Detección de texto en un vídeo almacenado

Para detectar texto en un vídeo almacenado en un bucket de Amazon S3 (SDK)

1. Realice los pasos que se indican en Análisis de un vídeo almacenado en un bucket de Amazon S3 con Java o Python (SDK).

2. Agregue el siguiente código a la clase VideoDetect en el paso 1.

   Java

   //Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
   //PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)
   
   
   private static void StartTextDetection(String bucket, String video) throws Exception{
              
       NotificationChannel channel= new NotificationChannel()
               .withSNSTopicArn(snsTopicArn)
               .withRoleArn(roleArn);
       
       StartTextDetectionRequest req = new StartTextDetectionRequest()
               .withVideo(new Video()
                       .withS3Object(new S3Object()
                           .withBucket(bucket)
                           .withName(video)))
               .withNotificationChannel(channel);
       
       
       StartTextDetectionResult startTextDetectionResult = rek.startTextDetection(req);
       startJobId=startTextDetectionResult.getJobId();
       
   } 
   
   private static void GetTextDetectionResults() throws Exception{
       
       int maxResults=10;
       String paginationToken=null;
       GetTextDetectionResult textDetectionResult=null;
       
       do{
           if (textDetectionResult !=null){
               paginationToken = textDetectionResult.getNextToken();
   
           }
           
       
           textDetectionResult = rek.getTextDetection(new GetTextDetectionRequest()
                .withJobId(startJobId)
                .withNextToken(paginationToken)
                .withMaxResults(maxResults));
       
           VideoMetadata videoMetaData=textDetectionResult.getVideoMetadata();
               
           System.out.println("Format: " + videoMetaData.getFormat());
           System.out.println("Codec: " + videoMetaData.getCodec());
           System.out.println("Duration: " + videoMetaData.getDurationMillis());
           System.out.println("FrameRate: " + videoMetaData.getFrameRate());
               
               
           //Show text, confidence values
           List<TextDetectionResult> textDetections = textDetectionResult.getTextDetections();
   
   
           for (TextDetectionResult text: textDetections) {
               long seconds=text.getTimestamp()/1000;
               System.out.println("Sec: " + Long.toString(seconds) + " ");
               TextDetection detectedText=text.getTextDetection();
               
               System.out.println("Text Detected: " + detectedText.getDetectedText());
                   System.out.println("Confidence: " + detectedText.getConfidence().toString());
                   System.out.println("Id : " + detectedText.getId());
                   System.out.println("Parent Id: " + detectedText.getParentId());
                   System.out.println("Bounding Box" + detectedText.getGeometry().getBoundingBox().toString());
                   System.out.println("Type: " + detectedText.getType());
                   System.out.println();
           }
       } while (textDetectionResult !=null && textDetectionResult.getNextToken() != null);
         
           
   }

   En la función main, reemplace las líneas:

           StartLabelDetection(bucket, video);
   
           if (GetSQSMessageSuccess()==true)
           	GetLabelDetectionResults();

   por:

           StartTextDetection(bucket, video);
   
           if (GetSQSMessageSuccess()==true)
           	GetTextDetectionResults();

   Java V2

   Este código se ha tomado del GitHub repositorio de ejemplos del SDK de AWS documentación. Consulte el ejemplo completo aquí.

   //snippet-start:[rekognition.java2.recognize_video_text.import]
   import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider;
   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;
   //snippet-end:[rekognition.java2.recognize_video_text.import]
   
   /**
   * 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 DetectTextVideo {
   
    private static String startJobId ="";
    public static void main(String[] args) {
   
        final String usage = "\n" +
            "Usage: " +
            "   <bucket> <video> <topicArn> <roleArn>\n\n" +
            "Where:\n" +
            "   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket). \n\n"+
            "   video - The name of video (for example, people.mp4). \n\n" +
            "   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic. \n\n" +
            "   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use. \n\n" ;
   
        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)
            .credentialsProvider(ProfileCredentialsProvider.create("profile-name"))
            .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();
    }
   
    // snippet-start:[rekognition.java2.recognize_video_text.main]
    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);
        }
    }
    // snippet-end:[rekognition.java2.recognize_video_text.main]
   }

   Python

   #Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
   #PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)
   
       def StartTextDetection(self):
           response=self.rek.start_text_detection(Video={'S3Object': {'Bucket': self.bucket, 'Name': self.video}},
               NotificationChannel={'RoleArn': self.roleArn, 'SNSTopicArn': self.snsTopicArn})
   
           self.startJobId=response['JobId']
           print('Start Job Id: ' + self.startJobId)
     
       def GetTextDetectionResults(self):
           maxResults = 10
           paginationToken = ''
           finished = False
   
           while finished == False:
               response = self.rek.get_text_detection(JobId=self.startJobId,
                                               MaxResults=maxResults,
                                               NextToken=paginationToken)
   
               print('Codec: ' + response['VideoMetadata']['Codec'])
               
               print('Duration: ' + str(response['VideoMetadata']['DurationMillis']))
               print('Format: ' + response['VideoMetadata']['Format'])
               print('Frame rate: ' + str(response['VideoMetadata']['FrameRate']))
               print()
   
               for textDetection in response['TextDetections']:
                   text=textDetection['TextDetection']
   
                   print("Timestamp: " + str(textDetection['Timestamp']))
                   print("   Text Detected: " + text['DetectedText'])
                   print("   Confidence: " +  str(text['Confidence']))
                   print ("      Bounding box")
                   print ("        Top: " + str(text['Geometry']['BoundingBox']['Top']))
                   print ("        Left: " + str(text['Geometry']['BoundingBox']['Left']))
                   print ("        Width: " +  str(text['Geometry']['BoundingBox']['Width']))
                   print ("        Height: " +  str(text['Geometry']['BoundingBox']['Height']))
                   print ("   Type: " + str(text['Type']) )
                   print()
   
               if 'NextToken' in response:
                   paginationToken = response['NextToken']
               else:
                   finished = True

   En la función main, reemplace las líneas:

       analyzer.StartLabelDetection()
       if analyzer.GetSQSMessageSuccess()==True:
           analyzer.GetLabelDetectionResults()

   por:

       analyzer.StartTextDetection()
       if analyzer.GetSQSMessageSuccess()==True:
           analyzer.GetTextDetectionResults()

   CLI

   Ejecute el siguiente AWS CLI comando para empezar a detectar texto en un vídeo.

    aws rekognition start-text-detection --video "{"S3Object":{"Bucket":"bucket-name","Name":"video-name"}}"\
    --notification-channel "{"SNSTopicArn":"topic-arn","RoleArn":"role-arn"}" \
    --region region-name --profile profile-name

   Actualice los siguientes valores:

   • Cambie bucket-name y video-name por el nombre del bucket de Amazon S3 y el nombre de archivo que especificó en el paso 2.

   • Cambie region-name por la región de AWS que está utilizando.

   • Sustituya el valor de profile-name de por el nombre de su perfil de desarrollador.

   • Reemplace topic-ARN por el ARN del tema de Amazon SNS que creó en el paso 3 de Configuración de Amazon Rekognition Video.

   • Cambie role-ARN por el ARN del rol de servicio de IAM que creó en el paso 7 de Configuración de Amazon Rekognition Video.

   Si accede a la CLI en un dispositivo Windows, utilice comillas dobles en lugar de comillas simples y evite las comillas dobles internas con una barra invertida (es decir, \) para corregir cualquier error del analizador que pueda encontrar. Consulte a continuación un ejemplo:

   aws rekognition start-text-detection --video \
    "{\"S3Object\":{\"Bucket\":\"bucket-name\",\"Name\":\"video-name\"}}" \
    --notification-channel "{\"SNSTopicArn\":\"topic-arn\",\"RoleArn\":\"role-arn\"}" \
    --region region-name --profile profile-name

   Tras ejecutar el ejemplo de código de procedimiento, copie el jobID devuelto y envíelo al siguiente comando GetTextDetection para obtener los resultados, sustituyendo job-id-number por el jobID que recibió anteriormente:

   aws rekognition get-text-detection --job-id job-id-number --profile profile-name             

   nota

   Si ya ha ejecutado un ejemplo de vídeo distinto de Análisis de un vídeo almacenado en un bucket de Amazon S3 con Java o Python (SDK), el código que se va a reemplazar podría ser diferente.

3. Ejecute el código. El texto que se detectó en el vídeo se muestra en una lista.