cutoff = np.array([5, 20, 45, 60])
order = np.array([1, 2, 3, 4])
for i in cutoff:
for j in order:
p6mask = noise.butterworthLowpassFilter(brain_size, i, j)
p6applied = util.applyMask(dft_brain, p6mask)
p6image = util.getImage(p6applied)
p6fimage = util.post_process_image(p6image)
filename = "p6_Masked_Image_" + str(i) + "_" + str(j) + ".jpg"
snr_p6 = util.signalToNoise(brain, p6fimage)
print(filename, snr_p6)
util.saveImage(filename, p6fimage)
glhp = np.array([200, 40, 120, 10])
for k in glhp:
p7lmask = noise.gaussianLowpassFilter(brain_size, k)
p7lapplied = util.applyMask(dft_brain, p7lmask)
p7limage = util.getImage(p7lapplied)
p7lfimage = util.post_process_image(p7limage)
filename = "p7_GLP_Masked_Image_" + str(k) + ".jpg"
util.saveImage(filename, p7lfimage)
p7hmask = noise.gaussianHighpassFilter(brain_size, k)
p7happlied = util.applyMask(dft_brain, p7hmask)
p7himage = util.getImage(p7happlied)
p7hfimage = util.post_process_image(p7himage)
filename = "p7_GHP_Masked_Image_" + str(k) + ".jpg"
util.saveImage(filename, p7hfimage)
noisy = util.loadMatrix("images/noisyimage.npy")
noisy_2 = noisy.real.astype(np.complex128)
# Use this file as you wish to generate the images needed to answer the report
import src.project.Utilities as util
import src.project.ImageSynthesisNoise as isn
import cv2
import numpy as np
# image = util.loadImage('images/brain.png')
matrix = util.loadMatrix('images/noisyimage.npy')
rows, cols = matrix.shape
mask = isn.gaussianLowpassFilter((rows, cols), cutoff=40)
im = np.multiply(matrix, mask)
im = util.post_process_images(util.getImage(im))
# im = np.abs(matrix)
# im = util.post_process_images(im)
# rows, cols = image.shape
util.displayImage(im)
# mask = isn.butterworthLowpassFilter((rows, cols), cutoff=40, order=7)
# mask = isn.gaussianHighpassFilter((rows, cols), cutoff=150)
# shift_fft = util.getDFT(image)
# filtered_image_fft = np.multiply(mask, shift_fft)
# filtered_image = util.post_process_images(util.getImage(filtered_image_fft))
# util.saveImage('butterworthLowpassFilter.png', filtered_image)
# print(util.signalToNoise(filtered_image))
# util.displayImage(filtered_image)
def test_gaussianLowpassFilter_half_width(self):
expected = self.setup.getExpectedOutput(self._testMethodName)
self.actual = noise.gaussianLowpassFilter(self.emptymask, 100)
self.actual = self.setup.normalizeImage(self.actual)
self.assertTrue(self.setup.imagesEqual(expected, self.actual))