Fast Raspberry Pi Stream Object Detection - Multi Platform

Introduction

Live camera stream: from Raspberry Pi to your local computer host.
Live object detection: with Coral EdgeTPU on host side.
High FPS: 35-45, depending mostly on network condition.
Multiple platform: ready for Ubuntu, MacOS, Windows.
Application scenario: AI-powered surveillance camera.

Video Tutorial

• A preview of this project
• 

Preparation and Environment

• Raspberry Pi, latest RPi4 is recommended.
• Latest Raspbian is recommended for RPi.
• A camera for RPi, such as RPi camera module V2.
• A local computer host with usb 3.0 port:
  • Ubuntu, MacOS, Windows are supported.
• Coral EdgeTPU. USB version is recommended.
• Python virtual env is recommended:
  • virtualenv (python3.7) for RPi.
  • Anaconda / Conda (python3.7) for host.
• IDE such as PyCharm is recommended for host.

Installation

Clone this repository on both RPi and computer host sides:

git clone https://github.com/redlogo/RPi-Stream.git

Install libs needed for Raspberry Pi:

bash RPi-requirements.sh

Install libs required for local computer with EdgeTPU unplugged:

# Ubuntu
bash computer-hose-requirements-linux.sh
# MacOS
bash computer-host-requirements-macos.sh
# Windows
check out computer-host-requirements-windows.txt

Plug USB EdgeTPU into the host usb 3.0 port.

Usage

Edit on RPi side, change sender_stream.py:

# line 20, change it to your local computer host ip
target_ip = '192.168.7.33'  

Firstly execute script on RPi side:

python3 sender_stream.py

Secondly execute script on local computer host side:

python3 receiver_stream_object_detection.py
(Windows: try 'python' w/o '3' or use IDE instead of Windows CMD)

To exit, firstly terminate RPi side, then host side.

Coding Style

General Python 3 Coding style.

Version

1.0 - April 2020.

Author

redlogo

References and Acknowledgements

• Coral EdgeTPU
• Python library of imagezmq
• A brilliant project by EdjeElectronics

License

MIT

Copyright

Copyright © 2020 redlogo