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• lavfi/dnn_classify : add filter dnn_classify for classification based on detection...

  17 mars 2021, par Guo, Yejun

  lavfi/dnn_classify : add filter dnn_classify for classification based on detection bounding boxes
  classification is done on every detection bounding box in frame's side data,
  
  which are the results of object detection (filter dnn_detect).
  Please refer to commit log of dnn_detect for the material for detection,
  
  and see below for classification.
   download material for classifcation :
  
  wget https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/emotions-recognition-retail-0003.bin
  
  wget https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/emotions-recognition-retail-0003.xml
  
  wget https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/emotions-recognition-retail-0003.label
   run command as :
  
  ./ffmpeg -i cici.jpg -vf dnn_detect=dnn_backend=openvino:model=face-detection-adas-0001.xml:input=data:output=detection_out:confidence=0.6:labels=face-detection-adas-0001.label,dnn_classify=dnn_backend=openvino:model=emotions-recognition-retail-0003.xml:input=data:output=prob_emotion:confidence=0.3:labels=emotions-recognition-retail-0003.label:target=face,showinfo -f null -
  We'll see the detect&classify result as below :
  
  [Parsed_showinfo_2 @ 0x55b7d25e77c0]   side data - detection bounding boxes :
  
  [Parsed_showinfo_2 @ 0x55b7d25e77c0] source : face-detection-adas-0001.xml, emotions-recognition-retail-0003.xml
  
  [Parsed_showinfo_2 @ 0x55b7d25e77c0] index : 0,  region : (1005, 813) -> (1086, 905), label : face, confidence : 10000/10000.
  
  [Parsed_showinfo_2 @ 0x55b7d25e77c0]            classify :  label : happy, confidence : 6757/10000.
  
  [Parsed_showinfo_2 @ 0x55b7d25e77c0] index : 1,  region : (888, 839) -> (967, 926), label : face, confidence : 6917/10000.
  
  [Parsed_showinfo_2 @ 0x55b7d25e77c0]            classify :  label : anger, confidence : 4320/10000.
  Signed-off-by : Guo, Yejun <yejun.guo@intel.com>
  

  • [DH] configure
  • [DH] doc/filters.texi
  • [DH] libavfilter/Makefile
  • [DH] libavfilter/allfilters.c
  • [DH] libavfilter/vf_dnn_classify.c

• lavd/v4l2 : detect device name truncation

  25 novembre 2021, par Anton Khirnov

  lavd/v4l2 : detect device name truncation
  Silences the following warning with gcc 10 :
  
  src/libavdevice/v4l2.c : In function ‘v4l2_get_device_list’ :
  
  src/libavdevice/v4l2.c:1042:64 : warning : ‘%s’ directive output may be truncated writing up to 255 bytes into a region of size 251 [-Wformat-truncation=]
  
   1042 |         ret = snprintf(device_name, sizeof(device_name), "/dev/%s", entry->d_name) ;
  
        |                                                                ^ 
  
  src/libavdevice/v4l2.c:1042:15 : note : ‘snprintf’ output between 6 and 261 bytes into a destination of size 256
  
   1042 |         ret = snprintf(device_name, sizeof(device_name), "/dev/%s", entry->d_name) ;
  
        |               ^ 
  Previous patches intending to silence it have proposed increasing the
  
  buffer size, but doing that correctly seems to be tricky. Failing on
  
  truncation is simpler and just as effective (as excessively long device
  
  names are unlikely).
  

  • [DH] libavdevice/v4l2.c

• ffmpeg node js s3 stream screenshots qestion

  20 octobre 2015, par user3564443

  On my Node js server I need get video from s3, generate thumbnail for it, set thumbnail to s3, without saving video or thumbnail on my server. So I need to use streams for this. For thumbnail generation I use fluent-ffmpeg.

  Is it possible to get screenshots from streams using fluent-ffmpeg ?

  Is the correct way to intercept s3 getObject stream, that there was no need to download full video ?

  var config = require('../config');
  
  var AWS = require('aws-sdk');
  
  AWS.config.update({
  
    accessKeyId: config.AWS_accessKeyId,
  
    secretAccessKey: config.AWS_secretAccessKey,
  
    region: 'eu-west-1'
  
  });
  
  var s3 = new AWS.S3();
  
  var fs = require('fs');
  
  var ffmpeg = require('fluent-ffmpeg');
  
  
  
  exports.generateVideoThumbnail = function(fileId, url, done) {
  
    var params = {
  
      Bucket: config.AWS_bucket,
  
      Key: url
  
    };
  
    var input = s3.getObject(params);
  
    var stream = fs.createWriteStream('./screenshot.png');
  
    return ffmpeg(input).screenshots({
  
      timestamps: ['0.1', '0.2'],
  
      size: '200x200'
  
    }).output('./screenshot.png').output(stream).on('error', function(err) {
  
      return done(err);
  
    }).on('end', function() {
  
      input.close();
  
      var _key = "files/" + fileId + "/thumbnail.png";
  
      return s3.putObject({
  
        Bucket: config.AWS_bucket,
  
        Key: _key,
  
        Body: stream,
  
        ContentType: 'image/jpeg'
  
      }, function(err) {
  
        return done(err);
  
      });
  
    });
  
  };