Merge pull request #404 from roboflow/develop

`supervision-0.15.0` release
👊 bump develop version to 0.15.0
2023-10-05 09:23:31 +02:00 · 2023-10-05 09:21:05 +02:00 · 2023-10-04 23:57:03 +02:00 · 2023-10-04 23:54:48 +02:00 · 2023-10-04 21:52:50 +00:00 · 2023-10-04 23:50:47 +02:00
20 changed files with 837 additions and 317 deletions
@@ -74,7 +74,7 @@ repos:
      -   id: black

  -   repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.0.291
+      rev: v0.0.292
      hooks:
      -   id: ruff
          args: [--fix, --exit-non-zero-on-fix]
@@ -0,0 +1,134 @@
+
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, caste, color, religion, or sexual
+identity and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the overall
+  community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or advances of
+  any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email address,
+  without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+community-reports@roboflow.com.
+
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series of
+actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or permanent
+ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior, harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within the
+community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.1, available at
+[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1].
+
+Community Impact Guidelines were inspired by
+[Mozilla's code of conduct enforcement ladder][Mozilla CoC].
+
+For answers to common questions about this code of conduct, see the FAQ at
+[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at
+[https://www.contributor-covenant.org/translations][translations].
+
+[homepage]: https://www.contributor-covenant.org
+[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
+[Mozilla CoC]: https://github.com/mozilla/diversity
+[FAQ]: https://www.contributor-covenant.org/faq
+[translations]: https://www.contributor-covenant.org/translations
@@ -0,0 +1,199 @@
+=== "BoundingBox"
+
+    ```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
+    ... )
+    ```
+
+    <div class="result" markdown>
+
+    ![bounding-box-annotator-example](https://media.roboflow.com/supervision-annotator-examples/bounding-box-annotator-example.png){ align=center width="800" }
+
+    </div>
+
+=== "Mask"
+
+    ```python
+    >>> import supervision as sv
+
+    >>> image = ...
+    >>> detections = sv.Detections(...)
+
+    >>> mask_annotator = sv.MaskAnnotator()
+    >>> annotated_frame = mask_annotator.annotate(
+    ...     scene=image.copy(),
+    ...     detections=detections
+    ... )
+    ```
+
+    <div class="result" markdown>
+
+    ![mask-annotator-example](https://media.roboflow.com/supervision-annotator-examples/mask-annotator-example.png){ align=center width="800" }
+
+    </div>
+
+=== "Ellipse"
+
+    ```python
+    >>> import supervision as sv
+
+    >>> image = ...
+    >>> detections = sv.Detections(...)
+
+    >>> ellipse_annotator = sv.EllipseAnnotator()
+    >>> annotated_frame = ellipse_annotator.annotate(
+    ...     scene=image.copy(),
+    ...     detections=detections
+    ... )
+    ```
+
+    <div class="result" markdown>
+
+    ![ellipse-annotator-example](https://media.roboflow.com/supervision-annotator-examples/ellipse-annotator-example.png){ align=center width="800" }
+
+    </div>
+
+=== "BoxCorner"
+
+    ```python
+    >>> import supervision as sv
+
+    >>> image = ...
+    >>> detections = sv.Detections(...)
+
+    >>> corner_annotator = sv.BoxCornerAnnotator()
+    >>> annotated_frame = corner_annotator.annotate(
+    ...     scene=image.copy(),
+    ...     detections=detections
+    ... )
+    ```
+
+    <div class="result" markdown>
+
+    ![box-corner-annotator-example](https://media.roboflow.com/supervision-annotator-examples/box-corner-annotator-example.png){ align=center width="800" }
+
+    </div>
+
+=== "Circle"
+
+    ```python
+    >>> import supervision as sv
+
+    >>> image = ...
+    >>> detections = sv.Detections(...)
+
+    >>> circle_annotator = sv.CircleAnnotator()
+    >>> annotated_frame = circle_annotator.annotate(
+    ...     scene=image.copy(),
+    ...     detections=detections
+    ... )
+    ```
+
+    <div class="result" markdown>
+
+    ![circle-annotator-example](https://media.roboflow.com/supervision-annotator-examples/circle-annotator-example.png){ align=center width="800" }
+
+    </div>
+
+=== "Label"
+
+    ```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
+    ... )
+    ```
+
+    <div class="result" markdown>
+
+    ![label-annotator-example](https://media.roboflow.com/supervision-annotator-examples/label-annotator-example-2.png){ align=center width="800" }
+
+    </div>
+
+=== "Blur"
+
+    ```python
+    >>> import supervision as sv
+
+    >>> image = ...
+    >>> detections = sv.Detections(...)
+
+    >>> blur_annotator = sv.BlurAnnotator()
+    >>> annotated_frame = blur_annotator.annotate(
+    ...     scene=image.copy(),
+    ...     detections=detections
+    ... )
+    ```
+
+    <div class="result" markdown>
+
+    ![blur-annotator-example](https://media.roboflow.com/supervision-annotator-examples/blur-annotator-example-2.png){ align=center width="800" }
+
+    </div>
+
+=== "Trace"
+
+    ```python
+    >>> import supervision as sv
+
+    >>> image = ...
+    >>> detections = sv.Detections(...)
+
+    >>> trace_annotator = sv.TraceAnnotator()
+    >>> annotated_frame = trace_annotator.annotate(
+    ...     scene=image.copy(),
+    ...     detections=detections
+    ... )
+    ```
+
+    <div class="result" markdown>
+
+    ![trace-annotator-example](https://media.roboflow.com/supervision-annotator-examples/trace-annotator-example.png){ align=center width="800" }
+
+    </div>
+
+## BoundingBoxAnnotator
+
+:::supervision.annotators.core.BoundingBoxAnnotator
+
+## MaskAnnotator
+
+:::supervision.annotators.core.MaskAnnotator
+
+## EllipseAnnotator
+
+:::supervision.annotators.core.EllipseAnnotator
+
+## BoxCornerAnnotator
+
+:::supervision.annotators.core.BoxCornerAnnotator
+
+## CircleAnnotator
+
+:::supervision.annotators.core.CircleAnnotator
+
+## LabelAnnotator
+
+:::supervision.annotators.core.LabelAnnotator
+
+## TraceAnnotator
+
+:::supervision.annotators.core.TraceAnnotator
+
+## BlurAnnotator
+
+:::supervision.annotators.core.BlurAnnotator
@@ -1,3 +1,42 @@
+### 0.15.0 <small>October 5, 2023</small>
+
+- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.BoundingBoxAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.BoundingBoxAnnotator) allowing to annotate images and videos with bounding boxes.
+
+- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.BoxCornerAnnotator `](https://supervision.roboflow.com/annotators/#supervision.annotators.core.BoxCornerAnnotator) allowing to annotate images and videos with just bounding box corners.
+
+- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.MaskAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.MaskAnnotator) allowing to annotate images and videos with segmentation masks.
+
+- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.EllipseAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.EllipseAnnotator) allowing to annotate images and videos with ellipses (sports game style).
+
+- Added [#386](https://github.com/roboflow/supervision/pull/386): [`sv.CircleAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.CircleAnnotator) allowing to annotate images and videos with circles.
+
+- Added [#354](https://github.com/roboflow/supervision/pull/354): [`sv.TraceAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.TraceAnnotator) allowing to draw path of moving objects on videos.
+
+- Added [#405](https://github.com/roboflow/supervision/pull/405): [`sv.BlurAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.BlurAnnotator) allowing to blur objects on images and videos.
+
+```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
+... )
+```
+
+- Added [#354](https://github.com/roboflow/supervision/pull/354): Supervision usage [example](https://github.com/roboflow/supervision/tree/develop/examples/traffic_analysis). You can now learn how to perform traffic flow analysis with Supervision.
+
+- Changed [#399](https://github.com/roboflow/supervision/pull/399): [`sv.Detections.from_roboflow`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_roboflow) now does not require `class_list` to be specified. The `class_id` value can be extracted directly from the [inference](https://github.com/roboflow/inference) response.
+
+- Changed [#381](https://github.com/roboflow/supervision/pull/381): [`sv.VideoSink`](https://supervision.roboflow.com/utils/video/#videosink) now allows to customize the output codec.
+
+- Changed [#361](https://github.com/roboflow/supervision/pull/361): [`sv.InferenceSlicer`](https://supervision.roboflow.com/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer) can now operate in multithreading mode.
+
+- Fixed [#348](https://github.com/roboflow/supervision/pull/348): [`sv.Detections.from_deepsparse`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_deepsparse) to allow processing empty [deepsparse](https://github.com/neuralmagic/deepsparse) result object.
+
 ### 0.14.0 <small>August 31, 2023</small>

 - Added [#282](https://github.com/roboflow/supervision/pull/282): support for SAHI inference technique with [`sv.InferenceSlicer`](https://supervision.roboflow.com/detection/tools/inference_slicer).
@@ -1,19 +0,0 @@
-## BoundingBoxAnnotator
-
-:::supervision.annotators.core.BoundingBoxAnnotator
-
-## MaskAnnotator
-
-:::supervision.annotators.core.MaskAnnotator
-
-## EllipseAnnotator
-
-:::supervision.annotators.core.EllipseAnnotator
-
-## BoxCornerAnnotator
-
-:::supervision.annotators.core.BoxCornerAnnotator
-
-## LabelAnnotator
-
-:::supervision.annotators.core.LabelAnnotator
@@ -28,29 +28,27 @@ nav:
  - Home: index.md
  - Quickstart:
    - Detections: quickstart/detections.md
-  - API reference:
-    - Classifications:
-        - Core: classification/core.md
-    - Detections:
-        - Core: detection/core.md
-        - Annotate: detection/annotate.md
-        - Utils: detection/utils.md
-        - Tools:
-            - Polygon Zone: detection/tools/polygon_zone.md
-            - Inference Slicer: detection/tools/inference_slicer.md
-    - Trackers:
-        - Core: tracker/core.md
-    - Dataset:
-        - Core: dataset/core.md
-    - Metrics:
-      - Detection Models: metrics/detection.md
-    - Draw:
-        - Utils: draw/utils.md
-    - Utils:
-        - Video: utils/video.md
-        - Image: utils/image.md
-        - Notebook: utils/notebook.md
-        - File: utils/file.md
+  - Classifications:
+      - Core: classification/core.md
+  - Detections:
+      - Core: detection/core.md
+      - Annotate: detection/annotate.md
+      - Utils: detection/utils.md
+      - Tools:
+          - Polygon Zone: detection/tools/polygon_zone.md
+          - Inference Slicer: detection/tools/inference_slicer.md
+  - Annotators: annotators.md
+  - Trackers: trackers.md
+  - Datasets: datasets.md
+  - Metrics:
+    - Object Detection: metrics/detection.md
+  - Draw:
+      - Utils: draw/utils.md
+  - Utils:
+      - Video: utils/video.md
+      - Image: utils/image.md
+      - Notebook: utils/notebook.md
+      - File: utils/file.md
  - Changelog: changelog.md

 theme:
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 [package.extras]
@@ -3370,13 +3358,13 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake

 [[package]]
 name = "urllib3"
-version = "2.0.4"
+version = "2.0.6"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "urllib3-2.0.4-py3-none-any.whl", hash = "sha256:de7df1803967d2c2a98e4b11bb7d6bd9210474c46e8a0401514e3a42a75ebde4"},
-    {file = "urllib3-2.0.4.tar.gz", hash = "sha256:8d22f86aae8ef5e410d4f539fde9ce6b2113a001bb4d189e0aed70642d602b11"},
+    {file = "urllib3-2.0.6-py3-none-any.whl", hash = "sha256:7a7c7003b000adf9e7ca2a377c9688bbc54ed41b985789ed576570342a375cd2"},
+    {file = "urllib3-2.0.6.tar.gz", hash = "sha256:b19e1a85d206b56d7df1d5e683df4a7725252a964e3993648dd0fb5a1c157564"},
 ]

 [package.extras]
@@ -3532,4 +3520,4 @@ desktop = ["opencv-python"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8,<3.12.0"
-content-hash = "fcefb11e72c40fa7defc13f4a9cb1fe24b9d3d171b64bb54fc263d8add9ef40f"
+content-hash = "beb692d89611ce85f8b2992f4e6da71fd14fa2ca75742ddae1a1c7b77df134f7"
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supervision"
-version = "0.15.0rc2"
+version = "0.15.0"
 description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
 authors = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
 maintainers = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
@@ -13,7 +13,9 @@ documentation = "https://github.com/roboflow/supervision/blob/main/README.md"
 keywords = ["machine-learning", "deep-learning", "vision", "ML", "DL", "AI", "YOLOv5", "YOLOv8", "Roboflow"]

 classifiers=[
+    'Development Status :: 4 - Beta',
    'Intended Audience :: Developers',
+    'Intended Audience :: Education',
    'Intended Audience :: Science/Research',
    'License :: OSI Approved :: MIT License',
    'Programming Language :: Python :: 3',
@@ -28,8 +30,7 @@ classifiers=[
    "Topic :: Scientific/Engineering :: Image Recognition",
    'Typing :: Typed',
    'Operating System :: Microsoft :: Windows',
-    'Operating System :: POSIX',
-    'Operating System :: Unix',
+    'Operating System :: POSIX :: Linux',
    'Operating System :: MacOS'
 ]

@@ -38,7 +39,7 @@ python = ">=3.8,<3.12.0"
 numpy = "^1.20.0"
 matplotlib = "^3.7.1"
 pyyaml = "^6.0"
-pillow = "^9.4.0"
+pillow = ">=9.4,<11.0"
 opencv-python = { version = "^4.8.0.74", optional = true }
 opencv-python-headless = "^4.8.0.74"
 scipy = "^1.9.0"
@@ -7,11 +7,14 @@ except importlib_metadata.PackageNotFoundError:
    __version__ = "development"

 from supervision.annotators.core import (
+    BlurAnnotator,
    BoundingBoxAnnotator,
    BoxCornerAnnotator,
+    CircleAnnotator,
    EllipseAnnotator,
    LabelAnnotator,
    MaskAnnotator,
+    TraceAnnotator,
 )
 from supervision.classification.core import Classifications
 from supervision.dataset.core import (
@@ -19,7 +22,7 @@ from supervision.dataset.core import (
    ClassificationDataset,
    DetectionDataset,
 )
-from supervision.detection.annotate import BoxAnnotator, TraceAnnotator
+from supervision.detection.annotate import BoxAnnotator
 from supervision.detection.core import Detections
 from supervision.detection.line_counter import LineZone, LineZoneAnnotator
 from supervision.detection.tools.inference_slicer import InferenceSlicer
@@ -1,55 +1,8 @@
 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):
@@ -1,11 +1,13 @@
-from typing import List, Tuple, Union
+from math import sqrt
+from typing import List, Optional, Tuple, Union

 import cv2
 import numpy as np

-from supervision.annotators.base import (
-    BaseAnnotator,
+from supervision.annotators.base import BaseAnnotator
+from supervision.annotators.utils import (
    ColorMap,
+    Trace,
    resolve_color,
    resolve_color_idx,
 )
@@ -315,6 +317,91 @@ class BoxCornerAnnotator(BaseAnnotator):
        return scene


+class CircleAnnotator(BaseAnnotator):
+    """
+    A class for drawing circle on an image using provided detections.
+    """
+
+    def __init__(
+        self,
+        color: Union[Color, ColorPalette] = ColorPalette.default(),
+        thickness: int = 4,
+        color_map: str = "class",
+    ):
+        """
+        Args:
+            color (Union[Color, ColorPalette]): The color or color palette to use for
+                annotating detections.
+            thickness (int): Thickness of the circle 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.color_map: ColorMap = ColorMap(color_map)
+
+    def annotate(
+        self,
+        scene: np.ndarray,
+        detections: Detections,
+    ) -> np.ndarray:
+        """
+        Annotates the given scene with circles 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(...)
+
+            >>> circle_annotator = sv.CircleAnnotator()
+            >>> annotated_frame = circle_annotator.annotate(
+            ...     scene=image.copy(),
+            ...     detections=detections
+            ... )
+            ```
+
+
+        ![circle-annotator-example](https://media.roboflow.com/
+        supervision-annotator-examples/circle-annotator-example.png)
+        """
+        for detection_idx in range(len(detections)):
+            x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
+            center = ((x1 + x2) // 2, (y1 + y2) // 2)
+            distance = sqrt((x1 - center[0]) ** 2 + (y1 - center[1]) ** 2)
+
+            idx = resolve_color_idx(
+                detections=detections,
+                detection_idx=detection_idx,
+                color_map=self.color_map,
+            )
+
+            color = (
+                self.color.by_idx(idx)
+                if isinstance(self.color, ColorPalette)
+                else self.color
+            )
+
+            cv2.circle(
+                img=scene,
+                center=center,
+                radius=int(distance),
+                color=color.as_bgr(),
+                thickness=self.thickness,
+            )
+
+        return scene
+
+
 class LabelAnnotator:
    """
    A class for annotating labels on an image using provided detections.
@@ -476,3 +563,145 @@ class LabelAnnotator:
                lineType=cv2.LINE_AA,
            )
        return scene
+
+
+class BlurAnnotator(BaseAnnotator):
+    """
+    A class for blurring regions in an image using provided detections.
+    """
+
+    def __init__(self, kernel_size: int = 15):
+        """
+        Args:
+            kernel_size (int): The size of the average pooling kernel used for blurring.
+        """
+        self.kernel_size: int = kernel_size
+
+    def annotate(
+        self,
+        scene: np.ndarray,
+        detections: Detections,
+    ) -> np.ndarray:
+        """
+        Annotates the given scene by blurring regions based on the provided detections.
+
+        Args:
+            scene (np.ndarray): The image where blurring will be applied.
+            detections (Detections): Object detections to annotate.
+
+        Returns:
+            np.ndarray: The annotated image.
+
+        Example:
+            ```python
+            >>> import supervision as sv
+
+            >>> image = ...
+            >>> detections = sv.Detections(...)
+
+            >>> blur_annotator = sv.BlurAnnotator()
+            >>> annotated_frame = blur_annotator.annotate(
+            ...     scene=image.copy(),
+            ...     detections=detections
+            ... )
+            ```
+
+        ![blur-annotator-example](https://media.roboflow.com/
+        supervision-annotator-examples/blur-annotator-example-2.png)
+        """
+        for detection_idx in range(len(detections)):
+            x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
+            roi = scene[y1:y2, x1:x2]
+
+            roi = cv2.blur(roi, (self.kernel_size, self.kernel_size))
+            scene[y1:y2, x1:x2] = roi
+
+        return scene
+
+
+class TraceAnnotator:
+    """
+    A class for drawing trace paths on an image based on detection coordinates.
+
+    !!! warning
+
+        This annotator utilizes the `tracker_id`. Read
+        [here](https://supervision.roboflow.com/trackers/) to learn how to plug
+        tracking into your inference pipeline.
+    """
+
+    def __init__(
+        self,
+        color: Union[Color, ColorPalette] = ColorPalette.default(),
+        position: Optional[Position] = Position.CENTER,
+        trace_length: int = 30,
+        thickness: int = 2,
+        color_map: str = "class",
+    ):
+        """
+        Args:
+            color (Union[Color, ColorPalette]): The color to draw the trace, can be
+                a single color or a color palette.
+            position (Optional[Position]): The position of the trace.
+                Defaults to `CENTER`.
+            trace_length (int): The maximum length of the trace in terms of historical
+                points. Defaults to `30`.
+            thickness (int): The thickness of the trace lines. Defaults to `2`.
+            color_map (str): Strategy for mapping colors to annotations.
+                Options are `index`, `class`, or `track`.
+        """
+        self.color: Union[Color, ColorPalette] = color
+        self.position = position
+        self.trace = Trace(max_size=trace_length)
+        self.thickness = thickness
+        self.color_map: ColorMap = ColorMap(color_map)
+
+    def annotate(self, scene: np.ndarray, detections: Detections) -> np.ndarray:
+        """
+        Draws trace paths on the frame based on the detection coordinates provided.
+
+        Args:
+            scene (np.ndarray): The image on which the traces will be drawn.
+            detections (Detections): The detections which include coordinates for
+                which the traces will be drawn.
+
+        Returns:
+            np.ndarray: The image with the trace paths drawn on it.
+
+        Example:
+            ```python
+            >>> import supervision as sv
+
+            >>> image = ...
+            >>> detections = sv.Detections(...)
+
+            >>> trace_annotator = sv.TraceAnnotator()
+            >>> annotated_frame = trace_annotator.annotate(
+            ...     scene=image.copy(),
+            ...     detections=detections
+            ... )
+            ```
+
+        ![trace-annotator-example](https://media.roboflow.com/
+        supervision-annotator-examples/trace-annotator-example.png)
+        """
+        self.trace.put(detections)
+
+        for detection_idx in range(len(detections)):
+            tracker_id = int(detections.tracker_id[detection_idx])
+            idx = resolve_color_idx(
+                detections=detections,
+                detection_idx=detection_idx,
+                color_map=self.color_map,
+            )
+            color = resolve_color(color=self.color, idx=idx)
+            xy = self.trace.get(tracker_id=tracker_id)
+            if len(xy) > 1:
+                scene = cv2.polylines(
+                    scene,
+                    [xy.astype(np.int32)],
+                    False,
+                    color=color.as_bgr(),
+                    thickness=self.thickness,
+                )
+        return scene
@@ -0,0 +1,89 @@
+from enum import Enum
+from typing import Optional, Union
+
+import numpy as np
+
+from supervision.detection.core import Detections
+from supervision.draw.color import Color, ColorPalette
+from supervision.geometry.core import Position
+
+
+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)
+    return color
+
+
+class Trace:
+    def __init__(
+        self,
+        max_size: Optional[int] = None,
+        start_frame_id: int = 0,
+        anchor: Position = Position.CENTER,
+    ) -> None:
+        self.current_frame_id = start_frame_id
+        self.max_size = max_size
+        self.anchor = anchor
+
+        self.frame_id = np.array([], dtype=int)
+        self.xy = np.empty((0, 2), dtype=np.float32)
+        self.tracker_id = np.array([], dtype=int)
+
+    def put(self, detections: Detections) -> None:
+        frame_id = np.full(len(detections), self.current_frame_id, dtype=int)
+        self.frame_id = np.concatenate([self.frame_id, frame_id])
+        self.xy = np.concatenate(
+            [self.xy, detections.get_anchor_coordinates(self.anchor)]
+        )
+        self.tracker_id = np.concatenate([self.tracker_id, detections.tracker_id])
+
+        unique_frame_id = np.unique(self.frame_id)
+
+        if 0 < self.max_size < len(unique_frame_id):
+            max_allowed_frame_id = self.current_frame_id - self.max_size + 1
+            filtering_mask = self.frame_id >= max_allowed_frame_id
+            self.frame_id = self.frame_id[filtering_mask]
+            self.xy = self.xy[filtering_mask]
+            self.tracker_id = self.tracker_id[filtering_mask]
+
+        self.current_frame_id += 1
+
+    def get(self, tracker_id: int) -> np.ndarray:
+        return self.xy[self.tracker_id == tracker_id]
@@ -5,7 +5,6 @@ import numpy as np

 from supervision.detection.core import Detections
 from supervision.draw.color import Color, ColorPalette
-from supervision.geometry.core import Position


 class BoxAnnotator:
@@ -145,116 +144,3 @@ class BoxAnnotator:
                lineType=cv2.LINE_AA,
            )
        return scene
-
-
-class Trace:
-    def __init__(
-        self,
-        max_size: Optional[int] = None,
-        start_frame_id: int = 0,
-        anchor: Position = Position.CENTER,
-    ) -> None:
-        self.current_frame_id = start_frame_id
-        self.max_size = max_size
-        self.anchor = anchor
-
-        self.frame_id = np.array([], dtype=int)
-        self.xy = np.empty((0, 2), dtype=np.float32)
-        self.tracker_id = np.array([], dtype=int)
-
-    def put(self, detections: Detections) -> None:
-        frame_id = np.full(len(detections), self.current_frame_id, dtype=int)
-        self.frame_id = np.concatenate([self.frame_id, frame_id])
-        self.xy = np.concatenate(
-            [self.xy, detections.get_anchor_coordinates(self.anchor)]
-        )
-        self.tracker_id = np.concatenate([self.tracker_id, detections.tracker_id])
-
-        unique_frame_id = np.unique(self.frame_id)
-
-        if 0 < self.max_size < len(unique_frame_id):
-            max_allowed_frame_id = self.current_frame_id - self.max_size + 1
-            filtering_mask = self.frame_id >= max_allowed_frame_id
-            self.frame_id = self.frame_id[filtering_mask]
-            self.xy = self.xy[filtering_mask]
-            self.tracker_id = self.tracker_id[filtering_mask]
-
-        self.current_frame_id += 1
-
-    def get(self, tracker_id: int) -> np.ndarray:
-        return self.xy[self.tracker_id == tracker_id]
-
-
-class TraceAnnotator:
-    """
-    A class for drawing trace paths on an image based on detection coordinates.
-
-    Attributes:
-        color (Union[Color, ColorPalette]): The color to draw the trace, can be
-            a single color or a color palette.
-        position (Optional[Position]): The position of the trace. Defaults to `CENTER`.
-        trace_length (int): The maximum length of the trace in terms of historical
-            points. Defaults to `30`.
-        thickness (int): The thickness of the trace lines. Defaults to `2`.
-
-    """
-
-    def __init__(
-        self,
-        color: Union[Color, ColorPalette] = ColorPalette.default(),
-        position: Optional[Position] = Position.CENTER,
-        trace_length: int = 30,
-        thickness: int = 2,
-    ):
-        self.color: Union[Color, ColorPalette] = color
-        self.position = position
-        self.trace = Trace(max_size=trace_length)
-        self.thickness = thickness
-
-    def annotate(self, scene: np.ndarray, detections: Detections) -> np.ndarray:
-        """
-        Draws trace paths on the frame based on the detection coordinates provided.
-
-        Args:
-            scene (np.ndarray): The image on which the traces will be drawn.
-            detections (Detections): The detections which include coordinates for
-                which the traces will be drawn.
-
-        Returns:
-            np.ndarray: The image with the trace paths drawn on it.
-
-        Example:
-            ```python
-            >>> import supervision as sv
-
-            >>> image = ...
-            >>> detections = sv.Detections(...)
-
-            >>> trace_annotator = sv.TraceAnnotator()
-            >>> annotated_frame = trace_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
-            ```
-        """
-        self.trace.put(detections)
-
-        for i, (xyxy, mask, confidence, class_id, tracker_id) in enumerate(detections):
-            class_id = detections.class_id[i] if 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
-            )
-
-            xy = self.trace.get(tracker_id=tracker_id)
-            if len(xy) > 1:
-                scene = cv2.polylines(
-                    scene,
-                    [xy.astype(np.int32)],
-                    False,
-                    color=color.as_bgr(),
-                    thickness=self.thickness,
-                )
-        return scene
@@ -432,7 +432,7 @@ class Detections:
        )

    @classmethod
-    def from_roboflow(cls, roboflow_result: dict, class_list: List[str]) -> Detections:
+    def from_roboflow(cls, roboflow_result: dict) -> Detections:
        """
        Create a Detections object from the [Roboflow](https://roboflow.com/)
            API inference result.
@@ -440,8 +440,6 @@ class Detections:
        Args:
            roboflow_result (dict): The result from the
                Roboflow API containing predictions.
-            class_list (List[str]): A list of class names
-                corresponding to the class IDs in the API result.

        Returns:
            (Detections): A Detections object containing the bounding boxes, class IDs,
@@ -458,19 +456,19 @@ class Detections:
            ...             "y": 0.5,
            ...             "width": 0.2,
            ...             "height": 0.3,
+            ...             "class_id": 0,
            ...             "class": "person",
            ...             "confidence": 0.9
            ...         },
            ...         # ... more predictions ...
            ...     ]
            ... }
-            >>> class_list = ["person", "car", "dog"]

-            >>> detections = sv.Detections.from_roboflow(roboflow_result, class_list)
+            >>> detections = sv.Detections.from_roboflow(roboflow_result)
            ```
        """
        xyxy, confidence, class_id, masks = process_roboflow_result(
-            roboflow_result=roboflow_result, class_list=class_list
+            roboflow_result=roboflow_result
        )

        if np.asarray(xyxy).shape[0] == 0:
@@ -1,3 +1,4 @@
+from concurrent.futures import ThreadPoolExecutor, as_completed
 from typing import Callable, Optional, Tuple

 import numpy as np
@@ -28,15 +29,16 @@ class InferenceSlicer:
    involves dividing a larger image into smaller slices, performing inference on each
    slice, and then merging the detections.

-    Attributes:
+    Args:
        slice_wh (Tuple[int, int]): Dimensions of each slice in the format
            `(width, height)`.
        overlap_ratio_wh (Tuple[float, float]): Overlap ratio between consecutive
            slices in the format `(width_ratio, height_ratio)`.
-        iou_threshold (Optional[float]): Intersection over Union (IoU) threshold used
-            for non-max suppression.
-        callback (Callable): A function that performs inference on a given image slice
-            and returns detections.
+        iou_threshold (Optional[float]): Intersection over Union (IoU) threshold
+            used for non-max suppression.
+        callback (Callable): A function that performs inference on a given image
+            slice and returns detections.
+        thread_workers (int): Number of threads for parallel execution.

    Note:
        The class ensures that slices do not exceed the boundaries of the original
@@ -51,11 +53,13 @@ class InferenceSlicer:
        slice_wh: Tuple[int, int] = (320, 320),
        overlap_ratio_wh: Tuple[float, float] = (0.2, 0.2),
        iou_threshold: Optional[float] = 0.5,
+        thread_workers: int = 1,
    ):
        self.slice_wh = slice_wh
        self.overlap_ratio_wh = overlap_ratio_wh
        self.iou_threshold = iou_threshold
        self.callback = callback
+        self.thread_workers = thread_workers
        validate_inference_callback(callback=callback)

    def __call__(self, image: np.ndarray) -> Detections:
@@ -98,15 +102,35 @@ class InferenceSlicer:
            overlap_ratio_wh=self.overlap_ratio_wh,
        )

-        for offset in offsets:
-            image_slice = crop_image(image=image, xyxy=offset)
-            detections = self.callback(image_slice)
-            detections = move_detections(detections=detections, offset=offset[:2])
-            detections_list.append(detections)
+        with ThreadPoolExecutor(max_workers=self.thread_workers) as executor:
+            futures = [
+                executor.submit(self._run_callback, image, offset) for offset in offsets
+            ]
+            for future in as_completed(futures):
+                detections_list.append(future.result())
+
        return Detections.merge(detections_list=detections_list).with_nms(
            threshold=self.iou_threshold
        )

+    def _run_callback(self, image, offset) -> Detections:
+        """
+        Run the provided callback on a slice of an image.
+
+        Args:
+            image (np.ndarray): The input image on which inference needs to run
+            offset (np.ndarray): An array of shape `(4,)` containing coordinates
+                for the slice.
+
+        Returns:
+            Detections: A collection of detections for the slice.
+        """
+        image_slice = crop_image(image=image, xyxy=offset)
+        detections = self.callback(image_slice)
+        detections = move_detections(detections=detections, offset=offset[:2])
+
+        return detections
+
    @staticmethod
    def _generate_offset(
        resolution_wh: Tuple[int, int],
@@ -332,7 +332,7 @@ def extract_ultralytics_masks(yolov8_results) -> Optional[np.ndarray]:


 def process_roboflow_result(
-    roboflow_result: dict, class_list: List[str]
+    roboflow_result: dict,
 ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, Optional[np.ndarray]]:
    if not roboflow_result["predictions"]:
        return np.empty((0, 4)), np.empty(0), np.empty(0), None
@@ -357,7 +357,7 @@ def process_roboflow_result(

        if "points" not in prediction:
            xyxy.append([x_min, y_min, x_max, y_max])
-            class_id.append(class_list.index(prediction["class"]))
+            class_id.append(prediction["class_id"])
            confidence.append(prediction["confidence"])
        elif len(prediction["points"]) >= 3:
            polygon = np.array(
@@ -365,7 +365,7 @@ def process_roboflow_result(
            )
            mask = polygon_to_mask(polygon, resolution_wh=(image_width, image_height))
            xyxy.append([x_min, y_min, x_max, y_max])
-            class_id.append(class_list.index(prediction["class"]))
+            class_id.append(prediction["class_id"])
            confidence.append(prediction["confidence"])
            masks.append(mask)

@@ -4,7 +4,7 @@ from typing import Optional

 import pytest

-from supervision.annotators.core import ColorMap, resolve_color_idx
+from supervision.annotators.utils import ColorMap, resolve_color_idx
 from supervision.detection.core import Detections


@@ -260,11 +260,10 @@ def test_filter_polygons_by_area(


@pytest.mark.parametrize(
-    "roboflow_result, class_list, expected_result, exception",
+    "roboflow_result, expected_result, exception",
    [
        (
            {"predictions": [], "image": {"width": 1000, "height": 1000}},
-            ["person", "car", "truck"],
            (np.empty((0, 4)), np.empty(0), np.empty(0), None),
            DoesNotRaise(),
        ),  # empty result
@@ -277,12 +276,12 @@ def test_filter_polygons_by_area(
                        "width": 50.0,
                        "height": 50.0,
                        "confidence": 0.9,
+                        "class_id": 0,
                        "class": "person",
                    }
                ],
                "image": {"width": 1000, "height": 1000},
            },
-            ["person", "car", "truck"],
            (
                np.array([[175.0, 275.0, 225.0, 325.0]]),
                np.array([0.9]),
@@ -300,6 +299,7 @@ def test_filter_polygons_by_area(
                        "width": 50.0,
                        "height": 50.0,
                        "confidence": 0.9,
+                        "class_id": 0,
                        "class": "person",
                    },
                    {
@@ -308,16 +308,16 @@ def test_filter_polygons_by_area(
                        "width": 100.0,
                        "height": 100.0,
                        "confidence": 0.8,
+                        "class_id": 7,
                        "class": "truck",
                    },
                ],
                "image": {"width": 1000, "height": 1000},
            },
-            ["person", "car", "truck"],
            (
                np.array([[175.0, 275.0, 225.0, 325.0], [450.0, 450.0, 550.0, 550.0]]),
                np.array([0.9, 0.8]),
-                np.array([0, 2]),
+                np.array([0, 7]),
                None,
            ),
            DoesNotRaise(),
@@ -331,13 +331,13 @@ def test_filter_polygons_by_area(
                        "width": 50.0,
                        "height": 50.0,
                        "confidence": 0.9,
+                        "class_id": 0,
                        "class": "person",
                        "points": [],
                    }
                ],
                "image": {"width": 1000, "height": 1000},
            },
-            ["person", "car", "truck"],
            (np.empty((0, 4)), np.empty(0), np.empty(0), None),
            DoesNotRaise(),
        ),  # single incorrect instance segmentation result with no points
@@ -350,13 +350,13 @@ def test_filter_polygons_by_area(
                        "width": 50.0,
                        "height": 50.0,
                        "confidence": 0.9,
+                        "class_id": 0,
                        "class": "person",
                        "points": [{"x": 200.0, "y": 300.0}, {"x": 250.0, "y": 300.0}],
                    }
                ],
                "image": {"width": 1000, "height": 1000},
            },
-            ["person", "car", "truck"],
            (np.empty((0, 4)), np.empty(0), np.empty(0), None),
            DoesNotRaise(),
        ),  # single incorrect instance segmentation result with no enough points
@@ -369,6 +369,7 @@ def test_filter_polygons_by_area(
                        "width": 50.0,
                        "height": 50.0,
                        "confidence": 0.9,
+                        "class_id": 0,
                        "class": "person",
                        "points": [
                            {"x": 200.0, "y": 300.0},
@@ -380,7 +381,6 @@ def test_filter_polygons_by_area(
                ],
                "image": {"width": 1000, "height": 1000},
            },
-            ["person", "car", "truck"],
            (
                np.array([[175.0, 275.0, 225.0, 325.0]]),
                np.array([0.9]),
@@ -398,6 +398,7 @@ def test_filter_polygons_by_area(
                        "width": 50.0,
                        "height": 50.0,
                        "confidence": 0.9,
+                        "class_id": 0,
                        "class": "person",
                        "points": [
                            {"x": 200.0, "y": 300.0},
@@ -412,13 +413,13 @@ def test_filter_polygons_by_area(
                        "width": 100.0,
                        "height": 100.0,
                        "confidence": 0.8,
+                        "class_id": 7,
                        "class": "truck",
                        "points": [],
                    },
                ],
                "image": {"width": 1000, "height": 1000},
            },
-            ["person", "car", "truck"],
            (
                np.array([[175.0, 275.0, 225.0, 325.0]]),
                np.array([0.9]),
@@ -431,14 +432,11 @@ def test_filter_polygons_by_area(
 )
 def test_process_roboflow_result(
    roboflow_result: dict,
-    class_list: List[str],
    expected_result: Tuple[np.ndarray, np.ndarray, np.ndarray, Optional[np.ndarray]],
    exception: Exception,
 ) -> None:
    with exception:
-        result = process_roboflow_result(
-            roboflow_result=roboflow_result, class_list=class_list
-        )
+        result = process_roboflow_result(roboflow_result=roboflow_result)
        assert np.array_equal(result[0], expected_result[0])
        assert np.array_equal(result[1], expected_result[1])
        assert np.array_equal(result[2], expected_result[2])
Author	SHA1	Message	Date
Piotr Skalski	1bddf26f34	Merge pull request #404 from roboflow/develop `supervision-0.15.0` release	2023-10-05 09:23:31 +02:00
SkalskiP	73ed3f5f5e	👊 bump `develop` version to `0.15.0`	2023-10-05 09:21:05 +02:00
SkalskiP	88e7fb76d2	Fixed link targeting in changelog documentation.	2023-10-04 23:57:03 +02:00
Piotr Skalski	09c5407347	Merge pull request #410 from roboflow/feature/blur-annotator-docs-update Refactor BlurAnnotator class and update documentation	2023-10-04 23:54:48 +02:00
pre-commit-ci[bot]	bfcf6469d4	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-10-04 21:52:50 +00:00
SkalskiP	ec72f81749	Refactor BlurAnnotator class and update documentation	2023-10-04 23:50:47 +02:00
Piotr Skalski	3a52f7b082	Merge pull request #405 from Rajarshi-Misra/new1 Create sv.BlurAnnotator #395	2023-10-04 23:29:26 +02:00
SkalskiP	5efb3e2df1	👊 bump `develop` version to `0.15.0rc3`	2023-10-04 19:43:03 +02:00
SkalskiP	6e33cb04ca	🪵 `supervision-0.15.0` `changelog.md` update	2023-10-04 19:35:01 +02:00
Rajarshi-Misra	2455ef7cfe	Merge branch 'roboflow:develop' into new1	2023-10-04 22:24:53 +05:30
Rajarshi-Misra	1d0ffeba72	Comments corrected	2023-10-04 22:23:54 +05:30
Piotr Skalski	55d14cf33e	Merge pull request #407 from roboflow/feature/color_map_strategy_support_for_trace_annotator Add color mapping strategy to InferenceSlicer	2023-10-04 18:49:45 +02:00
pre-commit-ci[bot]	a66d835a57	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-10-04 16:37:40 +00:00
SkalskiP	440be5d416	Add color mapping strategy to InferenceSlicer Added a new argument, 'color_map' in InferenceSlicer to provide more options for color coding while annotating. This includes mapping colors by 'index', 'class' or 'track'. Also refactored the way colors are resolved by separating the responsibility into a new function 'resolve_color_idx' and 'resolve_color'.	2023-10-04 18:36:30 +02:00
Hardik Dava	4fd1ba4dd3	Update var type	2023-10-04 18:34:51 +02:00
Piotr Skalski	61108048e1	Merge pull request #361 from roboflow/add-concurrent-slicer Add concurrent processing for `sv.InferenceSlicer`	2023-10-04 18:17:52 +02:00
pre-commit-ci[bot]	a0b64342ca	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-10-04 16:17:35 +00:00
Rajarshi-Misra	7cf593b55d	Replaced with blur filter	2023-10-04 21:47:24 +05:30
SkalskiP	1c1936e411	Refactor args description in InferenceSlicer	2023-10-04 18:15:08 +02:00
pre-commit-ci[bot]	331ccb8dde	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-10-04 16:09:38 +00:00
Rajarshi-Misra	7366ba9036	Error resolved	2023-10-04 21:38:44 +05:30
pre-commit-ci[bot]	9d5e53030a	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-10-04 14:25:37 +00:00
Piotr Skalski	d67fa3ae9b	Merge branch 'develop' into new1	2023-10-04 16:24:13 +02:00
Piotr Skalski	10ad27d726	Merge pull request #406 from roboflow/feature/annotators-refactor-before-0.15.0-release Refactor TraceAnnotator to separate utility class	2023-10-04 16:18:49 +02:00
SkalskiP	8511bbb762	Add Tracker ID warning and image to TraceAnnotator doc Added a warning about the requirement of `tracker_id` for TraceAnnotator in the `core.py`. Also, included an example image in the TraceAnnotator initialization for better understanding in both `core.py` and `annotators.md`.	2023-10-04 16:15:38 +02:00
SkalskiP	14388e56a2	Merge remote-tracking branch 'origin/feature/annotators-refactor-before-0.15.0-release' into feature/annotators-refactor-before-0.15.0-release	2023-10-04 15:35:17 +02:00
SkalskiP	1eb74ed88a	Add TraceAnnotator description to the documentation The documentation has been updated to include the `TraceAnnotator` class.	2023-10-04 15:35:07 +02:00
pre-commit-ci[bot]	87d830d3a1	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-10-04 13:31:17 +00:00
SkalskiP	d7a5624d60	Refactor TraceAnnotator to separate utility class	2023-10-04 15:28:00 +02:00
Rajarshi-Misra	d3c71c2119	Merge branch 'roboflow:develop' into new1	2023-10-04 18:01:13 +05:30
Rajarshi-Misra	9ad5e50ae9	Addition of gaussian blur filter	2023-10-04 17:59:03 +05:30
Piotr Skalski	67fa66c7e7	Merge pull request #399 from akashAD98/update_detection_sv Remove class list dependency for roboflow to detection conversion	2023-10-04 12:08:04 +02:00
dependabot[bot]	492f033882	chore(deps): bump pillow from 9.5.0 to 10.0.1 (#403 ) Bumps [pillow](https://github.com/python-pillow/Pillow) from 9.5.0 to 10.0.1. - [Release notes](https://github.com/python-pillow/Pillow/releases) - [Changelog](https://github.com/python-pillow/Pillow/blob/main/CHANGES.rst) - [Commits](https://github.com/python-pillow/Pillow/compare/9.5.0...10.0.1) --- updated-dependencies: - dependency-name: pillow dependency-type: direct:production ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2023-10-04 13:07:50 +03:00
SkalskiP	e911516180	Change prediction class handling from text to ids Updated the prediction parsing logic in supervision/detection/utils.py and supervision/detection/core.py to use 'class_id' instead of 'class' names. Adapted tests in test/detection/test_utils.py accordingly to reflect the same change.	2023-10-04 10:44:49 +02:00
pre-commit-ci[bot]	ed30ef68af	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-10-03 17:47:44 +00:00
Hardik Dava	fefd273a0f	removing class list from test inputs	2023-10-03 19:45:38 +02:00
pre-commit-ci[bot]	8746dd3d0e	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-10-03 17:34:30 +00:00
Hardik Dava	7cb57c9cc9	Fixing int conversion bug	2023-10-03 19:33:56 +02:00
James	ddc036ea3a	Update CODE_OF_CONDUCT.md (#400 )	2023-10-03 19:40:56 +03:00
pre-commit-ci[bot]	c83959bb16	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-10-03 16:18:29 +00:00
Akash A Desai	4f513ce275	Update core.py remoed class_list arg from core.py file	2023-10-03 21:41:13 +05:30
Piotr Skalski	6f30c87612	Merge pull request #392 from roboflow/docs/documentation-refactoring Refactor documentation structure and improve annotator details	2023-10-03 11:53:09 +02:00
SkalskiP	93a8110908	Refactor documentation structure and improve annotator details Update the file and section structure of the project documentation for better readability. Removed 'annotate.md' and replaced it with an expanded 'annotators.md'. The new document provides examples of each annotator and includes preview images for better understanding. The API reference structure was flattened for simplicity and related documents 'core.py' and 'mkdocs.yml' were updated accordingly. The core tracker and dataset documents were renamed for consistency.	2023-10-03 11:38:28 +02:00
dependabot[bot]	410b7eb4fc	chore(deps-dev): bump urllib3 from 2.0.4 to 2.0.6 (#389 ) Bumps [urllib3](https://github.com/urllib3/urllib3) from 2.0.4 to 2.0.6. - [Release notes](https://github.com/urllib3/urllib3/releases) - [Changelog](https://github.com/urllib3/urllib3/blob/main/CHANGES.rst) - [Commits](https://github.com/urllib3/urllib3/compare/2.0.4...2.0.6) --- updated-dependencies: - dependency-name: urllib3 dependency-type: indirect ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2023-10-03 12:07:45 +03:00
Piotr Skalski	e6e1397f56	Merge pull request #386 from onuralpszr/feature/circle_annotator feat(annotator): 🚀 introduce circle annotator	2023-10-02 23:56:24 +02:00
Piotr Skalski	0ca9a3ecdf	Merge pull request #384 from roboflow/code-of-conduct docs: 📝 code of conduct added	2023-10-02 23:54:36 +02:00
pre-commit-ci[bot]	c5016bbd8f	chore(pre_commit): ⬆ pre_commit autoupdate (#388 ) updates: - [github.com/astral-sh/ruff-pre-commit: v0.0.291 → v0.0.292](https://github.com/astral-sh/ruff-pre-commit/compare/v0.0.291...v0.0.292) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>	2023-10-03 00:16:20 +03:00
Onuralp SEZER	9fbd0ddf62	docs(annotator): 📝 circle annotator example picture added Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2023-10-03 00:03:20 +03:00
Onuralp SEZER	a630870968	feat(annotator): 🚀 introduce circle annotator Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2023-10-01 20:51:46 +03:00
Onuralp SEZER	06416607cc	docs: 📝 code of conduct added Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2023-09-29 14:54:56 +03:00
Onuralp SEZER	c6b8fbed31	fix(pre-commit): formatting fix Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2023-09-08 21:29:37 +03:00
Onuralp SEZER	b9598f54b6	feat: ✨ control thread worker and set default 1 Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2023-09-08 15:08:37 +03:00
pre-commit-ci[bot]	21e5d5139c	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-09-08 12:04:17 +00:00
Onuralp SEZER	8f583404ce	fix(import): 📦 import only ThreadPoolExecutor,as_completed Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2023-09-08 15:03:59 +03:00
pre-commit-ci[bot]	bb42753e23	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-09-08 11:46:17 +00:00
James Gallagher	ea0ec29c81	add concurrent processing for inference slicer	2023-09-08 12:43:20 +01:00