mask = sia.cartesianPattern(mask_size2, percent)
mask1 = sia.cartesianPattern(mask_size2, .05)
mask2 = sia.cartesianPattern(mask_size2, .15)
mask3 = sia.cartesianPattern(mask_size2, .35)
mask4 = sia.cartesianPattern(mask_size2, .64)
masks = (mask1, mask2, mask3, mask4)
count = 0
for mask in masks:
a = util.applyMask(img_copy2, mask)
b = util.writableDFT(a)
c = util.normalizeImage(b)
d = util.post_process_image(c)
count += 1
print(count)
util.displayImage(d)
############## Part 4 #####################
#change img(heart) to img2(brain)
# img_copy = util.getDFT(img2)
# rays = (1, 15, 50, 100, 360)
#
# mask = sia.radialPattern(mask_size2, rays[0])
#
# mask1 = sia.radialPattern(mask_size2, rays[1])
# mask2 = sia.radialPattern(mask_size2, rays[2])
# mask3 = sia.radialPattern(mask_size2, rays[3])
normal_cardiac = util.normalizeImage(cardiac)
dft_cardiac = util.getDFT(normal_cardiac)
write_dft = util.writableDFT(dft_cardiac)
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)
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)
p7hmask = noise.gaussianHighpassFilter(brain_size, k)