Overview of Azure Cognitive Services Custom Vision
- Azure Custom Vision Service allows developers to build custom image classifiers.
- It provides a REST API that can be integrated into applications to predict, classify, and tag images based on custom training data.
Setting Up Your Environment
- Ensure your Java development environment is ready. You’ll need JDK 8 or higher and a dependency management tool like Maven or Gradle.
- Obtain your Azure Custom Vision subscription key and endpoint from the Azure portal. These will be needed to authenticate your requests.
- Configure your project to include the necessary libraries. You can use libraries like `Apache HttpClient` for HTTP operations or `REST Assured` for more flexibility.
Adding Maven Dependencies
To integrate Azure Custom Vision APIs, add the following dependencies to your Maven project's pom.xml file:
<dependency>
<groupId>com.squareup.okhttp3</groupId>
<artifactId>okhttp</artifactId>
<version>4.9.1</version>
</dependency>
Adding the library for JSON parsing is also useful:
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-databind</artifactId>
<version>2.12.1</version>
</dependency>
Initialize HTTP Client
- Use OkHttpClient from the OkHttp library for making HTTP requests to Azure’s API.
- Initialize this client once and reuse it throughout your application to manage resources efficiently.
OkHttpClient client = new OkHttpClient();
Create Your Image Prediction Request
- Compose a request by specifying the URL, setting headers, and defining the image data as the body of the request.
- Set the content type header to `application/json` or `application/octet-stream` depending on how you are sending the image data.
String url = "https://your-custom-vision-endpoint.com/customvision/v3.0/Prediction/project-id/image";
String predictionKey = "your-subscription-key";
RequestBody body = new MultipartBody.Builder()
.setType(MultipartBody.FORM)
.addFormDataPart("file", "image.jpg",
RequestBody.create(MediaType.parse("application/octet-stream"),
new File("path-to-your-image")))
.build();
Request request = new Request.Builder()
.url(url)
.post(body)
.addHeader("Prediction-Key", predictionKey)
.build();
Execute the Request and Handle Response
- Use the previously initialized OkHttpClient object to execute the request.
- Parse the response to handle the prediction results.
try (Response response = client.newCall(request).execute()) {
if (!response.isSuccessful()) throw new IOException("Unexpected code " + response);
// Parse response as JSON
ObjectMapper mapper = new ObjectMapper();
JsonNode root = mapper.readTree(response.body().string());
System.out.println("Prediction Result: " + root.toPrettyString());
}
Debugging and Handling Errors
- Inspect HTTP response codes to handle errors such as authentication failures, validation issues, or server errors.
- Log significant events to help diagnose issues. Consider using a logging framework like SLF4J or Logback.
By following these steps, you integrate Azure Custom Vision Service into your Java application, enabling robust image prediction capabilities directly within your software solution.
