Detect moderation labels in an image with Amazon Rekognition using an AWS SDK - AWS SDK Code Examples

There are more AWS SDK examples available in the AWS Doc SDK Examples GitHub repo.

Detect moderation labels in an image with Amazon Rekognition using an AWS SDK

The following code examples show how to detect moderation labels in an image with Amazon Rekognition. Moderation labels identify content that may be inappropriate for some audiences.

For more information, see Detecting inappropriate images.

.NET
AWS SDK for .NET
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. 

    using System;
    using System.Threading.Tasks;
    using Amazon.Rekognition;
    using Amazon.Rekognition.Model;

    /// <summary>
    /// Uses the Amazon Rekognition Service to detect unsafe content in a
    /// JPEG or PNG format image. This example was created using the AWS SDK
    /// for .NET version 3.7 and .NET Core 5.0.
    /// </summary>
    public class DetectModerationLabels
    {
        public static async Task Main(string[] args)
        {
            string photo = "input.jpg";
            string bucket = "bucket";

            var rekognitionClient = new AmazonRekognitionClient();

            var detectModerationLabelsRequest = new DetectModerationLabelsRequest()
            {
                Image = new Image()
                {
                    S3Object = new S3Object()
                    {
                        Name = photo,
                        Bucket = bucket,
                    },
                },
                MinConfidence = 60F,
            };

            try
            {
                var detectModerationLabelsResponse = await rekognitionClient.DetectModerationLabelsAsync(detectModerationLabelsRequest);
                Console.WriteLine("Detected labels for " + photo);
                foreach (ModerationLabel label in detectModerationLabelsResponse.ModerationLabels)
                {
                    Console.WriteLine($"Label: {label.Name}");
                    Console.WriteLine($"Confidence: {label.Confidence}");
                    Console.WriteLine($"Parent: {label.ParentName}");
                }
            }
            catch (Exception ex)
            {
                Console.WriteLine(ex.Message);
            }
        }
    }
Java
SDK for Java 2.x
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. 

    public static void detectModLabels(RekognitionClient rekClient, String sourceImage) {

        try {
            InputStream sourceStream = new FileInputStream(sourceImage);
            SdkBytes sourceBytes = SdkBytes.fromInputStream(sourceStream);
            Image souImage = Image.builder()
                .bytes(sourceBytes)
                .build();

            DetectModerationLabelsRequest moderationLabelsRequest = DetectModerationLabelsRequest.builder()
                .image(souImage)
                .minConfidence(60F)
                .build();

            DetectModerationLabelsResponse moderationLabelsResponse = rekClient.detectModerationLabels(moderationLabelsRequest);
            List<ModerationLabel> labels = moderationLabelsResponse.moderationLabels();
            System.out.println("Detected labels for image");

            for (ModerationLabel label : labels) {
                System.out.println("Label: " + label.name()
                    + "\n Confidence: " + label.confidence().toString() + "%"
                    + "\n Parent:" + label.parentName());
            }

        } catch (RekognitionException | FileNotFoundException e) {
            e.printStackTrace();
            System.exit(1);
        }
    }
Kotlin
SDK for Kotlin
Note

This is prerelease documentation for a feature in preview release. It is subject to change.

Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. 

suspend fun detectModLabels(sourceImage: String) {

    val myImage = Image {
        this.bytes = (File(sourceImage).readBytes())
    }

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

    RekognitionClient { 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}")
        }
    }
}
Python
SDK for Python (Boto3)
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. 

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_moderation_labels(self):
        """
        Detects moderation labels in the image. Moderation labels identify content
        that may be inappropriate for some audiences.

        :return: The list of moderation labels found in the image.
        """
        try:
            response = self.rekognition_client.detect_moderation_labels(
                Image=self.image)
            labels = [RekognitionModerationLabel(label)
                      for label in response['ModerationLabels']]
            logger.info(
                "Found %s moderation labels in %s.", len(labels), self.image_name)
        except ClientError:
            logger.exception(
                "Couldn't detect moderation labels in %s.", self.image_name)
            raise
        else:
            return labels