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• Converting 3gp (amr) to mp3 using ffmpeg api calls

  13 mars 2012, par Sebastian L.

  Converting 3gp (amr) to mp3 using ffmpeg api calls

  I try to use libavformat (ffmpeg) to build my own function that converts 3gp audio files (recorded with an android mobile device) into mp3 files.

  I use av_read_frame() to read a frame from the input file and use avcodec_decode_audio3() to decode the data
  into a buffer and use this buffer to encode the data into mp3 with avcodec_encode_audio.
  This seems to give me a correct result for converting wav to mp3 and mp3 to wav (Or decode one mp3 and encode to another mp3) but not for amr to mp3.
  My resulting mp3 file seems to has the right length but only consists of noise.

  In another post I read that amr-decoder does not use the same sample format than mp3 does.
  AMR uses FLT and mp3 S16 or S32 und that I have to do resampling.
  So I call av_audio_resample_init() and audio_resample for each frame that has been decoded.
  But that does not solve my problem completely. Now I can hear my recorded voice and unsterstand what I was saying, but the quality is very low and there is still a lot of noise.
  I am not sure if I set the parameters of av_audio_resample correctly, especially the last 4 parameters (I think not) or if I miss something else.

  ReSampleContext* reSampleContext = av_audio_resample_init(1, 1, 44100, 8000, AV_SAMPLE_FMT_S32, AV_SAMPLE_FMT_FLT, 0, 0, 0, 0.0);
  
  
  
  while(1)
  
  {
  
      if(av_read_frame(ic, &avpkt) < 0)
  
      {
  
          break;
  
      }
  
  
  
      out_size = AVCODEC_MAX_AUDIO_FRAME_SIZE;
  
      int count;
  
  
  
      count = avcodec_decode_audio3(audio_stream->codec, (short *)decodedBuffer, &out_size, &avpkt);
  
  
  
      if(count < 0)
  
      {
  
          break;
  
      }
  
  
  
      if((audio_resample(reSampleContext, (short *)resampledBuffer, (short *)decodedBuffer, out_size / 4)) < 0)
  
      {
  
          fprintf(stderr, "Error\n");
  
          exit(1);
  
      }
  
  
  
      out_size = AVCODEC_MAX_AUDIO_FRAME_SIZE;
  
  
  
      pktOut.size = avcodec_encode_audio(c, outbuf, out_size, (short *)resampledBuffer);
  
  
  
      if(c->coded_frame && c->coded_frame->pts != AV_NOPTS_VALUE)
  
      {
  
          pktOut.pts = av_rescale_q(c->coded_frame->pts, c->time_base, outStream->time_base);
  
          //av_res
  
      }
  
  
  
      pktOut.pts = AV_NOPTS_VALUE;
  
      pktOut.dts = AV_NOPTS_VALUE;
  
  
  
      pktOut.flags |= AV_PKT_FLAG_KEY;
  
      pktOut.stream_index = audio_stream->index;
  
      pktOut.data = outbuf;
  
  
  
      if(av_write_frame(oc, &pktOut) != 0)
  
      {
  
          fprintf(stderr, "Error while writing audio frame\n");
  
          exit(1);
  
      }
  
  }

• Undefined reference to av_log

  12 novembre 2017, par Dana Prakoso

  I am cross-compiling FFMPEG source in Linux for Windows using i686-w64-mingw32 using this script :

  ../ffmpeg/configure --disable-ffmpeg --disable-ffprobe --disable-ffplay --disable-shared --enable-static --arch=x86 --target-os=mingw32 --cross-prefix=i686-w64-mingw32- --enable-cross-compile

  And it generates all static library files with extension *.a inside "lib" folder. Then I try to link those libraries with my own source, with this script :

  i686-w64-mingw32-gcc -m32 -c videoplayer.c -o videoplayer.o -w $(INCLUDEDIR) -Wl,--add-stdcall-alias
  
  i686-w64-mingw32-gcc -static -m32 -o libvideoplayer.dll videoplayer.o -Wl,--out-implib,libvideoplayer_dll.lib -Wl,--add-stdcall-alias -L../../ffmpeg/lib -lavdevice -lavformat -lavcodec -lavutil -lwsock32 -lswresample ../../ffmpeg/lib/libWs2_32.lib

  And I got this bunch of error messages :

  ../../ffmpeg/lib/libavformat.a(rtpproto.o): In function `rtp_resolve_host':
  
  /home/dana/Sources/build-ffmpeg/src/libavformat/rtpproto.c:140: undefined reference to `_imp__getaddrinfo@16'
  
  ../../ffmpeg/lib/libavformat.a(rtpproto.o): In function `rtp_parse_addr_list':
  
  /home/dana/Sources/build-ffmpeg/src/libavformat/rtpproto.c:282: undefined reference to `_imp__freeaddrinfo@4'
  
  /home/dana/Sources/build-ffmpeg/src/libavformat/rtpproto.c:277: undefined reference to `_imp__freeaddrinfo@4'
  
  ../../ffmpeg/lib/libavformat.a(tcp.o): In function `tcp_open':
  
  /home/dana/Sources/build-ffmpeg/src/libavformat/tcp.c:112: undefined reference to `_imp__getaddrinfo@16'
  
  /home/dana/Sources/build-ffmpeg/src/libavformat/tcp.c:114: undefined reference to `_imp__getaddrinfo@16'
  
  /home/dana/Sources/build-ffmpeg/src/libavformat/tcp.c:177: undefined reference to `_imp__freeaddrinfo@4'
  
  /home/dana/Sources/build-ffmpeg/src/libavformat/tcp.c:192: undefined reference to `_imp__freeaddrinfo@4'

  How do I resolve this ? Do I miss something in my script ? Big thanks for someone replying.

• 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)