Open Source Linux Background Removers

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. Introducing our Offline AI-powered Background Remover Desktop App, featuring one-click background removal for effortless image editing. Simply drag and drop your image to instantly remove the background with our automatic tool. Our app also includes basic editing features, allowing you to change the background image and color. Experience the power of AI and streamline your photo editing process.

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.

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.

Our online background remover instantly detects the subject from any photo and gives you a smooth & clear cutout. Now you can save an incredible amount of time as our AI is capable of handling hair, fur or any complex edges in just a few seconds. Install our desktop application to drag and drop 1000s of images at once. Click "Start" and watch as each image gets cut out automatically. Boost your efficiency and replace background from multiple images to get thousands of AI-powered cut outs for all your design needs. Looking to process millions of images? We've made it super easy to integrate our API into your project with just a few lines of code so you can remove bg at scale! We've also built plugins directly in the best design tools, programs, apps, and eCommerce platforms to improve your efficiency and workflow.

This program can automatically remove the background from images, replace it with any color you want (including transparent), scale the image to a desired size, and save it. Best of all, it can do it in bulk, so if you have hundred images to crop, this processor can do them all at once.

Digital Image Processing Toy process in real time live images captured from video4linux compatible hardware. Remove background from captured tv camera images, invert image colors, split rgb field, display coor istograms, etc.... Strong changes in light i

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.

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.