Reading and Writing Videos
Fork me on GitHub

Reading and Writing Videos is a module created for using a FFmpeg/LibAV backend to read and write videos. Depending on the available backend, the appropriate probing tool (ffprobe, avprobe, or even mediainfo) will be used to parse metadata from videos.


Use to load any video (here bigbuckbunny) into memory as a single ndarray. Note that this function assumes you have enough memory to do so, and should only be used for small videos.

import skvideo.datasets
videodata =

Running this code prints:

(132, 720, 1280, 3)

Use to load any video (here bigbuckbunny) frame-by-frame. This is the function to be used for larger files, and is actually faster than loading a video as 1 ndarray. However, it requires handling each frame as it is loaded. An example snippet:

import skvideo.datasets
videogen =
for frame in videogen:

The output:

(720, 1280, 3)
(720, 1280, 3)
(720, 1280, 3)

Sometimes, particular use cases require fine tuning FFmpeg’s reading parameters. For this, you can use

import skvideo.datasets

# here you can set keys and values for parameters in ffmpeg
inputparameters = {}
outputparameters = {}
reader =,

# iterate through the frames
accumulation = 0
for frame in reader.nextFrame():
        # do something with the ndarray frame
        accumulation += np.sum(frame)

For example, FFmpegReader will by default return an RGB representation of a video file, but you may want some other color space that FFmpeg supports, by setting appropriate key/values in outputparameters. Since FFmpeg output is piped into stdin, all FFmpeg commands can be used here.

inputparameters may be useful for raw video which has no header information. Then you should FFmpeg exactly how to interpret your data.


To write an ndarray to a video file, use

import numpy as np

outputdata = np.random.random(size=(5, 480, 680, 3)) * 255
outputdata = outputdata.astype(np.uint8)"outputvideo.mp4", outputdata)

Often, writing videos requires fine tuning FFmpeg’s writing parameters to select encoders, framerates, bitrates, etc. For this, you can use

import numpy as np

outputdata = np.random.random(size=(5, 480, 680, 3)) * 255
outputdata = outputdata.astype(np.uint8)

writer ="outputvideo.mp4")
for i in xrange(5):
        writer.writeFrame(outputdata[i, :, :, :])

Reading Video Metadata

Use to find video metadata. As below:

import skvideo.datasets
import json
metadata =
print(json.dumps(metadata["video"], indent=4)) returns a dictionary, which can be passed into json.dumps for pretty printing. See the below output:

[u'audio', u'video']
    "@index": "0",
    "@codec_name": "h264",
    "@codec_long_name": "H.264 / AVC / MPEG-4 AVC / MPEG-4 part 10",
    "@profile": "Main",
    "@codec_type": "video",
    "@codec_time_base": "1/50",
    "@codec_tag_string": "avc1",
    "@codec_tag": "0x31637661",
    "@width": "1280",
    "@height": "720",
    "@coded_width": "1280",
    "@coded_height": "720",
    "@has_b_frames": "0",
    "@sample_aspect_ratio": "1:1",
    "@display_aspect_ratio": "16:9",
    "@pix_fmt": "yuv420p",
    "@level": "31",
    "@chroma_location": "left",
    "@refs": "1",
    "@is_avc": "1",
    "@nal_length_size": "4",
    "@r_frame_rate": "25/1",
    "@avg_frame_rate": "25/1",
    "@time_base": "1/12800",
    "@start_pts": "0",
    "@start_time": "0.000000",
    "@duration_ts": "67584",
    "@duration": "5.280000",
    "@bit_rate": "1205959",
    "@bits_per_raw_sample": "8",
    "@nb_frames": "132",
    "disposition": {
        "@default": "1",
        "@dub": "0",
        "@original": "0",
        "@comment": "0",
        "@lyrics": "0",
        "@karaoke": "0",
        "@forced": "0",
        "@hearing_impaired": "0",
        "@visual_impaired": "0",
        "@clean_effects": "0",
        "@attached_pic": "0"
    "tag": [
            "@key": "creation_time",
            "@value": "1970-01-01 00:00:00"
            "@key": "language",
            "@value": "und"
            "@key": "handler_name",
            "@value": "VideoHandler"