PYTHON CODING:

import time

import cv2 import numpy as np from numpy.fft import fftshift

# Placeholder to stop auto-syntac checking from complaining TODO = None

def main():

# Read Image in grayscale and show img = cv2.imread("input.jpg", 0) cv2.imwrite("orig.png", img)

# ---------------------------------------------------------------------- # Create Filter # # TODO: 3 Marks: Create sharpen filter, but with a # Gaussian filter form the averaging instead of the mean filter. For the # Gaussian filter, use a kernel with size 31x31 with sigma 5. For the unit # impulse set the multiplier to be 2. kernel = TODO

# ---------------------------------------------------------------------- # Filter with FFT # # TODO: 1 Mark: Pad filter with zeros to have the same size as the image, # but with the filter in the center. This creates a larger filter, that # effectively does the same thing as the original image. kernel_padded = TODO

# Shift filter image to have origin on 0,0. This one is done for you. The # exact theory behind this was not explained in class so you may skip this # part. Drop by my office hours if you are interested. kernel_padded_shifted = fftshift(kernel_padded)

# TODO: 1 Mark: Move all signal to Fourier space (DFT). img_fft = TODO kernel_fft = TODO

# Display signals in Fourier Space # I put some visualization here to help debugging :-) cv2.imwrite( "orig_fft.png", np.minimum(1e-5 * np.abs(fftshift(img_fft)), 1.0) * 255.) cv2.imwrite( "filt_fft.png", np.minimum(1e-1 * np.abs(fftshift(kernel_fft)), 1.0) * 255.)

# TODO: 1 Mark: Do filtering in Fourier space img_filtered_fft = TODO

# TODO: 1 Mark: Bring back to Spatial domain (Inverse DFT) # TODO: 2 Marks: Throw away the imaginary part and clip between 0 and 255 # to make it a real image. img_sharpened = TODO cv2.imwrite("res_fft.png", img_sharpened.astype(np.uint8))

# ---------------------------------------------------------------------- # Filter with OpenCV # TODO: 1 Mark: Use padded filter and cyclic padding (wrap) to get exact results # TOOD: 1 Mark: Clip image for display img_sharpened = TODO cv2.imwrite("res_opencv.png", img_sharpened.astype(np.uint8))

cv2.waitKey(-1)

if __name__ == "__main__": main() exit(0)