# 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)
import cv2
import numpy as np
import src.project.SelectiveImageAcquisition as aqc
import src.project.Utilities as util
cardiac = util.loadImage("images/cardiac.jpg")
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)
def test_getDFT(self):
expected = self.setup.getExpectedOutputMatrix(self._testMethodName)
input = self.setup.getInputImage('chaplin.png')
self.actual = util.getDFT(input)
self.assertTrue(self.setup.matrixEqual(expected, self.actual))
# # util.displayImage(mask4) # masks = (mask1, mask2, mask3, mask4) # # count = 0 # for mask in masks: # 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) ################# Part 3 ##################### img_copy2 = util.getDFT(img) #mask_size2 = (h, w) percent = .5 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: