Code Along for Building a Serverless Image Recognition App with AWS Rekognition and JavaScript

AI Project Engine Code-Along Discussions
1

Step 1: Set up AWS Account and Services

To set up an AWS account, you can follow these steps:

  1. Go to the AWS website (https://aws.amazon.com/) and click on “Create an AWS Account”.
  2. Follow the instructions to create a new account.
  3. Once your account is set up, log in to the AWS Management Console.

To set up the required AWS services (S3, Lambda, API Gateway, and IAM), you can follow these steps:

  1. Open the AWS Management Console.
  2. Search for the service you want to set up (e.g., S3) and click on it.
  3. Follow the instructions to create the service. For example, to create an S3 bucket, you can click on “Create bucket” and provide a unique name for your bucket.
  4. Repeat the process for each service you need to set up.

Step 2: Configure AWS SDK and API Access

To configure the AWS SDK for JavaScript, you need to install it using npm (Node Package Manager). Open your command line interface and navigate to your project directory. Then, run the following command to install the AWS SDK:

npm install aws-sdk

Once the installation is complete, you can configure the SDK with your AWS credentials. You will need your Access Key ID and Secret Access Key, which can be obtained from the AWS Management Console.

Create a new file in your project directory called config.js and add the following code:

// config.js
window._config = {
  region: 'YOUR_REGION',
  s3Bucket: 'YOUR_S3_BUCKET',
  rekognitionBucket: 'YOUR_REKOGNITION_BUCKET',
  apiGateway: 'YOUR_API_GATEWAY_URL',
  accessKeyId: 'YOUR_ACCESS_KEY_ID',
  secretAccessKey: 'YOUR_SECRET_ACCESS_KEY'
};

Replace 'YOUR_REGION' with the AWS region you are using (e.g., ‘us-east-1’), 'YOUR_S3_BUCKET' with the name of your S3 bucket, 'YOUR_REKOGNITION_BUCKET' with the name of the bucket you will use for storing Rekognition analysis results, and 'YOUR_API_GATEWAY_URL' with the URL of your API Gateway.

Next, open your HTML file and add the following script tag before your own JavaScript code:

<script src="https://sdk.amazonaws.com/js/aws-sdk-2.966.0.min.js"></script>
<script src="config.js"></script>
<script src="your-script.js"></script>

Replace 'your-script.js' with the filename of your own JavaScript file.

Now, you can access your AWS configuration in your JavaScript code using the window._config object. For example, to access your Access Key ID, you can use window._config.accessKeyId.

To enable CORS for your API, you need to add the appropriate headers to your API’s responses. This can be done using an AWS Lambda function. We will cover this in more detail in the next steps.

Step 3: Build the User Interface

To build the user interface, you can follow these steps:

  1. Create an HTML file for the user interface. You can name it index.html.
  2. Add a file input element for image uploads. You can use the following code:
<input type="file" id="imageInput" accept="image/*">
  1. Implement JavaScript functions for handling user interactions. For example, you can add the following code to your JavaScript file:
// your-script.js
document.getElementById('imageInput').addEventListener('change', handleImageUpload);

function handleImageUpload(event) {
  const file = event.target.files[0];
  // Process the uploaded image
  processImage(file);
}

function processImage(file) {
  // Implement image processing logic here
}

Step 4: Process and Upload Images

To process and upload images, you can follow these steps:

  1. Write JavaScript code to process user-selected images. You can use libraries like canvas or FileReader to read and manipulate the image data.
  2. Generate a unique key for each image. You can use a combination of a timestamp and a random string to ensure uniqueness.
  3. Use the AWS SDK to upload the processed image to your S3 bucket. You can use the following code as a starting point:
// your-script.js
function processImage(file) {
  const reader = new FileReader();
  reader.onload = function(event) {
    const imageData = event.target.result;
    // Implement image processing logic here
    const processedImage = processImageData(imageData);
    uploadImage(processedImage);
  };
  reader.readAsDataURL(file);
}

function processImageData(imageData) {
  // Implement image processing logic here
  // For example, you can use the canvas API to resize or crop the image
  const canvas = document.createElement('canvas');
  // ...
  return processedImageData;
}

function uploadImage(imageData) {
  const s3 = new AWS.S3();
  const key = generateUniqueKey();
  const params = {
    Bucket: window._config.s3Bucket,
    Key: key,
    Body: imageData
  };
  s3.upload(params, function(err, data) {
    if (err) {
      console.error('Error uploading image:', err);
    } else {
      console.log('Image uploaded successfully:', data.Location);
    }
  });
}

function generateUniqueKey() {
  // Implement logic to generate a unique key
  // For example, you can use a combination of a timestamp and a random string
  const timestamp = Date.now();
  const randomString = Math.random().toString(36).substring(2, 15);
  return `${timestamp}-${randomString}`;
}

Step 5: Create a Serverless Function

To create a serverless function using AWS Lambda, you can follow these steps:

  1. Set up a new Lambda function in the AWS Management Console.
  2. Configure the function to receive HTTP requests from API Gateway. You can use the following code as a starting point:
// your-lambda-function.js
exports.handler = function(event, context, callback) {
  // Implement your Lambda function logic here
  // For example, you can call the AWS Rekognition API to analyze the uploaded image
  const imageKey = event.queryStringParameters.key;
  analyzeImage(imageKey, function(err, analysisResults) {
    if (err) {
      callback(err);
    } else {
      callback(null, {
        statusCode: 200,
        body: JSON.stringify(analysisResults)
      });
    }
  });
};

function analyzeImage(imageKey, callback) {
  // Implement code to analyze the uploaded image using AWS Rekognition
  // For example, you can use the AWS SDK for Rekognition to call the DetectLabels API
  const rekognition = new AWS.Rekognition();
  const params = {
    Image: {
      S3Object: {
        Bucket: window._config.s3Bucket,
        Name: imageKey
      }
    }
  };
  rekognition.detectLabels(params, function(err, data) {
    if (err) {
      callback(err);
    } else {
      callback(null, data.Labels);
    }
  });
}

Step 6: Implement Image Analysis

To implement image analysis using AWS Rekognition, you can follow these steps:

  1. Write the code to analyze the uploaded image using AWS Rekognition. You can use the AWS SDK for Rekognition to call the appropriate API, such as detectLabels for object detection or detectFaces for facial analysis.
  2. Extract relevant information from the analysis results. For example, you can extract the detected labels or facial attributes.

Step 7: Return Analysis Results

To return the analysis results from your Lambda function, you can follow these steps:

  1. Configure the Lambda function to return the analysis results. You can use the callback function provided by the Lambda runtime to send the results back to the caller.
  2. Handle any errors that may occur during the analysis process. You can use the callback function to send error messages back to the caller.

Step 8: Display Results in the User Interface

To display the analysis results in the user interface, you can follow these steps:

  1. Update your HTML and JavaScript code to display the analysis results. You can create HTML elements dynamically or update existing elements with the analysis data.
  2. Handle different types of analysis results, such as object detection, scene detection, and facial analysis. You can use conditional statements or switch cases to handle different types of analysis results.

Step 9: Test and Deploy Your Application

To test and deploy your application, you can follow these steps:

  1. Test your application locally to ensure everything is working correctly. You can use a local development server or open your HTML file directly in a web browser.
  2. Deploy your application to AWS using the AWS Command Line Interface (CLI) or the AWS Management Console. You can use services like AWS Amplify or AWS CloudFormation to automate the deployment process.

Remember to refer to the AWS documentation and other learning resources mentioned in the project description for more detailed information and examples.