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• Run docker container with FFMPEG rstp stream on websockets

  18 février, par bmvr

  I have create a node js application that uses ffmpeg with spawn node library.

  


  Here's the backend sample :

  


  const startStreamWs = (cameraId, rtsp_url) => {
  console.log(`Starting stream for camera: ${cameraId}`);

  const ffmpeg = spawn("ffmpeg", [
    "-rtsp_transport", "tcp", // Use TCP for reliable streaming
    "-i", rtsp_url,
    "-analyzeduration", "5000000",  // Increase analyzeduration
    "-probesize", "5000000",       // Increase probesize
    "-fflags", "nobuffer",         // Reduce buffering
    "-flags", "low_delay",         // Low latency
    "-strict", "experimental",
    "-max_delay", "200000",        // Reduce max delay for faster response
    "-bufsize", "2M",              // Buffer size for smoother streaming
    "-f", "mpegts",                // MPEG-TS container for streaming
    "-codec:v", "mpeg1video",         // MPEG-1 video codec
    "-s", "1280x720",              // Video resolution
    "-r", "25",                    // Frame rate (25 fps)
    "-b:v", "1500k",               // Bitrate for video
    "-maxrate", "2000k",           // Maximum bitrate
    "-bufsize", "2M",              // Buffer size (needed with maxrate)
    "-bf", "0",                    // Disable B-frames for lower latency
    "-an",                         // Disable audio
    "-"
]);


  ffmpeg.stdout.on("data", (data) => {
    if (cameraStreams[cameraId]) {
      console.log(`Data sent for camera ${cameraId}`);
      // Broadcast stream data to all connected clients
      for (const client of cameraStreams[cameraId].clients) {
        if (client.readyState === ws.OPEN) {
          client.send(data);
        }
      }
    }
  });

  ffmpeg.stderr.on("data", (data) => {
    console.error(`FFmpeg stderr (Camera ${cameraId}): ${data.toString()}`);
    logErrorToFile(data);
  });

  ffmpeg.on("close", (code) => {
    console.log(`FFmpeg process exited for Camera ${cameraId} with code ${code}`);
    if (cameraStreams[cameraId]) {
      // Close all remaining clients
      for (const client of cameraStreams[cameraId].clients) {
        client.close();
      }
      delete cameraStreams[cameraId];
    }
  });

  return ffmpeg;
};


  


  Front End Sample my angular component

  


  import { Component, OnDestroy, OnInit } from &#x27;@angular/core&#x27;;&#xA;import { FormsModule } from &#x27;@angular/forms&#x27;;&#xA;import { ActivatedRoute } from &#x27;@angular/router&#x27;;&#xA;&#xA;declare var JSMpeg: any; // Declare JSMpeg from the global script&#xA;&#xA;@Component({&#xA;  selector: &#x27;app-video-player&#x27;,&#xA;  templateUrl: &#x27;./video-player.component.html&#x27;,&#xA;  styleUrls: [&#x27;./video-player.component.css&#x27;],&#xA;  standalone: false&#xA;})&#xA;export class VideoPlayerComponent implements OnInit, OnDestroy {&#xA;  stepSize: number = 0.1;&#xA;  private player: any;&#xA;  cameraId: string | null = null;&#xA;  ws: WebSocket | null = null; &#xA;  wsUrl: string | null = null;&#xA;&#xA;  constructor(private route: ActivatedRoute) {&#xA;    this.cameraId = this.route.snapshot.paramMap.get(&#x27;id&#x27;);&#xA;    this.wsUrl = `ws://localhost:8085?cameraId=${this.cameraId}`;&#xA;    this.ws = new WebSocket(this.wsUrl);&#xA;  }&#xA;&#xA;  async ngOnInit(): Promise<void> {&#xA;    const canvas = document.getElementById(&#x27;videoCanvas&#x27;) as HTMLCanvasElement;&#xA;    this.player = new JSMpeg.Player(this.wsUrl, { canvas: canvas });&#xA;  }&#xA;&#xA;  async ngOnDestroy(): Promise<void> {&#xA;    this.ws?.close(1000, "Exiting");&#xA;  }&#xA;&#xA;  getStepSize(): number {&#xA;    return this.stepSize;&#xA;  }&#xA;}&#xA;&#xA;&#xA;</void></void>

  


  On localhost is fine, once i containerize, it's not. I can serve the website but not the stream.
I have the same version FFMPEG 7.1 and the codec is available.
Although I run localhost macosx and unbutu on docker.

  


• 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)

• Error being thrown by ffmpeg and ffserver, not getting a stream

  2 novembre 2015, par rotheman

  i am trying to stream a file that I have in my directory using ffmpeg and ffserver. But an error occurs in both ffmpeg and ffserver. The following is my ffserver config file.

  HTTPPort 8092
  
   HTTPBindAddress 0.0.0.0
  
   MaxHTTPConnections 2000
  
   MaxClients 1000
  
   MaxBandwidth 2000
  
   CustomLog -
  
   #NoDaemon
  
   NoDefaults
  
  
  
  <feed>
  
    File /tmp/feed1.ffm
  
    FileMaxSize 20M 
  
    ACL allow 127.0.0.1
  
  </feed>
  
  
  
  <stream>      
  
    Feed feed1.ffm             
  
    Format webm
  
  
  
    AudioCodec vorbis
  
    AudioBitRate 64            
  
  
  
    VideoCodec libvpx
  
    VideoSize 720x576           
  
    VideoFrameRate 25          
  
    AVOptionVideo flags +global_header  
  
    AVOptionVideo cpu-used 0
  
    AVOptionVideo qmin 10
  
    AVOptionVideo qmax 42
  
    AVOptionVideo quality good
  
    AVOptionAudio flags +global_header
  
    PreRoll 15
  
    StartSendOnKey
  
    VideoBitRate 400
  
    AudioSampleRate 44100           
  
  </stream>

  I am able to start ffserver properly with no problems but when i try to serve ffserver with a file using ffmpeg, this happens

  ffmpeg -i sam.webm http://127.0.0.1:8092/feed1.ffm -vcodec copy
  
    ffmpeg version N-72738-g7630cce Copyright (c) 2000-2015 the FFmpeg developers
  
    built with gcc 4.8 (Ubuntu 4.8.2-19ubuntu1)
  
    configuration: --enable-libvpx --enable-libvorbis --enable-libx264 --enable-gpl --enable-nonfree
  
    libavutil      54. 27.100 / 54. 27.100
  
    libavcodec     56. 41.100 / 56. 41.100
  
    libavformat    56. 36.100 / 56. 36.100
  
    libavdevice    56.  4.100 / 56.  4.100
  
    libavfilter     5. 16.101 /  5. 16.101
  
    libswscale      3.  1.101 /  3.  1.101
  
    libswresample   1.  2.100 /  1.  2.100
  
    libpostproc    53.  3.100 / 53.  3.100
  
    Trailing options were found on the commandline.
  
    Input #0, matroska,webm, from 'sam.webm':
  
    Metadata:
  
      title           : Sintel Trailer
  
      encoder         : Lavf56.25.101
  
    Duration: 00:00:52.21, start: 0.000000, bitrate: 305 kb/s
  
    Stream #0:0: Video: vp8, yuv420p, 854x480, SAR 1:1 DAR 427:240, 24 fps, 24 tbr, 1k tbn, 1k tbc (default)
  
  Stream #0:1: Audio: vorbis, 48000 Hz, stereo, fltp (default)
  
  [libvpx @ 0x367e180] v1.3.0
  
  Output #0, ffm, to 'http://127.0.0.1:8092/feed1.ffm':
  
  Metadata:
  
    title           : Sintel Trailer
  
    creation_time   : now
  
    encoder         : Lavf56.36.100
  
  Stream #0:0: Audio: vorbis (libvorbis), 44100 Hz, stereo, fltp, 64 kb/s (default)
  
  Metadata:
  
    encoder         : Lavc56.41.100 libvorbis
  
  Stream #0:1: Video: vp8 (libvpx), yuv420p, 720x576 [SAR 427:300 DAR 427:240], q=10-42, 400 kb/s, 24 fps, 1000k tbn, 25 tbc (default)
  
  Metadata:
  
    encoder         : Lavc56.41.100 libvpx
  
  Stream mapping:
  
    Stream #0:1 -> #0:0 (vorbis (native) -> vorbis (libvorbis))
  
    Stream #0:0 -> #0:1 (vp8 (native) -> vp8 (libvpx))
  
  Press [q] to stop, [?] for help
  
  av_interleaved_write_frame(): Connection reset by peer
  
      Last message repeated 2 times
  
     frame=   14 fps=0.0 q=0.0 Lsize=      16kB time=00:00:00.56 bitrate= 234.1kbits/s dup=1 drop=0    
  
     video:1kB audio:2kB subtitle:0kB other streams:0kB global headers:4kB muxing overhead: 327.780670%
  
     Conversion failed!

  And this is the message i get in ffserver when this error happens in ffmpeg.

  ffserver
  
   ffserver version N-72738-g7630cce Copyright (c) 2000-2015 the FFmpeg developers
  
   built with gcc 4.8 (Ubuntu 4.8.2-19ubuntu1)
  
   configuration: --enable-libvpx --enable-libvorbis --enable-libx264 --enable-gpl --enable-nonfree
  
   libavutil      54. 27.100 / 54. 27.100
  
   libavcodec     56. 41.100 / 56. 41.100
  
   libavformat    56. 36.100 / 56. 36.100
  
   libavdevice    56.  4.100 / 56.  4.100
  
   libavfilter     5. 16.101 /  5. 16.101
  
   libswscale      3.  1.101 /  3.  1.101
  
   libswresample   1.  2.100 /  1.  2.100
  
   libpostproc    53.  3.100 / 53.  3.100
  
   Mon Jun  8 18:38:28 2015 FFserver started.
  
   Mon Jun  8 18:38:40 2015 127.0.0.1 - - [GET] "/feed1.ffm HTTP/1.1" 200 4175
  
   Mon Jun  8 18:38:40 2015 [NULL @ 0x29878a0]Missing key or no key/value separator found after key 'pkt_timebase'
  
  Mon Jun  8 18:38:40 2015 Feed '/tmp/feed1.ffm' stream number does not match registered feed
  
  Mon Jun  8 18:38:40 2015 127.0.0.1 - - [POST] "/feed1.ffm HTTP/1.1" 200 4096

  Can someone please help me ?