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. For a list of moderation labels in Amazon Rekognition, see Using the image and video moderation APIs.

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.

    //Copyright 2018, Inc. or its affiliates. All Rights Reserved. //PDX-License-Identifier: MIT-0 (For details, see //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();


    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.

    import; import; import; import; import; import; import; import; import; import; import; import; 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: * * */ public class VideoDetectInappropriate { private static String startJobId = ""; public static void main(String[] args) { final String usage = """ Usage: <bucket> <video> <topicArn> <roleArn> 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<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); } } }
    #Copyright 2018, Inc. or its affiliates. All Rights Reserved. #PDX-License-Identifier: MIT-0 (For details, see # ============== Unsafe content =============== def StartUnsafeContent(self): response=self.rek.start_content_moderation(Video={'S3Object': {'Bucket': self.bucket, 'Name':}}, 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, SortBy="NAME", AggregateBy="TIMESTAMPS") 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()


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

    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 Moderating content.

The following is an example response from GetContentModeration, sorted by NAME and aggregated by TIMESTAMPS.

{ "JobStatus": "SUCCEEDED", "VideoMetadata": { "Codec": "h264", "DurationMillis": 54100, "Format": "QuickTime / MOV", "FrameRate": 30.0, "FrameHeight": 462, "FrameWidth": 884, "ColorRange": "LIMITED" }, "ModerationLabels": [ { "Timestamp": 36000, "ModerationLabel": { "Confidence": 52.451576232910156, "Name": "Alcohol", "ParentName": "", "TaxonomyLevel": 1 }, "ContentTypes": [ { "Confidence": 99.9999008178711, "Name": "Animated" } ] }, { "Timestamp": 36000, "ModerationLabel": { "Confidence": 52.451576232910156, "Name": "Alcoholic Beverages", "ParentName": "Alcohol", "TaxonomyLevel": 2 }, "ContentTypes": [ { "Confidence": 99.9999008178711, "Name": "Animated" } ] } ], "ModerationModelVersion": "7.0", "JobId": "a1b2c3d4...", "Video": { "S3Object": { "Bucket": "bucket-name", "Name": "video-name.mp4" } }, "GetRequestMetadata": { "SortBy": "TIMESTAMP", "AggregateBy": "TIMESTAMPS" } }

The following is an example response from GetContentModeration, sorted by NAME and aggregated by SEGMENTS.

{ "JobStatus": "SUCCEEDED", "VideoMetadata": { "Codec": "h264", "DurationMillis": 54100, "Format": "QuickTime / MOV", "FrameRate": 30.0, "FrameHeight": 462, "FrameWidth": 884, "ColorRange": "LIMITED" }, "ModerationLabels": [ { "Timestamp": 0, "ModerationLabel": { "Confidence": 0.0003000000142492354, "Name": "Alcohol Use", "ParentName": "Alcohol", "TaxonomyLevel": 2 }, "StartTimestampMillis": 0, "EndTimestampMillis": 29520, "DurationMillis": 29520, "ContentTypes": [ { "Confidence": 99.9999008178711, "Name": "Illustrated" }, { "Confidence": 99.9999008178711, "Name": "Animated" } ] } ], "ModerationModelVersion": "7.0", "JobId": "a1b2c3d4...", "Video": { "S3Object": { "Bucket": "bucket-name", "Name": "video-name.mp4" } }, "GetRequestMetadata": { "SortBy": "TIMESTAMP", "AggregateBy": "SEGMENTS" } }