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Pictolab (online HDR image editor)

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Pictolab (online HDR image editor)

Source code: http://github.com/anchpop/pictolab I love HDR, but until recently dynamic HDR content was hard to render in the browser. But now, with browsers shipping support for WebGPU and canvases with extended color spaces, it's actually feasible. So what I wanted to was make an image editor capable of doing the simple edits I often need to do, but with full HDR support, which is rare in browser editors. Turning up the brightness on an HDR-capable monitor is a great way to see what your monitor can do :P I tried to go overboard in every way I could. So brightness/saturation/hue adjustments use the okLCH color space, and there's an option to do a "content-aware resize" with seam carving, which is fast because it's parallelized using Sam Westrick's triangle-blocking algorithm [0]. Also, the site supports importing HEIC photos as taken with an iPhone, which is surprisingly hard to do correctly. Libraries like libheic don't fully support the format used by iPhones, so if used naively the HDR colors come out wrong. I'm working on a blog post about this, but the short version is that you have to use ISO 21496-1 gain map composition (which isn't even what apple documents). I'm pretty sure that pictolab is the *only* website that allows you to convert HEIC to JPEG or AVIF without messing up the HDR. There is also a local background-removal model. Enjoy! [0]: https://shwestrick.github.io/2020/07/29/seam-carve.html

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, para · Missing: reddit linkedin, podcasting, created
86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, apple, using · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% 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.

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