Skvideo io install python

Write Videos From Images using Scikit Video

There are different libraries in python for processing images and videos. We can read videos using python modules like moviepy, opencv, sckit video etc. These libraries also provide tools to write videos in required specification. In this tutorial we are using pillow to read images scikit video to write frames to a video. To get started, you must have python installed and install these libraries.

pip install pillow scikit-video numpy

http://www.scikit-video.org/stable/ If you have alread images stored in a numpy array, you can simple write them in output mp4 file.

import skvideo.io import numpy as np # we create frames using numpy random method outputdata = np.random.random(size=(5, 720, 1280, 3)) * 255 outputdata = outputdata.astype(np.uint8) # save array to output file skvideo.io.vwrite("outputvideo.mp4", outputdata)

Now if we want to customize writer and we need to writer data using writer, we can create a video writer using scikit video. We will provide video save path and frames per second with video encoding quality and preset. You can change these settings according to your requirements and also you can view complete options on scikit video library homepage.

import skvideo.io # save path and fps video_save_path = "output.mp4" fps = 30 # create writer using FFmpegWriter writer = skvideo.io.FFmpegWriter(video_save_path, inputdict=, outputdict=) 

Now we iterate over all images in directory and read images using pillow. After we read an image, we can convert it to numpy array and write using writer.writeFrame() method.

import numpy as np import os from PIL import Image base_path = "IMAGES_DIRECTORY" # iterate over each image using os module for img in os.listdir(base_path): image = Image.open(os.path.join(base_path, img)) # read image image = np.array(image, dtype=np.uint8) #convert to unit8 numpy array # write frame writer.writeFrame(image) # close writer writer.close()

This will write it to path we provided while writing video file. There are different options available for input and output dictionary of writer that we can futher explore.tpg

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sk-video 1.1.10

This library provides easy access to common as well as state-of-the-art video processing routines. Check out the website for more details.

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Лицензия: BSD License (BSD)

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Reading and Writing Videos¶

skvideo.io 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.

Reading¶

Use skvideo.io.vread 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.io import skvideo.datasets videodata = skvideo.io.vread(skvideo.datasets.bigbuckbunny()) print(videodata.shape) 

Use skvideo.io.vreader 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.io import skvideo.datasets videogen = skvideo.io.vreader(skvideo.datasets.bigbuckbunny()) for frame in videogen: print(frame.shape) 

(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 skvideo.io.FFmpegReader

import skvideo.io import skvideo.datasets # here you can set keys and values for parameters in ffmpeg inputparameters = <> outputparameters = <> reader = skvideo.io.FFmpegReader(skvideo.datasets.bigbuckbunny(), inputdict=inputparameters, outputdict=outputparameters) # 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.

Writing¶

To write an ndarray to a video file, use skvideo.io.write

import skvideo.io import numpy as np outputdata = np.random.random(size=(5, 480, 680, 3)) * 255 outputdata = outputdata.astype(np.uint8) skvideo.io.vwrite("outputvideo.mp4", outputdata) 

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

import skvideo.io import numpy as np outputdata = np.random.random(size=(5, 480, 680, 3)) * 255 outputdata = outputdata.astype(np.uint8) writer = skvideo.io.FFmpegWriter("outputvideo.mp4") for i in xrange(5): writer.writeFrame(outputdata[i, :, :, :]) writer.close() 

Reading Video Metadata¶

Use skvideo.io.ffprobe to find video metadata. As below:

import skvideo.io import skvideo.datasets import json metadata = skvideo.io.ffprobe(skvideo.datasets.bigbuckbunny()) print(metadata.keys()) print(json.dumps(metadata["video"], indent=4)) 

skvideo.io.ffprobe 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" > ] > 

© Copyright 2015-2017, scikit-video developers (BSD License).
Created using Sphinx 1.7.9.

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Video Processing in Python

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README.rst

Borrowing coding styles and conventions from scikit-image and scikit-learn, scikit-video is a Python module for video processing built on top of scipy, numpy, and ffmpeg/libav.

This project is distributed under the 3-clause BSD.

Dependencies and Installation

Here are the requirements needed to use scikit-video.

• Either ffmpeg (version >= 2.8) or libav (either version 10 or 11)
• python (2.7, 3.3 <=)
• numpy (version >= 1.9.2)
• scipy (version >= 0.16.0)
• PIL/Pillow (version >= 3.1)
• scikit-learn (version >= 0.18)
• mediainfo (optional)

$ sudo pip install scikit-video

1. Make sure minimum dependencies (above) are installed. In addition, install setuptools (python-setuptools or python2-setuptools).
2. Clone the scikit-video repository, enter the project directory, then run:

$ sudo python setup.py install

where python may refer to either python2 or python3.

If you installed scikit-video prior to version 1.1.10, you may have an import conflict. Run the following command(s) to fix it:

$ sudo pip uninstall sk-video

Then To check that the conflict no longer exists, import skvideo and print the file path:

import skvideo print(skvideo.__file__)

if setup correctly, you should see scikit_video in the path:

/usr/lib/python*/site-packages/scikit_video-*.*.*-py*.egg/skvideo/__init__.pyc

• Spatial-Temporal filtering helper functions
• Speedup routines (using cython and/or opencl)
• More ffmpeg/avconv interfacing
• Wrapping ffmpeg/avconv inside a subprocess to reduce memory overhead
• Add additional algorithms and maintain more comprehensive benchmarks

Quick tutorial on how to go about setting up your environment to contribute to scikit-video:

After installation, you can launch the test suite from outside the source directory (you will need to have the nose package installed). To ensure that both python2 and python3 versions pass:

$ nosetests2 -v skvideo $ nosetests3 -v skvideo

Copyright 2015-2019, scikit-video developers (BSD license).

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