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。 影像分析服務提供 AI 演算法，讓您處理影像並傳回其視覺特徵的相關信息。 請遵循下列步驟，將套件安裝到您的應用程式，並試用範例程序代碼。
使用適用於 .NET 的影像分析用戶端 SDK 來分析影像以讀取文字併產生影像 標題。 本快速入門會分析遠端影像，並將結果列印至主控台。
參考檔 | 套件 （NuGet） | 範例
提示
Analysis 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，請參閱分析影像作法指南。
必要條件
- Azure 訂用帳戶 - 免費建立一個訂用帳戶
- 已啟用工作負載 .NET 桌面開發的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 訂用帳戶之後，請在 Azure 入口網站 中建立 電腦視覺 資源。 若要使用本快速入門中的 標題 功能，您必須在其中一個支援的 Azure 區域中建立資源（請參閱映像 標題）。 部署之後，請選取 [移至資源]。
- 您需要從您建立的資源取得密鑰和端點，以將應用程式連線到 Azure AI 視覺服務。
- 您可以使用免費定價層 （
F0） 來試用服務，稍後再升級至生產環境的付費層。
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。 開啟的套件管理員中，選取 [ 瀏覽]，勾選 [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。
提示
請勿將金鑰直接包含在您的程式代碼中，且絕不會公開發佈。 如需更多驗證選項 (例如 Azure Key Vault)，請參閱 Azure AI 服務安全性文章。
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，請將 取代your-key為您資源的其中一個密鑰。 - 若要設定
VISION_ENDPOINT環境變數，請將 取代your-endpoint為您資源的端點。
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
從項目目錄中，開啟先前使用新專案建立的Program.cs檔案。 貼上下列程式代碼：
提示
此程式代碼會顯示分析影像 URL。 您也可以分析本機圖像檔案，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。
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，您已瞭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，深入瞭解 Analysis 4.0 API 功能。
使用適用於 Python 的影像分析用戶端 SDK 來分析影像以讀取文字併產生影像 標題。 本快速入門會分析遠端影像，並將結果列印至主控台。
提示
Analysis 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，請參閱分析影像作法指南。
必要條件
- Azure 訂用帳戶 - 免費建立一個訂用帳戶
- Python 3.x。 您的 Python 安裝應該包含 pip。 您可以藉由在命令列上執行
pip --version來檢查您是否已安裝 pip。 安裝最新版本的 Python 以取得 pip。 - 擁有 Azure 訂用帳戶之後，請在 Azure 入口網站 中建立 電腦視覺 資源。 若要使用本快速入門中的 標題 功能，您必須在其中一個支援的 Azure 區域中建立您的資源（請參閱映像 標題 以取得區域清單）。 部署之後，請選取 [移至資源]。
- 您需要從您建立的資源取得密鑰和端點，以將應用程式連線到 Azure AI 視覺服務。
- 您可以使用免費定價層 （
F0） 來試用服務，稍後再升級至生產環境的付費層。
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。
提示
請勿將金鑰直接包含在您的程式代碼中，且絕不會公開發佈。 如需更多驗證選項 (例如 Azure Key Vault)，請參閱 Azure AI 服務安全性文章。
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，請將 取代your-key為您資源的其中一個密鑰。 - 若要設定
VISION_ENDPOINT環境變數，請將 取代your-endpoint為您資源的端點。
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
開啟您想要新專案的命令提示字元，然後建立名為 quickstart.py 的新檔案。
執行此指令以安裝影像分析 SDK：
pip install azure-ai-vision-imageanalysis將下列程式碼複製到 quickstart.py：
提示
此程式代碼會顯示分析影像 URL。 您也可以從程式記憶體緩衝區分析影像。 如需詳細資訊，請參閱 分析影像操作指南。
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，您已瞭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，深入瞭解 Analysis 4.0 API 功能。
使用適用於 Java 的影像分析用戶端 SDK 來分析影像以讀取文字併產生影像 標題。 本快速入門會分析遠端影像，並將結果列印至主控台。
提示
Analysis 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，請參閱分析影像作法指南。
必要條件
- Windows 10 （或更新版本） x64 或 Linux x64 計算機。
- 已安裝 Java 開發工具套件 （JDK） 第 8 版或更新版本，例如 Azul Zulu OpenJDK、Microsoft Build of OpenJDK、Oracle Java 或您慣用的 JDK。 從命令行執行
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 。 在Linux上，如果有的話，請從散發存放庫安裝。 執行
mvn -v以確認安裝成功。 - Azure 訂用帳戶 - 免費建立一個訂用帳戶
- 擁有 Azure 訂用帳戶之後，請在 Azure 入口網站 中建立 電腦視覺 資源。 若要使用本快速入門中的 標題 功能，您必須在其中一個支援的 Azure 區域中建立資源（請參閱映像 標題）。 部署之後，請選取 [移至資源]。
- 您需要從您建立的資源取得密鑰和端點，以將應用程式連線到 Azure AI 視覺服務。
- 您可以使用免費定價層 （
F0） 來試用服務，稍後再升級至生產環境的付費層。
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。
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
pom.xml您的項目目錄中<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>根據 Maven 存放庫中最新可用 azure-ai-vision-imageanalysis 套件的可用版本來更新版本值 （
1.0.0-beta.2）。在專案目錄中執行下列命令，以安裝 SDK 和相依性：
mvn clean dependency:copy-dependencies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。
提示
請勿將金鑰直接包含在您的程式代碼中，且絕不會公開發佈。 如需更多驗證選項 (例如 Azure Key Vault)，請參閱 Azure AI 服務安全性文章。
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，請將 取代your-key為您資源的其中一個密鑰。 - 若要設定
VISION_ENDPOINT環境變數，請將 取代your-endpoint為您資源的端點。
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
程序代碼會從 URL 分析影像。 您也可以從程式記憶體緩衝區分析影像。 如需詳細資訊，請參閱 分析影像操作指南。
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，您已瞭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，深入瞭解 Analysis 4.0 API 功能。
使用適用於 JavaScript 的影像分析用戶端 SDK 來分析影像以讀取文字併產生影像 標題。 本快速入門會分析遠端影像，並將結果列印至主控台。
提示
Analysis 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，請參閱分析影像作法指南。
必要條件
- Azure 訂用帳戶 - 免費建立一個訂用帳戶
- 目前版本的 Node.js
- Edge、Chrome、Firefox 或 Safari 因特網瀏覽器的目前版本。
- 擁有 Azure 訂用帳戶之後，請在 Azure 入口網站 中建立 電腦視覺 資源，以取得您的密鑰和端點。 若要使用本快速入門中的 標題 功能，您必須在其中一個支援的 Azure 區域中建立資源（請參閱映像 標題 以取得區域清單）。 部署之後，請選取 [移至資源]。
- 您需要從您建立的資源取得密鑰和端點，以將應用程式連線到 Azure AI 視覺服務。
- 您可以使用免費定價層 （
F0） 來試用服務，稍後再升級至生產環境的付費層。
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。
提示
請勿將金鑰直接包含在您的程式代碼中，且絕不會公開發佈。 如需更多驗證選項 (例如 Azure Key Vault)，請參閱 Azure AI 服務安全性文章。
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，請將 取代your-key為您資源的其中一個密鑰。 - 若要設定
VISION_ENDPOINT環境變數，請將 取代your-endpoint為您資源的端點。
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
建立新的Node.js應用程式
在主控台視窗中（例如 cmd、PowerShell 或 Bash），為您的應用程式建立新的目錄，然後流覽至它。
mkdir myapp && cd myappnpm init執行 命令以使用package.json檔案建立節點應用程式。npm init安裝客戶端連結庫
安裝
@azure-rest/ai-vision-image-analysisnpm 套件：npm install @azure-rest/ai-vision-image-analysis同時安裝 dotenv 套件：
npm install dotenv您的應用程式檔案
package.json將會隨著相依性更新。建立新的檔案， index.js。 在文字編輯器中開啟它，並貼上下列程序代碼。
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();在
node快速入門檔案上使用 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，您已瞭解如何安裝影像分析用戶端連結庫，並進行基本的影像分析呼叫。 接下來，深入瞭解分析 API 功能。
使用影像分析 REST API 來讀取文字，併產生影像的 標題（僅限 4.0 版）。
提示
Analysis 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，請參閱分析影像作法指南。
必要條件
- Azure 訂用帳戶 - 免費建立一個訂用帳戶
- 擁有 Azure 訂用帳戶之後，請在 Azure 入口網站 中建立 電腦視覺 資源，以取得您的密鑰和端點。 若要使用本快速入門中的 標題 功能，您必須在下列其中一個 Azure 區域中建立資源：美國東部、法國中部、韓國中部、北歐、東南亞、西歐、美國西部、東亞。 部署之後，請選取 [移至資源]。
- 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代碼中。
- 您可以使用免費定價層 （
F0） 來試用服務，稍後再升級至生產環境的付費層。
- 已安裝 cURL
分析影像
若要分析各種視覺功能的影像，請執行下列步驟：
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" "https://<endpoint>/computervision/imageanalysis:analyze?features=caption,read&model-version=latest&language=en&api-version=2023-10-01" -d "{'url':'https://learn.microsoft.com/azure/ai-services/computer-vision/media/quickstarts/presentation.png'}"視需要在命令中進行下列變更：
- 以您的視覺資源金鑰取代
<subscriptionKey>的值。 - 以您的視覺資源端點取代
<endpoint>的值。 例如：https://YourResourceName.cognitiveservices.azure.com。 - 或者，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。
- 以您的視覺資源金鑰取代
開啟 [命令提示字元] 視窗。
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
JSON 中傳回成功的回應，類似於下列範例：
{
"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，您已瞭解如何使用 REST API 進行基本影像分析呼叫。 接下來，深入瞭解 Analysis 4.0 API 功能。