Introduction

Thus far we have been drawing things (lines, colored zones, circles, graphs) to model the world. In this module, we turn the situation upside down and start with a photograph of a real-world situation and use computation to make sense of what is in the image. Our case study has to do with forest management: which trees are healthy and which are infested with beetles. We will develop competencies in image processing by adjusting the image resolution (size in pixels), cropping (cutting out regions that are not of interest), color enhancement, and filters.

Learning outcomes

This module will help you do the following:

• Load an image as input into a program
• Adjust the saturation, brightness, and contrast of an image with code
• Manipulate individual pixels of an image
• Combine two or more images by blending them into one
• Apply filters with various mask sizes (median, mode, minimum, maximum, blur)
• Alter the color scheme from RGB to black-and-white by thresholding
• Use one image ask a mask for another image to create a composite overlay

Warm-up

Warm-up assessment

When looking at the images of the forest fires for high above, you must have noticed that the structure of the forest looks like a close-up image of a single leaf. COnnecting the topic of this module to the fractal structures of the first module, write down some original (crazy, unfiltered — please do not look it up online, just let your creativity run wild) ideas on how would one be able to distinguish whether a photo is of macroscopic object (like a forest or a mountain range) or of a microscopic object like the surface of a leaf or a rock?

Concepts

After this module, you should be familiar with the following concepts:

• Resolution
• Pixel
• Color
• Saturation
• Brightness
• Contrast
• Kernel
• Filter
• Mask
• Opacity

Remember that you can always look concepts up in the glossary. Should anything be missing or insufficient, please report it.