Aspect Ratio Image Sampler

I’ve written previously about my basic image sampler setup, and about how to make sure that your image sampler produces information with the proper aspect ratio. Both definitions were based on an initial grid of points, generated by ranges and domains. Today, I had a bit of trouble getting the definition to work for an image that was taller than it was wide. I’m still not sure what the problem was, exactly, but I found a work-around that I’m quite happy with. This image sampler sequence uses a referenced surface, with the same aspect ratio as the initial image, to generate a grid of points to be sampled. It’s a much cleaner definition, you can see:

Above is the old definition. Below is the new one.

Definition

UPDATE: Dividing the surface with Us and Vs simplifies quite a bit in the early stages of things, but as I move forward, I’m finding that it restructures all of the point data in quite a big way. I’ve got to wrap my brain around it first, but I’ll try to come back with some diagrams discussing the differences between the two approaches.

Expanding Panels

Remember these? I’m happy to report that I have the Grasshopper file under control, and in a friendly, won’t-crash-computers format.

Here’s my initial image sampler setup:

A series of image samplers filters for different color, saturation, and brightness information:

You can see that the values at each point vary by filter:

Use this information to alter the grid structure and generate voronoi cells:

Print and draw. (You saw this last week.)

Set up a rotation sequence to manipulate surfaces formed by the voronoi cells:

Pull three layers of the rotated cells apart:

UPDATE: So What?

  • I’m interested in the idea that one process (in this case, an image sampler) repeated, but adjusted for new priorities (color, saturation, brightness), can yield a variety of results (overlapping voronoi cells). The ability to arrive at a theme and variation through a testing of variables has some scientific undertones, but what I like about these images is that the different layers of results are all made apparent. Changes in color and rotation can emphasize certain image sampler filters over others, but the structure of each remains.
  • Making the move from 2-dimensional drawings to 3-dimensional (rhino) models has been quite helpful in understanding the potential of grasshopper as a way of looking. All of a sudden, the grasshopper manipulations feel less diagrammatic, and more like spatial fields with possible social/environmental potential.