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• I want to save a video with effects applied on it. OnSucccess is not calling [duplicate]

  10 novembre 2016, par shriya

  This question already has an answer here :

  • What is a NullPointerException, and how do I fix it ?
    
    12 answers
    

  This is command string which i want to process.

  String[] command = new String[]"ffmpeg -y -i " + old_video_path + " -strict experimental -vf curves=vintage -s 240x320 -r 30 -aspect 4:3 -ab 48000 /storage/emulated/0/video.mp4" ;
  executeFFmpeg(command) ;

  private void executeFFmpeg(String[] cmd) {
  
      try {
  
          ffmpeg.execute(cmd, new ExecuteBinaryResponseHandler() {
  
  
  
              @Override
  
              public void onStart() {
  
                  Log.e("Progress", "onStart");
  
              }
  
  
  
              @Override
  
              public void onProgress(String message) {
  
                  Log.e("Progress", "onProgress");
  
              }
  
  
  
              @Override
  
              public void onSuccess(String message) {
  
                  Log.e("Progress", "onSuccess");
  
              }
  
  
  
              @Override
  
              public void onFailure(String message) {
  
                  Log.e("Progress", "onFailure" + message);
  
              }
  
  
  
              @Override
  
              public void onFinish() {
  
  
  
                  Log.e("Progress", "onFinish");
  
  
  
              }
  
          });
  
      } catch (FFmpegCommandAlreadyRunningException e) {
  
          // Handle if FFmpeg is already running
  
      }
  
  }

  in Log i am getting onProcess call and onFininsh call. How do i write code to save this video in sdcard and where ?

• Python : ani.save very slow. Any alternatives to create videos ?

  14 novembre 2023, par Czeskleba

  Im doing some simple diffusion calculations. I save 2 matrices to 2 datasets every so many steps (every 2s or so) to a single .h5 file. After that I then load the file in another script, create some figures (2 subplots etc., see/run code - i know could be prettier). Then I use matplotlib.animation to make the animation. In the code below, in the very last lines, I then run the ani.save command from matplotlib.

  


  And that's where the problem is. The animation is created within 2 seconds, even for my longer animations (14.755 frames, done in under 2s at 8284 it/s) but after that, ani.save in line 144 takes forever (it didn't finish over night). It reserves/uses about 10gb of my RAM constantly but seemingly takes forever. If you run the code below be sure to set the frames_to_do (line 20) to something like 30 or 60 to see that it does in fact save an mp4 for shorter videos. You can set it higher to see how fast the time to save stuff increases to something unreasonable.

  


  I've been fiddling this for 2 days now and I cant figure it out. I guess my question is : Is there any way to create the video in a reasonable time like this ? Or do I need something other than animation ?

  


  You should be able to just run the code. Ill provide a diffusion_array.h5 with 140 frames so you dont have to create a dummy file, if I can figure out how to upload something like this safely. (The results are with dummy numbers for now, diffusion coefficients etc. are not right yet.)
I used dropbox. Not sure if thats allowed, if not I'll delete the link and uhh PM me or something ?

  


  https://www.dropbox.com/scl/fi/fv9stfqkm4trmt3zwtvun/diffusion_array.h5?rlkey=2oxuegnlcxq0jt6ed77rbskyu&dl=0

  


  Here is the code :

  


  import h5py
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.animation import FuncAnimation
from tqdm import tqdm
import numpy as np


# saving the .mp4 after tho takes forever

# Create an empty figure and axis
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 9), dpi=96)

# Load all saved arrays into a list
file_name = 'diffusion_array.h5'
loaded_u_arrays = []
loaded_h_arrays = []
frames_to_do = 14755  # for now like this, use # version once the slow mp4 convert is cleared up

# with h5py.File(file_name, 'r') as hf:
#     for key in hf.keys():
#         if key.startswith('u_snapshot_'):
#             loaded_u_arrays.append(hf[key][:])
#         elif key.startswith('h_snapshot_'):
#             loaded_h_arrays.append(hf[key][:])

with h5py.File(file_name, 'r') as hf:
    for i in range(frames_to_do):
        target_key1 = f'u_snapshot_{i:05d}'
        target_key2 = f'h_snapshot_{i:05d}'
        if target_key1 in hf:
            loaded_u_arrays.append(hf[target_key1][:])
        else:
            print(f'Dataset u for time step {i} not found in the file.')
        if target_key2 in hf:
            loaded_h_arrays.append(hf[target_key2][:])
        else:
            print(f'Dataset h for time step {i} not found in the file.')

# Create "empty" imshow objects
# First one
norm1 = mcolors.Normalize(vmin=140, vmax=400)
cmap1 = plt.get_cmap('hot')
cmap1.set_under('0.85')
im1 = ax1.imshow(loaded_u_arrays[0], cmap=cmap1, norm=norm1)
ax1.set_title('Diffusion Heatmap')
ax1.set_xlabel('X')
ax1.set_ylabel('Y')
cbar_ax = fig.add_axes([0.05, 0.15, 0.03, 0.7])
cbar_ax.set_xlabel('$T$ / K', labelpad=20)
fig.colorbar(im1, cax=cbar_ax)


# Second one
ax2 = plt.subplot(1, 2, 2)
norm2 = mcolors.Normalize(vmin=-0.1, vmax=5)
cmap2 = plt.get_cmap('viridis')
cmap2.set_under('0.85')
im2 = ax2.imshow(loaded_h_arrays[0], cmap=cmap2, norm=norm2)
ax2.set_title('Diffusion Hydrogen')
ax2.set_xlabel('X')
ax2.set_ylabel('Y')
cbar_ax = fig.add_axes([0.9, 0.15, 0.03, 0.7])
cbar_ax.set_xlabel('HD in ml/100g', labelpad=20)
fig.colorbar(im2, cax=cbar_ax)

# General
fig.subplots_adjust(right=0.85)
time_text = ax2.text(-15, 0.80, f'Time: {0} s', transform=plt.gca().transAxes, color='black', fontsize=20)

# Annotations
# Heat 1
marker_style = dict(marker='o', markersize=6, markerfacecolor='black', markeredgecolor='black')
ax1.scatter(*[10, 40], s=marker_style['markersize'], c=marker_style['markerfacecolor'],
            edgecolors=marker_style['markeredgecolor'])
ann_heat1 = ax1.annotate(f'Temp: {loaded_u_arrays[0][40, 10]:.0f}', xy=[10, 40], xycoords='data',
             xytext=([10, 40][0], [10, 40][1] + 48), textcoords='data',
             arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=0.3"), fontsize=12, color='black')
# Heat 2
ax1.scatter(*[140, 85], s=marker_style['markersize'], c=marker_style['markerfacecolor'],
            edgecolors=marker_style['markeredgecolor'])
ann_heat2 = ax1.annotate(f'Temp: {loaded_u_arrays[0][85, 140]:.0f}', xy=[140, 85], xycoords='data',
             xytext=([140, 85][0] + 55, [140, 85][1] + 3), textcoords='data',
             arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=0.3"), fontsize=12, color='black')

# Diffusion 1
marker_style = dict(marker='o', markersize=6, markerfacecolor='black', markeredgecolor='black')
ax2.scatter(*[10, 40], s=marker_style['markersize'], c=marker_style['markerfacecolor'],
            edgecolors=marker_style['markeredgecolor'])
ann_diff1 = ax2.annotate(f'HD: {loaded_h_arrays[0][40, 10]:.0f}', xy=[10, 40], xycoords='data',
             xytext=([10, 40][0], [10, 40][1] + 48), textcoords='data',
             arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=0.3"), fontsize=12, color='black')
# Diffusion 2
ax2.scatter(*[140, 85], s=marker_style['markersize'], c=marker_style['markerfacecolor'],
            edgecolors=marker_style['markeredgecolor'])
ann_diff2 = ax2.annotate(f'HD: {loaded_h_arrays[0][85, 140]:.0f}', xy=[140, 85], xycoords='data',
             xytext=([140, 85][0] + 55, [140, 85][1] + 3), textcoords='data',
             arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=0.3"), fontsize=12, color='black')


# Function to update the animation
def update(frame, *args):
    loaded_u_array, loaded_h_array = args

    s_per_frame = 2  # during weld/cooling you save a state every 2s
    frames_to_room_temp = 7803  # that means this many frames need to be animated
    dt_big = 87  # during "just diffusion" you save every 10 frame but 87s pass in those

    # Update the time step shown
    if frame <= frames_to_room_temp:
        im1.set_data(loaded_u_array[frame])
        im2.set_data(loaded_h_array[frame])
        time_text.set_text(f'Time: {frame * s_per_frame} s')

    else:
        im1.set_data(loaded_u_array[frame])
        im2.set_data(loaded_h_array[frame])
        calc_time = int(((2 * frames_to_room_temp) + (frame - frames_to_room_temp) * 87) / 3600)
        time_text.set_text(f'Time: {calc_time} s')

    # Annotate some points
    ann_heat1.set_text(f'Temp: {loaded_u_arrays[frame][40, 10]:.0f}')
    ann_heat2.set_text(f'Temp: {loaded_u_arrays[frame][85, 140]:.0f}')
    ann_diff1.set_text(f'HD: {loaded_h_arrays[frame][40, 10]:.0f}')
    ann_diff2.set_text(f'HD: {loaded_h_arrays[frame][85, 140]:.0f}')

    return im1, im2  # Return the updated artists


# Create the animation without displaying it
ani = FuncAnimation(fig, update, frames=frames_to_do, repeat=False, blit=True, interval=1,
                    fargs=(loaded_u_arrays, loaded_h_arrays))  # frames=len(loaded_u_arrays)

# Create the progress bar with tqdm
with tqdm(total=frames_to_do, desc='Creating Animation') as pbar:  # total=len(loaded_u_arrays)
    for i in range(frames_to_do):  # for i in range(len(loaded_u_arrays)):
        update(i, loaded_u_arrays, loaded_h_arrays)  # Manually update the frame with both datasets
        pbar.update(1)  # Update the progress bar

# Save the animation as a video file (e.g., MP4)
print("Converting to .mp4 now. This may take some time. This is normal, wait for Python to finish this process.")
ani.save('diffusion_animation.mp4', writer='ffmpeg', dpi=96, fps=60)

# Close the figure to prevent it from being displayed
plt.close(fig)



  


• Configure FFmpeg to save the recording at specified time intervals as separate output files

  27 septembre 2023, par John Doe

  How can I configure FFmpeg to save the recording into separate output files at specific time intervals, for example, every 25 seconds without interrupting the recording ? By default, FFmpeg saves the entire recorded video in a single output file, but I want to create separate output files for each 25-second segment.
What steps should I follow to achieve this ?

  


  I tried python multiprogramming to run the recording and saving task simultanously but didn't work.

  


  How about using the @ffmpeg.on("progress") event ?