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diff --git a/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/code/task2/task2.py b/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/code/task2/task2.py
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--- /dev/null
+++ b/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/code/task2/task2.py	
@@ -0,0 +1,31 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Task 2.1: Spatial Domain
+image = cv2.imread("../../Task2.jpg")
+
+kernel_size = (15, 15)
+variance = 2
+
+smoothed_image = cv2.GaussianBlur(image, kernel_size, variance)
+
+cv2.imwrite("./output/1_spatial_domain.jpg", smoothed_image)
+
+# Task 2.2: Frequency Domain Low-Pass Filter
+gaussian_kernel = cv2.getGaussianKernel(kernel_size[0], variance)
+gaussian_kernel_2d = gaussian_kernel @ gaussian_kernel.T
+fft_gaussian = np.fft.fft2(gaussian_kernel_2d)
+
+# shift zero frequency component to center
+fft_gaussian_shifted = np.fft.fftshift(fft_gaussian)
+
+# calculate the magnitude spectrum for visualization
+magnitude_spectrum = np.log(np.abs(fft_gaussian_shifted) + 1)
+
+# Plot the magnitude spectrum (Frequency Domain Representation)
+plt.imshow(magnitude_spectrum, cmap='gray')
+plt.axis('off')
+plt.savefig("./output/2_frequency_domain_low-pass_filter.jpg", bbox_inches='tight', pad_inches=0)
+
+# Task 2.3: Frequency Domain Filtering
diff --git a/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/latex/CT404-Assignment-2.pdf b/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/latex/CT404-Assignment-2.pdf
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diff --git a/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/latex/CT404-Assignment-2.tex b/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/latex/CT404-Assignment-2.tex
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--- a/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/latex/CT404-Assignment-2.tex	
+++ b/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/latex/CT404-Assignment-2.tex	
@@ -295,6 +295,39 @@ I chose to go with \mintinline{python}{kernel_size = 25} as it seemed to give th
 
 \subsection{Extraction of Binary Regions of Interest / Connected Components}
 
+\newpage
 
+\section{Filtering of Images in Spatial \& Frequency Domains}
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.5\textwidth]{../Task2.jpg}
+    \caption{Original Facial Image}
+\end{figure}
+
+\subsection{Spatial Domain}
+\begin{code}
+\inputminted[firstline=5, lastline=13, linenos, breaklines, frame=single]{python}{../code/task2/task2.py}
+\caption{Task 2.1 section of \mintinline{python}{task2.py}}
+\end{code}
+
+After some experimentation, I chose parameter values of \mintinline{python}{kernel_size = (15,15)} and \mintinline{python}{variance = 2} as, in my opinion, these yielded the best balance between blurring imperfections like wrinkles without causing the entire image to become too blurry.
+
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.5\textwidth]{../code/task2/output/1_spatial_domain.jpg}
+    \caption{Output of \mintinline{python}{1_spatial_domain.jpg}}
+\end{figure}
+
+\subsection{Frequency Domain Filtering}
+\begin{code}
+\inputminted[firstline=15, lastline=29, linenos, breaklines, frame=single]{python}{../code/task2/task2.py}
+\caption{Task 2.2 section of \mintinline{python}{task2.py}}
+\end{code}
+
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.5\textwidth]{../code/task2/output/2_frequency_domain_low-pass_filter.jpg}
+    \caption{Zero-centered low-pass filter of Gaussian Kernel}
+\end{figure}
 
 \end{document}