def goBilateral(f):
g = like(f)
sigmaS = 24.0 # spatial filter sigma
sigmaR = computeSigmaR(f)
blf = BilateralFilter(sigmaS, sigmaR)
blf.apply(f, g)
plot(g, cmin=-1, cmax=1, name='bilateral filter')
def bilateralFilterQC(sigmaS, sigmaX, t, x):
y = like(x)
bf = BilateralFilter(sigmaS, sigmaX)
if t:
qc = bf.applyQC(t, x, y)
else:
qc = bf.applyQC(x, y)
return qc.sx, qc.sn, qc.sd, qc.tn, qc.td
def bilateralFilter(sigmaS, sigmaX, t, x):
y = like(x)
bf = BilateralFilter(sigmaS, sigmaX)
if t:
bf.apply(t, x, y)
else:
bf.apply(x, y)
return y
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 goRandomBlocks():
n1 = 801
x = makeBlocks(n1)
plotB(x, x, -12, 12, "rbx")
return
y = add(x, makeNoiseForBlocks(3141, 8.0, 3.0, n1))
plotB(x, y, -12, 12, "rbxy")
sigmaS = 20.0
yqqd = qqd(y)
z = zerofloat(n1)
for scale in [0.01, 0.5, 1.0, 1.5, 10, 100.0]:
sigmaX = scale * yqqd
print "sigmaS =", sigmaS, " sigmaX =", sigmaX
bf = BilateralFilter(sigmaS, sigmaX)
bf.setType(BilateralFilter.Type.TUKEY)
bf.apply(y, z)
png = "rbxz" + str(int(scale * 10 + 0.5))
if len(png) == 1: png = "0" + png
plotB(x, z, -12, 12, png=png)
