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.
We also assume that you have cloned this repository using the
--recursive flag (if not then issue
git submodule update --init --recursive).
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/ ... ...
README.md file will describe the background, contents and tasks of the assignment.
CMakeLists.txt file setups up the cmake build routine for this assignment.
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.
include/ directory contains one file for each function that you will implement as part of the assignment. Do not change these files.
src/ directory contains empty implementations of the functions specified in the
include/ directory. This is where you will implement the parts of the assignment.
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.
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:
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?
Once built, you can execute the assignment from inside the
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.
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
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
would you access the green value at the pixel on the 36th row and 89th
data[1 + 3*(88+767*35)](Remember C++ starts counting with
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 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.
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
To simplify the implementation and to help with debugging, we will use the text-based .ppm formats for this assignment.
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?
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?
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.
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.
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).
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
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.
Feel free and encouraged to use standard template library functions in
#include <algorithm> and
#include <cmath> such as
Extract the 3-channel rgb data from a 4-channel rgba image.
Write an rgb or grayscale image to a .ppm file.
At this point, you should start seeing output files:
Horizontally reflect an image (like a mirror)
Rotate an image 90^\circ counter-clockwise
Convert a 3-channel RGB image to a 1-channel grayscale image.
Simulate an image acquired from the Bayer mosaic by taking a 3-channel rgb image and creating a single channel grayscale image composed of interleaved red/green/blue channels. The output image should be the same size as the input but only one channel.
Given a mosaiced image (interleaved GBRG colors in a single channel), created a 3-channel rgb image.
Convert a color represented by red, green and blue intensities to its representation using hue, saturation and value.
Convert a color represented by hue, saturation and value to its representation using red, green and blue intensities.
Shift the hue of a color rgb image.
Hint: Use your
Desaturate a given rgb color image by a given factor.
Hint: Use your
Submit your completed homework on MarkUs. Open the MarkUs course page and submit all the
.cpp files in your
src/ directory under Assignment 1: Raster Images in the
Direct your questions to the Issues page of this repository.
Help your fellow students by answering questions or positions helpful tips on Issues page of this repository.
You will need to install Xcode if you haven’t already.
Many linux distributions do not include gcc and the basic development tools
in their default installation. On Ubuntu, you need to install the following
packages (more than needed for this assignment but should cover the whole
sudo apt-get install git sudo apt-get install build-essential sudo apt-get install cmake sudo apt-get install libx11-dev sudo apt-get install mesa-common-dev libgl1-mesa-dev libglu1-mesa-dev sudo apt-get install libxinerama1 libxinerama-dev sudo apt-get install libxcursor-dev sudo apt-get install libxrandr-dev sudo apt-get install libxi-dev sudo apt-get install libxmu-dev sudo apt-get install libblas-dev
Our assignments only support the Microsoft Visual Studio 2015 (and later) compiler in
64bit mode. It will not work with a 32bit build and it will not work with
older versions of visual studio.