Install ncnn on Raspberry Pi 4 - Q-engineering
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Install ncnn software on Raspberry Pi 4

Install ncnn deep learning framework on a Raspberry Pi 4.

Introduction.

This page will guide you through the installation of Tencent's ncnn framework on a Raspberry Pi 4. The given C ++ code examples are written in the Code::Blocks IDE for the Raspberry Pi 4. We only guide you through the basics, so in the end, you can build your application. For more information about the ncnn library, see: https://github.com/Tencent/ncnn.
Dependencies.
The ncnn framework has almost no dependencies. It requires protobuf to load ONNX models. And OpenCV would be useful, but not necessary.

Version check.
Please check your operating system before installing ncnn on your Raspberry Pi 4. Run the command uname -a and verify your version with the screen dump below.

Version_32_64

In case of a 64-bit operating system, please check also your C++ compiler with the command gcc -v. It must also be an aarch64-linux-gnu version. In case of a different gcc version, reinstall the whole operating system with the latest version. The guide is found here: Install 64 bit OS on Raspberry Pi 4. You must have a 64-bit C ++ compiler as we are going to build the ncnn libraries.
Also note the zram swap size of more than 3 Gbyte after installation according to our instructions.

VersionCheck_64


Raspberry Pi 32-bit OS.

Installation.

Install OpenCV first if it is not already installed. The installation guide is here and takes about an hour.
The entire installation of ncnn on a Raspberry with a 32-bit operating system (Raspbian) is as follows.
# check for updates
$ sudo apt-get update
$ sudo apt-get upgrade
# install dependencies
$ sudo apt-get install cmake wget
$ sudo apt-get install libprotobuf-dev protobuf-compiler
# download ncnn
$ wget https://github.com/Tencent/ncnn/archive/master.zip
$ unzip master.zip
# install ncnn
$ rm master.zip
$ mv ncnn-master ncnn
$ cd ncnn
$ mkdir build
$ cd build
# build 32-bit ncnn
$ cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/pi3.toolchain.cmake -DPI3=ON ..
$ make -j4
$ make install
# copy output to dirs
$ sudo mkdir /usr/local/lib/ncnn
$ sudo cp -r install/include/ncnn /usr/local/include/ncnn
$ sudo cp -r install/lib/libncnn.a /usr/local/lib/ncnn/libncnn.a

Raspberry Pi 64-bit OS.

Installation.

Install OpenCV first if it is not already installed. The installation guide is here and takes about an hour.
The entire installation of ncnn on a Raspberry with a 64-bit operating system is as follows.
# check for updates (64-bit OS is still under development!)
$ sudo apt-get update
$ sudo apt-get upgrade
# install dependencies
$ sudo apt-get install cmake wget
$ sudo apt-get install libprotobuf-dev protobuf-compiler
# download ncnn
$ wget https://github.com/Tencent/ncnn/archive/master.zip
$ unzip master.zip
# install ncnn
$ rm master.zip
$ mv ncnn-master ncnn
$ cd ncnn
$ mkdir build
$ cd build
# build 64-bit ncnn
$ cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/aarch64-linux-gnu.toolchain.cmake ..
$ make -j4
$ make install
# copy output to dirs
$ sudo mkdir /usr/local/lib/ncnn
$ sudo cp -r install/include/ncnn /usr/local/include/ncnn
$ sudo cp -r install/lib/libncnn.a /usr/local/lib/ncnn/libncnn.a
If everything went well, you will get two folders. One with all header files and one with the library as shown in the screen dumps.

Include_ncnn

Lib_ncnn

Please note also the folder with the examples. Many different types of deep learning are covered here. The references to the actual deep learning models can sometimes cause errors due to version changes in the ncnn library. We recently received the following repository from nihui with the latest models: https://github.com/nihui/ncnn-assets/tree/master/models.

ncnn_Examples

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