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• Configure ffmpeg to work with native client

  11 juin 2016, par Mohammad Abu Musa

  I followed the steps https://chromium.googlesource.com/webports/+/master to port ffmpeg to native client. I do not know what is the issue here. here are the configuration and here what I get

  NACL_ARCH=pnacl TOOLCHAIN=pnacl make ffmpeg

  Unescaped left brace in regex is deprecated, passed through in regex; marked by <-- HERE in m/\@anchor{ <-- HERE (?:[^\}]*)\}/ at /home/user/Desktop/webports/src/out/build/ffmpeg/ffmpeg-2.6.1/doc/texi2pod.pl line 387.
MAN doc/ffmpeg.1
MAN doc/ffprobe.1
MAN doc/ffmpeg-all.1
MAN doc/ffprobe-all.1
LD  ffmpeg_g
LD  ffprobe_g
/home/user/Desktop/webports/src/out/build/ffmpeg/ffmpeg-2.6.1/Makefile:124: recipe for target 'ffprobe_g' failed
make[1]: *** [ffprobe_g] Error 247
make[1]: *** Waiting for unfinished jobs....
/home/user/Desktop/webports/src/out/build/ffmpeg/ffmpeg-2.6.1/Makefile:124: recipe for target 'ffmpeg_g' failed
make[1]: *** [ffmpeg_g] Error 247
make[1]: Leaving directory '/home/user/Desktop/webports/src/out/build/ffmpeg/build_pnacl'
webports: Build failed: 'ffmpeg' [pnacl/release]
Makefile:89: recipe for target 'ffmpeg' failed
make: *** [ffmpeg] Error 1

• Affectiva drops every second frame

  19 juin 2019, par machinery

  I am running Affectiva SDK 4.0 on a GoPro video recording. I’m using a C++ program on Ubuntu 16.04. The GoPro video was recorded with 60 fps. The problem is that Affectiva only provides results for around half of the frames (i.e. 30 fps). If I look at the timestamps provided by Affectiva, the last timestamp matches the video duration, that means Affectiva somehow skips around every second frame.

  Before running Affectiva I was running ffmpeg with the following command to make sure that the video has a constant frame rate of 60 fps :

  ffmpeg -i in.MP4 -vf -y -vcodec libx264 -preset medium -r 60 -map_metadata 0:g -strict -2 out.MP4 null 2>&1

  When I inspect the presentation timestamp using ffprobe -show_entries frame=pict_type,pkt_pts_time -of csv -select_streams v in.MP4 I’m getting for the raw video the following values :

  Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '/media/GoPro_concat/GoPro_concat.MP4':
  
    Metadata:
  
      major_brand     : isom
  
      minor_version   : 512
  
      compatible_brands: isomiso2avc1mp41
  
      encoder         : Lavf58.20.100
  
    Duration: 01:14:46.75, start: 0.000000, bitrate: 15123 kb/s
  
      Stream #0:0(eng): Video: h264 (Main) (avc1 / 0x31637661), yuvj420p(pc, bt709), 1280x720 [SAR 1:1 DAR 16:9], 14983 kb/s, 59.94 fps, 59.94 tbr, 60k tbn, 119.88 tbc (default)
  
      Metadata:
  
        handler_name    :  GoPro AVC
  
        timecode        : 13:17:26:44
  
      Stream #0:1(eng): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 127 kb/s (default)
  
      Metadata:
  
        handler_name    :  GoPro AAC
  
      Stream #0:2(eng): Data: none (tmcd / 0x64636D74)
  
      Metadata:
  
        handler_name    :  GoPro AVC
  
        timecode        : 13:17:26:44
  
  Unsupported codec with id 0 for input stream 2
  
  frame,0.000000,I
  
  frame,0.016683,P
  
  frame,0.033367,P
  
  frame,0.050050,P
  
  frame,0.066733,P
  
  frame,0.083417,P
  
  frame,0.100100,P
  
  frame,0.116783,P
  
  frame,0.133467,I
  
  frame,0.150150,P
  
  frame,0.166833,P
  
  frame,0.183517,P
  
  frame,0.200200,P
  
  frame,0.216883,P
  
  frame,0.233567,P
  
  frame,0.250250,P
  
  frame,0.266933,I
  
  frame,0.283617,P
  
  frame,0.300300,P
  
  frame,0.316983,P
  
  frame,0.333667,P
  
  frame,0.350350,P
  
  frame,0.367033,P
  
  frame,0.383717,P
  
  frame,0.400400,I
  
  frame,0.417083,P
  
  frame,0.433767,P
  
  frame,0.450450,P
  
  frame,0.467133,P
  
  frame,0.483817,P
  
  frame,0.500500,P
  
  frame,0.517183,P
  
  frame,0.533867,I
  
  frame,0.550550,P
  
  frame,0.567233,P
  
  frame,0.583917,P
  
  frame,0.600600,P
  
  frame,0.617283,P
  
  frame,0.633967,P
  
  frame,0.650650,P
  
  frame,0.667333,I
  
  frame,0.684017,P
  
  frame,0.700700,P
  
  frame,0.717383,P
  
  frame,0.734067,P
  
  frame,0.750750,P
  
  frame,0.767433,P
  
  frame,0.784117,P
  
  frame,0.800800,I
  
  frame,0.817483,P
  
  frame,0.834167,P
  
  frame,0.850850,P
  
  frame,0.867533,P
  
  frame,0.884217,P
  
  frame,0.900900,P
  
  frame,0.917583,P
  
  frame,0.934267,I
  
  frame,0.950950,P
  
  frame,0.967633,P
  
  frame,0.984317,P
  
  frame,1.001000,P
  
  frame,1.017683,P
  
  frame,1.034367,P
  
  frame,1.051050,P
  
  frame,1.067733,I
  
  ...

  I have uploaded the full output on OneDrive.

  If I run Affectiva on the raw video (not processed by ffmpeg) I face the same problem of dropped frames. I was using Affectiva with affdex::VideoDetector detector(60);

  Is there a problem with the ffmpeg command or with Affectiva ?

  Edit : I think I have found out where the problem could be. It seems that Affectiva is not processing the whole video but just stops after a certain amount of processed frames without any error message. Below I have posted the C++ code I’m using. In the onProcessingFinished() method I’m printing something to the console when the processing is finished. But this message is never printed, so Affectiva never comes to the end.

  Is there something wrong with my code or should I encode the videos into another format than MP4 ?

  #include "VideoDetector.h"
  
  #include "FrameDetector.h"
  
  
  
  #include <iostream>
  
  #include <fstream>
  
  #include <mutex>
  
  #include 
  
  
  
  std::mutex m;
  
  std::condition_variable conditional_variable;
  
  bool processed = false;
  
  
  
  class Listener : public affdex::ImageListener {
  
  public:
  
      Listener(std::ofstream * fout) {
  
          this->fout = fout;
  
    }
  
    virtual void onImageCapture(affdex::Frame image){
  
        //std::cout &lt;&lt; "called";
  
    }
  
    virtual void onImageResults(std::map faces, affdex::Frame image){
  
        //std::cout &lt;&lt; faces.size() &lt;&lt; " faces detected:" &lt;&lt; std::endl;
  
  
  
        for(auto&amp; kv : faces){
  
          (*this->fout) &lt;&lt; image.getTimestamp() &lt;&lt; ",";
  
          (*this->fout) &lt;&lt; kv.first &lt;&lt; ",";
  
          (*this->fout) &lt;&lt; kv.second.emotions.joy &lt;&lt; ",";
  
          (*this->fout) &lt;&lt; kv.second.emotions.fear &lt;&lt; ",";
  
          (*this->fout) &lt;&lt; kv.second.emotions.disgust &lt;&lt; ",";
  
          (*this->fout) &lt;&lt; kv.second.emotions.sadness &lt;&lt; ",";
  
          (*this->fout) &lt;&lt; kv.second.emotions.anger &lt;&lt; ",";
  
          (*this->fout) &lt;&lt; kv.second.emotions.surprise &lt;&lt; ",";
  
          (*this->fout) &lt;&lt; kv.second.emotions.contempt &lt;&lt; ",";
  
          (*this->fout) &lt;&lt; kv.second.emotions.valence &lt;&lt; ",";
  
          (*this->fout) &lt;&lt; kv.second.emotions.engagement &lt;&lt; ",";
  
          (*this->fout) &lt;&lt; kv.second.measurements.orientation.pitch &lt;&lt; ",";
  
          (*this->fout) &lt;&lt; kv.second.measurements.orientation.yaw &lt;&lt; ",";
  
          (*this->fout) &lt;&lt; kv.second.measurements.orientation.roll &lt;&lt; ",";
  
          (*this->fout) &lt;&lt; kv.second.faceQuality.brightness &lt;&lt; std::endl;
  
  
  
  
  
          //std::cout &lt;&lt;  kv.second.emotions.fear &lt;&lt; std::endl;
  
          //std::cout &lt;&lt;  kv.second.emotions.surprise  &lt;&lt; std::endl;
  
          //std::cout &lt;&lt;  (int) kv.second.emojis.dominantEmoji;
  
        }
  
    }
  
  private:
  
      std::ofstream * fout;
  
  };
  
  
  
  class ProcessListener : public affdex::ProcessStatusListener{
  
  public:
  
      virtual void onProcessingException (affdex::AffdexException ex){
  
          std::cerr &lt;&lt; "[Error] " &lt;&lt; ex.getExceptionMessage();
  
      }
  
      virtual void onProcessingFinished (){
  
          {
  
              std::lock_guard lk(m);
  
              processed = true;
  
              std::cout &lt;&lt; "[Affectiva] Video processing finised." &lt;&lt; std::endl;
  
          }
  
          conditional_variable.notify_one();
  
      }
  
  };
  
  
  
  int main(int argc, char ** argsv)
  
  {
  
      affdex::VideoDetector detector(60, 1, affdex::FaceDetectorMode::SMALL_FACES);
  
      //affdex::VideoDetector detector(60, 1, affdex::FaceDetectorMode::LARGE_FACES);
  
      std::string classifierPath="/home/wrafael/affdex-sdk/data";
  
      detector.setClassifierPath(classifierPath);
  
      detector.setDetectAllEmotions(true);
  
  
  
      // Output
  
      std::ofstream fout(argsv[2]);
  
      fout &lt;&lt; "timestamp" &lt;&lt; ",";
  
      fout &lt;&lt; "faceId" &lt;&lt; ",";
  
      fout &lt;&lt; "joy" &lt;&lt; ",";
  
      fout &lt;&lt; "fear" &lt;&lt; ",";
  
      fout &lt;&lt; "disgust" &lt;&lt; ",";
  
      fout &lt;&lt; "sadness" &lt;&lt; ",";
  
      fout &lt;&lt; "anger" &lt;&lt; ",";
  
      fout &lt;&lt; "surprise" &lt;&lt; ",";
  
      fout &lt;&lt; "contempt" &lt;&lt; ",";
  
      fout &lt;&lt; "valence" &lt;&lt; ",";
  
      fout &lt;&lt; "engagement"  &lt;&lt; ",";
  
      fout &lt;&lt; "pitch" &lt;&lt; ",";
  
      fout &lt;&lt; "yaw" &lt;&lt; ",";
  
      fout &lt;&lt; "roll" &lt;&lt; ",";
  
      fout &lt;&lt; "brightness" &lt;&lt; std::endl;
  
  
  
      Listener l(&amp;fout);
  
      ProcessListener pl;
  
      detector.setImageListener(&amp;l);
  
      detector.setProcessStatusListener(&amp;pl);
  
  
  
      detector.start();
  
      detector.process(argsv[1]);
  
  
  
      // wait for the worker
  
      {
  
      std::unique_lock lk(m);
  
      conditional_variable.wait(lk, []{return processed;});
  
      }
  
      fout.flush();
  
      fout.close();
  
  }
  
  </mutex></fstream></iostream>

  Edit 2 : I have now digged further into the problem and looked only at one GoPro file with a duration of 19min 53s (GoPro splits the recordings). When I run Affectiva with affdex::VideoDetector detector(60, 1, affdex::FaceDetectorMode::SMALL_FACES); on that raw video the following file is produced. Affectiva stops after 906s without any error message and without printing "[Affectiva] Video processing finised".

  When I now transform the video using ffmpeg -i raw.MP4 -y -vcodec libx264 -preset medium -r 60 -map_metadata 0:g -strict -2 out.MP4 and then run Affectiva with affdex::VideoDetector detector(60, 1, affdex::FaceDetectorMode::SMALL_FACES);, Affectiva runs until the end and prints
  "[Affectiva] Video processing finised" but the frame rate is only at 23 fps. Here is the file.

  When I now run Affectiva with affdex::VideoDetector detector(62, 1, affdex::FaceDetectorMode::SMALL_FACES); on this transformed file, Affectiva stops after 509s and "[Affectiva] Video processing finised" is not printed. Here is the file.

• No such file or directory - the ffprobe binary could not be found error

  22 juin 2016, par rahul

  I am using carrierwave-video gem uploading videos through carrierwave and it’s not working.

  video_uploader.rb

  class VideoUploader < CarrierWave::Uploader::Base
  
    include CarrierWave::Video
  
    storage :file
  
    def store_dir
  
      "uploads/#{model.class.to_s.underscore}/#{mounted_as}/#{model.id}"
  
    end
  
  end

  video.rb

  class Video < ActiveRecord::Base
  
    mount_uploader :file, VideoUploader
  
  
  
    def set_success(format, opts)
  
      self.success = true
  
    end
  
  end

  The error I am getting is :

  No such file or directory - the ffprobe binary could not be found in /home/administrator/.rvm/gems/ruby-2.3.0/bin:/home/administrator/.rvm/gems/ruby-2.3.0@global/bin:/usr/share/rvm/rubies/ruby-2.3.0/bin:/usr/share/rvm/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games