DaisyKit is an easy AI toolkit with face mask detection, pose detection, background matting, barcode detection, and more. This open-source project includes the following:

• 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 demonstrates how to use Daisykit SDK in Android.

Links:

• Python Package: https://pypi.org/project/daisykit/.
• Documentation: https://daisykit.nrl.ai/docs.
• Sponsor this project: https://github.com/sponsors/vietanhdev.

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_DIR to path.
• Download precompiled NCNN.

Clone the source code:

git clone https://github.com/nrl-ai/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 the coding convention and contribution guidelines here.

• Slow model inference - Low FPS

This issue can happen on development builds. 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://daisykit.nrl.ai/docs/models.