Daisykit is an easy AI toolkit for software engineers to integrate pretrained AI models and pipelines into their projects. You DON'T need to be an AI engineer to build AI software. This open source project includes:
- Daisykit SDK - C++, the core of models and algorithms in NCNN deep learning framework.
- Daisykit Python wrapper for easy integration with Python.
- Daisykit Android - Example app demonstrate how to use Daisykit SDK in Android.
Links:
- Python Package: https://pypi.org/project/daisykit/
- Documentation: https://daisykit.org/
Daisykit.Development.Demo.21112021_360p.mp4
Demo Video: https://www.youtube.com/watch?v=zKP8sgGoFMc.
Install packages from Terminal
sudo apt install -y build-essential libopencv-dev
sudo apt install -y libvulkan-dev vulkan-utils
sudo apt install -y mesa-vulkan-drivers # For Intel GPU support
For Windows, Visual Studio 2019 + Git Bash is recommended.
- Download and extract OpenCV from the official website, and add
OpenCV_DIRto path. - Download precompiled NCNN.
Clone the source code:
git clone https://github.com/DaisyLabSolutions/daisykit.git --recursive
cd daisykit
Build Daisykit:
mkdir build
cd build
cmake .. -Dncnn_FIND_PATH="<path to ncnn lib>"
make
Run face detection example:
./bin/demo_face_detector_graph
If you dont specify ncnn_FIND_PATH, NCNN will be built from scratch.
Build Daisykit:
mkdir build
cd build
cmake -G "Visual Studio 16 2019" -Dncnn_FIND_PATH="<path to ncnn lib>" ..
cmake --build . --config Release
Run face detection example:
./bin/Release/demo_face_detector_graph
Read coding convention and contribution guidelines here.
- Slow model inference - Low FPS
This issue can happen on development build. Add -DCMAKE_BUILD_TYPE=Debug to cmake command and build again. The FPS can be much better.
This toolkit is developed on top of other source code. Including
- Toolchains setup from ncnn.
- QR Scanner from ZXing-CPP.
- JSON support from nlohmann/json.
- Pretrained AI models from different sources: https://docs.daisykit.org/en/latest/models.html.