def goFilterImpulses():
xa, s1, s2, clip = getImage()
xb = makeImpulses(12, len(xa[0]), len(xa))
t = diffusionTensors(2.0, xa)
plot(xa, s1, s2, 1.3)
#plot(xb,s1,s2,0.9)
for sigmaR in [0.1, 1.0, 100.0]:
#for sigmaR in [100.0]:
y = like(xa)
bf = BilateralFilter(30.0, sigmaR)
bf.setType(BilateralFilter.Type.TUKEY)
bf.applyAB(t, xa, xb, y)
y = smoothS(y)
#plot(y,s1,s2,0.5*max(y))
plot(y, s1, s2, 0.1)
def goFilterImpulses():
xa,s1,s2,clip = getImage()
xb = makeImpulses(12,len(xa[0]),len(xa))
t = diffusionTensors(2.0,xa)
plot(xa,s1,s2,1.3)
#plot(xb,s1,s2,0.9)
for sigmaR in [0.1,1.0,100.0]:
#for sigmaR in [100.0]:
y = like(xa)
bf = BilateralFilter(30.0,sigmaR)
bf.setType(BilateralFilter.Type.TUKEY)
bf.applyAB(t,xa,xb,y)
y = smoothS(y)
#plot(y,s1,s2,0.5*max(y))
plot(y,s1,s2,0.1)
def goFilterRandom():
xa, s1, s2, clip = getImage()
plot(xa, s1, s2, clip)
n1, n2 = len(xa[0]), len(xa)
xb = makeRandom(n1, n2)
t = diffusionTensors(2.0, xa)
#plot(xa,s1,s2,1.3)
#plot(xb,s1,s2,0.5)
xqqd = qqd(x)
for sigmaR in [xqqd, 100 * xqqd]:
y = like(xa)
bf = BilateralFilter(30.0, sigmaR)
bf.setType(BilateralFilter.Type.TUKEY)
bf.applyAB(t, xa, xb, y)
plot(y, s1, s2, 0.1)
bf.apply(t, xa, y)
plot(y, s1, s2, clip)
def goFilterRandom():
xa,s1,s2,clip = getImage()
plot(xa,s1,s2,clip)
n1,n2 = len(xa[0]),len(xa)
xb = makeRandom(n1,n2)
t = diffusionTensors(2.0,xa)
#plot(xa,s1,s2,1.3)
#plot(xb,s1,s2,0.5)
xqqd = qqd(x)
for sigmaR in [xqqd,100*xqqd]:
y = like(xa)
bf = BilateralFilter(30.0,sigmaR)
bf.setType(BilateralFilter.Type.TUKEY)
bf.applyAB(t,xa,xb,y)
plot(y,s1,s2,0.1)
bf.apply(t,xa,y)
plot(y,s1,s2,clip)
