def __getAccessor__(self):
""" This reads in the input unwrapped file from Snaphu and Connected Components """
# Snaphu Unwrapped Phase
inphase = IML.mmapFromISCE(self.inpFile, logging)
if len(inphase.bands) == 1:
self.inpAcc = inphase.bands[0]
else:
self.inpAcc = inphase.bands[1]
# Connected Component
inConnComp = IML.mmapFromISCE(self.ccFile, logging)
if len(inConnComp.bands) == 1:
# Gdal dependency for computing proximity
self.__createGDALHDR__(inConnComp.name, inConnComp.width,
inConnComp.length)
self.cc_ds = gdal.Open(inConnComp.name, GA_ReadOnly)
self.ccband = self.cc_ds.GetRasterBand(1)
self.conncompAcc = self.ccband.ReadAsArray()
else:
raise Exception(
"Connected Component Input File has 2 bands: Expected only one"
)
return
def __getAccessor__(self):
""" This reads in the input unwrapped file from Snaphu and Connected Components """
# Snaphu Unwrapped Phase
inphase = IML.mmapFromISCE(self.inpFile, logging)
if len(inphase.bands) == 1:
self.inpAcc = inphase.bands[0]
self.inpAmp = False # D. Bekaert track if two-band file or not
else:
self.inpAcc = inphase.bands[1]
self.inpAmp = True
self.outFile_final = self.outFile
self.outFile = self.outFile + "_temp"
# Connected Component
inConnComp = IML.mmapFromISCE(self.ccFile, logging)
if len(inConnComp.bands) == 1:
# --D. Bekaert
# problem with using .hdr files see next item. Below is no longer needed
# Gdal dependency for computing proximity
# self.__createGDALHDR__(inConnComp.name, inConnComp.width, inConnComp.length)
# --Done
# --D. Bekaert - make sure gdal is using the vrt file to load the data.
# i.e. gdal will default to envi headers first, for which the convention is filename.dat => filename.hdr.
# for the connected component this load the wrong header: topophase.unw.conncomp => topophase.unw.hdr
# force therefore to use the vrt file instead.
inConnComp_filename, inConnComp_ext = os.path.splitext(
inConnComp.name)
# fix the file to be .vrt
if inConnComp_ext != '.vrt':
if inConnComp_ext != '.conncomp' and inConnComp_ext != '.geo':
inConnComp.name = inConnComp_filename + ".vrt"
else:
inConnComp.name = inConnComp.name + ".vrt"
if not os.path.isfile(inConnComp.name):
raise Exception("Connected Component vrt file does not exist")
print("GDAL using " + inConnComp.name)
# --Done
self.cc_ds = gdal.Open(inConnComp.name, GA_ReadOnly)
self.ccband = self.cc_ds.GetRasterBand(1)
self.conncompAcc = self.ccband.ReadAsArray()
else:
raise Exception(
"Connected Component Input File has 2 bands: Expected only one"
)
return
def makeOnePlot(filename, pos):
'''
Make plots.
'''
import matplotlib.pyplot as plt
from imageMath import IML
win = 100
mm = IML.mmapFromISCE(filename, logging)
data = mm.bands[0]
nl, npix = data.shape
pos = np.array(pos)
miny = np.clip(np.min(pos[:,1])-win, 0 , nl-1)
maxy = np.clip(np.max(pos[:,1])+win, 0 , nl-1)
minx = np.clip(np.min(pos[:,2])-win, 0, npix-1)
maxx = np.clip(np.max(pos[:,2])+win, 0, npix-1)
box = np.power(np.abs(data[int(miny):int(maxy), int(minx):int(maxx)]), 0.4)
plt.figure('CR analysis')
plt.imshow(box, cmap=plt.cm.gray)
plt.colorbar()
# plt.scatter(pos[:,2]-minx, pos[:,1]-miny, marker='+', c='b', s=200)
plt.scatter(pos[:,2]-minx, pos[:,1]-miny, marker='o',
facecolors='none', edgecolors='b', s=100)
plt.title(os.path.basename(os.path.dirname(filename)))
plt.show()
def makePlot(filename, pos):
'''
Make plots.
'''
import matplotlib.pyplot as plt
from imageMath import IML
win = 8
mm = IML.mmapFromISCE(filename, logging)
data = mm.bands[0]
plt.figure('CR analysis')
for index, (num, line, pixel) in enumerate(pos):
print(line, pixel)
xx = np.int(pixel)
yy = np.int(line)
box = 10 * np.log10(np.abs(data[yy-win:yy+win, yy-win:yy+win]))
plt.subplot(7,3,index+1)
plt.imshow(box, cmap=plt.cm.gray)
plt.colorbar()
plt.scatter(pixel-xx+win, line-yy+win, marker='+', c='b')
plt.show()
def loadProduct(filename):
'''
Load the product using Product Manager.
'''
import isce
import logging
from imageMath import IML
IMG = IML.mmapFromISCE(filename, logging)
img = IMG.bands[0]
# pdb.set_trace()
return img
def genRawImg(rawFile, iBias, qBias, rangeStart):
raw = IML.mmapFromISCE(rawFile, logging)
cj = np.complex64(1j)
rawImg = []
for i in range(len(raw.bands[0])):
line = raw.bands[0][i, rangeStart::2] + cj * raw.bands[0][
i, rangeStart + 1::2] - iBias - cj * qBias
rawImg.append(line)
rawImg = np.array(rawImg)
rfat = []
for i in range(len(rawImg)):
line = np.fft.fftshift(np.fft.fft(rawImg[i]) / len(rawImg[i]))
rfat.append(line)
rfat = np.array(rfat)
return rfat
def cropFrame(frame, limits, outname, israw=False):
'''
Crop the frame.
Parameters to change:
startingRange
farRange
sensingStart
sensingStop
sensingMid
numberOfLines
numberOfSamples
dopplerVsPixel
'''
outframe = copy.deepcopy(frame)
if not israw:
img = isceobj.createImage()
img.load(frame.image.filename + '.xml')
outframe.image = img
if israw:
factor = 2
else:
factor = 1
####sensing start
ymin = np.floor(
(limits[0] - frame.sensingStart).total_seconds() * frame.PRF)
print('Line start: ', ymin)
ymin = np.int(np.clip(ymin, 0, frame.numberOfLines - 1))
####sensing stop
ymax = np.ceil(
(limits[1] - frame.sensingStart).total_seconds() * frame.PRF) + 1
print('Line stop: ', ymax)
ymax = np.int(np.clip(ymax, 1, frame.numberOfLines))
print('Line limits: ', ymin, ymax)
print('Original Line Limits: ', 0, frame.numberOfLines)
if (ymax - ymin) <= 1:
raise Exception('Azimuth limits appear to not overlap with the scene')
outframe.sensingStart = frame.sensingStart + datetime.timedelta(
seconds=ymin / frame.PRF)
outframe.numberOfLines = ymax - ymin
outframe.sensingStop = frame.sensingStop + datetime.timedelta(
seconds=(ymax - 1) / frame.PRF)
outframe.sensingMid = outframe.sensingStart + 0.5 * (outframe.sensingStop -
outframe.sensingStart)
####starting range
xmin = np.floor(
(limits[2] - frame.startingRange) / frame.instrument.rangePixelSize)
print('Pixel start: ', xmin)
xmin = np.int(np.clip(xmin, 0, (frame.image.width // factor) - 1))
####far range
xmax = np.ceil((limits[3] - frame.startingRange) /
frame.instrument.rangePixelSize) + 1
print('Pixel stop: ', xmax)
xmax = np.int(np.clip(xmax, 1, frame.image.width // factor))
print('Pixel limits: ', xmin, xmax)
print('Original Pixel Limits: ', 0, frame.image.width // factor)
if (xmax - xmin) <= 1:
raise Exception('Range limits appear to not overlap with the scene')
outframe.startingRange = frame.startingRange + xmin * frame.instrument.rangePixelSize
outframe.numberOfSamples = (xmax - xmin) * factor
outframe.setFarRange(frame.startingRange +
(xmax - xmin - 1) * frame.instrument.rangePixelSize)
####Adjust Doppler centroid coefficients
coeff = frame._dopplerVsPixel
rng = np.linspace(xmin, xmax, len(coeff) + 1)
dops = np.polyval(coeff[::-1], rng)
rng = rng - xmin ###Adjust the start
pol = np.polyfit(rng, dops, len(coeff) - 1)
outframe._dopplerVsPixel = list(pol[::-1])
####Adjusting the image now
####Can potentially use israw to apply more logic but better to use new version
if frame.image.xmin != 0:
raise Exception(
'Looks like you are still using an old version of ISCE. The new version completely strips out the header bytes. Please switch to the latest ...'
)
inname = frame.image.filename
suffix = os.path.splitext(inname)[1]
outdirname = os.path.dirname(outname)
os.makedirs(outdirname, exist_ok=True)
indata = IML.mmapFromISCE(inname, logging)
indata.bands[0][ymin:ymax, xmin * factor:xmax * factor].tofile(outname)
indata = None
outframe.image.filename = outname
outframe.image.width = outframe.numberOfSamples
outframe.image.length = outframe.numberOfLines
outframe.image.xmax = outframe.numberOfSamples
outframe.image.coord1.coordSize = outframe.numberOfSamples
outframe.image.coord1.coordEnd = outframe.numberOfSamples
outframe.image.coord2.coordSize = outframe.numberOfLines
outframe.image.coord2.coordEnd = outframe.numberOfLines
outframe.image.renderHdr()
return outframe
if __name__ == '__main__':
inps = cmdLineParse()
print('################################################')
print('Multilook Start!!!')
print(time.strftime("%H:%M:%S"))
nanval = 0.0
# Read amp files in radar coordinates
# ampfile = "./interferogram/topophase.amp"
ampfile = inps.ampname
inty = IML.mmapFromISCE(ampfile, logging)
inty1 = inty.bands[0].copy()
inty2 = inty.bands[1].copy()
inty1[inty1 == nanval] = np.NaN
inty2[inty2 == nanval] = np.NaN
width = inty1.shape[1]
length = inty1.shape[0]
# multi-look filtering amp file
inty1 = np.power(inty1, 2)
inty2 = np.power(inty2, 2)
mask = np.ones((2, 2))
INTY1 = cv2.filter2D(inty1, -1, mask,
borderType=cv2.BORDER_CONSTANT) / np.sum(mask)
# INTY1 = signal.convolve2d(inty1, mask, boundary='symm', mode='same')/mask.size
def resampleOffset(maskedFiltOffset, geometryOffset, outName):
'''
Oversample offset and add.
'''
from imageMath import IML
import logging
resampledOffset = maskedFiltOffset + ".resampled"
inimg = isceobj.createImage()
inimg.load(geometryOffset + '.xml')
length = inimg.getLength()
width = inimg.getWidth()
###Currently making the assumption that top left of dense offsets and interfeorgrams are the same.
###This is not true for now. We need to update DenseOffsets to have the ability to have same top left
###As the input images. Once that is implemente, the math here should all be consistent.
###However, this is not too far off since the skip for doing dense offsets is generally large.
###The offset is not too large to worry about right now. If the skip is decreased, this could be an issue.
print(
'oversampling the filtered and masked offsets to the width and length:',
width, ' ', length)
cmd = 'gdal_translate -of ENVI -ot Float64 -outsize ' + str(
width) + ' ' + str(
length) + ' ' + maskedFiltOffset + '.vrt ' + resampledOffset
print(cmd)
os.system(cmd)
img = isceobj.createImage()
img.setFilename(resampledOffset)
img.setWidth(width)
img.setLength(length)
img.setAccessMode('READ')
img.bands = 1
img.dataType = 'DOUBLE'
img.scheme = 'BIP'
img.renderHdr()
###Adding the geometry offset and oversampled offset
geomoff = IML.mmapFromISCE(geometryOffset, logging)
osoff = IML.mmapFromISCE(resampledOffset, logging)
fid = open(outName, 'w')
for ll in range(length):
val = geomoff.bands[0][ll, :] + osoff.bands[0][ll, :]
val.tofile(fid)
fid.close()
img = isceobj.createImage()
img.setFilename(outName)
img.setWidth(width)
img.setLength(length)
img.setAccessMode('READ')
img.bands = 1
img.dataType = 'DOUBLE'
img.scheme = 'BIP'
img.renderHdr()
return None
def runCropOffsetGeo(self):
'''
Crops and resamples lat/lon/los/z images created by topsApp to the
same grid as the offset field image.
'''
print('\n====================================')
print('Cropping topo products to offset grid...')
print('====================================')
suffix = '.full'
if (self.numberRangeLooks == 1) and (self.numberAzimuthLooks == 1):
suffix = ''
flist1b = ['lat.rdr' + suffix, 'lon.rdr' + suffix, 'z.rdr' + suffix]
flist2b = [self._insar.mergedLosName + suffix]
wend = (self.offset_width * self.skipwidth) + self.offset_left
lend = (self.offset_length * self.skiphgt) + self.offset_top
for filename in flist1b:
print('\nCropping %s to %s ...\n' % (filename, filename + '.crop'))
f = os.path.join(self._insar.mergedDirname, filename)
outArr = []
mmap = IML.mmapFromISCE(f, logging)
'''
for i in range(self.offset_top, mmap.length, self.skiphgt):
outArr.append(mmap.bands[0][i][self.offset_left::self.skipwidth])
'''
for i in range(self.offset_top, lend, self.skiphgt):
outArr.append(
mmap.bands[0][i][self.offset_left:wend:self.skipwidth])
outFile = os.path.join(self._insar.mergedDirname, filename + '.crop')
outImg = isceobj.createImage()
outImg.bands = 1
outImg.scheme = 'BIP'
outImg.dataType = 'DOUBLE'
outImg.setWidth(len(outArr[0]))
outImg.setLength(len(outArr))
outImg.setFilename(outFile)
with open(outFile, 'wb') as fid:
for i in range(len(outArr)):
np.array(outArr[i]).astype(np.double).tofile(
fid) ### WAY easier to write to file like this
outImg.renderHdr()
print('Cropped %s' % (filename))
for filename in flist2b:
print('\nCropping %s to %s ...\n' % (filename, filename + '.crop'))
f = os.path.join(self._insar.mergedDirname, filename)
outArrCh1 = []
outArrCh2 = []
mmap = IML.mmapFromISCE(f, logging)
'''
for i in range(self.offset_top, mmap.length, self.skiphgt):
outArrCh1.append(mmap.bands[0][i][self.offset_left::self.skipwidth])
outArrCh2.append(mmap.bands[1][i][self.offset_left::self.skipwidth])
'''
for i in range(self.offset_top, lend, self.skiphgt):
outArrCh1.append(
mmap.bands[0][i][self.offset_left:wend:self.skipwidth])
outArrCh2.append(
mmap.bands[1][i][self.offset_left:wend:self.skipwidth])
outFile = os.path.join(self._insar.mergedDirname, filename + '.crop')
outImg = isceobj.createImage()
outImg.bands = 2
outImg.scheme = 'BIL'
outImg.dataType = 'FLOAT'
outImg.setWidth(len(outArrCh1[0]))
outImg.setLength(len(outArrCh1))
outImg.setFilename(outFile)
with open(outFile, 'wb') as fid:
for i in range(len(outArrCh1)):
np.array(outArrCh1[i]).astype(np.float32).tofile(fid)
np.array(outArrCh2[i]).astype(np.float32).tofile(fid)
outImg.renderHdr()
print('Cropped %s' % (filename))
def main(argv):
inps = cmdLineParse()
####Default names
int_file = "filt_topophase.flat"
cor_file = "phsig.cor"
unw_file = "filt_topophase.unw"
rdr_file = "los.rdr"
if inps.geocoord:
int_file += ".geo"
cor_file += ".geo"
unw_file += ".geo"
rdr_file += ".geo"
print(unw_file)
####Gather some basic information
unw = IML.mmapFromISCE(unw_file, logging)
shape = unw.bands[0].shape
h5file = inps.outhe5
## OPEN HDF5 FILE ##
f = h5py.File(h5file)
hdfeos = f.create_group("HDFEOS")
if inps.geocoord:
## CREATE GRIDS GROUP ##
group = hdfeos.create_group("GRIDS")
else:
## CREATE SWATHS GROUP ##
group = hdfeos.create_group("SWATHS")
insar = group.create_group("InSAR")
data = group.create_group("Data Fields")
## CREATE UNWRAPPED INTERFEROGRAM ##
dset = data.create_dataset(
"UnwrappedInterferogram", data=unw.bands[1], shape=shape, chunks=(128, 128), compression="gzip"
)
dset.attrs["Title"] = "Unwrapped phase"
dset.attrs["MissingValue"] = fzero
dset.attrs["Units"] = "radians"
dset.attrs["_FillValue"] = fzero
unw = None
#### CREATE COHERENCE ####
cor = IML.mmapFromISCE(cor_file, logging)
dset = data.create_dataset("Coherence", data=cor.bands[0], shape=shape, chunks=(128, 128), compression="gzip")
dset.attrs["Title"] = "Phase Sigma Coherence"
dset.attrs["MissingValue"] = fzero
dset.attrs["Units"] = "None"
dset.attrs["_FillValue"] = fzero
cor = None
#### CREATE WRAPPED INTERFEROGRAM
wrap = IML.mmapFromISCE(int_file, logging)
dset = data.create_dataset(
"WrappedInterferogram", data=wrap.bands[0], shape=shape, chunks=(64, 64), compression="gzip"
)
dset.attrs["Title"] = "Wrapped interferogram"
dset.attrs["MissingValue"] = czero
dset.attrs["Units"] = "None"
dset.attrs["_FillValue"] = czero
wrap = None
#### CREATE ILLUMINATION ANGLE
ang = IML.mmapFromISCE(rdr_file, logging)
dset = data.create_dataset("Illimunation", data=ang.bands[0], shape=shape, chunks=(128, 128), compression="gzip")
dset.attrs["Title"] = "Illumination angle"
dset.attrs[
"Description"
] = "Vertical angle of the vector from target to sensor, w.r.t to the normal of the ellipse at the target"
dset.attrs["MissingValue"] = fzero
dset.attrs["Units"] = "degrees"
dset.attrs["_FillValue"] = fzero
#### CREATE AZIMUTH ANGLE
dset = data.create_dataset("Azimuth", data=360.0 - ang.bands[1], shape=shape, chunks=(128, 128), compression="gzip")
dset.attrs["Title"] = "Azimuth angle"
dset.attrs[
"Description"
] = "Angle of the vector from target to sensor, measured clockwise w.r.t North at the target"
dset.attrs["MissingValue"] = fzero
dset.attrs["Units"] = "degrees"
dset.attrs["_FillValue"] = fzero
ang = None
## WRITE ATTRIBUTES TO THE HDF ##
if inps.geocoord:
geoinfo = IML.getGeoInfo(unw_file)
if geoinfo is None:
raise Exception("No geocoding information found")
north = geoinfo[0]
south = geoinfo[0] + (shape[1] - 1) * geoinfo[2]
west = geoinfo[1]
east = geoinfo[1] + (shape[0] - 1) * geoinfo[3]
insar.attrs["GCTPProjectionCode"] = np.zeros(1, dtype=np.int32)
insar.attrs["GCTPSpheroidCode"] = str(12)
insar.attrs["Projection"] = "Geographic"
insar.attrs["GridOrigin"] = "Center"
insar.attrs["GridSpacing"] = str((geoinfo[-1], geoinfo[-2]))
insar.attrs["GridSpacingUnit"] = "deg"
insar.attrs["GridSpan"] = str((west, east, north, south))
insar.attrs["GridSpanUnit"] = "deg"
insar.attrs["NumberOfLongitudesInGrid"] = np.array([shape[0]], dtype=np.int32)
insar.attrs["NumberOfLatitudesInGrid"] = np.array([shape[1]], dtype=np.int32)
f.close()