C++代写:CSC418 Raster Images


Image Processing


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.

Prerequisite installation

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:


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.


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 build/ using



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

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 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?

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?

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.

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
include//src/ directories.


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 <algorithm> and #include <cmath> such as std::fmod and std::fabs.


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:

  • bayer.ppm
  • composite.ppm
  • demosaicked.ppm
  • desaturated.ppm
  • gray.ppm
  • reflected.ppm
  • rgb.ppm
  • rotated.ppm
  • shifted.ppm


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 rgb_to_hsv and hsv_to_rgb functions.


Desaturate a given rgb color image by a given factor.

Hint: Use your rgb_to_hsv and hsv_to_rgb functions.



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 raster-images repository.


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.

Mac Users

You will need to install Xcode if you haven’t already.

Linux Users

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

Windows Users

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.