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Automatic Background Removal technology #17

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InternetMaster1 opened this issue Dec 15, 2019 · 1 comment
Open

Automatic Background Removal technology #17

InternetMaster1 opened this issue Dec 15, 2019 · 1 comment

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@InternetMaster1
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Hey,

Loved your library!!

I am looking for a deep learning library/sdk which can be used to remove the background from any image automatically (with quality as good as www.remove.bg).

I tried some image segmentation SDKs with pre-trained models such as Tensorflow Lite & Fritz AI, but the accuracy of the cutout mask was very low, amongst other issues.

Criteria :-

  1. Background Removal rather than just Human/Portrait Segmentation

If the foreground consists of person holding a balloon, sittting on a chair, with a pet on his side, then I want all of this to get extracted. Not just the human cutout. The segmentation SDKs I tried are only extracting humans (the chair gets vanished), that too with a very low quality mask (hair gets cut, parts of ear gets cut, etc).

  1. Mask quality should be Super-Accurate

I want even the finer details like the hair, delicate clothes, etc to be extracted perfectly.

  1. Fast & Lightweight (for mobile phone)

I want to use this technology on mobile phones (in an Android app) which should ideally work even in an offline environment. If this option is difficult to achieve, then plan B would be install the technoloy on our server.

  1. Technology
    What technology should I be exploring to achieve this? Is it called image segmentation or the better term would be image matting? (e.g. http://alphamatting.com/eval_25.php)

I have been reading a lot and I am currently lost in the sea of various technologies out there (OpenCV, Deep Matting, Mask RCNN, Instance Segmentation, Detectron2, Tensorflow, Pytorch, etc). I wonder what magic is happening behind the curtains of www.remove.bg

Would your library help me me to achieve what I am looking for? Any help you could provide would be awesome.

Thanks a ton!

@develoboi
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You got the solution ? @InternetMaster1

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