Merge pull request #15 from roboflow/feature/polygon-zone

feature/polygon-zone
- make linter happy
2023-02-07 19:28:51 +01:00 · 2023-02-07 19:03:25 +01:00 · 2023-02-07 19:02:19 +01:00 · 2023-02-07 14:14:04 +01:00 · 2023-02-07 13:42:42 +01:00 · 2023-02-07 13:24:52 +01:00
41 changed files with 1292 additions and 405 deletions
@@ -0,0 +1,65 @@
+name: 🐞 Bug Report
+# title: " "
+description: Problems with Supervision
+labels: [bug]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thank you for submitting a Supervision 🐞 Bug Report!
+
+  - type: checkboxes
+    attributes:
+      label: Search before asking
+      description: >
+        Please search the [issues](https://github.com/roboflow/supervision/issues) to see if a similar bug report already exists.
+      options:
+        - label: >
+            I have searched the Supervision [issues](https://github.com/roboflow/supervision/issues) and found no similar bug report.
+          required: true
+
+  - type: textarea
+    attributes:
+      label: Bug
+      description: Provide console output with error messages and/or screenshots of the bug.
+      placeholder: |
+        💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response.
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Environment
+      description: Please specify the software and hardware you used to produce the bug.
+      placeholder: |
+        - Supervision: 0.1.0
+        - OS: Ubuntu 20.04
+        - Python: 3.8.10
+    validations:
+      required: false
+
+  - type: textarea
+    attributes:
+      label: Minimal Reproducible Example
+      description: >
+        When asking a question, people will be better able to provide help if you provide code that they can easily understand and use to **reproduce** the problem.
+        This is referred to by community members as creating a [minimal reproducible example](https://stackoverflow.com/help/minimal-reproducible-example).
+      placeholder: |
+        ```
+        # Code to reproduce your issue here
+        ```
+    validations:
+      required: false
+
+  - type: textarea
+    attributes:
+      label: Additional
+      description: Anything else you would like to share?
+
+  - type: checkboxes
+    attributes:
+      label: Are you willing to submit a PR?
+      description: >
+        (Optional) We encourage you to submit a [Pull Request](https://github.com/roboflow/supervision/pulls) (PR) to help improve Supervision for everyone, especially if you have a good understanding of how to implement a fix or feature.
+      options:
+        - label: Yes I'd like to help by submitting a PR!
@@ -0,0 +1,49 @@
+name: 🤩 Feature Request
+description: Suggest a Supervision idea
+# title: " "
+labels: [enhancement]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thank you for submitting a Supervision 🤩 Feature Request!
+
+  - type: checkboxes
+    attributes:
+      label: Search before asking
+      description: >
+        Please search the [issues](https://github.com/roboflow/supervision/issues) to see if a similar feature request already exists.
+      options:
+        - label: >
+            I have searched the Supervision [issues](https://github.com/roboflow/supervision/issues) and found no similar feature requests.
+          required: true
+
+  - type: textarea
+    attributes:
+      label: Description
+      description: A short description of your feature.
+      placeholder: |
+        What new feature would you like to see in Supervision?
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Use case
+      description: |
+        Describe the use case of your feature request. It will help us understand and prioritize the feature request.
+      placeholder: |
+        How would this feature be used, and who would use it?
+
+  - type: textarea
+    attributes:
+      label: Additional
+      description: Anything else you would like to share?
+
+  - type: checkboxes
+    attributes:
+      label: Are you willing to submit a PR?
+      description: >
+        (Optional) We encourage you to submit a [Pull Request](https://github.com/roboflow/supervision/pulls) (PR) to help improve Supervision for everyone, especially if you have a good understanding of how to implement a fix or feature.
+      options:
+        - label: Yes I'd like to help by submitting a PR!
@@ -0,0 +1,33 @@
+name: ❓ Question
+description: Ask a Supervision question
+# title: " "
+labels: [question]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thank you for asking a Supervision ❓ Question!
+
+  - type: checkboxes
+    attributes:
+      label: Search before asking
+      description: >
+        Please search the [issues](https://github.com/roboflow/supervision/issues) to see if a similar feature request already exists.
+      options:
+        - label: >
+            I have searched the Supervision [issues](https://github.com/roboflow/supervision/issues) and found no similar feature requests.
+          required: true
+
+  - type: textarea
+    attributes:
+      label: Question
+      description: What is your question?
+      placeholder: |
+        💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response.
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Additional
+      description: Anything else you would like to share?
@@ -0,0 +1,18 @@
+name: Docs WorkFlow
+
+on:
+  push:
+    branches:
+      - master
+      - main
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: actions/setup-python@v2
+        with:
+          python-version: 3.x
+      - run: pip install mkdocs-material
+      - run: pip install "mkdocstrings[python]"
+      - run: mkdocs gh-deploy --force
@@ -9,7 +9,7 @@ jobs:
    runs-on: ubuntu-latest
    strategy:
      matrix:
-        python-version: [3.7, 3.8, 3.9]
+        python-version: [3.7, 3.8, 3.9, '3.10']
    steps:
      - name: 🛎️ Checkout
        uses: actions/checkout@v3
@@ -22,7 +22,11 @@ jobs:
      - name: 🦾 Install dependencies
        run: |
          python -m pip install --upgrade pip
-          pip install ".[dev]"
+          pip install -e .
+          pip install pytest
+          pip install isort
+          pip install flake8
+          pip install "black==22.3.0"
      - name: 🧹 Lint with flake8
        run: |
          make check_code_quality
@@ -1,3 +1,5 @@
+name: Welcome WorkFlow
+
 on:
  issues:
    types: [opened]
@@ -0,0 +1,12 @@
+cff-version: 1.2.0
+author: Roboflow
+message: If you use this software, please cite it as below.
+title: supervision
+version: 0.1.0
+date-released: 2023-01-19
+license: MIT
+repository-code: https://github.com/roboflow/supervision
+keywords:
+  - computer vision
+  - image processing
+  - video processing
@@ -0,0 +1,38 @@
+# Contributing to Supervision 🛠️
+
+Thank you for your interest in contributing to Supervision!
+
+We are actively improving this library to reduce the amount of work you need to do to solve common computer vision problems.
+
+## Contribution Guidelines
+
+We welcome contributions to:
+
+1. Add a new feature to the library (guidance below).
+2. Improve our documentation and add examples to make it clear how to leverage the supervision library.
+3. Report bugs and issues in the project.
+4. Submit a request for a new feature.
+5. Improve our test coverage.
+
+### Contributing Features
+
+Supervision is designed to provide generic utilities to solve problems. Thus, we focus on contributions that can have an impact on a wide range of projects.
+
+For example, counting objects that cross a line anywhere on an image is a common problem in computer vision, but counting objects that cross a line 75% of the way through is less useful.
+
+Before you contribute a new feature, consider submitting an Issue to discuss the feature so the community can weigh in and assist.
+
+## How to Contribute Changes
+
+First, fork this repository to your own GitHub account. Create a new branch that describes your changes (i.e. `line-counter-docs`). Push your changes to the branch on your fork and then submit a pull request to this repository.
+
+When creating new functions, please ensure you have the following:
+
+1. Docstrings for the function and all parameters.
+2. Unit tests for the function.
+3. Examples in the documentation for the function.
+4. Created an entry in our docs to autogenerate the documentation for the function.
+
+All pull requests will be reviewed by the maintainers of the project. We will provide feedback and ask for changes if necessary.
+
+PRs must pass all tests and linting requirements before they can be merged.
@@ -16,34 +16,34 @@
            width="3%"
          />
      </a>
-      <img src="https://github.com/SkalskiP/SkalskiP/blob/master/icons/transparent.png" width="3%"/>
+      <img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
      <a href="https://roboflow.com">
          <img
            src="https://media.roboflow.com/notebooks/template/icons/purple/roboflow-app.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949746649"
            width="3%"
          />
      </a>
-      <img src="https://github.com/SkalskiP/SkalskiP/blob/master/icons/transparent.png" width="3%"/>
+      <img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
      <a href="https://www.linkedin.com/company/roboflow-ai/">
          <img
            src="https://media.roboflow.com/notebooks/template/icons/purple/linkedin.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633691"
            width="3%"
          />
      </a>
-      <img src="https://github.com/SkalskiP/SkalskiP/blob/master/icons/transparent.png" width="3%"/>
+      <img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
      <a href="https://docs.roboflow.com">
          <img
            src="https://media.roboflow.com/notebooks/template/icons/purple/knowledge.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949634511"
            width="3%"
          />
      </a>
-      <img src="https://github.com/SkalskiP/SkalskiP/blob/master/icons/transparent.png" width="3%"/>
+      <img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
      <a href="https://disuss.roboflow.com">
          <img
            src="https://media.roboflow.com/notebooks/template/icons/purple/forum.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633584"
            width="3%"
          />
-      <img src="https://github.com/SkalskiP/SkalskiP/blob/master/icons/transparent.png" width="3%"/>
+      <img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
      <a href="https://blog.roboflow.com">
          <img
            src="https://media.roboflow.com/notebooks/template/icons/purple/blog.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633605"
@@ -63,9 +63,19 @@ and may change.

 ## 💻 install

-```console
+Pip install the supervision package in a
+[**3.10>=Python>=3.7**](https://www.python.org/) environment.
+
+```bash
+pip install supervision
+```
+
+<details close>
+<summary>Install from source</summary>
+
+```bash
 # clone repository and navigate to root directory
-git clone git@github.com:roboflow/supervision.git
+git clone https://github.com/roboflow/supervision.git
 cd supervision

 # setup python environment and activate it
@@ -76,7 +86,24 @@ source venv/bin/activate
 pip install -e ".[dev]"
 ```

-## 🧹 Code Quality 
+</details>
+
+## 📖 documentation
+
+Curious how Supervision can help you solve problems on your project? Visit our [documentation](https://roboflow.github.io/supervision) page!
+
+## 🎬 videos
+
+Learn how to use YOLOv8, ByteTrack and **Supervision** to detect, track and count objects. 🔥
+[Subscribe](https://www.youtube.com/@Roboflow), and stay up to date with our latest YouTube videos!
+
+<p align="center">
+    <a href="https://youtu.be/OS5qI9YBkfk">
+        <img src="https://user-images.githubusercontent.com/26109316/213702005-ddd568f0-b902-46c2-9af9-e6ff33db23bf.jpg" alt="latest-roboflow-tutorial">
+    </a>
+</p>
+
+## 🧹 code quality 

 We provide two handy commands inside the `Makefile`, namely:

@@ -85,6 +112,6 @@ We provide two handy commands inside the `Makefile`, namely:

 So far, **there is no types checking with mypy**. See [issue](https://github.com/roboflow-ai/template-python/issues/4). 

-## 🧪 Tests 
+## 🧪 tests 

 [`pytests`](https://docs.pytest.org/en/7.1.x/) is used to run our tests.
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+	c17.71-16.34,38.08-29.87,59.94-39.98C259.6,88,321.47,83.13,377.64,99.73c44.11,13.04,87.79,43.7,95.59,92.01
+	c1.65,10.25,1.63,20.72,0.17,31c-5.19,36.67-27.24,69.44-52.24,95.78c-26.59,28.02-60.44,46.35-88.52,72.44
+	c-16.43,15.27-23.13,34.01-3.29,49.87c27.1,13.81,49.64,6.64,74.27-8.31c14.52-8.82,26.96-20.57,37.76-33.61
+	c5.48-6.61,10.56-13.55,15.41-20.63c4.13-6.02,7.94-12.94,13.43-17.91c3.38-3.55,7.53-6.23,12.55-6.68
+	c7.35-0.66,14.61,3.23,17.51,10.15C503.23,370.87,500.61,379.84,497.44,386.39z"/>
+</svg>
@@ -0,0 +1,3 @@
+## Detections
+
+:::supervision.detection.core.Detections
@@ -0,0 +1,3 @@
+## generate_2d_mask
+
+:::supervision.detection.utils.generate_2d_mask
@@ -0,0 +1,7 @@
+## draw_line
+
+:::supervision.draw.utils.draw_line
+
+## draw_rectangle
+
+:::supervision.draw.utils.draw_rectangle
@@ -0,0 +1,37 @@
+<div align="center">
+  <p>
+    <a align="center" href="" target="_blank">
+      <img
+        width="850"
+        src="https://media.roboflow.com/open-source/supervision/roboflow-supervision-banner.png?ik-sdk-version=javascript-1.4.3&updatedAt=1674062891088"
+      >
+    </a>
+  </p>
+</div>
+
+## 👋 Welcome
+
+Supervision is a set of easy-to-use utilities that will come in handy in any computer vision project. 
+
+**Supervision** is still in 
+pre-release stage 🚧 Keep your eyes open for potential bugs and be aware that at this stage our API is still fluid and may change.
+
+## 💻 How to Install
+
+You can install `supervision` with pip in a 
+[**3.10>=Python>=3.7**](https://www.python.org/) environment.
+
+!!! example "Pip install method (recommended)"
+
+    ```bash
+    pip install supervision
+    ```
+
+!!! example "Git clone method (for development)"
+
+    ```bash
+    git https://github.com/roboflow/supervision.git
+    cd supervision
+    pip install -e '.[dev]'
+    ```
+    See contributing section to know more about contributing to the project
@@ -0,0 +1,3 @@
+## show_frame_in_notebook
+
+:::supervision.notebook.utils.show_frame_in_notebook
@@ -0,0 +1,15 @@
+## VideoInfo
+
+:::supervision.video.VideoInfo
+
+## VideoSink
+
+:::supervision.video.VideoSink
+
+## get_video_frames_generator
+
+:::supervision.video.get_video_frames_generator
+
+## process_video
+
+:::supervision.video.process_video
@@ -0,0 +1,53 @@
+site_name: Supervision
+site_url: https://roboflow.github.io/supervision
+site_author: Roboflow
+site_description: A set of easy-to-use utils that will come in handy in any Computer Vision project
+repo_name: roboflow/supervision
+repo_url: https://github.com/roboflow/supervision
+edit_uri: https://github.com/roboflow/supervision/tree/main/docs
+copyright: Roboflow 2023. All rights reserved.
+
+extra:
+  social:
+    - icon: fontawesome/brands/github
+      link: https://github.com/roboflow
+    - icon: fontawesome/brands/youtube
+      link: https://www.youtube.com/roboflow
+    - icon: fontawesome/brands/linkedin
+      link: https://www.linkedin.com/company/roboflow-ai/mycompany/
+    - icon: fontawesome/brands/twitter
+      link: https://twitter.com/roboflow
+  analytics:
+    provider: google
+    property: G-P7ZG0Y19G5
+
+nav:
+  - Home: index.md
+  - API reference:
+    - Video: video.md
+    - Detection:
+        - Core: detection_core.md
+        - Utils: detection_utils.md
+    - Draw: draw.md
+    - Geometry: geometry.md
+    - Notebook: notebook.md
+
+theme:
+  name: 'material'
+  logo: https://raw.githubusercontent.com/roboflow/supervision/main/docs/assets/roboflow_logomark_white.svg
+  favicon: https://raw.githubusercontent.com/roboflow/supervision/main/docs/assets/roboflow_logomark_color.svg
+  palette:
+    primary: 'deep purple'
+    accent: 'teal'
+  font:
+    text: Roboto
+    code: Roboto Mono
+
+plugins:
+  - mkdocstrings
+  - search
+
+markdown_extensions:
+  - admonition
+  - pymdownx.details
+  - pymdownx.superfences
@@ -0,0 +1,30 @@
+certifi==2022.12.7
+charset-normalizer==3.0.1
+click==8.1.3
+colorama==0.4.6
+ghp-import==2.1.0
+griffe==0.25.4
+idna==3.4
+importlib-metadata==6.0.0
+Jinja2==3.1.2
+Markdown==3.3.7
+MarkupSafe==2.1.2
+mergedeep==1.3.4
+mkdocs==1.4.2
+mkdocs-autorefs==0.4.1
+mkdocs-material==9.0.9
+mkdocs-material-extensions==1.1.1
+mkdocstrings==0.20.0
+mkdocstrings-python==0.8.3
+packaging==23.0
+Pygments==2.14.0
+pymdown-extensions==9.9.2
+python-dateutil==2.8.2
+PyYAML==6.0
+pyyaml_env_tag==0.1
+regex==2022.10.31
+requests==2.28.2
+six==1.16.0
+urllib3==1.26.14
+watchdog==2.2.1
+zipp==3.12.0
@@ -24,8 +24,9 @@ setuptools.setup(
    long_description_content_type='text/markdown',
    url='https://github.com/roboflow/supervision',
    install_requires=[
-            'numpy',
-            'opencv-python'
+        'numpy',
+        'opencv-python',
+        'matplotlib'
    ],
    packages=find_packages(exclude=("tests",)),
    extras_require={
@@ -36,7 +37,9 @@ setuptools.setup(
            'twine',
            'pytest',
            'wheel',
-            'notebook'
+            'notebook',
+            'mkdocs-material',
+            'mkdocstrings[python]'
        ],
    },
    classifiers=[
@@ -1 +1,16 @@
-__version__ = "0.0.2"
+__version__ = "0.2.0"
+
+from supervision.detection.core import BoxAnnotator, Detections
+from supervision.detection.polygon_zone import PolygonZone, PolygonZoneAnnotator
+from supervision.detection.utils import generate_2d_mask
+from supervision.draw.color import Color, ColorPalette
+from supervision.draw.utils import draw_filled_rectangle, draw_polygon, draw_text
+from supervision.geometry.core import Point, Position, Rect
+from supervision.geometry.utils import get_polygon_center
+from supervision.notebook.utils import show_frame_in_notebook
+from supervision.video import (
+    VideoInfo,
+    VideoSink,
+    get_video_frames_generator,
+    process_video,
+)
@@ -0,0 +1,293 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import List, Optional, Union
+
+import cv2
+import numpy as np
+
+from supervision.draw.color import Color, ColorPalette
+from supervision.geometry.core import Position
+
+
+@dataclass
+class Detections:
+    """
+    Data class containing information about the detections in a video frame.
+
+    Attributes:
+        xyxy (np.ndarray): An array of shape `(n, 4)` containing the bounding boxes coordinates in format `[x1, y1, x2, y2]`
+        confidence (np.ndarray): An array of shape `(n,)` containing the confidence scores of the detections.
+        class_id (np.ndarray): An array of shape `(n,)` containing the class ids of the detections.
+        tracker_id (Optional[np.ndarray]): An array of shape `(n,)` containing the tracker ids of the detections.
+    """
+
+    xyxy: np.ndarray
+    confidence: np.ndarray
+    class_id: np.ndarray
+    tracker_id: Optional[np.ndarray] = None
+
+    def __post_init__(self):
+        n = len(self.xyxy)
+        validators = [
+            (isinstance(self.xyxy, np.ndarray) and self.xyxy.shape == (n, 4)),
+            (isinstance(self.confidence, np.ndarray) and self.confidence.shape == (n,)),
+            (isinstance(self.class_id, np.ndarray) and self.class_id.shape == (n,)),
+            self.tracker_id is None
+            or (
+                isinstance(self.tracker_id, np.ndarray)
+                and self.tracker_id.shape == (n,)
+            ),
+        ]
+        if not all(validators):
+            raise ValueError(
+                "xyxy must be 2d np.ndarray with (n, 4) shape, "
+                "confidence must be 1d np.ndarray with (n,) shape, "
+                "class_id must be 1d np.ndarray with (n,) shape, "
+                "tracker_id must be None or 1d np.ndarray with (n,) shape"
+            )
+
+    def __len__(self):
+        """
+        Returns the number of detections in the Detections object.
+        """
+        return len(self.xyxy)
+
+    def __iter__(self):
+        """
+        Iterates over the Detections object and yield a tuple of `(xyxy, confidence, class_id, tracker_id)` for each detection.
+        """
+        for i in range(len(self.xyxy)):
+            yield (
+                self.xyxy[i],
+                self.confidence[i],
+                self.class_id[i],
+                self.tracker_id[i] if self.tracker_id is not None else None,
+            )
+
+    @classmethod
+    def from_yolov5(cls, yolov5_detections):
+        """
+        Creates a Detections instance from a YOLOv5 output Detections
+
+        Attributes:
+            yolov5_detections (yolov5.models.common.Detections): The output Detections instance from YOLOv5
+
+        Returns:
+
+        Example:
+            ```python
+            >>> import torch
+            >>> from supervision import Detections
+
+            >>> model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
+            >>> results = model(frame)
+            >>> detections = Detections.from_yolov5(results)
+            ```
+        """
+        yolov5_detections_predictions = yolov5_detections.pred[0].cpu().cpu().numpy()
+        return cls(
+            xyxy=yolov5_detections_predictions[:, :4],
+            confidence=yolov5_detections_predictions[:, 4],
+            class_id=yolov5_detections_predictions[:, 5].astype(int),
+        )
+
+    @classmethod
+    def from_yolov8(cls, yolov8_results):
+        """
+        Creates a Detections instance from a YOLOv8 output Results
+
+        Attributes:
+            yolov8_results (ultralytics.yolo.engine.results.Results): The output Results instance from YOLOv8
+
+        Returns:
+
+        Example:
+            ```python
+            >>> from ultralytics import YOLO
+            >>> from supervision import Detections
+
+            >>> model = YOLO('yolov8s.pt')
+            >>> results = model(frame)
+            >>> detections = Detections.from_yolov8(results)
+            ```
+        """
+        return cls(
+            xyxy=yolov8_results.boxes.xyxy.cpu().numpy(),
+            confidence=yolov8_results.boxes.conf.cpu().numpy(),
+            class_id=yolov8_results.boxes.cls.cpu().numpy().astype(int),
+        )
+
+    def filter(self, mask: np.ndarray, inplace: bool = False) -> Optional[Detections]:
+        """
+        Filter the detections by applying a mask.
+
+        Attributes:
+            mask (np.ndarray): A mask of shape `(n,)` containing a boolean value for each detection indicating if it should be included in the filtered detections
+            inplace (bool): If True, the original data will be modified and self will be returned.
+
+        Returns:
+            Optional[np.ndarray]: A new instance of Detections with the filtered detections, if inplace is set to `False`. `None` otherwise.
+        """
+        if inplace:
+            self.xyxy = self.xyxy[mask]
+            self.confidence = self.confidence[mask]
+            self.class_id = self.class_id[mask]
+            self.tracker_id = (
+                self.tracker_id[mask] if self.tracker_id is not None else None
+            )
+            return self
+        else:
+            return Detections(
+                xyxy=self.xyxy[mask],
+                confidence=self.confidence[mask],
+                class_id=self.class_id[mask],
+                tracker_id=self.tracker_id[mask]
+                if self.tracker_id is not None
+                else None,
+            )
+
+    def get_anchor_coordinates(self, anchor: Position) -> np.ndarray:
+        """
+        Returns the bounding box coordinates for a specific anchor.
+
+        Properties:
+            anchor (Position): Position of bounding box anchor for which to return the coordinates.
+
+        Returns:
+            np.ndarray: An array of shape `(n, 2)` containing the bounding box anchor coordinates in format `[x, y]`.
+        """
+        if anchor == Position.CENTER:
+            return np.array(
+                [
+                    (self.xyxy[:, 0] + self.xyxy[:, 2]) / 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()
+
+        raise ValueError(f"{anchor} is not supported.")
+
+    def __getitem__(self, index: np.ndarray) -> Detections:
+        if isinstance(index, np.ndarray) and index.dtype == np.bool:
+            return Detections(
+                xyxy=self.xyxy[index],
+                confidence=self.confidence[index],
+                class_id=self.class_id[index],
+                tracker_id=self.tracker_id[index]
+                if self.tracker_id is not None
+                else None,
+            )
+        raise TypeError(
+            f"Detections.__getitem__ not supported for index of type {type(index)}."
+        )
+
+
+class BoxAnnotator:
+    def __init__(
+        self,
+        color: Union[Color, ColorPalette] = ColorPalette.default(),
+        thickness: int = 2,
+        text_color: Color = Color.black(),
+        text_scale: float = 0.5,
+        text_thickness: int = 1,
+        text_padding: int = 10,
+    ):
+        """
+        A class for drawing bounding boxes on an image using detections provided.
+
+        Attributes:
+            color (Union[Color, ColorPalette]): The color to draw the bounding box, can be a single color or a color palette
+            thickness (int): The thickness of the bounding box lines, default is 2
+            text_color (Color): The color of the text on the bounding box, default is white
+            text_scale (float): The scale of the text on the bounding box, default is 0.5
+            text_thickness (int): The thickness of the text on the bounding box, default is 1
+            text_padding (int): The padding around the text on the bounding box, default is 5
+
+        """
+        self.color: Union[Color, ColorPalette] = color
+        self.thickness: int = thickness
+        self.text_color: Color = text_color
+        self.text_scale: float = text_scale
+        self.text_thickness: int = text_thickness
+        self.text_padding: int = text_padding
+
+    def annotate(
+        self,
+        scene: np.ndarray,
+        detections: Detections,
+        labels: Optional[List[str]] = None,
+        skip_label: bool = False,
+    ) -> np.ndarray:
+        """
+        Draws bounding boxes on the frame using the detections provided.
+
+        Parameters:
+            scene (np.ndarray): The image on which the bounding boxes will be drawn
+            detections (Detections): The detections for which the bounding boxes will be drawn
+            labels (Optional[List[str]]): An optional list of labels corresponding to each detection. If labels is provided, the confidence score of the detection will be replaced with the label.
+            skip_label (bool): Is set to True, skips bounding box label annotation.
+        Returns:
+            np.ndarray: The image with the bounding boxes drawn on it
+        """
+        font = cv2.FONT_HERSHEY_SIMPLEX
+        for i, (xyxy, confidence, class_id, tracker_id) in enumerate(detections):
+            x1, y1, x2, y2 = xyxy.astype(int)
+            color = (
+                self.color.by_idx(class_id)
+                if isinstance(self.color, ColorPalette)
+                else self.color
+            )
+            cv2.rectangle(
+                img=scene,
+                pt1=(x1, y1),
+                pt2=(x2, y2),
+                color=color.as_bgr(),
+                thickness=self.thickness,
+            )
+            if skip_label:
+                continue
+
+            text = (
+                f"{confidence:0.2f}"
+                if (labels is None or len(detections) != len(labels))
+                else labels[i]
+            )
+
+            text_width, text_height = cv2.getTextSize(
+                text=text,
+                fontFace=font,
+                fontScale=self.text_scale,
+                thickness=self.text_thickness,
+            )[0]
+
+            text_x = x1 + self.text_padding
+            text_y = y1 - self.text_padding
+
+            text_background_x1 = x1
+            text_background_y1 = y1 - 2 * self.text_padding - text_height
+
+            text_background_x2 = x1 + 2 * self.text_padding + text_width
+            text_background_y2 = y1
+
+            cv2.rectangle(
+                img=scene,
+                pt1=(text_background_x1, text_background_y1),
+                pt2=(text_background_x2, text_background_y2),
+                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
@@ -3,29 +3,37 @@ from typing import Dict
 import cv2
 import numpy as np

+from supervision.detection.core import Detections
 from supervision.draw.color import Color
-from supervision.geometry.dataclasses import Point, Rect, Vector
-from supervision.tools.detections import Detections
+from supervision.geometry.core import Point, Rect, Vector


-class LineCounter:
+class LineZone:
+    """
+    Count the number of objects that cross a line.
+    """
+
    def __init__(self, start: Point, end: Point):
        """
        Initialize a LineCounter object.

-        :param start: Point : The starting point of the line.
-        :param end: Point : The ending point of the line.
+        Attributes:
+            start (Point): The starting point of the line.
+            end (Point): The ending point of the line.
+
        """
        self.vector = Vector(start=start, end=end)
        self.tracker_state: Dict[str, bool] = {}
        self.in_count: int = 0
        self.out_count: int = 0

-    def update(self, detections: Detections):
+    def trigger(self, detections: Detections):
        """
        Update the in_count and out_count for the detections that cross the line.

-        :param detections: Detections : The detections for which to update the counts.
+        Attributes:
+            detections (Detections): The detections for which to update the counts.
+
        """
        for xyxy, confidence, class_id, tracker_id in detections:
            # handle detections with no tracker_id
@@ -63,7 +71,7 @@ class LineCounter:
                self.out_count += 1


-class LineCounterAnnotator:
+class LineZoneAnnotator:
    def __init__(
        self,
        thickness: float = 2,
@@ -77,13 +85,15 @@ class LineCounterAnnotator:
        """
        Initialize the LineCounterAnnotator object with default values.

-        :param thickness: float : The thickness of the line that will be drawn.
-        :param color: Color : The color of the line that will be drawn.
-        :param text_thickness: float : The thickness of the text that will be drawn.
-        :param text_color: Color : The color of the text that will be drawn.
-        :param text_scale: float : The scale of the text that will be drawn.
-        :param text_offset: float : The offset of the text that will be drawn.
-        :param text_padding: int : The padding of the text that will be drawn.
+        Attributes:
+            thickness (float): The thickness of the line that will be drawn.
+            color (Color): The color of the line that will be drawn.
+            text_thickness (float): The thickness of the text that will be drawn.
+            text_color (Color): The color of the text that will be drawn.
+            text_scale (float): The scale of the text that will be drawn.
+            text_offset (float): The offset of the text that will be drawn.
+            text_padding (int): The padding of the text that will be drawn.
+
        """
        self.thickness: float = thickness
        self.color: Color = color
@@ -93,13 +103,17 @@ class LineCounterAnnotator:
        self.text_offset: float = text_offset
        self.text_padding: int = text_padding

-    def annotate(self, frame: np.ndarray, line_counter: LineCounter) -> np.ndarray:
+    def annotate(self, frame: np.ndarray, line_counter: LineZone) -> np.ndarray:
        """
        Draws the line on the frame using the line_counter provided.

-        :param frame: np.ndarray : The image on which the line will be drawn
-        :param line_counter: LineCounter : The line counter that will be used to draw the line
-        :return: np.ndarray : The image with the line drawn on it
+        Attributes:
+            frame (np.ndarray): The image on which the line will be drawn.
+            line_counter (LineCounter): The line counter that will be used to draw the line.
+
+        Returns:
+            np.ndarray: The image with the line drawn on it.
+
        """
        cv2.line(
            frame,
@@ -0,0 +1,80 @@
+from typing import Optional, Tuple
+
+import cv2
+import numpy as np
+
+from supervision import Detections
+from supervision.detection.utils import generate_2d_mask
+from supervision.draw.color import Color
+from supervision.draw.utils import draw_polygon, draw_text
+from supervision.geometry.core import Position
+from supervision.geometry.utils import get_polygon_center
+
+
+class PolygonZone:
+    def __init__(
+        self,
+        polygon: np.ndarray,
+        frame_resolution_wh: Tuple[int, int],
+        triggering_position: Position = Position.BOTTOM_CENTER,
+    ):
+        self.polygon = polygon
+        self.frame_resolution_wh = frame_resolution_wh
+        self.triggering_position = triggering_position
+        self.mask = generate_2d_mask(polygon=polygon, resolution_wh=frame_resolution_wh)
+        self.current_count = 0
+
+    def trigger(self, detections: Detections) -> np.ndarray:
+        anchors = (
+            np.ceil(
+                detections.get_anchor_coordinates(anchor=self.triggering_position)
+            ).astype(int)
+            - 1
+        )
+        is_in_zone = self.mask[anchors[:, 1], anchors[:, 0]]
+        self.current_count = np.sum(is_in_zone)
+        return is_in_zone.astype(bool)
+
+
+class PolygonZoneAnnotator:
+    def __init__(
+        self,
+        zone: PolygonZone,
+        color: Color,
+        thickness: int = 2,
+        text_color: Color = Color.black(),
+        text_scale: float = 0.5,
+        text_thickness: int = 1,
+        text_padding: int = 10,
+    ):
+        self.zone = zone
+        self.color = color
+        self.thickness = thickness
+        self.text_color = text_color
+        self.text_scale = text_scale
+        self.text_thickness = text_thickness
+        self.text_padding = text_padding
+        self.font = cv2.FONT_HERSHEY_SIMPLEX
+        self.center = get_polygon_center(polygon=zone.polygon)
+
+    def annotate(self, scene: np.ndarray, label: Optional[str] = None) -> np.ndarray:
+        annotated_frame = draw_polygon(
+            scene=scene,
+            polygon=self.zone.polygon,
+            color=self.color,
+            thickness=self.thickness,
+        )
+
+        annotated_frame = draw_text(
+            scene=annotated_frame,
+            text=str(self.zone.current_count) if label is None else label,
+            text_anchor=self.center,
+            background_color=self.color,
+            text_color=self.text_color,
+            text_scale=self.text_scale,
+            text_thickness=self.text_thickness,
+            text_padding=self.text_padding,
+            text_font=self.font,
+        )
+
+        return annotated_frame
@@ -0,0 +1,20 @@
+from typing import Tuple
+
+import cv2
+import numpy as np
+
+
+def generate_2d_mask(polygon: np.ndarray, resolution_wh: Tuple[int, int]) -> np.ndarray:
+    """Generate a 2D mask from a polygon.
+
+    Properties:
+        polygon (np.ndarray): The polygon for which the mask should be generated, given as a list of vertices.
+        resolution_wh (Tuple[int, int]): The width and height of the desired resolution.
+
+    Returns:
+        np.ndarray: The generated 2D mask, where the polygon is marked with `1`'s and the rest is filled with `0`'s.
+    """
+    width, height = resolution_wh
+    mask = np.zeros((height, width), dtype=np.uint8)
+    cv2.fillPoly(mask, [polygon], color=1)
+    return mask
@@ -1,6 +1,6 @@
 from __future__ import annotations

-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from typing import List, Tuple

 DEFAULT_COLOR_PALETTE = [
@@ -94,11 +94,11 @@ class Color:

@dataclass
 class ColorPalette:
-    colors: List[Color] = field(
-        default_factory=lambda: [
-            Color.from_hex(color_hex) for color_hex in DEFAULT_COLOR_PALETTE
-        ]
-    )
+    colors: List[Color]
+
+    @classmethod
+    def default(cls) -> ColorPalette:
+        return ColorPalette.from_hex(color_hex_list=DEFAULT_COLOR_PALETTE)

    @classmethod
    def from_hex(cls, color_hex_list: List[str]):
@@ -1,8 +1,10 @@
+from typing import Optional
+
 import cv2
 import numpy as np

-from supervision.commons.dataclasses import Point, Rect
 from supervision.draw.color import Color
+from supervision.geometry.core import Point, Rect


 def draw_line(
@@ -11,12 +13,15 @@ def draw_line(
    """
    Draws a line on a given scene.

-    :param scene: np.ndarray : The scene on which the line will be drawn
-    :param start: Point : The starting point of the line
-    :param end: Point : The end point of the line
-    :param color: Color : The color of the line
-    :param thickness: int : The thickness of the line
-    :return: np.ndarray : The scene with the line drawn on it
+    Parameters:
+        scene (np.ndarray): The scene on which the line will be drawn
+        start (Point): The starting point of the line
+        end (Point): The end point of the line
+        color (Color): The color of the line
+        thickness (int): The thickness of the line
+
+    Returns:
+        np.ndarray: The scene with the line drawn on it
    """
    cv2.line(
        scene,
@@ -34,11 +39,14 @@ def draw_rectangle(
    """
    Draws a rectangle on an image.

-    :param scene: np.ndarray : The image on which to draw the rectangle.
-    :param rect: Rect : The rectangle to draw.
-    :param color: Color : The color of the rectangle.
-    :param thickness: int : The thickness of the rectangle border.
-    :return: np.ndarray : The image with the rectangle drawn on it.
+    Attributes:
+        scene (np.ndarray): The scene on which the rectangle will be drawn
+        rect (Rect): The rectangle to be drawn
+        color (Color): The color of the rectangle
+        thickness (int): The thickness of the rectangle border
+
+    Returns:
+        np.ndarray: The scene with the rectangle drawn on it
    """
    cv2.rectangle(
        scene,
@@ -58,6 +66,14 @@ def draw_filled_rectangle(scene: np.ndarray, rect: Rect, color: Color) -> np.nda
    :param rect: Rect : The rectangle to be drawn.
    :param color: Color : The color of the rectangle.
    :return: np.ndarray : The updated scene with the filled rectangle drawn on it.
+
+    Attributes:
+        scene (np.ndarray): The scene on which the rectangle will be drawn
+        rect (Rect): The rectangle to be drawn
+        color (Color): The color of the rectangle
+
+    Returns:
+        np.ndarray: The scene with the rectangle drawn on it
    """
    cv2.rectangle(
        scene,
@@ -67,3 +83,91 @@ def draw_filled_rectangle(scene: np.ndarray, rect: Rect, color: Color) -> np.nda
        -1,
    )
    return scene
+
+
+def draw_polygon(
+    scene: np.ndarray, polygon: np.ndarray, color: Color, thickness: int = 2
+) -> np.ndarray:
+    """Draw a polygon on a scene.
+
+    Attributes:
+        scene (np.ndarray): The scene to draw the polygon on.
+        polygon (np.ndarray): The polygon to be drawn, given as a list of vertices.
+        color (Color): The color of the polygon.
+        thickness (int, optional): The thickness of the polygon lines, by default 2.
+
+    Returns:
+        np.ndarray: The scene with the polygon drawn on it.
+    """
+    cv2.polylines(
+        scene, [polygon], isClosed=True, color=color.as_bgr(), thickness=thickness
+    )
+    return scene
+
+
+def draw_text(
+    scene: np.ndarray,
+    text: str,
+    text_anchor: Point,
+    text_color: Color = Color.black(),
+    text_scale: float = 0.5,
+    text_thickness: int = 1,
+    text_padding: int = 10,
+    text_font: int = cv2.FONT_HERSHEY_SIMPLEX,
+    background_color: Optional[Color] = None,
+) -> np.ndarray:
+    """
+    Draw text on a scene.
+
+    This function takes in a 2-dimensional numpy ndarray representing an image or scene, and draws text on it using OpenCV's putText function. The text is anchored at a specified Point, and its appearance can be customized using arguments such as color, scale, and font. An optional background color and padding can be specified to draw a rectangle behind the text.
+
+    Parameters:
+        scene (np.ndarray): A 2-dimensional numpy ndarray representing an image or scene.
+        text (str): The text to be drawn.
+        text_anchor (Point): The anchor point for the text, represented as a Point object with x and y attributes.
+        text_color (Color, optional): The color of the text. Defaults to black.
+        text_scale (float, optional): The scale of the text. Defaults to 0.5.
+        text_thickness (int, optional): The thickness of the text. Defaults to 1.
+        text_padding (int, optional): The amount of padding to add around the text when drawing a rectangle in the background. Defaults to 10.
+        text_font (int, optional): The font to use for the text. Defaults to cv2.FONT_HERSHEY_SIMPLEX.
+        background_color (Color, optional): The color of the background rectangle, if one is to be drawn. Defaults to None.
+
+    Returns:
+        np.ndarray: The input scene with the text drawn on it.
+
+    Examples:
+        ```python
+        >>> scene = np.zeros((100, 100, 3), dtype=np.uint8)
+        >>> text_anchor = Point(x=50, y=50)
+        >>> scene = draw_text(scene=scene, text="Hello, world!", text_anchor=text_anchor)
+        ```
+    """
+    text_width, text_height = cv2.getTextSize(
+        text=text,
+        fontFace=text_font,
+        fontScale=text_scale,
+        thickness=text_thickness,
+    )[0]
+    text_rect = Rect(
+        x=text_anchor.x - text_width // 2,
+        y=text_anchor.y - text_height // 2,
+        width=text_width,
+        height=text_height,
+    ).pad(text_padding)
+
+    if background_color is not None:
+        scene = draw_filled_rectangle(
+            scene=scene, rect=text_rect, color=background_color
+        )
+
+    cv2.putText(
+        img=scene,
+        text=text,
+        org=(text_anchor.x - text_width // 2, text_anchor.y + text_height // 2),
+        fontFace=text_font,
+        fontScale=text_scale,
+        color=text_color.as_bgr(),
+        thickness=text_thickness,
+        lineType=cv2.LINE_AA,
+    )
+    return scene
@@ -1,9 +1,19 @@
 from __future__ import annotations

 from dataclasses import dataclass
+from enum import Enum
 from typing import Tuple


+class Position(Enum):
+    CENTER = "CENTER"
+    BOTTOM_CENTER = "BOTTOM_CENTER"
+
+    @classmethod
+    def list(cls):
+        return list(map(lambda c: c.value, cls))
+
+
@dataclass
 class Point:
    x: float
@@ -0,0 +1,28 @@
+import numpy as np
+
+from supervision.geometry.core import Point
+
+
+def get_polygon_center(polygon: np.ndarray) -> Point:
+    """
+    Calculate the center of a polygon.
+
+    This function takes in a polygon as a 2-dimensional numpy ndarray and returns the center of the polygon as a Point object. The center is calculated as the mean of the polygon's vertices along each axis, and is rounded down to the nearest integer.
+
+    Parameters:
+        polygon (np.ndarray): A 2-dimensional numpy ndarray representing the vertices of the polygon.
+
+    Returns:
+        Point: The center of the polygon, represented as a Point object with x and y attributes.
+
+    Examples:
+        ```python
+        >>> from supervision.geometry.utils import get_polygon_center
+
+        >>> vertices = np.array([[0, 0], [0, 1], [1, 1], [1, 0]])
+        >>> get_center(vertices)
+        Point(x=0.5, y=0.5)
+        ```
+    """
+    center = np.mean(polygon, axis=0).astype(int)
+    return Point(x=center[0], y=center[1])
@@ -11,9 +11,18 @@ def show_frame_in_notebook(
    """
    Display a frame in Jupyter Notebook using Matplotlib

-    :param frame: np.ndarray : The frame to be displayed.
-    :param size: Tuple[int, int] : The size of the plot. default:(10,10)
-    :param cmap: str : the colormap to use for single channel images. default:gray
+    Attributes:
+        frame (np.ndarray): The frame to be displayed.
+        size (Tuple[int, int]): The size of the plot. default:(10,10)
+        cmap (str): the colormap to use for single channel images. default:gray
+
+    Examples:
+        ```python
+        >>> from supervision.notebook.utils import show_frame_in_notebook
+
+        %matplotlib inline
+        show_frame_in_notebook(frame, (16, 16))
+        ```
    """
    if frame.ndim == 2:
        plt.figure(figsize=size)
@@ -1,205 +0,0 @@
-from typing import List, Optional, Union
-
-import cv2
-import numpy as np
-
-from supervision.draw.color import Color, ColorPalette
-
-
-class Detections:
-    def __init__(
-        self,
-        xyxy: np.ndarray,
-        confidence: np.ndarray,
-        class_id: np.ndarray,
-        tracker_id: Optional[np.ndarray] = None,
-    ):
-        """
-        Data class containing information about the detections in a video frame.
-
-        :param xyxy: np.ndarray : An array of shape (n, 4) containing the bounding boxes coordinates in format [x1, y1, x2, y2]
-        :param confidence: np.ndarray : An array of shape (n,) containing the confidence scores of the detections.
-        :param class_id: np.ndarray : An array of shape (n,) containing the class ids of the detections.
-        :param tracker_id: Optional[np.ndarray] : An array of shape (n,) containing the tracker ids of the detections.
-        """
-        self.xyxy: np.ndarray = xyxy
-        self.confidence: np.ndarray = confidence
-        self.class_id: np.ndarray = class_id
-        self.tracker_id: Optional[np.ndarray] = tracker_id
-
-        n = len(self.xyxy)
-        validators = [
-            (isinstance(self.xyxy, np.ndarray) and self.xyxy.shape == (n, 4)),
-            (isinstance(self.confidence, np.ndarray) and self.confidence.shape == (n,)),
-            (isinstance(self.class_id, np.ndarray) and self.class_id.shape == (n,)),
-            self.tracker_id is None
-            or (
-                isinstance(self.tracker_id, np.ndarray)
-                and self.tracker_id.shape == (n,)
-            ),
-        ]
-        if not all(validators):
-            raise ValueError(
-                "xyxy must be 2d np.ndarray with (n, 4) shape, "
-                "confidence must be 1d np.ndarray with (n,) shape, "
-                "class_id must be 1d np.ndarray with (n,) shape, "
-                "tracker_id must be None or 1d np.ndarray with (n,) shape"
-            )
-
-    def __len__(self):
-        """
-        Returns the number of detections in the Detections object.
-        """
-        return len(self.xyxy)
-
-    def __iter__(self):
-        """
-        Iterates over the Detections object and yield a tuple of (xyxy, confidence, class_id, tracker_id) for each detection.
-        """
-        for i in range(len(self.xyxy)):
-            yield (
-                self.xyxy[i],
-                self.confidence[i],
-                self.class_id[i],
-                self.tracker_id[i] if self.tracker_id is not None else None,
-            )
-
-    @classmethod
-    def from_yolov5(cls, yolov5_output: np.ndarray):
-        """
-        Creates a Detections instance from a YOLOv5 output tensor
-
-        :param yolov5_output: np.ndarray : The output tensor from YOLOv5
-        :return: Detections : A Detections instance representing the detections in the frame
-
-        Example:
-        detections = Detections.from_yolov5(yolov5_output)
-        """
-        xyxy = yolov5_output[:, :4]
-        confidence = yolov5_output[:, 4]
-        class_id = yolov5_output[:, 5].astype(int)
-        return cls(xyxy, confidence, class_id)
-
-    def filter(self, mask: np.ndarray, inplace: bool = False) -> Optional[np.ndarray]:
-        """
-        Filter the detections by applying a mask
-
-        :param mask: np.ndarray : A mask of shape (n,) containing a boolean value for each detection indicating if it should be included in the filtered detections
-        :param inplace: bool : If True, the original data will be modified and self will be returned.
-        :return: Optional[np.ndarray] : A new instance of Detections with the filtered detections, if inplace is set to False. None otherwise.
-        """
-        if inplace:
-            self.xyxy = self.xyxy[mask]
-            self.confidence = self.confidence[mask]
-            self.class_id = self.class_id[mask]
-            self.tracker_id = (
-                self.tracker_id[mask] if self.tracker_id is not None else None
-            )
-            return self
-        else:
-            return Detections(
-                xyxy=self.xyxy[mask],
-                confidence=self.confidence[mask],
-                class_id=self.class_id[mask],
-                tracker_id=self.tracker_id[mask]
-                if self.tracker_id is not None
-                else None,
-            )
-
-
-class BoxAnnotator:
-    def __init__(
-        self,
-        color: Union[Color, ColorPalette],
-        thickness: int = 2,
-        text_color: Color = Color.black(),
-        text_scale: float = 0.5,
-        text_thickness: int = 1,
-        text_padding: int = 10,
-    ):
-        """
-        A class for drawing bounding boxes on an image using detections provided.
-
-        :param color: Union[Color, ColorPalette] :  The color to draw the bounding box, can be a single color or a color palette
-        :param thickness: int :  The thickness of the bounding box lines, default is 2
-        :param text_color: Color :  The color of the text on the bounding box, default is white
-        :param text_scale: float :  The scale of the text on the bounding box, default is 0.5
-        :param text_thickness: int :  The thickness of the text on the bounding box, default is 1
-        :param text_padding: int :  The padding around the text on the bounding box, default is 5
-        """
-        self.color: Union[Color, ColorPalette] = color
-        self.thickness: int = thickness
-        self.text_color: Color = text_color
-        self.text_scale: float = text_scale
-        self.text_thickness: int = text_thickness
-        self.text_padding: int = text_padding
-
-    def annotate(
-        self,
-        frame: np.ndarray,
-        detections: Detections,
-        labels: Optional[List[str]] = None,
-    ) -> np.ndarray:
-        """
-        Draws bounding boxes on the frame using the detections provided.
-
-        :param frame: np.ndarray : The image on which the bounding boxes will be drawn
-        :param detections: Detections : The detections for which the bounding boxes will be drawn
-        :param labels: Optional[List[str]] :  An optional list of labels corresponding to each detection. If labels is provided, the confidence score of the detection will be replaced with the label.
-        :return: np.ndarray : The image with the bounding boxes drawn on it
-        """
-        font = cv2.FONT_HERSHEY_SIMPLEX
-        for i, (xyxy, confidence, class_id, tracker_id) in enumerate(detections):
-            color = (
-                self.color.by_idx(class_id)
-                if isinstance(self.color, ColorPalette)
-                else self.color
-            )
-            text = (
-                f"{confidence:0.2f}"
-                if (labels is None or len(detections) != len(labels))
-                else labels[i]
-            )
-
-            x1, y1, x2, y2 = xyxy.astype(int)
-            text_width, text_height = cv2.getTextSize(
-                text=text,
-                fontFace=font,
-                fontScale=self.text_scale,
-                thickness=self.text_thickness,
-            )[0]
-
-            text_x = x1 + self.text_padding
-            text_y = y1 - self.text_padding
-
-            text_background_x1 = x1
-            text_background_y1 = y1 - 2 * self.text_padding - text_height
-
-            text_background_x2 = x1 + 2 * self.text_padding + text_width
-            text_background_y2 = y1
-
-            cv2.rectangle(
-                img=frame,
-                pt1=(x1, y1),
-                pt2=(x2, y2),
-                color=color.as_bgr(),
-                thickness=self.thickness,
-            )
-            cv2.rectangle(
-                img=frame,
-                pt1=(text_background_x1, text_background_y1),
-                pt2=(text_background_x2, text_background_y2),
-                color=color.as_bgr(),
-                thickness=cv2.FILLED,
-            )
-            cv2.putText(
-                img=frame,
-                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 frame
@@ -0,0 +1,161 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Callable, Generator, Optional, Tuple
+
+import cv2
+import numpy as np
+
+
+@dataclass
+class VideoInfo:
+    """
+    A class to store video information, including width, height, fps and total number of frames.
+
+    Attributes:
+        width (int): width of the video in pixels
+        height (int): height of the video in pixels
+        fps (int): frames per second of the video
+        total_frames (int, optional): total number of frames in the video, default is None
+
+    Examples:
+        ```python
+        >>> from supervision import VideoInfo
+
+        >>> video_info = VideoInfo.from_video_path(video_path='video.mp4')
+
+        >>> video_info
+        VideoInfo(width=3840, height=2160, fps=25, total_frames=538)
+
+        >>> video_info.resolution_wh
+        (3840, 2160)
+        ```
+    """
+
+    width: int
+    height: int
+    fps: int
+    total_frames: Optional[int] = None
+
+    @classmethod
+    def from_video_path(cls, video_path: str) -> VideoInfo:
+        video = cv2.VideoCapture(video_path)
+        if not video.isOpened():
+            raise Exception(f"Could not open video at {video_path}")
+
+        width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        fps = int(video.get(cv2.CAP_PROP_FPS))
+        total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+        video.release()
+        return VideoInfo(width, height, fps, total_frames)
+
+    @property
+    def resolution_wh(self) -> Tuple[int, int]:
+        return self.width, self.height
+
+
+class VideoSink:
+    """
+    Context manager that saves video frames to a file using OpenCV.
+
+    Attributes:
+        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.
+
+    Examples:
+        ```python
+        >>> from supervision import VideoInfo, VideoSink
+
+        >>> video_info = VideoInfo.from_video_path(video_path='source_video.mp4')
+
+        >>> with VideoSink(target_path='target_video.mp4', video_info=video_info) as s:
+        ...     frame = ...
+        ...     s.write_frame(frame=frame)
+        ```
+    """
+
+    def __init__(self, target_path: str, video_info: VideoInfo):
+        self.target_path = target_path
+        self.video_info = video_info
+        self.__fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+        self.__writer = None
+
+    def __enter__(self):
+        self.__writer = cv2.VideoWriter(
+            self.target_path,
+            self.__fourcc,
+            self.video_info.fps,
+            self.video_info.resolution_wh,
+        )
+        return self
+
+    def write_frame(self, frame: np.ndarray):
+        self.__writer.write(frame)
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.__writer.release()
+
+
+def get_video_frames_generator(source_path: str) -> Generator[np.ndarray, None, None]:
+    """
+    Get a generator that yields the frames of the video.
+
+    Args:
+        source_path (str): The path of the video file.
+
+    Returns:
+        (Generator[np.ndarray, None, None]): A generator that yields the frames of the video.
+
+    Examples:
+        ```python
+        >>> from supervision import get_video_frames_generator
+
+        >>> for frame in get_video_frames_generator(source_path='source_video.mp4'):
+        ...     ...
+        ```
+    """
+    video = cv2.VideoCapture(source_path)
+    if not video.isOpened():
+        raise Exception(f"Could not open video at {source_path}")
+    success, frame = video.read()
+    while success:
+        yield frame
+        success, frame = video.read()
+    video.release()
+
+
+def process_video(
+    source_path: str,
+    target_path: str,
+    callback: Callable[[np.ndarray, int], np.ndarray],
+) -> None:
+    """
+    Process a video file by applying a callback function on each frame and saving the result to a target video file.
+
+    Args:
+        source_path (str): The path to the source video file.
+        target_path (str): The path to the target video file.
+        callback (Callable[[np.ndarray, int], np.ndarray]): A function that takes in a numpy ndarray representation of a video frame and an int index of the frame and returns a processed numpy ndarray representation of the frame.
+
+    Examples:
+        ```python
+        >>> from supervision import process_video
+
+        >>> def process_frame(scene: np.ndarray) -> np.ndarray:
+        ...     ...
+
+        >>> process_video(
+        ...     source_path='source_video.mp4',
+        ...     target_path='target_video.mp4',
+        ...     callback=process_frame
+        ... )
+        ```
+    """
+    source_video_info = VideoInfo.from_video_path(video_path=source_path)
+    with VideoSink(target_path=target_path, video_info=source_video_info) as sink:
+        for index, frame in enumerate(
+            get_video_frames_generator(source_path=source_path)
+        ):
+            result_frame = callback(frame, index)
+            sink.write_frame(frame=result_frame)
@@ -1,46 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Optional, Tuple
-
-import cv2
-
-
-@dataclass
-class VideoInfo:
-    """
-    Data class containing information about the video resolution, fps, and total frame count.
-
-    :param width: int : The width of the video frames in pixels.
-    :param height: int : The height of the video frames in pixels.
-    :param fps: int : The frames per second of the video.
-    :param total_frames: int : The total number of frames in the video.
-    """
-
-    width: int
-    height: int
-    fps: int
-    total_frames: Optional[int] = None
-
-    @classmethod
-    def from_video_path(cls, video_path: str) -> VideoInfo:
-        """
-        Returns a VideoInfo data class containing information about the video resolution, fps, and total frame count.
-
-        :param video_path: str : The path of the video file.
-        :return: VideoInfo : A data class containing information about the video resolution, fps, and total frame count.
-        """
-        video = cv2.VideoCapture(video_path)
-        if not video.isOpened():
-            raise Exception(f"Could not open video at {video_path}")
-
-        width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
-        height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
-        fps = int(video.get(cv2.CAP_PROP_FPS))
-        total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
-        video.release()
-        return VideoInfo(width, height, fps, total_frames)
-
-    @property
-    def resolution(self) -> Tuple[int, int]:
-        return self.width, self.height
@@ -1,48 +0,0 @@
-import cv2
-import numpy as np
-
-from supervision.video.dataclasses import VideoInfo
-
-
-class VideoSink:
-    """
-    A context manager that uses OpenCV to save video frames to a file.
-
-    :param output_path: str : The path to the output file where the video will be saved.
-    :param video_info: VideoInfo : An instance of VideoInfo containing information about the video resolution, fps, and total frame count.
-    """
-
-    def __init__(self, output_path: str, video_info: VideoInfo):
-        """
-        Initializes the VideoSink with the specified output path and video information.
-        """
-        self.output_path = output_path
-        self.video_info = video_info
-        self.fourcc = cv2.VideoWriter_fourcc(*"mp4v")
-        self.writer = None
-
-    def __enter__(self):
-        """
-        Opens the output file and returns the VideoSink instance.
-        """
-        self.writer = cv2.VideoWriter(
-            self.output_path,
-            self.fourcc,
-            self.video_info.fps,
-            self.video_info.resolution,
-        )
-        return self
-
-    def write_frame(self, frame: np.ndarray):
-        """
-        Writes a frame to the output video file.
-
-        :param frame: np.ndarray : The frame to be written.
-        """
-        self.writer.write(frame)
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        """
-        Closes the output file.
-        """
-        self.writer.release()
@@ -1,20 +0,0 @@
-from typing import Generator
-
-import cv2
-
-
-def get_video_frames_generator(video_path: str) -> Generator[int, None, None]:
-    """
-    Returns a generator that yields the frames of the video.
-
-    :param video_path: str : The path of the video file.
-    :return: Generator[int, None, None] : Generator that yields the frames of the video.
-    """
-    video = cv2.VideoCapture(video_path)
-    if not video.isOpened():
-        raise Exception(f"Could not open video at {video_path}")
-    success, frame = video.read()
-    while success:
-        yield frame
-        success, frame = video.read()
-    video.release()
@@ -7,27 +7,25 @@ from supervision.draw.color import Color


@pytest.mark.parametrize(
-    'color_hex, expected_result, exception',
+    "color_hex, expected_result, exception",
    [
-        ('fff', Color.white(), DoesNotRaise()),
-        ('#fff', Color.white(), DoesNotRaise()),
-        ('ffffff', Color.white(), DoesNotRaise()),
-        ('#ffffff', Color.white(), DoesNotRaise()),
-        ('f00', Color.red(), DoesNotRaise()),
-        ('0f0', Color.green(), DoesNotRaise()),
-        ('00f', Color.blue(), DoesNotRaise()),
-        ('#808000', Color(r=128, g=128, b=0), DoesNotRaise()),
-        ('', None, pytest.raises(ValueError)),
-        ('00', None, pytest.raises(ValueError)),
-        ('0000', None, pytest.raises(ValueError)),
-        ('0000000', None, pytest.raises(ValueError)),
-        ('ffg', None, pytest.raises(ValueError)),
-    ]
+        ("fff", Color.white(), DoesNotRaise()),
+        ("#fff", Color.white(), DoesNotRaise()),
+        ("ffffff", Color.white(), DoesNotRaise()),
+        ("#ffffff", Color.white(), DoesNotRaise()),
+        ("f00", Color.red(), DoesNotRaise()),
+        ("0f0", Color.green(), DoesNotRaise()),
+        ("00f", Color.blue(), DoesNotRaise()),
+        ("#808000", Color(r=128, g=128, b=0), DoesNotRaise()),
+        ("", None, pytest.raises(ValueError)),
+        ("00", None, pytest.raises(ValueError)),
+        ("0000", None, pytest.raises(ValueError)),
+        ("0000000", None, pytest.raises(ValueError)),
+        ("ffg", None, pytest.raises(ValueError)),
+    ],
 )
 def test_color_from_hex(
-    color_hex,
-    expected_result: Optional[Color],
-    exception: Exception
+    color_hex, expected_result: Optional[Color], exception: Exception
 ) -> None:
    with exception:
        result = Color.from_hex(color_hex=color_hex)
@@ -1,40 +1,31 @@
 import pytest

-from supervision.geometry.dataclasses import Vector, Point
+from supervision.geometry.core import Vector, Point


@pytest.mark.parametrize(
-    'vector, point, expected_result',
+    "vector, point, expected_result",
    [
        (Vector(start=Point(x=0, y=0), end=Point(x=5, y=5)), Point(x=-1, y=1), False),
        (Vector(start=Point(x=0, y=0), end=Point(x=5, y=5)), Point(x=6, y=6), False),
        (Vector(start=Point(x=0, y=0), end=Point(x=5, y=5)), Point(x=3, y=6), False),
-
        (Vector(start=Point(x=5, y=5), end=Point(x=0, y=0)), Point(x=-1, y=1), True),
        (Vector(start=Point(x=5, y=5), end=Point(x=0, y=0)), Point(x=6, y=6), False),
        (Vector(start=Point(x=5, y=5), end=Point(x=0, y=0)), Point(x=3, y=6), True),
-
        (Vector(start=Point(x=0, y=0), end=Point(x=1, y=0)), Point(x=0, y=0), False),
        (Vector(start=Point(x=0, y=0), end=Point(x=1, y=0)), Point(x=0, y=-1), True),
        (Vector(start=Point(x=0, y=0), end=Point(x=1, y=0)), Point(x=0, y=1), False),
-
        (Vector(start=Point(x=1, y=0), end=Point(x=0, y=0)), Point(x=0, y=0), False),
        (Vector(start=Point(x=1, y=0), end=Point(x=0, y=0)), Point(x=0, y=-1), False),
        (Vector(start=Point(x=1, y=0), end=Point(x=0, y=0)), Point(x=0, y=1), True),
-
        (Vector(start=Point(x=1, y=1), end=Point(x=1, y=3)), Point(x=0, y=0), False),
        (Vector(start=Point(x=1, y=1), end=Point(x=1, y=3)), Point(x=1, y=4), False),
        (Vector(start=Point(x=1, y=1), end=Point(x=1, y=3)), Point(x=2, y=4), True),
-
        (Vector(start=Point(x=1, y=3), end=Point(x=1, y=1)), Point(x=0, y=0), True),
        (Vector(start=Point(x=1, y=3), end=Point(x=1, y=1)), Point(x=1, y=4), False),
        (Vector(start=Point(x=1, y=3), end=Point(x=1, y=1)), Point(x=2, y=4), False),
-    ]
+    ],
 )
-def test_vector_is_in(
-    vector: Vector,
-    point: Point,
-    expected_result: bool
-) -> None:
+def test_vector_is_in(vector: Vector, point: Point, expected_result: bool) -> None:
    result = vector.is_in(point=point)
    assert result == expected_result
Author	SHA1	Message	Date
Piotr Skalski	2e76e3db0f	Merge pull request #15 from roboflow/feature/polygon-zone feature/polygon-zone	2023-02-07 19:28:51 +01:00
SkalskiP	a0eb6c3237	- make linter happy	2023-02-07 19:03:25 +01:00
SkalskiP	fd35ad9653	- set supervision version to 0.2.0	2023-02-07 19:02:19 +01:00
SkalskiP	14d23eb9df	- fix bug in BoxAnnotator	2023-02-07 14:14:04 +01:00
SkalskiP	602d3af009	- fix bug in VideoSink	2023-02-07 13:42:42 +01:00
SkalskiP	b8b08cacd3	- make it simpler to import process_video function	2023-02-07 13:24:52 +01:00
SkalskiP	c8868c3412	- small API fixes in PolygonZoneAnnotator and BoxAnnotator	2023-02-07 12:34:59 +01:00
SkalskiP	52a8193b4a	- default value for triggering_position in PolygonZone added	2023-02-07 12:24:56 +01:00
SkalskiP	51a1655bd4	- improve ease of import of supervision components	2023-02-07 12:06:56 +01:00
SkalskiP	38bf4b0f54	- matplotlib added to setup.py	2023-02-07 11:52:07 +01:00
SkalskiP	05705bd4f2	- Fix linter issues	2023-02-07 11:49:16 +01:00
SkalskiP	940bcb8bc5	- BoxAnnotator.annotate() fixed	2023-02-07 11:39:11 +01:00
SkalskiP	a64a252ae3	- Detections.from_yolov8() added	2023-02-07 11:31:37 +01:00
SkalskiP	4afc650eb2	- skip_label argument added to BoxAnnotator.annotate()	2023-02-07 11:18:00 +01:00
SkalskiP	8d091a7a00	- Rename LineCounter to LineZone - Rename LineCounter.update() to LineZone.trigger() to make the API consistent with LineZone - Rename LineCounterAnnotator to LineZoneAnnotator	2023-02-07 09:30:53 +01:00
SkalskiP	ccafa3339d	- PolygonZone and PolygonZoneAnnotator added	2023-02-06 23:05:17 +01:00
SkalskiP	807785a820	- draw_text added	2023-02-06 22:35:00 +01:00
SkalskiP	66f81ceeb9	- ColorPalette.default() added - Detections.from_yolov5() updated - Detection.__getitem__ added - get_polygon_center added	2023-02-06 22:26:44 +01:00
SkalskiP	b7c1791236	🕵️ update Detection API	2023-02-06 16:14:24 +01:00
SkalskiP	0ac184b01d	👊 bump Supervision version to 0.2.0.dev0	2023-02-06 15:23:20 +01:00
SkalskiP	307ec8a042	📄 Documentation updates before 0.2.0 release	2023-02-06 14:49:09 +01:00
SkalskiP	d5d7187fe4	Detection core API reformat	2023-02-06 14:28:16 +01:00
SkalskiP	0e3851506c	Update VideoInfo API 🎬	2023-02-06 14:20:56 +01:00
SkalskiP	301f1df386	🎉 initial commit 0.2.0 API commit	2023-02-06 13:55:57 +01:00
Piotr Skalski	ceabb83ac6	Merge pull request #11 from roboflow/add-citation Create CITATION.cff file	2023-02-06 12:36:15 +01:00
Piotr Skalski	dfb757b8cd	Merge pull request #10 from roboflow/add-contributing Create CONTRIBUTING.md file	2023-02-06 12:33:10 +01:00
James Gallagher	ed0fd7b768	move analytics to main extra section in yaml	2023-02-02 09:29:10 +00:00
James Gallagher	5b2f8e4dcc	create citation.cff file	2023-02-02 09:24:08 +00:00
James Gallagher	465830f3f6	create contribution guidelines	2023-02-01 16:26:08 +00:00
Piotr Skalski	ae84cdeb02	Merge pull request #8 from roboflow/documentation-improvements Refactor documentation for Supervision	2023-02-01 16:07:37 +01:00
James Gallagher	4fbec12b06	run linter	2023-02-01 14:37:54 +00:00
Piotr Skalski	b5e33c5b30	Merge pull request #9 from roboflow/add-google-analytics Add Google Analytics to documentation	2023-02-01 15:05:54 +01:00
James Gallagher	5a3df85fc6	add ga script	2023-02-01 10:33:30 +00:00
James Gallagher	61a2f4e0e6	add requirements.txt	2023-02-01 10:29:00 +00:00
James Gallagher	90a26747b6	create stub doc files, refactor docstrings, add placeholders for examples	2023-02-01 10:28:49 +00:00
Piotr Skalski	507f08f27f	Update README.md	2023-01-31 13:58:04 +01:00
Piotr Skalski	fcf30b79d4	Merge pull request #7 from roboflow/feature/github-issue-templates 🐞 Bug Report, 🤩 Feature Request and ❓ Question issue templates	2023-01-31 13:51:39 +01:00
SkalskiP	a7b26abe0e	🧪 python 3.7 test still fail	2023-01-31 13:50:36 +01:00
SkalskiP	9590f3845e	🧪 python 3.7 test still fail	2023-01-31 13:49:28 +01:00
SkalskiP	8d8bd3b997	🧪 python 3.7 test still fail	2023-01-31 13:48:31 +01:00
SkalskiP	6b11a1fdad	🧪 python 3.7 test still fail	2023-01-31 13:31:28 +01:00
SkalskiP	cb485878cc	🧪 python 3.7 test still fail	2023-01-31 13:29:47 +01:00
SkalskiP	8e70a3cd46	🧪 python 3.7 test still fail	2023-01-31 13:21:55 +01:00
SkalskiP	123dd77ba4	🧪 python 3.7 test fail	2023-01-31 13:19:34 +01:00
SkalskiP	7687d4a873	🐞 Bug Report, 🤩 Feature Request and ❓ Question issue templates	2023-01-31 13:09:53 +01:00
Piotr Skalski	d941fe256d	Merge pull request #6 from roboflow/feature/add-initial-docs feature/add-initial-docs	2023-01-31 10:04:29 +01:00
SkalskiP	d4a2adde9e	Merge remote-tracking branch 'origin/feature/add-initial-docs' into feature/add-initial-docs # Conflicts: # .github/workflows/test.yml	2023-01-31 10:02:09 +01:00
SkalskiP	8b229427e5	🧪 tests	2023-01-31 10:01:52 +01:00
SkalskiP	b4641245c1	🧪 tests	2023-01-31 09:57:41 +01:00
SkalskiP	bfcc3e40b2	🖤 make black happy	2023-01-31 09:44:32 +01:00
SkalskiP	1561bd0027	🎬 poc of video utils documentation	2023-01-31 09:36:39 +01:00
SkalskiP	8d14a4d93b	Merge branch 'main' into feature/add-initial-docs	2023-01-30 22:44:45 +01:00
SkalskiP	bd889ae3cc	🚩 add roboflow logo to docs	2023-01-30 22:40:51 +01:00
SkalskiP	02c20e81a9	📚 initial documentation commit	2023-01-30 19:50:25 +01:00
Piotr Skalski	c236bb71d1	Update README.md	2023-01-20 14:08:53 +01:00
SkalskiP	22fa6f0a3a	📚 update README.md	2023-01-19 02:22:50 +01:00
SkalskiP	c2ac59f992	👊 bump package version to 0.1.0	2023-01-19 02:02:42 +01:00