[CT404]: Finish Assignment 2

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

@@ -0,0 +1,40 @@
+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

@@ -1,23 +0,0 @@
-# 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)

year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/code/task2/task2.py

@@ -28,4 +28,42 @@ 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
+# Task 2.3: Frequency Domain Filtering for
+channels = cv2.split(image)
+filtered_channels = []
+
+for channel in channels:
+    fft_channel = np.fft.fft2(channel)
+
+    # shift the zero frequency component to the center
+    fft_channel_shifted = np.fft.fftshift(fft_channel)
+
+    # create a Gaussian filter the same size as the channel
+    gaussian_kernel = cv2.getGaussianKernel(kernel_size[0], variance)
+    gaussian_kernel_2d = gaussian_kernel @ gaussian_kernel.T
+
+    # pad the Gaussian filter to match the size of the image channel
+    gaussian_kernel_padded = np.pad(gaussian_kernel_2d, 
+                                    ((0, fft_channel_shifted.shape[0] - gaussian_kernel_2d.shape[0]), 
+                                     (0, fft_channel_shifted.shape[1] - gaussian_kernel_2d.shape[1])), 
+                                    mode='constant', constant_values=0)
+
+    # shift the padded filter in the frequency domain
+    fft_gaussian_padded_shifted = np.fft.fftshift(np.fft.fft2(gaussian_kernel_padded))
+
+    # apply the low-pass filter to the channel
+    low_pass_filtered = fft_channel_shifted * fft_gaussian_padded_shifted
+
+    # perform the inverse FFT to get the filtered channel in the spatial domain
+    ifft_filtered = np.fft.ifft2(np.fft.ifftshift(low_pass_filtered))
+
+    # take the real part and normalize it
+    filtered_channel = np.real(ifft_filtered)
+    filtered_channel = np.clip(filtered_channel, 0, 255).astype(np.uint8)
+
+    # append the filtered channel to the list
+    filtered_channels.append(filtered_channel)
+
+filtered_image_color = cv2.merge(filtered_channels)
+
+cv2.imwrite("./output/3_filtered_color_image.jpg", filtered_image_color)

year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/code/task2/test.py

@@ -0,0 +1,53 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Task 2.1: Spatial Domain
+image = cv2.imread("jennifer.jpg")
+
+kernel_size = (15, 15)
+variance = 20
+
+smoothed_image = cv2.GaussianBlur(image, kernel_size, variance)
+
+cv2.imwrite("./output/jennifer_spatial.jpg", smoothed_image)
+
+# Task 2.3: Frequency Domain Filtering for
+channels = cv2.split(image)
+filtered_channels = []
+
+for channel in channels:
+    fft_channel = np.fft.fft2(channel)
+
+    # shift the zero frequency component to the center
+    fft_channel_shifted = np.fft.fftshift(fft_channel)
+
+    # create a Gaussian filter the same size as the channel
+    gaussian_kernel = cv2.getGaussianKernel(kernel_size[0], variance)
+    gaussian_kernel_2d = gaussian_kernel @ gaussian_kernel.T
+
+    # pad the Gaussian filter to match the size of the image channel
+    gaussian_kernel_padded = np.pad(gaussian_kernel_2d, 
+                                    ((0, fft_channel_shifted.shape[0] - gaussian_kernel_2d.shape[0]), 
+                                     (0, fft_channel_shifted.shape[1] - gaussian_kernel_2d.shape[1])), 
+                                    mode='constant', constant_values=0)
+
+    # shift the padded filter in the frequency domain
+    fft_gaussian_padded_shifted = np.fft.fftshift(np.fft.fft2(gaussian_kernel_padded))
+
+    # apply the low-pass filter to the channel
+    low_pass_filtered = fft_channel_shifted * fft_gaussian_padded_shifted
+
+    # perform the inverse FFT to get the filtered channel in the spatial domain
+    ifft_filtered = np.fft.ifft2(np.fft.ifftshift(low_pass_filtered))
+
+    # take the real part and normalize it
+    filtered_channel = np.real(ifft_filtered)
+    filtered_channel = np.clip(filtered_channel, 0, 255).astype(np.uint8)
+
+    # append the filtered channel to the list
+    filtered_channels.append(filtered_channel)
+
+filtered_image_color = cv2.merge(filtered_channels)
+
+cv2.imwrite("./output/jennifer_freq.jpg", filtered_image_color)