def getInputImage(self, imagename):
return util.loadImage(self.pathInput + imagename)
def getExpectedOutput(self, testname):
self.testname = testname
return util.loadImage(self.pathExpectOutput + self.testname + '.png')
# 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)
# 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)
def test_loadImage_jpg(self):
expected = self.setup.getExpectedOutput(self._testMethodName)
self.actual = util.loadImage("resources/inputs/test_loadImage.jpg")
self.assertTrue(self.setup.imagesEqual(expected, self.actual))
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)