Recherche avancée

Sur d’autres sites (5875)

• Anomalie #4449 (Nouveau) : Taille d’image erronné des logos si un redimensionnement de l’image

  25 février 2020

  Pour le contexte, le problème est signalé là https://github.com/marcimat/bigup/issues/9 mais ne provient pas de Bigup.

  Pour reproduire :¶

  - définir dans mes_options.php les constantes :
  

  <span class="CodeRay"><span class="predefined">define</span>(<span class="string"><span class="delimiter">'</span><span class="content">_IMG_MAX_WIDTH</span><span class="delimiter">'</span></span>, <span class="integer">3000</span>);
  <span class="predefined">define</span>(<span class="string"><span class="delimiter">'</span><span class="content">_IMG_MAX_HEIGHT</span><span class="delimiter">'</span></span>, <span class="integer">1000</span>);
  </span>
  Télécharger

  - ajouter un plus grand logo (en SPIP 3.3+) sur un élément, par exemple un article
  - l’aperçu au retour retourne une image erronée, avec les dimensions de l’images d’origine (alors que l’image a réellement été redimensionnée sur le disque)
  - au rechargement la taille est affichée correctement, mais la miniature est toujours incorrecte

  Suppositions¶

  Il semblerait que les filtres largeur() et hauteur() utilisés par image_reduire, et par l’affichage du logo aient un cache qui enregistre la taille de l’image originale la première fois qu’ils sont appelés, mais si cette image est modifiée (réduite) ensuite, un appel ultérieur à ces fonctions retourne la valeur en cache.

  La réduction se fait dans verifier_taille_document_acceptable() de action/ajouter_documents.php du plugin medias, qui prend en comptel les constantes _IMG_MAX_WIDTH et _IMG_MAX_HEIGHT indiquées.

• How add Data Stream into MXF(using mpeg2video) file with FFmpeg and C/C++

  26 mars 2019, par Helmuth Schmitz

  I’m a little bit stuck here trying create a MXF file
  with data stream on it. I have several MXF video files that contain
  this standard

  **1 Video Stream:
  
       Stream #0:0: Video: mpeg2video (4:2:2), yuv422p(tv, bt709, top first), 1920x1080 [SAR 1:1 DAR 16:9], 50000 kb/s, 29.9
  
  16 audio streams
  
       Audio: pcm_s24le, 48000 Hz, 1 channels, s32 (24 bit), 1152 kb/s
  
  1 Data Stream:
  
       Data: none**

  This data stream, contain personal data inside video file. I can
  open this stream and data is really there. Is all ok. But, when i try
  to create a file exactly like this, everytime i call "avformat_write_header"
  it returns an error.

  If i do comment the creation of this data streams the video file is succeffully
  created.

  If i change to "mpegts" with this data stream, the video file is also succeffully
  created.

  But, i can’t use mpets and i need this data stream.

  I know that is possible MXF with data stream cause i have this originals files
  that have this combination.

  So, i know that i missing something in my code.

  This is the way i create this Data Stream :

   void CFFmpegVideoWriter::addDataStream(EOutputStream *ost, AVFormatContext *oc, AVCodec **codec, enum AVCodecID codec_id)
  
      {
  
          AVCodecParameters *par;
  
  
  
          ost->stream = avformat_new_stream(oc, NULL);
  
          if (ost->stream == NULL)
  
          {
  
              fprintf(stderr, "OOooohhh man: avformat_new_stream() failed.\n");
  
              return;
  
          }
  
  
  
          par = ost->stream->codecpar;
  
          ost->stream->index = 17;
  
          par->codec_id = AV_CODEC_ID_NONE;
  
          par->codec_type = AVMEDIA_TYPE_DATA;
  
  
  
          ost->stream->codec->flags |= AV_CODEC_FLAG_GLOBAL_HEADER;
  
      }

  the file openning is this :

  CFFMpegVideoWriter::CFFMpegVideoWriter(QString outputfilename) : QThread()
  
  {
  
      av_register_all();
  
      avcodec_register_all();
  
  
  
      isOpen = false;
  
      shouldClose = false;
  
  
  
      frameIndex = 0;
  
  
  
  #ifdef __linux__
  
      QByteArray bFilename = outputfilename.toUtf8();
  
  #else
  
      QByteArray bFilename = outputfilename.toLatin1();
  
  #endif
  
  
  
      const char* filename = bFilename.data();
  
  
  
      codecContext = NULL;
  
  
  
      //encontra o formato desejado...
  
      outputFormat = av_guess_format("mp2v", filename, nullptr);
  
      if (!outputFormat)
  
      {
  
          qDebug("Could not find suitable output format\n");
  
          return;
  
      }
  
  
  
      //encontra o codec...
  
      codec = avcodec_find_encoder(outputFormat->video_codec);
  
      if (!codec)
  
      {
  
          qDebug( "Codec not found\n");
  
          return;
  
      }
  
  
  
      //aloca o contexto do codec...
  
      codecContext = avcodec_alloc_context3(codec);
  
      codecContext->field_order = AV_FIELD_TT;
  
      codecContext->profile = FF_PROFILE_MPEG2_422;
  
  
  
      //aloca o contexto do formato...
  
      formatContext = avformat_alloc_context();
  
      formatContext->oformat = outputFormat;
  
  
  
      //aloca o contexto da midia de saida...
  
      avformat_alloc_output_context2(&formatContext, NULL, NULL, filename);
  
      if (!formatContext)
  
      {
  
          qDebug("Erro");
  
          return;
  
      }
  
  
  
      videoStream.tmp_frame = NULL;
  
      videoStream.swr_ctx = NULL;
  
  
  
      //adiciona a stream de video...
  
      if (outputFormat->video_codec != AV_CODEC_ID_NONE)
  
      {
  
          addVideoStream(&videoStream, formatContext, &video_codec, outputFormat->video_codec);       
  
      }
  
  
  
      //adiciona as 16 streams de audio...
  
      if (outputFormat->audio_codec != AV_CODEC_ID_NONE)
  
      {
  
          for (int i = 0; i < 16; i++)
  
          {
  
              addAudioStream(&audioStream[i], formatContext, &audio_codec, outputFormat->audio_codec);
  
          }       
  
      }
  
  
  
      addDataStream(&datastream, formatContext, &video_codec, outputFormat->video_codec);     
  
  
  
      videoStream.sws_ctx = NULL;
  
      for (int i = 0; i < 16; i++)
  
      {
  
          audioStream[i].sws_ctx = NULL;
  
      }   
  
      opt = NULL;
  
  
  
  
  
      //carreca o codec de video para stream de video...      
  
      initVideoCodec(formatContext, video_codec, &videoStream, opt);
  
  
  
  
  
      //carrega o codec de audio para stream de audio...s
  
      for (int i = 0; i < 16; i++)
  
      {
  
          initAudioCodec(formatContext, audio_codec, &audioStream[i], opt);
  
      }
  
  
  
  
  
      av_dump_format(formatContext, 0, filename, 1);
  
  
  
      //abrea o arquivo de saida..
  
      if (!(outputFormat->flags & AVFMT_NOFILE))
  
      {
  
          ret = avio_open(&formatContext->pb, filename, AVIO_FLAG_WRITE);
  
          if (ret < 0)
  
          {
  
              qDebug("Could not open'%s", filename);
  
              return;
  
          }
  
      }
  
  
  
      //escreve o cabecalho do arquivo...
  
      ret = avformat_write_header(formatContext, &opt);
  
      if (ret < 0)
  
      {
  
          qDebug("Error occurred when opening output file");
  
          return;
  
      }
  
  
  
      isOpen = true;
  
  
  
      QThread::start();
  
  }

  The code always fails at "avformat_write_header" call.

  But if i remove "datastream" or change it to mpegts everything runs fine.

  Any ideia of what am i doing wrong here ?

  Thanks for reading this.

  Helmuth

• WARN : Tried to pass invalid video frame, marking as broken : Your frame has data type int64, but we require uint8

  5 septembre 2019, par Tavo Diaz

  I am doing some Udemy AI courses and came across with one that "teaches" a bidimensional cheetah how to walk. I was doing the exercises on my computer, but it takes too much time. I decided to use Google Cloud to run the code and see the results some hours after. Nevertheless, when I run the code I get the following error " WARN : Tried to pass
  invalid video frame, marking as broken : Your frame has data type int64, but we require uint8 (i.e. RGB values from 0-255)".

  After the code is executed, I see into the folder and I don’t see any videos (just the meta info).

  Some more info (if it helps) :
  I have a 1 CPU (4g), SSD Ubuntu 16.04 LTS

  I have not tried anything yet to solve it because I don´t know what to try. Im looking for solutions on the web, but nothing I could try.

  This is the code

  import os 
  
  import numpy as np
  
  import gym
  
  from gym import wrappers
  
  import pybullet_envs
  
  
  
  
  
  class Hp():
  
      def __init__(self):
  
          self.nb_steps = 1000
  
          self.episode_lenght =   1000
  
          self.learning_rate = 0.02
  
          self.nb_directions = 32
  
          self.nb_best_directions = 32
  
          assert self.nb_best_directions <= self.nb_directions
  
          self.noise = 0.03
  
          self.seed = 1
  
          self.env_name = 'HalfCheetahBulletEnv-v0'
  
  
  
  
  
  class Normalizer():
  
      def __init__(self, nb_inputs):
  
          self.n = np.zeros(nb_inputs)
  
          self.mean = np.zeros(nb_inputs)
  
          self.mean_diff = np.zeros(nb_inputs)
  
          self.var = np.zeros(nb_inputs)
  
  
  
      def observe(self, x):
  
          self.n += 1.
  
          last_mean = self.mean.copy()
  
          self.mean += (x - self.mean) / self.n
  
          #abajo es el online numerator update
  
          self.mean_diff += (x - last_mean) * (x - self.mean)
  
          #abajo online computation de la varianza
  
          self.var = (self.mean_diff / self.n).clip(min = 1e-2)  
  
  
  
      def normalize(self, inputs):
  
          obs_mean = self.mean
  
          obs_std = np.sqrt(self.var)
  
          return (inputs - obs_mean) / obs_std
  
  
  
  class Policy():
  
      def __init__(self, input_size, output_size):
  
          self.theta = np.zeros((output_size, input_size))
  
  
  
      def evaluate(self, input, delta = None, direction = None):
  
          if direction is None:
  
              return self.theta.dot(input)
  
          elif direction == 'positive':
  
              return (self.theta + hp.noise * delta).dot(input)
  
          else:
  
              return (self.theta - hp.noise * delta).dot(input)
  
  
  
      def sample_deltas(self):
  
          return [np.random.randn(*self.theta.shape) for _ in range(hp.nb_directions)]
  
  
  
      def update (self, rollouts, sigma_r):
  
          step = np.zeros(self.theta.shape)
  
          for r_pos, r_neg, d in rollouts:
  
              step += (r_pos - r_neg) * d
  
          self.theta += hp.learning_rate / (hp.nb_best_directions * sigma_r) * step
  
  
  
  
  
  def explore(env, normalizer, policy, direction = None, delta = None):
  
      state = env.reset()
  
      done = False
  
      num_plays = 0.
  
      #abajo puede ser promedio de las rewards
  
      sum_rewards = 0
  
      while not done and num_plays < hp.episode_lenght:
  
          normalizer.observe(state)
  
          state = normalizer.normalize(state)
  
          action = policy.evaluate(state, delta, direction)
  
          state, reward, done, _ = env.step(action)
  
          reward = max(min(reward, 1), -1)
  
          #abajo sería poner un promedio
  
          sum_rewards += reward
  
          num_plays += 1
  
      return sum_rewards
  
  
  
  def train (env, policy, normalizer, hp):
  
      for step in range(hp.nb_steps):
  
          #iniciar las perturbaciones deltas y los rewards positivos/negativos
  
          deltas = policy.sample_deltas()
  
          positive_rewards = [0] * hp.nb_directions
  
          negative_rewards = [0] * hp.nb_directions
  
          #sacar las rewards en la dirección positiva
  
          for k in range(hp.nb_directions):
  
              positive_rewards[k] = explore(env, normalizer, policy, direction = 'positive', delta = deltas[k])
  
          #sacar las rewards en dirección negativo
  
          for k in range(hp.nb_directions):
  
              negative_rewards[k] = explore(env, normalizer, policy, direction = 'negative', delta = deltas[k])
  
          #sacar todas las rewards para sacar la desvest
  
          all_rewards = np.array(positive_rewards + negative_rewards)
  
          sigma_r = all_rewards.std()
  
          #acomodar los rollauts por el max (r_pos, r_neg) y seleccionar la mejor dirección
  
          scores = {k:max(r_pos, r_neg) for k, (r_pos, r_neg) in enumerate(zip(positive_rewards, negative_rewards))}
  
          order = sorted(scores.keys(), key = lambda x:scores[x])[:hp.nb_best_directions]
  
          rollouts = [(positive_rewards[k], negative_rewards[k], deltas[k]) for k in order]
  
          #actualizar policy
  
          policy.update (rollouts, sigma_r)
  
          #poner el final reward del policy luego del update
  
          reward_evaluation = explore (env, normalizer, policy)
  
          print('Paso: ', step, 'Lejania: ', reward_evaluation)
  
  
  
  def mkdir(base, name):
  
      path = os.path.join(base, name)
  
      if not os.path.exists(path):
  
          os.makedirs(path)
  
      return path
  
  work_dir = mkdir('exp', 'brs')
  
  monitor_dir = mkdir(work_dir, 'monitor')
  
  
  
  hp = Hp()
  
  np.random.seed(hp.seed)
  
  env = gym.make(hp.env_name)
  
  env = wrappers.Monitor(env, monitor_dir, force = True)
  
  nb_inputs = env.observation_space.shape[0]
  
  nb_outputs = env.action_space.shape[0]
  
  policy = Policy(nb_inputs, nb_outputs)
  
  normalizer = Normalizer(nb_inputs)
  
  train(env, policy, normalizer, hp)