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• Revision 37455 : Une meilleure vérification... On ne disposait pas encore de l’id_orig ici

  20 avril 2010, par kent1@… — Log

  Une meilleure vérification... On ne disposait pas encore de l’id_orig ici

• Lambda/ffmpeg timelapse generation - output zero bytes, can't debug ffmpeg

  25 août 2021, par GoOutside

  I am attempting to use an AWS Lambda FFMPEG layer to build a timelapse of static images in an S3 bucket. To begin, I am basing my project off of the tutorial located here.

  


  I can replicate the steps in the tutorial, so I know the FFMPEG layer is working in Lambda. I have replicated the FFMPEG commands on a standalone server, so I know they are correct.

  


  Here is my setup : I have two S3 buckets, lambda-source-bucket and lambda-destination-bucket. The contents of lambda-source-bucket are :

  


  1.jpg
2.jpg
3.jpg
4.jpg
5.jpg
6.jpg
7.jpg
files.txt


  


  The files.txt contains this :

  


  file 'https://lambda-source-bucket.s3.us-west-2.amazonaws.com/1.jpg'
file 'https://lambda-source-bucket.s3.us-west-2.amazonaws.com/2.jpg'
file 'https://lambda-source-bucket.s3.us-west-2.amazonaws.com/3.jpg'
file 'https://lambda-source-bucket.s3.us-west-2.amazonaws.com/4.jpg'
file 'https://lambda-source-bucket.s3.us-west-2.amazonaws.com/5.jpg'
file 'https://lambda-source-bucket.s3.us-west-2.amazonaws.com/6.jpg'
file 'https://lambda-source-bucket.s3.us-west-2.amazonaws.com/7.jpg'


  


  This is my Lambda function code (in Python) :

  


  import json
import os
import subprocess
import shlex
import boto3

S3_DESTINATION_BUCKET = "lambda-destination-bucket"
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 = "timelapse.mp4"

    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 -y -r 24 -f concat -safe 0 -protocol_whitelist file,http,tcp,https,tls -I ""https://lambda-source-bucket.s3.us-west-2.amazonaws.com/files.txt"" -c copy -s 1024x576 -vcodec libx264 -"    
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')
    }


  


  The trigger for the Lambda function is when a new files.txt file is added to lambda-source-bucket.

  


  So far I have been able to get the trigger to fire, the function supposedly runs without errors (in Cloudwatch), and the function creates a new timelapse.mp4 in the lambda-destination-bucket. But this file is 0 bytes. I see no FFMPEG errors in the Cloudwatch console, though I am not sure I know how to configure my Lambda function code to log FFMPEG errors.

  


  Also : if I'm going about this in a totally wrong way, I'd love to hear feedback. I'm guessing that the concat and files.txt method of looping through https:// is not the most efficient way to do this, but it's the only way I can figure this out so far.

  


  Any help is most sincerely and humbly appreciated.

  


• Finding Optimal Code Coverage

  7 mars 2012, par Multimedia Mike — Programming

  A few months ago, I published a procedure for analyzing code coverage of the test suites exercised in FFmpeg and Libav. I used it to add some more tests and I have it on good authority that it has helped other developers fill in some gaps as well (beginning with students helping out with the projects as part of the Google Code-In program). Now I’m wondering about ways to do better.

  Current Process
  When adding a test that depends on a sample (like a demuxer or decoder test), it’s ideal to add a sample that’s A) small, and B) exercises as much of the codebase as possible. When I was studying code coverage statistics for the WC4-Xan video decoder, I noticed that the sample didn’t exercise one of the 2 possible frame types. So I scouted samples until I found one that covered both types, trimmed the sample down, and updated the coverage suite.

  I started wondering about a method for finding the optimal test sample for a given piece of code, one that exercises every code path in a module. Okay, so that’s foolhardy in the vast majority of cases (although I was able to add one test spec that pushed a module’s code coverage from 0% all the way to 100% — but the module in question only had 2 exercisable lines). Still, given a large enough corpus of samples, how can I find the smallest set of samples that exercise the complete codebase ?

  This almost sounds like an NP-complete problem. But why should that stop me from trying to find a solution ?

  Science Project
  Here’s the pitch :

  • Instrument FFmpeg with code coverage support
  • Download lots of media to exercise a particular module
  • Run FFmpeg against each sample and log code coverage statistics
  • Distill the resulting data in some meaningful way in order to obtain more optimal code coverage

  That first step sounds harsh– downloading lots and lots of media. Fortunately, there is at least one multimedia format in the projects that tends to be extremely small : ANSI. These are files that are designed to display elaborate scrolling graphics using text mode. Further, the FATE sample currently deployed for this test (TRE_IOM5.ANS) only exercises a little less than 50% of the code in libavcodec/ansi.c. I believe this makes the ANSI video decoder a good candidate for this experiment.

  Procedure
  First, find a site that hosts a lot ANSI files. Hi, sixteencolors.net. This site has lots (on the order of 4000) artpacks, which are ZIP archives that contain multiple ANSI files (and sometimes some other files). I scraped a list of all the artpack names.

  In an effort to be responsible, I randomized the list of artpacks and downloaded periodically and with limited bandwidth ('wget --limit-rate=20k').

  Run ‘gcov’ on ansi.c in order to gather the full set of line numbers to be covered.

  For each artpack, unpack the contents, run the instrumented FFmpeg on each file inside, run ‘gcov’ on ansi.c, and log statistics including the file’s size, the file’s location (artpack.zip:filename), and a comma-separated list of line numbers touched.

  Definition of ‘Optimal’
  The foregoing procedure worked and yielded useful, raw data. Now I have to figure out how to analyze it.

  I think it’s most desirable to have the smallest files (in terms of bytes) that exercise the most lines of code. To that end, I sorted the results by filesize, ascending. A Python script initializes a set of all exercisable line numbers in ansi.c, then iterates through each each file’s stats line, adding the file to the list of candidate samples if its set of exercised lines can remove any line numbers from the overall set of lines. Ideally, that set of lines should devolve to an empty set.

  I think a second possible approach is to find the single sample that exercises the most code and then proceed with the previously described method.

  Initial Results
  So far, I have analyzed 13324 samples from 357 different artpacks provided by sixteencolors.net.

  Using the first method, I can find a set of samples that covers nearly 80% of ansi.c :

  <br />
0 bytes: bad-0494.zip:5<br />
1 bytes: grip1293.zip:-ANSI---.---<br />
1 bytes: pur-0794.zip:.<br />
2 bytes: awe9706.zip:-ANSI───.───<br />
61 bytes: echo0197.zip:-(ART)-<br />
62 bytes: hx03.zip:HX005.DAT<br />
76 bytes: imp-0494.zip:IMPVIEW.CFG<br />
82 bytes: ice0010b.zip:_cont'd_.___<br />
101 bytes: bdp-0696.zip:BDP2.WAD<br />
112 bytes: plain12.zip:--------.---<br />
181 bytes: ins1295v.zip:-°VGA°-.  н<br />
219 bytes: purg-22.zip:NEM-SHIT.ASC<br />
289 bytes: srg1196.zip:HOWTOREQ.JNK<br />
315 bytes: karma-04.zip:FASHION.COM<br />
318 bytes: buzina9.zip:ox-rmzzy.ans<br />
411 bytes: solo1195.zip:FU-BLAH1.RIP<br />
621 bytes: ciapak14.zip:NA-APOC1.ASC<br />
951 bytes: lght9404.zip:AM-TDHO1.LIT<br />
1214 bytes: atb-1297.zip:TX-ROKL.ASC<br />
2332 bytes: imp-0494.zip:STATUS.ANS<br />
3218 bytes: acepak03.zip:TR-STAT5.ANS<br />
6068 bytes: lgc-0193.zip:LGC-0193.MEM<br />
16778 bytes: purg-20.zip:EZ-HIR~1.JPG<br />
20582 bytes: utd0495.zip:LT-CROW3.ANS<br />
26237 bytes: quad0597.zip:MR-QPWP.GIF<br />
29208 bytes: mx-pack17.zip:mx-mobile-source-logo.jpg<br />
----<br />
109440 bytes total<br />

  A few notes about that list : Some of those filenames are comprised primarily of control characters. 133t, and all that. The first file is 0 bytes. I wondered if I should discard 0-length files but decided to keep those in, especially if they exercise lines that wouldn’t normally be activated. Also, there are a few JPEG and GIF files in the set. I should point out that I forced the tty demuxer using -f tty and there isn’t much in the way of signatures for this format. So, again, whatever exercises more lines is better.

  Using this same corpus, I tried approach 2– which single sample exercises the most lines of the decoder ? Answer : blde9502.zip:REQUEST.EXE. Huh. I checked it out and ‘file’ ID’s it as a MS-DOS executable. So, that approach wasn’t fruitful, at least not for this corpus since I’m forcing everything through this narrow code path.

  Think About The Future
  Where can I take this next ? The cloud ! I have people inside the search engine industry who have furnished me with extensive lists of specific types of multimedia files from around the internet. I also see that Amazon Web Services Elastic Compute Cloud (AWS EC2) instances don’t charge for incoming bandwidth.

  I think you can see where I’m going with this.

  See Also :

  • Running this experiment on 4000 artpacks / 146,000 files