azure-ai-vision-imageanalysis-java
microsoft/skills
Java용 Azure AI Vision SDK를 사용하여 이미지를 분석하고, 캡션 생성, OCR, 객체 탐지, 태깅 및 스마트 자르기 기능을 활용할 수 있습니다.
...모든 것을 확장하십시오Java용 Azure AI Vision 이미지 분석 SDK
Java용 Azure AI Vision 이미지 분석 SDK를 사용하여 이미지 분석 애플리케이션을 구축하세요.
설치
com.azure
azure-ai-vision-imageanalysis
1.1.0-beta.1
클라이언트 생성
API 키 사용
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();
비동기 클라이언트
import com.azure.ai.vision.imageanalysis.ImageAnalysisAsyncClient;
ImageAnalysisAsyncClient asyncClient = new ImageAnalysisClientBuilder()
.endpoint(endpoint)
.credential(new KeyCredential(key))
.buildAsyncClient();
DefaultAzureCredential 사용
import com.azure.core.credential.TokenCredential;
import com.azure.identity.AzureIdentityEnvVars;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.identity.ManagedIdentityCredentialBuilder;
TokenCredential credential = new DefaultAzureCredentialBuilder()
.requireEnvVars(AzureIdentityEnvVars.AZURE_TOKEN_CREDENTIALS)
.build();
// 또는 프로덕션 환경에서 특정 자격 증명을 직접 사용:
// https://learn.microsoft.com/java/api/overview/azure/identity-readme?view=azure-java-stable#credential-classes 참조
// TokenCredential credential = new ManagedIdentityCredentialBuilder().build();
ImageAnalysisClient client = new ImageAnalysisClientBuilder()
.endpoint(endpoint)
.credential(credential)
.buildClient();
시각적 특징
| 특징 | 설명 |
|---|---|
캡션 |
사람이 읽을 수 있는 이미지 설명 생성 |
DENSE_CAPTIONS |
최대 10개 영역에 대한 캡션 |
읽기 |
OCR - 이미지에서 텍스트 추출 |
TAGS |
사물, 장면, 동작에 대한 콘텐츠 태그 |
객체 |
바운딩 박스를 사용하여 객체 감지 |
SMART_CROPS |
스마트 썸네일 영역 |
사람 |
위치를 포함한 사람 감지 |
핵심 패턴
캡션 생성
import com.azure.ai.vision.imageanalysis.models.*;
import com.azure.core.util.BinaryData;
import java.io.File;
import java.util.Arrays;
// 파일에서 불러오기
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("캡션: \"%s\" (신뢰도: %.4f)%n",
result.getCaption().getText(),
result.getCaption().getConfidence());
URL에서 캡션 생성
ImageAnalysisResult result = client.analyzeFromUrl(
"https://example.com/image.jpg",
Arrays.asList(VisualFeatures.CAPTION),
new ImageAnalysisOptions().setGenderNeutralCaption(true));
System.out.printf("캡션: \"%s\"%n", result.getCaption().getText());
텍스트 추출 (OCR)
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("줄: '%s'%n", line.getText());
System.out.printf(" 경계 다각형: %s%n", line.getBoundingPolygon());
for (DetectedTextWord word : line.getWords()) {
System.out.printf(" 단어: '%s' (신뢰도: %.4f)%n",
word.getText(),
word.getConfidence());
}
}
}
객체 감지
ImageAnalysisResult result = client.analyzeFromUrl(
imageUrl,
Arrays.asList(VisualFeatures.OBJECTS),
null);
for (DetectedObject obj : result.getObjects()) {
System.out.printf("물체: %s (신뢰도: %.4f)%n",
obj.getTags().get(0).getName(),
obj.getTags().get(0).getConfidence());
ImageBoundingBox box = obj.getBoundingBox();
System.out.printf(" 위치: x=%d, y=%d, w=%d, h=%d%n",
box.getX(), box.getY(), box.getWidth(), box.getHeight());
}
태그 가져오기
ImageAnalysisResult result = client.analyzeFromUrl(
imageUrl,
Arrays.asList(VisualFeatures.TAGS),
null);
for (DetectedTag tag : result.getTags()) {
System.out.printf("태그: %s (신뢰도: %.4f)%n",
tag.getName(),
tag.getConfidence());
}
사람 감지
ImageAnalysisResult result = client.analyzeFromUrl(
imageUrl,
Arrays.asList(VisualFeatures.PEOPLE),
null);
for (DetectedPerson person : result.getPeople()) {
ImageBoundingBox box = person.getBoundingBox();
System.out.printf("x=%d, y=%d 위치의 사람 (신뢰도: %.4f)%n",
box.getX(), box.getY(), person.getConfidence());
}
스마트 크롭
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("자르기 영역: 종횡비=%.2f, x=%d, y=%d, w=%d, h=%d%n",
crop.getAspectRatio(),
crop.getBoundingBox().getX(),
crop.getBoundingBox().getY(),
crop.getBoundingBox().getWidth(),
crop.getBoundingBox().getHeight());
}
밀도 높은 캡션
ImageAnalysisResult result = client.analyzeFromUrl(
imageUrl,
Arrays.asList(VisualFeatures.DENSE_CAPTIONS),
new ImageAnalysisOptions().setGenderNeutralCaption(true));
for (DenseCaption caption : result.getDenseCaptions()) {
System.out.printf("캡션: \"%s\" (신뢰도: %.4f)%n",
caption.getText(),
caption.getConfidence());
System.out.printf(" 영역: x=%d, y=%d, w=%d, h=%d%n",
caption.getBoundingBox().getX(),
caption.getBoundingBox().getY(),
caption.getBoundingBox().getWidth(),
caption.getBoundingBox().getHeight());
}
여러 특징
ImageAnalysisResult result = client.analyzeFromUrl(
imageUrl,
Arrays.asList(
VisualFeatures.CAPTION,
VisualFeatures.TAGS,
VisualFeatures.OBJECTS,
VisualFeatures.READ),
new ImageAnalysisOptions()
.setGenderNeutralCaption(true)
.setLanguage("en"));
// 모든 결과 가져오기
System.out.println("캡션: " + result.getCaption().getText());
System.out.println("태그: " + result.getTags().size());
System.out.println("사물: " + result.getObjects().size());
System.out.println("텍스트 블록: " + result.getRead().getBlocks().size());
비동기 분석
asyncClient.analyzeFromUrl(
imageUrl,
Arrays.asList(VisualFeatures.CAPTION),
null)
.subscribe(
result -> System.out.println("캡션: " + result.getCaption().getText()),
error -> System.err.println("오류: " + error.getMessage()),
() -> System.out.println("완료")
);
오류 처리
import com.azure.core.exception.HttpResponseException;
try {
client.analyzeFromUrl(imageUrl, Arrays.asList(VisualFeatures.CAPTION), null);
} catch (HttpResponseException e) {
System.out.println("상태: " + e.getResponse().getStatusCode());
System.out.println("오류: " + e.getMessage());
}
환경 변수
VISION_ENDPOINT=https://.cognitiveservices.azure.com/ # 모든 인증 방식에 필수
VISION_KEY= # AzureKeyCredential 인증 시에만 필요
AZURE_TOKEN_CREDENTIALS=prod # 프로덕션 환경에서 DefaultAzureCredential을 사용하는 경우에만 필요
이미지 요구 사항
- 형식: JPEG, PNG, GIF, BMP, WEBP, ICO, TIFF, MPO
- 크기: 20MB 미만
- 크기: 50x50 ~ 16000x16000 픽셀
지역별 사용 가능 여부
캡션 및 밀집 캡션을 사용하려면 GPU가 지원되는 지역이어야 합니다. 배포 전에 지원되는 지역을 확인하십시오.
트리거 문구
- "이미지 분석 Java"
- "Azure Vision SDK"
- "이미지 캡션 생성"
- "OCR 이미지 텍스트 추출"
- "물체 탐지 이미지"
- "스마트 자르기 썸네일"
- "사람 탐지 이미지"
---
name: azure-ai-vision-imageanalysis-java
description: Analyze images using Azure AI Vision SDK for Java, enabling captioning, OCR, object detection, tagging, and smart cropping.
license: MIT
---
# 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.core.credential.TokenCredential;
import com.azure.identity.AzureIdentityEnvVars;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.identity.ManagedIdentityCredentialBuilder;
TokenCredential credential = new DefaultAzureCredentialBuilder()
.requireEnvVars(AzureIdentityEnvVars.AZURE_TOKEN_CREDENTIALS)
.build();
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/java/api/overview/azure/identity-readme?view=azure-java-stable#credential-classes
// TokenCredential credential = new ManagedIdentityCredentialBuilder().build();
ImageAnalysisClient client = new ImageAnalysisClientBuilder()
.endpoint(endpoint)
.credential(credential)
.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/ # Required for all auth methods
VISION_KEY=<your-api-key> # Only required for AzureKeyCredential auth
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
```
## 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"
azure-ai-vision-imageanalysis-java 설치
스킬 파일을 다운로드한 후 .claude/skills/ 디렉터리에 압축을 풀어주세요.
ZIP 다운로드저장소를 클론하고 스킬 파일을 프로젝트에 복사하세요.
git clone https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-java/skills/azure-ai-vision-imageanalysis-java # Copy SKILL.md to your .claude/skills/ directory
복사





집
