[CT404]: Assignment 2 progress

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

@@ -5,7 +5,7 @@ import cv2
 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):
+for kernel_size in range(1, 32, 2):
     # define a disk-shaped structuring element
     structuring_element = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size, kernel_size))
 

year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/code/task1/5_extraction_of_binary_region_of_interest.py

@@ -0,0 +1,23 @@
+# Task 1.5: Extraction of Binary Regions of Interest / Connected Components
+import cv2
+import numpy as np
+
+# read in noise-reduced image
+image = cv2.imread("./output/kernel_size_25.jpg", cv2.IMREAD_GRAYSCALE)
+
+# Find connected components
+num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(image, connectivity=4)
+
+# Create an output image (color) to label components
+output_img = np.zeros((image.shape[0], image.shape[1], 3), dtype=np.uint8)
+
+# Apply a single color (e.g., gray) to each component in the output image
+for label in range(1, num_labels):  # Skip background (label 0)
+    output_img[labels == label] = (200, 200, 200)  # Light gray color for each component
+
+# Overlay red text labels at component centroids
+for i in range(1, num_labels):  # Skip background (label 0)
+    x, y = int(centroids[i][0]), int(centroids[i][1])
+    cv2.putText(output_img, str(i), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)  # Red color (BGR: (0, 0, 255))
+
+cv2.imwrite("./output/region_of_interest.jpg", output_img)