@azure-rest/ai-vision-image-analysis v1.0.0-beta.2

Azure AI Vision Image Analysis client library for JavaScript

The Image Analysis service provides AI algorithms for processing images and returning information about their content. In a single service call, you can extract one or more visual features from the image simultaneously, including getting a caption for the image, extracting text shown in the image (OCR) and detecting objects. For more information on the service and the supported visual features, see Image Analysis overview, and the Concepts page.

Use the Image Analysis client library to:

• Authenticate against the service
• Set what features you would like to extract
• Upload an image for analysis, or send an image URL
• Get the analysis result

Product documentation | Samples | Vision Studio | API reference documentation

Getting started

Currently supported environments

• LTS versions of Node.js
• Latest versions of Safari, Chrome, Edge, and Firefox.

See our support policy for more details.

Prerequisites

• An Azure subscription.
• A Computer Vision resource in your Azure subscription.
  • You will need the key and endpoint from this resource to authenticate against the service.
  • You can use the free pricing tier (F0) to try the service, and upgrade later to a paid tier for production.
  • Note that in order to run Image Analysis with the Caption or Dense Captions features, the Azure resource needs to be from one of the following GPU-supported regions: East US, France Central, Korea Central, North Europe, Southeast Asia, West Europe, or West US.

Install the @azure-rest/ai-vision-image-analysis package

Install the Image Analysis client library for JavaScript with npm:

npm install @azure-rest/ai-vision-image-analysis

Browser support

JavaScript Bundle

To use this client library in the browser, first, you need to use a bundler. For details on how to do this, please refer to our bundling documentation.

Key concepts

Once you've initialized an ImageAnalysisClient, you need to select one or more visual features to analyze. The options are specified by the enum class VisualFeatures. The following features are supported:

1. VisualFeatures.Caption: (Examples | Samples) Generate a human-readable sentence that describes the content of an image.
2. VisualFeatures.Read: (Examples | Samples) Also known as Optical Character Recognition (OCR). Extract printed or handwritten text from images.
3. VisualFeatures.DenseCaptions: Dense Captions provides more details by generating one-sentence captions for up to 10 different regions in the image, including one for the whole image.
4. VisualFeatures.Tags: Extract content tags for thousands of recognizable objects, living beings, scenery, and actions that appear in images.
5. VisualFeatures.Objects: Object detection. This is similar to tagging, but focused on detecting physical objects in the image and returning their location.
6. VisualFeatures.SmartCrops: Used to find a representative sub-region of the image for thumbnail generation, with priority given to include faces.
7. VisualFeatures.People: Locate people in the image and return their location.

For more information about these features, see Image Analysis overview, and the Concepts page.

Supported image formats

Image Analysis works on images that meet the following requirements:

• The image must be presented in JPEG, PNG, GIF, BMP, WEBP, ICO, TIFF, or MPO format
• The file size of the image must be less than 20 megabytes (MB)
• The dimensions of the image must be greater than 50 x 50 pixels and less than 16,000 x 16,000 pixels

ImageAnalysisClient

The ImageAnalysisClient is the primary interface for developers interacting with the Image Analysis service. It serves as the gateway from which all interaction with the library will occur.

Examples

Authenticate the client

Here's an example of how to create an ImageAnalysisClient instance using a key-based authentication and an Azure Active Directory authentication.

const { ImageAnalysisClient, KeyCredential } = require("@azure-rest/ai-image-analysis");

const endpoint = "<your_endpoint>";
const key = "<your_key>";
const credential = new KeyCredential(key);

const client = new ImageAnalysisClient(endpoint, credential);

Analyze an image from URL

The following example demonstrates how to analyze an image using the Image Analysis client library for JavaScript.

const imageUrl = "https://example.com/image.jpg";
const features = ["Caption", "DenseCaptions", "Objects", "People", "Read", "SmartCrops", "Tags"];

async function analyzeImageFromUrl() {
  const result = await client.path("/imageanalysis:analyze").post({
    body: {
      url: imageUrl,
    },
    queryParameters: {
      features: features,
      "smartCrops-aspect-ratios": [0.9, 1.33],
    },
    contentType: "application/json",
  });

  console.log("Image analysis result:", result.body);
}

analyzeImageFromUrl();

Analyze an image from a local file

In this example, we will analyze an image from a local file using the Image Analysis client library for JavaScript.

const fs = require("fs");

const imagePath = "./path/to/your/image.jpg";
const features = ["Caption", "DenseCaptions", "Objects", "People", "Read", "SmartCrops", "Tags"];

async function analyzeImageFromFile() {
  const imageBuffer = fs.readFileSync(imagePath);

  const result = await client.path("/imageanalysis:analyze").post({
    body: imageBuffer,
    queryParameters: {
      features: features,
      "smartCrops-aspect-ratios": [0.9, 1.33],
    },
    contentType: "application/octet-stream",
  });

  console.log("Image analysis result:", result.body);
}

analyzeImageFromFile();

Extract text from an image Url

This example demonstrates how to extract printed or hand-written text for the image file sample.jpg using the ImageAnalysisClient. The method call returns an ImageAnalysisResult object. The ReadResult property on the returned object includes a list of text lines and a bounding polygon surrounding each text line. For each line, it also returns a list of words in the text line and a bounding polygon surrounding each word.

const client: ImageAnalysisClient = createImageAnalysisClient(endpoint, credential);

const features: string[] = [
  'Read'
];

const imageUrl: string = 'https://aka.ms/azai/vision/image-analysis-sample.jpg';

client.path('/imageanalysis:analyze').post({
  body: { url: imageUrl },
  queryParameters: { features: features },
  contentType: 'application/json'
}).then(result => {
  const iaResult: ImageAnalysisResultOutput = result.body as ImageAnalysisResultOutput;

  // Process the response
  if (iaResult.readResult && iaResult.readResult.blocks.length > 0) {
    iaResult.readResult.blocks.forEach(block => {
      console.log(`Detected text block: ${JSON.stringify(block)}`);
    });
  } else {
    console.log('No text blocks detected.');
  }

Troubleshooting

Logging

Enabling logging may help uncover useful information about failures. In order to see a log of HTTP requests and responses, set the AZURE_LOG_LEVEL environment variable to info. Alternatively, logging can be enabled at runtime by calling setLogLevel in the @azure/logger:

const { setLogLevel } = require("@azure/logger");

setLogLevel("info");

For more detailed instructions on how to enable logs, you can look at the @azure/logger package docs.

Next steps

Please take a look at the samples directory for detailed examples that demonstrate how to use the client libraries.

Contributing

If you'd like to contribute to this library, please read the contributing guide to learn more about how to build and test the code.

Related projects

• Microsoft Azure SDK for JavaScript