ffmpegio-core 0.4.3

Media I/O with FFmpeg

Python ffmpegio package aims to bring the full capability of FFmpeg to read, write, and manipulate multimedia data to Python. FFmpeg is an open-source cross-platform multimedia framework, which can handle most of the multimedia formats available today.

Since v0.3.0, ffmpegio Python distribution package has been split into ffmpegio-core and ffmpegio to allow Numpy-independent installation.

Install the full ffmpegio package via pip:

pip install ffmpegio

If numpy.ndarray data I/O is not needed, instead use

pip install ffmpegio-core

Main Features

• Pure-Python light-weight package interacting with FFmpeg executable found in the system

• Transcode a media file to another in Python

• Read, write, filter, and create functions for audio, image, and video data

• Context-managing ffmpegio.open to perform stream read/write operations of video and audio

• Automatically detect and convert audio & video formats to and from numpy.ndarray properties

• Probe media file information

• Accepts all FFmpeg options including filter graphs

• Supports a user callback whenever FFmpeg updates its progress information file (see -progress FFmpeg option)

• Advanced users can gain finer controls of FFmpeg I/O with ffmpegio.ffmpegprocess submodule

• More features to follow

Documentation

Visit our GitHub page here

Examples

To import ffmpegio

>>> import ffmpegio

Transcoding

>>> # transcode, overwrite output file if exists, showing the FFmpeg log
>>> ffmpegio.transcode('input.avi', 'output.mp4', overwrite=True, show_log=True)

>>> # 1-pass H.264 transcoding
>>> ffmpegio.transcode('input.avi', 'output.mkv', vcodec='libx264', show_log=True,
>>>                    preset='slow', crf=22, acodec='copy')

>>> # 2-pass H.264 transcoding
>>> ffmpegio.transcode('input.avi', 'output.mkv', two_pass=True, show_log=True,
>>>                    **{'c:v':'libx264', 'b:v':'2600k', 'c:a':'aac', 'b:a':'128k'})

Read Audio Files

>>> # read audio samples in its native sample format and return all channels
>>> fs, x = ffmpegio.audio.read('myaudio.wav')
>>> # fs: sampling rate in samples/second, x: [nsamples x nchannels] numpy array

>>> # read audio samples from 24.15 seconds to 63.2 seconds, pre-convert to mono in float data type
>>> fs, x = ffmpegio.audio.read('myaudio.flac', ss=24.15, to=63.2, sample_fmt='dbl', ac=1)

>>> # read filtered audio samples first 10 seconds
>>> #   filter: equalizer which attenuate 10 dB at 1 kHz with a bandwidth of 200 Hz
>>> fs, x = ffmpegio.audio.read('myaudio.mp3', t=10.0, af='equalizer=f=1000:t=h:width=200:g=-10')

Read Image Files / Capture Video Frames

>>> # list supported image extensions
>>> ffmpegio.caps.muxer_info('image2')['extensions']
['bmp', 'dpx', 'exr', 'jls', 'jpeg', 'jpg', 'ljpg', 'pam', 'pbm', 'pcx', 'pfm', 'pgm', 'pgmyuv',
 'png', 'ppm', 'sgi', 'tga', 'tif', 'tiff', 'jp2', 'j2c', 'j2k', 'xwd', 'sun', 'ras', 'rs', 'im1',
 'im8', 'im24', 'sunras', 'xbm', 'xface', 'pix', 'y']

>>> # read BMP image with auto-detected pixel format (rgb24, gray, rgba, or ya8)
>>> I = ffmpegio.image.read('myimage.bmp') # I: [height x width x ncomp] numpy array

>>> # read JPEG image, then convert to grayscale and proportionally scale so the width is 480 pixels
>>> I = ffmpegio.image.read('myimage.jpg', pix_fmt='grayscale', s='480x-1')

>>> # read PNG image with transparency, convert it to plain RGB by filling transparent pixels orange
>>> I = ffmpegio.image.read('myimage.png', pix_fmt='rgb24', fill_color='orange')

>>> # capture video frame at timestamp=4:25.3 and convert non-square pixels to square
>>> I = ffmpegio.image.read('myvideo.mpg', ss='4:25.3', square_pixels='upscale')

>>> # capture 5 video frames and tile them on 3x2 grid with 7px between them, and 2px of initial margin
>>> I = ffmpegio.image.read('myvideo.mp4', vf='tile=3x2:nb_frames=5:padding=7:margin=2')

>>> # create spectrogram of the audio input (must specify pix_fmt if input is audio)
>>> I = ffmpegio.image.read('myaudio.mp3', filter_complex='showspectrumpic=s=960x540', pix_fmt='rgb24')

Read Video Files

>>> # read 50 video frames at t=00:32:40 then convert to grayscale
>>> fs, F = ffmpegio.video.read('myvideo.mp4', ss='00:32:40', vframes=50, pix_fmt='gray')
>>> #  fs: frame rate in frames/second, F: [nframes x height x width x ncomp] numpy array

>>> # get running spectrogram of audio input (must specify pix_fmt if input is audio)
>>> fs, F = ffmpegio.video.read('myvideo.mp4', pix_fmt='rgb24', filter_complex='showspectrum=s=1280x480')

Read Multiple Files or Streams

>>> # read both video and audio streams (1 ea)
>>> rates, data = ffmpegio.media.read('mymedia.mp4')
>>> #  rates: dict of frame rate and sampling rate: keys="v:0" and "a:0"
>>> #  data: dict of video frame array and audio sample array: keys="v:0" and "a:0"

>>> # combine video and audio files
>>> rates, data = ffmpegio.media.read('myvideo.mp4','myaudio.mp3')

>>> # get output of complex filtergraph (can take multiple inputs)
>>> expr = "[v:0]split=2[out0][l1];[l1]edgedetect[out1]"
>>> rates, data = ffmpegio.media.read('myvideo.mp4',filter_complex=expr,map=['[out0]','[out1]'])
>>> #  rates: dict of frame rates: keys="v:0" and "v:1"
>>> #  data: dict of video frame arrays: keys="v:0" and "v:1"

Write Audio, Image, & Video Files

>>> # create a video file from a numpy array
>>> ffmpegio.video.write('myvideo.mp4', rate, F)

>>> # create an image file from a numpy array
>>> ffmpegio.image.write('myimage.png', F)

>>> # create an audio file from a numpy array
>>> ffmpegio.audio.write('myaudio.mp3', rate, x)

Filter Audio, Image, & Video data

>>> # Add fade-in and fade-out effects to audio data
>>> fs_out, y = ffmpegio.audio.filter('afade=t=in:ss=0:d=15,afade=t=out:st=875:d=25', fs_in, x)

>>> # Apply mirror effect to an image
>>> I_out = ffmpegio.image.filter('crop=iw/2:ih:0:0,split[left][tmp];[tmp]hflip[right];[left][right] hstack', I_in)

>>> # Add text at the center of the video frame
>>> filter = "drawtext=fontsize=30:fontfile=FreeSerif.ttf:text='hello world':x=(w-text_w)/2:y=(h-text_h)/2"
>>> fs_out, F_out = ffmpegio.video.filter(filter, fs_in, F_in)

Stream I/O

>>> # process video 100 frames at a time and save output as a new video
>>> # with the same frame rate
>>> with ffmpegio.open('myvideo.mp4', 'rv', blocksize=100) as fin,
>>>      ffmpegio.open('myoutput.mp4', 'wv', rate=fin.frame_rate) as fout:
>>>     for frames in fin:
>>>         fout.write(myprocess(frames))

Progress callback

>>> import pprint

>>> # progress callback
>>> def progress(info, done):
>>>     pprint(info) # bunch of stats
>>>     if done:
>>>        print('video decoding completed')
>>>     else:
>>>        return check_cancel_command(): # return True to kill immediately

>>> # can be used in any butch processing
>>> rate, F = ffmpegio.video.read('myvideo.mp4', progress=progress)

>>> # as well as for stream processing
>>> with ffmpegio.open('myvideo.mp4', 'rv', blocksize=100, progress=progress) as fin:
>>>     for frames in fin:
>>>         myprocess(frames)