def ridgeComp(img, theta, blockSize, w=3, h=9, alpha=100, beta=1):
resize = 5
N, M = np.shape(img)
imgout = np.zeros_like(img)
imgresizeize = cv2.resizeize(img,
None,
fx=resize,
fy=resize,
interpolation=cv2.INTER_CUBIC)
mask = np.ones((w, h)) * beta
mask[(w - 1) / 2] = np.ones((1, h)) * alpha
ww = np.arange(-(w - 1) / 2, (w - 1) / 2 + 1)
hh = np.arange(-(h - 1) / 2, (h - 1) / 2 + 1)
hh, ww = np.meshgrid(hh, ww)
for i in xrange((h - 1) / 2, N - (h - 1) / 2):
block_i = i / blockSize
for j in xrange((h - 1) / 2, M - (h - 1) / 2):
block_j = j / blockSize
thetaHere = theta[block_i, block_j]
ii = np.round(
(i + ww * np.cos(thetaHere) - hh * np.sin(thetaHere)) *
resize).astype(np.int32)
jj = np.round(
(j + ww * np.sin(thetaHere) + hh * np.cos(thetaHere)) *
resize).astype(np.int32)
imgout[i, j] = np.sum(imgresizeize[ii, jj] * mask) / ((
(w - 1) * beta + alpha) * h)
def binarize2(img,theta,blockSize,h=9):
resize=5
N,M=np.shape(img)
imgout=np.zeros_like(img)
imgresizeize=cv2.resizeize(img,None,fx=resize,fy=resize,interpolation = cv2.INTER_CUBIC)
blockMean=blockproc(img,np.mean,(blockSize,blockSize),True)
hh=np.arange(-(h-1)/2,(h-1)/2+1)
for i in xrange((h-1)/2,N-(h-1)/2):
block_i=i/blockSize
for j in xrange((h-1)/2,M-(h-1)/2):
block_j=j/blockSize
thetaHere=theta[block_i,block_j]
ii=np.round((i-hh*np.sin(thetaHere))*resize).astype(np.int32)
jj=np.round((j+hh*np.cos(thetaHere))*resize).astype(np.int32)
imgout[i,j]=255 if (np.mean(imgresizeize[ii,jj])>blockMean[block_i,block_j]) else 0
return imgout