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‎articles/ai-services/computer-vision/how-to/background-removal.md

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**VisionSource** implements **IDisposable**, therefore create the object with a **using** statement or explicitly call **Dispose** method after analysis completes.
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> [!TIP]
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> You can also analyze a local image by passing in the full-path image file name (see [VisionSource.FromFile](/dotnet/api/azure.ai.vision.common.visionsource.fromfile)), or by copying the image into the SDK's input buffer (see [VisionSource.FromImageSourceBuffer](/dotnet/api/azure.ai.vision.common.visionsource.fromimagesourcebuffer)). For more details, see [Call the Analyze API](./call-analyze-image-40?pivots=programming-language-csharp#select-the-image-to-analyze).
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> You can also analyze a local image by passing in the full-path image file name (see [VisionSource.FromFile](/dotnet/api/azure.ai.vision.common.visionsource.fromfile)), or by copying the image into the SDK's input buffer (see [VisionSource.FromImageSourceBuffer](/dotnet/api/azure.ai.vision.common.visionsource.fromimagesourcebuffer)). For more details, see [Call the Analyze API](./call-analyze-image-40.md?pivots=programming-language-csharp#select-the-image-to-analyze).
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#### [Python](#tab/python)
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[!code-python[](~/azure-ai-vision-sdk/docs/learn.microsoft.com/python/image-analysis/how-to/main.py?name=vision_source)]
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> [!TIP]
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> You can also analyze a local image by passing in the full-path image file name to the **VisionSource** constructor instead of the image URL (see argument name **filename**). Alternatively, you can analyze an image in a memory buffer by constructing **VisionSource** using the argument **image_source_buffer**. For more details, see [Call the Analyze API](./call-analyze-image-40?pivots=programming-language-python#select-the-image-to-analyze).
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> You can also analyze a local image by passing in the full-path image file name to the **VisionSource** constructor instead of the image URL (see argument name **filename**). Alternatively, you can analyze an image in a memory buffer by constructing **VisionSource** using the argument **image_source_buffer**. For more details, see [Call the Analyze API](./call-analyze-image-40.md?pivots=programming-language-python#select-the-image-to-analyze).
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#### [Java](#tab/java)
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**VisionSource** implements **AutoCloseable**, therefore create the object in a try-with-resources block, or explicitly call the **close** method on this object when you're done analyzing the image.
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> [!TIP]
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> You can also analyze a local image by passing in the full-path image file name (see [VisionSource.fromFile](/java/api/com.azure.ai.vision.common.visionsource#com-azure-ai-vision-common-visionsource-fromfile(java-lang-string)), or by copying the image into the SDK's input buffer (see [VisionSource.fromImageSourceBuffer](/java/api/com.azure.ai.vision.common.visionsource#com-azure-ai-vision-common-visionsource-fromimagesourcebuffer(com-azure-ai-vision-common-imagesourcebuffer))). For more details, see [Call the Analyze API](./call-analyze-image-40?pivots=programming-language-java#select-the-image-to-analyze).
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> You can also analyze a local image by passing in the full-path image file name (see [VisionSource.fromFile](/java/api/com.azure.ai.vision.common.visionsource#com-azure-ai-vision-common-visionsource-fromfile(java-lang-string)), or by copying the image into the SDK's input buffer (see [VisionSource.fromImageSourceBuffer](/java/api/com.azure.ai.vision.common.visionsource#com-azure-ai-vision-common-visionsource-fromimagesourcebuffer(com-azure-ai-vision-common-imagesourcebuffer))). For more details, see [Call the Analyze API](./call-analyze-image-40.md?pivots=programming-language-java#select-the-image-to-analyze).
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#### [C++](#tab/cpp)
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[!code-cpp[](~/azure-ai-vision-sdk/docs/learn.microsoft.com/cpp/image-analysis/how-to/how-to.cpp?name=vision_source)]
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> [!TIP]
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> You can also analyze a local image by passing in the full-path image file name (see [VisionSource::FromFile](/cpp/cognitive-services/vision/input-visionsource#fromfile)), or by copying the image into the SDK's input buffer (see [VisionSource::FromImageSourceBuffer](/cpp/cognitive-services/vision/input-visionsource#fromimagesourcebuffer)). For more details, see [Call the Analyze API](./call-analyze-image-40?pivots=programming-language-cpp#select-the-image-to-analyze).
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> You can also analyze a local image by passing in the full-path image file name (see [VisionSource::FromFile](/cpp/cognitive-services/vision/input-visionsource#fromfile)), or by copying the image into the SDK's input buffer (see [VisionSource::FromImageSourceBuffer](/cpp/cognitive-services/vision/input-visionsource#fromimagesourcebuffer)). For more details, see [Call the Analyze API](./call-analyze-image-40.md?pivots=programming-language-cpp#select-the-image-to-analyze).
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#### [REST API](#tab/rest)
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‎articles/ai-services/computer-vision/includes/quickstarts-sdk/image-analysis-java-sdk-40.md

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</dependencies>
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</project>
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```
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2. Install the SDK and dependencies by running the following in the project directory:
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1. Install the SDK and dependencies by running the following in the project directory:
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```console
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mvn clean dependency:copy-dependencies
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```
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3. Once the operation succeeds, verify that the folders `target\dependency` were creating and they contain `.jar` files.
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1. Once the operation succeeds, verify that the folders `target\dependency` were creating and they contain `.jar` files.
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For more information, see the [SDK installation guide](../../sdk/install-sdk.md?pivots=programming-language-java).
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‎articles/ai-services/computer-vision/includes/setup-sdk/java-requirements.md

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ms.author: pafarley
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---
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The Vision SDK for C# is compatible with Windows and Linux.
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The Vision SDK for Java is compatible with Windows and Linux.
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# [Windows](#tab/windows)
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On Windows, you must use the 64-bit target architecture. Windows 10 or later is required.
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You must install the [Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017, 2019, and 2022](/cpp/windows/latest-supported-vc-redist?view=msvc-170&preserve-view=true) for your platform. Installing this package for the first time might require a restart.
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The Java SDK uses native binaries. You must install the [Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017, 2019, and 2022](/cpp/windows/latest-supported-vc-redist?view=msvc-170&preserve-view=true) for your platform. Installing this package for the first time might require a restart.
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# [Linux](#tab/linux)
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The Vision SDK for C# only supports **Ubuntu 18.04/20.04/22.04** and **Debian 9/10/11** on the x64 architecture when used with Linux.
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The Vision SDK for Java only supports **Ubuntu 18.04/20.04/22.04** and **Debian 9/10/11** on the x64 architecture when used with Linux.
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[!INCLUDE [Linux distributions](linux-distributions.md)]
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---
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## JAVA Development Kit
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Java 8 or above is required.
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Install a Java Development Kit (JDK) such as [Azul Zulu OpenJDK](https://www.azul.com/downloads/?package=jdk), [Microsoft Build of OpenJDK](https://www.microsoft.com/openjdk), [Oracle Java](https://www.java.com/download/), or your preferred JDK.
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Run `java -version` from a command line to confirm successful installation and see the version. Make sure that the Java installation is native to the system architecture and not running through emulation.
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‎articles/ai-services/computer-vision/includes/setup-sdk/java.md

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---
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[Reference documentation](/dotnet/api/azure.ai.vision.imageanalysis) | [Packages (NuGet)](https://www.nuget.org/packages/Azure.AI.Vision.ImageAnalysis) | [Samples](https://github.com/Azure-Samples/azure-ai-vision-sdk)
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[Reference documentation](/java/api/com.azure.ai.vision.imageanalysis) | [Maven Package](https://mvnrepository.com/artifact/com.azure/azure-ai-vision-imageanalysis) | [Samples](https://github.com/Azure-Samples/azure-ai-vision-sdk)
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This guide shows how to install the Vision SDK for C#.
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This guide shows how to install the Vision SDK for Java.
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## Platform requirements
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[!INCLUDE [Requirements](csharp-requirements.md)]
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## Install the Vision SDK for C#
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The Vision SDK for C# is available as a NuGet package and implements .NET Standard 2.0. For more information, see <a href="https://www.nuget.org/packages/Azure.AI.Vision.ImageAnalysis" target="_blank">Azure.AI.Vision.ImageAnalysis</a>.
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# [Terminal](#tab/dotnetcli)
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The Vision SDK for C# can be installed from the [.NET CLI](https://dotnet.microsoft.com/download/dotnet/). To add a package reference in your project file, run this command in the folder where your `.csproj` file is located:
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```dotnetcli
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dotnet add package Azure.AI.Vision.ImageAnalysis --prerelease
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[!INCLUDE [Requirements](java-requirements.md)]
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## Install the Vision SDK for Java
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The Vision SDK for Java is available as a Maven package. For more information, see <a href="https://www.nuget.org/packages/Azure.AI.Vision.ImageAnalysis" target="_blank">Azure.AI.Vision.ImageAnalysis</a>.
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# [Maven](#tab/maven)
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## Apache Maven
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Follow these steps to install the Azure AI Vision SDK for Java using Apache Maven:
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1. Install [Apache Maven](https://maven.apache.org/download.cgi). On Linux, install from the distribution repositories if available.
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1. Open a command prompt and run `mvn -v` to confirm successful installation.
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1. Open a command prompt where you want to place the new project, and create a new pom.xml file.
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1. Copy the following XML content into pom.xml
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```xml
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<project xmlns="http://maven.apache.org/POM/4.0.0"
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xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
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<modelVersion>4.0.0</modelVersion>
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<groupId>azure.ai.vision.imageanalysis.samples</groupId>
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<artifactId>image-analysis-quickstart</artifactId>
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<version>0.0</version>
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<dependencies>
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<!-- https://mvnrepository.com/artifact/com.azure/azure-ai-vision-imageanalysis -->
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<dependency>
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<groupId>com.azure</groupId>
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<artifactId>azure-ai-vision-imageanalysis</artifactId>
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<version>0.15.1-beta.1</version>
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</dependency>
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<!-- https://mvnrepository.com/artifact/com.azure/azure-core-http-netty -->
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<dependency>
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<groupId>com.azure</groupId>
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<artifactId>azure-core-http-netty</artifactId>
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<version>1.13.6</version>
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</dependency>
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<!-- https://mvnrepository.com/artifact/org.slf4j/slf4j-api -->
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<dependency>
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<groupId>org.slf4j</groupId>
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<artifactId>slf4j-api</artifactId>
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<version>2.0.7</version>
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</dependency>
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<!-- https://mvnrepository.com/artifact/org.slf4j/slf4j-simple -->
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<dependency>
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<groupId>org.slf4j</groupId>
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<artifactId>slf4j-simple</artifactId>
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<version>2.0.7</version>
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</dependency>
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</dependencies>
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</project>
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```
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1. Update the version value in `<version>0.15.1-beta.1</version>` based on the latest version you find in the [Maven repository](https://mvnrepository.com/artifact/com.azure/azure-ai-vision-imageanalysis).
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1. Run the following Maven command to install the Vision SDK and dependencies.
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mvn clean dependency:copy-dependencies
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# [PowerShell](#tab/powershell)
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The Vision SDK for C# can be installed from the [.NET CLI](https://dotnet.microsoft.com/download/dotnet/). To add a package reference in your project file, run this command in the folder where your `.csproj` file is located:
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Install-Package Azure.AI.Vision.ImageAnalysis --prerelease
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```
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# [Gradle](#tab/gradle)
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# [Visual Studio](#tab/vs)
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Open Visual Studio and create a new application project. Then 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**.
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Gradle configurations require an explicit reference to the .jar dependency extension:
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```gradle
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// build.gradle
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dependencies {
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implementation group: 'com.azure', name: ' azure-ai-vision-imageanalysis', version: "0.15.1-beta.1", ext: "jar"
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}
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```
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Update the version number based on the latest version you find in the [Maven repository](https://mvnrepository.com/artifact/com.azure/azure-ai-vision-imageanalysis).
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