def SubsetSpectrum(parameters, subset_input):
#--------------------------------------------------------------------------------------
# function that process a given subset image estimate the wave incident direction
# and compute the wave spectrum
#--------------------------------------------------------------------------------------
#--------------------------
# A) Subset Processing
#--------------------------
# scale sub-images
IP_Parameters = parameters.SubsetProcessingParameters
image = IP.ScaleImage(subset_input.image, IP_Parameters.IntensityType)
subset = CL.Subset(subset_input.CenterPoint, image,
subset_input.coordinates, subset_input.resolution,
subset_input.FlagFlip)
#stretch contrast
if IP_Parameters.ConstrastStretchFlag:
image = IP.ContrastStretch(image, IP_Parameters.IntensityType)
subset = CL.Subset(subset.CenterPoint, image, subset.coordinates,
subset.resolution, subset.FlagFlip)
#--------------------------
# B) Subset Spectrum
#--------------------------
Image_Spectrum = ImageSpectrum(IP_Parameters, subset)
#--------------------------
# C) Direction Estimate
#--------------------------
DE_Parameters = parameters.DirectionEstimateParameters
direction = DirectionEstimate(DE_Parameters, subset, Image_Spectrum)
#--------------------------------------
# D) Apply Butterworth Elliptic Filter
#-------------------------------------
IF_Parameters = parameters.FilterParameters
if IF_Parameters.FlagFilter:
FilteredImage = ImageFilter(IF_Parameters, subset, direction)
image = IP.ScaleImage(FilteredImage, 32)
subset = CL.Subset(subset.CenterPoint, image, subset.coordinates,
subset.resolution, subset.FlagFlip)
Image_Spectrum = ImageSpectrum(IP_Parameters, subset)
#---------------------------
# C1) Direction re-estimate
#---------------------------
DE_Parameters = parameters.DirectionEstimateParameters
direction = DirectionEstimate(DE_Parameters, subset, Image_Spectrum)
#---------------------------------------------
# E) Extract Wave Series and Compute Spectrum
#---------------------------------------------
SP_Parameters = parameters.SpectrumParameters.WaveSpectrumParameters
K, Ks, S, Sp, Sstd = WavesMeanSpectrum(SP_Parameters, subset, direction)
#---------------------------------------------
# F) Store data
#---------------------------------------------
Spectrum = CL.SpectrumData(Ks, Sp)
subset_output = subset
Spectrum_Computed_Data = CL.SpectrumComputedData(direction, Spectrum,
Image_Spectrum,
subset_output)
return Spectrum_Computed_Data
def RadialSpectrum(parameters, subset_input):
#--------------------------
# A) Subset Processing
#--------------------------
# scale sub-images
IP_Parameters = parameters.SubsetProcessingParameters
image = IP.ScaleImage(subset_input.image, IP_Parameters.IntensityType)
subset = CL.Subset(subset_input.CenterPoint, image,
subset_input.coordinates, subset_input.resolution,
subset_input.FlagFlip)
#stretch contrast
if IP_Parameters.ConstrastStretchFlag > 0:
image = IP.ContrastStretch(image, IP_Parameters.IntensityType)
subset = CL.Subset(subset.CenterPoint, image, subset.coordinates,
subset.resolution, subset.FlagFlip)
#--------------------------
# B) Subset Spectrum
#--------------------------
Image_Spectrum = ImageSpectrum(IP_Parameters, subset)
#--------------------------
# C) Direction Estimate
#--------------------------
# determine radius of influence
R, _, _ = Influence_Radius(Image_Spectrum)
DE_Parameters = parameters.DirectionEstimateParameters
direction = DirectionEstimate(DE_Parameters, subset, Image_Spectrum)
#--------------------------------------
# D) Apply Butterworth Elliptic Filter
#-------------------------------------
IF_Parameters = parameters.FilterParameters
DE_Parameters = parameters.DirectionEstimateParameters
if IF_Parameters.FlagFilter:
FilteredImage = ImageFilter(IF_Parameters, subset, direction)
image = IP.ScaleImage(FilteredImage, 32)
subset = CL.Subset(subset.CenterPoint, image, subset.coordinates,
subset.resolution, subset.FlagFlip)
#------------------------------
# E) Perform Image Padding
#------------------------------
RI_Parameters = parameters.SpectrumParameters.WaveSpectrumParameters
#---------------------------------------------------
# F) Re-Compute Image Spectrum and wave direction
#----------------------------------------------------
Image_Spectrum = ImageSpectrum(IP_Parameters, subset)
direction = DirectionEstimate(DE_Parameters, subset, Image_Spectrum)
#------------------------------------------------
# G) Process Spectrum
#------------------------------------------------
Spectrum = Image_Spectrum.Spectrum
k = Image_Spectrum.k
mask = MaskPlot(R, subset)
Spectrum[mask] = 0
#---------------------------------------------
# H) Compute radial integration and
#---------------------------------------------
Rr, RadialIntegration = RadialProjection(Spectrum)
#---------------------------------------------
# I) Evaluate k-axis in agreement with Radius
#---------------------------------------------
dk = np.sum(np.diff(k)) / (k.shape[0] - 1)
kr = Rr * dk
"""
fig, ax = plt.subplots(1)
ax.plot(kr, RadialIntegration)
ax.plot(kr, RadialIntegration,'or')
plt.show()
"""
#---------------------------------------------
# I) store data
#---------------------------------------------
Spectrum = CL.SpectrumData(kr, RadialIntegration)
subset_output = subset
Spectrum_Computed_Data = CL.SpectrumComputedData(direction, Spectrum,
Image_Spectrum,
subset_output)
return Spectrum_Computed_Data
def ComputeSpectrum(SubsetParameters, ComputingParameters, Subsets):
#--------------------------------------------------------------------------
# Estimate Subsets Spectra and directions and determine their mean values
#--------------------------------------------------------------------------
WaveSpectra, Directions, ImageSpectra = [], [], []
for i in range(SubsetParameters.BoxNb):
subset = Subsets[i]
# Compute wave related spectrum
if ComputingParameters.SpectrumParameters.WaveSpectrumParameters.SpectrumType in {
'waves', 'Waves'
}:
# use a multiple of wave signal
SpectrumComputedData = SubsetSpectrum(ComputingParameters, subset)
elif ComputingParameters.SpectrumParameters.WaveSpectrumParameters.SpectrumType in {
'radial', 'Radial'
}:
# use the radial integration of the Image Spectrum
SpectrumComputedData = RadialSpectrum(ComputingParameters, subset)
else:
sys.exit("Method for Spectrum Estimation not defined")
# store data
Directions.append(SpectrumComputedData.WaveDirection)
WaveSpectra.append(SpectrumComputedData.WaveSpectrum)
ImageSpectra.append(SpectrumComputedData.ImageSpectrum)
# compute mean directions
direction = np.nanmean(Directions)
# compute mean Spectrum
K = np.zeros((SubsetParameters.BoxNb, WaveSpectra[0].k.shape[0]))
WSP = np.zeros((SubsetParameters.BoxNb, WaveSpectra[0].Spectrum.shape[0]))
for i in range(SubsetParameters.BoxNb):
K[i, :] = WaveSpectra[i].k
WSP[i, :] = WaveSpectra[i].Spectrum
k = np.mean(K, axis=0)
spectrum = np.mean(WSP, axis=0)
spectrumstd = np.std(WSP, axis=0)
"""
fig, ax = plt.subplots(1)
for i in range(SubsetParameters.BoxNb):
ax.plot(K[i,:], WSP[i,:])
plt.show()
"""
# compute peak wavelength
PeakWavelength = WavelengthEstimate(ComputingParameters, k, spectrum)
# store processed data
Spectrum = CL.SpectrumProcessedData(k, spectrum, spectrumstd,
PeakWavelength)
# post-processing data for figures and statistics
SubscenesSpectrum = CL.SpectrumComputedData(Directions, WaveSpectra,
ImageSpectra, Subsets)
# store mean data
ComputedSpectrumData = CL.SpectrumComputedData(direction, Spectrum)
return ComputedSpectrumData, SubscenesSpectrum