菜单
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.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
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npx skills add https://github.com/BEKO2210/Firstbrain --skill azure-ai-vision-imageanalysis-java命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
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Arquitecto de Soluciones Principal y Consultor Tecnológico de Andru.ia. Diagnostica y traza la hoja de ruta óptima para proyectos de IA en español.
Security audit, hardening, threat modeling (STRIDE/PASTA), Red/Blue Team, OWASP checks, code review, incident response, and infrastructure security for any project.
Ingeniero de Sistemas de Andru.ia. Diseña, redacta y despliega nuevas habilidades (skills) dentro del repositorio siguiendo el Estándar de Diamante.
| 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. |
| type | skill |
| created | 2026-02-27T00:00:00.000Z |
| domain | ai-ml |
| category | computer-vision |
| risk | unknown |
| source | community |
| tags | ["skill","ai-ml","computer-vision","azure","vision","imageanalysis"] |
Build image analysis applications using the Azure AI Vision Image Analysis SDK for Java.
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-ai-vision-imageanalysis</artifactId>
<version>1.1.0-beta.1</version>
</dependency>
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();
import com.azure.ai.vision.imageanalysis.ImageAnalysisAsyncClient;
ImageAnalysisAsyncClient asyncClient = new ImageAnalysisClientBuilder()
.endpoint(endpoint)
.credential(new KeyCredential(key))
.buildAsyncClient();
import com.azure.identity.DefaultAzureCredentialBuilder;
ImageAnalysisClient client = new ImageAnalysisClientBuilder()
.endpoint(endpoint)
.credential(new DefaultAzureCredentialBuilder().build())
.buildClient();
| 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 |
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());
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());
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());
}
}
}
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());
}
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());
}
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());
}
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());
}
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());
}
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());
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")
);
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());
}
VISION_ENDPOINT=https://<resource>.cognitiveservices.azure.com/
VISION_KEY=<your-api-key>
Caption and Dense Captions require GPU-supported regions. Check supported regions before deployment.
This skill is applicable to execute the workflow or actions described in the overview.