95 lines
4.0 KiB
Markdown
95 lines
4.0 KiB
Markdown
# Amazon Rekognition People Label Detection via KVS
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This is the README for the whole people detection pipeline designed for real-time video analysis.
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There are three Lambda functions placed inside this repository:
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### 1. Kinesis Video Stream to Rekognition (`cctv-people-rekogniton`)
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This function utilizes Amazon Rekognition's stream processor, which is configured to monitor an incoming Amazon Kinesis Video Stream for people label detection.
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Note that this Lambda function is intended to process **a single Kinesis video stream only**. For a multi-camera setup, multiple deployments of this Lambda function are necessary.
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This is built on top of AWS KVS Consumer Library for Python. The original repository can be found here: https://github.com/aws-samples/amazon-kinesis-video-streams-consumer-library-for-python/
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### 2. SNS to DynamoDB and OpenSearch (`cctv-people-sns-dynamodb`)
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This function writes a new entry to DynamoDB and Opensearch tables whenever there is a detection coming from Rekognition via SNS.
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### 3. DynamoDB to Supabase (`cctv-people-dynamodb-supabase`)
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This function acts an extension of the previous function, writing a new entry to Supabase whenever there is a new entry in DynamoDB.
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## Prerequites
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The following must already be set up before deploying this pipeline:
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1. A working Kinesis video stream as input
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2. An S3 Bucket where all frames with detection go
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3. A SNS topic that will receive the notification coming from Rekognition
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4. A DynamoDB table
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5. An OpenSearch Service collection
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It is advised to have all above components in the same region for easy configuration.
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## Setup
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### Part I. Kinesis Video Stream to Rekognition (`\cctv-people-rekogniton`)
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1. Create an IAM role for the Rekognition stream processor. This role must contain the following AWS-managed policies:
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- AmazonRekognitionServiceRole
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- AmazonS3FullAccess
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2. Provide the following details in `lambda_function.py`:
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- Kinesis video stream name and ARN
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- Stream processor name (must be unique to the KVS stream)
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- S3 bucket name
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- IAM role ARN
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- SNS topic ARN
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3. Package the folder as a ZIP file.
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4. Create an AWS Lambda function and upload the ZIP file.
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5. Add an EventBridge trigger with the schedule expression of `rate(5 minutes)`
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### Part II. SNS to DynamoDB and OpenSearch (`\cctv-people-sns-dynamodb`)
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1. Create a Lambda function and upload the `lambda_function.py` file.
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2. Add a trigger to receive notifications from the SNS topic.
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3. Add the following AWS-managed permissions to this Lambda function:
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- AmazonDynamoDBFullAccess_v2
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- AmazonOpenSearchIngestionFullAccess
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- AmazonOpenSearchServiceFullAccess
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- AmazonS3FullAccess
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4. Add an inline policy for OpenSearch Serverless:
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```
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{
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"Version": "2012-10-17",
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"Statement": [
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{
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"Sid": "Statement1",
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"Effect": "Allow",
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"Action": "aoss:*",
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"Resource": "*"
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}
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]
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}
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```
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5. Create an AWS Managed Grafana workspace.
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6. Access the workspace and install opensearch aws sig4 and opensearch extension.
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7. Point it to the opensearch url and make sure Aws SigV4 is enabled.
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Configure the correct index.
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### 3. DynamoDB to Supabase (`\cctv-people-dynamodb-supabase`)
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1. On a local machine, prepare a folder for Supabase installation.
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2. Open a terminal pointed at this directory and run the following command:
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```
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pip3 install supabase --platform manylinux2014_x86_64 --python-version 3.12 --only-binary=:all: -t .
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```
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3. Place the `lambda_function.py` inside this folder.
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4. Package the folder as a ZIP file.
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5. Create an AWS Lambda function and upload the ZIP file.
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6. Add the following AWS-managed permission to the IAM role for this Lambda:
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- AmazonDynamoDBFullAccess_v2
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7. Enable stream on the DynamoDB table. Set view type to **New image**.
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8. Add trigger to the Lambda function to connect to the DynamoDB stream.
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