There are more AWS SDK examples available in the [AWS Doc SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples) GitHub repo.

# Amazon Rekognition examples using SDK for Kotlin
<a name="kotlin_1_rekognition_code_examples"></a>

The following code examples show you how to perform actions and implement common scenarios by using the AWS SDK for Kotlin with Amazon Rekognition.

*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.

Each example includes a link to the complete source code, where you can find instructions on how to set up and run the code in context.

**Topics**
+ [Actions](#actions)
+ [Scenarios](#scenarios)

## Actions
<a name="actions"></a>

### `CompareFaces`
<a name="rekognition_CompareFaces_kotlin_1_topic"></a>

The following code example shows how to use `CompareFaces`.

For more information, see [Comparing faces in images](https://docs.aws.amazon.com/rekognition/latest/dg/faces-comparefaces.html).

**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*. 

### `CreateCollection`
<a name="rekognition_CreateCollection_kotlin_1_topic"></a>

The following code example shows how to use `CreateCollection`.

For more information, see [Creating a collection](https://docs.aws.amazon.com/rekognition/latest/dg/create-collection-procedure.html).

**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*. 

### `DeleteCollection`
<a name="rekognition_DeleteCollection_kotlin_1_topic"></a>

The following code example shows how to use `DeleteCollection`.

For more information, see [Deleting a collection](https://docs.aws.amazon.com/rekognition/latest/dg/delete-collection-procedure.html).

**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*. 

### `DeleteFaces`
<a name="rekognition_DeleteFaces_kotlin_1_topic"></a>

The following code example shows 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).

**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*. 

### `DescribeCollection`
<a name="rekognition_DescribeCollection_kotlin_1_topic"></a>

The following code example shows how to use `DescribeCollection`.

For more information, see [Describing a collection](https://docs.aws.amazon.com/rekognition/latest/dg/describe-collection-procedure.html).

**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*. 

### `DetectFaces`
<a name="rekognition_DetectFaces_kotlin_1_topic"></a>

The following code example shows 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).

**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*. 

### `DetectLabels`
<a name="rekognition_DetectLabels_kotlin_1_topic"></a>

The following code example shows 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).

**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 detectImageLabels(sourceImage: String) {
    val souImage =
        Image {
            bytes = (File(sourceImage).readBytes())
        }
    val request =
        DetectLabelsRequest {
            image = souImage
            maxLabels = 10
        }

    RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
        val response = rekClient.detectLabels(request)
        response.labels?.forEach { label ->
            println("${label.name} : ${label.confidence}")
        }
    }
}
```
+  For API details, see [DetectLabels](https://sdk.amazonaws.com/kotlin/api/latest/index.html) in *AWS SDK for Kotlin API reference*. 

### `DetectModerationLabels`
<a name="rekognition_DetectModerationLabels_kotlin_1_topic"></a>

The following code example shows how to use `DetectModerationLabels`.

For more information, see [Detecting inappropriate images](https://docs.aws.amazon.com/rekognition/latest/dg/procedure-moderate-images.html).

**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 detectModLabels(sourceImage: String) {
    val myImage =
        Image {
            this.bytes = (File(sourceImage).readBytes())
        }

    val request =
        DetectModerationLabelsRequest {
            image = myImage
            minConfidence = 60f
        }

    RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
        val response = rekClient.detectModerationLabels(request)
        response.moderationLabels?.forEach { label ->
            println("Label: ${label.name} - Confidence: ${label.confidence} % Parent: ${label.parentName}")
        }
    }
}
```
+  For API details, see [DetectModerationLabels](https://sdk.amazonaws.com/kotlin/api/latest/index.html) in *AWS SDK for Kotlin API reference*. 

### `DetectText`
<a name="rekognition_DetectText_kotlin_1_topic"></a>

The following code example shows how to use `DetectText`.

For more information, see [Detecting text in an image](https://docs.aws.amazon.com/rekognition/latest/dg/text-detecting-text-procedure.html).

**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 detectTextLabels(sourceImage: String?) {
    val souImage =
        Image {
            bytes = (File(sourceImage).readBytes())
        }

    val request =
        DetectTextRequest {
            image = souImage
        }

    RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
        val response = rekClient.detectText(request)
        response.textDetections?.forEach { text ->
            println("Detected: ${text.detectedText}")
            println("Confidence: ${text.confidence}")
            println("Id: ${text.id}")
            println("Parent Id:  ${text.parentId}")
            println("Type: ${text.type}")
        }
    }
}
```
+  For API details, see [DetectText](https://sdk.amazonaws.com/kotlin/api/latest/index.html) in *AWS SDK for Kotlin API reference*. 

### `IndexFaces`
<a name="rekognition_IndexFaces_kotlin_1_topic"></a>

The following code example shows how to use `IndexFaces`.

For more information, see [Adding faces to a collection](https://docs.aws.amazon.com/rekognition/latest/dg/add-faces-to-collection-procedure.html).

**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 addToCollection(
    collectionIdVal: String?,
    sourceImage: String,
) {
    val souImage =
        Image {
            bytes = (File(sourceImage).readBytes())
        }

    val request =
        IndexFacesRequest {
            collectionId = collectionIdVal
            image = souImage
            maxFaces = 1
            qualityFilter = QualityFilter.Auto
            detectionAttributes = listOf(Attribute.Default)
        }

    RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
        val facesResponse = rekClient.indexFaces(request)

        // Display the results.
        println("Results for the image")
        println("\n Faces indexed:")
        facesResponse.faceRecords?.forEach { faceRecord ->
            println("Face ID: ${faceRecord.face?.faceId}")
            println("Location: ${faceRecord.faceDetail?.boundingBox}")
        }

        println("Faces not indexed:")
        facesResponse.unindexedFaces?.forEach { unindexedFace ->
            println("Location: ${unindexedFace.faceDetail?.boundingBox}")
            println("Reasons:")

            unindexedFace.reasons?.forEach { reason ->
                println("Reason:  $reason")
            }
        }
    }
}
```
+  For API details, see [IndexFaces](https://sdk.amazonaws.com/kotlin/api/latest/index.html) in *AWS SDK for Kotlin API reference*. 

### `ListCollections`
<a name="rekognition_ListCollections_kotlin_1_topic"></a>

The following code example shows how to use `ListCollections`.

For more information, see [Listing collections](https://docs.aws.amazon.com/rekognition/latest/dg/list-collection-procedure.html).

**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 listAllCollections() {
    val request =
        ListCollectionsRequest {
            maxResults = 10
        }

    RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
        val response = rekClient.listCollections(request)
        response.collectionIds?.forEach { resultId ->
            println(resultId)
        }
    }
}
```
+  For API details, see [ListCollections](https://sdk.amazonaws.com/kotlin/api/latest/index.html) in *AWS SDK for Kotlin API reference*. 

### `ListFaces`
<a name="rekognition_ListFaces_kotlin_1_topic"></a>

The following code example shows how to use `ListFaces`.

For more information, see [Listing faces in a collection](https://docs.aws.amazon.com/rekognition/latest/dg/list-faces-in-collection-procedure.html).

**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 listFacesCollection(collectionIdVal: String?) {
    val request =
        ListFacesRequest {
            collectionId = collectionIdVal
            maxResults = 10
        }

    RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
        val response = rekClient.listFaces(request)
        response.faces?.forEach { face ->
            println("Confidence level there is a face: ${face.confidence}")
            println("The face Id value is ${face.faceId}")
        }
    }
}
```
+  For API details, see [ListFaces](https://sdk.amazonaws.com/kotlin/api/latest/index.html) in *AWS SDK for Kotlin API reference*. 

### `RecognizeCelebrities`
<a name="rekognition_RecognizeCelebrities_kotlin_1_topic"></a>

The following code example shows how to use `RecognizeCelebrities`.

For more information, see [Recognizing celebrities in an image](https://docs.aws.amazon.com/rekognition/latest/dg/celebrities-procedure-image.html).

**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 recognizeAllCelebrities(sourceImage: String?) {
    val souImage =
        Image {
            bytes = (File(sourceImage).readBytes())
        }

    val request =
        RecognizeCelebritiesRequest {
            image = souImage
        }

    RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
        val response = rekClient.recognizeCelebrities(request)
        response.celebrityFaces?.forEach { celebrity ->
            println("Celebrity recognized: ${celebrity.name}")
            println("Celebrity ID:${celebrity.id}")
            println("Further information (if available):")
            celebrity.urls?.forEach { url ->
                println(url)
            }
        }
        println("${response.unrecognizedFaces?.size} face(s) were unrecognized.")
    }
}
```
+  For API details, see [RecognizeCelebrities](https://sdk.amazonaws.com/kotlin/api/latest/index.html) in *AWS SDK for Kotlin API reference*. 

## Scenarios
<a name="scenarios"></a>

### Create a serverless application to manage photos
<a name="cross_PAM_kotlin_1_topic"></a>

The following code example shows how to create a serverless application that lets users manage photos using labels.

**SDK for Kotlin**  
 Shows how to develop a photo asset management application that detects labels in images using Amazon Rekognition and stores them for later retrieval.   
For complete source code and instructions on how to set up and run, see the full example on [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/usecases/creating_pam).  
For a deep dive into the origin of this example see the post on [AWS Community](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app).  

**Services used in this example**
+ API Gateway
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

### Detect information in videos
<a name="rekognition_VideoDetection_kotlin_1_topic"></a>

The following code example shows how to:
+ Start Amazon Rekognition jobs to detect elements like people, objects, and text in videos.
+ Check job status until jobs finish.
+ Output the list of elements detected by each job.

**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). 
Detect faces in a video stored in an Amazon S3 bucket.  

```
suspend fun startFaceDetection(
    channelVal: NotificationChannel?,
    bucketVal: String,
    videoVal: String,
) {
    val s3Obj =
        S3Object {
            bucket = bucketVal
            name = videoVal
        }
    val vidOb =
        Video {
            s3Object = s3Obj
        }

    val request =
        StartFaceDetectionRequest {
            jobTag = "Faces"
            faceAttributes = FaceAttributes.All
            notificationChannel = channelVal
            video = vidOb
        }

    RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
        val startLabelDetectionResult = rekClient.startFaceDetection(request)
        startJobId = startLabelDetectionResult.jobId.toString()
    }
}

suspend fun getFaceResults() {
    var finished = false
    var status: String
    var yy = 0
    RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
        var response: GetFaceDetectionResponse? = null

        val recognitionRequest =
            GetFaceDetectionRequest {
                jobId = startJobId
                maxResults = 10
            }

        // Wait until the job succeeds.
        while (!finished) {
            response = rekClient.getFaceDetection(recognitionRequest)
            status = response.jobStatus.toString()
            if (status.compareTo("Succeeded") == 0) {
                finished = true
            } else {
                println("$yy status is: $status")
                delay(1000)
            }
            yy++
        }

        // Proceed when the job is done - otherwise VideoMetadata is null.
        val videoMetaData = response?.videoMetadata
        println("Format: ${videoMetaData?.format}")
        println("Codec: ${videoMetaData?.codec}")
        println("Duration: ${videoMetaData?.durationMillis}")
        println("FrameRate: ${videoMetaData?.frameRate}")

        // Show face information.
        response?.faces?.forEach { face ->
            println("Age: ${face.face?.ageRange}")
            println("Face: ${face.face?.beard}")
            println("Eye glasses: ${face?.face?.eyeglasses}")
            println("Mustache: ${face.face?.mustache}")
            println("Smile: ${face.face?.smile}")
        }
    }
}
```
Detect inappropriate or offensive content in a video stored in an Amazon S3 bucket.  

```
suspend fun startModerationDetection(
    channel: NotificationChannel?,
    bucketVal: String?,
    videoVal: String?,
) {
    val s3Obj =
        S3Object {
            bucket = bucketVal
            name = videoVal
        }
    val vidOb =
        Video {
            s3Object = s3Obj
        }
    val request =
        StartContentModerationRequest {
            jobTag = "Moderation"
            notificationChannel = channel
            video = vidOb
        }

    RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
        val startModDetectionResult = rekClient.startContentModeration(request)
        startJobId = startModDetectionResult.jobId.toString()
    }
}

suspend fun getModResults() {
    var finished = false
    var status: String
    var yy = 0
    RekognitionClient { region = "us-east-1" }.use { rekClient ->
        var modDetectionResponse: GetContentModerationResponse? = null

        val modRequest =
            GetContentModerationRequest {
                jobId = startJobId
                maxResults = 10
            }

        // Wait until the job succeeds.
        while (!finished) {
            modDetectionResponse = rekClient.getContentModeration(modRequest)
            status = modDetectionResponse.jobStatus.toString()
            if (status.compareTo("Succeeded") == 0) {
                finished = true
            } else {
                println("$yy status is: $status")
                delay(1000)
            }
            yy++
        }

        // Proceed when the job is done - otherwise VideoMetadata is null.
        val videoMetaData = modDetectionResponse?.videoMetadata
        println("Format: ${videoMetaData?.format}")
        println("Codec: ${videoMetaData?.codec}")
        println("Duration: ${videoMetaData?.durationMillis}")
        println("FrameRate: ${videoMetaData?.frameRate}")

        modDetectionResponse?.moderationLabels?.forEach { mod ->
            val seconds: Long = mod.timestamp / 1000
            print("Mod label: $seconds ")
            println(mod.moderationLabel)
        }
    }
}
```
+ For API details, see the following topics in *AWS SDK for Kotlin API reference*.
  + [GetCelebrityRecognition](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [GetContentModeration](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [GetLabelDetection](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [GetPersonTracking](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [GetSegmentDetection](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [GetTextDetection](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [StartCelebrityRecognition](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [StartContentModeration](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [StartLabelDetection](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [StartPersonTracking](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [StartSegmentDetection](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [StartTextDetection](https://sdk.amazonaws.com/kotlin/api/latest/index.html)

### Detect objects in images
<a name="cross_RekognitionPhotoAnalyzer_kotlin_1_topic"></a>

The following code example shows how to build an app that uses Amazon Rekognition to detect objects by category in images.

**SDK for Kotlin**  
 Shows how to use Amazon Rekognition Kotlin API to create an app that uses Amazon Rekognition to identify objects by category in images located in an Amazon Simple Storage Service (Amazon S3) bucket. The app sends the admin an email notification with the results using Amazon Simple Email Service (Amazon SES).   
 For complete source code and instructions on how to set up and run, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/usecases/creating_photo_analyzer_app).   

**Services used in this example**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES