IoT Twinmaker/IoT Sitewise

AWS IoT TwinMaker can collect real-time image data as input from components as a data source. Here's the general process for it:

• Set up IoT TwinMaker: First, you need to set up and configure the AWS IoT TwinMaker service in your AWS environment. This includes creating the necessary resources, such as a workspace, components, and connectors.
• Configure the Image Data Source: To collect real-time image data, you'll need to set up a data source that can provide the image data. This could be a camera, sensor, or any other device that can capture and transmit image data. Ref: https://docs.aws.amazon.com/iot-twinmaker/latest/guide/data-connector-interface.html
• Integrate the Image Data Source with IoT TwinMaker: You'll need to create a connector in IoT TwinMaker that can connect to and retrieve data from the image data source. IoT TwinMaker supports various data source types, including Amazon S3, Amazon DynamoDB, and AWS IoT Core. For example, if you're using a camera as the image data source, you could set up an AWS IoT Core device to capture the images and publish them to an MQTT topic. Then, you can create an IoT TwinMaker connector that subscribes to that MQTT topic and retrieves the image data. -- To ingest near real-time data into SiteWise:
• Set up IoT SiteWise: First, you need to set up and configure the AWS IoT SiteWise service in your AWS environment. This includes creating the necessary resources, such as an IoT SiteWise application, asset models, and asset properties.
• Configure the Image Data Source: You'll need to have a device or sensor that can capture and transmit real-time image data. This could be a camera, a sensor with image capture capabilities, or any other device that can generate image data. -Integrate the Image Data Source with IoT SiteWise: To ingest the image data into IoT SiteWise, you'll need to set up a data source connector that can connect to and retrieve the data from the image data source. -IoT SiteWise supports various data source types, including AWS IoT Core, Amazon S3, and others. Depending on your setup, you might need to create an AWS IoT Core device or an Amazon S3 bucket to store the image data. Once the data source is configured, IoT SiteWise will be able to ingest the real-time image data from the source. Ref: https://docs.aws.amazon.com/iot-sitewise/latest/userguide/industrial-data-ingestion.html
• Define the Asset Model and Properties: In IoT SiteWise, you'll need to create an asset model that represents the image data source. Within this asset model, you'll define properties that represent the image data itself.
• You can define the property as a custom data type, such as an image or a binary data type, depending on how the image data is being stored and transmitted.
• Ingest the Image Data: Once the data source connector and the asset properties are set up, IoT SiteWise will automatically ingest the real-time image data from the source and store it within the asset's properties.. -- To connect TwinMaker with Rekognition:
• Create an AWS Rekognition Integration: In IoT TwinMaker, you'll need to create a custom integration with AWS Rekognition to enable the image analysis capabilities. This involves the following steps:

Define a custom component in IoT TwinMaker that represents the image data source and the Rekognition analysis.

• Create a custom connector in IoT TwinMaker that can communicate with the AWS Rekognition service. Configure the connector to send the image data from the data source to Rekognition for analysis.
• Integrate the Rekognition Analysis: Once the Rekognition integration is set up, you can configure IoT TwinMaker to leverage the Rekognition capabilities: Define properties within the custom component to store the results of the Rekognition analysis, such as detected objects, labels, or other insights. Configure the custom connector to receive the analysis results from Rekognition and update the corresponding properties in the IoT TwinMaker component..