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• TypeError : 'function' object is not subscriptable Python with ffmpeg

  25 novembre 2019, par S1mple

  clips = []
  
  
  
  def clipFinder(CurrentDir, fileType):
  
      clips.clear()
  
      for r,d,f in os.walk(CurrentDir):
  
          for file in f:
  
              if fileType in file:
  
                  clips.append(r+file)
  
      random.shuffle(clips)
  
  
  
  def removeVods(r):
  
      for f in clips:
  
          if 'vod' in clips:
  
              os.remove(r+f)
  
  
  
  def clipString():
  
      string = 'intermediate'
  
      clipList = []
  
      clipNum = 1
  
      for f in clips:
  
          clipList.append(string+str(clipNum)+'.ts'+'|')
  
          clipNum+=1
  
      string1 = ''.join(clipList)
  
      string2 = string1[0:len(string1)-1]
  
      return string2
  
  
  
  def concatFiles():
  
      clipFinder('***', '.mp4')
  
      removeVods('***')
  
      i = 0
  
      intermediates = []
  
      for f in clips:
  
          subprocess.call(['***', '-i', clips[i], '-c', 'copy', '-bsf:v', 'h264_mp4toannexb', '-f', 'mpegts', 'intermediate'+ str(i+1) +'.ts'])
  
          i += 1 
  
      clipsLength = len(clips)
  
      subprocess.call['***', '-i', '"concat:' + clipString() + '"', '-c', 'copy', '-bsf:a 
  
      aac_adtstoasc', 'output.mp4']

  I am trying to make a clip concatenator, but the last subprocess call won’t run and gives me the error shown in the question.

  Problematic code :

  subprocess.call(['***', '-i', '"concat:' + clipString() + '"', '-c', 'copy', '-bsf:a aac_adtstoasc', 'output.mp4'])

  all places with * were paths such as :
  /davidscomputer/bin/ffmpeg/

• Concatenate list of streams with ffmpeg

  16 novembre 2019, par oioioi

  I’d like to write python code, automatically rearranging the scenes of a video. How do I use ffmpeg-python to concatenate a list of n streams/scenes ? What is the easiest way to merge those scenes with their audio counterpart ?
  Unfortunately, I couldn’t figure out how to do it properly.

  The status quo :

  import os
  
  
  
  import scenedetect
  
  from scenedetect.video_manager import VideoManager
  
  from scenedetect.scene_manager import SceneManager
  
  from scenedetect.frame_timecode import FrameTimecode
  
  from scenedetect.stats_manager import StatsManager
  
  from scenedetect.detectors import ContentDetector
  
  
  
  import ffmpeg
  
  
  
  STATS_FILE_PATH = 'stats.csv'
  
  
  
  def main():
  
  
  
      for root, dirs, files in os.walk('material'):
  
          for file in files:
  
              file = os.path.join(root, file)
  
              print(file)
  
              video_manager = VideoManager([file])
  
              stats_manager = StatsManager()
  
              scene_manager = SceneManager(stats_manager)
  
              scene_manager.add_detector(ContentDetector())
  
              base_timecode = video_manager.get_base_timecode()
  
              end_timecode = video_manager.get_duration()
  
  
  
              start_time = base_timecode
  
              end_time = base_timecode + 100.0
  
              #end_time = end_timecode[2]
  
  
  
              video_manager.set_duration(start_time=start_time, end_time=end_time)
  
              video_manager.set_downscale_factor()
  
              video_manager.start()
  
              scene_manager.detect_scenes(frame_source=video_manager)
  
  
  
              scene_list = scene_manager.get_scene_list(base_timecode)
  
  
  
              print('List of scenes obtained:')
  
              for i, scene in enumerate(scene_list):
  
                  print('    Scene %2d: Start %s / Frame %d, End %s / Frame %d' % (
  
                      i+1,
  
                      scene[0].get_timecode(), scene[0].get_frames(),
  
                      scene[1].get_timecode(), scene[1].get_frames(),))
  
  
  
                  start = scene[0].get_frames()
  
                  end = scene[1].get_frames()
  
  
  
                  print(start)
  
                  print(end)
  
  
  
                  (
  
                      ffmpeg
  
                      .input(file)
  
                      .trim(start_frame=start, end_frame=end)
  
                      .setpts ('PTS-STARTPTS')
  
                      .output('scene %d.mp4' % (i+1)) 
  
                      .run()
  
                  )
  
  
  
              if stats_manager.is_save_required():
  
                  with open(STATS_FILE_PATH, 'w') as stats_file:
  
                      stats_manager.save_to_csv(stats_file, base_timecode)
  
  
  
  if __name__ == "__main__":
  
      main()

• 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)