def ImageSpectrum(parameters, subset):
#----------------------------------------------
# Compute Image Spectrum or Power Spectrum
#----------------------------------------------
image = subset.image # given subset image
if parameters.MeanSubstractionFlag:
image = image - np.mean(image) # substract mean value
Spectrum = imfft(image) # FFT of the given image
mag = cv2.magnitude(Spectrum[:, :, 0], Spectrum[:, :, 1])
if parameters.DecibelRepresentationFlag:
mag = 20 * np.log(mag) # amplitude spectrum (sqrt(Im**2 +Re**2))
#power Spectrum
S = Spectrum[:, :, 0] + 1j * Spectrum[:, :, 1]
mag2 = np.real(S * np.conjugate(S))
if parameters.DecibelRepresentationFlag:
mag2 = 20 * np.log(mag2)
#mag2 = np.abs(mag)**2
# selected spectrum to be plotted
if parameters.PowerSpectrumFlag == True:
spectrum = mag2
else:
spectrum = mag
#--------------------------------
# define the wavenumber axis
#--------------------------------
step = 1 / subset.resolution[0]
freqs = np.linspace(0, (spectrum.shape[1] - 1) * step / spectrum.shape[1],
spectrum.shape[1])
fCenter = 0.5 * (freqs[np.int(spectrum.shape[1] / 2)] +
freqs[np.int(spectrum.shape[1] / 2) - 1])
freqs = freqs - fCenter # axis centered on 0
k = 2 * np.pi * (freqs)
#store data
ImageSpectrum = CL.SpectrumData(k, spectrum)
return ImageSpectrum
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 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