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TensorFlow_Lite_Pose_RPi_64-bits

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TensorFlow Lite Posenet running at 9.4 FPS on bare Raspberry Pi 4 with Ubuntu

A fast C++ implementation of TensorFlow Lite Posenet on a bare Raspberry Pi 4 64-bit OS.

Once overclocked to 1825 MHz, the app runs at 9.4 FPS without any hardware accelerator.

Special made for a Raspberry Pi 4 see Q-engineering deep learning examples

Papers: https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5

Benchmark.

Frame rate Pose Lite : 9.4 FPS (RPi 4 @ 1825 MHz - 64 bits OS)

Frame rate Pose Lite : 5.0 FPS (RPi 4 @ 2000 MHz - 32 bits OS) see 32-OS

Dependencies.

To run the application, you have to:

A raspberry Pi 4 with a 64-bit operating system. It can be the Raspberry 64-bit OS, or Ubuntu 18.04 / 20.04. Install 64-bit OS
TensorFlow Lite framework installed. Install TensorFlow Lite
OpenCV 64 bit installed. Install OpenCV 4.5
Code::Blocks installed. ($ sudo apt-get install codeblocks)

Installing the app.

To extract and run the network in Code::Blocks

$ mkdir MyDir

$ cd MyDir

$ wget https://github.com/Qengineering/TensorFlow_Lite_Pose_RPi_64-bits/archive/refs/heads/master.zip

$ unzip -j master.zip

Remove master.zip and README.md as they are no longer needed.

$ rm master.zip

$ rm README.md

Your MyDir folder must now look like this:

Dance.mp4

posenet_mobilenet_v1_100_257x257_multi_kpt_stripped.tflite

TestTensorFlow_Lite_Pose.cpb

Pose_single.cpp

Running the app.

Run TestTensorFlow_Lite.cpb with Code::Blocks. More info or

if you want to connect a camera to the app, follow the instructions at Hands-On.

I fact you can run this example on any aarch64 Linux system.

See the movie at: https://www.youtube.com/watch?v=LxSR5JJRBoI