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.
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=)[:, :, :, 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=)[:, :, :, 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.
Как использовать Python для обработки аудио и видео
Узнайте, как использовать Python для обработки аудио и видео с помощью популярных библиотек в нашей информативной статье!
Python — мощный и гибкий язык программирования, который можно использовать для обработки аудио и видео файлов. В этой статье мы рассмотрим некоторые основные библиотеки и инструменты, которые позволят вам работать с медиа-файлами.
Обработка аудио
Pydub
Pydub — это простая и удобная библиотека для работы с аудиофайлами. Она поддерживает различные форматы и позволяет выполнять множество операций, таких как обрезка, склеивание и изменение громкости.
Пример использования Pydub для обрезки аудиофайла:
from pydub import AudioSegment audio_file = AudioSegment.from_file("input.mp3") start_time = 10000 # начало обрезки в миллисекундах end_time = 20000 # конец обрезки в миллисекундах trimmed_audio = audio_file[start_time:end_time] trimmed_audio.export("output.mp3", format="mp3") Librosa
Librosa — это библиотека для анализа музыки и аудио. Она предоставляет функции для извлечения музыкальных характеристик, таких как темп, тональность и гармонии.
Пример использования Librosa для анализа аудиофайла:
import librosa audio_file = "input.wav" y, sr = librosa.load(audio_file) tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr) print(f"Темп: BPM")
Обработка видео
OpenCV
OpenCV (Open Source Computer Vision Library) — это библиотека для работы с компьютерным зрением, которая позволяет обрабатывать видео и изображения. С помощью OpenCV можно выполнять такие операции, как обнаружение объектов, отслеживание движения и распознавание лиц.
Пример использования OpenCV для обнаружения лиц на видео:
import cv2 face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml") video_capture = cv2.VideoCapture(0) while True: ret, frame = video_capture.read() gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.3, 5) for (x, y, w, h) in faces: cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2) cv2.imshow("Video", frame) if cv2.waitKey(1) & 0xFF == ord("q"): break video_capture.release() cv2.destroyAllWindows() MoviePy
MoviePy — это библиотека для редактирования видео, которая позволяет выполнять различные операции, такие как обрезка, склеивание и применение фильтров.
Пример использования MoviePy для создания видео с обратным воспроизведением:
from moviepy.editor import VideoFileClip input_video = VideoFileClip("input.mp4") reversed_video = input_video.fx(vfx.time_mirror) reversed_video.write_videofile("output.mp4") Теперь вы знакомы с некоторыми основными инструментами и библиотеками для работы с аудио и видео в Python. Не останавливайтесь на достигнутом и продолжайте изучать возможности Python для работы с медиа-файлами! 😉