加速 matplotlib 动画到视频文件 [英] Speedup matplotlib animation to video file

问题描述

在 Raspbian (Raspberry Pi 2) 上，从我的脚本中删除的以下最小示例正确生成了一个 mp4 文件:

On Raspbian (Raspberry Pi 2), the following minimal example stripped from my script correctly produces an mp4 file:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import animation

def anim_lift(x, y):

    #set up the figure
    fig = plt.figure(figsize=(15, 9))

    def animate(i):
        # update plot
        pointplot.set_data(x[i], y[i])

        return  pointplot

    # First frame
    ax0 = plt.plot(x,y)
    pointplot, = ax0.plot(x[0], y[0], 'or')

    anim = animation.FuncAnimation(fig, animate, repeat = False,
                                   frames=range(1,len(x)), 
                                   interval=200,
                                   blit=True, repeat_delay=1000)

    anim.save('out.mp4')
    plt.close(fig)

# Number of frames
nframes = 200

# Generate data
x = np.linspace(0, 100, num=nframes)
y = np.random.random_sample(np.size(x))

anim_lift(x, y)

现在，制作的文件质量很好，文件很小，但是制作一部 170 帧的电影需要 15 分钟，这对我的应用程序来说是不可接受的.我正在寻找显着的加速，视频文件大小增加不是问题.

Now, the file is produced with good quality and pretty small file size, but it takes 15 minutes to produce a 170 frames movie, which is not acceptable for my application. i'm looking for a significant speedup, video file size increase is not a problem.

我认为视频制作的瓶颈在于以 png 格式临时保存帧.在处理过程中，我可以看到 png 文件出现在我的工作目录中，CPU 负载仅为 25%.

I believe the bottleneck in the video production is in the temporary saving of the frames in png format. During processing I can see the png files apprearing in my working directory, with the CPU load at 25% only.

请提出一个解决方案，该解决方案也可能基于不同的包，而不是简单的 matplotlib.animation，例如 OpenCV(无论如何已经导入到我的项目中)或 moviepy.

Please suggest a solution, that might also be based on a different package rather than simply matplotlib.animation, like OpenCV (which is anyway already imported in my project) or moviepy.

正在使用的版本:

• python 2.7.3
• matplotlib 1.1.1rc2
• ffmpeg 0.8.17-6:0.8.17-1+rpi1

推荐答案

将动画保存到文件的瓶颈在于figure.savefig()的使用.这是 matplotlib 的一个自制子类 FFMpegWriter，灵感来自 gaggio 的回答.它不使用 savefig(因此忽略了 savefig_kwargs)，但只需要对您的动画脚本进行最少的更改.

The bottleneck of saving an animation to file lies in the use of figure.savefig(). Here is a homemade subclass of matplotlib's FFMpegWriter, inspired by gaggio's answer. It doesn't use savefig (and thus ignores savefig_kwargs) but requires minimal changes to whatever your animation script are.

from matplotlib.animation import FFMpegWriter

class FasterFFMpegWriter(FFMpegWriter):
    '''FFMpeg-pipe writer bypassing figure.savefig.'''
    def __init__(self, **kwargs):
        '''Initialize the Writer object and sets the default frame_format.'''
        super().__init__(**kwargs)
        self.frame_format = 'argb'

    def grab_frame(self, **savefig_kwargs):
        '''Grab the image information from the figure and save as a movie frame.

        Doesn't use savefig to be faster: savefig_kwargs will be ignored.
        '''
        try:
            # re-adjust the figure size and dpi in case it has been changed by the
            # user.  We must ensure that every frame is the same size or
            # the movie will not save correctly.
            self.fig.set_size_inches(self._w, self._h)
            self.fig.set_dpi(self.dpi)
            # Draw and save the frame as an argb string to the pipe sink
            self.fig.canvas.draw()
            self._frame_sink().write(self.fig.canvas.tostring_argb()) 
        except (RuntimeError, IOError) as e:
            out, err = self._proc.communicate()
            raise IOError('Error saving animation to file (cause: {0}) '
                      'Stdout: {1} StdError: {2}. It may help to re-run '
                      'with --verbose-debug.'.format(e, out, err)) 

与使用默认的 FFMpegWriter 相比，我能够用一半或更短的时间创建动画.

I was able to create animation in half the time or less than with the default FFMpegWriter.

您可以按照本示例中的说明使用.

You can use is as explained in this example.

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