[CT404]: Assignment 2 progress

year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/code/task1/2_image_enhancement.py

@@ -0,0 +1,18 @@
+# Task 1.2: Image Enhancement
+import cv2
+
+# read in chosen single-channel greyscale image
+image = cv2.imread("./output/g_channel_greyscale.jpg", cv2.IMREAD_GRAYSCALE)
+
+# apply histogram equalisation
+equalised_image = cv2.equalizeHist(image)
+equalised_image_contrast = equalised_image.std()
+cv2.imwrite("./output/histogram_equalised.jpg", equalised_image)
+
+# apply contrast stretching
+stretched_image = cv2.normalize(image, None, 0, 255, cv2.NORM_MINMAX)
+stretched_image_contrast = stretched_image.std()
+cv2.imwrite("./output/contrast_stretched.jpg", stretched_image)
+
+print("Histogram Equalisation Contrast: " + str(equalised_image_contrast))
+print("Contrast Stretching Contrast: "    + str(stretched_image_contrast))

year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/code/task1/3_thresholding.py

@@ -0,0 +1,11 @@
+# Task 1.3: Thresholding
+import cv2
+
+# read in chosen enhanced image
+image = cv2.imread("./output/histogram_equalised.jpg", cv2.IMREAD_GRAYSCALE)
+
+# perform otsu thresholding to find the optimal threshold
+threshold_value, otsu_thresholded = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+cv2.imwrite("./output/otsu.jpg", otsu_thresholded)
+
+print("Threshold value used: " + str(threshold_value))

year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/code/task1/4_noise_removal.py

@@ -0,0 +1,14 @@
+# Task 1.4: Noise Removal
+import cv2
+
+# read in thresholded image
+image = cv2.imread("./output/otsu.jpg", cv2.IMREAD_GRAYSCALE)
+
+# try several different sizes of structuring element (must be odd)
+for kernel_size in range(1, 16, 2):
+    # define a disk-shaped structuring element
+    structuring_element = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size, kernel_size))
+
+    # apply morphological opening to remove noise
+    opened_image = cv2.morphologyEx(image, cv2.MORPH_OPEN, structuring_element)
+    cv2.imwrite(f"./output/kernel_size_{kernel_size}.jpg", opened_image)

year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/latex/CT404-Assignment-2.tex

@@ -134,5 +134,89 @@ My selected single-channel image is the greyscale version of the green-channel-o
 \end{figure}
 
 \subsection{Image Enhancement}
+\begin{code}
+\inputminted[linenos, breaklines, frame=single]{python}{../code/task1/2_image_enhancement.py}
+\caption{\mintinline{python}{2_image_enhancement.py}}
+\end{code}
+
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=\textwidth]{./images/2_image_enhancement_output.png}
+    \caption{Output of \mintinline{python}{2_image_enhancement.py}}
+\end{figure}
+
+I chose to use the histogram equalisation technique as it gave the best contrast, as seen from the calculated standard deviation in contrast above and in the output images below.
+
+\begin{minipage}{0.49\textwidth}
+    \centering
+    \includegraphics[width=\textwidth]{../code/task1/output/histogram_equalised.jpg}
+    \captionof{figure}{Histogram-equalised image}
+\end{minipage}
+\hfill
+\begin{minipage}{0.49\textwidth}
+    \centering
+    \includegraphics[width=\textwidth]{../code/task1/output/contrast_stretched.jpg}
+    \captionof{figure}{Contrast-stretched image}
+\end{minipage}
+
+\subsection{Thresholding}
+\begin{code}
+\inputminted[linenos, breaklines, frame=single]{python}{../code/task1/3_thresholding.py}
+\caption{\mintinline{python}{3_thresholding.py}}
+\end{code}
+
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=\textwidth]{./images/3_thresholding_output.png}
+    \caption{Output of \mintinline{python}{3_thresholding.py}}
+\end{figure}
+
+I used Otsu's algorithm to find the optimal threshold value that best separated the foreground (objects of interest) from the background.
+As can be seen from the above output, the optimal value chosen was 129.
+
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.5\textwidth]{../code/task1/output/otsu.jpg}
+    \caption{Image with Otsu thresholding}
+\end{figure}
+
+\subsection{Noise Removal}
+\begin{code}
+    \inputminted[linenos, breaklines, frame=single]{python}{../code/task1/4_noise_removal.py}
+    \caption{\mintinline{python}{4_noise_removal.py}}
+\end{code}
+
+\noindent
+\begin{minipage}{0.24\textwidth}
+    \centering
+    \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_1.jpg}
+    \captionof{figure}{\mintinline{python}{kernel_size = 1}}
+    \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_3.jpg}
+    \captionof{figure}{\mintinline{python}{kernel_size = 3}}
+\end{minipage}
+\hfill
+\begin{minipage}{0.24\textwidth}
+    \centering
+    \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_5.jpg}
+    \captionof{figure}{\mintinline{python}{kernel_size = 5}}
+    \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_7.jpg}
+    \captionof{figure}{\mintinline{python}{kernel_size = 7}}
+\end{minipage}
+\hfill
+\begin{minipage}{0.24\textwidth}
+    \centering
+    \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_9.jpg}
+    \captionof{figure}{\mintinline{python}{kernel_size = 9}}
+    \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_11.jpg}
+    \captionof{figure}{\mintinline{python}{kernel_size = 11}}
+\end{minipage}
+\hfill
+\begin{minipage}{0.24\textwidth}
+    \centering
+    \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_13.jpg}
+    \captionof{figure}{\mintinline{python}{kernel_size = 13}}
+    \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_15.jpg}
+    \captionof{figure}{\mintinline{python}{kernel_size = 15}}
+\end{minipage}
 
 \end{document}