PA 1 – Saving Dr Elara

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BBM 203 – PA 1 – Saving Dr Elara
An adventure into the cosmos to uncover secrets.
BBM 203: Software Laboratory I –
Subject: Arrays and Matrices
Programming Language: C++11
1 Introduction 2
1.1 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Prerequisites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.1 Image Matrix Representation: . . . . . . . . . . . . . . . . . . . . 2
1.2.2 Convolution: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.3 Sobel Operator: . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.4 Image Sharpening: . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2.5 Edge Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Your Quest 8
2.1 Task 1: Unlocking the Celestial Code . . . . . . . . . . . . . . . . . . . . 9
2.1.1 Restoring the Astral Clarity . . . . . . . . . . . . . . . . . . . . . 9
2.1.2 Tracing the Stellar Pathways . . . . . . . . . . . . . . . . . . . . . 9
2.1.3 Decoding the Alien Message . . . . . . . . . . . . . . . . . . . . . 10
2.2 Task 2: Re-Formulating and Embedding the Response . . . . . . . . . . . 11
2.2.1 Character Transformation . . . . . . . . . . . . . . . . . . . . . . 11
2.2.2 Right Circular Shifting . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.3 Message Embedding . . . . . . . . . . . . . . . . . . . . . . . . . 11
3 Problem Statements and Classes 12
3.1 ImageLoader Class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.2 ImageMatrix Class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.3 Class Implementation: Convolution . . . . . . . . . . . . . . . . . . . . . 13
3.4 Class Implementation: ImageSharpening . . . . . . . . . . . . . . . . . . 14
3.5 Class Implementation: EdgeDetector . . . . . . . . . . . . . . . . . . . . 14
3.6 Class Implementation: DecodeMessage . . . . . . . . . . . . . . . . . . . 15
3.7 Class Implementation: EncodeMessage . . . . . . . . . . . . . . . . . . . 15
3.8 Class Implementation: EncodeMessage . . . . . . . . . . . . . . . . . . . 15
3.9 ImageProcessor Class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4 Must-Use Starter Codes and Sample Input/Output 16
5 How to Run Your Code 17
6 Notes: 17
7 Submission Guidelines 18
8 Grading Criteria 18
1 Introduction
1.1 Objective
Welcome to your first assignment! This assignment is to familiarize you with C++ and
basic data structures – high-dimensional matrices. Here, you will also gain hands-on
experience with classes, constructors, destructors, and various matrix operations. Additionally, you are asked to implement these for a basic image processing task, melding
theoretical knowledge with real-world application.
1.2 Prerequisites
If you’re unfamiliar with image processing, no worries! We will walk through the terminology and operations you are to implement step by step together. Let’s touch on a few
key concepts you’ll be delving into:
1.2.1 Image Matrix Representation:
Grayscale images can be represented as two-dimensional (2D) matrices, where each element of the matrix corresponds to the intensity of a pixel, ranging from 0 (black) to 255
(white). For colored images, typically in RGB format, a three-dimensional (3D) matrix
is used. Here, the third dimension is the channel, corresponding to the intensity values
of the Red, Green, and Blue color channels respectively. Thus, an element [i, j, k] gives
the intensity of the k-th color channel at the pixel located at (i, j) in the image.
1.2.2 Convolution:
Convolution is a mathematical operation that blends two functions (or signals). In image
processing, convolution is employed to modify the appearance of an image by using a
filter (or kernel), which is another matrix. The kernel is an operator used in image processing tasks, such as blurring, sharpening, edge detection, and more. The convolution
operation involves placing the kernel at a pixel, multiplying the neighboring pixels by the
corresponding values in the kernel, and then summing up those results to produce a new
pixel in the output image.
The convolution operation I ∗ K involves sliding the kernel K over the image I and
at each position, calculating the sum of the element-wise product of the overlapped numbers, assigning the result to the corresponding position in a new matrix. An example of
Figure 1: Convolution on Images.
convolution can be seen in Figure 1. Also, a basic convolution example is provided below
for your convenience.
(I ∗ K)i,j =
Ii−m,j−n · Km,n
• I is the input image.
• K is the kernel.
• m and n are the row and column indices to slide the kernel K across the image I.
• The summation P runs over all possible valid m and n values where the kernel and
image overlap.
The (i, j)th entry of the resulting image, I ∗ K, is the sum of the elementwise product of the overlapped values in I and K at position (i, j).
• Abuse of Terminology: Technically, the operation described here is crosscorrelation, not convolution. True convolution involves rotating the kernel by 180
degrees before applying. However, they result in the same matrix given symmetric
kernels. Throughout this assignment, the operation referred to as convolution is in
fact cross-correlation. But be aware of the distinction in broader contexts.
• Parameters of convolution
– Kernel size: The size of the kernel.
– Padding: Adding extra pixels around the input image to deal with the edge
cases. This is also quite of use to handle the shape of the output image.
Padding can be ”valid” (no padding) or ”same” (pad to keep output size same
as input), generally image is pad with 0’s.
In the context of this homework, we will only deal with 0 padding
of size 1 or no padding at all.
Consider an image I without padding and I’ with padding.
I =

1 2
3 4
and I
′ =

0 0 0 0
0 1 2 0
0 3 4 0
0 0 0 0

– Stride: The number of pixel shifts over the input matrix. Stride of 1 moves
the kernel one pixel at a time, while stride of 2 moves it two pixels at a time.
– When striding the kernel, if a portion of it passes through the image
boundaries, move on with the next row.
– Source: You can check the operation via various sources. Here’s an example
• Shape of the matrix after convolution: Padding is either 0 or 1.
Output height =

Input height − Kernel height + 2 × Padding
+ 1
Output width =

Input width − Kernel width + 2 × Padding
+ 1
• Example convolution operation: Here, the operation on the edges are avoided
(no padding) and stride is 1, resulting in a lower-dimensional matrix. Given an
image matrix I and a kernel K as below:
I =

1 2 3
4 5 6
7 8 9

 , K =

1 1
1 1
, (I ∗ K)0,0 = 12
I =

1 2 3
4 5 6
7 8 9

 , K =

1 1
1 1
, (I ∗ K)0,1 = 16
I =

1 2 3
4 5 6
7 8 9

 , K =

1 1
1 1
, (I ∗ K)1,0 = 24
I =

1 2 3
4 5 6
7 8 9

 , K =

1 1
1 1
, (I ∗ K)1,1 = 28
(I ∗ K) = 
12 16
24 28
1.2.3 Sobel Operator:
The Sobel Operator is a discrete differentiation operator, computing an approximate of
the gradient of the image intensity function. It is utilized for edge detection and operates
by convolving the image with a pair of 3 × 3 kernels which are designed to respond
maximally to edges running vertically and horizontally relative to the pixel grid. Below
are the Sobel Operators:
Gx =

−1 0 1
−2 0 2
−1 0 1

 and Gy =

−1 −2 −1
0 0 0
1 2 1

• Gx is used for detecting edges that are vertically oriented.
• Gy is used for detecting edges that are horizontally oriented.
Given an image I, convolutions are performed for both Gx and Gy:
Ix = I ∗ Gx and Iy = I ∗ Gy
• Ix and Iy are the images that result after convolution, representing the horizontal
and vertical derivative approximations respectively.
Then, the gradient magnitude G and direction Θ can be determined as follows:
G =
x + I
Θ = arctan 

• G gives the magnitude or intensity of the gradient at each point in the image. It
signifies the amount of variation or edge content in the local neighborhood of a
• Θ gives the direction of the gradient, pointing in the direction of the most rapid
increase in intensity.
The approximate magnitude of the gradient is sometimes computed using:
G ≈ |Ix| + |Iy|
(a) Original Image (b) The horizontal response (c) The vertical response
Figure 2: Original image vs horizontal and vertical responses after applying the corresponding sobel operators.
1.2.4 Image Sharpening:
Below is a basic image sharpening algorithm, where we initially arrive at an image with
highlighted the edge information by subtracting the blurred image from the original one.
Then, adding this highlighted edge information back to the original matrix results in the
sharpened image. For this operation, steps are:
1. Generate Noisy (Blurred) Image: Given the original image, Iorig, a blurring
operation is applied to generate a noisy image, Inoisy. Though there are other kernels
that can be employed for this operation, in the context of this assignment, we
will opt for a simple 3×3 averaging kernel for blurring:
K =

1 1 1
1 1 1
1 1 1

• To arrive at the blurred image, convolve the original image with this
averaging kernel.
• Preserving the Image Dimensions:
Using the above given 3×3 kernel with padding and a stride of value 1 preserves
the image dimensions as seen below. The given equation for height also applies
to width.
Output Height =

Input Height − 3 + 2

+ 1 = Input Height
2. Sharp Image Calculation: The sharpened image, Isharp, is derived using the
Isharp = Iorig + k · (Iorig − Inoisy)
where k is a sharpening factor that scales the contribution of the high-frequency
components extracted from the original image. Adjusting k allows for control over
the degree of sharpening applied.
Figure 3: Blurring operation
3. Clipping: Ensure that pixel intensity values in Isharp lie within acceptable bounds
– [0, 255]. The multiplication with the sharpening factor, k, may cause overflow.
Values that fall outside this range should be clipped to ensure visual coherence, ie, clip the values greater than 255 back to 255.
1.2.5 Edge Detection
• Define the Sobel Operators as above: Gx and Gy
• Use zero padding to preserve the image shape.
• Convolution
1. Convolve the original image matrix with Gx to produce a new image matrix
representing the change rates in the x-direction (horizontal changes). Let’s
call this matrix Ix.
2. Convolve the original image matrix with Gy to produce a new image matrix
representing the change rates in the y-direction (vertical changes). Let’s call
this matrix Iy.
• Gradient Magnitude Calculation:
– For each pixel position (i, j), calculate the gradient magnitude:
G(i, j) = q
(Ix(i, j))2 + (Iy(i, j))2
• Gradient Direction Calculation:
– [Omitted in this assignment] Normally, the gradient direction is calculated
to provide information about the edge orientation:
Θ(i, j) = arctan 
Iy(i, j)
Ix(i, j)

– We are omitting this step, as we will consider all pixels with a gradient magnitude greater than a certain threshold as edge pixels,
regardless of their orientation for simplicity.
• Thresholding:
– Determine a threshold value. Any pixel G(i, j) with a gradient magnitude
larger than this threshold is considered an edge. Various strategies can be
employed to determine this threshold.
– The thresholding policy: Though there are more advanced methods to
determine the threshold which yield better results, we will opt for a rather
naive policy where we will use the average of the gradient magnitudes
across all pixels as the threshold for the sake of simplicity and determinism. It’s important to note that, in practice, this method is unlikely to yield
optimal edge detection, especially for images with large uniform regions or
varied intensity profiles.
– Those pixels with a gradient greater than the threshold are on the edges.
• Non-maximum Suppression:
– [Omitted in this assignment] Typically, to obtain thin and well-defined
edges, non-maximum suppression is applied. However, for simplicity and to
maintain a deterministic outcome, we will skip this step in the assignment.
• Online resource: You can check this algorithm via various sources. Here’s
an example source. However, stick to the given Algorithm below for any
• Task: Implement the Sobel edge detection based on the steps outlined (omitting
the gradient direction and non-maximum suppression steps). Subsequently, you
will use the pixels on these edges to decypher a hidden message. Details are below!
2 Your Quest
In an era where interstellar communication was a far-fetched dream, Dr. Elara, a pioneering astrophysicist, dedicated her existence to decoding the mysteries of the universe.
Rooted in the belief that the cosmos concealed secret messages, she stood staunchly at
the intersection of rigorous science and the mystical unknown.
Despite being recognized for her brilliance, her speculative methods and fearless explorations into undiscovered realms were often met with skepticism from her contemporaries.
Undeterred, she assembled a covert group, a unique blend of daring scientists, astute codebreakers, and skilled astrophotographers, operating secretly and free from the doubting
glances of the scientific community.
After perceiving a mysterious “ripple from the cosmos” she disappeared, leaving behind a perplexing celestial image — what she referred to as the “Celestial Tapestry” —
and her team, now enveloped in a cloud of mystery and resolute determination.
The celestial tapestry was not merely a message but potentially a medium of communication, a conduit through which Elara might be reaching out, possibly navigating through
dimensions unknown. She always believed that if humans managed to traverse through
time or alternate dimensions, they would likely leave messages hidden in plain sight,
layered within the familiar, waiting to be uncovered by those keen enough to look deeper.
2.1 Task 1: Unlocking the Celestial Code
The steps to solve the assignment are explained below in a structured way,
presented as pseudo-codes. Please follow them thoroughly. For any details
about specific operations in the provided pseudo-code, please refer to the
Prerequisites Section.
2.1.1 Restoring the Astral Clarity
Knowing Elara’s penchant for hidden details and layered meanings, the first step is to
reveal the details embedded within the cosmic tapestry. You believe a message is concealed in the edges of the image, as Elara often mused about the edges of the cosmos,
suggesting that the path to other civilizations was woven into the edges of our perception.
However, as edges are noisy, you first need to sharpen the image. Notice that we will
use zero-padding and a stride of value 1 for convolution for this algorithm.
Algorithm 1 Image Sharpening Algorithm
Require: Input Image: InputImg, Sharpening parameter: k
Ensure: Sharpened Image: SharpImg
1: Kernel ← 1
9 ×

1 1 1
1 1 1
1 1 1

 {Define the blurring kernel}
2: BlurredImg ← CONV OLV E(InputImg, Kernel, stride = 1, padding = T rue)
{Convolve with Kernel, applying zero padding with a stride of 1}
3: SharpImg ← InputImg + k × (InputImg − BlurredImg)
4: return SharpImg
2.1.2 Tracing the Stellar Pathways
After sharpening the image, you need to detect the pixels on the edges. For this, you
will be running an edge detection algorithm using Sobel Operators. The details are
explained above and the pseudo-code is below. Notice that the we will use fixed
zero-padding and a stride of value 1 for convolution for this algorithm, which
preserves the output dimensions as per the kernel size and other parameters
(See Preserving the Image Dimensions bullet in 1.2.4 Image Sharpening).
Algorithm 2 Stellar Pathways: Edge Detection using Sobel Operators
Require: Image: Img
Ensure: List of edge pixels: EdgeP ixels
1: Gx, Gy ← initialize the Sobel operators
2: Ix ← CONV OLV E(Img, Gx, stride = 1, padding = T rue) {Horizontal edge response}
3: Iy ← CONV OLV E(Img, Gy, stride = 1, padding = T rue) {Vertical edge response}
4: for all pixel (i,j) in Img do
5: magnitude[i, j] ←
(Ix[i, j])2 + (Iy[i, j])2
6: end for
7: threshold ← average of all values in magnitude
8: Initialize EdgeP ixels ← []
9: Append to EdgeP ixels all pixels with gradient above the threshold
10: return EdgeP ixels
Note: Processing each pixel sequentially (starting from the top left corner to bottom
right). The order of pixels to be decoded matters!
2.1.3 Decoding the Alien Message
Elara, while a visionary, was constrained by the technology of her time and thus, resorted
to a simple yet ingenious method of encoding: LSB steganography. In this method, the
message is hidden in the least significant bits (lsb) of the pixels on the image’s edges.
These, when concatenated, form a binary string. Each segment of 7 bits in this binary
string corresponds to a character in the ASCII table, which can then be converted into a
string to reveal the hidden message
Below are the steps of how the process works in detail:
1. Extracting LSBs from Edge Pixels: First, we gather a list of edge pixels from
the image. For each pixel, we extract its least significant bit (lsb). These lsb values
are collected and concatenated to form a binary string.
2. Converting Binary to ASCII: The binary string obtained in the previous step
might not have segments of exactly 7 bits. To ensure proper conversion, we pad
this binary string with leading zeros to form complete 7-bit bytes. We
then proceed to convert each 7-bit segment into its corresponding decimal value.
Note that not all elements in the ASCII table are printable, namely, those with
indices ≤ 32 and ≥ 127. To ensure all characters are printable, if the resulting
ASCII value is less than or equal to 32, we adjust it by adding 33 and if the
ASCII value is 127, clip it back to 126.
3. Revealing the Message: The final step is to convert the decimal ASCII values
into their corresponding characters. These characters are concatenated to form the
decoded message, revealing the hidden information.
Elara’s legacy lies within these celestial codes, hidden in plain sight. Your task is to
decrypt these codes, navigate through the starlit pathways, and carry forth her wisdom
into the great cosmic beyond.
Note 1: The order in which you decode the pixels matters. Start from the top-left
corner of the image and proceed to the right and then down.
Note 2: Padding the binary string with leading zeros ensures consistent 7-bit segments
for accurate decoding. Additionally, adjustments are made to ASCII values to ensure
printable characters are obtained during the conversion process.
In short, you’ll extract the least significant bit from each pixel along the detected edges, decode the binary to ASCII, and reveal the messages concealed
2.2 Task 2: Re-Formulating and Embedding the Response
In decoding Elara’s message, the team becomes the stewards of a potent secret, a conduit
through space and possibly time. To forge a connection, they must navigate through
the metaphysical and scientific, encoding their message back into the celestial image
without diminishing its original integrity. Having decoded Elara’s message, your duty
now evolves from mere interpretation to active participation in this celestial dialogue,
crafting a message embedded seamlessly back into the original image.
2.2.1 Character Transformation
For each character of the extracted message in a position indexed with a prime number,
adjust its ASCII value by adding the Fibonacci number corresponding to that index, i.e.,
the ith position means adding the ith Fibonacci number. Ensure that the transformed
character is printable within the ASCII range of 33 to 126. Apply the same adjustment in the Decoding step, ie, add 33 to those ≤ 32 and clip those ≥ 127 back
to 126.
2.2.2 Right Circular Shifting
Apply right circular shifting to the manipulated message by ⌊len(message)/2⌋ characters.
For example, shifting ”HELLO” by 2 results in ”LOHEL”.
2.2.3 Message Embedding
The restructured binary message must be delicately embedded back into the celestial
tapestry. To maintain the visual salience of the image while encoding the modified
message, begin the encoding process at the top-left edge pixel and continue
in a sequential order down to the bottom-right edge pixel. (Use only the
pixels on the edge). Each bit should be placed into the least significant bit (LSB) of
the edge pixel, preserving the original sequence while ensuring the seamless integration
of the modified message.
3 Problem Statements and Classes
To tackle with the above problem, you are developing a software with the classes introduced in the following section. However, as responsible scientists, you opt for a structured
code with classes, which are to be unit tested, ie, each method in each class will be tested
and evaluated.
In this assignment, you will create several classes to handle different aspects of the task.
For each class, you will provide both the declarations (in a ‘.h‘ header file) and implementations (in a ‘.cpp‘ file).
You are required to implement the given functions in the template files, as
they will be graded. DO NOT CHANGE THEIR NAMES! Other than these,
you can define as many functions, variables as you wish.
For the ImageMatrix and Convolution constructors, you have to dynamically
allocate memory using the given matrix, ie, you cannot use STL library –
vectors, in particular. However, you are allowed to use them in the member functions.
3.1 ImageLoader Class
An ImageLoader class is available to simplify image loading from a specified path.
You can utilize the ImageMatrix(const std::string &filepath) constructor of the
ImageMatrix class to create an ImageMatrix directly from a file. Please be aware that
this template employs PNG images for loading, which involves non-standard library dependencies. To accommodate, a Python script (convert pil to under the data
directory) is also shared that converts PNG images into text files. You can use these
text files as input for the constructors of both the ImageLoader and ImageMatrix classes.
Additionally, note that the Python script relies on the PIL library, which must also be
installed. For your convenience, each PNG image is accompanied by a corresponding
text file, eliminating the need for manual conversion. However, you are free to use the
provided Python script if preferred.
3.2 ImageMatrix Class
The ImageMatrix class encapsulates the pixel data of an image and provides functionalities for basic matrix operations.
• Constructors and Destructor:
– ImageMatrix(): Default constructor
– ImageMatrix(const std::string &filepath): Parametrized constructor that
accepts a file path as an argument, loads the image from the specified path,
and initializes the matrix with the image data. The implementation is
– ImageMatrix(int imgHeight, int imgWidth): Parametrized constructor for
creating a blank image of the given size.
– ImageMatrix(const double** inputMatrix, int imgHeight, int imgWidth)
Parameterized constructor that accepts a 2D matrix and directly initializes the
matrix with the provided data.
– ImageMatrix(const ImageMatrix &other): Copy constructor
– ImageMatrix & operator=(const ImageMatrix &other): Copy Assignment
– ImageMatrix(): Destructor
• Overloaded Operators:
– ImageMatrix operator+(const ImageMatrix &other) const : Overloaded
addition operartor that performs element-wise addition of matrices.
– ImageMatrix operator-(const ImageMatrix &other) const : Overloaded
subtraction operartor that performs element-wise subtraction of matrices.
– ImageMatrix operator*(const double &scalar) const : Overloaded scalar
multiplication operator.
• Getter Functions:
– double **get data() const: Retrieve the private data
– double get data(int i, int j) const: Retrieve the specific element at the
given index.
• Private Data Members:
– double **data: 2D array to store image data.
3.3 Class Implementation: Convolution
The Convolution class provides functionalities related to the convolution operation on
an image with the parameters: kernel, stride, and padding.
• Constructors and Destructors
– Convolution(): Default constructor
– Convolution(double** customKernel, int kernelHeight, int kernelWidth,
int stride, bool padding): Parametrized constructor for a custom kernel
and other parameters.
– Convolution(const Convolution &other): Copy constructor
– Convolution &operator=(const Convolution &other): Copy assignment
– ∼Convolution(): Destructor
• Public Member Functions
– ImageMatrix convolve(const ImageMatrix &input image) const: Responsible for convolving the input image with the kernel and returning the convolved image.
Note: Padding is a boolean value, where ‘true‘ indicates that zero padding
should be applied and ‘false‘ indicates no padding.
Note: For the sake of simplicity, in the context of this PA, you can assume
all kernels are square matrices.
3.4 Class Implementation: ImageSharpening
The ImageSharpening class is designed to apply a basic image sharpening algorithm to
an input image using a predefined kernel.
• Constructors and Destructors
– ImageSharpening(): Default constructor
– ∼ImageSharpening(): Destructor
• Public Member Functions
– ImageMatrix sharpen(const ImageMatrix &input image, double k): Performs image sharpening on the input image using the specified sharpening
factor (k) and returns the sharpened image.
• Private Data Members
– double** blurring kernel: Kernel for blurring
– int kernel height: Height of the kernel
– int kernel width: Width of the kernel
Use the above given Algorithm 1, strictly abiding by the specs!
3.5 Class Implementation: EdgeDetector
The EdgeDetector class is designed to detect edges in an image using Sobel operators.
• Constructors and Destructors
– EdgeDetector(): Default constructor
– ∼EdgeDetector(): Destructor
• Public Member Functions
– std::vector<std::pair<int, int>> detectEdges(const ImageMatrix &input image):Detects edges in the input image using the Sobel operators and returns a vector
of edge pixel positions as pairs of (row, column) indices.
Use the above given Algorithm 2, strictly abiding by the specs!
3.6 Class Implementation: DecodeMessage
The DecodeMessage class is designed to decode a binary string into its ASCII representation using the above algorithm.
• Constructors and Destructors
– DecodeMessage(): The default constructor.
– ∼ DecodeMessage(): Destructor to manage any resources or memory the
class might allocate.
• Public Member Functions
– std::string binaryToASCII(const std::string& binaryString): This function takes a binary string and decodes it into its ASCII representation and
returns the final string.
3.7 Class Implementation: EncodeMessage
The EncodeMessage class is devised to embed a binary message back into an image
matrix, making sure that the bits of the message are stored in the least significant bit
(LSB) of the pixel values from which the original message was extracted. The encoded
bits must correspond to the pixel locations they are originally extracted from!
3.8 Class Implementation: EncodeMessage
The EncodeMessage is designed to embed a binary message after applying the above
operations on the extracted one back to the image. The updated message needs to
be embedded in the image starting from the top left edge pixel to the bottom
• Constructors and Destructors
– EncodeMessage(): The default constructor.
– ∼EncodeMessage(): Destructor
• Public Member Functions
– ImageMatrix encodeMessageToImage(const ImageMatrix &img, const std::string
&message, const std::vector<std::pair<int, int>>& positions): This
function accepts an image matrix, a message string, and a vector of pixel positions as inputs. It returns a new image matrix wherein the input message is
embedded into the positions specified.
3.9 ImageProcessor Class
The ImageProcessor Class is to be used as the final step, where a message decoded
and the updated message is encoded.
• Constructors and Destructors
– ImageProcessor(): The default constructor.
– ∼ImageProcessor(): Destructor
• Public Member Functions
– std::string decodeHiddenMessage(const ImageMatrix &img): Given an
image of type ImageMatrix, it decodes and returns the message embedded
– ImageMatrix encodeHiddenMessage(const ImageMatrix &img, const std::string
&message): Encodes the given message back to the image and returns the image.
4 Must-Use Starter Codes and Sample Input/Output
You MUST use this starter code for this assignment. Under src folder, you will find the
template .h and .cpp files. You need to implement the functions in the .cpp files. And,
under the data directory, you will find a .png image, its corresponding .txt conversion,
and the python script to convert a png image to txt.
Your implementations need to work both with images loaded from a path and direct
matrix initialization.
• Loading from a file: An ImageLoader class to load the image in the given path
is provided for your convenience. You can use the constructor that takes file path argument ImageMatrix(const std::string &filepath) of the ImageMatrix class
to create an ImageMatrix from the file directly as is. Please note that this template
uses a PNG image for loading, which is not a trivial task as it requires libraries
that are not part of the standard library. To simplify the process, a Python script
(convert pil to that loads a PNG image and converts it into a text file
is provided. You can then use this text file as an argument for the constructors of
the ImageLoader and ImageMatrix classes. Additionally, please be aware that the
Python script relies on the PIL library, which also needs to be installed. For each
PNG image provided, you will also find the corresponding text file. That is, you
do not need to perform the conversion manually, although you are welcome to use
the provided Python script if you prefer. All files regarding images are provided in
a separate folder: data, including the .PNG, .txt files and the python script for the
• Loading image data from the main function: You will find a main function
provided with several ImageMatrix data examples for testing your classes. However, a comprehensive testing platform, Tur3Bo Grader, is available at https:
// Please use it, as it will also be used for
5 How to Run Your Code
All classes will be unit tested. An example compile and run is as follows (those for the
other classes are of the same structure):
g ++ – std = c ++11 -o test TestImageMatrixConstructor . cpp ImageMatrix
. cpp ImageLoader . cpp
./ test
6 Notes:
• Note on Constructors and Destructor: In C++, constructors and destructor
play a crucial role in managing resources and ensuring that objects are correctly
initialized and cleaned up. Here’s a general guideline on when to implement your
– Default Constructor: If your class has members that need to be initialized
to specific values (other than zero or default-constructed), or if you need to
perform some setup (like allocating memory or initializing resources), provide
a default constructor.
– Copy Constructor: If your class manages resources, a shallow copy of pointers does not suffice – provide a copy constructor that performs a deep copy.
Please further check deep vs shallow copy: one example resource.
– Destructor: If your class acquires resources (like memory, file handles, or
network connections) or holds ownership to resources, provide a destructor
to release those resources and prevent resource leaks. If your class does not
manage resources or if all member variables automatically manage their own
resources (like ‘std::vector‘ or ‘std::string‘), the compiler-generated destructor
is sufficient.
• For the sake of simplicity, in the context of this PA, we will stick to the grayscale
images only. Also, you can assume all kernels are square matrices.
• For edge detection, we will omit the gradient direction calculation and nonmaximum suppression steps.
• Abuse of Terminology: Technically, the operation described here is crosscorrelation, not convolution. True convolution involves rotating the kernel by
180 degrees before applying. Yet, their results are the same if the kernels are symmetrical. Throughout this assignment, the operation referred to as convolution is
in fact cross-correlation. But be aware of the distinction in broader contexts.
• You must test your code via Tur3Bo Grader https://test-grader.cs.hacettepe. (does not count as submission!).
• You must submit your work via
7 Submission Guidelines
Create a .zip file including all the implementation files. The given classes and the function
prints are to be tested. Do not change the given class names and function names
to be tested! You must submit your work via
tr/. Do not upload your main functions or other any other file. The submissions should
be in the follow the hieararchy below:
• b<studentID>.zip
– ImageMatrix.h
– ImageMatrix.cpp
– Convolution.h
– Convolution.cpp
– ImageSharpening.h
– ImageSharpening.cpp
– EdgeDetector.h
– EdgeDetector.cpp
– DecodeMessage.h
– DecodeMessage.cpp
– EncodeMessage.h
– EncodeMessage.cpp
– ImageProcessor.h
– ImageProcessor.cpp
8 Grading Criteria
Each class will be evaluated using unit testing, so each method you implement, you gain
points. Here’s a breakdown of the grading criteria:
Task / Requirement Points
Implementing ImageMatrix Class 10
Implementing Convolution Class 10
Implementing ImageSharpening Class 10
Implementing EdgeDetector Class 10
Implementing DecodeMessage Class 15
Implementing EncodeMessage Class 15
Implementing ImageProcessor Class 10
No STL Vector Usage in ImageMatrix Class 10
No Memory Leaks/Errors 10


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