How to use t6-Iot image preprocessor to trigger action based on image facial expression recognition
Tags: tutorial, iot
Published: 2022-07-24 18:44:37

In this recipe, the goal is to trigger an Email action after t6-IoT detect a human facial expression from a provided image.

## Check prerequisites
In this recipe, we’ll use the following concepts:

- an [aidc Preprocessor](https://www.internetcollaboratif.info/features/data-acquisition-preprocessor/) to identify the facial expression (Automatic identification and data capture)
- a [Flow](https://www.internetcollaboratif.info/features/flow/) to store specific measurements
- a [Rule](https://www.internetcollaboratif.info/features/decision-rule/) to trigger an email after the facial expression is identified on the image
- We’ll also set [datatype as a String](https://www.internetcollaboratif.info/features/data-types/) so that the Flow will contains the facial expression

## Setup the Flow container
This step is straight forward and does not require anything special. We’ll customize this Flow with a [String datatype](https://www.internetcollaboratif.info/features/data-types/).

So, the first step is to create this Flow using the following payload. To have more details on Flows, read the [technical documentation](https://doc.internetcollaboratif.info/#api-Flow-Create_new_Flow).

`{
    "name": "My AIDC Flow to identify facial expression from images",
    "data_type": "a394e18f-12bd-4c22-b9c3-74c387d1a8db",
    "preprocessor": [
        {
            "name": "aidc",
            "mode": "faceExpressionRecognition"
        }
    ]
}`

Once your Flow is created, take note of the flow.data.id on the Api results. This value will be used on datapoints creation as the referring variable `{{$flow_id}}`.

Create the Rule that will trigger the Email
`{
    "name": "Trigger an email when aidc identify a sad facial expression",
    "rule": {
        "conditions": {
            "all": [
                {
                    "fact": "flow",
                    "operator": "equal",
                    "value": "65e2ca88-adf1-431b-a2f4-82497f54f32f"
                },
                {
                    "fact": "value",
                    "operator": "equal",
                    "value": "sad"
                }
            ]
        },
        "event": {
            "type": "email",
            "params": {
                "to": "{{$your_own_email@domain.invalid}}",
                "subject": "Facial recognition on t6 Flow {flow}",
                "text": "Facial recognition on t6 Flow {value}",
                "html": "<h1>Hello</h1>Facial recognition on t6 Flow<br />Value: {value}"
            }
        },
        "priority": 1
    },
    "active": true
}`
Need more details on Rules? read the [technical documentation](https://doc.internetcollaboratif.info/#api-Rule-Create_new_Rule).

## Let’s put it all together, post image datapoint
Before posting the [datapoint](https://doc.internetcollaboratif.info/#api-DataPoint_Measure-Create_a_DataPoint), you’ll need to make sure the payload contains a valid base64 image encoded string. You can use an [online service](https://doc.internetcollaboratif.info/#api-DataPoint_Measure-Create_a_DataPoint) to do that.

`{
    "save": false,
    "publish": true,
    "flow_id": "{{$flow_id}}",
    "mqtt_topic": "image-test-processing",
    "preprocessor": [
        {
            "name": "aidc",
            "mode": "faceExpressionRecognition"
        }
    ],
    "value": "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"
}`

And voilà, you’ll notice the Api results will transform the initial value into a String value telling about the recognized expression found in the image. Additionally, the preprocessor is having a specific expressions node in the result providing with the full expressions scores. The Rule identified and use that value to trigger the Email as notification.

`"value": "sad",
"preprocessor": [
    {
        "name": "aidc",
        "mode": "faceExpressionRecognition",
        "initialValue": "1658669719797000000-faceExpressionRecognition.png",
        "status": "completed",
        "expressions": {
            "neutral": 9.418351254453228e-8,
            "happy": 1.1365385715889076e-10,
            "sad": 0.9999997615814209,
            "angry": 7.31789351338108e-10,
            "fearful": 1.6018465487377398e-7,
            "disgusted": 8.681204626687089e-13,
            "surprised": 6.788646977895496e-9
        },
        "recognizedValue": "sad",
        "expressionValue": 0.9999997615814209
    },
],`

To have more details on Datapoints, read the [technical documentation](https://doc.internetcollaboratif.info/#api-DataPoint_Measure-Create_a_DataPoint).