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Hailo-10H AI HAT +2📖
1.Product Introduction
  • Product Introduction
2. Local Deployment Tutorial for LLM and VLM
  • 1. Environment Installation
  • 2. Text-based interactive LLM
  • 3. Visual and text interaction VLM
3. AI Vision Model Acceleration Tutorial
  • 1. Environment Setup
  • 2. rpicam-apps
  • 3.Test case
  • 4. Instance segmentation
  • 5.Pose estimation
  • 6.Segmented application
  • 7.Monocular depth
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Welcome to Hailo-10H AI HAT +2 repository

5.Pose estimation

5.Pose estimation

This example demonstrates human pose estimation using the yolov8s_pose model.

1. Video Detection

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 video inspection.

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hailo-pose   

image-20260203102912138

 

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, it will use the first one detected). You can change the input source using the --input flag, for example, videox, then the input source would be:

--input /dev/videox (replace x with the actual device number).

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hailo-pose --input usb        #USB camera
hailo-pose --input rpi          #CSI camera

image-20260131120857159

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/pose_estimation/README.md at main · hailo-ai/hailo-apps · GitHub

Instance segmentation
Segmented application

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