偵測已儲存影片中的文字

偵測存放於 Amazon S3 儲存貯體 (SDK) 之影片中的文字

1. 請執行 使用 Java 或 Python（SDK）分析存儲在 Amazon S3 存儲桶中的視頻 中的步驟。

2. 將下列程式碼加入至步驟 1 中的類別 VideoDetect。

   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);
         
           
   }

   在函數 main 中，將下行:

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

   取代為：

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

   Java V2

   此代碼取自 AWS 文檔 SDK 示例 GitHub 存儲庫。請參閱此處的完整範例。

   //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

   在函數 main 中，將下行:

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

   取代為：

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

   CLI

   運行以下 AWS CLI 命令以開始檢測視頻中的文本。

    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

   更新下列的值：

   • 將 bucket-name 與 video-name 變更為您在步驟 2 中指定的 Amazon S3 儲存貯體與文檔名稱。

   • 將 region-name 變更為您正在使用的 AWS 區域。

   • 使用您開發人員設定檔的名稱取代 profile-name 的值。

   • 將 topic-ARN 變更為您在 設定 Amazon Rekognition Video 步驟 3 建立的 Amazon SNS 主題 ARN。

   • 將 role-ARN 變更為您在步驟 7 建立的 設定 Amazon Rekognition Video IAM 服務角色的 ARN。

   如果您在 Windows 裝置上存取 CLI，請使用雙引號而非單引號，並以反斜線 (即\) 替代內部雙引號，以解決您可能遇到的任何剖析器錯誤。如需範例，請參閱下列內容：

   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

   執行正在進行的程式碼範例之後，複製傳回的程式碼 jobID，並將其提供給下列 GetTextDetection 命令，以 job-id-number 取代您之前收到的 jobID 結果：

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

   注意

   如果您已執行 使用 Java 或 Python（SDK）分析存儲在 Amazon S3 存儲桶中的視頻 以外的影片範例，要取代的程式碼可能會不同。

3. 執行程式碼。在影片中偵測到的文字會顯示在清單中。