首页
网站开发
桌面应用
管理软件
微信开发
App开发
嵌入式软件
工具软件
数据采集与分析
其他
首页
>
> 详细
代做COMP27112、代写C/C++程序语言
项目预算:
开发周期:
发布时间:
要求地区:
COMP27112: Visual Computing Lab 4
COMP27112: Visual Computing
Lab 4
Lecturer: Terence Morley
1 Introduction
For this practical assignment you should use C/C++ and OpenCV to develop the code. Your code, results and
comments MUST be submitted in a single PDF file. Only submit the PDF file.
You should use the supplied images for your processing and include them in your report.
For ease of marking, please lay out your report in sections using the titles given in this document.
You will probably need to refer to the OpenCV documentation website: https://docs.opencv.org/4.8.0/.
2 Intended Learning Outcomes
By the end of this assignment, you should be able to:
• Implement image processing code using C/C++ and OpenCV
• Create an image processing function that you can add to your own library
• Choose combinations of techniques in order to solve an image processing problem (that is, a image processing pipeline, or workflow)
3 Image Histogram and Segmentation [10 Marks]
3.1 Histogram
Image segmentation is the process of partitioning an image into distinct parts (regions or objects). Thresholding
is a simple way to do this, and the result is a binary image (that is, one that consists of two levels: black and
white for example).
Imagine that you want to write software to help a drone navigate in a desert by following roads. You might
want to segment the drone camera image into sand and road. This is shown in the following image where sand
is labelled white and the road is labelled black.
The grey-level threshold that was used in the above thresholding problem was 110. But how do you decide
which threshold value to use? It is often done by examining a histogram of the image, as shown below.
Department of Computer Science, The University of Manchester (Jan 2024) Page 1 of 7
COMP27112: Visual Computing Lab 4
In this histogram image, a vertical bar is drawn in black to represent the number of pixels in the image with
a particular grey level (0–255). The grey vertical gridlines mark 0, 64, 128, 192, and 255. Two peaks can be
seen in this image: a large one at the lighter end (the sand), and a small one at the darker end (the road). A
suitable threshold value, between the two peaks, can be estimated at around 110.
⋆ TASK
Write a function that creates a histogram image like the one shown above. The image must be 400 pixels high
and 512 pixels wide. Because it is 512 pixels wide and the horizontal axis should represent 256 grey levels
(0–255), each bar should be two pixels wide. You can use the supplied histogram.cpp as a starting point.
The highest count in the histogram should be drawn to the full height of the image. If the count for a grey-level
is zero, no bar should be drawn.
You must write your own code to count the number of occurrences of each grey level, and to scale the counts to
fit the image. You must not use the OpenCV calcHist() function nor something similar from other libraries.
You can draw the bars by setting pixels in the image or by drawing lines or rectangles with OpenCV functions.
Drawing the gridlines is optional, but if you do add them, it will make your function more helpful. The gridlines
in the above image were drawn in light grey to avoid them being mistaken for bars, and the bars were drawn
over the top of the gridlines (that is, the gridlines were drawn first).
Your REPORT should contain the source code for your histogram function and the histogram image for these
two supplied images:
circuit board.jpg science person.jpg
NOTE If you use LATEX to write your report, you might include your code using the listings package:
\usepackage{listings}
...
\begin{lstlisting}[language=C++]
int x = 123;
\end{lstlisting}
3.2 Thresholding
For this part, you should use your histogram function to help you choose suitable threshold values. If you failed
to do that part of the assignment, you can use an image manipulation program such as gimp that provides a
Department of Computer Science, The University of Manchester (Jan 2024) Page 2 of 7
COMP27112: Visual Computing Lab 4
histogram display. The gimp program is available for Linux, Windows and macOS.
You may also use the programs that you created in the previous lab for doing OTSU thresholding and interactive
thresholding.
⋆ TASK
Choose an appropriate threshold value for each of the following problems, and give a brief description of any
problems encountered or any observations.
Filename Problem
fundus.tif This is taken from a set of images used to train and test algorithms
for recognising the effects of diabetes on the retina. The aim of the
processing is to identify the blood vessels
glaucoma.jpg This is an image taken from a set used to train ophthalmologists to
recognise glaucoma. The aim of processing is to find the diffuse bright
region towards the middle and the brighter area inside it.
optic nerve head.jpg This was an image captured by a bespoke device that gives a tightly
framed image of the optic nerve head. The aim of processing is to find
the outlines of the large, slightly bright area and the smaller brighter
area inside it.
motorway.png This is an image from the internet of a motorway destination sign. Car
manufacturers have deployed systems that read speed limit signs. A next
step would be to read these signs. So the aim would be to find the white
text.
In your REPORT, include the following for each problem:
• the original image
• the image histogram
• thresholded image
• threshold value that was chosen
• a brief description of any observations you made
4 Horizon Detection [10 Marks]
This section asks you to find, and plot, a polynomial that represents the horizon in the following images:
horizon1.jpg horizon2.png horizon3.jpg
4.1 Processing Pipeline
You will need to do some form of edge detection that finds the edge between the Earth and space in two of the
images, and sea and sky in the final one. You might think that you could do binary thresholding first and then
Department of Computer Science, The University of Manchester (Jan 2024) Page 3 of 7
COMP27112: Visual Computing Lab 4
do edge detection, but you will find that there are lots of areas of misclassification that you will need to deal
with.
This lab asks you to use the Canny edge detector and the Hough line transform in your processing pipeline.
You should attempt to process the images in the following way:
• Convert the image into greyscale (if necessary)
• Apply a Canny filter on the image, leaving us with an image of the edges
• Apply a probabilistic Hough transformation that will return a list of pairs of Points defining the start and
end coordinates for line segments.
• Filter out the short lines, use Pythagoras to compute the lines’ lengths.
• Filter out the vertical lines. You could do that by either calculating the inverse tangent of each line
(use atan2), finding its angle from the horizontal, or check whether the x co-ordinates of the segment’s
endpoints are similar.
• Now that you are left with all the (nearly) horizontal lines’ points, find a curve that best fits all those
points. This is called polynomial regression. It takes some points and calculates the best polynomial of
any order that you choose that fits all the points. Be careful not to overfit the points though; since the
horizon curve best matches a quadratic function choosing a higher order polynomial can give you unstable
results, i.e. a very wavy line.
Your program will need to use a number of parameters. It would be ideal if a single set of parameters
could be used to process all images of this type, but that often isn’t possible.
You should allow for your program to use different parameter values. This might be achieved by passing
them in as command-line parameters, using a switch-statement, or just by using three set of constants,
two of which being commented out for each image.
NOTE You are supplied with some code to help you with this lab, see horizon.cpp. The contents of this file
are shown below.
The fitPoly function, shown below, accepts a list of points. It calculates a line (curve) of best fit through
those points. You also specify an order for the poylonimial, n (if you are expecting a straight line, you would
set n to 1, for example). The function returns the polymomial as a vector of doubles, the order of which is the
order of coefficients: a + bx + cx2 + . . .. You might want to set up a vector with a few points and try out this
function so that you are clear on its operation.
1 //Polynomial regression function
2 std::vector
fitPoly(std::vector
points, int n)
3 {
4 //Number of points
5 int nPoints = points.size();
6
7 //Vectors for all the points’ xs and ys
8 std::vector
xValues = std::vector
();
9 std::vector
yValues = std::vector
();
10
11 //Split the points into two vectors for x and y values
12 for(int i = 0; i < nPoints; i++)
13 {
14 xValues.push_back(points[i].x);
15 yValues.push_back(points[i].y);
16 }
17
18 //Augmented matrix
19 double matrixSystem[n+1][n+2];
20 for(int row = 0; row < n+1; row++)
Department of Computer Science, The University of Manchester (Jan 2024) Page 4 of 7
COMP27112: Visual Computing Lab 4
21 {
22 for(int col = 0; col < n+1; col++)
23 {
24 matrixSystem[row][col] = 0;
25 for(int i = 0; i < nPoints; i++)
26 matrixSystem[row][col] += pow(xValues[i], row + col);
27 }
28
29 matrixSystem[row][n+1] = 0;
30 for(int i = 0; i < nPoints; i++)
31 matrixSystem[row][n+1] += pow(xValues[i], row) * yValues[i];
32
33 }
34
35 //Array that holds all the coefficients
36 double coeffVec[n+2] = {}; // the "= {}" is needed in visual studio, but not in Linux
37
38 //Gauss reduction
39 for(int i = 0; i <= n-1; i++)
40 for (int k=i+1; k <= n; k++)
41 {
42 double t=matrixSystem[k][i]/matrixSystem[i][i];
43
44 for (int j=0;j<=n+1;j++)
45 matrixSystem[k][j]=matrixSystem[k][j]-t*matrixSystem[i][j];
46
47 }
48
49 //Back-substitution
50 for (int i=n;i>=0;i--)
51 {
52 coeffVec[i]=matrixSystem[i][n+1];
53 for (int j=0;j<=n+1;j++)
54 if (j!=i)
55 coeffVec[i]=coeffVec[i]-matrixSystem[i][j]*coeffVec[j];
56
57 coeffVec[i]=coeffVec[i]/matrixSystem[i][i];
58 }
59
60 //Construct the vector and return it
61 std::vector
result = std::vector
();
62 for(int i = 0; i < n+1; i++)
63 result.push_back(coeffVec[i]);
64 return result;
65 }
As part of this lab, you will be asked to draw the detected horizon from the polynomial onto the image. The
function pointAtX(), shown below, will help you with that. If you provide it with an x-coordinate and the
polynomial coefficients, it will return a point (x, y) (that is, it will calculate the y-coordinate for you and return
it as a point that you might use in an OpenCV function).
1 //Returns the point for the equation determined
2 //by a vector of coefficents, at a certain x location
3 cv::Point pointAtX(std::vector
coeff, double x)
4 {
5 double y = 0;
6 for(int i = 0; i < coeff.size(); i++)
7 y += pow(x, i) * coeff[i];
8 return cv::Point(x, y);
9 }
⋆ TASK
Write a program to perform the processing pipeline described above. Use the supplied fitPoly() and pointAtX()
as well as the OpenCV functions Canny() and HoughLinesP().
Department of Computer Science, The University of Manchester (Jan 2024) Page 5 of 7
COMP27112: Visual Computing Lab 4
You will need to experiment with the parameters for the Canny and Hough functions and other processing that
you do. You will probably need different values for each image that you process, so make them easier to work
with in your program. That is, don’t just hard-code values into your function calls.
Make sure that you understand the purpose of the Canny parameters lowerThreshold and upperThreshold;
and the Hough parameters rho, theta, threshold, minLen, maxGap.
Once you have obtained a polynomial that follows the line of the horizon, draw the line/curve on the original
colour image. Make it stand out by drawing it in a bright colour and don’t draw the line too narrow (nor so
thick that it disguises an inaccurate detection). You might draw this line by setting pixels in the image, or by
using the OpenCV functions circle() or line().
Your REPORT should include your code.
4.2 Processing
Now that you have written your program to detect the horizon in an image, use it on the provided horizon
images.
⋆ TASK
Run your program on each of the three supplied horizon images. You may need to choose different parameter
values to get a good result in each image.
You might have trouble with noise in a couple of the images. If that is the case (and you can’t select parameter
values to achieve the aim), try adding some extra processing to your pipeline, but make sure that the horizon
is detected accurately.
In your REPORT, include the following for each horizon image:
• The original image
• The Canny edge image
• The image with all of the probabilistic Hough lies drawn
• The image with the short lines removed
• The image with only the (approximately) horizontal lines
• The image with the horizon drawn
• The set of parameter values with an obvious name for the parameter
• If you needed to add extra processing, give a brief description
NOTE Draw your Hough lines and the final horizon in colour onto the original colour image.
Optional: The horizon in the oil-rig image needs rotating clockwise to make it horizontal. Calculate the angle
that it needs to be rotated by (in degrees). Hint: use your calculated polynomial. Show your working and state
an OpenCV function that can rotate the image. Rotate the image to make the horizon horizontal. Do you have
any observations on your result?
Department of Computer Science, The University of Manchester (Jan 2024) Page 6 of 7
COMP27112: Visual Computing Lab 4
5 Marking Scheme
3.1 Write a function to create a histogram image. 5 marks
3.1 Show histogram for the images circuit board.jpg and science person.jpg. 1 marks
3.2 Threshold the images fundus.tif, glaucoma.jpg, optic nerve head.jpg,
and motorway.png. One mark for each image for supplying the output image,
threshold value, and observations.
4 marks
4.1 Write code that performs the stated horizon-detection processing pipeline. 4 marks
4.2 Process the three images (horizon1, horizon2, horizon3) and draw the detected
horizon from the polynomial on the original colour image. Include the requested
images at the intermediate stages as well as the parameter values used. Two
marks for each horizon image.
6 marks
Total 20 marks
Check-list
Have you:
• Created a well-formatted report?
• Included input, intermediate, and output images with useful labels?
• Chosen a size for your images so that the details can be seen, but not so big that the document covers
too many pages? (See the images in this document.)
• Included the code?
Submit your report as a single pdf document on Blackboard.
Do not include any other files or zip it – just submit a pdf.
Department of Computer Science, The University of Manchester (Jan 2024) Page 7 of 7
软件开发、广告设计客服
QQ:99515681
邮箱:99515681@qq.com
工作时间:8:00-23:00
微信:codinghelp
热点项目
更多
cis432代做、代写python/java程...
2024-05-04
eeen3007j代写、c++程序设计代...
2024-05-04
代写data程序、代做c/c++, jav...
2024-05-04
comp2006代做、代写c++程序语言
2024-05-04
comp26020代做、java/c++设计编...
2024-05-04
csci251 advanced programming...
2024-05-03
cs 6290: high-performance co...
2024-05-03
assignment 2: executing and ...
2024-05-03
ecse427/comp310 programmin...
2024-05-03
cs 452 (fall 22): operating...
2024-05-03
comp9414 23t2 assignment 2 ...
2024-05-03
dpst1091 23t1 assignment 2 ...
2024-05-03
program代做、代写python设计编...
2024-05-03
热点标签
finm8007
comp2006
comp26020
comp1721
eeen3007j
cis432
csci251
comp5125m
com398sust
32022
mth6158
comp328
finn41615
2024
mec302
mgmt3004
mgt7158
com160
as.640.440
econ3016
finm7405
econ7021
fin600
infs4205/7205
mktg2510-
f27sb
csse2310/csse7231
rv32i
eecs 113
comp1117b
cs 412
comp 315
econ7300
comp2017
ecs 116
fit5046
com6511
comp30024
acs341
econ1020
isys3014
acc408
comp1047
csc 256
cs 6347
finm7008
comp34212
csmde21
estr2520
comp285/comp220
mds5130/iba6205
finc6010
is3s665
busi2194
125.785
iom209
msin0041
econ339
cmt218
mast10007
comp5349
ecx2953/ecx5953
bios706
comp3310
mth6150
comp30027
comp20005
eec286
busi2211
bff2401
fnce90046
visu2001
mang6554
finc6001
125785
data423-24s1
engi 1331
fint2100
(520|600).666
can202
cs 61b
mast20029
info20003
stat512
econ3208
cmpsc311
engg1340
ecmt1010
fit5216
basc0003
ee3121
acct2002
comp5313
busi2131
ise529
elec372/472
csit940/csit440
cenv6141
comp3027/comp3927
ftec5580
comp1433
msci223
mark203
en3098
eden1000
ece6483
econ4410
mats16302
cs 6476
com6521
comp222
comp3211
comp10002
csc1002
chc6186
cs 161
comp27112
comp282
swen20003
comm1190
elec9764
acfi3308
acct7101
fin6035
comp2048
geog0163
comp2013
coen 146
dts101tc
sehh2042
comp30023
comp4880/8880
cs 455
07
stat0045.
fil-30023
celen085
psyc40005
math40082
are271
comp9311
ee5311
imse2113
comp 2322
acct2102
fnd109
int102
is3s664
is6153
data4000
accfin5034
fit5212
cs536-s24
fit5225
ecos3006
mes202tc
finc5001
stat3061
csc171
cs1b
7ssmm712
bu.450.760
cs170
comp3411
swen90004
cpt206
comp5313/comp4313—large
bl5611
kxo206
comp532
elec207
kxo151
cs 2820
cpt108
math2319
dts204tc
qm222
comp2511
ccs599
infs1001
mat2355
eeee4123
25721
ifn647
pols0010
hpm 573
qbus6860
comp9417
csci 1100
stat0023
cse340
comp2003j
cs 2550
cs360
fin 3080
ierg 4080
cs6238
cit 594
finm7406
hw6
联系我们
- QQ: 9951568
© 2021
www.rj363.com
软件定制开发网!