyca/cryptography) from 41.0.3 to 41.0.4. - [Changelog](https://github.com/pyca/cryptography/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pyca/cryptography/compare/41.0.3...41.0.4) --- updated-dependencies: - dependency-name: cryptography dependency-type: indirect ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2023-09-22 10:17:28 +03:00
Onuralp SEZER	a39c92016f	docs(example): 📝 example LabelAnnotator doc fix Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2023-09-21 15:45:04 +03:00
Onuralp SEZER	4e88e26d91	chore(formatting): pre-commit fixes Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2023-09-21 15:43:07 +03:00
pre-commit-ci[bot]	484e53324e	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-09-21 12:36:21 +00:00
Onuralp SEZER	e5c4d8e3b9	docs(example): 📝 Update tracking example with new annotators Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2023-09-21 15:34:17 +03:00
Onuralp SEZER	52ef3fadcb	fix(redefinition): 🐞 fix redefinition for MaskAnnotator TraceAnnotator Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2023-09-21 14:46:35 +03:00
pre-commit-ci[bot]	472ddd8d0e	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-09-21 11:35:41 +00:00
Onuralp SEZER	90b23430d2	Merge branch 'develop' into feature/redesigned_annotators Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2023-09-21 14:35:24 +03:00
pre-commit-ci[bot]	e91f0cb496	chore(pre_commit): ⬆ pre_commit autoupdate (#366 ) updates: - [github.com/astral-sh/ruff-pre-commit: v0.0.288 → v0.0.290](https://github.com/astral-sh/ruff-pre-commit/compare/v0.0.288...v0.0.290) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>	2023-09-19 16:34:16 +03:00
pre-commit-ci[bot]	fa6238dbc4	chore(pre_commit): ⬆ pre_commit autoupdate (#362 ) updates: - [github.com/psf/black: 23.7.0 → 23.9.1](https://github.com/psf/black/compare/23.7.0...23.9.1) - [github.com/astral-sh/ruff-pre-commit: v0.0.287 → v0.0.288](https://github.com/astral-sh/ruff-pre-commit/compare/v0.0.287...v0.0.288) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>	2023-09-12 00:32:39 +03:00
Onuralp SEZER	1a7306c4ae	ci(test): 🔥 removal formatters from test ci (#317 ) Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2023-09-10 23:29:45 +03:00
Onuralp SEZER	3d4326c79b	Merge pull request #349 from onuralpszr/empty_box_checks fix: 🐛 Empty check for xyxy results for prevent ValueError and return as empty detection	2023-09-05 20:17:17 +03:00
pre-commit-ci[bot]	ca212b70c8	chore(pre_commit): ⬆ pre_commit autoupdate (#355 ) updates: - [github.com/astral-sh/ruff-pre-commit: v0.0.286 → v0.0.287](https://github.com/astral-sh/ruff-pre-commit/compare/v0.0.286...v0.0.287) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>	2023-09-05 20:15:19 +03:00
Piotr Skalski	0008b33eb1	Add traffic analysis demo to `README.md`	2023-09-05 17:52:29 +02:00
SkalskiP	77190f287c	📍 Pin `supervision==0.15.0rc1` in `examples/traffic_analysis`	2023-09-04 23:08:12 +02:00
pre-commit-ci[bot]	bbd9a955f8	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-09-02 19:45:43 +00:00
Onuralp SEZER	7b2bcf00ad	fix: 🐛 sam xyxy check added Signed-off-by: Onuralp SEZER <thunderbirdtr@fedoraproject.org>	2023-09-02 22:45:29 +03:00
Onuralp SEZER	775a3f2795	fix: 🐛 cpu().numpy() converts not require empty check Signed-off-by: Onuralp SEZER <thunderbirdtr@fedoraproject.org>	2023-09-02 22:43:27 +03:00
Onuralp SEZER	5dae02e93e	fix: 🐛 deepsparse model_stub to model_path docs fix Signed-off-by: Onuralp SEZER <thunderbirdtr@fedoraproject.org>	2023-09-02 20:40:35 +03:00
Onuralp SEZER	666427df08	fix: 🐛 empty check for xyxy results for prevent ValueError and return as empty detection Signed-off-by: Onuralp SEZER <thunderbirdtr@fedoraproject.org>	2023-09-02 20:36:00 +03:00
Hardik Dava	92413d3cc2	Merge pull request #277 from hardikdava/feature/trace_annotators tracer in consistent format	2023-08-14 21:20:56 +02:00
Hardik Dava	8b764218eb	tracer in consistent format	2023-08-10 10:00:26 +02:00
SkalskiP	117ba2fcc0	🧪 initial API tests	2023-07-24 23:17:21 +02:00
Piotr Skalski	1d28b8ae26	Merge pull request #171 from hardikdava/advanced_box_annotator cornered box annotator	2023-07-23 20:44:29 +02:00
SkalskiP	cc2e08ac72	🖤 make black happy	2023-07-23 20:40:46 +02:00
SkalskiP	ba03fefbd8	🧹‍💨 cleanup in progress	2023-07-23 20:39:12 +02:00
SkalskiP	4cd9f05fab	🧪 reformatted tests	2023-07-23 20:08:34 +02:00
SkalskiP	8066125f03	🧹‍💨 clean up	2023-07-23 19:43:51 +02:00
SkalskiP	aa772dde8d	🧹‍💨 clean up	2023-07-23 19:42:47 +02:00
Piotr Skalski	1b5e6db196	Merge pull request #159 from Ucag/feature/unicode-label-support Add unicode label support	2023-07-21 14:13:40 +02:00
hd	fc4bb27aa8	removing extra spacing	2023-07-21 12:54:44 +02:00
hd	ace6ce363f	Tests added for anchor fn	2023-07-21 12:54:19 +02:00
hd	f0cef99de7	ready for review	2023-07-21 11:53:44 +02:00
hd	c4d296fbc6	Ready for review	2023-07-19 10:58:39 +02:00
hd	9c4555a800	Added extra anchor points calculation	2023-07-19 09:47:49 +02:00
hd	4b423fd402	Fixed typo in annotator	2023-07-07 22:40:30 +02:00
hd	27efbc044d	Adding EllipseAnotator and added backward compatibility for box and mask annotator	2023-07-07 19:18:12 +02:00
hd	2a2c259964	🔥 Consistent code style	2023-07-07 09:37:03 +02:00
hd	c705d50e74	Marked as ready to review	2023-07-05 15:39:22 +02:00
hd	d5b8a3aa1a	Adding cornered box annotator and pillow label annotator	2023-07-05 15:01:16 +02:00
hd	d9f6c5c73c	tracked detection method added along with trackstorage	2023-07-04 20:42:33 +02:00
Hardik Dava	65a93f8287	Merge pull request #2 from hardikdava/redesigned_annotators updated code	2023-07-04 16:16:53 +02:00
hd	1bcf8346dc	updated code	2023-07-04 16:12:30 +02:00
Hardik Dava	aba6ff57a9	Merge pull request #1 from hardikdava/redesigned_annotators Redesigned annotators	2023-07-04 13:47:15 +02:00
hd	b5e1342038	Updating refactored annotators	2023-07-04 13:38:10 +02:00
SkalskiP	64d223b76d	🖤 make black happy	2023-07-02 23:28:28 +02:00
SkalskiP	21968e53c6	✏️initial PR for redesigned annotators	2023-07-02 23:25:43 +02:00
hd	9b77b2a1c6	initial commit for advanced annotator	2023-06-30 18:25:13 +02:00
dong.li	ddabe04409	add unicode label support	2023-06-28 09:56:53 +08:00