Fundamentals of Computer Science I: Media Computing (CS151.01 2008S)
As you may have noted from our initial discussions of drawing, there are a number of ways to think about how one creates and how one represents an image. And, as we noted in our discussion of computer science, there is a strong relationship between the way we organize or represent information and the algorithms we write to manipulate that information. The representations we have explored so far are rather coarse-grained; that is, they refer to relatively large drawing objects or techniques. Let us now turn our attention to a somewhat finer-grained approach. In particular, we will explore raster graphics, one of the simplest ways to represent and think about images on the computer.
In the raster graphics format, an image is represented as a grid of colors. That is, we segment the image into a large number of uniformly sized squares, which we arrange side-by-side and top-to-bottom, and we assign a color to each square. We call this grouping a “grid” because, well, it looks like the grid on graph paper.
Variations on raster graphics are used in a wide variety of image file formats, including JPG, Bitmap, GIF, and PNG.
When we describe a raster graphics image, we need to indicate the width and height of the image. Because raster images are a grid, we typically indicate the width and height in terms of the number of columns and rows, not in terms of inches. We also need to describe the color at each point in the grid. While there are a number of ways to indicate colors of different grid points, it is easiest if we specify the color of each one separately, particularly if we are going to write programs that process raster graphics images. We call one grid point in an image a pixel.
In essence, we need to assign an index to each point in the grid. As you've already discovered in your exploration of GIMP, in assigning these indices, we typically number the rows of the grid from top to bottom, and number the columns of the grid from left to right. We also start both column numbers and row numbers with 0. When we refer to one pixel on the grid, we do so in terms of its column number and its row number.
Of course, in order to write programs that manipulate raster images, we
need to know more than how images and colors are represented - we also
need to know what operations are available.
DrFu provides a few core operations to build and analyze images.
You've already encountered a few, such as
Now, let us turn to the procedures that work with individual pixels.
extracts the color of a particular pixel in the image. For example,
we might get the initial color of the top-left pixel of an image with
(define sample-image (load "..."))
(define top-left-color (image-get-pixel sample-image 0 0))
the color of a particular pixel in the image. We might change the
top-right pixel of the sample image to the same color as the center with
(image-set-pixel! sample-image (- (image-width sample-image) 1) 0 top-left-color)
As you should have figured out by now, the exclamation point at
the end of a procedure indicates that the procedure is intended to
change something, instead of just computing a result. In this case,
image-set-pixel! changes the image. Because changing
things can be dangerous, Scheme programmers remind themselves that a
procedure changes things by suffixing them with that exclamation point.
Of course, we also want to set pixels using existing
( procedure will find the
internal representation of a color for the given name. (As you'll see
in the next reading, those representations are typically RGB colors.)
(define a-color-i-like (cname->rgb "deep purple"))
We can also use this technique to set colors.
(image-set-pixel! sample-image 2 3 (cname->rgb "blood orange"))
What if we want to find out what color names are available to us? As
you may recall,
will give a list of every available color,
( will give a list of the colors
name. For example, the following
will list a number of colors whose name includes “red”.
"dark cherry red"
Finally, when we've obtained a color from an image, we can find the
name of a similar color using
(. Why is it a similar color,
rather than exactly the same color? Because there are sixteen million,
seven hundred seventy seven thousand, two hundred and sixteen different
colors possible in the standard simple color scheme, and no one is
mentally ill enough to try to name them all (at least so far).
Copyright (c) 2007-8 Janet Davis, Matthew Kluber, and Samuel A. Rebelsky. (Selected materials copyright by John David Stone and Henry Walker and used by permission.)
This material is based upon work partially supported by the National Science Foundation under Grant No. CCLI-0633090. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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