def test_getImage(self):
expected = self.setup.getExpectedOutput(self._testMethodName)
input = self.setup.getInputMatrix('chaplin_dft.npy')
self.actual = util.getImage(input)
self.assertTrue(self.setup.imagesEqual(expected, self.actual))
def test_getImage_from_noisy_dft(self):
expected = self.setup.getExpectedOutput(self._testMethodName)
input = self.setup.getInputMatrix('noisy_dft.npy')
self.actual = util.getImage(input)
#cv2.imshow("image",self.actual)
#cv2.waitKey(0)
self.assertTrue(self.setup.imagesEqual(expected, self.actual))
# 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)
# Use this file as you wish to generate the images needed to answer the report
import src.project.Utilities as util
import numpy as np
import src.project.SelectiveImageAcquisition as sia
image = util.loadImage('images/brain.png')
rows, cols = image.shape
print(image.shape)
# code for cardiac cartesian
# mask = sia.cartesianPattern((rows, cols), 0.7)
mask = sia.circlePattern((rows, cols), 90)
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.displayImage(filtered_image)
# cutout = (250, 200, 150, 100)
# count = 0
# for cut in cutout:
# print(cut)
# mask = isn.gaussianHighpassFilter(mask_size2, cut)
# a = util.applyMask(img_copy, mask)
# b = util.writableDFT(a)
# c = util.normalizeImage(b)
# d = util.post_process_image(c)
# count += 1
# print(count)
# util.displayImage(d)
nmask_size = (h2, w2)
img_copy = util.getImage(img3)
mask = isn.idealHighpassFilter(nmask_size, 100)
mask1 = isn.idealLowpassFilter(nmask_size, 100)
mask2 = isn.gaussianHighpassFilter(nmask_size, 100)
mask3 = isn.ringLowpassFilter(nmask_size, 100, 10)
mask4 = isn.butterworthHighpassFilter(nmask_size, 50, 2)
masks = (mask, mask1, mask2, mask3, mask4)
count = 0
for mask in masks:
a = util.applyMask(mask, img_copy)
count += 1
print(count)
util.displayImage(a)
util.saveMatrix("Cardiac_DFT.jpg", write_dft)
height, width = cardiac.shape
cardiac_size = np.array([height, width])
brain = util.loadImage("images/brain.png")
normal_brain = util.normalizeImage(brain)
dft_brain = util.getDFT(normal_brain)
#util.saveMatrix("Brain_DFT.jpg", dft_brain)
height, width = brain.shape
brain_size = np.array([height, width])
p1mask = aqc.bandPattern(cardiac_size, 15, 128, 35)
p1fmask = util.post_process_image(p1mask)
util.saveImage("p1fmask.jpg", p1fmask)
p1applied = util.applyMask(dft_cardiac, p1mask)
p1image = util.getImage(p1applied)
p1fimage = util.post_process_image(p1image)
util.saveImage('p1_Masked_Image.jpg', p1fimage)
p2mask1 = aqc.bandPattern(cardiac_size, 15, 128, 10)
p2applied1 = util.applyMask(dft_cardiac, p2mask1)
p2image1 = util.getImage(p2applied1)
p2fimage1 = util.post_process_image(p2image1)
util.saveImage("p2_Masked_Image_1.jpg", p2fimage1)
p2mask2 = aqc.bandPattern(cardiac_size, 10, 171, 135)
p2applied2 = util.applyMask(dft_cardiac, p2mask2)
p2image2 = util.getImage(p2applied2)
p2fimage2 = util.post_process_image(p2image2)
util.saveImage("p2_Masked_Image_2.jpg", p2fimage2)
height, width = cardiac.shape
cardiac_size = np.array([height, width])
brain = util.loadImage("images/brain.png")
normal_brain = util.normalizeImage(brain)
dft_brain = util.getDFT(normal_brain)
height, width = brain.shape
brain_size = np.array([height, width])
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)