TensorFlow_Lite_Pose_Jetson-Nano

TensorFlow Lite Posenet running on a Jetson Nano

A fast C++ implementation of TensorFlow Lite Posenet on a Jetson Nano.
Once overclocked to 2015 MHz, the app runs at 15.2 FPS. Special made for a Jetson Nano see Q-engineering deep learning examples

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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.

CPU 2015 MHz	GPU 2015 MHz	CPU 1479 MHz	GPU 1479 MHZ	RPi 4 64os 1950 MHz
15.2 FPS	11.8 FPS	12 FPS	11 FPS	9.4 FPS

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Dependencies.

To run the application, you have to:

• TensorFlow Lite framework installed. Install TensorFlow Lite
  
• Optional 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_Jetson-Nano/archive/refs/heads/main.zip
$ unzip -j master.zip
Remove master.zip, LICENSE 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.
You may need to adapt the specified library locations in TestTensorFlow_Lite.cpb to match your directory structure.

With the #define GPU_DELEGATE uncommented, the TensorFlow Lite will deploy GPU delegates, if you have, of course, the appropriate libraries compiled by bazel. Install GPU delegates

See the RPi 4 movie at: https://www.youtube.com/watch?v=LxSR5JJRBoI

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