def goRangeFunctions():
xmin, xmax, sigma = -3.5, 3.5, 1.0
nx = 351
dx = (xmax - xmin) / (nx - 1)
fx = xmin
sx = Sampling(nx, dx, fx)
yg = BilateralFilter.sampleRangeFunction(gauss, sigma, sx)
yt = BilateralFilter.sampleRangeFunction(tukey, sigma, sx)
#yh = BilateralFilter.sampleRangeFunction(huber,sigma,sx)
#x = rampfloat(fx,dx,nx)
#yg = mul(x,yg)
#yh = mul(x,yh)
#yt = mul(x,yt)
sp = SimplePlot()
sp.setBackground(backgroundColor)
sp.setFontSizeForSlide(1.0, 0.9)
sp.setSize(700, 500)
sp.setHLabel(" ")
sp.setVLabel(" ")
solid = PointsView.Line.SOLID
dash = PointsView.Line.DASH
dot = PointsView.Line.DOT
#pv = sp.addPoints(sx,yh); pv.setLineStyle(dot); pv.setLineWidth(4)
pv = sp.addPoints(sx, yg)
pv.setLineStyle(dash)
pv.setLineWidth(4)
pv = sp.addPoints(sx, yt)
pv.setLineStyle(solid)
pv.setLineWidth(4)
if pngDir:
sp.paintToPng(720, 3.3, pngDir + "blftg.png")
def goRangeFunctions():
xmin,xmax,sigma = -3.5,3.5,1.0
nx = 351
dx = (xmax-xmin)/(nx-1)
fx = xmin
sx = Sampling(nx,dx,fx)
yg = BilateralFilter.sampleRangeFunction(gauss,sigma,sx)
yt = BilateralFilter.sampleRangeFunction(tukey,sigma,sx)
#yh = BilateralFilter.sampleRangeFunction(huber,sigma,sx)
#x = rampfloat(fx,dx,nx)
#yg = mul(x,yg)
#yh = mul(x,yh)
#yt = mul(x,yt)
sp = SimplePlot()
sp.setBackground(backgroundColor)
sp.setFontSizeForSlide(1.0,0.9)
sp.setSize(700,500)
sp.setHLabel(" ")
sp.setVLabel(" ")
solid = PointsView.Line.SOLID
dash = PointsView.Line.DASH
dot = PointsView.Line.DOT
#pv = sp.addPoints(sx,yh); pv.setLineStyle(dot); pv.setLineWidth(4)
pv = sp.addPoints(sx,yg); pv.setLineStyle(dash); pv.setLineWidth(4)
pv = sp.addPoints(sx,yt); pv.setLineStyle(solid); pv.setLineWidth(4)
if pngDir:
sp.paintToPng(720,3.3,pngDir+"blftg.png")
def goApproximation():
factor = 0.67448 # 3rd quartile of standard normal distribution (sigma = 1)
sigma = 1.401114
pmin, pmax = -9.0, 9.0
np, dp, fp = 301, 0.06, pmin
nk, dk, fk = 13, 1.5, pmin
sp = Sampling(np, dp, fp)
sk = Sampling(nk, dk, fk)
type = BilateralFilter.Type.GAUSS
rf = BilateralFilter.sampleRangeFunction(type, sigma, sp)
rg = BilateralFilter.sampleRangeFunction(type, sigma / sqrt(2.0), sp)
method = CubicInterpolator.Method.MONOTONIC
rfi = CubicInterpolator(method, np, rampfloat(fp, dp, np), rf)
rgi = CubicInterpolator(method, np, rampfloat(fp, dp, np), rg)
rr = zerofloat(np, np)
ra = zerofloat(np, np)
r0 = zerofloat(np, np)
rh = zerofloat(np, np)
for i in range(np):
pi = sp.getValue(i)
for j in range(np):
pj = sp.getValue(j)
rr[i][j] = rfi.interpolate(pi - pj)
for k in range(nk):
pk = sk.getValue(k)
rjk = rgi.interpolate(pj - pk)
rik = rgi.interpolate(pi - pk)
#hik = hat(dk,pi-pk)
#ra[i][j] += hik*rjk
ra[i][j] += rik * rjk
if k == nk / 2:
r0[i][j] = rjk
rh[i][j] = rik * rjk
#rh[i][j] = hik*rjk
rr = div(rr, max(rr))
r0 = div(r0, max(r0))
rh = div(rh, max(rh))
ra = div(ra, max(ra))
plotR2(sp, rr, "blfrr")
plotR2(sp, r0, "blfr0")
plotR2(sp, rh, "blfrh")
plotR2(sp, ra, "blfra")
def goApproximation():
factor = 0.67448 # 3rd quartile of standard normal distribution (sigma = 1)
sigma = 1.401114
pmin,pmax = -9.0,9.0
np,dp,fp = 301,0.06,pmin
nk,dk,fk = 13,1.5,pmin
sp = Sampling(np,dp,fp)
sk = Sampling(nk,dk,fk)
type = BilateralFilter.Type.GAUSS
rf = BilateralFilter.sampleRangeFunction(type,sigma,sp)
rg = BilateralFilter.sampleRangeFunction(type,sigma/sqrt(2.0),sp)
method = CubicInterpolator.Method.MONOTONIC
rfi = CubicInterpolator(method,np,rampfloat(fp,dp,np),rf)
rgi = CubicInterpolator(method,np,rampfloat(fp,dp,np),rg)
rr = zerofloat(np,np)
ra = zerofloat(np,np)
r0 = zerofloat(np,np)
rh = zerofloat(np,np)
for i in range(np):
pi = sp.getValue(i)
for j in range(np):
pj = sp.getValue(j)
rr[i][j] = rfi.interpolate(pi-pj)
for k in range(nk):
pk = sk.getValue(k)
rjk = rgi.interpolate(pj-pk)
rik = rgi.interpolate(pi-pk)
#hik = hat(dk,pi-pk)
#ra[i][j] += hik*rjk
ra[i][j] += rik*rjk
if k==nk/2:
r0[i][j] = rjk
rh[i][j] = rik*rjk
#rh[i][j] = hik*rjk
rr = div(rr,max(rr))
r0 = div(r0,max(r0))
rh = div(rh,max(rh))
ra = div(ra,max(ra))
plotR2(sp,rr,"blfrr")
plotR2(sp,r0,"blfr0")
plotR2(sp,rh,"blfrh")
plotR2(sp,ra,"blfra")