In this workshop you will learn how to build a sentiment analysis project for your DeepLens.
In this project you will learn to build a deep learning model to identify and analyze the sentiments of your audience
- How to build and train a face detection model in SageMaker
- Modify the DeepLens inference lambda function to upload cropped faces to S3
- Deploy the inference lambda function and face detection model to DeepLens
- Create a lambda function to trigger Rekognition to identify emotions
- Create a DynamoDB table to store the recognized emotions
- Analyze using CloudWatch
The workshop consists of 3 steps:
In this step, you will build and train a face detection model. Follow instructions here: SageMaker lab
Recall the bucket name you created in the SageMaker lab, we'll be using it shortly.
A DeepLens Project consists of two things:
- A model artifact: This is the model that is used for inference.
- A Lambda function: This is the script that runs inference on the device.
Before we deploy a project to DeepLens, we need to create a custom lambda function that will use the face-detection model on the device to detect faces and push crops to S3.
Go to AWS Management console and search for Lambda
Click Create function
Choose Blueprints
In the search bar, type greengrass-hello-world and hit Enter.
Choose the python blueprint and click Configure/
Name the function: DeepLens-sentiment-your-name
Role: Choose an existing role
Existing Role: AWSDeepLensLambdaRole
Click Create Function.
Scroll down to Function Code. Once generated, replace the default script with the inference script here by copying and pasting.
You can select the inference script, by selecting Raw in the Github page and choosing the script using ctrl+A/ cmd+A . Copy the script and paste it into the lambda function (make sure you delete the default code).
Note: In the script, you will have to provide the name for your S3 bucket. Insert your bucket name in the code below
Click Save
You'll also click "Actions" and then "Publish new version".
Then, enter a brief description and click "Publish."
With the lambda created, we can now make a project using it and the built-in face detection model.
From the DeepLens homepage dashboard, select "Projects" from the left side-bar:
Then select "Create new project"
Next, select "Create a new blank project" then click "Next".
Now, name your deeplens project.
Next, select "Add model". From the pop-up window, select "deeplens-face-detection" then click "Add model".
Next, select "Add function". from the pop-up window, select your deeplens lambda function and click "Add function".
Finally, click "Create".
Now that the project has been created, you will select your project from the project dashboard and click "Deploy to device".
Select the device you're deploying too, then click "Review" (your screen will look different here).
Finally, click "Deploy" on the next screen to begin project deployment.
You should now start to see deployment status. Once the project has been deployed, your deeplens will now start processing frames and running face-detection locally. When faces are detected, it will push to your S3 bucket. Everything else in the pipeline remains the same, so return to your dashboard to see the new results coming in!
Note: If your model download progress hangs at a blank state (Not 0%, but blank) then you may need to reset greengrass on DeepLens. To do this, log onto the DeepLens device, open up a terminal, and type the following command:
sudo systemctl restart greengrassd.service --no-block. After a couple minutes, you model should start to download.
Confirmation/ verification
To confirm your model is running, check the bucket you created to see if images of cropped faces are being uploaded.
Step 3.1 - Create DynamoDB table
Go to AWS Management console and search for Dynamo
Click on Create Table.
Table name: rekognize-faces-your-name
Primary key: s3key
Click on Create. This will create a table in your DynamoDB.
Step 3.2 - Create a lambda function that runs in the cloud
The inference lambda function that you deployed earlier will upload the cropped faces to your S3. You will now create a new lambda function in the cloud that triggers on this S3 upload. This new lambda function gets triggered and runs the Rekognize Emotions API by integrating with Amazon Rekognition.
Go to AWS Management console and search for Lambda
Click Create function
Choose Author from scratch
Name the function: recognize-emotion-your-name.
Runtime: Choose Python 2.7
Role: Choose an existing role
Existing role: rekognizeEmotions
Choose Create function
Replace the default script with the script in recognize-emotions.py. Copy the script and paste it into the lambda function (make sure you delete the default code).
Make sure you enter the table name you created earlier in the section highlighted below:
Next, we need to add the event that triggers this lambda function. This will be an “S3:ObjectCreated” event that happens every time a face is uploaded to the face S3 bucket. Add S3 trigger from designer section on the left.
Configure with the following:
Bucket name: (you created this bucket earlier)
Event type: Object Created
Prefix: faces/
Suffix: .jpg
Enable trigger: ON (keep the checkbox on)
Press Add button
Save the lambda function
Under Actions tab choose Publish
Step 3.3 - View the emotions on a dashboard
Go to AWS Management console and search for Cloudwatch
Create a dashboard called “sentiment-dashboard-your-name”
Choose Line in the widget
Under Custom Namespaces, select “string”, “Metrics with no dimensions”, and then select all metrics.
Next, set “Auto-refresh” to the smallest interval possible (10 Seconds), the horizontal axis to Relative and 1 Hour(s), and change the “Period” to whatever works best for you (1 second or 5 seconds)
You should get a dashboard similar to this:
You can now see average emotion scores from faces detected at the edge in a dashboard in real time.
NOTE: These metrics will only appear once they have been sent to Cloudwatch via the Rekognition Lambda. It may take some time for them to appear after your model is deployed and running locally. If they do not appear, then there is a problem somewhere in the pipeline.
You can also take a look at the DynamoDB table you created, to see the results of our emotion detection being written to your table.
With this we have come to the end of the session. As part of building this project, you learnt the following:
- How to build and train a face detection model in SageMaker
- Modify the DeepLens inference lambda function to upload cropped faces to S3
- Deploy the inference lambda function and face detection model to DeepLens
- Create a lambda function to trigger Rekognition to identify emotions
- Create a DynamoDB table to store the recognized emotions
- Analyze using CloudWatch