Search Results for "code e-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! ...

...With your batch in hand, you can use PyTorch to develop and train your model using gradient descent. For example, check out this example code for training on the Stanford Natural Language Inference (SNLI) Corpus. Now you've setup your pipeline, you may want to ensure that some functions run deterministically. Wrap any code that's random, with fork_rng and you'll be good to go. Now that you've computed your vocabulary, you may want to make use of pre-trained word vectors to set your embeddings.

A comprehensive software suite for reading barcodes. Supports EAN/UPC, Code 128, Code 39, Interleaved 2 of 5 and QR Code. Includes libraries and applications for decoding captured barcode images and using a video device (eg, webcam) as a barcode scanner.

code in this repository is now deprecated, vvvv has moved to github. please see: https://github.com/vvvv/vvvv-sdk --- a collection of standards-based plugins compatible with (among others) vvvv: video tracking plugins based on the freeframe api, audio/video directshow filters, and plugin nodes based on vvvvs own plugin interface. for vvvv see: http://vvvv.org

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).

The webcam computer vision (WCCV) project focuses on computer vision algorithms and systems that follow the web cam paradigm: cheap, robust and efficient. The project comprises designs, patterns, code, tutorials and examples.