文件 AWS 開發套件範例 GitHub 儲存庫中有更多可用的 [AWS SDK 範例](https://github.com/awsdocs/aws-doc-sdk-examples)。
本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。
# 使用 AWS SDKs Amazon Rekognition 程式碼範例
下列程式碼範例示範如何使用 Amazon Rekognition 搭配 AWS 軟體開發套件 (SDK)。
*Actions* 是大型程式的程式碼摘錄，必須在內容中執行。雖然動作會告訴您如何呼叫個別服務函數，但您可以在其相關情境中查看內容中的動作。
*案例*是向您展示如何呼叫服務中的多個函數或與其他 AWS 服務組合來完成特定任務的程式碼範例。
**其他資源**
+ **[Amazon Rekognition 開發人員指南](https://docs.aws.amazon.com/rekognition/latest/dg/what-is.html)** – Amazon Rekognition 的詳細資訊。
+ **[Amazon Rekognition API 參考](https://docs.aws.amazon.com/rekognition/latest/APIReference/Welcome.html)** – 所有可用 Amazon Rekognition 動作的詳細資訊。
+ **[AWS 開發人員中心](https://aws.amazon.com/developer/code-examples/?awsf.sdk-code-examples-product=product%23rekognition)** – 您可以依類別或全文搜尋篩選的程式碼範例。
+ **[AWS SDK 範例](https://github.com/awsdocs/aws-doc-sdk-examples)** – GitHub 儲存庫使用慣用語言的完整程式碼。包含設定和執行程式碼的指示。
**Contents**
+ [基本概念](rekognition_code_examples_basics.md)
+ [Hello Amazon Rekognition](rekognition_example_rekognition_Hello_section.md)
+ [動作](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)
+ [案例](rekognition_code_examples_scenarios.md)
+ [建立集合並在其中尋找人臉](rekognition_example_rekognition_Usage_FindFacesInCollection_section.md)
+ [建立無伺服器應用程式來管理相片](rekognition_example_cross_PAM_section.md)
+ [偵測映像中的 PPE](rekognition_example_cross_RekognitionPhotoAnalyzerPPE_section.md)
+ [偵測並顯示映像中的元素](rekognition_example_rekognition_Usage_DetectAndDisplayImage_section.md)
+ [偵測映像中的人臉](rekognition_example_cross_DetectFaces_section.md)
+ [偵測映像中的資訊](rekognition_example_rekognition_VideoDetection_section.md)
+ [偵測映像中的物件](rekognition_example_cross_RekognitionPhotoAnalyzer_section.md)
+ [偵測映像中的人物和物件](rekognition_example_cross_RekognitionVideoDetection_section.md)
+ [儲存 EXIF 和其他映像資訊](rekognition_example_cross_DetectLabels_section.md)
# 使用 AWS SDKs Amazon Rekognition 基本範例
下列程式碼範例示範如何搭配 AWS SDK 使用 Amazon Rekognition 的基本功能。
**Contents**
+ [Hello Amazon Rekognition](rekognition_example_rekognition_Hello_section.md)
+ [動作](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
下列程式碼範例示範如何開始使用 Amazon Rekognition。
------
#### [ C\$1\$1 ]
**適用於 C\$1\$1 的 SDK**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/cpp/example_code/rekognition/hello_rekognition#code-examples)中設定和執行。
CMakeLists.txt CMake 檔案的程式碼。
```
# 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 來源檔案的程式碼。
```
#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;
}
```
+ 如需 API 詳細資訊，請參閱《適用於 C\$1\$1 的 AWS SDK API 參考》**中的 [ListCollections](https://docs.aws.amazon.com/goto/SdkForCpp/rekognition-2016-06-27/ListCollections)。
------
# 使用 AWS SDKs的 Amazon Rekognition 動作
下列程式碼範例示範如何使用 AWS SDKs 執行個別 Amazon Rekognition 動作。每個範例均包含 GitHub 的連結，您可以在連結中找到設定和執行程式碼的相關說明。
這些摘錄會呼叫 Amazon Rekognition API，是必須在內容中執行的大型程式的程式碼摘錄。您可以在 [使用 AWS SDKs Amazon Rekognition 案例](rekognition_code_examples_scenarios.md) 中查看內容中的動作。
下列範例僅包含最常使用的動作。如需完整清單，請參閱 [Amazon Rekognition API 參考](https://docs.aws.amazon.com/rekognition/latest/APIReference/Welcome.html)。
**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)
# `CompareFaces` 搭配 AWS SDK 或 CLI 使用
下列程式碼範例示範如何使用 `CompareFaces`。
如需詳細資訊，請參閱[比較映像中的人臉](https://docs.aws.amazon.com/rekognition/latest/dg/faces-comparefaces.html)。
------
#### [ .NET ]
**適用於 .NET 的 SDK**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples)中設定和執行。
```
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.");
}
}
```
+ 如需 API 詳細資訊，請參閱《適用於 .NET 的 AWS SDK API 參考》**中的 [CompareFaces](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/CompareFaces)。
------
#### [ CLI ]
**AWS CLI**
**比較兩個影像中的人臉**
下列 `compare-faces` 命令會比較存放在 Amazon S3 儲存貯體中兩個影像中的人臉。
```
aws rekognition compare-faces \
--source-image '{"S3Object":{"Bucket":"MyImageS3Bucket","Name":"source.jpg"}}' \
--target-image '{"S3Object":{"Bucket":"MyImageS3Bucket","Name":"target.jpg"}}'
```
輸出：
```
{
"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
}
}
```
如需詳細資訊，請參閱《Amazon Rekognition 開發人員指南》**中的[比較影像中的人臉](https://docs.aws.amazon.com/rekognition/latest/dg/faces-comparefaces.html)。
+ 如需 API 詳細資訊，請參閱《AWS CLI 命令參考》**中的 [CompareFaces](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/compare-faces.html)。
------
#### [ Java ]
**SDK for Java 2.x**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples)中設定和執行。
```
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);
}
}
}
```
+ 如需 API 詳細資訊，請參閱《AWS SDK for Java 2.x API 參考》**中的 [CompareFaces](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/CompareFaces)。
------
#### [ Kotlin ]
**適用於 Kotlin 的 SDK**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples)中設定和執行。
```
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}")
}
}
```
+ 如需 API 詳細資訊，請參閱《AWS 適用於 Kotlin 的 SDK API 參考》**中的 [CompareFaces](https://sdk.amazonaws.com/kotlin/api/latest/index.html)。
------
#### [ Python ]
**適用於 Python (Boto3) 的 SDK**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples)中設定和執行。
```
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
```
+ 如需 API 詳細資訊，請參閱《AWS 適用於 Python (Boto3) 的 SDK API 參考》**中的 [CompareFaces](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/CompareFaces)。
------
#### [ SAP ABAP ]
**適用於 SAP ABAP 的開發套件**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples)中設定和執行。
```
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.
```
+ 如需 API 詳細資訊，請參閱《適用於 *AWS SAP ABAP 的 SDK API 參考*》中的 [CompareFaces](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)。
------
# `CreateCollection` 搭配 AWS SDK 或 CLI 使用
下列程式碼範例示範如何使用 `CreateCollection`。
如需更多資訊，請參閱[建立集合](https://docs.aws.amazon.com/rekognition/latest/dg/create-collection-procedure.html)。
------
#### [ .NET ]
**適用於 .NET 的 SDK**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples)中設定和執行。
```
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}");
}
}
```
+ 如需 API 詳細資訊，請參閱《適用於 .NET 的 AWS SDK API 參考》**中的 [CreateCollection](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/CreateCollection)。
------
#### [ CLI ]
**AWS CLI**
**建立集合**
下列 `create-collection` 命令會建立具有指定名稱的集合。
```
aws rekognition create-collection \
--collection-id "MyCollection"
```
輸出：
```
{
"CollectionArn": "aws:rekognition:us-west-2:123456789012:collection/MyCollection",
"FaceModelVersion": "4.0",
"StatusCode": 200
}
```
如需詳細資訊，請參閱《*Amazon Rekognition 開發人員指南*》中的[建立集合](https://docs.aws.amazon.com/rekognition/latest/dg/create-collection-procedure.html)。
+ 如需 API 詳細資訊，請參閱《AWS CLI 命令參考》**中的 [CreateCollection](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/create-collection.html)。
------
#### [ Java ]
**SDK for Java 2.x**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples)中設定和執行。
```
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);
}
}
}
```
+ 如需 API 詳細資訊，請參閱《AWS SDK for Java 2.x API 參考》**中的 [CreateCollection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/CreateCollection)。
------
#### [ Kotlin ]
**適用於 Kotlin 的 SDK **
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples)中設定和執行。
```
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}")
}
}
```
+ 如需 API 詳細資訊，請參閱《AWS 適用於 Kotlin 的 SDK API 參考》**中的 [CreateCollection](https://sdk.amazonaws.com/kotlin/api/latest/index.html)。
------
#### [ Python ]
**適用於 Python 的 SDK (Boto3)**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples)中設定和執行。
```
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
```
+ 如需 API 詳細資訊，請參閱《AWS 適用於 Python (Boto3) 的 SDK API 參考》**中的 [CreateCollection](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/CreateCollection)。
------
#### [ SAP ABAP ]
**適用於 SAP ABAP 的開發套件**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples)中設定和執行。
```
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.
```
+ 如需 API 詳細資訊，請參閱《適用於 *AWS SAP ABAP 的 SDK API 參考*》中的 [CreateCollection](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)。
------
# `DeleteCollection` 搭配 AWS SDK 或 CLI 使用
下列程式碼範例示範如何使用 `DeleteCollection`。
如需更多資訊，請參閱[刪除集合](https://docs.aws.amazon.com/rekognition/latest/dg/delete-collection-procedure.html)。
------
#### [ .NET ]
**適用於 .NET 的 SDK**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples)中設定和執行。
```
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}");
}
}
```
+ 如需 API 詳細資訊，請參閱《適用於 .NET 的 AWS SDK API 參考》**中的 [DeleteCollection](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/DeleteCollection)。
------
#### [ CLI ]
**AWS CLI**
**刪除集合**
下列 `delete-collection` 命令會刪除指定的集合。
```
aws rekognition delete-collection \
--collection-id MyCollection
```
輸出：
```
{
"StatusCode": 200
}
```
如需詳細資訊，請參閱《*Amazon Rekognition 開發人員指南*》中的[刪除集合](https://docs.aws.amazon.com/rekognition/latest/dg/delete-collection-procedure.html)。
+ 如需 API 詳細資訊，請參閱《AWS CLI 命令參考》**中的 [DeleteCollection](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/delete-collection.html)。
------
#### [ Java ]
**SDK for Java 2.x**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples)中設定和執行。
```
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);
}
}
}
```
+ 如需 API 詳細資訊，請參閱《AWS SDK for Java 2.x API 參考》**中的 [DeleteCollection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DeleteCollection)。
------
#### [ Kotlin ]
**適用於 Kotlin 的 SDK **
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples)中設定和執行。
```
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}")
}
}
```
+ 如需 API 詳細資訊，請參閱《AWS 適用於 Kotlin 的 SDK API 參考》**中的 [DeleteCollection](https://sdk.amazonaws.com/kotlin/api/latest/index.html)。
------
#### [ Python ]
**適用於 Python 的 SDK (Boto3)**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples)中設定和執行。
```
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
```
+ 如需 API 詳細資訊，請參閱《AWS 適用於 Python (Boto3) 的 API 參考》**中的 [DeleteCollection](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DeleteCollection)。
------
#### [ SAP ABAP ]
**適用於 SAP ABAP 的開發套件**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples)中設定和執行。
```
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.
```
+ 如需 API 詳細資訊，請參閱《適用於 *AWS SAP ABAP 的 SDK API 參考*》中的 [DeleteCollection](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)。
------
# `DeleteFaces` 搭配 AWS SDK 或 CLI 使用
下列程式碼範例示範如何使用 `DeleteFaces`。
如需詳細資訊，請參閱[從集合中刪除人臉](https://docs.aws.amazon.com/rekognition/latest/dg/delete-faces-procedure.html)。
------
#### [ .NET ]
**適用於 .NET 的 SDK**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples)中設定和執行。
```
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}");
});
}
}
```
+ 如需 API 詳細資訊，請參閱《適用於 .NET 的 AWS SDK API 參考》**中的 [DeleteFaces](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/DeleteFaces)。
------
#### [ CLI ]
**AWS CLI**
**從集合中刪除人臉**
下列 `delete-faces` 命令會從集合中刪除指定的人臉。
```
aws rekognition delete-faces \
--collection-id MyCollection
--face-ids '["0040279c-0178-436e-b70a-e61b074e96b0"]'
```
輸出：
```
{
"DeletedFaces": [
"0040279c-0178-436e-b70a-e61b074e96b0"
]
}
```
如需詳細資訊，請參閱《Amazon Rekognition 開發人員指南》**中的[從集合中刪除人臉](https://docs.aws.amazon.com/rekognition/latest/dg/delete-faces-procedure.html)。
+ 如需 API 詳細資訊，請參閱《AWS CLI 命令參考》**中的 [DeleteFaces](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/delete-faces.html)。
------
#### [ Java ]
**SDK for Java 2.x**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples)中設定和執行。
```
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);
}
}
}
```
+ 如需 API 詳細資訊，請參閱《AWS SDK for Java 2.x API 參考》**中的 [DeleteFaces](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DeleteFaces)。
------
#### [ Kotlin ]
**適用於 Kotlin 的 SDK **
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples)中設定和執行。
```
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")
}
}
```
+ 如需 API 詳細資訊，請參閱《AWS 適用於 Kotlin 的 SDK API 參考》**中的 [DeleteFaces](https://sdk.amazonaws.com/kotlin/api/latest/index.html)。
------
#### [ Python ]
**適用於 Python 的 SDK (Boto3)**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples)中設定和執行。
```
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
```
+ 如需 API 詳細資訊，請參閱《AWS 適用於 Python (Boto3) 的 SDK API 參考》**中的 [DeleteFaces](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DeleteFaces)。
------
#### [ SAP ABAP ]
**適用於 SAP ABAP 的開發套件**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples)中設定和執行。
```
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.
```
+ 如需 API 詳細資訊，請參閱《適用於 *AWS SAP ABAP 的 SDK API 參考*》中的 [DeleteFaces](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)。
------
# `DescribeCollection` 搭配 AWS SDK 或 CLI 使用
下列程式碼範例示範如何使用 `DescribeCollection`。
如需詳細資訊，請參閱[描述集合](https://docs.aws.amazon.com/rekognition/latest/dg/describe-collection-procedure.html)。
------
#### [ .NET ]
**適用於 .NET 的 SDK**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples)中設定和執行。
```
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}");
}
}
```
+ 如需 API 詳細資訊，請參閱《適用於 .NET 的 AWS SDK API 參考》**中的 [DescribeCollection](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/DescribeCollection)。
------
#### [ CLI ]
**AWS CLI**
**描述集合**
下列 `describe-collection` 範例顯示指定集合的詳細資訊。
```
aws rekognition describe-collection \
--collection-id MyCollection
```
輸出：
```
{
"FaceCount": 200,
"CreationTimestamp": 1569444828.274,
"CollectionARN": "arn:aws:rekognition:us-west-2:123456789012:collection/MyCollection",
"FaceModelVersion": "4.0"
}
```
如需詳細資訊，請參閱《*Amazon Rekognition 開發人員指南*》中的[描述集合](https://docs.aws.amazon.com/rekognition/latest/dg/describe-collection-procedure.html)。
+ 如需 API 詳細資訊，請參閱《AWS CLI 命令參考》**中的 [DescribeCollection](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/describe-collection.html)。
------
#### [ Java ]
**SDK for Java 2.x**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples)中設定和執行。
```
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);
}
}
}
```
+ 如需 API 詳細資訊，請參閱《AWS SDK for Java 2.x API 參考》**中的 [DescribeCollection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DescribeCollection)。
------
#### [ Kotlin ]
**適用於 Kotlin 的 SDK **
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples)中設定和執行。
```
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}")
}
}
```
+ 如需 API 詳細資訊，請參閱《適用於 *AWS Kotlin 的 SDK API 參考*》中的 [DescribeCollection](https://sdk.amazonaws.com/kotlin/api/latest/index.html)。
------
#### [ Python ]
**適用於 Python 的 SDK (Boto3)**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples)中設定和執行。
```
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()
```
+ 如需 API 詳細資訊，請參閱《AWS SDK for Python (Boto3) API 參考》**中的 [DescribeCollection](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DescribeCollection)。
------
#### [ SAP ABAP ]
**適用於 SAP ABAP 的開發套件**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples)中設定和執行。
```
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.
```
+ 如需 API 詳細資訊，請參閱《適用於 *AWS SAP ABAP 的 SDK API 參考*》中的 [DescribeCollection](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)。
------
# `DetectFaces` 搭配 AWS SDK 或 CLI 使用
下列程式碼範例示範如何使用 `DetectFaces`。
如需詳細資訊，請參閱[在映像中偵測人臉](https://docs.aws.amazon.com/rekognition/latest/dg/faces-detect-images.html)。
------
#### [ .NET ]
**適用於 .NET 的 SDK**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples)中設定和執行。
```
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($"Estimated age is between {face.AgeRange.Low} and {face.AgeRange.High} years old.");
}
}
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
}
}
}
```
顯示映像中所有人臉的邊界框資訊。
```
using System;
using System.Collections.Generic;
using System.Drawing;
using System.IO;
using System.Threading.Tasks;
using Amazon.Rekognition;
using Amazon.Rekognition.Model;
///
/// Uses the Amazon Rekognition Service to display the details of the
/// bounding boxes around the faces detected in an image.
///
public class ImageOrientationBoundingBox
{
public static async Task Main()
{
string photo = @"D:\Development\AWS-Examples\Rekognition\target.jpg"; // "photo.jpg";
var rekognitionClient = new AmazonRekognitionClient();
var image = new Amazon.Rekognition.Model.Image();
try
{
using var fs = new FileStream(photo, FileMode.Open, FileAccess.Read);
byte[] data = null;
data = new byte[fs.Length];
fs.Read(data, 0, (int)fs.Length);
image.Bytes = new MemoryStream(data);
}
catch (Exception)
{
Console.WriteLine("Failed to load file " + photo);
return;
}
int height;
int width;
// Used to extract original photo width/height
using (var imageBitmap = new Bitmap(photo))
{
height = imageBitmap.Height;
width = imageBitmap.Width;
}
Console.WriteLine("Image Information:");
Console.WriteLine(photo);
Console.WriteLine("Image Height: " + height);
Console.WriteLine("Image Width: " + width);
try
{
var detectFacesRequest = new DetectFacesRequest()
{
Image = image,
Attributes = new List() { "ALL" },
};
DetectFacesResponse detectFacesResponse = await rekognitionClient.DetectFacesAsync(detectFacesRequest);
detectFacesResponse.FaceDetails.ForEach(face =>
{
Console.WriteLine("Face:");
ShowBoundingBoxPositions(
height,
width,
face.BoundingBox,
detectFacesResponse.OrientationCorrection);
Console.WriteLine($"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}");
}
}
```
+ 如需 API 詳細資訊，請參閱《適用於 .NET 的 AWS SDK API 參考》**中的 [DetectFaces](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/DetectFaces)。
------
#### [ CLI ]
**AWS CLI**
**偵測影像中的人臉**
下列 `detect-faces` 命令會偵測存放在 Amazon S3 儲存貯體中指定影像中的人臉。
```
aws rekognition detect-faces \
--image '{"S3Object":{"Bucket":"MyImageS3Bucket","Name":"MyFriend.jpg"}}' \
--attributes "ALL"
```
輸出：
```
{
"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
}
}
]
}
```
如需詳細資訊，請參閱《Amazon Rekognition 開發人員指南》**中的[偵測影像中的人臉](https://docs.aws.amazon.com/rekognition/latest/dg/faces-detect-images.html)。
+ 如需 API 詳細資訊，請參閱《AWS CLI 命令參考》**中的 [DetectFaces](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/detect-faces.html)。
------
#### [ Java ]
**SDK for Java 2.x**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples)中設定和執行。
```
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);
}
}
}
```
+ 如需 API 詳細資訊，請參閱《AWS SDK for Java 2.x API 參考》**中的 [DetectFaces](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DetectFaces)。
------
#### [ Kotlin ]
**適用於 Kotlin 的 SDK **
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples)中設定和執行。
```
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}")
}
}
}
```
+ 如需 API 詳細資訊，請參閱《AWS 適用於 Kotlin 的 SDK API 參考》**中的 [DetectFaces](https://sdk.amazonaws.com/kotlin/api/latest/index.html)。
------
#### [ Python ]
**適用於 Python 的 SDK (Boto3)**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples)中設定和執行。
```
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
```
+ 如需 API 詳細資訊，請參閱《AWS 適用於 Python (Boto3) 的 SDK API 參考》**中的 [DetectFaces](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DetectFaces)。
------
#### [ SAP ABAP ]
**適用於 SAP ABAP 的開發套件**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples)中設定和執行。
```
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.
```
+ 如需 API 詳細資訊，請參閱《適用於 *AWS SAP ABAP 的 SDK API 參考*》中的 [DetectFaces](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)。
------
# `DetectLabels` 搭配 AWS SDK 或 CLI 使用
下列程式碼範例示範如何使用 `DetectLabels`。
如需詳細資訊，請參閱[偵測映像中的標籤](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detect-labels-image.html)。
------
#### [ .NET ]
**適用於 .NET 的 SDK**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples)中設定和執行。
```
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);
}
}
}
```
偵測儲存於您計算機的映像檔案中的標籤。
```
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);
}
}
}
```
+ 如需 API 詳細資訊，請參閱《適用於 .NET 的 AWS SDK API 參考》**中的 [DetectLabels](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/DetectLabels)。
------
#### [ C\$1\$1 ]
**適用於 C\$1\$1 的 SDK**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/cpp/example_code/rekognition#code-examples)中設定和執行。
```
//! 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 &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();
}
```
+ 如需 API 詳細資訊，請參閱《適用於 C\$1\$1 的 AWS SDK API 參考》**中的 [DetectLabels](https://docs.aws.amazon.com/goto/SdkForCpp/rekognition-2016-06-27/DetectLabels)。
------
#### [ CLI ]
**AWS CLI**
**偵測影像中的標籤**
下列 `detect-labels` 範例會偵測存放在 Amazon S3 儲存貯體之影像中的場景和物件。
```
aws rekognition detect-labels \
--image '{"S3Object":{"Bucket":"bucket","Name":"image"}}'
```
輸出：
```
{
"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"
}
```
如需詳細資訊，請參閱《*Amazon Rekognition 開發人員指南*》中的[偵測影像中的標籤](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detect-labels-image.html)。
+ 如需 API 詳細資訊，請參閱《AWS CLI 命令參考》**中的 [DetectLabels](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/detect-labels.html)。
------
#### [ Java ]
**SDK for Java 2.x**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples)中設定和執行。
```
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