[CT404]: Finish Assignment 2

year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/code/task1/5-7.py

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+import cv2
+import numpy as np
+
+# Load and pre-process binary image
+binary_image = cv2.imread("./output/kernel_size_17.jpg", cv2.IMREAD_GRAYSCALE)
+binary_image = cv2.medianBlur(binary_image, 3)
+
+# Step 1.5: Extraction of Binary Regions of Interest / connected components
+num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(binary_image, connectivity=8)
+
+# Initialize an empty mask for filtered regions
+filtered_mask = np.zeros(binary_image.shape, dtype=np.uint8)
+
+total_globules = 0
+
+# Task 1.6: Filtering of Fat Globules
+for i in range(1, num_labels):
+    area = stats[i, cv2.CC_STAT_AREA]
+
+    # Calculate compactness
+    perimeter = cv2.arcLength(cv2.findContours((labels == i).astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0][0], True)
+    compactness = (perimeter ** 2) / area if area > 0 else 0
+
+    if (300 < area) and (compactness < 27):
+        total_globules += 1
+        filtered_mask[labels == i] = 255
+
+cv2.imwrite("./output/filtered_fat_globules.jpg", filtered_mask)
+print("Total globules: " + str(total_globules))
+
+# Task 1.7: Calculation of the Fat Area
+# Total area of the image in pixels (excluding the background)
+total_image_area = binary_image.shape[0] * binary_image.shape[1]
+
+# Total fat area (in pixels)
+fat_area = np.sum(filtered_mask == 255)
+
+# Calculate fat percentage
+fat_percentage = (fat_area / total_image_area) * 100
+print(f"Fat Area Percentage: {fat_percentage:.2f}%")

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

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-# 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)