def _filter(self, FT):
""" Helper function to apply Butterworth filter in
frequensy domain.
"""
filterSize = numpy.max(self._size)
pin=filters.makeRadialMatrix(matrixSize=filterSize, center=(0,0), radius=1.0)
pin[int(filterSize / 2)][int(filterSize / 2)] = 0.00000001 # Prevents divide by zero error. This is DC and is set to zero later anyway.
FT = numpy.multiply(FT,(pin) ** self.noiseFractalPower)
if self.noiseFilterOrder > 0.01:
if self._upsf<(filterSize/2.0):
filter = filters.butter2d_lp_elliptic(size = [filterSize,filterSize],
cutoff_x = self._upsf / filterSize,
cutoff_y = self._upsf / filterSize,
n=self.noiseFilterOrder,
alpha=0,
offset_x = 0.5/filterSize, #becuase FFTs are slightly off centred.
offset_y = 0.5/filterSize)
else:
filter = numpy.ones((int(filterSize),int(filterSize)))
if self._lowsf > 0:
if self._lowsf > filterSize/2:
msg = ('Lower cut off frequency for filtered '
'noise is too high (exceeds Nyquist limit).')
raise Warning(msg)
filter = filter-filters.butter2d_lp_elliptic(size = [filterSize,filterSize],
cutoff_x = self._lowsf / filterSize,
cutoff_y = self._lowsf / filterSize,
n = self.noiseFilterOrder,
alpha = 0,
offset_x = 0.5/filterSize, #becuase FFTs are slightly off centred.
offset_y = 0.5/filterSize)
return FT * filter
else:
return FT
def _filter(self, FT):
""" Helper function to apply Butterworth filter in
frequensy domain.
"""
filterSize = numpy.max(self._size)
pin = filters.makeRadialMatrix(matrixSize=filterSize,
center=(0, 0),
radius=1.0)
pin[int(filterSize / 2)][int(
filterSize / 2
)] = 0.00000001 # Prevents divide by zero error. This is DC and is set to zero later anyway.
FT = numpy.multiply(FT, (pin)**self.noiseFractalPower)
if self.noiseFilterOrder > 0.01:
if self._upsf < (filterSize / 2.0):
filter = filters.butter2d_lp_elliptic(
size=[filterSize, filterSize],
cutoff_x=self._upsf / filterSize,
cutoff_y=self._upsf / filterSize,
n=self.noiseFilterOrder,
alpha=0,
offset_x=0.5 /
filterSize, #becuase FFTs are slightly off centred.
offset_y=0.5 / filterSize)
else:
filter = numpy.ones((int(filterSize), int(filterSize)))
if self._lowsf > 0:
if self._lowsf > filterSize / 2:
msg = ('Lower cut off frequency for filtered '
'noise is too high (exceeds Nyquist limit).')
raise Warning(msg)
filter = filter - filters.butter2d_lp_elliptic(
size=[filterSize, filterSize],
cutoff_x=self._lowsf / filterSize,
cutoff_y=self._lowsf / filterSize,
n=self.noiseFilterOrder,
alpha=0,
offset_x=0.5 /
filterSize, #becuase FFTs are slightly off centred.
offset_y=0.5 / filterSize)
return FT * filter
else:
return FT
def buildNoise(self):
"""build a new noise sample. Required to act on changes to any noise parameters or texRes.
"""
if self.units == 'pix':
if not (self.noiseType in ['Binary','binary','Normal','normal','uniform','Uniform']):
mysize = numpy.max(self.size)
else:
mysize = self.size
sampleSize = self.noiseElementSize
mysf = self.__dict__['noiseBaseSf']*mysize
lowsf = self.noiseFilterLower*mysize
upsf = self.noiseFilterUpper*mysize
else:
mysize = self.texRes
pixSize = self.size/self.texRes
sampleSize = self.noiseElementSize/pixSize
mysf = self.size[0]*self.noiseBaseSf
lowsf = self.size[0]*self.noiseFilterLower
upsf = self.size[0]*self.noiseFilterUpper
self._size = mysize # store for use by updateNoise()
self._sf = mysf
if self.noiseType in ['binary','Binary','normal','Normal','uniform','Uniform']:
self._sideLength = numpy.round(mysize/sampleSize) # dummy side length for use when unpacking noise samples in updateNoise()
self._sideLength.astype(int)
if ((self._sideLength[0] < 2) and (self._sideLength[1] < 2)):
msg=('Noise sample size '
'must result in more than '
'1 sample per image dimension.')
raise ValueError(msg)
totalSamples = self._sideLength[0]*self._sideLength[1]
if self.noiseType in ['binary','Binary']:
self.noiseTex=numpy.append(numpy.ones(int(numpy.round(totalSamples/2.0))),-1*numpy.ones(int(numpy.round(totalSamples/2.0))))
elif self.noiseType in ['White','white']:
self.noiseTex = numpy.ones((int(mysize),int(mysize)))
self.noiseTex[0][0] = 0
#elif self.noiseType in ['Coloured','coloured']:
# pin=filters.makeRadialMatrix(matrixSize=mysize, center=(0,0), radius=1.0)
# self.noiseTex=numpy.multiply(numpy.ones((int(mysize),int(mysize))),(pin)**self.noiseFractalPower)
# self.noiseTex=fftshift(self.noiseTex)
# self.noiseTex[0][0]=0
elif self.noiseType in ['Isotropic','isotropic']:
if mysf > mysize/2:
msg = ('Base frequency for isotropic '
'noise is definitely too high.')
raise Warning(msg)
localf = mysf/mysize
linbw = 2**self.noiseBW
lowf = 2.0*localf/(linbw+1.0)
highf = linbw*lowf
FWF = highf-lowf
sigmaF = FWF/(2*numpy.sqrt(2*numpy.log(2)))
self.noiseTex = numpy.zeros(int(mysize**2))
self.noiseTex = numpy.reshape(self.noiseTex,(int(mysize),int(mysize)))
pin = filters.makeRadialMatrix(matrixSize=mysize, center=(0,0), radius=2)
self.noiseTex = filters.makeGauss(pin, mean=localf, sd=sigmaF)
self.noiseTex = fftshift(self.noiseTex)
self.noiseTex[0][0] = 0
elif self.noiseType in ['Gabor','gabor']:
if mysf > mysize/2:
msg = ('Base frequency for Gabor '
'noise is definitely too high.')
raise Warning(msg)
localf = mysf/mysize
linbw = 2**self.noiseBW
lowf = 2.0*localf/(linbw+1.0)
highf = linbw*lowf
FWF = highf-lowf
sigmaF = FWF/(2*numpy.sqrt(2*numpy.log(2)))
FWO = 2.0*localf*numpy.tan(numpy.pi*self.noiseBWO/360.0)
sigmaO = FWO/(2*numpy.sqrt(2*numpy.log(2)))
self.noiseTex=numpy.zeros(int(mysize**2))
self.noiseTex=numpy.reshape(self.noiseTex,(int(mysize),int(mysize)))
yy, xx = numpy.mgrid[0:mysize, 0:mysize]
xx = (0.5 - 1.0 / mysize * xx)
yy = (0.5 - 1.0 / mysize * yy)
self.noiseTex=filters.make2DGauss(xx,yy,mean=(localf,0), sd=(sigmaF,sigmaO))
self.noiseTex=self.noiseTex+filters.make2DGauss(xx,yy, mean=(-localf,0), sd=(sigmaF,sigmaO))
self.noiseTex=fftshift(self.noiseTex)
self.noiseTex[0][0]=0
elif self.noiseType in ['Image','image']:
if not(self.noiseImage in ['None','none']):
im = Image.open(self.noiseImage)
im = im.transpose(Image.FLIP_TOP_BOTTOM)
im = im.convert("L") # FORCE TO LUMINANCE
intensity = numpy.array(im).astype(
numpy.float32) * 0.0078431372549019607 - 1.0
self.noiseTex = numpy.absolute(fft2(intensity))
else:
self.noiseTex = numpy.ones((int(mysize),int(mysize))) # if image is 'None' will make white noise as tempary measure
self.noiseTex[0][0]=0
elif self.noiseType in ['filtered','Filtered']:
pin=filters.makeRadialMatrix(matrixSize=mysize, center=(0,0), radius=1.0)
self.noiseTex = numpy.multiply(numpy.ones((int(mysize),int(mysize))),(pin)**self.noiseFractalPower)
if lowsf > mysize/2:
msg = ('Lower cut off frequency for filtered '
'noise is definitely too high.')
raise Warning(msg)
if self.noiseFilterOrder > 0.01:
if upsf<(mysize/2.0):
filter = filters.butter2d_lp_elliptic(size=[mysize,mysize], cutoff_x=upsf/mysize, cutoff_y=upsf/mysize, n=self.noiseFilterOrder, alpha=0, offset_x=2/(mysize-1),offset_y=2/(mysize-1))
else:
filter = numpy.ones((int(mysize),int(mysize)))
if lowsf>0:
filter = filter-filters.butter2d_lp_elliptic(size=[mysize,mysize], cutoff_x=lowsf/mysize, cutoff_y=lowsf/mysize, n=self.noiseFilterOrder, alpha=0, offset_x=2/(mysize-1),offset_y=2/(mysize-1))
self.noiseTex = self.noiseTex*filter
self.noiseTex = fftshift(self.noiseTex)
self.noiseTex[0][0] = 0
else:
raise ValueError('Noise type not recognised.')
self._needBuild = False # prevent noise from being re-built at next draw() unless a parameter is chnaged in the mean time.
self.updateNoise() # now choose the initial random sample.
def buildNoise(self):
"""build a new noise sample. Required to act on changes to any noise parameters or texRes.
"""
if self.units == 'pix':
if not (self.noiseType in ['Binary','binary','Normal','normal','uniform','Uniform']):
mysize = numpy.max(self.size)
else:
mysize = self.size
sampleSize = self.noiseElementSize
mysf = self.__dict__['noiseBaseSf']*mysize
lowsf = self.noiseFilterLower*mysize
upsf = self.noiseFilterUpper*mysize
else:
mysize = self.texRes
pixSize = self.size/self.texRes
sampleSize = self.noiseElementSize/pixSize
mysf = self.size[0]*self.noiseBaseSf
lowsf = self.size[0]*self.noiseFilterLower
upsf = self.size[0]*self.noiseFilterUpper
self._size = mysize # store for use by updateNoise()
self._sf = mysf
if self.noiseType in ['binary','Binary','normal','Normal','uniform','Uniform']:
self._sideLength = numpy.round(mysize/sampleSize) # dummy side length for use when unpacking noise samples in updateNoise()
self._sideLength.astype(int)
if ((self._sideLength[0] < 2) and (self._sideLength[1] < 2)):
msg=('Noise sample size '
'must result in more than '
'1 sample per image dimension.')
raise ValueError(msg)
totalSamples = self._sideLength[0]*self._sideLength[1]
if self.noiseType in ['binary','Binary']:
self.noiseTex=numpy.append(numpy.ones(int(numpy.round(totalSamples/2.0))),-1*numpy.ones(int(numpy.round(totalSamples/2.0))))
elif self.noiseType in ['White','white']:
self.noiseTex = numpy.ones((int(mysize),int(mysize)))
self.noiseTex[0][0] = 0
#elif self.noiseType in ['Coloured','coloured']:
# pin=filters.makeRadialMatrix(matrixSize=mysize, center=(0,0), radius=1.0)
# self.noiseTex=numpy.multiply(numpy.ones((int(mysize),int(mysize))),(pin)**self.noiseFractalPower)
# self.noiseTex=fftshift(self.noiseTex)
# self.noiseTex[0][0]=0
elif self.noiseType in ['Isotropic','isotropic']:
if mysf > mysize/2:
msg = ('Base frequency for isotropic '
'noise is definitely too high.')
raise Warning(msg)
localf = mysf/mysize
linbw = 2**self.noiseBW
lowf = 2.0*localf/(linbw+1.0)
highf = linbw*lowf
FWF = highf-lowf
sigmaF = FWF/(2*numpy.sqrt(2*numpy.log(2)))
self.noiseTex = numpy.zeros(int(mysize**2))
self.noiseTex = numpy.reshape(self.noiseTex,(int(mysize),int(mysize)))
pin = filters.makeRadialMatrix(matrixSize=mysize, center=(0,0), radius=2)
self.noiseTex = filters.makeGauss(pin, mean=localf, sd=sigmaF)
self.noiseTex = fftshift(self.noiseTex)
self.noiseTex[0][0] = 0
elif self.noiseType in ['Gabor','gabor']:
if mysf > mysize/2:
msg = ('Base frequency for Gabor '
'noise is definitely too high.')
raise Warning(msg)
localf = mysf/mysize
linbw = 2**self.noiseBW
lowf = 2.0*localf/(linbw+1.0)
highf = linbw*lowf
FWF = highf-lowf
sigmaF = FWF/(2*numpy.sqrt(2*numpy.log(2)))
FWO = 2.0*localf*numpy.tan(numpy.pi*self.noiseBWO/360.0)
sigmaO = FWO/(2*numpy.sqrt(2*numpy.log(2)))
self.noiseTex=numpy.zeros(int(mysize**2))
self.noiseTex=numpy.reshape(self.noiseTex,(int(mysize),int(mysize)))
yy, xx = numpy.mgrid[0:mysize, 0:mysize]
xx = (0.5 - 1.0 / mysize * xx)
yy = (0.5 - 1.0 / mysize * yy)
self.noiseTex=filters.make2DGauss(xx,yy,mean=(localf,0), sd=(sigmaF,sigmaO))
self.noiseTex=self.noiseTex+filters.make2DGauss(xx,yy, mean=(-localf,0), sd=(sigmaF,sigmaO))
self.noiseTex=fftshift(self.noiseTex)
self.noiseTex[0][0]=0
elif self.noiseType in ['Image','image']:
if not(self.noiseImage in ['None','none']):
im = Image.open(self.noiseImage)
im = im.transpose(Image.FLIP_TOP_BOTTOM)
im = im.convert("L") # FORCE TO LUMINANCE
intensity = numpy.array(im).astype(
numpy.float32) * 0.0078431372549019607 - 1.0
self.noiseTex = numpy.absolute(fft2(intensity))
else:
self.noiseTex = numpy.ones((int(mysize),int(mysize))) # if image is 'None' will make white noise as tempary measure
self.noiseTex[0][0]=0
elif self.noiseType in ['filtered','Filtered']:
pin=filters.makeRadialMatrix(matrixSize=mysize, center=(0,0), radius=1.0)
self.noiseTex = numpy.multiply(numpy.ones((int(mysize),int(mysize))),(pin)**self.noiseFractalPower)
if lowsf > mysize/2:
msg = ('Lower cut off frequency for filtered '
'noise is definitely too high.')
raise Warning(msg)
if self.noiseFilterOrder > 0.01:
if upsf<(mysize/2.0):
filter = filters.butter2d_lp_elliptic(size=[mysize,mysize], cutoff_x=upsf/mysize, cutoff_y=upsf/mysize, n=self.noiseFilterOrder, alpha=0, offset_x=2/(mysize-1),offset_y=2/(mysize-1))
else:
filter = numpy.ones((int(mysize),int(mysize)))
if lowsf>0:
filter = filter-filters.butter2d_lp_elliptic(size=[mysize,mysize], cutoff_x=lowsf/mysize, cutoff_y=lowsf/mysize, n=self.noiseFilterOrder, alpha=0, offset_x=2/(mysize-1),offset_y=2/(mysize-1))
self.noiseTex = self.noiseTex*filter
self.noiseTex = fftshift(self.noiseTex)
self.noiseTex[0][0] = 0
else:
raise ValueError('Noise type not recognised.')
self._needBuild = False # prevent noise from being re-built at next draw() unless a parameter is chnaged in the mean time.
self.updateNoise() # now choose the inital random sample.
