Quickstart: Image Analysis 4.0

Article
09/17/2023

Get started with the Image Analysis 4.0 REST API or client SDK to set up a basic image analysis application. The Image Analysis service provides you with AI algorithms for processing images and returning information on their visual features. Follow these steps to install a package to your application and try out the sample code.

Use the Image Analysis client SDK for C# to analyze an image to read text and generate an image caption. This quickstart calls a function AnalyzeImage(), which uses the client object to analyze a remote image and print the results to the console.

Reference documentation | Packages (NuGet) | Samples

Tip

The Analysis 4.0 API can do many different operations. See the Analyze Image how-to guide for examples that showcase all of the available features.

Prerequisites

An Azure subscription - Create one for free
The Visual Studio IDE with workload .NET desktop development enabled. Or if you don't plan on using Visual Studio IDE, you need .NET 6.0 SDK or higher installed.
.NET Runtime installed.
Once you have your Azure subscription, create a Vision resource in the Azure portal. In order to use the captioning feature in this quickstart, you must create your resource in one of the following Azure regions: East US, France Central, Korea Central, North Europe, Southeast Asia, West Europe, West US. After it deploys, select Go to resource.
- You need the key and endpoint from the resource you create to connect your application to the Azure AI Vision service.
- You can use the free pricing tier (F0) to try the service, and upgrade later to a paid tier for production.

Set up application

Create a new C# application.

Visual Studio IDE
CLI

Open Visual Studio, and under Get started select Create a new project. Set the template filters to C#/All Platforms/Console. Select Console App (command-line application that can run on .NET on Windows, Linux and macOS) and choose Next. Update the project name to ImageAnalysisQuickstart and choose Next. Select .NET 6.0 or above, and choose Create to create the project.

Install the client SDK

Once you've created a new project, install the client SDK by right-clicking on the project solution in the Solution Explorer and selecting Manage NuGet Packages. In the package manager that opens select Browse, check Include prerelease, and search for Azure.AI.Vision.ImageAnalysis. Select Install. For more information, see the SDK installation guide.

In a console window (such as cmd, PowerShell, or Bash), use the dotnet new command to create a new console app with the name image-analysis-quickstart. This command creates a simple "Hello World" C# project with a single source file: Program.cs.

dotnet new console -n image-analysis-quickstart

Change your directory to the newly created app folder. You can build the application with:

dotnet build

The build output should contain no warnings or errors.

...
Build succeeded.
  0 Warning(s)
  0 Error(s)
...

Install the client SDK

Within the application directory, install the Azure AI Vision client SDK for .NET with the following command:

dotnet add package  Azure.AI.Vision.ImageAnalysis --prerelease

For more information, see the SDK installation guide.

Create environment variables

In this example, write your credentials to environment variables on the local machine that runs the application.

Go to the Azure portal. If the resource you created in the Prerequisites section deployed successfully, select Go to resource under Next Steps. You can find your key and endpoint under Resource Management in the Keys and Endpoint page. Your resource key isn't the same as your Azure subscription ID.

Tip

Don't include the key directly in your code, and never post it publicly. See the Azure AI services security article for more authentication options like Azure Key Vault.

To set the environment variable for your key and endpoint, open a console window and follow the instructions for your operating system and development environment.

To set the VISION_KEY environment variable, replace your-key with one of the keys for your resource.
To set the VISION_ENDPOINT environment variable, replace your-endpoint with the endpoint for your resource.

Windows
Linux

setx VISION_KEY your-key

setx VISION_ENDPOINT your-endpoint

After you add the environment variables, you may need to restart any running programs that will read the environment variables, including the console window.

export VISION_KEY=your-key

export VISION_ENDPOINT=your-endpoint

After you add the environment variables, run source ~/.bashrc from your console window to make the changes effective.

Analyze Image

From the project directory, open the Program.cs file that was created previously with your new project. Paste in the following code:

Tip

The code shows analyzing an image URL. You can also analyze a local image file, or an image from a memory buffer. For more information, see the Analyze Image how-to guide.

using Azure;
using Azure.AI.Vision.Common;
using Azure.AI.Vision.ImageAnalysis;

class Program
{
    static void AnalyzeImage()
    {
        var serviceOptions = new VisionServiceOptions(
            Environment.GetEnvironmentVariable("VISION_ENDPOINT"),
            new AzureKeyCredential(Environment.GetEnvironmentVariable("VISION_KEY")));

        using var imageSource = VisionSource.FromUrl(
            new Uri("https://learn.microsoft.com/azure/ai-services/computer-vision/media/quickstarts/presentation.png"));

        var analysisOptions = new ImageAnalysisOptions()
        {
            Features = ImageAnalysisFeature.Caption | ImageAnalysisFeature.Text,

            Language = "en",

            GenderNeutralCaption = true
        };

        using var analyzer = new ImageAnalyzer(serviceOptions, imageSource, analysisOptions);

        var result = analyzer.Analyze();

        if (result.Reason == ImageAnalysisResultReason.Analyzed)
        {
            if (result.Caption != null)
            {
                Console.WriteLine(" Caption:");
                Console.WriteLine($"   \"{result.Caption.Content}\", Confidence {result.Caption.Confidence:0.0000}");
            }

            if (result.Text != null)
            {
                Console.WriteLine($" Text:");
                foreach (var line in result.Text.Lines)
                {
                    string pointsToString = "{" + string.Join(',', line.BoundingPolygon.Select(pointsToString => pointsToString.ToString())) + "}";
                    Console.WriteLine($"   Line: '{line.Content}', Bounding polygon {pointsToString}");

                    foreach (var word in line.Words)
                    {
                        pointsToString = "{" + string.Join(',', word.BoundingPolygon.Select(pointsToString => pointsToString.ToString())) + "}";
                        Console.WriteLine($"     Word: '{word.Content}', Bounding polygon {pointsToString}, Confidence {word.Confidence:0.0000}");
                    }
                }
            }
        }
        else
        {
            var errorDetails = ImageAnalysisErrorDetails.FromResult(result);
            Console.WriteLine(" Analysis failed.");
            Console.WriteLine($"   Error reason : {errorDetails.Reason}");
            Console.WriteLine($"   Error code : {errorDetails.ErrorCode}");
            Console.WriteLine($"   Error message: {errorDetails.Message}");
        }
    }

    static void Main()
    {
        try
        {
            AnalyzeImage();
        }
        catch (Exception e)
        {
            Console.WriteLine(e);
        }
    }
}

Then, build and run the application. You should see output similar to the one shown here.

Visual Studio IDE
CLI

Build and run the application by selecting Start Debugging from the Debug menu at the top of the IDE window (or press F5).

Build and run the application from your application directory with these commands:

dotnet build
dotnet run

Output

The console output should show something similar to the following text:

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

Clean up resources

If you want to clean up and remove an Azure AI services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

Next steps

In this quickstart, you learned how to install the Image Analysis client SDK and make basic image analysis calls. Next, learn more about the Analysis 4.0 API features.

Call the Analyze Image 4.0 API

Image Analysis overview
Sample source code can be found on GitHub.

Use the Image Analysis client SDK for Python to analyze a remote image to read text and generate an image caption. This quickstart uses the client object to analyze a remote image and print the results to the console.

Reference documentation | Package (PyPi) | Samples

Tip

The Analysis 4.0 API can do many different operations. See the Analyze Image how-to guide for examples that showcase all of the available features.

Prerequisites

An Azure subscription - Create one for free
Python 3.x. Your Python installation should include pip. You can check if you have pip installed by running pip --version on the command line. Get pip by installing the latest version of Python.
Once you have your Azure subscription, create a Vision resource in the Azure portal. In order to use the captioning feature in this quickstart, you must create your resource in one of the following Azure regions: East US, France Central, Korea Central, North Europe, Southeast Asia, West Europe, West US, East Asia. After it deploys, select Go to resource.
- You need the key and endpoint from the resource you create to connect your application to the Azure AI Vision service.
- You can use the free pricing tier (F0) to try the service, and upgrade later to a paid tier for production.

Create environment variables

In this example, write your credentials to environment variables on the local machine that runs the application.

Tip

Don't include the key directly in your code, and never post it publicly. See the Azure AI services security article for more authentication options like Azure Key Vault.

To set the environment variable for your key and endpoint, open a console window and follow the instructions for your operating system and development environment.

To set the VISION_KEY environment variable, replace your-key with one of the keys for your resource.
To set the VISION_ENDPOINT environment variable, replace your-endpoint with the endpoint for your resource.

Windows
Linux

setx VISION_KEY your-key

setx VISION_ENDPOINT your-endpoint

After you add the environment variables, you may need to restart any running programs that will read the environment variables, including the console window.

export VISION_KEY=your-key

export VISION_ENDPOINT=your-endpoint

After you add the environment variables, run source ~/.bashrc from your console window to make the changes effective.

Analyze Image

Open a command prompt where you want the new project, and create a new file named quickstart.py.
Run this command to install the Azure AI Vision client SDK:
```
python -m pip install azure-ai-vision
```
For more information, see the SDK installation guide.
Copy the following code into quickstart.py:

Tip

The code shows analyzing an image URL. You can also analyze a local image file, or an image from a memory buffer. For more information, see the Analyze Image how-to guide.

import os
import azure.ai.vision as sdk

service_options = sdk.VisionServiceOptions(os.environ["VISION_ENDPOINT"],
                                           os.environ["VISION_KEY"])

vision_source = sdk.VisionSource(
    url="https://learn.microsoft.com/azure/ai-services/computer-vision/media/quickstarts/presentation.png")

analysis_options = sdk.ImageAnalysisOptions()

analysis_options.features = (
    sdk.ImageAnalysisFeature.CAPTION |
    sdk.ImageAnalysisFeature.TEXT
)

analysis_options.language = "en"

analysis_options.gender_neutral_caption = True

image_analyzer = sdk.ImageAnalyzer(service_options, vision_source, analysis_options)

result = image_analyzer.analyze()

if result.reason == sdk.ImageAnalysisResultReason.ANALYZED:

    if result.caption is not None:
        print(" Caption:")
        print("   '{}', Confidence {:.4f}".format(result.caption.content, result.caption.confidence))

    if result.text is not None:
        print(" Text:")
        for line in result.text.lines:
            points_string = "{" + ", ".join([str(int(point)) for point in line.bounding_polygon]) + "}"
            print("   Line: '{}', Bounding polygon {}".format(line.content, points_string))
            for word in line.words:
                points_string = "{" + ", ".join([str(int(point)) for point in word.bounding_polygon]) + "}"
                print("     Word: '{}', Bounding polygon {}, Confidence {:.4f}"
                      .format(word.content, points_string, word.confidence))

else:

    error_details = sdk.ImageAnalysisErrorDetails.from_result(result)
    print(" Analysis failed.")
    print("   Error reason: {}".format(error_details.reason))
    print("   Error code: {}".format(error_details.error_code))
    print("   Error message: {}".format(error_details.message))

Then run the application with the python command on your quickstart file.
```
python quickstart.py
```

Output

The console output should show something similar to the following text:

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

Clean up resources

If you want to clean up and remove an Azure AI services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

Next steps

In this quickstart, you learned how to install the Image Analysis client SDK and make basic image analysis calls. Next, learn more about the Analysis 4.0 API features.

Call the Analyze Image 4.0 API

Image Analysis overview
Sample source code can be found on GitHub.

Use the Image Analysis client SDK for C++ to analyze an image to read text and generate an image caption. This quickstart calls a function AnalyzeImage(), which uses the client object to analyze a remote image and print the results to the console.

Reference documentation | Package (NuGet) | Samples

Tip

The Analysis 4.0 API can do many different operations. See the Analyze Image how-to guide for examples that showcase all of the available features.

Prerequisites

An Azure subscription - Create one for free
For Windows development, the Visual Studio IDE with workload Desktop development with C++ enabled.
Once you have your Azure subscription, create a Vision resource in the Azure portal. In order to use the captioning feature in this quickstart, you must create your resource in one of the following Azure regions: East US, France Central, Korea Central, North Europe, Southeast Asia, West Europe, West US, East Asia, East Asia. After it deploys, select Go to resource.
- You need the key and endpoint from the resource you create to connect your application to the Azure AI Vision service.
- You can use the free pricing tier (F0) to try the service, and upgrade later to a paid tier for production.

Set up application

Create a new C++ application.

Windows
Linux

Open Visual Studio, and under Get started select Create a new project. Set the template filters to C++/Windows/Console. Select Console App and choose Next. Update the project name to ImageAnalysisQuickstart and choose Create to create the project.

Install the client SDK

Create environment variables

In this example, write your credentials to environment variables on the local machine that runs the application.

Tip

Don't include the key directly in your code, and never post it publicly. See the Azure AI services security article for more authentication options like Azure Key Vault.

To set the environment variable for your key and endpoint, open a console window and follow the instructions for your operating system and development environment.

To set the VISION_KEY environment variable, replace your-key with one of the keys for your resource.
To set the VISION_ENDPOINT environment variable, replace your-endpoint with the endpoint for your resource.

Windows
Linux

setx VISION_KEY your-key

setx VISION_ENDPOINT your-endpoint

After you add the environment variables, you may need to restart any running programs that will read the environment variables, including the console window.

export VISION_KEY=your-key

export VISION_ENDPOINT=your-endpoint

After you add the environment variables, run source ~/.bashrc from your console window to make the changes effective.

Analyze Image

From the project directory, open the ImageAnalysisQuickstart.cpp file that was created previously with your new project. Clear its contents and paste in the following code:

Tip

The code shows analyzing an image URL. You can also analyze a local image file, or an image from a memory buffer. For more information, see the Analyze Image how-to guide.

#include <vision_api_cxx_image_analyzer.hpp>

using namespace Azure::AI::Vision::ImageAnalysis;
using namespace Azure::AI::Vision::Input;
using namespace Azure::AI::Vision::Service;

std::string PolygonToString(std::vector<int32_t> boundingPolygon);
std::string GetEnvironmentVariable(const std::string name);

void AnalyzeImage()
{
    auto serviceOptions = VisionServiceOptions::FromEndpoint(
        GetEnvironmentVariable("VISION_ENDPOINT"),
        GetEnvironmentVariable("VISION_KEY"));

    auto imageSource = VisionSource::FromUrl(
        "https://learn.microsoft.com/azure/ai-services/computer-vision/media/quickstarts/presentation.png");

    auto analysisOptions = ImageAnalysisOptions::Create();

    analysisOptions->SetFeatures(
        {
            ImageAnalysisFeature::Caption,
            ImageAnalysisFeature::Text,
        });

    analysisOptions->SetLanguage("en");

    analysisOptions->SetGenderNeutralCaption(true);

    auto analyzer = ImageAnalyzer::Create(serviceOptions, imageSource, analysisOptions);

    auto result = analyzer->Analyze();

    if (result->GetReason() == ImageAnalysisResultReason::Analyzed)
    {
        const auto caption = result->GetCaption();
        if (caption.HasValue())
        {
            std::cout << " Caption:" << std::endl;
            std::cout << "   \"" << caption.Value().Content << "\", Confidence " << caption.Value().Confidence << std::endl;
        }

        const auto detectedText = result->GetText();
        if (detectedText.HasValue())
        {
            std::cout << " Text:\n";
            for (const auto line : detectedText.Value().Lines)
            {
                std::cout << "   Line: \"" << line.Content << "\"";
                std::cout << ", Bounding polygon " << PolygonToString(line.BoundingPolygon) << std::endl;

                for (const auto word : line.Words)
                {
                    std::cout << "     Word: \"" << word.Content << "\"";
                    std::cout << ", Bounding polygon " << PolygonToString(word.BoundingPolygon);
                    std::cout << ", Confidence " << word.Confidence << std::endl;
                }
            }
        }
    }
    else
    {
        auto errorDetails = ImageAnalysisErrorDetails::FromResult(result);
        std::cout << " Analysis failed." << std::endl;
        std::cout << "   Error reason = " << (int)errorDetails->GetReason() << std::endl;
        std::cout << "   Error code = " << errorDetails->GetErrorCode() << std::endl;
        std::cout << "   Error message = " << errorDetails->GetMessage() << std::endl;
    }
}

std::string PolygonToString(std::vector<int32_t> boundingPolygon)
{
    std::string out = "{";
    for (int i = 0; i < boundingPolygon.size(); i += 2)
    {
        out += ((i == 0) ? "{" : ",{") +
            std::to_string(boundingPolygon[i]) + "," +
            std::to_string(boundingPolygon[i + 1]) + "}";
    }
    out += "}";
    return out;
}

std::string GetEnvironmentVariable(const std::string name)
{
#if defined(_MSC_VER)
    size_t size = 0;
    char buffer[1024];
    getenv_s(&size, nullptr, 0, name.c_str());
    if (size > 0 && size < sizeof(buffer))
    {
        getenv_s(&size, buffer, size, name.c_str());
        return std::string{ buffer };
    }
#else
    const char* value = getenv(name.c_str());
    if (value != nullptr)
    {
        return std::string{ value };
    }
#endif
    return std::string{ "" };
}

int main()
{
    try
    {
        AnalyzeImage();
    }
    catch (std::exception e)
    {
        std::cout << e.what();
    }

    return 0;
}

Then, compile and run the application by selecting Start Debugging from the Debug menu at the top of the IDE window (or press F5). You should see output similar to the one shown here.

Output

The console output should show something similar to the following text:

Caption:
   "a person pointing at a screen", Confidence 0.489159
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 0.993
     Word: "AM", Bounding polygon {{179,130},{204,130},{203,149},{178,149}}, Confidence 0.998
   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 0.104
     Word: "Conference", Bounding polygon {{142,154},{174,154},{173,161},{141,161}}, Confidence 0.902
     Word: "room", Bounding polygon {{175,154},{189,155},{188,161},{175,161}}, Confidence 0.796
     Word: "154584354", Bounding polygon {{192,155},{224,154},{223,162},{191,161}}, Confidence 0.864
   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 0.036
     Word: "555-173-4547", Bounding polygon {{142,164},{182,165},{181,171},{142,171}}, Confidence 0.597
   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 0.981
     Word: "Hall", Bounding polygon {{570,181},{590,181},{590,191},{570,190}}, Confidence 0.991
   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 0.09
     Word: "AM", Bounding polygon {{557,192},{565,192},{565,200},{557,200}}, Confidence 0.991
     Word: "-", Bounding polygon {{567,192},{569,192},{569,200},{567,200}}, Confidence 0.691
     Word: "10:00", Bounding polygon {{570,192},{585,193},{584,200},{570,200}}, Confidence 0.885
     Word: "AM", Bounding polygon {{586,193},{593,194},{593,200},{586,200}}, Confidence 0.991
   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 0.602
     Word: "Buaion", Bounding polygon {{561,202},{580,202},{579,208},{560,208}}, Confidence 0.291
   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 0.175
     Word: "SCRUM", Bounding polygon {{552,260},{570,260},{570,266},{551,266}}, Confidence 0.114
   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 0.857
     Word: "AM", Bounding polygon {{554,267},{561,267},{560,273},{553,273}}, Confidence 0.998
     Word: "11:00", Bounding polygon {{564,267},{578,267},{577,273},{563,273}}, Confidence 0.479
     Word: "AM", Bounding polygon {{579,267},{586,267},{585,273},{578,273}}, Confidence 0.994
   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 0.464
     Word: "de", Bounding polygon {{563,274},{569,274},{568,279},{562,279}}, Confidence 0.81
     Word: "Crum", Bounding polygon {{570,274},{582,273},{581,279},{569,279}}, Confidence 0.885
   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 0.523
     Word: "NI", Bounding polygon {{563,296},{570,296},{570,302},{563,302}}, Confidence 0.303
     Word: "Hands", Bounding polygon {{572,296},{588,296},{588,302},{571,302}}, Confidence 0.613
   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 0.618
     Word: "AM-12:00", Bounding polygon {{554,304},{578,304},{577,310},{553,310}}, Confidence 0.27
     Word: "PM", Bounding polygon {{579,304},{586,304},{586,309},{578,310}}, Confidence 0.662
   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 0.611
     Word: "Shaman", Bounding polygon {{555,310},{576,311},{576,317},{555,317}}, Confidence 0.605
   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 0.606
     Word: "stand", Bounding polygon {{558,333},{572,334},{571,340},{557,339}}, Confidence 0.489
     Word: "up", Bounding polygon {{574,334},{580,334},{580,340},{573,340}}, Confidence 0.815
   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 0.826
     Word: "PM-1:00", Bounding polygon {{554,341},{575,341},{574,347},{553,347}}, Confidence 0.209
     Word: "PM", Bounding polygon {{576,341},{583,341},{582,347},{575,347}}, Confidence 0.039
   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 0.58
     Word: "Marckre", Bounding polygon {{560,347},{582,348},{582,353},{559,353}}, Confidence 0.275
   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 0.615
     Word: "review", Bounding polygon {{560,371},{576,371},{575,376},{559,376}}, Confidence 0.04

Clean up resources

If you want to clean up and remove an Azure AI services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

Next steps

In this quickstart, you learned how to install the Image Analysis client SDK and make basic image analysis calls. Next, learn more about the Analysis 4.0 API features.

Call the Analyze Image 4.0 API

Image Analysis overview
Sample source code can be found on GitHub.

Use the Image Analysis client SDK for Java to analyze an image to read text and generate an image caption. This quickstart analyzes a remote image and prints the results to the console.

Reference documentation | Maven Package | Samples

Tip

The Analysis 4.0 API can do many different operations. See the Analyze Image how-to guide for examples that showcase all of the available features.

Prerequisites

A Windows 10 (or higher) x64, or Linux x64 machine.
Java Development Kit (JDK) version 8 or above installed, such as Azul Zulu OpenJDK, Microsoft Build of OpenJDK, Oracle Java, or your preferred JDK. Run java -version from a command line to see your version and confirm a successful installation. Make sure that the Java installation is native to the system architecture and not running through emulation.
Apache Maven installed. On Linux, install from the distribution repositories if available. Run mvn -v to confirm successful installation.
An Azure subscription - Create one for free
Once you have your Azure subscription, create a Vision resource in the Azure portal. In order to use the captioning feature in this quickstart, you must create your resource in one of the following Azure regions: East US, France Central, Korea Central, North Europe, Southeast Asia, West Europe, West US. After it deploys, select Go to resource.
- You need the key and endpoint from the resource you create to connect your application to the Azure AI Vision service.
- You can use the free pricing tier (F0) to try the service, and upgrade later to a paid tier for production.

Set up application

Open a console window and create a new folder for your quickstart application.

Open a text editor and copy the following content to a new file. Save the file as pom.xml in your project directory

<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>azure.ai.vision.imageanalysis.samples</groupId>
  <artifactId>image-analysis-quickstart</artifactId>
  <version>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>0.15.1-beta.1</version>
    </dependency>
    <!-- https://mvnrepository.com/artifact/com.azure/azure-core-http-netty -->
    <dependency>
      <groupId>com.azure</groupId>
      <artifactId>azure-core-http-netty</artifactId>
      <version>1.13.6</version>
    </dependency>
    <!-- https://mvnrepository.com/artifact/org.slf4j/slf4j-api -->
    <dependency>
      <groupId>org.slf4j</groupId>
      <artifactId>slf4j-api</artifactId>
      <version>2.0.7</version>
    </dependency>
    <!-- https://mvnrepository.com/artifact/org.slf4j/slf4j-simple -->
    <dependency>
      <groupId>org.slf4j</groupId>
      <artifactId>slf4j-simple</artifactId>
      <version>2.0.7</version>
    </dependency>
  </dependencies>
</project>

Install the SDK and dependencies by running the following in the project directory:
```
mvn clean dependency:copy-dependencies
```
Once the operation succeeds, verify that the folders target\dependency were creating and they contain .jar files.

For more information, see the SDK installation guide.

Create environment variables

In this example, write your credentials to environment variables on the local machine that runs the application.

Tip

Don't include the key directly in your code, and never post it publicly. See the Azure AI services security article for more authentication options like Azure Key Vault.

To set the environment variable for your key and endpoint, open a console window and follow the instructions for your operating system and development environment.

To set the VISION_KEY environment variable, replace your-key with one of the keys for your resource.
To set the VISION_ENDPOINT environment variable, replace your-endpoint with the endpoint for your resource.

Windows
Linux

setx VISION_KEY your-key

setx VISION_ENDPOINT your-endpoint

After you add the environment variables, you may need to restart any running programs that will read the environment variables, including the console window.

export VISION_KEY=your-key

export VISION_ENDPOINT=your-endpoint

After you add the environment variables, run source ~/.bashrc from your console window to make the changes effective.

Analyze Image

Open a text editor and copy the following content to a new file. Save the file as ImageAnalysis.java

import java.net.URL;
import java.util.EnumSet;

import com.azure.ai.vision.common.*;
import com.azure.ai.vision.imageanalysis.*;

public class ImageAnalysis {

    public static void main(String[] args) {

        try (
            VisionServiceOptions serviceOptions = new VisionServiceOptions(
                new URL(System.getenv("VISION_ENDPOINT")),
                System.getenv("VISION_KEY"));

            VisionSource imageSource = VisionSource.fromUrl(
                new URL("https://learn.microsoft.com/azure/ai-services/computer-vision/media/quickstarts/presentation.png"));

            ImageAnalysisOptions analysisOptions = new ImageAnalysisOptions()) {

            analysisOptions.setFeatures(EnumSet.of(ImageAnalysisFeature.CAPTION, ImageAnalysisFeature.TEXT));

            analysisOptions.setLanguage("en");

            analysisOptions.setGenderNeutralCaption(true);

            try (
                ImageAnalyzer analyzer = new ImageAnalyzer(serviceOptions, imageSource, analysisOptions);

                ImageAnalysisResult result = analyzer.analyze()) {

                if (result.getReason() == ImageAnalysisResultReason.ANALYZED) {

                    if (result.getCaption() != null) {
                        System.out.println(" Caption:");
                        System.out.println("   \"" + result.getCaption().getContent() + "\", Confidence "
                                + String.format("%.4f", result.getCaption().getConfidence()));
                    }

                    if (result.getText() != null) {
                        System.out.println(" Text:");
                        for (DetectedTextLine line : result.getText()) {
                            String pointsToString = "{" + String.join(",",
                                    line.getBoundingPolygon().stream().map(Object::toString).toArray(String[]::new))
                                    + "}";
                            System.out.println(
                                    "   Line: '" + line.getContent() + "', Bounding polygon " + pointsToString);

                            for (DetectedTextWord word : line.getWords()) {
                                pointsToString = "{" + String.join(",",
                                        word.getBoundingPolygon().stream().map(Object::toString).toArray(String[]::new))
                                        + "}";
                                System.out.println(
                                        "     Word: '" + word.getContent() + "', Bounding polygon " + pointsToString +
                                                ", Confidence " + String.format("%.4f", word.getConfidence()));
                            }
                        }
                    }
                } else { // result.Reason == ImageAnalysisResultReason.Error
                    ImageAnalysisErrorDetails errorDetails = ImageAnalysisErrorDetails.fromResult(result);
                    System.out.println(" Analysis failed.");
                    System.out.println("   Error reason: " + errorDetails.getReason());
                    System.out.println("   Error code: " + errorDetails.getErrorCode());
                    System.out.println("   Error message: " + errorDetails.getMessage());
                }
            }
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

Tip

The code shows analyzing an image URL. You can also analyze a local image file, or an image from a memory buffer. For more information, see the Analyze Image how-to guide.

To compile the Java file, run the following:

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

You should see the file ImageAnalysis.class created in the current folder.

To run the application:

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

Output

The console output should show something similar to the following text:

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

Clean up resources

If you want to clean up and remove an Azure AI services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

Next steps

In this quickstart, you learned how to install the Image Analysis client SDK and make basic image analysis calls. Next, learn more about the Analysis 4.0 API features.

Call the Analyze Image 4.0 API

Image Analysis overview
Sample source code can be found on GitHub.

Use the Image Analysis REST API to read text and generate captions for the image (version 4.0 only).

Tip

The Analysis 4.0 API can do many different operations. See the Analyze Image how-to guide for examples that showcase all of the available features.

Prerequisites

An Azure subscription - Create one for free
Once you have your Azure subscription, create a Vision resource in the Azure portal to get your key and endpoint. In order to use the captioning feature in this quickstart, you must create your resource in one of the following Azure regions: East US, France Central, Korea Central, North Europe, Southeast Asia, West Europe, West US, East Asia. After it deploys, select Go to resource.
- You'll need the key and endpoint from the resource you create to connect your application to the Azure AI Vision service. You'll paste your key and endpoint into the code below later in the quickstart.
- You can use the free pricing tier (F0) to try the service, and upgrade later to a paid tier for production.
cURL installed

Analyze an image

To analyze an image for various visual features, do the following steps:

Copy the following curl command into a text editor.

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=2023-02-01-preview" -d "{'url':'https://learn.microsoft.com/azure/ai-services/computer-vision/media/quickstarts/presentation.png'}"

Make the following changes in the command where needed:
1. Replace the value of <subscriptionKey> with your Vision resource key.
2. Replace the value of <endpoint> with your Vision resource endpoint. For example: https://YourResourceName.cognitiveservices.azure.com.
3. Optionally, change the image URL in the request body (https://learn.microsoft.com/azure/ai-services/computer-vision/media/quickstarts/presentation.png) to the URL of a different image to be analyzed.
Open a command prompt window.
Paste your edited curl command from the text editor into the command prompt window, and then run the command.

Examine the response

A successful response is returned in JSON, similar to the following example:

{
    "captionResult":
    {
        "text": "a man pointing at a screen",
        "confidence": 0.4891590476036072
    },
    "modelVersion": "2023-02-01-preview",
    "metadata":
    {
        "width": 1038,
        "height": 692
    },
    "readResult":
    {
        "stringIndexType": "TextElements",
        "content": "9:35 AM\nE Conference room 154584354\n#: 555-173-4547\nTown Hall\n9:00 AM - 10:00 AM\nAaron Buaion\nDaily SCRUM\n10:00 AM 11:00 AM\nChurlette de Crum\nQuarterly NI Hands\n11.00 AM-12:00 PM\nBebek Shaman\nWeekly stand up\n12:00 PM-1:00 PM\nDelle Marckre\nProduct review",
        "pages":
        [
            {
                "height": 692,
                "width": 1038,
                "angle": 0.3048,
                "pageNumber": 1,
                "words":
                [
                    {"content":"9:35","boundingBox":[131,130,171,130,171,149,130,149],"confidence":0.993,"span":{"offset":0,"length":4}},
                    {"content":"AM","boundingBox":[179,130,204,130,203,149,178,149],"confidence":0.998,"span":{"offset":5,"length":2}},
                    {"content":"E","boundingBox":[131,154,135,154,135,161,131,161],"confidence":0.104,"span":{"offset":8,"length":1}},
                    {"content":"Conference","boundingBox":[142,154,174,154,173,161,141,161],"confidence":0.902,"span":{"offset":10,"length":10}},
                    {"content":"room","boundingBox":[175,154,189,155,188,161,175,161],"confidence":0.796,"span":{"offset":21,"length":4}},
                    {"content":"154584354","boundingBox":[192,155,224,154,223,162,191,161],"confidence":0.864,"span":{"offset":26,"length":9}},
                    {"content":"#:","boundingBox":[131,163,139,164,139,171,131,171],"confidence":0.036,"span":{"offset":36,"length":2}},
                    {"content":"555-173-4547","boundingBox":[142,164,182,165,181,171,142,171],"confidence":0.597,"span":{"offset":39,"length":12}},
                    {"content":"Town","boundingBox":[547,181,568,181,568,190,546,191],"confidence":0.981,"span":{"offset":52,"length":4}},
                    {"content":"Hall","boundingBox":[570,181,590,181,590,191,570,190],"confidence":0.991,"span":{"offset":57,"length":4}},
                    {"content":"9:00","boundingBox":[546,192,555,192,555,200,546,200],"confidence":0.09,"span":{"offset":62,"length":4}},
                    {"content":"AM","boundingBox":[557,192,565,192,565,200,557,200],"confidence":0.991,"span":{"offset":67,"length":2}},
                    {"content":"-","boundingBox":[567,192,569,192,569,200,567,200],"confidence":0.691,"span":{"offset":70,"length":1}},
                    {"content":"10:00","boundingBox":[570,192,585,193,584,200,570,200],"confidence":0.885,"span":{"offset":72,"length":5}},
                    {"content":"AM","boundingBox":[586,193,593,194,593,200,586,200],"confidence":0.991,"span":{"offset":78,"length":2}},
                    {"content":"Aaron","boundingBox":[545,202,560,202,559,208,544,208],"confidence":0.602,"span":{"offset":81,"length":5}},
                    {"content":"Buaion","boundingBox":[561,202,580,202,579,208,560,208],"confidence":0.291,"span":{"offset":87,"length":6}},
                    {"content":"Daily","boundingBox":[538,259,551,260,550,266,538,265],"confidence":0.175,"span":{"offset":94,"length":5}},
                    {"content":"SCRUM","boundingBox":[552,260,570,260,570,266,551,266],"confidence":0.114,"span":{"offset":100,"length":5}},
                    {"content":"10:00","boundingBox":[539,267,553,267,552,273,538,272],"confidence":0.857,"span":{"offset":106,"length":5}},
                    {"content":"AM","boundingBox":[554,267,561,267,560,273,553,273],"confidence":0.998,"span":{"offset":112,"length":2}},
                    {"content":"11:00","boundingBox":[564,267,578,267,577,273,563,273],"confidence":0.479,"span":{"offset":115,"length":5}},
                    {"content":"AM","boundingBox":[579,267,586,267,585,273,578,273],"confidence":0.994,"span":{"offset":121,"length":2}},
                    {"content":"Churlette","boundingBox":[539,274,562,274,561,279,538,279],"confidence":0.464,"span":{"offset":124,"length":9}},
                    {"content":"de","boundingBox":[563,274,569,274,568,279,562,279],"confidence":0.81,"span":{"offset":134,"length":2}},
                    {"content":"Crum","boundingBox":[570,274,582,273,581,279,569,279],"confidence":0.885,"span":{"offset":137,"length":4}},
                    {"content":"Quarterly","boundingBox":[540,296,562,296,562,302,539,302],"confidence":0.523,"span":{"offset":142,"length":9}},
                    {"content":"NI","boundingBox":[563,296,570,296,570,302,563,302],"confidence":0.303,"span":{"offset":152,"length":2}},
                    {"content":"Hands","boundingBox":[572,296,588,296,588,302,571,302],"confidence":0.613,"span":{"offset":155,"length":5}},
                    {"content":"11.00","boundingBox":[538,304,552,304,552,310,538,310],"confidence":0.618,"span":{"offset":161,"length":5}},
                    {"content":"AM-12:00","boundingBox":[554,304,578,304,577,310,553,310],"confidence":0.27,"span":{"offset":167,"length":8}},
                    {"content":"PM","boundingBox":[579,304,586,304,586,309,578,310],"confidence":0.662,"span":{"offset":176,"length":2}},
                    {"content":"Bebek","boundingBox":[539,310,554,310,554,317,539,316],"confidence":0.611,"span":{"offset":179,"length":5}},
                    {"content":"Shaman","boundingBox":[555,310,576,311,576,317,555,317],"confidence":0.605,"span":{"offset":185,"length":6}},
                    {"content":"Weekly","boundingBox":[538,332,557,333,556,339,538,338],"confidence":0.606,"span":{"offset":192,"length":6}},
                    {"content":"stand","boundingBox":[558,333,572,334,571,340,557,339],"confidence":0.489,"span":{"offset":199,"length":5}},
                    {"content":"up","boundingBox":[574,334,580,334,580,340,573,340],"confidence":0.815,"span":{"offset":205,"length":2}},
                    {"content":"12:00","boundingBox":[539,341,553,341,552,347,538,347],"confidence":0.826,"span":{"offset":208,"length":5}},
                    {"content":"PM-1:00","boundingBox":[554,341,575,341,574,347,553,347],"confidence":0.209,"span":{"offset":214,"length":7}},
                    {"content":"PM","boundingBox":[576,341,583,341,582,347,575,347],"confidence":0.039,"span":{"offset":222,"length":2}},
                    {"content":"Delle","boundingBox":[540,348,559,347,558,353,539,353],"confidence":0.58,"span":{"offset":225,"length":5}},
                    {"content":"Marckre","boundingBox":[560,347,582,348,582,353,559,353],"confidence":0.275,"span":{"offset":231,"length":7}},
                    {"content":"Product","boundingBox":[539,370,559,371,558,376,539,376],"confidence":0.615,"span":{"offset":239,"length":7}},
                    {"content":"review","boundingBox":[560,371,576,371,575,376,559,376],"confidence":0.04,"span":{"offset":247,"length":6}}
                ],
                "spans":
                [
                    {"offset":0,"length":253}
                ],
                "lines":
                [
                    {"content":"9:35 AM","boundingBox":[130,129,215,130,215,149,130,148],"spans":[{"offset":0,"length":7}]},
                    {"content":"E Conference room 154584354","boundingBox":[130,153,224,154,224,161,130,161],"spans":[{"offset":8,"length":27}]},
                    {"content":"#: 555-173-4547","boundingBox":[130,163,182,164,181,171,130,170],"spans":[{"offset":36,"length":15}]},
                    {"content":"Town Hall","boundingBox":[546,180,590,180,590,190,546,190],"spans":[{"offset":52,"length":9}]},
                    {"content":"9:00 AM - 10:00 AM","boundingBox":[546,191,596,192,596,200,546,199],"spans":[{"offset":62,"length":18}]},
                    {"content":"Aaron Buaion","boundingBox":[543,201,581,201,581,208,543,208],"spans":[{"offset":81,"length":12}]},
                    {"content":"Daily SCRUM","boundingBox":[537,259,575,260,575,266,537,265],"spans":[{"offset":94,"length":11}]},
                    {"content":"10:00 AM 11:00 AM","boundingBox":[536,266,590,266,590,272,536,272],"spans":[{"offset":106,"length":17}]},
                    {"content":"Churlette de Crum","boundingBox":[538,273,584,273,585,279,538,279],"spans":[{"offset":124,"length":17}]},
                    {"content":"Quarterly NI Hands","boundingBox":[538,295,588,295,588,301,538,302],"spans":[{"offset":142,"length":18}]},
                    {"content":"11.00 AM-12:00 PM","boundingBox":[536,304,588,303,588,309,536,310],"spans":[{"offset":161,"length":17}]},
                    {"content":"Bebek Shaman","boundingBox":[538,310,577,310,577,316,538,316],"spans":[{"offset":179,"length":12}]},
                    {"content":"Weekly stand up","boundingBox":[537,332,582,333,582,339,537,338],"spans":[{"offset":192,"length":15}]},
                    {"content":"12:00 PM-1:00 PM","boundingBox":[537,340,583,340,583,347,536,346],"spans":[{"offset":208,"length":16}]},
                    {"content":"Delle Marckre","boundingBox":[538,347,582,347,582,352,538,353],"spans":[{"offset":225,"length":13}]},
                    {"content":"Product review","boundingBox":[538,370,577,370,577,376,538,375],"spans":[{"offset":239,"length":14}]}
                ]
            }
        ],
        "styles": [],
        "modelVersion": "2022-04-30"
    }
}

Next steps

In this quickstart, you learned how to make basic image analysis calls using the REST API. Next, learn more about the Analysis 4.0 API features.

Call the Analyze Image 4.0 API

Image Analysis overview

Quickstart: Image Analysis 4.0

Prerequisites

Set up application

Install the client SDK

Install the client SDK

Create environment variables

Analyze Image

Output

Clean up resources

Next steps

Prerequisites

Create environment variables

Analyze Image

Output

Clean up resources

Next steps

Prerequisites

Set up application

Install the client SDK

Create environment variables

Analyze Image

Output

Clean up resources

Next steps

Prerequisites

Set up application

Create environment variables

Analyze Image

Output

Clean up resources

Next steps

Prerequisites

Analyze an image

Examine the response

Next steps

Feedback

Additional resources

Additional resources