Top 3 Python Libraries for video Editing

Python programming can make your video editing project easy by providing the Python libraries for video manipulation. You can manipulate videos and gifs with python with only a few lines of code. 

Python programming is best known for the rich libraries that the python community is committed to building continuously. With these python libraries, one is able to create a project without creating the building from the scratch but instead using the libraries and projects that someone has already created. 

Video Editing is one of the tedious tasks that is very hard if you are doing it manually and not using any library. Though it is not impossible. You will be working on the mathematical aspects of the manipulation. 

In this article, i will introduce you to the Best 3 python libraries for video editing and video manipulating. Feel free to add anything else you know about these projects or you know any other best python library for video editing and video manipulating. The comment section is open for suggestion. 

1. MoviePy

MoviePy is a Python module for video editing, which can be used for basic operations (like cuts, concatenations, title insertions), video compositing (a.k.a. non-linear editing), video processing, or to create advanced effects.

It can read and write the most common video formats, including GIF. with MoviePy Basic operations can be done in one line.

The code is easy to learn and easy to understand for newcomers. with MoviePy You have total control over the frames of the video and audio, and creating your own effects is easy as Py. The code uses very common software (Numpy and FFMPEG) and can run on (almost) any machine with (almost) any version of Python.

For learning about this awesome python library for video editing please feel free to check out their official documentation.

Example of MoviePy Library for Video Editing

The following code show you how we can use moviepy library in python to edit video.

# Import everything needed to edit video clips
from moviepy.editor import *

# Load myHolidays.mp4 and select the subclip 00:00:50 - 00:00:60
clip = VideoFileClip("myvideo.mp4")

# Reduce the audio volume (volume x 0.8)
# clip = clip.volumex(0.8)

# Generate a text clip. You can customize the font, color, etc.
txt_clip = TextClip("Edited By AlixaProDev Using Python",fontsize=30,color='white')

# Say that you want it to appear 10s at the center of the screen
txt_clip = txt_clip.set_pos('center').set_duration(10)

# Overlay the text clip on the first video clip
video = CompositeVideoClip([clip, txt_clip])

# Write the result to a file (many options available !)
video.write_videofile("Edited.webm") 

2. Scikit-video

scikit-video is a collection of Video processing algorithms, including I/O, quality metrics, temporal filtering, motion/object detection, and motion estimation. This is intended as a companion to scikit-image, containing all the algorithms which deal with video.There is a certain degree of overlap between image and video algorithms, for example a PSNR quality metric could be applied to pairs of images or pairs of video frames just as well. However, other algorithms are video-specific, for example, a temporal denoise.This is the future home of the video-specific algorithms, as well as some of the algorithms which are not strictly video specific but are usually seen in a video context.To learn more about this video editing python library check out this github repository.

Example of scicket-vide0

It is used for for the video processining than the video editing.

import skvideo.io
import skvideo.datasets
import skvideo.utils

filename = skvideo.datasets.bigbuckbunny()

print("Loading only luminance channel")
vid = skvideo.io.vread(filename, outputdict={"-pix_fmt": "gray"})[:, :, :, 0]
print(vid.shape)
print("Enforcing video shape")
vid = skvideo.utils.vshape(vid)
print(vid.shape)
print("")

print("Loading only first 5 luminance channel frames")
vid = skvideo.io.vread(filename, num_frames=5, outputdict={"-pix_fmt": "gray"})[:, :, :, 0]
print(vid.shape)
print("Enforcing video shape")
vid = skvideo.utils.vshape(vid)
print(vid.shape)
print("")

3. VidGear

VidGear is a High-Performance Video Processing Python Library that provides an easy-to-use, highly extensible, thoroughly optimised Multi-Threaded + Asyncio API Framework on top of many state-of-the-art specialized librariesThese Libraries are OpenCV, FFmpeg, ZeroMQ, picamera, starlette, streamlink, pafy, pyscreenshot, aiortc and python-mss serving at its backend, and enable us to flexibly exploit their internal parameters and methods, while silently delivering robust error-handling and real-time performance 🔥 VidGear primarily focuses on simplicity and thereby lets programmers and software developers to easily integrate and perform Complex Video Processing Tasks, in just a few lines of code.

Example of vidGear Library for Video Editing

If the camera as moving and shaking, python vidGear module can be used to stabilize the video.

# import required libraries
from vidgear.gears import VideoGear
import numpy as np
import cv2

# open any valid video stream with stabilization enabled(`stabilize = True`)
stream_stab = VideoGear(source="test.mp4", stabilize=True).start()

# open same stream without stabilization for comparison
stream_org = VideoGear(source="test.mp4").start()

# loop over
while True:

    # read stabilized frames
    frame_stab = stream_stab.read()

    # check for stabilized frame if Nonetype
    if frame_stab is None:
        break

    # read un-stabilized frame
    frame_org = stream_org.read()

    # concatenate both frames
    output_frame = np.concatenate((frame_org, frame_stab), axis=1)

    # put text over concatenated frame
    cv2.putText(
        output_frame,
        "Before",
        (10, output_frame.shape[0] - 10),
        cv2.FONT_HERSHEY_SIMPLEX,
        0.6,
        (0, 255, 0),
        2,
    )
    cv2.putText(
        output_frame,
        "After",
        (output_frame.shape[1] // 2 + 10, output_frame.shape[0] - 10),
        cv2.FONT_HERSHEY_SIMPLEX,
        0.6,
        (0, 255, 0),
        2,
    )

    # Show output window
    cv2.imshow("Stabilized Frame", output_frame)

    # check for 'q' key if pressed
    key = cv2.waitKey(1) & 0xFF
    if key == ord("q"):
        break

# close output window
cv2.destroyAllWindows()

# safely close both video streams
stream_org.stop()
stream_stab.stop()

Summary and Conclusion:-

This was all about Video Editing libraries. The first one is the best libra among them. feel free to ask any questions in the comment section. If you are interested in other python tutorials please visit my youtube channel Code with Ali.