I’m Kazuhito Yokoi at OSS Solution Center in Hitachi, Ltd. In the previous article, I introduced how to use Node-RED Operator on Red Hat OpenShift. In this article, I will explain the procedures to connect the Node-RED environment with the TensorFlow container on the OpenShift.
Here, we will use the example of Node-RED flow that realizes the alert system for when driving a car.
In Japan, cars run on the left side road and cyclists also run on the same side of the car line as you may know. Therefore, drivers always want to avoid collisions with them…
I’m Kazuhito Yokoi at OSS Solution Center in Hitachi, Ltd. In this article, I will explain how to use Node-RED on OpenShift. Thanks to the OpenShift web console, users can easily deploy the Node-RED environment without command-line operations even if they are beginners. Recently, the Node-RED operator has been available on the OpenShift catalog as below.
As of December 2020, there are four items in the catalog. Let’s deploy “Node-RED operator” from them to the OpenShift environment on the Katacoda.
(1) Access OpenShift web console
Firstly, access the OpenShift Playground 4.6 via the following URL. This playground environment is available…
In this post, I will explain how to use the “Node generator” which makes it easy to create Node-RED nodes.
What is the Node generator?
In this post, I will introduce how to use image recognition using TensorFlow.js and Node-RED. Node-RED flow can answer what image contains (For example, person, dog, car and bottle) using TensorFlow.js.
Settings in Node-RED
To use Tensorflow.js easily, I will use the TensorFlow.js module which contains the learned model from the function node in Node-RED. When we use 3rd party npm modules in function nodes, the document, “Loading additional modules” section in the official Node-RED website will be useful.
(1) Install npm module
After opening the command prompt, I will install the external npm module, “max-image-segmenter” on the home directory…