Computer Graphics – Raster Images

To get started: Clone this repository:

git clone  https://github.com/karansher/computer-graphics-raster-images.git

Do not fork: Clicking "Fork" will create a public repository. If you'd like to use GitHub while you work on your assignment, then mirror this repo as a new private repository: https://stackoverflow.com/questions/10065526/github-how-to-make-a-fork-of-public-repository-private

Introduction

Welcome to Computer Graphics! The main purpose of this assignment will be to get you up and running with C++ and the cmake build setup used for our assignments.

For this assignment, and all future assignments, please read the following:

• this entire document (the README)
• the comments in the header files (the .h files located in the include folder)

Some articles linked here may also contain useful information that you should read, so use your judgement there.

Prerequisite installation

On all platforms, we will assume you have installed cmake and a modern c++ compiler on Mac OS X¹, Linux², or Windows³.

We also assume that you have cloned this repository using the --recursive flag (if not then issue git submodule update --init --recursive).

Layout

All assignments will have a similar directory and file layout:

README.md
CMakeLists.txt
main.cpp
include/
  function1.h
  function2.h
  ...
src/
  function1.cpp
  function2.cpp
  ...
data/
  ...
...

The README.md file will describe the background, contents and tasks of the assignment.

The CMakeLists.txt file setups up the cmake build routine for this assignment.

The main.cpp file will include the headers in the include/ directory and link to the functions compiled in the src/ directory. This file contains the main function that is executed when the program is run from the command line.

The include/ directory contains one file for each function that you will implement as part of the assignment. Do not change these files.

The src/ directory contains empty implementations of the functions specified in the include/ directory. This is where you will implement the parts of the assignment.

The data/ directory contains sample input data for your program. Keep in mind you should create your own test data to verify your program as you write it. It is not necessarily sufficient that your program only works on the given sample data.

Compilation

This and all following assignments will follow a typical cmake/make build routine. Starting in this directory, issue:

mkdir build
cd build
cmake ..

If you are using Mac or Linux, then issue:

make

If you are using Windows, then running cmake .. should have created a Visual Studio solution file called raster.sln that you can open and build from there. Building the raster project will generate an .exe file.

Why don't you try this right now?

Execution

Once built, you can execute the assignment from inside the build/ using

./raster

Background

Every assignment, including this one, will start with a Background section. This will cite a chapter of the book to read or review the math and algorithms behind the task in the assignment. Students following the lectures should already be familiar with this material.

Read Chapter 3 of Fundamentals of Computer Graphics (4th Edition).

The most common digital representation of a color image is a 2D array of red/green/blue intensities at pixels. Since each entry in the array is actually a 3-vector of color values, we can interpret an image as a 3-tensor or 3D array. Memory on the computer is addressed linear, so an RGB image with a certain width and height will be represented as width*height*3 numbers. How these numbers are ordered is a matter of convention. In our assignment we use the convention that the red value of pixel in the top-left corner comes first, then its green value, then its blue value, and then the rgb values of its neighbor to the right and so on across the row of pixels, and then moving to the next row down the columns of rows.

Q: Suppose you have a 767\times 772 rgb image stored in an array called data. How would you access the green value at the pixel on the 36th row and 89th column?

A: data[1 + 3*(88+767*35)] (Remember C++ starts counting with 0).

Alpha map

Natural images (e.g., photographs) only require color information, but to manipulate images it is often useful to also store a value representing how much of a pixel is "covered" by the given color. Intuitively this value (called alpha or represents how opaque (the opposite of transparent) each pixel is. When we store rgb + α image as a 4-channel rgba image. Just like rgb images, rgba images are 3D arrays unrolled into a linear array in memory.

.png files can store rgba images, whereas our simpler .ppm file format only stores grayscale or rgb images.

.ppm files

We'll use a very basic uncompressed image file format to write out the results of our tasks: the .ppm.

Like many image file formats, .ppm uses 8 bits per color value. Color intensities are represented as an integer between 0 (0% intensity) and 255 (100% intensity). In our programs we will use unsigned char to represent these values when reading, writing and doing simple operations. For numerically sensitive computations (e.g., conversion between rgb and hsv), it is convenient to convert values to decimal representations using double precision floating point numbers 0 is converted to 0.0 and 255 to 1.0.

To simplify the implementation and to help with debugging, we will use the text-based .ppm formats for this assignment.

Grayscale Images

Surprisingly there are many acceptable and reasonable ways to convert a color image into a grayscale ("black and white") image. The complexity of each method scales with the amount that method accommodates for human perception. For example, a very naive method is to average red, green and blue intensities. A slightly better (and very popular method) is to take a weighted average giving higher priority to green:

Q: Why are humans more sensitive to green?

Hint: 🐒

Mosaic images

The raw color measurements made by modern digital cameras are typically stored with a single color channel per pixel. This information is stored as a seemingly 1-channel image, but with an understood convention for interpreting each pixel as the red, green or blue intensity value given some pattern. The most common is the Bayer pattern. In this assignment, we'll assume the top left pixel is green, its right neighbor is blue and neighbor below is red, and its kitty-corner neighbor is also green.

Q: Why are more sensors devoted to green?

Hint: 🐒

To demosaic an image, we would like to create a full rgb image without downsampling the image resolution. So for each pixel, we'll use the exact color sample when it's available and average available neighbors (in all 8 directions) to fill in missing colors. This simple linear interpolation-based method has some blurring artifacts and can be improved with more complex methods.

Color representation

RGB is just one way to represent a color. Another useful representation is store the hue, saturation, and value of a color. This "hsv" representation also has 3-channels: typically, the hue or h channel is stored in degrees (i.e., on a periodic scale) in the range and the saturation s and value v are given as absolute values in .

Converting between rgb and hsv is straightforward and makes it easy to implement certain image changes such as shifting the hue of an image (e.g., Instagram's "warmth" filter) and the saturation of an image (e.g., Instagram's "saturation" filter).

Tasks

Every assignment, including this one, will contain a Tasks section. This will enumerate all of the tasks a student will need to complete for this assignment. These tasks will match the header/implementation pairs in the include//src/ directories.

Groundrules

Implementations of nearly any task you're asked to implemented in this course can be found online. Do not copy these and avoid googling for code; instead, search the internet for explanations. Many topics have relevant wikipedia articles. Use these as references. Always remember to cite any references in your comments.

White List

Feel free and encouraged to use standard template library functions in `#include