Doc AWS SDK Examples GitHub リポジトリには、他にも SDK の例があります。 [AWS](https://github.com/awsdocs/aws-doc-sdk-examples)
翻訳は機械翻訳により提供されています。提供された翻訳内容と英語版の間で齟齬、不一致または矛盾がある場合、英語版が優先します。
# SDK を使用した Amazon Rekognition のコード例 AWS SDKs
次のコード例は、 AWS Software Development Kit (SDK) で Amazon Rekognition を使用する方法を示しています。
*アクション*はより大きなプログラムからのコードの抜粋であり、コンテキスト内で実行する必要があります。アクションは個々のサービス機能を呼び出す方法を示していますが、コンテキスト内のアクションは、関連するシナリオで確認できます。
*シナリオ*は、1 つのサービス内から、または他の AWS のサービスと組み合わせて複数の関数を呼び出し、特定のタスクを実行する方法を示すコード例です。
**その他のリソース**
+ **[ Amazon Rekognition デベロッパーガイド](https://docs.aws.amazon.com/rekognition/latest/dg/what-is.html)** — Amazon Rekognition に関する詳細情報。
+ **[Amazon Rekognition API リファレンス](https://docs.aws.amazon.com/rekognition/latest/APIReference/Welcome.html)** — 使用可能なすべての Amazon Rekognition アクションに関する詳細。
+ **[AWS デベロッパーセンター](https://aws.amazon.com/developer/code-examples/?awsf.sdk-code-examples-product=product%23rekognition)** – カテゴリまたは全文検索でフィルタリングできるコード例。
+ **[AWS SDK の例](https://github.com/awsdocs/aws-doc-sdk-examples)** – 完全なコードを優先言語で含む GitHub リポジトリ。コードの設定と実行に関する説明が記載されています。
**Contents**
+ [基本](rekognition_code_examples_basics.md)
+ [Hello Amazon Rekognition](rekognition_example_rekognition_Hello_section.md)
+ [アクション](rekognition_code_examples_actions.md)
+ [`CompareFaces`](rekognition_example_rekognition_CompareFaces_section.md)
+ [`CreateCollection`](rekognition_example_rekognition_CreateCollection_section.md)
+ [`DeleteCollection`](rekognition_example_rekognition_DeleteCollection_section.md)
+ [`DeleteFaces`](rekognition_example_rekognition_DeleteFaces_section.md)
+ [`DescribeCollection`](rekognition_example_rekognition_DescribeCollection_section.md)
+ [`DetectFaces`](rekognition_example_rekognition_DetectFaces_section.md)
+ [`DetectLabels`](rekognition_example_rekognition_DetectLabels_section.md)
+ [`DetectModerationLabels`](rekognition_example_rekognition_DetectModerationLabels_section.md)
+ [`DetectText`](rekognition_example_rekognition_DetectText_section.md)
+ [`GetCelebrityInfo`](rekognition_example_rekognition_GetCelebrityInfo_section.md)
+ [`IndexFaces`](rekognition_example_rekognition_IndexFaces_section.md)
+ [`ListCollections`](rekognition_example_rekognition_ListCollections_section.md)
+ [`ListFaces`](rekognition_example_rekognition_ListFaces_section.md)
+ [`RecognizeCelebrities`](rekognition_example_rekognition_RecognizeCelebrities_section.md)
+ [`SearchFaces`](rekognition_example_rekognition_SearchFaces_section.md)
+ [`SearchFacesByImage`](rekognition_example_rekognition_SearchFacesByImage_section.md)
+ [シナリオ](rekognition_code_examples_scenarios.md)
+ [コレクションを構築し、その中に顔を検索します。](rekognition_example_rekognition_Usage_FindFacesInCollection_section.md)
+ [サーバーレスアプリケーションを作成して写真の管理](rekognition_example_cross_PAM_section.md)
+ [画像内の PPE を検出する](rekognition_example_cross_RekognitionPhotoAnalyzerPPE_section.md)
+ [イメージ内の要素の検出と表示](rekognition_example_rekognition_Usage_DetectAndDisplayImage_section.md)
+ [イメージ内の顔を検出します](rekognition_example_cross_DetectFaces_section.md)
+ [ビデオ内の情報を検出する](rekognition_example_rekognition_VideoDetection_section.md)
+ [画像内のオブジェクトを検出する](rekognition_example_cross_RekognitionPhotoAnalyzer_section.md)
+ [動画内の人物や物体を検出する](rekognition_example_cross_RekognitionVideoDetection_section.md)
+ [EXIF およびその他のイメージ情報を保存します](rekognition_example_cross_DetectLabels_section.md)
# AWS SDKs を使用した Amazon Rekognition の基本的な例
以下のコード例では、 AWS SDK を利用した Amazon Rekognition の基本的な使用方法を示しています。
**Contents**
+ [Hello Amazon Rekognition](rekognition_example_rekognition_Hello_section.md)
+ [アクション](rekognition_code_examples_actions.md)
+ [`CompareFaces`](rekognition_example_rekognition_CompareFaces_section.md)
+ [`CreateCollection`](rekognition_example_rekognition_CreateCollection_section.md)
+ [`DeleteCollection`](rekognition_example_rekognition_DeleteCollection_section.md)
+ [`DeleteFaces`](rekognition_example_rekognition_DeleteFaces_section.md)
+ [`DescribeCollection`](rekognition_example_rekognition_DescribeCollection_section.md)
+ [`DetectFaces`](rekognition_example_rekognition_DetectFaces_section.md)
+ [`DetectLabels`](rekognition_example_rekognition_DetectLabels_section.md)
+ [`DetectModerationLabels`](rekognition_example_rekognition_DetectModerationLabels_section.md)
+ [`DetectText`](rekognition_example_rekognition_DetectText_section.md)
+ [`GetCelebrityInfo`](rekognition_example_rekognition_GetCelebrityInfo_section.md)
+ [`IndexFaces`](rekognition_example_rekognition_IndexFaces_section.md)
+ [`ListCollections`](rekognition_example_rekognition_ListCollections_section.md)
+ [`ListFaces`](rekognition_example_rekognition_ListFaces_section.md)
+ [`RecognizeCelebrities`](rekognition_example_rekognition_RecognizeCelebrities_section.md)
+ [`SearchFaces`](rekognition_example_rekognition_SearchFaces_section.md)
+ [`SearchFacesByImage`](rekognition_example_rekognition_SearchFacesByImage_section.md)
# Hello Amazon Rekognition
以下のコード例では、Amazon Rekognition の使用を開始する方法を示しています。
------
#### [ C\$1\$1 ]
**SDK for C\$1\$1**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/cpp/example_code/rekognition/hello_rekognition#code-examples)での設定と実行の方法を確認してください。
CMakeLists.txt CMake ファイルのコード。
```
# 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 ソースファイルのコード。
```
#include
#include
#include
#include
/*
* 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& 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;
}
```
+ API の詳細については、**「AWS SDK for C\$1\$1 API リファレンス」の「[ListCollections](https://docs.aws.amazon.com/goto/SdkForCpp/rekognition-2016-06-27/ListCollections)」を参照してください。
------
# SDK を使用した Amazon Rekognition のアクション AWS SDKs
次のコード例は、 AWS SDKs を使用して個々の Amazon Rekognition アクションを実行する方法を示しています。それぞれの例には、GitHub へのリンクがあり、そこにはコードの設定と実行に関する説明が記載されています。
これらの抜粋は Amazon Rekognition API を呼び出すもので、コンテキスト内で実行する必要がある大規模なプログラムからのコードの抜粋です。アクションは [SDK を使用した Amazon Rekognition のシナリオ AWS SDKs](rekognition_code_examples_scenarios.md) のコンテキスト内で確認できます。
以下の例には、最も一般的に使用されるアクションのみ含まれています。詳細な一覧については、「[Amazon Rekognition API Reference](https://docs.aws.amazon.com/rekognition/latest/APIReference/Welcome.html)」を参照してください。
**Topics**
+ [`CompareFaces`](rekognition_example_rekognition_CompareFaces_section.md)
+ [`CreateCollection`](rekognition_example_rekognition_CreateCollection_section.md)
+ [`DeleteCollection`](rekognition_example_rekognition_DeleteCollection_section.md)
+ [`DeleteFaces`](rekognition_example_rekognition_DeleteFaces_section.md)
+ [`DescribeCollection`](rekognition_example_rekognition_DescribeCollection_section.md)
+ [`DetectFaces`](rekognition_example_rekognition_DetectFaces_section.md)
+ [`DetectLabels`](rekognition_example_rekognition_DetectLabels_section.md)
+ [`DetectModerationLabels`](rekognition_example_rekognition_DetectModerationLabels_section.md)
+ [`DetectText`](rekognition_example_rekognition_DetectText_section.md)
+ [`GetCelebrityInfo`](rekognition_example_rekognition_GetCelebrityInfo_section.md)
+ [`IndexFaces`](rekognition_example_rekognition_IndexFaces_section.md)
+ [`ListCollections`](rekognition_example_rekognition_ListCollections_section.md)
+ [`ListFaces`](rekognition_example_rekognition_ListFaces_section.md)
+ [`RecognizeCelebrities`](rekognition_example_rekognition_RecognizeCelebrities_section.md)
+ [`SearchFaces`](rekognition_example_rekognition_SearchFaces_section.md)
+ [`SearchFacesByImage`](rekognition_example_rekognition_SearchFacesByImage_section.md)
# AWS SDK または CLI `CompareFaces`で を使用する
次のサンプルコードは、`CompareFaces` を使用する方法を説明しています。
詳細については、「[イメージ内の顔を比較する](https://docs.aws.amazon.com/rekognition/latest/dg/faces-comparefaces.html)」を参照してください。
------
#### [ .NET ]
**SDK for .NET**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples)での設定と実行の方法を確認してください。
```
using System;
using System.IO;
using System.Threading.Tasks;
using Amazon.Rekognition;
using Amazon.Rekognition.Model;
///
/// Uses the Amazon Rekognition Service to compare faces in two images.
///
public class CompareFaces
{
public static async Task Main()
{
float similarityThreshold = 70F;
string sourceImage = "source.jpg";
string targetImage = "target.jpg";
var rekognitionClient = new AmazonRekognitionClient();
Amazon.Rekognition.Model.Image imageSource = new Amazon.Rekognition.Model.Image();
try
{
using FileStream fs = new FileStream(sourceImage, FileMode.Open, FileAccess.Read);
byte[] data = new byte[fs.Length];
fs.Read(data, 0, (int)fs.Length);
imageSource.Bytes = new MemoryStream(data);
}
catch (Exception)
{
Console.WriteLine($"Failed to load source image: {sourceImage}");
return;
}
Amazon.Rekognition.Model.Image imageTarget = new Amazon.Rekognition.Model.Image();
try
{
using FileStream fs = new FileStream(targetImage, FileMode.Open, FileAccess.Read);
byte[] data = new byte[fs.Length];
data = new byte[fs.Length];
fs.Read(data, 0, (int)fs.Length);
imageTarget.Bytes = new MemoryStream(data);
}
catch (Exception ex)
{
Console.WriteLine($"Failed to load target image: {targetImage}");
Console.WriteLine(ex.Message);
return;
}
var compareFacesRequest = new CompareFacesRequest
{
SourceImage = imageSource,
TargetImage = imageTarget,
SimilarityThreshold = similarityThreshold,
};
// Call operation
var compareFacesResponse = await rekognitionClient.CompareFacesAsync(compareFacesRequest);
// Display results
compareFacesResponse.FaceMatches.ForEach(match =>
{
ComparedFace face = match.Face;
BoundingBox position = face.BoundingBox;
Console.WriteLine($"Face at {position.Left} {position.Top} matches with {match.Similarity}% confidence.");
});
Console.WriteLine($"Found {compareFacesResponse.UnmatchedFaces.Count} face(s) that did not match.");
}
}
```
+ API の詳細については、**「AWS SDK for .NET API リファレンス」の「[CompareFaces](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/CompareFaces)」を参照してください。
------
#### [ CLI ]
**AWS CLI**
**2 つの画像内の顔を比較するには**
次の `compare-faces` コマンドは、Amazon S3 バケットに保存されている 2 つの画像の顔を比較します。
```
aws rekognition compare-faces \
--source-image '{"S3Object":{"Bucket":"MyImageS3Bucket","Name":"source.jpg"}}' \
--target-image '{"S3Object":{"Bucket":"MyImageS3Bucket","Name":"target.jpg"}}'
```
出力:
```
{
"UnmatchedFaces": [],
"FaceMatches": [
{
"Face": {
"BoundingBox": {
"Width": 0.12368916720151901,
"Top": 0.16007372736930847,
"Left": 0.5901257991790771,
"Height": 0.25140416622161865
},
"Confidence": 100.0,
"Pose": {
"Yaw": -3.7351467609405518,
"Roll": -0.10309021919965744,
"Pitch": 0.8637830018997192
},
"Quality": {
"Sharpness": 95.51618957519531,
"Brightness": 65.29893493652344
},
"Landmarks": [
{
"Y": 0.26721030473709106,
"X": 0.6204193830490112,
"Type": "eyeLeft"
},
{
"Y": 0.26831310987472534,
"X": 0.6776827573776245,
"Type": "eyeRight"
},
{
"Y": 0.3514654338359833,
"X": 0.6241428852081299,
"Type": "mouthLeft"
},
{
"Y": 0.35258132219314575,
"X": 0.6713621020317078,
"Type": "mouthRight"
},
{
"Y": 0.3140771687030792,
"X": 0.6428444981575012,
"Type": "nose"
}
]
},
"Similarity": 100.0
}
],
"SourceImageFace": {
"BoundingBox": {
"Width": 0.12368916720151901,
"Top": 0.16007372736930847,
"Left": 0.5901257991790771,
"Height": 0.25140416622161865
},
"Confidence": 100.0
}
}
```
詳細については、「Amazon Rekognition ディベロッパーガイド」の「[イメージ間の顔の比較](https://docs.aws.amazon.com/rekognition/latest/dg/faces-comparefaces.html)」を参照してください。**
+ API の詳細については、AWS CLI コマンドリファレンスの「*CompareFaces*」を参照してください。[https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/compare-faces.html](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/compare-faces.html)
------
#### [ Java ]
**SDK for Java 2.x**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples)での設定と実行の方法を確認してください。
```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.*;
import software.amazon.awssdk.core.SdkBytes;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.InputStream;
import java.util.List;
/**
* Before running this Java V2 code example, set up your development
* environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class CompareFaces {
public static void main(String[] args) {
final String usage = """
Usage:
Where:
bucketName - The name of the S3 bucket where the images are stored.
sourceKey - The S3 key (file name) for the source image.
targetKey - The S3 key (file name) for the target image.
""";
if (args.length != 3) {
System.out.println(usage);
System.exit(1);
}
String bucketName = args[0];
String sourceKey = args[1];
String targetKey = args[2];
Region region = Region.US_WEST_2;
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.build();
compareTwoFaces(rekClient, bucketName, sourceKey, targetKey);
}
/**
* Compares two faces from images stored in an Amazon S3 bucket using AWS Rekognition.
*
*
This method takes two image keys from an S3 bucket and compares the faces within them.
* It prints out the confidence level of matched faces and reports the number of unmatched faces.
*
* @param rekClient The {@link RekognitionClient} used to call AWS Rekognition.
* @param bucketName The name of the S3 bucket containing the images.
* @param sourceKey The object key (file path) for the source image in the S3 bucket.
* @param targetKey The object key (file path) for the target image in the S3 bucket.
* @throws RuntimeException If the Rekognition service returns an error.
*/
public static void compareTwoFaces(RekognitionClient rekClient, String bucketName, String sourceKey, String targetKey) {
try {
Float similarityThreshold = 70F;
S3Object s3ObjectSource = S3Object.builder()
.bucket(bucketName)
.name(sourceKey)
.build();
Image sourceImage = Image.builder()
.s3Object(s3ObjectSource)
.build();
S3Object s3ObjectTarget = S3Object.builder()
.bucket(bucketName)
.name(targetKey)
.build();
Image targetImage = Image.builder()
.s3Object(s3ObjectTarget)
.build();
CompareFacesRequest facesRequest = CompareFacesRequest.builder()
.sourceImage(sourceImage)
.targetImage(targetImage)
.similarityThreshold(similarityThreshold)
.build();
// Compare the two images.
CompareFacesResponse compareFacesResult = rekClient.compareFaces(facesRequest);
List faceDetails = compareFacesResult.faceMatches();
for (CompareFacesMatch match : faceDetails) {
ComparedFace face = match.face();
BoundingBox position = face.boundingBox();
System.out.println("Face at " + position.left().toString()
+ " " + position.top()
+ " matches with " + face.confidence().toString()
+ "% confidence.");
}
List unmatchedFaces = compareFacesResult.unmatchedFaces();
System.out.println("There were " + unmatchedFaces.size() + " face(s) that did not match.");
} catch (RekognitionException e) {
System.err.println("Error comparing faces: " + e.awsErrorDetails().errorMessage());
throw new RuntimeException(e);
}
}
}
```
+ API の詳細については、**「AWS SDK for Java 2.x API リファレンス」の「[CompareFaces](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/CompareFaces)」を参照してください。
------
#### [ Kotlin ]
**SDK for Kotlin**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples)での設定と実行の方法を確認してください。
```
suspend fun compareTwoFaces(
similarityThresholdVal: Float,
sourceImageVal: String,
targetImageVal: String,
) {
val sourceBytes = (File(sourceImageVal).readBytes())
val targetBytes = (File(targetImageVal).readBytes())
// Create an Image object for the source image.
val souImage =
Image {
bytes = sourceBytes
}
val tarImage =
Image {
bytes = targetBytes
}
val facesRequest =
CompareFacesRequest {
sourceImage = souImage
targetImage = tarImage
similarityThreshold = similarityThresholdVal
}
RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
val compareFacesResult = rekClient.compareFaces(facesRequest)
val faceDetails = compareFacesResult.faceMatches
if (faceDetails != null) {
for (match: CompareFacesMatch in faceDetails) {
val face = match.face
val position = face?.boundingBox
if (position != null) {
println("Face at ${position.left} ${position.top} matches with ${face.confidence} % confidence.")
}
}
}
val uncompared = compareFacesResult.unmatchedFaces
if (uncompared != null) {
println("There was ${uncompared.size} face(s) that did not match")
}
println("Source image rotation: ${compareFacesResult.sourceImageOrientationCorrection}")
println("target image rotation: ${compareFacesResult.targetImageOrientationCorrection}")
}
}
```
+ API の詳細については、「AWS SDK for Kotlin API Reference」の「[CompareFaces](https://sdk.amazonaws.com/kotlin/api/latest/index.html)」を参照してください。
------
#### [ Python ]
**SDK for Python (Boto3)**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples)での設定と実行の方法を確認してください。
```
class RekognitionImage:
"""
Encapsulates an Amazon Rekognition image. This class is a thin wrapper
around parts of the Boto3 Amazon Rekognition API.
"""
def __init__(self, image, image_name, rekognition_client):
"""
Initializes the image object.
:param image: Data that defines the image, either the image bytes or
an Amazon S3 bucket and object key.
:param image_name: The name of the image.
:param rekognition_client: A Boto3 Rekognition client.
"""
self.image = image
self.image_name = image_name
self.rekognition_client = rekognition_client
def compare_faces(self, target_image, similarity):
"""
Compares faces in the image with the largest face in the target image.
:param target_image: The target image to compare against.
:param similarity: Faces in the image must have a similarity value greater
than this value to be included in the results.
:return: A tuple. The first element is the list of faces that match the
reference image. The second element is the list of faces that have
a similarity value below the specified threshold.
"""
try:
response = self.rekognition_client.compare_faces(
SourceImage=self.image,
TargetImage=target_image.image,
SimilarityThreshold=similarity,
)
matches = [
RekognitionFace(match["Face"]) for match in response["FaceMatches"]
]
unmatches = [RekognitionFace(face) for face in response["UnmatchedFaces"]]
logger.info(
"Found %s matched faces and %s unmatched faces.",
len(matches),
len(unmatches),
)
except ClientError:
logger.exception(
"Couldn't match faces from %s to %s.",
self.image_name,
target_image.image_name,
)
raise
else:
return matches, unmatches
```
+ API の詳細については、[AWS SDK for Python (Boto3) API リファレンス](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/CompareFaces)の「*CompareFaces*」を参照してください。
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples)での設定と実行の方法を確認してください。
```
TRY.
" Create S3 object reference for the source image
DATA(lo_source_s3obj) = NEW /aws1/cl_reks3object(
iv_bucket = iv_source_s3_bucket
iv_name = iv_source_s3_key ).
" Create source image object
DATA(lo_source_image) = NEW /aws1/cl_rekimage(
io_s3object = lo_source_s3obj ).
" Create S3 object reference for the target image
DATA(lo_target_s3obj) = NEW /aws1/cl_reks3object(
iv_bucket = iv_target_s3_bucket
iv_name = iv_target_s3_key ).
" Create target image object
DATA(lo_target_image) = NEW /aws1/cl_rekimage(
io_s3object = lo_target_s3obj ).
" Compare faces
oo_result = lo_rek->comparefaces(
io_sourceimage = lo_source_image
io_targetimage = lo_target_image
iv_similaritythreshold = iv_similarity ).
DATA(lt_face_matches) = oo_result->get_facematches( ).
DATA(lt_unmatched_faces) = oo_result->get_unmatchedfaces( ).
" Get counts of matched and unmatched faces
DATA(lv_matched_count) = lines( lt_face_matches ).
DATA(lv_unmatched_count) = lines( lt_unmatched_faces ).
" Output detailed comparison results
DATA(lv_message) = |Face comparison completed: | &&
|{ lv_matched_count } matched face(s), | &&
|{ lv_unmatched_count } unmatched face(s).|.
MESSAGE lv_message TYPE 'I'.
CATCH /aws1/cx_rekinvalids3objectex.
MESSAGE 'Invalid S3 object.' TYPE 'E'.
CATCH /aws1/cx_rekinvalidparameterex.
MESSAGE 'Invalid parameter value.' TYPE 'E'.
ENDTRY.
```
+ API の詳細については、 *AWS SDK for SAP ABAP API リファレンス*の[CompareFaces](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)」を参照してください。
------
# AWS SDK または CLI `CreateCollection`で を使用する
次のサンプルコードは、`CreateCollection` を使用する方法を説明しています。
詳細については、「[コレクションを作成する](https://docs.aws.amazon.com/rekognition/latest/dg/create-collection-procedure.html)」を参照してください。
------
#### [ .NET ]
**SDK for .NET**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples)での設定と実行の方法を確認してください。
```
using System;
using System.Threading.Tasks;
using Amazon.Rekognition;
using Amazon.Rekognition.Model;
///
/// Uses Amazon Rekognition to create a collection to which you can add
/// faces using the IndexFaces operation.
///
public class CreateCollection
{
public static async Task Main()
{
var rekognitionClient = new AmazonRekognitionClient();
string collectionId = "MyCollection";
Console.WriteLine("Creating collection: " + collectionId);
var createCollectionRequest = new CreateCollectionRequest
{
CollectionId = collectionId,
};
CreateCollectionResponse createCollectionResponse = await rekognitionClient.CreateCollectionAsync(createCollectionRequest);
Console.WriteLine($"CollectionArn : {createCollectionResponse.CollectionArn}");
Console.WriteLine($"Status code : {createCollectionResponse.StatusCode}");
}
}
```
+ API の詳細については、**「AWS SDK for .NET API リファレンス」の「[CreateCollection](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/CreateCollection)」を参照してください。
------
#### [ CLI ]
**AWS CLI**
**コレクションを作成するには**
次の `create-collection` コマンドは、指定された名前のコレクションを作成します。
```
aws rekognition create-collection \
--collection-id "MyCollection"
```
出力:
```
{
"CollectionArn": "aws:rekognition:us-west-2:123456789012:collection/MyCollection",
"FaceModelVersion": "4.0",
"StatusCode": 200
}
```
詳細については、「Amazon Rekognition 開発者ガイド」の「[コレクションの作成](https://docs.aws.amazon.com/rekognition/latest/dg/create-collection-procedure.html)」を参照してください。**
+ API の詳細については、AWS CLI コマンドリファレンスの「[CreateCollection](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/create-collection.html)」を参照してください。**
------
#### [ Java ]
**SDK for Java 2.x**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples)での設定と実行の方法を確認してください。
```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.CreateCollectionResponse;
import software.amazon.awssdk.services.rekognition.model.CreateCollectionRequest;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
/**
* Before running this Java V2 code example, set up your development
* environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class CreateCollection {
public static void main(String[] args) {
final String usage = """
Usage: \s
Where:
collectionName - The name of the collection.\s
""";
if (args.length != 1) {
System.out.println(usage);
System.exit(1);
}
String collectionId = args[0];
Region region = Region.US_WEST_2;
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.build();
System.out.println("Creating collection: " + collectionId);
createMyCollection(rekClient, collectionId);
rekClient.close();
}
/**
* Creates a new Amazon Rekognition collection.
*
* @param rekClient the Amazon Rekognition client used to interact with the Rekognition service
* @param collectionId the unique identifier for the collection to be created
*/
public static void createMyCollection(RekognitionClient rekClient, String collectionId) {
try {
CreateCollectionRequest collectionRequest = CreateCollectionRequest.builder()
.collectionId(collectionId)
.build();
CreateCollectionResponse collectionResponse = rekClient.createCollection(collectionRequest);
System.out.println("CollectionArn: " + collectionResponse.collectionArn());
System.out.println("Status code: " + collectionResponse.statusCode().toString());
} catch (RekognitionException e) {
System.out.println(e.getMessage());
System.exit(1);
}
}
}
```
+ API の詳細については、**「AWS SDK for Java 2.x API リファレンス」の「[CreateCollection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/CreateCollection)」を参照してください。
------
#### [ Kotlin ]
**SDK for Kotlin**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples)での設定と実行の方法を確認してください。
```
suspend fun createMyCollection(collectionIdVal: String) {
val request =
CreateCollectionRequest {
collectionId = collectionIdVal
}
RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
val response = rekClient.createCollection(request)
println("Collection ARN is ${response.collectionArn}")
println("Status code is ${response.statusCode}")
}
}
```
+ API の詳細については、「AWS SDK for Kotlin API Reference」の「[CreateCollection](https://sdk.amazonaws.com/kotlin/api/latest/index.html)」を参照してください。
------
#### [ Python ]
**SDK for Python (Boto3)**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples)での設定と実行の方法を確認してください。
```
class RekognitionCollectionManager:
"""
Encapsulates Amazon Rekognition collection management functions.
This class is a thin wrapper around parts of the Boto3 Amazon Rekognition API.
"""
def __init__(self, rekognition_client):
"""
Initializes the collection manager object.
:param rekognition_client: A Boto3 Rekognition client.
"""
self.rekognition_client = rekognition_client
def create_collection(self, collection_id):
"""
Creates an empty collection.
:param collection_id: Text that identifies the collection.
:return: The newly created collection.
"""
try:
response = self.rekognition_client.create_collection(
CollectionId=collection_id
)
response["CollectionId"] = collection_id
collection = RekognitionCollection(response, self.rekognition_client)
logger.info("Created collection %s.", collection_id)
except ClientError:
logger.exception("Couldn't create collection %s.", collection_id)
raise
else:
return collection
```
+ API の詳細については、[AWS SDK for Python (Boto3) API リファレンス](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/CreateCollection) の「*CreateCollection*」を参照してください。
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples)での設定と実行の方法を確認してください。
```
TRY.
oo_result = lo_rek->createcollection(
iv_collectionid = iv_collection_id ).
MESSAGE 'Collection created successfully.' TYPE 'I'.
CATCH /aws1/cx_rekresrcalrdyexistsex.
MESSAGE 'Collection already exists.' TYPE 'E'.
CATCH /aws1/cx_rekinvalidparameterex.
MESSAGE 'Invalid parameter value.' TYPE 'E'.
ENDTRY.
```
+ API の詳細については、 *AWS SDK for SAP ABAP API リファレンス*の「[CreateCollection](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)」を参照してください。
------
# AWS SDK または CLI `DeleteCollection`で を使用する
次のサンプルコードは、`DeleteCollection` を使用する方法を説明しています。
詳細については、「[コレクションを削除する](https://docs.aws.amazon.com/rekognition/latest/dg/delete-collection-procedure.html)」を参照してください。
------
#### [ .NET ]
**SDK for .NET**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples)での設定と実行の方法を確認してください。
```
using System;
using System.Threading.Tasks;
using Amazon.Rekognition;
using Amazon.Rekognition.Model;
///
/// Uses the Amazon Rekognition Service to delete an existing collection.
///
public class DeleteCollection
{
public static async Task Main()
{
var rekognitionClient = new AmazonRekognitionClient();
string collectionId = "MyCollection";
Console.WriteLine("Deleting collection: " + collectionId);
var deleteCollectionRequest = new DeleteCollectionRequest()
{
CollectionId = collectionId,
};
var deleteCollectionResponse = await rekognitionClient.DeleteCollectionAsync(deleteCollectionRequest);
Console.WriteLine($"{collectionId}: {deleteCollectionResponse.StatusCode}");
}
}
```
+ API の詳細については、**「AWS SDK for .NET API リファレンス」の「[DeleteCollection](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/DeleteCollection)」を参照してください。
------
#### [ CLI ]
**AWS CLI**
**コレクションを削除するには**
次の `delete-collection` コマンドは、指定されたコレクションを削除します。
```
aws rekognition delete-collection \
--collection-id MyCollection
```
出力:
```
{
"StatusCode": 200
}
```
詳細については、「Amazon Rekognition ディベロッパーガイド」の「[コレクションの削除](https://docs.aws.amazon.com/rekognition/latest/dg/delete-collection-procedure.html)」を参照してください。**
+ API の詳細については、AWS CLI コマンドリファレンスの「[DeleteCollection](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/delete-collection.html)」を参照してください。**
------
#### [ Java ]
**SDK for Java 2.x**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples)での設定と実行の方法を確認してください。
```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.DeleteCollectionRequest;
import software.amazon.awssdk.services.rekognition.model.DeleteCollectionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
/**
* Before running this Java V2 code example, set up your development
* environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class DeleteCollection {
public static void main(String[] args) {
final String usage = """
Usage: \s
Where:
collectionId - The id of the collection to delete.\s
""";
if (args.length != 1) {
System.out.println(usage);
System.exit(1);
}
String collectionId = args[0];
Region region = Region.US_EAST_1;
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.build();
System.out.println("Deleting collection: " + collectionId);
deleteMyCollection(rekClient, collectionId);
rekClient.close();
}
/**
* Deletes an Amazon Rekognition collection.
*
* @param rekClient An instance of the {@link RekognitionClient} class, which is used to interact with the Amazon Rekognition service.
* @param collectionId The ID of the collection to be deleted.
*/
public static void deleteMyCollection(RekognitionClient rekClient, String collectionId) {
try {
DeleteCollectionRequest deleteCollectionRequest = DeleteCollectionRequest.builder()
.collectionId(collectionId)
.build();
DeleteCollectionResponse deleteCollectionResponse = rekClient.deleteCollection(deleteCollectionRequest);
System.out.println(collectionId + ": " + deleteCollectionResponse.statusCode().toString());
} catch (RekognitionException e) {
System.out.println(e.getMessage());
System.exit(1);
}
}
}
```
+ API の詳細については、**「AWS SDK for Java 2.x API リファレンス」の「[DeleteCollection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DeleteCollection)」を参照してください。
------
#### [ Kotlin ]
**SDK for Kotlin**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples)での設定と実行の方法を確認してください。
```
suspend fun deleteMyCollection(collectionIdVal: String) {
val request =
DeleteCollectionRequest {
collectionId = collectionIdVal
}
RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
val response = rekClient.deleteCollection(request)
println("The collectionId status is ${response.statusCode}")
}
}
```
+ API の詳細については、「AWS SDK for Kotlin API Reference」の「[DeleteCollection](https://sdk.amazonaws.com/kotlin/api/latest/index.html)」を参照してください。
------
#### [ Python ]
**SDK for Python (Boto3)**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples)での設定と実行の方法を確認してください。
```
class RekognitionCollection:
"""
Encapsulates an Amazon Rekognition collection. This class is a thin wrapper
around parts of the Boto3 Amazon Rekognition API.
"""
def __init__(self, collection, rekognition_client):
"""
Initializes a collection object.
:param collection: Collection data in the format returned by a call to
create_collection.
:param rekognition_client: A Boto3 Rekognition client.
"""
self.collection_id = collection["CollectionId"]
self.collection_arn, self.face_count, self.created = self._unpack_collection(
collection
)
self.rekognition_client = rekognition_client
@staticmethod
def _unpack_collection(collection):
"""
Unpacks optional parts of a collection that can be returned by
describe_collection.
:param collection: The collection data.
:return: A tuple of the data in the collection.
"""
return (
collection.get("CollectionArn"),
collection.get("FaceCount", 0),
collection.get("CreationTimestamp"),
)
def delete_collection(self):
"""
Deletes the collection.
"""
try:
self.rekognition_client.delete_collection(CollectionId=self.collection_id)
logger.info("Deleted collection %s.", self.collection_id)
self.collection_id = None
except ClientError:
logger.exception("Couldn't delete collection %s.", self.collection_id)
raise
```
+ API の詳細については、 [AWS SDK for Python (Boto3) API リファレンス](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DeleteCollection) で「*DeleteCollection*」を参照してください。
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples)での設定と実行の方法を確認してください。
```
TRY.
lo_rek->deletecollection(
iv_collectionid = iv_collection_id ).
MESSAGE 'Collection deleted successfully.' TYPE 'I'.
CATCH /aws1/cx_rekresourcenotfoundex.
MESSAGE 'Collection not found.' TYPE 'E'.
CATCH /aws1/cx_rekinvalidparameterex.
MESSAGE 'Invalid parameter value.' TYPE 'E'.
ENDTRY.
```
+ API の詳細については、 *AWS SDK for SAP ABAP API リファレンス*の[DeleteCollection](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)」を参照してください。
------
# AWS SDK または CLI `DeleteFaces`で を使用する
次のサンプルコードは、`DeleteFaces` を使用する方法を説明しています。
詳細については、「[コレクションから顔を削除する](https://docs.aws.amazon.com/rekognition/latest/dg/delete-faces-procedure.html)」を参照してください。
------
#### [ .NET ]
**SDK for .NET**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples)での設定と実行の方法を確認してください。
```
using System;
using System.Collections.Generic;
using System.Threading.Tasks;
using Amazon.Rekognition;
using Amazon.Rekognition.Model;
///
/// Uses the Amazon Rekognition Service to delete one or more faces from
/// a Rekognition collection.
///
public class DeleteFaces
{
public static async Task Main()
{
string collectionId = "MyCollection";
var faces = new List { "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx" };
var rekognitionClient = new AmazonRekognitionClient();
var deleteFacesRequest = new DeleteFacesRequest()
{
CollectionId = collectionId,
FaceIds = faces,
};
DeleteFacesResponse deleteFacesResponse = await rekognitionClient.DeleteFacesAsync(deleteFacesRequest);
deleteFacesResponse.DeletedFaces.ForEach(face =>
{
Console.WriteLine($"FaceID: {face}");
});
}
}
```
+ API の詳細については、**「AWS SDK for .NET API リファレンス」の「[DeleteFaces](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/DeleteFaces)」を参照してください。
------
#### [ CLI ]
**AWS CLI**
**コレクションから顔を削除するには**
次の `delete-faces` コマンドは、コレクションから指定された顔を削除します。
```
aws rekognition delete-faces \
--collection-id MyCollection
--face-ids '["0040279c-0178-436e-b70a-e61b074e96b0"]'
```
出力:
```
{
"DeletedFaces": [
"0040279c-0178-436e-b70a-e61b074e96b0"
]
}
```
詳細については、「Amazon Rekognition ディベロッパーガイド」の「[コレクションからの顔の削除](https://docs.aws.amazon.com/rekognition/latest/dg/delete-faces-procedure.html)」を参照してください。**
+ API の詳細については、AWS CLI コマンドリファレンス**の「[DeleteFaces](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/delete-faces.html)」を参照してください。
------
#### [ Java ]
**SDK for Java 2.x**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples)での設定と実行の方法を確認してください。
```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.DeleteFacesRequest;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
/**
* Before running this Java V2 code example, set up your development
* environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class DeleteFacesFromCollection {
public static void main(String[] args) {
final String usage = """
Usage: \s
Where:
collectionId - The id of the collection from which faces are deleted.\s
faceId - The id of the face to delete.\s
""";
if (args.length != 2) {
System.out.println(usage);
System.exit(1);
}
String collectionId = args[0];
String faceId = args[1];
Region region = Region.US_EAST_1;
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.build();
System.out.println("Deleting collection: " + collectionId);
deleteFacesCollection(rekClient, collectionId, faceId);
rekClient.close();
}
/**
* Deletes a face from the specified Amazon Rekognition collection.
*
* @param rekClient an instance of the Amazon Rekognition client
* @param collectionId the ID of the collection from which the face should be deleted
* @param faceId the ID of the face to be deleted
* @throws RekognitionException if an error occurs while deleting the face
*/
public static void deleteFacesCollection(RekognitionClient rekClient,
String collectionId,
String faceId) {
try {
DeleteFacesRequest deleteFacesRequest = DeleteFacesRequest.builder()
.collectionId(collectionId)
.faceIds(faceId)
.build();
rekClient.deleteFaces(deleteFacesRequest);
System.out.println("The face was deleted from the collection.");
} catch (RekognitionException e) {
System.out.println(e.getMessage());
System.exit(1);
}
}
}
```
+ API の詳細については、**「AWS SDK for Java 2.x API リファレンス」の「[DeleteFaces](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DeleteFaces)」を参照してください。
------
#### [ Kotlin ]
**SDK for Kotlin**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples)での設定と実行の方法を確認してください。
```
suspend fun deleteFacesCollection(
collectionIdVal: String?,
faceIdVal: String,
) {
val deleteFacesRequest =
DeleteFacesRequest {
collectionId = collectionIdVal
faceIds = listOf(faceIdVal)
}
RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
rekClient.deleteFaces(deleteFacesRequest)
println("$faceIdVal was deleted from the collection")
}
}
```
+ API の詳細については、「AWS SDK for Kotlin API Reference」の「[DeleteFaces](https://sdk.amazonaws.com/kotlin/api/latest/index.html)」を参照してください。
------
#### [ Python ]
**SDK for Python (Boto3)**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples)での設定と実行の方法を確認してください。
```
class RekognitionCollection:
"""
Encapsulates an Amazon Rekognition collection. This class is a thin wrapper
around parts of the Boto3 Amazon Rekognition API.
"""
def __init__(self, collection, rekognition_client):
"""
Initializes a collection object.
:param collection: Collection data in the format returned by a call to
create_collection.
:param rekognition_client: A Boto3 Rekognition client.
"""
self.collection_id = collection["CollectionId"]
self.collection_arn, self.face_count, self.created = self._unpack_collection(
collection
)
self.rekognition_client = rekognition_client
@staticmethod
def _unpack_collection(collection):
"""
Unpacks optional parts of a collection that can be returned by
describe_collection.
:param collection: The collection data.
:return: A tuple of the data in the collection.
"""
return (
collection.get("CollectionArn"),
collection.get("FaceCount", 0),
collection.get("CreationTimestamp"),
)
def delete_faces(self, face_ids):
"""
Deletes faces from the collection.
:param face_ids: The list of IDs of faces to delete.
:return: The list of IDs of faces that were deleted.
"""
try:
response = self.rekognition_client.delete_faces(
CollectionId=self.collection_id, FaceIds=face_ids
)
deleted_ids = response["DeletedFaces"]
logger.info(
"Deleted %s faces from %s.", len(deleted_ids), self.collection_id
)
except ClientError:
logger.exception("Couldn't delete faces from %s.", self.collection_id)
raise
else:
return deleted_ids
```
+ API の詳細については、[AWS SDK for Python (Boto3) API リファレンス](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DeleteFaces) で「*DeleteFaces*」を参照してください。
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples)での設定と実行の方法を確認してください。
```
TRY.
oo_result = lo_rek->deletefaces(
iv_collectionid = iv_collection_id
it_faceids = it_face_ids ).
DATA(lt_deleted_faces) = oo_result->get_deletedfaces( ).
DATA(lv_deleted_count) = lines( lt_deleted_faces ).
DATA(lv_msg6) = |{ lv_deleted_count } face(s) deleted successfully.|.
MESSAGE lv_msg6 TYPE 'I'.
CATCH /aws1/cx_rekresourcenotfoundex.
MESSAGE 'Collection not found.' TYPE 'E'.
CATCH /aws1/cx_rekinvalidparameterex.
MESSAGE 'Invalid parameter value.' TYPE 'E'.
ENDTRY.
```
+ API の詳細については、 *AWS SDK for SAP ABAP API リファレンス*の[DeleteFaces](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)」を参照してください。
------
# AWS SDK または CLI `DescribeCollection`で を使用する
次のサンプルコードは、`DescribeCollection` を使用する方法を説明しています。
詳細については、「[コレクションを定義する](https://docs.aws.amazon.com/rekognition/latest/dg/describe-collection-procedure.html)」を参照してください。
------
#### [ .NET ]
**SDK for .NET**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples)での設定と実行の方法を確認してください。
```
using System;
using System.Threading.Tasks;
using Amazon.Rekognition;
using Amazon.Rekognition.Model;
///
/// Uses the Amazon Rekognition Service to describe the contents of a
/// collection.
///
public class DescribeCollection
{
public static async Task Main()
{
var rekognitionClient = new AmazonRekognitionClient();
string collectionId = "MyCollection";
Console.WriteLine($"Describing collection: {collectionId}");
var describeCollectionRequest = new DescribeCollectionRequest()
{
CollectionId = collectionId,
};
var describeCollectionResponse = await rekognitionClient.DescribeCollectionAsync(describeCollectionRequest);
Console.WriteLine($"Collection ARN: {describeCollectionResponse.CollectionARN}");
Console.WriteLine($"Face count: {describeCollectionResponse.FaceCount}");
Console.WriteLine($"Face model version: {describeCollectionResponse.FaceModelVersion}");
Console.WriteLine($"Created: {describeCollectionResponse.CreationTimestamp}");
}
}
```
+ API の詳細については、**「AWS SDK for .NET API リファレンス」の「[DescribeCollection](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/DescribeCollection)」を参照してください。
------
#### [ CLI ]
**AWS CLI**
**コレクションを記述するには**
次の `describe-collection` の例は、指定されたコレクションの詳細を表示します。
```
aws rekognition describe-collection \
--collection-id MyCollection
```
出力:
```
{
"FaceCount": 200,
"CreationTimestamp": 1569444828.274,
"CollectionARN": "arn:aws:rekognition:us-west-2:123456789012:collection/MyCollection",
"FaceModelVersion": "4.0"
}
```
詳細については、「Amazon Rekognition 開発者ガイド」の「[コレクションの定義](https://docs.aws.amazon.com/rekognition/latest/dg/describe-collection-procedure.html)」を参照してください。**
+ API の詳細については、AWS CLI コマンドリファレンスの「[DescribeCollection](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/describe-collection.html)」を参照してください。**
------
#### [ Java ]
**SDK for Java 2.x**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples)での設定と実行の方法を確認してください。
```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.DescribeCollectionRequest;
import software.amazon.awssdk.services.rekognition.model.DescribeCollectionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
/**
* Before running this Java V2 code example, set up your development
* environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class DescribeCollection {
public static void main(String[] args) {
final String usage = """
Usage:
Where:
collectionName - The name of the Amazon Rekognition collection.\s
""";
if (args.length != 1) {
System.out.println(usage);
System.exit(1);
}
String collectionName = args[0];
Region region = Region.US_EAST_1;
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.build();
describeColl(rekClient, collectionName);
rekClient.close();
}
/**
* Describes an Amazon Rekognition collection.
*
* @param rekClient The Amazon Rekognition client used to make the request.
* @param collectionName The name of the collection to describe.
*
* @throws RekognitionException If an error occurs while describing the collection.
*/
public static void describeColl(RekognitionClient rekClient, String collectionName) {
try {
DescribeCollectionRequest describeCollectionRequest = DescribeCollectionRequest.builder()
.collectionId(collectionName)
.build();
DescribeCollectionResponse describeCollectionResponse = rekClient
.describeCollection(describeCollectionRequest);
System.out.println("Collection Arn : " + describeCollectionResponse.collectionARN());
System.out.println("Created : " + describeCollectionResponse.creationTimestamp().toString());
} catch (RekognitionException e) {
System.out.println(e.getMessage());
System.exit(1);
}
}
}
```
+ API の詳細については、**「AWS SDK for Java 2.x API リファレンス」の「[DescribeCollection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DescribeCollection)」を参照してください。
------
#### [ Kotlin ]
**SDK for Kotlin**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples)での設定と実行の方法を確認してください。
```
suspend fun describeColl(collectionName: String) {
val request =
DescribeCollectionRequest {
collectionId = collectionName
}
RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
val response = rekClient.describeCollection(request)
println("The collection Arn is ${response.collectionArn}")
println("The collection contains this many faces ${response.faceCount}")
}
}
```
+ API の詳細については、「AWS SDK for Kotlin API Reference」の「[DescribeCollection](https://sdk.amazonaws.com/kotlin/api/latest/index.html)」を参照してください。
------
#### [ Python ]
**SDK for Python (Boto3)**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples)での設定と実行の方法を確認してください。
```
class RekognitionCollection:
"""
Encapsulates an Amazon Rekognition collection. This class is a thin wrapper
around parts of the Boto3 Amazon Rekognition API.
"""
def __init__(self, collection, rekognition_client):
"""
Initializes a collection object.
:param collection: Collection data in the format returned by a call to
create_collection.
:param rekognition_client: A Boto3 Rekognition client.
"""
self.collection_id = collection["CollectionId"]
self.collection_arn, self.face_count, self.created = self._unpack_collection(
collection
)
self.rekognition_client = rekognition_client
@staticmethod
def _unpack_collection(collection):
"""
Unpacks optional parts of a collection that can be returned by
describe_collection.
:param collection: The collection data.
:return: A tuple of the data in the collection.
"""
return (
collection.get("CollectionArn"),
collection.get("FaceCount", 0),
collection.get("CreationTimestamp"),
)
def describe_collection(self):
"""
Gets data about the collection from the Amazon Rekognition service.
:return: The collection rendered as a dict.
"""
try:
response = self.rekognition_client.describe_collection(
CollectionId=self.collection_id
)
# Work around capitalization of Arn vs. ARN
response["CollectionArn"] = response.get("CollectionARN")
(
self.collection_arn,
self.face_count,
self.created,
) = self._unpack_collection(response)
logger.info("Got data for collection %s.", self.collection_id)
except ClientError:
logger.exception("Couldn't get data for collection %s.", self.collection_id)
raise
else:
return self.to_dict()
```
+ API の詳細については、[AWS SDK for Python (Boto3) API リファレンス](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DescribeCollection)の「*DescribeCollection*」を参照してください。
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples)での設定と実行の方法を確認してください。
```
TRY.
oo_result = lo_rek->describecollection(
iv_collectionid = iv_collection_id ).
DATA(lv_face_count) = oo_result->get_facecount( ).
DATA(lv_msg) = |Collection described: { lv_face_count } face(s) indexed.|.
MESSAGE lv_msg TYPE 'I'.
CATCH /aws1/cx_rekresourcenotfoundex.
MESSAGE 'Collection not found.' TYPE 'E'.
CATCH /aws1/cx_rekinvalidparameterex.
MESSAGE 'Invalid parameter value.' TYPE 'E'.
ENDTRY.
```
+ API の詳細については、 *AWS SDK for SAP ABAP API リファレンス*の「[DescribeCollection](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)」を参照してください。
------
# AWS SDK または CLI `DetectFaces`で を使用する
次のサンプルコードは、`DetectFaces` を使用する方法を説明しています。
詳細については、「[イメージ内の顔を検出する](https://docs.aws.amazon.com/rekognition/latest/dg/faces-detect-images.html)」を参照してください。
------
#### [ .NET ]
**SDK for .NET**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples)での設定と実行の方法を確認してください。
```
using System;
using System.Collections.Generic;
using System.Threading.Tasks;
using Amazon.Rekognition;
using Amazon.Rekognition.Model;
///
/// Uses the Amazon Rekognition Service to detect faces within an image
/// stored in an Amazon Simple Storage Service (Amazon S3) bucket.
///
public class DetectFaces
{
public static async Task Main()
{
string photo = "input.jpg";
string bucket = "amzn-s3-demo-bucket";
var rekognitionClient = new AmazonRekognitionClient();
var detectFacesRequest = new DetectFacesRequest()
{
Image = new Image()
{
S3Object = new S3Object()
{
Name = photo,
Bucket = bucket,
},
},
// Attributes can be "ALL" or "DEFAULT".
// "DEFAULT": BoundingBox, Confidence, Landmarks, Pose, and Quality.
// "ALL": See https://docs.aws.amazon.com/sdkfornet/v3/apidocs/items/Rekognition/TFaceDetail.html
Attributes = new List() { "ALL" },
};
try
{
DetectFacesResponse detectFacesResponse = await rekognitionClient.DetectFacesAsync(detectFacesRequest);
bool hasAll = detectFacesRequest.Attributes.Contains("ALL");
foreach (FaceDetail face in detectFacesResponse.FaceDetails)
{
Console.WriteLine($"BoundingBox: top={face.BoundingBox.Left} left={face.BoundingBox.Top} width={face.BoundingBox.Width} height={face.BoundingBox.Height}");
Console.WriteLine($"Confidence: {face.Confidence}");
Console.WriteLine($"Landmarks: {face.Landmarks.Count}");
Console.WriteLine($"Pose: pitch={face.Pose.Pitch} roll={face.Pose.Roll} yaw={face.Pose.Yaw}");
Console.WriteLine($"Brightness: {face.Quality.Brightness}\tSharpness: {face.Quality.Sharpness}");
if (hasAll)
{
Console.WriteLine($"Estimated age is between {face.AgeRange.Low} and {face.AgeRange.High} years old.");
}
}
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
}
}
}
```
イメージ内のすべての顔の境界ボックス情報を表示します。
```
using System;
using System.Collections.Generic;
using System.Drawing;
using System.IO;
using System.Threading.Tasks;
using Amazon.Rekognition;
using Amazon.Rekognition.Model;
///
/// Uses the Amazon Rekognition Service to display the details of the
/// bounding boxes around the faces detected in an image.
///
public class ImageOrientationBoundingBox
{
public static async Task Main()
{
string photo = @"D:\Development\AWS-Examples\Rekognition\target.jpg"; // "photo.jpg";
var rekognitionClient = new AmazonRekognitionClient();
var image = new Amazon.Rekognition.Model.Image();
try
{
using var fs = new FileStream(photo, FileMode.Open, FileAccess.Read);
byte[] data = null;
data = new byte[fs.Length];
fs.Read(data, 0, (int)fs.Length);
image.Bytes = new MemoryStream(data);
}
catch (Exception)
{
Console.WriteLine("Failed to load file " + photo);
return;
}
int height;
int width;
// Used to extract original photo width/height
using (var imageBitmap = new Bitmap(photo))
{
height = imageBitmap.Height;
width = imageBitmap.Width;
}
Console.WriteLine("Image Information:");
Console.WriteLine(photo);
Console.WriteLine("Image Height: " + height);
Console.WriteLine("Image Width: " + width);
try
{
var detectFacesRequest = new DetectFacesRequest()
{
Image = image,
Attributes = new List() { "ALL" },
};
DetectFacesResponse detectFacesResponse = await rekognitionClient.DetectFacesAsync(detectFacesRequest);
detectFacesResponse.FaceDetails.ForEach(face =>
{
Console.WriteLine("Face:");
ShowBoundingBoxPositions(
height,
width,
face.BoundingBox,
detectFacesResponse.OrientationCorrection);
Console.WriteLine($"BoundingBox: top={face.BoundingBox.Left} left={face.BoundingBox.Top} width={face.BoundingBox.Width} height={face.BoundingBox.Height}");
Console.WriteLine($"The detected face is estimated to be between {face.AgeRange.Low} and {face.AgeRange.High} years old.\n");
});
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
}
}
///
/// Display the bounding box information for an image.
///
/// The height of the image.
/// The width of the image.
/// The bounding box for a face found within the image.
/// The rotation of the face's bounding box.
public static void ShowBoundingBoxPositions(int imageHeight, int imageWidth, BoundingBox box, string rotation)
{
float left;
float top;
if (rotation == null)
{
Console.WriteLine("No estimated orientation. Check Exif data.");
return;
}
// Calculate face position based on image orientation.
switch (rotation)
{
case "ROTATE_0":
left = imageWidth * box.Left;
top = imageHeight * box.Top;
break;
case "ROTATE_90":
left = imageHeight * (1 - (box.Top + box.Height));
top = imageWidth * box.Left;
break;
case "ROTATE_180":
left = imageWidth - (imageWidth * (box.Left + box.Width));
top = imageHeight * (1 - (box.Top + box.Height));
break;
case "ROTATE_270":
left = imageHeight * box.Top;
top = imageWidth * (1 - box.Left - box.Width);
break;
default:
Console.WriteLine("No estimated orientation information. Check Exif data.");
return;
}
// Display face location information.
Console.WriteLine($"Left: {left}");
Console.WriteLine($"Top: {top}");
Console.WriteLine($"Face Width: {imageWidth * box.Width}");
Console.WriteLine($"Face Height: {imageHeight * box.Height}");
}
}
```
+ API の詳細については、**「AWS SDK for .NET API リファレンス」の「[DetectFaces](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/DetectFaces)」を参照してください。
------
#### [ CLI ]
**AWS CLI**
**画像内の顔を検出するには**
次の `detect-faces` コマンドは、Amazon S3 バケットに保存されている指定された画像の顔を検出します。
```
aws rekognition detect-faces \
--image '{"S3Object":{"Bucket":"MyImageS3Bucket","Name":"MyFriend.jpg"}}' \
--attributes "ALL"
```
出力:
```
{
"FaceDetails": [
{
"Confidence": 100.0,
"Eyeglasses": {
"Confidence": 98.91107940673828,
"Value": false
},
"Sunglasses": {
"Confidence": 99.7966537475586,
"Value": false
},
"Gender": {
"Confidence": 99.56611633300781,
"Value": "Male"
},
"Landmarks": [
{
"Y": 0.26721030473709106,
"X": 0.6204193830490112,
"Type": "eyeLeft"
},
{
"Y": 0.26831310987472534,
"X": 0.6776827573776245,
"Type": "eyeRight"
},
{
"Y": 0.3514654338359833,
"X": 0.6241428852081299,
"Type": "mouthLeft"
},
{
"Y": 0.35258132219314575,
"X": 0.6713621020317078,
"Type": "mouthRight"
},
{
"Y": 0.3140771687030792,
"X": 0.6428444981575012,
"Type": "nose"
},
{
"Y": 0.24662546813488007,
"X": 0.6001564860343933,
"Type": "leftEyeBrowLeft"
},
{
"Y": 0.24326619505882263,
"X": 0.6303644776344299,
"Type": "leftEyeBrowRight"
},
{
"Y": 0.23818562924861908,
"X": 0.6146903038024902,
"Type": "leftEyeBrowUp"
},
{
"Y": 0.24373626708984375,
"X": 0.6640064716339111,
"Type": "rightEyeBrowLeft"
},
{
"Y": 0.24877218902111053,
"X": 0.7025929093360901,
"Type": "rightEyeBrowRight"
},
{
"Y": 0.23938551545143127,
"X": 0.6823262572288513,
"Type": "rightEyeBrowUp"
},
{
"Y": 0.265746533870697,
"X": 0.6112898588180542,
"Type": "leftEyeLeft"
},
{
"Y": 0.2676128149032593,
"X": 0.6317071914672852,
"Type": "leftEyeRight"
},
{
"Y": 0.262735515832901,
"X": 0.6201658248901367,
"Type": "leftEyeUp"
},
{
"Y": 0.27025148272514343,
"X": 0.6206279993057251,
"Type": "leftEyeDown"
},
{
"Y": 0.268223375082016,
"X": 0.6658390760421753,
"Type": "rightEyeLeft"
},
{
"Y": 0.2672517001628876,
"X": 0.687832236289978,
"Type": "rightEyeRight"
},
{
"Y": 0.26383838057518005,
"X": 0.6769183874130249,
"Type": "rightEyeUp"
},
{
"Y": 0.27138751745224,
"X": 0.676596462726593,
"Type": "rightEyeDown"
},
{
"Y": 0.32283174991607666,
"X": 0.6350004076957703,
"Type": "noseLeft"
},
{
"Y": 0.3219289481639862,
"X": 0.6567046642303467,
"Type": "noseRight"
},
{
"Y": 0.3420318365097046,
"X": 0.6450609564781189,
"Type": "mouthUp"
},
{
"Y": 0.3664324879646301,
"X": 0.6455618143081665,
"Type": "mouthDown"
},
{
"Y": 0.26721030473709106,
"X": 0.6204193830490112,
"Type": "leftPupil"
},
{
"Y": 0.26831310987472534,
"X": 0.6776827573776245,
"Type": "rightPupil"
},
{
"Y": 0.26343393325805664,
"X": 0.5946047306060791,
"Type": "upperJawlineLeft"
},
{
"Y": 0.3543180525302887,
"X": 0.6044883728027344,
"Type": "midJawlineLeft"
},
{
"Y": 0.4084877669811249,
"X": 0.6477024555206299,
"Type": "chinBottom"
},
{
"Y": 0.3562754988670349,
"X": 0.707981526851654,
"Type": "midJawlineRight"
},
{
"Y": 0.26580461859703064,
"X": 0.7234612107276917,
"Type": "upperJawlineRight"
}
],
"Pose": {
"Yaw": -3.7351467609405518,
"Roll": -0.10309021919965744,
"Pitch": 0.8637830018997192
},
"Emotions": [
{
"Confidence": 8.74203109741211,
"Type": "SURPRISED"
},
{
"Confidence": 2.501944065093994,
"Type": "ANGRY"
},
{
"Confidence": 0.7378743290901184,
"Type": "DISGUSTED"
},
{
"Confidence": 3.5296201705932617,
"Type": "HAPPY"
},
{
"Confidence": 1.7162904739379883,
"Type": "SAD"
},
{
"Confidence": 9.518536567687988,
"Type": "CONFUSED"
},
{
"Confidence": 0.45474427938461304,
"Type": "FEAR"
},
{
"Confidence": 72.79895782470703,
"Type": "CALM"
}
],
"AgeRange": {
"High": 48,
"Low": 32
},
"EyesOpen": {
"Confidence": 98.93987274169922,
"Value": true
},
"BoundingBox": {
"Width": 0.12368916720151901,
"Top": 0.16007372736930847,
"Left": 0.5901257991790771,
"Height": 0.25140416622161865
},
"Smile": {
"Confidence": 93.4493179321289,
"Value": false
},
"MouthOpen": {
"Confidence": 90.53053283691406,
"Value": false
},
"Quality": {
"Sharpness": 95.51618957519531,
"Brightness": 65.29893493652344
},
"Mustache": {
"Confidence": 89.85221099853516,
"Value": false
},
"Beard": {
"Confidence": 86.1991195678711,
"Value": true
}
}
]
}
```
詳細については、「Amazon Rekognition 開発者ガイド」の「[イメージ内の顔の検出](https://docs.aws.amazon.com/rekognition/latest/dg/faces-detect-images.html)」を参照してください。**
+ API の詳細については、AWS CLI コマンドリファレンスの「[DetectFaces](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/detect-faces.html)」を参照してください。**
------
#### [ Java ]
**SDK for Java 2.x**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples)での設定と実行の方法を確認してください。
```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.*;
import java.util.List;
/**
* Before running this Java V2 code example, set up your development
* environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class DetectFaces {
public static void main(String[] args) {
final String usage = """
Usage:
Where:
bucketName = The name of the Amazon S3 bucket where the source image is stored.
sourceImage - The name of the source image file in the Amazon S3 bucket. (for example, pic1.png).\s
""";
if (args.length != 2) {
System.out.println(usage);
System.exit(1);
}
String bucketName = args[0];
String sourceImage = args[1];
Region region = Region.US_WEST_2;
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.build();
detectFacesinImage(rekClient, bucketName, sourceImage);
rekClient.close();
}
/**
* Detects faces in an image stored in an Amazon S3 bucket using the Amazon Rekognition service.
*
* @param rekClient The Amazon Rekognition client used to interact with the Rekognition service.
* @param bucketName The name of the Amazon S3 bucket where the source image is stored.
* @param sourceImage The name of the source image file in the Amazon S3 bucket.
*/
public static void detectFacesinImage(RekognitionClient rekClient, String bucketName, String sourceImage) {
try {
S3Object s3ObjectTarget = S3Object.builder()
.bucket(bucketName)
.name(sourceImage)
.build();
Image targetImage = Image.builder()
.s3Object(s3ObjectTarget)
.build();
DetectFacesRequest facesRequest = DetectFacesRequest.builder()
.attributes(Attribute.ALL)
.image(targetImage)
.build();
DetectFacesResponse facesResponse = rekClient.detectFaces(facesRequest);
List faceDetails = facesResponse.faceDetails();
for (FaceDetail face : faceDetails) {
AgeRange ageRange = face.ageRange();
System.out.println("The detected face is estimated to be between "
+ ageRange.low().toString() + " and " + ageRange.high().toString()
+ " years old.");
System.out.println("There is a smile : " + face.smile().value().toString());
}
} catch (RekognitionException e) {
System.out.println(e.getMessage());
System.exit(1);
}
}
}
```
+ API の詳細については、**「AWS SDK for Java 2.x API リファレンス」の「[DetectFaces](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DetectFaces)」を参照してください。
------
#### [ Kotlin ]
**SDK for Kotlin**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples)での設定と実行の方法を確認してください。
```
suspend fun detectFacesinImage(sourceImage: String?) {
val souImage =
Image {
bytes = (File(sourceImage).readBytes())
}
val request =
DetectFacesRequest {
attributes = listOf(Attribute.All)
image = souImage
}
RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
val response = rekClient.detectFaces(request)
response.faceDetails?.forEach { face ->
val ageRange = face.ageRange
println("The detected face is estimated to be between ${ageRange?.low} and ${ageRange?.high} years old.")
println("There is a smile ${face.smile?.value}")
}
}
}
```
+ API の詳細については、「AWS SDK for Kotlin API Reference」の「[DetectFaces](https://sdk.amazonaws.com/kotlin/api/latest/index.html)」を参照してください。
------
#### [ Python ]
**SDK for Python (Boto3)**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples)での設定と実行の方法を確認してください。
```
class RekognitionImage:
"""
Encapsulates an Amazon Rekognition image. This class is a thin wrapper
around parts of the Boto3 Amazon Rekognition API.
"""
def __init__(self, image, image_name, rekognition_client):
"""
Initializes the image object.
:param image: Data that defines the image, either the image bytes or
an Amazon S3 bucket and object key.
:param image_name: The name of the image.
:param rekognition_client: A Boto3 Rekognition client.
"""
self.image = image
self.image_name = image_name
self.rekognition_client = rekognition_client
def detect_faces(self):
"""
Detects faces in the image.
:return: The list of faces found in the image.
"""
try:
response = self.rekognition_client.detect_faces(
Image=self.image, Attributes=["ALL"]
)
faces = [RekognitionFace(face) for face in response["FaceDetails"]]
logger.info("Detected %s faces.", len(faces))
except ClientError:
logger.exception("Couldn't detect faces in %s.", self.image_name)
raise
else:
return faces
```
+ API の詳細については、[AWS SDK for Python (Boto3) API リファレンス](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DetectFaces) の「*DetectFaces*」を参照してください。
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples)での設定と実行の方法を確認してください。
```
TRY.
" Create S3 object reference for the image
DATA(lo_s3object) = NEW /aws1/cl_reks3object(
iv_bucket = iv_s3_bucket
iv_name = iv_s3_key ).
" Create image object
DATA(lo_image) = NEW /aws1/cl_rekimage(
io_s3object = lo_s3object ).
" Detect faces in the image with all attributes
DATA(lt_attributes) = VALUE /aws1/cl_rekattributes_w=>tt_attributes( ).
DATA(lo_attr_wrapper) = NEW /aws1/cl_rekattributes_w( iv_value = 'ALL' ).
INSERT lo_attr_wrapper INTO TABLE lt_attributes.
oo_result = lo_rek->detectfaces(
io_image = lo_image
it_attributes = lt_attributes ).
DATA(lt_face_details) = oo_result->get_facedetails( ).
DATA(lv_detected_count) = lines( lt_face_details ).
DATA(lv_msg8) = |{ lv_detected_count } face(s) detected in image.|.
MESSAGE lv_msg8 TYPE 'I'.
CATCH /aws1/cx_rekinvalids3objectex.
MESSAGE 'Invalid S3 object.' TYPE 'E'.
CATCH /aws1/cx_rekinvalidparameterex.
MESSAGE 'Invalid parameter value.' TYPE 'E'.
ENDTRY.
```
+ API の詳細については、 *AWS SDK for SAP ABAP API リファレンス*の[DetectFaces](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)」を参照してください。
------
# AWS SDK または CLI `DetectLabels`で を使用する
次のサンプルコードは、`DetectLabels` を使用する方法を説明しています。
詳細については、「[イメージ内のラベルを検出する](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detect-labels-image.html)」を参照してください。
------
#### [ .NET ]
**SDK for .NET**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples)での設定と実行の方法を確認してください。
```
using System;
using System.Threading.Tasks;
using Amazon.Rekognition;
using Amazon.Rekognition.Model;
///
/// Uses the Amazon Rekognition Service to detect labels within an image
/// stored in an Amazon Simple Storage Service (Amazon S3) bucket.
///
public class DetectLabels
{
public static async Task Main()
{
string photo = "del_river_02092020_01.jpg"; // "input.jpg";
string bucket = "amzn-s3-demo-bucket"; // "bucket";
var rekognitionClient = new AmazonRekognitionClient();
var detectlabelsRequest = new DetectLabelsRequest
{
Image = new Image()
{
S3Object = new S3Object()
{
Name = photo,
Bucket = bucket,
},
},
MaxLabels = 10,
MinConfidence = 75F,
};
try
{
DetectLabelsResponse detectLabelsResponse = await rekognitionClient.DetectLabelsAsync(detectlabelsRequest);
Console.WriteLine("Detected labels for " + photo);
foreach (Label label in detectLabelsResponse.Labels)
{
Console.WriteLine($"Name: {label.Name} Confidence: {label.Confidence}");
}
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
}
}
}
```
コンピュータに保存されているイメージファイル内のラベルを検出します。
```
using System;
using System.IO;
using System.Threading.Tasks;
using Amazon.Rekognition;
using Amazon.Rekognition.Model;
///
/// Uses the Amazon Rekognition Service to detect labels within an image
/// stored locally.
///
public class DetectLabelsLocalFile
{
public static async Task Main()
{
string photo = "input.jpg";
var image = new Amazon.Rekognition.Model.Image();
try
{
using var fs = new FileStream(photo, FileMode.Open, FileAccess.Read);
byte[] data = null;
data = new byte[fs.Length];
fs.Read(data, 0, (int)fs.Length);
image.Bytes = new MemoryStream(data);
}
catch (Exception)
{
Console.WriteLine("Failed to load file " + photo);
return;
}
var rekognitionClient = new AmazonRekognitionClient();
var detectlabelsRequest = new DetectLabelsRequest
{
Image = image,
MaxLabels = 10,
MinConfidence = 77F,
};
try
{
DetectLabelsResponse detectLabelsResponse = await rekognitionClient.DetectLabelsAsync(detectlabelsRequest);
Console.WriteLine($"Detected labels for {photo}");
foreach (Label label in detectLabelsResponse.Labels)
{
Console.WriteLine($"{label.Name}: {label.Confidence}");
}
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
}
}
}
```
+ API の詳細については、**「AWS SDK for .NET API リファレンス」の「[DetectLabels](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/DetectLabels)」を参照してください。
------
#### [ C\$1\$1 ]
**SDK for C\$1\$1**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/cpp/example_code/rekognition#code-examples)での設定と実行の方法を確認してください。
```
//! 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 &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();
}
```
+ API の詳細については、**「AWS SDK for C\$1\$1 API リファレンス」の「[DetectLabels](https://docs.aws.amazon.com/goto/SdkForCpp/rekognition-2016-06-27/DetectLabels)」を参照してください。
------
#### [ CLI ]
**AWS CLI**
**画像内のラベルを検出するには**
次の `detect-labels` の例は、Amazon S3 バケットに保存されている画像内のシーンとオブジェクトを検出します。
```
aws rekognition detect-labels \
--image '{"S3Object":{"Bucket":"bucket","Name":"image"}}'
```
出力:
```
{
"Labels": [
{
"Instances": [],
"Confidence": 99.15271759033203,
"Parents": [
{
"Name": "Vehicle"
},
{
"Name": "Transportation"
}
],
"Name": "Automobile"
},
{
"Instances": [],
"Confidence": 99.15271759033203,
"Parents": [
{
"Name": "Transportation"
}
],
"Name": "Vehicle"
},
{
"Instances": [],
"Confidence": 99.15271759033203,
"Parents": [],
"Name": "Transportation"
},
{
"Instances": [
{
"BoundingBox": {
"Width": 0.10616336017847061,
"Top": 0.5039216876029968,
"Left": 0.0037978808395564556,
"Height": 0.18528179824352264
},
"Confidence": 99.15271759033203
},
{
"BoundingBox": {
"Width": 0.2429988533258438,
"Top": 0.5251884460449219,
"Left": 0.7309805154800415,
"Height": 0.21577216684818268
},
"Confidence": 99.1286392211914
},
{
"BoundingBox": {
"Width": 0.14233611524105072,
"Top": 0.5333095788955688,
"Left": 0.6494812965393066,
"Height": 0.15528248250484467
},
"Confidence": 98.48368072509766
},
{
"BoundingBox": {
"Width": 0.11086395382881165,
"Top": 0.5354844927787781,
"Left": 0.10355594009160995,
"Height": 0.10271988064050674
},
"Confidence": 96.45606231689453
},
{
"BoundingBox": {
"Width": 0.06254628300666809,
"Top": 0.5573825240135193,
"Left": 0.46083059906959534,
"Height": 0.053911514580249786
},
"Confidence": 93.65448760986328
},
{
"BoundingBox": {
"Width": 0.10105438530445099,
"Top": 0.534368634223938,
"Left": 0.5743985772132874,
"Height": 0.12226245552301407
},
"Confidence": 93.06217193603516
},
{
"BoundingBox": {
"Width": 0.056389667093753815,
"Top": 0.5235804319381714,
"Left": 0.9427769780158997,
"Height": 0.17163699865341187
},
"Confidence": 92.6864013671875
},
{
"BoundingBox": {
"Width": 0.06003860384225845,
"Top": 0.5441341400146484,
"Left": 0.22409997880458832,
"Height": 0.06737709045410156
},
"Confidence": 90.4227066040039
},
{
"BoundingBox": {
"Width": 0.02848697081208229,
"Top": 0.5107086896896362,
"Left": 0,
"Height": 0.19150497019290924
},
"Confidence": 86.65286254882812
},
{
"BoundingBox": {
"Width": 0.04067881405353546,
"Top": 0.5566273927688599,
"Left": 0.316415935754776,
"Height": 0.03428703173995018
},
"Confidence": 85.36471557617188
},
{
"BoundingBox": {
"Width": 0.043411049991846085,
"Top": 0.5394920110702515,
"Left": 0.18293385207653046,
"Height": 0.0893595889210701
},
"Confidence": 82.21705627441406
},
{
"BoundingBox": {
"Width": 0.031183116137981415,
"Top": 0.5579366683959961,
"Left": 0.2853088080883026,
"Height": 0.03989990055561066
},
"Confidence": 81.0157470703125
},
{
"BoundingBox": {
"Width": 0.031113790348172188,
"Top": 0.5504819750785828,
"Left": 0.2580395042896271,
"Height": 0.056484755128622055
},
"Confidence": 56.13441467285156
},
{
"BoundingBox": {
"Width": 0.08586374670267105,
"Top": 0.5438792705535889,
"Left": 0.5128012895584106,
"Height": 0.08550430089235306
},
"Confidence": 52.37760925292969
}
],
"Confidence": 99.15271759033203,
"Parents": [
{
"Name": "Vehicle"
},
{
"Name": "Transportation"
}
],
"Name": "Car"
},
{
"Instances": [],
"Confidence": 98.9914321899414,
"Parents": [],
"Name": "Human"
},
{
"Instances": [
{
"BoundingBox": {
"Width": 0.19360728561878204,
"Top": 0.35072067379951477,
"Left": 0.43734854459762573,
"Height": 0.2742200493812561
},
"Confidence": 98.9914321899414
},
{
"BoundingBox": {
"Width": 0.03801717236638069,
"Top": 0.5010883808135986,
"Left": 0.9155802130699158,
"Height": 0.06597328186035156
},
"Confidence": 85.02790832519531
}
],
"Confidence": 98.9914321899414,
"Parents": [],
"Name": "Person"
},
{
"Instances": [],
"Confidence": 93.24951934814453,
"Parents": [],
"Name": "Machine"
},
{
"Instances": [
{
"BoundingBox": {
"Width": 0.03561960905790329,
"Top": 0.6468243598937988,
"Left": 0.7850857377052307,
"Height": 0.08878646790981293
},
"Confidence": 93.24951934814453
},
{
"BoundingBox": {
"Width": 0.02217046171426773,
"Top": 0.6149078607559204,
"Left": 0.04757237061858177,
"Height": 0.07136218994855881
},
"Confidence": 91.5025863647461
},
{
"BoundingBox": {
"Width": 0.016197510063648224,
"Top": 0.6274210214614868,
"Left": 0.6472989320755005,
"Height": 0.04955997318029404
},
"Confidence": 85.14686584472656
},
{
"BoundingBox": {
"Width": 0.020207518711686134,
"Top": 0.6348286867141724,
"Left": 0.7295016646385193,
"Height": 0.07059963047504425
},
"Confidence": 83.34547424316406
},
{
"BoundingBox": {
"Width": 0.020280985161662102,
"Top": 0.6171894669532776,
"Left": 0.08744934946298599,
"Height": 0.05297485366463661
},
"Confidence": 79.9981460571289
},
{
"BoundingBox": {
"Width": 0.018318990245461464,
"Top": 0.623889148235321,
"Left": 0.6836880445480347,
"Height": 0.06730121374130249
},
"Confidence": 78.87144470214844
},
{
"BoundingBox": {
"Width": 0.021310249343514442,
"Top": 0.6167286038398743,
"Left": 0.004064912907779217,
"Height": 0.08317798376083374
},
"Confidence": 75.89361572265625
},
{
"BoundingBox": {
"Width": 0.03604431077837944,
"Top": 0.7030032277107239,
"Left": 0.9254803657531738,
"Height": 0.04569442570209503
},
"Confidence": 64.402587890625
},
{
"BoundingBox": {
"Width": 0.009834849275648594,
"Top": 0.5821820497512817,
"Left": 0.28094568848609924,
"Height": 0.01964157074689865
},
"Confidence": 62.79907989501953
},
{
"BoundingBox": {
"Width": 0.01475677452981472,
"Top": 0.6137543320655823,
"Left": 0.5950819253921509,
"Height": 0.039063986390829086
},
"Confidence": 59.40483474731445
}
],
"Confidence": 93.24951934814453,
"Parents": [
{
"Name": "Machine"
}
],
"Name": "Wheel"
},
{
"Instances": [],
"Confidence": 92.61514282226562,
"Parents": [],
"Name": "Road"
},
{
"Instances": [],
"Confidence": 92.37877655029297,
"Parents": [
{
"Name": "Person"
}
],
"Name": "Sport"
},
{
"Instances": [],
"Confidence": 92.37877655029297,
"Parents": [
{
"Name": "Person"
}
],
"Name": "Sports"
},
{
"Instances": [
{
"BoundingBox": {
"Width": 0.12326609343290329,
"Top": 0.6332163214683533,
"Left": 0.44815489649772644,
"Height": 0.058117982000112534
},
"Confidence": 92.37877655029297
}
],
"Confidence": 92.37877655029297,
"Parents": [
{
"Name": "Person"
},
{
"Name": "Sport"
}
],
"Name": "Skateboard"
},
{
"Instances": [],
"Confidence": 90.62931060791016,
"Parents": [
{
"Name": "Person"
}
],
"Name": "Pedestrian"
},
{
"Instances": [],
"Confidence": 88.81334686279297,
"Parents": [],
"Name": "Asphalt"
},
{
"Instances": [],
"Confidence": 88.81334686279297,
"Parents": [],
"Name": "Tarmac"
},
{
"Instances": [],
"Confidence": 88.23201751708984,
"Parents": [],
"Name": "Path"
},
{
"Instances": [],
"Confidence": 80.26520538330078,
"Parents": [],
"Name": "Urban"
},
{
"Instances": [],
"Confidence": 80.26520538330078,
"Parents": [
{
"Name": "Building"
},
{
"Name": "Urban"
}
],
"Name": "Town"
},
{
"Instances": [],
"Confidence": 80.26520538330078,
"Parents": [],
"Name": "Building"
},
{
"Instances": [],
"Confidence": 80.26520538330078,
"Parents": [
{
"Name": "Building"
},
{
"Name": "Urban"
}
],
"Name": "City"
},
{
"Instances": [],
"Confidence": 78.37934875488281,
"Parents": [
{
"Name": "Car"
},
{
"Name": "Vehicle"
},
{
"Name": "Transportation"
}
],
"Name": "Parking Lot"
},
{
"Instances": [],
"Confidence": 78.37934875488281,
"Parents": [
{
"Name": "Car"
},
{
"Name": "Vehicle"
},
{
"Name": "Transportation"
}
],
"Name": "Parking"
},
{
"Instances": [],
"Confidence": 74.37590026855469,
"Parents": [
{
"Name": "Building"
},
{
"Name": "Urban"
},
{
"Name": "City"
}
],
"Name": "Downtown"
},
{
"Instances": [],
"Confidence": 69.84622955322266,
"Parents": [
{
"Name": "Road"
}
],
"Name": "Intersection"
},
{
"Instances": [],
"Confidence": 57.68518829345703,
"Parents": [
{
"Name": "Sports Car"
},
{
"Name": "Car"
},
{
"Name": "Vehicle"
},
{
"Name": "Transportation"
}
],
"Name": "Coupe"
},
{
"Instances": [],
"Confidence": 57.68518829345703,
"Parents": [
{
"Name": "Car"
},
{
"Name": "Vehicle"
},
{
"Name": "Transportation"
}
],
"Name": "Sports Car"
},
{
"Instances": [],
"Confidence": 56.59492111206055,
"Parents": [
{
"Name": "Path"
}
],
"Name": "Sidewalk"
},
{
"Instances": [],
"Confidence": 56.59492111206055,
"Parents": [
{
"Name": "Path"
}
],
"Name": "Pavement"
},
{
"Instances": [],
"Confidence": 55.58770751953125,
"Parents": [
{
"Name": "Building"
},
{
"Name": "Urban"
}
],
"Name": "Neighborhood"
}
],
"LabelModelVersion": "2.0"
}
```
詳細については、「Amazon Rekognition 開発者ガイド」の「[イメージ内のラベルの検出](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detect-labels-image.html)」を参照してください。**
+ API の詳細については、AWS CLI コマンドリファレンスの「[DetectLabels](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/detect-labels.html)」を参照してください。**
------
#### [ Java ]
**SDK for Java 2.x**
GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples)での設定と実行の方法を確認してください。
```
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.*;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.InputStream;
import java.util.List;
/**
* Before running this Java V2 code example, set up your development
* environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class DetectLabels {
public static void main(String[] args) {
final String usage = """
Usage:
Where:
bucketName - The name of the Amazon S3 bucket where the image is stored
sourceImage - The name of the image file (for example, pic1.png).\s
""";
if (args.length != 2) {
System.out.println(usage);
System.exit(1);
}
String bucketName = args[0] ;
String sourceImage = args[1] ;
Region region = Region.US_WEST_2;
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.build();
detectImageLabels(rekClient, bucketName, sourceImage);
rekClient.close();
}
/**
* Detects the labels in an image stored in an Amazon S3 bucket using the Amazon Rekognition service.
*
* @param rekClient the Amazon Rekognition client used to make the detection request
* @param bucketName the name of the Amazon S3 bucket where the image is stored
* @param sourceImage the name of the image file to be analyzed
*/
public static void detectImageLabels(RekognitionClient rekClient, String bucketName, String sourceImage) {
try {
S3Object s3ObjectTarget = S3Object.builder()
.bucket(bucketName)
.name(sourceImage)
.build();
Image souImage = Image.builder()
.s3Object(s3ObjectTarget)
.build();
DetectLabelsRequest detectLabelsRequest = DetectLabelsRequest.builder()
.image(souImage)
.maxLabels(10)
.build();
DetectLabelsResponse labelsResponse = rekClient.detectLabels(detectLabelsRequest);
List