You can use LibRealSense and OpenCV* to stream RGB and depth data from your connected Intel® RealSense™ camera. This tutorial and code sample shows how to do this, based on the Ubuntu* operating system. In the end you will have a nice starting point where you use this code base to build upon to create your own LibRealSense / OpenCV applications.
This article shows you how you can use LibRealSense and OpenCV to stream RGB and depth data. In the end you will have a nice starting point where you use this code base to build upon to create your own LibRealSense / OpenCV applications.
In support of computer vision development efforts, we've created a GitHub* repository of twelve computer vision code samples. These code samples are a good starting point for developers who wish to develop more robust computer vision and analytic solutions. We use the Retail, Digital Signage market in these examples but the technology can be used in a variety of different markets.
OpenCV, Python* This sample application is useful to see movement patterns over time. For example, it could be used to see the usage of entrances to a factory floor over time, or patterns of shoppers in a store. color/heat map background subtraction
Intel introduces the Intel® Distribution of OpenVINO™ toolkit for deep learning inference applications.
智能视频可用于多个不同的行业，比如数字安全和监控、零售、智慧城市、工业和制造、供应链管理、医学研究和个性化医疗。这些行业均使用深度学习处理视频数据，以获取关键洞察，并在竞争中发展壮大。英特尔推出了 OpenVINO™ 工具套件。
The TensorFlow* image classification sample codes below describe a step-by-step approach to modify the code in order to scale the deep learning training across multiple nodes of HPC data centers.
Learn how to use OpenCV* to count people using edge detection rather than using server farms.
Get all the code samples, documentation, and training you need to emulate human vision in this toolkit.