文档 AWS SDK 示例 GitHub 存储库中还有更多 [S AWS DK 示例](https://github.com/awsdocs/aws-doc-sdk-examples)。

本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。

# 使用适用于 C\$1\$1 的 SDK 的 Amazon Rekognition 示例
<a name="cpp_1_rekognition_code_examples"></a>

以下代码示例向您展示了如何使用 适用于 C\$1\$1 的 AWS SDK 与 Amazon Rekognition 配合使用来执行操作和实现常见场景。

*操作*是大型程序的代码摘录，必须在上下文中运行。您可以通过操作了解如何调用单个服务函数，还可以通过函数相关场景的上下文查看操作。

*场景*是向您演示如何通过在一个服务中调用多个函数或与其他 AWS 服务结合来完成特定任务的代码示例。

每个示例都包含一个指向完整源代码的链接，您可以从中找到有关如何在上下文中设置和运行代码的说明。

**Topics**
+ [开始使用](#get_started)
+ [操作](#actions)
+ [场景](#scenarios)

## 开始使用
<a name="get_started"></a>

### Hello Amazon Rekognition
<a name="rekognition_Hello_cpp_1_topic"></a>

以下代码示例展示了如何开始使用 Amazon Rekognition。

**SDK for C\$1\$1**  
 还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/cpp/example_code/rekognition/hello_rekognition#code-examples)中查找完整示例，了解如何进行设置和运行。
 CMakeLists.txt CMake 文件的代码。  

```
# Set the minimum required version of CMake for this project.
cmake_minimum_required(VERSION 3.13)

# Set the AWS service components used by this project.
set(SERVICE_COMPONENTS rekognition)

# Set this project's name.
project("hello_rekognition")

# Set the C++ standard to use to build this target.
# At least C++ 11 is required for the AWS SDK for C++.
set(CMAKE_CXX_STANDARD 11)

# Use the MSVC variable to determine if this is a Windows build.
set(WINDOWS_BUILD ${MSVC})

if (WINDOWS_BUILD) # Set the location where CMake can find the installed libraries for the AWS SDK.
    string(REPLACE ";" "/aws-cpp-sdk-all;" SYSTEM_MODULE_PATH "${CMAKE_SYSTEM_PREFIX_PATH}/aws-cpp-sdk-all")
    list(APPEND CMAKE_PREFIX_PATH ${SYSTEM_MODULE_PATH})
endif ()

# Find the AWS SDK for C++ package.
find_package(AWSSDK REQUIRED COMPONENTS ${SERVICE_COMPONENTS})

if (WINDOWS_BUILD AND AWSSDK_INSTALL_AS_SHARED_LIBS) 
     # Copy relevant AWS SDK for C++ libraries into the current binary directory for running and debugging.

     # set(BIN_SUB_DIR "/Debug") # If you are building from the command line, you may need to uncomment this 
                                    # and set the proper subdirectory to the executables' location.

     AWSSDK_CPY_DYN_LIBS(SERVICE_COMPONENTS "" ${CMAKE_CURRENT_BINARY_DIR}${BIN_SUB_DIR})
endif ()

add_executable(${PROJECT_NAME}
        hello_rekognition.cpp)

target_link_libraries(${PROJECT_NAME}
        ${AWSSDK_LINK_LIBRARIES})
```
hello\$1rekognition.cpp 源文件的代码。  

```
#include <aws/core/Aws.h>
#include <aws/rekognition/RekognitionClient.h>
#include <aws/rekognition/model/ListCollectionsRequest.h>
#include <iostream>

/*
 *  A "Hello Rekognition" starter application which initializes an Amazon Rekognition client and
 *  lists the Amazon Rekognition collections in the current account and region.
 *
 *  main function
 *
 *  Usage: 'hello_rekognition'
 *
 */

int main(int argc, char **argv) {
    Aws::SDKOptions options;
    //  Optional: change the log level for debugging.
    //  options.loggingOptions.logLevel = Aws::Utils::Logging::LogLevel::Debug;
    Aws::InitAPI(options); // Should only be called once.
    {
        Aws::Client::ClientConfiguration clientConfig;
        // Optional: Set to the AWS Region (overrides config file).
        // clientConfig.region = "us-east-1";

        Aws::Rekognition::RekognitionClient rekognitionClient(clientConfig);
        Aws::Rekognition::Model::ListCollectionsRequest request;
        Aws::Rekognition::Model::ListCollectionsOutcome outcome =
                rekognitionClient.ListCollections(request);

        if (outcome.IsSuccess()) {
            const Aws::Vector<Aws::String>& collectionsIds = outcome.GetResult().GetCollectionIds();
            if (!collectionsIds.empty()) {
                std::cout << "collectionsIds: " << std::endl;
                for (auto &collectionId : collectionsIds) {
                    std::cout << "- " << collectionId << std::endl;
                }
            } else {
                std::cout << "No collections found" << std::endl;
            }
        } else {
            std::cerr << "Error with ListCollections: " << outcome.GetError()
                      << std::endl;
        }
    }


    Aws::ShutdownAPI(options); // Should only be called once.
    return 0;
}
```
+  有关 API 的详细信息，请参阅 *适用于 C\$1\$1 的 AWS SDK API 参考[ListCollections](https://docs.aws.amazon.com/goto/SdkForCpp/rekognition-2016-06-27/ListCollections)*中的。

## 操作
<a name="actions"></a>

### `DetectLabels`
<a name="rekognition_DetectLabels_cpp_1_topic"></a>

以下代码示例演示了如何使用 `DetectLabels`。

有关更多信息，请参阅[检测图像中的标签](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detect-labels-image.html)。

**SDK for C\$1\$1**  
 还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/cpp/example_code/rekognition#code-examples)中查找完整示例，了解如何进行设置和运行。

```
//! Detect instances of real-world entities within an image by using Amazon Rekognition
/*!
  \param imageBucket: The Amazon Simple Storage Service (Amazon S3) bucket containing an image.
  \param imageKey: The Amazon S3 key of an image object.
  \param clientConfiguration: AWS client configuration.
  \return bool: Function succeeded.
 */
bool AwsDoc::Rekognition::detectLabels(const Aws::String &imageBucket,
                                       const Aws::String &imageKey,
                                       const Aws::Client::ClientConfiguration &clientConfiguration) {
    Aws::Rekognition::RekognitionClient rekognitionClient(clientConfiguration);

    Aws::Rekognition::Model::DetectLabelsRequest request;
    Aws::Rekognition::Model::S3Object s3Object;
    s3Object.SetBucket(imageBucket);
    s3Object.SetName(imageKey);

    Aws::Rekognition::Model::Image image;
    image.SetS3Object(s3Object);

    request.SetImage(image);

    const Aws::Rekognition::Model::DetectLabelsOutcome outcome = rekognitionClient.DetectLabels(request);

    if (outcome.IsSuccess()) {
        const Aws::Vector<Aws::Rekognition::Model::Label> &labels = outcome.GetResult().GetLabels();
        if (labels.empty()) {
            std::cout << "No labels detected" << std::endl;
        } else {
            for (const Aws::Rekognition::Model::Label &label: labels) {
                std::cout << label.GetName() << ": " << label.GetConfidence() << std::endl;
            }
        }
    } else {
        std::cerr << "Error while detecting labels: '"
                  << outcome.GetError().GetMessage()
                  << "'" << std::endl;
    }

    return outcome.IsSuccess();
}
```
+  有关 API 的详细信息，请参阅 *适用于 C\$1\$1 的 AWS SDK API 参考[DetectLabels](https://docs.aws.amazon.com/goto/SdkForCpp/rekognition-2016-06-27/DetectLabels)*中的。

## 场景
<a name="scenarios"></a>

### 创建无服务器应用程序来管理照片
<a name="cross_PAM_cpp_1_topic"></a>

以下代码示例演示如何创建无服务器应用程序，让用户能够使用标签管理照片。

**SDK for C\$1\$1**  
 演示如何开发照片资产管理应用程序，该应用程序使用 Amazon Rekognition 检测图像中的标签并将其存储以供日后检索。  
有关如何设置和运行的完整源代码和说明，请参阅上的完整示例[ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/cpp/example_code/cross-service/photo_asset_manager)。  
要深入了解这个例子的起源，请参阅 [AWS 社区](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app)上的博文。  

**本示例中使用的服务**
+ API Gateway
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS