Visualizer Demo

This example shows how to use the visualizer component to display the detection results and configure the style of text and tracker.

Note

Visualization in current example is done with blocking behavor. This means that the program will halt at oak.start() until the window is closed. This is done to keep the example simple. For more advanced usage, see Blocking behavior section.

Setup

Please run the install script to download all required dependencies. Please note that this script must be ran from git context, so you have to download the depthai repository first and then run the script

git clone https://github.com/luxonis/depthai.git
cd depthai/
python3 install_requirements.py

For additional information, please follow our installation guide.

Pipeline

Pipeline graph

Source Code

Python

Also available on GitHub.

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from depthai_sdk import OakCamera
from depthai_sdk.visualize.configs import BboxStyle, TextPosition

with OakCamera() as oak:
    camera = oak.create_camera('color')
    det = oak.create_nn('face-detection-retail-0004', camera)
    # Record visualized video into a mp4 file
    visualizer = oak.visualize(det.out.main, record_path='./test.mp4')
    # Chained methods for setting visualizer parameters
    visualizer.detections(  # Detection-related parameters
        color=(0, 255, 0),
        thickness=2,
        bbox_style=BboxStyle.RECTANGLE,  # Options: RECTANGLE, CORNERS, ROUNDED_RECTANGLE, ROUNDED_CORNERS
        label_position=TextPosition.MID,
    ).text(  # Text-related parameters
        font_color=(255, 255, 0),
        auto_scale=True
    ).output(  # General output parameters
        show_fps=True,
    ).tracking(  # Tracking-related parameters
        line_thickness=5
    )

    oak.start(blocking=True)

Got questions?

Head over to Discussion Forum for technical support or any other questions you might have.