def sharpen(im: np.array):
laplacian = np.array([
[0, -1, 0],
[-1, 4, -1],
[0, -1, 0]
])
### START YOUR CODE HERE ### (You can change anything inside this block)
im_filt = convolve_im(im, laplacian, verbose=True)
# JUST FOR TESTING
# im_filt = im_filt-im_filt.min()
# im_filt = im_filt/im_filt.max()
#
# im = im-im.min()
# im = im/im.max()
# im_filt = np.where(im+im_filt > im.max(), im.max(), im_filt)
# im_filt = np.where(im+im_filt < im.min(), im.min(), im_filt)
#
im_sharp = im+im_filt
#im_sharp = np.where(im_sharp > im.max(), im.max(), im_sharp)
plt.imshow(im_sharp,cmap="gray")
### END YOUR CODE HERE ###
return im_sharp
def sharpen(im: np.array):
""" Sharpens a grayscale image using the laplacian operator.
Args:
im: np.array of shape [H, W]
Returns:
im: np.array of shape [H, W]
"""
laplacian = np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]])
im = im + convolve_im(im, laplacian)
plt.show()
return im
def sharpen(im: np.array):
""" Sharpens a grayscale image using the laplacian operator.
Args:
im: np.array of shape [H, W]
Returns:
im: np.array of shape [H, W]
"""
laplacian = np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]])
### START YOUR CODE HERE ### (You can change anything inside this block)
conv_im = convolve_im(im, laplacian, verbose=False)
im = im + conv_im
### END YOUR CODE HERE ###
return im
def sharpen(im: np.array):
""" Sharpens a grayscale image using the laplacian operator.
Args:
im: np.array of shape [H, W]
Returns:
im: np.array of shape [H, W]
"""
laplacian = np.array([
[0, -1, 0],
[-1, 4, -1],
[0, -1, 0]
])
### START YOUR CODE HERE ### (You can change anything inside this block)
###just adds the image and the convolve_im result together and returns it
im=np.add(im,convolve_im(im, laplacian, True))
### END YOUR CODE HERE ###
return im
def sharpen(im: np.array):
""" Sharpens a grayscale image using the laplacian operator.
Args:
im: np.array of shape [H, W]
Returns:
im: np.array of shape [H, W]
"""
laplacian = np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]])
### START YOUR CODE HERE ### (You can change anything inside this block)
# Convolute image and Laplacian to find image details.
sharpened = convolve_im(im, laplacian)
# Attenuate the details in the image.
im = im + sharpened
### END YOUR CODE HERE ###
return im
def sharpen(im: np.array):
""" Sharpens a grayscale image using the laplacian operator.
Args:
im: np.array of shape [H, W]
Returns:
im: np.array of shape [H, W]
"""
laplacian = np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]])
### START YOUR CODE HERE ### (You can change anything inside this block)
# Convolve img with laplacian kernel
convolved_im = convolve_im(im, laplacian, verbose=True)
# Use equation 6.
resulting_im = np.add(im, (im * convolved_im))
# Limit values between [0,1]
im = np.add(resulting_im, resulting_im.min())
im = np.multiply(im, 1 / im.max())
### END YOUR CODE HERE ###
return im
import skimage
import os
import numpy as np
import utils
from task4b import convolve_im
from matplotlib import pyplot as plt
if __name__ == "__main__":
# DO NOT CHANGE
impath = os.path.join("images", "clown.jpg")
im = skimage.io.imread(impath)
im = utils.uint8_to_float(im)
kernel = np.load("images/notch_filter.npy")
### START YOUR CODE HERE ### (You can change anything inside this block)
im_filtered = convolve_im(im, kernel)
plt.show()
### END YOUR CODE HERE ###
utils.save_im("clown_filtered.png", im_filtered)
plt.imshow(conv_result, cmap="gray")
### END YOUR CODE HERE ###
return conv_result
if __name__ == "__main__":
# DO NOT CHANGE
impath = os.path.join("images", "clown.jpg")
im = skimage.io.imread(impath)
im = utils.uint8_to_float(im)
kernel = np.load("images/notch_filter.npy")
### START YOUR CODE HERE ### (You can change anything inside this block)
im_filtered = convolve_im(im, kernel, verbose = True)
### END YOUR CODE HERE ###
utils.save_im("clown_filtered.png", im_filtered)
H,W = np.shape(im)
h,w = np.shape(kernel)
t_b = (H-h)//2
l_r = (W-w)//2
kernel_padded = np.pad(kernel, ((0, 2*t_b),(0, 2*l_r)), 'constant')
plt.figure(figsize=(20, 5))
plt.subplot(1, 3, 1)
f_im = np.fft.fftshift(np.fft.fft2(im, s=None, axes=(-2, -1), norm=None))
plt.imshow(np.abs(f_im), norm=LogNorm(vmin=1, vmax = 1000), aspect='auto')
import matplotlib.pyplot as plt
from task4b import convolve_im
if __name__ == "__main__":
# DO NOT CHANGE
impath = os.path.join("images", "noisy_moon.png")
im = utils.read_im(impath)
# START YOUR CODE HERE ### (You can change anything inside this block)
im_freq = np.fft.fft2(im)
im_freq_old = im_freq.copy()
neigboor_average = np.ones((5, 5))/25
neigboor_average[2, 2] = 0
neigboor_convolved = convolve_im(np.abs(im_freq), neigboor_average, False)
ratio = np.abs(im_freq) / neigboor_convolved
noise_arg = np.unravel_index(
np.argmax(ratio), im_freq.shape)
print(noise_arg)
im_freq[noise_arg] = 0
im_freq[-noise_arg[0], -noise_arg[1]] = 0
im_filtered = np.fft.ifft2(im_freq)
fig, ax = plt.subplots(2, 2)
ax[0, 0].imshow(im, cmap="gray")
ax[0, 1].imshow(np.abs(im_filtered), cmap="gray")
# ax[0, 2].set_axis_off()
ax[1, 0].imshow(np.abs(np.fft.fftshift(im_freq_old)),
cmap="gray", norm=SymLogNorm(1))