Open Source Python Image Processing Software
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Python Image Processing Software
View 5039 business solutionsBrowse free open source Python Image Processing Software and projects below. Use the toggles on the left to filter open source Python Image Processing Software by OS, license, language, programming language, and project status.
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Gwyddion
Scanning probe microscopy data visualisation and analysis
A data visualization and processing tool for scanning probe microscopy (SPM, i.e. AFM, STM, MFM, SNOM/NSOM, ...) and profilometry data, useful also for general image and 2D data analysis. -
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Pillow
The friendly Python Imaging Library fork
If you've ever wondered or worried about the future of Python's Imaging Library, it's time to stop. Pillow is here to answer your concerns, and offer you more. Pillow is the friendly fork of the Python Imaging Library or PIL, a library that adds image processing capabilities to your Python interpreter. Why turn to Pillow? Aside from offering extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities, Pillow is setuptools compatible. While PIL is not officially over yet, with Pillow you can be assured of continuous integration testing, publicized development activity, and regular releases to the Python Package Index. -
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rembg
Rembg is a tool to remove images background
Rembg is a powerful tool that utilizes AI (specifically U^2-Net) to automatically remove backgrounds from images, offering a streamlined command-line interface and Docker support. It's ideal for batch processing and integrates smoothly into workflows -
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SciPy
SciPy library main repository
This is the main repository for the SciPy library, one of the core packages that make up the SciPy stack. SciPy is an open source software used in the fields of mathematics, science, and engineering, with modules for statistics, optimization, integration, linear algebra, signal and image processing, and many more. The SciPy library contains many of the user-friendly and efficient numerical routines, including those for numerical integration, interpolation, and optimization. SciPy is built to work with NumPy, a software that provides convenient and fast N-dimensional array manipulation. Both SciPy and NumPy run on all popular operating systems, are fast and easy to install, and are powerful yet easy to use. They’re currently depended upon by numerous leading scientists and engineers all over the world. Try them for yourself! -
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MMDeploy
OpenMMLab Model Deployment Framework
MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project. Models can be exported and run in several backends, and more will be compatible. All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on. Install and build your target backend. ONNX Runtime is a cross-platform inference and training accelerator compatible with many popular ML/DNN frameworks. Please read getting_started for the basic usage of MMDeploy. -
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SMILI
Scientific Visualisation Made Easy
The Simple Medical Imaging Library Interface (SMILI), pronounced 'smilie', is an open-source, light-weight and easy-to-use medical imaging viewer and library for all major operating systems. The main sMILX application features for viewing n-D images, vector images, DICOMs, anonymizing, shape analysis and models/surfaces with easy drag and drop functions. It also features a number of standard processing algorithms for smoothing, thresholding, masking etc. images and models, both with graphical user interfaces and/or via the command-line. See our YouTube channel for tutorial videos via the homepage. The applications are all built out of a uniform user-interface framework that provides a very high level (Qt) interface to powerful image processing and scientific visualisation algorithms from the Insight Toolkit (ITK) and Visualisation Toolkit (VTK). The framework allows one to build stand-alone medical imaging applications quickly and easily. -
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NOTE: the project has moved to https://scipy.org/scipylib/ --- go there to find latest versions. This sourceforge project contains only old historical versions of the software. SciPy is package of tools for science and engineering for Python. It includes modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, ODE solvers, and more.
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BioImageXD - free open source software for analysis, processing and 3D rendering of multi dimensional, multi data channel, time series image data from microscopy and other sources.
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itom
itom - an Open Source Measurement, Automation and Evaluation Software
itom is an open source software suite for operating measurement systems, laboratory automation and data evaluation. One main application of itom is the development and operation of sensor and measurement system for instance in a laboratory environment. Therefore, the software has to be able to communicate with a wide range of different hardware systems, such as cameras or actuators and should provide a diversified and as complete as possible set of evaluation and data processing methods. Additionally, the rapid prototyping of modern measurement and inspection setups requires a system, where parameters or components can easily be changed at runtime, necessitating the availability of an embedded scripting language. Finally, when operating a measurement system, it is also desirable to extend the graphical user interface by system adapted dialogs and windows. The project has been moved mid 2023 to github: https://itom-project.github.io https://github.com/itom-project -
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Exposure
Learning infinite-resolution image processing with GAN and RL
Learning infinite-resolution image processing with GAN and RL from unpaired image datasets, using a differentiable photo editing model. ACM Transactions on Graphics (presented at SIGGRAPH 2018) Exposure is originally designed for RAW photos, which assumes 12+ bit color depth and linear "RGB" color space (or whatever we get after demosaicing). jpg and png images typically have only 8-bit color depth (except 16-bit pngs) and the lack of information (dynamic range/activation resolution) may lead to suboptimal results such as posterization. Moreover, jpg and most pngs assume an sRGB color space, which contains a roughly 1/2.2 Gamma correction, making the data distribution different from training images (which are linear). Exposure is just a prototype (proof-of-concept) of our latest research, and there are definitely a lot of engineering efforts required to make it suitable for a real product. -
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GIMP ML
AI for GNU Image Manipulation Program
This repository introduces GIMP3-ML, a set of Python plugins for the widely popular GNU Image Manipulation Program (GIMP). It enables the use of recent advances in computer vision to the conventional image editing pipeline. Applications from deep learning such as monocular depth estimation, semantic segmentation, mask generative adversarial networks, image super-resolution, de-noising and coloring have been incorporated with GIMP through Python-based plugins. Additionally, operations on images such as edge detection and color clustering have also been added. GIMP-ML relies on standard Python packages such as numpy, scikit-image, pillow, pytorch, open-cv, scipy. In addition, GIMP-ML also aims to bring the benefits of using deep learning networks used for computer vision tasks to routine image processing workflows. -
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Surface Defect Detection Dataset Papers
Constantly summarizing open source dataset and critical papers
At present, surface defect equipment based on machine vision has widely replaced artificial visual inspection in various industrial fields, including 3C, automobiles, home appliances, machinery manufacturing, semiconductors and electronics, chemical, pharmaceutical, aerospace, light industry and other industries. Traditional surface defect detection methods based on machine vision often use conventional image processing algorithms or artificially designed features plus classifiers. Generally speaking, imaging schemes are usually designed by using the different properties of the inspected surface or defects. A reasonable imaging scheme helps to obtain images with uniform illumination and clearly reflect the surface defects of the object. In recent years, many defect detection methods based on deep learning have also been widely used in various industrial scenarios. -
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scikit-image
Image processing in Python
scikit-image is a collection of algorithms for image processing. It is available free of charge and free of restriction. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers. scikit-image builds on scipy.ndimage to provide a versatile set of image processing routines in Python. This library is developed by its community, and contributions are most welcome! Read about our mission, vision, and values and how we govern the project. Major proposals to the project are documented in SKIPs. The scikit-image community consists of anyone using or working with the project in any way. A community member can become a contributor by interacting directly with the project in concrete ways. -
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Robean
Rodent Neurobehavioral Analysis Software
Robean is a standalone freeware application for automated rodent neurobehavioural analysis. The software provides an intuitive graphical interface for analysing Morris Water Maze, Elevated Plus Maze, and Open Field Test experiments from prerecorded videos or live camera feeds. Robean integrates arena definition, zone management, centroid tracking, trajectory visualization, occupancy heatmap generation, automated PDF reporting, and batch study summaries within a single desktop application requiring no programming expertise. -
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TextureAtlas Toolbox
A powerful, free and open-source tool for TextureAtlases/Spritesheets
TextureAtlas Toolbox is an all-in-one solution for working with texture atlases and sprite sheets. Extract sprites into organized frame collections and GIF/WebP/APNG animations, generate optimized atlases from individual frames, or convert between 15+ atlas formats. Perfect for game developers, modders, and anyone creating showcases of game sprites. Formerly known as TextureAtlas to GIFs and Frames Licensed under AGPL-3.0 Third-party licenses: See https://github.com/MeguminBOT/TextureAtlas-Toolbox/blob/main/docs/licenses.md The GitHub has most things documented, please have a look there if you want to find out more! Documentation Hub: https://github.com/MeguminBOT/TextureAtlas-Toolbox/tree/main/docs List of supported formats; https://github.com/MeguminBOT/TextureAtlas-Toolbox?tab=readme-ov-file#supported-formats List of supported formats (in-depth): https://github.com/MeguminBOT/TextureAtlas-Toolbox/blob/main/docs/format-reference.md -
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medical image analysis
Libraries and command line tools for medical image processing.
This software provides libraries and command line tools for the processing and analysis of gray scale medical images. -
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OpenCMISS-Zinc
A Library for Interactive Modelling and Visualisation
The OpenCMISS-Zinc Library (opencmiss.org/zinc), or Zinc, is a software library for building interactive graphical modelling and visualisation applications. Models are represented in Zinc as mathematical fields defined over domains, including finite elements with support for high-order basis functions, complex parameter mappings and time variation, and image-based fields. Further fields can be defined by mathematical expressions and algorithms on existing fields, including image processing filters. Zinc model data structures are dynamic, supporting interactive applications which programmatically create, destroy and modify parts of models. -
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ImageJ is a public domain Java image processing program inspired by NIH Image for the Macintosh. It runs, either as an online applet or as a downloadable application, on any computer with a Java 1.1 or later. HOME @ http://rsb.info.nih.gov/ij/
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Comic panel detection
Program, written in python, which detect panels in comics
Program, written in python, which detect panels in comics, based on Max Halford's tutorial "https://maxhalford.github.io/blog/comic-book-panel-segmentation/". It allows you to process Jpg/Png images in a folder , and then save the individual panels in a folder or in a single pdf file. Unfortunately it is far from perfect and makes many mistakes especially with "black and white" comics. -
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MIPPY
Modular Image Processing in Python
MIPPY is a minimalistic DICOM image browser with built-in image processing modules. Its modular design means it can be extended with any number of user-created modules for image processing and analysis. -
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Medical Img Segmentation Suite
A software providing easy ways to segment structures in medical images
- 15 segmentation tools (FloodFill Strict, Adaptive Gaussian Threshold, Manual Brush (Freehand Lasso), Polygon Lasso (Click Points), Manual Bounding Box, Auto Bounding Box, Local Otsu Auto-Threshold, K-Means Clustering, Watershed, GrabCut AI, Canny Edge + Morphological Close, Morphologic Active Contours, Interactive Heatmap, Topographical Relief Map, Segment Anything AI) - Hybrid Layout (1 window with editor tools or Split-View with a preview window) - Intelligent file management (once a folder is loaded, the system recognizes which files already have masks and leaves them in the background) - Real-time filter application (CLAHE, Global Histogram Equalization, Top-Hat, Bottom-Hat, Multi-Scale Retinex, Homomorphic Filter, Sharpness Control) - Three mask and label options (conventional masks in - Lesion shape, lesion region bounding boxes, and heatmap to outline the lesion and its secondary components) - Brush/cursor sensitivity control. - Global brightness and contrast adjustments -
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PI-Based Image Encoder / Converter
Python code able to convert / compress image to PI (3.14, π) Indexes
Image processing tool that encodes pixel data as indices within the first 16.7 million digits of PI (π). Features high-performance Numba-accelerated search and a signature 'film-grain' aesthetic upon reconstruction. ZIP also include 16 MB file with 16,7 mil numbers of PI Benchmark(Single-Thread): Hardware & Environment Apple Silicon: Apple M2 (Mac mini/MacBook) x86_64 Platform: Intel Core Ultra 5 225F (Arrow Lake, 10 Cores) OS 1: Fedora 43 (GNOME) OS 2: Windows 11 Pro (23H2/24H2) Software: Python 3.14.3 + Numba JIT (latest) Results (Lower is better) Platform / OS CPU Time (Seconds) macOS (Native) Apple M2 52.151311 s (in default setup) Fedora Linux Intel Core Ultra 5 225F 58.536457 s (in default Power Management: Balanced) Windows 11 Intel Core Ultra 5 225F 59.681427 s (important! Power Management set as High Performance .. in Balanced is slow) -
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ARK AR Sticker Generator
ARK is an AR Sticker Generator
In Augmented Reality Development we often need high quality markers for image targets compatible with accurate detection. ARK is an AR sticker generator made using various image processing libraries & scientific tools. It creates a colorful sticker similar to a QR code with each sticker being completely unique from other. It also allows to add an image overlay for branding or other stuff. Generated stickers can be found in Output folder. Extension is .png *GitHub Listing is in process* -
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AiHound
AI powered image classification for nudity and documents / id-cards
AI Hound is designed to run from an USB pendrive or any other kind of removeable and writeable media. The programm checks all Office-documents, Images and videos for various categories for images. Actually It can recognice nudity/porn and scanned or photographed documents / ID- and credit-cards. I am working on a model that also recognice various types of drugs in images. -
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**Zastosowanie**: Głównym celem projektu jest optymalizacja wykorzystania pamięci w graficznych bazach danych poprzez analizę podobieństwa cosinusowego wektorów cech obrazów. Umożliwia identyfikację duplikatów (obrazów bardzo do siebie podobnych), co prowadzi do efektywniejszego zarządzania zasobami. **Program** W chwili obecnej program przeszukuje obrazy w katalogu, do którego ścieżka została zapisana w pliku 'Data\settings.txt'. **Użycie**: Program obsługuje się w terminalu. Po piewszym wykonaniu programu, zostaje utworzony katalog Data, w miejscu gdzie zapisany jest program. Aby wybrać ścieżkę do katalogu z obrazami należy w pliku 'settings.txt' zapisać ścieżkę. Następnie można wykonywać program z: -an, -mnb, -c, -i Link do GitHub: https://github.com/Duke-Axer/Duplicate-Finder Wszystkie pytania proszę pisać na b.gabka.nkn@gmail.com
