node-canvas VS Konva

Assuming SWR (stale while revalidate) is working, we could definitely use the ability to edit images in workers. We would need support for node canvas or something similar.

So at this point, I have all the images and the data required to align the images. I used node-canvas to do that. So here's how I aligned the images:

ECharts was originally a Baidu project, released under https://github.com/ecomfe/echarts (ecomfe = "E-commerce frontend"?). They still maintain some of the auxiliary libraries.

I've been using it since around ... 2018-ish[0]? ... as a replacement for Google Charts. It was my first time using a big library from one of the Chinese tech giants -- basic docs and tutorials in English, then all the advanced stuff (and comments) written in Chinese. I was impressed by how comprehensive the charts library was, and how they'd obviously invested a lot of brainwork into the configuration system. IMO it's one of the highest-quality data visualization libraries in JavaScript unless you're willing to dive deep into something like d3.js.

The blog post's author describes running echarts in a headless Chrome, though, which seems insane to me. It's JavaScript rendering to a -- can't it run in Node with https://github.com/Automattic/node-canvas ?

[0] A small publicly-accessible example: https://john-millikin.com/reddit-front-page-2018#by-domain

node-pre-gyp ERR! install response status 404 Not Found on https://github.com/Automattic/node-canvas/releases/download/v2.11.0/canvas-v2.11.0-node-v93-darwin-unknown-arm64.tar.gz

Another approach is it to create a background image and insert the texts and the metadata with a canvas library like node-canvas. This approach is very well described in this Learn With Jason episode. But with a canvas library we have to specify on which coordinates we want to render our texts and it does not wrap automatically if it becomes to long.

Never used it but probably this: https://github.com/Automattic/node-canvas

I found this issue: https://github.com/Automattic/node-canvas/issues/1974 and somebody commented with a link about node buffers. So I decided to test these parts.

Some popular Node.js libraries for manipulating images are node-canvas and sharp.

node.js + imagemagick + node canvas + node canvas libraries (depends on your OS)

If you want to go the same route check out the node-canvas library from the Automattic folks. That essentially lets you use the Browser Canvas API for drawing (but server side instead of in the browser).

Based on this, I found that some of the libraries are dead and no longer have any support. Only two libraries are still alive and have significant amount of stars on GitHub and downloads on NPM. They are Fabric.js and Konva.js.

I have been assigned a task to create a sort of a canva clone which will have almost same features as canva with authentication, access control and rating system(not in this phase). I need help in finding libraries similar to https://konvajs.org/ which has updated docs and great support for Nextjs.

I'm currently using konva (& react-konva) package, to utilize it in Node.js enviroment I also need canvas package installed in. However, when running the code encountering this error:

used goJS in one project and konva in another

All the UI part would make sense to do in React. The actual drawing board you likely would need to implement in canvas or SVG. It still could be a React component, but for actual drawing, you'd probably use something like Konva (https://konvajs.org/).

Konvajs - is an HTML5 Canvas JavaScript framework that enables high performance animations, transitions, node nesting, layering, filtering, caching, event handling for desktop and mobile applications, and much more.

During the development of a complex interactive UI for the configuration of a digital laboratory ecosystem, we were utilizing the Konva.js library. Konva is a wrapper around the HTML canvas that simplifies working with shapes and interacting with the canvas a lot. Everybody dealing with the plain canvas API knows how much code certain tasks require, especially when user interaction with the drawn shapes is required. The most important features Konva offers to me are

Konva.js - example

Have a go at petswitch.com if you wish... I made the original Petswitch almost ten years ago, and it's had mild success since then, including CNET writing an article about it and it receiving the prestigious honour of 'most useless website' in week 41 of 2018, as determined by theuselesswebindex.com. Aside from the obvious question of why I even made this, it was getting pretty creaky – I originally built it with PHP and ImageMagick, with the facial features being manually selected via jQuery UI. So I decided to rebuild the whole thing with a full face-to-pet ML pipeline, on static hosting. To get the human face features, the app renders the upload to a temporary img element. This is a handy way to orient the image correctly via the browser, and saves having to deal with EXIF data. It's then resized, rendered to a canvas element, converted to a base64 string, then sent via fetch to Google's Cloud Vision API, which returns landmark coordinates of the face. I use these coordinates to correct any tilt on the face, mask the eyes and mouth via a mask image, then store each masked element as an additional canvas. Detecting pet faces was trickier. Google, Amazon and Microsoft all offer object detection APIs via transfer learning, and the approach is largely the same: you supply a series of images with bounding boxes around the objects you want to detect, either added via a web interface or uploaded via their API. You train a model online from these supplied images, then the service will return the estimated coordinates of any detected objects in an uploaded image. I found a dataset of both cats and dogs that had been labelled with landmarks on their faces, then wrote a script to convert the landmarks into bounding boxes around their eyes and nose, the dimensions based on a simple formula around the distance between the eyes in each image. All in all it's been trained on about 17,000 images of cats and dogs, and the accuracy seems to be pretty good. I was pleased to discover it actually works pretty well on other pets too. I've also added some friendly pets to the Petswitch family for those that don't have a pet on hand. I decided not to use a framework for this, it's written from scratch using a series of ES6 modules – although I did use Konva to handle the manual selection of facial features if the API can't detect a face. I used ParcelJS as my task runner, and my detection APIs are hosted on Firebase Cloud Functions. Let me know if you have any questions, although I can offer no good explanation for why I created this monstrosity...