Laravel package for AWS Rekognition API (PHP 8)
Amazon Rekognition is a cloud-based image and video analysis service that makes it easy to add advanced computer vision capabilities to your applications. The service is powered by proven deep learning technology and it requires no machine learning expertise to use. Amazon Rekognition includes a simple, easy-to-use API that can quickly analyze any image or video file that’s stored in Amazon S3.
- PHP 8.2 or higher
You can install the package via composer:
composer require moe-mizrak/aws-rekognitionYou can publish the aws-rekognition config file with:
php artisan vendor:publish --tag=aws-rekognitionThis is the contents of the published config file:
return [
'credentials' => [
'key' => env('AWS_ACCESS_KEY_ID'),
'secret' => env('AWS_SECRET_ACCESS_KEY'),
],
'region' => env('AWS_REGION', 'us-east-1'),
'version' => env('AWS_VERSION', 'latest'),
];After publishing the aws-rekognition config file, you'll need to add the following environment variables to your .env file:
AWS_ACCESS_KEY_ID=your_aws_access_key_id
AWS_SECRET_ACCESS_KEY=your_aws_secret_access_key
AWS_REGION=your_aws_region
AWS_VERSION=your_aws_version- credentials: AWS credentials for accessing the Rekognition API. Please refer to the
Get Access Key ID and Secret Access Key for AWS:
- AWS_ACCESS_KEY_ID: The AWS access key ID.
- AWS_SECRET_ACCESS_KEY: The AWS secret access key.
Important
Give following Permissions to the IAM user for accessing the Rekognition API:
AmazonRekognitionFullAccessAmazonS3FullAccess
- AWS_REGION: The AWS region where the Rekognition API is located (default: us-east-1).
Caution
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
- AWS_VERSION: The version of the Rekognition API (default: latest).
For more info, please refer to AWS Client.
The Rekognition facade offers a convenient way to make AWS Rekognition API requests.
Note
AWS Rekognition API offers over 10 primary operations across various categories for image and video analysis, but this package currently supports only a handful of them.
Contributions are highly encouraged! If you'd like to add support for more operations, feel free to contribute to the package.
Check out the full list of Rekognition operations Amazon Rekognition.
Following Rekognition API operations are supported:
Tip
All classes include comprehensive DocBlock comments and detailed documentation to enhance readability and understanding.
Refer to the class definitions for a complete overview of methods, parameters, and their usage.
Detects instances of real-world entities within an image (JPEG or PNG) provided as input. This includes objects like flower, tree, and table; events like wedding, graduation, and birthday party; and concepts like landscape, evening, and nature.
Labels supported by Rekognition label detection operations can be found in Detecting Objects and Concepts.
First of all, you need to create an instance of ImageData object by providing the image bytes of an image file.
// Path to the image file
$imagePath = __DIR__.'/resources/images/test_labels.jpg';
// Read the image file into bytes
$image = file_get_contents($imagePath);
// Create an ImageData object
$imageData = new ImageData(
bytes: $image,
);Alternatively, you can use S3 as the image source:
// Create an S3ObjectData object
$s3Object = new S3ObjectData(
bucket: 'your_bucket_name',
name : 'your_image_name.jpg',
);
// Create an ImageData object by providing the S3 object
$imageData = new ImageData(
s3Object: $s3Object,
);For more details, see S3Object section.
To send a detect labels request, you need to create an instance of DetectLabelsData object.
// Create a DetectLabelsData object
$detectLabelsData = new DetectLabelsData(
image: $imageData,
);More optional parameters can be provided to the DetectLabelsData object:
// Create a DetectLabelsData object with optional parameters
$detectLabelsData = new DetectLabelsData(
image : $imageData,
maxLabels : 10, // Maximum number of labels to return
minConfidence: 80.0, // Minimum confidence level for the labels to return
settings : new SettingsData(
generalLabels: new GeneralLabelsSettingsData(
labelCategoryExclusionFilters: ['Person Description'],
labelCategoryInclusionFilters: ['Animals and Pets'],
labelExclusionFilters : ['Man', 'Woman'],
labelInclusionFilters : ['Dog', 'Cat'],
),
),
);Check out DetectLabelsData class for optional parameters and their descriptions.
For more details, see DetectLabels section.
Then, you can send the request using the Rekognition facade detectLabels method.
$response = Rekognition::detectLabels($detectLabelsData);Response will be an instance of DetectLabelsResultData object.
This is the sample DetectLabelsResultData:
DetectLabelsResultData(
labels: DataCollection([
LabelData(
name: 'Adult',
parents: DataCollection([
ParentData(name: 'Person'),
]),
categories: DataCollection([
CategoryData(name: 'Person Description'),
]),
confidence: 99.406089782715,
instances: DataCollection([
InstanceData(
boundingBox: BoundingBoxData(
width: 0.4137507379055,
height: 0.74068546295166,
left: 0.0,
top: 0.25919502973557,
),
confidence: 99.406089782715,
),
InstanceData(
boundingBox: BoundingBoxData(
width: 0.4726165831089,
height: 0.55402708053589,
left: 0.29312029480934,
top: 0.23203137516975,
),
confidence: 98.74324798584,
),
InstanceData(
boundingBox: BoundingBoxData(
width: 0.29476174712181,
height: 0.62268280982971,
left: 0.64589500427246,
top: 0.26460602879524,
),
confidence: 98.648498535156,
),
]),
),
LabelData(
name: 'Male',
parents: DataCollection([
ParentData(name: 'Person'),
]),
categories: DataCollection([
CategoryData(name: 'Person Description'),
]),
confidence: 99.406089782715,
instances: DataCollection([
InstanceData(
boundingBox: BoundingBoxData(
width: 0.4137507379055,
height: 0.74068546295166,
left: 0.0,
top: 0.25919502973557,
),
confidence: 99.406089782715,
),
InstanceData(
boundingBox: BoundingBoxData(
width: 0.40260022878647,
height: 0.50842136144638,
left: 0.5948948264122,
top: 0.49154290556908,
),
confidence: 98.609413146973,
),
]),
),
LabelData(
name: 'Man',
parents: DataCollection([
ParentData(name: 'Adult'),
ParentData(name: 'Male'),
ParentData(name: 'Person'),
]),
categories: DataCollection([
CategoryData(name: 'Person Description'),
]),
confidence: 99.406089782715,
instances: DataCollection([
InstanceData(
boundingBox: BoundingBoxData(
width: 0.4137507379055,
height: 0.74068546295166,
left: 0.0,
top: 0.25919502973557,
),
confidence: 99.406089782715,
),
]),
),
LabelData(
name: 'Person',
categories: DataCollection([
CategoryData(name: 'Person Description'),
]),
confidence: 99.406089782715,
instances: DataCollection([
InstanceData(
boundingBox: BoundingBoxData(
width: 0.4137507379055,
height: 0.74068546295166,
left: 0.0,
top: 0.25919502973557,
),
confidence: 99.406089782715,
),
InstanceData(
boundingBox: BoundingBoxData(
width: 0.4726165831089,
height: 0.55402708053589,
left: 0.29312029480934,
top: 0.23203137516975,
),
confidence: 98.74324798584,
),
InstanceData(
boundingBox: BoundingBoxData(
width: 0.29476174712181,
height: 0.62268280982971,
left: 0.64589500427246,
top: 0.26460602879524,
),
confidence: 98.648498535156,
),
InstanceData(
boundingBox: BoundingBoxData(
width: 0.40260022878647,
height: 0.50842136144638,
left: 0.5948948264122,
top: 0.49154290556908,
),
confidence: 98.609413146973,
),
]),
),
LabelData(
name: 'Woman',
parents: DataCollection([
ParentData(name: 'Adult'),
ParentData(name: 'Female'),
ParentData(name: 'Person'),
]),
categories: DataCollection([
CategoryData(name: 'Person Description'),
]),
confidence: 98.74324798584,
instances: DataCollection([
InstanceData(
boundingBox: BoundingBoxData(
width: 0.4726165831089,
height: 0.55402708053589,
left: 0.29312029480934,
top: 0.23203137516975,
),
confidence: 98.74324798584,
),
InstanceData(
boundingBox: BoundingBoxData(
width: 0.29476174712181,
height: 0.62268280982971,
left: 0.64589500427246,
top: 0.26460602879524,
),
confidence: 98.648498535156,
),
]),
),
]),
labelModelVersion: "3.0",
metadata: MetadataData(
statusCode: 200,
effectiveUri: "https://rekognition.us-east-1.amazonaws.com/",
headers: [
"x-amzn-requestid" => "8dc27697-dc77-4d24-9f68-1f5080b536c9",
"content-type" => "application/x-amz-json-1.1",
"content-length" => "2658",
"date" => "Fri, 17 Jan 2025 18:05:24 GMT",
],
transferStats: [
"http" => [
[],
],
],
),
);The face collection is the primary Amazon Rekognition resource. A collection is a container for faces that you want to search, compare, or store.
Check out Managing Face Collections, Faces, and Users for more details.
Creates a collection in an AWS Region.
To create a collection, you need to create an instance of CreateCollectionData object:
// Create a CreateCollectionData object
$createCollectionData = new CreateCollectionData(
collectionId: 'your_collection_id', // Unique identifier for the collection
tags : ['tag_key' => 'tag_value'], // Optional tags for the collection (A set of tags key-value pairs that you want to attach to the collection)
);Then, you can send the request using the Rekognition facade createCollection method:
$response = Rekognition::createCollection($createCollectionData);Response will be an instance of CreateCollectionResultData object.
Tip
CreateCollectionResultData contains the collectionArn - Amazon Resource Name (ARN) of the collection. You can use this to manage permissions on your resources.
Please refer to Identify AWS resources with Amazon Resource Names (ARNs) for more details.
This is the sample CreateCollectionResultData:
CreateCollectionResultData(
collectionArn: "arn:aws:rekognition:us-east-1:123456789010:collection/your_collection_id",
faceModelVersion: "7.0",
statusCode: 200,
metadata: MetadataData(
statusCode: 200,
effectiveUri: "https://rekognition.us-east-1.amazonaws.com/",
headers: [
"x-amzn-requestid" => "8dc27697-dc77-4d24-9f68-1f5080b536c9",
"content-type" => "application/x-amz-json-1.1",
"content-length" => "2658",
"date" => "Fri, 17 Jan 2025 18:05:24 GMT",
],
transferStats: [
"http" => [
[],
],
],
),
);Deletes the specified collection with the provided collectionId.
Warning
Deleting a collection also removes all faces in the collection.
To delete a collection, you need to create an instance of DeleteCollectionData object:
// Create a DeleteCollectionData object
$deleteCollectionData = new DeleteCollectionData(
collectionId: 'your_collection_id', // Unique identifier for the collection
);Then, you can send the request using the Rekognition facade deleteCollection method:
$response = Rekognition::deleteCollection($deleteCollectionData);Response will be an instance of DeleteCollectionResultData object.
This is the sample DeleteCollectionResultData:
DeleteCollectionResultData(
statusCode: 200,
metadata: MetadataData(
statusCode: 200,
effectiveUri: "https://rekognition.us-east-1.amazonaws.com/",
headers: [
"x-amzn-requestid" => "8dc27697-dc77-4d24-9f68-1f5080b536c9",
"content-type" => "application/x-amz-json-1.1",
"content-length" => "2658",
"date" => "Fri, 17 Jan 2025 18:05:24 GMT",
],
transferStats: [
"http" => [
[],
],
],
),
);Returns the list of collections in the AWS account.
To list collections, you need to create an instance of ListCollectionsData object:
// Create a ListCollectionsData object.
$listCollectionsData = new ListCollectionsData(
maxResults: 10, // Maximum number of collection IDs to return - optional
nextToken : 'your_next_token', // Pagination token from the previous response - optional
);Then, you can send the request using the Rekognition facade listCollections method:
$response = Rekognition::listCollections($listCollectionsData);Response will be an instance of ListCollectionsResultData object.
This is the sample ListCollectionsResultData:
ListCollectionsResultData(
collectionIds: [
"your_collection_id_0",
"your_collection_id_1",
],
faceModelVersions: [
"7.0",
"7.0",
],
nextToken: "your_next_token",
metadata: MetadataData(
statusCode: 200,
effectiveUri: "https://rekognition.us-east-1.amazonaws.com/",
headers: [
"x-amzn-requestid" => "8dc27697-dc77-4d24-9f68-1f5080b536c9",
"content-type" => "application/x-amz-json-1.1",
"content-length" => "2658",
"date" => "Fri, 17 Jan 2025 18:05:24 GMT",
],
transferStats: [
"http" => [
[],
],
],
),
);Creates a new user within a collection specified by collectionId and unique userId.
To create a user, you need to create an instance of CreateUserData object:
// Create a CreateUserData object
$createUserData = new CreateUserData(
collectionId : 'your_collection_id', // The ID of an existing collection - required
userId : 'your_user_id', // ID for the user id to be created. This user id needs to be unique within the collection. - required
/*
* Optional - Idempotent token used to identify the request to createUser.
* If you use the same token with multiple createUser requests, the same response is returned.
* Use clientRequestToken to prevent the same request from being processed more than once.
*/
clientRequestToken: 'your_client_request_token',
);Note
userId needs to be unique within the collection.
Then, you can send the request using the Rekognition facade createUser method:
$response = Rekognition::createUser($createUserData);Response will be an instance of CreateUserResultData object.
Note
The result for the createUser request is always empty, only metadata is returned in CreateUserResultData.
This is the sample CreateUserResultData:
CreateUserResultData(
metadata: MetadataData(
statusCode: 200,
effectiveUri: "https://rekognition.us-east-1.amazonaws.com/",
headers: [
"x-amzn-requestid" => "8dc27697-dc77-4d24-9f68-1f5080b536c9",
"content-type" => "application/x-amz-json-1.1",
"content-length" => "2658",
"date" => "Fri, 17 Jan 2025 18:05:24 GMT",
],
transferStats: [
"http" => [
[],
],
],
),
);Detects faces in the input image and adds them to the specified collection.
First of all, you need to create an instance of ImageData object by providing the image bytes of an image file.
// Path to the image file
$imagePath = __DIR__.'/resources/images/test_labels.jpg';
// Read the image file into bytes
$image = file_get_contents($imagePath);
// Create an ImageData object
$imageData = new ImageData(
bytes: $image,
);Alternatively, you can use S3 as the image source:
// Create an S3ObjectData object
$s3Object = new S3ObjectData(
bucket: 'your_bucket_name',
name : 'your_image_name.jpg',
);
// Create an ImageData object by providing the S3 object
$imageData = new ImageData(
s3Object: $s3Object,
);For more details, see S3Object section.
Then, in order to index faces, you need to create an instance of IndexFacesData object:
// Create an IndexFacesData object
$indexFacesData = new IndexFacesData(
collectionId : 'your_collection_id', // The ID of an existing collection - required
image : $imageData, // The input image as ImageData object - required
maxFaces : 2, // Maximum number of faces to index - optional
qualityFilter : 'AUTO', // The quality filter for the face detection - optional
externalImageId : 'your_external_image_id', // ID you want to assign to all the faces detected in the image - optional
detectionAttributes: ['ALL'], // An array of facial attributes you want to be returned - optional
);Note
detectionAttributes: Requesting more attributes may increase response time.maxFaces: IndexFaces returns no more than 100 detected faces in an image, even if you specify a larger value for maxFaces.
Then, you can send the request using the Rekognition facade indexFaces method:
$response = Rekognition::indexFaces($indexFacesData);Response will be an instance of IndexFacesResultData object.
This is the sample IndexFacesResultData:
IndexFacesResultData(
faceModelVersion: "7.0",
faceRecords: DataCollection([
FaceRecordData(
face: FaceData(
confidence: 99.406089782715,
boundingBox: BoundingBoxData(
width: 0.4137507379055,
height: 0.74068546295166,
left: 0.0,
top: 0.25919502973557,
),
faceId: "558388f6-221a-4f3f-aab5-1ccd8256f7e5",
userId: "your_user_id",
imageId: "970575de-6fc3-3762-8644-3b393cef2743",
externalImageId: "your_external_image_id",
indexFacesModelVersion: "7.0",
),
faceDetail: FaceDetailData(
confidence: 99.406089782715,
boundingBox: BoundingBoxData(
width: 0.4737507379055,
height: 0.72068546295166,
left: 0.0,
top: 0.28919502973557,
),
ageRange: AgeRangeData(
low: 20,
high: 30,
),
emotions: DataCollection([
EmotionData(
type: "HAPPY",
confidence: 99.406089782715,
),
EmotionData(
type: "SURPRISED",
confidence: 98.74324798584,
),
]),
eyeDirection: EyeDirectionData(
confidence: 99.406089782715,
yaw: 0.0,
pitch: 0.0,
),
...
)
),
FaceRecordData(
face: FaceData(
confidence: 97.406089782715,
boundingBox: BoundingBoxData(
width: 0.4237507379055,
height: 0.70068546295166,
left: 0.8,
top: 0.28919502973557,
),
faceId: "038388f6-221a-4f3f-aab5-1ccd8256f7e8",
userId: "your_user_id",
imageId: "8d0575de-6fc3-3762-8644-3b393cef2741",
externalImageId: "your_external_image_id",
indexFacesModelVersion: "7.0",
),
faceDetail: FaceDetailData(
confidence: 96.406089782715,
boundingBox: BoundingBoxData(
width: 0.4737507379055,
height: 0.72068546295166,
left: 0.0,
top: 0.28919502973557,
),
ageRange: AgeRangeData(
low: 25,
high: 35,
),
emotions: DataCollection([
EmotionData(
type: "CALM",
confidence: 96.406089782715,
),
EmotionData(
type: "CONFUSED",
confidence: 99.74324798584,
),
]),
eyeglasses: EyeglassesData(
confidence: 99.406089782715,
value: true,
),
eyesOpen: EyesOpenData(
confidence: 99.496089782715,
value: true,
),
...
)
),
...
]),
unindexedFaces: DataCollection([
UnindexedFaceData(
reason: "LOW_CONFIDENCE",
faceDetail: FaceDetailData(
confidence: 66.406089782715,
boundingBox: BoundingBoxData(
width: 0.4737507379055,
height: 0.72068546295166,
left: 0.0,
top: 0.28919502973557,
),
ageRange: AgeRangeData(
low: 25,
high: 35,
),
...
)
),
UnindexFaceData(
reason: "EXCEEDS_MAX_FACES",
faceDetail: FaceDetailData(
confidence: 86.406089782715,
boundingBox: BoundingBoxData(
width: 0.4737507379055,
height: 0.72068546295166,
left: 0.0,
top: 0.28919502973557,
),
ageRange: AgeRangeData(
low: 45,
high: 55,
),
...
)
),
...
]),
metadata: MetadataData(
statusCode: 200,
effectiveUri: "https://rekognition.us-east-1.amazonaws.com/",
headers: [
"x-amzn-requestid" => "8dc27697-dc77-4d24-9f68-1f5080b536c9",
"content-type" => "application/x-amz-json-1.1",
"content-length" => "2658",
"date" => "Fri, 17 Jan 2025 18:05:24 GMT",
],
transferStats: [
"http" => [
[],
],
],
),
);Associates one or more faces with an existing userId.
To associate faces, you need to create an instance of AssociateFacesData object:
// Create an AssociateFacesData object
$associateFacesData = new AssociateFacesData(
collectionId : 'test_collection_id', // The id of an existing collection containing the userId - required
faceIds : ['8e2ad714-4d23-43c0-b9ad-9fab136bef13', 'ed49afb4-b45b-468e-9614-d652c924cd4a'], // An array of faceIds to associate with the userId - required
userId : 'test_user_id', // The userId to associate with the faceIds. (The id for the existing userId.) - required
userMatchThreshold: 80.0, // An optional value specifying the minimum confidence in the userId match to return - optional
clientRequestToken: 'test_client_request_token', // Idempotent token used to identify the request to associate faces - optional
);Note
- The maximum number of total faceIds per userId is 100.
userMatchThreshold: The default value is 75.0.clientRequestToken: If you use the same token with multiple associate faces requests, the same response is returned. Use clientRequestToken to prevent the same request from being processed more than once.
Then, you can send the request using the Rekognition facade associateFaces method:
$response = Rekognition::associateFaces($associateFacesData);Response will be an instance of AssociateFacesResultData object.
This is the sample AssociateFacesResultData:
AssociateFacesResultData(
associatedFaces: DataCollection([
AssociatedFaceData(
faceId: "8e2ad714-4d23-43c0-b9ad-9fab136bef13",
),
AssociatedFaceData(
faceId: "ed49afb4-b45b-468e-9614-d652c924cd4a",
),
]),
unsuccessfulFaceAssociations: DataCollection([
UnsuccessfulFaceAssociationData(
confidence: 70.0,
faceId: "6e2ad714-4d23-43c0-b9ad-9fab136bef19",
reasons: ["LOW_CONFIDENCE"],
userId: "test_user_id",
),
UnsuccessfulFaceAssociationData(
confidence: 80.0,
faceId: "5f2ad714-4d23-43c0-b9ad-9fab136bef15",
reasons: ["LOW_QUALITY"],
userId: "test_user_id",
),
]),
userStatus: "UPDATING",
metadata: MetadataData(
statusCode: 200,
effectiveUri: "https://rekognition.us-east-1.amazonaws.com/",
headers: [
"x-amzn-requestid" => "8dc27697-dc77-4d24-9f68-1f5080b536c9",
"content-type" => "application/x-amz-json-1.1",
"content-length" => "2658",
"date" => "Fri, 17 Jan 2025 18:05:24 GMT",
],
transferStats: [
"http" => [
[],
],
],
),
);Searches for userIds using a supplied image. It first detects the largest face in the image, and then searches a specified collection for matching userIds.
First of all, you need to create an instance of ImageData object by providing the image bytes of an image file.
// Path to the image file
$imagePath = __DIR__.'/resources/images/test_labels.jpg';
// Read the image file into bytes
$image = file_get_contents($imagePath);
// Create an ImageData object
$imageData = new ImageData(
bytes: $image,
);Alternatively, you can use S3 as the image source:
// Create an S3ObjectData object
$s3Object = new S3ObjectData(
bucket: 'your_bucket_name',
name : 'your_image_name.jpg',
);
// Create an ImageData object by providing the S3 object
$imageData = new ImageData(
s3Object: $s3Object,
);For more details, see S3Object section.
Then, in order to search users by image, you need to create an instance of SearchUsersByImageData object:
// Create a SearchUsersByImageData object
$searchUsersByImageData = new SearchUsersByImageData(
collectionId : 'test_collection_id', // The id of an existing collection containing the userId - required
image : $imageData, // Provides the input image either as bytes or an S3 object - required
maxUsers : 2, // Maximum number of userIds to return - optional
userMatchThreshold: 80.0, // Specifies the minimum confidence in the UserID match to return - optional
qualityFilter : 'MEDIUM', // A filter that specifies a quality bar for how much filtering is done to identify faces - optional
);Note
userMatchThreshold: The default value is 80.0qualityFilter: The default value is NONE.
Then, you can send the request using the Rekognition facade searchUsersByImage method:
$response = Rekognition::searchUsersByImage($searchUsersByImageData);Response will be an instance of SearchUsersByImageResultData object.
This is the sample SearchUsersByImageResultData:
SearchUsersByImageResultData(
faceModelVersion: "7.0",
userMatches: DataCollection([
UserMatchData(
similarity: 99.406089782715,
user: MatchedUserData(
userId: "test_user_id",
userStatus: "ACTIVE",
)
),
UserMatchData(
similarity: 96.406089782715,
user: MatchedUserData(
userId: "test_user_id_1",
userStatus: "ACTIVE",
)
),
...
]),
searchedFace: SearchedFaceData(
faceDetail: FaceDetailData(
confidence: 99.406089782715,
boundingBox: BoundingBoxData(
width: 0.4137507379055,
height: 0.74068546295166,
left: 0.0,
top: 0.25919502973557,
),
ageRange: AgeRangeData(
low: 20,
high: 30,
),
emotions: DataCollection([
EmotionData(
type: "HAPPY",
confidence: 99.406089782715,
),
EmotionData(
type: "SURPRISED",
confidence: 98.74324798584,
),
...
]),
eyeDirection: EyeDirectionData(
confidence: 99.406089782715,
yaw: 0.0,
pitch: 0.0,
),
...
)
),
unsearchedFaces: DataCollection([
UnsearchedFaceData(
faceDetail: FaceDetailData(
confidence: 66.406089782715,
boundingBox: BoundingBoxData(
width: 0.4737507379055,
height: 0.72068546295166,
left: 0.0,
top: 0.28919502973557,
),
ageRange: AgeRangeData(
low: 25,
high: 35,
),
...
),
reasons: ["FACE_NOT_LARGEST"],
),
...
]),
metadata: MetadataData(
statusCode: 200,
effectiveUri: "https://rekognition.us-east-1.amazonaws.com/",
headers: [
"x-amzn-requestid" => "8dc27697-dc77-4d24-9f68-1f5080b536c9",
"content-type" => "application/x-amz-json-1.1",
"content-length" => "2658",
"date" => "Fri, 17 Jan 2025 18:05:24 GMT",
],
transferStats: [
"http" => [
[],
],
],
),
);Your contributions are welcome! If you'd like to improve this package, simply create a pull request with your changes. Your efforts help enhance its functionality and documentation.
If you find this package useful, please consider ⭐ it to show your support!
AWS Rekognition API for Laravel is an open-sourced software licensed under the MIT license.