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xabe.ffmpeg not working in azure app service
28 décembre 2020, par Sripathi RajaI have a .net core application which generates video thumbnails for uploaded videos. I am using xabe.ffmpeg for this . The code runs fine in the local system and Iam able to generate the thumbnails . I have containerized this application and pushed it to azure app service . But when the code is deployed on azure app service its throws an error . I have placed the ffmpeg.exe ,ffprobe.exe , ffplay.exe inside a folder ffmpeg in wwwroot folder .
the app service logs give this error


the first line is the path : /app/wwwroot/ffmpeg


2020-12-25T14:34:01.347103909Z /app/wwwroot/ffmpeg
2020-12-25T14:34:01.443016709Z [41m[30mfail[39m[22m[49m : Microsoft.AspNetCore.Server.Kestrel[13]
2020-12-25T14:34:01.443066009Z Connection id "0HM58S8AE014U", Request id "0HM58S8AE014U:00000002" : An unhandled exception was thrown by the application.
2020-12-25T14:34:01.456821709Z System.ComponentModel.Win32Exception (2) : No such file or directory
2020-12-25T14:34:01.462537109Z at System.Diagnostics.Process.ForkAndExecProcess(String filename, String[] argv, String[] envp, String cwd, Boolean redirectStdin, Boolean redirectStdout, Boolean redirectStderr, Boolean setCredentials, UInt32 userId, UInt32 groupId, UInt32[] groups, Int32& stdinFd, Int32& stdoutFd, Int32& stderrFd, Boolean usesTerminal, Boolean throwOnNoExec)
2020-12-25T14:34:01.462555709Z at System.Diagnostics.Process.StartCore(ProcessStartInfo startInfo)
2020-12-25T14:34:01.462560609Z at System.Diagnostics.Process.Start()
2020-12-25T14:34:01.463739309Z at Xabe.FFmpeg.FFmpeg.RunProcess(String args, String processPath, Nullable
1 priority, Boolean standardInput, Boolean standardOutput, Boolean standardError) 2020-12-25T14:34:01.463755809Z at Xabe.FFmpeg.FFprobeWrapper.<>c__DisplayClass9_0.<runprocess>b__0() 2020-12-25T14:34:01.463761809Z at System.Threading.Tasks.Task</runprocess>1.InnerInvoke()
2020-12-25T14:34:01.463766309Z at System.Threading.Tasks.Task.<>c.<.cctor>b__274_0(Object obj)
2020-12-25T14:34:01.463781109Z at System.Threading.ExecutionContext.RunInternal(ExecutionContext executionContext, ContextCallback callback, Object state)
2020-12-25T14:34:01.463786509Z --- End of stack trace from previous location where exception was thrown ---
2020-12-25T14:34:01.463790909Z at System.Threading.ExecutionContext.RunInternal(ExecutionContext executionContext, ContextCallback callback, Object state)
2020-12-25T14:34:01.463795509Z at System.Threading.Tasks.Task.ExecuteWithThreadLocal(Task& currentTaskSlot, Thread threadPoolThread)
2020-12-25T14:34:01.463800009Z --- End of stack trace from previous location where exception was thrown ---
2020-12-25T14:34:01.463813709Z at Xabe.FFmpeg.FFprobeWrapper.RunProcess(String args, CancellationToken cancellationToken)
2020-12-25T14:34:01.463818109Z at Xabe.FFmpeg.FFprobeWrapper.GetStreams(String videoPath, CancellationToken cancellationToken)
2020-12-25T14:34:01.463821909Z at Xabe.FFmpeg.FFprobeWrapper.SetProperties(MediaInfo mediaInfo, CancellationToken cancellationToken)
2020-12-25T14:34:01.463825509Z at Xabe.FFmpeg.MediaInfo.Get(String filePath, CancellationToken cancellationToken)
2020-12-25T14:34:01.463829109Z at Xabe.FFmpeg.MediaInfo.Get(String filePath)
2020-12-25T14:34:01.463832609Z at Xabe.FFmpeg.FFmpeg.GetMediaInfo(String fileName)
2020-12-25T14:34:01.467009509Z at root2webAPI.Controllers.AzureStorageControllers.BlobExplorerController.GetVideoThumbnailAsync(IFormFile file, Int32 frameTarget) in /src/root2webAPI/Controllers/AzureStorageControllers/BlobExplorerController.cs:line 271
2020-12-25T14:34:01.467023809Z at root2webAPI.Controllers.AzureStorageControllers.BlobExplorerController.UploadMediaBlob(IFormFile file, String parentId) in /src/root2webAPI/Controllers/AzureStorageControllers/BlobExplorerController.cs:line 96
2020-12-25T14:34:01.467028709Z at lambda_method(Closure , Object )
2020-12-25T14:34:01.467032109Z at Microsoft.Extensions.Internal.ObjectMethodExecutorAwaitable.Awaiter.GetResult()
2020-12-25T14:34:01.467035909Z at Microsoft.AspNetCore.Mvc.Infrastructure.ActionMethodExecutor.AwaitableObjectResultExecutor.Execute(IActionResultTypeMapper mapper, ObjectMethodExecutor executor, Object controller, Object[] arguments)
2020-12-25T14:34:01.467039409Z at Microsoft.AspNetCore.Mvc.Infrastructure.ControllerActionInvoker.g__Awaited|12_0(ControllerActionInvoker invoker, ValueTask`1 actionResultValueTask)
2020-12-25T14:34:01.467043209Z at Microsoft.AspNetCore.Mvc.Infrastructure.ControllerActionInvoker.g__Awaited|10_0(ControllerActionInvoker invoker, Task lastTask, State next, Scope scope, Object state, Boolean isCompleted)
2020-12-25T14:34:01.467055709Z at Microsoft.AspNetCore.Mvc.Infrastructure.ControllerActionInvoker.Rethrow(ActionExecutedContextSealed context)
2020-12-25T14:34:01.467060609Z at Microsoft.AspNetCore.Mvc.Infrastructure.ControllerActionInvoker.Next(State& next, Scope& scope, Object& state, Boolean& isCompleted)
2020-12-25T14:34:01.467067409Z at Microsoft.AspNetCore.Mvc.Infrastructure.ControllerActionInvoker.g__Awaited|13_0(ControllerActionInvoker invoker, Task lastTask, State next, Scope scope, Object state, Boolean isCompleted)
2020-12-25T14:34:01.467071609Z at Microsoft.AspNetCore.Mvc.Infrastructure.ResourceInvoker.g__Awaited|24_0(ResourceInvoker invoker, Task lastTask, State next, Scope scope, Object state, Boolean isCompleted)
2020-12-25T14:34:01.467075209Z at Microsoft.AspNetCore.Mvc.Infrastructure.ResourceInvoker.Rethrow(ResourceExecutedContextSealed context)
2020-12-25T14:34:01.467078709Z at Microsoft.AspNetCore.Mvc.Infrastructure.ResourceInvoker.Next(State& next, Scope& scope, Object& state, Boolean& isCompleted)
2020-12-25T14:34:01.467089009Z at Microsoft.AspNetCore.Mvc.Infrastructure.ResourceInvoker.g__Awaited|19_0(ResourceInvoker invoker, Task lastTask, State next, Scope scope, Object state, Boolean isCompleted)
2020-12-25T14:34:01.467093409Z at Microsoft.AspNetCore.Mvc.Infrastructure.ResourceInvoker.g__Awaited|17_0(ResourceInvoker invoker, Task task, IDisposable scope)
2020-12-25T14:34:01.467096909Z at Microsoft.AspNetCore.Routing.EndpointMiddleware.g__AwaitRequestTask|6_0(Endpoint endpoint, Task requestTask, ILogger logger)
2020-12-25T14:34:01.467100509Z at Microsoft.AspNetCore.Authorization.AuthorizationMiddleware.Invoke(HttpContext context)
2020-12-25T14:34:01.467103809Z at Microsoft.AspNetCore.Server.Kestrel.Core.Internal.Http.HttpProtocol.ProcessRequests[TContext]

here is my relevant .net application code


private async Task<mediametadata> GetVideoThumbnailAsync(IFormFile file,int frameTarget)
 {
 var fileName = file.FileName;
 var filePath = Path.Combine(_rootPath, "videos", fileName);
 var fileExtension = Path.GetExtension(filePath);
 
 // the xabe wrapper works with only mp4 extension to create thumbnail , if the file is any other format first convert it to
 //the mp4 format and then goahead with creating the thumbnail. 
 var thumbnailImageName = fileName.Replace(fileExtension, ".jpg");
 var thumbnailImagePath = Path.Combine(_rootPath, "thumbnails", thumbnailImageName);
 
 using (Stream fileStream = new FileStream(filePath, FileMode.Create)) {
 
 await file.CopyToAsync(fileStream);
 }
 Console.WriteLine(Path.Combine(_rootPath,"ffmpeg"));
 FFmpeg.SetExecutablesPath(Path.Combine(_rootPath,"ffmpeg"));
 IMediaInfo mediaInfo = await FFmpeg.GetMediaInfo(filePath);
 var videoDuration = mediaInfo.VideoStreams.First().Duration;
 IConversion conversion = await FFmpeg.Conversions.FromSnippet.Snapshot(filePath, thumbnailImagePath , TimeSpan.FromSeconds(frameTarget));
 IConversionResult result = await conversion.Start();
 MediaMetadata media = new MediaMetadata();
 media.DurationSeconds=Convert.ToInt32(videoDuration.TotalMilliseconds);
 // media.DurationSeconds=10;
 media.ThumbnailImagePath= thumbnailImagePath;
 return media;
 
 }
</mediametadata>

could you help me out with this ..many thanks


-
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 DiazI 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 LTSI 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) -
Ubuntu ffmpeg can't use libx264
27 mai 2021, par user1315621Command


ffprobe rtsp://localhost/myvideo -codec:v libx264 -show_frames -of csv


Output


Failed to set value 'libx264' for option 'codec:v': Option not found


But
libx264seems to be installed -
sudo apt install libx264-dev:

Reading package lists... Done
Building dependency tree 
Reading state information... Done
libx264-dev is already the newest version (2:0.152.2854+gite9a5903-2).


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