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)