Open Source Mac Video Editing Software - Page 2
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Video Editing Software for Mac
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Virtualdub filter blenderM
Mixes current masked Virtualdub frame with a previous frame
This version of Filter Blender has the capability of mixing modifications with a previous video frame. The original version by Steven Don could only use one mask or one set of percentage mixes. This version supports multiple masks and mixes. You can apply other filters to one area using a mask, then apply other mods to a different area using another mask, and so on. The original had, in my opinion, confusing options which mixed the current frame with saved frame B and applied the mask to frame B. The current version mixes the masked current frame with saved frame A. To enable this filter: Move fblenderM.vdf to the plugin folder for virtualdub. To use it: 1. Add "filter blenderM" in your virtualdub filter chain. This first instance must be to Store the current frame to A 2. Then add the filters you wish to modify the video. 3. Put it in your filter chain and select "Blend" a. You can blend the current video frame with the one stored in A by selecting the percent -
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AI Models
A repository of trained models
All models (at least currently) are supported by chaiNNer, an upscaling GUI that allows for both very simple and very complex tasks to be completed in a nice manner where you "chain" nodes together. Highly recommended for images. If you're looking to upscale videos using the models then use enhancr simply due to the fact that it supports TensorRT, which will allow you to upscale videos at incredible speeds! The GUI is one of the best looking applications out there and is personally my go to option. While yes builds are paid, it is well worth your money and beats any other GUI out there currently. -
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Aeyae Remux
GOP-based video editor (remuxer)
Aeyae Remux can be used to extract portions of original video into a separate file. Aeyae Remux is a GOP-based editor (GOP = Group Of Pictures), which means it doesn't do any transcoding, only remuxing. However, it can truncate a GOP. Aeyae Remux can be used to stitch together files... within reason. Since it is only a remuxer -- stitched files must be of compatible format. This means stitched files must have the same audio/video codecs, and codec properties (width, height, sample rate, etc...). That said... Aeyae Remux currently doesn't check or enforce this restriction, so beware -- garbage input will likely produce garbage output. Linux binaries are provided as AppImage, built on Ubuntu 14.04. 8 GB of RAM (but more is better) is highly recommended if working with source files longer than 2 hours. -
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Anime4K-rs
An attempt to write Anime4K in Rust
An attempt to write Anime4K in Rust. Anime4K is a state-of-the-art*, open-source, high-quality real-time anime upscaling algorithm that can be implemented in any programming language. -
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Anime4kSharp
Anime4K implemented in C#
Anime4KSharp is a .Net Core library that implements bloc97's Anime4K Algorithm version 0.9 and 1.0 RC2. The Algorithm is executed on the CPU, but utilizing all available CPU Cores. This yields to a conversion time of "only" 4432 ms when upscaling from 1080p to 2160p. This time could possibly be reduced with further optimization. Images are processed in four phases that are executed on a pixel- per- pixel basis. Each phase takes a input image and renders it to a output image. This makes it easy to port the algorithm (back) to GLSL fragment shaders. As bloc97 described in his pseudo-preprint, the Anime4K algorithm is actually quite simple. -
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Border Control Mod Virtualdub
Create pleasing border to video using Virtualdub
Filter to modify the edges of a video frame. 1. Fill a fixed border with selected color 2. Smear a single line to the edge of the border, next line to smear region used 3. Stretch N lines to fill to the edge of border, N lines above smear region used 4. Fade video from selected line to edge of border fade can be to black or to border color 5. Cross smear progressively softens the smeared lines to make smear less obvious 6. Range of values is suitable for HD videos While this was intended for edge modification it can be used for special effects such as fading the edges of a scene to frame a center portion ... GNU General Public license Copyright (C) 2000-2002 Simon Walters Extensive Modifications by John M. Clement (C) 2014 -
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Easy Upscale
A simple image upscaler application using EDSR, ESPCN, FSRCNN, etc.
This application was made to fulfill the assignment for the Data Structures course. The concept of the application is an application to upgrade/enhance image quality. The main theme is queues, we implement circular queues for pooling/storing a list of images to be upscaled. Gui creation is made manually using the tkinter library. For the upscale process itself, it uses the OpenCV library with a model obtained from open source. Checked using vermin. Minimum required versions: 3.6 Incompatible versions: 2. -
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Editly
Slick, declarative command line video editing & API
Editly is a tool and framework for declarative NLE (non-linear video editing) using Node.js and FFmpeg. Editly allows you to easily and programmatically create a video from a set of clips, images, audio and titles, with smooth transitions and music overlaid. Editly has a simple CLI for quickly assembling a video from a set of clips or images, or you can use its more flexible JavaScript API. Inspired by ffmpeg-concat, Editly is much faster and doesn't require much storage because it uses streaming editing. Editly aims to be very extensible and feature-rich with a pluggable interface for adding new dynamic content. -
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FFCreator
A fast video processing library based on node.js
FFCreator is a lightweight and flexible short video processing library based on Node.js. You only need to add some pictures, music, or video clips, you can use it to quickly create a very exciting video album. Nowadays, short video is an increasingly popular form of media communication. Like Weishi and Tiktok is full of all kinds of wonderful short videos. So how can users visually create video clips on the web easily and quickly? Based on pictures and Text content, dynamic batch generation of short videos is a technical problem. FFCreator is a lightweight and flexible solution that requires few dependencies and low machine configuration to start working quickly. It simulates 90% animation effects of animate.css. You can easily convert the animation effects on the web page side into videos. -
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G2SConverter
Convert models from GoldSource engine to Source engine with AI
Convert models from GoldSource engine to the Source engine with AI. This utility converts GoldSource engine models to Source engine models. A feature of this utility is the ability to improve the quality of textures of models using Upscaling, deblurring, and normal map generating. All operations to improve the quality of textures are performed by neural networks. To improve the quality of the texture, it is first Upscaled using RealESRGAN. The user can select scaling factor: x2, x4 or x8. After the Upscaling procedure, the texture is deblured using the Scale-recurrent Network for Deep Image Deblurring. An example of a processed texture is shown in the following image (parameters used: scaling-factor = 4 and deblur iterations = 4) besides upscaling and debluring the utility also generates normal maps for each texture. This is implemented using the DeepBump by HugoTiny model. Examples of normal maps are shown in the following images. -
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MNIST-WGAN
A C# WGAN
This project generates hand-written digits from the MNIST dataset using WGAN architecture. While debugged, a few lingering issues may remain, if you encounter any please submit them and they will be resolved. The sources and features of the project can be found in the wiki. Before use, one should verify that the network architecture is as-desired. This may be done with the GUI to the right of the number display. The "Default" button resets the network to hard-coded values which I have verified function. The "Reset" button sets the ACTIVE network to whatever architecture is displayed. In order to change which network is displayed, use the "Critic [1] or Generator [0]" checkbox. To begin training the network, press the "Train" button, after which you may use the "Clear" button to reset the average error and average percent correct value textboxes. In order to use the project on an alternative dataset, one must replace the MNIST files, then rewrite IO.FindNextNumber. -
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RenderTune
Free open-source high quality video rendering app
RenderTune is a powerful and versatile video rendering tool that allows you to effortlessly create stunning videos from your audio and image files. With RenderTune, you can combine single or multiple audio files into one video and even choose the order in which they appear. Customize your output video location, resolution, and padding options to achieve the perfect look for your video. Our latest version (v1.1.3) includes important bug fixes and improvements, such as better support for mp4 video format on iOS mobile uploading, the option to choose your default video render output format, and enhanced ffmpeg support for older Mac versions (High Sierra 10.13+). RenderTune is free and open-source, making it accessible to everyone. Whether you're a content creator, musician, or just someone who loves to make videos, RenderTune is the ideal tool to bring your vision to life. Download RenderTune now and start creating stunning videos today! -
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Robust Video Matting (RVM)
Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX
We introduce a robust, real-time, high-resolution human video matting method that achieves new state-of-the-art performance. Our method is much lighter than previous approaches and can process 4K at 76 FPS and HD at 104 FPS on an Nvidia GTX 1080Ti GPU. Unlike most existing methods that perform video matting frame-by-frame as independent images, our method uses a recurrent architecture to exploit temporal information in videos and achieves significant improvements in temporal coherence and matting quality. Furthermore, we propose a novel training strategy that enforces our network on both matting and segmentation objectives. This significantly improves our model's robustness. Our method does not require any auxiliary inputs such as a trimap or a pre-captured background image, so it can be widely applied to existing human matting applications. RVM is specifically designed for robust human video matting. -
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Super-résolution via CNN
Super resolution using a CNN, based on the work of the DGtal team
Super-resolution using a CNN, based on the work of the DGtal team. First of all, an Nvidia graphics card (neither AMD nor Intel integrated) is highly recommended to parallelize the CNN. You will then need to install CUDA. No CUDA = dozens of times slower. This program will generate "model_epoch_ .pth" files corresponding to the model at epoch n, in a folder saved_model_u t_bs bs_tbs tbs_lr lr, where corresponds to the scale factor, bsthe size of the training batch, tbsthe size of the test batch and lrto the learning rate. Low res images should be located in a "dataset/input" folder, and high res targets in a "dataset/target" folder, where each different quality image has the same name in both folders. -
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TD-Anime4K
Implementation of Anime4K in TouchDesigner
Anime4K is an upscaler specifically for anime style. This repository is an implementation of Anime4K in TouchDesigner that can be used to upscale low-resolution textures to higher textures. Reduced texture loss, aliasing and banding. -
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Upscale
This program is upscaling any image by a factor 2 using an algorithm
This program is upscaling any image by a factor 2 using an algorithm of cubic interpolation. You may need to install the following libraries to run the program, tqdm, itertools, and OpenCV. -
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UpscalerJS
Image Upscaling in Javascript. Increase image resolution up to 4x
Image Upscaling in Javascript. Increase image resolution up to 4x using Tensorflow.js. Open source, browser/Node compatibility, and completely free to use under the MIT license. Scale images up to 4x their original size, all in Javascript. UpscalerJS ships with pre-trained models in the box covering a wide variety of use cases. Or bring your own! Browser, Node (CPU and GPU-accelerated), and Service Worker environments all supported. Supports inputs in a wide variety of formats - URL, HTMLImageElement, and more, and by default exports a base64 upscaled string. Pick your Javascript flavor! UpscalerJS ships with ESM and UMD (browser) and ESM and CJS (Node). Close to 100% test coverage, Typescript support, examples covering a wide variety of use cases, and thick documentation. -
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VSGAN
VapourSynth Single Image Super-Resolution Generative Adversarial
Single Image Super-Resolution Generative Adversarial Network (GAN) which uses the VapourSynth processing framework to handle input and output image data. Transform, Filter, or Enhance your input video, or the VSGAN result with VapourSynth, a Script-based NLE. You can chain models or re-run the model twice-over (or more). Have low VRAM? Don’t worry! The Network will be applied in quadrants of the image to reduce up-front VRAM usage. You can use any RGB video input, including float32 (e.g., RGBS) inputs. Using VapourSynth you can pass a Video directly to VSGAN, without any frame extraction needed. Any edit you make in the VapourSynth script with or without VSGAN can be re-used for any other video. VSGAN is released under the MIT License, ensuring it will stay free, with the ability to be used commercially. -
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textureAtlasTools
A set of tools for slicing a texture atlas to individual components
A set of tools for slicing a texture atlas to individual components and merging back. These tools simplify the approach for upscaling a texture atlas. Manually select some tiles in the texture atlas (that are placed in difficult positions) Add them to a json file following this pattern: json file; you can use any image editing tool such as GIMP to manually select and inspect the location, width and height of each selection. Run the tool with the split; this will automatically create a folder with separate files from the selections, as well a [basefilename]_sliced.png file containing the texture without the sliced files. Manually upscale all the resulting files with the correct transparency / seamless mode, including the [basefilename]_sliced.png from the previous step. it is important to use the same scale factor for all the files. you can leave some of the files unscaled (including the basefile).
