KJ135
/
ISCE_INSAR
mirror of http://172.16.0.12:5000/WBJY/ISCE_INSAR.git
4
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Modified algorithm for ionospheric phase estimation (polar regions)

LT1AB
vbrancat 2020-02-10 17:07:09 -08:00
parent d7444e83ca f864b582a4
commit 3f01fd2f07
82 changed files with 2018 additions and 1333 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
.circleci/config.yml
View File

@ -72,7 +72,7 @@ jobs:
set -ex
pwd
. /opt/conda/bin/activate root
export ISCE_HOME=/root/project/install/isce
ISCE_HOME=/root/project/install/isce
export PATH="$ISCE_HOME/bin:$ISCE_HOME/applications:/opt/conda/bin:$PATH"
export PYTHONPATH="/root/project/install:$PYTHONPATH"
export LD_LIBRARY_PATH="/opt/conda/lib:$LD_LIBRARY_PATH"

35
SConstruct
View File

@ -216,43 +216,12 @@ else:
### End of GPU branch-specific modifications
file = '__init__.py'
if not os.path.exists(file):
fout = open(file,"w")
fout.write("#!/usr/bin/env python3")
fout.close()
env.Install(inst,file)
try:
from subprocess import check_output
svn_revision = check_output('svnversion').strip() or 'Unknown'
if sys.version_info[0] == 3:
svn_revision = svn_revision.decode('utf-8')
except ImportError:
try:
import popen2
stdout, stdin, stderr = popen2.popen3('svnversion')
svn_revision = stdout.read().strip()
if stderr.read():
raise Exception
except Exception:
svn_revision = 'Unknown'
except OSError:
svn_revision = 'Unknown'
env.Install(inst, '__init__.py')
env.Install(inst, 'release_history.py')
if not os.path.exists(inst):
os.makedirs(inst)
fvers = open(os.path.join(inst,'version.py'),'w')
from release_history import release_version, release_svn_revision, release_date
fvers_lines = ["release_version = '"+release_version+"'\n",
"release_svn_revision = '"+release_svn_revision+"'\n",
"release_date = '"+release_date+"'\n",
"svn_revision = '"+svn_revision+"'\n\n"]
fvers.write(''.join(fvers_lines))
fvers.close()
v = 0
if isrerun == 'no':
cmd = 'scons -Q install --isrerun=yes'

17
__init__.py
View File

@ -25,18 +25,19 @@
# Author: Giangi Sacco
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
from __future__ import print_function
from .version import release_version, release_svn_revision, release_date
from .version import svn_revision
from .release_history import release_version, release_svn_revision, release_date
svn_revision = release_svn_revision
version = release_history # compatibility alias
__version__ = release_version
import sys, os
isce_path = os.path.split(os.path.abspath(__file__))[0]
isce_path = os.path.dirname(os.path.abspath(__file__))
import logging
from logging.config import fileConfig as _fc
_fc(os.path.join(isce_path, 'defaults', 'logging', 'logging.conf'))
sys.path.insert(1,isce_path)
sys.path.insert(1,os.path.join(isce_path,'applications'))
sys.path.insert(1,os.path.join(isce_path,'components'))

6
applications/CalculatePegPoint.py
View File

@ -31,12 +31,8 @@
import os
import math
import logging
import logging.config
logging.config.fileConfig(os.path.join(os.environ['ISCE_HOME'], 'defaults',
'logging', 'logging.conf'))
from isce import logging
from iscesys.Compatibility import Compatibility
Compatibility.checkPythonVersion()
from isceobj.Location.Peg import Peg

6
applications/calculateBaseline.py
View File

@ -30,11 +30,7 @@
import os
import logging
import logging.config
logging.config.fileConfig(os.path.join(os.environ['ISCE_HOME'], 'defaults',
'logging', 'logging.conf'))
from isce import logging
from iscesys.Compatibility import Compatibility
Compatibility.checkPythonVersion()
from iscesys.Component.FactoryInit import FactoryInit

6
applications/createGeneric.py
View File

@ -30,11 +30,7 @@
import os
import logging
import logging.config
logging.config.fileConfig(os.path.join(os.environ['ISCE_HOME'], 'defaults',
'logging', 'logging.conf'))
from isce import logging
import isceobj
from iscesys.Component.FactoryInit import FactoryInit

6
applications/extractHDROrbit.py
View File

@ -30,12 +30,8 @@
import os
import datetime
import logging
import logging.config
logging.config.fileConfig(os.path.join(os.environ['ISCE_HOME'], 'defaults',
'logging', 'logging.conf'))
from isce import logging
from iscesys.Compatibility import Compatibility
Compatibility.checkPythonVersion()
from iscesys.Component.FactoryInit import FactoryInit

6
applications/focus.py
View File

@ -30,12 +30,8 @@
import os
import math
import logging
import logging.config
logging.config.fileConfig(os.path.join(os.environ['ISCE_HOME'], 'defaults',
'logging', 'logging.conf'))
from isce import logging
import isceobj
from iscesys.Component.FactoryInit import FactoryInit
from iscesys.DateTimeUtil.DateTimeUtil import DateTimeUtil as DTU

8
applications/insarApp.py
View File

@ -34,8 +34,7 @@ from __future__ import print_function
import time
import os
import sys
import logging
import logging.config
from isce import logging
import isce
import isceobj
@ -46,11 +45,6 @@ from iscesys.Component.Configurable import SELF
import isceobj.InsarProc as InsarProc
from isceobj.Scene.Frame import FrameMixin
logging.config.fileConfig(
os.path.join(os.environ['ISCE_HOME'], 'defaults', 'logging',
'logging.conf')
)
logger = logging.getLogger('isce.insar')

8
applications/isceApp.py
View File

@ -41,8 +41,7 @@ import datetime
import os
import sys
import math
import logging
import logging.config
from isce import logging
import isce
import isceobj
@ -1438,11 +1437,6 @@ class IsceApp(Application, FrameMixin):
sys.exit("Could not find the output directory: %s" % self.outputDir)
os.chdir(self.outputDir) ##change working directory to given output directory
##read configfile only here so that log path is in output directory
logging.config.fileConfig(
os.path.join(os.environ['ISCE_HOME'], 'defaults', 'logging',
'logging.conf')
)
logger = logging.getLogger('isce.isceProc')
logger.info(self.intromsg)
self._isce.dataDirectory = self.outputDir

10
applications/make_raw.py
View File

@ -27,16 +27,8 @@
# Author: Walter Szeliga
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
import os
import logging
import logging.config
logging.config.fileConfig(os.path.join(os.environ['ISCE_HOME'], 'defaults',
'logging', 'logging.conf'))
import isce
from isce import logging
from iscesys.Compatibility import Compatibility
from iscesys.Component.Component import Component, Port
from isceobj.Planet.Ellipsoid import Ellipsoid

9
applications/rtcApp.py
View File

@ -30,10 +30,8 @@
import time
import os
import sys
import logging
import logging.config
from isce import logging
import isce
import isceobj
@ -44,11 +42,6 @@ from iscesys.Component.Configurable import SELF
from isceobj import RtcProc
from isceobj.Util.decorators import use_api
logging.config.fileConfig(
os.path.join(os.environ['ISCE_HOME'], 'defaults', 'logging',
'logging.conf')
)
logger = logging.getLogger('isce.grdsar')

11
applications/scansarApp.py
View File

@ -27,13 +27,9 @@
# Authors: Giangi Sacco, Eric Gurrola
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
import time
import os
import sys
import logging
import logging.config
from isce import logging
import isce
import isceobj
@ -43,11 +39,6 @@ from iscesys.Compatibility import Compatibility
from iscesys.Component.Configurable import SELF
from isceobj import ScansarProc
logging.config.fileConfig(
os.path.join(os.environ['ISCE_HOME'], 'defaults', 'logging',
'logging.conf')
)
logger = logging.getLogger('isce.insar')

11
applications/stripmapApp.py
View File

@ -35,10 +35,8 @@
from __future__ import print_function
import time
import os
import sys
import logging
import logging.config
from isce import logging
import isce
import isceobj
@ -50,11 +48,6 @@ import isceobj.StripmapProc as StripmapProc
from isceobj.Scene.Frame import FrameMixin
from isceobj.Util.decorators import use_api
logging.config.fileConfig(
os.path.join(os.environ['ISCE_HOME'], 'defaults', 'logging',
'logging.conf')
)
logger = logging.getLogger('isce.insar')
@ -265,7 +258,7 @@ RUBBERSHEET_SNR_THRESHOLD = Application.Parameter('rubberSheetSNRThreshold',
RUBBERSHEET_FILTER_SIZE = Application.Parameter('rubberSheetFilterSize',
public_name='rubber sheet filter size',
default = 8,
default = 9,
type = int,
mandatory = False,
doc = '')

9
applications/topsApp.py
View File

@ -34,10 +34,8 @@
import time
import os
import sys
import logging
import logging.config
from isce import logging
import isce
import isceobj
@ -47,11 +45,6 @@ from iscesys.Compatibility import Compatibility
from iscesys.Component.Configurable import SELF
from isceobj import TopsProc
logging.config.fileConfig(
os.path.join(os.environ['ISCE_HOME'], 'defaults', 'logging',
'logging.conf')
)
logger = logging.getLogger('isce.insar')

9
applications/topsOffsetApp.py
View File

@ -30,10 +30,8 @@
import time
import os
import sys
import logging
import logging.config
from isce import logging
import isce
import isceobj
@ -42,11 +40,6 @@ from isce.applications.topsApp import TopsInSAR
from iscesys.Component.Application import Application
from isceobj.Util.decorators import use_api
logging.config.fileConfig(
os.path.join(os.environ['ISCE_HOME'], 'defaults', 'logging',
'logging.conf')
)
logger = logging.getLogger('isce.insar')
WINDOW_SIZE_WIDTH = Application.Parameter(

9
applications/viewMetadata.py
View File

@ -27,14 +27,7 @@
# Author: Walter Szeliga
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
import os
import logging
import logging.config
logging.config.fileConfig(os.path.join(os.environ['ISCE_HOME'], 'defaults',
'logging', 'logging.conf'))
from isce import logging
from iscesys.Compatibility import Compatibility
Compatibility.checkPythonVersion()
from iscesys.Component.FactoryInit import FactoryInit

6
components/isceobj/Location/Offset.py
View File

@ -29,11 +29,7 @@
import math
import os
import logging
import logging.config
logging.config.fileConfig(os.path.join(os.environ['ISCE_HOME'], 'defaults',
'logging', 'logging.conf'))
from isce import logging
from isceobj.Util.decorators import type_check, force, pickled, logged
import numpy as np

2
components/isceobj/Orbit/Orbit.py
View File

@ -1061,7 +1061,7 @@ class Orbit(Component):
###This wont break the old interface but could cause
###issues at midnight crossing
if reference is None:
reference = self.minTime()
reference = self.minTime
refEpoch = reference.replace(hour=0, minute=0, second=0, microsecond=0)

9
components/isceobj/Scene/test/testTrack.py
View File

@ -27,14 +27,7 @@
# Author: Walter Szeliga
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
import os
import logging
import logging.config
logging.config.fileConfig(os.path.join(os.environ['ISCE_HOME'], 'defaults',
'logging', 'logging.conf'))
from isce import logging
from isceobj.Sensor.ERS import ERS
from isceobj.Scene.Track import Track
logger = logging.getLogger("testTrack")

10
components/isceobj/StripmapProc/runDenseOffsets.py
View File

@ -62,7 +62,7 @@ def estimateOffsetField(master, slave, denseOffsetFileName,
else:
objOffset.setImageDataType2('real')
objOffset.offsetImageName = denseOffsetFileName + '.bil'
objOffset.snrImageName = denseOffsetFileName +'_snr.bil'
objOffset.covImageName = denseOffsetFileName +'_cov.bil'
@ -75,11 +75,11 @@ def estimateOffsetField(master, slave, denseOffsetFileName,
def runDenseOffsets(self):
if self.doDenseOffsets or self.doRubbersheeting:
if self.doDenseOffsets or self.doRubbersheetingAzimuth:
if self.doDenseOffsets:
print('Dense offsets explicitly requested')
if self.doRubbersheeting:
if self.doRubbersheetingAzimuth:
print('Generating offsets as rubber sheeting requested')
else:
return
@ -96,7 +96,7 @@ def runDenseOffsets(self):
os.makedirs(dirname)
denseOffsetFilename = os.path.join(dirname , self.insar.denseOffsetFilename)
field = estimateOffsetField(masterSlc, slaveSlc, denseOffsetFilename,
ww = self.denseWindowWidth,
wh = self.denseWindowHeight,
@ -107,5 +107,5 @@ def runDenseOffsets(self):
self._insar.offset_top = field[0]
self._insar.offset_left = field[1]
return None

5
components/isceobj/StripmapProc/runDispersive.py
View File

@ -8,10 +8,13 @@ import isceobj
from isceobj.Constants import SPEED_OF_LIGHT
import numpy as np
import gdal
<<<<<<< HEAD
from scipy.ndimage import median_filter
from astropy.convolution import convolve
from scipy import ndimage
import numpy as np
=======
>>>>>>> upstream/master
try:
import cv2
@ -299,6 +302,8 @@ def fill(data, invalid=None):
Output:
Return a filled array.
"""
from scipy import ndimage
if invalid is None: invalid = np.isnan(data)
ind = ndimage.distance_transform_edt(invalid,

15
components/isceobj/StripmapProc/runInterferogram.py
View File

@ -56,7 +56,7 @@ def compute_FlatEarth(self,ifgFilename,width,length,radarWavelength):
# Open the interferogram
#ifgFilename= os.path.join(self.insar.ifgDirname, self.insar.ifgFilename)
intf = np.memmap(ifgFilename+'.full',dtype=np.complex64,mode='r+',shape=(length,width))
intf = np.memmap(ifgFilename,dtype=np.complex64,mode='r+',shape=(length,width))
for ll in range(length):
intf[ll,:] *= np.exp(cJ*fact*rng2[ll,:])
@ -155,10 +155,13 @@ def generateIgram(self,imageSlc1, imageSlc2, resampName, azLooks, rgLooks,radarW
else:
resampAmp += '.amp'
resampInt = resampName
if not self.doRubbersheetingRange:
resampInt = resampName
else:
resampInt = resampName + ".full"
objInt = isceobj.createIntImage()
objInt.setFilename(resampInt+'.full')
objInt.setFilename(resampInt)
objInt.setWidth(intWidth)
imageInt = isceobj.createIntImage()
IU.copyAttributes(objInt, imageInt)
@ -166,7 +169,7 @@ def generateIgram(self,imageSlc1, imageSlc2, resampName, azLooks, rgLooks,radarW
objInt.createImage()
objAmp = isceobj.createAmpImage()
objAmp.setFilename(resampAmp+'.full')
objAmp.setFilename(resampAmp)
objAmp.setWidth(intWidth)
imageAmp = isceobj.createAmpImage()
IU.copyAttributes(objAmp, imageAmp)
@ -196,8 +199,8 @@ def generateIgram(self,imageSlc1, imageSlc2, resampName, azLooks, rgLooks,radarW
compute_FlatEarth(self,resampInt,intWidth,lines,radarWavelength)
# Perform Multilook
multilook(resampInt+'.full', outname=resampInt, alks=azLooks, rlks=rgLooks) #takeLooks(objAmp,azLooks,rgLooks)
multilook(resampAmp+'.full', outname=resampAmp, alks=azLooks, rlks=rgLooks) #takeLooks(objInt,azLooks,rgLooks)
multilook(resampInt, outname=resampName, alks=azLooks, rlks=rgLooks) #takeLooks(objAmp,azLooks,rgLooks)
multilook(resampAmp, outname=resampAmp.replace(".full",""), alks=azLooks, rlks=rgLooks) #takeLooks(objInt,azLooks,rgLooks)
#os.system('rm ' + resampInt+'.full* ' + resampAmp + '.full* ')
# End of modification

1
components/isceobj/StripmapProc/runResampleSlc.py
View File

@ -75,6 +75,7 @@ def runResampleSlc(self, kind='coarse'):
if kind in ['coarse', 'refined']:
azname = os.path.join(offsetsDir, self.insar.azimuthOffsetFilename)
rgname = os.path.join(offsetsDir, self.insar.rangeOffsetFilename)
flatten = True
else:
azname = os.path.join(offsetsDir, self.insar.azimuthRubbersheetFilename)
if self.doRubbersheetingRange:

22
components/isceobj/StripmapProc/runRubbersheet.py
View File

@ -6,7 +6,6 @@
import isce
import isceobj
from osgeo import gdal
from scipy import ndimage
import numpy as np
import os
@ -14,16 +13,19 @@ def fill(data, invalid=None):
"""
Replace the value of invalid 'data' cells (indicated by 'invalid')
by the value of the nearest valid data cell
Input:
data: numpy array of any dimension
invalid: a binary array of same shape as 'data'.
data value are replaced where invalid is True
If None (default), use: invalid = np.isnan(data)
Output:
Return a filled array.
"""
from scipy import ndimage
if invalid is None: invalid = np.isnan(data)
ind = ndimage.distance_transform_edt(invalid,
@ -35,6 +37,8 @@ def fill(data, invalid=None):
def mask_filter(denseOffsetFile, snrFile, band, snrThreshold, filterSize, outName):
#masking and Filtering
from scipy import ndimage
##Read in the offset file
ds = gdal.Open(denseOffsetFile + '.vrt', gdal.GA_ReadOnly)
Offset = ds.GetRasterBand(1).ReadAsArray()
@ -93,7 +97,7 @@ def resampleOffset(maskedFiltOffset, geometryOffset, outName):
###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.
###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 )
@ -121,7 +125,7 @@ def resampleOffset(maskedFiltOffset, geometryOffset, outName):
for ll in range(length):
val = geomoff.bands[0][ll,:] + osoff.bands[0][ll,:]
val.tofile(fid)
fid.close()
img = isceobj.createImage()
@ -134,13 +138,13 @@ def resampleOffset(maskedFiltOffset, geometryOffset, outName):
img.scheme = 'BIP'
img.renderHdr()
return None
def runRubbersheet(self):
if not self.doRubbersheeting:
if not self.doRubbersheetingAzimuth:
print('Rubber sheeting not requested ... skipping')
return
@ -164,11 +168,9 @@ def runRubbersheet(self):
sheetOffset = os.path.join(offsetsDir, self.insar.azimuthRubbersheetFilename)
# oversampling the filtAzOffsetFile to the same size of geometryAzimuthOffset
# and then update the geometryAzimuthOffset by adding the oversampled
# and then update the geometryAzimuthOffset by adding the oversampled
# filtAzOffsetFile to it.
resampleOffset(filtAzOffsetFile, geometryAzimuthOffset, sheetOffset)
print("I'm here")
return None

69
components/isceobj/StripmapProc/runRubbersheetAzimuth.py
View File

@ -9,52 +9,52 @@
import isce
import isceobj
from osgeo import gdal
from scipy import ndimage
from astropy.convolution import convolve
import numpy as np
import os
def mask_filterNoSNR(denseOffsetFile,filterSize,outName):
# Masking the offsets with a data-based approach
from scipy import ndimage
# Open the offsets
ds = gdal.Open(denseOffsetFile+'.vrt',gdal.GA_ReadOnly)
off_az = ds.GetRasterBand(1).ReadAsArray()
off_rg = ds.GetRasterBand(2).ReadAsArray()
ds = None
# Remove missing values from ampcor
# Remove missing values from ampcor
off_rg[np.where(off_rg < -9999)]=0
off_az[np.where(off_az < -9999)]=0
# Store the offsets in a complex variable
off = off_rg + 1j*off_az
# Mask the azimuth offsets based on the MAD
mask = off_masking(off,filterSize,thre=3)
mask = off_masking(off,filterSize,thre=3)
xoff_masked = np.ma.array(off.real,mask=mask)
yoff_masked = np.ma.array(off.imag,mask=mask)
# Delete unused variables
mask = None
off = None
# Remove residual noisy spots with a median filter on the azimuth offmap
yoff_masked.mask = yoff_masked.mask | \
(ndimage.median_filter(xoff_masked.filled(fill_value=0),3) == 0) | \
(ndimage.median_filter(yoff_masked.filled(fill_value=0),3) == 0)
# Fill the data by iteratively using smoothed values
# Fill the data by iteratively using smoothed values
data = yoff_masked.data
data[yoff_masked.mask]=np.nan
off_az_filled = fill_with_smoothed(data,filterSize)
# Apply median filter to smooth the azimuth offset map
off_az_filled = ndimage.median_filter(off_az_filled,filterSize)
# Save the filtered offsets
length, width = off_az_filled.shape
@ -74,11 +74,14 @@ def mask_filterNoSNR(denseOffsetFile,filterSize,outName):
img.scheme = 'BIP'
img.renderHdr()
return
return
def off_masking(off,filterSize,thre=2):
from scipy import ndimage
# Define the mask to fill the offsets
vram = ndimage.median_filter(off.real, filterSize)
vazm = ndimage.median_filter(off.imag, filterSize)
@ -91,16 +94,18 @@ def fill(data, invalid=None):
"""
Replace the value of invalid 'data' cells (indicated by 'invalid')
by the value of the nearest valid data cell
Input:
data: numpy array of any dimension
invalid: a binary array of same shape as 'data'.
data value are replaced where invalid is True
If None (default), use: invalid = np.isnan(data)
Output:
Return a filled array.
"""
from scipy import ndimage
if invalid is None: invalid = np.isnan(data)
ind = ndimage.distance_transform_edt(invalid,
@ -112,6 +117,8 @@ def fill(data, invalid=None):
def mask_filter(denseOffsetFile, snrFile, band, snrThreshold, filterSize, outName):
#masking and Filtering
from scipy import ndimage
##Read in the offset file
ds = gdal.Open(denseOffsetFile + '.vrt', gdal.GA_ReadOnly)
Offset = ds.GetRasterBand(band).ReadAsArray()
@ -154,12 +161,14 @@ def mask_filter(denseOffsetFile, snrFile, band, snrThreshold, filterSize, outNam
return None
def fill_with_smoothed(off,filterSize):
from astropy.convolution import convolve
off_2filt=np.copy(off)
kernel = np.ones((filterSize,filterSize),np.float32)/(filterSize*filterSize)
loop = 0
cnt2=1
while (cnt2!=0 & loop<100):
loop += 1
idx2= np.isnan(off_2filt)
@ -171,9 +180,9 @@ def fill_with_smoothed(off,filterSize):
idx3 = np.where(off_filt == 0)
off_2filt[idx3]=np.nan
off_filt=None
return off_2filt
def resampleOffset(maskedFiltOffset, geometryOffset, outName):
'''
Oversample offset and add.
@ -191,7 +200,7 @@ def resampleOffset(maskedFiltOffset, geometryOffset, outName):
###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.
###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 )
@ -219,7 +228,7 @@ def resampleOffset(maskedFiltOffset, geometryOffset, outName):
for ll in range(length):
val = geomoff.bands[0][ll,:] + osoff.bands[0][ll,:]
val.tofile(fid)
fid.close()
img = isceobj.createImage()
@ -232,7 +241,7 @@ def resampleOffset(maskedFiltOffset, geometryOffset, outName):
img.scheme = 'BIP'
img.renderHdr()
return None
@ -257,7 +266,7 @@ def runRubbersheetAzimuth(self):
if not self.doRubbersheetingRange:
print('Rubber sheeting in range is off, filtering the offsets with a SNR-based mask')
mask_filter(denseOffsetFile, snrFile, band[0], snrThreshold, filterSize, filtAzOffsetFile)
else:
else:
print('Rubber sheeting in range is on, filtering the offsets with data-based mask')
mask_filterNoSNR(denseOffsetFile, filterSize, filtAzOffsetFile)
@ -267,10 +276,8 @@ def runRubbersheetAzimuth(self):
sheetOffset = os.path.join(offsetsDir, self.insar.azimuthRubbersheetFilename)
# oversampling the filtAzOffsetFile to the same size of geometryAzimuthOffset
# and then update the geometryAzimuthOffset by adding the oversampled
# and then update the geometryAzimuthOffset by adding the oversampled
# filtAzOffsetFile to it.
resampleOffset(filtAzOffsetFile, geometryAzimuthOffset, sheetOffset)
return None

20
components/isceobj/StripmapProc/runRubbersheetRange.py
View File

@ -9,15 +9,14 @@
import isce
import isceobj
from osgeo import gdal
from scipy import ndimage
import numpy as np
import os
from astropy.convolution import convolve
def mask_filterNoSNR(denseOffsetFile,filterSize,outName):
# Masking the offsets with a data-based approach
from scipy import ndimage
# Open the offsets
ds = gdal.Open(denseOffsetFile+'.vrt',gdal.GA_ReadOnly)
off_az = ds.GetRasterBand(1).ReadAsArray()
@ -78,6 +77,9 @@ def mask_filterNoSNR(denseOffsetFile,filterSize,outName):
return
def off_masking(off,filterSize,thre=2):
from scipy import ndimage
vram = ndimage.median_filter(off.real, filterSize)
vazm = ndimage.median_filter(off.imag, filterSize)
@ -100,6 +102,8 @@ def fill(data, invalid=None):
Output:
Return a filled array.
"""
from scipy import ndimage
if invalid is None: invalid = np.isnan(data)
ind = ndimage.distance_transform_edt(invalid,
@ -108,7 +112,9 @@ def fill(data, invalid=None):
return data[tuple(ind)]
def fill_with_smoothed(off,filterSize):
from astropy.convolution import convolve
off_2filt=np.copy(off)
kernel = np.ones((filterSize,filterSize),np.float32)/(filterSize*filterSize)
loop = 0
@ -131,6 +137,8 @@ def fill_with_smoothed(off,filterSize):
def mask_filter(denseOffsetFile, snrFile, band, snrThreshold, filterSize, outName):
#masking and Filtering
from scipy import ndimage
##Read in the offset file
ds = gdal.Open(denseOffsetFile + '.vrt', gdal.GA_ReadOnly)
Offset = ds.GetRasterBand(band).ReadAsArray()
@ -236,6 +244,8 @@ def resampleOffset(maskedFiltOffset, geometryOffset, outName):
def runRubbersheetRange(self):
from scipy import ndimage
if not self.doRubbersheetingRange:
print('Rubber sheeting in azimuth not requested ... skipping')
return

12
components/isceobj/TopsProc/runIon.py
View File

@ -9,9 +9,6 @@ import shutil
import datetime
import numpy as np
import numpy.matlib
import scipy.signal as ss
from scipy import interpolate
from scipy.interpolate import interp1d
import isceobj
import logging
@ -638,6 +635,7 @@ def cal_coherence(inf, win=5, edge=0):
4: keep all samples
'''
import scipy.signal as ss
if win % 2 != 1:
raise Exception('window size must be odd!')
@ -1682,6 +1680,9 @@ def computeDopplerOffset(burst, firstline, lastline, firstcolumn, lastcolumn, nr
output: first lines > 0, last lines < 0
'''
from scipy import interpolate
from scipy.interpolate import interp1d
Vs = np.linalg.norm(burst.orbit.interpolateOrbit(burst.sensingMid, method='hermite').getVelocity())
Ks = 2 * Vs * burst.azimuthSteeringRate / burst.radarWavelength
@ -1830,6 +1831,7 @@ def adaptive_gaussian(ionos, wgt, size_max, size_min):
size_max: maximum window size
size_min: minimum window size
'''
import scipy.signal as ss
length = (ionos.shape)[0]
width = (ionos.shape)[1]
@ -1892,6 +1894,8 @@ def filt_gaussian(self, ionParam):
currently not implemented.
a less accurate method is to use ionsphere without any projection
'''
from scipy import interpolate
from scipy.interpolate import interp1d
#################################################
#SET PARAMETERS HERE
@ -2659,5 +2663,3 @@ def runIon(self):
#esd_noion(self, ionParam)
return

3
components/isceobj/TopsProc/runOffsetFilter.py
View File

@ -3,7 +3,6 @@
# Copyright 2016
#
from scipy.ndimage.filters import median_filter
import numpy as np
import isce
import isceobj
@ -20,6 +19,8 @@ def runOffsetFilter(self):
if not self.doDenseOffsets:
return
from scipy.ndimage.filters import median_filter
offsetfile = os.path.join(self._insar.mergedDirname, self._insar.offsetfile)
snrfile = os.path.join(self._insar.mergedDirname, self._insar.snrfile)
print('\n======================================')

2
components/isceobj/TopsProc/runPrepESD.py
View File

@ -8,7 +8,6 @@ import numpy as np
import os
import isceobj
import logging
import scipy.signal as SS
from isceobj.Util.ImageUtil import ImageLib as IML
import datetime
import pprint
@ -177,6 +176,7 @@ def createCoherence(intfile, win=5):
'''
Compute coherence using scipy convolve 2D.
'''
import scipy.signal as SS
corfile = os.path.splitext(intfile)[0] + '.cor'
filt = np.ones((win,win))/ (1.0*win*win)

2
components/isceobj/Unwrap/snaphu.py
View File

@ -54,7 +54,7 @@ class snaphu(Component):
self.azimuthLooks = obj.insar.topo.numberAzimuthLooks
azres = obj.insar.masterFrame.platform.antennaLength/2.0
azfact = obj.insar.topo.numberAzimuthLooks *azres / obj.insar.topo.azimuthSpacing
azfact = azres / obj.insar.topo.azimuthSpacing
rBW = obj.insar.masterFrame.instrument.pulseLength * obj.insar.masterFrame.instrument.chirpSlope
rgres = abs(SPEED_OF_LIGHT / (2.0 * rBW))

2
components/isceobj/Unwrap/snaphu_mcf.py
View File

@ -54,7 +54,7 @@ class snaphu_mcf(Component):
self.azimuthLooks = obj.insar.topo.numberAzimuthLooks
azres = obj.insar.masterFrame.platform.antennaLength/2.0
azfact = obj.insar.topo.numberAzimuthLooks *azres / obj.insar.topo.azimuthSpacing
azfact = azres / obj.insar.topo.azimuthSpacing
rBW = obj.insar.masterFrame.instrument.pulseLength * obj.insar.masterFrame.instrument.chirpSlope
rgres = abs(SPEED_OF_LIGHT / (2.0 * rBW))

4
components/isceobj/Util/geo/__init__.py
View File

@ -45,10 +45,6 @@ ellipsoid oblate ellipsoid of revolution (e.g, WGS84) with all the
See mainpage.txt for a complete dump of geo's philosophy-- otherwise,
use the docstrings.
"""
import os
isce_path = os.getenv("ISCE_HOME")
## \namespace geo Vector- and Affine-spaces, on Earth
__all__ = ['euclid', 'coordinates', 'ellipsoid', 'charts', 'affine', 'motion']

5
components/iscesys/Component/Configurable.py
View File

@ -32,10 +32,7 @@ from __future__ import print_function
import os
import sys
import operator
import logging
import logging.config
logging.config.fileConfig(os.path.join(os.environ['ISCE_HOME'], 'defaults',
'logging', 'logging.conf'))
from isce import logging
from iscesys.DictUtils.DictUtils import DictUtils as DU
from iscesys.Compatibility import Compatibility
Compatibility.checkPythonVersion()

7
components/iscesys/DataRetriever/DataRetriever.py
View File

@ -37,8 +37,7 @@ import isce
import zipfile
import os
import sys
import logging
import logging.config
from isce import logging
from iscesys.Component.Component import Component
import shutil
from urllib import request
@ -325,8 +324,4 @@ class DataRetriever(Component):
# logger not defined until baseclass is called
if not self.logger:
logging.config.fileConfig(
os.path.join(os.environ['ISCE_HOME'], 'defaults',
'logging', 'logging.conf')
)
self.logger = logging.getLogger('isce.iscesys.DataRetriever')

1
components/stdproc/alosreformat/ALOS_lib/src/cfft1d_fftpack.c
View File

@ -1,7 +1,6 @@
#include <stdio.h>
#include <stdlib.h>
#include <complex.h>
#include <malloc.h>
/************************************************************************
* cfft1d is a subroutine used to call and initialize perflib Fortran FFT *
* routines. *

5
components/stdproc/orbit/orbitLib/CalcSchHeightVel.py
View File

@ -29,10 +29,8 @@
import os
import logging
import math
import logging.config
from iscesys.Compatibility import Compatibility
@ -40,9 +38,6 @@ from isceobj.Planet import Planet
from isceobj import Constants as CN
from iscesys.Component.Component import Component, Port
logging.config.fileConfig(os.path.join(os.environ['ISCE_HOME'], 'defaults',
'logging', 'logging.conf'))
RANGE_SAMPLING_RATE = Component.Parameter('rangeSamplingRate',
public_name='range sampling rate',
type=float,

10
components/zerodop/GPUampcor/cuda/SConscript
View File

@ -2,19 +2,19 @@
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Copyright 2010 California Institute of Technology. ALL RIGHTS RESERVED.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#
# United States Government Sponsorship acknowledged. This software is subject to
# U.S. export control laws and regulations and has been classified as 'EAR99 NLR'
# (No [Export] License Required except when exporting to an embargoed country,
@ -49,7 +49,7 @@ if envGPUampcor['GPU_ACC_ENABLED']:
build_base += "-ccbin " + envGPUampcor['NVCC_CCBIN'] + " "
else:
print('Assuming default system compiler for nvcc.')
build_base += "-arch=sm_35 -shared -Xcompiler -fPIC -O3 "
build_base += "-shared -Xcompiler -fPIC -O3 "
build_cmd = build_base + "-dc -m64 -o $TARGET -c $SOURCE"
built_path = os.path.join(build, 'gpu-ampcor.o')
linked_path = os.path.join(build, 'gpu-ampcor-linked.o')

2
components/zerodop/GPUtopozero/cuda/compilation
View File

@ -1,2 +1,2 @@
nvcc -arch=sm_35 -Xcompiler -fPIC -o gpu-topo.o -c Topo.cu
nvcc -Xcompiler -fPIC -o gpu-topo.o -c Topo.cu
cp -f gpu-topo.o ..

4
contrib/PyCuAmpcor/SConscript
View File

@ -1,4 +1,4 @@
#!/usr/bin/env python
#!/usr/bin/env python3
import os
@ -28,7 +28,7 @@ if envPyCuAmpcor['GPU_ACC_ENABLED']:
if not os.path.exists(initFile):
with open(initFile, 'w') as fout:
fout.write("#!/usr/bin/env python")
fout.write("#!/usr/bin/env python3")
listFiles = [initFile]
envPyCuAmpcor.Install(install, listFiles)

63
contrib/PyCuAmpcor/examples/GeoTiffSample.py Normal file
View File

@ -0,0 +1,63 @@
#!/usr/bin/env python3
#
# Test program to run ampcor with GPU
# For two GeoTiff images
#
import argparse
import numpy as np
from PyCuAmpcor import PyCuAmpcor
def main():
'''
main program
'''
objOffset = PyCuAmpcor() # create the processor
objOffset.algorithm = 0 # cross-correlation method 0=freq 1=time
objOffset.deviceID = 0 # GPU device id to be used
objOffset.nStreams = 2 # cudaStreams; multiple streams to overlap data transfer with gpu calculations
objOffset.masterImageName = "master.tif"
objOffset.masterImageHeight = 16480 # RasterYSize
objOffset.masterImageWidth = 17000 # RasterXSize
objOffset.slaveImageName = "slave.tif"
objOffset.slaveImageHeight = 16480
objOffset.slaveImageWidth = 17000
objOffset.windowSizeWidth = 64 # template window size
objOffset.windowSizeHeight = 64
objOffset.halfSearchRangeDown = 20 # search range
objOffset.halfSearchRangeAcross = 20
objOffset.derampMethod = 1 # deramping for complex signal, set to 1 for real images
objOffset.skipSampleDown = 128 # strides between windows
objOffset.skipSampleAcross = 64
# gpu processes several windows in one batch/Chunk
# total windows in Chunk = numberWindowDownInChunk*numberWindowAcrossInChunk
# the max number of windows depending on gpu memory and type
objOffset.numberWindowDownInChunk = 1
objOffset.numberWindowAcrossInChunk = 10
objOffset.corrSurfaceOverSamplingFactor = 8 # oversampling factor for correlation surface
objOffset.corrSurfaceZoomInWindow = 16 # area in correlation surface to be oversampled
objOffset.corrSufaceOverSamplingMethod = 1 # fft or sinc oversampler
objOffset.useMmap = 1 # default using memory map as buffer, if having troubles, set to 0
objOffset.mmapSize = 1 # mmap or buffer size used for transferring data from file to gpu, in GB
objOffset.numberWindowDown = 40 # number of windows to be processed
objOffset.numberWindowAcross = 100
# if to process the whole image; some math needs to be done
# margin = 0 # margins to be neglected
#objOffset.numberWindowDown = (objOffset.slaveImageHeight - 2*margin - 2*objOffset.halfSearchRangeDown - objOffset.windowSizeHeight) // objOffset.skipSampleDown
#objOffset.numberWindowAcross = (objOffset.slaveImageWidth - 2*margin - 2*objOffset.halfSearchRangeAcross - objOffset.windowSizeWidth) // objOffset.skipSampleAcross
objOffset.setupParams()
objOffset.masterStartPixelDownStatic = objOffset.halfSearchRangeDown # starting pixel offset
objOffset.masterStartPixelAcrossStatic = objOffset.halfSearchRangeDown
objOffset.setConstantGrossOffset(0, 0) # gross offset between master and slave images
objOffset.checkPixelInImageRange() # check whether there is something wrong with
objOffset.runAmpcor()
if __name__ == '__main__':

21
contrib/PyCuAmpcor/examples/fixedGrossOffsetSample.py → contrib/PyCuAmpcor/examples/glacierSample.py
View File

@ -1,14 +1,14 @@
#!/usr/bin/env python3
#
#
# test_cuAmpcor.py
# Test program to run ampcor with GPU
#
#
#
import argparse
import numpy as np
#from PyCuAmpcor import PyCuAmpcor
from isce.components.contrib.PyCuAmpcor import PyCuAmpcor
from PyCuAmpcor import PyCuAmpcor
def main():
'''
@ -20,10 +20,10 @@ def main():
objOffset.algorithm = 0
objOffset.deviceID = 0 # -1:let system find the best GPU
objOffset.nStreams = 2 #cudaStreams
objOffset.masterImageName = "master.slc"
objOffset.masterImageName = "20131213.slc.vrt"
objOffset.masterImageHeight = 43008
objOffset.masterImageWidth = 24320
objOffset.slaveImageName = "slave.slc"
objOffset.slaveImageName = "20131221.slc.vrt"
objOffset.slaveImageHeight = 43008
objOffset.slaveImageWidth = 24320
objOffset.windowSizeWidth = 64
@ -38,8 +38,9 @@ def main():
objOffset.numberWindowDownInChunk = 10
objOffset.numberWindowAcrossInChunk = 10
objOffset.corrSurfaceOverSamplingFactor = 8
objOffset.corrSurfaceZoomInWindow = 16
objOffset.corrSufaceOverSamplingMethod = 1
objOffset.corrSurfaceZoomInWindow = 16
objOffset.corrSufaceOverSamplingMethod = 1
objOffset.useMmap = 1
objOffset.mmapSize = 8
objOffset.setupParams()
@ -48,8 +49,8 @@ def main():
objOffset.setConstantGrossOffset(642, -30)
objOffset.checkPixelInImageRange()
objOffset.runAmpcor()
if __name__ == '__main__':
main()

26
contrib/PyCuAmpcor/examples/varyGrossOffsetSample.py
View File

@ -1,27 +1,27 @@
#!/usr/bin/env python3
#
#
from PyCuAmpcor import PyCuAmpcor
import numpy as np
def main():
def main():
'''
Set parameters manually and run ampcor
'''
objOffset = PyCuAmpcor()
#step 1 set constant parameters
objOffset.masterImageName = "master.slc"
objOffset.masterImageName = "master.slc.vrt"
objOffset.masterImageHeight = 128
objOffset.masterImageWidth = 128
objOffset.slaveImageName = "slave.slc"
objOffset.slaveImageName = "slave.slc.vrt"
objOffset.masterImageHeight = 128
objOffset.masterImageWidth = 128
objOffset.masterImageWidth = 128
objOffset.skipSampleDown = 2
objOffset.skipSampleAcross = 2
objOffset.windowSizeHeight = 16
objOffset.windowSizeWidth = 16
objOffset.halfSearchRangeDown = 20
objOffset.halfSearchRangeDown = 20
objOffset.halfSearchRangeAcross = 20
objOffset.numberWindowDown = 2
objOffset.numberWindowAcross = 2
@ -29,19 +29,19 @@ def main():
objOffset.numberWindowAcrossInChunk = 2
# 2 set other dependent parameters and allocate aray parameters
objOffset.setupParams()
#3 set gross offsets: constant or varying
objOffset.masterStartPixelDownStatic = objOffset.halfSearchRangeDown
objOffset.masterStartPixelDownStatic = objOffset.halfSearchRangeDown
objOffset.masterStartPixelAcrossStatic = objOffset.halfSearchRangeAcross
vD = np.random.randint(0, 10, size =objOffset.numberWindows, dtype=np.int32)
vD = np.random.randint(0, 10, size =objOffset.numberWindows, dtype=np.int32)
vA = np.random.randint(0, 1, size = objOffset.numberWindows, dtype=np.int32)
objOffset.setVaryingGrossOffset(vD, vA)
objOffset.checkPixelInImageRange()
#4 run ampcor
objOffset.runAmpcor()
if __name__ == '__main__':
main()

154
contrib/PyCuAmpcor/src/GDALImage.cu Normal file
View File

@ -0,0 +1,154 @@
#include "GDALImage.h"
#include <iostream>
#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <fcntl.h>
#include <assert.h>
#include <cublas_v2.h>
#include "cudaError.h"
#include <errno.h>
#include <unistd.h>
/**
* \brief Constructor
*
* @param filename a std::string with the raster image file name
*/
GDALImage::GDALImage(std::string filename, int band, int cacheSizeInGB, int useMmap)
: _useMmap(useMmap)
{
// open the file as dataset
_poDataset = (GDALDataset *) GDALOpen(filename.c_str(), GA_ReadOnly );
// if something is wrong, throw an exception
// GDAL reports the error message
if(!_poDataset)
throw;
// check the band info
int count = _poDataset->GetRasterCount();
if(band > count)
{
std::cout << "The desired band " << band << " is greated than " << count << " bands available";
throw;
}
// get the desired band
_poBand = _poDataset->GetRasterBand(band);
if(!_poBand)
throw;
// get the width(x), and height(y)
_width = _poBand->GetXSize();
_height = _poBand->GetYSize();
_dataType = _poBand->GetRasterDataType();
// determine the image type
_isComplex = GDALDataTypeIsComplex(_dataType);
// determine the pixel size in bytes
_pixelSize = GDALGetDataTypeSize(_dataType);
_bufferSize = 1024*1024*cacheSizeInGB;
// checking whether using memory map
if(_useMmap) {
char **papszOptions = NULL;
// if cacheSizeInGB = 0, use default
// else set the option
if(cacheSizeInGB > 0)
papszOptions = CSLSetNameValue( papszOptions,
"CACHE_SIZE",
std::to_string(_bufferSize).c_str());
// space between two lines
GIntBig pnLineSpace;
// set up the virtual mem buffer
_poBandVirtualMem = GDALGetVirtualMemAuto(
static_cast<GDALRasterBandH>(_poBand),
GF_Read,
&_pixelSize,
&pnLineSpace,
papszOptions);
// check it
if(!_poBandVirtualMem)
throw;
// get the starting pointer
_memPtr = CPLVirtualMemGetAddr(_poBandVirtualMem);
}
else { // use a buffer
checkCudaErrors(cudaMallocHost((void **)&_memPtr, _bufferSize));
}
// make sure memPtr is not Null
if (!_memPtr)
throw;
// all done
}
/// load a tile of data h_tile x w_tile from CPU (mmap) to GPU
/// @param dArray pointer for array in device memory
/// @param h_offset Down/Height offset
/// @param w_offset Across/Width offset
/// @param h_tile Down/Height tile size
/// @param w_tile Across/Width tile size
/// @param stream CUDA stream for copying
void GDALImage::loadToDevice(void *dArray, size_t h_offset, size_t w_offset, size_t h_tile, size_t w_tile, cudaStream_t stream)
{
size_t tileStartOffset = (h_offset*_width + w_offset)*_pixelSize;
char * startPtr = (char *)_memPtr ;
startPtr += tileStartOffset;
// @note
// We assume down/across directions as rows/cols. Therefore, SLC mmap and device array are both row major.
// cuBlas assumes both source and target arrays are column major.
// To use cublasSetMatrix, we need to switch w_tile/h_tile for rows/cols
// checkCudaErrors(cublasSetMatrixAsync(w_tile, h_tile, sizeof(float2), startPtr, width, dArray, w_tile, stream));
if (_useMmap)
checkCudaErrors(cudaMemcpy2DAsync(dArray, w_tile*_pixelSize, startPtr, _width*_pixelSize,
w_tile*_pixelSize, h_tile, cudaMemcpyHostToDevice,stream));
else {
// get the total tile size in bytes
size_t tileSize = h_tile*w_tile*_pixelSize;
// if the size is bigger than existing buffer, reallocate
if (tileSize > _bufferSize) {
// maybe we need to make it to fit the pagesize
_bufferSize = tileSize;
checkCudaErrors(cudaFree(_memPtr));
checkCudaErrors(cudaMallocHost((void **)&_memPtr, _bufferSize));
}
// copy from file to buffer
CPLErr err = _poBand->RasterIO(GF_Read, //eRWFlag
w_offset, h_offset, //nXOff, nYOff
w_tile, h_tile, // nXSize, nYSize
_memPtr, // pData
w_tile*h_tile, 1, // nBufXSize, nBufYSize
_dataType, //eBufType
0, 0, //nPixelSpace, nLineSpace in pData
NULL //psExtraArg extra resampling callback
);
if(err != CE_None)
throw;
// copy from buffer to gpu
checkCudaErrors(cudaMemcpyAsync(dArray, _memPtr, tileSize, cudaMemcpyHostToDevice, stream));
}
}
GDALImage::~GDALImage()
{
// free the virtual memory
CPLVirtualMemFree(_poBandVirtualMem),
// free the GDAL Dataset, close the file
delete _poDataset;
}
// end of file

79
contrib/PyCuAmpcor/src/GDALImage.h Normal file
View File

@ -0,0 +1,79 @@
// -*- c++ -*-
/**
* \brief Class for an image described GDAL vrt
*
* only complex (pixelOffset=8) or real(pixelOffset=4) images are supported, such as SLC and single-precision TIFF
*/
#ifndef __GDALIMAGE_H
#define __GDALIMAGE_H
#include <cublas_v2.h>
#include <string>
#include <gdal/gdal_priv.h>
#include <gdal/cpl_conv.h>
class GDALImage{
public:
using size_t = std::size_t;
private:
size_t _fileSize;
int _height;
int _width;
// buffer pointer
void * _memPtr = NULL;
int _pixelSize; //in bytes
int _isComplex;
size_t _bufferSize;
int _useMmap;
GDALDataType _dataType;
CPLVirtualMem * _poBandVirtualMem = NULL;
GDALDataset * _poDataset = NULL;
GDALRasterBand * _poBand = NULL;
public:
GDALImage() = delete;
GDALImage(std::string fn, int band=1, int cacheSizeInGB=0, int useMmap=1);
void * getmemPtr()
{
return(_memPtr);
}
size_t getFileSize()
{
return (_fileSize);
}
size_t getHeight() {
return (_height);
}
size_t getWidth()
{
return (_width);
}
int getPixelSize()
{
return _pixelSize;
}
bool isComplex()
{
return _isComplex;
}
void loadToDevice(void *dArray, size_t h_offset, size_t w_offset, size_t h_tile, size_t w_tile, cudaStream_t stream);
~GDALImage();
};
#endif //__GDALIMAGE_H

25
contrib/PyCuAmpcor/src/Makefile
View File

@ -3,23 +3,24 @@ PROJECT = CUAMPCOR
LDFLAGS = -lcuda -lcudart -lcufft -lcublas
CXXFLAGS = -std=c++11 -fpermissive -fPIC -shared
NVCCFLAGS = -ccbin g++ -m64 \
-gencode arch=compute_35,code=sm_35 \
-gencode arch=compute_35,code=sm_35 \
-gencode arch=compute_60,code=sm_60 \
-Xcompiler -fPIC -shared -Wno-deprecated-gpu-targets \
-ftz=false -prec-div=true -prec-sqrt=true
CXX=g++
NVCC=nvcc
DEPS = cudaUtil.h cudaError.h cuArrays.h SlcImage.h cuAmpcorParameter.h
OBJS = SlcImage.o cuArrays.o cuArraysCopy.o cuArraysPadding.o cuOverSampler.o \
DEPS = cudaUtil.h cudaError.h cuArrays.h GDALImage.h cuAmpcorParameter.h
OBJS = GDALImage.o cuArrays.o cuArraysCopy.o cuArraysPadding.o cuOverSampler.o \
cuSincOverSampler.o cuDeramp.o cuOffset.o \
cuCorrNormalization.o cuAmpcorParameter.o cuCorrTimeDomain.o cuCorrFrequency.o \
cuAmpcorChunk.o cuAmpcorController.o cuEstimateStats.o
all: cuampcor
all: pyampcor
SlcImage.o: SlcImage.cu $(DEPS)
$(NVCC) $(NVCCFLAGS) -c -o $@ SlcImage.cu
GDALImage.o: GDALImage.cu $(DEPS)
$(NVCC) $(NVCCFLAGS) -c -o $@ GDALImage.cu
cuArrays.o: cuArrays.cu $(DEPS)
$(NVCC) $(NVCCFLAGS) -c -o $@ cuArrays.cu
@ -45,8 +46,8 @@ cuOffset.o: cuOffset.cu $(DEPS)
cuCorrNormalization.o: cuCorrNormalization.cu $(DEPS)
$(NVCC) $(NVCCFLAGS) -c -o $@ cuCorrNormalization.cu
cuAmpcorParameter.o: cuAmpcorParameter.cu
$(NVCC) $(NVCCFLAGS) -c -o $@ cuAmpcorParameter.cu
cuAmpcorParameter.o: cuAmpcorParameter.cu
$(NVCC) $(NVCCFLAGS) -c -o $@ cuAmpcorParameter.cu
cuCorrTimeDomain.o: cuCorrTimeDomain.cu $(DEPS)
$(NVCC) $(NVCCFLAGS) -c -o $@ cuCorrTimeDomain.cu
@ -54,8 +55,8 @@ cuCorrTimeDomain.o: cuCorrTimeDomain.cu $(DEPS)
cuCorrFrequency.o: cuCorrFrequency.cu $(DEPS) cuCorrFrequency.h
$(NVCC) $(NVCCFLAGS) -c -o $@ cuCorrFrequency.cu
cuAmpcorChunk.o: cuAmpcorChunk.cu cuAmpcorUtil.h $(DEPS)
$(NVCC) $(NVCCFLAGS) -c -o $@ cuAmpcorChunk.cu
cuAmpcorChunk.o: cuAmpcorChunk.cu cuAmpcorUtil.h $(DEPS)
$(NVCC) $(NVCCFLAGS) -c -o $@ cuAmpcorChunk.cu
cuAmpcorController.o: cuAmpcorController.cu
$(NVCC) $(NVCCFLAGS) -c -o $@ cuAmpcorController.cu
@ -64,8 +65,8 @@ cuEstimateStats.o: cuEstimateStats.cu
$(NVCC) $(NVCCFLAGS) -c -o $@ cuEstimateStats.cu
cuampcor: $(OBJS)
pyampcor: $(OBJS)
rm -f PyCuAmpcor.cpp && python3 setup.py build_ext --inplace
clean:
rm -rf *.o *so build *~ PyCuAmpcor.cpp ctest *.dat
rm -rf *.o *so build *~ PyCuAmpcor.cpp ctest *.dat

190
contrib/PyCuAmpcor/src/PyCuAmpcor.pyx
View File

@ -1,6 +1,6 @@
#
#
# PYX file to control Python module interface to underlying CUDA-Ampcor code
#
#
from libcpp.string cimport string
import numpy as np
cimport numpy as np
@ -9,13 +9,13 @@ cimport numpy as np
cdef extern from "cudaUtil.h":
int gpuDeviceInit(int)
void gpuDeviceList()
int gpuGetMaxGflopsDeviceId()
int gpuGetMaxGflopsDeviceId()
def listGPU():
gpuDeviceList()
def findGPU():
return gpuGetMaxGflopsDeviceId()
return gpuGetMaxGflopsDeviceId()
def setGPU(int id):
return gpuDeviceInit(id)
@ -24,90 +24,92 @@ def setGPU(int id):
cdef extern from "cuAmpcorParameter.h":
cdef cppclass cuAmpcorParameter:
cuAmpcorParameter() except +
int algorithm ## Cross-correlation algorithm: 0=freq domain 1=time domain
int deviceID ## Targeted GPU device ID: use -1 to auto select
int nStreams ## Number of streams to asynchonize data transfers and compute kernels
int algorithm ## Cross-correlation algorithm: 0=freq domain 1=time domain
int deviceID ## Targeted GPU device ID: use -1 to auto select
int nStreams ## Number of streams to asynchonize data transfers and compute kernels
int derampMethod ## Method for deramping 0=None, 1=average, 2=phase gradient
## chip or window size for raw data
int windowSizeHeightRaw ## Template window height (original size)
int windowSizeWidthRaw ## Template window width (original size)
int searchWindowSizeHeightRaw ## Search window height (original size)
int windowSizeWidthRaw ## Template window width (original size)
int searchWindowSizeHeightRaw ## Search window height (original size)
int searchWindowSizeWidthRaw ## Search window width (orignal size)
int halfSearchRangeDownRaw ##(searchWindowSizeHeightRaw-windowSizeHeightRaw)/2
int halfSearchRangeDownRaw ##(searchWindowSizeHeightRaw-windowSizeHeightRaw)/2
int halfSearchRangeAcrossRaw ##(searchWindowSizeWidthRaw-windowSizeWidthRaw)/2
## chip or window size after oversampling
int rawDataOversamplingFactor ## Raw data overampling factor (from original size to oversampled size)
## strides between chips/windows
## strides between chips/windows
int skipSampleDownRaw ## Skip size between neighboring windows in Down direction (original size)
int skipSampleAcrossRaw ## Skip size between neighboring windows in across direction (original size)
## Zoom in region near location of max correlation
int zoomWindowSize ## Zoom-in window size in correlation surface (same for down and across directions)
int zoomWindowSize ## Zoom-in window size in correlation surface (same for down and across directions)
int oversamplingFactor ## Oversampling factor for interpolating correlation surface
int oversamplingMethod
float thresholdSNR ## Threshold of Signal noise ratio to remove noisy data
int oversamplingMethod
float thresholdSNR ## Threshold of Signal noise ratio to remove noisy data
##master image
string masterImageName ## master SLC image name
int imageDataType1 ## master image data type, 2=cfloat=complex=float2 1=float
int masterImageHeight ## master image height
int masterImageHeight ## master image height
int masterImageWidth ## master image width
##slave image
string slaveImageName ## slave SLC image name
int imageDataType2 ## slave image data type, 2=cfloat=complex=float2 1=float
int slaveImageHeight ## slave image height
int slaveImageHeight ## slave image height
int slaveImageWidth ## slave image width
int mmapSizeInGB ## mmap buffer size in unit of Gigabytes
int useMmap ## whether to use mmap
int mmapSizeInGB ## mmap buffer size in unit of Gigabytes (if not mmmap, the buffer size)
## total number of chips/windows
int numberWindowDown ## number of total windows (down)
int numberWindowAcross ## number of total windows (across)
int numberWindows ## numberWindowDown*numberWindowAcross
## number of chips/windows in a batch/chunk
int numberWindowDownInChunk ## number of windows processed in a chunk (down)
int numberWindowAcrossInChunk ## number of windows processed in a chunk (across)
int numberWindowsInChunk ## numberWindowDownInChunk*numberWindowAcrossInChunk
int numberChunkDown ## number of chunks (down)
int numberChunkAcross ## number of chunks (across)
int numberChunks
int numberChunks
int *masterStartPixelDown ## master starting pixels for each window (down)
int *masterStartPixelDown ## master starting pixels for each window (down)
int *masterStartPixelAcross ## master starting pixels for each window (across)
int *slaveStartPixelDown ## slave starting pixels for each window (down)
int *slaveStartPixelAcross ## slave starting pixels for each window (across)
int *slaveStartPixelDown ## slave starting pixels for each window (down)
int *slaveStartPixelAcross ## slave starting pixels for each window (across)
int *grossOffsetDown ## Gross offsets between master and slave windows (down) : slaveStartPixel - masterStartPixel
int *grossOffsetAcross ## Gross offsets between master and slave windows (across)
int *grossOffsetAcross ## Gross offsets between master and slave windows (across)
int grossOffsetDown0 ## constant gross offset (down)
int grossOffsetAcross0 ## constant gross offset (across)
int masterStartPixelDown0 ## the first pixel of master image (down), be adjusted with margins and gross offset
int masterStartPixelDown0 ## the first pixel of master image (down), be adjusted with margins and gross offset
int masterStartPixelAcross0 ## the first pixel of master image (across)
int *masterChunkStartPixelDown ## array of starting pixels for all master chunks (down)
int *masterChunkStartPixelAcross ## array of starting pixels for all master chunks (across)
int *slaveChunkStartPixelDown ## array of starting pixels for all slave chunks (down)
int *slaveChunkStartPixelAcross ## array of starting pixels for all slave chunks (across)
int *masterChunkHeight ## array of heights of all master chunks, required when loading chunk to GPU
int *masterChunkHeight ## array of heights of all master chunks, required when loading chunk to GPU
int *masterChunkWidth ## array of width of all master chunks
int *slaveChunkHeight ## array of width of all master chunks
int *slaveChunkWidth ## array of width of all slave chunks
int maxMasterChunkHeight ## max height for all master/slave chunks, determine the size of reading cache in GPU
int maxMasterChunkWidth ## max width for all master chunks, determine the size of reading cache in GPU
int maxMasterChunkHeight ## max height for all master/slave chunks, determine the size of reading cache in GPU
int maxMasterChunkWidth ## max width for all master chunks, determine the size of reading cache in GPU
int maxSlaveChunkHeight
int maxSlaveChunkWidth
string grossOffsetImageName
string offsetImageName ## Output Offset fields filename
string grossOffsetImageName
string offsetImageName ## Output Offset fields filename
string snrImageName ## Output SNR filename
void setStartPixels(int*, int*, int*, int*)
void setStartPixels(int, int, int*, int*)
void setStartPixels(int, int, int, int)
void checkPixelInImageRange() ## check whether
string covImageName ## Output COV filename
void setStartPixels(int*, int*, int*, int*)
void setStartPixels(int, int, int*, int*)
void setStartPixels(int, int, int, int)
void checkPixelInImageRange() ## check whether
void setupParameters() ## Process other parameters after Python Inpu
cdef extern from "cuAmpcorController.h":
@ -115,34 +117,40 @@ cdef extern from "cuAmpcorController.h":
cuAmpcorController() except +
cuAmpcorParameter *param
void runAmpcor()
cdef class PyCuAmpcor(object):
'''
Python interface for cuda Ampcor
Python interface for cuda Ampcor
'''
cdef cuAmpcorController c_cuAmpcor
def __cinit__(self):
return
return
@property
def algorithm(self):
return self.c_cuAmpcor.param.algorithm
return self.c_cuAmpcor.param.algorithm
@algorithm.setter
def algorithm(self, int a):
self.c_cuAmpcor.param.algorithm = a
@property
def deviceID(self):
return self.c_cuAmpcor.param.deviceID
return self.c_cuAmpcor.param.deviceID
@deviceID.setter
def deviceID(self, int a):
self.c_cuAmpcor.param.deviceID = a
@property
def nStreams(self):
return self.c_cuAmpcor.param.nStreams
return self.c_cuAmpcor.param.nStreams
@nStreams.setter
def nStreams(self, int a):
self.c_cuAmpcor.param.nStreams = a
@property
@property
def useMmap(self):
return self.c_cuAmpcor.param.useMmap
@useMmap.setter
def useMmap(self, int a):
self.c_cuAmpcor.param.useMmap = a
@property
def mmapSize(self):
return self.c_cuAmpcor.param.mmapSizeInGB
@mmapSize.setter
@ -150,19 +158,19 @@ cdef class PyCuAmpcor(object):
self.c_cuAmpcor.param.mmapSizeInGB = a
@property
def derampMethod(self):
return self.c_cuAmpcor.param.derampMethod
return self.c_cuAmpcor.param.derampMethod
@derampMethod.setter
def derampMethod(self, int a):
self.c_cuAmpcor.param.derampMethod = a
@property
def windowSizeHeight(self):
return self.c_cuAmpcor.param.windowSizeHeightRaw
return self.c_cuAmpcor.param.windowSizeHeightRaw
@windowSizeHeight.setter
def windowSizeHeight(self, int a):
self.c_cuAmpcor.param.windowSizeHeightRaw = a
@property
def windowSizeWidth(self):
return self.c_cuAmpcor.param.windowSizeWidthRaw
return self.c_cuAmpcor.param.windowSizeWidthRaw
@windowSizeWidth.setter
def windowSizeWidth(self, int a):
self.c_cuAmpcor.param.windowSizeWidthRaw = a
@ -200,7 +208,7 @@ cdef class PyCuAmpcor(object):
@skipSampleAcross.setter
def skipSampleAcross(self, int a):
self.c_cuAmpcor.param.skipSampleAcrossRaw = a
@property
def rawDataOversamplingFactor(self):
"""anti-aliasing oversampling factor"""
@ -229,7 +237,7 @@ cdef class PyCuAmpcor(object):
@corrSufaceOverSamplingMethod.setter
def corrSufaceOverSamplingMethod(self, int a):
self.c_cuAmpcor.param.oversamplingMethod = a
@property
@property
def masterImageName(self):
return self.c_cuAmpcor.param.masterImageName
@masterImageName.setter
@ -241,12 +249,12 @@ cdef class PyCuAmpcor(object):
@slaveImageName.setter
def slaveImageName(self, str a):
self.c_cuAmpcor.param.slaveImageName = <string> a.encode()
@property
@property
def masterImageName(self):
return self.c_cuAmpcor.param.masterImageName
@masterImageName.setter
def masterImageName(self, str a):
self.c_cuAmpcor.param.masterImageName = <string> a.encode()
self.c_cuAmpcor.param.masterImageName = <string> a.encode()
@property
def masterImageHeight(self):
return self.c_cuAmpcor.param.masterImageHeight
@ -258,7 +266,7 @@ cdef class PyCuAmpcor(object):
return self.c_cuAmpcor.param.masterImageWidth
@masterImageWidth.setter
def masterImageWidth(self, int a):
self.c_cuAmpcor.param.masterImageWidth=a
self.c_cuAmpcor.param.masterImageWidth=a
@property
def slaveImageHeight(self):
return self.c_cuAmpcor.param.slaveImageHeight
@ -270,8 +278,8 @@ cdef class PyCuAmpcor(object):
return self.c_cuAmpcor.param.slaveImageWidth
@slaveImageWidth.setter
def slaveImageWidth(self, int a):
self.c_cuAmpcor.param.slaveImageWidth=a
self.c_cuAmpcor.param.slaveImageWidth=a
@property
def numberWindowDown(self):
return self.c_cuAmpcor.param.numberWindowDown
@ -283,11 +291,11 @@ cdef class PyCuAmpcor(object):
return self.c_cuAmpcor.param.numberWindowAcross
@numberWindowAcross.setter
def numberWindowAcross(self, int a):
self.c_cuAmpcor.param.numberWindowAcross = a
self.c_cuAmpcor.param.numberWindowAcross = a
@property
def numberWindows(self):
return self.c_cuAmpcor.param.numberWindows
@property
def numberWindowDownInChunk(self):
return self.c_cuAmpcor.param.numberWindowDownInChunk
@ -299,7 +307,7 @@ cdef class PyCuAmpcor(object):
return self.c_cuAmpcor.param.numberWindowAcrossInChunk
@numberWindowAcrossInChunk.setter
def numberWindowAcrossInChunk(self, int a):
self.c_cuAmpcor.param.numberWindowAcrossInChunk = a
self.c_cuAmpcor.param.numberWindowAcrossInChunk = a
@property
def numberChunkDown(self):
return self.c_cuAmpcor.param.numberChunkDown
@ -309,9 +317,9 @@ cdef class PyCuAmpcor(object):
@property
def numberChunks(self):
return self.c_cuAmpcor.param.numberChunks
## gross offets
## gross offets
@property
def grossOffsetImageName(self):
return self.c_cuAmpcor.param.grossOffsetImageName
@ -324,13 +332,21 @@ cdef class PyCuAmpcor(object):
@offsetImageName.setter
def offsetImageName(self, str a):
self.c_cuAmpcor.param.offsetImageName = <string> a.encode()
@property
def snrImageName(self):
return self.c_cuAmpcor.param.snrImageName
@snrImageName.setter
def snrImageName(self, str a):
self.c_cuAmpcor.param.snrImageName = <string> a.encode()
@property
def covImageName(self):
return self.c_cuAmpcor.param.covImageName
@covImageName.setter
def covImageName(self, str a):
self.c_cuAmpcor.param.covImageName = <string> a.encode()
@property
def masterStartPixelDownStatic(self):
return self.c_cuAmpcor.param.masterStartPixelDown0
@ -342,20 +358,20 @@ cdef class PyCuAmpcor(object):
return self.c_cuAmpcor.param.masterStartPixelAcross0
@masterStartPixelAcrossStatic.setter
def masterStartPixelAcrossStatic(self, int a):
self.c_cuAmpcor.param.masterStartPixelAcross0 = a
self.c_cuAmpcor.param.masterStartPixelAcross0 = a
@property
def grossOffsetDownStatic(self):
return self.c_cuAmpcor.param.grossOffsetDown0
@grossOffsetDownStatic.setter
def grossOffsetDownStatic(self, int a):
self.c_cuAmpcor.param.grossOffsetDown0 =a
self.c_cuAmpcor.param.grossOffsetDown0 =a
@property
def grossOffsetAcrossStatic(self):
return self.c_cuAmpcor.param.grossOffsetAcross0
@grossOffsetAcrossStatic.setter
def grossOffsetAcrossStatic(self, int a):
self.c_cuAmpcor.param.grossOffsetAcross0 =a
self.c_cuAmpcor.param.grossOffsetAcross0 =a
@property
def grossOffsetDownDynamic(self):
cdef int *c_data
@ -366,12 +382,12 @@ cdef class PyCuAmpcor(object):
return p_data
@grossOffsetDownDynamic.setter
def grossOffsetDownDynamic (self, np.ndarray[np.int32_t,ndim=1,mode="c"] pa):
cdef int *c_data
cdef int *c_data
cdef int *p_data
c_data = self.c_cuAmpcor.param.grossOffsetDown
p_data = <int *> pa.data
for i in range (self.numberWindows):
c_data[i] = p_data[i]
c_data[i] = p_data[i]
@property
def grossOffsetAcrossDynamic(self):
cdef int *c_data
@ -382,23 +398,23 @@ cdef class PyCuAmpcor(object):
return p_data
@grossOffsetAcrossDynamic.setter
def grossOffsetAcrossDynamic (self, np.ndarray[np.int32_t,ndim=1,mode="c"] pa):
cdef int *c_data
cdef int *c_data
cdef int *p_data
c_data = self.c_cuAmpcor.param.grossOffsetAcross
p_data = <int *> pa.data
for i in range (self.numberWindows):
c_data[i] = p_data[i]
c_data[i] = p_data[i]
return
def setConstantGrossOffset(self, int goDown, int goAcross):
"""
"""
constant gross offsets
param goDown gross offset in azimuth direction
param goAcross gross offset in range direction
"""
self.c_cuAmpcor.param.setStartPixels(<int>self.masterStartPixelDownStatic, <int>self.masterStartPixelAcrossStatic, goDown, goAcross)
def setVaryingGrossOffset(self, np.ndarray[np.int32_t,ndim=1,mode="c"] vD, np.ndarray[np.int32_t,ndim=1,mode="c"] vA):
"""
varying gross offsets for each window
@ -411,21 +427,21 @@ cdef class PyCuAmpcor(object):
def checkPixelInImageRange(self):
""" check whether each window is with image range """
self.c_cuAmpcor.param.checkPixelInImageRange()
def setupParams(self):
"""
set up constant parameters and allocate array parameters (offsets)
should be called after number of windows is set and before setting varying gross offsets
"""
self.c_cuAmpcor.param.setupParameters()
self.c_cuAmpcor.param.setupParameters()
def runAmpcor(self):
""" main procedure to run ampcor """
self.c_cuAmpcor.runAmpcor()

2
contrib/PyCuAmpcor/src/SConscript
View File

@ -6,7 +6,7 @@ package = envPyCuAmpcor['PACKAGE']
project = envPyCuAmpcor['PROJECT']
build = envPyCuAmpcor['PRJ_LIB_DIR']
install = envPyCuAmpcor['PRJ_SCONS_INSTALL'] + '/' + package + '/' + project
listFiles = ['SlcImage.cu', 'cuArrays.cu', 'cuArraysCopy.cu',
listFiles = ['GDALImage.cu', 'cuArrays.cu', 'cuArraysCopy.cu',
'cuArraysPadding.cu', 'cuOverSampler.cu',
'cuSincOverSampler.cu', 'cuDeramp.cu',
'cuOffset.cu', 'cuCorrNormalization.cu',

408
contrib/PyCuAmpcor/src/cuAmpcorChunk.cu
View File

@ -2,58 +2,74 @@
#include "cuAmpcorUtil.h"
/**
* Run ampcor process for a batch of images (a chunk)
* Run ampcor process for a batch of images (a chunk)
* @param[in] idxDown_ index oIDIVUP(i,j) ((i+j-1)/j)f the chunk along Down/Azimuth direction
* @param[in] idxAcross_ index of the chunk along Across/Range direction
*/
*/
void cuAmpcorChunk::run(int idxDown_, int idxAcross_)
{
// set chunk index
setIndex(idxDown_, idxAcross_);
// load master image chunk
loadMasterChunk();
// load master image chunk
loadMasterChunk();
//std::cout << "load master chunk ok\n";
cuArraysAbs(c_masterBatchRaw, r_masterBatchRaw, stream);
cuArraysSubtractMean(r_masterBatchRaw, stream);
// load slave image chunk
loadSlaveChunk();
cuArraysAbs(c_slaveBatchRaw, r_slaveBatchRaw, stream);
//std::cout << "load slave chunk ok\n";
//cross correlation for none-oversampled data
if(param->algorithm == 0) {
cuCorrFreqDomain->execute(r_masterBatchRaw, r_slaveBatchRaw, r_corrBatchRaw);
}
else {
cuCorrTimeDomain(r_masterBatchRaw, r_slaveBatchRaw, r_corrBatchRaw, stream); //time domain cross correlation
}
}
cuCorrNormalize(r_masterBatchRaw, r_slaveBatchRaw, r_corrBatchRaw, stream);
//find the maximum location of none-oversampled correlation
cuArraysMaxloc2D(r_corrBatchRaw, offsetInit, stream);
// Estimate SNR (Minyan Zhong)
//std::cout<< "flag stats 1" <<std::endl;
//cuArraysCopyExtractCorr(r_corrBatchRaw, r_corrBatchZoomIn, i_corrBatchZoomInValid, offsetInit, stream);
// find the maximum location of none-oversampled correlation
// 41 x 41, if halfsearchrange=20
//cuArraysMaxloc2D(r_corrBatchRaw, offsetInit, stream);
cuArraysMaxloc2D(r_corrBatchRaw, offsetInit, r_maxval, stream);
//std::cout<< "flag stats 2" <<std::endl;
//cuArraysSumCorr(r_corrBatchZoomIn, i_corrBatchZoomInValid, r_corrBatchSum, i_corrBatchValidCount, stream);
offsetInit->outputToFile("offsetInit1", stream);
//std::cout<< "flag stats 3" <<std::endl;
//cuEstimateSnr(r_corrBatchSum, i_corrBatchValidCount, r_maxval, r_snrValue, stream);
// Estimation of statistics
// Author: Minyan Zhong
// Extraction of correlation surface around the peak
cuArraysCopyExtractCorr(r_corrBatchRaw, r_corrBatchRawZoomIn, i_corrBatchZoomInValid, offsetInit, stream);
//
cudaDeviceSynchronize();
// debug: output the intermediate results
r_maxval->outputToFile("r_maxval",stream);
r_corrBatchRaw->outputToFile("r_corrBatchRaw",stream);
r_corrBatchRawZoomIn->outputToFile("r_corrBatchRawZoomIn",stream);
i_corrBatchZoomInValid->outputToFile("i_corrBatchZoomInValid",stream);
// Summation of correlation and data point values
cuArraysSumCorr(r_corrBatchRawZoomIn, i_corrBatchZoomInValid, r_corrBatchSum, i_corrBatchValidCount, stream);
// SNR
cuEstimateSnr(r_corrBatchSum, i_corrBatchValidCount, r_maxval, r_snrValue, stream);
// Variance
// cuEstimateVariance(r_corrBatchRaw, offsetInit, r_maxval, r_covValue, stream);
// Using the approximate estimation to adjust slave image (half search window size becomes only 4 pixels)
//offsetInit->debuginfo(stream);
// determine the starting pixel to extract slave images around the max location
cuDetermineSlaveExtractOffset(offsetInit,
cuDetermineSlaveExtractOffset(offsetInit,
param->halfSearchRangeDownRaw, // old range
param->halfSearchRangeAcrossRaw,
param->halfSearchRangeAcrossRaw,
param->halfZoomWindowSizeRaw, // new range
param->halfZoomWindowSizeRaw,
stream);
@ -63,58 +79,67 @@ void cuAmpcorChunk::run(int idxDown_, int idxAcross_)
masterBatchOverSampler->execute(c_masterBatchRaw, c_masterBatchOverSampled, param->derampMethod);
cuArraysAbs(c_masterBatchOverSampled, r_masterBatchOverSampled, stream);
cuArraysSubtractMean(r_masterBatchOverSampled, stream);
// extract slave and oversample
cuArraysCopyExtract(c_slaveBatchRaw, c_slaveBatchZoomIn, offsetInit, stream);
slaveBatchOverSampler->execute(c_slaveBatchZoomIn, c_slaveBatchOverSampled, param->derampMethod);
cuArraysAbs(c_slaveBatchOverSampled, r_slaveBatchOverSampled, stream);
// correlate oversampled images
if(param->algorithm == 0) {
cuCorrFreqDomain_OverSampled->execute(r_masterBatchOverSampled, r_slaveBatchOverSampled, r_corrBatchZoomIn);
}
else {
cuCorrTimeDomain(r_masterBatchOverSampled, r_slaveBatchOverSampled, r_corrBatchZoomIn, stream);
}
cuCorrTimeDomain(r_masterBatchOverSampled, r_slaveBatchOverSampled, r_corrBatchZoomIn, stream);
}
cuCorrNormalize(r_masterBatchOverSampled, r_slaveBatchOverSampled, r_corrBatchZoomIn, stream);
//std::cout << "debug correlation oversample\n";
//std::cout << r_masterBatchOverSampled->height << " " << r_masterBatchOverSampled->width << "\n";
//std::cout << r_slaveBatchOverSampled->height << " " << r_slaveBatchOverSampled->width << "\n";
//std::cout << r_corrBatchZoomIn->height << " " << r_corrBatchZoomIn->width << "\n";
// oversample the correlation surface
// oversample the correlation surface
cuArraysCopyExtract(r_corrBatchZoomIn, r_corrBatchZoomInAdjust, make_int2(0,0), stream);
//std::cout << "debug oversampling " << r_corrBatchZoomInAdjust << " " << r_corrBatchZoomInOverSampled << "\n";
if(param->oversamplingMethod) {
corrSincOverSampler->execute(r_corrBatchZoomInAdjust, r_corrBatchZoomInOverSampled);
}
else {
corrOverSampler->execute(r_corrBatchZoomInAdjust, r_corrBatchZoomInOverSampled);
corrOverSampler->execute(r_corrBatchZoomInAdjust, r_corrBatchZoomInOverSampled);
}
//find the max again
cuArraysMaxloc2D(r_corrBatchZoomInOverSampled, offsetZoomIn, corrMaxValue, stream);
// determine the final offset from non-oversampled (pixel) and oversampled (sub-pixel)
cuSubPixelOffset(offsetInit, offsetZoomIn, offsetFinal,
param->oversamplingFactor, param->rawDataOversamplingFactor,
// determine the final offset from non-oversampled (pixel) and oversampled (sub-pixel)
cuSubPixelOffset(offsetInit, offsetZoomIn, offsetFinal,
param->oversamplingFactor, param->rawDataOversamplingFactor,
param->halfSearchRangeDownRaw, param->halfSearchRangeAcrossRaw,
param->halfZoomWindowSizeRaw, param->halfZoomWindowSizeRaw,
stream);
//offsetInit->debuginfo(stream);
//offsetZoomIn->debuginfo(stream);
//offsetFinal->debuginfo(stream);
//offsetFinal->debuginfo(stream);
// Do insertion.
// Offsetfields.
cuArraysCopyInsert(offsetFinal, offsetImage, idxDown_*param->numberWindowDownInChunk, idxAcross_*param->numberWindowAcrossInChunk,stream);
// Minyan Zhong
//cuArraysCopyInsert(corrMaxValue, snrImage, idxDown_*param->numberWindowDownInChunk, idxAcross_*param->numberWindowAcrossInChunk,stream);
//cuArraysCopyInsert(r_snrValue, snrImage, idxDown_*param->numberWindowDownInChunk, idxAcross_*param->numberWindowAcrossInChunk,stream);
// Debugging matrix.
cuArraysCopyInsert(r_corrBatchSum, floatImage1, idxDown_*param->numberWindowDownInChunk, idxAcross_*param->numberWindowAcrossInChunk,stream);
cuArraysCopyInsert(i_corrBatchValidCount, intImage1, idxDown_*param->numberWindowDownInChunk, idxAcross_*param->numberWindowAcrossInChunk,stream);
// Old: save max correlation coefficients.
//cuArraysCopyInsert(corrMaxValue, snrImage, idxDown_*param->numberWindowDownInChunk, idxAcross_*param->numberWindowAcrossInChunk,stream);
// New: save SNR
cuArraysCopyInsert(r_snrValue, snrImage, idxDown_*param->numberWindowDownInChunk, idxAcross_*param->numberWindowAcrossInChunk,stream);
// Variance.
cuArraysCopyInsert(r_covValue, covImage, idxDown_*param->numberWindowDownInChunk, idxAcross_*param->numberWindowAcrossInChunk,stream);
}
void cuAmpcorChunk::setIndex(int idxDown_, int idxAcross_)
@ -122,14 +147,14 @@ void cuAmpcorChunk::setIndex(int idxDown_, int idxAcross_)
idxChunkDown = idxDown_;
idxChunkAcross = idxAcross_;
idxChunk = idxChunkAcross + idxChunkDown*param->numberChunkAcross;
if(idxChunkDown == param->numberChunkDown -1) {
nWindowsDown = param->numberWindowDown - param->numberWindowDownInChunk*(param->numberChunkDown -1);
}
else {
nWindowsDown = param->numberWindowDownInChunk;
}
if(idxChunkAcross == param->numberChunkAcross -1) {
nWindowsAcross = param->numberWindowAcross - param->numberWindowAcrossInChunk*(param->numberChunkAcross -1);
}
@ -137,20 +162,20 @@ void cuAmpcorChunk::setIndex(int idxDown_, int idxAcross_)
nWindowsAcross = param->numberWindowAcrossInChunk;
}
//std::cout << "DEBUG setIndex" << idxChunk << " " << nWindowsDown << " " << nWindowsAcross << "\n";
}
/// obtain the starting pixels for each chip
/// @param[in] oStartPixel
/// @param[in] oStartPixel
///
void cuAmpcorChunk::getRelativeOffset(int *rStartPixel, const int *oStartPixel, int diff)
{
for(int i=0; i<param->numberWindowDownInChunk; ++i) {
int iDown = i;
if(i>=nWindowsDown) iDown = nWindowsDown-1;
if(i>=nWindowsDown) iDown = nWindowsDown-1;
for(int j=0; j<param->numberWindowAcrossInChunk; ++j){
int iAcross = j;
if(j>=nWindowsAcross) iAcross = nWindowsAcross-1;
if(j>=nWindowsAcross) iAcross = nWindowsAcross-1;
int idxInChunk = iDown*param->numberWindowAcrossInChunk+iAcross;
int idxInAll = (iDown+idxChunkDown*param->numberWindowDownInChunk)*param->numberWindowAcross
+ idxChunkAcross*param->numberWindowAcrossInChunk+iAcross;
@ -158,108 +183,179 @@ void cuAmpcorChunk::getRelativeOffset(int *rStartPixel, const int *oStartPixel,
//fprintf(stderr, "relative offset %d %d %d %d\n", i, j, rStartPixel[idxInChunk], diff);
}
}
}
}
void cuAmpcorChunk::loadMasterChunk()
{
//load a chunk from mmap to gpu
int startD = param->masterChunkStartPixelDown[idxChunk];
int startA = param->masterChunkStartPixelAcross[idxChunk];
int height = param->masterChunkHeight[idxChunk];
int width = param->masterChunkWidth[idxChunk];
masterImage->loadToDevice(c_masterChunkRaw->devData, startD, startA, height, width, stream);
std::cout << "debug load master: " << startD << " " << startA << " " << height << " " << width << "\n";
//copy the chunk to a batch of images format (nImages, height, width)
//use cpu for some simple math
// we first load the whole chunk of image from cpu to a gpu buffer c(r)_masterChunkRaw
// then copy to a batch of windows with (nImages, height, width) (leading dimension on the right)
// get the chunk size to be loaded to gpu
int startD = param->masterChunkStartPixelDown[idxChunk]; //start pixel down (along height)
int startA = param->masterChunkStartPixelAcross[idxChunk]; // start pixel across (along width)
int height = param->masterChunkHeight[idxChunk]; // number of pixels along height
int width = param->masterChunkWidth[idxChunk]; // number of pixels along width
//use cpu to compute the starting positions for each window
getRelativeOffset(ChunkOffsetDown->hostData, param->masterStartPixelDown, param->masterChunkStartPixelDown[idxChunk]);
// copy the positions to gpu
ChunkOffsetDown->copyToDevice(stream);
// same for the across direction
getRelativeOffset(ChunkOffsetAcross->hostData, param->masterStartPixelAcross, param->masterChunkStartPixelAcross[idxChunk]);
ChunkOffsetAcross->copyToDevice(stream);
// if derampMethod = 0 (no deramp), take amplitudes; otherwise, copy complex data
if(param->derampMethod == 0) {
cuArraysCopyToBatchAbsWithOffset(c_masterChunkRaw, param->masterChunkWidth[idxChunk],
c_masterBatchRaw, ChunkOffsetDown->devData, ChunkOffsetAcross->devData, stream);
// check whether the image is complex (e.g., SLC) or real( e.g. TIFF)
if(masterImage->isComplex())
{
// allocate a gpu buffer to load data from cpu/file
// try allocate/deallocate the buffer on the fly to save gpu memory 07/09/19
c_masterChunkRaw = new cuArrays<float2> (param->maxMasterChunkHeight, param->maxMasterChunkWidth);
c_masterChunkRaw->allocate();
// load the data from cpu
masterImage->loadToDevice((void *)c_masterChunkRaw->devData, startD, startA, height, width, stream);
//std::cout << "debug load master: " << startD << " " << startA << " " << height << " " << width << "\n";
//copy the chunk to a batch format (nImages, height, width)
// if derampMethod = 0 (no deramp), take amplitudes; otherwise, copy complex data
if(param->derampMethod == 0) {
cuArraysCopyToBatchAbsWithOffset(c_masterChunkRaw, param->masterChunkWidth[idxChunk],
c_masterBatchRaw, ChunkOffsetDown->devData, ChunkOffsetAcross->devData, stream);
}
else {
cuArraysCopyToBatchWithOffset(c_masterChunkRaw, param->masterChunkWidth[idxChunk],
c_masterBatchRaw, ChunkOffsetDown->devData, ChunkOffsetAcross->devData, stream);
}
// deallocate the gpu buffer
delete c_masterChunkRaw;
}
// if the image is real
else {
cuArraysCopyToBatchWithOffset(c_masterChunkRaw, param->masterChunkWidth[idxChunk],
c_masterBatchRaw, ChunkOffsetDown->devData, ChunkOffsetAcross->devData, stream);
r_masterChunkRaw = new cuArrays<float> (param->maxMasterChunkHeight, param->maxMasterChunkWidth);
r_masterChunkRaw->allocate();
// load the data from cpu
masterImage->loadToDevice((void *)r_masterChunkRaw->devData, startD, startA, height, width, stream);
// copy the chunk (real) to a batch format (complex)
cuArraysCopyToBatchWithOffsetR2C(r_masterChunkRaw, param->masterChunkWidth[idxChunk],
c_masterBatchRaw, ChunkOffsetDown->devData, ChunkOffsetAcross->devData, stream);
// deallocate the gpu buffer
delete r_masterChunkRaw;
}
}
void cuAmpcorChunk::loadSlaveChunk()
{
//load a chunk from mmap to gpu
slaveImage->loadToDevice(c_slaveChunkRaw->devData,
param->slaveChunkStartPixelDown[idxChunk],
param->slaveChunkStartPixelAcross[idxChunk],
param->slaveChunkHeight[idxChunk],
param->slaveChunkWidth[idxChunk],
stream);
//copy to a batch format (nImages, height, width)
getRelativeOffset(ChunkOffsetDown->hostData, param->slaveStartPixelDown, param->slaveChunkStartPixelDown[idxChunk]);
ChunkOffsetDown->copyToDevice(stream);
getRelativeOffset(ChunkOffsetAcross->hostData, param->slaveStartPixelAcross, param->slaveChunkStartPixelAcross[idxChunk]);
ChunkOffsetAcross->copyToDevice(stream);
if(param->derampMethod == 0) {
cuArraysCopyToBatchAbsWithOffset(c_slaveChunkRaw, param->slaveChunkWidth[idxChunk],
c_slaveBatchRaw, ChunkOffsetDown->devData, ChunkOffsetAcross->devData, stream);
}
else
if(slaveImage->isComplex())
{
cuArraysCopyToBatchWithOffset(c_slaveChunkRaw, param->slaveChunkWidth[idxChunk],
c_slaveBatchRaw, ChunkOffsetDown->devData, ChunkOffsetAcross->devData, stream);
}
c_slaveChunkRaw = new cuArrays<float2> (param->maxSlaveChunkHeight, param->maxSlaveChunkWidth);
c_slaveChunkRaw->allocate();
//load a chunk from mmap to gpu
slaveImage->loadToDevice(c_slaveChunkRaw->devData,
param->slaveChunkStartPixelDown[idxChunk],
param->slaveChunkStartPixelAcross[idxChunk],
param->slaveChunkHeight[idxChunk],
param->slaveChunkWidth[idxChunk],
stream);
if(param->derampMethod == 0) {
cuArraysCopyToBatchAbsWithOffset(c_slaveChunkRaw, param->slaveChunkWidth[idxChunk],
c_slaveBatchRaw, ChunkOffsetDown->devData, ChunkOffsetAcross->devData, stream);
}
else {
cuArraysCopyToBatchWithOffset(c_slaveChunkRaw, param->slaveChunkWidth[idxChunk],
c_slaveBatchRaw, ChunkOffsetDown->devData, ChunkOffsetAcross->devData, stream);
}
delete c_slaveChunkRaw;
}
else { //real image
//allocate the gpu buffer
r_slaveChunkRaw = new cuArrays<float> (param->maxSlaveChunkHeight, param->maxSlaveChunkWidth);
r_slaveChunkRaw->allocate();
//load a chunk from mmap to gpu
slaveImage->loadToDevice(r_slaveChunkRaw->devData,
param->slaveChunkStartPixelDown[idxChunk],
param->slaveChunkStartPixelAcross[idxChunk],
param->slaveChunkHeight[idxChunk],
param->slaveChunkWidth[idxChunk],
stream);
// convert to the batch format
cuArraysCopyToBatchWithOffsetR2C(r_slaveChunkRaw, param->slaveChunkWidth[idxChunk],
c_slaveBatchRaw, ChunkOffsetDown->devData, ChunkOffsetAcross->devData, stream);
delete r_slaveChunkRaw;
}
}
cuAmpcorChunk::cuAmpcorChunk(cuAmpcorParameter *param_, SlcImage *master_, SlcImage *slave_,
cuArrays<float2> *offsetImage_, cuArrays<float> *snrImage_, cudaStream_t stream_)
cuAmpcorChunk::cuAmpcorChunk(cuAmpcorParameter *param_, GDALImage *master_, GDALImage *slave_,
cuArrays<float2> *offsetImage_, cuArrays<float> *snrImage_, cuArrays<float3> *covImage_, cuArrays<int> *intImage1_, cuArrays<float> *floatImage1_, cudaStream_t stream_)
{
param = param_;
masterImage = master_;
slaveImage = slave_;
slaveImage = slave_;
offsetImage = offsetImage_;
snrImage = snrImage_;
covImage = covImage_;
intImage1 = intImage1_;
floatImage1 = floatImage1_;
stream = stream_;
std::cout << "debug Chunk creator " << param->maxMasterChunkHeight << " " << param->maxMasterChunkWidth << "\n";
c_masterChunkRaw = new cuArrays<float2> (param->maxMasterChunkHeight, param->maxMasterChunkWidth);
c_masterChunkRaw->allocate();
c_slaveChunkRaw = new cuArrays<float2> (param->maxSlaveChunkHeight, param->maxSlaveChunkWidth);
c_slaveChunkRaw->allocate();
// std::cout << "debug Chunk creator " << param->maxMasterChunkHeight << " " << param->maxMasterChunkWidth << "\n";
// try allocate/deallocate on the fly to save gpu memory 07/09/19
// c_masterChunkRaw = new cuArrays<float2> (param->maxMasterChunkHeight, param->maxMasterChunkWidth);
// c_masterChunkRaw->allocate();
// c_slaveChunkRaw = new cuArrays<float2> (param->maxSlaveChunkHeight, param->maxSlaveChunkWidth);
// c_slaveChunkRaw->allocate();
ChunkOffsetDown = new cuArrays<int> (param->numberWindowDownInChunk, param->numberWindowAcrossInChunk);
ChunkOffsetDown->allocate();
ChunkOffsetDown->allocateHost();
ChunkOffsetAcross = new cuArrays<int> (param->numberWindowDownInChunk, param->numberWindowAcrossInChunk);
ChunkOffsetAcross->allocate();
ChunkOffsetAcross->allocateHost();
c_masterBatchRaw = new cuArrays<float2> (
param->windowSizeHeightRaw, param->windowSizeWidthRaw,
param->numberWindowDownInChunk, param->numberWindowAcrossInChunk);
c_masterBatchRaw->allocate();
c_slaveBatchRaw = new cuArrays<float2> (
param->searchWindowSizeHeightRaw, param->searchWindowSizeWidthRaw,
param->numberWindowDownInChunk, param->numberWindowAcrossInChunk);
c_slaveBatchRaw->allocate();
r_masterBatchRaw = new cuArrays<float> (
param->windowSizeHeightRaw, param->windowSizeWidthRaw,
param->numberWindowDownInChunk, param->numberWindowAcrossInChunk);
r_masterBatchRaw->allocate();
r_slaveBatchRaw = new cuArrays<float> (
param->searchWindowSizeHeightRaw, param->searchWindowSizeWidthRaw,
param->numberWindowDownInChunk, param->numberWindowAcrossInChunk);
r_slaveBatchRaw->allocate();
c_slaveBatchZoomIn = new cuArrays<float2> (
param->searchWindowSizeHeightRawZoomIn, param->searchWindowSizeWidthRawZoomIn,
param->numberWindowDownInChunk, param->numberWindowAcrossInChunk);
c_slaveBatchZoomIn->allocate();
c_masterBatchOverSampled = new cuArrays<float2> (
param->windowSizeHeight, param->windowSizeWidth,
param->numberWindowDownInChunk, param->numberWindowAcrossInChunk);
@ -269,7 +365,7 @@ cuAmpcorChunk::cuAmpcorChunk(cuAmpcorParameter *param_, SlcImage *master_, SlcIm
param->searchWindowSizeHeight, param->searchWindowSizeWidth,
param->numberWindowDownInChunk, param->numberWindowAcrossInChunk);
c_slaveBatchOverSampled->allocate();
r_masterBatchOverSampled = new cuArrays<float> (
param->windowSizeHeight, param->windowSizeWidth,
param->numberWindowDownInChunk, param->numberWindowAcrossInChunk);
@ -279,66 +375,114 @@ cuAmpcorChunk::cuAmpcorChunk(cuAmpcorParameter *param_, SlcImage *master_, SlcIm
param->searchWindowSizeHeight, param->searchWindowSizeWidth,
param->numberWindowDownInChunk, param->numberWindowAcrossInChunk);
r_slaveBatchOverSampled->allocate();
masterBatchOverSampler = new cuOverSamplerC2C(
c_masterBatchRaw->height, c_masterBatchRaw->width, //orignal size
c_masterBatchOverSampled->height, c_masterBatchOverSampled->width, //oversampled size
c_masterBatchOverSampled->height, c_masterBatchOverSampled->width, //oversampled size
c_masterBatchRaw->count, stream);
slaveBatchOverSampler = new cuOverSamplerC2C(c_slaveBatchZoomIn->height, c_slaveBatchZoomIn->width,
slaveBatchOverSampler = new cuOverSamplerC2C(c_slaveBatchZoomIn->height, c_slaveBatchZoomIn->width,
c_slaveBatchOverSampled->height, c_slaveBatchOverSampled->width, c_slaveBatchRaw->count, stream);
r_corrBatchRaw = new cuArrays<float> (
param->searchWindowSizeHeightRaw-param->windowSizeHeightRaw+1,
param->searchWindowSizeWidthRaw-param->windowSizeWidthRaw+1,
param->numberWindowDownInChunk,
param->searchWindowSizeHeightRaw-param->windowSizeHeightRaw+1,
param->searchWindowSizeWidthRaw-param->windowSizeWidthRaw+1,
param->numberWindowDownInChunk,
param->numberWindowAcrossInChunk);
r_corrBatchRaw->allocate();
r_corrBatchZoomIn = new cuArrays<float> (
param->searchWindowSizeHeight - param->windowSizeHeight+1,
param->searchWindowSizeWidth - param->windowSizeWidth+1,
param->numberWindowDownInChunk,
param->searchWindowSizeHeight - param->windowSizeHeight+1,
param->searchWindowSizeWidth - param->windowSizeWidth+1,
param->numberWindowDownInChunk,
param->numberWindowAcrossInChunk);
r_corrBatchZoomIn->allocate();
r_corrBatchZoomInAdjust = new cuArrays<float> (
param->searchWindowSizeHeight - param->windowSizeHeight,
param->searchWindowSizeWidth - param->windowSizeWidth,
param->numberWindowDownInChunk,
param->searchWindowSizeHeight - param->windowSizeHeight,
param->searchWindowSizeWidth - param->windowSizeWidth,
param->numberWindowDownInChunk,
param->numberWindowAcrossInChunk);
r_corrBatchZoomInAdjust->allocate();
r_corrBatchZoomInOverSampled = new cuArrays<float> (
param->zoomWindowSize * param->oversamplingFactor,
param->zoomWindowSize * param->oversamplingFactor,
param->numberWindowDownInChunk,
param->zoomWindowSize * param->oversamplingFactor,
param->numberWindowDownInChunk,
param->numberWindowAcrossInChunk);
r_corrBatchZoomInOverSampled->allocate();
offsetInit = new cuArrays<int2> (param->numberWindowDownInChunk, param->numberWindowAcrossInChunk);
offsetInit->allocate();
offsetZoomIn = new cuArrays<int2> (param->numberWindowDownInChunk, param->numberWindowAcrossInChunk);
offsetZoomIn->allocate();
offsetFinal = new cuArrays<float2> (param->numberWindowDownInChunk, param->numberWindowAcrossInChunk);
offsetFinal->allocate();
corrMaxValue = new cuArrays<float> (param->numberWindowDownInChunk, param->numberWindowAcrossInChunk);
corrMaxValue->allocate();
// new arrays due to snr estimation
std::cout<< "corrRawZoomInHeight: " << param->corrRawZoomInHeight << "\n";
std::cout<< "corrRawZoomInWidth: " << param->corrRawZoomInWidth << "\n";
r_corrBatchRawZoomIn = new cuArrays<float> (
param->corrRawZoomInHeight,
param->corrRawZoomInWidth,
param->numberWindowDownInChunk,
param->numberWindowAcrossInChunk);
r_corrBatchRawZoomIn->allocate();
i_corrBatchZoomInValid = new cuArrays<int> (
param->corrRawZoomInHeight,
param->corrRawZoomInWidth,
param->numberWindowDownInChunk,
param->numberWindowAcrossInChunk);
i_corrBatchZoomInValid->allocate();
r_corrBatchSum = new cuArrays<float> (
param->numberWindowDownInChunk,
param->numberWindowAcrossInChunk);
r_corrBatchSum->allocate();
i_corrBatchValidCount = new cuArrays<int> (
param->numberWindowDownInChunk,
param->numberWindowAcrossInChunk);
i_corrBatchValidCount->allocate();
i_maxloc = new cuArrays<int2> (param->numberWindowDownInChunk, param->numberWindowAcrossInChunk);
i_maxloc->allocate();
r_maxval = new cuArrays<float> (param->numberWindowDownInChunk, param->numberWindowAcrossInChunk);
r_maxval->allocate();
r_snrValue = new cuArrays<float> (param->numberWindowDownInChunk, param->numberWindowAcrossInChunk);
r_snrValue->allocate();
r_covValue = new cuArrays<float3> (param->numberWindowDownInChunk, param->numberWindowAcrossInChunk);
r_covValue->allocate();
// end of new arrays
if(param->oversamplingMethod) {
corrSincOverSampler = new cuSincOverSamplerR2R(param->zoomWindowSize, param->oversamplingFactor, stream);
}
else {
else {
corrOverSampler= new cuOverSamplerR2R(param->zoomWindowSize, param->zoomWindowSize,
(param->zoomWindowSize)*param->oversamplingFactor,
(param->zoomWindowSize)*param->oversamplingFactor,
(param->zoomWindowSize)*param->oversamplingFactor,
param->numberWindowDownInChunk*param->numberWindowAcrossInChunk,
stream);
}
param->numberWindowDownInChunk*param->numberWindowAcrossInChunk,
stream);
}
if(param->algorithm == 0) {
cuCorrFreqDomain = new cuFreqCorrelator(
param->searchWindowSizeHeightRaw, param->searchWindowSizeWidthRaw,
@ -347,10 +491,10 @@ cuAmpcorChunk::cuAmpcorChunk(cuAmpcorParameter *param_, SlcImage *master_, SlcIm
cuCorrFreqDomain_OverSampled = new cuFreqCorrelator(
param->searchWindowSizeHeight, param->searchWindowSizeWidth,
param->numberWindowDownInChunk*param->numberWindowAcrossInChunk,
stream);
stream);
}
debugmsg("all objects in chunk are created ...\n");

81
contrib/PyCuAmpcor/src/cuAmpcorChunk.h
View File

@ -1,4 +1,4 @@
/*
/*
* cuAmpcorChunk.h
* Purpose: a group of chips processed at the same time
*/
@ -6,7 +6,7 @@
#ifndef __CUAMPCORCHUNK_H
#define __CUAMPCORCHUNK_H
#include "SlcImage.h"
#include "GDALImage.h"
#include "cuArrays.h"
#include "cuAmpcorParameter.h"
#include "cuOverSampler.h"
@ -22,64 +22,81 @@ private:
int nWindowsAcross;
int devId;
cudaStream_t stream;
SlcImage *masterImage;
SlcImage *slaveImage;
cudaStream_t stream;
GDALImage *masterImage;
GDALImage *slaveImage;
cuAmpcorParameter *param;
cuArrays<float2> *offsetImage;
cuArrays<float> *snrImage;
cuArrays<float2> * c_masterChunkRaw, * c_slaveChunkRaw;
cuArrays<float3> *covImage;
// added for test
cuArrays<int> *intImage1;
cuArrays<float> *floatImage1;
// gpu buffer
cuArrays<float2> * c_masterChunkRaw, * c_slaveChunkRaw;
cuArrays<float> * r_masterChunkRaw, * r_slaveChunkRaw;
// gpu windows raw data
cuArrays<float2> * c_masterBatchRaw, * c_slaveBatchRaw, * c_slaveBatchZoomIn;
cuArrays<float> * r_masterBatchRaw, * r_slaveBatchRaw;
cuArrays<float2> * c_masterBatchOverSampled, * c_slaveBatchOverSampled;
// gpu windows oversampled data
cuArrays<float2> * c_masterBatchOverSampled, * c_slaveBatchOverSampled;
cuArrays<float> * r_masterBatchOverSampled, * r_slaveBatchOverSampled;
cuArrays<float> * r_corrBatchRaw, * r_corrBatchZoomIn, * r_corrBatchZoomInOverSampled, * r_corrBatchZoomInAdjust;
cuArrays<int> *ChunkOffsetDown, *ChunkOffsetAcross;
cuOverSamplerC2C *masterBatchOverSampler, *slaveBatchOverSampler;
cuOverSamplerR2R *corrOverSampler;
cuSincOverSamplerR2R *corrSincOverSampler;
cuSincOverSamplerR2R *corrSincOverSampler;
//for frequency domain
cuFreqCorrelator *cuCorrFreqDomain, *cuCorrFreqDomain_OverSampled;
cuArrays<int2> *offsetInit;
cuArrays<int2> *offsetZoomIn;
cuArrays<float2> *offsetFinal;
cuArrays<float> *corrMaxValue;
//SNR estimation
cuArrays<float> *r_corrBatchRawZoomIn;
cuArrays<float> *r_corrBatchSum;
cuArrays<int> *i_corrBatchZoomInValid, *i_corrBatchValidCount;
cuArrays<float> *r_snrValue;
//corr statistics
cuArrays<int2> *i_maxloc;
cuArrays<float> *r_maxval;
cuArrays<float> *r_corrBatchSum;
cuArrays<int> *i_corrBatchZoomInValid, *i_corrBatchValidCount;
cuArrays<float> *corrMaxValue;
cuArrays<float> *r_snrValue;
// Varince estimation.
cuArrays<float3> *r_covValue;
public:
cuAmpcorChunk() {}
//cuAmpcorChunk(cuAmpcorParameter *param_, SlcImage *master_, SlcImage *slave_);
void setIndex(int idxDown_, int idxAcross_);
cuAmpcorChunk(cuAmpcorParameter *param_, GDALImage *master_, GDALImage *slave_, cuArrays<float2> *offsetImage_,
cuArrays<float> *snrImage_, cuArrays<float3> *covImage_, cuArrays<int> *intImage1_, cuArrays<float> *floatImage1_, cudaStream_t stream_);
cuAmpcorChunk(cuAmpcorParameter *param_, SlcImage *master_, SlcImage *slave_, cuArrays<float2> *offsetImage_,
cuArrays<float> *snrImage_, cudaStream_t stream_);
void loadMasterChunk();
void loadSlaveChunk();
void getRelativeOffset(int *rStartPixel, const int *oStartPixel, int diff);
~cuAmpcorChunk();
void run(int, int);
~cuAmpcorChunk();
void run(int, int);
};
#endif
#endif

131
contrib/PyCuAmpcor/src/cuAmpcorController.cu
View File

@ -1,113 +1,142 @@
// Implementation of cuAmpcorController
#include "cuAmpcorController.h"
#include "SlcImage.h"
#include "GDALImage.h"
#include "cuArrays.h"
#include "cudaUtil.h"
#include "cuAmpcorChunk.h"
#include "cuAmpcorUtil.h"
#include <iostream>
cuAmpcorController::cuAmpcorController() { param = new cuAmpcorParameter();}
cuAmpcorController::~cuAmpcorController() { delete param; }
cuAmpcorController::cuAmpcorController() { param = new cuAmpcorParameter();}
cuAmpcorController::~cuAmpcorController() { delete param; }
void cuAmpcorController::runAmpcor() {
void cuAmpcorController::runAmpcor() {
// set the gpu id
param->deviceID = gpuDeviceInit(param->deviceID);
SlcImage *masterImage;
SlcImage *slaveImage;
// initialize the gdal driver
GDALAllRegister();
// master and slave images; use band=1 as default
// TODO: selecting band
GDALImage *masterImage = new GDALImage(param->masterImageName, 1, param->mmapSizeInGB);
GDALImage *slaveImage = new GDALImage(param->slaveImageName, 1, param->mmapSizeInGB);
cuArrays<float2> *offsetImage, *offsetImageRun;
cuArrays<float> *snrImage, *snrImageRun;
// cuArrays<float> *floatImage;
// cuArrays<int> *intImage;
cuArrays<float3> *covImage, *covImageRun;
// For debugging.
cuArrays<int> *intImage1;
cuArrays<float> *floatImage1;
int nWindowsDownRun = param->numberChunkDown * param->numberWindowDownInChunk;
int nWindowsAcrossRun = param->numberChunkAcross * param->numberWindowAcrossInChunk;
masterImage = new SlcImage(param->masterImageName, param->masterImageHeight, param->masterImageWidth, param->mmapSizeInGB);
slaveImage = new SlcImage(param->slaveImageName, param->slaveImageHeight, param->slaveImageWidth, param->mmapSizeInGB);
int nWindowsDownRun = param->numberChunkDown*param->numberWindowDownInChunk;
int nWindowsAcrossRun = param->numberChunkAcross*param->numberWindowAcrossInChunk;
std::cout << "Debug " << nWindowsDownRun << " " << param->numberWindowDown << "\n";
offsetImageRun = new cuArrays<float2>(nWindowsDownRun, nWindowsAcrossRun);
snrImageRun = new cuArrays<float>(nWindowsDownRun, nWindowsAcrossRun);
offsetImageRun->allocate();
snrImageRun = new cuArrays<float>(nWindowsDownRun, nWindowsAcrossRun);
snrImageRun->allocate();
covImageRun = new cuArrays<float3>(nWindowsDownRun, nWindowsAcrossRun);
covImageRun->allocate();
// intImage 1 and floatImage 1 are added for debugging issues
intImage1 = new cuArrays<int>(nWindowsDownRun, nWindowsAcrossRun);
intImage1->allocate();
floatImage1 = new cuArrays<float>(nWindowsDownRun, nWindowsAcrossRun);
floatImage1->allocate();
// Offsetfields.
offsetImage = new cuArrays<float2>(param->numberWindowDown, param->numberWindowAcross);
snrImage = new cuArrays<float>(param->numberWindowDown, param->numberWindowAcross);
offsetImage->allocate();
// SNR.
snrImage = new cuArrays<float>(param->numberWindowDown, param->numberWindowAcross);
snrImage->allocate();
// Minyan Zhong
// floatImage = new cuArrays<float>(param->numberWindowDown, param->numberWindowAcross);
// intImage = new cuArrays<int>(param->numberWindowDown, param->numberWindowAcross);
// Variance.
covImage = new cuArrays<float3>(param->numberWindowDown, param->numberWindowAcross);
covImage->allocate();
// floatImage->allocate();
// intImage->allocate();
//
cudaStream_t streams[param->nStreams];
cuAmpcorChunk *chunk[param->nStreams];
for(int ist=0; ist<param->nStreams; ist++)
for(int ist=0; ist<param->nStreams; ist++)
{
cudaStreamCreate(&streams[ist]);
chunk[ist]= new cuAmpcorChunk(param, masterImage, slaveImage, offsetImageRun, snrImageRun, streams[ist]);
chunk[ist]= new cuAmpcorChunk(param, masterImage, slaveImage, offsetImageRun, snrImageRun, covImageRun, intImage1, floatImage1, streams[ist]);
}
int nChunksDown = param->numberChunkDown;
int nChunksAcross = param->numberChunkAcross;
int nChunksAcross = param->numberChunkAcross;
std::cout << "Total number of windows (azimuth x range): " <<param->numberWindowDown << " x " << param->numberWindowAcross << std::endl;
std::cout << "to be processed in the number of chunks: " <<nChunksDown << " x " << nChunksAcross << std::endl;
for(int i = 60; i<nChunksDown; i++)
for(int i = 0; i<nChunksDown; i++)
{
std::cout << "Processing chunk (" << i <<", x" << ")" << std::endl;
std::cout << "Processing chunk (" << i <<", x" << ")" << std::endl;
for(int j=0; j<nChunksAcross; j+=param->nStreams)
{
//std::cout << "Processing chunk(" << i <<", " << j <<")" << std::endl;
for(int ist = 0; ist<param->nStreams; ist++)
{
{
if(j+ist < nChunksAcross) {
chunk[ist]->run(i, j+ist);
}
}
}
}
}
cudaDeviceSynchronize();
// Do extraction.
cuArraysCopyExtract(offsetImageRun, offsetImage, make_int2(0,0), streams[0]);
cuArraysCopyExtract(snrImageRun, snrImage, make_int2(0,0), streams[0]);
cuArraysCopyExtract(snrImageRun, snrImage, make_int2(0,0), streams[0]);
cuArraysCopyExtract(covImageRun, covImage, make_int2(0,0), streams[0]);
offsetImage->outputToFile(param->offsetImageName, streams[0]);
snrImage->outputToFile(param->snrImageName, streams[0]);
covImage->outputToFile(param->covImageName, streams[0]);
// Minyan Zhong
// floatImage->allocate();
// intImage->allocate();
//
// Output debugging arrays.
intImage1->outputToFile("intImage1", streams[0]);
floatImage1->outputToFile("floatImage1", streams[0]);
outputGrossOffsets();
// Delete arrays.
delete offsetImage;
delete snrImage;
delete covImage;
delete intImage1;
delete floatImage1;
delete offsetImageRun;
delete snrImageRun;
delete covImageRun;
for (int ist=0; ist<param->nStreams; ist++)
delete chunk[ist];
delete masterImage;
delete slaveImage;
}
delete slaveImage;
}
void cuAmpcorController::outputGrossOffsets()
{
cuArrays<float2> *grossOffsets = new cuArrays<float2>(param->numberWindowDown, param->numberWindowAcross);
grossOffsets->allocateHost();
for(int i=0; i< param->numberWindows; i++)
grossOffsets->hostData[i] = make_float2(param->grossOffsetDown[i], param->grossOffsetAcross[i]);
grossOffsets->outputHostToFile(param->grossOffsetImageName);
@ -176,7 +205,7 @@ void cuAmpcorController::setGrossOffsets(int *in, int size) {
param->grossOffsets = (int *)malloc(size*sizeof(int));
mempcpy(param->grossOffsets, in, size*sizeof(int));
fprintf(stderr, "copy grossOffsets %d\n", size);
}
}
void cuAmpcorController::setOffsetImageName(std::string s) { param->offsetImageName = s; }
void cuAmpcorController::setSNRImageName(std::string s) { param->snrImageName = s; }
//void cuAmpcorController::setMargin(int n) { param->margin = n; }

164
contrib/PyCuAmpcor/src/cuAmpcorParameter.cu
View File

@ -1,6 +1,6 @@
/**
* cuAmpcorParameter.cu
* Input parameters for ampcor
* Input parameters for ampcor
*/
#include "cuAmpcorParameter.h"
@ -11,17 +11,19 @@
#endif
///
/// Constructor for cuAmpcorParameter class
/// Constructor for cuAmpcorParameter class
/// also sets the default/initial values of various parameters
///
cuAmpcorParameter::cuAmpcorParameter()
{
algorithm = 0; //0 freq; 1 time
deviceID = 0;
nStreams = 1;
// default settings
// will be changed if they are set by python scripts
algorithm = 0; //0 freq; 1 time
deviceID = 0;
nStreams = 1;
derampMethod = 1;
windowSizeWidthRaw = 64;
windowSizeHeightRaw = 64;
halfSearchRangeDownRaw = 20;
@ -31,9 +33,9 @@ cuAmpcorParameter::cuAmpcorParameter()
skipSampleDownRaw = 64;
rawDataOversamplingFactor = 2;
zoomWindowSize = 8;
oversamplingFactor = 16;
oversamplingMethod = 0;
oversamplingFactor = 16;
oversamplingMethod = 0;
masterImageName = "master.slc";
masterImageWidth = 1000;
masterImageHeight = 1000;
@ -43,50 +45,58 @@ cuAmpcorParameter::cuAmpcorParameter()
offsetImageName = "DenseOffset.off";
grossOffsetImageName = "GrossOffset.off";
snrImageName = "snr.snr";
covImageName = "cov.cov";
numberWindowDown = 1;
numberWindowAcross = 1;
numberWindowAcross = 1;
numberWindowDownInChunk = 1;
numberWindowAcrossInChunk = 1 ;
numberWindowAcrossInChunk = 1 ;
masterStartPixelDown0 = 0;
masterStartPixelAcross0 = 0;
corrRawZoomInHeight = 17; // 8*2+1
corrRawZoomInWidth = 17;
useMmap = 1; // use mmap
mmapSizeInGB = 1;
}
/**
* To determine other process parameters after reading essential parameters from python
*/
* To determine other process parameters after reading essential parameters from python
*/
void cuAmpcorParameter::setupParameters()
{
{
zoomWindowSize *= rawDataOversamplingFactor; //8 * 2
halfZoomWindowSizeRaw = zoomWindowSize/(2*rawDataOversamplingFactor); // 8*2/(2*2) = 4
halfZoomWindowSizeRaw = zoomWindowSize/(2*rawDataOversamplingFactor); // 8*2/(2*2) = 4
windowSizeWidth = windowSizeWidthRaw*rawDataOversamplingFactor; //
windowSizeHeight = windowSizeHeightRaw*rawDataOversamplingFactor;
searchWindowSizeWidthRaw = windowSizeWidthRaw + 2*halfSearchRangeDownRaw;
searchWindowSizeWidthRaw = windowSizeWidthRaw + 2*halfSearchRangeDownRaw;
searchWindowSizeHeightRaw = windowSizeHeightRaw + 2*halfSearchRangeAcrossRaw;
searchWindowSizeWidthRawZoomIn = windowSizeWidthRaw + 2*halfZoomWindowSizeRaw;
searchWindowSizeHeightRawZoomIn = windowSizeHeightRaw + 2*halfZoomWindowSizeRaw;
searchWindowSizeWidth = searchWindowSizeWidthRawZoomIn*rawDataOversamplingFactor;
searchWindowSizeHeight = searchWindowSizeHeightRawZoomIn*rawDataOversamplingFactor;
numberWindows = numberWindowDown*numberWindowAcross;
if(numberWindows <=0) {
fprintf(stderr, "Incorrect number of windows! (%d, %d)\n", numberWindowDown, numberWindowAcross);
exit(EXIT_FAILURE);
}
}
// modified 02/12/2018 to include one more chunk
// e.g. numberWindowDownInChunk=102, numberWindowDown=10, results in numberChunkDown=11
// the last chunk will include 2 windows, numberWindowDownInChunkRun = 2.
// the last chunk will include 2 windows, numberWindowDownInChunkRun = 2.
numberChunkDown = IDIVUP(numberWindowDown, numberWindowDownInChunk);
numberChunkAcross = IDIVUP(numberWindowAcross, numberWindowAcrossInChunk);
numberChunks = numberChunkDown*numberChunkAcross;
allocateArrays();
allocateArrays();
}
@ -99,7 +109,7 @@ void cuAmpcorParameter::allocateArrays()
masterStartPixelAcross = (int *)malloc(arraySize);
slaveStartPixelDown = (int *)malloc(arraySize);
slaveStartPixelAcross = (int *)malloc(arraySize);
int arraySizeChunk = numberChunks*sizeof(int);
masterChunkStartPixelDown = (int *)malloc(arraySizeChunk);
masterChunkStartPixelAcross = (int *)malloc(arraySizeChunk);
@ -130,18 +140,18 @@ void cuAmpcorParameter::deallocateArrays()
}
/// Set starting pixels for master and slave windows from arrays
/// Set starting pixels for master and slave windows from arrays
/// set also gross offsets between master and slave windows
///
///
void cuAmpcorParameter::setStartPixels(int *mStartD, int *mStartA, int *gOffsetD, int *gOffsetA)
{
for(int i=0; i<numberWindows; i++)
{
masterStartPixelDown[i] = mStartD[i];
grossOffsetDown[i] = gOffsetD[i];
grossOffsetDown[i] = gOffsetD[i];
slaveStartPixelDown[i] = masterStartPixelDown[i] + grossOffsetDown[i] - halfSearchRangeDownRaw;
masterStartPixelAcross[i] = mStartA[i];
grossOffsetAcross[i] = gOffsetA[i];
grossOffsetAcross[i] = gOffsetA[i];
slaveStartPixelAcross[i] = masterStartPixelAcross[i] + grossOffsetAcross[i] - halfSearchRangeAcrossRaw;
}
setChunkStartPixels();
@ -160,7 +170,7 @@ void cuAmpcorParameter::setStartPixels(int mStartD, int mStartA, int *gOffsetD,
masterStartPixelAcross[i] = mStartA + col*skipSampleAcrossRaw;
grossOffsetAcross[i] = gOffsetA[i];
slaveStartPixelAcross[i] = masterStartPixelAcross[i] + grossOffsetAcross[i] - halfSearchRangeAcrossRaw;
}
}
}
setChunkStartPixels();
}
@ -179,60 +189,60 @@ void cuAmpcorParameter::setStartPixels(int mStartD, int mStartA, int gOffsetD, i
masterStartPixelAcross[i] = mStartA + col*skipSampleAcrossRaw;
grossOffsetAcross[i] = gOffsetA;
slaveStartPixelAcross[i] = masterStartPixelAcross[i] + grossOffsetAcross[i] - halfSearchRangeAcrossRaw;
}
}
}
setChunkStartPixels();
}
void cuAmpcorParameter::setChunkStartPixels()
{
maxMasterChunkHeight = 0;
maxMasterChunkWidth = 0;
maxSlaveChunkHeight = 0;
maxSlaveChunkWidth = 0;
for(int ichunk=0; ichunk <numberChunkDown; ichunk++)
{
for (int jchunk =0; jchunk<numberChunkAcross; jchunk++)
{
int idxChunk = ichunk*numberChunkAcross+jchunk;
int mChunkSD = masterImageHeight;
int mChunkSA = masterImageWidth;
int mChunkSD = masterImageHeight;
int mChunkSA = masterImageWidth;
int mChunkED = 0;
int mChunkEA = 0;
int sChunkSD = slaveImageHeight;
int sChunkSA = slaveImageWidth;
int sChunkED = 0;
int sChunkEA = 0;
// modified 02/12/2018
int numberWindowDownInChunkRun = numberWindowDownInChunk;
int numberWindowAcrossInChunkRun = numberWindowAcrossInChunk;
// modify the number of windows in last chunk
if(ichunk == numberChunkDown -1)
int numberWindowDownInChunkRun = numberWindowDownInChunk;
int numberWindowAcrossInChunkRun = numberWindowAcrossInChunk;
// modify the number of windows in last chunk
if(ichunk == numberChunkDown -1)
numberWindowDownInChunkRun = numberWindowDown - numberWindowDownInChunk*(numberChunkDown -1);
if(jchunk == numberChunkAcross -1)
if(jchunk == numberChunkAcross -1)
numberWindowAcrossInChunkRun = numberWindowAcross - numberWindowAcrossInChunk*(numberChunkAcross -1);
for(int i=0; i<numberWindowDownInChunkRun; i++)
for(int i=0; i<numberWindowDownInChunkRun; i++)
{
for(int j=0; j<numberWindowAcrossInChunkRun; j++)
{
{
int idxWindow = (ichunk*numberWindowDownInChunk+i)*numberWindowAcross + (jchunk*numberWindowAcrossInChunk+j);
int vpixel = masterStartPixelDown[idxWindow];
if(mChunkSD > vpixel) mChunkSD = vpixel;
if(mChunkSD > vpixel) mChunkSD = vpixel;
if(mChunkED < vpixel) mChunkED = vpixel;
vpixel = masterStartPixelAcross[idxWindow];
if(mChunkSA > vpixel) mChunkSA = vpixel;
if(mChunkSA > vpixel) mChunkSA = vpixel;
if(mChunkEA < vpixel) mChunkEA = vpixel;
vpixel = slaveStartPixelDown[idxWindow];
if(sChunkSD > vpixel) sChunkSD = vpixel;
if(sChunkSD > vpixel) sChunkSD = vpixel;
if(sChunkED < vpixel) sChunkED = vpixel;
vpixel = slaveStartPixelAcross[idxWindow];
if(sChunkSA > vpixel) sChunkSA = vpixel;
if(sChunkSA > vpixel) sChunkSA = vpixel;
if(sChunkEA < vpixel) sChunkEA = vpixel;
}
}
@ -261,58 +271,58 @@ void cuAmpcorParameter::checkPixelInImageRange()
for(int col = 0; col < numberWindowAcross; col++)
{
int i = row*numberWindowAcross + col;
if(masterStartPixelDown[i] <0)
if(masterStartPixelDown[i] <0)
{
fprintf(stderr, "Master Window start pixel out ot range in Down, window (%d,%d), pixel %d\n", row, col, masterStartPixelDown[i]);
exit(EXIT_FAILURE); //or raise range error
}
if(masterStartPixelAcross[i] <0)
}
if(masterStartPixelAcross[i] <0)
{
fprintf(stderr, "Master Window start pixel out ot range in Across, window (%d,%d), pixel %d\n", row, col, masterStartPixelAcross[i]);
exit(EXIT_FAILURE);
}
endPixel = masterStartPixelDown[i] + windowSizeHeightRaw;
if(endPixel >= masterImageHeight)
}
endPixel = masterStartPixelDown[i] + windowSizeHeightRaw;
if(endPixel >= masterImageHeight)
{
fprintf(stderr, "Master Window end pixel out ot range in Down, window (%d,%d), pixel %d\n", row, col, endPixel);
exit(EXIT_FAILURE);
}
endPixel = masterStartPixelAcross[i] + windowSizeWidthRaw;
if(endPixel >= masterImageWidth)
}
endPixel = masterStartPixelAcross[i] + windowSizeWidthRaw;
if(endPixel >= masterImageWidth)
{
fprintf(stderr, "Master Window end pixel out ot range in Across, window (%d,%d), pixel %d\n", row, col, endPixel);
exit(EXIT_FAILURE);
}
}
//slave
if(slaveStartPixelDown[i] <0)
if(slaveStartPixelDown[i] <0)
{
fprintf(stderr, "Slave Window start pixel out ot range in Down, window (%d,%d), pixel %d\n", row, col, slaveStartPixelDown[i]);
exit(EXIT_FAILURE);
}
if(slaveStartPixelAcross[i] <0)
exit(EXIT_FAILURE);
}
if(slaveStartPixelAcross[i] <0)
{
fprintf(stderr, "Slave Window start pixel out ot range in Across, window (%d,%d), pixel %d\n", row, col, slaveStartPixelAcross[i]);
exit(EXIT_FAILURE);
}
endPixel = slaveStartPixelDown[i] + searchWindowSizeHeightRaw;
if(endPixel >= slaveImageHeight)
}
endPixel = slaveStartPixelDown[i] + searchWindowSizeHeightRaw;
if(endPixel >= slaveImageHeight)
{
fprintf(stderr, "Slave Window end pixel out ot range in Down, window (%d,%d), pixel %d\n", row, col, endPixel);
exit(EXIT_FAILURE);
}
endPixel = slaveStartPixelAcross[i] + searchWindowSizeWidthRaw;
if(endPixel >= slaveImageWidth)
}
endPixel = slaveStartPixelAcross[i] + searchWindowSizeWidthRaw;
if(endPixel >= slaveImageWidth)
{
fprintf(stderr, "Slave Window end pixel out ot range in Across, window (%d,%d), pixel %d\n", row, col, endPixel);
exit(EXIT_FAILURE);
}
}
}
}
}
}
cuAmpcorParameter::~cuAmpcorParameter()
cuAmpcorParameter::~cuAmpcorParameter()
{
deallocateArrays();
}

110
contrib/PyCuAmpcor/src/cuAmpcorParameter.h
View File

@ -1,7 +1,7 @@
/**
* cuAmpcorParameter.h
* Header file for Ampcor Parameter Class
*
*
* Author: Lijun Zhu @ Seismo Lab, Caltech
* March 2017
*/
@ -12,13 +12,13 @@
#include <string>
/// Class container for all parameters
///
///
/// @note
/// The dimension/direction names used are:
/// The dimension/direction names used are:
/// The inner-most dimension: x, row, height, down, azimuth, along the track.
/// The outer-most dimension: y, column, width, across, range, along the sight.
/// C/C++/Python use row-major indexing: a[i][j] -> a[i*WIDTH+j]
/// FORTRAN/BLAS/CUBLAS use column-major indexing: a[i][j]->a[i+j*LENGTH]
/// C/C++/Python use row-major indexing: a[i][j] -> a[i*WIDTH+j]
/// FORTRAN/BLAS/CUBLAS use column-major indexing: a[i][j]->a[i+j*LENGTH]
/// @note
/// Common procedures to use cuAmpcorParameter
@ -27,72 +27,74 @@
/// 3. Call setupParameters() to determine related parameters and allocate starting pixels for each window: param->setupParameters()
/// 4. Provide/set Master window starting pixel(s), and gross offset(s): param->setStartPixels(masterStartDown, masterStartAcross, grossOffsetDown, grossOffsetAcross)
/// 4a. Optionally, check the range of windows is within the SLC image range: param->checkPixelInImageRange()
/// Steps 1, 3, 4 are mandatory. If step 2 is missing, default values will be used
/// Steps 1, 3, 4 are mandatory. If step 2 is missing, default values will be used
class cuAmpcorParameter{
public:
int algorithm; /// Cross-correlation algorithm: 0=freq domain (default) 1=time domain
int deviceID; /// Targeted GPU device ID: use -1 to auto select
int nStreams; /// Number of streams to asynchonize data transfers and compute kernels
int algorithm; /// Cross-correlation algorithm: 0=freq domain (default) 1=time domain
int deviceID; /// Targeted GPU device ID: use -1 to auto select
int nStreams; /// Number of streams to asynchonize data transfers and compute kernels
int derampMethod; /// Method for deramping 0=None, 1=average, 2=phase gradient
// chip or window size for raw data
int windowSizeHeightRaw; /// Template window height (original size)
int windowSizeWidthRaw; /// Template window width (original size)
int searchWindowSizeHeightRaw; /// Search window height (original size)
int windowSizeWidthRaw; /// Template window width (original size)
int searchWindowSizeHeightRaw; /// Search window height (original size)
int searchWindowSizeWidthRaw; /// Search window width (orignal size)
int halfSearchRangeDownRaw; ///(searchWindowSizeHeightRaw-windowSizeHeightRaw)/2
int halfSearchRangeDownRaw; ///(searchWindowSizeHeightRaw-windowSizeHeightRaw)/2
int halfSearchRangeAcrossRaw; ///(searchWindowSizeWidthRaw-windowSizeWidthRaw)/2
// search range is (-halfSearchRangeRaw, halfSearchRangeRaw)
int searchWindowSizeHeightRawZoomIn;
int searchWindowSizeWidthRawZoomIn;
int corrRawZoomInHeight; // window to estimate snr
int corrRawZoomInWidth;
// chip or window size after oversampling
int rawDataOversamplingFactor; /// Raw data overampling factor (from original size to oversampled size)
int windowSizeHeight; /// Template window length (oversampled size)
int windowSizeWidth; /// Template window width (original size)
int searchWindowSizeHeight; /// Search window height (oversampled size)
int searchWindowSizeWidth; /// Search window width (oversampled size)
// strides between chips/windows
int searchWindowSizeHeight; /// Search window height (oversampled size)
int searchWindowSizeWidth; /// Search window width (oversampled size)
// strides between chips/windows
int skipSampleDownRaw; /// Skip size between neighboring windows in Down direction (original size)
int skipSampleAcrossRaw; /// Skip size between neighboring windows in across direction (original size)
//int skipSampleDown; /// Skip size between neighboring windows in Down direction (oversampled size)
//int skipSampleAcross; /// Skip size between neighboring windows in Across direction (oversampled size)
// Zoom in region near location of max correlation
int zoomWindowSize; /// Zoom-in window size in correlation surface (same for down and across directions)
int halfZoomWindowSizeRaw; /// = half of zoomWindowSize/rawDataOversamplingFactor
int zoomWindowSize; /// Zoom-in window size in correlation surface (same for down and across directions)
int halfZoomWindowSizeRaw; /// = half of zoomWindowSize/rawDataOversamplingFactor
int oversamplingFactor; /// Oversampling factor for interpolating correlation surface
int oversamplingMethod; /// 0 = fft (default) 1 = sinc
float thresholdSNR; /// Threshold of Signal noise ratio to remove noisy data
int oversamplingMethod; /// 0 = fft (default) 1 = sinc
float thresholdSNR; /// Threshold of Signal noise ratio to remove noisy data
//master image
std::string masterImageName; /// master SLC image name
int imageDataType1; /// master image data type, 2=cfloat=complex=float2 1=float
int masterImageHeight; /// master image height
int masterImageHeight; /// master image height
int masterImageWidth; /// master image width
//slave image
std::string slaveImageName; /// slave SLC image name
int imageDataType2; /// slave image data type, 2=cfloat=complex=float2 1=float
int slaveImageHeight; /// slave image height
int slaveImageHeight; /// slave image height
int slaveImageWidth; /// slave image width
// total number of chips/windows
int numberWindowDown; /// number of total windows (down)
int numberWindowAcross; /// number of total windows (across)
int numberWindows; /// numberWindowDown*numberWindowAcross
// number of chips/windows in a batch/chunk
int numberWindowDownInChunk; /// number of windows processed in a chunk (down)
int numberWindowAcrossInChunk; /// number of windows processed in a chunk (across)
@ -100,20 +102,21 @@ public:
int numberChunkDown; /// number of chunks (down)
int numberChunkAcross; /// number of chunks (across)
int numberChunks;
int mmapSizeInGB;
int useMmap; /// whether to use mmap 0=not 1=yes (default = 0)
int mmapSizeInGB; /// size for mmap buffer(useMmap=1) or a cpu memory buffer (useMmap=0)
int masterStartPixelDown0;
int masterStartPixelAcross0;
int *masterStartPixelDown; /// master starting pixels for each window (down)
int *masterStartPixelDown; /// master starting pixels for each window (down)
int *masterStartPixelAcross;/// master starting pixels for each window (across)
int *slaveStartPixelDown; /// slave starting pixels for each window (down)
int *slaveStartPixelAcross; /// slave starting pixels for each window (across)
int *slaveStartPixelDown; /// slave starting pixels for each window (down)
int *slaveStartPixelAcross; /// slave starting pixels for each window (across)
int grossOffsetDown0;
int grossOffsetAcross0;
int *grossOffsetDown; /// Gross offsets between master and slave windows (down) : slaveStartPixel - masterStartPixel
int *grossOffsetAcross; /// Gross offsets between master and slave windows (across)
int *grossOffsetAcross; /// Gross offsets between master and slave windows (across)
int *masterChunkStartPixelDown;
int *masterChunkStartPixelAcross;
int *slaveChunkStartPixelDown;
@ -124,18 +127,19 @@ public:
int *slaveChunkWidth;
int maxMasterChunkHeight, maxMasterChunkWidth;
int maxSlaveChunkHeight, maxSlaveChunkWidth;
std::string grossOffsetImageName;
std::string offsetImageName; /// Output Offset fields filename
std::string grossOffsetImageName;
std::string offsetImageName; /// Output Offset fields filename
std::string snrImageName; /// Output SNR filename
std::string covImageName;
cuAmpcorParameter(); /// Class constructor and default parameters setter
~cuAmpcorParameter(); /// Class descontructor
~cuAmpcorParameter(); /// Class descontructor
void allocateArrays(); /// Allocate various arrays after the number of Windows is given
void deallocateArrays(); /// Deallocate arrays on exit
/// Three methods to set master/slave starting pixels and gross offsets from input master start pixel(s) and gross offset(s)
/// 1 (int *, int *, int *, int *): varying master start pixels and gross offsets
/// 2 (int, int, int *, int *): fixed master start pixel (first window) and varying gross offsets
@ -144,7 +148,7 @@ public:
void setStartPixels(int, int, int*, int*);
void setStartPixels(int, int, int, int);
void setChunkStartPixels();
void checkPixelInImageRange(); /// check whether
void checkPixelInImageRange(); /// check whether
void setupParameters(); /// Process other parameters after Python Input
};

39
contrib/PyCuAmpcor/src/cuAmpcorUtil.h
View File

@ -1,10 +1,10 @@
/*
/*
* cuAmpcorUtil.h
* header file to include the various routines for ampcor
* serves as an index
* serves as an index
*/
#ifndef __CUAMPCORUTIL_H
#define __CUMAPCORUTIL_H
@ -18,20 +18,27 @@
//in cuArraysCopy.cu: various utitlies for copy images file in gpu memory
void cuArraysCopyToBatch(cuArrays<float2> *image1, cuArrays<float2> *image2, int strideH, int strideW, cudaStream_t stream);
void cuArraysCopyToBatchWithOffset(cuArrays<float2> *image1, const int lda1, cuArrays<float2> *image2,
void cuArraysCopyToBatchWithOffset(cuArrays<float2> *image1, const int lda1, cuArrays<float2> *image2,
const int *offsetH, const int* offsetW, cudaStream_t stream);
void cuArraysCopyToBatchAbsWithOffset(cuArrays<float2> *image1, const int lda1, cuArrays<float2> *image2,
void cuArraysCopyToBatchAbsWithOffset(cuArrays<float2> *image1, const int lda1, cuArrays<float2> *image2,
const int *offsetH, const int* offsetW, cudaStream_t stream);
void cuArraysCopyToBatchWithOffsetR2C(cuArrays<float> *image1, const int lda1, cuArrays<float2> *image2,
const int *offsetH, const int* offsetW, cudaStream_t stream);
void cuArraysCopyC2R(cuArrays<float2> *image1, cuArrays<float> *image2, int strideH, int strideW, cudaStream_t stream);
// same routine name overloaded for different data type
void cuArraysCopyExtract(cuArrays<float> *imagesIn, cuArrays<float> *imagesOut, cuArrays<int2> *offset, cudaStream_t stream);
void cuArraysCopyExtract(cuArrays<float> *imagesIn, cuArrays<float> *imagesOut, int2 offset, cudaStream_t stream);
void cuArraysCopyExtract(cuArrays<float2> *imagesIn, cuArrays<float> *imagesOut, int2 offset, cudaStream_t stream);
void cuArraysCopyExtract(cuArrays<float2> *imagesIn, cuArrays<float2> *imagesOut, int2 offset, cudaStream_t stream);
void cuArraysCopyExtract(cuArrays<float2> *imagesIn, cuArrays<float2> *imagesOut, cuArrays<int2> *offsets, cudaStream_t stream);
void cuArraysCopyExtract(cuArrays<float3> *imagesIn, cuArrays<float3> *imagesOut, int2 offset, cudaStream_t stream);
void cuArraysCopyInsert(cuArrays<float2> *imageIn, cuArrays<float2> *imageOut, int offsetX, int offersetY, cudaStream_t stream);
void cuArraysCopyInsert(cuArrays<float3> *imageIn, cuArrays<float3> *imageOut, int offsetX, int offersetY, cudaStream_t stream);
void cuArraysCopyInsert(cuArrays<float> *imageIn, cuArrays<float> *imageOut, int offsetX, int offsetY, cudaStream_t stream);
void cuArraysCopyInsert(cuArrays<int> *imageIn, cuArrays<int> *imageOut, int offsetX, int offersetY, cudaStream_t stream);
void cuArraysCopyInversePadded(cuArrays<float> *imageIn, cuArrays<float> *imageOut,cudaStream_t stream);
void cuArraysCopyPadded(cuArrays<float> *imageIn, cuArrays<float> *imageOut,cudaStream_t stream);
@ -46,8 +53,8 @@ void cuDerampMethod2(cuArrays<float2> *images, cudaStream_t stream);
void cpuDerampMethod3(cuArrays<float2> *images, cudaStream_t stream);
//in cuArraysPadding.cu: various utilities for oversampling padding
void cuArraysPadding(cuArrays<float2> *image1, cuArrays<float2> *image2, cudaStream_t stream);
void cuArraysPaddingMany(cuArrays<float2> *image1, cuArrays<float2> *image2, cudaStream_t stream);
void cuArraysPadding(cuArrays<float2> *image1, cuArrays<float2> *image2, cudaStream_t stream);
void cuArraysPaddingMany(cuArrays<float2> *image1, cuArrays<float2> *image2, cudaStream_t stream);
void cuArraysR2C(cuArrays<float> *image1, cuArrays<float2> *image2, cudaStream_t stream);
void cuArraysC2R(cuArrays<float2> *image1, cuArrays<float> *image2, cudaStream_t stream);
void cuArraysAbs(cuArrays<float2> *image1, cuArrays<float> *image2, cudaStream_t stream);
@ -57,21 +64,21 @@ void cuArraysSubtractMean(cuArrays<float> *images, cudaStream_t stream);
void cuCorrNormalize(cuArrays<float> *templates, cuArrays<float> *images, cuArrays<float> *results, cudaStream_t stream);
//in cuOffset.cu: utitilies for determining the max locaiton of cross correlations or the offset
void cuArraysMaxloc2D(cuArrays<float> *images, cuArrays<int2> *maxloc, cuArrays<float> *maxval, cudaStream_t stream);
void cuArraysMaxloc2D(cuArrays<float> *images, cuArrays<int2> *maxloc, cudaStream_t stream);
void cuArraysMaxloc2D(cuArrays<float> *images, cuArrays<int2> *maxloc, cuArrays<float> *maxval, cudaStream_t stream);
void cuArraysMaxloc2D(cuArrays<float> *images, cuArrays<int2> *maxloc, cudaStream_t stream);
void cuSubPixelOffset(cuArrays<int2> *offsetInit, cuArrays<int2> *offsetZoomIn, cuArrays<float2> *offsetFinal,
int OverSampleRatioZoomin, int OverSampleRatioRaw,
int xHalfRangeInit, int yHalfRangeInit, int xHalfRangeZoomIn, int yHalfRangeZoomIn,
cudaStream_t stream);
void cuDetermineInterpZone(cuArrays<int2> *maxloc, cuArrays<int2> *zoomInOffset, cuArrays<float> *corrOrig, cuArrays<float> *corrZoomIn, cudaStream_t stream);
void cuDetermineSlaveExtractOffset(cuArrays<int2> *maxLoc, int xOldRange, int yOldRange, int xNewRange, int yNewRange, cudaStream_t stream);
//in cuCorrTimeDomain.cu: cross correlation in time domain
void cuCorrTimeDomain(cuArrays<float> *templates, cuArrays<float> *images, cuArrays<float> *results, cudaStream_t stream);
void cuCorrTimeDomain(cuArrays<float> *templates, cuArrays<float> *images, cuArrays<float> *results, cudaStream_t stream);
//in cuCorrFrequency.cu: cross correlation in freq domain, also include fft correlatior class
void cuArraysElementMultiply(cuArrays<float2> *image1, cuArrays<float2> *image2, cudaStream_t stream);
void cuArraysElementMultiply(cuArrays<float2> *image1, cuArrays<float2> *image2, cudaStream_t stream);
void cuArraysElementMultiplyConjugate(cuArrays<float2> *image1, cuArrays<float2> *image2, float coef, cudaStream_t stream);
@ -80,7 +87,11 @@ void cuArraysElementMultiplyConjugate(cuArrays<float2> *image1, cuArrays<float2>
void cuArraysCopyExtractCorr(cuArrays<float> *imagesIn, cuArrays<float> *imagesOut, cuArrays<int> *imagesValid, cuArrays<int2> *maxloc, cudaStream_t stream);
// implemented in cuCorrNormalization.cu
void cuArraysSumCorr(cuArrays<float> *images, cuArrays<int> *imagesValid, cuArrays<float> *imagesSum, cuArrays<int> *imagesValidCount, cudaStream_t stream);
// implemented in cuEstimateStats.cu
void cuEstimateSnr(cuArrays<float> *corrSum, cuArrays<int> *corrValidCount, cuArrays<float> *maxval, cuArrays<float> *snrValue, cudaStream_t stream);
#endif
// implemented in cuEstimateStats.cu
void cuEstimateVariance(cuArrays<float> *corrBatchRaw, cuArrays<int2> *maxloc, cuArrays<float> *maxval, cuArrays<float3> *covValue, cudaStream_t stream);
#endif

13
contrib/PyCuAmpcor/src/cuArrays.cu
View File

@ -154,8 +154,21 @@
file.write((char *)data, size*count*sizeof(float2));
file.close();
}
template<>
void cuArrays<float3>::outputToFile(std::string fn, cudaStream_t stream)
{
float *data;
data = (float *)malloc(size*count*sizeof(float3));
checkCudaErrors(cudaMemcpyAsync(data, devData, size*count*sizeof(float3), cudaMemcpyDeviceToHost, stream));
std::ofstream file;
file.open(fn.c_str(), std::ios_base::binary);
file.write((char *)data, size*count*sizeof(float3));
file.close();
}
template class cuArrays<float>;
template class cuArrays<float2>;
template class cuArrays<float3>;
template class cuArrays<int2>;
template class cuArrays<int>;

316
contrib/PyCuAmpcor/src/cuArraysCopy.cu
View File

@ -4,7 +4,7 @@
* Lijun Zhu @ Seismo Lab, Caltech
* v1.0 Jan 2017
*/
#include "cuArrays.h"
#include "cudaUtil.h"
#include "cudaError.h"
@ -16,14 +16,14 @@ inline __device__ float cuAbs(float2 a)
return sqrtf(a.x*a.x+a.y*a.y);
}*/
//copy a chunk into a series of chips
__global__ void cuArraysCopyToBatch_kernel(const float2 *imageIn, const int inNX, const int inNY,
float2 *imageOut, const int outNX, const int outNY,
const int nImagesX, const int nImagesY,
// copy a chunk into a batch of chips for a given stride
__global__ void cuArraysCopyToBatch_kernel(const float2 *imageIn, const int inNX, const int inNY,
float2 *imageOut, const int outNX, const int outNY,
const int nImagesX, const int nImagesY,
const int strideX, const int strideY)
{
int idxImage = blockIdx.z;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outy = threadIdx.y + blockDim.y*blockIdx.y;
if(idxImage >=nImagesX*nImagesY|| outx >= outNX || outy >= outNY) return;
int idxOut = idxImage*outNX*outNY + outx*outNY + outy;
@ -33,8 +33,7 @@ __global__ void cuArraysCopyToBatch_kernel(const float2 *imageIn, const int inNX
imageOut[idxOut] = imageIn[idxIn];
}
//tested
void cuArraysCopyToBatch(cuArrays<float2> *image1, cuArrays<float2> *image2,
void cuArraysCopyToBatch(cuArrays<float2> *image1, cuArrays<float2> *image2,
int strideH, int strideW, cudaStream_t stream)
{
const int nthreads = NTHREADS2D;
@ -48,12 +47,14 @@ void cuArraysCopyToBatch(cuArrays<float2> *image1, cuArrays<float2> *image2,
getLastCudaError("cuArraysCopyToBatch_kernel");
}
__global__ void cuArraysCopyToBatchWithOffset_kernel(const float2 *imageIn, const int inNY,
float2 *imageOut, const int outNX, const int outNY, const int nImages,
// copy a chunk into a batch of chips for a set of offsets (varying strides), from complex to complex
__global__ void cuArraysCopyToBatchWithOffset_kernel(const float2 *imageIn, const int inNY,
float2 *imageOut, const int outNX, const int outNY, const int nImages,
const int *offsetX, const int *offsetY)
{
int idxImage = blockIdx.z;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outy = threadIdx.y + blockDim.y*blockIdx.y;
if(idxImage>=nImages || outx >= outNX || outy >= outNY) return;
int idxOut = idxImage*outNX*outNY + outx*outNY + outy;
@ -61,11 +62,8 @@ __global__ void cuArraysCopyToBatchWithOffset_kernel(const float2 *imageIn, cons
imageOut[idxOut] = imageIn[idxIn];
}
/// @param[in] image1 input image in a large chunk
/// @param[in] lda1 width of image 1
/// @param[out] image2 output image with a batch of small windows
void cuArraysCopyToBatchWithOffset(cuArrays<float2> *image1, const int lda1, cuArrays<float2> *image2,
// lda1 (inNY) is the leading dimension of image1, usually, its width
void cuArraysCopyToBatchWithOffset(cuArrays<float2> *image1, const int lda1, cuArrays<float2> *image2,
const int *offsetH, const int* offsetW, cudaStream_t stream)
{
const int nthreads = 16;
@ -79,12 +77,13 @@ void cuArraysCopyToBatchWithOffset(cuArrays<float2> *image1, const int lda1, cuA
getLastCudaError("cuArraysCopyToBatchAbsWithOffset_kernel");
}
__global__ void cuArraysCopyToBatchAbsWithOffset_kernel(const float2 *imageIn, const int inNY,
float2 *imageOut, const int outNX, const int outNY, const int nImages,
// copy a chunk into a batch of chips for a set of offsets (varying strides), from complex to real(take amplitudes)
__global__ void cuArraysCopyToBatchAbsWithOffset_kernel(const float2 *imageIn, const int inNY,
float2 *imageOut, const int outNX, const int outNY, const int nImages,
const int *offsetX, const int *offsetY)
{
int idxImage = blockIdx.z;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outy = threadIdx.y + blockDim.y*blockIdx.y;
if(idxImage>=nImages || outx >= outNX || outy >= outNY) return;
int idxOut = idxImage*outNX*outNY + outx*outNY + outy;
@ -92,7 +91,7 @@ __global__ void cuArraysCopyToBatchAbsWithOffset_kernel(const float2 *imageIn, c
imageOut[idxOut] = make_float2(complexAbs(imageIn[idxIn]), 0.0);
}
void cuArraysCopyToBatchAbsWithOffset(cuArrays<float2> *image1, const int lda1, cuArrays<float2> *image2,
void cuArraysCopyToBatchAbsWithOffset(cuArrays<float2> *image1, const int lda1, cuArrays<float2> *image2,
const int *offsetH, const int* offsetW, cudaStream_t stream)
{
const int nthreads = 16;
@ -106,14 +105,42 @@ void cuArraysCopyToBatchAbsWithOffset(cuArrays<float2> *image1, const int lda1,
getLastCudaError("cuArraysCopyToBatchAbsWithOffset_kernel");
}
// copy a chunk into a batch of chips for a set of offsets (varying strides), from real to complex(to real part)
__global__ void cuArraysCopyToBatchWithOffsetR2C_kernel(const float *imageIn, const int inNY,
float2 *imageOut, const int outNX, const int outNY, const int nImages,
const int *offsetX, const int *offsetY)
{
int idxImage = blockIdx.z;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outy = threadIdx.y + blockDim.y*blockIdx.y;
if(idxImage>=nImages || outx >= outNX || outy >= outNY) return;
int idxOut = idxImage*outNX*outNY + outx*outNY + outy;
int idxIn = (offsetX[idxImage]+outx)*inNY + offsetY[idxImage] + outy;
imageOut[idxOut] = make_float2(imageIn[idxIn], 0.0f);
}
void cuArraysCopyToBatchWithOffsetR2C(cuArrays<float> *image1, const int lda1, cuArrays<float2> *image2,
const int *offsetH, const int* offsetW, cudaStream_t stream)
{
const int nthreads = 16;
dim3 blockSize(nthreads, nthreads, 1);
dim3 gridSize(IDIVUP(image2->height,nthreads), IDIVUP(image2->width,nthreads), image2->count);
//fprintf(stderr, "copy tile to batch, %d %d\n", lda1, image2->count);
cuArraysCopyToBatchWithOffsetR2C_kernel<<<gridSize,blockSize, 0 , stream>>> (
image1->devData, lda1,
image2->devData, image2->height, image2->width, image2->count,
offsetH, offsetW);
getLastCudaError("cuArraysCopyToBatchWithOffsetR2C_kernel");
}
//copy a chunk into a series of chips
__global__ void cuArraysCopyC2R_kernel(const float2 *imageIn, const int inNX, const int inNY,
float *imageOut, const int outNX, const int outNY,
const int nImagesX, const int nImagesY,
__global__ void cuArraysCopyC2R_kernel(const float2 *imageIn, const int inNX, const int inNY,
float *imageOut, const int outNX, const int outNY,
const int nImagesX, const int nImagesY,
const int strideX, const int strideY, const float factor)
{
int idxImage = blockIdx.z;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outy = threadIdx.y + blockDim.y*blockIdx.y;
if(idxImage >=nImagesX*nImagesY|| outx >= outNX || outy >= outNY) return;
int idxOut = idxImage*outNX*outNY + outx*outNY + outy;
@ -121,17 +148,17 @@ __global__ void cuArraysCopyC2R_kernel(const float2 *imageIn, const int inNX, co
int idxImageY = idxImage%nImagesY;
int idxIn = (idxImageX*strideX+outx)*inNY + idxImageY*strideY+outy;
imageOut[idxOut] = complexAbs(imageIn[idxIn])*factor;
//printf( "%d\n", idxOut);
//printf( "%d\n", idxOut);
}
//tested
void cuArraysCopyC2R(cuArrays<float2> *image1, cuArrays<float> *image2,
void cuArraysCopyC2R(cuArrays<float2> *image1, cuArrays<float> *image2,
int strideH, int strideW, cudaStream_t stream)
{
const int nthreads = 16;
dim3 blockSize(nthreads, nthreads, 1);
dim3 gridSize(IDIVUP(image2->height,nthreads), IDIVUP(image2->width,nthreads), image2->count);
float factor = 1.0f/image1->size; //the FFT factor
float factor = 1.0f/image1->size; //the FFT factor
cuArraysCopyC2R_kernel<<<gridSize,blockSize, 0 , stream>>> (
image1->devData, image1->height, image1->width,
image2->devData, image2->height, image2->width,
@ -141,22 +168,22 @@ void cuArraysCopyC2R(cuArrays<float2> *image1, cuArrays<float> *image2,
}
__global__ void cuArraysCopyExtractVaryingOffset(const float *imageIn, const int inNX, const int inNY,
float *imageOut, const int outNX, const int outNY, const int nImages,
float *imageOut, const int outNX, const int outNY, const int nImages,
const int2 *offsets)
{
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outy = threadIdx.y + blockDim.y*blockIdx.y;
if(outx < outNX && outy < outNY)
{
int idxImage = blockIdx.z;
int idxImage = blockIdx.z;
int idxOut = (blockIdx.z * outNX + outx)*outNY+outy;
int idxIn = (blockIdx.z*inNX + outx + offsets[idxImage].x)*inNY + outy + offsets[idxImage].y;
int idxIn = (blockIdx.z*inNX + outx + offsets[idxImage].x)*inNY + outy + offsets[idxImage].y;
imageOut[idxOut] = imageIn[idxIn];
}
}
/* copy a tile of images to another image, with starting pixels offsets
/* copy a tile of images to another image, with starting pixels offsets
* param[in] imageIn inut images
* param[out] imageOut output images of dimension nImages*outNX*outNY
*/
@ -166,24 +193,24 @@ void cuArraysCopyExtract(cuArrays<float> *imagesIn, cuArrays<float> *imagesOut,
const int nthreads = 16;
dim3 threadsperblock(nthreads, nthreads,1);
dim3 blockspergrid(IDIVUP(imagesOut->height,nthreads), IDIVUP(imagesOut->width,nthreads), imagesOut->count);
cuArraysCopyExtractVaryingOffset<<<blockspergrid, threadsperblock,0, stream>>>(imagesIn->devData, imagesIn->height, imagesIn->width,
cuArraysCopyExtractVaryingOffset<<<blockspergrid, threadsperblock,0, stream>>>(imagesIn->devData, imagesIn->height, imagesIn->width,
imagesOut->devData, imagesOut->height, imagesOut->width, imagesOut->count, offsets->devData);
getLastCudaError("cuArraysCopyExtract error");
}
__global__ void cuArraysCopyExtractVaryingOffset_C2C(const float2 *imageIn, const int inNX, const int inNY,
float2 *imageOut, const int outNX, const int outNY, const int nImages,
float2 *imageOut, const int outNX, const int outNY, const int nImages,
const int2 *offsets)
{
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outy = threadIdx.y + blockDim.y*blockIdx.y;
if(outx < outNX && outy < outNY)
{
int idxImage = blockIdx.z;
int idxImage = blockIdx.z;
int idxOut = (blockIdx.z * outNX + outx)*outNY+outy;
int idxIn = (blockIdx.z*inNX + outx + offsets[idxImage].x)*inNY + outy + offsets[idxImage].y;
int idxIn = (blockIdx.z*inNX + outx + offsets[idxImage].x)*inNY + outy + offsets[idxImage].y;
imageOut[idxOut] = imageIn[idxIn];
}
}
@ -194,7 +221,7 @@ void cuArraysCopyExtract(cuArrays<float2> *imagesIn, cuArrays<float2> *imagesOut
const int nthreads = 16;
dim3 threadsperblock(nthreads, nthreads,1);
dim3 blockspergrid(IDIVUP(imagesOut->height,nthreads), IDIVUP(imagesOut->width,nthreads), imagesOut->count);
cuArraysCopyExtractVaryingOffset_C2C<<<blockspergrid, threadsperblock,0, stream>>>(imagesIn->devData, imagesIn->height, imagesIn->width,
cuArraysCopyExtractVaryingOffset_C2C<<<blockspergrid, threadsperblock,0, stream>>>(imagesIn->devData, imagesIn->height, imagesIn->width,
imagesOut->devData, imagesOut->height, imagesOut->width, imagesOut->count, offsets->devData);
getLastCudaError("cuArraysCopyExtractC2C error");
@ -202,26 +229,29 @@ void cuArraysCopyExtract(cuArrays<float2> *imagesIn, cuArrays<float2> *imagesOut
// correlation surface extraction (Minyan Zhong)
__global__ void cuArraysCopyExtractVaryingOffsetCorr(const float *imageIn, const int inNX, const int inNY,
float *imageOut, const int outNX, const int outNY, int *imageValid, const int nImages,
float *imageOut, const int outNX, const int outNY, int *imageValid, const int nImages,
const int2 *maxloc)
{
int idxImage = blockIdx.z;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
// One thread per out point. Find the coordinates within the current image.
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outy = threadIdx.y + blockDim.y*blockIdx.y;
// Find the correponding input.
int inx = outx + maxloc[idxImage].x - outNX/2;
int iny = outy + maxloc[idxImage].y - outNY/2;
if (outx < outNX && outy < outNY)
if (outx < outNX && outy < outNY)
{
// Find the location in full array.
int idxOut = ( blockIdx.z * outNX + outx ) * outNY + outy;
int idxIn = ( blockIdx.z * inNX + inx ) * inNY + iny;
if (inx>=0 && iny>=0 && inx<inNX && iny<inNY) {
imageOut[idxOut] = imageIn[idxIn];
imageValid[idxOut] = 1;
}
@ -255,21 +285,21 @@ void cuArraysCopyExtractCorr(cuArrays<float> *imagesIn, cuArrays<float> *imagesO
__global__ void cuArraysCopyExtractFixedOffset(const float *imageIn, const int inNX, const int inNY,
float *imageOut, const int outNX, const int outNY, const int nImages,
float *imageOut, const int outNX, const int outNY, const int nImages,
const int offsetX, const int offsetY)
{
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outy = threadIdx.y + blockDim.y*blockIdx.y;
if(outx < outNX && outy < outNY)
{
{
int idxOut = (blockIdx.z * outNX + outx)*outNY+outy;
int idxIn = (blockIdx.z*inNX + outx + offsetX)*inNY + outy + offsetY;
int idxIn = (blockIdx.z*inNX + outx + offsetX)*inNY + outy + offsetY;
imageOut[idxOut] = imageIn[idxIn];
}
}
/* copy a tile of images to another image, with starting pixels offsets
/* copy a tile of images to another image, with starting pixels offsets
* param[in] imageIn inut images
* param[out] imageOut output images of dimension nImages*outNX*outNY
*/
@ -279,23 +309,24 @@ void cuArraysCopyExtract(cuArrays<float> *imagesIn, cuArrays<float> *imagesOut,
const int nthreads = 16;
dim3 threadsperblock(nthreads, nthreads,1);
dim3 blockspergrid(IDIVUP(imagesOut->height,nthreads), IDIVUP(imagesOut->width,nthreads), imagesOut->count);
cuArraysCopyExtractFixedOffset<<<blockspergrid, threadsperblock,0, stream>>>(imagesIn->devData, imagesIn->height, imagesIn->width,
cuArraysCopyExtractFixedOffset<<<blockspergrid, threadsperblock,0, stream>>>(imagesIn->devData, imagesIn->height, imagesIn->width,
imagesOut->devData, imagesOut->height, imagesOut->width, imagesOut->count, offset.x, offset.y);
getLastCudaError("cuArraysCopyExtract error");
}
//
__global__ void cuArraysCopyExtract_C2C_FixedOffset(const float2 *imageIn, const int inNX, const int inNY,
float2 *imageOut, const int outNX, const int outNY, const int nImages,
float2 *imageOut, const int outNX, const int outNY, const int nImages,
const int offsetX, const int offsetY)
{
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outy = threadIdx.y + blockDim.y*blockIdx.y;
if(outx < outNX && outy < outNY)
{
{
int idxOut = (blockIdx.z * outNX + outx)*outNY+outy;
int idxIn = (blockIdx.z*inNX + outx + offsetX)*inNY + outy + offsetY;
int idxIn = (blockIdx.z*inNX + outx + offsetX)*inNY + outy + offsetY;
imageOut[idxOut] = imageIn[idxIn];
}
}
@ -311,27 +342,64 @@ void cuArraysCopyExtract(cuArrays<float2> *imagesIn, cuArrays<float2> *imagesOut
//imagesIn->debuginfo(stream);
//imagesOut->debuginfo(stream);
cuArraysCopyExtract_C2C_FixedOffset<<<blockspergrid, threadsperblock,0, stream>>>
(imagesIn->devData, imagesIn->height, imagesIn->width,
(imagesIn->devData, imagesIn->height, imagesIn->width,
imagesOut->devData, imagesOut->height, imagesOut->width, imagesOut->count, offset.x, offset.y);
getLastCudaError("cuArraysCopyExtractC2C error");
}
//
__global__ void cuArraysCopyExtract_C2R_FixedOffset(const float2 *imageIn, const int inNX, const int inNY,
float *imageOut, const int outNX, const int outNY, const int nImages,
// float3
__global__ void cuArraysCopyExtract_C2C_FixedOffset(const float3 *imageIn, const int inNX, const int inNY,
float3 *imageOut, const int outNX, const int outNY, const int nImages,
const int offsetX, const int offsetY)
{
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outy = threadIdx.y + blockDim.y*blockIdx.y;
if(outx < outNX && outy < outNY)
{
{
int idxOut = (blockIdx.z * outNX + outx)*outNY+outy;
int idxIn = (blockIdx.z*inNX + outx + offsetX)*inNY + outy + offsetY;
int idxIn = (blockIdx.z*inNX + outx + offsetX)*inNY + outy + offsetY;
imageOut[idxOut] = imageIn[idxIn];
}
}
void cuArraysCopyExtract(cuArrays<float3> *imagesIn, cuArrays<float3> *imagesOut, int2 offset, cudaStream_t stream)
{
//assert(imagesIn->height >= imagesOut && inNY >= outNY);
const int nthreads = NTHREADS2D;
dim3 threadsperblock(nthreads, nthreads,1);
dim3 blockspergrid(IDIVUP(imagesOut->height,nthreads), IDIVUP(imagesOut->width,nthreads), imagesOut->count);
//std::cout << "debug copyExtract" << imagesOut->width << imagesOut->height << "\n";
//imagesIn->debuginfo(stream);
//imagesOut->debuginfo(stream);
cuArraysCopyExtract_C2C_FixedOffset<<<blockspergrid, threadsperblock,0, stream>>>
(imagesIn->devData, imagesIn->height, imagesIn->width,
imagesOut->devData, imagesOut->height, imagesOut->width, imagesOut->count, offset.x, offset.y);
getLastCudaError("cuArraysCopyExtractFloat3 error");
}
//
__global__ void cuArraysCopyExtract_C2R_FixedOffset(const float2 *imageIn, const int inNX, const int inNY,
float *imageOut, const int outNX, const int outNY, const int nImages,
const int offsetX, const int offsetY)
{
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outy = threadIdx.y + blockDim.y*blockIdx.y;
if(outx < outNX && outy < outNY)
{
int idxOut = (blockIdx.z * outNX + outx)*outNY+outy;
int idxIn = (blockIdx.z*inNX + outx + offsetX)*inNY + outy + offsetY;
imageOut[idxOut] = imageIn[idxIn].x;
}
}
void cuArraysCopyExtract(cuArrays<float2> *imagesIn, cuArrays<float> *imagesOut, int2 offset, cudaStream_t stream)
{
//assert(imagesIn->height >= imagesOut && inNY >= outNY);
@ -339,16 +407,16 @@ void cuArraysCopyExtract(cuArrays<float2> *imagesIn, cuArrays<float> *imagesOut,
dim3 threadsperblock(nthreads, nthreads,1);
dim3 blockspergrid(IDIVUP(imagesOut->height,nthreads), IDIVUP(imagesOut->width,nthreads), imagesOut->count);
cuArraysCopyExtract_C2R_FixedOffset<<<blockspergrid, threadsperblock,0, stream>>>
(imagesIn->devData, imagesIn->height, imagesIn->width,
(imagesIn->devData, imagesIn->height, imagesIn->width,
imagesOut->devData, imagesOut->height, imagesOut->width, imagesOut->count, offset.x, offset.y);
getLastCudaError("cuArraysCopyExtractC2C error");
}
//
__global__ void cuArraysCopyInsert_kernel(const float2* imageIn, const int inNX, const int inNY,
float2* imageOut, const int outNY, const int offsetX, const int offsetY)
{
int inx = threadIdx.x + blockDim.x*blockIdx.x;
int inx = threadIdx.x + blockDim.x*blockIdx.x;
int iny = threadIdx.y + blockDim.y*blockIdx.y;
if(inx < inNX && iny < inNY) {
int idxOut = IDX2R(inx+offsetX, iny+offsetY, outNY);
@ -363,16 +431,40 @@ void cuArraysCopyInsert(cuArrays<float2> *imageIn, cuArrays<float2> *imageOut, i
const int nthreads = 16;
dim3 threadsperblock(nthreads, nthreads);
dim3 blockspergrid(IDIVUP(imageIn->height,nthreads), IDIVUP(imageIn->width,nthreads));
cuArraysCopyInsert_kernel<<<blockspergrid, threadsperblock,0, stream>>>(imageIn->devData, imageIn->height, imageIn->width,
cuArraysCopyInsert_kernel<<<blockspergrid, threadsperblock,0, stream>>>(imageIn->devData, imageIn->height, imageIn->width,
imageOut->devData, imageOut->width, offsetX, offsetY);
getLastCudaError("cuArraysCopyInsert error");
}
//
// float3
__global__ void cuArraysCopyInsert_kernel(const float3* imageIn, const int inNX, const int inNY,
float3* imageOut, const int outNY, const int offsetX, const int offsetY)
{
int inx = threadIdx.x + blockDim.x*blockIdx.x;
int iny = threadIdx.y + blockDim.y*blockIdx.y;
if(inx < inNX && iny < inNY) {
int idxOut = IDX2R(inx+offsetX, iny+offsetY, outNY);
int idxIn = IDX2R(inx, iny, inNY);
imageOut[idxOut] = make_float3(imageIn[idxIn].x, imageIn[idxIn].y, imageIn[idxIn].z);
}
}
void cuArraysCopyInsert(cuArrays<float3> *imageIn, cuArrays<float3> *imageOut, int offsetX, int offsetY, cudaStream_t stream)
{
const int nthreads = 16;
dim3 threadsperblock(nthreads, nthreads);
dim3 blockspergrid(IDIVUP(imageIn->height,nthreads), IDIVUP(imageIn->width,nthreads));
cuArraysCopyInsert_kernel<<<blockspergrid, threadsperblock,0, stream>>>(imageIn->devData, imageIn->height, imageIn->width,
imageOut->devData, imageOut->width, offsetX, offsetY);
getLastCudaError("cuArraysCopyInsert error");
}
//
__global__ void cuArraysCopyInsert_kernel(const float* imageIn, const int inNX, const int inNY,
float* imageOut, const int outNY, const int offsetX, const int offsetY)
{
int inx = threadIdx.x + blockDim.x*blockIdx.x;
int inx = threadIdx.x + blockDim.x*blockIdx.x;
int iny = threadIdx.y + blockDim.y*blockIdx.y;
if(inx < inNX && iny < inNY) {
int idxOut = IDX2R(inx+offsetX, iny+offsetY, outNY);
@ -387,18 +479,44 @@ void cuArraysCopyInsert(cuArrays<float> *imageIn, cuArrays<float> *imageOut, int
const int nthreads = 16;
dim3 threadsperblock(nthreads, nthreads);
dim3 blockspergrid(IDIVUP(imageIn->height,nthreads), IDIVUP(imageIn->width,nthreads));
cuArraysCopyInsert_kernel<<<blockspergrid, threadsperblock,0, stream>>>(imageIn->devData, imageIn->height, imageIn->width,
cuArraysCopyInsert_kernel<<<blockspergrid, threadsperblock,0, stream>>>(imageIn->devData, imageIn->height, imageIn->width,
imageOut->devData, imageOut->width, offsetX, offsetY);
getLastCudaError("cuArraysCopyInsert Float error");
}
//
__global__ void cuArraysCopyInsert_kernel(const int* imageIn, const int inNX, const int inNY,
int* imageOut, const int outNY, const int offsetX, const int offsetY)
{
int inx = threadIdx.x + blockDim.x*blockIdx.x;
int iny = threadIdx.y + blockDim.y*blockIdx.y;
if(inx < inNX && iny < inNY) {
int idxOut = IDX2R(inx+offsetX, iny+offsetY, outNY);
int idxIn = IDX2R(inx, iny, inNY);
imageOut[idxOut] = imageIn[idxIn];
}
}
void cuArraysCopyInsert(cuArrays<int> *imageIn, cuArrays<int> *imageOut, int offsetX, int offsetY, cudaStream_t stream)
{
const int nthreads = 16;
dim3 threadsperblock(nthreads, nthreads);
dim3 blockspergrid(IDIVUP(imageIn->height,nthreads), IDIVUP(imageIn->width,nthreads));
cuArraysCopyInsert_kernel<<<blockspergrid, threadsperblock,0, stream>>>(imageIn->devData, imageIn->height, imageIn->width,
imageOut->devData, imageOut->width, offsetX, offsetY);
getLastCudaError("cuArraysCopyInsert Integer error");
}
//
__global__ void cuArraysCopyInversePadded_kernel(float *imageIn, int inNX, int inNY, int sizeIn,
float *imageOut, int outNX, int outNY, int sizeOut, int nImages)
{
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outy = threadIdx.y + blockDim.y*blockIdx.y;
if(outx < outNX && outy < outNY)
{
int idxImage = blockIdx.z;
@ -409,27 +527,27 @@ __global__ void cuArraysCopyInversePadded_kernel(float *imageIn, int inNX, int i
}
else
{ imageOut[idxOut] = 0.0f; }
}
}
}
void cuArraysCopyInversePadded(cuArrays<float> *imageIn, cuArrays<float> *imageOut,cudaStream_t stream)
{
const int nthreads = 16;
int nImages = imageIn->count;
int nImages = imageIn->count;
dim3 blockSize(nthreads, nthreads,1);
dim3 gridSize(IDIVUP(imageOut->height,nthreads), IDIVUP(imageOut->width,nthreads), nImages);
cuArraysCopyInversePadded_kernel<<<gridSize, blockSize, 0, stream>>>(imageIn->devData, imageIn->height, imageIn->width, imageIn->size,
imageOut->devData, imageOut->height, imageOut->width, imageOut->size, nImages);
getLastCudaError("cuArraysCopyInversePadded error");
getLastCudaError("cuArraysCopyInversePadded error");
}
__global__ void cuArraysCopyPadded_R2R_kernel(float *imageIn, int inNX, int inNY, int sizeIn,
float *imageOut, int outNX, int outNY, int sizeOut, int nImages)
{
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outy = threadIdx.y + blockDim.y*blockIdx.y;
if(outx < outNX && outy < outNY)
{
int idxImage = blockIdx.z;
@ -440,26 +558,26 @@ __global__ void cuArraysCopyPadded_R2R_kernel(float *imageIn, int inNX, int inNY
}
else
{ imageOut[idxOut] = 0.0f; }
}
}
}
void cuArraysCopyPadded(cuArrays<float> *imageIn, cuArrays<float> *imageOut,cudaStream_t stream)
{
const int nthreads = 16;
int nImages = imageIn->count;
int nImages = imageIn->count;
dim3 blockSize(nthreads, nthreads,1);
dim3 gridSize(IDIVUP(imageOut->height,nthreads), IDIVUP(imageOut->width,nthreads), nImages);
cuArraysCopyPadded_R2R_kernel<<<gridSize, blockSize, 0, stream>>>(imageIn->devData, imageIn->height, imageIn->width, imageIn->size,
imageOut->devData, imageOut->height, imageOut->width, imageOut->size, nImages);
getLastCudaError("cuArraysCopyPaddedR2R error");
getLastCudaError("cuArraysCopyPaddedR2R error");
}
__global__ void cuArraysCopyPadded_C2C_kernel(float2 *imageIn, int inNX, int inNY, int sizeIn,
float2 *imageOut, int outNX, int outNY, int sizeOut, int nImages)
{
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outy = threadIdx.y + blockDim.y*blockIdx.y;
if(outx < outNX && outy < outNY)
{
int idxImage = blockIdx.z;
@ -468,31 +586,31 @@ __global__ void cuArraysCopyPadded_C2C_kernel(float2 *imageIn, int inNX, int inN
int idxIn = IDX2R(outx, outy, inNY)+idxImage*sizeIn;
imageOut[idxOut] = imageIn[idxIn];
}
else{
imageOut[idxOut] = make_float2(0.0f, 0.0f);
else{
imageOut[idxOut] = make_float2(0.0f, 0.0f);
}
}
}
}
void cuArraysCopyPadded(cuArrays<float2> *imageIn, cuArrays<float2> *imageOut,cudaStream_t stream)
{
const int nthreads = NTHREADS2D;
int nImages = imageIn->count;
int nImages = imageIn->count;
dim3 blockSize(nthreads, nthreads,1);
dim3 gridSize(IDIVUP(imageOut->height,nthreads), IDIVUP(imageOut->width,nthreads), nImages);
cuArraysCopyPadded_C2C_kernel<<<gridSize, blockSize, 0, stream>>>
(imageIn->devData, imageIn->height, imageIn->width, imageIn->size,
imageOut->devData, imageOut->height, imageOut->width, imageOut->size, nImages);
getLastCudaError("cuArraysCopyInversePadded error");
getLastCudaError("cuArraysCopyInversePadded error");
}
__global__ void cuArraysCopyPadded_R2C_kernel(float *imageIn, int inNX, int inNY, int sizeIn,
float2 *imageOut, int outNX, int outNY, int sizeOut, int nImages)
{
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outy = threadIdx.y + blockDim.y*blockIdx.y;
if(outx < outNX && outy < outNY)
{
int idxImage = blockIdx.z;
@ -501,42 +619,42 @@ __global__ void cuArraysCopyPadded_R2C_kernel(float *imageIn, int inNX, int inNY
int idxIn = IDX2R(outx, outy, inNY)+idxImage*sizeIn;
imageOut[idxOut] = make_float2(imageIn[idxIn], 0.0f);
}
else{
imageOut[idxOut] = make_float2(0.0f, 0.0f);
else{
imageOut[idxOut] = make_float2(0.0f, 0.0f);
}
}
}
}
void cuArraysCopyPadded(cuArrays<float> *imageIn, cuArrays<float2> *imageOut,cudaStream_t stream)
{
const int nthreads = NTHREADS2D;
int nImages = imageIn->count;
int nImages = imageIn->count;
dim3 blockSize(nthreads, nthreads,1);
dim3 gridSize(IDIVUP(imageOut->height,nthreads), IDIVUP(imageOut->width,nthreads), nImages);
cuArraysCopyPadded_R2C_kernel<<<gridSize, blockSize, 0, stream>>>
(imageIn->devData, imageIn->height, imageIn->width, imageIn->size,
imageOut->devData, imageOut->height, imageOut->width, imageOut->size, nImages);
getLastCudaError("cuArraysCopyPadded error");
getLastCudaError("cuArraysCopyPadded error");
}
__global__ void cuArraysSetConstant_kernel(float *image, int size, float value)
{
int idx = threadIdx.x + blockDim.x*blockIdx.x;
int idx = threadIdx.x + blockDim.x*blockIdx.x;
if(idx < size)
{
image[idx] = value;
}
}
}
void cuArraysSetConstant(cuArrays<float> *imageIn, float value, cudaStream_t stream)
{
const int nthreads = 256;
int size = imageIn->getSize();
int size = imageIn->getSize();
cuArraysSetConstant_kernel<<<IDIVUP(size, nthreads), nthreads, 0, stream>>>
(imageIn->devData, imageIn->size, value);
getLastCudaError("cuArraysCopyPadded error");
getLastCudaError("cuArraysCopyPadded error");
}

8
contrib/PyCuAmpcor/src/cuCorrNormalization.cu
View File

@ -195,7 +195,6 @@ __device__ float2 partialSums(const float v, volatile float* shmem, const int st
return make_float2(Sum, Sum2);
}
__forceinline__ __device__ int __mul(const int a, const int b) { return a*b; }
template<const int Nthreads2>
__global__ void cuCorrNormalize_kernel(
@ -232,7 +231,7 @@ __global__ void cuCorrNormalize_kernel(
templateSum += templateD[i];
}
templateSum = sumReduceBlock<Nthreads>(templateSum, shmem);
__syncthreads();
float templateSum2 = 0.0f;
for (int i = tid; i < templateSize; i += Nthreads)
@ -241,11 +240,12 @@ __global__ void cuCorrNormalize_kernel(
templateSum2 += t*t;
}
templateSum2 = sumReduceBlock<Nthreads>(templateSum2, shmem);
__syncthreads();
//if(tid ==0) printf("template sum %d %g %g \n", imageIdx, templateSum, templateSum2);
/*********/
shmem[tid] = shmem[tid + Nthreads] = 0.0f;
shmem[tid] = shmem[tid + Nthreads] = shmem[tid + 2*Nthreads] = 0.0f;
__syncthreads();
float imageSum = 0.0f;
@ -281,7 +281,7 @@ __global__ void cuCorrNormalize_kernel(
if (tid < resultNY)
{
const int ix = iaddr/imageNY;
const int addr = __mul(ix-templateNX, resultNY);
const int addr = (ix-templateNX)*resultNY;
//printf("test norm %d %d %d %d %f\n", tid, ix, addr, addr+tid, resultD[addr + tid]);

81
contrib/PyCuAmpcor/src/cuEstimateStats.cu
View File

@ -25,7 +25,7 @@ __global__ void cudaKernel_estimateSnr(const float* corrSum, const int* corrVali
float mean = (corrSum[idx] - maxval[idx] * maxval[idx]) / (corrValidCount[idx] - 1);
snrValue[idx] = maxval[idx] / mean;
snrValue[idx] = maxval[idx] * maxval[idx] / mean;
}
void cuEstimateSnr(cuArrays<float> *corrSum, cuArrays<int> *corrValidCount, cuArrays<float> *maxval, cuArrays<float> *snrValue, cudaStream_t stream)
@ -55,7 +55,7 @@ void cuEstimateSnr(cuArrays<float> *corrSum, cuArrays<int> *corrValidCount, cuAr
//for (int i=0; i<size; i++){
// std::cout<<corrSum->hostData[i]<<std::endl;
// std::cout<<corrValidCount->hostData[i]<<std::endl;
@ -68,3 +68,80 @@ void cuEstimateSnr(cuArrays<float> *corrSum, cuArrays<int> *corrValidCount, cuAr
getLastCudaError("cuda kernel estimate stats error\n");
}
template <const int BLOCKSIZE> // number of threads per block.
__global__ void cudaKernel_estimateVar(const float* corrBatchRaw, const int NX, const int NY, const int2* maxloc, const float* maxval, float3* covValue, const int size)
{
// Find image id.
int idxImage = threadIdx.x + blockDim.x*blockIdx.x;
if (idxImage >= size) return;
// Preparation.
int px = maxloc[idxImage].x;
int py = maxloc[idxImage].y;
float peak = maxval[idxImage];
// Check if maxval is on the margin.
if (px-1 < 0 || py-1 <0 || px + 1 >=NX || py+1 >=NY) {
covValue[idxImage] = make_float3(99.0, 99.0, 99.0);
}
else {
int offset = NX * NY * idxImage;
int idx00 = offset + (px - 1) * NY + py - 1;
int idx01 = offset + (px - 1) * NY + py ;
int idx02 = offset + (px - 1) * NY + py + 1;
int idx10 = offset + (px ) * NY + py - 1;
int idx11 = offset + (px ) * NY + py ;
int idx12 = offset + (px ) * NY + py + 1;
int idx20 = offset + (px + 1) * NY + py - 1;
int idx21 = offset + (px + 1) * NY + py ;
int idx22 = offset + (px + 1) * NY + py + 1;
float dxx = - ( corrBatchRaw[idx21] + corrBatchRaw[idx01] - 2*corrBatchRaw[idx11] ) * 0.5;
float dyy = - ( corrBatchRaw[idx12] + corrBatchRaw[idx10] - 2*corrBatchRaw[idx11] ) * 0.5;
float dxy = - ( corrBatchRaw[idx22] + corrBatchRaw[idx00] - corrBatchRaw[idx20] - corrBatchRaw[idx02] ) *0.25;
float n2 = fmaxf(1 - peak, 0.0);
int winSize = NX*NY;
dxx = dxx * winSize;
dyy = dyy * winSize;
dxy = dxy * winSize;
float n4 = n2*n2;
n2 = n2 * 2;
n4 = n4 * 0.5 * winSize;
float u = dxy * dxy - dxx * dyy;
float u2 = u*u;
if (fabsf(u) < 1e-2) {
covValue[idxImage] = make_float3(99.0, 99.0, 99.0);
}
else {
float cov_xx = (- n2 * u * dyy + n4 * ( dyy*dyy + dxy*dxy) ) / u2;
float cov_yy = (- n2 * u * dxx + n4 * ( dxx*dxx + dxy*dxy) ) / u2;
float cov_xy = ( n2 * u * dxy - n4 * ( dxx + dyy ) * dxy ) / u2;
covValue[idxImage] = make_float3(cov_xx, cov_yy, cov_xy);
}
}
}
void cuEstimateVariance(cuArrays<float> *corrBatchRaw, cuArrays<int2> *maxloc, cuArrays<float> *maxval, cuArrays<float3> *covValue, cudaStream_t stream)
{
int size = corrBatchRaw->count;
// One dimensional launching parameters to loop over every correlation surface.
cudaKernel_estimateVar<NTHREADS><<< IDIVUP(size, NTHREADS), NTHREADS, 0, stream>>>
(corrBatchRaw->devData, corrBatchRaw->height, corrBatchRaw->width, maxloc->devData, maxval->devData, covValue->devData, size);
getLastCudaError("cudaKernel_estimateVar error\n");
}

13
contrib/PyCuAmpcor/src/setup.py
View File

@ -7,20 +7,21 @@
from distutils.core import setup
from distutils.extension import Extension
from Cython.Build import cythonize
import os
os.environ["CC"] = "g++"
import numpy
setup( name = 'PyCuAmpcor',
ext_modules = cythonize(Extension(
"PyCuAmpcor",
sources=['PyCuAmpcor.pyx'],
include_dirs=['/usr/local/cuda/include'], # REPLACE WITH YOUR PATH TO YOUR CUDA LIBRARY HEADERS
include_dirs=['/usr/local/cuda/include', numpy.get_include()], # REPLACE WITH YOUR PATH TO YOUR CUDA LIBRARY HEADERS
extra_compile_args=['-fPIC','-fpermissive'],
extra_objects=['SlcImage.o','cuAmpcorChunk.o','cuAmpcorParameter.o','cuCorrFrequency.o',
extra_objects=['GDALImage.o','cuAmpcorChunk.o','cuAmpcorParameter.o','cuCorrFrequency.o',
'cuCorrNormalization.o','cuCorrTimeDomain.o','cuArraysCopy.o',
'cuArrays.o','cuArraysPadding.o','cuOffset.o','cuOverSampler.o',
'cuSincOverSampler.o', 'cuDeramp.o','cuAmpcorController.o'],
extra_link_args=['-L/usr/local/cuda/lib64','-lcuda','-lcudart','-lcufft','-lcublas'], # REPLACE FIRST PATH WITH YOUR PATH TO YOUR CUDA LIBRARIES
'cuSincOverSampler.o', 'cuDeramp.o','cuAmpcorController.o','cuEstimateStats.o'],
extra_link_args=['-L/usr/local/cuda/lib64',
'-L/usr/lib64/nvidia',
'-lcuda','-lcudart','-lcufft','-lcublas','-lgdal'], # REPLACE FIRST PATH WITH YOUR PATH TO YOUR CUDA LIBRARIES
language='c++'
)))

3
contrib/SConscript
View File

@ -78,3 +78,6 @@ SConscript(rfi)
SConscript('PyCuAmpcor/SConscript')
SConscript('splitSpectrum/SConscript')
SConscript('alos2proc/SConscript')
if os.path.exists('geo_autoRIFT'):
SConscript('geo_autoRIFT/SConscript')

7
contrib/demUtils/demstitcher/DemStitcher.py
View File

@ -43,8 +43,7 @@ import os
import sys
import math
import urllib.request, urllib.parse, urllib.error
import logging
import logging.config
from isce import logging
from iscesys.Component.Component import Component
import xml.etree.ElementTree as ET
@ -1013,10 +1012,6 @@ class DemStitcher(Component):
# logger not defined until baseclass is called
if not self.logger:
logging.config.fileConfig(
os.path.join(os.environ['ISCE_HOME'], 'defaults',
'logging', 'logging.conf')
)
self.logger = logging.getLogger('isce.contrib.demUtils.DemStitcher')
url = property(getUrl,setUrl)

6
contrib/demUtils/demstitcher/DemStitcherV3.py
View File

@ -39,8 +39,7 @@ from ctypes import cdll
import os
import sys
import urllib.request, urllib.error, urllib.parse
import logging
import logging.config
from isce import logging
from iscesys.Component.Component import Component
from contrib.demUtils.DemStitcher import DemStitcher as DS
#Parameters definitions
@ -291,7 +290,4 @@ class DemStitcher(DS):
#it's /srtm/version2_1/SRTM(1,3)
self._remove = ['.jpg','.xml']
if not self.logger:
logging.config.fileConfig(
os.environ['ISCE_HOME'] + '/library/applications/logging.conf'
)
self.logger = logging.getLogger('isce.contrib.demUtils.DemStitcherV3')

6
contrib/demUtils/swbdstitcher/SWBDStitcher.py
View File

@ -39,9 +39,8 @@ from ctypes import cdll
import numpy as np
import os
import sys
import logging
from isce import logging
import math
import logging.config
import urllib.request, urllib.parse, urllib.error
from iscesys.Component.Component import Component
from contrib.demUtils.DemStitcher import DemStitcher
@ -315,9 +314,6 @@ class SWBDStitcher(DemStitcher):
#it's /srtm/version2_1/SRTM(1,3)
self._remove = ['.jpg','.xml']
if not self.logger:
logging.config.fileConfig(
os.environ['ISCE_HOME'] + '/library/applications/logging.conf'
)
self.logger = logging.getLogger('isce.contrib.demUtils.SWBDStitcher')
self.parameter_list = self.parameter_list + super(DemStitcher,self).parameter_list

7
contrib/demUtils/watermask/WaterMask.py
View File

@ -35,8 +35,7 @@ import sys
import math
from html.parser import HTMLParser
import urllib.request, urllib.parse, urllib.error
import logging
import logging.config
from isce import logging
from iscesys.Component.Component import Component
import zipfile
import os
@ -979,10 +978,6 @@ class MaskStitcher(Component):
# logger not defined until baseclass is called
if not self.logger:
logging.config.fileConfig(
os.path.join(os.environ['ISCE_HOME'], 'defaults',
'logging', 'logging.conf')
)
self.logger = logging.getLogger('isce.contrib.demUtils.MaskStitcher')
utl = property(getUrl,setUrl)

5
contrib/issi/applications/ISSI.py
View File

@ -32,10 +32,7 @@
import os
import math
import logging
import logging.config
logging.config.fileConfig(os.path.join(os.environ['ISCE_HOME'], 'defaults',
'logging', 'logging.conf'))
from isce import logging
import isce
from iscesys.Component.FactoryInit import FactoryInit

17
contrib/stack/INSTALL.txt
View File

@ -1,17 +0,0 @@
To use the TOPS or Stripmap stack processors you need to:
1- Install ISCE as usual
2- Depending on which stack processor you need to try, add the path of the folder containing the python scripts to your $PATH environment variable as follows:
- to use the topsStack for processing a stack of Sentinel-1 tops data add the full path of your "contrib/stack/topsStack" to your $PATH environemnt variable
- to use the stripmapStack for processing a stack of Stripmap data, add the full path of your "contrib/stack/stripmapStack" to your $PATH environemnt variableu
NOTE:
The stack processors do not show up in the install directory of your isce software. They can be found in the isce source directory.
Important Note:
There might be conflicts between topsStack and stripmapStack scripts (due to comman names of different scripts). Therefore users MUST only have the path of one stack processor in their $PATH environment at a time, to avoid conflicts between the two stack processors.

34
contrib/stack/README.md Normal file
View File

@ -0,0 +1,34 @@
## Stack Processors
Read the document for each stack processor for details.
+ [stripmapStack](./stripmapStack/README.md)
+ [topsStack](./topsStack/README.md)
### Installation
To use the TOPS or Stripmap stack processors you need to:
1. Install ISCE as usual
2. Depending on which stack processor you need to try, add the path of the folder containing the python scripts to your `$PATH` environment variable as follows:
- add the full path of your **contrib/stack/topsStack** to `$PATH` to use the topsStack for processing a stack of Sentinel-1 TOPS data
- add the full path of your **contrib/stack/stripmapStack** to `$PATH` to use the stripmapStack for processing a stack of StripMap data
Note: The stack processors do not show up in the install directory of your isce software. They can be found in the isce source directory.
#### Important Note: ####
There might be conflicts between topsStack and stripmapStack scripts (due to comman names of different scripts). Therefore users **MUST only** have the path of **one stack processor in their $PATH environment at a time**, to avoid conflicts between the two stack processors.
### References
Users who use the stack processors may refer to the following literatures:
For StripMap stack processor and ionospheric phase estimation:
+ H. Fattahi, M. Simons, and P. Agram, "InSAR Time-Series Estimation of the Ionospheric Phase Delay: An Extension of the Split Range-Spectrum Technique", IEEE Trans. Geosci. Remote Sens., vol. 55, no. 10, 5984-5996, 2017. (https://ieeexplore.ieee.org/abstract/document/7987747/)
For TOPS stack processing:
+ H. Fattahi, P. Agram, and M. Simons, “A network-based enhanced spectral diversity approach for TOPS time-series analysis,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 2, pp. 777–786, Feb. 2017. (https://ieeexplore.ieee.org/abstract/document/7637021/)

64
contrib/stack/README_stripmapStack.txt
View File

@ -1,64 +0,0 @@
The detailed algorithms for stack processing of stripmap data can be found here:
H. Fattahi, M. Simons, and P. Agram, "InSAR Time-Series Estimation of the Ionospheric Phase Delay: An Extension of the Split Range-Spectrum Technique", IEEE Trans. Geosci. Remote Sens., vol. 55, no. 10, 5984-5996, 2017. (https://ieeexplore.ieee.org/abstract/document/7987747/)
-----------------------------------
Notes on stripmap stack processor:
Here are some notes to get started with processing stacks of stripmap data with ISCE.
1- create a folder somewhere for your project
mkdir MauleT111
cd MauleT111
2- create a DEM:
dem.py -a stitch -b -37 -31 -72 -69 -r -s 1 -c
3- Keep only ".dem.wgs84", ".dem.wgs84.vrt" and ".dem.wgs84.xml" and remove unnecessary files
4- fix the path of the file in the xml file of the DEM by using this command:
fixImageXml.py -f -i demLat_S37_S31_Lon_W072_W069.dem.wgs84
5- create a folder to download the ALOS-1 data from ASF:
mkdir download
cd download
6- Download the data that that you want to process to the downlowd directory.
7- once all data have been downloaded, we need to unzip them and move them to different folders and getting ready for unpacking and then SLC generation.
This can be done by running the following command in a directory above "download":
prepRawALOS.py -i download/ -o SLC
This command generates an empty SLC folder and a run file called: "run_unPackALOS".
You could also run prepRawSensor.py which aims to recognize the sensor data automatically followed by running the sensor specific preparation script. For now we include support for ALOS and CSK raw data, but it is trivial to expand and include other sensors as unpacking routines are already included in the distribution.
prepRawSensor.py -i download/ -o SLC
8- execute the commands inside run_unPackALOS file. If you have a cluster that you can submit jobs, you can submit each line of command to a processor. The commands are independent and can be run in parallel.
9- After successfully running the previous step, you should see acquisition dates in the SLC folder and the ".raw" files for each acquisition
Note: For ALOS-1, If there is an acquisition that does not include .raw file, this is most likely due to PRF change between frames and can not be currently handled by ISCE. You have to ignore those.
10- run stackStripmap.py which will generate many config and run files that need to be executed. Here is an example:
stackStripMap.py -s SLC/ -d demLat_S37_S31_Lon_W072_W069.dem.wgs84 -t 250 -b 1000 -a 14 -r 4 -u snaphu
This will produce:
a) baseline folder, which contains baseline information
b) pairs.png which is a baseline-time plot of the network of interferograms
c) configs: which contains the configuration parameter to run different InSAR processing steps
d) run_files: a folder that includes several run and job files that needs to be run in order
11- execute the commands in run files (run_1, run_2, etc) in the run_files folder

117
contrib/stack/README_topsStack.txt
View File

@ -1,117 +0,0 @@
The detailed algorithm for stack processing of TOPS data can be find here:
H. Fattahi, P. Agram, and M. Simons, “A network-based enhanced spectral diversity approach for TOPS time-series analysis,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 2, pp. 777–786, Feb. 2017. (https://ieeexplore.ieee.org/abstract/document/7637021/)
<<<<<< Sentinel-1 TOPS stack processor >>>>>>
To use the sentinel stack processor, make sure to add the path of your "contrib/stack/topsStack" folder to your $PATH environment varibale.
The scripts provides support for Sentinel-1 TOPS stack processing. Currently supported workflows include a coregistered stack of SLC, interferograms, offsets, and coherence.
stackSentinel.py generates all configuration and run files required to be executed on a stack of Sentinel-1 TOPS data. When stackSentinel.py is executed for a given workflow (-W option) a “configs” and “run_files” folder is generated. No processing is performed at this stage. Within the “run_files” folder different run_#_description files are contained which are to be executed as shell scripts in the run number order. Each of these run scripts call specific configure files contained in the “configs” folder which call ISCE in a modular fashion. The configure and run files will change depending on the selected workflow. To make run_# files executable, change the file permission accordingly (e.g., chmod +x run_1_unpack_slc).
To see workflow examples, type “stackSentinel.py -H”
To get an overview of all the configurable parameters, type “stackSentinel.py -h”
Required parameters of stackSentinel.py include:
-s SLC_DIRNAME A folder with downloaded Sentinel-1 SLC’s.
-o ORBIT_DIRNAME A folder containing the Sentinel-1 orbits.
Missing orbit files will be downloaded automatically
-a AUX_DIRNAME A folder containing the Sentinel-1 Auxiliary files
-d DEM A DEM (Digital Elevation Model) referenced to wgs84
In the following, different workflow examples are provided. Note that stackSentinel.py only generates the run and configure files. To perform the actual processing, the user will need to execute each run file in their numbered order.
In all workflows, coregistration (-C option) can be done using only geometry (set option = geometry) or with geometry plus refined azimuth offsets through NESD (set option = NESD) approach, the latter being the default. For the NESD coregistrstion the user can control the ESD coherence threshold (-e option) and the number of overlap interferograms (-O) to be used in NESD estimation.
------------------------------ Example 1: Coregistered stack of SLC ----------------------------
Generate the run and configure files needed to generate a coregistered stack of SLCs.
In this example, a pre-defined bounding box is specified. Note, if the bounding box is not provided it is set by default to the common SLC area among all SLCs. We recommend that user always set the processing bounding box. Since ESA does not have a fixed frame definition, we suggest to download data for a larger bounding box compared to the actual bounding box used in stackSentinel.py. This way user can ensure to have required data to cover the region of interest. Here is an example command to create configuration files for a stack of SLCs:
stackSentinel.py -s ../SLC/ -d ../DEM/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -a ../../AuxDir/ -o ../../Orbits -b '19 20 -99.5 -98.5' -W slc
by running the command above, the configs and run_files folders are created. User needs to execute each run file in order. The order is specified by the index number of the run file name. For the example above, the run_files folder includes the following files:
- run_1_unpack_slc_topo_master
- run_2_average_baseline
- run_3_extract_burst_overlaps
- run_4_overlap_geo2rdr_resample
- run_5_pairs_misreg
- run_6_timeseries_misreg
- run_7_geo2rdr_resample
- run_8_extract_stack_valid_region
- run_9_merge
- run_10_grid_baseline
The generated run files are self descriptive. Below is a short explanation on what each run_file does:
***run_1_unpack_slc_topo_master:***
Includes commands to unpack Sentinel-1 TOPS SLCs using ISCE readers. For older SLCs which need antenna elevation pattern correction, the file is extracted and written to disk. For newer version of SLCs which don’t need the elevation antenna pattern correction, only a gdal virtual “vrt” file (and isce xml file) is generated. The “.vrt” file points to the Sentinel SLC file and reads them whenever required during the processing. If a user wants to write the “.vrt” SLC file to disk, it can be done easily using gdal_translate (e.g. gdal_translate –of ENVI File.vrt File.slc).
The “run_1_unpack_slc_topo_master” also includes a command that refers to the config file of the stack master, which includes configuration for running topo for the stack master. Note that in the pair-wise processing strategy one should run topo (mapping from range-Doppler to geo coordinate) for all pairs. However, with stackSentinel, topo needs to be run only one time for the master in the stack.
***run_2_average_baseline: ***
Computes average baseline for the stack. These baselines are not used for processing anywhere. They are only an approximation and can be used for plotting purposes. A more precise baseline grid is estimated later in run_10.
***run_3_extract_burst_overlaps: ***
Burst overlaps are extracted for estimating azimuth misregistration using NESD technique. If coregistration method is chosen to be “geometry”, then this run file won’t exist and the overlaps are not extracted.
***run_4_overlap_geo2rdr_resample: ***
Running geo2rdr to estimate geometrical offsets between slave burst overlaps and the stack master burst overlaps. The slave burst overlaps are then resampled to the stack master burst overlaps.
***run_5_pairs_misreg: ***
Using the coregistered stack burst overlaps generated from the previous step, differential overlap interferograms are generated and are used for estimating azimuth misregistration using Enhanced Spectral Diversity (ESD) technique.
***run_6_timeseries_misreg: ***
A time-series of azimuth and range misregistration is estimated with respect to the stack master. The time-series is a least squares esatimation from the pair misregistration from the previous step.
***run_7_geo2rdr_resample: ***
Using orbit and DEM, geometrical offsets among all slave SLCs and the stack master is computed. The goometrical offsets, together with the misregistration time-series (from previous step) are used for precise coregistration of each burst SLC.
***run_8_extract_stack_valid_region: ***
The valid region between burst SLCs at the overlap area of the bursts slightly changes for different acquisitions. Therefore we need to keep track of these overlaps which will be used during merging bursts. Without these knowledge, lines of invalid data may appear in the merged products at the burst overlaps.
***run_9_merge: ***
Merges all bursts for the master and coregistered SLCs. The geometry files are also merged including longitude, latitude, shadow and layer mask, line-of-sight files, etc. .
***run_10_grid_baseline: ***
A coarse grid of baselines between each slave SLC and the stack master is generated. This is not used in any computation.
-------- Example 2: Coregistered stack of SLC with modified parameters -----------
In the following example, the same stack generation is requested but where the threshold of the default coregistration approach (NESD) is relaxed from its default 0.85 value 0.7.
stackSentinel.py -s ../SLC/ -d ../DEM/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -a ../../AuxDir/ -o ../../Orbits -b '19 20 -99.5 -98.5' -W slc -e 0.7
When running all the run files, the final products are located in the merge folder which has subdirectories “geom_master”, “baselines” and “SLC”. The “geom_master” folder contains geometry products such as longitude, latitude, height, local incidence angle, look angle, heading, and shadowing/layover mask files. The “baselines” folder contains sparse grids of the perpendicular baseline for each acquisition, while the “SLC” folder contains the coregistered SLCs
------------------------------ Example 3: Stack of interferograms ------------------------------
Generate the run and configure files needed to generate a stack of interferograms.
In this example, a stack of interferograms is requested for which up to 2 nearest neighbor connections are included.
stackSentinel.py -s ../SLC/ -d ../../MexicoCity/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -b '19 20 -99.5 -98.5' -a ../../AuxDir/ -o ../../Orbits -c 2
In the following example, all possible interferograms are being generated and in which the coregistration approach is set to use geometry and not the default NESD.
stackSentinel.py -s ../SLC/ -d ../../MexicoCity/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -b '19 20 -99.5 -98.5' -a ../../AuxDir/ -o ../../Orbits -C geometry -c all
When executing all the run files, a coregistered stack of slcs are produced, the burst interferograms are generated and then merged. Merged interferograms are multilooked, filtered and unwrapped. Geocoding is not applied. If users need to geocode any product, they can use the geocodeGdal.py script.
-------------------- Example 4: Correlation stack example ----------------------------
Generate the run and configure files needed to generate a stack of coherence.
In this example, a correlation stack is requested considering all possible coherence pairs and where the coregistration approach is done using geometry only.
stackSentinel.py -s ../SLC/ -d ../../MexicoCity/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -b '19 20 -99.5 -98.5' -a ../../AuxDir/ -o ../../Orbits -C geometry -c all -W correlation
This workflow is basically similar to the previous one. The difference is that the interferograms are not unwrapped.
----------------------------------- DEM download example -----------------------------------
Download of DEM (need to use wgs84 version) using the ISCE DEM download script.
dem.py -a stitch -b 18 20 -100 -97 -r -s 1 –c
Updating DEM’s wgs84 xml to include full path to the DEM
fixImageXml.py -f -i demLat_N18_N20_Lon_W100_W097.dem.wgs84

11
contrib/stack/References.txt
View File

@ -1,11 +0,0 @@
Users who use the stack processors may refer to the following literatures:
for stripmap stack processor and ionospheric phase estimation:
H. Fattahi, M. Simons, and P. Agram, "InSAR Time-Series Estimation of the Ionospheric Phase Delay: An Extension of the Split Range-Spectrum Technique", IEEE Trans. Geosci. Remote Sens., vol. 55, no. 10, 5984-5996, 2017. (https://ieeexplore.ieee.org/abstract/document/7987747/)
For TOPS stack processing:
H. Fattahi, P. Agram, and M. Simons, “A network-based enhanced spectral diversity approach for TOPS time-series analysis,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 2, pp. 777–786, Feb. 2017. (https://ieeexplore.ieee.org/abstract/document/7637021/)

85
contrib/stack/stripmapStack/README.md Normal file
View File

@ -0,0 +1,85 @@
## StripMap stack processor
The detailed algorithms and workflow for stack processing of stripmap SAR data can be found here:
+ Fattahi, H., M. Simons, and P. Agram (2017), InSAR Time-Series Estimation of the Ionospheric Phase Delay: An Extension of the Split Range-Spectrum Technique, IEEE Transactions on Geoscience and Remote Sensing, 55(10), 5984-5996, doi:[10.1109/TGRS.2017.2718566](https://ieeexplore.ieee.org/abstract/document/7987747/).
-----------------------------------
To use the stripmap stack processor, make sure to add the path of your `contrib/stack/stripmapStack` folder to your `$PATH` environment varibale.
Currently supported workflows include a coregistered stack of SLC, interferograms, ionospheric delays.
Here are some notes to get started with processing stacks of stripmap data with ISCE.
#### 1. Create your project folder somewhere
```
mkdir MauleAlosDT111
cd MauleAlosDT111
```
#### 2. Prepare DEM
a) create a folder for DEM;
b) create a DEM using dem.py with SNWE of your study area in integer;
d) Keep only ".dem.wgs84", ".dem.wgs84.vrt" and ".dem.wgs84.xml" and remove unnecessary files;
d) fix the path of the file in the xml file of the DEM by using fixImageXml.py.
```
mkdir DEM; cd DEM
dem.py -a stitch -b -37 -31 -72 -69 -r -s 1 -c
rm demLat*.dem demLat*.dem.xml demLat*.dem.vrt
fixImageXml.py -f -i demLat*.dem.wgs84
cd ..
```
#### 3. Download data
##### 3.1 create a folder to download SAR data (i.e. ALOS-1 data from ASF)
```
mkdir download
cd download
```
##### 3.2 Download the data that that you want to process to the "downlowd" directory
#### 4. Prepare SAR data
Once all data have been downloaded, we need to unzip them and move them to different folders and getting ready for unpacking and then SLC generation. This can be done by running the following command in a directory above "download":
```
prepRawALOS.py -i download/ -o SLC
```
This command generates an empty SLC folder and a run file called: "run_unPackALOS".
You could also run prepRawSensor.py which aims to recognize the sensor data automatically followed by running the sensor specific preparation script. For now we include support for ALOS and CSK raw data, but it is trivial to expand and include other sensors as unpacking routines are already included in the distribution.
```
prepRawSensor.py -i download/ -o SLC
```
#### 5. Execute the commands in "run_unPackALOS" file
If you have a cluster that you can submit jobs, you can submit each line of command to a processor. The commands are independent and can be run in parallel.
After successfully running the previous step, you should see acquisition dates in the SLC folder and the ".raw" files for each acquisition.
Note: For ALOS-1, If there is an acquisition that does not include .raw file, this is most likely due to PRF change between frames and can not be currently handled by ISCE. You have to ignore those.
#### 6. Run "stackStripmap.py"
This will generate many config and run files that need to be executed. Here is an example:
```
stackStripMap.py -s SLC/ -d DEM/demLat*.dem.wgs84 -t 250 -b 1000 -a 14 -r 4 -u snaphu
```
This will produce:
a) baseline folder, which contains baseline information
b) pairs.png which is a baseline-time plot of the network of interferograms
c) configs: which contains the configuration parameter to run different InSAR processing steps
d) run_files: a folder that includes several run and job files that needs to be run in order
#### 7. Execute the commands in run files (run_1*, run_2*, etc) in the "run_files" folder

64
contrib/stack/stripmapStack/README.txt
View File

@ -1,64 +0,0 @@
The detailed algorithms for stack processing of stripmap data can be found here:
H. Fattahi, M. Simons, and P. Agram, "InSAR Time-Series Estimation of the Ionospheric Phase Delay: An Extension of the Split Range-Spectrum Technique", IEEE Trans. Geosci. Remote Sens., vol. 55, no. 10, 5984-5996, 2017. (https://ieeexplore.ieee.org/abstract/document/7987747/)
-----------------------------------
Notes on stripmap stack processor:
Here are some notes to get started with processing stacks of stripmap data with ISCE.
1- create a folder somewhere for your project
mkdir MauleT111
cd MauleT111
2- create a DEM:
dem.py -a stitch -b -37 -31 -72 -69 -r -s 1 -c
3- Keep only ".dem.wgs84", ".dem.wgs84.vrt" and ".dem.wgs84.xml" and remove unnecessary files
4- fix the path of the file in the xml file of the DEM by using this command:
fixImageXml.py -f -i demLat_S37_S31_Lon_W072_W069.dem.wgs84
5- create a folder to download the ALOS-1 data from ASF:
mkdir download
cd download
6- Download the data that that you want to process to the downlowd directory.
7- once all data have been downloaded, we need to unzip them and move them to different folders and getting ready for unpacking and then SLC generation.
This can be done by running the following command in a directory above "download":
prepRawALOS.py -i download/ -o SLC
This command generates an empty SLC folder and a run file called: "run_unPackALOS".
You could also run prepRawSensor.py which aims to recognize the sensor data automatically followed by running the sensor specific preparation script. For now we include support for ALOS and CSK raw data, but it is trivial to expand and include other sensors as unpacking routines are already included in the distribution.
prepRawSensor.py -i download/ -o SLC
8- execute the commands inside run_unPackALOS file. If you have a cluster that you can submit jobs, you can submit each line of command to a processor. The commands are independent and can be run in parallel.
9- After successfully running the previous step, you should see acquisition dates in the SLC folder and the ".raw" files for each acquisition
Note: For ALOS-1, If there is an acquisition that does not include .raw file, this is most likely due to PRF change between frames and can not be currently handled by ISCE. You have to ignore those.
10- run stackStripmap.py which will generate many config and run files that need to be executed. Here is an example:
stackStripMap.py -s SLC/ -d demLat_S37_S31_Lon_W072_W069.dem.wgs84 -t 250 -b 1000 -a 14 -r 4 -u snaphu
This will produce:
a) baseline folder, which contains baseline information
b) pairs.png which is a baseline-time plot of the network of interferograms
c) configs: which contains the configuration parameter to run different InSAR processing steps
d) run_files: a folder that includes several run and job files that needs to be run in order
11- execute the commands in run files (run_1, run_2, etc) in the run_files folder

38
contrib/stack/stripmapStack/Stack.py
View File

@ -65,6 +65,8 @@ class config(object):
self.f.write('master : ' + self.slcDir +'\n')
self.f.write('dem : ' + self.dem +'\n')
self.f.write('output : ' + self.geometryDir +'\n')
self.f.write('alks : ' + self.alks +'\n')
self.f.write('rlks : ' + self.rlks +'\n')
if self.nativeDoppler:
self.f.write('native : True\n')
if self.useGPU:
@ -72,7 +74,18 @@ class config(object):
else:
self.f.write('useGPU : False\n')
self.f.write('##########################'+'\n')
def createWaterMask(self, function):
self.f.write('##########################'+'\n')
self.f.write(function+'\n')
self.f.write('createWaterMask : '+'\n')
self.f.write('dem_file : ' + self.dem +'\n')
self.f.write('lat_file : ' + self.latFile +'\n')
self.f.write('lon_file : ' + self.lonFile +'\n')
self.f.write('output : ' + self.waterMaskFile + '\n')
self.f.write('##########################'+'\n')
def geo2rdr(self, function):
self.f.write('##########################'+'\n')
@ -197,6 +210,8 @@ class config(object):
self.f.write('nomcf : ' + self.noMCF + '\n')
self.f.write('master : ' + self.master + '\n')
self.f.write('defomax : ' + self.defoMax + '\n')
self.f.write('alks : ' + self.alks + '\n')
self.f.write('rlks : ' + self.rlks + '\n')
self.f.write('method : ' + self.unwMethod + '\n')
self.f.write('##########################'+'\n')
@ -307,8 +322,7 @@ class run(object):
self.runf.write(self.text_cmd+'stripmapWrapper.py -c '+ configName+'\n')
def master_focus_split_geometry(self, stackMaster, config_prefix, split=False, focus=True, native=True):
########
# focusing master and producing geometry files
"""focusing master and producing geometry files"""
configName = os.path.join(self.configDir, config_prefix + stackMaster)
configObj = config(configName)
configObj.configure(self)
@ -325,11 +339,19 @@ class run(object):
counter += 1
if split:
configObj.slc = os.path.join(configObj.slcDir,stackMaster+self.raw_string+'.slc')
configObj.outDir = configObj.slcDir
configObj.shelve = os.path.join(configObj.slcDir, 'data')
configObj.splitRangeSpectrum('[Function-{0}]'.format(counter))
configObj.slc = os.path.join(configObj.slcDir,stackMaster+self.raw_string+'.slc')
configObj.outDir = configObj.slcDir
configObj.shelve = os.path.join(configObj.slcDir, 'data')
configObj.splitRangeSpectrum('[Function-{0}]'.format(counter))
counter += 1
# generate water mask in radar coordinates
configObj.latFile = os.path.join(self.workDir, 'geom_master/lat.rdr')
configObj.lonFile = os.path.join(self.workDir, 'geom_master/lon.rdr')
configObj.waterMaskFile = os.path.join(self.workDir, 'geom_master/waterMask.rdr')
configObj.createWaterMask('[Function-{0}]'.format(counter))
counter += 1
configObj.finalize()
del configObj
self.runf.write(self.text_cmd+'stripmapWrapper.py -c '+ configName+'\n')

95
contrib/stack/stripmapStack/createWaterMask.py
View File

@ -2,30 +2,45 @@
#Author: Heresh Fattahi
import isce
import isceobj
from contrib.demUtils.SWBDStitcher import SWBDStitcher
from iscesys.DataManager import createManager
import os
import argparse
import configparser
from numpy import round
import numpy as np
import isce
import isceobj
from iscesys.DataManager import createManager
from contrib.demUtils.SWBDStitcher import SWBDStitcher
EXAMPLE = """example:
createWaterMask.py -b 31 33 130 132
createWaterMask.py -b 31 33 130 132 -l lat.rdr -L lon.rdr -o waterMask.rdr
createWaterMask.py -d ../DEM/demLat_N31_N33_Lon_E130_E132.dem.wgs84 -l lat.rdr -L lon.rdr -o waterMask.rdr
"""
def createParser():
'''
Create command line parser.
'''
parser = argparse.ArgumentParser( description='extracts the overlap geometry between master bursts')
# parser.add_argument('-b', '--bbox', dest='bbox', type=str, default=None,
# help='Lat/Lon Bounding SNWE')
parser.add_argument('-b', '--bbox', type = int, default = None, nargs = '+', dest = 'bbox', help = 'Defines the spatial region in the format south north west east.\
The values should be integers from (-90,90) for latitudes and (0,360) or (-180,180) for longitudes.')
parser = argparse.ArgumentParser(description='Create water body mask in geo and/or radar coordinates',
formatter_class=argparse.RawTextHelpFormatter,
epilog=EXAMPLE)
parser.add_argument('-b', '--bbox', dest='bbox', type=int, default=None, nargs=4, metavar=('S','N','W','E'),
help = 'Defines the spatial region in the format south north west east.\n'
'The values should be integers from (-90,90) for latitudes '
'and (0,360) or (-180,180) for longitudes.')
parser.add_argument('-d','--dem_file', dest='demName', type=str, default=None,
help='DEM file in geo coordinates, i.e. demLat*.dem.wgs84.')
parser.add_argument('-l', '--lat_file', dest='latName', type=str, default=None,
help='pixel by pixel lat file in radar coordinate')
parser.add_argument('-L', '--lon_file', dest='lonName', type=str, default=None,
help='pixel by pixel lat file in radar coordinate')
parser.add_argument('-o', '--output', dest='outfile', type=str,
help='output filename of water mask in radar coordinates')
return parser
def cmdLineParse(iargs = None):
'''
Command line parser.
@ -33,37 +48,69 @@ def cmdLineParse(iargs = None):
parser = createParser()
inps = parser.parse_args(args=iargs)
#inps.bbox = [int(round(val)) for val in inps.bbox.split()]
if not inps.bbox and not inps.demName:
parser.print_usage()
raise SystemExit('ERROR: no --bbox/--dem_file input, at least one is required.')
if not inps.outfile and (inps.latName and inps.lonName):
inps.outfile = os.path.join(os.path.dirname(inps.latName), 'waterMask.rdr')
return inps
def download_waterMask(inps):
def dem2bbox(dem_file):
"""Grab bbox from DEM file in geo coordinates"""
demImage = isceobj.createDemImage()
demImage.load(dem_file + '.xml')
demImage.setAccessMode('read')
N = demImage.getFirstLatitude()
W = demImage.getFirstLongitude()
S = N + demImage.getDeltaLatitude() * demImage.getLength()
E = W + demImage.getDeltaLongitude() * demImage.getWidth()
bbox = [np.floor(S).astype(int), np.ceil(N).astype(int),
np.floor(W).astype(int), np.ceil(E).astype(int)]
return bbox
def download_waterMask(bbox, dem_file):
out_dir = os.getcwd()
# update out_dir and/or bbox if dem_file is input
if dem_file:
out_dir = os.path.dirname(dem_file)
if not bbox:
bbox = dem2bbox(dem_file)
sw = createManager('wbd')
sw.configure()
inps.waterBodyGeo = sw.defaultName(inps.bbox)
#inps.waterBodyGeo = sw.defaultName(inps.bbox)
sw.outputFile = os.path.join(out_dir, sw.defaultName(bbox))
sw._noFilling = False
#sw._fillingValue = -1.0
sw._fillingValue = 0.0
sw.stitch(inps.bbox[0:2],inps.bbox[2:])
sw._fillingValue = -1.0 #fill pixels without DEM data with value of -1, same as water body
#sw._fillingValue = 0.0
sw.stitch(bbox[0:2], bbox[2:])
return sw.outputFile
return inps
def geo2radar(inps):
inps.waterBodyRadar = inps.waterBodyGeo + '.rdr'
def geo2radar(geo_file, rdr_file, lat_file, lon_file):
#inps.waterBodyRadar = inps.waterBodyGeo + '.rdr'
sw = SWBDStitcher()
sw.toRadar(inps.waterBodyGeo, inps.latName, inps.lonName, inps.waterBodyRadar)
sw.toRadar(geo_file, lat_file, lon_file, rdr_file)
return rdr_file
#looks.py -i watermask.msk -r 4 -a 14 -o 'waterMask.14alks_4rlks.msk'
#imageMath.py -e='a*b' --a=filt_20100911_20101027.int --b=watermask.14alks_4rlks.msk -o filt_20100911_20101027_masked.int -t cfloat -s BIL
def main(iargs=None):
inps = cmdLineParse(iargs)
inps = download_waterMask(inps)
if inps.latName and inps.lonName:
inps = geo2radar(inps)
inps = cmdLineParse(iargs)
geo_file = download_waterMask(inps.bbox, inps.demName)
if inps.latName and inps.lonName:
geo2radar(geo_file, inps.outfile, inps.latName, inps.lonName)
return
if __name__ == '__main__' :
'''

65
contrib/stack/stripmapStack/stackStripMap.py
View File

@ -5,7 +5,7 @@
import os, imp, sys, glob
import argparse
import configparser
import datetime
import datetime
import numpy as np
import shelve
import isce
@ -20,7 +20,7 @@ defoMax = '2'
maxNodes = 72
def createParser():
parser = argparse.ArgumentParser( description='Preparing the directory structure and config files for stack processing of Sentinel data')
parser = argparse.ArgumentParser( description='Preparing the directory structure and config files for stack processing of StripMap data')
parser.add_argument('-s', '--slc_directory', dest='slcDir', type=str, required=True,
help='Directory with all stripmap SLCs')
@ -31,7 +31,7 @@ def createParser():
help='Working directory ')
parser.add_argument('-d', '--dem', dest='dem', type=str, required=True,
help='Directory with the DEM (.xml and .vrt files)')
help='DEM file (with .xml and .vrt files)')
parser.add_argument('-m', '--master_date', dest='masterDate', type=str, default=None,
help='Directory with master acquisition')
@ -43,47 +43,54 @@ def createParser():
help='Baseline threshold (max bperp in meters)')
parser.add_argument('-a', '--azimuth_looks', dest='alks', type=str, default='10',
help='Number of looks in azimuth (automaticly computed as AspectR*looks when "S" or "sensor" is defined to give approximately square multi-look pixels)')
help='Number of looks in azimuth (automaticly computed as AspectR*looks when '
'"S" or "sensor" is defined to give approximately square multi-look pixels)')
parser.add_argument('-r', '--range_looks', dest='rlks', type=str, default='10',
help='Number of looks in range')
parser.add_argument('-S', '--sensor', dest='sensor', type=str, required=False,
help='SAR sensor used to define square multi-look pixels')
parser.add_argument('-L', '--low_band_frequency', dest='fL', type=str, default=None,
help='low band frequency')
parser.add_argument('-H', '--high_band_frequency', dest='fH', type=str, default=None,
help='high band frequency')
parser.add_argument('-B', '--subband_bandwidth ', dest='bandWidth', type=str, default=None,
help='sub-band band width')
parser.add_argument('-u', '--unw_method', dest='unwMethod', type=str, default='snaphu'
, help='unwrapping method (icu, snaphu, or snaphu2stage)')
parser.add_argument('-u', '--unw_method', dest='unwMethod', type=str, default='snaphu',
help='unwrapping method (icu, snaphu, or snaphu2stage)')
parser.add_argument('-f','--filter_strength', dest='filtStrength', type=str, default=filtStrength,
help='strength of Goldstein filter applied to the wrapped phase before spatial coherence estimation.'
' Default: {}'.format(filtStrength))
parser.add_argument('--filter_sigma_x', dest='filterSigmaX', type=str, default='100'
, help='filter sigma for gaussian filtering the dispersive and nonDispersive phase')
iono = parser.add_argument_group('Ionosphere', 'Configurationas for ionospheric correction')
iono.add_argument('-L', '--low_band_frequency', dest='fL', type=str, default=None,
help='low band frequency')
iono.add_argument('-H', '--high_band_frequency', dest='fH', type=str, default=None,
help='high band frequency')
iono.add_argument('-B', '--subband_bandwidth ', dest='bandWidth', type=str, default=None,
help='sub-band band width')
parser.add_argument('--filter_sigma_y', dest='filterSigmaY', type=str, default='100.0',
help='sigma of the gaussian filter in Y direction, default=100')
iono.add_argument('--filter_sigma_x', dest='filterSigmaX', type=str, default='100',
help='filter sigma for gaussian filtering the dispersive and nonDispersive phase')
parser.add_argument('--filter_size_x', dest='filterSizeX', type=str, default='800.0',
help='size of the gaussian kernel in X direction, default = 800')
iono.add_argument('--filter_sigma_y', dest='filterSigmaY', type=str, default='100.0',
help='sigma of the gaussian filter in Y direction, default=100')
parser.add_argument('--filter_size_y', dest='filterSizeY', type=str, default='800.0',
help='size of the gaussian kernel in Y direction, default=800')
iono.add_argument('--filter_size_x', dest='filterSizeX', type=str, default='800.0',
help='size of the gaussian kernel in X direction, default = 800')
parser.add_argument('--filter_kernel_rotation', dest='filterKernelRotation', type=str, default='0.0',
help='rotation angle of the filter kernel in degrees (default = 0.0)')
iono.add_argument('--filter_size_y', dest='filterSizeY', type=str, default='800.0',
help='size of the gaussian kernel in Y direction, default=800')
parser.add_argument('-W', '--workflow', dest='workflow', type=str, default='slc'
, help='The InSAR processing workflow : (slc, interferogram, ionosphere)')
iono.add_argument('--filter_kernel_rotation', dest='filterKernelRotation', type=str, default='0.0',
help='rotation angle of the filter kernel in degrees (default = 0.0)')
parser.add_argument('-z', '--zero', dest='zerodop', action='store_true', default=False, help='Use zero doppler geometry for processing - Default : No')
parser.add_argument('--nofocus', dest='nofocus', action='store_true', default=False, help='If input data is already focused to SLCs - Default : do focus')
parser.add_argument('-c', '--text_cmd', dest='text_cmd', type=str, default=''
, help='text command to be added to the beginning of each line of the run files. Example : source ~/.bash_profile;')
parser.add_argument('-useGPU', '--useGPU', dest='useGPU',action='store_true', default=False, help='Allow App to use GPU when available')
parser.add_argument('-W', '--workflow', dest='workflow', type=str, default='slc',
help='The InSAR processing workflow : (slc, interferogram, ionosphere)')
parser.add_argument('-z', '--zero', dest='zerodop', action='store_true', default=False,
help='Use zero doppler geometry for processing - Default : No')
parser.add_argument('--nofocus', dest='nofocus', action='store_true', default=False,
help='If input data is already focused to SLCs - Default : do focus')
parser.add_argument('-c', '--text_cmd', dest='text_cmd', type=str, default='',
help='text command to be added to the beginning of each line of the run files. Example : source ~/.bash_profile;')
parser.add_argument('-useGPU', '--useGPU', dest='useGPU',action='store_true', default=False,
help='Allow App to use GPU when available')
parser.add_argument('--summary', dest='summary', action='store_true', default=False, help='Show summary only')
return parser

154
contrib/stack/stripmapStack/topo.py
View File

@ -1,13 +1,16 @@
#!/usr/bin/env python3
import os
import argparse
import shelve
import datetime
import shutil
import numpy as np
import isce
import isceobj
import numpy as np
import shelve
import os
import datetime
from isceobj.Constants import SPEED_OF_LIGHT
from isceobj.Util.Poly2D import Poly2D
from mroipac.looks.Looks import Looks
def createParser():
'''
@ -45,7 +48,7 @@ class Dummy(object):
def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
from isceobj.Planet.Planet import Planet
from zerodop.GPUtopozero.GPUtopozero import PyTopozero
from isceobj import Constants as CN
@ -81,14 +84,14 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
omethod = 2 # LEGENDRE INTERPOLATION
else:
omethod = 0 # HERMITE INTERPOLATION
# tracking doppler specifications
if nativedop and (dop is not None):
try:
coeffs = dop._coeffs
except:
coeffs = dop
polyDoppler = Poly2D()
polyDoppler.setWidth(width)
polyDoppler.setLength(length)
@ -106,10 +109,10 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
polyDoppler.createPoly2D()
# dem
# dem
demImage.setCaster('read','FLOAT')
demImage.createImage()
# slant range file
slantRangeImage = Poly2D()
slantRangeImage.setWidth(width)
@ -127,12 +130,12 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
dataType = 'DOUBLE'
latImage.initImage(latFilename,accessMode,width,dataType)
latImage.createImage()
# lon file
lonImage = isceobj.createImage()
lonImage.initImage(lonFilename,accessMode,width,dataType)
lonImage.createImage()
# LOS file
losImage = isceobj.createImage()
dataType = 'FLOAT'
@ -141,7 +144,7 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
losImage.initImage(losFilename,accessMode,width,dataType,bands=bands,scheme=scheme)
losImage.setCaster('write','DOUBLE')
losImage.createImage()
# height file
heightImage = isceobj.createImage()
dataType = 'DOUBLE'
@ -155,7 +158,7 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
incImage.initImage(incFilename,accessMode,width,dataType,bands=bands,scheme=scheme)
incImage.createImage()
incImagePtr = incImage.getImagePointer()
maskImage = isceobj.createImage()
dataType = 'BYTE'
bands = 1
@ -165,7 +168,7 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
else:
incImagePtr = 0
maskImagePtr = 0
# initalize planet
elp = Planet(pname='Earth').ellipsoid
@ -211,14 +214,14 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
topo.set_orbitBasis(1) # Is this ever different?
topo.createOrbit() # Initializes the empty orbit to the right allocated size
count = 0
for sv in info.orbit.stateVectors.list:
td = DTU.seconds_since_midnight(sv.getTime())
pos = sv.getPosition()
vel = sv.getVelocity()
topo.set_orbitVector(count,td,pos[0],pos[1],pos[2],vel[0],vel[1],vel[2])
count += 1
# run topo
topo.runTopo()
@ -241,13 +244,13 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
# los file
descr = '''Two channel Line-Of-Sight geometry image (all angles in degrees). Represents vector drawn from target to platform.
Channel 1: Incidence angle measured from vertical at target (always +ve).
Channel 1: Incidence angle measured from vertical at target (always +ve).
Channel 2: Azimuth angle measured from North in Anti-clockwise direction.'''
losImage.setImageType('bil')
losImage.addDescription(descr)
losImage.finalizeImage()
losImage.renderHdr()
# dem/ height file
demImage.finalizeImage()
@ -256,7 +259,7 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
descr = '''Two channel angle file.
Channel 1: Angle between ray to target and the vertical at the sensor
Channel 2: Local incidence angle accounting for DEM slope at target'''
incImage.addDescription(descr)
incImage.finalizeImage()
incImage.renderHdr()
@ -265,7 +268,7 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
maskImage.addDescription(descr)
maskImage.finalizeImage()
maskImage.renderHdr()
if slantRangeImage:
try:
slantRangeImage.finalizeImage()
@ -273,7 +276,7 @@ def runTopoGPU(info, demImage, dop=None, nativedop=False, legendre=False):
pass
def runTopoCPU(info, demImage, dop=None,
def runTopoCPU(info, demImage, dop=None,
nativedop=False, legendre=False):
from zerodop.topozero import createTopozero
from isceobj.Planet.Planet import Planet
@ -295,7 +298,7 @@ def runTopoCPU(info, demImage, dop=None,
topo.numberRangeLooks = info.numberRangeLooks
topo.numberAzimuthLooks = info.numberAzimuthLooks
topo.lookSide = info.lookSide
topo.sensingStart = info.sensingStart + datetime.timedelta(seconds = ((info.numberAzimuthLooks - 1) /2) / info.prf)
topo.sensingStart = info.sensingStart + datetime.timedelta(seconds = ((info.numberAzimuthLooks - 1) /2) / info.prf)
topo.rangeFirstSample = info.rangeFirstSample + ((info.numberRangeLooks - 1)/2) * info.slantRangePixelSpacing
topo.demInterpolationMethod='BIQUINTIC'
@ -328,9 +331,10 @@ def runTopoCPU(info, demImage, dop=None,
topo.topo()
return
def runSimamp(outdir, hname='z.rdr'):
from iscesys.StdOEL.StdOELPy import create_writer
#####Run simamp
stdWriter = create_writer("log","",True,filename='sim.log')
objShade = isceobj.createSimamplitude()
@ -354,6 +358,86 @@ def runSimamp(outdir, hname='z.rdr'):
simImage.renderHdr()
hgtImage.finalizeImage()
simImage.finalizeImage()
return
def runMultilook(in_dir, out_dir, alks, rlks):
print('generate multilooked geometry files with alks={} and rlks={}'.format(alks, rlks))
from iscesys.Parsers.FileParserFactory import createFileParser
FP = createFileParser('xml')
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
print('create directory: {}'.format(out_dir))
for fbase in ['hgt', 'incLocal', 'lat', 'lon', 'los', 'shadowMask', 'waterMask']:
fname = '{}.rdr'.format(fbase)
in_file = os.path.join(in_dir, fname)
out_file = os.path.join(out_dir, fname)
if os.path.isfile(in_file):
xmlProp = FP.parse(in_file+'.xml')[0]
if('image_type' in xmlProp and xmlProp['image_type'] == 'dem'):
inImage = isceobj.createDemImage()
else:
inImage = isceobj.createImage()
inImage.load(in_file+'.xml')
inImage.filename = in_file
lkObj = Looks()
lkObj.setDownLooks(alks)
lkObj.setAcrossLooks(rlks)
lkObj.setInputImage(inImage)
lkObj.setOutputFilename(out_file)
lkObj.looks()
# copy the full resolution xml/vrt file from ./merged/geom_master to ./geom_master
# to facilitate the number of looks extraction
# the file path inside .xml file is not, but should, updated
shutil.copy(in_file+'.xml', out_file+'.full.xml')
shutil.copy(in_file+'.vrt', out_file+'.full.vrt')
return out_dir
def runMultilookGdal(in_dir, out_dir, alks, rlks):
print('generate multilooked geometry files with alks={} and rlks={}'.format(alks, rlks))
import gdal
# create 'geom_master' directory
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
print('create directory: {}'.format(out_dir))
# multilook files one by one
for fbase in ['hgt', 'incLocal', 'lat', 'lon', 'los', 'shadowMask', 'waterMask']:
fname = '{}.rdr'.format(fbase)
in_file = os.path.join(in_dir, fname)
out_file = os.path.join(out_dir, fname)
if os.path.isfile(in_file):
ds = gdal.Open(in_file, gdal.GA_ReadOnly)
in_wid = ds.RasterXSize
in_len = ds.RasterYSize
out_wid = int(in_wid / rlks)
out_len = int(in_len / alks)
src_wid = out_wid * rlks
src_len = out_len * alks
cmd = 'gdal_translate -of ENVI -a_nodata 0 -outsize {ox} {oy} '.format(ox=out_wid, oy=out_len)
cmd += ' -srcwin 0 0 {sx} {sy} {fi} {fo} '.format(sx=src_wid, sy=src_len, fi=in_file, fo=out_file)
print(cmd)
os.system(cmd)
# copy the full resolution xml/vrt file from ./merged/geom_master to ./geom_master
# to facilitate the number of looks extraction
# the file path inside .xml file is not, but should, updated
shutil.copy(in_file+'.xml', out_file+'.full.xml')
shutil.copy(in_file+'.vrt', out_file+'.full.vrt')
return out_dir
def extractInfo(frame, inps):
@ -369,8 +453,8 @@ def extractInfo(frame, inps):
info.lookSide = frame.instrument.platform.pointingDirection
info.rangeFirstSample = frame.startingRange
info.numberRangeLooks = inps.rlks
info.numberAzimuthLooks = inps.alks
info.numberRangeLooks = 1 #inps.rlks
info.numberAzimuthLooks = 1 #inps.alks
fsamp = frame.rangeSamplingRate
@ -378,9 +462,9 @@ def extractInfo(frame, inps):
info.prf = frame.PRF
info.radarWavelength = frame.radarWavelegth
info.orbit = frame.getOrbit()
info.width = frame.getNumberOfSamples()
info.length = frame.getNumberOfLines()
info.width = frame.getNumberOfSamples()
info.length = frame.getNumberOfLines()
info.sensingStop = frame.getSensingStop()
info.outdir = inps.outdir
@ -389,7 +473,7 @@ def extractInfo(frame, inps):
def main(iargs=None):
inps = cmdLineParse(iargs)
# see if the user compiled isce with GPU enabled
@ -419,11 +503,9 @@ def main(iargs=None):
doppler = db['doppler']
except:
doppler = frame._dopplerVsPixel
db.close()
####Setup dem
demImage = isceobj.createDemImage()
demImage.load(inps.dem + '.xml')
@ -439,14 +521,20 @@ def main(iargs=None):
info.incFilename = os.path.join(info.outdir, 'incLocal.rdr')
info.maskFilename = os.path.join(info.outdir, 'shadowMask.rdr')
runTopo(info,demImage,dop=doppler,nativedop=inps.nativedop, legendre=inps.legendre)
runSimamp(os.path.dirname(info.heightFilename),os.path.basename(info.heightFilename))
# write multilooked geometry files in "geom_master" directory, same level as "Igrams"
if inps.rlks * inps.rlks > 1:
out_dir = os.path.join(os.path.dirname(os.path.dirname(info.outdir)), 'geom_master')
runMultilookGdal(in_dir=info.outdir, out_dir=out_dir, alks=inps.alks, rlks=inps.rlks)
#runMultilook(in_dir=info.outdir, out_dir=out_dir, alks=inps.alks, rlks=inps.rlks)
return
if __name__ == '__main__':
'''
Main driver.
'''
main()

7
contrib/stack/stripmapStack/trackCRs.py
View File

@ -73,8 +73,7 @@ def makeOnePlot(filename, pos):
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[miny:maxy, minx:maxx]), 0.4)
box = np.power(np.abs(data[int(miny):int(maxy), int(minx):int(maxx)]), 0.4)
plt.figure('CR analysis')
@ -104,7 +103,7 @@ def getAzRg(frame,llh):
pol._normRange = frame.instrument.rangePixelSize
pol.initPoly(azimuthOrder=0, rangeOrder=len(coeffs)-1, coeffs=[coeffs])
taz, rgm = frame.orbit.geo2rdr(list(llh), side=frame.instrument.platform.pointingDirection,
taz, rgm = frame.orbit.geo2rdr(list(llh)[1:], side=frame.instrument.platform.pointingDirection,
doppler=pol, wvl=frame.instrument.getRadarWavelength())
line = (taz - frame.sensingStart).total_seconds() * frame.PRF
@ -145,7 +144,7 @@ if __name__ == '__main__':
# frame.startingRange = frame.startingRange + 100.0
###Load CRS positions
llhs = np.loadtxt(inps.posfile)
llhs = np.loadtxt(inps.posfile, delimiter=',')
crs = []

106
contrib/stack/stripmapStack/unwrap.py
View File

@ -113,9 +113,19 @@ def extractInfoFromPickle(pckfile, inps):
data['earthRadius'] = elp.local_radius_of_curvature(llh.lat, hdg)
#azspacing = burst.azimuthTimeInterval * sv.getScalarVelocity()
azres = 20.0
#azres = 20.0
azspacing = sv.getScalarVelocity() / burst.PRF
azres = burst.platform.antennaLength / 2.0
azfact = azres / azspacing
burst.getInstrument()
rgBandwidth = burst.instrument.pulseLength * burst.instrument.chirpSlope
rgres = abs(SPEED_OF_LIGHT / (2.0 * rgBandwidth))
rgspacing = burst.instrument.rangePixelSize
rgfact = rgres / rgspacing
#data['corrlooks'] = inps.rglooks * inps.azlooks * azspacing / azres
data['corrlooks'] = inps.rglooks * inps.azlooks / (azfact * rgfact)
data['rglooks'] = inps.rglooks
data['azlooks'] = inps.azlooks
@ -149,7 +159,7 @@ def runUnwrap(infile, outfile, corfile, config, costMode = None,initMethod = Non
altitude = config['altitude']
rangeLooks = config['rglooks']
azimuthLooks = config['azlooks']
#corrLooks = config['corrlooks']
corrLooks = config['corrlooks']
maxComponents = 20
snp = Snaphu()
@ -163,7 +173,7 @@ def runUnwrap(infile, outfile, corfile, config, costMode = None,initMethod = Non
snp.setAltitude(altitude)
snp.setCorrfile(corfile)
snp.setInitMethod(initMethod)
# snp.setCorrLooks(corrLooks)
snp.setCorrLooks(corrLooks)
snp.setMaxComponents(maxComponents)
snp.setDefoMaxCycles(defomax)
snp.setRangeLooks(rangeLooks)
@ -248,33 +258,34 @@ def runUnwrapIcu(infile, outfile):
unwImage.finalizeImage()
unwImage.renderHdr()
def runUnwrap2Stage(unwrappedIntFilename,connectedComponentsFilename,unwrapped2StageFilename, unwrapper_2stage_name=None, solver_2stage=None):
def runUnwrap2Stage(unwrappedIntFilename,connectedComponentsFilename,unwrapped2StageFilename,
unwrapper_2stage_name=None, solver_2stage=None):
if unwrapper_2stage_name is None:
unwrapper_2stage_name = 'REDARC0'
if solver_2stage is None:
# If unwrapper_2state_name is MCF then solver is ignored
# and relaxIV MCF solver is used by default
solver_2stage = 'pulp'
print('Unwrap 2 Stage Settings:')
print('Name: %s'%unwrapper_2stage_name)
print('Solver: %s'%solver_2stage)
inpFile = unwrappedIntFilename
ccFile = connectedComponentsFilename
outFile = unwrapped2StageFilename
# Hand over to 2Stage unwrap
unw = UnwrapComponents()
unw.setInpFile(inpFile)
unw.setConnCompFile(ccFile)
unw.setOutFile(outFile)
unw.setSolver(solver_2stage)
unw.setRedArcs(unwrapper_2stage_name)
unw.unwrapComponents()
return
if unwrapper_2stage_name is None:
unwrapper_2stage_name = 'REDARC0'
if solver_2stage is None:
# If unwrapper_2state_name is MCF then solver is ignored
# and relaxIV MCF solver is used by default
solver_2stage = 'pulp'
print('Unwrap 2 Stage Settings:')
print('Name: %s'%unwrapper_2stage_name)
print('Solver: %s'%solver_2stage)
inpFile = unwrappedIntFilename
ccFile = connectedComponentsFilename
outFile = unwrapped2StageFilename
# Hand over to 2Stage unwrap
unw = UnwrapComponents()
unw.setInpFile(inpFile)
unw.setConnCompFile(ccFile)
unw.setOutFile(outFile)
unw.setSolver(solver_2stage)
unw.setRedArcs(unwrapper_2stage_name)
unw.unwrapComponents()
return
def main(iargs=None):
@ -293,24 +304,26 @@ def main(iargs=None):
if inps.method != 'icu':
masterShelveDir = os.path.join(interferogramDir , 'masterShelve')
if not os.path.exists(masterShelveDir):
os.makedirs(masterShelveDir)
masterShelveDir = os.path.join(interferogramDir , 'masterShelve')
if not os.path.exists(masterShelveDir):
os.makedirs(masterShelveDir)
inps.master = os.path.dirname(inps.master)
cpCmd='cp ' + os.path.join(inps.master, 'data*') +' '+masterShelveDir
os.system(cpCmd)
pckfile = os.path.join(masterShelveDir,'data')
print(pckfile)
metadata = extractInfoFromPickle(pckfile, inps)
inps.master = os.path.dirname(inps.master)
cpCmd='cp ' + os.path.join(inps.master, 'data*') +' '+masterShelveDir
os.system(cpCmd)
pckfile = os.path.join(masterShelveDir,'data')
print(pckfile)
metadata = extractInfoFromPickle(pckfile, inps)
########
print ('unwrapping method : ' , inps.method)
if inps.method == 'snaphu':
if inps.nomcf:
fncall = runUnwrap
else:
fncall = runUnwrapMcf
fncall(inps.intfile, inps.unwprefix + '_snaphu.unw', inps.cohfile, metadata, defomax=inps.defomax)
if inps.nomcf:
fncall = runUnwrap
else:
fncall = runUnwrapMcf
fncall(inps.intfile, inps.unwprefix + '_snaphu.unw', inps.cohfile, metadata, defomax=inps.defomax)
elif inps.method == 'snaphu2stage':
if inps.nomcf:
fncall = runUnwrap
@ -319,11 +332,12 @@ def main(iargs=None):
fncall(inps.intfile, inps.unwprefix + '_snaphu.unw', inps.cohfile, metadata, defomax=inps.defomax)
# adding in the two-stage
runUnwrap2Stage(inps.unwprefix + '_snaphu.unw', inps.unwprefix + '_snaphu.unw.conncomp',inps.unwprefix + '_snaphu2stage.unw')
runUnwrap2Stage(inps.unwprefix + '_snaphu.unw',
inps.unwprefix + '_snaphu.unw.conncomp',
inps.unwprefix + '_snaphu2stage.unw')
elif inps.method == 'icu':
runUnwrapIcu(inps.intfile, inps.unwprefix + '_icu.unw')
runUnwrapIcu(inps.intfile, inps.unwprefix + '_icu.unw')
if __name__ == '__main__':

129
contrib/stack/topsStack/README.txt → contrib/stack/topsStack/README.md
View File

@ -1,38 +1,80 @@
## Sentinel-1 TOPS stack processor
The detailed algorithm for stack processing of TOPS data can be find here:
H. Fattahi, P. Agram, and M. Simons, “A network-based enhanced spectral diversity approach for TOPS time-series analysis,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 2, pp. 777–786, Feb. 2017. (https://ieeexplore.ieee.org/abstract/document/7637021/)
+ Fattahi, H., P. Agram, and M. Simons (2016), A Network-Based Enhanced Spectral Diversity Approach for TOPS Time-Series Analysis, IEEE Transactions on Geoscience and Remote Sensing, 55(2), 777-786, doi:[10.1109/TGRS.2016.2614925](https://ieeexplore.ieee.org/abstract/document/7637021).
-----------------------------------
<<<<<< Sentinel-1 TOPS stack processor >>>>>>
To use the sentinel stack processor, make sure to add the path of your "contrib/stack/topsStack" folder to your $PATH environment varibale.
To use the sentinel stack processor, make sure to add the path of your `contrib/stack/topsStack` folder to your `$PATH` environment varibale.
The scripts provides support for Sentinel-1 TOPS stack processing. Currently supported workflows include a coregistered stack of SLC, interferograms, offsets, and coherence.
stackSentinel.py generates all configuration and run files required to be executed on a stack of Sentinel-1 TOPS data. When stackSentinel.py is executed for a given workflow (-W option) a “configs” and “run_files” folder is generated. No processing is performed at this stage. Within the “run_files” folder different run_#_description files are contained which are to be executed as shell scripts in the run number order. Each of these run scripts call specific configure files contained in the “configs” folder which call ISCE in a modular fashion. The configure and run files will change depending on the selected workflow. To make run_# files executable, change the file permission accordingly (e.g., chmod +x run_1_unpack_slc).
`stackSentinel.py` generates all configuration and run files required to be executed on a stack of Sentinel-1 TOPS data. When stackSentinel.py is executed for a given workflow (-W option) a **configs** and **run_files** folder is generated. No processing is performed at this stage. Within the run_files folder different run\_#\_description files are contained which are to be executed as shell scripts in the run number order. Each of these run scripts call specific configure files contained in the “configs” folder which call ISCE in a modular fashion. The configure and run files will change depending on the selected workflow. To make run_# files executable, change the file permission accordingly (e.g., `chmod +x run_1_unpack_slc`).
To see workflow examples, type “stackSentinel.py -H”
To get an overview of all the configurable parameters, type “stackSentinel.py -h”
```bash
stackSentinel.py -H #To see workflow examples,
stackSentinel.py -h #To get an overview of all the configurable parameters
```
Required parameters of stackSentinel.py include:
-s SLC_DIRNAME A folder with downloaded Sentinel-1 SLC’s.
-o ORBIT_DIRNAME A folder containing the Sentinel-1 orbits.
Missing orbit files will be downloaded automatically
-a AUX_DIRNAME A folder containing the Sentinel-1 Auxiliary files
-d DEM A DEM (Digital Elevation Model) referenced to wgs84
```cfg
-s SLC_DIRNAME #A folder with downloaded Sentinel-1 SLC’s.
-o ORBIT_DIRNAME #A folder containing the Sentinel-1 orbits. Missing orbit files will be downloaded automatically
-a AUX_DIRNAME #A folder containing the Sentinel-1 Auxiliary files
-d DEM_FILENAME #A DEM (Digital Elevation Model) referenced to wgs84
```
In the following, different workflow examples are provided. Note that stackSentinel.py only generates the run and configure files. To perform the actual processing, the user will need to execute each run file in their numbered order.
In all workflows, coregistration (-C option) can be done using only geometry (set option = geometry) or with geometry plus refined azimuth offsets through NESD (set option = NESD) approach, the latter being the default. For the NESD coregistrstion the user can control the ESD coherence threshold (-e option) and the number of overlap interferograms (-O) to be used in NESD estimation.
------------------------------ Example 1: Coregistered stack of SLC ----------------------------
Generate the run and configure files needed to generate a coregistered stack of SLCs.
In this example, a pre-defined bounding box is specified. Note, if the bounding box is not provided it is set by default to the common SLC area among all SLCs. We recommend that user always set the processing bounding box. Since ESA does not have a fixed frame definition, we suggest to download data for a larger bounding box compared to the actual bounding box used in stackSentinel.py. This way user can ensure to have required data to cover the region of interest. Here is an example command to create configuration files for a stack of SLCs:
#### AUX_CAL file download ####
The following calibration auxliary (AUX_CAL) file is used for **antenna pattern correction** to compensate the range phase offset of SAFE products with **IPF verison 002.36** (mainly for images acquired before March 2015). If all your SAFE products are from another IPF version, then no AUX files are needed. Check [ESA document](https://earth.esa.int/documents/247904/1653440/Sentinel-1-IPF_EAP_Phase_correction) for details.
Run the command below to download the AUX_CAL file once and store it somewhere (_i.e._ ~/aux/aux_cal) so that you can use it all the time, for `stackSentinel.py -a` or `auxiliary data directory` in `topsApp.py`.
```
wget https://qc.sentinel1.eo.esa.int/product/S1A/AUX_CAL/20140908T000000/S1A_AUX_CAL_V20140908T000000_G20190626T100201.SAFE.TGZ
tar zxvf S1A_AUX_CAL_V20140908T000000_G20190626T100201.SAFE.TGZ
rm S1A_AUX_CAL_V20140908T000000_G20190626T100201.SAFE.TGZ
```
#### 1. Create your project folder somewhere ####
```
mkdir MexicoSenAT72
cd MexicoSenAT72
```
#### 2. Prepare DEM ####
Download of DEM (need to use wgs84 version) using the ISCE DEM download script.
```
mkdir DEM; cd DEM
dem.py -a stitch -b 18 20 -100 -97 -r -s 1 –c
rm demLat*.dem demLat*.dem.xml demLat*.dem.vrt
fixImageXml.py -f -i demLat*.dem.wgs84 #Updating DEM’s wgs84 xml to include full path to the DEM
cd ..
```
#### 3. Download Sentinel-1 data to SLC ####
#### 4.1 Example workflow: Coregistered stack of SLC ####
Generate the run and configure files needed to generate a coregistered stack of SLCs. In this example, a pre-defined bounding box is specified. Note, if the bounding box is not provided it is set by default to the common SLC area among all SLCs. We recommend that user always set the processing bounding box. Since ESA does not have a fixed frame definition, we suggest to download data for a larger bounding box compared to the actual bounding box used in stackSentinel.py. This way user can ensure to have required data to cover the region of interest. Here is an example command to create configuration files for a stack of SLCs:
```
stackSentinel.py -s ../SLC/ -d ../DEM/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -a ../../AuxDir/ -o ../../Orbits -b '19 20 -99.5 -98.5' -W slc
```
by running the command above, the configs and run_files folders are created. User needs to execute each run file in order. The order is specified by the index number of the run file name. For the example above, the run_files folder includes the following files:
- run_1_unpack_slc_topo_master
- run_2_average_baseline
- run_3_extract_burst_overlaps
@ -46,72 +88,83 @@ by running the command above, the configs and run_files folders are created. Use
The generated run files are self descriptive. Below is a short explanation on what each run_file does:
***run_1_unpack_slc_topo_master:***
**run_1_unpack_slc_topo_master:**
Includes commands to unpack Sentinel-1 TOPS SLCs using ISCE readers. For older SLCs which need antenna elevation pattern correction, the file is extracted and written to disk. For newer version of SLCs which don’t need the elevation antenna pattern correction, only a gdal virtual “vrt” file (and isce xml file) is generated. The “.vrt” file points to the Sentinel SLC file and reads them whenever required during the processing. If a user wants to write the “.vrt” SLC file to disk, it can be done easily using gdal_translate (e.g. gdal_translate –of ENVI File.vrt File.slc).
The “run_1_unpack_slc_topo_master” also includes a command that refers to the config file of the stack master, which includes configuration for running topo for the stack master. Note that in the pair-wise processing strategy one should run topo (mapping from range-Doppler to geo coordinate) for all pairs. However, with stackSentinel, topo needs to be run only one time for the master in the stack.
***run_2_average_baseline: ***
**run_2_average_baseline:**
Computes average baseline for the stack. These baselines are not used for processing anywhere. They are only an approximation and can be used for plotting purposes. A more precise baseline grid is estimated later in run_10.
***run_3_extract_burst_overlaps: ***
**run_3_extract_burst_overlaps:**
Burst overlaps are extracted for estimating azimuth misregistration using NESD technique. If coregistration method is chosen to be “geometry”, then this run file won’t exist and the overlaps are not extracted.
***run_4_overlap_geo2rdr_resample: ***
**run_4_overlap_geo2rdr_resample:***
Running geo2rdr to estimate geometrical offsets between slave burst overlaps and the stack master burst overlaps. The slave burst overlaps are then resampled to the stack master burst overlaps.
***run_5_pairs_misreg: ***
**run_5_pairs_misreg:**
Using the coregistered stack burst overlaps generated from the previous step, differential overlap interferograms are generated and are used for estimating azimuth misregistration using Enhanced Spectral Diversity (ESD) technique.
***run_6_timeseries_misreg: ***
**run_6_timeseries_misreg:**
A time-series of azimuth and range misregistration is estimated with respect to the stack master. The time-series is a least squares esatimation from the pair misregistration from the previous step.
***run_7_geo2rdr_resample: ***
**run_7_geo2rdr_resample:**
Using orbit and DEM, geometrical offsets among all slave SLCs and the stack master is computed. The goometrical offsets, together with the misregistration time-series (from previous step) are used for precise coregistration of each burst SLC.
***run_8_extract_stack_valid_region: ***
**run_8_extract_stack_valid_region:**
The valid region between burst SLCs at the overlap area of the bursts slightly changes for different acquisitions. Therefore we need to keep track of these overlaps which will be used during merging bursts. Without these knowledge, lines of invalid data may appear in the merged products at the burst overlaps.
***run_9_merge: ***
**run_9_merge:**
Merges all bursts for the master and coregistered SLCs. The geometry files are also merged including longitude, latitude, shadow and layer mask, line-of-sight files, etc. .
***run_10_grid_baseline: ***
**run_10_grid_baseline:**
A coarse grid of baselines between each slave SLC and the stack master is generated. This is not used in any computation.
#### 4.2 Example workflow: Coregistered stack of SLC with modified parameters ####
-------- Example 2: Coregistered stack of SLC with modified parameters -----------
In the following example, the same stack generation is requested but where the threshold of the default coregistration approach (NESD) is relaxed from its default 0.85 value 0.7.
```
stackSentinel.py -s ../SLC/ -d ../DEM/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -a ../../AuxDir/ -o ../../Orbits -b '19 20 -99.5 -98.5' -W slc -e 0.7
```
When running all the run files, the final products are located in the merge folder which has subdirectories “geom_master”, “baselines” and “SLC”. The “geom_master” folder contains geometry products such as longitude, latitude, height, local incidence angle, look angle, heading, and shadowing/layover mask files. The “baselines” folder contains sparse grids of the perpendicular baseline for each acquisition, while the “SLC” folder contains the coregistered SLCs
When running all the run files, the final products are located in the merge folder which has subdirectories **geom_master**, **baselines** and **SLC**. The **geom_master** folder contains geometry products such as longitude, latitude, height, local incidence angle, look angle, heading, and shadowing/layover mask files. The **baselines** folder contains sparse grids of the perpendicular baseline for each acquisition, while the **SLC** folder contains the coregistered SLCs
#### 4.3 Example workflow: Stack of interferograms ####
------------------------------ Example 3: Stack of interferograms ------------------------------
Generate the run and configure files needed to generate a stack of interferograms.
In this example, a stack of interferograms is requested for which up to 2 nearest neighbor connections are included.
```
stackSentinel.py -s ../SLC/ -d ../../MexicoCity/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -b '19 20 -99.5 -98.5' -a ../../AuxDir/ -o ../../Orbits -c 2
```
In the following example, all possible interferograms are being generated and in which the coregistration approach is set to use geometry and not the default NESD.
```
stackSentinel.py -s ../SLC/ -d ../../MexicoCity/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -b '19 20 -99.5 -98.5' -a ../../AuxDir/ -o ../../Orbits -C geometry -c all
```
When executing all the run files, a coregistered stack of slcs are produced, the burst interferograms are generated and then merged. Merged interferograms are multilooked, filtered and unwrapped. Geocoding is not applied. If users need to geocode any product, they can use the geocodeGdal.py script.
#### 4.4 Example workflow: Stack of correlation ####
-------------------- Example 4: Correlation stack example ----------------------------
Generate the run and configure files needed to generate a stack of coherence.
In this example, a correlation stack is requested considering all possible coherence pairs and where the coregistration approach is done using geometry only.
```
stackSentinel.py -s ../SLC/ -d ../../MexicoCity/demLat_N18_N20_Lon_W100_W097.dem.wgs84 -b '19 20 -99.5 -98.5' -a ../../AuxDir/ -o ../../Orbits -C geometry -c all -W correlation
```
This workflow is basically similar to the previous one. The difference is that the interferograms are not unwrapped.
----------------------------------- DEM download example -----------------------------------
Download of DEM (need to use wgs84 version) using the ISCE DEM download script.
dem.py -a stitch -b 18 20 -100 -97 -r -s 1 –c
Updating DEM’s wgs84 xml to include full path to the DEM
fixImageXml.py -f -i demLat_N18_N20_Lon_W100_W097.dem.wgs84
#### 5. Execute the commands in run files (run_1*, run_2*, etc) in the "run_files" folder ####

3
examples/input_files/stripmapApp.xml
View File

@ -12,7 +12,8 @@
<value>/Users/fattahi/process/test_roiApp/Alos_Maule_T116/demLat_S39_S35_Lon_W074_W071.dem.wgs84</value>
</property>
<!--
<property name="do rubbersheeting">True</property>
<property name="do rubbersheetingAzimuth">True</property>
<property name="do rubbersheetingRange">False</property>
-->
<property name="do denseoffsets">True</property>
<property name="do split spectrum">True</property>

4
scons_tools/cuda.py
View File

@ -52,7 +52,7 @@ def generate(env):
# default flags for the NVCC compiler
env['STATICNVCCFLAGS'] = ''
env['SHAREDNVCCFLAGS'] = ''
env['ENABLESHAREDNVCCFLAG'] = '-arch=sm_35 -shared -Xcompiler -fPIC'
env['ENABLESHAREDNVCCFLAG'] = '-shared -Xcompiler -fPIC'
# default NVCC commands
env['STATICNVCCCMD'] = '$NVCC -o $TARGET -c $NVCCFLAGS $STATICNVCCFLAGS $SOURCES'
@ -153,7 +153,7 @@ def generate(env):
#env.Append(LIBPATH=[cudaSDKPath + '/lib', cudaSDKPath + '/common/lib' + cudaSDKSubLibDir, cudaToolkitPath + '/lib'])
env.Append(CUDACPPPATH=[cudaToolkitPath + '/include'])
env.Append(CUDALIBPATH=[cudaToolkitPath + '/lib', cudaToolkitPath + '/lib64'])
env.Append(CUDALIBPATH=[cudaToolkitPath + '/lib', cudaToolkitPath + '/lib64', '/lib64'])
env.Append(CUDALIBS=['cudart'])
def exists(env):

14
setup/setup.py
View File

@ -12,7 +12,7 @@
from __future__ import print_function
import sys
import os
import urllib2
import urllib
import getopt
import re
import shutil
@ -57,7 +57,7 @@ def print2log(msg, withtime=True, cmd=False):
if withtime:
now = datetime.datetime.today()
msg = "%s >> %s" % (now.isoformat(), msg)
LOGFILE.write(msg + '\n')
LOGFILE.write((msg + '\n').encode('utf-8'))
LOGFILE.flush()
os.fsync(LOGFILE)
@ -157,9 +157,9 @@ def downloadfile(url, fname, repeat=1):
counter = 0
while counter < repeat:
try:
response = urllib2.urlopen(url)
response = urllib.request.urlopen(url)
break
except urllib2.URLError, e:
except urllib.request.URLError as e:
counter += 1
if hasattr(e, 'reason'):
print2log("Failed to reach server. Reason: %s" % e.reason)
@ -851,7 +851,7 @@ class ISCEDeps(object):
f = open(self.config, 'rb')
lines = f.readlines()
for line in lines:
m = re.match("([^#].*?)=([^#]+?)$", line.strip())
m = re.match("([^#].*?)=([^#]+?)$", line.strip().decode('utf-8'))
if m:
var = m.group(1).strip()
val = m.group(2).strip()
@ -867,7 +867,7 @@ def readSetupConfig(setup_config):
f = open(setup_config, 'rb')
lines = f.readlines()
for line in lines:
m = re.match("([^#].*?)=([^#]+?)$", line.strip())
m = re.match("([^#].*?)=([^#]+?)$", line.strip().decode('utf-8'))
if m:
var = m.group(1).strip()
val = m.group(2).strip().replace('"', '')
@ -885,7 +885,7 @@ def checkArgs(args):
"""
try:
opts, args = getopt.getopt(args, "h", ["help", "prefix=", "ping=", "config=", "uname=", "download=", "unpack=", "install=", "gcc=", "gpp=", "verbose"])
except getopt.GetoptError, err:
except getopt.GetoptError as err:
print2log("ProgError: %s" % str(err))
usage()
sys.exit(2)