I made a WebGL-based app that traces images using circles

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I was fascinated by this [0] and this video [1]. After many struggles, I finally built this app that traces images using circles similar to what these videos had shown. The most challenging part (to me) is to find a way to convert images to vector lines. I had tried Potrace, but its output is not suitable for my use case: too many small elements share the same border. Potrace's goal is to represent the original image faithfully using vector lines. But I want to trace the image edges. After searching and trying some Potrace alternatives in vain, I finally found my keyword. Surprisingly (to me), it lies at the end of the wiki page of the very topic [2]. Then I found a paper [3] that has nice pseudocode and a C implementation. I rewrote the pseudocode in Rust because I wanted to experiment with rustwasm. Honestly, I didn't care much about the math behind it. From then, I could continue to finish the app and show it to the world. This app is also my chance to learn about rustwasm and WebGL. FYI: this app is offline-only; your images never leave your browser [0] https://www.youtube.com/watch?v=r6sGWTCMz2k [1] https://www.youtube.com/watch?v=-qgreAUpPwM [2] https://en.wikipedia.org/wiki/Edge_detection#Subpixel [3] https://www.ipol.im/pub/art/2017/216/

Visit product View on Hacker NewsMar 2, 2022

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: using, code · Missing: mac, agents, macos

36%36% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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