Displaying 1 to 4 from 4 results

labelme - Image Polygonal Annotation with Python (polygon, rectangle, line, point and image-level flag annotation)

  •    Python

Labelme is a graphical image annotation tool inspired by http://labelme.csail.mit.edu. It is written in Python and uses Qt for its graphical interface. Fig 2. VOC dataset example of instance segmentation.

cvat - Computer Vision Annotation Tool (CVAT) is a web-based tool which helps to annotate video and images for Computer Vision algorithms

  •    Javascript

CVAT is completely re-designed and re-implemented version of Video Annotation Tool from Irvine, California tool. It is free, online, interactive video and image annotation tool for computer vision. It is being used by our team to annotate million of objects with different properties. Many UI and UX decisions are based on feedbacks from professional data annotation team. Code released under the MIT License.

vatic - Efficiently Scaling Up Video Annotation with Crowdsourced Marketplaces. IJCV 2012

  •    HTML

VATIC is an online video annotation tool for computer vision research that crowdsources work to Amazon's Mechanical Turk. Our tool makes it easy to build massive, affordable video data sets. Note: VATIC has only been tested on Ubuntu with Apache 2.2 HTTP server and a MySQL server. This document will describe installation on this platform, however it should work any operating system and with any server.

VIAME - Video and Image Analytics for Marine Environments

  •    C++

VIAME is a computer vision application designed for do-it-yourself artificial intelligence including object detection, object tracking, image/video annotation, image/video search, image mosaicing, size measurement, rapid model generation, and tools for the evaluation of different algorithms. Originally targetting marine species analytics, it now contains many common algorithms and libraries, and is also useful as a generic computer vision toolkit. It contains a number of standalone tools for accomplishing the above, a pipeline framework which can connect C/C++, python, and matlab nodes together in a multi-threaded fashion, and, lastly, multiple algorithms resting on top of the pipeline infrastructure. Both a desktop and web version exist for deployments in different types of environments. For a full installation guide and description of the various flavors of VIAME, see the quick-start guide, above. The desktop version is provided as either a .msi, .zip or .tar file. Alternatively, docker files are available for both VIAME Desktop and Web (below). A sample instance of VIAME Web is also online, hosted at viame.kitware.com. For desktop installs, extract the binaries (or use the msi Windows installation wizard) and place them in a directory of your choosing, for example /opt/noaa/viame on Linux or C:\Program Files\VIAME on Windows. If using packages built with GPU support, make sure to have sufficient video drivers installed, version 451.82 or higher. The best way to install drivers depends on your operating system, see below. Lastly, run through some of the examples to validate the installation. The binaries are quite large, in terms of disk space, due to the inclusion of multiple default model files and programs, but if just building your desired features from source (e.g. for embedded apps) they are much smaller.









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