peppyguy / deepstream-facenet

Demo app on using Deepstream 5.0 with Facenet

Deepstream Facenet

Face Recognition on Jetson Nano using DeepStream and Python.

DeepStream Installation

install-deepstream.sh will install DeepStream and its dependencies

1. Download DeepStream using this link
2. get Jetpack version

$ dpkg-query --show nvidia-l4t-core
nvidia-l4t-core 32.3.1-20191209225816

3. export needed variables

export JETPACK_VERSION=32.3
export PLATFORM=<platform>
export DEEPSTREAM_SDK_TAR_PATH=<path>

Where identifies the platform’s processor:

• t186 for Jetson TX2 series
• t194 for Jetson AGX Xavier series or Jetson Xavier NX
• t210 for Jetson Nano or Jetson TX1

4. running installation script

chmod +x install-deepstream.sh
sudo -E ./install-deepstream.sh

5. Making sure installation is fine by running a sample app

cd /opt/nvidia/deepsteream/deepstream-5.0/sources/deepstream_python_apps/apps/deepstream-test1
python3 deepstream-test1.py /opt/nvidia/deepstream/deepstream-5.0/samples/streams/sample_720p.h264

take some time to compile the model and running the application for first time.

App

This demo is built on top of Python sample app deepstream-test2

• Download back-to-back-detectors (the mode can detect faces). It is primary inference.
• The secondary inference facenet engine.
• No changes regarding the tracker.
• Note: embedding dataset (npz file) should be generate by your dataset.
• Note: you should count avg mean and avg std for your dataset:
  • Put avg mean in offsets parameter and in the net-scale-factor parameter put (1/avg std) in classifier_config.txt to make facenet model work efficient.

Steps to run the demo:

• Generate the engine file for Facenet model

  • facenet_keras.h5 can be found in the models folder. The model is taken from nyoki-mtl/keras-facenet

  • Convert facenet model to TensorRT engine using this jupyter notebook. The steps in the jupyter notebook is taken from Nvidia official tutorial.

  • when converting pb file to onnx use below command instead: python -m tf2onnx.convert --input facenet.pb --inputs input_1:0[1,160,160,3] --inputs-as-nchw input_1:0 --outputs Bottleneck_BatchNorm/batchnorm_1/add_1:0 --output facenet.onnx Note: make sure to use this command --inputs-as-nchw input_1:0 while converting to ONNX to avoid having this error: Error in NvDsInferContextImpl::preparePreprocess() <nvdsinfer_context_impl.cpp:874> [UID = 2]: RGB/BGR input format specified but network input channels is not 3

• Change the model-engine-file path to the your facenet engine file in classifier_config.txt.

• python3 deepstream_test_2.py <h264_elementary_stream_contains_faces

Resources

You can find more resources about our face recognition work and inference results at https://www.riotu-lab.org/face/