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Face Recognition for NVIDIA Jetson AGX Orin using TensorRT

  • This project is based on the implementation of this repo: Face Recognition for NVIDIA Jetson (Nano) using TensorRT. Since the original author is no longer updating his content, and many of the original content cannot be applied to the new Jetpack version and the new Jetson device. Therefore, I have modified the original author's content slightly to make it work for face recognition on the Jetson AGX Orin.
  • Face recognition with Google FaceNet architecture and retrained model by David Sandberg (github.com/davidsandberg/facenet) using TensorRT and OpenCV.
  • Moreover, this project uses an adapted version of PKUZHOU's implementation of the mtCNN for face detection. More info below.

Hardware

  • Nvidia Jetson AGX Orin DVK
  • Logitech C922 Pro HD Stream Webcam

If you want to use a CSI camera instead of USB Camera, set the boolean isCSICam to true in main.cpp.

Dependencies

  • JetPack 5.1
  • CUDA 11.4.19 + cuDNN 8.6.0
  • TensorRT 8.5.2
  • OpenCV 4.5.4
  • Tensorflow 2.11

Installation

1. Install Tensorflow

The following shows the steps to install Tensorflow for Jetpack 5.1. This was copied from the official NVIDIA documentation. I'm assuming you don't need to install it in a virtual environment. If yes, please refer to the documentation linked above. If you are not installing this on a jetson, please refer to the official tensorflow documentation.

# Install system packages required by TensorFlow:
sudo apt update
sudo apt install libhdf5-serial-dev hdf5-tools libhdf5-dev zlib1g-dev zip libjpeg8-dev liblapack-dev libblas-dev gfortran

# Install and upgrade pip3
sudo apt install python3-pip
sudo python3 -m pip install --upgrade pip
sudo pip3 install -U testresources setuptools==65.5.0

# Install the Python package dependencies
sudo pip3 install -U numpy==1.22 future==0.18.2 mock==3.0.5 keras_preprocessing==1.1.2 keras_applications==1.0.8 gast==0.4.0 protobuf pybind11 cython pkgconfig packaging h5py==3.6.0

# Install TensorFlow using the pip3 command. This command will install the latest version of TensorFlow compatible with JetPack 5.1.
sudo pip3 install --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v51 tensorflow==2.11.0+nv23.01

3. Prune and freeze TensorFlow model or get frozen model in the link

The inputs to the original model are an input tensor consisting of a single or multiple faces and a phase train tensor telling all batch normalisation layers that model is not in train mode. Batch normalisation uses a switch layer to decide if the model is currently trained or just used for inference. This switch layer cannot be processed in TensorRT which is why it needs to be removed. Apparently this can be done using freeze_graph from TensorFlow, but here is a link to model where the phase train tensor has already been removed from the saved model github.com/apollo-time/facenet/raw/master/model/resnet/facenet.pb

4. Convert frozen protobuf (.pb) model to UFF

Use the convert-to-uff tool which is installed with tensorflow installation to convert the *.pb model to *.uff. The script will replace unsupported layers with custom layers implemented by github.com/r7vme/tensorrt_l2norm_helper. Please check the file for the user defined values and update them if needed. Do not worry if there are a few warnings about the TRT_L2NORM_HELPER plugin.

cd path/to/project
python3 step01_pb_to_uff.py

You should now have a facenet.uff file in the facenetModels folder which will be used as the input model to TensorRT.

4. Get mtCNN models

This repo uses an implementation by PKUZHOU of the multi-task Cascaded Convolutional Neural Network (mtCNN) for face detection. The original implementation was adapted to return the bounding boxes such that it can be used as input to my FaceNet TensorRT implementation. You will need all models from the repo in the mtCNNModels folder so please do this to download them:

# go to one above project,
cd path/to/project/..
# clone PKUZHOUs repo,
git clone https://github.com/PKUZHOU/MTCNN_FaceDetection_TensorRT
# and move models into mtCNNModels folder
mv MTCNN_FaceDetection_TensorRT/det* path/to/project/mtCNNModels

After doing so you should have the following files in your mtCNNModels folder:

  • det1_relu.caffemodel
  • det1_relu.prototxt
  • det2_relu.caffemodel
  • det2_relu.prototxt
  • det3_relu.caffemodel
  • det3_relu.prototxt
  • README.md

Done you are ready to build the project!

5. Build the project

mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j${nproc}

If not run on Jetson platform set the path to your CUDA and TensorRT installation using -DCUDA_TOOLKIT_ROOTDIR=path/to/cuda and -DTENSORRT_ROOT=path/to/tensorRT.

NOTE

.uff and .engine files are GPU specific, so if you use want to run this project on a different GPU or on another machine, always start over at step 3. above.

Usage

Put images of people in the imgs folder. Please only use images that contain one face.
NEW FEATURE:You can now add faces while the algorithm is running. When you see the OpenCV GUI, press "N" on your keyboard to add a new face. The camera input will stop until you have opened your terminal and put in the name of the person you want to add.

./face_recogition_tensorRT

Press "Q" to quit and to show the stats (fps).

NOTE: This step might take a while when done the first time. TensorRT now parses and serializes the model from .uff to a runtime engine (.engine file).

Performance

Performance on NVIDIA Jetson AGX Orin

  • ~24ms for face detection using mtCNN
  • ~4ms per face for facenet inference
  • Total: ~30fps

License

Please respect all licenses of OpenCV and the data the machine learning models (mtCNN and Google FaceNet) were trained on.