284 lines
8.6 KiB
Python
284 lines
8.6 KiB
Python
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#
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# Author: Heresh Fattahi
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# Copyright 2017
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#
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# Modified by V. Brancato
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# Included offset filtering with no SNR
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#
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import isce
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import isceobj
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from osgeo import gdal
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import numpy as np
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import os
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def mask_filterNoSNR(denseOffsetFile,filterSize,outName):
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# Masking the offsets with a data-based approach
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from scipy import ndimage
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# Open the offsets
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ds = gdal.Open(denseOffsetFile+'.vrt',gdal.GA_ReadOnly)
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off_az = ds.GetRasterBand(1).ReadAsArray()
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off_rg = ds.GetRasterBand(2).ReadAsArray()
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ds = None
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# Remove missing values from ampcor
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off_rg[np.where(off_rg < -9999)]=0
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off_az[np.where(off_az < -9999)]=0
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# Store the offsets in a complex variable
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off = off_rg + 1j*off_az
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# Mask the azimuth offsets based on the MAD
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mask = off_masking(off,filterSize,thre=3)
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xoff_masked = np.ma.array(off.real,mask=mask)
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yoff_masked = np.ma.array(off.imag,mask=mask)
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# Delete unused variables
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mask = None
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off = None
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# Remove residual noisy spots with a median filter on the azimuth offmap
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yoff_masked.mask = yoff_masked.mask | \
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(ndimage.median_filter(xoff_masked.filled(fill_value=0),3) == 0) | \
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(ndimage.median_filter(yoff_masked.filled(fill_value=0),3) == 0)
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# Fill the data by iteratively using smoothed values
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data = yoff_masked.data
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data[yoff_masked.mask]=np.nan
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off_az_filled = fill_with_smoothed(data,filterSize)
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# Apply median filter to smooth the azimuth offset map
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off_az_filled = ndimage.median_filter(off_az_filled,filterSize)
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# Save the filtered offsets
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length, width = off_az_filled.shape
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# writing the masked and filtered offsets to a file
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print ('writing masked and filtered offsets to: ', outName)
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##Write array to offsetfile
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off_az_filled.tofile(outName)
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# write the xml file
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img = isceobj.createImage()
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img.setFilename(outName)
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img.setWidth(width)
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img.setAccessMode('READ')
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img.bands = 1
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img.dataType = 'FLOAT'
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img.scheme = 'BIP'
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img.renderHdr()
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return
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def off_masking(off,filterSize,thre=2):
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from scipy import ndimage
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# Define the mask to fill the offsets
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vram = ndimage.median_filter(off.real, filterSize)
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vazm = ndimage.median_filter(off.imag, filterSize)
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mask = (np.abs(off.real-vram) > thre) | (np.abs(off.imag-vazm) > thre) | (off.imag == 0) | (off.real == 0)
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return mask
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def fill(data, invalid=None):
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"""
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Replace the value of invalid 'data' cells (indicated by 'invalid')
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by the value of the nearest valid data cell
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Input:
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data: numpy array of any dimension
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invalid: a binary array of same shape as 'data'.
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data value are replaced where invalid is True
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If None (default), use: invalid = np.isnan(data)
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Output:
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Return a filled array.
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"""
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from scipy import ndimage
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if invalid is None: invalid = np.isnan(data)
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ind = ndimage.distance_transform_edt(invalid,
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return_distances=False,
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return_indices=True)
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return data[tuple(ind)]
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def mask_filter(denseOffsetFile, snrFile, band, snrThreshold, filterSize, outName):
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#masking and Filtering
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from scipy import ndimage
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##Read in the offset file
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ds = gdal.Open(denseOffsetFile + '.vrt', gdal.GA_ReadOnly)
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Offset = ds.GetRasterBand(band).ReadAsArray()
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ds = None
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##Read in the SNR file
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ds = gdal.Open(snrFile + '.vrt', gdal.GA_ReadOnly)
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snr = ds.GetRasterBand(1).ReadAsArray()
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ds = None
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# Masking the dense offsets based on SNR
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print ('masking the dense offsets with SNR threshold: ', snrThreshold)
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Offset[snr<snrThreshold]=np.nan
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# Fill the masked region using valid neighboring pixels
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Offset = fill(Offset)
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############
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# Median filtering the masked offsets
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print ('Filtering with median filter with size : ', filterSize)
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Offset = ndimage.median_filter(Offset, size=filterSize)
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length, width = Offset.shape
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# writing the masked and filtered offsets to a file
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print ('writing masked and filtered offsets to: ', outName)
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##Write array to offsetfile
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Offset.tofile(outName)
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# write the xml file
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img = isceobj.createImage()
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img.setFilename(outName)
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img.setWidth(width)
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img.setAccessMode('READ')
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img.bands = 1
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img.dataType = 'FLOAT'
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img.scheme = 'BIP'
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img.renderHdr()
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return None
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def fill_with_smoothed(off,filterSize):
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from astropy.convolution import convolve
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off_2filt=np.copy(off)
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kernel = np.ones((filterSize,filterSize),np.float32)/(filterSize*filterSize)
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loop = 0
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cnt2=1
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while (cnt2!=0 & loop<100):
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loop += 1
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idx2= np.isnan(off_2filt)
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cnt2 = np.sum(np.count_nonzero(np.isnan(off_2filt)))
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print(cnt2)
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if cnt2 != 0:
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off_filt= convolve(off_2filt,kernel,boundary='extend',nan_treatment='interpolate')
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off_2filt[idx2]=off_filt[idx2]
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idx3 = np.where(off_filt == 0)
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off_2filt[idx3]=np.nan
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off_filt=None
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return off_2filt
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def resampleOffset(maskedFiltOffset, geometryOffset, outName):
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'''
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Oversample offset and add.
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'''
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from imageMath import IML
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import logging
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resampledOffset = maskedFiltOffset + ".resampled"
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inimg = isceobj.createImage()
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inimg.load(geometryOffset + '.xml')
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length = inimg.getLength()
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width = inimg.getWidth()
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###Currently making the assumption that top left of dense offsets and interfeorgrams are the same.
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###This is not true for now. We need to update DenseOffsets to have the ability to have same top left
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###As the input images. Once that is implemente, the math here should all be consistent.
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###However, this is not too far off since the skip for doing dense offsets is generally large.
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###The offset is not too large to worry about right now. If the skip is decreased, this could be an issue.
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print('oversampling the filtered and masked offsets to the width and length:', width, ' ', length )
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cmd = 'gdal_translate -of ENVI -ot Float64 -outsize ' + str(width) + ' ' + str(length) + ' ' + maskedFiltOffset + '.vrt ' + resampledOffset
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print(cmd)
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os.system(cmd)
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img = isceobj.createImage()
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img.setFilename(resampledOffset)
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img.setWidth(width)
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img.setLength(length)
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img.setAccessMode('READ')
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img.bands = 1
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img.dataType = 'DOUBLE'
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img.scheme = 'BIP'
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img.renderHdr()
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###Adding the geometry offset and oversampled offset
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geomoff = IML.mmapFromISCE(geometryOffset, logging)
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osoff = IML.mmapFromISCE(resampledOffset, logging)
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fid = open(outName, 'w')
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for ll in range(length):
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val = geomoff.bands[0][ll,:] + osoff.bands[0][ll,:]
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val.tofile(fid)
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fid.close()
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img = isceobj.createImage()
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img.setFilename(outName)
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img.setWidth(width)
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img.setLength(length)
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img.setAccessMode('READ')
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img.bands = 1
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img.dataType = 'DOUBLE'
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img.scheme = 'BIP'
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img.renderHdr()
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return None
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def runRubbersheetAzimuth(self):
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if not self.doRubbersheetingAzimuth:
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print('Rubber sheeting in azimuth not requested ... skipping')
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return
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# denseOffset file name computeed from cross-correlation
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denseOffsetFile = os.path.join(self.insar.denseOffsetsDirname , self.insar.denseOffsetFilename)
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snrFile = denseOffsetFile + "_snr.bil"
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denseOffsetFile = denseOffsetFile + ".bil"
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# we want the azimuth offsets only which are the first band
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band = [1]
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snrThreshold = self.rubberSheetSNRThreshold
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filterSize = self.rubberSheetFilterSize
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filtAzOffsetFile = os.path.join(self.insar.denseOffsetsDirname, self._insar.filtAzimuthOffsetFilename)
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# masking and median filtering the dense offsets
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if not self.doRubbersheetingRange:
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print('Rubber sheeting in range is off, filtering the offsets with a SNR-based mask')
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mask_filter(denseOffsetFile, snrFile, band[0], snrThreshold, filterSize, filtAzOffsetFile)
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else:
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print('Rubber sheeting in range is on, filtering the offsets with data-based mask')
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mask_filterNoSNR(denseOffsetFile, filterSize, filtAzOffsetFile)
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# azimuth offsets computed from geometry
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offsetsDir = self.insar.offsetsDirname
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geometryAzimuthOffset = os.path.join(offsetsDir, self.insar.azimuthOffsetFilename)
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sheetOffset = os.path.join(offsetsDir, self.insar.azimuthRubbersheetFilename)
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# oversampling the filtAzOffsetFile to the same size of geometryAzimuthOffset
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# and then update the geometryAzimuthOffset by adding the oversampled
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# filtAzOffsetFile to it.
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resampleOffset(filtAzOffsetFile, geometryAzimuthOffset, sheetOffset)
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return None
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