Open Source Computer Vision Libraries - Page 6

GAAS (Generalized Autonomy Aviation System) is an open source software platform for autonomous drones and VTOLs. GAAS was built to provide a common infrastructure for computer-vision based drone intelligence. In the long term, GAAS aims to accelerate the coming of autonomous VTOLs. Being a BSD-licensed product, GAAS makes it easy for enterprises, researches, and drone enthusiasts to modify the code to suit specific use cases. Our long-term vision is to implement GAAS in autonomous passenger carrying VTOLs (or "flying cars"). The first step of this vision is to make Unmanned Aerial Vehicles truly "unmanned", and thus make drones ubiquitous. We currently support manned and unmanned multi-rotor drones and helicopters. Our next step is to support VTOLs and eVTOLs.

Computer Vision library using GPU environment acceleration. Based on openCV and openGL

computer vision based obstacle detection

GPUVision is a framework for creating GPU based general purpose programs, image processing programs, and computer vision programs in C++. Supported libraries include matrix operations, graph partitioning, kernels, corner detection, edge detection etc.

Gandalf is a computer vision and numerical algorithm library, written in C, which allows you to develop new applications that will be portable and run FAST. Dynamically reconfigurable vector, matrix and image structures allow efficient use of memory.

Genjutsu-vision is a networked computer vision framework for OpenGL. Based upon GLFW and IPP for optimization, networked computer vision development technologies enable vision analysis of data from multiple sources.

Geometric Computer Vision library in C++. Provides functions and structures of projective geometry, taylored for 3D computer vision.

GluonCV provides implementations of state-of-the-art (SOTA) deep learning algorithms in computer vision. It aims to help engineers, researchers, and students quickly prototype products, validate new ideas and learn computer vision. It features training scripts that reproduce SOTA results reported in latest papers, a large set of pre-trained models, carefully designed APIs and easy-to-understand implementations and community support. From fundamental image classification, object detection, semantic segmentation and pose estimation, to instance segmentation and video action recognition. The model zoo is the one-stop shopping center for many models you are expecting. GluonCV embraces a flexible development pattern while is super easy to optimize and deploy without retaining a heavyweight deep learning framework.

GoCV gives programmers who use the Go programming language access to the OpenCV 4 computer vision library. The GoCV package supports the latest releases of Go and OpenCV v4.5.4 on Linux, macOS, and Windows. Our mission is to make the Go language a “first-class” client compatible with the latest developments in the OpenCV ecosystem. Computer Vision (CV) is the ability of computers to process visual information, and perform tasks normally associated with those performed by humans. CV software typically processes video images, then uses the data to extract information in order to do something useful. Since memory allocations for images in GoCV are done through C based code, the go garbage collector will not clean all resources associated with a Mat. As a result, any Mat created must be closed to avoid memory leaks.

HaViMo is a compact vision module designed to add computer vision capabilities to low power microcontrollers. HaViMoGUI is the PC-side application to calibrate and setup the module.

Video processing and computer vision library for GNU/Linux offering interfaces to do image- and video-I/O with ImageMagick/Magick++, Xine, firewire digital camera (DC1394), and video for linux (V4L2). Note that this version of HornetsEye is deprecated. HornetsEye now is released as multiple packages on RubyGems.org. The source code is available on Github.com (see https://wedesoft.github.io/hornetseye-doc/ for more information).

A Python script doing basic computer vision that takes control the webcam and with OpenCV processes the video stream to capture what is told to do.

This repository is only used for the purpose of dataset storage for Team IGVC, IITK. For the relevant code, see our GitHub repositories. (https://github.com/igvc-iitk). The recorded data is used for testing various algorithms related to Computer Vision, SLAM, Motion Planning etc.

Computer Vision Project

this is a project for the computer vision class. We intend to track multi-targets while preserving the identity of the targets. We propose to represent this as a Flow optimization problem and solve it using K-shortest path algorithm.

Igovision is an experimental project investigating applications of computer vision technology to the board game Go.

The program for image classification,processing and computer vision.

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,...

This repository is a survey / code collection centered on deep learning–based image fusion (e.g. fusing infrared + visible light images, multi-modal fusion) methods. It catalogs many fusion algorithms (e.g. DenseFuse, FusionGAN, NestFuse, etc.), links to code implementations, and describes evaluation metrics. The repository includes a “General Evaluation Metric” subfolder containing objective fusion metrics. It is not a single monolithic tool, but rather a curated reference and aggregation of methods, code and performance comparisons in the domain of image fusion. Survey style description of method taxonomy, architectures, loss types. Compilation of many state-of-the-art image fusion methods (infrared + visible, multi-focus, multi-exposure). Survey style description of method taxonomy, architectures, loss types.

The project is aimed at automatic target following using a camera , a computer vision system and a microcontroller that moves the cam. The project should mainly work under linux and it might be ported into windows,

The present invention is an application for Android platform that recognizes Jakim’s Halal logo and acknowledge users whether certain logo is Jakim’s Halal logo or not using embedded system technology, real-time processing, computer vision, pattern recognition, and machine learning algorithm just by taking the picture of any Halal logo.

The present invention is an application for Android platform that recognizes Jakim’s Halal logo and acknowledge users whether certain logo is Jakim’s Halal logo or not using embedded system technology, real-time processing, computer vision, pattern recognition, and machine learning algorithm just by taking the picture of any Halal logo.

JOptical is a free library for generic computer vision applications. It will include most well known algorithms around this subject and expand on that.

This Java native library wraps OpenCV (Computer Vision Lib.) function cvMatchTemplate and implements methods for utilities result visualization. It allows efficient images template matching using Normalized Cross-Correlation (NCC) and others algorithms.

KLT-IV v1.0 has been designed to offer a user-friendly graphical user interface for the determination of river flow velocity and river discharge using videos acquired using a variety of fixed and moving platforms. KLT-IV employs optical flow and computer vision techniques for the tracking of features present on the river surface with pixel displacements being converted to real-world velocities using an optimised camera model approach. Movement of the platform can be accounted for when ground control points and/or stable features are present, or where the platform is equipped with GPS and IMU sensors. Please direct queries or comments to: https://groups.google.com/forum/#!forum/klt-iv-image-velocimetry-software