共用方式為


快速入門:影像分析 4.0

開始使用影像分析 4.0 REST API 或用戶端 SDK 來設定基本影像分析應用程式。 影像分析服務為您提供 AI 演算法,以便處理影像及傳回其視覺功能的資訊。 請遵循下列步驟將套件安裝至您的應用程式,並試用範例程式碼。

使用適用於 .NET 的影像分析用戶端 SDK 來讀取影像中的文字,並產生影像標題。 本快速入門會分析遠端影像,並將結果列印至主控台。

參考文件 | 套件 (NuGet) | 範例

提示

分析 4.0 API 可以執行許多不同的作業。 如需展示所有可用功能的範例,請參閱分析影像操作指南

必要條件

  • 已啟用具有工作負載 .NET 桌面開發Visual Studio IDE。 或者,如果您不打算使用 Visual Studio IDE,則需要安裝 .NET SDK
  • 擁有 Azure 訂閱之後,請在 Azure 入口網站中建立電腦視覺資源。 為了使用本快速入門中的輔助字幕功能,您必須在其中一個受支援的 Azure 區域中建立資源 (請參閱影像標題 (部分機器翻譯))。 在其部署後,選取 [前往資源]
    • 您需要來自所建立資源的金鑰和端點,以便將應用程式連線至 Azure AI 視覺服務。
    • 您可以使用免費定價層 (F0) 來試用服務,之後可升級至付費層以用於實際執行環境。

設定應用程式

建立新的 C# 應用程式。

開啟 Visual Studio,然後在 [開始] 下,選取 [建立新專案]。 將範本篩選設定為 C#/所有平台/主控台。 選取 [主控台應用程式] (可在 Windows、Linux 和 macOS 上的 .NET 上執行的命令列應用程式),然後選擇 [下一步]。 將專案名稱更新為 ImageAnalysisQuickstart,然後選擇 [下一步]。 選取 .NET 6.0 或更新版本,然後選擇 [建立] 以建立專案。

安裝用戶端 SDK

建立新專案後,以滑鼠右鍵按一下 [方案總管] 中的專案解決方案,然後選取 [管理 NuGet 套件],以安裝用戶端 SDK。 在開啟的套件管理員中,選取 [瀏覽]、核取 [包含發行前版本],然後搜尋 Azure.AI.Vision.ImageAnalysis。 選取安裝

建立環境變數

在此範例中,在執行應用程式的本機電腦上將認證寫入環境變數。

前往 Azure 入口網站。 如果已成功部署您在 [必要條件] 區段中建立的資源,請選取 [後續步驟] 下的 [前往資源] 按鈕。 您可以在 [金鑰和端點] 頁面中 [資源管理] 底下找到金鑰和端點。 您的資源金鑰與您的 Azure 訂用帳戶識別碼不同。

若要設定金鑰和端點的環境變數,請開啟主控台視窗,然後遵循作業系統和開發環境的指示進行。

  • 若要設定 VISION_KEY 環境變數,請以您其中一個資源索引碼取代 <your_key>
  • 若要設定 VISION_ENDPOINT 環境變數,請將 <your_endpoint> 取代為您資源的端點。

重要

如果您使用 API 金鑰,請將其安全地儲存在別處,例如 Azure Key Vault。 請勿在程式碼中直接包含 API 索引碼,且切勿公開張貼索引碼。

如需 AI 服務安全性的詳細資訊,請參閱驗證對 Azure AI 服務的要求

setx VISION_KEY <your_key>
setx VISION_ENDPOINT <your_endpoint>

新增環境變數之後,您可能需要重新啟動任何將讀取環境變數的執行中程式,包括主控台視窗。

分析影像

從專案目錄,開啟先前隨著您的新專案建立的 Program.cs 檔案。 貼上下列程式碼:

提示

此程式碼顯示分析影像 URL。 您也可以分析本機影像檔案,或記憶體緩衝區的影像。 如需詳細資訊,請參閱分析影像操作指南 (部分機器翻譯)。

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);
        }
    }
}

從 IDE 視窗頂端的 [偵錯] 功能表選取 [開始偵錯] (或按 F5) 來建置並執行應用程式。

輸出

主控台輸出應該會顯示類似下列文字的內容:

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 

清除資源

如果您想要清除和移除 Azure AI 服務訂用帳戶,則可以刪除資源或資源群組。 刪除資源群組也會刪除與其相關聯的任何其他資源。

下一步

在本快速入門中,您已了解如何安裝影像分析用戶端 SDK,並進行基本的影像分析呼叫。 接下來,深入了解分析 4.0 API 功能。

使用適用於 Python 的影像分析用戶端 SDK 來讀取影像中的文字,並產生影像標題。 本快速入門會分析遠端影像,並將結果列印至主控台。

參考文件 | 套件 (PyPi) | 範例

提示

分析 4.0 API 可以執行許多不同的作業。 如需展示所有可用功能的範例,請參閱分析影像操作指南

必要條件

  • Azure 訂用帳戶 - 建立免費帳戶
  • Python 3.x。 您安裝的 Python 應包含 pip。 您可以在命令列上執行 pip --version 來檢查是否已安裝 pip。 安裝最新版本的 Python 以取得 pip。
  • 擁有 Azure 訂閱之後,請在 Azure 入口網站中建立電腦視覺資源。 為了使用本快速入門中的輔助字幕功能,您必須在其中一個受支援的 Azure 區域中建立資源 (請參閱影像標題 (部分機器翻譯) 以取得區域清單)。 在其部署後,選取 [前往資源]
    • 您需要來自所建立資源的金鑰和端點,以便將應用程式連線至 Azure AI 視覺服務。
    • 您可以使用免費定價層 (F0) 來試用服務,之後可升級至付費層以用於實際執行環境。

建立環境變數

在此範例中,在執行應用程式的本機電腦上將認證寫入環境變數。

前往 Azure 入口網站。 如果已成功部署您在 [必要條件] 區段中建立的資源,請選取 [後續步驟] 下的 [前往資源] 按鈕。 您可以在 [金鑰和端點] 頁面中 [資源管理] 底下找到金鑰和端點。 您的資源金鑰與您的 Azure 訂用帳戶識別碼不同。

若要設定金鑰和端點的環境變數,請開啟主控台視窗,然後遵循作業系統和開發環境的指示進行。

  • 若要設定 VISION_KEY 環境變數,請以您其中一個資源索引碼取代 <your_key>
  • 若要設定 VISION_ENDPOINT 環境變數,請將 <your_endpoint> 取代為您資源的端點。

重要

如果您使用 API 金鑰,請將其安全地儲存在別處,例如 Azure Key Vault。 請勿在程式碼中直接包含 API 索引碼,且切勿公開張貼索引碼。

如需 AI 服務安全性的詳細資訊,請參閱驗證對 Azure AI 服務的要求

setx VISION_KEY <your_key>
setx VISION_ENDPOINT <your_endpoint>

新增環境變數之後,您可能需要重新啟動任何將讀取環境變數的執行中程式,包括主控台視窗。

分析影像

  1. 開啟要新增專案的命令提示字元,然後建立名為 quickstart.py 的新檔案。

  2. 執行此命令以安裝影像分析 SDK:

    pip install azure-ai-vision-imageanalysis
    
  3. 將下列程式碼複製到 quickstart.py

    提示

    此程式碼顯示分析影像 URL。 您也可以分析來自程式記憶體緩衝區的影像。 如需詳細資訊,請參閱分析影像操作指南 (部分機器翻譯)。

    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}")
    
  4. 然後使用快速入門檔案上的 python 命令執行應用程式。

    python quickstart.py
    

輸出

主控台輸出應該會顯示類似下列文字的內容:

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

清除資源

如果您想要清除和移除 Azure AI 服務訂用帳戶,則可以刪除資源或資源群組。 刪除資源群組也會刪除與其相關聯的任何其他資源。

下一步

在本快速入門中,您已了解如何安裝影像分析用戶端 SDK,並進行基本的影像分析呼叫。 接下來,深入了解分析 4.0 API 功能。

使用適用於 Java 的影像分析用戶端 SDK 來讀取影像中的文字,並產生影像標題。 本快速入門會分析遠端影像,並將結果列印至主控台。

參考文件 | Maven 套件 | 範例

提示

分析 4.0 API 可以執行許多不同的作業。 如需展示所有可用功能的範例,請參閱分析影像操作指南

必要條件

  • Windows 10 (或更高版本) x64,或 Linux x64 機器。
  • 已安裝 JAVA 開發套件 (JDK) 第 8 版或更新版本,例如 Azul Zulu OpenJDKMicrosoft Build of OpenJDKOracle JAVA 或您慣用的 JDK。 從命令列執行 java -version,以查看您的版本並確認安裝成功。 請確定 JAVA 安裝是系統架構的原生安裝,而且不會透過模擬執行。
  • 已安裝 Apache Maven (英文)。 在 Linux上,從發行版本存放庫安裝 (如有)。 執行 mvn -v 以確認安裝成功。
  • Azure 訂用帳戶 - 建立免費帳戶
  • 擁有 Azure 訂閱之後,請在 Azure 入口網站中建立電腦視覺資源。 為了使用本快速入門中的輔助字幕功能,您必須在其中一個受支援的 Azure 區域中建立資源 (請參閱影像標題 (部分機器翻譯))。 在其部署後,選取 [前往資源]
    • 您需要來自所建立資源的金鑰和端點,以便將應用程式連線至 Azure AI 視覺服務。
    • 您可以使用免費定價層 (F0) 來試用服務,之後可升級至付費層以用於實際執行環境。

設定應用程式

開啟主控台視窗,並為快速入門應用程式建立新的資料夾。

  1. 開啟文字編輯器,並將下列內容複製到新的檔案。 將檔案儲存為您專案目錄中的 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 -->
        <dependency>
          <groupId>org.slf4j</groupId>
          <artifactId>slf4j-nop</artifactId>
          <version>1.7.36</version>
        </dependency>
      </dependencies>
    </project>
    
  2. 根據 Maven 存放庫中 azure-ai-vision-imageanalysis 套件的最新可用版本來更新版本值 (1.0.0-beta.2)。

  3. 在專案目錄中執行下列動作,以安裝 SDK 和相依性:

    mvn clean dependency:copy-dependencies
    
  4. 作業成功之後,請確認資料夾 target\dependency 已建立,且包含 .jar 檔案。

建立環境變數

在此範例中,在執行應用程式的本機電腦上將認證寫入環境變數。

前往 Azure 入口網站。 如果已成功部署您在 [必要條件] 區段中建立的資源,請選取 [後續步驟] 下的 [前往資源] 按鈕。 您可以在 [金鑰和端點] 頁面中 [資源管理] 底下找到金鑰和端點。 您的資源金鑰與您的 Azure 訂用帳戶識別碼不同。

若要設定金鑰和端點的環境變數,請開啟主控台視窗,然後遵循作業系統和開發環境的指示進行。

  • 若要設定 VISION_KEY 環境變數,請以您其中一個資源索引碼取代 <your_key>
  • 若要設定 VISION_ENDPOINT 環境變數,請將 <your_endpoint> 取代為您資源的端點。

重要

如果您使用 API 金鑰,請將其安全地儲存在別處,例如 Azure Key Vault。 請勿在程式碼中直接包含 API 索引碼,且切勿公開張貼索引碼。

如需 AI 服務安全性的詳細資訊,請參閱驗證對 Azure AI 服務的要求

setx VISION_KEY <your_key>
setx VISION_ENDPOINT <your_endpoint>

新增環境變數之後,您可能需要重新啟動任何將讀取環境變數的執行中程式,包括主控台視窗。

分析影像

開啟文字編輯器,並將下列內容複製到新的檔案。 將檔案儲存為 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

該程式碼會分析來自 URL 的影像。 您也可以分析來自程式記憶體緩衝區的影像。 如需詳細資訊,請參閱分析影像操作指南 (部分機器翻譯)。

若要編譯 JAVA 檔案,請執行下列命令:

javac ImageAnalysis.java -cp ".;target/dependency/*"

您應該會看到在目前資料夾中建立的檔案 ImageAnalysis.class

若要執行應用程式,請執行下列命令:

java -cp ".;target/dependency/*" ImageAnalysis

輸出

主控台輸出應該會顯示類似下列文字的內容:

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

清除資源

如果您想要清除和移除 Azure AI 服務訂用帳戶,則可以刪除資源或資源群組。 刪除資源群組也會刪除與其相關聯的任何其他資源。

下一步

在本快速入門中,您已了解如何安裝影像分析用戶端 SDK,並進行基本的影像分析呼叫。 接下來,深入了解分析 4.0 API 功能。

使用適用於 JavaScript 的影像分析用戶端 SDK 來讀取影像中的文字,並產生影像標題。 本快速入門會分析遠端影像,並將結果列印至主控台。

參考文件 | 套件 (npm) | 範例

提示

分析 4.0 API 可以執行許多不同的作業。 如需展示所有可用功能的範例,請參閱分析影像操作指南

必要條件

  • Azure 訂用帳戶 - 建立免費帳戶
  • 最新版的 Node.js
  • 目前版本的 Edge、Chrome、Firefox 或 Safari 網際網路瀏覽器。
  • 擁有 Azure 訂用帳戶之後,在 Azure 入口網站中建立電腦視覺資源,以取得您的金鑰和端點。 為了使用本快速入門中的輔助字幕功能,您必須在其中一個受支援的 Azure 區域中建立資源 (請參閱影像標題 (部分機器翻譯) 以取得區域清單)。 在其部署後,選取 [前往資源]
    • 您需要來自所建立資源的金鑰和端點,以便將應用程式連線至 Azure AI 視覺服務。
    • 您可以使用免費定價層 (F0) 來試用服務,之後可升級至付費層以用於實際執行環境。

建立環境變數

在此範例中,在執行應用程式的本機電腦上將認證寫入環境變數。

前往 Azure 入口網站。 如果已成功部署您在 [必要條件] 區段中建立的資源,請選取 [後續步驟] 下的 [前往資源] 按鈕。 您可以在 [金鑰和端點] 頁面中 [資源管理] 底下找到金鑰和端點。 您的資源金鑰與您的 Azure 訂用帳戶識別碼不同。

若要設定金鑰和端點的環境變數,請開啟主控台視窗,然後遵循作業系統和開發環境的指示進行。

  • 若要設定 VISION_KEY 環境變數,請以您其中一個資源索引碼取代 <your_key>
  • 若要設定 VISION_ENDPOINT 環境變數,請將 <your_endpoint> 取代為您資源的端點。

重要

如果您使用 API 金鑰,請將其安全地儲存在別處,例如 Azure Key Vault。 請勿在程式碼中直接包含 API 索引碼,且切勿公開張貼索引碼。

如需 AI 服務安全性的詳細資訊,請參閱驗證對 Azure AI 服務的要求

setx VISION_KEY <your_key>
setx VISION_ENDPOINT <your_endpoint>

新增環境變數之後,您可能需要重新啟動任何將讀取環境變數的執行中程式,包括主控台視窗。

分析影像

  1. 建立新的 Node.js 應用程式

    在主控台視窗 (例如 cmd、PowerShell 或 Bash) 中,為您的應用程式建立新的目錄,並瀏覽至該目錄。

    mkdir myapp && cd myapp
    

    執行命令 npm init,以使用 package.json 檔案建立節點應用程式。

    npm init
    
  2. 安裝用戶端程式庫

    安裝 @azure-rest/ai-vision-image-analysis npm 套件:

    npm install @azure-rest/ai-vision-image-analysis
    

    一併安裝 dotenv 套件:

    npm install dotenv
    

    您應用程式的 package.json 檔案會隨著相依性而更新。

  3. 建立新檔案 index.js。 在文字編輯器中將其開啟,然後貼上下列程式碼。

    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();
    
  4. 使用快速入門檔案上使用 node 命令執行應用程式。

    node index.js
    

清除資源

如果您想要清除和移除 Azure AI 服務訂用帳戶,則可以刪除資源或資源群組。 刪除資源群組也會刪除與其相關聯的任何其他資源。

下一步

在本快速入門中,您已了解如何安裝影像分析用戶端程式庫,並進行基本的影像分析呼叫。 接下來,深入了解分析 API 功能。

使用影像分析 REST API 來讀取文字並產生影像的標題 (僅限版本 4.0)。

提示

分析 4.0 API 可以執行許多不同的作業。 如需展示所有可用功能的範例,請參閱分析影像操作指南

必要條件

  • Azure 訂用帳戶 - 建立免費帳戶
  • 擁有 Azure 訂用帳戶之後,在 Azure 入口網站中建立電腦視覺資源,以取得您的金鑰和端點。 為了使用本快速入門中的輔助字幕功能,您必須在特定 Azure 區域中建立您的資源。 請參閱區域可用性 (英文)。 在其部署後,選取 [前往資源]
    • 您將需要來自所建立資源的金鑰和端點,以便將應用程式連線至 Azure AI 視覺服務。 您稍後會在快速入門中將金鑰和端點貼到下列程式碼中。
    • 您可以使用免費定價層 (F0) 來試用服務,之後可升級至付費層以用於實際執行環境。
  • 已安裝 cURL

分析影像

若要分析影像中的各種視覺特徵,請執行下列步驟:

  1. 將下列 curl 命令複製到文字編輯器。

    curl.exe -H "Ocp-Apim-Subscription-Key: <subscriptionKey>" -H "Content-Type: application/json" "<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'}"
    
  2. 視需要在命令中進行下列變更:

    1. 以您的視覺資源金鑰取代 <subscriptionKey> 的值。
    2. 以您的視覺資源端點 URL 取代 <endpoint> 的值。 例如: https://YourResourceName.cognitiveservices.azure.com
    3. (選擇性) 將要求本文 (https://learn.microsoft.com/azure/ai-services/computer-vision/media/quickstarts/presentation.png) 中的影像 URL 變更為要分析之不同影像的 URL。
  3. 開啟 [命令提示字元] 視窗。

  4. 從文字編輯器將經過編輯的 curl 命令貼上到命令提示字元視窗中,然後執行該命令。

檢查回應

成功的回應會以 JSON 格式傳回,類似下列範例:

{
    "modelVersion": "2023-10-01",
    "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}]
                    }
                ]
            }
        ]
    }
}

下一步

在本快速入門中,您已了解如何使用 REST API 進行基本影像分析呼叫。 接下來,深入了解分析 4.0 API 功能。

必要條件

分析影像

  1. 選取 [分析影像] 索引標籤,然後選取標題為 [從影像擷取一般標記] 的面板。
  2. 若要使用立即試用體驗,您需要選擇資源並確認其會根據您的定價層產生使用量。
  3. 從可用的集合中選取影像,或上傳您自己的影像。
  4. 選取影像之後,您會看到偵測到的標記以及其信賴分數出現在輸出視窗中。 您也可以選取 [JSON] 索引標籤,以查看 API 呼叫傳回的 JSON 輸出。
  5. 立即試用體驗之後的後續步驟,是在您自己的應用程式中開始使用此功能。

下一步

在本快速入門中,您已使用 Vision Studio 來執行基本影像分析工作。 接下來,深入了解分析影像 API 功能。

呼叫分析 4.0 API (部分機器翻譯)