# Azure AI Vision Imageanalysis Java > 使用 Azure AI Vision Java SDK 构建图像分析应用程序。适用于实现图像描述生成、OCR文字提取、物体检测、标签识别或智能裁剪。触发词：图像分析Java、Azure Vision SDK、图像描述、OCR文字提取、物体检测、智能裁剪、人脸检测 - Skill: `kscz0000/azure-ai-vision-imageanalysis-java` (Agent Skill) - Install (CLI): `npx skillmds@latest add kscz0000/azure-ai-vision-imageanalysis-java` - Raw SKILL.md: https://api.skillmd.com/api/skills/kscz0000/azure-ai-vision-imageanalysis-java/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: kscz0000 (https://skillmd.com/u/kscz0000) - Updated: 2026-09-21 - Page: https://skillmd.com/skills/kscz0000/azure-ai-vision-imageanalysis-java --- # Azure AI Vision Image Analysis SDK for Java 使用 Azure AI Vision Image Analysis SDK for Java 构建图像分析应用程序。 ## 安装 ```xml com.azure azure-ai-vision-imageanalysis 1.1.0-beta.1 ``` ## 创建客户端 ### 使用 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(); ``` ### 异步客户端 ```java import com.azure.ai.vision.imageanalysis.ImageAnalysisAsyncClient; ImageAnalysisAsyncClient asyncClient = new ImageAnalysisClientBuilder() .endpoint(endpoint) .credential(new KeyCredential(key)) .buildAsyncClient(); ``` ### 使用 DefaultAzureCredential ```java import com.azure.identity.DefaultAzureCredentialBuilder; ImageAnalysisClient client = new ImageAnalysisClientBuilder() .endpoint(endpoint) .credential(new DefaultAzureCredentialBuilder().build()) .buildClient(); ``` ## 视觉功能 | 功能 | 描述 | |---------|-------------| | `CAPTION` | 生成人类可读的图像描述 | | `DENSE_CAPTIONS` | 为最多10个区域生成描述 | | `READ` | OCR - 从图像中提取文字 | | `TAGS` | 物体、场景、动作的内容标签 | | `OBJECTS` | 检测物体并返回边界框 | | `SMART_CROPS` | 智能缩略图区域 | | `PEOPLE` | 检测人物及其位置 | ## 核心模式 ### 生成描述 ```java import com.azure.ai.vision.imageanalysis.models.*; import com.azure.core.util.BinaryData; import java.io.File; import java.util.Arrays; // 从文件读取 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()); ``` ### 从 URL 生成描述 ```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()); ``` ### 提取文字 (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()); } } } ``` ### 检测物体 ```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()); } ``` ### 获取标签 ```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()); } ``` ### 检测人物 ```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()); } ``` ### 智能裁剪 ```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()); } ``` ### 密集描述 ```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()); } ``` ### 多功能分析 ```java ImageAnalysisResult result = client.analyzeFromUrl( imageUrl, Arrays.asList( VisualFeatures.CAPTION, VisualFeatures.TAGS, VisualFeatures.OBJECTS, VisualFeatures.READ), new ImageAnalysisOptions() .setGenderNeutralCaption(true) .setLanguage("en")); // 访问所有结果 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()); ``` ### 异步分析 ```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") ); ``` ## 错误处理 ```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()); } ``` ## 环境变量 ```bash VISION_ENDPOINT=https://.cognitiveservices.azure.com/ VISION_KEY= ``` ## 图像要求 - 格式：JPEG, PNG, GIF, BMP, WEBP, ICO, TIFF, MPO - 大小：< 20 MB - 尺寸：50x50 到 16000x16000 像素 ## 区域可用性 描述和密集描述功能需要支持 GPU 的区域。部署前请查看[支持的区域](https://learn.microsoft.com/azure/ai-services/computer-vision/concept-describe-images-40)。 ## 触发词 - "图像分析 Java" - "Azure Vision SDK" - "图像描述生成" - "OCR 图像文字提取" - "图像物体检测" - "智能裁剪缩略图" - "图像人物检测" ## 适用场景 本技能适用于执行概述中描述的工作流程或操作。 ## 限制 - 仅当任务明确符合上述范围时使用本技能。 - 输出不应替代特定环境的验证、测试或专家审查。 - 如果缺少必要的输入、权限、安全边界或成功标准，请停止并请求澄清。