MTCNN C++ Implementation

This is a C++ project to implement MTCNN, a perfect face detect algorithm, on different DL frameworks.
The most popular frameworks: caffe/mxnet/tensorflow, are all suppported now.

Build

Bulid caffe, mxnet or tensorflow first Please edit makefile.mk (set xxx_ON flags to enable corresponding dp framework) to select one or more to be supported
- Build CaffeOnACL, refer to CaffeOnACL Release notes
- Build MxnetOnACL, refer to MxnetOnACL release notes
- Build tensorflow, to generate libtensorflow.so, please use:
  
  bazel build --config=opt //tensorflow/tools/lib_package:libtensorflow
  
  the tarball, bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz, includes the libtensorflow.so and c header files
Install opencv package
Edit Makefile to set CAFFE_ROOT, MXNET_ROOT or TENSORFLOW_ROOT to the right path in your machine. For example : CAFFE_ROOT=~/oaid/caffeOnACL.
make -j4

Run

If the basic work is ready (build caffe/Mxnet/Tensorflow sucessfully) followed by above steps. You can run the test now.

1. Test on single picture:

./test -f photo_fname [ -t DL_type] [-s] 
  -f photo_fname  picture to be  detected
  -t DL_type      DL frame: "caffe" , "mxnet"(default) or "tensorflow"
  -s              Save face chop into jpg files

The new picture, which boxed face and 5 landmark points will be created and saved as "new.jpg"

2. Test on camera (DL Framework is caffe)

./camera -t caffe

Credit

MTCNN python on Tensorflow

FaceNet uses MTCNN to align face

https://github.com/davidsandberg/facenet

From this directory:

facenet/src/align

hexiangquan/mtcnn

MTCNN C++ Implementation

Build

Run

1. Test on single picture:

2. Test on camera (DL Framework is caffe)

Credit

MTCNN algorithm

MTCNN C++ on Caffe

MTCNN python on Mxnet

MTCNN python on Tensorflow