def callAsymmetricFrangi(img, sigma, beta, degreeOfAsymmetrie):
if (degreeOfAsymmetrie == 1.0):
return frangi(img,
sigma_x=sigma,
sigma_y=sigma,
beta1=beta,
black_ridges=True)
else:
return frangi(img,
sigma_x=sigma,
sigma_y=sigma * degreeOfAsymmetrie,
beta1=beta,
black_ridges=True), frangi(img,
sigma_x=sigma *
degreeOfAsymmetrie,
sigma_y=sigma,
beta1=beta,
black_ridges=True)
#thresh= [0.2,0.25]
class_result = ""
#apply the filters
for i in range(len(param_x)):
start_time1 = time.time()
for l in range(len(thresh)):
onlyFPvec = []
sensitivityVec = []
for k in range(len(param_y)):
img_fr = frangi(img_fft,
sigma_x=param_x[i],
sigma_y=param_y[k],
beta1=0.5,
black_ridges=True)
#save the frangi image in order to check it with IJ
#imsave(str(im)+'_img_fr_bl_x_y_'+str(param_x[k])+'_thresh_'+str(thresh[l])+'.tif', (img_fr).astype(np.float32))
# THE part below can be optimized??????????
for t in range(len(img_fr)):
for z in range(len(img_fr)):
if ((img_fr[t][z]) > thresh[l]):
img_fr[t][z] = 1
else:
img_fr[t][z] = 0
img_roi = createROI(img_Eq)
#sigma x-y8
param_x = [1.0]
degree = np.arange(1, 10.1, 1)
thresh = 0.20
for x_i in range(len(param_x)):
#v=[]
for d_i in range(len(degree)):
if (degree[d_i] == 1.0):
start_time1 = time.time()
img_fr = frangi(img_fft,
sigma_x=param_x[x_i],
sigma_y=param_x[x_i],
beta1=0.5,
black_ridges=True)
print(time.time() - start_time1)
"""
for t in range(len(img_fr)):
for z in range(len(img_fr)):
if((img_fr[t][z])>thresh):
img_fr[t][z] = 1
else:
img_fr[t][z] = 0
img_fr_max_roi = (img_fr*img_roi).astype(np.float32)