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displayTumor.py
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displayTumor.py
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import numpy as np
import cv2 as cv
class DisplayTumor:
curImg = 0
Img = 0
def readImage(self, img):
self.Img = np.array(img)
self.curImg = np.array(img)
gray = cv.cvtColor(np.array(img), cv.COLOR_BGR2GRAY)
self.ret, self.thresh = cv.threshold(gray, 0, 255, cv.THRESH_BINARY_INV + cv.THRESH_OTSU)
def getImage(self):
return self.curImg
# noise removal
def removeNoise(self):
self.kernel = np.ones((3, 3), np.uint8)
opening = cv.morphologyEx(self.thresh, cv.MORPH_OPEN, self.kernel, iterations=2)
self.curImg = opening
def displayTumor(self):
# sure background area
sure_bg = cv.dilate(self.curImg, self.kernel, iterations=3)
# Finding sure foreground area
dist_transform = cv.distanceTransform(self.curImg, cv.DIST_L2, 5)
ret, sure_fg = cv.threshold(dist_transform, 0.7 * dist_transform.max(), 255, 0)
# Find unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv.subtract(sure_bg, sure_fg)
# Marker labelling
ret, markers = cv.connectedComponents(sure_fg)
# Add one to all labels so that sure background is not 0, but 1
markers = markers + 1
# Now mark the region of unknown with zero
markers[unknown == 255] = 0
markers = cv.watershed(self.Img, markers)
self.Img[markers == -1] = [255, 0, 0]
tumorImage = cv.cvtColor(self.Img, cv.COLOR_HSV2BGR)
self.curImg = tumorImage