Azure Image Analysis client library for Java - version 1.0.0-beta.2

  • Article

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 | Maven Package | SDK source code

Getting started

Prerequisites

  • Java Development Kit (JDK) with version 8 or above.
  • 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.
    • Note that in order to run Image Analysis with the Caption or Dense Captions features, the Azure resource needs to be from a GPU-supported region. See this document for a list of supported regions.

Adding the package to your product

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-ai-vision-imageanalysis</artifactId>
    <version>1.0.0-beta.2</version>
</dependency>

Set environment variables

To authenticate the ImageAnalysisClient, you will need the endpoint and key from your Azure Computer Vision resource in the Azure Portal. The code snippet below assumes these values are stored in environment variables:

  • Set the environment variable VISION_ENDPOINT to the endpoint URL. It has the form https://your-resource-name.cognitiveservices.azure.com, where your-resource-name is your unique Azure Computer Vision resource name.

  • Set the environment variable VISION_KEY to the key. The key is a 32-character Hexadecimal number.

Note that the client library does not directly read these environment variable at run time. The endpoint and key must be provided to the ImageAnalysisClientBuilder in your code. The code snippet below reads environment variables to promote the practice of not hard-coding secrets in your source code.

Create and authenticate the client

Once you define the environment variables, this Java code will create and authenticate a synchronous ImageAnalysisClient:

import com.azure.ai.vision.imageanalysis.ImageAnalysisClient;
import com.azure.ai.vision.imageanalysis.ImageAnalysisClientBuilder;
import com.azure.core.credential.KeyCredential;

String endpoint = System.getenv("VISION_ENDPOINT");
String key = System.getenv("VISION_KEY");

if (endpoint == null || key == null) {
    System.out.println("Missing environment variable 'VISION_ENDPOINT' or 'VISION_KEY'.");
    System.out.println("Set them before running this sample.");
    System.exit(1);
}

// Create a synchronous Image Analysis client.
ImageAnalysisClient client = new ImageAnalysisClientBuilder()
    .endpoint(endpoint)
    .credential(new KeyCredential(key))
    .buildClient();

A synchronous client supports synchronous analysis methods, meaning they will block until the service responds with analysis results. The code snippets below all use synchronous methods because it's easier for a getting-started guide. The SDK offers equivalent asynchronous APIs which are often preferred. To create an ImageAnalysisAsyncClient, simply import com.azure.ai.vision.imageanalysis.ImageAnalysisAsyncClient and call buildAsyncClient() instead of buildClient():

// Create an asynchronous Image Analysis client.
ImageAnalysisAsyncClient client = new ImageAnalysisClientBuilder()
    .endpoint(endpoint)
    .credential(new KeyCredential(key))
    .buildAsyncClient();

Key concepts

Visual features

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. Note: For extracting text from PDF, Office, and HTML documents and document images, use the Document Intelligence service with the Read model. This model is optimized for text-heavy digital and scanned documents with an asynchronous REST API that makes it easy to power your intelligent document processing scenarios. This service is separate from the Image Analysis service and has its own SDK.
  3. VisualFeatures.DENSE_CAPTIONS (Samples): 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 (Samples): Extract content tags for thousands of recognizable objects, living beings, scenery, and actions that appear in images.
  5. VisualFeatures.OBJECTS (Samples): Object detection. This is similar to tagging, but focused on detecting physical objects in the image and returning their location.
  6. VisualFeatures.SMART_CROPS (Samples): Used to find a representative sub-region of the image for thumbnail generation, with priority given to include faces.
  7. VisualFeatures.PEOPLE (Samples): Detect people in the image and return their location.

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

Analyze from image buffer or URL

The ImageAnalysisClient has two overloads for the method analyze:

  • Analyze an image from a memory buffer, using the BinaryData object. The client will upload the image to the service as the REQUEST body.
  • Analyze an image from a publicly-accessible URL, using the java.lang.URL object. The client will send the image URL to the service. The service will fetch the image.

The examples below show how to do both. The analyze examples populate the input BinaryData object by loading an image from a file on disk.

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

Examples

The following sections provide code snippets covering these common Image Analysis scenarios:

These snippets use the synchronous client from Create and authenticate the client.

See the Samples folder for fully working samples for all visual features, including asynchronous clients.

Generate an image caption for an image file

This example demonstrates how to generate a one-sentence caption for the image file sample.jpg using the ImageAnalysisClient. The synchronous (blocking) analyze method call returns an ImageAnalysisResult object. A call to getCaption() on this result will return a CaptionResult object. It contains the generated caption and its confidence score in the range [0, 1]. By default the caption may contain gender terms such as "man", "woman", or "boy", "girl". You have the option to request gender-neutral terms such as "person" or "child" by setting genderNeutralCaption to true when calling analyze, as shown in this example.

Notes:

  • Caption is only available in some Azure regions. See this document for a list of supported regions.
  • Caption is only supported in English at the moment.
import com.azure.ai.vision.imageanalysis.models.ImageAnalysisOptions;
import com.azure.ai.vision.imageanalysis.models.ImageAnalysisResult;
import com.azure.ai.vision.imageanalysis.models.VisualFeatures;
import com.azure.core.util.BinaryData;
import java.io.File;
import java.util.Arrays;

ImageAnalysisResult result = client.analyze(
    BinaryData.fromFile(new File("sample.jpg").toPath()), // imageData: Image file loaded into memory as BinaryData
    Arrays.asList(VisualFeatures.CAPTION), // visualFeatures
    new ImageAnalysisOptions().setGenderNeutralCaption(true)); // options:  Set to 'true' or 'false' (relevant for CAPTION or DENSE_CAPTIONS visual features)

// Print analysis results to the console
System.out.println("Image analysis results:");
System.out.println(" Caption:");
System.out.println("   \"" + result.getCaption().getText() + "\", Confidence " 
    + String.format("%.4f", result.getCaption().getConfidence()));

To generate captions for additional images, simply call analyze multiple times. You can use the same ImageAnalysisClient do to multiple analysis calls.

Generate an image caption for an image URL

This example is similar to the above, except it calls the analyze method and provides a publicly accessible image URL instead of a file name.

import com.azure.ai.vision.imageanalysis.models.ImageAnalysisOptions;
import com.azure.ai.vision.imageanalysis.models.ImageAnalysisResult;
import com.azure.ai.vision.imageanalysis.models.VisualFeatures;
import java.util.Arrays;

ImageAnalysisResult result = client.analyzeFromUrl(
    "https://aka.ms/azsdk/image-analysis/sample.jpg", // imageUrl: the URL of the image to analyze
    Arrays.asList(VisualFeatures.CAPTION), // visualFeatures
    new ImageAnalysisOptions().setGenderNeutralCaption(true)); // options:  Set to 'true' or 'false' (relevant for CAPTION or DENSE_CAPTIONS visual features)

// Print analysis results to the console
System.out.println("Image analysis results:");
System.out.println(" Caption:");
System.out.println("   \"" + result.getCaption().getText() + "\", Confidence "
    + String.format("%.4f", result.getCaption().getConfidence()));

Extract text from an image file

This example demonstrates how to extract printed or hand-written text for the image file sample.jpg using the ImageAnalysisClient. The synchronous (blocking) analyze method call returns an ImageAnalysisResult object. A call to getRead() on the result will return a ReadResult object. It 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.

import com.azure.ai.vision.imageanalysis.models.DetectedTextLine;
import com.azure.ai.vision.imageanalysis.models.DetectedTextWord;
import com.azure.ai.vision.imageanalysis.models.ImageAnalysisResult;
import com.azure.ai.vision.imageanalysis.models.VisualFeatures;
import com.azure.core.util.BinaryData;
import java.io.File;
import java.util.Arrays;

ImageAnalysisResult result = client.analyze(
    BinaryData.fromFile(new File("sample.jpg").toPath()), // imageData: Image file loaded into memory as BinaryData
    Arrays.asList(VisualFeatures.READ), // visualFeatures
    null); // options: There are no options for READ visual feature

// Print analysis results to the console
System.out.println("Image analysis results:");
System.out.println(" Read:");
for (DetectedTextLine line : result.getRead().getBlocks().get(0).getLines()) {
    System.out.println("   Line: '" + line.getText()
        + "', Bounding polygon " + line.getBoundingPolygon());
    for (DetectedTextWord word : line.getWords()) {
        System.out.println("     Word: '" + word.getText()
            + "', Bounding polygon " + word.getBoundingPolygon()
            + ", Confidence " + String.format("%.4f", word.getConfidence()));
    }
}

To extract text for additional images, simply call the analyze multiple times. You can use the same ImageAnalysisClient do to multiple analysis calls.

Note: For extracting text from PDF, Office, and HTML documents and document images, use the Document Intelligence service with the Read model. This model is optimized for text-heavy digital and scanned documents with an asynchronous REST API that makes it easy to power your intelligent document processing scenarios. This service is separate from the Image Analysis service and has its own SDK.

Extract text from an image URL

This example is similar to the above, except it calls the analyze method and provides a publicly accessible image URL instead of a file name.

import com.azure.ai.vision.imageanalysis.models.DetectedTextLine;
import com.azure.ai.vision.imageanalysis.models.DetectedTextWord;
import com.azure.ai.vision.imageanalysis.models.ImageAnalysisResult;
import com.azure.ai.vision.imageanalysis.models.VisualFeatures;
import java.util.Arrays;

ImageAnalysisResult result = client.analyzeFromUrl(
    "https://aka.ms/azsdk/image-analysis/sample.jpg", // imageUrl: the URL of the image to analyze
    Arrays.asList(VisualFeatures.READ), // visualFeatures
    null); // options: There are no options for READ visual feature

// Print analysis results to the console
System.out.println("Image analysis results:");
System.out.println(" Read:");
for (DetectedTextLine line : result.getRead().getBlocks().get(0).getLines()) {
    System.out.println("   Line: '" + line.getText()
        + "', Bounding polygon " + line.getBoundingPolygon());
    for (DetectedTextWord word : line.getWords()) {
        System.out.println("     Word: '" + word.getText()
            + "', Bounding polygon " + word.getBoundingPolygon()
            + ", Confidence " + String.format("%.4f", word.getConfidence()));
    }
}

Troubleshooting

Exceptions

The analyze methods throw HttpResponseException when the service responds with a non-success HTTP status code. The exception's getResponse().getStatusCode() will hold the HTTP response status code. The exception's getMessage() contains a detailed message that will allow you to diagnose the issue:

try {
    ImageAnalysisResult result = client.analyze(...)
} catch (HttpResponseException e) {
    System.out.println("Exception: " + e.getClass().getSimpleName());
    System.out.println("Status code: " + e.getResponse().getStatusCode());
    System.out.println("Message: " + e.getMessage());
} catch (Exception e) {
    System.out.println("Message: " + e.getMessage());
}

For example, when you provide a wrong authentication key:

Exception: ClientAuthenticationException
Status code: 401
Message: Status code 401, "{"error":{"code":"401","message":"Access denied due to invalid subscription key or wrong API endpoint. Make sure to provide a valid key for an active subscription and use a correct regional API endpoint for your resource."}}"

Or when you provide an image in a format that is not recognized:

Exception: HttpResponseException
Status code: 400
Message: Status code 400, "{"error":{"code":"InvalidRequest","message":"Image format is not valid.","innererror":{"code":"InvalidImageFormat","message":"Input data is not a valid image."}}}"

Enable HTTP request/response logging

Reviewing the HTTP request sent or response received over the wire to the Image Analysis service can be useful in troubleshooting. This can be done in two ways:

  1. The Image Analysis client library supports a built-in console logging framework for temporary debugging purposes. It also supports more advanced logging using the SLF4J interface. For detailed information see Use logging in the Azure SDK for Java.
  2. By getting access to the Response object, and from it the HttpRequest object, and printing information provided by these objects. See SampleCaptionImageFileWithResponse.java and SampleOcrImageUrlWithResponseAsync.java.

We recommend you enable console logging (option #1). The sections below discusses enabling console logging using the built-in framework.

By setting environment variables

You can enable console logging of HTTP request and response for your entire application by setting the following two environment variables. Note that this change will affect every Azure client that supports logging HTTP request and response.

  • Set environment variable AZURE_LOG_LEVEL to debug
  • Set environment variable AZURE_HTTP_LOG_DETAIL_LEVEL to one of the following values:
Value Logging level
none HTTP request/response logging is disabled
basic Logs only URLs, HTTP methods, and time to finish the request.
headers Logs everything in BASIC, plus all the request and response headers.
body Logs everything in BASIC, plus all the request and response body.
body_and_headers Logs everything in HEADERS and BODY.

By setting httpLogOptions

To enable console logging of HTTP request and response for a single client

  • Set environment variable AZURE_LOG_LEVEL to debug
  • Add a call to httpLogOptions when building the ImageAnalysisClient:
ImageAnalysisClient client = new ImageAnalysisClientBuilder()
    .endpoint(endpoint)
    .credential(new KeyCredential(key))
    .httpLogOptions(new HttpLogOptions().setLogLevel(HttpLogDetailLevel.BODY_AND_HEADERS))
    .buildClient();

The enum HttpLogDetailLevel defines the supported logging levels.

By default, when logging, certain HTTP header and query parameter values are redacted. It is possible to override this default by specifying which headers and query parameters are safe to log:

ImageAnalysisClient client = new ImageAnalysisClientBuilder()
    .endpoint(endpoint)
    .credential(new KeyCredential(key))
    .httpLogOptions(new HttpLogOptions().setLogLevel(HttpLogDetailLevel.BODY_AND_HEADERS)
        .addAllowedHeaderName("safe-to-log-header-name")
        .addAllowedQueryParamName("safe-to-log-query-parameter-name"))
    .buildClient();

For example, to get a complete un-redacted log of the HTTP request, apply the following:

    .httpLogOptions(new HttpLogOptions().setLogLevel(HttpLogDetailLevel.BODY_AND_HEADERS)
        .addAllowedHeaderName("Ocp-Apim-Subscription-Key")
        .addAllowedQueryParamName("features")
        .addAllowedQueryParamName("language")
        .addAllowedQueryParamName("gender-neutral-caption")
        .addAllowedQueryParamName("smartcrops-aspect-ratios")
        .addAllowedQueryParamName("model-version"))

Add more to the above to get a completely un-redacted HTTP response. When you share an un-redacted log, make sure it does not contain secrets such as your subscription key.

Next steps

  • Have a look at the Samples folder, containing fully runnable Java code for Image Analysis (all visual features, synchronous and asynchronous clients, from image file or URL).

Contributing

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