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COMP27112 Introduction to Visual Computing

创建日期：2024-05-21 17:29:08

所属分类：计算机computer science

标签： COMP27112

Introduction to Visual Computing

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COMP27112

Introduction to Visual Computing
Coursework Assignment 3
Image Processing Exercise 1
Introduction
The aim of this exercise is to get you started with OpenCV and to do some very simple
image processing.
OpenCV (opencv.org) is an open source computer vision and machine learning library that’s
released under the BSD licence. It can be used freely for academic and commercial
purposes. The first version was released in 2001, it now has a user community of over
47,000 users and the libraries have been downloaded over 18 million times.
Since it’s a well-established and widely used library, there are a lot of resources on the
internet. If you run into problems with the coursework, this should be one of the first places
to seek help. Other useful websites are listed in Blackboard.
This piece of coursework is worth 7.5% of the unit’s assessment, so you should spend no
more than about 7.5 hours on it.
Getting Started
Follow the instructions on the Coursework area of Blackboard to download and install the
version of OpenCV for your operating system. In this lab, you’ll write two simple
programmes, one to prove to yourself that you’ve installed OpenCV correctly, the second to
perform thresholding.
Hello OpenCV
To check that everything is working correctly, print out the installed OpenCV major and
minor versions in the console. To do that, OpenCV provides two macros representing the
two integers: CV_MAJOR_VERSION and CV_MINOR_VERSION.

Use your favourite text editor to create a new C++ file. (Although this is technically a C++
file, you’re using the C subset only.) Type the following code:
#include
#include
int main(int argc, char ∗argv[])
{
//Print the OpenCV version
printf ("OpenCV version: %d.%d\n", CV_MAJOR_VERSION,
CV_MINOR_VERSION);
return 0;
}
Now compile it by running:
g++ HelloCV.cpp -o HelloCV ’pkg-config --cflags --libs
opencv4’

(If you copy and paste, then be aware of the problem with quotation marks.) Run the output
programme, you should get:

OpenCV version 4.5

You can, of course, make editing and compiling much easier by using an IDE, instructions for
doing that can be found on the web.
Using OpenCV

The first part of this exercise is to load an image file into memory and display it in a window
using OpenCV. Along with the standard input/output library, and core.hpp that was included
in the example you will also need to include the highgui.hpp headers. core.hpp provides
basic OpenCV structures and operations, while highgui.hpp gives you the ability to create
and work with windows to display information to the user.

First, you will need to provide the code with an image path, either by using command-line
arguments, user input in the terminal or any other way that you can think of. Make sure to
check that the path has been provided, if it hasn’t you should exit the program. Now that
you have a path to the image, you need to load the image. For this, OpenCV has a function
named imread() in the cv namespace which takes the path as the first argument and some
flags that determine how the data is interpreted as the second (it’s safest to use
IMREAD_UNCHANGED). You can find all the details in the OpenCV documentation, links are
given below. This function loads the image into a cv::Mat object. Now that you have the
image (you should check that the image is loaded, if imread fails, it will return NULL), you
need a window to display it in. OpenCV has a function called cv::namedWindow() which
does this for you. This takes the window name as the first argument and the window flags as
the second argument. You want the window size to match the image size, so use
CV_WINDOW_AUTOSIZE as the flag. All that’s left is to add the image to the window.
cv::imshow() does just that. The first argument is the name of the window you want to add
the image to and the second argument is a pointer to the image. At this point, if you
compile and run the program you will notice that nothing shows (try it). That is because
your program terminates and closes the window before you can see anything. A useful
command in this case is cv::waitKey(0). The function waits for the user to press a key in
order to move on with the code. The numerical argument defines how long the function will
wait, look up the documentation to see how it’s interpreted. Compile and run the code. A
window should pop up with the title that you gave and the image.
Note that you must also include the imgcodecs.hpp header to use imread.
To summarise, the steps involved are:
1. Load an image using imread()
2. If imread has returned NULL, the reading has failed, so exit the programme.
3. Create a display window using namedWindow
4. Load the image into that window using imshow
5. Use waitKey to pause the execution until a key is pressed
The final step in this lab is to threshold the image and display the result. If you loaded a
colour image, you must first convert it to an 8-bit grey scale image using cv::cvtColor which
takes four arguments: the input and output images, a flag that indicates the conversion
being used (use COLOR_BGR2GRAY) and the last one indicates the number of channels in the
output (use 0). Thresholding is achieved using the function cv::threshold. You will need to
include imgproc.hpp to use threshold.
Threshold takes five arguments: the input and output images, a value for the threshold, a
maxval and a flag to indicate the type of thresholding being applied. You will probably use
THRESH_OTSU as the flag – in this case the function returns the value of the threshold that’s
computed and maxval is ignored. If you set the flag to THRESH_BINARY, then
dst(x, y) = *maxval if src(x, y) > thresh0 otherwise
Finally, save the threshold image using cv::imwrite()
You might want to try to add a trackbar to control the thresholding to see the effect of
different thresholds.
Finally, apply the thresholding programme to the sample images that are provided.
Queries
Experiment with your code to find the answers to the following questions. Write your
answers in a separate document.
1. Is Otsu’s method successful in thresholding all the images?
2. How would you modify the thresholding algorithm to address any problems?
3. What metrics are there for assessing the success of thresholding?
Submission
Once you have a working solution, simply ZIP the C++ file and your answer document and
submit it to the Lab 3 area in Blackboard.

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