Die vorliegende Übersetzung wurde maschinell erstellt. Im Falle eines Konflikts oder eines Widerspruchs zwischen dieser übersetzten Fassung und der englischen Fassung (einschließlich infolge von Verzögerungen bei der Übersetzung) ist die englische Fassung maßgeblich.
Beispiel: Erkennen von Segmenten in einem gespeicherten Video
Das folgende Verfahren zeigt, wie technische Signal-Segmente und Einstellungserkennungssegmente in einem Video, das in einem Amazon-S3-Bucket gespeichert ist, erkannt werden. Das Verfahren zeigt auch, wie erkannte Segmente basierend auf der Erkennungssicherheit von Amazon Rekognition Video gefiltert werden.
Das Beispiel erweitert den Code in Analysieren eines in einem Amazon S3-Bucket gespeicherten Videos mit Java oder Python (SDK), der eine Amazon-Simple-Queue-Service-Warteschlange verwendet, um den Abschlussstatus einer Videoanalyseanforderung zu erhalten.
So erkennen Sie Segmente in einem Video, das in einem Amazon-S3-Bucket gespeichert ist (SDK)
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Führen Sie Analysieren eines in einem Amazon S3-Bucket gespeicherten Videos mit Java oder Python (SDK) aus.
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Fügen Sie dem Code, den Sie in Schritt 1 verwendet haben, Folgendes hinzu.
- Java
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Fügen Sie die folgenden Importe hinzu.
import com.amazonaws.services.rekognition.model.GetSegmentDetectionRequest; import com.amazonaws.services.rekognition.model.GetSegmentDetectionResult; import com.amazonaws.services.rekognition.model.SegmentDetection; import com.amazonaws.services.rekognition.model.SegmentType; import com.amazonaws.services.rekognition.model.SegmentTypeInfo; import com.amazonaws.services.rekognition.model.ShotSegment; import com.amazonaws.services.rekognition.model.StartSegmentDetectionFilters; import com.amazonaws.services.rekognition.model.StartSegmentDetectionRequest; import com.amazonaws.services.rekognition.model.StartSegmentDetectionResult; import com.amazonaws.services.rekognition.model.StartShotDetectionFilter; import com.amazonaws.services.rekognition.model.StartTechnicalCueDetectionFilter; import com.amazonaws.services.rekognition.model.TechnicalCueSegment; import com.amazonaws.services.rekognition.model.AudioMetadata; -
Fügen Sie den folgenden Code zur Klasse
VideoDetecthinzu.//Copyright 2020 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 StartSegmentDetection(String bucket, String video) throws Exception{ NotificationChannel channel= new NotificationChannel() .withSNSTopicArn(snsTopicArn) .withRoleArn(roleArn); float minTechnicalCueConfidence = 80F; float minShotConfidence = 80F; StartSegmentDetectionRequest req = new StartSegmentDetectionRequest() .withVideo(new Video() .withS3Object(new S3Object() .withBucket(bucket) .withName(video))) .withSegmentTypes("TECHNICAL_CUE" , "SHOT") .withFilters(new StartSegmentDetectionFilters() .withTechnicalCueFilter(new StartTechnicalCueDetectionFilter() .withMinSegmentConfidence(minTechnicalCueConfidence)) .withShotFilter(new StartShotDetectionFilter() .withMinSegmentConfidence(minShotConfidence))) .withJobTag("DetectingVideoSegments") .withNotificationChannel(channel); StartSegmentDetectionResult startLabelDetectionResult = rek.startSegmentDetection(req); startJobId=startLabelDetectionResult.getJobId(); } private static void GetSegmentDetectionResults() throws Exception{ int maxResults=10; String paginationToken=null; GetSegmentDetectionResult segmentDetectionResult=null; Boolean firstTime=true; do { if (segmentDetectionResult !=null){ paginationToken = segmentDetectionResult.getNextToken(); } GetSegmentDetectionRequest segmentDetectionRequest= new GetSegmentDetectionRequest() .withJobId(startJobId) .withMaxResults(maxResults) .withNextToken(paginationToken); segmentDetectionResult = rek.getSegmentDetection(segmentDetectionRequest); if(firstTime) { System.out.println("\nStatus\n------"); System.out.println(segmentDetectionResult.getJobStatus()); System.out.println("\nRequested features\n------------------"); for (SegmentTypeInfo requestedFeatures : segmentDetectionResult.getSelectedSegmentTypes()) { System.out.println(requestedFeatures.getType()); } int count=1; List<VideoMetadata> videoMetaDataList = segmentDetectionResult.getVideoMetadata(); System.out.println("\nVideo Streams\n-------------"); for (VideoMetadata videoMetaData: videoMetaDataList) { System.out.println("Stream: " + count++); System.out.println("\tFormat: " + videoMetaData.getFormat()); System.out.println("\tCodec: " + videoMetaData.getCodec()); System.out.println("\tDuration: " + videoMetaData.getDurationMillis()); System.out.println("\tFrameRate: " + videoMetaData.getFrameRate()); } List<AudioMetadata> audioMetaDataList = segmentDetectionResult.getAudioMetadata(); System.out.println("\nAudio streams\n-------------"); count=1; for (AudioMetadata audioMetaData: audioMetaDataList) { System.out.println("Stream: " + count++); System.out.println("\tSample Rate: " + audioMetaData.getSampleRate()); System.out.println("\tCodec: " + audioMetaData.getCodec()); System.out.println("\tDuration: " + audioMetaData.getDurationMillis()); System.out.println("\tNumber of Channels: " + audioMetaData.getNumberOfChannels()); } System.out.println("\nSegments\n--------"); firstTime=false; } //Show segment information List<SegmentDetection> detectedSegments= segmentDetectionResult.getSegments(); for (SegmentDetection detectedSegment: detectedSegments) { if (detectedSegment.getType().contains(SegmentType.TECHNICAL_CUE.toString())) { System.out.println("Technical Cue"); TechnicalCueSegment segmentCue=detectedSegment.getTechnicalCueSegment(); System.out.println("\tType: " + segmentCue.getType()); System.out.println("\tConfidence: " + segmentCue.getConfidence().toString()); } if (detectedSegment.getType().contains(SegmentType.SHOT.toString())) { System.out.println("Shot"); ShotSegment segmentShot=detectedSegment.getShotSegment(); System.out.println("\tIndex " + segmentShot.getIndex()); System.out.println("\tConfidence: " + segmentShot.getConfidence().toString()); } long seconds=detectedSegment.getDurationMillis(); System.out.println("\tDuration : " + Long.toString(seconds) + " milliseconds"); System.out.println("\tStart time code: " + detectedSegment.getStartTimecodeSMPTE()); System.out.println("\tEnd time code: " + detectedSegment.getEndTimecodeSMPTE()); System.out.println("\tDuration time code: " + detectedSegment.getDurationSMPTE()); System.out.println(); } } while (segmentDetectionResult !=null && segmentDetectionResult.getNextToken() != null); } -
Ersetzen Sie in der Funktion
maindie folgenden Zeilen:StartLabelDetection(bucket, video); if (GetSQSMessageSuccess()==true) GetLabelDetectionResults();mit:
StartSegmentDetection(bucket, video); if (GetSQSMessageSuccess()==true) GetSegmentDetectionResults();
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- Java V2
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//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 DetectVideoSegments { 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_WEST_2; 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
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Fügen Sie den folgenden Code in der Klasse
VideoDetectein, die Sie in Schritt 1 erstellt haben.# Copyright 2020 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 StartSegmentDetection(self): min_Technical_Cue_Confidence = 80.0 min_Shot_Confidence = 80.0 max_pixel_threshold = 0.1 min_coverage_percentage = 60 response = self.rek.start_segment_detection( Video={"S3Object": {"Bucket": self.bucket, "Name": self.video}}, NotificationChannel={ "RoleArn": self.roleArn, "SNSTopicArn": self.snsTopicArn, }, SegmentTypes=["TECHNICAL_CUE", "SHOT"], Filters={ "TechnicalCueFilter": { "BlackFrame": { "MaxPixelThreshold": max_pixel_threshold, "MinCoveragePercentage": min_coverage_percentage, }, "MinSegmentConfidence": min_Technical_Cue_Confidence, }, "ShotFilter": {"MinSegmentConfidence": min_Shot_Confidence}, } ) self.startJobId = response["JobId"] print(f"Start Job Id: {self.startJobId}") def GetSegmentDetectionResults(self): maxResults = 10 paginationToken = "" finished = False firstTime = True while finished == False: response = self.rek.get_segment_detection( JobId=self.startJobId, MaxResults=maxResults, NextToken=paginationToken ) if firstTime == True: print(f"Status\n------\n{response['JobStatus']}") print("\nRequested Types\n---------------") for selectedSegmentType in response['SelectedSegmentTypes']: print(f"\tType: {selectedSegmentType['Type']}") print(f"\t\tModel Version: {selectedSegmentType['ModelVersion']}") print() print("\nAudio metadata\n--------------") for audioMetadata in response['AudioMetadata']: print(f"\tCodec: {audioMetadata['Codec']}") print(f"\tDuration: {audioMetadata['DurationMillis']}") print(f"\tNumber of Channels: {audioMetadata['NumberOfChannels']}") print(f"\tSample rate: {audioMetadata['SampleRate']}") print() print("\nVideo metadata\n--------------") for videoMetadata in response["VideoMetadata"]: print(f"\tCodec: {videoMetadata['Codec']}") print(f"\tColor Range: {videoMetadata['ColorRange']}") print(f"\tDuration: {videoMetadata['DurationMillis']}") print(f"\tFormat: {videoMetadata['Format']}") print(f"\tFrame rate: {videoMetadata['FrameRate']}") print("\nSegments\n--------") firstTime = False for segment in response['Segments']: if segment["Type"] == "TECHNICAL_CUE": print("Technical Cue") print(f"\tConfidence: {segment['TechnicalCueSegment']['Confidence']}") print(f"\tType: {segment['TechnicalCueSegment']['Type']}") if segment["Type"] == "SHOT": print("Shot") print(f"\tConfidence: {segment['ShotSegment']['Confidence']}") print(f"\tIndex: " + str(segment["ShotSegment"]["Index"])) print(f"\tDuration (milliseconds): {segment['DurationMillis']}") print(f"\tStart Timestamp (milliseconds): {segment['StartTimestampMillis']}") print(f"\tEnd Timestamp (milliseconds): {segment['EndTimestampMillis']}") print(f"\tStart timecode: {segment['StartTimecodeSMPTE']}") print(f"\tEnd timecode: {segment['EndTimecodeSMPTE']}") print(f"\tDuration timecode: {segment['DurationSMPTE']}") print(f"\tStart frame number {segment['StartFrameNumber']}") print(f"\tEnd frame number: {segment['EndFrameNumber']}") print(f"\tDuration frames: {segment['DurationFrames']}") print() if "NextToken" in response: paginationToken = response["NextToken"] else: finished = True -
Ersetzen Sie in der Funktion
maindie folgenden Zeilen:analyzer.StartLabelDetection() if analyzer.GetSQSMessageSuccess()==True: analyzer.GetLabelDetectionResults()mit:
analyzer.StartSegmentDetection() if analyzer.GetSQSMessageSuccess()==True: analyzer.GetSegmentDetectionResults()
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Anmerkung
Wenn Sie zusätzlich zu Analysieren eines in einem Amazon S3-Bucket gespeicherten Videos mit Java oder Python (SDK) bereits ein anderes Videobeispiel ausgeführt haben, ist der zu ersetzende Code möglicherweise anders.
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Führen Sie den Code aus. Es werden Informationen über die im Eingabevideo erkannten Segmente angezeigt.
Verwenden der Amazon-Rekognition-Segment-API
Echtheit von Gesichtern erkennen