is there extra setup needed outside of CF for Elasticsearch?
@inacubicle_twitter Kibana access is disabled by default, but you can set it up like this: https://github.com/awslabs/aws-media-insights-engine/blob/development/IMPLEMENTATION_GUIDE.md#validate-metadata-in-elasticsearch
the other issue I have is I'm getting a 403 from the UI still, it errors with the identity pool, but I can log in fine with no errors/do the upload process
then when I hit the search endpoint I get a 403. The data and index is there in the cluster, checked in Kibana
They are in the state force change password, and it seems like the client Vue app doesn't have a state for that
is there something unique about the originally created user?
actually I got it working, I just needed to relogin, I don't think the user pool had caught up to the changes I made
thanks so much for the help, everything is working as expecting :)
https://github.com/awslabs/aws-media-insights-engine/blob/master/IMPLEMENTATION_GUIDE.md
What are the triple boxes next to the kinesis streams?
- CDC stream from DynamoDB
- Notify Lambda function that new data was created. This Lambda loads metadata from json files in s3 (reminder: lots of MIE operators save metadata as json files in s3, then put the path to said file in dynamodb), then reformats those json records in a format that Elasticsearch likes (e.g. it’ll flatten nested json arrays because Elasticsearch doesn’t search well over nested arrays)
- This is a hypothetical line. You can use the same kinesis stream endpoints to publish to a new lambda that feeds data into another data store. We use Elasticsearch for the MIE webapp, but as this diagram suggests, you can use this same architectural pattern to populate another data store, e.g. like a graph DB to facilitate some kind of new app for graphing relationships in metadata.
Timestamp is the time, in milliseconds from the start of the video, that the face was recognized.
Hey Ian! I changed the mediaconvert operator to extract frames into a folder in the dataplane location by updating the create_job call parameters. For some reason, I am unable to see any metadata tags populated in the UI for any of the existing videos that were uploaded. Is there a way to debug this?
The mediaconvert operator ran and frames were extracted but the overall job status shows an error in the UI. I was operating under the assumption that the mediaconvert operator is never called in the workflow created through the UI.
To debug a workflow error, click the error status in the UI. That will take you to the state machine in AWS Step Functions. Then in the graph for the state machine you should see an orange or red state. Click that state. (Depending on your mousing skills, you might have to zoom in to click on it). Then expand the Output text in the Step details, and look for error info that explains why it failed.
@rajesh1993 Does that help?
The mie cloudformation failed on me with the following error message:
I built and deployed it from the source. Anyone knows what's happening behind those error message.
Btw, I was able to deploy it successfully yesterday and I deleted the stack (successfully) and got the error message when I tried to redeploy
The following resource(s) failed to create: [MediaconvertOperation, GenericDataLookupOperation, technicalCueDetectionOperation, contentModerationOperation, WebCaptionsOperation, TranscribeOperation, comprehendPhrasesOperation, textDetectionOperation, CreateSRTCaptionsOperation, CreateVTTCaptionsOperation, ThumbnailOperation, contentModerationOperationImage, comprehendEntitiesOperation].I built and deployed it from the source. Anyone knows what's happening behind those error message.
Btw, I was able to deploy it successfully yesterday and I deleted the stack (successfully) and got the error message when I tried to redeploy
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The AWS Content Analysis solution was released today. This is the first MIE based solution to be released as an official AWS solution. The Content Analysis solution is designed for video search use cases, and for people who want to test-drive AWS AI services using their own video content.
Landing page:
https://aws.amazon.com/solutions/implementations/aws-content-analysis/
Documentation:
https://docs.aws.amazon.com/solutions/latest/aws-content-analysis/
Source Code:
https://github.com/awslabs/aws-content-analysis
Landing page:
https://aws.amazon.com/solutions/implementations/aws-content-analysis/
Documentation:
https://docs.aws.amazon.com/solutions/latest/aws-content-analysis/
Source Code:
https://github.com/awslabs/aws-content-analysis
A blog article was published today describing the AWS Content Analysis application: https://aws.amazon.com/blogs/media/introducing-aws-content-analysis-solution/
Yesterday we published a blog article summarizing why we built MIE and showcasing some of the applications that use it: https://aws.amazon.com/blogs/media/how-to-rapidly-prototype-multimedia-applications-on-aws-with-the-media-insights-engine/
Hello, I am working in a student project at HPI in Potsdam, Germany. Our task in our project is to develop a pipeline for the automated transcription and translation of videos. We are building an application which integrates different services for transcription and translation. Currently we try to use the aws media insights engine. What is your suggestion to call the aws mie api from our application? How does the authentication work? We are using python with django. We appreciate any help to find a good solution. Thanks a lot!
@olivernordemann MIE’s api is a REST API, so you interact with it via HTTP requests. Since you’re using python you’ll probably want to use the urllib package to do that. Those calls will include a security token. The IMPLEMENTATION GUIDE shows how to get the token and make HTTP requests: https://github.com/awslabs/aws-media-insights-engine/blob/master/IMPLEMENTATION_GUIDE.md#obtain-a-cognito-token
No, let's say I have a set of videos I've already broken down into frames, that have a specific partition structure, eg:
s3://my-path/parent_id={}/child_id={}
Where the parent_id holds a set frames partitioned by a specific set of ID's. This is for performance reasons doing sagemaker inference (if I uploaded a 25 minute video and tried to do inference on all the frames, that would be very time consuming without splitting the jobs parallel)
The frames will match the video, its just partitioned in a specific way for performance and metadata labeling reasons
the parent ID would be corresponding to a specific video