Please consider using http://lygia.xyz instead of copy/pasting this functions. It expand suport for voronoi, voronoise, fbm, noise, worley, noise, derivatives and much more, through simple file dependencies. Take a look to https://github.com/patriciogonzalezvivo/lygia/tree/main/generative
float rand(float n){return fract(sin(n) * 43758.5453123);}
float noise(float p){
float fl = floor(p);
float fc = fract(p);
I've taken a bunch of RAMDisk solutions from this 3year old article, and tried them on my own machine (Ivy Bridge i7/16GB RAM).
Using Crystal Disk Mark with 50MiB files on 1GB RAMDisk images, formatted with FAT32. Here are my results (yours may vary):
Note: 14 day trial
This guide/log is based off my experience attempting to build and install LibreOffice Online and it's dependencies on my system.
The end goal is to get LibreOffice Online integrated with Karoshi Server.
LibreOffice Online is still in development (17/06/16).
| import json | |
| import requests | |
| import csv | |
| # Authentication for user filing issue (must have read/write access to | |
| # repository to add issue to) | |
| USERNAME = 'username' | |
| PASSWORD = 'password' | |
| # The repository to add this issue to |
| /** | |
| * @author knee-cola / https://github.com/knee-cola | |
| * Original file URL: https://gist.github.com/knee-cola/37875bc4359609b96c9f329cd2a68fa1 | |
| */ | |
| // This is a simple progressive image loader for Three.js | |
| // It enables the smaller image files to be loaded first, | |
| // before the big texture image is fully loaded. | |
| // | |
| // The images are loaded in the order they are passed |
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FFmpeg is one of the most powerful tools for video transcoding and manipulation, but it's fairly complex and confusing to use. That's why I decided to create this cheat sheet which shows some of the most often used commands.
Let's start with some basics:
ffmpeg calls the FFmpeg application in the command line window, could also be the full path to the FFmpeg binary or .exe file
| Service | SSL | status | Response Type | Allowed methods | Allowed headers |
|---|
Here's a simple implementation of bilinear interpolation on tensors using PyTorch.
I wrote this up since I ended up learning a lot about options for interpolation in both the numpy and PyTorch ecosystems. More generally than just interpolation, too, it's also a nice case study in how PyTorch magically can put very numpy-like code on the GPU (and by the way, do autodiff for you too).
For interpolation in PyTorch, this open issue calls for more interpolation features. There is now a nn.functional.grid_sample() feature but at least at first this didn't look like what I needed (but we'll come back to this later).
In particular I wanted to take an image, W x H x C, and sample it many times at different random locations. Note also that this is different than upsampling which exhaustively samples and also doesn't give us fle
| """ | |
| Copyright 2020 Justin Braaten | |
| Licensed under the Apache License, Version 2.0 (the "License"); | |
| you may not use this file except in compliance with the License. | |
| You may obtain a copy of the License at | |
| https://www.apache.org/licenses/LICENSE-2.0 | |
| Unless required by applicable law or agreed to in writing, software |
Notes about gsplat / 3DGS: 3D Gaussian Splatting (~WIP)
I'm planning to turn this into an "awesome-gsplat" repository (https://github.com/sindresorhus/awesome) or a wiki on some repository.
Feel free to add comments to this gist for now.
I'm contiuining to update this, and will hopefully get to reformatting it with thumbnails and further information (especially on performance and capabilities of implementations).
