create stub doc files, refactor docstrings, add placeholders for examples
This commit is contained in:
James Gallagher
parent
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Utilities for drawing on images.
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## Draw Line
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:::supervision.draw.utils.draw_line
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## Draw Rectangle
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:::supervision.draw.utils.draw_rectangle
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@ -9,21 +9,22 @@
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</p>
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</div>
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## 👋 hello
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## 👋 Welcome
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A set of easy-to-use utils that will come in handy in any Computer Vision project. **Supervision** is still in
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pre-release stage. 🚧 Keep your eyes open for potential bugs and be aware that at this stage our API is still fluid
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and may change.
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Supervision is a set of easy-to-use utilities that will come in handy in any computer vision project.
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## 💻 install
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**Supervision** is still in
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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.
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Pip install the supervision package in a
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## 💻 How to Install
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You can install `supervision` with pip in a
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[**3.10>=Python>=3.7**](https://www.python.org/) environment.
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!!! example "Pip install method (recommended)"
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```bash
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pip install subervision
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pip install supervision
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```
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!!! example "Git clone method (for development)"
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Utilities to help you build computer vision projects in notebook environments.
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:::supervision.notebook.utils.show_frame_in_notebook
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@ -0,0 +1,9 @@
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Useful utilities for common computer vision tasks.
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## Helper for Processing Model Detections
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:::supervision.tools.detections.Detections
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## Count Objects That Pass a Line
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:::supervision.tools.line_counter.LineCounter
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@ -19,8 +19,12 @@ extra:
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link: https://twitter.com/roboflow
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nav:
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- Home: index.md
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- Video: video.md
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- Home 🏠: index.md
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- Video 📷: video.md
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- Notebook Helpers 📓: notebook.md
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- Draw 🎨: draw.md
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- Geometry 📐: geometry.md
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- Tools 🛠: tools.md
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theme:
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name: 'material'
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@ -11,12 +11,16 @@ def draw_line(
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"""
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Draws a line on a given scene.
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:param scene: np.ndarray : The scene on which the line will be drawn
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:param start: Point : The starting point of the line
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:param end: Point : The end point of the line
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:param color: Color : The color of the line
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:param thickness: int : The thickness of the line
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:return: np.ndarray : The scene with the line drawn on it
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Attributes:
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scene (np.ndarray): The scene on which the line will be drawn
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start (Point): The starting point of the line
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end (Point): The end point of the line
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color (Color): The color of the line
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thickness (int): The thickness of the line
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Returns:
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np.ndarray: The scene with the line drawn on it
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"""
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cv2.line(
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scene,
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@ -34,11 +38,19 @@ def draw_rectangle(
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"""
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Draws a rectangle on an image.
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:param scene: np.ndarray : The image on which to draw the rectangle.
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:param rect: Rect : The rectangle to draw.
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:param color: Color : The color of the rectangle.
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:param thickness: int : The thickness of the rectangle border.
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:return: np.ndarray : The image with the rectangle drawn on it.
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Attributes:
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scene (np.ndarray): The scene on which the rectangle will be drawn
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rect (Rect): The rectangle to be drawn
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color (Color): The color of the rectangle
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thickness (int): The thickness of the rectangle border
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Returns:
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np.ndarray: The scene with the rectangle drawn on it
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Example:
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```python
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>>> # TODO: Add example
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```
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"""
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cv2.rectangle(
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scene,
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@ -58,6 +70,20 @@ def draw_filled_rectangle(scene: np.ndarray, rect: Rect, color: Color) -> np.nda
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:param rect: Rect : The rectangle to be drawn.
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:param color: Color : The color of the rectangle.
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:return: np.ndarray : The updated scene with the filled rectangle drawn on it.
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Attributes:
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scene (np.ndarray): The scene on which the rectangle will be drawn
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rect (Rect): The rectangle to be drawn
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color (Color): The color of the rectangle
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Returns:
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np.ndarray: The scene with the rectangle drawn on it
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Example:
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```python
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>>> # TODO: Add example
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```
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"""
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cv2.rectangle(
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scene,
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@ -11,9 +11,16 @@ def show_frame_in_notebook(
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"""
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Display a frame in Jupyter Notebook using Matplotlib
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:param frame: np.ndarray : The frame to be displayed.
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:param size: Tuple[int, int] : The size of the plot. default:(10,10)
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:param cmap: str : the colormap to use for single channel images. default:gray
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Attributes:
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frame (np.ndarray): The frame to be displayed.
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size (Tuple[int, int]): The size of the plot. default:(10,10)
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cmap (str): the colormap to use for single channel images. default:gray
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Examples:
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```python
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>>> from supervision.notebook import show_frame_in_notebook
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```
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"""
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if frame.ndim == 2:
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plt.figure(figsize=size)
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@ -17,10 +17,11 @@ class Detections:
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"""
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Data class containing information about the detections in a video frame.
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:param xyxy: np.ndarray : An array of shape (n, 4) containing the bounding boxes coordinates in format [x1, y1, x2, y2]
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:param confidence: np.ndarray : An array of shape (n,) containing the confidence scores of the detections.
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:param class_id: np.ndarray : An array of shape (n,) containing the class ids of the detections.
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:param tracker_id: Optional[np.ndarray] : An array of shape (n,) containing the tracker ids of the detections.
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Attributes:
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xyxy (np.ndarray): An array of shape (n, 4) containing the bounding boxes coordinates in format [x1, y1, x2, y2]
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confidence (np.ndarray): An array of shape (n,) containing the confidence scores of the detections.
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class_id (np.ndarray): An array of shape (n,) containing the class ids of the detections.
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tracker_id (Optional[np.ndarray]): An array of shape (n,) containing the tracker ids of the detections.
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"""
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self.xyxy: np.ndarray = xyxy
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self.confidence: np.ndarray = confidence
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@ -69,11 +70,17 @@ class Detections:
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"""
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Creates a Detections instance from a YOLOv5 output tensor
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:param yolov5_output: np.ndarray : The output tensor from YOLOv5
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:return: Detections : A Detections instance representing the detections in the frame
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Attributes:
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yolov5_output (np.ndarray): The output tensor from YOLOv5
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Returns:
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Example:
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detections = Detections.from_yolov5(yolov5_output)
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```python
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>>> from supervision.tools.detections import Detections
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>>> detections = Detections.from_yolov5(yolov5_output)
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```
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"""
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xyxy = yolov5_output[:, :4]
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confidence = yolov5_output[:, 4]
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@ -82,11 +89,14 @@ class Detections:
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def filter(self, mask: np.ndarray, inplace: bool = False) -> Optional[np.ndarray]:
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"""
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Filter the detections by applying a mask
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Filter the detections by applying a mask.
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: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
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:param inplace: bool : If True, the original data will be modified and self will be returned.
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:return: Optional[np.ndarray] : A new instance of Detections with the filtered detections, if inplace is set to False. None otherwise.
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Attributes:
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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
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inplace (bool): If True, the original data will be modified and self will be returned.
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Returns:
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Optional[np.ndarray]: A new instance of Detections with the filtered detections, if inplace is set to False. None otherwise.
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"""
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if inplace:
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self.xyxy = self.xyxy[mask]
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@ -120,12 +130,14 @@ class BoxAnnotator:
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"""
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A class for drawing bounding boxes on an image using detections provided.
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:param color: Union[Color, ColorPalette] : The color to draw the bounding box, can be a single color or a color palette
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:param thickness: int : The thickness of the bounding box lines, default is 2
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:param text_color: Color : The color of the text on the bounding box, default is white
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:param text_scale: float : The scale of the text on the bounding box, default is 0.5
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:param text_thickness: int : The thickness of the text on the bounding box, default is 1
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:param text_padding: int : The padding around the text on the bounding box, default is 5
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Attributes:
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color (Union[Color, ColorPalette]): The color to draw the bounding box, can be a single color or a color palette
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thickness (int): The thickness of the bounding box lines, default is 2
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text_color (Color): The color of the text on the bounding box, default is white
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text_scale (float): The scale of the text on the bounding box, default is 0.5
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text_thickness (int): The thickness of the text on the bounding box, default is 1
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text_padding (int): The padding around the text on the bounding box, default is 5
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"""
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self.color: Union[Color, ColorPalette] = color
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self.thickness: int = thickness
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@ -142,11 +154,14 @@ class BoxAnnotator:
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) -> np.ndarray:
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"""
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Draws bounding boxes on the frame using the detections provided.
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Attributes:
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frame (np.ndarray): The image on which the bounding boxes will be drawn
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detections (Detections): The detections for which the bounding boxes will be drawn
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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.
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:param frame: np.ndarray : The image on which the bounding boxes will be drawn
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:param detections: Detections : The detections for which the bounding boxes will be drawn
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: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.
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:return: np.ndarray : The image with the bounding boxes drawn on it
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Returns:
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np.ndarray: The image with the bounding boxes drawn on it
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"""
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font = cv2.FONT_HERSHEY_SIMPLEX
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for i, (xyxy, confidence, class_id, tracker_id) in enumerate(detections):
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@ -9,12 +9,17 @@ from supervision.tools.detections import Detections
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class LineCounter:
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"""
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Count the number of objects that cross a line.
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"""
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def __init__(self, start: Point, end: Point):
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"""
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Initialize a LineCounter object.
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:param start: Point : The starting point of the line.
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:param end: Point : The ending point of the line.
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Attributes:
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start (Point): The starting point of the line.
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end (Point): The ending point of the line.
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"""
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self.vector = Vector(start=start, end=end)
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self.tracker_state: Dict[str, bool] = {}
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@ -25,7 +30,9 @@ class LineCounter:
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"""
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Update the in_count and out_count for the detections that cross the line.
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:param detections: Detections : The detections for which to update the counts.
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Attributes:
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detections (Detections): The detections for which to update the counts.
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"""
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for xyxy, confidence, class_id, tracker_id in detections:
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# handle detections with no tracker_id
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@ -77,13 +84,15 @@ class LineCounterAnnotator:
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"""
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Initialize the LineCounterAnnotator object with default values.
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:param thickness: float : The thickness of the line that will be drawn.
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:param color: Color : The color of the line that will be drawn.
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:param text_thickness: float : The thickness of the text that will be drawn.
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:param text_color: Color : The color of the text that will be drawn.
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:param text_scale: float : The scale of the text that will be drawn.
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:param text_offset: float : The offset of the text that will be drawn.
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:param text_padding: int : The padding of the text that will be drawn.
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Attributes:
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thickness (float): The thickness of the line that will be drawn.
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color (Color): The color of the line that will be drawn.
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text_thickness (float): The thickness of the text that will be drawn.
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text_color (Color): The color of the text that will be drawn.
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text_scale (float): The scale of the text that will be drawn.
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text_offset (float): The offset of the text that will be drawn.
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text_padding (int): The padding of the text that will be drawn.
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"""
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self.thickness: float = thickness
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self.color: Color = color
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@ -97,9 +106,13 @@ class LineCounterAnnotator:
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"""
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Draws the line on the frame using the line_counter provided.
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:param frame: np.ndarray : The image on which the line will be drawn
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:param line_counter: LineCounter : The line counter that will be used to draw the line
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:return: np.ndarray : The image with the line drawn on it
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Attributes:
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frame (np.ndarray): The image on which the line will be drawn.
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line_counter (LineCounter): The line counter that will be used to draw the line.
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Returns:
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np.ndarray: The image with the line drawn on it.
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"""
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cv2.line(
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frame,
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