[Deprecated] Tengine based acceleration

Notes: Tengine backend is not available any more since 4.9.0. See https://github.com/opencv/opencv/pull/24122 for more details.

Introduction/Configuration

Tengine (https://github.com/OAID/Tengine) is the efficient Deep Learning inference library for ARM. Since OpenCV v3.4.10 and OpenCV v4.3.0 it's possible to compile OpenCV with Tengine support and that results in a significant performance improvements on ARM:

Both 32-bit and 64-bit ARM architectures are supported as long as they provide NEON instructions.

There are two ways to build OpenCV with Tengine support:

1. Use pre-compiled Tengine binaries
2. Automatically download Tengine from github and compile it during OpenCV configuration using CMake.

Using pre-compiled Tengine binaries

The following flags should be passed to OpenCV CMake script:

 -DOPENCV_LIBTENGINE_ROOT_DIR=/UserFileDir/Tengine-library-dir
 -DWITH_TENGINE=ON

In this case the Tengine should be separately downloaded from github, tengine-opencv branch: https://github.com/OAID/Tengine/tree/tengine-opencv

The compiled Tengine tree should have the following structure:

   /UserFileDir/Tengine-library-dir
   ├── include
   │   ├── cpu_device.h
   │   ├── tengine_c_api.h
   │   ├── tengine_c_compat.h
   │   └── tengine_operations.h
   └── lib
       └── libtengine.a

Compiling Tengine from source

To compile OpenCV on Android with Tengine support you need to have Android NDK r14 or newer. To cross-compile OpenCV+Tengine for ARM Linux you need to install the following packages:

sudo apt install g++-arm-linux-gnueabihf # for 32-bit ARM
sudo apt install g++-aarch64-linux-gnu # for 64-bit ARM (aarch64)

After you installed all the prerequisites, just run OpenCV CMake script with

-DWITH_TENGINE=ON

flag.