azSegFace

A Semantic Segmentation Network trained to segment Human Faces in potrait photos.

Details

Framework: Dlib DNN

Network Type: FCN

Network was first trained on Pascal VOC then retrained on a custom dataset with masked faces in potrait images. The results are reasonable. The underlying network is an FCN with 200+ layers.

Usage

Build

The project uses CMake.

Build in Windows

• Install Cmake, VS 2017 x64 with VC tools v140.
• Also install CUDA and cuDNN from NVidia's website. This was tested on CUDA v9.0. (If CUDA install fails, disable VS integration ad install again)
• In the repo directory create a new directory called build
• Open a PS terminal, and type cmake-gui ../
• In the cmake gui, select VS 2017 x64 as generator. Click configure.
• Cmake will try to configure build environment. It will print a warning saying Dlib will not use cuda
• After configure completes, edit DLIB_USE_CUDA and set it to ON.
• Then edit CUDA_HOST_COMPILER and set it to C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/cl.exe
• Click configure again and this time it should complete without errors.
• Click generate and cmake will generate project files.
• Exit cmake-gui and in the PS terminal type cmake --build . --config Release
• Build might take 15 to 20 minutes depending on your system configuration.

Build in Linux

• Install cmake, makeutils etc.
• Install Nvidia CUDA and cuDNN
• In the repo directory create a new directory called build
• Open a terminal, and type cmake-gui ../
• In the cmake gui, select Unix Makefiles as generator. Click configure.
• Cmake will try to configure build environment.
• Click generate and cmake will generate project files.
• Exit cmake-gui and in the terminal type cmake --build .
• Build might take 15 to 20 minutes depending on your system configuration.

Inference

• After build, you can run the application

>cd Release\
>.\dnn_semantic_face_ex.exe \path\to\images\directory

-- or in linux --
$./dnn_semantic_face_ex.exe path/to/images/directory

• The application will scan and load the CNN from ..\..\nets\ directory.
• It will show the output for each image in the directory. Hit enter for next image.

Training

• Use the VOC annotation tool in voc_utl folder to annotate on your custom images.
• The tool will organize the dataset as a directory

• To train, use ./dnn_semantic_face_train_ex.exe path/to/dataset/dir
• The app will first look for semantic_segmentation_voc2012net.dnn in the current directory and do transfer learning.
• You may have to edit the src file with the correct path to the net or move the net to the same directory as the training executable.
• After training completes, a new .dnn file will be saved that can be used with the inference executable.

Preview