Open Source Python Multimedia Software - Page 10
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Python Multimedia Software
View 4990 business solutionsBrowse free open source Python Multimedia Software and projects below. Use the toggles on the left to filter open source Python Multimedia Software by OS, license, language, programming language, and project status.
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Consistent Depth
We estimate dense, flicker-free, geometrically consistent depth
Consistent Depth is a research project developed by Facebook Research that presents an algorithm for reconstructing dense and geometrically consistent depth information for all pixels in a monocular video. The system builds upon traditional structure-from-motion (SfM) techniques to provide geometric constraints while integrating a convolutional neural network trained for single-image depth estimation. During inference, the model fine-tunes itself to align with the geometric constraints of a specific input video, ensuring stable and realistic depth maps even in less-constrained regions. This approach achieves improved geometric consistency and visual stability compared to prior monocular reconstruction methods. The project can process challenging hand-held video footage, including those with moderate dynamic motion, making it practical for real-world usage. -
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FastFlix
FastFlix is a free GUI for H.264, HEVC and AV1 hardware and software
FastFlix is a graphical user interface for AV1 video encoding that simplifies the process of converting video files into the modern AV1 format. It acts as a frontend for encoding tools such as SVT-AV1 and AOM, allowing users to configure advanced encoding parameters without using the command line. The application focuses on balancing quality and encoding speed, offering presets and tuning options for different use cases. It supports batch encoding, enabling users to process multiple files efficiently. FastFlix also integrates filtering and preprocessing steps to improve output quality. Its interface provides real-time feedback on encoding progress and estimated completion times. Overall, it serves as a user-friendly solution for high-quality video compression workflows. -
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FeelUOwn
Trying to be a robust, user-friendly and hackable music player
FeelUOwn is a user-friendly, and hackable music player. -
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FrankMocap
A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator
FrankMocap is a monocular 3D human capture system that estimates body, hand, and optionally face pose from a single RGB image or video. It regresses parametric human models (e.g., SMPL/SMPL-X) directly, producing temporally stable meshes and joint angles suitable for animation or analytics. The pipeline couples a robust 2D keypoint detector with 3D mesh regression networks and priors that keep results anatomically plausible. It can run frame-by-frame or with temporal smoothing, and includes demo apps for live webcam capture as well as batch processing. Outputs include textured meshes, joint locations, and model parameters that can be exported to common DCC tools and game engines. The codebase offers pretrained models, clear inference scripts, and utilities to visualize results, making single-camera motion capture approachable on commodity hardware. Researchers and creators use it for motion studies, AR/VR prototyping, character animation, and human-in-the-loop editing. -
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Image Super-Resolution (ISR)
Super-scale your images and run experiments with Residual Dense
The goal of this project is to upscale and improve the quality of low-resolution images. This project contains Keras implementations of different Residual Dense Networks for Single Image Super-Resolution (ISR) as well as scripts to train these networks using content and adversarial loss components. Docker scripts and Google Colab notebooks are available to carry training and prediction. Also, we provide scripts to facilitate training on the cloud with AWS and Nvidia-docker with only a few commands. When training your own model, start with only PSNR loss (50+ epochs, depending on the dataset) and only then introduce GANS and feature loss. This can be controlled by the loss weights argument. The weights used to produce these images are available directly when creating the model object. ISR is compatible with Python 3.6 and is distributed under the Apache 2.0 license. -
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Imogen
GPU Texture Generator
Imogen is a real-time, node-based procedural texture generation tool aimed at artists, developers, and shader enthusiasts. It allows users to build complex material textures using a graph-based interface, combining operations like blending, noise, filters, and color correction in a non-destructive workflow. Built with Vulkan and ImGui, Imogen provides immediate visual feedback and supports GPU acceleration for high-resolution texture output. It's particularly useful in game development, VFX, and digital art where procedural workflows are valued for their flexibility and speed. -
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Kindle_download_helper
Download all your kindle books script
Kindle_download_helper is a Python script for downloading Kindle books and personal documents from a user’s Amazon content library. It supports several Amazon regions, including Amazon, Amazon.cn, Amazon.de, Amazon.co.uk, and Amazon.co.jp. The workflow requires the user to log in through the browser, retrieve a CSRF token, and then run the script with the correct regional option. It can download purchased Kindle content and, depending on the flag used, personal document files as well. The repository is archived, so it should be treated as a historical utility rather than an actively maintained tool. It is best understood as a convenience script for users who need bulk access to their own Kindle library files. -
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Kornia
Open Source Differentiable Computer Vision Library
Kornia is a differentiable computer vision library for PyTorch. It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. Inspired by existing packages, this library is composed by a subset of packages containing operators that can be inserted within neural networks to train models to perform image transformations, epipolar geometry, depth estimation, and low-level image processing such as filtering and edge detection that operate directly on tensors. With Kornia we fill the gap between classical and deep computer vision that implements standard and advanced vision algorithms for AI. Our libraries and initiatives are always according to the community needs. -
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Manim Skills Repository
Agent skills for Manim to create 3Blue1Brown style animations
Manim Skills Repository provides a well-crafted set of “agent skills” and reusable components that make it easier to produce animations in Manim similar to the iconic style seen in 3Blue1Brown videos. It supplies templates, best-practice patterns, and example scenes that help users quickly get started with both Manim Community Edition and the older ManimGL framework, allowing for expressive mathematical and conceptual animations without reinventing the wheel. The project aims to wrap common animation logic into structured skills that can be invoked programmatically, reducing boilerplate and allowing creators to focus on creative content rather than low-level animation setup. With clear installation instructions and a collection of test cases, it’s approachable for developers comfortable with Python and interested in educational or technical visualization. -
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MediaCrawler
Self-media platform crawler
MediaCrawler is an open-source tool for collecting public content from major Chinese social media platforms. It supports Xiaohongshu, Douyin, Kuaishou, Bilibili, Weibo, Tieba, Zhihu, and other services. The project uses Playwright to automate browser login and preserve authenticated sessions. It obtains required request signatures from the active browser context, avoiding complex JavaScript reverse engineering. Users can search by keyword, crawl specific posts, collect nested comments, and retrieve creator profiles. It also supports cached login states, proxy pools, and comment word-cloud generation. The repository is intended for learning and research, with clear restrictions against illegal, large-scale, or commercial scraping. -
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Mkchromecast
Cast macOS and Linux Audio/Video to your Google Cast and Sonos Devices
This is a program to cast audio and video from your macOS, or Linux desktop to your Google Cast devices or Sonos speakers. It is written in Python, and it streams via node.js, ffmpeg, or avconv. Mkchromecast is capable of using lossy and lossless audio formats provided that ffmpeg, avconv (Linux), or parec (Linux) are installed. It also supports Multi-room group playback, and 24-bits/96kHz high audio resolution. Linux users also can configure ALSA to capture audio. -
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MystiQ
Qt5/C++ FFmpeg Media Converter
MystiQ is a cross-platform multimedia converter built with Qt and FFmpeg, designed to provide a modern graphical interface for video and audio processing tasks. It allows users to perform operations such as transcoding, trimming, and format conversion without needing to use command-line tools. The application supports a wide range of codecs and formats, enabling compatibility across devices and platforms. It includes batch processing capabilities, allowing multiple files to be converted simultaneously. MystiQ also provides customizable encoding parameters, giving users control over quality and performance. Its interface is designed to be intuitive while still exposing advanced features for experienced users. Overall, it combines ease of use with powerful multimedia processing capabilities. -
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NovaSR
A lightning fast audio upsampler
NovaSR is an extremely lightweight and high-performance audio upsampling model that transforms low-quality 16 kHz audio into clearer, high-fidelity 48 kHz audio with remarkable speed and efficiency. At only about 50 KB in size, the model is orders of magnitude smaller than typical audio super-resolution networks, yet it achieves high quality and realtime performance thanks to its compact architecture and efficient convolutional design. NovaSR is especially valuable for post-processing tasks in speech enhancement, TTS pipelines, and dataset restoration where low sampling rates degrade perceived audio clarity; the minimal model size also makes it suitable for edge and embedded use cases where memory is at a premium. Its performance can reach thousands of times realtime on modern GPUs, allowing massive audio batches to be processed with negligible compute overhead. -
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Pixal3D
Pixel-Aligned 3D Generation from Images
Pixal3D is a TencentARC research project for generating high-fidelity 3D assets from a single input image. It addresses a key weakness in image-to-3D generation: many models produce plausible 3D shapes but fail to preserve pixel-level faithfulness to the original image. Pixal3D improves this by explicitly lifting image features into 3D through back-projection, creating clearer correspondences between the input pixels and the generated asset. The system is designed to produce detailed geometry and physically based rendering textures rather than only coarse 3D approximations. It is useful for researchers, 3D artists, game asset workflows, and generative AI experiments focused on image-conditioned reconstruction. Its main value is combining generative flexibility with reconstruction-like visual fidelity. -
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PyAV
Pythonic bindings for FFmpeg's libraries
PyAV is a Python library that provides direct bindings to FFmpeg, offering fine-grained control over multimedia processing through a Pythonic interface. It enables developers to work with media containers, streams, packets, codecs, and frames, exposing low-level operations while simplifying some of the complexity of FFmpeg. The library is designed for precise media manipulation, making it suitable for applications that require frame-level access or custom processing pipelines. PyAV integrates well with other Python libraries such as NumPy and Pillow, allowing seamless data transformation and analysis. It supports decoding, encoding, filtering, and resampling operations, covering a wide range of multimedia tasks. While powerful, it requires a solid understanding of FFmpeg concepts, as it prioritizes flexibility and control over abstraction. Overall, PyAV is a robust tool for developers building advanced video and audio processing systems in Python. -
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PyVista
3D plotting and mesh analysis through a streamlined interface
3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK). PyVista is a helper module for the Visualization Toolkit (VTK) that takes a different approach on interfacing with VTK through NumPy and direct array access. This package provides a Pythonic, well-documented interface exposing VTK’s powerful visualization backend to facilitate rapid prototyping, analysis, and visual integration of spatially referenced datasets. This module can be used for scientific plotting for presentations and research papers as well as a supporting module for other mesh-dependent Python modules. Easily integrate with NumPy and create a variety of geometries and plot them. You could use any geometry to create your glyphs, or even plot the points directly. Direct access to mesh analysis and transformation routines. Intuitive plotting routines with matplotlib similar syntax. -
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Pydub
Manipulate audio with a simple and easy high level interface
Manipulate audio with a simple and easy high level interface. You can pass an optional bitrate argument to export using any syntax ffmpeg supports. Any further arguments supported by ffmpeg can be passed as a list in a 'parameters' argument, with switch first, argument second. Note that no validation takes place on these parameters, and you may be limited by what your particular build of ffmpeg/avlib supports. You can open and save WAV files with pure python. For opening and saving non-wav files, like mp3, you'll need ffmpeg or libav. Any operations that combine multiple AudioSegment objects in any way will first ensure that they have the same number of channels, frame rate, sample rate, bit depth, etc. -
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Segmentation Models
Segmentation models with pretrained backbones. PyTorch
Segmentation models with pre trained backbones. High-level API (just two lines to create a neural network) 9 models architectures for binary and multi class segmentation (including legendary Unet) 124 available encoders (and 500+ encoders from timm) All encoders have pre-trained weights for faster and better convergence. Popular metrics and losses for training routines. All encoders have pretrained weights. Preparing your data the same way as during weights pre-training may give you better results (higher metric score and faster convergence). It is not necessary in case you train the whole model, not only the decoder. Pytorch Image Models (a.k.a. timm) has a lot of pretrained models and interface which allows using these models as encoders in smp, however, not all models are supported. Input channels parameter allows you to create models, which process tensors with an arbitrary number of channels. -
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Spaceship Generator
A Blender script to procedurally generate 3D spaceships
A Blender script to procedurally generate 3D spaceships from a random seed. Install Blender 2.80 or greater. Download newest add_mesh_SpaceshipGenerator.zip from the Releases section. Under Edit, Preferences, Add-ons, Install, open the downloaded ZIP file. Under Edit, Preferences, Add-ons enable the "Add Mesh Spaceship Generator" script (search for "spaceship"). Add a spaceship in the 3D View under Add, Mesh, Spaceship. Expand the Spaceship tab that appears in the bottom left of the viewport to adjust procedural generation settings. Build the hull. Extrude the front/rear faces several times, adding random translation/scaling/rotation along the way. Add asymmetry to the hull: Pick random faces and extrude them out in a similar manner, reducing in scale each time. Categorize each face by its orientation and generate details on it such as engines, antenna, weapon turrets, lights etc. -
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Vedo
A python module for scientific analysis of 3D data
A lightweight and powerful python module for scientific analysis and visualization of 3d objects. Inspired by the vpython manifesto "3D programming for ordinary mortals", vedo makes it easy to work with 3D pointclouds, meshes and volumes, in just a few lines of code, even for less experienced programmers. vedo is based on VTK and numpy, with no other dependencies. Import meshes from VTK format, STL, Wavefront OBJ, 3DS, Dolfin-XML, Neutral, GMSH, OFF, PCD (PointCloud). Export meshes as ASCII or binary to VTK, STL, OBJ, PLY formats. Analysis tools like Moving Least Squares, mesh morphing and more. Tools to visualize and edit meshes (cutting a mesh with another mesh, slicing, normalizing, moving vertex positions, etc..). Split mesh based on surface connectivity. Extract the largest connected area. Calculate areas, volumes, center of mass, average sizes etc. Calculate vertex and face normals, curvatures, feature edges. Fill mesh holes. -
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You-Get
Dumb downloader that scrapes the web
You-Get is a small command-line utility for downloading media (video, audio and images) from the Web when there are no other means to do so. It can download video and audio files from such popular web sites as YouTube, Twitter, Niconico, Vimeo, Flickr, Instagram and a whole lot more. You-Get is a great option for when you want to enjoy your favorite videos, audio or images from the internet without having to open any web browsers or get interrupted by ads. It’s also a good choice for when you want to get rid of any closed-source technology or proprietary JavaScript code from running on your computer. -
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asciinema
Open source terminal session recorder
asciinema is a free and open source terminal session recorder. It lets you easily record and play back terminal sessions in the terminal or in a web browser. Forget old screen recording methods and resulting blurry videos. asciinema lets you record your terminal sessions the right way, which is right where you work, in the terminal. Recording is as easy as running one command, and since it’s purely text-based you can copy and paste any content you want, simply pause the recording! You can also easily share your recordings on the web, embed an asciicast player in your blog post, project documentation page or in your conference talk slides. See plenty of example sessions recorded with asciinema here: https://asciinema.org/ -
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scikit-image
Image processing in Python
scikit-image is a collection of algorithms for image processing. It is available free of charge and free of restriction. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers. scikit-image builds on scipy.ndimage to provide a versatile set of image processing routines in Python. This library is developed by its community, and contributions are most welcome! Read about our mission, vision, and values and how we govern the project. Major proposals to the project are documented in SKIPs. The scikit-image community consists of anyone using or working with the project in any way. A community member can become a contributor by interacting directly with the project in concrete ways. -
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word_cloud
A little word cloud generator in Python
A little word cloud generator in Python. The code is tested against Python 2.7, 3.4, 3.5, 3.6 and 3.7. If you are using conda, you can install from the conda-forge channel. wordcloud depends on numpy and pillow. To save the wordcloud into a file, matplotlib can also be installed. If there are no wheels available for your version of python, installing the package requires having a C compiler set up. Before installing a compiler, report an issue describing the version of python and operating system being used. The wordcloud_cli tool can be used to generate word clouds directly from the command-line. If you're dealing with PDF files, then pdftotext, included by default with many Linux distribution, comes in handy. Use wordcloud_cli --help so see all available options. The wordcloud library is MIT licenced, but contains DroidSansMono.ttf, a true type font by Google, that is apache licensed. -
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youtube-dlG
A cross platform front-end GUI of the popular youtube-dl
A cross platform front-end GUI of the popular youtube-dl media downloader written in wxPython. Youtube-dl-gui uses youtube-dl in the backend to download files. Youtube-dl provides a list of extractors to work with each particular site. You can change the naming pattern by picking a different filename format under the Options>General tab. You can also use a custom pattern by setting the option to "Custom" and editing the output template field. You can change the number of max parallel downloads by editing the "workers_number" option in your settings.json file. You can add extra youtube-dl command line options in the commands box under the Options>Extra tab. You can use your own version of youtube-dl by editing the "youtubedl_path" option in your settings.json file and make it point to your own binary (e.g. /usr/local/bin).
