I am currently working on an image processing task in Python where I am trying to apply a bandpass filter to an image using the Fourier Transform. My goal is to convert the original image: To this output: Here is the code I have tried so far: import cv2 import numpy as np from matplotlib import pyplot as plt image = cv2.imread('path-to-image', cv2.IMREAD_GRAYSCALE) # Apply Fourier Transform f_transform = np.fft.fft2(image) f_transform_shifted = np.fft.fftshift(f_transform) # Create a bandpass filter rows, cols = image.shape crow, ccol = rows // 2, cols // 2 inner_radius = 0.1 * crow outer_radius = 0.8 * crow # Create a meshgrid for the frequency coordinates x = np.arange(-ccol, ccol) y = np.arange(-crow, crow) x, y = np.meshgrid(x, y) # Create the bandpass filter mask = ((x**2 + y**2 >= inner_radius**2) & (x**2 + y**2 <= outer_radius**2)) # Apply the mask to the shifted Fourier transform f_transform_shifted_filtered = f_transform_shifted * mask # Inverse Fourier Transform to get the image back f_inverse_shifted = np.fft.ifftshift(f_transform_shifted_filtered) image_filtered = np.fft.ifft2(f_inverse_shifted) image_filtered = np.abs(image_filtered) # Convert back to uint8 and normalize the values image_filtered = np.uint8(image_filtered) image_filtered = cv2.normalize(image_filtered, None, 0, 255, cv2.NORM_MINMAX) The problem I am facing is that the output image does not match my desired output. I believe the issue lies in the way I am defining and applying the bandpass filter, but I am not sure how to adjust it to achieve the desired result. Any suggestions on how I can improve this code to get closer to my desired output would be greatly appreciated. Continue reading...
