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# Esempi per Amazon Rekognition con SDK per Java 2.x
* 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:
Where:
bucketName - The name of the S3 bucket where the images are stored.
sourceKey - The S3 key (file name) for the source image.
targetKey - The S3 key (file name) for the target image.
""";
if (args.length != 3) {
System.out.println(usage);
System.exit(1);
}
String bucketName = args[0];
String sourceKey = args[1];
String targetKey = args[2];
Region region = Region.US_WEST_2;
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.build();
compareTwoFaces(rekClient, bucketName, sourceKey, targetKey);
}
/**
* Compares two faces from images stored in an Amazon S3 bucket using AWS Rekognition.
*
* This method takes two image keys from an S3 bucket and compares the faces within them.
* It prints out the confidence level of matched faces and reports the number of unmatched faces.
*
* @param rekClient The {@link RekognitionClient} used to call AWS Rekognition.
* @param bucketName The name of the S3 bucket containing the images.
* @param sourceKey The object key (file path) for the source image in the S3 bucket.
* @param targetKey The object key (file path) for the target image in the S3 bucket.
* @throws RuntimeException If the Rekognition service returns an error.
*/
public static void compareTwoFaces(RekognitionClient rekClient, String bucketName, String sourceKey, String targetKey) {
try {
Float similarityThreshold = 70F;
S3Object s3ObjectSource = S3Object.builder()
.bucket(bucketName)
.name(sourceKey)
.build();
Image sourceImage = Image.builder()
.s3Object(s3ObjectSource)
.build();
S3Object s3ObjectTarget = S3Object.builder()
.bucket(bucketName)
.name(targetKey)
.build();
Image targetImage = Image.builder()
.s3Object(s3ObjectTarget)
.build();
CompareFacesRequest facesRequest = CompareFacesRequest.builder()
.sourceImage(sourceImage)
.targetImage(targetImage)
.similarityThreshold(similarityThreshold)
.build();
// Compare the two images.
CompareFacesResponse compareFacesResult = rekClient.compareFaces(facesRequest);
List 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 unmatchedFaces = compareFacesResult.unmatchedFaces();
System.out.println("There were " + unmatchedFaces.size() + " face(s) that did not match.");
} catch (RekognitionException e) {
System.err.println("Error comparing faces: " + e.awsErrorDetails().errorMessage());
throw new RuntimeException(e);
}
}
}
```
+ Per i dettagli sull'API, [CompareFaces](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/CompareFaces)consulta *AWS SDK for Java 2.x API Reference*.
### `CreateCollection`
\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_WEST_2;
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.build();
System.out.println("Creating collection: " + collectionId);
createMyCollection(rekClient, collectionId);
rekClient.close();
}
/**
* Creates a new Amazon Rekognition collection.
*
* @param rekClient the Amazon Rekognition client used to interact with the Rekognition service
* @param collectionId the unique identifier for the collection to be created
*/
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);
}
}
}
```
+ Per i dettagli sull'API, [CreateCollection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/CreateCollection)consulta *AWS SDK for Java 2.x API Reference*.
### `DeleteCollection`
\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();
}
/**
* Deletes an Amazon Rekognition collection.
*
* @param rekClient An instance of the {@link RekognitionClient} class, which is used to interact with the Amazon Rekognition service.
* @param collectionId The ID of the collection to be deleted.
*/
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);
}
}
}
```
+ Per i dettagli sull'API, [DeleteCollection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DeleteCollection)consulta *AWS SDK for Java 2.x API Reference*.
### `DeleteFaces`
\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 != 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("Deleting collection: " + collectionId);
deleteFacesCollection(rekClient, collectionId, faceId);
rekClient.close();
}
/**
* Deletes a face from the specified Amazon Rekognition collection.
*
* @param rekClient an instance of the Amazon Rekognition client
* @param collectionId the ID of the collection from which the face should be deleted
* @param faceId the ID of the face to be deleted
* @throws RekognitionException if an error occurs while deleting the face
*/
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);
}
}
}
```
+ Per i dettagli sull'API, [DeleteFaces](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DeleteFaces)consulta *AWS SDK for Java 2.x API Reference*.
### `DescribeCollection`
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();
}
/**
* Describes an Amazon Rekognition collection.
*
* @param rekClient The Amazon Rekognition client used to make the request.
* @param collectionName The name of the collection to describe.
*
* @throws RekognitionException If an error occurs while describing the collection.
*/
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);
}
}
}
```
+ Per i dettagli sull'API, [DescribeCollection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DescribeCollection)consulta *AWS SDK for Java 2.x API Reference*.
### `DetectFaces`
* 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:
Where:
bucketName = The name of the Amazon S3 bucket where the source image is stored.
sourceImage - The name of the source image file in the Amazon S3 bucket. (for example, pic1.png).\s
""";
if (args.length != 2) {
System.out.println(usage);
System.exit(1);
}
String bucketName = args[0];
String sourceImage = args[1];
Region region = Region.US_WEST_2;
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.build();
detectFacesinImage(rekClient, bucketName, sourceImage);
rekClient.close();
}
/**
* Detects faces in an image stored in an Amazon S3 bucket using the Amazon Rekognition service.
*
* @param rekClient The Amazon Rekognition client used to interact with the Rekognition service.
* @param bucketName The name of the Amazon S3 bucket where the source image is stored.
* @param sourceImage The name of the source image file in the Amazon S3 bucket.
*/
public static void detectFacesinImage(RekognitionClient rekClient, String bucketName, String sourceImage) {
try {
S3Object s3ObjectTarget = S3Object.builder()
.bucket(bucketName)
.name(sourceImage)
.build();
Image targetImage = Image.builder()
.s3Object(s3ObjectTarget)
.build();
DetectFacesRequest facesRequest = DetectFacesRequest.builder()
.attributes(Attribute.ALL)
.image(targetImage)
.build();
DetectFacesResponse facesResponse = rekClient.detectFaces(facesRequest);
List 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 e) {
System.out.println(e.getMessage());
System.exit(1);
}
}
}
```
+ Per i dettagli sull'API, [DetectFaces](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DetectFaces)consulta *AWS SDK for Java 2.x API Reference*.
### `DetectLabels`
Where:
bucketName - The name of the Amazon S3 bucket where the image is stored
sourceImage - The name of the image file (for example, pic1.png).\s
""";
if (args.length != 2) {
System.out.println(usage);
System.exit(1);
}
String bucketName = args[0] ;
String sourceImage = args[1] ;
Region region = Region.US_WEST_2;
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.build();
detectImageLabels(rekClient, bucketName, sourceImage);
rekClient.close();
}
/**
* Detects the labels in an image stored in an Amazon S3 bucket using the Amazon Rekognition service.
*
* @param rekClient the Amazon Rekognition client used to make the detection request
* @param bucketName the name of the Amazon S3 bucket where the image is stored
* @param sourceImage the name of the image file to be analyzed
*/
public static void detectImageLabels(RekognitionClient rekClient, String bucketName, String sourceImage) {
try {
S3Object s3ObjectTarget = S3Object.builder()
.bucket(bucketName)
.name(sourceImage)
.build();
Image souImage = Image.builder()
.s3Object(s3ObjectTarget)
.build();
DetectLabelsRequest detectLabelsRequest = DetectLabelsRequest.builder()
.image(souImage)
.maxLabels(10)
.build();
DetectLabelsResponse labelsResponse = rekClient.detectLabels(detectLabelsRequest);
List 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 e) {
System.out.println(e.getMessage());
System.exit(1);
}
}
}
```
+ Per i dettagli sull'API, [DetectLabels](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DetectLabels)consulta *AWS SDK for Java 2.x API Reference*.
### `DetectModerationLabels`
Where:
bucketName - The name of the S3 bucket where the images are stored.
sourceImage - The name of the image (for example, pic1.png).\s
""";
if (args.length != 2) {
System.out.println(usage);
System.exit(1);
}
String bucketName = args[0];
String sourceImage = args[1];
Region region = Region.US_WEST_2;
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.build();
detectModLabels(rekClient, bucketName, sourceImage);
rekClient.close();
}
/**
* Detects moderation labels in an image stored in an Amazon S3 bucket.
*
* @param rekClient the Amazon Rekognition client to use for the detection
* @param bucketName the name of the Amazon S3 bucket where the image is stored
* @param sourceImage the name of the image file to be analyzed
*
* @throws RekognitionException if there is an error during the image detection process
*/
public static void detectModLabels(RekognitionClient rekClient, String bucketName, String sourceImage) {
try {
S3Object s3ObjectTarget = S3Object.builder()
.bucket(bucketName)
.name(sourceImage)
.build();
Image targetImage = Image.builder()
.s3Object(s3ObjectTarget)
.build();
DetectModerationLabelsRequest moderationLabelsRequest = DetectModerationLabelsRequest.builder()
.image(targetImage)
.minConfidence(60F)
.build();
DetectModerationLabelsResponse moderationLabelsResponse = rekClient
.detectModerationLabels(moderationLabelsRequest);
List 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 e) {
e.printStackTrace();
System.exit(1);
}
}
}
```
+ Per i dettagli sull'API, [DetectModerationLabels](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DetectModerationLabels)consulta *AWS SDK for Java 2.x API Reference*.
### `DetectText`
\n" +
"\n" +
"Where:\n" +
" bucketName - The name of the S3 bucket where the image is stored\n" +
" sourceImage - The path to the image that contains text (for example, pic1.png). \n";
if (args.length != 2) {
System.out.println(usage);
System.exit(1);
}
String bucketName = args[0];
String sourceImage = args[1];
Region region = Region.US_EAST_1;
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.build();
detectTextLabels(rekClient, bucketName, sourceImage);
rekClient.close();
}
/**
* Detects text labels in an image stored in an S3 bucket using Amazon Rekognition.
*
* @param rekClient an instance of the Amazon Rekognition client
* @param bucketName the name of the S3 bucket where the image is stored
* @param sourceImage the name of the image file in the S3 bucket
* @throws RekognitionException if an error occurs while calling the Amazon Rekognition API
*/
public static void detectTextLabels(RekognitionClient rekClient, String bucketName, String sourceImage) {
try {
S3Object s3ObjectTarget = S3Object.builder()
.bucket(bucketName)
.name(sourceImage)
.build();
Image souImage = Image.builder()
.s3Object(s3ObjectTarget)
.build();
DetectTextRequest textRequest = DetectTextRequest.builder()
.image(souImage)
.build();
DetectTextResponse textResponse = rekClient.detectText(textRequest);
List 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 e) {
System.out.println(e.getMessage());
System.exit(1);
}
}
}
```
+ Per i dettagli sull'API, [DetectText](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DetectText)consulta *AWS SDK for Java 2.x API Reference*.
### `IndexFaces`
Where:
collectionName - The name of the collection.
sourceImage - The name of the image (for example, pic1.png).
bucketName - The name of the S3 bucket.
""";
if (args.length != 3) {
System.out.println(usage);
System.exit(1);
}
String collectionId = args[0];
String sourceImage = args[1];
String bucketName = args[2];;
Region region = Region.US_EAST_1;
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.build();
addToCollection(rekClient, collectionId, bucketName, sourceImage);
rekClient.close();
}
/**
* Adds a face from an image to an Amazon Rekognition collection.
*
* @param rekClient the Amazon Rekognition client
* @param collectionId the ID of the collection to add the face to
* @param bucketName the name of the Amazon S3 bucket containing the image
* @param sourceImage the name of the image file to add to the collection
* @throws RekognitionException if there is an error while interacting with the Amazon Rekognition service
*/
public static void addToCollection(RekognitionClient rekClient, String collectionId, String bucketName, String sourceImage) {
try {
S3Object s3ObjectTarget = S3Object.builder()
.bucket(bucketName)
.name(sourceImage)
.build();
Image targetImage = Image.builder()
.s3Object(s3ObjectTarget)
.build();
IndexFacesRequest facesRequest = IndexFacesRequest.builder()
.collectionId(collectionId)
.image(targetImage)
.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 faceRecords = facesResponse.faceRecords();
for (FaceRecord faceRecord : faceRecords) {
System.out.println(" Face ID: " + faceRecord.face().faceId());
System.out.println(" Location:" + faceRecord.faceDetail().boundingBox().toString());
}
List 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 e) {
System.out.println(e.getMessage());
System.exit(1);
}
}
}
```
+ Per i dettagli sull'API, [IndexFaces](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/IndexFaces)consulta *AWS SDK for Java 2.x API Reference*.
### `ListCollections`
collectionIds = response.collectionIds();
for (String resultId : collectionIds) {
System.out.println(resultId);
}
} catch (RekognitionException e) {
System.out.println(e.getMessage());
System.exit(1);
}
}
}
```
+ Per i dettagli sull'API, [ListCollections](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/ListCollections)consulta *AWS SDK for Java 2.x API Reference*.
### `ListFaces`
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 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);
}
}
}
```
+ Per i dettagli sull'API, [ListFaces](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/ListFaces)consulta *AWS SDK for Java 2.x API Reference*.
### `RecognizeCelebrities`
Where:
bucketName - The name of the S3 bucket where the images are stored.
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 bucketName = args[0];;
String sourceImage = args[1];
Region region = Region.US_WEST_2;
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.build();
System.out.println("Locating celebrities in " + sourceImage);
recognizeAllCelebrities(rekClient, bucketName, sourceImage);
rekClient.close();
}
/**
* Recognizes all celebrities in an image stored in an Amazon S3 bucket.
*
* @param rekClient the Amazon Rekognition client used to perform the celebrity recognition operation
* @param bucketName the name of the Amazon S3 bucket where the source image is stored
* @param sourceImage the name of the source image file stored in the Amazon S3 bucket
*/
public static void recognizeAllCelebrities(RekognitionClient rekClient, String bucketName, String sourceImage) {
try {
S3Object s3ObjectTarget = S3Object.builder()
.bucket(bucketName)
.name(sourceImage)
.build();
Image souImage = Image.builder()
.s3Object(s3ObjectTarget)
.build();
RecognizeCelebritiesRequest request = RecognizeCelebritiesRequest.builder()
.image(souImage)
.build();
RecognizeCelebritiesResponse result = rekClient.recognizeCelebrities(request);
List 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 e) {
System.out.println(e.getMessage());
System.exit(1);
}
}
}
```
+ Per i dettagli sull'API, [RecognizeCelebrities](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/RecognizeCelebrities)consulta *AWS SDK for Java 2.x API Reference*.
### `SearchFaces`
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_WEST_2;
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 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);
}
}
}
```
+ Per i dettagli sull'API, [SearchFaces](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/SearchFaces)consulta *AWS SDK for Java 2.x API Reference*.
### `SearchFacesByImage`
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_WEST_2;
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 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);
}
}
}
```
+ Per i dettagli sull'API, [SearchFacesByImage](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/SearchFacesByImage)consulta *AWS SDK for Java 2.x API Reference*.
## Scenari
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 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);
}
}
}
```
Rileva le etichette in un video tramite un’operazione di rilevamento delle etichette.
```
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:
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 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 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 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 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);
}
}
}
```
Rileva i volti in un video archiviato in un bucket Amazon S3.
```
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:
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 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 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 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 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);
}
}
}
```
Rileva contenuti non appropriati o offensivi in un video archiviato in un bucket Amazon S3.
```
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:
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 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);
}
}
}
```
Rileva segmenti di segnali d’azione tecnici e segmenti di rilevamento delle riprese in un video archiviato in un bucket Amazon S3.
```
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:
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 = 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 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);
}
}
}
```
Rileva il testo in un video archiviato in un bucket Amazon S3.
```
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:
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 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);
}
}
}
```
Rileva le persone in un video archiviato in un bucket Amazon S3.
```
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:
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 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);
}
}
}
```
+ Per informazioni dettagliate sull’API, consulta i seguenti argomenti nella *documentazione di riferimento dell’API AWS SDK for Java 2.x *.
+ [GetCelebrityRecognition](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/GetCelebrityRecognition)
+ [GetContentModeration](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/GetContentModeration)
+ [GetLabelDetection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/GetLabelDetection)
+ [GetPersonTracking](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/GetPersonTracking)
+ [GetSegmentDetection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/GetSegmentDetection)
+ [GetTextDetection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/GetTextDetection)
+ [StartCelebrityRecognition](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/StartCelebrityRecognition)
+ [StartContentModeration](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/StartContentModeration)
+ [StartLabelDetection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/StartLabelDetection)
+ [StartPersonTracking](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/StartPersonTracking)
+ [StartSegmentDetection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/StartSegmentDetection)
+ [StartTextDetection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/StartTextDetection)
### Rilevamento di oggetti nelle immagini