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  • Language
    Python
  • Created over 6 years ago
  • Updated over 5 years ago

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Repository Details

Fast (aimed to "real time") Portrait Segmentation on mobile phone

Fast_Portrait_Segmentation

Fast (aimed to "real time") Portrait Segmentation at mobile phone

This project is not normal semantic segmentation but focus on real-time protrait segmentation.All the experimentals works with pytorch.

I hope to find a effcient network which can run on mobile phone. Currently, successfull application of person body/protrait segmentation can be find in APP like SNOW&B612, whose technology is proposed by a Korea company Nalbi.

Models

  • mobilenet_dilate_unet[1][2][7][9]

    Encoder : mobilenet_v2(os: 32)

    Decoder : unet(concat low level feature) use dilate convolution at different stage(d = 2, 6, 12, 18)

  • Shuffle_Seg_SkipNet[4][10][18]

    Encoder : shufflenet

    Decoder : skip connection (add low level feature)

  • esp_dense_seg[20][10][15][19]

  • residualdense_bisenet[15][23][24]

    Attention model is a potential module in the segmentation task. I use a very light residual-dense net as the backbone of the Context Path. The details about fussion of last features in Contxt Path is not clear in the paper(BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation).

  • Segmentation + Matting [7][12][15]

    Hard segmentation + Soft matting.(coming soon)

update 2019/04/10: The code and pre_trained model of final version of the portrait_segmentation is released ! ! ! mobile_phone_human_matting

Speed Analysis

⚑ Real-time ! ! ! πŸŽ‰πŸŽ‰πŸŽ‰

Platform : ncnn.

Mobile phone: Samsung Galaxy S8+(cpu).

model size (M) time(ms)
model_seg_matting 3.3 ~40

update : 2018/12/27: Demo video on my iphone 6 (baiduyun)

Result Examples

HUAWEI Mate 20 released recently can keep color on human and make the bacgrand gray in real time (click to view ). I test my model using cpu on my MAC, getting some videos here.

References

papers