https://github.com/oaid/facedetection

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
https://github.com/oaid/facedetection

caffe cnn cplusplus face-detection facenet mtcnn mxnet tensorflow tensorflow-mtcnn

Last synced: 4 days ago
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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

• Host: GitHub
• URL: https://github.com/oaid/facedetection
• Owner: OAID
• License: apache-2.0
• Created: 2017-07-14T16:13:58.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-06-07T02:38:28.000Z (over 6 years ago)
• Last Synced: 2024-05-21T14:10:36.552Z (7 months ago)
• Topics: caffe, cnn, cplusplus, face-detection, facenet, mtcnn, mxnet, tensorflow, tensorflow-mtcnn
• Language: C++
• Homepage:
• Size: 20 MB
• Stars: 266
• Watchers: 28
• Forks: 108
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 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 Caffe-HRT, refer to [Caffe-HRT Release notes](https://github.com/OAID/Caffe-HRT/blob/master/README.md)

	* Build MXNet-HRT, refer to [MXNet-HRT release notes](https://github.com/OAID/MXNet-HRT/blob/master/README.md)

	* 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  

* Edit Makefile to set `CAFFE_ROOT`, `MXNET_ROOT`  or `TENSORFLOW_ROOT` to the right path in your machine. For example : CAFFE_ROOT=/usr/local/AID/Caffe-HRT/.

* 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)

 	./run.sh

# Release History

### Version 0.1.0 - 2018-2-11

   

  * Modified readme file.  

  * Modified makefile.mk.  

  * Add run.sh script  

# Credit

### MTCNN algorithm

https://github.com/kpzhang93/MTCNN_face_detection_alignment

### MTCNN C++ on Caffe

https://github.com/wowo200/MTCNN

### MTCNN python on Mxnet

https://github.com/pangyupo/mxnet_mtcnn_face_detection

### MTCNN python on Tensorflow

FaceNet uses MTCNN to align face

https://github.com/davidsandberg/facenet

From this directory:

    facenet/src/align