Download :https://github.com/Tw1ddle/geometrize/releases
Features
Recreate images as geometric primitives.
Start with hundreds of images with preset settings.
Export geometrized images to SVG, PNG, JPG, GIF and more.
Export geometrized images as HTML5 canvas or WebGL webpages.
Export shape data as JSON for use in custom projects and creations.
Control the algorithm at the core of Geometrize with ChaiScript scripts.
Shrub is a tool for painting-and-traveling, and even for painting while moving your own body (for example to use the color of your own pants).
If you touch with two fingers, you can immediately send your drawing as an SMS message. Shrub is designed as a mobile communication tool as much as a mobile drawing tool.
More pro tips: For the best drawings, pinch with your fingers to change the brush size. Twist with your fingers to change the brush softness. And of course, tap with one finger to show and hide the viewfinder.
A light Rust API for Multiresolution Stochastic Texture Synthesis [1], a non-parametric example-based algorithm for image generation.
It's all a game of construction — some with a brush, some with a shovel, some choose a pen.
Jackson Pollock
…and some, including myself, choose neural networks. I’m an artist, and I've also been building commercial software for a long while. But art and software used to be two parallel tracks in my life; save for the occasional foray into generative art with Processing and computational photography, all my art was analog… until I discovered GANs (Generative Adversarial Networks).
Since the invention of GANs in 2014, the machine learning community has produced a number of deep, technical pieces about the technique (such as this one). This is not one of those pieces. Instead, I want to share in broad strokes some reasons why GANs are excellent artistic tools and the methods I have developed for creating my GAN-augmented art.
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/
https://github.com/eriklindernoren/PyTorch-GAN
https://heartbeat.fritz.ai/introduction-to-generative-adversarial-networks-gans-35ef44f21193
https://github.com/nightrome/really-awesome-gan
https://github.com/zhangqianhui/AdversarialNetsPapers
https://github.com/io99/Resources
https://github.com/yunjey/pytorch-tutorial
https://github.com/bharathgs/Awesome-pytorch-list
https://old.reddit.com/r/MachineLearning
http://www.codingwoman.com/generative-adversarial-networks-entertaining-intro/
https://medium.com/@jonathan_hui/gan-gan-series-2d279f906e7b
https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ/videos
https://www.youtube.com/watch?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi&v=aircAruvnKk
https://www.youtube.com/watch?list=PLxt59R_fWVzT9bDxA76AHm3ig0Gg9S3So&v=ZzWaow1Rvho
Repaint your picture in the style of your favorite artist.
About
Our mission is to provide a novel artistic painting tool that allows everyone to create and share artistic pictures with just a few clicks. All you need to do is upload a photo and choose your favorite style. Our servers will then render your artwork for you. We apply an algorithm developed by Leon Gatys, Alexander Ecker and Matthias Bethge. The website was originally created by Łukasz Kidziński and Michał Warchoł. We have now joined forces to provide you with the latest technology in even more accessible way.
Our Team
Five researchers from the Bethge lab at University of Tübingen (Germany), CHILI Lab at École polytechnique fédérale de Lausanne (Switzerland) and Université catholique de Louvain (Belgium).
Copying an element from a photo and pasting it into a painting is a challenging task. Applying photo compositing techniques in this context yields subpar results that look like a collage --- and existing painterly stylization algorithms, which are global, perform poorly when applied locally. We address these issues with a dedicated algorithm that carefully determines the local statistics to be transferred. We ensure both spatial and inter-scale statistical consistency and demonstrate that both aspects are key to generating quality results. To cope with the diversity of abstraction levels and types of paintings, we introduce a technique to adjust the parameters of the transfer depending on the painting. We show that our algorithm produces significantly better results than photo compositing or global stylization techniques and that it enables creative painterly edits that would be otherwise difficult to achieve.
Echo Nest Remix is the Internet Synthesizer. Make amazing things from music, automatically.
Turn any music or video into Python or JavaScript code.
Echo Nest Remix lets you remix, re-edit, and reimagine any piece of music and video, automatically and algorithmically.
Remix has done the following: played a song forever, walkenized and cowbellized hundreds of thousands of songs in a week, reversed basically everything, beat matched two songs, split apart DJ mixes by their individual tracks, made new kinds of video mashups, corrected sloppy drumming, synced video to a song, transitioned between multiple covers of the same song, made a cat play piano, and taught dogs to play dubstep. Check out all the examples here.
Remix is available as an open source SDK for you to use, for Mac, Linux, and Windows:
Install for Python: sudo pip install remix
. Full installation details, packages for Mac and Windows, and complete Python documentation are here.
Try JavaScript: Test out remix.js here.
Download JavaScript: remix.js. Full JavaScript install details and documentation are here.
Videogrep is a python script that searches through dialog in videos and then cuts together a new video based on what it finds. Basically, it’s a command-line “supercut” generator. The code is here on github.
The script searches through a video’s associated subtitle file (which needs to be in the same folder as the video, in standard .srt format), identifies timestamps for the dialog, and then uses the wonderful moviepy library to generate the new final cut.