# 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 heart = "images/cardiac.jpg" brain = "images/brain.png" matrix = "images/noisyimage.npy" ############## Utilities ################### img = util.loadImage(heart) img2 = util.loadImage(brain) img3 = util.loadMatrix(matrix) # util.displayImage(img) # util.displayImage(img2) #util.displayImage(img) h = img.shape[0] w = img.shape[1] h1 = img2.shape[0] w1 = img2.shape[1] w2 = img3.shape[1] h2 = img3.shape[0] print(h2) print(w2) mask_size = (h, w) mask_size2 = (h1, w1) #img_copy = util.getDFT(img2)
# 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)
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)
noisy_image = util.getImage(noisy_2)
noisyfi = util.post_process_image(noisy_image)
#util.displayImage(noisyfi)
noisy_2_write = util.writableDFT(noisy_2)
#util.displayImage_plt(noisy_2_write)
height_noisy, width_noisy = noisy_2.shape
noisy_size = np.array([height_noisy, width_noisy])
p8mask1 = noise.butterworthLowpassFilter(noisy_size, 99, 100)
p8mask2 = noise.gaussianLowpassFilter(noisy_size, 50)
p8mask3 = noise.idealLowpassFilter(noisy_size, 99)
p8applied1 = util.applyMask(noisy_2, p8mask1)
p8applied2 = util.applyMask(noisy_2, p8mask2)
p8applied3 = util.applyMask(noisy_2, p8mask3)
p8image1 = util.getImage(p8applied1)