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• Why doesn't the ffmpeg output display the stream in the browser ? [closed]

  10 mai 2024, par Tebyy

  Why is it that when I create a livestream in Python using ffmpeg, and then I open the browser and visit the page, the page keeps loading continuously, and in PyCharm logs, I see binary data ? There are no errors displayed, and the code seems correct to me. I even tried saving to a file for testing purposes, and when I play the video, everything works fine. Does anyone know what might be wrong here ?

  


  Code :

  


  def generate_frames():
    cap = cv2.VideoCapture(os.path.normpath(app_root_dir().joinpath("data/temp", "video-979257305707693982.mp4")))
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break

        yield frame


@app.route('/video_feed')
def video_feed():
    ffmpeg_command = [
        'ffmpeg', '-f', 'rawvideo', '-pix_fmt', 'bgr24',
        '-s:v', '1920x1080', '-r', '60',
        '-i', '-', '-vf', 'setpts=2.5*PTS', # Video Speed
        '-c:v', 'libvpx-vp9', '-g', '60', '-keyint_min', '60',
        '-b:v', '6M', '-minrate', '4M', '-maxrate', '12M', '-bufsize', '8M',
        '-crf', '0', '-deadline', 'realtime', '-tune', 'psnr', '-quality', 'good',
        '-tile-columns', '6', '-threads', '8', '-lag-in-frames', '16',
        '-f', 'webm', '-'
    ]
    ffmpeg_process = subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE, stderr=subprocess.PIPE, bufsize=-1)
    frames_generator = generate_frames()
    for frame in frames_generator:
        ffmpeg_process.stdin.write(frame)
        ffmpeg_process.stdin.flush()

    ffmpeg_process.stdin.close()
    ffmpeg_process.wait()

    def generate_video_stream(process):
        startTime = time.time()
        buffer = []
        sentBurst = False
        for chunk in iter(lambda: process.stderr.read(4096), b''):
            buffer.append(chunk)

            # Minimum buffer time, 3 seconds
            if sentBurst is False and time.time() > startTime + 3 and len(buffer) > 0:
                sentBurst = True
                for i in range(0, len(buffer) - 2):
                    print("Send initial burst #", i)
                    yield buffer.pop(0)

            elif time.time() > startTime + 3 and len(buffer) > 0:
                yield buffer.pop(0)

            process.poll()
            if isinstance(process.returncode, int):
                if process.returncode > 0:
                    print('FFmpeg Error', process.returncode)

                break

    return Response(stream_with_context(generate_video_stream(ffmpeg_process)), mimetype='video/webm', content_type="video/webm; codecs=vp9", headers=Headers([("Connection", "close")]))



  


• FFMPEG multi livestream - recorded stream send to different services like YT and Twitch at different time (on different button clicks )

  4 octobre 2022, par Ganesh

  Trying for the last 10 days and still no success, I am creating a python application that will accept the URL and visit that URL using chromium, capture that screen and send that real-time screen recording to different live stream acceptors as youtube live, twitch Twitter, Facebook live or some other sources and many of these could be multiple.

  


  There are two challenges (both challenges depend on a user action like different button clicks) -

  


  

  • The time of starting the Livestream we know only one Livestream acceptor and other acceptors could be sent via another API at any time or may not be sent on the whole live stream.

  


  • Any of the streams could be stopped at any moment including the first one which started the original live streaming service

  


  


  To Solve these challenges I am trying the following process (i took mp4 as a source for simplifying)

  


  

  • create a stream and store it into PIPE.stdout

  


  


  ffmpeg_Command_get_stream = 'ffmpeg -re -i test.mp4 -f flv pipe:1'
ffmpeg_Command_get_stream=ffmpeg_Command_get_stream.split()
pipe = sp.Popen(ffmpeg_Command_get_stream,
            stdout=sp.PIPE,
            stderr=sp.PIPE,
            bufsize=8000000,
            shell=True,
            universal_newlines=True
            )
out,err = pipe.communicate()


  


  


  • and send that stream with the help of FFMPEG to the Livestream acceptor with the click of the youtube Livestream button

    


    ffmpeg_Command_send_stream = ['ffmpeg','-i',pipe.stdout,'-f','flv',RTMPURL_YOUTUBE]

    


  


  


  Update Trying to Explain it a little more :

  


  step 1 - I need a real-time stream from the first command, so I used -re in FFMPEG

  


  step 2 - Use above stream as an input for other command and send that as an output as a Livestream to youtube (or twitch/Facebook), But the second step would happen only when the user click on the button "YT LiveStream", Here the tricky thing is there are multiple buttons (YT LiveStream, Twitch LiveStream, Facebook LiveStream) and user can click any time on any of button, also can click on all button one by one.

  


  

  


  sorry for bad explaination

  


  what I am doing wrong ? , Is this Possible ? or need to go with another process,

  


  any help would be greatly appreciated

  


• 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)