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快速入门：图像分析 4.0
开始使用图像分析 4.0 REST API 或客户端 SDK 来设置基本的图像分析应用程序。 图像分析服务提供 AI 算法，可用于处理图像并返回其视觉特征的相关信息。 请按照以下步骤将包安装到应用程序中并试用示例代码。
使用适用于 .NET 的图像分析客户端 SDK 分析图像以读取文本并生成图像描述文字。 此快速入门分析远程图像，并将结果输出到控制台。
提示
分析 4.0 API 可以执行许多不同的操作。 有关展示所有可用功能的示例，请参阅分析图像操作指南。
先决条件
- Azure 订阅 - 免费创建订阅
- 启用了“.NET 桌面开发”工作负载的 Visual Studio IDE。 或者，如果你不打算使用 Visual Studio IDE，则需要安装 .NET SDK。
- 拥有 Azure 订阅后，请在 Azure 门户中创建计算机视觉资源。 若要使用本快速入门中的描述文字功能，必须在某个受支持的 Azure 区域中创建资源（请参阅图像描述文字）。 部署后，选择”转到资源”。
- 需要从创建的资源获取密钥和终结点，以便将应用程序连接到 Azure AI 视觉服务。
- 可以使用免费定价层 (
F0) 试用该服务，然后再升级到付费层进行生产。
设置应用程序
创建一个新的 C# 应用程序。
打开 Visual Studio，在“开始”下选择“创建新项目”。 将模板筛选器设置为 C#/所有平台/控制台。 选择“控制台应用”（可在 Windows、Linux 和 macOS 版 .NET 上运行的命令行应用程序），然后选择“下一步”。 将项目名称更新为 ImageAnalysisQuickstart，然后选择“下一步”。 选择“.NET 6.0”或更高版本，然后选择“创建”以创建项目。
安装客户端 SDK
创建新项目后，右键单击“解决方案资源管理器”中的项目解决方案，然后选择“管理 NuGet 包”，以安装客户端 SDK。 在打开的包管理器中，选择“浏览”，选中“包括预发行版”并搜索 Azure.AI.Vision.ImageAnalysis。 选择“安装” 。
创建环境变量
在此示例中，将凭据写入运行应用程序的本地计算机上的环境变量。
转到 Azure 门户。 如果在“先决条件”部分创建的资源部署成功，请选择“后续步骤”下的“转到资源”。 在“密钥和终结点”页的“资源管理”下，可以找到密钥和终结点。 你的资源密钥与你的 Azure 订阅 ID 不同。
提示
请不要直接在代码中包含密钥，并且绝不公开发布密钥。 有关 Azure Key Vault 等更多身份验证选项，请参阅 Azure AI 服务安全性一文。
若要为密钥和终结点设置环境变量，请打开控制台窗口，并按照操作系统和开发环境的说明进行操作。
- 若要设置
VISION_KEY环境变量，请将your-key替换为资源的其中一个密钥。 - 若要设置
VISION_ENDPOINT环境变量，请将your-endpoint替换为资源的终结点。
setx VISION_KEY your-key
setx VISION_ENDPOINT your-endpoint
添加环境变量后，可能需要重启任何正在运行的、将读取环境变量的程序（包括控制台窗口）。
分析图像
从项目目录中，打开以前使用新项目创建的 Program.cs 文件。 粘贴以下代码。
提示
代码显示如何分析图像 URL。 还可以分析本地图像文件或内存缓冲区中的图像。 有关详细信息，请参阅分析图像操作指南。
using Azure;
using Azure.AI.Vision.ImageAnalysis;
using System;
public class Program
{
static void AnalyzeImage()
{
string endpoint = Environment.GetEnvironmentVariable("VISION_ENDPOINT");
string key = Environment.GetEnvironmentVariable("VISION_KEY");
ImageAnalysisClient client = new ImageAnalysisClient(
new Uri(endpoint),
new AzureKeyCredential(key));
ImageAnalysisResult result = client.Analyze(
new Uri("https://learn.microsoft.com/azure/ai-services/computer-vision/media/quickstarts/presentation.png"),
VisualFeatures.Caption | VisualFeatures.Read,
new ImageAnalysisOptions { GenderNeutralCaption = true });
Console.WriteLine("Image analysis results:");
Console.WriteLine(" Caption:");
Console.WriteLine($" '{result.Caption.Text}', Confidence {result.Caption.Confidence:F4}");
Console.WriteLine(" Read:");
foreach (DetectedTextBlock block in result.Read.Blocks)
foreach (DetectedTextLine line in block.Lines)
{
Console.WriteLine($" Line: '{line.Text}', Bounding Polygon: [{string.Join(" ", line.BoundingPolygon)}]");
foreach (DetectedTextWord word in line.Words)
{
Console.WriteLine($" Word: '{word.Text}', Confidence {word.Confidence.ToString("#.####")}, Bounding Polygon: [{string.Join(" ", word.BoundingPolygon)}]");
}
}
}
static void Main()
{
try
{
AnalyzeImage();
}
catch (Exception e)
{
Console.WriteLine(e);
}
}
}
从 IDE 窗口顶部的“调试”菜单中选择“开始调试”（或按 F5），以生成并运行应用程序。
输出
控制台输出应显示类似于以下文本的内容：
Caption:
"a person pointing at a screen", Confidence 0.4892
Text:
Line: '9:35 AM', Bounding polygon {{X=130,Y=129},{X=215,Y=130},{X=215,Y=149},{X=130,Y=148}}
Word: '9:35', Bounding polygon {{X=131,Y=130},{X=171,Y=130},{X=171,Y=149},{X=130,Y=149}}, Confidence 0.9930
Word: 'AM', Bounding polygon {{X=179,Y=130},{X=204,Y=130},{X=203,Y=149},{X=178,Y=149}}, Confidence 0.9980
Line: 'E Conference room 154584354', Bounding polygon {{X=130,Y=153},{X=224,Y=154},{X=224,Y=161},{X=130,Y=161}}
Word: 'E', Bounding polygon {{X=131,Y=154},{X=135,Y=154},{X=135,Y=161},{X=131,Y=161}}, Confidence 0.1040
Word: 'Conference', Bounding polygon {{X=142,Y=154},{X=174,Y=154},{X=173,Y=161},{X=141,Y=161}}, Confidence 0.9020
Word: 'room', Bounding polygon {{X=175,Y=154},{X=189,Y=155},{X=188,Y=161},{X=175,Y=161}}, Confidence 0.7960
Word: '154584354', Bounding polygon {{X=192,Y=155},{X=224,Y=154},{X=223,Y=162},{X=191,Y=161}}, Confidence 0.8640
Line: '#: 555-173-4547', Bounding polygon {{X=130,Y=163},{X=182,Y=164},{X=181,Y=171},{X=130,Y=170}}
Word: '#:', Bounding polygon {{X=131,Y=163},{X=139,Y=164},{X=139,Y=171},{X=131,Y=171}}, Confidence 0.0360
Word: '555-173-4547', Bounding polygon {{X=142,Y=164},{X=182,Y=165},{X=181,Y=171},{X=142,Y=171}}, Confidence 0.5970
Line: 'Town Hall', Bounding polygon {{X=546,Y=180},{X=590,Y=180},{X=590,Y=190},{X=546,Y=190}}
Word: 'Town', Bounding polygon {{X=547,Y=181},{X=568,Y=181},{X=568,Y=190},{X=546,Y=191}}, Confidence 0.9810
Word: 'Hall', Bounding polygon {{X=570,Y=181},{X=590,Y=181},{X=590,Y=191},{X=570,Y=190}}, Confidence 0.9910
Line: '9:00 AM - 10:00 AM', Bounding polygon {{X=546,Y=191},{X=596,Y=192},{X=596,Y=200},{X=546,Y=199}}
Word: '9:00', Bounding polygon {{X=546,Y=192},{X=555,Y=192},{X=555,Y=200},{X=546,Y=200}}, Confidence 0.0900
Word: 'AM', Bounding polygon {{X=557,Y=192},{X=565,Y=192},{X=565,Y=200},{X=557,Y=200}}, Confidence 0.9910
Word: '-', Bounding polygon {{X=567,Y=192},{X=569,Y=192},{X=569,Y=200},{X=567,Y=200}}, Confidence 0.6910
Word: '10:00', Bounding polygon {{X=570,Y=192},{X=585,Y=193},{X=584,Y=200},{X=570,Y=200}}, Confidence 0.8850
Word: 'AM', Bounding polygon {{X=586,Y=193},{X=593,Y=194},{X=593,Y=200},{X=586,Y=200}}, Confidence 0.9910
Line: 'Aaron Buaion', Bounding polygon {{X=543,Y=201},{X=581,Y=201},{X=581,Y=208},{X=543,Y=208}}
Word: 'Aaron', Bounding polygon {{X=545,Y=202},{X=560,Y=202},{X=559,Y=208},{X=544,Y=208}}, Confidence 0.6020
Word: 'Buaion', Bounding polygon {{X=561,Y=202},{X=580,Y=202},{X=579,Y=208},{X=560,Y=208}}, Confidence 0.2910
Line: 'Daily SCRUM', Bounding polygon {{X=537,Y=259},{X=575,Y=260},{X=575,Y=266},{X=537,Y=265}}
Word: 'Daily', Bounding polygon {{X=538,Y=259},{X=551,Y=260},{X=550,Y=266},{X=538,Y=265}}, Confidence 0.1750
Word: 'SCRUM', Bounding polygon {{X=552,Y=260},{X=570,Y=260},{X=570,Y=266},{X=551,Y=266}}, Confidence 0.1140
Line: '10:00 AM 11:00 AM', Bounding polygon {{X=536,Y=266},{X=590,Y=266},{X=590,Y=272},{X=536,Y=272}}
Word: '10:00', Bounding polygon {{X=539,Y=267},{X=553,Y=267},{X=552,Y=273},{X=538,Y=272}}, Confidence 0.8570
Word: 'AM', Bounding polygon {{X=554,Y=267},{X=561,Y=267},{X=560,Y=273},{X=553,Y=273}}, Confidence 0.9980
Word: '11:00', Bounding polygon {{X=564,Y=267},{X=578,Y=267},{X=577,Y=273},{X=563,Y=273}}, Confidence 0.4790
Word: 'AM', Bounding polygon {{X=579,Y=267},{X=586,Y=267},{X=585,Y=273},{X=578,Y=273}}, Confidence 0.9940
Line: 'Churlette de Crum', Bounding polygon {{X=538,Y=273},{X=584,Y=273},{X=585,Y=279},{X=538,Y=279}}
Word: 'Churlette', Bounding polygon {{X=539,Y=274},{X=562,Y=274},{X=561,Y=279},{X=538,Y=279}}, Confidence 0.4640
Word: 'de', Bounding polygon {{X=563,Y=274},{X=569,Y=274},{X=568,Y=279},{X=562,Y=279}}, Confidence 0.8100
Word: 'Crum', Bounding polygon {{X=570,Y=274},{X=582,Y=273},{X=581,Y=279},{X=569,Y=279}}, Confidence 0.8850
Line: 'Quarterly NI Hands', Bounding polygon {{X=538,Y=295},{X=588,Y=295},{X=588,Y=301},{X=538,Y=302}}
Word: 'Quarterly', Bounding polygon {{X=540,Y=296},{X=562,Y=296},{X=562,Y=302},{X=539,Y=302}}, Confidence 0.5230
Word: 'NI', Bounding polygon {{X=563,Y=296},{X=570,Y=296},{X=570,Y=302},{X=563,Y=302}}, Confidence 0.3030
Word: 'Hands', Bounding polygon {{X=572,Y=296},{X=588,Y=296},{X=588,Y=302},{X=571,Y=302}}, Confidence 0.6130
Line: '11.00 AM-12:00 PM', Bounding polygon {{X=536,Y=304},{X=588,Y=303},{X=588,Y=309},{X=536,Y=310}}
Word: '11.00', Bounding polygon {{X=538,Y=304},{X=552,Y=304},{X=552,Y=310},{X=538,Y=310}}, Confidence 0.6180
Word: 'AM-12:00', Bounding polygon {{X=554,Y=304},{X=578,Y=304},{X=577,Y=310},{X=553,Y=310}}, Confidence 0.2700
Word: 'PM', Bounding polygon {{X=579,Y=304},{X=586,Y=304},{X=586,Y=309},{X=578,Y=310}}, Confidence 0.6620
Line: 'Bebek Shaman', Bounding polygon {{X=538,Y=310},{X=577,Y=310},{X=577,Y=316},{X=538,Y=316}}
Word: 'Bebek', Bounding polygon {{X=539,Y=310},{X=554,Y=310},{X=554,Y=317},{X=539,Y=316}}, Confidence 0.6110
Word: 'Shaman', Bounding polygon {{X=555,Y=310},{X=576,Y=311},{X=576,Y=317},{X=555,Y=317}}, Confidence 0.6050
Line: 'Weekly stand up', Bounding polygon {{X=537,Y=332},{X=582,Y=333},{X=582,Y=339},{X=537,Y=338}}
Word: 'Weekly', Bounding polygon {{X=538,Y=332},{X=557,Y=333},{X=556,Y=339},{X=538,Y=338}}, Confidence 0.6060
Word: 'stand', Bounding polygon {{X=558,Y=333},{X=572,Y=334},{X=571,Y=340},{X=557,Y=339}}, Confidence 0.4890
Word: 'up', Bounding polygon {{X=574,Y=334},{X=580,Y=334},{X=580,Y=340},{X=573,Y=340}}, Confidence 0.8150
Line: '12:00 PM-1:00 PM', Bounding polygon {{X=537,Y=340},{X=583,Y=340},{X=583,Y=347},{X=536,Y=346}}
Word: '12:00', Bounding polygon {{X=539,Y=341},{X=553,Y=341},{X=552,Y=347},{X=538,Y=347}}, Confidence 0.8260
Word: 'PM-1:00', Bounding polygon {{X=554,Y=341},{X=575,Y=341},{X=574,Y=347},{X=553,Y=347}}, Confidence 0.2090
Word: 'PM', Bounding polygon {{X=576,Y=341},{X=583,Y=341},{X=582,Y=347},{X=575,Y=347}}, Confidence 0.0390
Line: 'Delle Marckre', Bounding polygon {{X=538,Y=347},{X=582,Y=347},{X=582,Y=352},{X=538,Y=353}}
Word: 'Delle', Bounding polygon {{X=540,Y=348},{X=559,Y=347},{X=558,Y=353},{X=539,Y=353}}, Confidence 0.5800
Word: 'Marckre', Bounding polygon {{X=560,Y=347},{X=582,Y=348},{X=582,Y=353},{X=559,Y=353}}, Confidence 0.2750
Line: 'Product review', Bounding polygon {{X=538,Y=370},{X=577,Y=370},{X=577,Y=376},{X=538,Y=375}}
Word: 'Product', Bounding polygon {{X=539,Y=370},{X=559,Y=371},{X=558,Y=376},{X=539,Y=376}}, Confidence 0.6150
Word: 'review', Bounding polygon {{X=560,Y=371},{X=576,Y=371},{X=575,Y=376},{X=559,Y=376}}, Confidence 0.0400
清理资源
如果想要清理并移除 Azure AI 服务订阅，可以删除资源或资源组。 删除资源组同时也会删除与之相关联的任何其他资源。
后续步骤
在本快速入门中了解了安装图像分析客户端 SDK 和进行基本的图像分析调用的方法。 接下来，详细了解分析 4.0 API 功能。
使用适用于 Python 的图像分析客户端 SDK 分析图像以读取文本并生成图像描述文字。 此快速入门分析远程图像，并将结果输出到控制台。
提示
分析 4.0 API 可以执行许多不同的操作。 有关展示所有可用功能的示例，请参阅分析图像操作指南。
先决条件
- Azure 订阅 - 免费创建订阅
- Python 3.x。 你的 Python 安装应包含 pip。 可以通过在命令行上运行
pip --version来检查是否安装了 pip。 通过安装最新版本的 Python 获取 pip。 - 拥有 Azure 订阅后，请在 Azure 门户中创建计算机视觉资源。 若要使用本快速入门中的描述文字功能，必须在某个受支持的 Azure 区域中创建资源（请参阅图像描述文字获取区域列表）。 部署后，选择”转到资源”。
- 需要从创建的资源获取密钥和终结点，以便将应用程序连接到 Azure AI 视觉服务。
- 可以使用免费定价层 (
F0) 试用该服务，然后再升级到付费层进行生产。
创建环境变量
在此示例中，将凭据写入运行应用程序的本地计算机上的环境变量。
转到 Azure 门户。 如果在“先决条件”部分创建的资源部署成功，请选择“后续步骤”下的“转到资源”。 在“密钥和终结点”页的“资源管理”下，可以找到密钥和终结点。 你的资源密钥与你的 Azure 订阅 ID 不同。
提示
请不要直接在代码中包含密钥，并且绝不公开发布密钥。 有关 Azure Key Vault 等更多身份验证选项，请参阅 Azure AI 服务安全性一文。
若要为密钥和终结点设置环境变量，请打开控制台窗口，并按照操作系统和开发环境的说明进行操作。
- 若要设置
VISION_KEY环境变量，请将your-key替换为资源的其中一个密钥。 - 若要设置
VISION_ENDPOINT环境变量，请将your-endpoint替换为资源的终结点。
setx VISION_KEY your-key
setx VISION_ENDPOINT your-endpoint
添加环境变量后，可能需要重启任何正在运行的、将读取环境变量的程序（包括控制台窗口）。
分析图像
在需要新项目的地方打开命令提示符，并创建名为 quickstart.py 的新文件。
运行以下命令以安装图像分析 SDK：
pip install azure-ai-vision-imageanalysis将以下代码复制到 quickstart.py 中：
提示
代码显示如何分析图像 URL。 还可以从程序内存缓冲区分析图像。 有关详细信息，请参阅分析图像操作指南。
import os from azure.ai.vision.imageanalysis import ImageAnalysisClient from azure.ai.vision.imageanalysis.models import VisualFeatures from azure.core.credentials import AzureKeyCredential # Set the values of your computer vision endpoint and computer vision key # as environment variables: try: endpoint = os.environ["VISION_ENDPOINT"] key = os.environ["VISION_KEY"] except KeyError: print("Missing environment variable 'VISION_ENDPOINT' or 'VISION_KEY'") print("Set them before running this sample.") exit() # Create an Image Analysis client client = ImageAnalysisClient( endpoint=endpoint, credential=AzureKeyCredential(key) ) # Get a caption for the image. This will be a synchronously (blocking) call. result = client.analyze_from_url( image_url="https://learn.microsoft.com/azure/ai-services/computer-vision/media/quickstarts/presentation.png", visual_features=[VisualFeatures.CAPTION, VisualFeatures.READ], gender_neutral_caption=True, # Optional (default is False) ) print("Image analysis results:") # Print caption results to the console print(" Caption:") if result.caption is not None: print(f" '{result.caption.text}', Confidence {result.caption.confidence:.4f}") # Print text (OCR) analysis results to the console print(" Read:") if result.read is not None: for line in result.read.blocks[0].lines: print(f" Line: '{line.text}', Bounding box {line.bounding_polygon}") for word in line.words: print(f" Word: '{word.text}', Bounding polygon {word.bounding_polygon}, Confidence {word.confidence:.4f}")然后，使用快速入门文件中的
python命令运行应用程序。python quickstart.py
输出
控制台输出应显示类似于以下文本的内容：
Caption:
'a person pointing at a screen', Confidence 0.4892
Text:
Line: '9:35 AM', Bounding polygon {130, 129, 215, 130, 215, 149, 130, 148}
Word: '9:35', Bounding polygon {131, 130, 171, 130, 171, 149, 130, 149}, Confidence 0.9930
Word: 'AM', Bounding polygon {179, 130, 204, 130, 203, 149, 178, 149}, Confidence 0.9980
Line: 'E Conference room 154584354', Bounding polygon {130, 153, 224, 154, 224, 161, 130, 161}
Word: 'E', Bounding polygon {131, 154, 135, 154, 135, 161, 131, 161}, Confidence 0.1040
Word: 'Conference', Bounding polygon {142, 154, 174, 154, 173, 161, 141, 161}, Confidence 0.9020
Word: 'room', Bounding polygon {175, 154, 189, 155, 188, 161, 175, 161}, Confidence 0.7960
Word: '154584354', Bounding polygon {192, 155, 224, 154, 223, 162, 191, 161}, Confidence 0.8640
Line: '#: 555-173-4547', Bounding polygon {130, 163, 182, 164, 181, 171, 130, 170}
Word: '#:', Bounding polygon {131, 163, 139, 164, 139, 171, 131, 171}, Confidence 0.0360
Word: '555-173-4547', Bounding polygon {142, 164, 182, 165, 181, 171, 142, 171}, Confidence 0.5970
Line: 'Town Hall', Bounding polygon {546, 180, 590, 180, 590, 190, 546, 190}
Word: 'Town', Bounding polygon {547, 181, 568, 181, 568, 190, 546, 191}, Confidence 0.9810
Word: 'Hall', Bounding polygon {570, 181, 590, 181, 590, 191, 570, 190}, Confidence 0.9910
Line: '9:00 AM - 10:00 AM', Bounding polygon {546, 191, 596, 192, 596, 200, 546, 199}
Word: '9:00', Bounding polygon {546, 192, 555, 192, 555, 200, 546, 200}, Confidence 0.0900
Word: 'AM', Bounding polygon {557, 192, 565, 192, 565, 200, 557, 200}, Confidence 0.9910
Word: '-', Bounding polygon {567, 192, 569, 192, 569, 200, 567, 200}, Confidence 0.6910
Word: '10:00', Bounding polygon {570, 192, 585, 193, 584, 200, 570, 200}, Confidence 0.8850
Word: 'AM', Bounding polygon {586, 193, 593, 194, 593, 200, 586, 200}, Confidence 0.9910
Line: 'Aaron Buaion', Bounding polygon {543, 201, 581, 201, 581, 208, 543, 208}
Word: 'Aaron', Bounding polygon {545, 202, 560, 202, 559, 208, 544, 208}, Confidence 0.6020
Word: 'Buaion', Bounding polygon {561, 202, 580, 202, 579, 208, 560, 208}, Confidence 0.2910
Line: 'Daily SCRUM', Bounding polygon {537, 259, 575, 260, 575, 266, 537, 265}
Word: 'Daily', Bounding polygon {538, 259, 551, 260, 550, 266, 538, 265}, Confidence 0.1750
Word: 'SCRUM', Bounding polygon {552, 260, 570, 260, 570, 266, 551, 266}, Confidence 0.1140
Line: '10:00 AM 11:00 AM', Bounding polygon {536, 266, 590, 266, 590, 272, 536, 272}
Word: '10:00', Bounding polygon {539, 267, 553, 267, 552, 273, 538, 272}, Confidence 0.8570
Word: 'AM', Bounding polygon {554, 267, 561, 267, 560, 273, 553, 273}, Confidence 0.9980
Word: '11:00', Bounding polygon {564, 267, 578, 267, 577, 273, 563, 273}, Confidence 0.4790
Word: 'AM', Bounding polygon {579, 267, 586, 267, 585, 273, 578, 273}, Confidence 0.9940
Line: 'Churlette de Crum', Bounding polygon {538, 273, 584, 273, 585, 279, 538, 279}
Word: 'Churlette', Bounding polygon {539, 274, 562, 274, 561, 279, 538, 279}, Confidence 0.4640
Word: 'de', Bounding polygon {563, 274, 569, 274, 568, 279, 562, 279}, Confidence 0.8100
Word: 'Crum', Bounding polygon {570, 274, 582, 273, 581, 279, 569, 279}, Confidence 0.8850
Line: 'Quarterly NI Hands', Bounding polygon {538, 295, 588, 295, 588, 301, 538, 302}
Word: 'Quarterly', Bounding polygon {540, 296, 562, 296, 562, 302, 539, 302}, Confidence 0.5230
Word: 'NI', Bounding polygon {563, 296, 570, 296, 570, 302, 563, 302}, Confidence 0.3030
Word: 'Hands', Bounding polygon {572, 296, 588, 296, 588, 302, 571, 302}, Confidence 0.6130
Line: '11.00 AM-12:00 PM', Bounding polygon {536, 304, 588, 303, 588, 309, 536, 310}
Word: '11.00', Bounding polygon {538, 304, 552, 304, 552, 310, 538, 310}, Confidence 0.6180
Word: 'AM-12:00', Bounding polygon {554, 304, 578, 304, 577, 310, 553, 310}, Confidence 0.2700
Word: 'PM', Bounding polygon {579, 304, 586, 304, 586, 309, 578, 310}, Confidence 0.6620
Line: 'Bebek Shaman', Bounding polygon {538, 310, 577, 310, 577, 316, 538, 316}
Word: 'Bebek', Bounding polygon {539, 310, 554, 310, 554, 317, 539, 316}, Confidence 0.6110
Word: 'Shaman', Bounding polygon {555, 310, 576, 311, 576, 317, 555, 317}, Confidence 0.6050
Line: 'Weekly stand up', Bounding polygon {537, 332, 582, 333, 582, 339, 537, 338}
Word: 'Weekly', Bounding polygon {538, 332, 557, 333, 556, 339, 538, 338}, Confidence 0.6060
Word: 'stand', Bounding polygon {558, 333, 572, 334, 571, 340, 557, 339}, Confidence 0.4890
Word: 'up', Bounding polygon {574, 334, 580, 334, 580, 340, 573, 340}, Confidence 0.8150
Line: '12:00 PM-1:00 PM', Bounding polygon {537, 340, 583, 340, 583, 347, 536, 346}
Word: '12:00', Bounding polygon {539, 341, 553, 341, 552, 347, 538, 347}, Confidence 0.8260
Word: 'PM-1:00', Bounding polygon {554, 341, 575, 341, 574, 347, 553, 347}, Confidence 0.2090
Word: 'PM', Bounding polygon {576, 341, 583, 341, 582, 347, 575, 347}, Confidence 0.0390
Line: 'Delle Marckre', Bounding polygon {538, 347, 582, 347, 582, 352, 538, 353}
Word: 'Delle', Bounding polygon {540, 348, 559, 347, 558, 353, 539, 353}, Confidence 0.5800
Word: 'Marckre', Bounding polygon {560, 347, 582, 348, 582, 353, 559, 353}, Confidence 0.2750
Line: 'Product review', Bounding polygon {538, 370, 577, 370, 577, 376, 538, 375}
Word: 'Product', Bounding polygon {539, 370, 559, 371, 558, 376, 539, 376}, Confidence 0.6150
Word: 'review', Bounding polygon {560, 371, 576, 371, 575, 376, 559, 376}, Confidence 0.0400
清理资源
如果想要清理并移除 Azure AI 服务订阅，可以删除资源或资源组。 删除资源组同时也会删除与之相关联的任何其他资源。
后续步骤
在本快速入门中了解了安装图像分析客户端 SDK 和进行基本的图像分析调用的方法。 接下来，详细了解分析 4.0 API 功能。
使用适用于 Java 的图像分析客户端 SDK 来分析图像，以读取文本并生成图像描述文字。 此快速入门分析远程图像，并将结果输出到控制台。
提示
分析 4.0 API 可以执行许多不同的操作。 有关展示所有可用功能的示例，请参阅分析图像操作指南。
先决条件
- Windows 10（或更高版本）x64 或 Linux x64 计算机。
- 已安装 Java 开发工具包 (JDK) 版本 8 或更高版本，例如 Azul Zulu OpenJDK、Microsoft Build of OpenJDK、Oracle Java 或首选 JDK。 从命令行运行
java -version以查看版本并确认安装成功。 请确保 Java 安装是系统体系结构的本机安装，并且不会通过仿真运行。 - 已安装 Apache Maven。 在 Linux 上，从分发存储库安装（如果可用）。 运行
mvn -v以确认安装成功。 - Azure 订阅 - 免费创建订阅
- 获取 Azure 订阅后，请在 Azure 门户中创建计算机视觉资源。 若要使用本快速入门中的描述文字功能，必须在某个受支持的 Azure 区域中创建资源（请参阅图像描述文字）。 部署后，选择”转到资源”。
- 需要从创建的资源获取密钥和终结点，以便将应用程序连接到 Azure AI 视觉服务。
- 可以使用免费定价层 (
F0) 试用该服务，然后再升级到付费层进行生产。
设置应用程序
打开控制台窗口，并为快速入门应用程序创建一个新文件夹。
打开文本编辑器，将以下内容复制到新文件。 在项目目录中将文件另存为
pom.xml<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <groupId>com.example</groupId> <artifactId>my-application-name</artifactId> <version>1.0.0</version> <dependencies> <!-- https://mvnrepository.com/artifact/com.azure/azure-ai-vision-imageanalysis --> <dependency> <groupId>com.azure</groupId> <artifactId>azure-ai-vision-imageanalysis</artifactId> <version>1.0.0-beta.2</version> </dependency> <!-- https://mvnrepository.com/artifact/org.slf4j/slf4j-nop --> <!-- Optional: provide a slf4j implementation. Here we use a no-op implementation just to make the slf4j console spew warning go away. We can still use the internal logger in azure.core library. See https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/vision/azure-ai-vision-imageanalysis#enable-http-requestresponse-logging --> <dependency> <groupId>org.slf4j</groupId> <artifactId>slf4j-nop</artifactId> <version>1.7.36</version> </dependency> </dependencies> </project>根据 Maven 存储库中 azure-ai-vision-imageanalysis 包的最新可用版本更新版本值 (
1.0.0-beta.2)。通过在项目目录中运行以下命令来安装 SDK 和依赖项：
mvn clean dependency:copy-dependencies操作成功后，验证是否在创建文件夹
target\dependency，以及该文件夹中是否包含.jar文件。
创建环境变量
在此示例中，将凭据写入运行应用程序的本地计算机上的环境变量。
转到 Azure 门户。 如果在“先决条件”部分创建的资源部署成功，请选择“后续步骤”下的“转到资源”。 在“密钥和终结点”页的“资源管理”下，可以找到密钥和终结点。 你的资源密钥与你的 Azure 订阅 ID 不同。
提示
请不要直接在代码中包含密钥，并且绝不公开发布密钥。 有关 Azure Key Vault 等更多身份验证选项，请参阅 Azure AI 服务安全性一文。
若要为密钥和终结点设置环境变量，请打开控制台窗口，并按照操作系统和开发环境的说明进行操作。
- 若要设置
VISION_KEY环境变量，请将your-key替换为资源的其中一个密钥。 - 若要设置
VISION_ENDPOINT环境变量，请将your-endpoint替换为资源的终结点。
setx VISION_KEY your-key
setx VISION_ENDPOINT your-endpoint
添加环境变量后，可能需要重启任何正在运行的、将读取环境变量的程序（包括控制台窗口）。
分析图像
打开文本编辑器，将以下内容复制到新文件。 将文件另存为 ImageAnalysis.java
import com.azure.ai.vision.imageanalysis.*;
import com.azure.ai.vision.imageanalysis.models.*;
import com.azure.core.credential.KeyCredential;
import java.util.Arrays;
public class ImageAnalysisQuickStart {
public static void main(String[] args) {
String endpoint = System.getenv("VISION_ENDPOINT");
String key = System.getenv("VISION_KEY");
if (endpoint == null || key == null) {
System.out.println("Missing environment variable 'VISION_ENDPOINT' or 'VISION_KEY'.");
System.out.println("Set them before running this sample.");
System.exit(1);
}
// Create a synchronous Image Analysis client.
ImageAnalysisClient client = new ImageAnalysisClientBuilder()
.endpoint(endpoint)
.credential(new KeyCredential(key))
.buildClient();
// This is a synchronous (blocking) call.
ImageAnalysisResult result = client.analyzeFromUrl(
"https://learn.microsoft.com/azure/ai-services/computer-vision/media/quickstarts/presentation.png",
Arrays.asList(VisualFeatures.CAPTION, VisualFeatures.READ),
new ImageAnalysisOptions().setGenderNeutralCaption(true));
// Print analysis results to the console
System.out.println("Image analysis results:");
System.out.println(" Caption:");
System.out.println(" \"" + result.getCaption().getText() + "\", Confidence "
+ String.format("%.4f", result.getCaption().getConfidence()));
System.out.println(" Read:");
for (DetectedTextLine line : result.getRead().getBlocks().get(0).getLines()) {
System.out.println(" Line: '" + line.getText()
+ "', Bounding polygon " + line.getBoundingPolygon());
for (DetectedTextWord word : line.getWords()) {
System.out.println(" Word: '" + word.getText()
+ "', Bounding polygon " + word.getBoundingPolygon()
+ ", Confidence " + String.format("%.4f", word.getConfidence()));
}
}
}
}
提示
该代码会从 URL 分析图像。 还可以从程序内存缓冲区分析图像。 有关详细信息，请参阅分析图像操作指南。
若要编译 Java 文件，请运行以下命令：
javac ImageAnalysis.java -cp ".;target/dependency/*"
你应当会看到在当前文件夹中创建的 ImageAnalysis.class 文件。
若要运行应用程序，请运行以下命令：
java -cp ".;target/dependency/*" ImageAnalysis
输出
控制台输出应显示类似于以下文本的内容：
Image analysis results:
Caption:
"a person pointing at a screen", Confidence 0.7768
Read:
Line: '9:35 AM', Bounding polygon [(x=131, y=130), (x=214, y=130), (x=214, y=148), (x=131, y=148)]
Word: '9:35', Bounding polygon [(x=132, y=130), (x=172, y=131), (x=171, y=149), (x=131, y=148)], Confidence 0.9770
Word: 'AM', Bounding polygon [(x=180, y=131), (x=203, y=131), (x=202, y=149), (x=180, y=149)], Confidence 0.9980
Line: 'Conference room 154584354', Bounding polygon [(x=132, y=153), (x=224, y=153), (x=224, y=161), (x=132, y=160)]
Word: 'Conference', Bounding polygon [(x=143, y=153), (x=174, y=154), (x=174, y=161), (x=143, y=161)], Confidence 0.6930
Word: 'room', Bounding polygon [(x=176, y=154), (x=188, y=154), (x=188, y=161), (x=176, y=161)], Confidence 0.9590
Word: '154584354', Bounding polygon [(x=192, y=154), (x=224, y=154), (x=223, y=161), (x=192, y=161)], Confidence 0.7050
Line: ': 555-123-4567', Bounding polygon [(x=133, y=164), (x=183, y=164), (x=183, y=170), (x=133, y=170)]
Word: ':', Bounding polygon [(x=134, y=165), (x=137, y=165), (x=136, y=171), (x=133, y=171)], Confidence 0.1620
Word: '555-123-4567', Bounding polygon [(x=143, y=165), (x=182, y=165), (x=181, y=171), (x=143, y=171)], Confidence 0.6530
Line: 'Town Hall', Bounding polygon [(x=545, y=178), (x=588, y=179), (x=588, y=190), (x=545, y=190)]
Word: 'Town', Bounding polygon [(x=545, y=179), (x=569, y=180), (x=569, y=190), (x=545, y=190)], Confidence 0.9880
Word: 'Hall', Bounding polygon [(x=571, y=180), (x=589, y=180), (x=589, y=190), (x=571, y=190)], Confidence 0.9900
Line: '9:00 AM - 10:00 AM', Bounding polygon [(x=545, y=191), (x=596, y=191), (x=596, y=199), (x=545, y=198)]
Word: '9:00', Bounding polygon [(x=546, y=191), (x=556, y=192), (x=556, y=199), (x=546, y=199)], Confidence 0.7580
Word: 'AM', Bounding polygon [(x=558, y=192), (x=565, y=192), (x=564, y=199), (x=558, y=199)], Confidence 0.9890
Word: '-', Bounding polygon [(x=567, y=192), (x=570, y=192), (x=569, y=199), (x=567, y=199)], Confidence 0.8960
Word: '10:00', Bounding polygon [(x=571, y=192), (x=585, y=192), (x=585, y=199), (x=571, y=199)], Confidence 0.7970
Word: 'AM', Bounding polygon [(x=587, y=192), (x=594, y=193), (x=593, y=199), (x=586, y=199)], Confidence 0.9940
Line: 'Aaron Blaion', Bounding polygon [(x=542, y=201), (x=581, y=201), (x=581, y=207), (x=542, y=207)]
Word: 'Aaron', Bounding polygon [(x=545, y=201), (x=560, y=202), (x=560, y=208), (x=545, y=208)], Confidence 0.7180
Word: 'Blaion', Bounding polygon [(x=562, y=202), (x=579, y=202), (x=579, y=207), (x=562, y=207)], Confidence 0.2740
Line: 'Daily SCRUM', Bounding polygon [(x=537, y=258), (x=574, y=259), (x=574, y=266), (x=537, y=265)]
Word: 'Daily', Bounding polygon [(x=538, y=259), (x=551, y=259), (x=551, y=266), (x=538, y=265)], Confidence 0.4040
Word: 'SCRUM', Bounding polygon [(x=553, y=259), (x=570, y=260), (x=570, y=265), (x=553, y=266)], Confidence 0.6970
Line: '10:00 AM-11:00 AM', Bounding polygon [(x=535, y=266), (x=589, y=265), (x=589, y=272), (x=535, y=273)]
Word: '10:00', Bounding polygon [(x=539, y=267), (x=553, y=266), (x=552, y=273), (x=539, y=274)], Confidence 0.2190
Word: 'AM-11:00', Bounding polygon [(x=554, y=266), (x=578, y=266), (x=578, y=272), (x=554, y=273)], Confidence 0.1750
Word: 'AM', Bounding polygon [(x=580, y=266), (x=587, y=266), (x=586, y=272), (x=580, y=272)], Confidence 1.0000
Line: 'Charlene de Crum', Bounding polygon [(x=538, y=272), (x=588, y=273), (x=588, y=279), (x=538, y=279)]
Word: 'Charlene', Bounding polygon [(x=538, y=273), (x=562, y=273), (x=562, y=280), (x=538, y=280)], Confidence 0.3220
Word: 'de', Bounding polygon [(x=563, y=273), (x=569, y=273), (x=569, y=280), (x=563, y=280)], Confidence 0.9100
Word: 'Crum', Bounding polygon [(x=570, y=273), (x=582, y=273), (x=583, y=280), (x=571, y=280)], Confidence 0.8710
Line: 'Quarterly NI Handa', Bounding polygon [(x=537, y=295), (x=588, y=295), (x=588, y=302), (x=537, y=302)]
Word: 'Quarterly', Bounding polygon [(x=539, y=296), (x=563, y=296), (x=563, y=302), (x=538, y=302)], Confidence 0.6030
Word: 'NI', Bounding polygon [(x=564, y=296), (x=570, y=296), (x=571, y=302), (x=564, y=302)], Confidence 0.7300
Word: 'Handa', Bounding polygon [(x=572, y=296), (x=588, y=296), (x=588, y=302), (x=572, y=302)], Confidence 0.9050
Line: '11.00 AM-12:00 PM', Bounding polygon [(x=538, y=303), (x=587, y=303), (x=587, y=309), (x=538, y=309)]
Word: '11.00', Bounding polygon [(x=539, y=303), (x=552, y=303), (x=553, y=309), (x=539, y=310)], Confidence 0.6710
Word: 'AM-12:00', Bounding polygon [(x=554, y=303), (x=578, y=303), (x=578, y=309), (x=554, y=309)], Confidence 0.6560
Word: 'PM', Bounding polygon [(x=579, y=303), (x=586, y=303), (x=586, y=309), (x=580, y=309)], Confidence 0.4540
Line: 'Bobek Shemar', Bounding polygon [(x=538, y=310), (x=577, y=310), (x=577, y=316), (x=538, y=316)]
Word: 'Bobek', Bounding polygon [(x=539, y=310), (x=554, y=311), (x=554, y=317), (x=539, y=317)], Confidence 0.6320
Word: 'Shemar', Bounding polygon [(x=556, y=311), (x=576, y=311), (x=577, y=317), (x=556, y=317)], Confidence 0.2190
Line: 'Weekly aband up', Bounding polygon [(x=538, y=332), (x=583, y=333), (x=583, y=339), (x=538, y=338)]
Word: 'Weekly', Bounding polygon [(x=539, y=333), (x=557, y=333), (x=557, y=339), (x=539, y=339)], Confidence 0.5750
Word: 'aband', Bounding polygon [(x=558, y=334), (x=573, y=334), (x=573, y=339), (x=558, y=339)], Confidence 0.4750
Word: 'up', Bounding polygon [(x=574, y=334), (x=580, y=334), (x=580, y=339), (x=574, y=339)], Confidence 0.8650
Line: '12:00 PM-1:00 PM', Bounding polygon [(x=538, y=339), (x=585, y=339), (x=585, y=346), (x=538, y=346)]
Word: '12:00', Bounding polygon [(x=539, y=339), (x=553, y=340), (x=553, y=347), (x=539, y=346)], Confidence 0.7090
Word: 'PM-1:00', Bounding polygon [(x=554, y=340), (x=575, y=340), (x=575, y=346), (x=554, y=347)], Confidence 0.9080
Word: 'PM', Bounding polygon [(x=576, y=340), (x=583, y=340), (x=583, y=346), (x=576, y=346)], Confidence 0.9980
Line: 'Danielle MarchTe', Bounding polygon [(x=538, y=346), (x=583, y=346), (x=583, y=352), (x=538, y=352)]
Word: 'Danielle', Bounding polygon [(x=539, y=347), (x=559, y=347), (x=559, y=352), (x=539, y=353)], Confidence 0.1960
Word: 'MarchTe', Bounding polygon [(x=560, y=347), (x=582, y=347), (x=582, y=352), (x=560, y=352)], Confidence 0.5710
Line: 'Product reviret', Bounding polygon [(x=537, y=370), (x=578, y=370), (x=578, y=375), (x=537, y=375)]
Word: 'Product', Bounding polygon [(x=539, y=370), (x=559, y=370), (x=559, y=376), (x=539, y=375)], Confidence 0.7000
Word: 'reviret', Bounding polygon [(x=560, y=370), (x=578, y=371), (x=578, y=375), (x=560, y=376)], Confidence 0.2180
清理资源
如果想要清理并移除 Azure AI 服务订阅，可以删除资源或资源组。 删除资源组同时也会删除与之相关联的任何其他资源。
后续步骤
在本快速入门中了解了安装图像分析客户端 SDK 和进行基本的图像分析调用的方法。 接下来，详细了解分析 4.0 API 功能。
使用适用于 JavaScript 的图像分析客户端 SDK 来分析图像，以读取文本并生成图像描述文字。 此快速入门分析远程图像，并将结果输出到控制台。
提示
分析 4.0 API 可以执行许多不同的操作。 有关展示所有可用功能的示例，请参阅分析图像操作指南。
先决条件
- Azure 订阅 - 免费创建订阅
- 最新版本的 Node.js
- Edge、Chrome、Firefox 或 Safari Internet 浏览器的当前版本。
- 拥有 Azure 订阅后，请在 Azure 门户中创建计算机视觉资源 ，以获取密钥和终结点。 若要使用本快速入门中的描述文字功能，必须在某个受支持的 Azure 区域中创建资源（请参阅图像描述文字获取区域列表）。 部署后，选择”转到资源”。
- 需要从创建的资源获取密钥和终结点，以便将应用程序连接到 Azure AI 视觉服务。
- 可以使用免费定价层 (
F0) 试用该服务，然后再升级到付费层进行生产。
创建环境变量
在此示例中，将凭据写入运行应用程序的本地计算机上的环境变量。
转到 Azure 门户。 如果在“先决条件”部分创建的资源部署成功，请选择“后续步骤”下的“转到资源”。 在“密钥和终结点”页的“资源管理”下，可以找到密钥和终结点。 你的资源密钥与你的 Azure 订阅 ID 不同。
提示
请不要直接在代码中包含密钥，并且绝不公开发布密钥。 有关 Azure Key Vault 等更多身份验证选项，请参阅 Azure AI 服务安全性一文。
若要为密钥和终结点设置环境变量，请打开控制台窗口，并按照操作系统和开发环境的说明进行操作。
- 若要设置
VISION_KEY环境变量，请将your-key替换为资源的其中一个密钥。 - 若要设置
VISION_ENDPOINT环境变量，请将your-endpoint替换为资源的终结点。
setx VISION_KEY your-key
setx VISION_ENDPOINT your-endpoint
添加环境变量后，可能需要重启任何正在运行的、将读取环境变量的程序（包括控制台窗口）。
分析图像
创建新的 Node.js 应用程序
在控制台窗口（例如 cmd、PowerShell 或 Bash）中，为应用创建一个新目录并导航到该目录。
mkdir myapp && cd myapp运行
npm init命令以使用package.json文件创建一个 node 应用程序。npm init安装客户端库
安装
@azure-rest/ai-vision-image-analysisnpm 包：npm install @azure-rest/ai-vision-image-analysis另请安装 dotenv 包：
npm install dotenv应用的
package.json文件将使用依赖项进行更新。创建新文件 index.js。 在文本编辑器中打开它，并粘贴以下代码。
const { ImageAnalysisClient } = require('@azure-rest/ai-vision-image-analysis'); const createClient = require('@azure-rest/ai-vision-image-analysis').default; const { AzureKeyCredential } = require('@azure/core-auth'); // Load the .env file if it exists require("dotenv").config(); const endpoint = process.env['VISION_ENDPOINT']; const key = process.env['VISION_KEY']; const credential = new AzureKeyCredential(key); const client = createClient(endpoint, credential); const features = [ 'Caption', 'Read' ]; const imageUrl = 'https://learn.microsoft.com/azure/ai-services/computer-vision/media/quickstarts/presentation.png'; async function analyzeImageFromUrl() { const result = await client.path('/imageanalysis:analyze').post({ body: { url: imageUrl }, queryParameters: { features: features }, contentType: 'application/json' }); const iaResult = result.body; if (iaResult.captionResult) { console.log(`Caption: ${iaResult.captionResult.text} (confidence: ${iaResult.captionResult.confidence})`); } if (iaResult.readResult) { iaResult.readResult.blocks.forEach(block => console.log(`Text Block: ${JSON.stringify(block)}`)); } } analyzeImageFromUrl();在快速入门文件中使用
node命令运行应用程序。node index.js
清理资源
如果想要清理并移除 Azure AI 服务订阅，可以删除资源或资源组。 删除资源组同时也会删除与之相关联的任何其他资源。
后续步骤
在本快速入门中了解了安装图像分析客户端库和进行基本的图像分析调用的方法。 接下来，详细了解分析 API 功能。
使用图像分析 REST API 读取文本并为图像生成描述文字（仅限 4.0 版）。
提示
分析 4.0 API 可以执行许多不同的操作。 有关展示所有可用功能的示例，请参阅分析图像操作指南。
先决条件
- Azure 订阅 - 免费创建订阅
- 拥有 Azure 订阅后，请在 Azure 门户中创建计算机视觉资源 ，以获取密钥和终结点。 要使用本快速入门中的描述文字功能，必须在以下 Azure 区域之一创建资源：美国东部、法国中部、韩国中部、北欧、东南亚、西欧、美国西部、东亚。 部署后，选择”转到资源”。
- 需要从创建的资源获取密钥和终结点，以便将应用程序连接到 Azure AI 视觉服务。 你稍后会在快速入门中将密钥和终结点粘贴到下方的代码中。
- 可以使用免费定价层 (
F0) 试用该服务，然后再升级到付费层进行生产。
- 已安装 cURL
分析图像
若要分析图像的各种视觉特征，请执行以下步骤：
将以下
curl命令复制到文本编辑器中。curl.exe -H "Ocp-Apim-Subscription-Key: <subscriptionKey>" -H "Content-Type: application/json" "https://<endpoint>/computervision/imageanalysis:analyze?features=caption,read&model-version=latest&language=en&api-version=2024-02-01" -d "{'url':'https://learn.microsoft.com/azure/ai-services/computer-vision/media/quickstarts/presentation.png'}"必要时在命令中进行如下更改：
- 将
<subscriptionKey>的值替换为视觉资源密钥。 - 将
<endpoint>的值替换为视觉资源终结点。 例如：https://YourResourceName.cognitiveservices.azure.com。 - （可选）将请求正文中的图像 URL (
https://learn.microsoft.com/azure/ai-services/computer-vision/media/quickstarts/presentation.png) 更改为要分析的其他图像的 URL。
- 将
打开命令提示符窗口。
将文本编辑器中编辑的
curl命令粘贴到命令提示符窗口，然后运行命令。
检查响应
在 JSON 中返回成功的响应，与以下示例类似：
{
"modelVersion": "2024-02-01",
"captionResult":
{
"text": "a man pointing at a screen",
"confidence": 0.7767987847328186
},
"metadata":
{
"width": 1038,
"height": 692
},
"readResult":
{
"blocks":
[
{
"lines":
[
{
"text": "9:35 AM",
"boundingPolygon": [{"x":131,"y":130},{"x":214,"y":130},{"x":214,"y":148},{"x":131,"y":148}],
"words": [{"text":"9:35","boundingPolygon":[{"x":132,"y":130},{"x":172,"y":131},{"x":171,"y":149},{"x":131,"y":148}],"confidence":0.977},{"text":"AM","boundingPolygon":[{"x":180,"y":131},{"x":203,"y":131},{"x":202,"y":149},{"x":180,"y":149}],"confidence":0.998}]
},
{
"text": "Conference room 154584354",
"boundingPolygon": [{"x":132,"y":153},{"x":224,"y":153},{"x":224,"y":161},{"x":132,"y":160}],
"words": [{"text":"Conference","boundingPolygon":[{"x":143,"y":153},{"x":174,"y":154},{"x":174,"y":161},{"x":143,"y":161}],"confidence":0.693},{"text":"room","boundingPolygon":[{"x":176,"y":154},{"x":188,"y":154},{"x":188,"y":161},{"x":176,"y":161}],"confidence":0.959},{"text":"154584354","boundingPolygon":[{"x":192,"y":154},{"x":224,"y":154},{"x":223,"y":161},{"x":192,"y":161}],"confidence":0.705}]
},
{
"text": ": 555-123-4567",
"boundingPolygon": [{"x":133,"y":164},{"x":183,"y":164},{"x":183,"y":170},{"x":133,"y":170}],
"words": [{"text":":","boundingPolygon":[{"x":134,"y":165},{"x":137,"y":165},{"x":136,"y":171},{"x":133,"y":171}],"confidence":0.162},{"text":"555-123-4567","boundingPolygon":[{"x":143,"y":165},{"x":182,"y":165},{"x":181,"y":171},{"x":143,"y":171}],"confidence":0.653}]
},
{
"text": "Town Hall",
"boundingPolygon": [{"x":545,"y":178},{"x":588,"y":179},{"x":588,"y":190},{"x":545,"y":190}],
"words": [{"text":"Town","boundingPolygon":[{"x":545,"y":179},{"x":569,"y":180},{"x":569,"y":190},{"x":545,"y":190}],"confidence":0.988},{"text":"Hall","boundingPolygon":[{"x":571,"y":180},{"x":589,"y":180},{"x":589,"y":190},{"x":571,"y":190}],"confidence":0.99}]
},
{
"text": "9:00 AM - 10:00 AM",
"boundingPolygon": [{"x":545,"y":191},{"x":596,"y":191},{"x":596,"y":199},{"x":545,"y":198}],
"words": [{"text":"9:00","boundingPolygon":[{"x":546,"y":191},{"x":556,"y":192},{"x":556,"y":199},{"x":546,"y":199}],"confidence":0.758},{"text":"AM","boundingPolygon":[{"x":558,"y":192},{"x":565,"y":192},{"x":564,"y":199},{"x":558,"y":199}],"confidence":0.989},{"text":"-","boundingPolygon":[{"x":567,"y":192},{"x":570,"y":192},{"x":569,"y":199},{"x":567,"y":199}],"confidence":0.896},{"text":"10:00","boundingPolygon":[{"x":571,"y":192},{"x":585,"y":192},{"x":585,"y":199},{"x":571,"y":199}],"confidence":0.797},{"text":"AM","boundingPolygon":[{"x":587,"y":192},{"x":594,"y":193},{"x":593,"y":199},{"x":586,"y":199}],"confidence":0.994}]
},
{
"text": "Aaron Blaion",
"boundingPolygon": [{"x":542,"y":201},{"x":581,"y":201},{"x":581,"y":207},{"x":542,"y":207}],
"words": [{"text":"Aaron","boundingPolygon":[{"x":545,"y":201},{"x":560,"y":202},{"x":560,"y":208},{"x":545,"y":208}],"confidence":0.718},{"text":"Blaion","boundingPolygon":[{"x":562,"y":202},{"x":579,"y":202},{"x":579,"y":207},{"x":562,"y":207}],"confidence":0.274}]
},
{
"text": "Daily SCRUM",
"boundingPolygon": [{"x":537,"y":258},{"x":574,"y":259},{"x":574,"y":266},{"x":537,"y":265}],
"words": [{"text":"Daily","boundingPolygon":[{"x":538,"y":259},{"x":551,"y":259},{"x":551,"y":266},{"x":538,"y":265}],"confidence":0.404},{"text":"SCRUM","boundingPolygon":[{"x":553,"y":259},{"x":570,"y":260},{"x":570,"y":265},{"x":553,"y":266}],"confidence":0.697}]
},
{
"text": "10:00 AM-11:00 AM",
"boundingPolygon": [{"x":535,"y":266},{"x":589,"y":265},{"x":589,"y":272},{"x":535,"y":273}],
"words": [{"text":"10:00","boundingPolygon":[{"x":539,"y":267},{"x":553,"y":266},{"x":552,"y":273},{"x":539,"y":274}],"confidence":0.219},{"text":"AM-11:00","boundingPolygon":[{"x":554,"y":266},{"x":578,"y":266},{"x":578,"y":272},{"x":554,"y":273}],"confidence":0.175},{"text":"AM","boundingPolygon":[{"x":580,"y":266},{"x":587,"y":266},{"x":586,"y":272},{"x":580,"y":272}],"confidence":1}]
},
{
"text": "Charlene de Crum",
"boundingPolygon": [{"x":538,"y":272},{"x":588,"y":273},{"x":588,"y":279},{"x":538,"y":279}],
"words": [{"text":"Charlene","boundingPolygon":[{"x":538,"y":273},{"x":562,"y":273},{"x":562,"y":280},{"x":538,"y":280}],"confidence":0.322},{"text":"de","boundingPolygon":[{"x":563,"y":273},{"x":569,"y":273},{"x":569,"y":280},{"x":563,"y":280}],"confidence":0.91},{"text":"Crum","boundingPolygon":[{"x":570,"y":273},{"x":582,"y":273},{"x":583,"y":280},{"x":571,"y":280}],"confidence":0.871}]
},
{
"text": "Quarterly NI Handa",
"boundingPolygon": [{"x":537,"y":295},{"x":588,"y":295},{"x":588,"y":302},{"x":537,"y":302}],
"words": [{"text":"Quarterly","boundingPolygon":[{"x":539,"y":296},{"x":563,"y":296},{"x":563,"y":302},{"x":538,"y":302}],"confidence":0.603},{"text":"NI","boundingPolygon":[{"x":564,"y":296},{"x":570,"y":296},{"x":571,"y":302},{"x":564,"y":302}],"confidence":0.73},{"text":"Handa","boundingPolygon":[{"x":572,"y":296},{"x":588,"y":296},{"x":588,"y":302},{"x":572,"y":302}],"confidence":0.905}]
},
{
"text": "11.00 AM-12:00 PM",
"boundingPolygon": [{"x":538,"y":303},{"x":587,"y":303},{"x":587,"y":309},{"x":538,"y":309}],
"words": [{"text":"11.00","boundingPolygon":[{"x":539,"y":303},{"x":552,"y":303},{"x":553,"y":309},{"x":539,"y":310}],"confidence":0.671},{"text":"AM-12:00","boundingPolygon":[{"x":554,"y":303},{"x":578,"y":303},{"x":578,"y":309},{"x":554,"y":309}],"confidence":0.656},{"text":"PM","boundingPolygon":[{"x":579,"y":303},{"x":586,"y":303},{"x":586,"y":309},{"x":580,"y":309}],"confidence":0.454}]
},
{
"text": "Bobek Shemar",
"boundingPolygon": [{"x":538,"y":310},{"x":577,"y":310},{"x":577,"y":316},{"x":538,"y":316}],
"words": [{"text":"Bobek","boundingPolygon":[{"x":539,"y":310},{"x":554,"y":311},{"x":554,"y":317},{"x":539,"y":317}],"confidence":0.632},{"text":"Shemar","boundingPolygon":[{"x":556,"y":311},{"x":576,"y":311},{"x":577,"y":317},{"x":556,"y":317}],"confidence":0.219}]
},
{
"text": "Weekly aband up",
"boundingPolygon": [{"x":538,"y":332},{"x":583,"y":333},{"x":583,"y":339},{"x":538,"y":338}],
"words": [{"text":"Weekly","boundingPolygon":[{"x":539,"y":333},{"x":557,"y":333},{"x":557,"y":339},{"x":539,"y":339}],"confidence":0.575},{"text":"aband","boundingPolygon":[{"x":558,"y":334},{"x":573,"y":334},{"x":573,"y":339},{"x":558,"y":339}],"confidence":0.475},{"text":"up","boundingPolygon":[{"x":574,"y":334},{"x":580,"y":334},{"x":580,"y":339},{"x":574,"y":339}],"confidence":0.865}]
},
{
"text": "12:00 PM-1:00 PM",
"boundingPolygon": [{"x":538,"y":339},{"x":585,"y":339},{"x":585,"y":346},{"x":538,"y":346}],
"words": [{"text":"12:00","boundingPolygon":[{"x":539,"y":339},{"x":553,"y":340},{"x":553,"y":347},{"x":539,"y":346}],"confidence":0.709},{"text":"PM-1:00","boundingPolygon":[{"x":554,"y":340},{"x":575,"y":340},{"x":575,"y":346},{"x":554,"y":347}],"confidence":0.908},{"text":"PM","boundingPolygon":[{"x":576,"y":340},{"x":583,"y":340},{"x":583,"y":346},{"x":576,"y":346}],"confidence":0.998}]
},
{
"text": "Danielle MarchTe",
"boundingPolygon": [{"x":538,"y":346},{"x":583,"y":346},{"x":583,"y":352},{"x":538,"y":352}],
"words": [{"text":"Danielle","boundingPolygon":[{"x":539,"y":347},{"x":559,"y":347},{"x":559,"y":352},{"x":539,"y":353}],"confidence":0.196},{"text":"MarchTe","boundingPolygon":[{"x":560,"y":347},{"x":582,"y":347},{"x":582,"y":352},{"x":560,"y":352}],"confidence":0.571}]
},
{
"text": "Product reviret",
"boundingPolygon": [{"x":537,"y":370},{"x":578,"y":370},{"x":578,"y":375},{"x":537,"y":375}],
"words": [{"text":"Product","boundingPolygon":[{"x":539,"y":370},{"x":559,"y":370},{"x":559,"y":376},{"x":539,"y":375}],"confidence":0.7},{"text":"reviret","boundingPolygon":[{"x":560,"y":370},{"x":578,"y":371},{"x":578,"y":375},{"x":560,"y":376}],"confidence":0.218}]
}
]
}
]
}
}
后续步骤
在本快速入门中，你了解了使用 REST API 进行基本的图像分析调用的方法。 接下来，详细了解分析 4.0 API 功能。
先决条件
- 使用 Azure 订阅和 Azure AI 服务资源登录 Vision Studio。 如果你需要有关此步骤的帮助，请参阅概述的入门部分。
分析图像
- 选择“分析图像”选项卡，然后选择标题为“从图像中提取常见标记”的面板。
- 若要尝试进行体验，需要选择一个资源，并确认它将根据定价层产生使用量。
- 从可用集合中选择图像，或上传你自己的图像。
- 选择图像后，会看到检测到的标记显示在输出窗口，同时还会显示其置信度分数。 你还可以选择 JSON 选项卡以查看 API 调用返回的 JSON 输出。
- 试用体验下方是在你自己的应用程序中开始使用此功能的后续步骤。
后续步骤
在本快速入门中，你使用 Vision Studio 完成了一项基本的图像分析任务。 接下来，详细了解分析图像 API 功能。