def GetImageSubset(subsetparameters, data, dimensions):
#------------------------------------------------------------------
# Create square image subset from predefined corner points
#------------------------------------------------------------------
Point = subsetparameters.Point
# get and scale image subset
BoxRange = dimensions.BoxRange
BoxRange = BoxRange.astype(int)
image = data.image[Point[1] - BoxRange[0]:Point[1] + BoxRange[1],
Point[0] - BoxRange[0]:Point[0] + BoxRange[1]]
image = IP.ScaleImage(image, 8)
# get and store corresponding coordinates
easting = data.coordinates.easting[Point[1] - BoxRange[0]:Point[1] +
BoxRange[1], Point[0] -
BoxRange[0]:Point[0] + BoxRange[1]]
northing = data.coordinates.northing[Point[1] - BoxRange[0]:Point[1] +
BoxRange[1], Point[0] -
BoxRange[0]:Point[0] + BoxRange[1]]
coordinates = CL.Coordinates(northing, easting)
#store subset data
FlagFlip = IP.CheckImageOrientation(coordinates)
subset = CL.Subset(Point, image, coordinates, data.resolution, FlagFlip)
return subset
def GetFFTBoxes(subsetparameters, data, dimension):
#---------------------------------------------------------------------------------------------------------------
# Create list of overlapping subset images to estimate spectrum at a given grid point (center of main subset)
#---------------------------------------------------------------------------------------------------------------
# initialisation
#images, northings, eastings= [],[],[]
Subsets = []
# Get the various subset images centers (box defined with an offset from the main subset, the overlapping is controlled by the offset)
offset = subsetparameters.Shift * dimension.BoxRange
subset_centers = GetSubsetCenters(subsetparameters.Point, offset,
subsetparameters.BoxNb)
# get information on subsets (coordinates, image footprint)
for i in subset_centers:
# get subset
subsetparameters.Point = i
subset = GetImageSubset(subsetparameters, data, dimension)
# scale image
image = IP.ScaleImage(subset.image, 8)
coordinates = CL.Coordinates(subset.coordinates.northing,
subset.coordinates.easting)
FlagFlip = IP.CheckImageOrientation(coordinates)
subsets = CL.Subset(i, image, coordinates, data.resolution, FlagFlip)
Subsets.append(subsets)
"""
# concatenate data
images.append(image); eastings.append(subset.coordinates.easting); northings.append(subset.coordinates.northing)
#store data
coordinates = Coordinates(northings,eastings)
subsets = Subset(subset_centers, images, coordinates, data.resolution)
"""
return Subsets
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