Detecting inappropriate stored videos - Amazon Rekognition

Detecting inappropriate stored videos

Amazon Rekognition Video inappropriate or offensive content detection in stored videos is an asynchronous operation. To start detecting inappropriate or offensive content, call StartContentModeration. Amazon Rekognition Video publishes the completion status of the video analysis to an Amazon Simple Notification Service topic. If the video analysis is successful, call GetContentModeration to get the analysis results. For more information about starting video analysis and getting the results, see Calling Amazon Rekognition Video operations.

This procedure expands on the code in Analyzing a video stored in an Amazon S3 bucket with Java or Python (SDK), which uses an Amazon Simple Queue Service queue to get the completion status of a video analysis request.

To detect inappropriate or offensive content in a video stored in an Amazon S3 bucket (SDK)

  1. Perform Analyzing a video stored in an Amazon S3 bucket with Java or Python (SDK).

  2. Add the following code to the class VideoDetect that you created in step 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.) //Content moderation ================================================================== private static void StartUnsafeContentDetection(String bucket, String video) throws Exception{ NotificationChannel channel= new NotificationChannel() .withSNSTopicArn(snsTopicArn) .withRoleArn(roleArn); StartContentModerationRequest req = new StartContentModerationRequest() .withVideo(new Video() .withS3Object(new S3Object() .withBucket(bucket) .withName(video))) .withNotificationChannel(channel); StartContentModerationResult startModerationLabelDetectionResult = rek.startContentModeration(req); startJobId=startModerationLabelDetectionResult.getJobId(); } private static void GetUnsafeContentDetectionResults() throws Exception{ int maxResults=10; String paginationToken=null; GetContentModerationResult moderationLabelDetectionResult =null; do{ if (moderationLabelDetectionResult !=null){ paginationToken = moderationLabelDetectionResult.getNextToken(); } moderationLabelDetectionResult = rek.getContentModeration( new GetContentModerationRequest() .withJobId(startJobId) .withNextToken(paginationToken) .withSortBy(ContentModerationSortBy.TIMESTAMP) .withMaxResults(maxResults)); VideoMetadata videoMetaData=moderationLabelDetectionResult.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 moderated content labels, confidence and detection times List<ContentModerationDetection> moderationLabelsInFrames= moderationLabelDetectionResult.getModerationLabels(); for (ContentModerationDetection label: moderationLabelsInFrames) { long seconds=label.getTimestamp()/1000; System.out.print("Sec: " + Long.toString(seconds)); System.out.println(label.getModerationLabel().toString()); System.out.println(); } } while (moderationLabelDetectionResult !=null && moderationLabelDetectionResult.getNextToken() != null); }

    In the function main, replace the lines:

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

    with:

    StartUnsafeContentDetection(bucket, video); if (GetSQSMessageSuccess()==true) GetUnsafeContentDetectionResults();
    Java V2

    This code is taken from the AWS Documentation SDK examples GitHub repository. See the full example here.

    public static void startModerationDetection(RekognitionClient rekClient, NotificationChannel channel, String bucket, String video) { try { S3Object s3Obj = S3Object.builder() .bucket(bucket) .name(video) .build(); Video vidOb = Video.builder() .s3Object(s3Obj) .build(); StartContentModerationRequest modDetectionRequest = StartContentModerationRequest.builder() .jobTag("Moderation") .notificationChannel(channel) .video(vidOb) .build(); StartContentModerationResponse startModDetectionResult = rekClient.startContentModeration(modDetectionRequest); startJobId=startModDetectionResult.jobId(); } catch(RekognitionException e) { System.out.println(e.getMessage()); System.exit(1); } } public static void GetModResults(RekognitionClient rekClient) { try { String paginationToken=null; GetContentModerationResponse modDetectionResponse=null; Boolean finished = false; String status=""; int yy=0 ; do{ if (modDetectionResponse !=null) paginationToken = modDetectionResponse.nextToken(); GetContentModerationRequest modRequest = GetContentModerationRequest.builder() .jobId(startJobId) .nextToken(paginationToken) .maxResults(10) .build(); // Wait until the job succeeds while (!finished) { modDetectionResponse = rekClient.getContentModeration(modRequest); status = modDetectionResponse.jobStatusAsString(); if (status.compareTo("SUCCEEDED") == 0) finished = true; else { System.out.println(yy + " status is: " + status); Thread.sleep(1000); } yy++; } finished = false; // Proceed when the job is done - otherwise VideoMetadata is null VideoMetadata videoMetaData=modDetectionResponse.videoMetadata(); System.out.println("Format: " + videoMetaData.format()); System.out.println("Codec: " + videoMetaData.codec()); System.out.println("Duration: " + videoMetaData.durationMillis()); System.out.println("FrameRate: " + videoMetaData.frameRate()); System.out.println("Job"); List<ContentModerationDetection> mods = modDetectionResponse.moderationLabels(); for (ContentModerationDetection mod: mods) { long seconds=mod.timestamp()/1000; System.out.print("Mod label: " + seconds + " "); System.out.println(mod.moderationLabel().toString()); System.out.println(); } } while (modDetectionResponse !=null && modDetectionResponse.nextToken() != null); } catch(RekognitionException | InterruptedException e) { System.out.println(e.getMessage()); System.exit(1); } }
    Python
    #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.) # ============== Unsafe content =============== def StartUnsafeContent(self): response=self.rek.start_content_moderation(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 GetUnsafeContentResults(self): maxResults = 10 paginationToken = '' finished = False while finished == False: response = self.rek.get_content_moderation(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 contentModerationDetection in response['ModerationLabels']: print('Label: ' + str(contentModerationDetection['ModerationLabel']['Name'])) print('Confidence: ' + str(contentModerationDetection['ModerationLabel']['Confidence'])) print('Parent category: ' + str(contentModerationDetection['ModerationLabel']['ParentName'])) print('Timestamp: ' + str(contentModerationDetection['Timestamp'])) print() if 'NextToken' in response: paginationToken = response['NextToken'] else: finished = True

    In the function main, replace the lines:

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

    with:

    analyzer.StartUnsafeContent() if analyzer.GetSQSMessageSuccess()==True: analyzer.GetUnsafeContentResults()
    Note

    If you've already run a video example other than Analyzing a video stored in an Amazon S3 bucket with Java or Python (SDK), the code to replace might be different.

  3. Run the code. A list of inappropriate content labels detected in the video is shown.

GetContentModeration operation response

The response from GetContentModeration is an array, ModerationLabels, of ContentModerationDetection objects. The array contains an element for each time an inappropriate content label is detected. Within a ContentModerationDetectionObject object, ModerationLabel contains information for a detected item of inappropriate or offensive content. Timestamp is the time, in milliseconds from the start of the video, when the label was detected. The labels are organized hierarchically in the same manner as the labels detected by inappropriate content image analysis. For more information, see Content moderation.

The following is an example response from GetContentModeration.

{ "JobStatus": "SUCCEEDED", "ModerationLabels": [ { "ModerationLabel": { "Confidence": 93.02153015136719, "Name": "Male Swimwear Or Underwear", "ParentName": "Suggestive" }, "Timestamp": 0 }, { "ModerationLabel": { "Confidence": 93.02153015136719, "Name": "Suggestive", "ParentName": "" }, "Timestamp": 0 }, { "ModerationLabel": { "Confidence": 98.29075622558594, "Name": "Male Swimwear Or Underwear", "ParentName": "Suggestive" }, "Timestamp": 1000 }, { "ModerationLabel": { "Confidence": 98.29075622558594, "Name": "Suggestive", "ParentName": "" }, "Timestamp": 1000 }, { "ModerationLabel": { "Confidence": 97.91191101074219, "Name": "Male Swimwear Or Underwear", "ParentName": "Suggestive" }, "Timestamp": 1999 } ], "NextToken": "w5xfYx64+QvCdGTidVVWtczKHe0JAcUFu2tJ1RgDevHRovJ+1xej2GUDfTMWrTVn1nwSMHi9", "VideoMetadata": { "Codec": "h264", "DurationMillis": 3533, "Format": "QuickTime / MOV", "FrameHeight": 1080, "FrameRate": 30, "FrameWidth": 1920 } }