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

The following code examples show how to detect labels in an image with Amazon Rekognition.

For more information, see Detecting labels in an image.

.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 labels within an image /// stored in an Amazon Simple Storage Service (Amazon S3) bucket. /// </summary> public class DetectLabels { public static async Task Main() { string photo = "del_river_02092020_01.jpg"; // "input.jpg"; string bucket = "igsmiths3photos"; // "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; /// <summary> /// Uses the Amazon Rekognition Service to detect labels within an image /// stored locally. /// </summary> 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 in AWS SDK for .NET 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 in the Amazon Rekognition Developer Guide.

  • For API details, see DetectLabels in AWS CLI Command Reference.

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.

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.Image; import software.amazon.awssdk.services.rekognition.model.DetectLabelsRequest; import software.amazon.awssdk.services.rekognition.model.DetectLabelsResponse; import software.amazon.awssdk.services.rekognition.model.Label; import software.amazon.awssdk.services.rekognition.model.RekognitionException; 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: <sourceImage> Where: sourceImage - The path to the image (for example, C:\\AWS\\pic1.png).\s """; if (args.length != 1) { System.out.println(usage); System.exit(1); } String sourceImage = args[0]; Region region = Region.US_EAST_1; RekognitionClient rekClient = RekognitionClient.builder() .region(region) .build(); detectImageLabels(rekClient, sourceImage); rekClient.close(); } public static void detectImageLabels(RekognitionClient rekClient, String sourceImage) { try { InputStream sourceStream = new FileInputStream(sourceImage); SdkBytes sourceBytes = SdkBytes.fromInputStream(sourceStream); // Create an Image object for the source image. Image souImage = Image.builder() .bytes(sourceBytes) .build(); DetectLabelsRequest detectLabelsRequest = DetectLabelsRequest.builder() .image(souImage) .maxLabels(10) .build(); DetectLabelsResponse labelsResponse = rekClient.detectLabels(detectLabelsRequest); List<Label> labels = labelsResponse.labels(); System.out.println("Detected labels for the given photo"); for (Label label : labels) { System.out.println(label.name() + ": " + label.confidence().toString()); } } catch (RekognitionException | FileNotFoundException e) { System.out.println(e.getMessage()); System.exit(1); } } }
  • For API details, see DetectLabels in AWS SDK for Java 2.x API Reference.

Kotlin
SDK for Kotlin
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 detectImageLabels(sourceImage: String) { val souImage = Image { bytes = (File(sourceImage).readBytes()) } val request = DetectLabelsRequest { image = souImage maxLabels = 10 } RekognitionClient { 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 in AWS SDK for Kotlin API reference.

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_labels(self, max_labels): """ Detects labels in the image. Labels are objects and people. :param max_labels: The maximum number of labels to return. :return: The list of labels detected in the image. """ try: response = self.rekognition_client.detect_labels( Image=self.image, MaxLabels=max_labels ) labels = [RekognitionLabel(label) for label in response["Labels"]] logger.info("Found %s labels in %s.", len(labels), self.image_name) except ClientError: logger.info("Couldn't detect labels in %s.", self.image_name) raise else: return labels
  • For API details, see DetectLabels in AWS SDK for Python (Boto3) API Reference.