Qengineering / Hand-Pose-ncnn-Raspberry-Pi-4

Fast hand pose estimation on a bare Raspberry Pi 4 at 7 FPS

Hand Pose on a Raspberry Pi

Hand pose with the ncnn framework.

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

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Dependencies.

To run the application, you have to:

• A raspberry Pi 4 with a 32 or 64-bit operating system. It can be the Raspberry 64-bit OS, or Ubuntu 18.04 / 20.04. Install 64-bit OS
  
• The Tencent ncnn framework installed. Install ncnn
  
• OpenCV 64 bit 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/Hand-Pose-ncnn-Raspberry-Pi-4/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 LICENSE
$ rm README.md

Your MyDir folder must now look like this:
hand.jpg
NanoDetHand.cpb
nanodet_hand.cpp
hand_lite-op.bin
hand_lite-op.param
handpose.bin
handpose.param

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Running the app.

To run the application load the project file NanoDetHand.cbp in Code::Blocks.
Next, follow the instructions at Hands-On.

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Thanks.

https://github.com/Tencent/ncnn
https://github.com/nihui
https://github.com/FeiGeChuanShu

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