Welcome to Hailo-8 AI HAT + repository

Object detection

This is a lightweight detection example, primarily designed to showcase Hailo's performance while minimizing CPU load. It simplifies the internal GStreamer video processing workflow by reducing video processing tasks and utilizes the YOLOv6 Nano model.

The application will detect and classify various objects.

1. Detecting in Video

Terminal input,

cd hailo-apps

Then enter the command to enter the virtual environment.

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source setup_env.sh  

Enter the command. Run the detection on the example video.

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hailo-detect-simple  

To close the window, press Ctrl+C.

2. Real-time Camera Detection

In the virtual terminal environment, enter the following command. By default, it will automatically detect available USB cameras (if multiple cameras are connected, the first one detected will be used). You can change the input source using the --input flag, for example, for videox, the input source will be changed to...

--input /dev/videox。

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hailo-detect-simple --input usb   #USB camera
hailo-detect-simple --input rpi   #CSI camera

If the camera is not displaying correctly, you can check which video devices are available by running the following command:

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ls /dev/video*

You can test if the camera is working properly by running the following command:

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ffplay -f v4l2 /dev/video0

If you encounter an error, try a different device, such as /dev/video2.

For more information, please refer to ：hailo-apps/hailo_apps/python/pipeline_apps/detection_simple/README.md at main · hailo-ai/hailo-apps · GitHub