Amazon Rekognition examples using SDK for Java 2.x
The following code examples show you how to perform actions and implement common scenarios by using the Amazon SDK for Java 2.x 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 Amazon Web Services 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.
Actions
The following code example shows how to use CompareFaces
.
For more information, see Comparing faces in images.
- SDK for Java 2.x
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Note
There's more on GitHub. Find the complete example and learn how to set up and run in the Amazon Code Examples Repository
. import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.RekognitionException; import software.amazon.awssdk.services.rekognition.model.Image; import software.amazon.awssdk.services.rekognition.model.CompareFacesRequest; import software.amazon.awssdk.services.rekognition.model.CompareFacesResponse; import software.amazon.awssdk.services.rekognition.model.CompareFacesMatch; import software.amazon.awssdk.services.rekognition.model.ComparedFace; import software.amazon.awssdk.services.rekognition.model.BoundingBox; 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: <pathSource> <pathTarget> Where: pathSource - The path to the source image (for example, C:\\AWS\\pic1.png).\s pathTarget - The path to the target image (for example, C:\\AWS\\pic2.png).\s """; if (args.length != 2) { System.out.println(usage); System.exit(1); } Float similarityThreshold = 70F; String sourceImage = args[0]; String targetImage = args[1]; Region region = Region.US_EAST_1; RekognitionClient rekClient = RekognitionClient.builder() .region(region) .build(); compareTwoFaces(rekClient, similarityThreshold, sourceImage, targetImage); rekClient.close(); } public static void compareTwoFaces(RekognitionClient rekClient, Float similarityThreshold, String sourceImage, String targetImage) { try { InputStream sourceStream = new FileInputStream(sourceImage); InputStream tarStream = new FileInputStream(targetImage); SdkBytes sourceBytes = SdkBytes.fromInputStream(sourceStream); SdkBytes targetBytes = SdkBytes.fromInputStream(tarStream); // Create an Image object for the source image. Image souImage = Image.builder() .bytes(sourceBytes) .build(); Image tarImage = Image.builder() .bytes(targetBytes) .build(); CompareFacesRequest facesRequest = CompareFacesRequest.builder() .sourceImage(souImage) .targetImage(tarImage) .similarityThreshold(similarityThreshold) .build(); // Compare the two images. CompareFacesResponse compareFacesResult = rekClient.compareFaces(facesRequest); List<CompareFacesMatch> 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<ComparedFace> uncompared = compareFacesResult.unmatchedFaces(); System.out.println("There was " + uncompared.size() + " face(s) that did not match"); System.out.println("Source image rotation: " + compareFacesResult.sourceImageOrientationCorrection()); System.out.println("target image rotation: " + compareFacesResult.targetImageOrientationCorrection()); } catch (RekognitionException | FileNotFoundException e) { System.out.println("Failed to load source image " + sourceImage); System.exit(1); } } }
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For API details, see CompareFaces in Amazon SDK for Java 2.x API Reference.
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The following code example shows how to use CreateCollection
.
For more information, see Creating a collection.
- SDK for Java 2.x
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Note
There's more on GitHub. Find the complete example and learn how to set up and run in the Amazon Code Examples Repository
. 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: <collectionName>\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_EAST_1; RekognitionClient rekClient = RekognitionClient.builder() .region(region) .build(); System.out.println("Creating collection: " + collectionId); createMyCollection(rekClient, collectionId); rekClient.close(); } 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); } } }
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For API details, see CreateCollection in Amazon SDK for Java 2.x API Reference.
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The following code example shows how to use DeleteCollection
.
For more information, see Deleting a collection.
- SDK for Java 2.x
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Note
There's more on GitHub. Find the complete example and learn how to set up and run in the Amazon Code Examples Repository
. 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: <collectionId>\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(); } 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); } } }
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For API details, see DeleteCollection in Amazon SDK for Java 2.x API Reference.
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The following code example shows how to use DeleteFaces
.
For more information, see Deleting faces from a collection.
- SDK for Java 2.x
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Note
There's more on GitHub. Find the complete example and learn how to set up and run in the Amazon Code Examples Repository
. 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: <collectionId> <faceId>\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 != 1) { 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(); } 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); } } }
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For API details, see DeleteFaces in Amazon SDK for Java 2.x API Reference.
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The following code example shows how to use DescribeCollection
.
For more information, see Describing a collection.
- SDK for Java 2.x
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Note
There's more on GitHub. Find the complete example and learn how to set up and run in the Amazon Code Examples Repository
. 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: <collectionName> 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(); } 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); } } }
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For API details, see DescribeCollection in Amazon SDK for Java 2.x API Reference.
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The following code example shows how to use DetectFaces
.
For more information, see Detecting faces in an image.
- SDK for Java 2.x
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Note
There's more on GitHub. Find the complete example and learn how to set up and run in the Amazon Code Examples Repository
. import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.RekognitionException; import software.amazon.awssdk.services.rekognition.model.DetectFacesRequest; import software.amazon.awssdk.services.rekognition.model.DetectFacesResponse; import software.amazon.awssdk.services.rekognition.model.Image; import software.amazon.awssdk.services.rekognition.model.Attribute; import software.amazon.awssdk.services.rekognition.model.FaceDetail; import software.amazon.awssdk.services.rekognition.model.AgeRange; 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 DetectFaces { 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(); detectFacesinImage(rekClient, sourceImage); rekClient.close(); } public static void detectFacesinImage(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(); DetectFacesRequest facesRequest = DetectFacesRequest.builder() .attributes(Attribute.ALL) .image(souImage) .build(); DetectFacesResponse facesResponse = rekClient.detectFaces(facesRequest); List<FaceDetail> 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 | FileNotFoundException e) { System.out.println(e.getMessage()); System.exit(1); } } }
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For API details, see DetectFaces in Amazon SDK for Java 2.x API Reference.
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The following code example shows how to use DetectLabels
.
For more information, see Detecting labels in an image.
- SDK for Java 2.x
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Note
There's more on GitHub. Find the complete example and learn how to set up and run in the Amazon 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); } } }
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For API details, see DetectLabels in Amazon SDK for Java 2.x API Reference.
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The following code example shows how to use DetectModerationLabels
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For more information, see Detecting inappropriate images.
- SDK for Java 2.x
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Note
There's more on GitHub. Find the complete example and learn how to set up and run in the Amazon 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.RekognitionException; import software.amazon.awssdk.services.rekognition.model.Image; import software.amazon.awssdk.services.rekognition.model.DetectModerationLabelsRequest; import software.amazon.awssdk.services.rekognition.model.DetectModerationLabelsResponse; import software.amazon.awssdk.services.rekognition.model.ModerationLabel; 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 DetectModerationLabels { 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(); detectModLabels(rekClient, sourceImage); rekClient.close(); } 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); } } }
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For API details, see DetectModerationLabels in Amazon SDK for Java 2.x API Reference.
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The following code example shows how to use DetectText
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For more information, see Detecting text in an image.
- SDK for Java 2.x
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Note
There's more on GitHub. Find the complete example and learn how to set up and run in the Amazon 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.DetectTextRequest; import software.amazon.awssdk.services.rekognition.model.Image; import software.amazon.awssdk.services.rekognition.model.DetectTextResponse; import software.amazon.awssdk.services.rekognition.model.TextDetection; 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 DetectText { public static void main(String[] args) { final String usage = """ Usage: <sourceImage> Where: sourceImage - The path to the image that contains text (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(); detectTextLabels(rekClient, sourceImage); rekClient.close(); } public static void detectTextLabels(RekognitionClient rekClient, String sourceImage) { try { InputStream sourceStream = new FileInputStream(sourceImage); SdkBytes sourceBytes = SdkBytes.fromInputStream(sourceStream); Image souImage = Image.builder() .bytes(sourceBytes) .build(); DetectTextRequest textRequest = DetectTextRequest.builder() .image(souImage) .build(); DetectTextResponse textResponse = rekClient.detectText(textRequest); List<TextDetection> textCollection = textResponse.textDetections(); System.out.println("Detected lines and words"); for (TextDetection text : textCollection) { System.out.println("Detected: " + text.detectedText()); System.out.println("Confidence: " + text.confidence().toString()); System.out.println("Id : " + text.id()); System.out.println("Parent Id: " + text.parentId()); System.out.println("Type: " + text.type()); System.out.println(); } } catch (RekognitionException | FileNotFoundException e) { System.out.println(e.getMessage()); System.exit(1); } } }
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For API details, see DetectText in Amazon SDK for Java 2.x API Reference.
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The following code example shows how to use IndexFaces
.
For more information, see Adding faces to a collection.
- SDK for Java 2.x
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Note
There's more on GitHub. Find the complete example and learn how to set up and run in the Amazon 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.IndexFacesResponse; import software.amazon.awssdk.services.rekognition.model.IndexFacesRequest; import software.amazon.awssdk.services.rekognition.model.Image; import software.amazon.awssdk.services.rekognition.model.QualityFilter; import software.amazon.awssdk.services.rekognition.model.Attribute; import software.amazon.awssdk.services.rekognition.model.FaceRecord; import software.amazon.awssdk.services.rekognition.model.UnindexedFace; import software.amazon.awssdk.services.rekognition.model.RekognitionException; import software.amazon.awssdk.services.rekognition.model.Reason; 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 AddFacesToCollection { public static void main(String[] args) { final String usage = """ Usage: <collectionId> <sourceImage> Where: collectionName - The name of the collection. sourceImage - The path to the image (for example, C:\\AWS\\pic1.png).\s """; if (args.length != 2) { System.out.println(usage); System.exit(1); } String collectionId = args[0]; String sourceImage = args[1]; Region region = Region.US_EAST_1; RekognitionClient rekClient = RekognitionClient.builder() .region(region) .build(); addToCollection(rekClient, collectionId, sourceImage); rekClient.close(); } public static void addToCollection(RekognitionClient rekClient, String collectionId, String sourceImage) { try { InputStream sourceStream = new FileInputStream(sourceImage); SdkBytes sourceBytes = SdkBytes.fromInputStream(sourceStream); Image souImage = Image.builder() .bytes(sourceBytes) .build(); IndexFacesRequest facesRequest = IndexFacesRequest.builder() .collectionId(collectionId) .image(souImage) .maxFaces(1) .qualityFilter(QualityFilter.AUTO) .detectionAttributes(Attribute.DEFAULT) .build(); IndexFacesResponse facesResponse = rekClient.indexFaces(facesRequest); System.out.println("Results for the image"); System.out.println("\n Faces indexed:"); List<FaceRecord> faceRecords = facesResponse.faceRecords(); for (FaceRecord faceRecord : faceRecords) { System.out.println(" Face ID: " + faceRecord.face().faceId()); System.out.println(" Location:" + faceRecord.faceDetail().boundingBox().toString()); } List<UnindexedFace> unindexedFaces = facesResponse.unindexedFaces(); System.out.println("Faces not indexed:"); for (UnindexedFace unindexedFace : unindexedFaces) { System.out.println(" Location:" + unindexedFace.faceDetail().boundingBox().toString()); System.out.println(" Reasons:"); for (Reason reason : unindexedFace.reasons()) { System.out.println("Reason: " + reason); } } } catch (RekognitionException | FileNotFoundException e) { System.out.println(e.getMessage()); System.exit(1); } } }
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For API details, see IndexFaces in Amazon SDK for Java 2.x API Reference.
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The following code example shows how to use ListCollections
.
For more information, see Listing collections.
- SDK for Java 2.x
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Note
There's more on GitHub. Find the complete example and learn how to set up and run in the Amazon Code Examples Repository
. import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.ListCollectionsRequest; import software.amazon.awssdk.services.rekognition.model.ListCollectionsResponse; import software.amazon.awssdk.services.rekognition.model.RekognitionException; 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 ListCollections { public static void main(String[] args) { Region region = Region.US_EAST_1; RekognitionClient rekClient = RekognitionClient.builder() .region(region) .build(); System.out.println("Listing collections"); listAllCollections(rekClient); rekClient.close(); } public static void listAllCollections(RekognitionClient rekClient) { try { ListCollectionsRequest listCollectionsRequest = ListCollectionsRequest.builder() .maxResults(10) .build(); ListCollectionsResponse response = rekClient.listCollections(listCollectionsRequest); List<String> collectionIds = response.collectionIds(); for (String resultId : collectionIds) { System.out.println(resultId); } } catch (RekognitionException e) { System.out.println(e.getMessage()); System.exit(1); } } }
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For API details, see ListCollections in Amazon SDK for Java 2.x API Reference.
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The following code example shows how to use ListFaces
.
For more information, see Listing faces in a collection.
- SDK for Java 2.x
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Note
There's more on GitHub. Find the complete example and learn how to set up and run in the Amazon Code Examples Repository
. import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.Face; import software.amazon.awssdk.services.rekognition.model.ListFacesRequest; import software.amazon.awssdk.services.rekognition.model.ListFacesResponse; import software.amazon.awssdk.services.rekognition.model.RekognitionException; 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 ListFacesInCollection { public static void main(String[] args) { final String usage = """ Usage: <collectionId> Where: collectionId - 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_EAST_1; RekognitionClient rekClient = RekognitionClient.builder() .region(region) .build(); System.out.println("Faces in collection " + collectionId); listFacesCollection(rekClient, collectionId); rekClient.close(); } public static void listFacesCollection(RekognitionClient rekClient, String collectionId) { try { ListFacesRequest facesRequest = ListFacesRequest.builder() .collectionId(collectionId) .maxResults(10) .build(); ListFacesResponse facesResponse = rekClient.listFaces(facesRequest); List<Face> faces = facesResponse.faces(); for (Face face : faces) { System.out.println("Confidence level there is a face: " + face.confidence()); System.out.println("The face Id value is " + face.faceId()); } } catch (RekognitionException e) { System.out.println(e.getMessage()); System.exit(1); } } }
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For API details, see ListFaces in Amazon SDK for Java 2.x API Reference.
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The following code example shows how to use RecognizeCelebrities
.
For more information, see Recognizing celebrities in an image.
- 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 Amazon Code Examples Repository
. import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.core.SdkBytes; import java.io.FileInputStream; import java.io.FileNotFoundException; import java.io.InputStream; import java.util.List; import software.amazon.awssdk.services.rekognition.model.RecognizeCelebritiesRequest; import software.amazon.awssdk.services.rekognition.model.RecognizeCelebritiesResponse; import software.amazon.awssdk.services.rekognition.model.RekognitionException; import software.amazon.awssdk.services.rekognition.model.Image; import software.amazon.awssdk.services.rekognition.model.Celebrity; /** * 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 RecognizeCelebrities { 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(); System.out.println("Locating celebrities in " + sourceImage); recognizeAllCelebrities(rekClient, sourceImage); rekClient.close(); } public static void recognizeAllCelebrities(RekognitionClient rekClient, String sourceImage) { try { InputStream sourceStream = new FileInputStream(sourceImage); SdkBytes sourceBytes = SdkBytes.fromInputStream(sourceStream); Image souImage = Image.builder() .bytes(sourceBytes) .build(); RecognizeCelebritiesRequest request = RecognizeCelebritiesRequest.builder() .image(souImage) .build(); RecognizeCelebritiesResponse result = rekClient.recognizeCelebrities(request); List<Celebrity> celebs = result.celebrityFaces(); System.out.println(celebs.size() + " celebrity(s) were recognized.\n"); for (Celebrity celebrity : celebs) { System.out.println("Celebrity recognized: " + celebrity.name()); System.out.println("Celebrity ID: " + celebrity.id()); System.out.println("Further information (if available):"); for (String url : celebrity.urls()) { System.out.println(url); } System.out.println(); } System.out.println(result.unrecognizedFaces().size() + " face(s) were unrecognized."); } catch (RekognitionException | FileNotFoundException e) { System.out.println(e.getMessage()); System.exit(1); } } }
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For API details, see RecognizeCelebrities in Amazon SDK for Java 2.x API Reference.
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The following code example shows how to use SearchFaces
.
For more information, see Searching for a face (face ID).
- 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 Amazon 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.RekognitionException; import software.amazon.awssdk.services.rekognition.model.SearchFacesByImageRequest; import software.amazon.awssdk.services.rekognition.model.Image; import software.amazon.awssdk.services.rekognition.model.SearchFacesByImageResponse; import software.amazon.awssdk.services.rekognition.model.FaceMatch; import java.io.File; 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 SearchFaceMatchingImageCollection { public static void main(String[] args) { final String usage = """ Usage: <collectionId> <sourceImage> Where: collectionId - The id of the collection. \s sourceImage - The path to the image (for example, C:\\AWS\\pic1.png).\s """; if (args.length != 2) { System.out.println(usage); System.exit(1); } String collectionId = args[0]; String sourceImage = args[1]; Region region = Region.US_EAST_1; RekognitionClient rekClient = RekognitionClient.builder() .region(region) .build(); System.out.println("Searching for a face in a collections"); searchFaceInCollection(rekClient, collectionId, sourceImage); rekClient.close(); } public static void searchFaceInCollection(RekognitionClient rekClient, String collectionId, String sourceImage) { try { InputStream sourceStream = new FileInputStream(new File(sourceImage)); SdkBytes sourceBytes = SdkBytes.fromInputStream(sourceStream); Image souImage = Image.builder() .bytes(sourceBytes) .build(); SearchFacesByImageRequest facesByImageRequest = SearchFacesByImageRequest.builder() .image(souImage) .maxFaces(10) .faceMatchThreshold(70F) .collectionId(collectionId) .build(); SearchFacesByImageResponse imageResponse = rekClient.searchFacesByImage(facesByImageRequest); System.out.println("Faces matching in the collection"); List<FaceMatch> faceImageMatches = imageResponse.faceMatches(); for (FaceMatch face : faceImageMatches) { System.out.println("The similarity level is " + face.similarity()); System.out.println(); } } catch (RekognitionException | FileNotFoundException e) { System.out.println(e.getMessage()); System.exit(1); } } }
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For API details, see SearchFaces in Amazon SDK for Java 2.x API Reference.
-
The following code example shows how to use SearchFacesByImage
.
For more information, see Searching for a face (image).
- 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 Amazon Code Examples Repository
. import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.SearchFacesRequest; import software.amazon.awssdk.services.rekognition.model.SearchFacesResponse; import software.amazon.awssdk.services.rekognition.model.FaceMatch; import software.amazon.awssdk.services.rekognition.model.RekognitionException; 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 SearchFaceMatchingIdCollection { public static void main(String[] args) { final String usage = """ Usage: <collectionId> <sourceImage> Where: collectionId - The id of the collection. \s sourceImage - The path to the image (for example, C:\\AWS\\pic1.png).\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("Searching for a face in a collections"); searchFacebyId(rekClient, collectionId, faceId); rekClient.close(); } public static void searchFacebyId(RekognitionClient rekClient, String collectionId, String faceId) { try { SearchFacesRequest searchFacesRequest = SearchFacesRequest.builder() .collectionId(collectionId) .faceId(faceId) .faceMatchThreshold(70F) .maxFaces(2) .build(); SearchFacesResponse imageResponse = rekClient.searchFaces(searchFacesRequest); System.out.println("Faces matching in the collection"); List<FaceMatch> faceImageMatches = imageResponse.faceMatches(); for (FaceMatch face : faceImageMatches) { System.out.println("The similarity level is " + face.similarity()); System.out.println(); } } catch (RekognitionException e) { System.out.println(e.getMessage()); System.exit(1); } } }
-
For API details, see SearchFacesByImage in Amazon SDK for Java 2.x API Reference.
-
Scenarios
The following code example shows how to create a serverless application that lets users manage photos using labels.
- SDK for Java 2.x
-
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
. For a deep dive into the origin of this example see the post on Amazon Community
. Services used in this example
API Gateway
DynamoDB
Lambda
Amazon Rekognition
Amazon S3
Amazon SNS
The following code example shows how to build an app that uses Amazon Rekognition to detect Personal Protective Equipment (PPE) in images.
- SDK for Java 2.x
-
Shows how to create an Amazon Lambda function that detects images with Personal Protective Equipment.
For complete source code and instructions on how to set up and run, see the full example on GitHub
. Services used in this example
DynamoDB
Amazon Rekognition
Amazon S3
Amazon SES
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 Java 2.x
-
Note
There's more on GitHub. Find the complete example and learn how to set up and run in the Amazon Code Examples Repository
. Get celebrity results from a video located in an Amazon S3 bucket.
import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.S3Object; import software.amazon.awssdk.services.rekognition.model.NotificationChannel; import software.amazon.awssdk.services.rekognition.model.Video; import software.amazon.awssdk.services.rekognition.model.StartCelebrityRecognitionResponse; import software.amazon.awssdk.services.rekognition.model.RekognitionException; import software.amazon.awssdk.services.rekognition.model.CelebrityRecognitionSortBy; import software.amazon.awssdk.services.rekognition.model.VideoMetadata; import software.amazon.awssdk.services.rekognition.model.CelebrityRecognition; import software.amazon.awssdk.services.rekognition.model.CelebrityDetail; import software.amazon.awssdk.services.rekognition.model.StartCelebrityRecognitionRequest; import software.amazon.awssdk.services.rekognition.model.GetCelebrityRecognitionRequest; import software.amazon.awssdk.services.rekognition.model.GetCelebrityRecognitionResponse; import java.util.List; /** * To run this code example, ensure that you perform the Prerequisites as stated * in the Amazon Rekognition Guide: * https://docs.aws.amazon.com/rekognition/latest/dg/video-analyzing-with-sqs.html * * Also, ensure that set up your development environment, including your * credentials. * * For information, see this documentation topic: * * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html */ public class VideoCelebrityDetection { private static String startJobId = ""; public static void main(String[] args) { final String usage = """ Usage: <bucket> <video> <topicArn> <roleArn> Where: bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s video - The name of video (for example, people.mp4).\s topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s """; if (args.length != 4) { System.out.println(usage); System.exit(1); } String bucket = args[0]; String video = args[1]; String topicArn = args[2]; String roleArn = args[3]; Region region = Region.US_EAST_1; RekognitionClient rekClient = RekognitionClient.builder() .region(region) .build(); NotificationChannel channel = NotificationChannel.builder() .snsTopicArn(topicArn) .roleArn(roleArn) .build(); startCelebrityDetection(rekClient, channel, bucket, video); getCelebrityDetectionResults(rekClient); System.out.println("This example is done!"); rekClient.close(); } public static void startCelebrityDetection(RekognitionClient rekClient, NotificationChannel channel, String bucket, String video) { try { S3Object s3Obj = S3Object.builder() .bucket(bucket) .name(video) .build(); Video vidOb = Video.builder() .s3Object(s3Obj) .build(); StartCelebrityRecognitionRequest recognitionRequest = StartCelebrityRecognitionRequest.builder() .jobTag("Celebrities") .notificationChannel(channel) .video(vidOb) .build(); StartCelebrityRecognitionResponse startCelebrityRecognitionResult = rekClient .startCelebrityRecognition(recognitionRequest); startJobId = startCelebrityRecognitionResult.jobId(); } catch (RekognitionException e) { System.out.println(e.getMessage()); System.exit(1); } } public static void getCelebrityDetectionResults(RekognitionClient rekClient) { try { String paginationToken = null; GetCelebrityRecognitionResponse recognitionResponse = null; boolean finished = false; String status; int yy = 0; do { if (recognitionResponse != null) paginationToken = recognitionResponse.nextToken(); GetCelebrityRecognitionRequest recognitionRequest = GetCelebrityRecognitionRequest.builder() .jobId(startJobId) .nextToken(paginationToken) .sortBy(CelebrityRecognitionSortBy.TIMESTAMP) .maxResults(10) .build(); // Wait until the job succeeds while (!finished) { recognitionResponse = rekClient.getCelebrityRecognition(recognitionRequest); status = recognitionResponse.jobStatusAsString(); if (status.compareTo("SUCCEEDED") == 0) finished = true; else { System.out.println(yy + " status is: " + status); Thread.sleep(1000); } yy++; } finished = false; // Proceed when the job is done - otherwise VideoMetadata is null. VideoMetadata videoMetaData = recognitionResponse.videoMetadata(); System.out.println("Format: " + videoMetaData.format()); System.out.println("Codec: " + videoMetaData.codec()); System.out.println("Duration: " + videoMetaData.durationMillis()); System.out.println("FrameRate: " + videoMetaData.frameRate()); System.out.println("Job"); List<CelebrityRecognition> celebs = recognitionResponse.celebrities(); for (CelebrityRecognition celeb : celebs) { long seconds = celeb.timestamp() / 1000; System.out.print("Sec: " + seconds + " "); CelebrityDetail details = celeb.celebrity(); System.out.println("Name: " + details.name()); System.out.println("Id: " + details.id()); System.out.println(); } } while (recognitionResponse.nextToken() != null); } catch (RekognitionException | InterruptedException e) { System.out.println(e.getMessage()); System.exit(1); } } }
Detect labels in a video by a label detection operation.
import com.fasterxml.jackson.core.JsonProcessingException; import com.fasterxml.jackson.databind.JsonMappingException; import com.fasterxml.jackson.databind.JsonNode; import com.fasterxml.jackson.databind.ObjectMapper; import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionResponse; import software.amazon.awssdk.services.rekognition.model.NotificationChannel; import software.amazon.awssdk.services.rekognition.model.S3Object; import software.amazon.awssdk.services.rekognition.model.Video; import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionRequest; import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionRequest; import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionResponse; import software.amazon.awssdk.services.rekognition.model.RekognitionException; import software.amazon.awssdk.services.rekognition.model.LabelDetectionSortBy; import software.amazon.awssdk.services.rekognition.model.VideoMetadata; import software.amazon.awssdk.services.rekognition.model.LabelDetection; import software.amazon.awssdk.services.rekognition.model.Label; import software.amazon.awssdk.services.rekognition.model.Instance; import software.amazon.awssdk.services.rekognition.model.Parent; import software.amazon.awssdk.services.sqs.SqsClient; import software.amazon.awssdk.services.sqs.model.Message; import software.amazon.awssdk.services.sqs.model.ReceiveMessageRequest; import software.amazon.awssdk.services.sqs.model.DeleteMessageRequest; 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 VideoDetect { private static String startJobId = ""; public static void main(String[] args) { final String usage = """ Usage: <bucket> <video> <queueUrl> <topicArn> <roleArn> Where: bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s video - The name of the video (for example, people.mp4).\s queueUrl- The URL of a SQS queue.\s topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s """; if (args.length != 5) { System.out.println(usage); System.exit(1); } String bucket = args[0]; String video = args[1]; String queueUrl = args[2]; String topicArn = args[3]; String roleArn = args[4]; Region region = Region.US_EAST_1; RekognitionClient rekClient = RekognitionClient.builder() .region(region) .build(); SqsClient sqs = SqsClient.builder() .region(Region.US_EAST_1) .build(); NotificationChannel channel = NotificationChannel.builder() .snsTopicArn(topicArn) .roleArn(roleArn) .build(); startLabels(rekClient, channel, bucket, video); getLabelJob(rekClient, sqs, queueUrl); System.out.println("This example is done!"); sqs.close(); rekClient.close(); } public static void startLabels(RekognitionClient rekClient, NotificationChannel channel, String bucket, String video) { try { S3Object s3Obj = S3Object.builder() .bucket(bucket) .name(video) .build(); Video vidOb = Video.builder() .s3Object(s3Obj) .build(); StartLabelDetectionRequest labelDetectionRequest = StartLabelDetectionRequest.builder() .jobTag("DetectingLabels") .notificationChannel(channel) .video(vidOb) .minConfidence(50F) .build(); StartLabelDetectionResponse labelDetectionResponse = rekClient.startLabelDetection(labelDetectionRequest); startJobId = labelDetectionResponse.jobId(); boolean ans = true; String status = ""; int yy = 0; while (ans) { GetLabelDetectionRequest detectionRequest = GetLabelDetectionRequest.builder() .jobId(startJobId) .maxResults(10) .build(); GetLabelDetectionResponse result = rekClient.getLabelDetection(detectionRequest); status = result.jobStatusAsString(); if (status.compareTo("SUCCEEDED") == 0) ans = false; else System.out.println(yy + " status is: " + status); Thread.sleep(1000); yy++; } System.out.println(startJobId + " status is: " + status); } catch (RekognitionException | InterruptedException e) { e.getMessage(); System.exit(1); } } public static void getLabelJob(RekognitionClient rekClient, SqsClient sqs, String queueUrl) { List<Message> messages; ReceiveMessageRequest messageRequest = ReceiveMessageRequest.builder() .queueUrl(queueUrl) .build(); try { messages = sqs.receiveMessage(messageRequest).messages(); if (!messages.isEmpty()) { for (Message message : messages) { String notification = message.body(); // Get the status and job id from the notification ObjectMapper mapper = new ObjectMapper(); JsonNode jsonMessageTree = mapper.readTree(notification); JsonNode messageBodyText = jsonMessageTree.get("Message"); ObjectMapper operationResultMapper = new ObjectMapper(); JsonNode jsonResultTree = operationResultMapper.readTree(messageBodyText.textValue()); JsonNode operationJobId = jsonResultTree.get("JobId"); JsonNode operationStatus = jsonResultTree.get("Status"); System.out.println("Job found in JSON is " + operationJobId); DeleteMessageRequest deleteMessageRequest = DeleteMessageRequest.builder() .queueUrl(queueUrl) .build(); String jobId = operationJobId.textValue(); if (startJobId.compareTo(jobId) == 0) { System.out.println("Job id: " + operationJobId); System.out.println("Status : " + operationStatus.toString()); if (operationStatus.asText().equals("SUCCEEDED")) getResultsLabels(rekClient); else System.out.println("Video analysis failed"); sqs.deleteMessage(deleteMessageRequest); } else { System.out.println("Job received was not job " + startJobId); sqs.deleteMessage(deleteMessageRequest); } } } } catch (RekognitionException e) { e.getMessage(); System.exit(1); } catch (JsonMappingException e) { e.printStackTrace(); } catch (JsonProcessingException e) { e.printStackTrace(); } } // Gets the job results by calling GetLabelDetection private static void getResultsLabels(RekognitionClient rekClient) { int maxResults = 10; String paginationToken = null; GetLabelDetectionResponse labelDetectionResult = null; try { do { if (labelDetectionResult != null) paginationToken = labelDetectionResult.nextToken(); GetLabelDetectionRequest labelDetectionRequest = GetLabelDetectionRequest.builder() .jobId(startJobId) .sortBy(LabelDetectionSortBy.TIMESTAMP) .maxResults(maxResults) .nextToken(paginationToken) .build(); labelDetectionResult = rekClient.getLabelDetection(labelDetectionRequest); VideoMetadata videoMetaData = labelDetectionResult.videoMetadata(); System.out.println("Format: " + videoMetaData.format()); System.out.println("Codec: " + videoMetaData.codec()); System.out.println("Duration: " + videoMetaData.durationMillis()); System.out.println("FrameRate: " + videoMetaData.frameRate()); List<LabelDetection> detectedLabels = labelDetectionResult.labels(); for (LabelDetection detectedLabel : detectedLabels) { long seconds = detectedLabel.timestamp(); Label label = detectedLabel.label(); System.out.println("Millisecond: " + seconds + " "); System.out.println(" Label:" + label.name()); System.out.println(" Confidence:" + detectedLabel.label().confidence().toString()); List<Instance> instances = label.instances(); System.out.println(" Instances of " + label.name()); if (instances.isEmpty()) { System.out.println(" " + "None"); } else { for (Instance instance : instances) { System.out.println(" Confidence: " + instance.confidence().toString()); System.out.println(" Bounding box: " + instance.boundingBox().toString()); } } System.out.println(" Parent labels for " + label.name() + ":"); List<Parent> parents = label.parents(); if (parents.isEmpty()) { System.out.println(" None"); } else { for (Parent parent : parents) { System.out.println(" " + parent.name()); } } System.out.println(); } } while (labelDetectionResult != null && labelDetectionResult.nextToken() != null); } catch (RekognitionException e) { e.getMessage(); System.exit(1); } } }
Detect faces in a video stored in an Amazon S3 bucket.
import com.fasterxml.jackson.core.JsonProcessingException; import com.fasterxml.jackson.databind.JsonMappingException; import com.fasterxml.jackson.databind.JsonNode; import com.fasterxml.jackson.databind.ObjectMapper; import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionResponse; import software.amazon.awssdk.services.rekognition.model.NotificationChannel; import software.amazon.awssdk.services.rekognition.model.S3Object; import software.amazon.awssdk.services.rekognition.model.Video; import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionRequest; import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionRequest; import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionResponse; import software.amazon.awssdk.services.rekognition.model.RekognitionException; import software.amazon.awssdk.services.rekognition.model.LabelDetectionSortBy; import software.amazon.awssdk.services.rekognition.model.VideoMetadata; import software.amazon.awssdk.services.rekognition.model.LabelDetection; import software.amazon.awssdk.services.rekognition.model.Label; import software.amazon.awssdk.services.rekognition.model.Instance; import software.amazon.awssdk.services.rekognition.model.Parent; import software.amazon.awssdk.services.sqs.SqsClient; import software.amazon.awssdk.services.sqs.model.Message; import software.amazon.awssdk.services.sqs.model.ReceiveMessageRequest; import software.amazon.awssdk.services.sqs.model.DeleteMessageRequest; 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 VideoDetect { private static String startJobId = ""; public static void main(String[] args) { final String usage = """ Usage: <bucket> <video> <queueUrl> <topicArn> <roleArn> Where: bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s video - The name of the video (for example, people.mp4).\s queueUrl- The URL of a SQS queue.\s topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s """; if (args.length != 5) { System.out.println(usage); System.exit(1); } String bucket = args[0]; String video = args[1]; String queueUrl = args[2]; String topicArn = args[3]; String roleArn = args[4]; Region region = Region.US_EAST_1; RekognitionClient rekClient = RekognitionClient.builder() .region(region) .build(); SqsClient sqs = SqsClient.builder() .region(Region.US_EAST_1) .build(); NotificationChannel channel = NotificationChannel.builder() .snsTopicArn(topicArn) .roleArn(roleArn) .build(); startLabels(rekClient, channel, bucket, video); getLabelJob(rekClient, sqs, queueUrl); System.out.println("This example is done!"); sqs.close(); rekClient.close(); } public static void startLabels(RekognitionClient rekClient, NotificationChannel channel, String bucket, String video) { try { S3Object s3Obj = S3Object.builder() .bucket(bucket) .name(video) .build(); Video vidOb = Video.builder() .s3Object(s3Obj) .build(); StartLabelDetectionRequest labelDetectionRequest = StartLabelDetectionRequest.builder() .jobTag("DetectingLabels") .notificationChannel(channel) .video(vidOb) .minConfidence(50F) .build(); StartLabelDetectionResponse labelDetectionResponse = rekClient.startLabelDetection(labelDetectionRequest); startJobId = labelDetectionResponse.jobId(); boolean ans = true; String status = ""; int yy = 0; while (ans) { GetLabelDetectionRequest detectionRequest = GetLabelDetectionRequest.builder() .jobId(startJobId) .maxResults(10) .build(); GetLabelDetectionResponse result = rekClient.getLabelDetection(detectionRequest); status = result.jobStatusAsString(); if (status.compareTo("SUCCEEDED") == 0) ans = false; else System.out.println(yy + " status is: " + status); Thread.sleep(1000); yy++; } System.out.println(startJobId + " status is: " + status); } catch (RekognitionException | InterruptedException e) { e.getMessage(); System.exit(1); } } public static void getLabelJob(RekognitionClient rekClient, SqsClient sqs, String queueUrl) { List<Message> messages; ReceiveMessageRequest messageRequest = ReceiveMessageRequest.builder() .queueUrl(queueUrl) .build(); try { messages = sqs.receiveMessage(messageRequest).messages(); if (!messages.isEmpty()) { for (Message message : messages) { String notification = message.body(); // Get the status and job id from the notification ObjectMapper mapper = new ObjectMapper(); JsonNode jsonMessageTree = mapper.readTree(notification); JsonNode messageBodyText = jsonMessageTree.get("Message"); ObjectMapper operationResultMapper = new ObjectMapper(); JsonNode jsonResultTree = operationResultMapper.readTree(messageBodyText.textValue()); JsonNode operationJobId = jsonResultTree.get("JobId"); JsonNode operationStatus = jsonResultTree.get("Status"); System.out.println("Job found in JSON is " + operationJobId); DeleteMessageRequest deleteMessageRequest = DeleteMessageRequest.builder() .queueUrl(queueUrl) .build(); String jobId = operationJobId.textValue(); if (startJobId.compareTo(jobId) == 0) { System.out.println("Job id: " + operationJobId); System.out.println("Status : " + operationStatus.toString()); if (operationStatus.asText().equals("SUCCEEDED")) getResultsLabels(rekClient); else System.out.println("Video analysis failed"); sqs.deleteMessage(deleteMessageRequest); } else { System.out.println("Job received was not job " + startJobId); sqs.deleteMessage(deleteMessageRequest); } } } } catch (RekognitionException e) { e.getMessage(); System.exit(1); } catch (JsonMappingException e) { e.printStackTrace(); } catch (JsonProcessingException e) { e.printStackTrace(); } } // Gets the job results by calling GetLabelDetection private static void getResultsLabels(RekognitionClient rekClient) { int maxResults = 10; String paginationToken = null; GetLabelDetectionResponse labelDetectionResult = null; try { do { if (labelDetectionResult != null) paginationToken = labelDetectionResult.nextToken(); GetLabelDetectionRequest labelDetectionRequest = GetLabelDetectionRequest.builder() .jobId(startJobId) .sortBy(LabelDetectionSortBy.TIMESTAMP) .maxResults(maxResults) .nextToken(paginationToken) .build(); labelDetectionResult = rekClient.getLabelDetection(labelDetectionRequest); VideoMetadata videoMetaData = labelDetectionResult.videoMetadata(); System.out.println("Format: " + videoMetaData.format()); System.out.println("Codec: " + videoMetaData.codec()); System.out.println("Duration: " + videoMetaData.durationMillis()); System.out.println("FrameRate: " + videoMetaData.frameRate()); List<LabelDetection> detectedLabels = labelDetectionResult.labels(); for (LabelDetection detectedLabel : detectedLabels) { long seconds = detectedLabel.timestamp(); Label label = detectedLabel.label(); System.out.println("Millisecond: " + seconds + " "); System.out.println(" Label:" + label.name()); System.out.println(" Confidence:" + detectedLabel.label().confidence().toString()); List<Instance> instances = label.instances(); System.out.println(" Instances of " + label.name()); if (instances.isEmpty()) { System.out.println(" " + "None"); } else { for (Instance instance : instances) { System.out.println(" Confidence: " + instance.confidence().toString()); System.out.println(" Bounding box: " + instance.boundingBox().toString()); } } System.out.println(" Parent labels for " + label.name() + ":"); List<Parent> parents = label.parents(); if (parents.isEmpty()) { System.out.println(" None"); } else { for (Parent parent : parents) { System.out.println(" " + parent.name()); } } System.out.println(); } } while (labelDetectionResult != null && labelDetectionResult.nextToken() != null); } catch (RekognitionException e) { e.getMessage(); System.exit(1); } } }
Detect inappropriate or offensive content in a video stored in an Amazon S3 bucket.
import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.NotificationChannel; import software.amazon.awssdk.services.rekognition.model.S3Object; import software.amazon.awssdk.services.rekognition.model.Video; import software.amazon.awssdk.services.rekognition.model.StartContentModerationRequest; import software.amazon.awssdk.services.rekognition.model.StartContentModerationResponse; import software.amazon.awssdk.services.rekognition.model.RekognitionException; import software.amazon.awssdk.services.rekognition.model.GetContentModerationResponse; import software.amazon.awssdk.services.rekognition.model.GetContentModerationRequest; import software.amazon.awssdk.services.rekognition.model.VideoMetadata; import software.amazon.awssdk.services.rekognition.model.ContentModerationDetection; 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 VideoDetectInappropriate { private static String startJobId = ""; public static void main(String[] args) { final String usage = """ Usage: <bucket> <video> <topicArn> <roleArn> Where: bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s video - The name of video (for example, people.mp4).\s topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s """; if (args.length != 4) { System.out.println(usage); System.exit(1); } String bucket = args[0]; String video = args[1]; String topicArn = args[2]; String roleArn = args[3]; Region region = Region.US_EAST_1; RekognitionClient rekClient = RekognitionClient.builder() .region(region) .build(); NotificationChannel channel = NotificationChannel.builder() .snsTopicArn(topicArn) .roleArn(roleArn) .build(); startModerationDetection(rekClient, channel, bucket, video); getModResults(rekClient); System.out.println("This example is done!"); rekClient.close(); } public static void startModerationDetection(RekognitionClient rekClient, NotificationChannel channel, String bucket, String video) { try { S3Object s3Obj = S3Object.builder() .bucket(bucket) .name(video) .build(); Video vidOb = Video.builder() .s3Object(s3Obj) .build(); StartContentModerationRequest modDetectionRequest = StartContentModerationRequest.builder() .jobTag("Moderation") .notificationChannel(channel) .video(vidOb) .build(); StartContentModerationResponse startModDetectionResult = rekClient .startContentModeration(modDetectionRequest); startJobId = startModDetectionResult.jobId(); } catch (RekognitionException e) { System.out.println(e.getMessage()); System.exit(1); } } public static void getModResults(RekognitionClient rekClient) { try { String paginationToken = null; GetContentModerationResponse modDetectionResponse = null; boolean finished = false; String status; int yy = 0; do { if (modDetectionResponse != null) paginationToken = modDetectionResponse.nextToken(); GetContentModerationRequest modRequest = GetContentModerationRequest.builder() .jobId(startJobId) .nextToken(paginationToken) .maxResults(10) .build(); // Wait until the job succeeds. while (!finished) { modDetectionResponse = rekClient.getContentModeration(modRequest); status = modDetectionResponse.jobStatusAsString(); if (status.compareTo("SUCCEEDED") == 0) finished = true; else { System.out.println(yy + " status is: " + status); Thread.sleep(1000); } yy++; } finished = false; // Proceed when the job is done - otherwise VideoMetadata is null. VideoMetadata videoMetaData = modDetectionResponse.videoMetadata(); System.out.println("Format: " + videoMetaData.format()); System.out.println("Codec: " + videoMetaData.codec()); System.out.println("Duration: " + videoMetaData.durationMillis()); System.out.println("FrameRate: " + videoMetaData.frameRate()); System.out.println("Job"); List<ContentModerationDetection> mods = modDetectionResponse.moderationLabels(); for (ContentModerationDetection mod : mods) { long seconds = mod.timestamp() / 1000; System.out.print("Mod label: " + seconds + " "); System.out.println(mod.moderationLabel().toString()); System.out.println(); } } while (modDetectionResponse != null && modDetectionResponse.nextToken() != null); } catch (RekognitionException | InterruptedException e) { System.out.println(e.getMessage()); System.exit(1); } } }
Detect technical cue segments and shot detection segments in a video stored in an Amazon S3 bucket.
import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.S3Object; import software.amazon.awssdk.services.rekognition.model.NotificationChannel; import software.amazon.awssdk.services.rekognition.model.Video; import software.amazon.awssdk.services.rekognition.model.StartShotDetectionFilter; import software.amazon.awssdk.services.rekognition.model.StartTechnicalCueDetectionFilter; import software.amazon.awssdk.services.rekognition.model.StartSegmentDetectionFilters; import software.amazon.awssdk.services.rekognition.model.StartSegmentDetectionRequest; import software.amazon.awssdk.services.rekognition.model.StartSegmentDetectionResponse; import software.amazon.awssdk.services.rekognition.model.RekognitionException; import software.amazon.awssdk.services.rekognition.model.GetSegmentDetectionResponse; import software.amazon.awssdk.services.rekognition.model.GetSegmentDetectionRequest; import software.amazon.awssdk.services.rekognition.model.VideoMetadata; import software.amazon.awssdk.services.rekognition.model.SegmentDetection; import software.amazon.awssdk.services.rekognition.model.TechnicalCueSegment; import software.amazon.awssdk.services.rekognition.model.ShotSegment; import software.amazon.awssdk.services.rekognition.model.SegmentType; import software.amazon.awssdk.services.sqs.SqsClient; 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 VideoDetectSegment { private static String startJobId = ""; public static void main(String[] args) { final String usage = """ Usage: <bucket> <video> <topicArn> <roleArn> Where: bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s video - The name of video (for example, people.mp4).\s topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s """; if (args.length != 4) { System.out.println(usage); System.exit(1); } String bucket = args[0]; String video = args[1]; String topicArn = args[2]; String roleArn = args[3]; Region region = Region.US_EAST_1; RekognitionClient rekClient = RekognitionClient.builder() .region(region) .build(); SqsClient sqs = SqsClient.builder() .region(Region.US_EAST_1) .build(); NotificationChannel channel = NotificationChannel.builder() .snsTopicArn(topicArn) .roleArn(roleArn) .build(); startSegmentDetection(rekClient, channel, bucket, video); getSegmentResults(rekClient); System.out.println("This example is done!"); sqs.close(); rekClient.close(); } public static void startSegmentDetection(RekognitionClient rekClient, NotificationChannel channel, String bucket, String video) { try { S3Object s3Obj = S3Object.builder() .bucket(bucket) .name(video) .build(); Video vidOb = Video.builder() .s3Object(s3Obj) .build(); StartShotDetectionFilter cueDetectionFilter = StartShotDetectionFilter.builder() .minSegmentConfidence(60F) .build(); StartTechnicalCueDetectionFilter technicalCueDetectionFilter = StartTechnicalCueDetectionFilter.builder() .minSegmentConfidence(60F) .build(); StartSegmentDetectionFilters filters = StartSegmentDetectionFilters.builder() .shotFilter(cueDetectionFilter) .technicalCueFilter(technicalCueDetectionFilter) .build(); StartSegmentDetectionRequest segDetectionRequest = StartSegmentDetectionRequest.builder() .jobTag("DetectingLabels") .notificationChannel(channel) .segmentTypes(SegmentType.TECHNICAL_CUE, SegmentType.SHOT) .video(vidOb) .filters(filters) .build(); StartSegmentDetectionResponse segDetectionResponse = rekClient.startSegmentDetection(segDetectionRequest); startJobId = segDetectionResponse.jobId(); } catch (RekognitionException e) { e.getMessage(); System.exit(1); } } public static void getSegmentResults(RekognitionClient rekClient) { try { String paginationToken = null; GetSegmentDetectionResponse segDetectionResponse = null; boolean finished = false; String status; int yy = 0; do { if (segDetectionResponse != null) paginationToken = segDetectionResponse.nextToken(); GetSegmentDetectionRequest recognitionRequest = GetSegmentDetectionRequest.builder() .jobId(startJobId) .nextToken(paginationToken) .maxResults(10) .build(); // Wait until the job succeeds. while (!finished) { segDetectionResponse = rekClient.getSegmentDetection(recognitionRequest); status = segDetectionResponse.jobStatusAsString(); if (status.compareTo("SUCCEEDED") == 0) finished = true; else { System.out.println(yy + " status is: " + status); Thread.sleep(1000); } yy++; } finished = false; // Proceed when the job is done - otherwise VideoMetadata is null. List<VideoMetadata> videoMetaData = segDetectionResponse.videoMetadata(); for (VideoMetadata metaData : videoMetaData) { System.out.println("Format: " + metaData.format()); System.out.println("Codec: " + metaData.codec()); System.out.println("Duration: " + metaData.durationMillis()); System.out.println("FrameRate: " + metaData.frameRate()); System.out.println("Job"); } List<SegmentDetection> detectedSegments = segDetectionResponse.segments(); for (SegmentDetection detectedSegment : detectedSegments) { String type = detectedSegment.type().toString(); if (type.contains(SegmentType.TECHNICAL_CUE.toString())) { System.out.println("Technical Cue"); TechnicalCueSegment segmentCue = detectedSegment.technicalCueSegment(); System.out.println("\tType: " + segmentCue.type()); System.out.println("\tConfidence: " + segmentCue.confidence().toString()); } if (type.contains(SegmentType.SHOT.toString())) { System.out.println("Shot"); ShotSegment segmentShot = detectedSegment.shotSegment(); System.out.println("\tIndex " + segmentShot.index()); System.out.println("\tConfidence: " + segmentShot.confidence().toString()); } long seconds = detectedSegment.durationMillis(); System.out.println("\tDuration : " + seconds + " milliseconds"); System.out.println("\tStart time code: " + detectedSegment.startTimecodeSMPTE()); System.out.println("\tEnd time code: " + detectedSegment.endTimecodeSMPTE()); System.out.println("\tDuration time code: " + detectedSegment.durationSMPTE()); System.out.println(); } } while (segDetectionResponse != null && segDetectionResponse.nextToken() != null); } catch (RekognitionException | InterruptedException e) { System.out.println(e.getMessage()); System.exit(1); } } }
Detect text in a video stored in a video stored in an Amazon S3 bucket.
import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.S3Object; import software.amazon.awssdk.services.rekognition.model.NotificationChannel; import software.amazon.awssdk.services.rekognition.model.Video; import software.amazon.awssdk.services.rekognition.model.StartTextDetectionRequest; import software.amazon.awssdk.services.rekognition.model.StartTextDetectionResponse; import software.amazon.awssdk.services.rekognition.model.RekognitionException; import software.amazon.awssdk.services.rekognition.model.GetTextDetectionResponse; import software.amazon.awssdk.services.rekognition.model.GetTextDetectionRequest; import software.amazon.awssdk.services.rekognition.model.VideoMetadata; import software.amazon.awssdk.services.rekognition.model.TextDetectionResult; 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 VideoDetectText { private static String startJobId = ""; public static void main(String[] args) { final String usage = """ Usage: <bucket> <video> <topicArn> <roleArn> Where: bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s video - The name of video (for example, people.mp4).\s topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s """; if (args.length != 4) { System.out.println(usage); System.exit(1); } String bucket = args[0]; String video = args[1]; String topicArn = args[2]; String roleArn = args[3]; Region region = Region.US_EAST_1; RekognitionClient rekClient = RekognitionClient.builder() .region(region) .build(); NotificationChannel channel = NotificationChannel.builder() .snsTopicArn(topicArn) .roleArn(roleArn) .build(); startTextLabels(rekClient, channel, bucket, video); getTextResults(rekClient); System.out.println("This example is done!"); rekClient.close(); } public static void startTextLabels(RekognitionClient rekClient, NotificationChannel channel, String bucket, String video) { try { S3Object s3Obj = S3Object.builder() .bucket(bucket) .name(video) .build(); Video vidOb = Video.builder() .s3Object(s3Obj) .build(); StartTextDetectionRequest labelDetectionRequest = StartTextDetectionRequest.builder() .jobTag("DetectingLabels") .notificationChannel(channel) .video(vidOb) .build(); StartTextDetectionResponse labelDetectionResponse = rekClient.startTextDetection(labelDetectionRequest); startJobId = labelDetectionResponse.jobId(); } catch (RekognitionException e) { System.out.println(e.getMessage()); System.exit(1); } } public static void getTextResults(RekognitionClient rekClient) { try { String paginationToken = null; GetTextDetectionResponse textDetectionResponse = null; boolean finished = false; String status; int yy = 0; do { if (textDetectionResponse != null) paginationToken = textDetectionResponse.nextToken(); GetTextDetectionRequest recognitionRequest = GetTextDetectionRequest.builder() .jobId(startJobId) .nextToken(paginationToken) .maxResults(10) .build(); // Wait until the job succeeds. while (!finished) { textDetectionResponse = rekClient.getTextDetection(recognitionRequest); status = textDetectionResponse.jobStatusAsString(); if (status.compareTo("SUCCEEDED") == 0) finished = true; else { System.out.println(yy + " status is: " + status); Thread.sleep(1000); } yy++; } finished = false; // Proceed when the job is done - otherwise VideoMetadata is null. VideoMetadata videoMetaData = textDetectionResponse.videoMetadata(); System.out.println("Format: " + videoMetaData.format()); System.out.println("Codec: " + videoMetaData.codec()); System.out.println("Duration: " + videoMetaData.durationMillis()); System.out.println("FrameRate: " + videoMetaData.frameRate()); System.out.println("Job"); List<TextDetectionResult> labels = textDetectionResponse.textDetections(); for (TextDetectionResult detectedText : labels) { System.out.println("Confidence: " + detectedText.textDetection().confidence().toString()); System.out.println("Id : " + detectedText.textDetection().id()); System.out.println("Parent Id: " + detectedText.textDetection().parentId()); System.out.println("Type: " + detectedText.textDetection().type()); System.out.println("Text: " + detectedText.textDetection().detectedText()); System.out.println(); } } while (textDetectionResponse != null && textDetectionResponse.nextToken() != null); } catch (RekognitionException | InterruptedException e) { System.out.println(e.getMessage()); System.exit(1); } } }
Detect people in a video stored in a video stored in an Amazon S3 bucket.
import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.S3Object; import software.amazon.awssdk.services.rekognition.model.NotificationChannel; import software.amazon.awssdk.services.rekognition.model.StartPersonTrackingRequest; import software.amazon.awssdk.services.rekognition.model.Video; import software.amazon.awssdk.services.rekognition.model.StartPersonTrackingResponse; import software.amazon.awssdk.services.rekognition.model.RekognitionException; import software.amazon.awssdk.services.rekognition.model.GetPersonTrackingResponse; import software.amazon.awssdk.services.rekognition.model.GetPersonTrackingRequest; import software.amazon.awssdk.services.rekognition.model.VideoMetadata; import software.amazon.awssdk.services.rekognition.model.PersonDetection; 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 VideoPersonDetection { private static String startJobId = ""; public static void main(String[] args) { final String usage = """ Usage: <bucket> <video> <topicArn> <roleArn> Where: bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s video - The name of video (for example, people.mp4).\s topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s """; if (args.length != 4) { System.out.println(usage); System.exit(1); } String bucket = args[0]; String video = args[1]; String topicArn = args[2]; String roleArn = args[3]; Region region = Region.US_EAST_1; RekognitionClient rekClient = RekognitionClient.builder() .region(region) .build(); NotificationChannel channel = NotificationChannel.builder() .snsTopicArn(topicArn) .roleArn(roleArn) .build(); startPersonLabels(rekClient, channel, bucket, video); getPersonDetectionResults(rekClient); System.out.println("This example is done!"); rekClient.close(); } public static void startPersonLabels(RekognitionClient rekClient, NotificationChannel channel, String bucket, String video) { try { S3Object s3Obj = S3Object.builder() .bucket(bucket) .name(video) .build(); Video vidOb = Video.builder() .s3Object(s3Obj) .build(); StartPersonTrackingRequest personTrackingRequest = StartPersonTrackingRequest.builder() .jobTag("DetectingLabels") .video(vidOb) .notificationChannel(channel) .build(); StartPersonTrackingResponse labelDetectionResponse = rekClient.startPersonTracking(personTrackingRequest); startJobId = labelDetectionResponse.jobId(); } catch (RekognitionException e) { System.out.println(e.getMessage()); System.exit(1); } } public static void getPersonDetectionResults(RekognitionClient rekClient) { try { String paginationToken = null; GetPersonTrackingResponse personTrackingResult = null; boolean finished = false; String status; int yy = 0; do { if (personTrackingResult != null) paginationToken = personTrackingResult.nextToken(); GetPersonTrackingRequest recognitionRequest = GetPersonTrackingRequest.builder() .jobId(startJobId) .nextToken(paginationToken) .maxResults(10) .build(); // Wait until the job succeeds while (!finished) { personTrackingResult = rekClient.getPersonTracking(recognitionRequest); status = personTrackingResult.jobStatusAsString(); if (status.compareTo("SUCCEEDED") == 0) finished = true; else { System.out.println(yy + " status is: " + status); Thread.sleep(1000); } yy++; } finished = false; // Proceed when the job is done - otherwise VideoMetadata is null. VideoMetadata videoMetaData = personTrackingResult.videoMetadata(); System.out.println("Format: " + videoMetaData.format()); System.out.println("Codec: " + videoMetaData.codec()); System.out.println("Duration: " + videoMetaData.durationMillis()); System.out.println("FrameRate: " + videoMetaData.frameRate()); System.out.println("Job"); List<PersonDetection> detectedPersons = personTrackingResult.persons(); for (PersonDetection detectedPerson : detectedPersons) { long seconds = detectedPerson.timestamp() / 1000; System.out.print("Sec: " + seconds + " "); System.out.println("Person Identifier: " + detectedPerson.person().index()); System.out.println(); } } while (personTrackingResult != null && personTrackingResult.nextToken() != null); } catch (RekognitionException | InterruptedException e) { System.out.println(e.getMessage()); System.exit(1); } } }
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For API details, see the following topics in Amazon SDK for Java 2.x API Reference.
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The following code example shows how to build an app that uses Amazon Rekognition to detect objects by category in images.
- SDK for Java 2.x
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Shows how to use Amazon Rekognition Java 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
. Services used in this example
Amazon Rekognition
Amazon S3
Amazon SES
The following code example shows how to detect people and objects in a video with Amazon Rekognition.
- SDK for Java 2.x
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Shows how to use Amazon Rekognition Java API to create an app to detect faces and objects in videos 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
. Services used in this example
Amazon Rekognition
Amazon S3
Amazon SES