def filtering(filterType, img_Eq, param1, param2):
if filterType == 'bilateral':
img_filtered= cv2.bilateralFilter(img_Eq,param1,param2,param2)
if filterType == 'anisotropic':
img_filtered = ad(img_Eq, niter=param2,step= param1, kappa=50,gamma=0.10, option=1)
img_filtered=np.uint8(img_filtered)# if not frangi does not accept img_filtered because it is float between -1 and 1
if filterType == 'guided':
img_filtered = guidedFilter(img_Eq, img_Eq, param1, param2)
return img_filtered
cv2.imwrite(str(im) + '_Frangi_bl_.tif', img_fr_roi)
#save the data
with open(str(im) + '_Frangi_bl_classification_results.txt',
'w') as output:
output.write(str(perform_result))
if (index == 1):
v = []
#Anisotropic
for i in range(len(param_ad[0])):
for j in range(len(param_ad[1])):
#Anisotropic Diffusion
img_filtered = ad(img_Eq,
niter=param_ad[0][i],
step=param_ad[1][j],
kappa=50,
gamma=0.10,
option=1)
img_filtered = np.uint8(
img_filtered
) # if not frangi does not accept img_filtered because it is float between -1 and 1
v.append(
img_as_ubyte(
frangi(img_filtered, (1.0, 2.1),
0.5,
0.5,
0.125,
black_ridges=True)))
img_fr = v[0]