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A Trimap-Free Portrait Matting Solution in Real Time [AAAI 2022]

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Differences between original repository and fork:

  • Compatibility with PyTorch >=2.1. (🔥)
  • Original pretrained models and converted ONNX models from GitHub releases page. (🔥)
  • Installation with requirements.txt file.
  • ONNX Simplifier integration in the export_onnx.py file.
  • Minor modifications in the inference.py file.
  • The following errors has been fixed:
    • AttributeError: module 'onnx' has no attribute 'load_from_string'.

Installation

pip install -r requirements.txt

Pretrained models

Name Link
MODNet (Photographic) PyTorch, ONNX
MODNet (Webcam) PyTorch, ONNX

Inference

python -m demo.image_matting.colab.inference --ckpt-path pretrained/modnet_photographic_portrait_matting.ckpt --image-path data/images/test.jpg --output-path result.jpg

Export to ONNX format

pip install onnx onnxsim
python -m onnx_model.export_onnx --ckpt-path pretrained/modnet_photographic_portrait_matting.ckpt --output-path pretrained/modnet_photographic_portrait_matting.onnx
python -m onnx_model.export_onnx --ckpt-path pretrained/modnet_webcam_portrait_matting.ckpt --output-path pretrained/modnet_webcam_portrait_matting.onnx

ONNX inference

python -m onnx_model.inference_onnx --model-path pretrained/modnet_photographic_portrait_matting.onnx --image-path data/images/test.jpg --output-path result.jpg

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A Trimap-Free Portrait Matting Solution in Real Time [AAAI 2022]

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