Merge pull request #404 from roboflow/develop

`supervision-0.15.0` release

.github/workflows/test.yml

@@ -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"

.pre-commit-config.yaml

@@ -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: v0.0.292
       hooks:
       -   id: ruff
           args: [--fix, --exit-non-zero-on-fix]

CODE_OF_CONDUCT.md

@@ -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

README.md

@@ -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.

docs/annotators.md

@@ -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

docs/changelog.md

@@ -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).

docs/detection/annotate.md

@@ -1,7 +0,0 @@
-## BoxAnnotator
-
-:::supervision.detection.annotate.BoxAnnotator
-
-## MaskAnnotator
-
-:::supervision.detection.annotate.MaskAnnotator

docs/geometry/core.md

@@ -0,0 +1,3 @@
+## Position
+
+:::supervision.geometry.core.Position

examples/tracking/script.py

@@ -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__":

examples/traffic_analysis/.gitignore

@@ -0,0 +1 @@
+data/

examples/traffic_analysis/README.md

@@ -0,0 +1,44 @@
+## 👋 hello
+
+This script performs traffic flow analysis using YOLOv8, an object-detection method and ByteTrack, a simple yet effective online multi-object tracking method. It uses the supervision package for multiple tasks such as tracking, annotations, etc.
+
+https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c-60e9c81b3900
+
+## 💻 install
+
+- clone repository and navigate to example directory
+
+    ```bash
+    git clone https://github.com/roboflow/supervision.git
+    cd supervision/examples/traffic_analysis
+    ```
+
+- setup python environment and activate it [optional]
+
+    ```bash
+    python3 -m venv venv
+    source venv/bin/activate
+    ```
+
+- install required dependencies
+
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+- download `traffic_analysis.pt` and `traffic_analysis.mov` files
+
+    ```bash
+    ./setup.sh
+    ```
+
+## ⚙️ run
+
+```bash
+python script.py \
+--source_weights_path data/traffic_analysis.pt \
+--source_video_path data/traffic_analysis.mov \
+--confidence_threshold 0.3 \
+--iou_threshold 0.5 \
+--target_video_path data/traffic_analysis_result.mov
+```

examples/traffic_analysis/requirements.txt

@@ -0,0 +1,4 @@
+supervision>=0.15.0rc1
+tqdm
+ultralytics
+gdown

examples/traffic_analysis/script.py

@@ -0,0 +1,219 @@
+import argparse
+from typing import Dict, List, Set, Tuple
+
+import cv2
+import numpy as np
+from tqdm import tqdm
+from ultralytics import YOLO
+
+import supervision as sv
+
+COLORS = sv.ColorPalette.default()
+
+ZONE_IN_POLYGONS = [
+    np.array([[592, 282], [900, 282], [900, 82], [592, 82]]),
+    np.array([[950, 860], [1250, 860], [1250, 1060], [950, 1060]]),
+    np.array([[592, 582], [592, 860], [392, 860], [392, 582]]),
+    np.array([[1250, 282], [1250, 530], [1450, 530], [1450, 282]]),
+]
+
+ZONE_OUT_POLYGONS = [
+    np.array([[950, 282], [1250, 282], [1250, 82], [950, 82]]),
+    np.array([[592, 860], [900, 860], [900, 1060], [592, 1060]]),
+    np.array([[592, 282], [592, 550], [392, 550], [392, 282]]),
+    np.array([[1250, 860], [1250, 560], [1450, 560], [1450, 860]]),
+]
+
+
+class DetectionsManager:
+    def __init__(self) -> None:
+        self.tracker_id_to_zone_id: Dict[int, int] = {}
+        self.counts: Dict[int, Dict[int, Set[int]]] = {}
+
+    def update(
+        self,
+        detections_all: sv.Detections,
+        detections_in_zones: List[sv.Detections],
+        detections_out_zones: List[sv.Detections],
+    ) -> sv.Detections:
+        for zone_in_id, detections_in_zone in enumerate(detections_in_zones):
+            for tracker_id in detections_in_zone.tracker_id:
+                self.tracker_id_to_zone_id.setdefault(tracker_id, zone_in_id)
+
+        for zone_out_id, detections_out_zone in enumerate(detections_out_zones):
+            for tracker_id in detections_out_zone.tracker_id:
+                if tracker_id in self.tracker_id_to_zone_id:
+                    zone_in_id = self.tracker_id_to_zone_id[tracker_id]
+                    self.counts.setdefault(zone_out_id, {})
+                    self.counts[zone_out_id].setdefault(zone_in_id, set())
+                    self.counts[zone_out_id][zone_in_id].add(tracker_id)
+
+        detections_all.class_id = np.vectorize(
+            lambda x: self.tracker_id_to_zone_id.get(x, -1)
+        )(detections_all.tracker_id)
+        return detections_all[detections_all.class_id != -1]
+
+
+def initiate_polygon_zones(
+    polygons: List[np.ndarray],
+    frame_resolution_wh: Tuple[int, int],
+    triggering_position: sv.Position = sv.Position.CENTER,
+) -> List[sv.PolygonZone]:
+    return [
+        sv.PolygonZone(
+            polygon=polygon,
+            frame_resolution_wh=frame_resolution_wh,
+            triggering_position=triggering_position,
+        )
+        for polygon in polygons
+    ]
+
+
+class VideoProcessor:
+    def __init__(
+        self,
+        source_weights_path: str,
+        source_video_path: str,
+        target_video_path: str = None,
+        confidence_threshold: float = 0.3,
+        iou_threshold: float = 0.7,
+    ) -> None:
+        self.conf_threshold = confidence_threshold
+        self.iou_threshold = iou_threshold
+        self.source_video_path = source_video_path
+        self.target_video_path = target_video_path
+
+        self.model = YOLO(source_weights_path)
+        self.tracker = sv.ByteTrack()
+
+        self.video_info = sv.VideoInfo.from_video_path(source_video_path)
+        self.zones_in = initiate_polygon_zones(
+            ZONE_IN_POLYGONS, self.video_info.resolution_wh, sv.Position.CENTER
+        )
+        self.zones_out = initiate_polygon_zones(
+            ZONE_OUT_POLYGONS, self.video_info.resolution_wh, sv.Position.CENTER
+        )
+
+        self.box_annotator = sv.BoxAnnotator(color=COLORS)
+        self.trace_annotator = sv.TraceAnnotator(
+            color=COLORS, position=sv.Position.CENTER, trace_length=100, thickness=2
+        )
+        self.detections_manager = DetectionsManager()
+
+    def process_video(self):
+        frame_generator = sv.get_video_frames_generator(
+            source_path=self.source_video_path
+        )
+
+        if self.target_video_path:
+            with sv.VideoSink(self.target_video_path, self.video_info) as sink:
+                for frame in tqdm(frame_generator, total=self.video_info.total_frames):
+                    annotated_frame = self.process_frame(frame)
+                    sink.write_frame(annotated_frame)
+        else:
+            for frame in tqdm(frame_generator, total=self.video_info.total_frames):
+                annotated_frame = self.process_frame(frame)
+                cv2.imshow("Processed Video", annotated_frame)
+                if cv2.waitKey(1) & 0xFF == ord("q"):
+                    break
+            cv2.destroyAllWindows()
+
+    def annotate_frame(
+        self, frame: np.ndarray, detections: sv.Detections
+    ) -> np.ndarray:
+        annotated_frame = frame.copy()
+        for i, (zone_in, zone_out) in enumerate(zip(self.zones_in, self.zones_out)):
+            annotated_frame = sv.draw_polygon(
+                annotated_frame, zone_in.polygon, COLORS.colors[i]
+            )
+            annotated_frame = sv.draw_polygon(
+                annotated_frame, zone_out.polygon, COLORS.colors[i]
+            )
+
+        labels = [f"#{tracker_id}" for tracker_id in detections.tracker_id]
+        annotated_frame = self.trace_annotator.annotate(annotated_frame, detections)
+        annotated_frame = self.box_annotator.annotate(
+            annotated_frame, detections, labels
+        )
+
+        for zone_out_id, zone_out in enumerate(self.zones_out):
+            zone_center = sv.get_polygon_center(polygon=zone_out.polygon)
+            if zone_out_id in self.detections_manager.counts:
+                counts = self.detections_manager.counts[zone_out_id]
+                for i, zone_in_id in enumerate(counts):
+                    count = len(self.detections_manager.counts[zone_out_id][zone_in_id])
+                    text_anchor = sv.Point(x=zone_center.x, y=zone_center.y + 40 * i)
+                    annotated_frame = sv.draw_text(
+                        scene=annotated_frame,
+                        text=str(count),
+                        text_anchor=text_anchor,
+                        background_color=COLORS.colors[zone_in_id],
+                    )
+
+        return annotated_frame
+
+    def process_frame(self, frame: np.ndarray) -> np.ndarray:
+        results = self.model(
+            frame, verbose=False, conf=self.conf_threshold, iou=self.iou_threshold
+        )[0]
+        detections = sv.Detections.from_ultralytics(results)
+        detections.class_id = np.zeros(len(detections))
+        detections = self.tracker.update_with_detections(detections)
+
+        detections_in_zones = []
+        detections_out_zones = []
+
+        for i, (zone_in, zone_out) in enumerate(zip(self.zones_in, self.zones_out)):
+            detections_in_zone = detections[zone_in.trigger(detections=detections)]
+            detections_in_zones.append(detections_in_zone)
+            detections_out_zone = detections[zone_out.trigger(detections=detections)]
+            detections_out_zones.append(detections_out_zone)
+
+        detections = self.detections_manager.update(
+            detections, detections_in_zones, detections_out_zones
+        )
+        return self.annotate_frame(frame, detections)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Traffic Flow Analysis with YOLO and ByteTrack"
+    )
+
+    parser.add_argument(
+        "--source_weights_path",
+        required=True,
+        help="Path to the source weights file",
+        type=str,
+    )
+    parser.add_argument(
+        "--source_video_path",
+        required=True,
+        help="Path to the source video file",
+        type=str,
+    )
+    parser.add_argument(
+        "--target_video_path",
+        default=None,
+        help="Path to the target video file (output)",
+        type=str,
+    )
+    parser.add_argument(
+        "--confidence_threshold",
+        default=0.3,
+        help="Confidence threshold for the model",
+        type=float,
+    )
+    parser.add_argument(
+        "--iou_threshold", default=0.7, help="IOU threshold for the model", type=float
+    )
+
+    args = parser.parse_args()
+    processor = VideoProcessor(
+        source_weights_path=args.source_weights_path,
+        source_video_path=args.source_video_path,
+        target_video_path=args.target_video_path,
+        confidence_threshold=args.confidence_threshold,
+        iou_threshold=args.iou_threshold,
+    )
+    processor.process_video()

examples/traffic_analysis/script.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+
+# Get the directory where the script is located
+DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+
+# Check if 'data' directory does not exist and then create it
+if [[ ! -e $DIR/data ]]; then
+    mkdir "$DIR/data"
+else
+    echo "'data' directory already exists."
+fi
+
+# Download the traffic_analysis.mov file from Google Drive
+gdown -O "$DIR/data/traffic_analysis.mov" "https://drive.google.com/uc?id=1qadBd7lgpediafCpL_yedGjQPk-FLK-W"
+
+# Download the traffic_analysis.pt file from Google Drive
+gdown -O "$DIR/data/traffic_analysis.pt" "https://drive.google.com/uc?id=1y-IfToCjRXa3ZdC1JpnKRopC7mcQW-5z"

mkdocs.yml

@@ -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:

poetry.lock

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@@ -539,34 +543,34 @@ test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
 
 [[package]]
 name = "cryptography"
-version = "41.0.2"
+version = "41.0.4"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
 python-versions = ">=3.7"
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+    {file = "Pillow-10.0.1.tar.gz", hash = "sha256:d72967b06be9300fed5cfbc8b5bafceec48bf7cdc7dab66b1d2549035287191d"},
 ]
 
 [package.extras]
@@ -3356,13 +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]
@@ -3518,4 +3520,4 @@ desktop = ["opencv-python"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8,<3.12.0"
-content-hash = "fcefb11e72c40fa7defc13f4a9cb1fe24b9d3d171b64bb54fc263d8add9ef40f"
+content-hash = "beb692d89611ce85f8b2992f4e6da71fd14fa2ca75742ddae1a1c7b77df134f7"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supervision"
-version = "0.14.0"
+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"

supervision/__init__.py

@@ -6,14 +6,23 @@ try:
 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 (
     BaseDataset,
     ClassificationDataset,
     DetectionDataset,
 )
-from supervision.detection.annotate import BoxAnnotator, MaskAnnotator
+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

supervision/annotators/base.py

@@ -0,0 +1,11 @@
+from abc import ABC, abstractmethod
+
+import numpy as np
+
+from supervision.detection.core import Detections
+
+
+class BaseAnnotator(ABC):
+    @abstractmethod
+    def annotate(self, scene: np.ndarray, detections: Detections) -> np.ndarray:
+        pass

supervision/annotators/core.py

@@ -0,0 +1,707 @@
+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.utils import (
+    ColorMap,
+    Trace,
+    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 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.
+    """
+
+    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
+
+
+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

supervision/annotators/utils.py

@@ -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]

supervision/detection/annotate.py

@@ -144,61 +144,3 @@ class BoxAnnotator:
                 lineType=cv2.LINE_AA,
             )
         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

supervision/detection/core.py

@@ -78,7 +78,7 @@ class Detections:
     """
 
     xyxy: np.ndarray
-    mask: np.Optional[np.ndarray] = None
+    mask: Optional[np.ndarray] = None
     confidence: Optional[np.ndarray] = None
     class_id: Optional[np.ndarray] = None
     tracker_id: Optional[np.ndarray] = None
@@ -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,
@@ -320,14 +326,16 @@ 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)
-            >>> result = list(pipeline_outputs.boxes[0])
-            >>> detections = sv.Detections.from_yolo_nas(result)
+            >>> detections = sv.Detections.from_deepsparse(result)
             ```
         """
+        if np.asarray(deepsparse_results.boxes[0]).shape[0] == 0:
+            return cls.empty()
+
         return cls(
             xyxy=np.array(deepsparse_results.boxes[0]),
             confidence=np.array(deepsparse_results.scores[0]),
@@ -360,6 +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(),
@@ -375,6 +384,7 @@ class Detections:
         Returns:
             Detections: A new Detections object.
         """
+
         return cls(
             xyxy=transformers_results["boxes"].cpu().numpy(),
             confidence=transformers_results["scores"].cpu().numpy(),
@@ -411,6 +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(),
@@ -421,7 +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.
@@ -429,8 +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,
@@ -447,21 +456,25 @@ 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
         )
-        return Detections(
+
+        if np.asarray(xyxy).shape[0] == 0:
+            return cls.empty()
+
+        return cls(
             xyxy=xyxy,
             confidence=confidence,
             class_id=class_id,
@@ -504,7 +517,11 @@ 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:
@@ -538,6 +555,10 @@ 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],
@@ -621,15 +642,23 @@ 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(
@@ -638,10 +667,36 @@ 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.")
 

supervision/detection/tools/inference_slicer.py

@@ -1,3 +1,4 @@
+from concurrent.futures import ThreadPoolExecutor, as_completed
 from typing import Callable, Optional, Tuple
 
 import numpy as np
@@ -23,19 +24,21 @@ 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:
+    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
@@ -50,11 +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:
@@ -97,15 +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],

supervision/detection/tools/polygon_zone.py

@@ -63,7 +63,7 @@ class PolygonZone:
             clipped_detections.get_anchor_coordinates(anchor=self.triggering_position)
         ).astype(int)
         is_in_zone = self.mask[clipped_anchors[:, 1], clipped_anchors[:, 0]]
-        self.current_count = np.sum(is_in_zone)
+        self.current_count = int(np.sum(is_in_zone))
         return is_in_zone.astype(bool)
 
 

supervision/detection/utils.py

@@ -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)
 

supervision/geometry/core.py

@@ -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):

supervision/utils/video.py

@@ -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,

test/annotators/test_utils.py

@@ -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.utils 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

test/detection/test_core.py

@@ -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)

test/detection/test_utils.py

@@ -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])