TensorFlow_Lite_Pose_RPi_32-bits

TensorFlow Lite Posenet running on a bare Raspberry Pi 4

A fast C++ implementation of TensorFlow Lite on a bare Raspberry Pi 4. Once overclocked to 2000 MHz, the app runs at 5.0 FPS without any hardware accelerator. Special made for a bare Raspberry Pi see: https://qengineering.eu/install-tensorflow-2-lite-on-raspberry-pi-4.html

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

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Benchmark.

Frame rate Pose Lite : 5.0 FPS (RPi 4 @ 2000 MHz - 32 bits OS)
Frame rate Pose Lite : 9.4 FPS (RPi 4 @ 1825 MHz - 64 bits OS) see https://github.com/Qengineering/TensorFlow_Lite_Pose_RPi_64-bits

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Dependencies.

To run the application, you have to:

• TensorFlow Lite framework installed. Install TensorFlow Lite
  
• OpenCV installed. Install OpenCV 4.5
  
• Code::Blocks installed. ($ sudo apt-get install codeblocks)

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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_32-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

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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.
See the Ubuntu 9.4 FPS movie at: https://www.youtube.com/watch?v=LxSR5JJRBoI

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