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azure-ai-vision-imageanalysis-java skill

/skills/azure-ai-vision-imageanalysis-java

This skill enables Java developers to analyze images using Azure Vision SDK, extracting captions, text, objects, and crops to power intelligent apps.

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---
name: azure-ai-vision-imageanalysis-java
description: Build image analysis applications with Azure AI Vision SDK for Java. Use when implementing image captioning, OCR text extraction, object detection, tagging, or smart cropping.
package: com.azure:azure-ai-vision-imageanalysis
---

# Azure AI Vision Image Analysis SDK for Java

Build image analysis applications using the Azure AI Vision Image Analysis SDK for Java.

## Installation

```xml
<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-ai-vision-imageanalysis</artifactId>
    <version>1.1.0-beta.1</version>
</dependency>
```

## Client Creation

### With API Key

```java
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");

ImageAnalysisClient client = new ImageAnalysisClientBuilder()
    .endpoint(endpoint)
    .credential(new KeyCredential(key))
    .buildClient();
```

### Async Client

```java
import com.azure.ai.vision.imageanalysis.ImageAnalysisAsyncClient;

ImageAnalysisAsyncClient asyncClient = new ImageAnalysisClientBuilder()
    .endpoint(endpoint)
    .credential(new KeyCredential(key))
    .buildAsyncClient();
```

### With DefaultAzureCredential

```java
import com.azure.identity.DefaultAzureCredentialBuilder;

ImageAnalysisClient client = new ImageAnalysisClientBuilder()
    .endpoint(endpoint)
    .credential(new DefaultAzureCredentialBuilder().build())
    .buildClient();
```

## Visual Features

| Feature | Description |
|---------|-------------|
| `CAPTION` | Generate human-readable image description |
| `DENSE_CAPTIONS` | Captions for up to 10 regions |
| `READ` | OCR - Extract text from images |
| `TAGS` | Content tags for objects, scenes, actions |
| `OBJECTS` | Detect objects with bounding boxes |
| `SMART_CROPS` | Smart thumbnail regions |
| `PEOPLE` | Detect people with locations |

## Core Patterns

### Generate Caption

```java
import com.azure.ai.vision.imageanalysis.models.*;
import com.azure.core.util.BinaryData;
import java.io.File;
import java.util.Arrays;

// From file
BinaryData imageData = BinaryData.fromFile(new File("image.jpg").toPath());

ImageAnalysisResult result = client.analyze(
    imageData,
    Arrays.asList(VisualFeatures.CAPTION),
    new ImageAnalysisOptions().setGenderNeutralCaption(true));

System.out.printf("Caption: \"%s\" (confidence: %.4f)%n",
    result.getCaption().getText(),
    result.getCaption().getConfidence());
```

### Generate Caption from URL

```java
ImageAnalysisResult result = client.analyzeFromUrl(
    "https://example.com/image.jpg",
    Arrays.asList(VisualFeatures.CAPTION),
    new ImageAnalysisOptions().setGenderNeutralCaption(true));

System.out.printf("Caption: \"%s\"%n", result.getCaption().getText());
```

### Extract Text (OCR)

```java
ImageAnalysisResult result = client.analyze(
    BinaryData.fromFile(new File("document.jpg").toPath()),
    Arrays.asList(VisualFeatures.READ),
    null);

for (DetectedTextBlock block : result.getRead().getBlocks()) {
    for (DetectedTextLine line : block.getLines()) {
        System.out.printf("Line: '%s'%n", line.getText());
        System.out.printf("  Bounding polygon: %s%n", line.getBoundingPolygon());
        
        for (DetectedTextWord word : line.getWords()) {
            System.out.printf("  Word: '%s' (confidence: %.4f)%n",
                word.getText(),
                word.getConfidence());
        }
    }
}
```

### Detect Objects

```java
ImageAnalysisResult result = client.analyzeFromUrl(
    imageUrl,
    Arrays.asList(VisualFeatures.OBJECTS),
    null);

for (DetectedObject obj : result.getObjects()) {
    System.out.printf("Object: %s (confidence: %.4f)%n",
        obj.getTags().get(0).getName(),
        obj.getTags().get(0).getConfidence());
    
    ImageBoundingBox box = obj.getBoundingBox();
    System.out.printf("  Location: x=%d, y=%d, w=%d, h=%d%n",
        box.getX(), box.getY(), box.getWidth(), box.getHeight());
}
```

### Get Tags

```java
ImageAnalysisResult result = client.analyzeFromUrl(
    imageUrl,
    Arrays.asList(VisualFeatures.TAGS),
    null);

for (DetectedTag tag : result.getTags()) {
    System.out.printf("Tag: %s (confidence: %.4f)%n",
        tag.getName(),
        tag.getConfidence());
}
```

### Detect People

```java
ImageAnalysisResult result = client.analyzeFromUrl(
    imageUrl,
    Arrays.asList(VisualFeatures.PEOPLE),
    null);

for (DetectedPerson person : result.getPeople()) {
    ImageBoundingBox box = person.getBoundingBox();
    System.out.printf("Person at x=%d, y=%d (confidence: %.4f)%n",
        box.getX(), box.getY(), person.getConfidence());
}
```

### Smart Cropping

```java
ImageAnalysisResult result = client.analyzeFromUrl(
    imageUrl,
    Arrays.asList(VisualFeatures.SMART_CROPS),
    new ImageAnalysisOptions().setSmartCropsAspectRatios(Arrays.asList(1.0, 1.5)));

for (CropRegion crop : result.getSmartCrops()) {
    System.out.printf("Crop region: aspect=%.2f, x=%d, y=%d, w=%d, h=%d%n",
        crop.getAspectRatio(),
        crop.getBoundingBox().getX(),
        crop.getBoundingBox().getY(),
        crop.getBoundingBox().getWidth(),
        crop.getBoundingBox().getHeight());
}
```

### Dense Captions

```java
ImageAnalysisResult result = client.analyzeFromUrl(
    imageUrl,
    Arrays.asList(VisualFeatures.DENSE_CAPTIONS),
    new ImageAnalysisOptions().setGenderNeutralCaption(true));

for (DenseCaption caption : result.getDenseCaptions()) {
    System.out.printf("Caption: \"%s\" (confidence: %.4f)%n",
        caption.getText(),
        caption.getConfidence());
    System.out.printf("  Region: x=%d, y=%d, w=%d, h=%d%n",
        caption.getBoundingBox().getX(),
        caption.getBoundingBox().getY(),
        caption.getBoundingBox().getWidth(),
        caption.getBoundingBox().getHeight());
}
```

### Multiple Features

```java
ImageAnalysisResult result = client.analyzeFromUrl(
    imageUrl,
    Arrays.asList(
        VisualFeatures.CAPTION,
        VisualFeatures.TAGS,
        VisualFeatures.OBJECTS,
        VisualFeatures.READ),
    new ImageAnalysisOptions()
        .setGenderNeutralCaption(true)
        .setLanguage("en"));

// Access all results
System.out.println("Caption: " + result.getCaption().getText());
System.out.println("Tags: " + result.getTags().size());
System.out.println("Objects: " + result.getObjects().size());
System.out.println("Text blocks: " + result.getRead().getBlocks().size());
```

### Async Analysis

```java
asyncClient.analyzeFromUrl(
    imageUrl,
    Arrays.asList(VisualFeatures.CAPTION),
    null)
    .subscribe(
        result -> System.out.println("Caption: " + result.getCaption().getText()),
        error -> System.err.println("Error: " + error.getMessage()),
        () -> System.out.println("Complete")
    );
```

## Error Handling

```java
import com.azure.core.exception.HttpResponseException;

try {
    client.analyzeFromUrl(imageUrl, Arrays.asList(VisualFeatures.CAPTION), null);
} catch (HttpResponseException e) {
    System.out.println("Status: " + e.getResponse().getStatusCode());
    System.out.println("Error: " + e.getMessage());
}
```

## Environment Variables

```bash
VISION_ENDPOINT=https://<resource>.cognitiveservices.azure.com/
VISION_KEY=<your-api-key>
```

## Image Requirements

- Formats: JPEG, PNG, GIF, BMP, WEBP, ICO, TIFF, MPO
- Size: < 20 MB
- Dimensions: 50x50 to 16000x16000 pixels

## Regional Availability

Caption and Dense Captions require GPU-supported regions. Check [supported regions](https://learn.microsoft.com/azure/ai-services/computer-vision/concept-describe-images-40) before deployment.

## Trigger Phrases

- "image analysis Java"
- "Azure Vision SDK"
- "image captioning"
- "OCR image text extraction"
- "object detection image"
- "smart crop thumbnail"
- "detect people image"

Overview

This skill lets you build image analysis applications using the Azure AI Vision Image Analysis SDK for Java. It provides straightforward client creation patterns (API key, DefaultAzureCredential, async) and examples for captioning, OCR, object detection, tagging, people detection, dense captions, and smart crops. The code samples focus on practical integration points and common visual features so you can get working quickly.

How this skill works

The skill shows how to create synchronous and asynchronous ImageAnalysis clients and call the analyze/analyzeFromUrl methods with specific VisualFeatures. Results return typed models (Caption, Read/OCR blocks, DetectedObject, DetectedTag, CropRegion, DetectedPerson, DenseCaption) that include confidence scores and bounding boxes. Examples include multi-feature analysis, error handling, environment variable setup, and recommended image formats and size limits.

When to use it

  • You need automatic human-readable captions for images or regions.
  • Extract printed or handwritten text from photos or scanned documents (OCR).
  • Detect, locate, and label objects or people with bounding boxes and confidence scores.
  • Generate tags and scene labels for indexing, search, or recommendations.
  • Produce smart thumbnails or aspect-aware crops for responsive UIs.

Best practices

  • Use environment variables for VISION_ENDPOINT and VISION_KEY or DefaultAzureCredential for managed identities.
  • Request only the VisualFeatures you need to minimize latency and cost (combine features when appropriate).
  • Check confidence scores and apply thresholds before acting on results in production.
  • Respect image size and format limits (<= 20 MB; 50x50 to 16000x16000 px) and prefer smaller, resized images for high throughput.
  • Handle HttpResponseException and other transient errors; implement retries and exponential backoff for network calls.

Example use cases

  • Generate accessible image captions for web content and alt text automation.
  • Process invoices, forms, or receipts with OCR to extract structured text and line items.
  • Automate inventory tagging by detecting and tagging products in e-commerce photos.
  • Create user-facing photo editors that suggest smart crops and focal points for thumbnails.
  • Build video analysis pipelines by sampling frames and running object detection and dense captions.

FAQ

Which authentication methods are supported?

You can authenticate with an API key (KeyCredential) or use DefaultAzureCredential for managed identities; both sync and async clients are available.

What image formats and size limits apply?

Supported formats include JPEG, PNG, GIF, BMP, WEBP, ICO, TIFF, MPO; files must be under 20 MB and dimensions between 50x50 and 16000x16000 pixels.