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• Background transparency lost on new machine using matplotlib and ffmpeg

  23 octobre 2023, par Jan Turowski

  I am creating animated physics graphs with a transparent background for later use in a NLE. On my old machine at work they display and render with background transparency just fine. The exact same code however loses background transparency in the ffmpeg render on both my Linux and my Windows machine at home. The animations are displayed just fine on all machines.

  


  As I first thought it was a Linux issue, I tried to run the code on my Windows machine expecting it to work again. Unfortunately it did not.

  


  Reduced code :

  


  import numpy as np
import matplotlib.pylab as plt
from matplotlib.animation import FuncAnimation
import matplotlib.animation as animation
from matplotlib.pyplot import figure
from matplotlib import style
import locale
from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)
# Set to German locale to get comma decimal separater
locale.setlocale(locale.LC_NUMERIC, "de_DE")
# Tell matplotlib to use the locale we set above
plt.rcParams['axes.formatter.use_locale'] = True

# plt.clf()
# plt.rcdefaults()

# Style und Font definieren

style.use('dark_background')

# Pfeile erstellen
def arrowed_spines(fig, ax):

    xmin, xmax = ax.get_xlim()
    ymin, ymax = ax.get_ylim()

    # removing the default axis on all sides:
    for side in ['bottom','right','top','left']:
        ax.spines[side].set_visible(False)

    # removing the axis ticks
    # plt.xticks([]) # labels
    # plt.yticks([])
    # ax.xaxis.set_ticks_position('none') # tick markers
    # ax.yaxis.set_ticks_position('none')

    # get width and height of axes object to compute
    # matching arrowhead length and width
    dps = fig.dpi_scale_trans.inverted()
    bbox = ax.get_window_extent().transformed(dps)
    width, height = bbox.width, bbox.height

    # manual arrowhead width and length
    hw = 1./20.*(ymax-ymin)
    hl = 1./20.*(xmax-xmin)
    lw = 1. # axis line width
    ohg = 0.3 # arrow overhang

    # compute matching arrowhead length and width
    yhw = hw/(ymax-ymin)*(xmax-xmin)* height/width
    yhl = hl/(xmax-xmin)*(ymax-ymin)* width/height

    # draw x and y axis
    ax.arrow(xmin, 0, xmax-xmin, 0., fc='w', ec='w', lw = lw,
             head_width=hw, head_length=hl, overhang = ohg,
             length_includes_head= True, clip_on = False)

    ax.arrow(0, ymin, 0., ymax-ymin, fc='w', ec='w', lw = lw,
             head_width=yhw, head_length=yhl, overhang = ohg,
             length_includes_head= True, clip_on = False)

# Meine easing-Funktion
def ease(n):
    if n < 0.0:
        return 0
    elif n > 1.0:
        return 1
    else:
        return 3*n**2-2*n**3

# Meine Floor/Warte Funktion
def wait(n):
    if n < 0.0:
        return 0
    else:
        return n

# Canvas erstellen
fig = plt.figure()
ax = fig.add_subplot(111)
fig.set_size_inches([8,9])

def f(x):
    return -0.05*x**2+125
xlin = np.linspace(0,60,100)


# Beschriftung und Optik

plt.xlabel(r"$x$ in $\rm{m}$", horizontalalignment='right', x=1.0)
plt.ylabel(r"$y$ in $\rm{m}$", horizontalalignment='right', y=1.0)
ax.set_xlim(0,100)
ax.set_ylim(0,139)
plt.grid(alpha=.4)
plt.xticks(np.arange(0, 100, 20))
plt.yticks(np.arange(0, 140, 20))
ax.yaxis.set_minor_locator(MultipleLocator(10))
ax.xaxis.set_minor_locator(MultipleLocator(10))
ax.tick_params(axis='x', direction = "inout", length= 10.0, which='both', width=3)
ax.tick_params(axis='y', direction = "inout", length= 10.0, which='both', width=3)


xsub = np.array([0])

# statische Linien definieren
line2, = ax.plot(xsub,f(xsub),linewidth=5,zorder=0,c = 'b')
arrowed_spines(fig, ax)
plt.tight_layout()

# Linien animieren
def animate(i):

    xsub = xlin[0:wait(i-20)]
    global line2
    line2.remove()
    line2, = ax.plot(xsub, f(xsub), linewidth=5, zorder=0,c = "b")
    plt.tight_layout()

animation = FuncAnimation(fig, animate, np.arange(0, 130, 1), interval=100)

plt.show()

# animation.save(r"YOUR\PATH\HERE\reduced_x-y.mov", codec="png",
         dpi=100, bitrate=-1,
         savefig_kwargs={'transparent': True, 'facecolor': 'none'})



  


• How to set background transparency for animation with ffmpeg

  23 octobre 2023, par Jan Turowski

  I am creating animated physics graphs with a transparent background for later use in a NLE. On my old machine at work they display and render with background transparency just fine. The exact same code however loses background transparency in the ffmpeg render on both my Linux and my Windows machine at home. The animations are displayed just fine on all machines.

  


  As I first thought it was a Linux issue, I tried to run the code on my Windows machine expecting it to work again. Unfortunately it did not.

  


  Reduced code :

  


  import numpy as np
import matplotlib.pylab as plt
from matplotlib.animation import FuncAnimation
import matplotlib.animation as animation
from matplotlib.pyplot import figure
from matplotlib import style
import locale
from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)
# Set to German locale to get comma decimal separater
locale.setlocale(locale.LC_NUMERIC, "de_DE")
# Tell matplotlib to use the locale we set above
plt.rcParams['axes.formatter.use_locale'] = True

# plt.clf()
# plt.rcdefaults()

# Style und Font definieren

style.use('dark_background')

# Pfeile erstellen
def arrowed_spines(fig, ax):

    xmin, xmax = ax.get_xlim()
    ymin, ymax = ax.get_ylim()

    # removing the default axis on all sides:
    for side in ['bottom','right','top','left']:
        ax.spines[side].set_visible(False)

    # removing the axis ticks
    # plt.xticks([]) # labels
    # plt.yticks([])
    # ax.xaxis.set_ticks_position('none') # tick markers
    # ax.yaxis.set_ticks_position('none')

    # get width and height of axes object to compute
    # matching arrowhead length and width
    dps = fig.dpi_scale_trans.inverted()
    bbox = ax.get_window_extent().transformed(dps)
    width, height = bbox.width, bbox.height

    # manual arrowhead width and length
    hw = 1./20.*(ymax-ymin)
    hl = 1./20.*(xmax-xmin)
    lw = 1. # axis line width
    ohg = 0.3 # arrow overhang

    # compute matching arrowhead length and width
    yhw = hw/(ymax-ymin)*(xmax-xmin)* height/width
    yhl = hl/(xmax-xmin)*(ymax-ymin)* width/height

    # draw x and y axis
    ax.arrow(xmin, 0, xmax-xmin, 0., fc='w', ec='w', lw = lw,
             head_width=hw, head_length=hl, overhang = ohg,
             length_includes_head= True, clip_on = False)

    ax.arrow(0, ymin, 0., ymax-ymin, fc='w', ec='w', lw = lw,
             head_width=yhw, head_length=yhl, overhang = ohg,
             length_includes_head= True, clip_on = False)

# Meine easing-Funktion
def ease(n):
    if n < 0.0:
        return 0
    elif n > 1.0:
        return 1
    else:
        return 3*n**2-2*n**3

# Meine Floor/Warte Funktion
def wait(n):
    if n < 0.0:
        return 0
    else:
        return n

# Canvas erstellen
fig = plt.figure()
ax = fig.add_subplot(111)
fig.set_size_inches([8,9])

def f(x):
    return -0.05*x**2+125
xlin = np.linspace(0,60,100)


# Beschriftung und Optik

plt.xlabel(r"$x$ in $\rm{m}$", horizontalalignment='right', x=1.0)
plt.ylabel(r"$y$ in $\rm{m}$", horizontalalignment='right', y=1.0)
ax.set_xlim(0,100)
ax.set_ylim(0,139)
plt.grid(alpha=.4)
plt.xticks(np.arange(0, 100, 20))
plt.yticks(np.arange(0, 140, 20))
ax.yaxis.set_minor_locator(MultipleLocator(10))
ax.xaxis.set_minor_locator(MultipleLocator(10))
ax.tick_params(axis='x', direction = "inout", length= 10.0, which='both', width=3)
ax.tick_params(axis='y', direction = "inout", length= 10.0, which='both', width=3)


xsub = np.array([0])

# statische Linien definieren
line2, = ax.plot(xsub,f(xsub),linewidth=5,zorder=0,c = 'b')
arrowed_spines(fig, ax)
plt.tight_layout()

# Linien animieren
def animate(i):

    xsub = xlin[0:wait(i-20)]
    global line2
    line2.remove()
    line2, = ax.plot(xsub, f(xsub), linewidth=5, zorder=0,c = "b")
    plt.tight_layout()

animation = FuncAnimation(fig, animate, np.arange(0, 130, 1), interval=100)

plt.show()

# animation.save(r"YOUR\PATH\HERE\reduced_x-y.mov", codec="png",
         dpi=100, bitrate=-1,
         savefig_kwargs={'transparent': True, 'facecolor': 'none'})



  


• Monitoring ffmpeg two-passes encoding

  31 décembre 2024, par Hodol

  I'm new in FFMPEG.

  


  According to the official guide, https://trac.ffmpeg.org/wiki/Encode/VP9 I use the following command to convert a large h.264 file :

  


  ffmpeg -i input.mp4 -c:v libvpx-vp9 -b:v 0 -crf 30 -pass 1 -an -f null /dev/null
ffmpeg -i input.mp4 -c:v libvpx-vp9 -b:v 0 -crf 30 -pass 2 -c:a libopus output.webm


  


  However, the pass-1 takes too long time and it does not log progress. With -report option I can see something is in progress but I don't know how long I should wait.

  


  Here's questions :

  


  

  1. Is there any way to see the progress of 1-pass ?

  


  2. Is there any way to speed up the process ?

  


  


  Thank you,