There are more AWS SDK examples available in the [AWS Doc SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples) GitHub repo.
# Code examples for Amazon Rekognition using AWS SDKs
The following code examples show you how to use Amazon Rekognition with an AWS software development kit (SDK).
*Actions* are code excerpts from larger programs and must be run in context. While actions show you how to call individual service functions, you can see actions in context in their related scenarios.
*Scenarios* are code examples that show you how to accomplish specific tasks by calling multiple functions within a service or combined with other AWS services.
**More resources**
+ **[ Amazon Rekognition Developer Guide](https://docs.aws.amazon.com/rekognition/latest/dg/what-is.html)** – More information about Amazon Rekognition.
+ **[Amazon Rekognition API Reference](https://docs.aws.amazon.com/rekognition/latest/APIReference/Welcome.html)** – Details about all available Amazon Rekognition actions.
+ **[AWS Developer Center](https://aws.amazon.com/developer/code-examples/?awsf.sdk-code-examples-product=product%23rekognition)** – Code examples that you can filter by category or full-text search.
+ **[AWS SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples)** – GitHub repo with complete code in preferred languages. Includes instructions for setting up and running the code.
**Contents**
+ [Basics](rekognition_code_examples_basics.md)
+ [Hello Amazon Rekognition](rekognition_example_rekognition_Hello_section.md)
+ [Actions](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)
+ [Scenarios](rekognition_code_examples_scenarios.md)
+ [Build a collection and find faces in it](rekognition_example_rekognition_Usage_FindFacesInCollection_section.md)
+ [Create a serverless application to manage photos](rekognition_example_cross_PAM_section.md)
+ [Detect PPE in images](rekognition_example_cross_RekognitionPhotoAnalyzerPPE_section.md)
+ [Detect and display elements in images](rekognition_example_rekognition_Usage_DetectAndDisplayImage_section.md)
+ [Detect faces in an image](rekognition_example_cross_DetectFaces_section.md)
+ [Detect information in videos](rekognition_example_rekognition_VideoDetection_section.md)
+ [Detect objects in images](rekognition_example_cross_RekognitionPhotoAnalyzer_section.md)
+ [Detect people and objects in a video](rekognition_example_cross_RekognitionVideoDetection_section.md)
+ [Save EXIF and other image information](rekognition_example_cross_DetectLabels_section.md)
# Basic examples for Amazon Rekognition using AWS SDKs
The following code examples show how to use the basics of Amazon Rekognition with AWS SDKs.
**Contents**
+ [Hello Amazon Rekognition](rekognition_example_rekognition_Hello_section.md)
+ [Actions](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
The following code example shows how to get started using Amazon Rekognition.
------
#### [ C\$1\$1 ]
**SDK for C\$1\$1**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/cpp/example_code/rekognition/hello_rekognition#code-examples).
Code for the CMakeLists.txt CMake file.
```
# 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})
```
Code for the hello\$1rekognition.cpp source file.
```
#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;
}
```
+ For API details, see [ListCollections](https://docs.aws.amazon.com/goto/SdkForCpp/rekognition-2016-06-27/ListCollections) in *AWS SDK for C\$1\$1 API Reference*.
------
# Actions for Amazon Rekognition using AWS SDKs
The following code examples demonstrate how to perform individual Amazon Rekognition actions with AWS SDKs. Each example includes a link to GitHub, where you can find instructions for setting up and running the code.
These excerpts call the Amazon Rekognition API and are code excerpts from larger programs that must be run in context. You can see actions in context in [Scenarios for Amazon Rekognition using AWS SDKs](rekognition_code_examples_scenarios.md).
The following examples include only the most commonly used actions. For a complete list, see the [Amazon Rekognition API Reference](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)
# Use `CompareFaces` with an AWS SDK or CLI
The following code examples show how to use `CompareFaces`.
For more information, see [Comparing faces in images](https://docs.aws.amazon.com/rekognition/latest/dg/faces-comparefaces.html).
------
#### [ .NET ]
**SDK for .NET**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples).
```
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.");
}
}
```
+ For API details, see [CompareFaces](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/CompareFaces) in *AWS SDK for .NET API Reference*.
------
#### [ CLI ]
**AWS CLI**
**To compare faces in two images**
The following `compare-faces` command compares faces in two images stored in an Amazon S3 bucket.
```
aws rekognition compare-faces \
--source-image '{"S3Object":{"Bucket":"MyImageS3Bucket","Name":"source.jpg"}}' \
--target-image '{"S3Object":{"Bucket":"MyImageS3Bucket","Name":"target.jpg"}}'
```
Output:
```
{
"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
}
}
```
For more information, see [Comparing Faces in Images](https://docs.aws.amazon.com/rekognition/latest/dg/faces-comparefaces.html) in the *Amazon Rekognition Developer Guide*.
+ For API details, see [CompareFaces](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/compare-faces.html) in *AWS CLI Command Reference*.
------
#### [ Java ]
**SDK for Java 2.x**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples).
```
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);
}
}
}
```
+ For API details, see [CompareFaces](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/CompareFaces) in *AWS SDK for Java 2.x API Reference*.
------
#### [ Kotlin ]
**SDK for Kotlin**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples).
```
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}")
}
}
```
+ For API details, see [CompareFaces](https://sdk.amazonaws.com/kotlin/api/latest/index.html) in *AWS SDK for Kotlin API reference*.
------
#### [ Python ]
**SDK for Python (Boto3)**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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
```
+ For API details, see [CompareFaces](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/CompareFaces) in *AWS SDK for Python (Boto3) API Reference*.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples).
```
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.
```
+ For API details, see [CompareFaces](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*.
------
# Use `CreateCollection` with an AWS SDK or CLI
The following code examples show how to use `CreateCollection`.
For more information, see [Creating a collection](https://docs.aws.amazon.com/rekognition/latest/dg/create-collection-procedure.html).
------
#### [ .NET ]
**SDK for .NET**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples).
```
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}");
}
}
```
+ For API details, see [CreateCollection](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/CreateCollection) in *AWS SDK for .NET API Reference*.
------
#### [ CLI ]
**AWS CLI**
**To create a collection**
The following `create-collection` command creates a collection with the specified name.
```
aws rekognition create-collection \
--collection-id "MyCollection"
```
Output:
```
{
"CollectionArn": "aws:rekognition:us-west-2:123456789012:collection/MyCollection",
"FaceModelVersion": "4.0",
"StatusCode": 200
}
```
For more information, see [Creating a Collection](https://docs.aws.amazon.com/rekognition/latest/dg/create-collection-procedure.html) in the *Amazon Rekognition Developer Guide*.
+ For API details, see [CreateCollection](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/create-collection.html) in *AWS CLI Command Reference*.
------
#### [ Java ]
**SDK for Java 2.x**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples).
```
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);
}
}
}
```
+ For API details, see [CreateCollection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/CreateCollection) in *AWS SDK for Java 2.x API Reference*.
------
#### [ Kotlin ]
**SDK for Kotlin**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples).
```
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}")
}
}
```
+ For API details, see [CreateCollection](https://sdk.amazonaws.com/kotlin/api/latest/index.html) in *AWS SDK for Kotlin API reference*.
------
#### [ Python ]
**SDK for Python (Boto3)**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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
```
+ For API details, see [CreateCollection](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/CreateCollection) in *AWS SDK for Python (Boto3) API Reference*.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples).
```
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.
```
+ For API details, see [CreateCollection](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*.
------
# Use `DeleteCollection` with an AWS SDK or CLI
The following code examples show how to use `DeleteCollection`.
For more information, see [Deleting a collection](https://docs.aws.amazon.com/rekognition/latest/dg/delete-collection-procedure.html).
------
#### [ .NET ]
**SDK for .NET**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples).
```
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}");
}
}
```
+ For API details, see [DeleteCollection](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/DeleteCollection) in *AWS SDK for .NET API Reference*.
------
#### [ CLI ]
**AWS CLI**
**To delete a collection**
The following `delete-collection` command deletes the specified collection.
```
aws rekognition delete-collection \
--collection-id MyCollection
```
Output:
```
{
"StatusCode": 200
}
```
For more information, see [Deleting a Collection](https://docs.aws.amazon.com/rekognition/latest/dg/delete-collection-procedure.html) in the *Amazon Rekognition Developer Guide*.
+ For API details, see [DeleteCollection](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/delete-collection.html) in *AWS CLI Command Reference*.
------
#### [ Java ]
**SDK for Java 2.x**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples).
```
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);
}
}
}
```
+ For API details, see [DeleteCollection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DeleteCollection) in *AWS SDK for Java 2.x API Reference*.
------
#### [ Kotlin ]
**SDK for Kotlin**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples).
```
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}")
}
}
```
+ For API details, see [DeleteCollection](https://sdk.amazonaws.com/kotlin/api/latest/index.html) in *AWS SDK for Kotlin API reference*.
------
#### [ Python ]
**SDK for Python (Boto3)**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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
```
+ For API details, see [DeleteCollection](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DeleteCollection) in *AWS SDK for Python (Boto3) API Reference*.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples).
```
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.
```
+ For API details, see [DeleteCollection](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*.
------
# Use `DeleteFaces` with an AWS SDK or CLI
The following code examples show how to use `DeleteFaces`.
For more information, see [Deleting faces from a collection](https://docs.aws.amazon.com/rekognition/latest/dg/delete-faces-procedure.html).
------
#### [ .NET ]
**SDK for .NET**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples).
```
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}");
});
}
}
```
+ For API details, see [DeleteFaces](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/DeleteFaces) in *AWS SDK for .NET API Reference*.
------
#### [ CLI ]
**AWS CLI**
**To delete faces from a collection**
The following `delete-faces` command deletes the specified face from a collection.
```
aws rekognition delete-faces \
--collection-id MyCollection
--face-ids '["0040279c-0178-436e-b70a-e61b074e96b0"]'
```
Output:
```
{
"DeletedFaces": [
"0040279c-0178-436e-b70a-e61b074e96b0"
]
}
```
For more information, see [Deleting Faces from a Collection](https://docs.aws.amazon.com/rekognition/latest/dg/delete-faces-procedure.html) in the *Amazon Rekognition Developer Guide*.
+ For API details, see [DeleteFaces](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/delete-faces.html) in *AWS CLI Command Reference*.
------
#### [ Java ]
**SDK for Java 2.x**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples).
```
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);
}
}
}
```
+ For API details, see [DeleteFaces](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DeleteFaces) in *AWS SDK for Java 2.x API Reference*.
------
#### [ Kotlin ]
**SDK for Kotlin**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples).
```
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")
}
}
```
+ For API details, see [DeleteFaces](https://sdk.amazonaws.com/kotlin/api/latest/index.html) in *AWS SDK for Kotlin API reference*.
------
#### [ Python ]
**SDK for Python (Boto3)**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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
```
+ For API details, see [DeleteFaces](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DeleteFaces) in *AWS SDK for Python (Boto3) API Reference*.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples).
```
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.
```
+ For API details, see [DeleteFaces](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*.
------
# Use `DescribeCollection` with an AWS SDK or CLI
The following code examples show how to use `DescribeCollection`.
For more information, see [Describing a collection](https://docs.aws.amazon.com/rekognition/latest/dg/describe-collection-procedure.html).
------
#### [ .NET ]
**SDK for .NET**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples).
```
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}");
}
}
```
+ For API details, see [DescribeCollection](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/DescribeCollection) in *AWS SDK for .NET API Reference*.
------
#### [ CLI ]
**AWS CLI**
**To describe a collection**
The following `describe-collection` example displays the details about the specified collection.
```
aws rekognition describe-collection \
--collection-id MyCollection
```
Output:
```
{
"FaceCount": 200,
"CreationTimestamp": 1569444828.274,
"CollectionARN": "arn:aws:rekognition:us-west-2:123456789012:collection/MyCollection",
"FaceModelVersion": "4.0"
}
```
For more information, see [Describing a Collection](https://docs.aws.amazon.com/rekognition/latest/dg/describe-collection-procedure.html) in the *Amazon Rekognition Developer Guide*.
+ For API details, see [DescribeCollection](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/describe-collection.html) in *AWS CLI Command Reference*.
------
#### [ Java ]
**SDK for Java 2.x**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples).
```
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);
}
}
}
```
+ For API details, see [DescribeCollection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DescribeCollection) in *AWS SDK for Java 2.x API Reference*.
------
#### [ Kotlin ]
**SDK for Kotlin**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples).
```
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}")
}
}
```
+ For API details, see [DescribeCollection](https://sdk.amazonaws.com/kotlin/api/latest/index.html) in *AWS SDK for Kotlin API reference*.
------
#### [ Python ]
**SDK for Python (Boto3)**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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()
```
+ For API details, see [DescribeCollection](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DescribeCollection) in *AWS SDK for Python (Boto3) API Reference*.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples).
```
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.
```
+ For API details, see [DescribeCollection](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*.
------
# Use `DetectFaces` with an AWS SDK or CLI
The following code examples show how to use `DetectFaces`.
For more information, see [Detecting faces in an image](https://docs.aws.amazon.com/rekognition/latest/dg/faces-detect-images.html).
------
#### [ .NET ]
**SDK for .NET**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples).
```
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($"Estimated age is between {face.AgeRange.Low} and {face.AgeRange.High} years old.");
}
}
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
}
}
}
```
Display bounding box information for all faces in an image.
```
using System;
using System.Collections.Generic;
using System.Drawing;
using System.IO;
using System.Threading.Tasks;
using Amazon.Rekognition;
using Amazon.Rekognition.Model;
///
/// Uses the Amazon Rekognition Service to display the details of the
/// bounding boxes around the faces detected in an image.
///
public class ImageOrientationBoundingBox
{
public static async Task Main()
{
string photo = @"D:\Development\AWS-Examples\Rekognition\target.jpg"; // "photo.jpg";
var rekognitionClient = new AmazonRekognitionClient();
var image = new Amazon.Rekognition.Model.Image();
try
{
using var fs = new FileStream(photo, FileMode.Open, FileAccess.Read);
byte[] data = null;
data = new byte[fs.Length];
fs.Read(data, 0, (int)fs.Length);
image.Bytes = new MemoryStream(data);
}
catch (Exception)
{
Console.WriteLine("Failed to load file " + photo);
return;
}
int height;
int width;
// Used to extract original photo width/height
using (var imageBitmap = new Bitmap(photo))
{
height = imageBitmap.Height;
width = imageBitmap.Width;
}
Console.WriteLine("Image Information:");
Console.WriteLine(photo);
Console.WriteLine("Image Height: " + height);
Console.WriteLine("Image Width: " + width);
try
{
var detectFacesRequest = new DetectFacesRequest()
{
Image = image,
Attributes = new List() { "ALL" },
};
DetectFacesResponse detectFacesResponse = await rekognitionClient.DetectFacesAsync(detectFacesRequest);
detectFacesResponse.FaceDetails.ForEach(face =>
{
Console.WriteLine("Face:");
ShowBoundingBoxPositions(
height,
width,
face.BoundingBox,
detectFacesResponse.OrientationCorrection);
Console.WriteLine($"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}");
}
}
```
+ For API details, see [DetectFaces](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/DetectFaces) in *AWS SDK for .NET API Reference*.
------
#### [ CLI ]
**AWS CLI**
**To detect faces in an image**
The following `detect-faces` command detects faces in the specified image stored in an Amazon S3 bucket.
```
aws rekognition detect-faces \
--image '{"S3Object":{"Bucket":"MyImageS3Bucket","Name":"MyFriend.jpg"}}' \
--attributes "ALL"
```
Output:
```
{
"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
}
}
]
}
```
For more information, see [Detecting Faces in an Image](https://docs.aws.amazon.com/rekognition/latest/dg/faces-detect-images.html) in the *Amazon Rekognition Developer Guide*.
+ For API details, see [DetectFaces](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/detect-faces.html) in *AWS CLI Command Reference*.
------
#### [ Java ]
**SDK for Java 2.x**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples).
```
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);
}
}
}
```
+ For API details, see [DetectFaces](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DetectFaces) in *AWS SDK for Java 2.x API Reference*.
------
#### [ Kotlin ]
**SDK for Kotlin**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples).
```
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}")
}
}
}
```
+ For API details, see [DetectFaces](https://sdk.amazonaws.com/kotlin/api/latest/index.html) in *AWS SDK for Kotlin API reference*.
------
#### [ Python ]
**SDK for Python (Boto3)**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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
```
+ For API details, see [DetectFaces](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DetectFaces) in *AWS SDK for Python (Boto3) API Reference*.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples).
```
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.
```
+ For API details, see [DetectFaces](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*.
------
# Use `DetectLabels` with an AWS SDK or CLI
The following code examples show how to use `DetectLabels`.
For more information, see [Detecting labels in an image](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detect-labels-image.html).
------
#### [ .NET ]
**SDK for .NET**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples).
```
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);
}
}
}
```
Detect labels in an image file stored on your computer.
```
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);
}
}
}
```
+ For API details, see [DetectLabels](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/DetectLabels) in *AWS SDK for .NET API Reference*.
------
#### [ C\$1\$1 ]
**SDK for C\$1\$1**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/cpp/example_code/rekognition#code-examples).
```
//! 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();
}
```
+ For API details, see [DetectLabels](https://docs.aws.amazon.com/goto/SdkForCpp/rekognition-2016-06-27/DetectLabels) in *AWS SDK for C\$1\$1 API Reference*.
------
#### [ CLI ]
**AWS CLI**
**To detect a label in an image**
The following `detect-labels` example detects scenes and objects in an image stored in an Amazon S3 bucket.
```
aws rekognition detect-labels \
--image '{"S3Object":{"Bucket":"bucket","Name":"image"}}'
```
Output:
```
{
"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"
}
```
For more information, see [Detecting Labels in an Image](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detect-labels-image.html) in the *Amazon Rekognition Developer Guide*.
+ For API details, see [DetectLabels](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/detect-labels.html) in *AWS CLI Command Reference*.
------
#### [ Java ]
**SDK for Java 2.x**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples).
```
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