Processing: Creative Coding and Computational Art

PROCESSING: CREATIVE CODING AND COMPUTATIONAL ART
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Figure 10-32. Contrast sketch (using bitwise operations)
This is a fairly simple contrast implementation that calculates the difference between the
color component value and some threshold—in this case, the midpoint of the 255-value
color range (127.5). This value is multiplied by a contrast value and then added to 1;
the color component is then multiplied by this number. So, for example, if the threshold/
contrast expression value evaluates to 1.2, and the color component value is 150, it will
become 180. The reason I subtract the threshold from the component is to generate a
nice gradient of values so that the contrast shift will be smooth. For example, a color in the
mid-range (e.g., 129) would be affected very little (multiplied by 1.00225). However, colors
approaching 0 or 255 would be more intensely affected (e.g., an initial component value
of 240, using .15 as the value of the contrast variable, would become 280.5). The difference
in values between the most minimal changes and the most severe would be across a
gradient proportional to the difference between the component’s original value and the
threshold.
In the example, I was also forced to convert the calculated contrast values from type float
to type int so that I could assign them back to the color component variables, declared as
type int. Finally, I used Processing’s constrain() function, which sets a minimum and
maximum for a value. Since my component values needed to stay within the range of 0 to
255, I clipped them if they exceeded these limits. With a starting value of 240 and a contrast
value of .15, the calculated value 280.5 would get constrained to 255. You can try
commenting out the constrain() function calls to see what happens when the values do
go out of range; it can be visually interesting. I’ll finish up this section by looking some
more at Processing’s built-in imaging and filtering capabilities.
Imaging filters
Processing’s PImage class has a method called mask() that allows you to use either an
image or a pixel array to mask out part of an image. The mask image should be grayscale.
(You can use an RGB image, but only the blue channel of the image will be used as the
mask.) My suggestion is to convert your mask image to grayscale before using it.