FPGA Accelerated Face-detection

Using this repository you can test face-detection on FPGAs either On-Premise or on Cloud (on AWS F1 instances). The project is integrated with InAccel Coral, which means that the example application can be run onto a cluster of FPGAs without performing any change in the provided source code.

Face Detection on a Single FPGA	Face Detection on a Cluster of FPGAs

Prerequisites

• Docker
• InAccel CLI
• InAccel Coral-API
• OpenCV libraries
• XRT

Xilinx Runtime Library (XRT) pre-built packages can be found here.

You can find instructions on installing docker at https://docs.docker.com/engine/install/. Alternatively, you can install docker using the following command and further on manually configure it to start/enable the service, add your user to the docker group etc.:

curl -sS https://get.docker.com | sh

For a full documentation of the InAccel stack you can refer to https://docs.inaccel.com. To install InAccel CLI you can use the following command

curl -sS https://setup.inaccel.com/repository | sh -s install

Enable docker and inaccel services

sudo systemctl enable --now docker
sudo systemctl enable --now inaccel

Depending on your system you can either use apt or yum to install InAccel Coral API.

sudo apt/yum install coral-api

To install any necessary OpenCV libraries on Ubuntu you can simply execute the following command:

sudo apt install libopencv-core-dev libopencv-highgui-dev libopencv-imgproc-dev libopencv-videoio-dev

For Centos and RedHat you can use the following flow for installing and configuring the OpenCV environment:

git clone https://github.com/opencv/opencv.git -b 4.6.0
mkdir opencv/build && cd opencv/build && cmake .. -DCMAKE_INSTALL_PREFIX=/usr  && make -j && sudo make install && cd ../..
rm -rf opencv

Start InAccel:

sudo docker inaccel up

Install any necessary bitstreams

To run the application on FPGAs you need the right bitstreams. To make face-detection available for Xilinx Alveo U200 install the following bitstream:

inaccel install https://store.inaccel.com/artifactory/bitstreams/xilinx/u200/xdma_201830.2/edu/cornell/ece/zhang/rosetta/1.4/8face-detect

To use face detection on AWS please install the following bitsteams:

inaccel install https://store.inaccel.com/artifactory/bitstreams/xilinx/aws-vu9p-f1/dynamic-shell/aws/edu/cornell/ece/zhang/rosetta/1.1/4face-detect
inaccel install https://store.inaccel.com/artifactory/bitstreams/xilinx/aws-vu9p-f1/shell-v04261818_201920.2/aws/edu/cornell/ece/zhang/rosetta/1.1/4face-detect

Run the Face Detection Application

Apart from the FPGA accelerated version we also provide a CPU (reference) version of Viola Jones algorithm. The version is specified using the TARGET variable. TARGET can either be 'hw' or 'sw'.

For example, to generate the binary for the FPGA accelerated version you can issue the following command:

TARGET=hw make

The binary takes any input videos or cameras as arguments. E.g:

./face_detect_hw /path/to/video1 /path/to/video2 ...

Resources

The Face Detection (object detection) FPGA kernel used in this repository is provided from Cornell Zhang, Rosetta GitHub repository (https://github.com/cornell-zhang/rosetta) and is tweaked to get the most out of it.

The CPU version for Viola Jones algorithm is provided from https://sites.google.com/site/5kk73gpu2012/assignment/viola-jones-face-detection.

Support

This project has received funding from the European Union Horizon 2020 Research and Innovation program under grant agreement No. 871643 MORPHEMIC.

For more information please check: MORPHEMIC