Browse free open source Python Background Removers and projects below. Use the toggles on the left to filter open source Python Background Removers by OS, license, language, programming language, and project status.

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  • 1
    Image-Editor

    Image-Editor

    AI based photo editing website for changing image background

    Welcome to Image-Editor, the AI-based photo editing website that lets you change backgrounds, colors, crop, sharpen images, and much more with just a single click. With exceptional image quality and fast processing times, Image-Editor is the ultimate tool for all your photo editing needs. To get started, simply run pip install -r requirements.txt to download all the necessary libraries. Then to, create a new Django project using django-admin startproject Website1, replacing 'Website1' with the name of your choice. Image-Editor uses Python's cv2 library, which provides an easy and efficient way to work with images and videos, including a wide range of image processing and computer vision algorithms. With cv2, you can easily read, write, filter, and display images, and much more. Image-Editor uses Mediapipe's selfie_segmentation model for background removal in real-time video streams. This advanced model uses deep neural networks to detect and remove the background.
    Downloads: 4 This Week
    Last Update:
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  • 2
    BG Remover - offline

    BG Remover - offline

    AI powered Offline Background Remover.

    Our Offline AI-powered Background Remover Desktop App effortlessly removes backgrounds from any image or photo. It utilizes the latest machine learning algorithms to provide accurate results within seconds. Download now and experience the ease and efficiency of our AI-powered solution.
    Downloads: 57 This Week
    Last Update:
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  • 3
    BackgroundRemover

    BackgroundRemover

    Background Remover lets you Remove Background from images and video

    BackgroundRemover is a command line tool to remove background from image and video, made by nadermx to power BackgroundRemoverAI. If you wonder why it was made read this short blog post.
    Downloads: 2 This Week
    Last Update:
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  • 4
    TRACER

    TRACER

    Extreme Attention Guided Salient Object Tracing Network

    Extreme Attention Guided Salient Object Tracing Network (AAAI 2022) implementation in PyTorch. Now, fast inference mode offers a salient object result with the mask. You can get the more clear salient object by tuning the threshold. We will release initializing TRACER with a version of pre-trained TE-x.
    Downloads: 2 This Week
    Last Update:
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  • 5
    Transparent Background

    Transparent Background

    This is a background removing tool powered by InSPyReNet

    This is a background-removing tool powered by InSPyReNet (ACCV 2022). You can easily remove the background from the image or video or bunch of other stuffs when you can make the background transparent! We basically follow the virtual camera settings from pyvirtualcam. If you do not choose to install virtual camera, it will visualize real-time output with cv2.imshow. Use another checkpoint file. Default is trained with composite dataset and will be automatically downloaded if not available.
    Downloads: 1 This Week
    Last Update:
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  • 6
    pybaselines

    pybaselines

    Library of algorithms for baseline correction of experimental data

    pybaselines is a Python library that provides many different algorithms for performing baseline correction on data from experimental techniques such as Raman, FTIR, NMR, XRD, XRF, PIXE, etc. The aim of the project is to provide a semi-unified API to allow quick testing and comparing multiple baseline correction algorithms to find the best one for a set of data. pybaselines has 50+ baseline correction algorithms. These include popular algorithms, such as AsLS, airPLS, ModPoly, and SNIP, as well as many lesser-known algorithms. Most algorithms are adapted directly from literature, although there are a few that are unique to pybaselines, such as penalized spline versions of Whittaker-smoothing-based algorithms. The full list of implemented algorithms can be found in the documentation.
    Downloads: 1 This Week
    Last Update:
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