canvas_ity - A tiny, single-header canvas-like 2D rasterizer for C++

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Hi HN! After a few years of off-and-on work on it, I'm pleased to release my canvas_ity library as open source. TLDR: This is a single-header C++ library with no dependencies beyond the standard C++ library. In about 2300 lines of 78-column code (not counting blanks or comments), or 1300 semicolons, it implements an API based on the basic W3C <canvas> specification to draw 2D vector graphics into an image buffer: - Strokes and fills (with antialiasing and gamma-correct blending) - Linear and radial gradients - Patterns (with repeat modes and bi-cubic resampling) - Line caps and line joins (handling high curvature) - Dash patterns and dash offsets - Transforms - Lines, quadratic and cubic Beziers, arcs, and rectangles - Text (very basic, but does its own TTF font file parsing) - Raster images (i.e., sprites) - Clipping (via masking) - Compositing modes (Porter-Duff) - Drop shadows with Gaussian blurs Why? Some years ago I needed to programmatically draw on an image file. I looked at Skia and was put off by its size and build complexity. Cairo was much better, but by then I was spoiled by the STB single-header libraries. I like tiny code and writing a single-header but powerful vector graphics library seemed like a fun challenge and a good way to pay things forward. I ended up with something that can compile down to less than 36 KiB of object code on x86-64 and uncovered a lot of interesting browser <canvas> quirks along the way with my testing suite. Enjoy!

Visit product View on Hacker NewsOct 10, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: single, tiny, code · Missing: mac, agents, macos

92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

68%68% 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

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% 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

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

14%14% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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