def load_image(self, path='images/pixelart.png', display=False):
img = cv.imread(path, cv.IMREAD_GRAYSCALE)
if display:
# Display image
plt.close('all')
view = viewer(img, title=path.split('/')[-1], subplots=(1, 1))
return img
def get_gaussian_kernel(self, size, sig, display=False):
# Initializing value of x-axis and y-axis
# in the range -1 to 1
x, y = np.meshgrid(np.linspace(-1, 1, size), np.linspace(-1, 1, size))
dst = np.sqrt(x * x + y * y)
# Calculating Gaussian array
gauss = np.exp(-(dst**2) / (2.0 * sig**2))
if display:
# Display kernel
plt.close('all')
view = viewer(gauss, title='Gaussian kernel', subplots=(1, 1))
return gauss
def visual_comparison(self, use_saved=False):
if not use_saved:
self.img_convolved = self.convolve_2d()
self.img_fft_filtered = self.FFT_filtering()
self.img_fft_filtered_padded = self.FFT_filtering(mode='full')
plt.close('all')
titles = [
'Original', 'FFT filtered (no padding)', 'Convolution',
'FFT filtered (zero padding)'
]
view = viewer([
self.img, self.img_fft_filtered, self.img_convolved,
self.img_fft_filtered_padded
],
title=titles,
subplots=(2, 2),
joint_zoom=True)
def convolve_2d(self,
kernel=None,
mode='same',
boundary='fill',
fillvalue=0,
display=False,
save=False):
kernel = self.kernel if kernel is None else kernel
img_convolved = signal.convolve2d(self.img,
kernel,
mode=mode,
boundary=boundary,
fillvalue=fillvalue)
if display:
plt.close('all')
view = viewer([self.img, img_convolved],
title=['Original image', 'Convolved image'],
subplots=(1, 2))
if save:
self.img_convolved = img_convolved
return img_convolved
def display_kernel(self):
# Display kernel
plt.close('all')
view = viewer(self.kernel, title='Gaussian kernel', subplots=(1, 1))