import numpy as np
import cv2
#from matplotlib import pyplot as plt
from skimage import morphology
class BloodVessels:
#img = cv2.imread("E:\\Rasheed Files\\Rasheed Data (DONOT DELETE)\\FYP\Dataset\\1\\30_left.jpeg")
jpegImg = 0
grayImg = 0
curImg = 0
def setImage(self, img):
self.jpegImg = img
self.curImg = np.array(img) ##Convert jpegFile to numpy array (Required for CV2)
def getImage(self):
return self.curImg
def greenComp(self):
###Extracting Green Component
gcImg = self.curImg[:,:,1]
self.curImg = gcImg
def histEqualize(self):
histEqImg = cv2.equalizeHist(self.curImg)
self.curImg = histEqImg
def applyKirschFilter(self):
gray = self.curImg
if gray.ndim > 2:
raise Exception("illegal argument: input must be a single channel image (gray)")
kernelG1 = np.array([[ 5, 5, 5],
[-3, 0, -3],
[-3, -3, -3]], dtype=np.float32)
kernelG2 = np.array([[ 5, 5, -3],
[ 5, 0, -3],
[-3, -3, -3]], dtype=np.float32)
kernelG3 = np.array([[ 5, -3, -3],
[ 5, 0, -3],
[ 5, -3, -3]], dtype=np.float32)
kernelG4 = np.array([[-3, -3, -3],
[ 5, 0, -3],
[ 5, 5, -3]], dtype=np.float32)
kernelG5 = np.array([[-3, -3, -3],
[-3, 0, -3],
[ 5, 5, 5]], dtype=np.float32)
kernelG6 = np.array([[-3, -3, -3],
[-3, 0, 5],
[-3, 5, 5]], dtype=np.float32)
kernelG7 = np.array([[-3, -3, 5],
[-3, 0, 5],
[-3, -3, 5]], dtype=np.float32)
kernelG8 = np.array([[-3, 5, 5],
[-3, 0, 5],
[-3, -3, -3]], dtype=np.float32)
g1 = cv2.normalize(cv2.filter2D(gray, cv2.CV_32F, kernelG1), None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8UC1)
g2 = cv2.normalize(cv2.filter2D(gray, cv2.CV_32F, kernelG2), None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8UC1)
g3 = cv2.normalize(cv2.filter2D(gray, cv2.CV_32F, kernelG3), None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8UC1)
g4 = cv2.normalize(cv2.filter2D(gray, cv2.CV_32F, kernelG4), None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8UC1)
g5 = cv2.normalize(cv2.filter2D(gray, cv2.CV_32F, kernelG5), None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8UC1)
g6 = cv2.normalize(cv2.filter2D(gray, cv2.CV_32F, kernelG6), None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8UC1)
g7 = cv2.normalize(cv2.filter2D(gray, cv2.CV_32F, kernelG7), None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8UC1)
g8 = cv2.normalize(cv2.filter2D(gray, cv2.CV_32F, kernelG8), None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8UC1)
magn = cv2.max(g1, cv2.max(g2, cv2.max(g3, cv2.max(g4, cv2.max(g5, cv2.max(g6, cv2.max(g7, g8)))))))
self.curImg = magn
def applyThreshold(self):
ret, threshImg = cv2.threshold(self.curImg,160,180,cv2.THRESH_BINARY_INV)
self.curImg = threshImg
def cleanSmallObjects(self):
cleanImg = morphology.remove_small_objects(self.curImg, min_size=130, connectivity=100)
self.curImg = cleanImg
#cv2.imwrite('Final123.jpg',threshImg)
