Open Source BSD Computer Vision Libraries
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Computer Vision Libraries for BSD
Browse free open source Computer Vision Libraries and projects for BSD below. Use the toggles on the left to filter open source Computer Vision Libraries by OS, license, language, programming language, and project status.
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OpenCV
Open Source Computer Vision Library
The Open Source Computer Vision Library has >2500 algorithms, extensive documentation and sample code for real-time computer vision. It works on Windows, Linux, Mac OS X, Android, iOS in your browser through JavaScript. Languages: C++, Python, Julia, Javascript Homepage: https://opencv.org Q&A forum: https://forum.opencv.org/ Documentation: https://docs.opencv.org Source code: https://github.com/opencv Please pay special attention to our tutorials! https://docs.opencv.org/master Books about the OpenCV are described here: https://opencv.org/books.html -
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Armadillo
fast C++ library for linear algebra & scientific computing
* Fast C++ library for linear algebra (matrix maths) and scientific computing * Easy to use functions and syntax, deliberately similar to Matlab / Octave * Uses template meta-programming techniques to increase efficiency * Provides user-friendly wrappers for OpenBLAS, Intel MKL, LAPACK, ATLAS, ARPACK, SuperLU and FFTW libraries * Useful for machine learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc. * Downloads: http://arma.sourceforge.net/download.html * Documentation: http://arma.sourceforge.net/docs.html * Bug reports: http://arma.sourceforge.net/faq.html * Git repo: https://gitlab.com/conradsnicta/armadillo-code -
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COLMAP
Structure-from-Motion and Multi-View Stereo
COLMAP is a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical and command-line interface. It offers a wide range of features for the reconstruction of ordered and unordered image collections. The software is licensed under the new BSD license. -
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SAM 2
The repository provides code for running inference with SAM 2
SAM2 is a next-generation version of the Segment Anything Model (SAM), designed to improve performance, generalization, and efficiency in promptable image segmentation tasks. It retains the core promptable interface—accepting points, boxes, or masks—but incorporates architectural and training enhancements to produce higher-fidelity masks, better boundary adherence, and robustness to complex scenes. The updated model is optimized for faster inference and lower memory use, enabling real-time interactivity even on larger images or constrained hardware. SAM2 comes with pretrained weights and easy-to-use APIs, enabling developers and researchers to integrate promptable segmentation into annotation tools, vision pipelines, or downstream tasks. The project also includes scripts and notebooks to compare SAM2 against SAM on edge cases, benchmarks showing improvements, and evaluation suites to measure mask quality metrics like IoU and boundary error. -
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Vision Transformer Pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA
This repository provides a from-scratch, minimalist implementation of the Vision Transformer (ViT) in PyTorch, focusing on the core architectural pieces needed for image classification. It breaks down the model into patch embedding, positional encoding, multi-head self-attention, feed-forward blocks, and a classification head so you can understand each component in isolation. The code is intentionally compact and modular, which makes it easy to tinker with hyperparameters, depth, width, and attention dimensions. Because it stays close to vanilla PyTorch, you can integrate custom datasets and training loops without framework lock-in. It’s widely used as an educational reference for people learning transformers in vision and as a lightweight baseline for research prototypes. The project encourages experimentation—swap optimizers, change augmentations, or plug the transformer backbone into downstream tasks. -
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Phi-3-MLX
Phi-3.5 for Mac: Locally-run Vision and Language Models
Phi-3-Vision-MLX is an Apple MLX (machine learning on Apple silicon) implementation of Phi-3 Vision, a lightweight multi-modal model designed for vision and language tasks. It focuses on running vision-language AI efficiently on Apple hardware like M1 and M2 chips. -
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PIFuHD
High-Resolution 3D Human Digitization from A Single Image
PIFuHD (Pixel-Aligned Implicit Function for 3D human reconstruction at high resolution) is a method and codebase to reconstruct high-fidelity 3D human meshes from a single image. It extends prior PIFu work by increasing resolution and detail, enabling fine geometry in cloth folds, hair, and subtle surface features. The method operates by learning an implicit occupancy / surface function conditioned on the image and camera projection; at inference time it queries dense points to reconstruct a mesh via marching cubes. It also uses a two-stage architecture: a coarse global model followed by local refinement patches to capture fine detail, balancing global consistency and local detail. The repo includes training pipelines, dataset loaders (for Multi-POP, etc.), and inference scripts for mesh output including depth maps for postprocessing. To help practical use, there are utilities for normal estimation, texture back-projection, mesh cleanup, and integration with rendering pipelines. -
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Hiera
A fast, powerful, and simple hierarchical vision transformer
Hiera is a hierarchical vision transformer designed to be fast, simple, and strong across image and video recognition tasks. The core idea is to use straightforward hierarchical attention with a minimal set of architectural “bells and whistles,” achieving competitive or superior accuracy while being markedly faster at inference and often faster to train. The repository provides installation options (from source or Torch Hub), a model zoo with pre-trained checkpoints, and code for evaluation and fine-tuning on standard benchmarks. Documentation emphasizes that model weights may have separate licensing and that the code targets practical experimentation for both research and downstream tasks. Community discussions cover topics like dataset pretrains, integration in other frameworks, and comparisons with related implementations. Security and contribution guidelines follow Meta’s open-source practices, and activity shows ongoing interest and usage across the community. -
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Screenshot to Code
A neural network that transforms a design mock-up into static websites
Screenshot-to-code is a tool or prototype that attempts to convert UI screenshots (e.g., of mobile or web UIs) into code representations, likely generating layouts, HTML, CSS, or markup from image inputs. It is part of a research/proof-of-concept domain in UI automation and image-to-UI code generation. Mapping visual design to code constructs. Code/UI layout (HTML, CSS, or markup). Examples/demo scripts showing “image UI code”. -
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Mobile Robot Programming Toolkit (MRPT)
**MOVED TO GITHUB** ==> https://github.com/MRPT/mrpt
**MOVED TO GITHUB** ==> https://github.com/MRPT/mrpt The Mobile Robot Programming Toolkit (MRPT) is an extensive, cross-platform, and open source C++ library aimed for robotics researchers to design and implement algorithms about Localization, SLAM, Navigation, computer vision. http://www.mrpt.org/ -
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OpenNN - Open Neural Networks Library
Machine learning algorithms for advanced analytics
OpenNN is a software library written in C++ for advanced analytics. It implements neural networks, the most successful machine learning method. Some typical applications of OpenNN are business intelligence (customer segmentation, churn prevention…), health care (early diagnosis, microarray analysis…) and engineering (performance optimization, predictive maitenance…). OpenNN does not deal with computer vision or natural language processing. The main advantage of OpenNN is its high performance. This library outstands in terms of execution speed and memory allocation. It is constantly optimized and parallelized in order to maximize its efficiency. The documentation is composed by tutorials and examples to offer a complete overview about the library. OpenNN is developed by Artelnics, a company specialized in artificial intelligence. -
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ConvNeXt
Code release for ConvNeXt model
ConvNeXt is a modernized convolutional neural network (CNN) architecture designed to rival Vision Transformers (ViTs) in accuracy and scalability while retaining the simplicity and efficiency of CNNs. It revisits classic ResNet-style backbones through the lens of transformer design trends—large kernel sizes, inverted bottlenecks, layer normalization, and GELU activations—to bridge the performance gap between convolutions and attention-based models. ConvNeXt’s clean, hierarchical structure makes it efficient for both pretraining and fine-tuning across a wide range of visual recognition tasks. It achieves competitive or superior results on ImageNet and downstream datasets while being easier to deploy and train than transformers. The repository provides pretrained models, training recipes, and ablation studies demonstrating how incremental design choices collectively yield state-of-the-art performance. -
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Segment Anything
Provides code for running inference with the SegmentAnything Model
Segment Anything (SAM) is a foundation model for image segmentation that’s designed to work “out of the box” on a wide variety of images without task-specific fine-tuning. It’s a promptable segmenter: you guide it with points, boxes, or rough masks, and it predicts high-quality object masks consistent with the prompt. The architecture separates a powerful image encoder from a lightweight mask decoder, so the heavy vision work can be computed once and the interactive part stays fast. A bundled automatic mask generator can sweep an image and propose many object masks, which is useful for dataset bootstrapping or bulk annotation. The repository includes ready-to-use weights, Python APIs, and example notebooks demonstrating both interactive and automatic modes. Because SAM was trained with an extremely large and diverse mask dataset, it tends to generalize well to new domains, making it a practical starting point for research and production annotation tools. -
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VGGT
[CVPR 2025 Best Paper Award] VGGT
VGGT is a transformer-based framework aimed at unifying classic visual geometry tasks—such as depth estimation, camera pose recovery, point tracking, and correspondence—under a single model. Rather than training separate networks per task, it shares an encoder and leverages geometric heads/decoders to infer structure and motion from images or short clips. The design emphasizes consistent geometric reasoning: outputs from one head (e.g., correspondences or tracks) reinforce others (e.g., pose or depth), making the system more robust to challenging viewpoints and textures. The repo provides inference pipelines to estimate geometry from monocular inputs, stereo pairs, or brief sequences, together with evaluation harnesses for common geometry benchmarks. Training utilities highlight data curation and augmentations that preserve geometric cues while improving generalization across scenes and cameras. -
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The Integrating Vision Toolkit (IVT) is a powerful and fast C++ computer vision library with an easy-to-use object-oriented architecture. It offers its own multi-platform GUI toolkit. OpenCV is integrated optionally. Website: http://ivt.sourceforge.net
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LTI-Lib is an object oriented computer vision library written in C++ for Windows/MS-VC++ and Linux/gcc. It provides lots of functionality to solve mathematical problems, many image processing algorithms, some classification tools and much more...
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Scilab Image Processing Toolbox
Advanced image processing toolbox for Scilab on Unix/Linux/Mac OS
SIP is the image processing and computer vision package for SciLab, a free Matlab-like programming environment. SIP reads/writes images in formats like JPEG, PNG, and BMP. It does filtering, segmentation, edge detection, morphology, and shape analysis. Download from Git http://siptoolbox.sourceforge.net/devel -
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OpenPR stands for Open Pattern Recognition project and is intended to be an open source library for algorithms of image processing, computer vision, natural language processing, pattern recognition, machine learning and the related fields.
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The Video Processing Evaluation Resource: A toolkit for evaluating computer vision algorithms on video, and a corresponding tool for annotating video streams with spatial metadata.
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Panzer Combat II
Computer-assisted miniature tank game.
Panzer Combat II is a multi-player voice and webcam enabled computer-assisted distributed miniature wargame of World War II tank combat. Firing is done by placing a webcam behind the aiming unit. Distance to target is computed using computer vision. Action inside the tanks is performed on the computer screen while battlefield strategy is played on the miniature terrain. Both camps can use a different laptop or tablet, the game will interconnect. You can try it online : http://server.panzercombat.com/PCII_Web/move.htm Look at battle reports : http://www.flickr.com/photos/panzercombatii Or watch a demo : http://www.youtube.com/watch?v=WcjfV8Odtss 100% CLEAN : http://games.softpedia.com/progClean/Panzer-Combat-II-Clean-95530.html -
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Blazeface
Blazeface is a lightweight model that detects faces in images
Blazeface is a lightweight, high-performance face detection model designed for mobile and embedded devices, developed by TensorFlow. It is optimized for real-time face detection tasks and runs efficiently on mobile CPUs, ensuring minimal latency and power consumption. Blazeface is based on a fast architecture and uses deep learning techniques to detect faces with high accuracy, even in challenging conditions. It supports multiple face detection in varying lighting and poses, and is designed to work in real-world applications like mobile apps, robotics, and other resource-constrained environments. -
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CVSharp (aka Computer Vision in C#) is a Computer Vision project. Until the present day just one part of the whole project was actually developed. It's called CVSharp Lab, an Image Processing Tool.
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The goal of cvtk is to provide an easy to use computer vision framework that allows real-time tracking of color-marked objects in 2 dimensions.
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i3D-converter creates a 3D representation from a couple of images (or a pair of stereo images). This program also performs other Computer Vision operations such as, edge and corner detection, image filtering, getting geometric shapes,...
