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The objective of this project is to run the DBFace, a real-time, single-stage face detector on Intel(r) Distribution of OpenVINO(tm) Toolkit.
Original DBFace GitHub site:https://github.com/dlunion/DBFace

Thie original developer used PyTorch to train the model. You need to convert the PyTorch model to OpenVINO IR model. You need to run 2 conversion steps to get the final IR model.

  PyTorch (.pth) -> ONNX (.onnx) -> IR (.xml, .bin)

このプロジェクトの目的はリアルタイムでシングルステージの顔検出モデル、DBFaceをIntel(r) Distribution of OpenVINO(tm) Toolkit上で実行できるようにすることです。
Original DBFace GitHub site:https://github.com/dlunion/DBFace

元の開発者はPyTorchを使ってモデルの学習を行っています。そのためPyTorchモデルからOpenVINO IRモデルへの変換を行わなくてはなりません。 IRモデルを得るためには２つの変換ステップを要します。

  PyTorch (.pth) -> ONNX (.onnx) -> IR (.xml, .bin)

Detection result with a 4VGA (1280x960) input picture

• Intel Distribution of OpenVINO toolkit 2020.2 (or newer)
• Model Downloader / Model Converter setup
  Model Downloader guide

pip3 -r${INTEL_OPENVINO_DIR}/deployment_tools/tools/model_downloader/requirements.inpip3 -r${INTEL_OPENVINO_DIR}/deployment_tools/tools/model_downloader/requirements-pytorch.in

If you fail to install PyTorch, go to PyTorch official web site and follow theQUICK START LOCALLY guide to install it. You need to have PyTorch >=1.4.

DownloadDBFace.py anddbface.pth fromoriginal developer's GitHub page.

Usepytorch_to_onnx.py in themodel_downloader directory.

python3${INTEL_OPENVINO_DIR}/deployment_tools/tools/model_downloader/pytorch_to_onnx.py \ --model-name DBFace \ --weights dbface.pth \ --import-module DBFace \ --input-shape 1,3,320,320 \ --output-file dbface.onnx \ --input-names x \ --output-names sigmoid_hm,tlrb,landmark

Hint: You can get the input and output node names from the model source code (in this case,DBFace.py in themodel directory)

defforward(self,x):out=self.hs1(self.bn1(self.conv1(x)))          :returnsigmoid_hm,tlrb,landmark

UseModel Optimizer (MO) to convert the ONNX model into IR model.

python3${INTEL_OPENVINO_DIR}/deployment_tools/model_optimizer/mo.py \ --input_model dbface.onnx \ --mean_values [180,154,150] \ --scale_values [73.7,70.0,70.9] \ --input_shape [1,3,960,1280] \ --output_dir dbface-4vga \ --data_type FP16

Hint: You can change the input shape with--input_shape option.
Hint: You can find the appropriate parameters (--mean_values and--scale_values) for input data preprocessing from the original source code (in this case, line 36-40 inmain.py from the origianl GitHub site)

mean= [0.408,0.447,0.47]std= [0.289,0.274,0.278]image=common.pad(image)image= ((image/255.0-mean)/std).astype(np.float32)

python3 dbface-infer.py -m model.xml -i input_image

A webCam #0 will be used when you specify 'cam' as the input file name.output.jpg will be ganarated in the current directry. (non-webCam version only)

Command line example:

 $ python3 dbface-infer.py -m dbface-4vga/dbface.xml -i image.jpg $ python3 dbface-infer.py -m dbface-vga/dbface.xml -i cam

• Ubuntu 18.04 / Windows 10 1909
• OpenVINO 2020.3 LTS

• GitHub, dlunion/DBFace
• Intel(r) Distribution of OpenVINO(tm) Toolkit
• Converting a ONNX* Model
• Model Downloader and other automation tools
• PyTorch official web page