|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Blurhash||13,295||29||61||11 days ago||9||September 12, 2022||46||mit||C|
|A very compact representation of a placeholder for an image.|
|Codable Diy Kit||197||3 years ago||2||mit||Swift|
|A template for creating your own Swift Codable encoders and decoders|
|Coteafs Datasource||10||a year ago||1||apache-2.0||Java|
|:ledger: Simple Data file to object parser supports JSON, YML, XML and properties file format.|
|As3 Blurhash||3||3 years ago||mit||ActionScript|
|En2fr_translate||2||6 years ago||HTML|
|Translating English to French with Sequence-to-sequence|
|a compact binary format for SVG based image placeholders|
|Language Translation||1||6 years ago||Jupyter Notebook|
|Train a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French|
|Dlnd Language Translation||1||6 years ago||HTML|
|Project 4 from the course https://br.udacity.com/course/deep-learning-nanodegree-foundation--nd101|
BlurHash is a compact representation of a placeholder for an image.
Does your designer cry every time you load their beautifully designed screen, and it is full of empty boxes because all the images have not loaded yet? Does your database engineer cry when you want to solve this by trying to cram little thumbnail images into your data to show as placeholders?
BlurHash will solve your problems! How? Like this:
You can also see nice examples and try it out yourself at blurha.sh!
In short, BlurHash takes an image, and gives you a short string (only 20-30 characters!) that represents the placeholder for this image. You do this on the backend of your service, and store the string along with the image. When you send data to your client, you send both the URL to the image, and the BlurHash string. Your client then takes the string, and decodes it into an image that it shows while the real image is loading over the network. The string is short enough that it comfortably fits into whatever data format you use. For instance, it can easily be added as a field in a JSON object.
Want to know all the gory technical details? Read the algorithm description.
Implementing the algorithm is actually quite easy! Implementations are short and easily ported to your favourite language or platform.
So far, we have created these implementations:
These cover our use cases, but could probably use polishing, extending and improving. There are also these third party implementations that we know of:
Can't find the language you're looking for? Try your luck with the GitHub search. For example, here are the search results for repos which have "blurhash" in their name.
Perhaps you'd like to help extend this list? Which brings us to...
We'd love contributions! The algorithm is very simple - less than two hundred lines of code - and can easily be ported to your platform of choice. And having support for more platforms would be wonderful! So, Java decoder? Golang encoder? Haskell? Rust? We want them all!
We will also try to tag any issues on our issue tracker that we'd love help with, so if you just want to dip in, go have a look.
You can file a pull request with us, or you can start your own repo and project if you want to run everything yourself, we don't mind.
If you do want to contribute to this project, we have a code of conduct.
Who uses BlurHash? Here are some projects we know about:
These implementations are not very optimised. Running them on very large images can be a bit slow. The performance of the encoder and decoder are about the same for the same input or output size, so decoding very large placeholders, especially on your UI thread, can also be a bit slow.
However! The trick to using the algorithm efficiently is to not run it on full-sized data. The fine detail of an image is all thrown away, so you should scale your images down before running BlurHash on them. If you are creating thumbnails, run BlurHash on those instead of the full images.
Similarly, when displaying the placeholders, very small images work very well when scaled up. We usually decode placeholders that are 32 or even 20 pixels wide, and then let the UI layer scale them up, which is indistinguishable from decoding them at full size.
It depends a bit on taste. The more components you pick, the more information is retained in the placeholder, but the longer the BlurHash string will be. Also, it doesn't always look good with too many components. We usually go with 4 by 3, which seems to strike a nice balance.
However, you should adjust the number of components depending on the aspect ratio of your images. For instance, very wide images should have more X components and fewer Y components.
The Swift example project contains a test app where you can play around with the parameters and see the results.
punchparameter in some of these implementations?
It is a parameter that adjusts the contrast on the decoded image. 1 means normal, smaller values will make the effect more subtle, and larger values will make it stronger. This is basically a design parameter, which lets you adjust the look.
Technically, what it does is scale the AC components up or down.
Well, that is what it was designed for originally, but it turns out to be useful for a few other things:
First, 83 seems to be about how many low-ASCII characters you can find that are safe for use in all of JSON, HTML and shells.
Secondly, 83 * 83 is very close to, and a little more than, 19 * 19 * 19, making it ideal for encoding three AC components in two characters.
We haven't looked into how much overhead UTF-8 encoding would introduce versus base 83 in single-byte characters, but the encoding and decoding would probably be a lot more complicated, so in the spirit of minimalism BlurHash uses the simpler option. It might also be awkward to copy-paste, depending on OS capabilities.
If you think it can be done and is worth it, though, do make your own version and show us! We'd love to see it in action.
This is something we'd love to try. The DCT looks quite ugly when you increase the number of components, probably because the shape of the basis functions becomes too visible. Using a different basis with more aesthetically pleasing shape might be a big win.
However, we have not managed come up with one. Some experimenting with a Fourier-Bessel base, targeted at images that are going to be cropped into circles has been done, but without much success. Here again we'd love to see what you can come up with!
Dag gren - Original algorithm design, Swift and C implementations
Mykhailo Shchurov - Kotlin decoder implementation
Hang Duy Khiem - Android demo app
Olli Mahlamki - TypeScript decoder and encoder implementations
Atte Lautanala - Python integration
Lorenz Diener - Pure Python implementation
Boris Momilovi - Pure PHP implementation
Hendrik Schnepel - Java encoder implementation
Tuomo Virolainen - Clojure implementation
Fotis Papadogeorgopoulos - Rust and WebAssembly implementation
Sam Protas - Pure Haskell implementation
Markus Sammallahti - Scala implementation
Kacper Perzankowski - Elixir encoder implementation
Belvi Nosakhare - Kotlin extensions of Blurhash for ImageView, Glide, and Piccasso optimized for Android.
Armedi - ReScript (BuckleScript) implementation.
Ricardo Cruz - Xojo implementation.
Marc Rousavy - React Native UI Component
Matt Howard - Zig implementation
Wajahat Iqbal - Jetpack compose-based implementation optimized for using any component as a painter.
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