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Quickstart: Generate a thumbnail using the Computer Vision REST API and Java

In this quickstart, you 'll generate a thumbnail from an image using the Computer Vision REST API. You specify the height and width, which can differ from the aspect ratio of the input image. Computer Vision uses smart cropping to intelligently identify the area of interest and generate cropping coordinates based on that region.


  • An Azure subscription - Create one for free
  • Java™ Platform, Standard Edition Development Kit 7 or 8 (JDK 7 or 8)
  • Once you have your Azure subscription, create a Computer Vision resource in the Azure portal to get your key and endpoint. After it deploys, click Go to resource.
    • You will need the key and endpoint from the resource you create to connect your application to the Computer Vision service. You'll paste your key and endpoint into the code below later in the quickstart.
    • You can use the free pricing tier (F0) to try the service, and upgrade later to a paid tier for production.
  • Create environment variables for the key and endpoint URL, named COMPUTER_VISION_SUBSCRIPTION_KEY and COMPUTER_VISION_ENDPOINT, respectively.

Create and run the sample application

To create and run the sample, do the following steps:

  1. Create a new Java project in your favorite IDE or editor. If the option is available, create the Java project from a command line application template.

  2. Import the following libraries into your Java project. If you're using Maven, the Maven coordinates are provided for each library.

  3. Add the following import statements to your main class:

     import java.awt.*;
     import javax.swing.*;
     import javax.imageio.ImageIO;
     import java.awt.image.BufferedImage;
     import org.apache.http.HttpEntity;
     import org.apache.http.HttpResponse;
     import org.apache.http.client.methods.HttpPost;
     import org.apache.http.entity.StringEntity;
     import org.apache.http.client.utils.URIBuilder;
     import org.apache.http.impl.client.CloseableHttpClient;
     import org.apache.http.impl.client.HttpClientBuilder;
     import org.apache.http.util.EntityUtils;
     import org.json.JSONObject;
  4. Add the rest of the sample code below, beneath the imports (change to your class name if needed).

  5. Add your Computer Vision subscription key and endpoint to your environment variables.

  6. Optionally, replace the value of imageToAnalyze with the URL of your own image.

  7. Save, then build the Java project.

  8. If you're using an IDE, run GenerateThumbnail. Otherwise, run from the command line (commands below).

 * This sample uses the following libraries (create a "lib" folder to place them in): 
 * Apache HTTP client:
 * org.apache.httpcomponents:httpclient:4.5.X 
 * Apache HTTP core:
 * org.apache.httpcomponents:httpccore:4.4.X 
 * JSON library:
 * org.json:json:20180130
 * To build/run from the command line: 
 *     javac -cp .;lib\*
 *     java -cp .;lib\* GenerateThumbnail

public class GenerateThumbnail {

    // Add your Computer Vision subscription key and endpoint to your environment
    // variables. Then, close and then re-open your command shell or project for the
    // changes to take effect.
    private static String subscriptionKey = System.getenv("COMPUTER_VISION_SUBSCRIPTION_KEY");
    private static String endpoint = System.getenv("COMPUTER_VISION_ENDPOINT");
    // The endpoint path
    private static final String uriBase = endpoint + "vision/v3.1/generateThumbnail";
    // It's optional if you'd like to use your own image instead of this one.
    private static final String imageToAnalyze = "";

    public static void main(String[] args) {
        CloseableHttpClient httpClient = HttpClientBuilder.create().build();

        try {
            URIBuilder uriBuilder = new URIBuilder(uriBase);

            // Request parameters.
            uriBuilder.setParameter("width", "100");
            uriBuilder.setParameter("height", "150");
            uriBuilder.setParameter("smartCropping", "true");

            // Prepare the URI for the REST API method.
            URI uri =;
            HttpPost request = new HttpPost(uri);

            // Request headers.
            request.setHeader("Content-Type", "application/json");
            request.setHeader("Ocp-Apim-Subscription-Key", subscriptionKey);

            // Request body.
            StringEntity requestEntity = new StringEntity("{\"url\":\"" + imageToAnalyze + "\"}");

            // Call the REST API method and get the response entity.
            HttpResponse response = httpClient.execute(request);
            HttpEntity entity = response.getEntity();

            System.out.println("status" + response.getStatusLine().getStatusCode());

            // Check for success.
            if (response.getStatusLine().getStatusCode() == 200) {
                // Display the thumbnail.
                System.out.println("\nDisplaying thumbnail.\n");
            } else {
                // Format and display the JSON error message.
                String jsonString = EntityUtils.toString(entity);
                JSONObject json = new JSONObject(jsonString);
        } catch (Exception e) {

    // Displays the given input stream as an image.
    public static void displayImage(InputStream inputStream) {
        try {
            BufferedImage bufferedImage =;

            ImageIcon imageIcon = new ImageIcon(bufferedImage);

            JLabel jLabel = new JLabel();

            JFrame jFrame = new JFrame();
            jFrame.setLayout(new FlowLayout());
            jFrame.setSize(100, 150);

        } catch (Exception e) {

Examine the response

A successful response is returned as binary data, which represents the image data for the thumbnail. If the request succeeds, the thumbnail is generated from the binary data in the response and displayed in a separate window created by the sample application. If the request fails, the response is displayed in the console window. The response for the failed request contains an error code and a message to help determine what went wrong.

Next steps

Explore a Java Swing application that uses Computer Vision to perform optical character recognition (OCR); create smart-cropped thumbnails; and detect, categorize, tag, and describe visual features in images.