Image processing is a critical component of many
applications, from social media to healthcare. However, processing large
volumes of image data can be time-consuming and resource-intensive. In this
tutorial, we'll show you how to use Azure Functions and Azure Blob Storage to
create a real-time image processing pipeline that can handle large volumes of
data with scalability and flexibility.
Prerequisites
Before we get started, you'll need to have the following:
1.
An Azure account
2.
Visual Studio Code
3.
Azure Functions extension for Visual Studio Code
4.
Azure Blob Storage extension for Visual Studio
Code
Creating the Azure
Functions App
The first step is to create an Azure Functions app. In
Visual Studio Code, select the Azure Functions extension and choose
"Create New Project". Follow the prompts to choose your programming
language and runtime.
Once your project is created, you can create a new function
by selecting the "Create Function" button in the Azure Functions
Explorer. Choose the Blob trigger template to create a function that responds
to new files added to Azure Blob Storage.
In this example, we'll create a function that recognizes
objects in images using Azure Cognitive Services. We'll use the Cognitive
Services extension for Visual Studio Code to connect to our Cognitive Services
account.
Creating the Azure
Blob Storage Account
Next, we'll create an Azure Blob Storage account to store
our image data. In the Azure portal, select "Create a resource" and
search for "Blob Storage". Choose "Storage account" and
follow the prompts to create a new account.
Once your account is created, select "Containers"
to create a new container for your image data. Choose a container name and
access level, and select "Create". You can now add images to your
container through the Azure portal or through your Azure Functions app.
Connecting the Azure
Functions App to Azure Cognitive Services
To connect your Azure Functions app to Azure Cognitive
Services, you'll need to add the Cognitive Services extension to your project.
In Visual Studio Code, select the Extensions icon and search for "Azure
Cognitive Services". Install the extension and reload Visual Studio Code.
Next, open your function code and add the following code to
your function:
const { ComputerVisionClient } = require("@azure/cognitiveservices-computervision");
const { BlobServiceClient } = require("@azure/storage-blob");
module.exports = async function (context, myBlob) {
const endpoint = process.env["ComputerVisionEndpoint"];
const key = process.env["ComputerVisionKey"];
const client = new ComputerVisionClient({ endpoint, key });
const blobEndpoint = process.env["BlobEndpoint"];
const blobKey = process.env["BlobKey"];
const blobServiceClient = BlobServiceClient.fromConnectionString(`BlobEndpoint=${blobEndpoint};BlobAccessKey=${blobKey}`);
const containerClient = blobServiceClient.getContainerClient("mycontainer");
const buffer = myBlob;
const result = await client.analyzeImageInStream(buffer, { visualFeatures: ["Objects"] });
const blobName = context.bindingData.name;
const blobClient = containerClient.getBlockBlobClient(blobName);
const metadata = { tags: result.objects.map(obj => obj.objectProperty) };
await blobClient.setMetadata(metadata);
}
This code connects to your Azure Cognitive Services account
and creates a new ComputerVisionClient object. It also connects to your Blob
Storage account and retrieves the image data from the blob trigger.
The code then uses the Computer Vision API to analyze the
image and extract any objects it detects. It adds these object tags to the
image metadata and saves the updated metadata to Blob Storage.
Testing the Image
Processing Pipeline
Now that our image processing pipeline is set up, we can
test it by uploading an image to our Blob Storage container. The function
should automatically trigger and process the image, adding object tags to the
metadata.
To view the updated metadata, select the image in the Azure
portal and choose "Properties". You should see a list of object tags
extracted from the image.
