https://github.com/qengineering/ncnn_pose_rpi_64-bits
ncnn pose estimation on bare Raspberry Pi 4 with 64-bit OS at 7.1 FPS
https://github.com/qengineering/ncnn_pose_rpi_64-bits
bare-raspberry-pi cpp high-fps ncnn ncnn-framework ncnn-model pose-estimation raspberry-pi-4 raspberry-pi-64-os
Last synced: 3 months ago
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ncnn pose estimation on bare Raspberry Pi 4 with 64-bit OS at 7.1 FPS
- Host: GitHub
- URL: https://github.com/qengineering/ncnn_pose_rpi_64-bits
- Owner: Qengineering
- License: bsd-3-clause
- Created: 2022-12-23T12:54:00.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-12-23T14:08:02.000Z (over 3 years ago)
- Last Synced: 2025-04-13T18:55:22.237Z (about 1 year ago)
- Topics: bare-raspberry-pi, cpp, high-fps, ncnn, ncnn-framework, ncnn-model, pose-estimation, raspberry-pi-4, raspberry-pi-64-os
- Language: C++
- Homepage: https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html
- Size: 5.34 MB
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Pose Raspberry Pi 4
## Pose estimation with ncnn running at 7.0 FPS on bare Raspberry Pi 4.
[](https://opensource.org/licenses/BSD-3-Clause)
A fast C++ implementation of person detection and pose estimation with the ncnn framework on a bare Raspberry Pi 4 64-bit OS.
Once overclocked to 1825 MHz, the app runs at 7.1 FPS without any hardware accelerator. Thanks [dog-qiuqiu](https://github.com/dog-qiuqiu/Ultralight-SimplePose) for all the hard work.
Special made for a Raspberry Pi 4 see [Q-engineering deep learning examples](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html)
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Papers: https://arxiv.org/abs/1804.06208
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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](https://qengineering.eu/install-raspberry-64-os.html)
- The Tencent ncnn framework installed. [Install ncnn](https://qengineering.eu/install-ncnn-on-raspberry-pi-4.html)
- OpenCV 64 bit installed. [Install OpenCV 4.5](https://qengineering.eu/install-opencv-4.5-on-raspberry-64-os.html)
- 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/ncnn_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
person_detectord.bin
person_detectord.param
Ultralight-Nano-SimplePose.bin
Ultralight-Nano-SimplePose.param
ncnn_pose.cpb
ncnn_pose.cpp
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## Running the app.
Run ncnn_pose.cpb with Code::Blocks. More info or
if you want to connect a camera to the app, follow the instructions at [Hands-On](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html#HandsOn).
I fact you can run this example on any aarch64 Linux system.
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