This sample application takes an image or frame of an analog gauge and reads the value using computer vision. It consists of two parts: the calibration and the measurement. During calibration, the user gives the application an image of the gauge to calibrate, and it prompts the user to enter the range of values in degrees. It then uses these calibrated values in the measurement stage to convert...
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
Access hardware and software that help you get started with computer vision.
To help grow deep learning inference applications at the edge, Intel developed the energy efficient and low cost Intel® Movidius™ Neural Compute Stick - a tiny fanless deep learning device powered by the Intel® Movidius™ Vision Processing Unit (VPU).
Deep Learning Inference Engine Workflow
This paper presents a quick hands-on tour of the Inference Engine Python API, using an image classification sample that is included in the OpenVINO™ toolkit 2018 R1.2. This sample uses a public SqueezeNet* model that contains around one thousand object classification labels.
Computer Vision is a fast growing technology being deployed in nearly every industry from factory floors to amusement parks to shopping malls, smart buildings, and smart homes.
This overview shows how to use computer vision, algorithms, and machine learning with Intel® architecture.
This article provides guidance for transitioning from the NCSDK to the Intel® Distribution of OpenVINO™ toolkit.