Assignment 5 Normalization


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// ~ Overview ~ //

In this assignment you will read and write image files, and
“normalize” them. Normalization is sometimes referred to as contrast
stretching, and is an image enhancement technique that (in general)
improves the viewing quality of washed out or dark images.

// ~ Learning Goals ~ //

(1) Structs
(2) Reading and writing binary files in a robust and safe manner
(3) Managing memory in functions with many if statements
(4) Introduction to hexidecimal numbers

// ~ Project Files ~ //

+ answer05.h: Header file contains the declarations that you must
+ diff-plus.c: A short computer program that you can compile and use
to test if two image files are “different”. (See the discussion on
linear normalization below.)
+ image-bmp.c: Code for reading and writing windows “bmp” files.
+ main.c: Some example code to get you started.
+ README: this file.
+ images: a directory of images, described below.
+ tester: for testing your assignment
+ tester-sample-output.text: the tester’s output for a working
+ output-tester: When you run the tester, it will save the output of
each testcase and the valgrind log for each testcase in separate files
inside of this directory. Please review these files if you
fail a testcase, so that you can recreate the problem yourself.

// ~ About the Image Files ~ //

The images directory contains four collections of images:
+ bmps: A set of five unnormalized (washed-out) and five normalized
images. Please look at these files before proceeding. Any standard
image viewer should do the trick.
+ originals: These are the original images that were used to generate
the bmps. You do not need to do anything with them.
+ corrupt-testcases: These files are all corrupted, but in different
ways. Your Image_load(…) function should return NULL when attempting
to read any of these files. Furthermore, you should have no memory
+ the ee264 images: There is an equivalent ee264 file for every bmp
image in the bmps directory. See below for a description of the ee264
file format. A major part of the assignment involves reading and
writing ee264 images.

// ~ The ee264 Format ~ //

The “ee264” image format supports 8-bit grayscale images. That
means that every pixel in the image has a single 8-bit value
[0..255], that denotes the intensity (amount of whiteness) at
that pixel.

The following diagram summarizes the byte layout in ee264 files:

| magic-number | width | height | comment-len | 16 bytes
| comment with null-byte | comment-len bytes
| pixel data | width*height bytes

The file has a 16 byte header whose binary layout is given by the
struct ImageHeader in the file “answer05.h”. The full explanation of
this header is:

+ 4 byte integer: “magic number” 0x21343632.
The first 4 bytes of the file always starts with
this number (in little-endian format). If this
number is absent, then there is something wrong
with the file.
+ 4 byte integer: width of the image
+ 4 byte integer: height of the image
+ 4 byte integer: length of an ASCII string file comment *including*
the NULL-byte

The next N bytes is a null-terminated ASCII string. The length of
the string is specified in the last field of the header. The length
*includes* the trailing NULL byte.

After the ASCII string, there are width*height bytes of pixel data.
Each byte is unsigned, and represents the intensity of a pixel in
the range [0..255]. The intensity of the pixel is its “whiteness”.
A value of 255 denotes a fully white pixel, and a value of 0
denotes a fully black pixel.

The pixels are stored in “row-major-order” from top-to-bottom.
That means that the first byte is the intensity of pixel [0, 0],
which is the top-left corner of the image. After reading “width”
number of pixels, you will arrive at the start of the second row
of pixels, which is the intensity of coordinate [0, 1]: the first
pixel of the second line of the image.

In general, pixel[x, y] == image-data[x + y * width] where (x, y)
is the x-y co-ordinate of the pixel, with x increasing left-to-
right, and y increasing top-to-bottom.

// ~ Part 1: Reading an Image File ~ //

This assignment is tricky because you cannot control data that is
coming from outside of your program. Because you cannot control this
data, you cannot trust it either, and so you must assume the worst,
and check *everything*.

Most dangerous real-world computer bugs are related to programmers not
sufficiently checking the inputs to their programs. So for this
assignment, you will need to think very carefully about what could go
wrong when reading a file. If anything goes wrong, you return NULL and
leak no resources. In this way your code will be bullet-proof; even a
malicious user cannot crash your program and thereby take control of
the computer.


You are supplied with a few test images, including corrupt test
images. These test-cases are provided in order to simplify and
standardize the assignment, and ease the learning process. However, in
real-world scenarios, you will need to generate your own testcases,
including corrupt test cases. By the end of this assignment, you
should understand how to go about doing precisely that.

A big, *big*, hint is to look at the code in image-bmp.c You don’t
have to do everything you see there, or even the same things; however,
it should give you a pretty good idea of where to start and how to go
about it.

The following is a list of things that you should check for. In
real-world scenarios, you’ll have to develop this list yourself. If
there are *any* errors, then return NULL, and leak no resources.

(1) Make sure you can open the file
(2) Read the header:
(2.a) Make sure that you can read all the bytes of the header
(2.b) Make sure that the magic_number in the header is correct
(2.c) Make sure that neither the width nor height is zero
(2.d) Make sure that the comment length is not zero. (Remember, it
includes the null-byte.)
(3) Allocate space for the image, comment, and pixels. Remember,
malloc will return NULL if it cannot do its job.
(4) Read the comment
(4.a) Make sure you read the entire comment
(4.b) Make sure the comment ends in a null-byte.
(5) Read the pixels
(5.a) Make sure you read *all* width*height pixels
(5.b) Make sure you’ve reached the end of the file. (i.e., there are
no trailing bytes of data past the end of the pixel data.)

A suite of corrupt image files is provided to help ensure that you get
all of this correct. Your code must successfully return NULL for
*every* corrupted image, and leak no resources.

// ~ Part 2: Linear Normalization ~ //

Imagine that the intensity values in your input image are in the
range [50..180]. The image looks gray and washed out. You can
“normalize” the image, which means applying a mathematical function
that “stretches” the intensity values to give a more satisfying
image. We are going to use “linear normalization” which is the
simplest normalization method. It works as follows:

(1) Find the min and max intensity values of the image pixels.
(2) Now scale each pixel so that its new intensity is:

pixel[i] = (pixel[i] – min) * 255.0 / (max – min)

That’s it! For the above example the equation is:

pixel[i] = (pixel[i] – 50) * 255.0 / (180 – 50)

To implement this, you need two for loops, one after the other.
The first for-loop completes step (1), and the second for-loop
to completes step (2).

// ~ Part 3: Saving an Image File ~ //

Saving an image is easier than reading an image, because you can
assume (as a precondition) that the Image struct is valid. Thus you do
not have to worry about the plethora of errors that may be encountered
when reading an input. (Remember, inputs come from outside of the
program, and hence you have no control over what is actually

In this part of the assignment, you merely have to take an Image
struct and write it out in the prescribed format.

// ~ Checking Your Results with diff-plus ~ //

Read the code for diff-plus. (It is short.) Use this program to check
if two files are the “same” as each other. Diff-plus is similar to the
unix “diff” command; however, it accepts a little wiggle room, which
may occur due to rounding errors in the linear normalization process.

You should use diff-plus to check at least the following:
(1) Read a bmp or ee264 file, write it as ee264 or bmp.
(2) Read a bmp file, apply normalization, and save it as bmp or ee264.
(3) Read an ee264 file, apply normalization, and save it as bmp.

Don’t forget that you must also test all of the corrupt testcases.

// ~ Determining Your Mark ~ //

This assignment is pass or fail. You pass if everything works, and
fail if there is one or more things wrong. You can use the tester to
find mistakes in answer05.c. No other files are marked.


Remember, the tester must be run on the ECE lab computers, and it
should not be run until after you are 100% confident that you already
have the correct answer.

// ~ Hints ~ //

(*) What are Headers?

Although a file is just a stream of bytes (1s and 0s), logically
speaking, they are generally divided into a “header” structure and a
“data” structure. The “header” explains the layout of the “data”.

(*) The learning goals mention hexidecimal??

When reading and writing binary files, it helps to look at the files
in a hex-editor. Hex editors show a file as a sequence
of hexidecimal (base-16) numbers. (If you don’t yet understand
base-16 notation, then now is a good time to learn this simple
and important concept.)

There are many hex-editors available. I prefer emacs, which is
installed on the lab computers. You can ask emacs to show you the raw
byte-structure of a file by using “hexl-mode”. (M-x hexl-mode) Please
ask the TAs for assistance if you are unsure how to do this.

(*) Help, I don’t know how to test my code!

Firstly, you have everything you need, including all the
testcases. You will need to compile the diff-plus.c program:

gcc -Wall -Wshadow -g diff-plus.c -o diff-plus

You use this program to test if two files are approximately the same:

./diff-plus –help

Now you need to write a short program that will read a file, apply
normalization to it, and write it out. The code in “main.c” should get
you started.

Once you have this program, you can apply normalization to various bmp
or ee264 images. Compare your results to the supplied file with

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