[CT404]: Finish Assignment 2

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)