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• OpenCV3 returning float FRAME_COUNT

  15 mars 2016, par mprat

  I am using OpenCV3 on OSX with Python 2.7 bindings. However, when I try to read the frame count of my video (a .mp4 video), it returns a float - I am expecting an int. Do I need to compile OpenCV3 with some special flags ? Am I missing some codecs ?

  import cv2
  
  vid = cv2.VideoCapture("vid.mp4")
  
  print vid.get(cv2.CAP_PROP_FRAME_WIDTH)

  And it returns a float.

  Installation details :

  • ffmpeg : brew install ffmpeg --with-dcadec --with-openh264 --with-openjpeg --with-openssl --with-tools --with-x265  --with-zimg --with-libvidstab --with-libvpx
  • opencv3 : brew install opencv3 --with-ffmpeg --with-contrib

• 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)



  


• FFmpeg cut a 16:9 video to a 9:16 ratio and put subtitle only in ratio [closed]

  8 septembre 2023, par LordzSpectron

  I want to cut a 16:9 video to take only the central part of it, vertical and what's left, fill it with black borders, example :

  


  

  


  I want only the part highlighted in red.

  


  How to make this using ffmpeg ?

  


  I've been trying using crop, some answers that taught how to blur what's left, but they all leave the full video and just cut it down to 9:16.

  


  I want to have only the middle part highlighted and the rest be black border.