def PreHamor2(src):
'''Preprocess image of Hamorrhage by morphology.
'''
print 'Preprocessing...'
img=src.astype(float)
b,g,r = cv2.split(img)
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS,(20, 20))
# eroded = cv2.erode(g,kernel)
# dilated = cv2.dilate(eroded,kernel)
blackhat = cv2.morphologyEx(g, cv2.MORPH_BLACKHAT, kernel)
tmp = g-blackhat*5
dst = g-blackhat*5
tmp.clip(0,255)
dst.clip(0,255)
f_dst = createFlatView(dst)
f_src = createFlatView(src)
#The background area must be removed
for i in xrange(f_dst.size):
if f_dst[i]<30 and (f_src[3*i]>10 or f_src[3*i+1]>10 or f_src[3*i+2]>10):
f_dst[i]=80
else:
f_dst[i]=0
# dst = dst.astype(np.uint8)
# dst = cv2.medianBlur(dst,3)
#
# tmp_xx=cv2.filter2D(tmp,-1, np.array([[0,0,0],[0,1,-1],[0,-1,1]]))
# beta=0
# alpha=30
# tmp_xx = 255/(1+np.exp(-(tmp_xx-beta)/alpha))
# tmp_xy=cv2.filter2D(tmp,-1, np.array([[0,0,0],[0,1,-1],[0,-1,1]]))
# beta=0
# alpha=30
# tmp_xy = 255/(1+np.exp(-(tmp_xy-beta)/alpha))
# tmp_yy=cv2.filter2D(tmp,-1, np.array([[0,1,0],[0,-2,0],[0,1,0]]))
# beta=0
# alpha=30
# tmp_yy = 255/(1+np.exp(-(tmp_yy-beta)/alpha))
featureImg = np.zeros(img.shape,img.dtype)
cv2.merge((tmp,g,tmp),featureImg)
tmpImg = np.zeros(img.shape,img.dtype)
cv2.merge((tmp,tmp,tmp),tmpImg)
return featureImg,dst,tmpImg
def PreHamor(src):
'''Preprocess image of Hamorrhage by morphology.
'''
print 'Preprocessing...'
img=src.astype(float)
b,g,r = cv2.split(img)
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS,(20, 20))
# eroded = cv2.erode(g,kernel)
# dilated = cv2.dilate(eroded,kernel)
blackhat = cv2.morphologyEx(g, cv2.MORPH_BLACKHAT, kernel)
tmp = g-blackhat*5
dst = g-blackhat*5
# blackhat = cv2.morphologyEx(dst, cv2.MORPH_BLACKHAT, kernel)
# tmp = g-blackhat
# wsize=img.shape[0]/30
# if wsize%2 is 0:
# wsize=wsize+1
#
# tmp=tmp.clip(0,255)
# tmp1 = tmp.astype(np.uint8)
# bgEst = cv2.medianBlur(tmp1,wsize)
#
# dst=(bgEst-tmp).clip(0,255)
#
# f_dst = createFlatView(dst)
# for i in xrange(f_dst.size):
# if f_dst[i]<20:
# f_dst[i]=0
# dst=dst*5
dst.clip(0,255)
f_dst = createFlatView(dst)
f_src = createFlatView(src)
#The background area must be removed
for i in xrange(f_dst.size):
if f_dst[i]<15 and (f_src[3*i]>10 or f_src[3*i+1]>10 or f_src[3*i+2]>10):
f_dst[i]=80
else:
f_dst[i]=0
# beta=10
# alpha=-0.05
# dst = 255/(1+np.exp(-(tmp-beta)/alpha))
dst = dst.astype(np.uint8)
dst = cv2.medianBlur(dst,3)
return dst
def ExudateSegment(originImg, ResultPath, presegPath=''):
presegImg = PreprocessingModule.PreExudate(originImg)
presegImg = presegImg.astype(np.uint8)
presegImg = cv2.medianBlur(presegImg, 3)
f_presegImg = createFlatView(presegImg)
for i in xrange(f_presegImg.size):
if f_presegImg[i]>100:
f_presegImg[i]=80
else:
f_presegImg[i]=0
#cv2.imwrite(presegPath,presegImg)
CNNSeg = CNNSegModule.ExudateCNNSegNetwork('./ConfExu.txt')
result=CNNSeg.segment(originImg, presegImg, 128)
#connect two Images
showImg = np.full((result.shape[0],result.shape[1]*2+3,result.shape[2]), 255, result.dtype)
showImg[0:originImg.shape[0],0:originImg.shape[1],:]=originImg[:,:,:]
showImg[0:result.shape[0],originImg.shape[1]+3:originImg.shape[1]+result.shape[1]+3,:]= result[:,:,:]
cv2.imwrite(ResultPath,showImg)
def apply(self, src, dst):
"""Apply the filter with a BGR or gray source/destination."""
srcFlatView = utils.createFlatView(src)
dstFlatView = utils.createFlatView(dst)
utils.applyLookupArray(self._vLookupArray, srcFlatView, dstFlatView)
def apply(self, src, dest):
flatSrc = utils.createFlatView(src)
flatDest = utils.createFlatView(dest)
utils.searchLookupTable(self._lookupArray, flatSrc, flatDest)
def HamorSegment(originImg, ResultPath, presegPath=''):
# cv2.imwrite("./temp_ori.jpg",originImg)
featureImg,presegImg,tmpImg = PreprocessingModule.PreHamor2(originImg)
# presegImg = presegImg.astype(np.uint8)
# presegImg = cv2.medianBlur(presegImg, 3)
#
# f_presegImg = createFlatView(presegImg)
#
# for i in xrange(f_presegImg.size):
# if f_presegImg[i]>100:
# f_presegImg[i]=80
# else:
# f_presegImg[i]=0
###Preprocess by Jinpeng
# originImg1 = cv2.imread("/home/linhuangjing/Desktop/Retina_Data/Group-1/DR/DR_102.jpg")
cv2.imwrite("./temp_ori.jpg",originImg)
originImg2 = cv2.imread("./temp_ori.jpg")
# cv2.imwrite("/home/linhuangjing/Desktop/Retina_Data/Group-1/results/strange.jpg",originImg1-originImg2)
import JPModule
JPModule.Preprocess("./temp_ori.jpg","./temp_jppre.jpg")
JPImg=cv2.imread("./temp_jppre.jpg")
b,g,r = cv2.split(JPImg)
cv2.imwrite("/home/linhuangjing/Desktop/Retina_Data/Group-1/results/MyPre_102.jpg",presegImg)
cv2.imwrite("/home/linhuangjing/Desktop/Retina_Data/Group-1/results/JPPre_102.jpg",JPImg)
#Mask two images
f_g = createFlatView(g)
f_presegImg = createFlatView(presegImg)
for i in xrange(f_presegImg.size):
if f_presegImg[i]>f_g[i]:
f_presegImg[i] = f_g[i]
cv2.imwrite("/home/linhuangjing/Desktop/Retina_Data/Group-1/results/Pre_102.jpg",presegImg)
preImg3 = np.zeros(originImg.shape,presegImg.dtype)
cv2.merge((presegImg,presegImg,presegImg),preImg3)
showTmpImg = np.full((originImg.shape[0],originImg.shape[1]*2+3,originImg.shape[2]), 255, tmpImg.dtype)
showTmpImg[0:preImg3.shape[0],0:preImg3.shape[1],:]=preImg3[:,:,:]
showTmpImg[0:tmpImg.shape[0],preImg3.shape[1]+3:preImg3.shape[1]+tmpImg.shape[1]+3,:]= tmpImg[:,:,:]
cv2.imwrite(presegPath,showTmpImg)
featureImg = featureImg.clip(0,255)
cv2.imwrite("/home/linhuangjing/Desktop/featureImg.jpg",featureImg)
CNNSeg = CNNSegModule.HamorCNNSegNetwork('./ConfHamor.txt')
result=CNNSeg.segment(featureImg, presegImg, 1)
#connect two Images
# showImg = np.full((result.shape[0],result.shape[1]*2+3,result.shape[2]), 255, result.dtype)
# showImg[0:originImg.shape[0],0:originImg.shape[1],:]=originImg[:,:,:]
# showImg[0:result.shape[0],originImg.shape[1]+3:originImg.shape[1]+result.shape[1]+3,:]= result[:,:,:]
result=result.astype(float)
b,g,r = cv2.split(result)
a = np.zeros(g.shape,g.dtype)
f_g = createFlatView(g)
f_a = createFlatView(a)
f_r = createFlatView(r)
f_b = createFlatView(b)
for i in xrange(f_g.size):
if f_g[i]>20 or f_r[i]>20 or f_b[i]>20:
f_a[i]=255
showImg = np.zeros((result.shape[0],result.shape[1],4),g.dtype)
cv2.merge((b,g,r,a),showImg)
cv2.imwrite(ResultPath,showImg)
def apply(self, src, dst):
"""Apply the filter with a BGR or gray source/destination."""
srcFlatView = utils.createFlatView(src)
dstFlatView = utils.createFlatView(dst)
utils.applyLookupArray(self._vLookupArray, srcFlatView,
dstFlatView)
def PreExudate(src):
'''Preprocess image of exudates by estimate the Background via a Median Filter,
Which is used for exudates preprocessing.
'''
# InImage=cv2.cvtColor(src,cv2.cv.CV_BGR2GRAY)
print 'Preprocessing...'
img=src.astype(float)
b,g,r = cv2.split(img)
InImage = np.zeros(g.shape,g.dtype)
cv2.addWeighted(b,0.333333,r,0.333333,0,InImage)
cv2.addWeighted(g,0.333333,InImage,1.0,0,InImage)
# cv2.addWeighted(b,0.5,r,0.5,0,InImage)
wsize=img.shape[0]/30
if wsize%2 is 0:
wsize=wsize+1
InImage = InImage.astype(np.uint8)
bgEst = cv2.medianBlur(InImage,wsize)
bgMask = np.zeros(bgEst.shape,bgEst.dtype)
bgMask2 = np.zeros(bgEst.shape,bgEst.dtype)
f_InImage = createFlatView(InImage)
f_bgEst = createFlatView(bgEst)
f_bgMask = createFlatView(bgMask)
f_bgMask2 = createFlatView(bgMask2)
for i in xrange(f_bgMask.size):
if f_InImage[i]>f_bgEst[i]:
f_bgMask[i] = f_InImage[i]
f_bgMask2[i] = f_bgEst[i]
else:
f_bgMask[i] = f_bgEst[i]
f_bgMask2[i] = f_InImage[i]
im1 = bgMask-bgMask2
im2 = g-bgEst
dst = np.zeros(im2.shape,im2.dtype)
f_im1 = createFlatView(im1)
f_im2 = createFlatView(im2)
f_dst = createFlatView(dst)
for i in xrange(f_im1.size):
if f_im1[i]<8:
f_im1[i] = 0
if f_im2[i]<6:
f_im2[i] = 0
if f_im1[i]<f_im2[i]:
f_dst[i] = f_im1[i]
else:
f_dst[i] = f_im2[i]
if f_dst[i]>5:
f_dst[i] = 255
else:
f_dst[i] = 0
return dst
def apply(self, src, dst):
''' Apply the filter with BGR or gray source/dest'''
srcFlatView = utils.createFlatView(src)
dstFlatView = utils.createFlatView(dst)
utils.applyLookupArray(self._vLookupArray, srcFlatView, dstFlatView)