Lab 20

# Lab 20 Instructions #

## Reminders #

• While students are coming into class, feel free to go ahead and download the lab materials in preparation for the start of the lab proper. (But please wait until you’re instructed before beginning work on the lab.)

• The format for this lab is quite different. You’ll be working together in your lab groups to understand and rewrite a piece of software.

• As this is the last lab (!), we’ll be distributing the final post-lab survey shortly. Please fill it out promptly.

• Do you have hints about the course? Let me know: shieber@seas.harvard.edu.

## Grayscale images and digital halftoning #

Today’s lab has you working with a piece of software to manipulate grayscale images, including performing "digital halftoning", the conversion of grayscale to black-and-white. Digital halftoning is useful when presenting images on devices that only support a limited color palette, for instance, Kindle ebook readers or black-and-white printers.

By way of example, Figure 1(a) is a grayscale image of Da Vinci’s Mona Lisa, rendered using OCaml’s Graphics module, which allows for 256 gray levels. Figures (b), (c), and (d) show versions that have been digitally halftoned into binary (black-and-white) images using three different algorithms. In (b), each pixel has been thresholded; pixels darker than the threshold value – 75% gray, that is, values 0.75 and greater – become black, and those lighter than the threshold become white. Thresholding tends to eliminate detail, as seen in the example. In (c), a technique called dithering is used, which retains some of the detail at the cost of some washing out of the image. Each pixel’s gray level is interpreted as a probability, and the pixel becomes black with that probability. The darker the pixel, the higher the probability it ends up as a black pixel in the binary version. In (d), a method called "error diffusion" is employed (in particular, one-dimensional error diffusion), which operates by thresholding pixels, but propagating the "error" introduced by the change in pixel value to adjacent pixels. As you can see, error diffusion is a much higher quality method for digital halftoning.

In the file lab20.ml, you’ll find some code that implements various image functions including the first two halftoning algorithms. You can see them in action by compiling and running the file:

 % ocamlbuild -use-ocamlfind lab20.byte
% ./lab20.byte


If your OCaml is set up properly and the graphics module is working, you should see images like those in the figure pop up in an X11 window on your computer. Each one should appear for a couple of seconds before moving on to the next one. Try it now.

Now take a look at the code in the file lab20.ml. It works (almost), but it’s a mess. Your job is to work with your lab group to clean it up and improve it.

You should feel free to make any changes you want, including arbitrary changes to the types and implementations of functions, how they’re structured, how they’re distributed among files – anything at all. You’ll want to make changes so that the code is improved along any and all of the dimensions that we’ve talked about in the class. That shouldn’t be difficult, because the code is in such rough shape at the moment.

As with the previous lab, you’ll submit your lab to Gradescope as a single group submission. Designate one person at your table as the lab submitter, who should submit the lab on behalf of the table and add all the members of your group to the submission so all will get credit for the lab. (Because of Gradescope limitations, the members of the group will need to be re-added every time the designated submitter submits.)

Given the open-ended nature of this lab (just like actual coding assignments in the real world!), you may wonder how we will be evaluating the lab. You’ll still submit the lab to Gradescope, but because we’ll have no way of knowing the functions and signatures that you’ll use, we won’t run any unit tests. Instead, we’ll just verify that the code compiles cleanly.