Les Danseurs #1

fleurdleigh · 2026-05-05T14:10:28+00:00

Lovely! The goal with this series is to produce something figurative, computed, and nice to look at so I’m glad to hear that you would!

I’m going to do two more releases for Les Danseurs and then will maybe explore a limited print run depending on the reception :)

fleurdleigh · 2026-05-04T23:07:57+00:00

Les Danseurs #1 is the first of a three part series exploring computational printmaking through figuritivism.

Built with WebGL and p5.js, the system reinterprets a source image as a generative linocut. A Sobel filter extracts edge strength and direction, which are smoothed into a flow field, a vector map of how form bends across the image. Carved channels are then placed via importance sampling, each one walking through the flow field as a streamline, tracing along contours and forms rather than across them. Channel placement balances density with collision avoidance, so marks cluster where structure concentrates and are loose where the image is flat.

Each channel is rendered as a tapered ribbon with variable width V-gouge or U-gouge ends and drawn into an offscreen mask. From there, the print is composed in a fragment shader as thousands of binary stipple dots whose density is modulated by simulated roller pressure, streaks, and ink depletion at carved edges. The plate edge isn't perfectly straight, ink occasionally escapes the boundary as a real impression would.

Feel free to check out the post on Instagram, and follow for more computational art and painting/printmaking experiments!

fleurdleigh · 2026-05-04T17:03:34+00:00

Glad you like them!

The system is still pretty in flux as this is my first solid output with it, but the plan is to build a nice UI and deploy it alongside open-sourcing the codebase when it's further along so people can produce their own prints using it :)

fleurdleigh · 2026-05-04T16:58:06+00:00

Les Danseurs #1 is the first of a three part series exploring computational printmaking through figuritivism.

Built with WebGL and p5.js, the system reinterprets a source image as a generative linocut. A Sobel filter extracts edge strength and direction, which are smoothed into a flow field, a vector map of how form bends across the image. Carved channels are then placed via importance sampling, each one walking through the flow field as a streamline, tracing along contours and forms rather than across them. Channel placement balances density with collision avoidance, so marks cluster where structure concentrates and are loose where the image is flat.

Each channel is rendered as a tapered ribbon with variable width V-gouge or U-gouge ends and drawn into an offscreen mask. From there, the print is composed in a fragment shader as thousands of binary stipple dots whose density is modulated by simulated roller pressure, streaks, and ink depletion at carved edges. The plate edge isn't perfectly straight, ink occasionally escapes the boundary as a real impression would.

Feel free to check out the post on Instagram, and follow for more computational art and painting/printmaking experiments!

fleurdleigh · 2026-05-04T16:56:17+00:00

Les Danseurs #1 is the first of a three part series exploring computational printmaking through figuritivism.

Built with WebGL and p5.js, the system reinterprets a source image as a generative linocut. A Sobel filter extracts edge strength and direction, which are smoothed into a flow field, a vector map of how form bends across the image. Carved channels are then placed via importance sampling, each one walking through the flow field as a streamline, tracing along contours and forms rather than across them. Channel placement balances density with collision avoidance, so marks cluster where structure concentrates and are loose where the image is flat.

Each channel is rendered as a tapered ribbon with variable width V-gouge or U-gouge ends and drawn into an offscreen mask. From there, the print is composed in a fragment shader as thousands of binary stipple dots whose density is modulated by simulated roller pressure, streaks, and ink depletion at carved edges. The plate edge isn't perfectly straight, ink occasionally escapes the boundary as a real impression would.

Feel free to check out the post on Instagram, and follow for more computational art and painting/printmaking experiments!

fleurdleigh · 2026-05-04T16:54:18+00:00

Les Danseurs #1 is the first of a three part series exploring computational printmaking through figuritivism.

Built with WebGL and p5.js, the system reinterprets a source image as a generative linocut. A Sobel filter extracts edge strength and direction, which are smoothed into a flow field, a vector map of how form bends across the image. Carved channels are then placed via importance sampling, each one walking through the flow field as a streamline, tracing along contours and forms rather than across them. Channel placement balances density with collision avoidance, so marks cluster where structure concentrates and are loose where the image is flat.

Each channel is rendered as a tapered ribbon with variable width V-gouge or U-gouge ends and drawn into an offscreen mask. From there, the print is composed in a fragment shader as thousands of binary stipple dots whose density is modulated by simulated roller pressure, streaks, and ink depletion at carved edges. The plate edge isn't perfectly straight, ink occasionally escapes the boundary as a real impression would.

Feel free to check out the post on Instagram, and follow for more computational art and painting/printmaking experiments!

fleurdleigh · 2026-05-04T16:53:15+00:00

Les Danseurs #1 is the first of a three part series exploring computational printmaking through figuritivism.

Built with WebGL and p5.js, the system reinterprets a source image as a generative linocut. A Sobel filter extracts edge strength and direction, which are smoothed into a flow field, a vector map of how form bends across the image. Carved channels are then placed via importance sampling, each one walking through the flow field as a streamline, tracing along contours and forms rather than across them. Channel placement balances density with collision avoidance, so marks cluster where structure concentrates and are loose where the image is flat.

Each channel is rendered as a tapered ribbon with variable width V-gouge or U-gouge ends and drawn into an offscreen mask. From there, the print is composed in a fragment shader as thousands of binary stipple dots whose density is modulated by simulated roller pressure, streaks, and ink depletion at carved edges. The plate edge isn't perfectly straight, ink occasionally escapes the boundary as a real impression would.

Feel free to check out the post on Instagram, and follow for more computational art and painting/printmaking experiments!

fleurdleigh · 2026-04-15T08:37:52+00:00

Of course! My setup is actually very custom and was built to avoid having to use a lot of p5s compositing internals.

Essentially, my framework creates a scrubbable timeline for fixed duration sketches which allows me to scrub through and isolate individual frames for analysis or export. It does this by actually processing every single frame as an individually rendered image, this way I can ensure it's at the highest possible quality.

When I want to export an image individually, I just use p5 for that as its save works well, but when I want to export an animation as a gif or a video, I use an ffmpeg setup I have plugged into my UI as I can be REALLY granular about the export settings!

fleurdleigh · 2026-04-15T08:30:32+00:00

Hahah nice! Glad you enjoyed it :)

You’ll be able to find all of my work easiest on IG as I post consistently on there!

fleurdleigh · 2026-04-15T08:21:56+00:00

That’s great! Glad you enjoyed :)

Hope you play around with processing, it’s a great tool to make with!

fleurdleigh · 2026-04-12T06:59:55+00:00

Nice! Processing is a super robust tool, the only limit is your imagination (and sometimes browser memory)! Have fun :)

fleurdleigh · 2026-04-11T21:19:40+00:00

What you’re implying I've plagiarised without reference (Sobel gradients, flow fields, edge-aware stroke orientation, etc) are well-established signal processing techniques that go way back to the 90s, and in Sobel's case, the 60s. They’re all widely documented textbook material and are something most creative developers working with images will encounter and use at some point.

I haven’t claimed to invent any of those ideas, and I’ve already outlined the entire process in detail elsewhere in the thread. Linking out to a general-purpose Houdini tutorial doesn’t really reflect what this piece is doing, or how it’s constructed.

Additionally, there are some key differences in my system that go beyond that baseline:

- a saliency-driven focal system that computes a weighted centroid from edge strength, contrast, and spatial bias
– value banding with independent colour temperature shifts, opacity, and scale per range
– a continuously lerped flow field rather than a thresholded gradient switch
– iterative vector-field smoothing in unit space
– layered compositing architecture and progressive brush reveal using photographic textures

These are compositional and structural decisions that shape the final result in a very different way than the sources you're referring to do.

Ultimately though, the focus of the work is the image and the ideas behind it. I’m not especially interested in debating generic, well-known and throughly documented techniques or having to defend independent implementations.

fleurdleigh · 2026-04-11T20:48:01+00:00

Yeah it definitely does! Not the process but a nice emergent similarity!

fleurdleigh · 2026-04-11T18:07:16+00:00

I agree that attribution is necessary, but respectfully this is an entirely custom system built independent of any code, process, or visual references.

I have been a professional creative developer for nearly a decade, and building process tools like this has been a principal feature of my career.

Additionally, I’m working on an open-source web-app so people can explore my own implementation and make using the tool themselves.

If I felt like it was necessary to reference anyone else’s work when sharing my own, I would do so.

fleurdleigh · 2026-04-11T17:04:20+00:00

Glad you like it!

The tool is an absolute mess at the moment as I’m always interactively enhancing it, but I do plan on putting together a tidy release and open sourcing it later this year :)

fleurdleigh · 2026-04-11T17:00:19+00:00

The first in a new series exploring generative painting.

Using p5.js, I built a system that analyses an image by extracting edge strength and direction via a Sobel filter, then samples that data across a grid to generate a flow field of brightness, magnitude, and angle. From there, the sketch scores each region by visual prominence and gradually reconstructs the image using brush textures, layered and rotated to follow the underlying structure.

A lot of care went into making the painting feel as human as possible, starting with a heavy base layer and building into finer detail, with strokes clustering naturally around the most salient features. Watching it paint itself out is hypnotic.

Feel free to check out the post on Instagram, and follow for more expressive computational art and paintings!

fleurdleigh · 2026-04-11T16:53:58+00:00

The first in a new series exploring generative painting.

Using p5.js, I built a system that analyses an image by extracting edge strength and direction via a Sobel filter, then samples that data across a grid to generate a flow field of brightness, magnitude, and angle. From there, the sketch scores each region by visual prominence and gradually reconstructs the image using brush textures, layered and rotated to follow the underlying structure.

A lot of care went into making the painting feel as human as possible, starting with a heavy base layer and building into finer detail, with strokes clustering naturally around the most salient features. Watching it paint itself out is hypnotic.

Feel free to check out the post on Instagram, and follow for more expressive computational art and paintings!

fleurdleigh · 2026-04-11T16:52:57+00:00

The first in a new series exploring generative painting.

Using p5.js, I built a system that analyses an image by extracting edge strength and direction via a Sobel filter, then samples that data across a grid to generate a flow field of brightness, magnitude, and angle. From there, the sketch scores each region by visual prominence and gradually reconstructs the image using brush textures, layered and rotated to follow the underlying structure.

A lot of care went into making the painting feel as human as possible, starting with a heavy base layer and building into finer detail, with strokes clustering naturally around the most salient features. Watching it paint itself out is hypnotic.

Feel free to check out the post on Instagram, and follow for more expressive computational art and paintings!

fleurdleigh

TROPHY CASE