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• Live streaming of processed frames to AWS

  22 avril 2021, par MinasCham

  I'm working on a project where i need to capture live video feed from an RTSP camera source, process the video frame-by-frame and stream the result to an AWS Service.

  


  So far, my solution :

  


  

  • Captures frames from the RTSP camera source using OpenCV and performs some processing.

  


  • Feeds the processed frames to an ffmpeg pipe that packages the content for online streaming (HTTP Live Streaming - hls) and saves it locally.

  


  • Transfers the media content to an Amazon Kinesis Video Stream using a Gstreamer pipeline element with kvssink as a sink element.

  


  


  My questions are :

  


  

  • Currently I'm saving the content both locally and on an Amazon Kinesis Video Stream. Is this efficient ?

  


  • Is it possible to directly stream the frames to the Amazon kinesis Video Stream (perhaps by connecting the ffmpeg output with the gstreamer pipeline element) ?

  


  • Is the file format suitable for this implementation or would it better to encode the media differently ?

  


  


• I want to print HLS files using ffmpeg in aws lambda (python)

  14 avril 2021, par 최우선

  I implemented it through the link(https://aws.amazon.com/ko/blogs/media/processing-user-generated-content-using-aws-lambda-and-ffmpeg/) here, and it works well.

  


  s3_source_bucket = event['Records'][0]['s3']['bucket']['name']
s3_source_key = event['Records'][0]['s3']['object']['key']

s3_source_basename = os.path.splitext(os.path.basename(s3_source_key))[0]
s3_destination_filename = s3_source_basename + ".m3u8"

s3_client = boto3.client('s3')
s3_source_signed_url = s3_client.generate_presigned_url('get_object',
    Params={'Bucket': s3_source_bucket, 'Key': s3_source_key},
    ExpiresIn=SIGNED_URL_TIMEOUT)


ffmpeg_cmd = "/opt/bin/ffmpeg -i \"" + s3_source_signed_url + "\" -codec: copy -start_number 0 -hls_time 10 -hls_list_size 0 -f hls -"
command1 = shlex.split(ffmpeg_cmd)
p1 = subprocess.run(command1, stdout=subprocess.PIPE, stderr=subprocess.PIPE)

resp = s3_client.put_object(Body=p1.stdout, Bucket=S3_DESTINATION_BUCKET, Key=s3_destination_filename)


  


  However, the actual output through ffmpeg is multiple files. For example test.m3u8, test0.ts, test1.ts .....

  


  But when I print p1.stdout, it looks like multiple files (test.m3u8,test0.ts....) are merged into one file.

  


  Is there a way to get the actual output multiple files (test.m3u8,test0.ts......) from p1.stdout ? Please help.

  


• ffmpeg file conversion AWS Lambda

  10 avril 2021, par eartoolbox

  I want a .webm file to be converted to a .wav file after it hits my S3 bucket. I followed this tutorial and tried to adapt it from my use case using the .webm -> .wav ffmpeg command described here.

  


  My AWS Lambda function generally works, in that when my .webm file hits the source bucket, it is converted to .wav and ends up in the destination bucket. However, the resulting file .wav is always 0 bytes (though the .webm not, including the appropriate audio). Did I adapt the code wrong ? I only changed the ffmpeg_cmd line from the first link.

  


  import json
import os
import subprocess
import shlex
import boto3

S3_DESTINATION_BUCKET = "hmtm-out"
SIGNED_URL_TIMEOUT = 60

def lambda_handler(event, context):

    s3_source_bucket = event['Records'][0]['s3']['bucket']['name']
    s3_source_key = event['Records'][0]['s3']['object']['key']

    s3_source_basename = os.path.splitext(os.path.basename(s3_source_key))[0]
    s3_destination_filename = s3_source_basename + ".wav"

    s3_client = boto3.client('s3')
    s3_source_signed_url = s3_client.generate_presigned_url('get_object',
        Params={'Bucket': s3_source_bucket, 'Key': s3_source_key},
        ExpiresIn=SIGNED_URL_TIMEOUT)
    
    ffmpeg_cmd = "/opt/bin/ffmpeg -i \"" + s3_source_signed_url + "\" -c:a pcm_f32le " + s3_destination_filename + " -"
    
    
    command1 = shlex.split(ffmpeg_cmd)
    p1 = subprocess.run(command1, stdout=subprocess.PIPE, stderr=subprocess.PIPE)

    resp = s3_client.put_object(Body=p1.stdout, Bucket=S3_DESTINATION_BUCKET, Key=s3_destination_filename)

    return {
        'statusCode': 200,
        'body': json.dumps('Processing complete successfully')
    }