First out is the original Afternow.
Followed by my own version.
And lastly the one I found tonight.

I also made a polar area diagram raster. As the sectors will grow, or shrink, depending on the amount of color there will be more background, thus yielding a slightly darker image compared to the pie chart raster.

When dividing the stroke width with a smaller number, a “thicker” pattern emerged.

Going below 1, another surprising effect happened…

Now, when I had an image to work with I quickly wrote a function that divide the image into a number of rectangles that then takes on the average color of its underlying area – I’ll be refering to this rectangle as the color sampling area. This function then calls other small functions that applies all the different rasters I could think of. Here is a run-down of the effects.

STANDARD MOSAIC RASTER
This is a pretty straight forward mosaic raster effect but I managed to optimize the process as it’s usually made by drawing a bunch of rectangles that acts like a pixel. This will put considerable strain to Illustrator because, as we all know, a rectangle has four corners that it has to keep track of. Therefore the amount of anchor points will be four times that of the number of objects.

The image above contains 16,384 rectangle objects which would, under normal circumstances, equal to 65,536 anchor points. But, instead of making rectangles I’m drawing the objects as vertical lines with strokes applied to the lines the size of the sample area. This way I get by with only half of the anchor points; 32,768. The two images below show the difference.

351 rectangles = 1,404 anchor points

351 paths = 702 anchor points

Another advantage, I think, is that I can choose to modify the width of the strokes. Below is an image that has the exact same properties as the previous only the stroke width is half of the color sampled area. This results in a kind of white, vertical interference pattern.

RGB PIXEL RASTER
This is supposed to imitate the pixels of old cathode ray TVs. Nothing fancy but just something that I had to try out.

It made for a somewhat nice effect but not very special. Not until I started to play around and rotating each pixel by 90 degrees increments yielding a very prominent pattern. While I was kinda surprised at the outcome it seems it’s too limited to be of any use.

In an effort to reduce the patterning a bit I let the “rgb-pixels” rotate randomly, again in 90 degrees increments.

This could have been it had I not kept on manipulating the outcome. I isolated the different color channels in Photoshop but neither the red nor the blue channel where very interesting. The green one, though, was way cooler.

Clearly this cross-hatch pattern has a lot more to offer than the previous two. So much so, in fact, that I imported the result to Photoshop where I ramped up the contrast and had the green color desaturated.

I feel the end-result is interesting enough to warrant its own script. Got to get to it.

DASHED RGB PIXEL RASTER
When thinking of CRT pixels I came up with yet another way. What if the red, green and blue intensity of each pixel was always at its maximum value and instead just activate parts of it. I don’t really know how to explain it, but I’ll give it a try. Below, anyway, is the final outcome and at first, it didn’t come out quite as I expected. It looks kinda inverted, but…

…the colors are still preserved and when adding a black background it turns the image into this.

Each of the sampled areas are divided into 3 x 8 “cells”. The higher value of each color channel the more of the “cells” are going to be filled in.

For instance, above is the average color of the complete Lenna image. The color components are, [177, 99, 106], so dividing by 32 and rounding to its nearest integer on each channel, gives [6, 3, 3]. So, for the red channel, 6 out of 8 “cells” are filled. Green channel, 3 out of 8 are filled and for the blue – yep you guessed it – 3 out of 8. These numbers are then randomly distributed which means that two colors with exactly the same channel values might not be diplayed in the same way.
Unfortunately, Illustrator can only make dashed strokes with a maximum of three dashes and three gaps. Therefore, a line in the form of;

dashArray = [dash, gap, dash, gap, dash, gap, dash]

will not display correctly as Illustrator simply ignores the input from the last dash and onwards. Instead – as a workaround – the last dash will have to be added to the first. So, let’s say a color channel array looks like this;

dashArray = [1, 0, 1, 0, 1, 0, 2]

In order to be a valid dash array it would have to be re-arranged like this;

dashArray = [3, 0, 1, 0, 1, 0]

I don’t know if I’m making any sense trying to explain how it works but the end-result is quite nice, I think.

RGB + CMYK PIXEL RASTER
Now this is starting to get a little too far fetched. I split each sample area into an 8 “cell” pixel, converted all color channels into binary and performed almost like a bitwise OR operation.

The same average image color as before. The color channels – in binary – reads like:

red   = 10110100
green = 01100011
blue  = 01101001

Then, taking one number from each channel in sequential order yields RGB values as such:

Red   = rgb(1, 0, 0)
Cyan  = rgb(0, 1, 1)
White = rgb(1, 1, 1)
Red   = rgb(1, 0, 0)
Blue  = rgb(0, 0, 1)
Red   = rgb(1, 0, 0)
Green = rgb(0, 1, 0)

and finally

Cyan  = rgb(0, 1, 1)

I know, I’m not making any sense here and I think the outcome is not very comprehensive either, or nice for that matter. Maybe it could used for glitch art or something. Anyway, one can BARELY work out some details in the image so I guess this one is the least useful of the bunch.

Lighting
Maybe the biggest addition to the script are the three sliders (one for each side of the blocks; top, left & right) to adjust the amount of light falling on each side. This can be used both as a way to increase the contrast – like in the image below – as well as change the entire lighting direction.

Elevate
This function was actually included in the previous version but I’ll consider it new anyway. What this list does is to simply tell the script that either ‘Light’ or ‘Dark’ colors should be elevated. The script checks the brightness level of the sampled area and uses that value as a multiplier with the Max Height value. Therefore, as the ‘Light’ value is default, lighter blocks are rendered higher, of course the same happens with dark blocks if Elevate is set to ‘Dark’.

Draw Non-Elevated & Threshold
Having this option turned OFF when running the script with images with an overall uniform background can be useful to not draw blocks of limited height that would otherwise be drawn as a thin flat object. The accompanying Threshold value – enabled ONLY if Draw Non-Elevated is turned OFF – is the amount by how high a block needs to be in order to be drawn by the script. Note that the threshold is limited to values between zero and the Max Height value, the maximum threshold value will vary accordingly.

Outlines
I’ve also added an option to turn the outlines ON and OFF. Not very big enhancment as it is very easy to just turn of the stroke of all selected objects directly inside Illustrator but I thought this way was more convenient.

My way about this problem is that I divided an image into smaller squares (in this case a roughly 800 x 750 image would be divided into 32 x 32 squares).
From each square I would sample its color. Based on that colors light intensity (a value somewhere between 0-1), a predefined maximum number of dots – I used 36 – was to be multiplied and randomly placed inside that square as such;

dot distribution = {more < intensity > less}
The result as you can see is an almost uniform distribution of some 40,000+ dots. There is virtually no contrast. If you squint your eyes you just might see some density variation, if you’re lucky! Next step was to try out a variable dot size version;

dot distribution = {more & larger < intensity > less & smaller}
This showed much improvement in terms of contrast but the details are absent because of the size of the sample area.
Reducing the sample area would greatly increase the number of dots. Lets say I subdivide the image into 64 x 64 squares instead of 32 x 32, and the maximum number of dots per square remains the same – 36 dots – the total dot count would go from 36,864 to 147,456.
In order to get some discernible details at all on a medium sized image of say 800 x 600, I wouldn’t go higher than 12 x 12 pixels per subdivision (~ 66 x 50 sample areas).An image subdivided into 66 x 50 areas with a maximum of 36 dots per area could yield a maximum of 118,800 dots. Each dot consists of 2-4 anchors (depending on how you choose to draw them little bastards). This brings the total number of anchorpoints to somewhere between 237,600-475,200. A staggering amount and not very pleasant to work with.
Finally I tried the different versions but keeping the color values.
Still no improvement on fidelity and what’s more is that the square mosaics (kinda like JPEG-artifacting) are even more evident when using colors. Not to mention when dot size is uniform and larger.
I’d say that unless some sort of voronoi black magic is used, hell even some image quantizing using halftone dithering, it’s going to be no easy task making a memory efficient stippler in Scriptographer.So, until someone comes up with a working script I would suggest using Vectoraster 4 instead – it’s only $25 and it comes with a ton of cool features, although it’s Mac only.

Now, this script is probably my least useable one as the outcome wasn’t very nice or functional. So what does it do then?

Well, the idea was to make a script that would produce a raster image with only one tone. Aptly named Monotone Raster. Since a raster will create dots of varying sizes corresponding to the level of gray of the target image pixels. This is not very hard in Scriptographer. But what if I wanted to make a raster image with something other than a circle, or, a raster with multiple objects?

Well, that wasn’t that hard either so I took it a bit further. What if the objects looked different, but in reality they all had the same amount of black?

These objects seem to differ but they all represent 50% black so a rasterized picture would, at a distance, seem to be just a gray square but at closer range some details would appear and reveal a picture. This turned out to be wrong. Close up it didn’t make any sense at all, just a bunch of squares cluttered together.

However, I encountered alot of problems.

First off, with every object being quite alot bigger than a pixel the resulting raster image would become huge!

Secondly, every object contains many anchors so there would be millions of them making the file size very big.

Thirdly, getting Java to handle this many objects, one per pixel, without running out of memory is virtually impossible. One solution to this problem would be to reduce the target image resolution but that would also make it harder to discern any of the finer details, thus making the raster quite useless.

Although it didn’t turn out as I had planned I still think it is a novel idea that was definately worth exploring.

Vectorraster 4 by Lost Minds is a great tool for anyone who wants to create beautiful vector-based rasters in all kinds of ways. It is user-friendly and allow for alot of experimentations by using text or custom shapes as raster dots. Well worth its $25 (Mac only) if you like to fiddle around with halftones.