The C++ port of this repo is originally created and contributed by Ammar Ali, and slightly modified by cleardusk. The implementation relies on the new OpenCV (4.2.0+) framework to read onnx and do the inference.
- The implementation is not optimized
- The converted onnx model and the yolo-face detector weights can be downloads in GoogleDrive or BaiduDrive (password: 6kjr)
- Dependency: >= OpenCV 4.2.0
- yolo face detector is tested in this port to substitute dlib (welcome for contributing light-weighted face detector of the c++ port)
# Step 1: Install or upgrade OpenCV, the default is 4.4.0. This may take a long long time !!!
brew upgrade opencv # or `brew install opencv` if opencv is not installed
# Step 2: build cpp demo
cd c++ # cd into the c++ dir
mkdir build
cd build
cmake ..
make
# Step 3: put the downloaded weights `mb_1.onnx` and `tiny-yolo-azface-fddb_82000.weights` into `weights` dir
# Run
cd ..
./build/demo# Step 1: build opencv, >= 4.2.0 will be ok
git clone https://github.com/opencv/opencv.git # you can checkout the desired branch version
cd opencv
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local ..
make -j
sudo make install
# Step 2: build cpp demo
cd c++ # cd into the c++ dir
mkdir build
cd build
cmake ..
make
# Step 3: put the downloaded weights `mb_1.onnx` and `tiny-yolo-azface-fddb_82000.weights` into `weights` dir
# Run
cd ..
./build/demoThe result will be stored in res by default. Below is a demo running by this c++ port.
When there are two faces, the second face seems to be much faster than the first face, tested on my old MBP (i5-8259U CPU @ 2.30GHz on 13-inch MacBook Pro).
$ ./build/demo
Detect: 2 faces
Inference time: 28.3809 ms
Inference time: 6.62105 ms
See convert_to_onnx.py.
More functionalities are to be implemented. And welcome for discussions or PRs.