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testautoRIFT.py
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testautoRIFT.py
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#!/usr/bin/env python3
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Copyright 2019 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,
# end user, or in support of a prohibited end use). By downloading this software,
# the user agrees to comply with all applicable U.S. export laws and regulations.
# The user has the responsibility to obtain export licenses, or other export
# authority as may be required before exporting this software to any 'EAR99'
# embargoed foreign country or citizen of those countries.
#
# Author: Yang Lei
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
import re
import warnings
from osgeo import gdal
from datetime import datetime, timedelta
def runCmd(cmd):
import subprocess
out = subprocess.getoutput(cmd)
return out
def cmdLineParse():
'''
Command line parser.
'''
import argparse
SUPPORTED_MISSIONS = ['S1', 'S2', 'L4', 'L5', 'L7', 'L8', 'L9']
parser = argparse.ArgumentParser(description='Output geo grid')
parser.add_argument('-m', '--input_m', dest='indir_m', type=str, required=True,
help='Input master image file name (in ISCE format and radar coordinates) or Input master image file name (in GeoTIFF format and Cartesian coordinates)')
parser.add_argument('-s', '--input_s', dest='indir_s', type=str, required=True,
help='Input slave image file name (in ISCE format and radar coordinates) or Input slave image file name (in GeoTIFF format and Cartesian coordinates)')
parser.add_argument('-g', '--input_g', dest='grid_location', type=str, required=False,
help='Input pixel indices file name')
parser.add_argument('-o', '--input_o', dest='init_offset', type=str, required=False,
help='Input search center offsets ("downstream" reach location) file name')
parser.add_argument('-sr', '--input_sr', dest='search_range', type=str, required=False,
help='Input search range file name')
parser.add_argument('-csmin', '--input_csmin', dest='chip_size_min', type=str, required=False,
help='Input chip size min file name')
parser.add_argument('-csmax', '--input_csmax', dest='chip_size_max', type=str, required=False,
help='Input chip size max file name')
parser.add_argument('-vx', '--input_vx', dest='offset2vx', type=str, required=False,
help='Input pixel offsets to vx conversion coefficients file name')
parser.add_argument('-vy', '--input_vy', dest='offset2vy', type=str, required=False,
help='Input pixel offsets to vy conversion coefficients file name')
parser.add_argument('-sf', '--input_scale_factor', dest='scale_factor', type=str, required=False,
help='Input map projection scale factor file name')
parser.add_argument('-ssm', '--input_ssm', dest='stable_surface_mask', type=str, required=False,
help='Input stable surface mask file name')
parser.add_argument('-fo', '--flag_optical', dest='optical_flag', type=bool, required=False, default=0,
help='flag for reading optical data (e.g. Landsat): use 1 for on and 0 (default) for off')
parser.add_argument('-nc', '--sensor_flag_netCDF', dest='nc_sensor', type=str, required=False, default=None, choices=SUPPORTED_MISSIONS,
help=f'flag for packaging output formatted for Satellite missions. Default is None; supported missions: {SUPPORTED_MISSIONS}')
parser.add_argument('-mpflag', '--mpflag', dest='mpflag', type=int, required=False, default=0,
help='number of threads for multiple threading (default is specified by 0, which uses the original single-core version and surpasses the multithreading routine)')
parser.add_argument('-ncname', '--ncname', dest='ncname', type=str, required=False, default=None,
help='User-defined filename for the NetCDF output to which the ROI percentage and the production version will be appended')
return parser.parse_args()
class Dummy(object):
pass
def loadProduct(filename):
'''
Load the product using Product Manager.
'''
import isce
import logging
from imageMath import IML
IMG = IML.mmapFromISCE(filename, logging)
img = IMG.bands[0]
# pdb.set_trace()
return img
def loadProductOptical(file_m, file_s):
import numpy as np
'''
Load the product using Product Manager.
'''
from geogrid import GeogridOptical
# import isce
# from components.contrib.geo_autoRIFT.geogrid import GeogridOptical
obj = GeogridOptical()
x1a, y1a, xsize1, ysize1, x2a, y2a, xsize2, ysize2, trans = obj.coregister(file_m, file_s)
DS1 = gdal.Open(file_m)
DS2 = gdal.Open(file_s)
I1 = DS1.ReadAsArray(xoff=x1a, yoff=y1a, xsize=xsize1, ysize=ysize1)
I2 = DS2.ReadAsArray(xoff=x2a, yoff=y2a, xsize=xsize2, ysize=ysize2)
I1 = I1.astype(np.float32)
I2 = I2.astype(np.float32)
DS1=None
DS2=None
return I1, I2
def runAutorift(I1, I2, xGrid, yGrid, Dx0, Dy0, SRx0, SRy0, CSMINx0, CSMINy0, CSMAXx0, CSMAXy0, noDataMask, optflag,
nodata, mpflag, geogrid_run_info=None, preprocessing_methods=('hps', 'hps'),
preprocessing_filter_width=5):
'''
Wire and run geogrid.
'''
# import isce
# from components.contrib.geo_autoRIFT.autoRIFT import autoRIFT_ISCE
from autoRIFT import autoRIFT
import numpy as np
# import isceobj
import time
import subprocess
obj = autoRIFT()
# obj.configure()
obj.WallisFilterWidth = preprocessing_filter_width
print(f'Setting Wallis Filter Width to {preprocessing_filter_width}')
# ########## uncomment if starting from preprocessed images
# I1 = I1.astype(np.uint8)
# I2 = I2.astype(np.uint8)
obj.MultiThread = mpflag
# take the amplitude only for the radar images
if optflag == 0:
I1 = np.abs(I1)
I2 = np.abs(I2)
obj.I1 = I1
obj.I2 = I2
# test with lena image (533 X 533)
# obj.ChipSizeMinX=16
# obj.ChipSizeMaxX=32
# obj.ChipSize0X=16
# obj.SkipSampleX=16
# obj.SkipSampleY=16
# test with Venus image (407 X 407)
# obj.ChipSizeMinX=8
# obj.ChipSizeMaxX=16
# obj.ChipSize0X=8
# obj.SkipSampleX=8
# obj.SkipSampleY=8
# test with small tiff images
# obj.SkipSampleX=16
# obj.SkipSampleY=16
# create the grid if it does not exist
if xGrid is None:
m,n = obj.I1.shape
xGrid = np.arange(obj.SkipSampleX+10,n-obj.SkipSampleX,obj.SkipSampleX)
yGrid = np.arange(obj.SkipSampleY+10,m-obj.SkipSampleY,obj.SkipSampleY)
nd = xGrid.__len__()
md = yGrid.__len__()
obj.xGrid = np.int32(np.dot(np.ones((md,1)),np.reshape(xGrid,(1,xGrid.__len__()))))
obj.yGrid = np.int32(np.dot(np.reshape(yGrid,(yGrid.__len__(),1)),np.ones((1,nd))))
noDataMask = np.logical_not(obj.xGrid)
else:
obj.xGrid = xGrid
obj.yGrid = yGrid
# NOTE: This assumes the zero values in the image are only outside the valid image "frame",
# but is not true for Landsat-7 after the failure of the Scan Line Corrector, May 31, 2003.
# We should not mask based on zero values in the L7 images as this percolates into SearchLimit{X,Y}
# and prevents autoRIFT from looking at large parts of the images, but untangling the logic here
# has proved too difficult, so lets just turn it off if `wallis_fill` preprocessing is going to be used.
# generate the nodata mask where offset searching will be skipped based on 1) imported nodata mask and/or 2) zero values in the image
if 'wallis_fill' not in preprocessing_methods:
for ii in range(obj.xGrid.shape[0]):
for jj in range(obj.xGrid.shape[1]):
if (obj.yGrid[ii,jj] != nodata)&(obj.xGrid[ii,jj] != nodata):
if (I1[obj.yGrid[ii,jj]-1,obj.xGrid[ii,jj]-1]==0)|(I2[obj.yGrid[ii,jj]-1,obj.xGrid[ii,jj]-1]==0):
noDataMask[ii,jj] = True
######### mask out nodata to skip the offset searching using the nodata mask (by setting SearchLimit to be 0)
if SRx0 is None:
# ########### uncomment to customize SearchLimit based on velocity distribution (i.e. Dx0 must not be None)
# obj.SearchLimitX = np.int32(4+(25-4)/(np.max(np.abs(Dx0[np.logical_not(noDataMask)]))-np.min(np.abs(Dx0[np.logical_not(noDataMask)])))*(np.abs(Dx0)-np.min(np.abs(Dx0[np.logical_not(noDataMask)]))))
# obj.SearchLimitY = 5
# ###########
obj.SearchLimitX = obj.SearchLimitX * np.logical_not(noDataMask)
obj.SearchLimitY = obj.SearchLimitY * np.logical_not(noDataMask)
else:
obj.SearchLimitX = SRx0
obj.SearchLimitY = SRy0
# ############ add buffer to search range
# obj.SearchLimitX[obj.SearchLimitX!=0] = obj.SearchLimitX[obj.SearchLimitX!=0] + 2
# obj.SearchLimitY[obj.SearchLimitY!=0] = obj.SearchLimitY[obj.SearchLimitY!=0] + 2
if CSMINx0 is not None:
obj.ChipSizeMaxX = CSMAXx0
obj.ChipSizeMinX = CSMINx0
if geogrid_run_info is None:
gridspacingx = float(str.split(runCmd('fgrep "Grid spacing in m:" testGeogrid.txt'))[-1])
chipsizex0 = float(str.split(runCmd('fgrep "Smallest Allowable Chip Size in m:" testGeogrid.txt'))[-1])
try:
pixsizex = float(str.split(runCmd('fgrep "Ground range pixel size:" testGeogrid.txt'))[-1])
except:
pixsizex = float(str.split(runCmd('fgrep "X-direction pixel size:" testGeogrid.txt'))[-1])
else:
gridspacingx = geogrid_run_info['gridspacingx']
chipsizex0 = geogrid_run_info['chipsizex0']
pixsizex = geogrid_run_info['XPixelSize']
obj.ChipSize0X = int(np.ceil(chipsizex0/pixsizex/4)*4)
obj.GridSpacingX = int(obj.ChipSize0X*gridspacingx/chipsizex0)
# obj.ChipSize0X = np.min(CSMINx0[CSMINx0!=nodata])
RATIO_Y2X = CSMINy0/CSMINx0
obj.ScaleChipSizeY = np.median(RATIO_Y2X[(CSMINx0!=nodata)&(CSMINy0!=nodata)])
# obj.ChipSizeMaxX = obj.ChipSizeMaxX / obj.ChipSizeMaxX * 544
# obj.ChipSizeMinX = obj.ChipSizeMinX / obj.ChipSizeMinX * 68
else:
if ((optflag == 1)&(xGrid is not None)):
obj.ChipSizeMaxX = 32
obj.ChipSizeMinX = 16
obj.ChipSize0X = 16
# create the downstream search offset if not provided as input
if Dx0 is not None:
obj.Dx0 = Dx0
obj.Dy0 = Dy0
else:
obj.Dx0 = obj.Dx0 * np.logical_not(noDataMask)
obj.Dy0 = obj.Dy0 * np.logical_not(noDataMask)
# replace the nodata value with zero
obj.xGrid[noDataMask] = 0
obj.yGrid[noDataMask] = 0
obj.Dx0[noDataMask] = 0
obj.Dy0[noDataMask] = 0
if SRx0 is not None:
obj.SearchLimitX[noDataMask] = 0
obj.SearchLimitY[noDataMask] = 0
if CSMINx0 is not None:
obj.ChipSizeMaxX[noDataMask] = 0
obj.ChipSizeMinX[noDataMask] = 0
# convert azimuth offset to vertical offset as used in autoRIFT convention
if optflag == 0:
obj.Dy0 = -1 * obj.Dy0
######## preprocessing
t1 = time.time()
print("Pre-process Start!!!")
print(f"Using Wallis Filter Width: {obj.WallisFilterWidth}")
# obj.zeroMask = 1
# TODO: Allow different filters to be applied images independently
# default to most stringent filtering
if 'wallis_fill' in preprocessing_methods:
obj.preprocess_filt_wal_nodata_fill()
elif 'wallis' in preprocessing_methods:
obj.preprocess_filt_wal()
elif 'fft' in preprocessing_methods:
# FIXME: The Landsat 4/5 FFT preprocessor looks for the image corners to
# determine the scene rotation, but Geogrid + autoRIFT rond the
# corners when co-registering and chop the non-overlapping corners
# when subsetting to the common image overlap. FFT filer needs to
# be applied to the native images before they are processed by
# Geogrid or autoRIFT.
# obj.preprocess_filt_wal()
# obj.preprocess_filt_fft()
warnings.warn('FFT filtering must be done before processing with geogrid! Be careful when using this method', UserWarning)
else:
obj.preprocess_filt_hps()
# obj.I1 = np.abs(I1)
# obj.I2 = np.abs(I2)
print("Pre-process Done!!!")
print(time.time()-t1)
t1 = time.time()
# obj.DataType = 0
obj.uniform_data_type()
print("Uniform Data Type Done!!!")
print(time.time()-t1)
# pdb.set_trace()
# obj.sparseSearchSampleRate = 16
obj.OverSampleRatio = 64
# obj.colfiltChunkSize = 4
# OverSampleRatio can be assigned as a scalar (such as the above line) or as a Python dictionary below for intellgient use (ChipSize-dependent).
# Here, four chip sizes are used: ChipSize0X*[1,2,4,8] and four OverSampleRatio are considered [16,32,64,128]. The intelligent selection of OverSampleRatio (as a function of chip size) was determined by analyzing various combinations of (OverSampleRatio and chip size) and comparing the resulting image quality and statistics with the reference scenario (where the largest OverSampleRatio of 128 and chip size of ChipSize0X*8 are considered).
# The selection for the optical data flag is based on Landsat-8 data over an inland region (thus stable and not moving much) of Greenland, while that for the radar flag (optflag = 0) is based on Sentinel-1 data over the same region of Greenland.
if CSMINx0 is not None:
if (optflag == 1):
obj.OverSampleRatio = {obj.ChipSize0X:16,obj.ChipSize0X*2:32,obj.ChipSize0X*4:64,obj.ChipSize0X*8:64}
else:
obj.OverSampleRatio = {obj.ChipSize0X:32,obj.ChipSize0X*2:64,obj.ChipSize0X*4:128,obj.ChipSize0X*8:128}
# ########## export preprocessed images to files; can be commented out if not debugging
#
# t1 = time.time()
#
# I1 = obj.I1
# I2 = obj.I2
#
# length,width = I1.shape
#
# filename1 = 'I1_uint8_hpsnew.off'
#
# slcFid = open(filename1, 'wb')
#
# for yy in range(length):
# data = I1[yy,:]
# data.astype(np.float32).tofile(slcFid)
#
# slcFid.close()
#
# img = isceobj.createOffsetImage()
# img.setFilename(filename1)
# img.setBands(1)
# img.setWidth(width)
# img.setLength(length)
# img.setAccessMode('READ')
# img.renderHdr()
#
#
# filename2 = 'I2_uint8_hpsnew.off'
#
# slcFid = open(filename2, 'wb')
#
# for yy in range(length):
# data = I2[yy,:]
# data.astype(np.float32).tofile(slcFid)
#
# slcFid.close()
#
# img = isceobj.createOffsetImage()
# img.setFilename(filename2)
# img.setBands(1)
# img.setWidth(width)
# img.setLength(length)
# img.setAccessMode('READ')
# img.renderHdr()
#
# print("output Done!!!")
# print(time.time()-t1)
########## run Autorift
t1 = time.time()
print("AutoRIFT Start!!!")
obj.runAutorift()
print("AutoRIFT Done!!!")
print(time.time()-t1)
import cv2
kernel = np.ones((3,3),np.uint8)
noDataMask = cv2.dilate(noDataMask.astype(np.uint8),kernel,iterations = 1)
noDataMask = noDataMask.astype(np.bool)
return obj.Dx, obj.Dy, obj.InterpMask, obj.ChipSizeX, obj.GridSpacingX, obj.ScaleChipSizeY, obj.SearchLimitX, obj.SearchLimitY, obj.origSize, noDataMask
def main():
'''
Main driver.
'''
inps = cmdLineParse()
generateAutoriftProduct(indir_m=inps.indir_m, indir_s=inps.indir_s, grid_location=inps.grid_location,
init_offset=inps.init_offset, search_range=inps.search_range,
chip_size_min=inps.chip_size_min,chip_size_max=inps.chip_size_max,
offset2vx=inps.offset2vx, offset2vy=inps.offset2vy, scale_factor=inps.scale_factor,
stable_surface_mask=inps.stable_surface_mask, optical_flag=inps.optical_flag,
nc_sensor=inps.nc_sensor, mpflag=inps.mpflag, ncname=inps.ncname)
def generateAutoriftProduct(indir_m, indir_s, grid_location, init_offset, search_range, chip_size_min, chip_size_max,
offset2vx, offset2vy, scale_factor, stable_surface_mask, optical_flag, nc_sensor, mpflag, ncname,
geogrid_run_info=None):
import numpy as np
import time
import os
# import isce
# from components.contrib.geo_autoRIFT.autoRIFT import __version__ as version
from autoRIFT import __version__ as version
if optical_flag == 1:
data_m, data_s = loadProductOptical(indir_m, indir_s)
# test with lena/Venus image
# import scipy.io as sio
# conts = sio.loadmat(indir_m)
# data_m = conts['I']
# data_s = conts['I1']
else:
data_m = loadProduct(indir_m)
data_s = loadProduct(indir_s)
xGrid = None
yGrid = None
Dx0 = None
Dy0 = None
SRx0 = None
SRy0 = None
CSMINx0 = None
CSMINy0 = None
CSMAXx0 = None
CSMAXy0 = None
SSM = None
noDataMask = None
nodata = None
if grid_location is not None:
ds = gdal.Open(grid_location)
tran = ds.GetGeoTransform()
proj = ds.GetProjection()
srs = ds.GetSpatialRef()
band = ds.GetRasterBand(1)
nodata = band.GetNoDataValue()
xGrid = band.ReadAsArray()
noDataMask = (xGrid == nodata)
band = ds.GetRasterBand(2)
yGrid = band.ReadAsArray()
band=None
ds=None
if init_offset is not None:
ds = gdal.Open(init_offset)
band = ds.GetRasterBand(1)
Dx0 = band.ReadAsArray()
band = ds.GetRasterBand(2)
Dy0 = band.ReadAsArray()
band=None
ds=None
if search_range is not None:
ds = gdal.Open(search_range)
band = ds.GetRasterBand(1)
SRx0 = band.ReadAsArray()
band = ds.GetRasterBand(2)
SRy0 = band.ReadAsArray()
band=None
ds=None
if chip_size_min is not None:
ds = gdal.Open(chip_size_min)
band = ds.GetRasterBand(1)
CSMINx0 = band.ReadAsArray()
band = ds.GetRasterBand(2)
CSMINy0 = band.ReadAsArray()
band=None
ds=None
if chip_size_max is not None:
ds = gdal.Open(chip_size_max)
band = ds.GetRasterBand(1)
CSMAXx0 = band.ReadAsArray()
band = ds.GetRasterBand(2)
CSMAXy0 = band.ReadAsArray()
band=None
ds=None
if stable_surface_mask is not None:
ds = gdal.Open(stable_surface_mask)
band = ds.GetRasterBand(1)
SSM = band.ReadAsArray()
SSM = SSM.astype('bool')
band=None
ds=None
intermediate_nc_file = 'autoRIFT_intermediate.nc'
if os.path.exists(intermediate_nc_file):
import netcdf_output as no
Dx, Dy, InterpMask, ChipSizeX, GridSpacingX, ScaleChipSizeY, SearchLimitX, SearchLimitY, origSize, noDataMask = no.netCDF_read_intermediate(intermediate_nc_file)
else:
m_name = os.path.basename(indir_m)
s_name = os.path.basename(indir_s)
# FIXME: Filter width is a magic variable here and not exposed well.
preprocessing_filter_width = 5
if nc_sensor == 'S1':
preprocessing_filter_width = 21
print(f'Preprocessing filter width {preprocessing_filter_width}')
preprocessing_methods = ['hps', 'hps']
for ii, name in enumerate((m_name, s_name)):
if len(re.findall("L[EO]07_", name)) > 0:
acquisition = datetime.strptime(name.split('_')[3], '%Y%m%d')
if acquisition >= datetime(2003, 5, 31):
preprocessing_methods[ii] = 'wallis_fill'
elif len(re.findall("LT0[45]_", name)) > 0:
preprocessing_methods[ii] = 'fft'
print(f'Using preprocessing methods {preprocessing_methods}')
Dx, Dy, InterpMask, ChipSizeX, GridSpacingX, ScaleChipSizeY, SearchLimitX, SearchLimitY, origSize, noDataMask = \
runAutorift(
data_m, data_s, xGrid, yGrid, Dx0, Dy0, SRx0, SRy0, CSMINx0, CSMINy0, CSMAXx0, CSMAXy0,
noDataMask, optical_flag, nodata, mpflag, geogrid_run_info=geogrid_run_info,
preprocessing_methods=preprocessing_methods, preprocessing_filter_width=preprocessing_filter_width,
)
if nc_sensor is not None:
import netcdf_output as no
no.netCDF_packaging_intermediate(Dx, Dy, InterpMask, ChipSizeX, GridSpacingX, ScaleChipSizeY, SearchLimitX, SearchLimitY, origSize, noDataMask, intermediate_nc_file)
if optical_flag == 0:
Dy = -Dy
DX = np.zeros(origSize,dtype=np.float32) * np.nan
DY = np.zeros(origSize,dtype=np.float32) * np.nan
INTERPMASK = np.zeros(origSize,dtype=np.float32)
CHIPSIZEX = np.zeros(origSize,dtype=np.float32)
SEARCHLIMITX = np.zeros(origSize,dtype=np.float32)
SEARCHLIMITY = np.zeros(origSize,dtype=np.float32)
DX[0:Dx.shape[0],0:Dx.shape[1]] = Dx
DY[0:Dy.shape[0],0:Dy.shape[1]] = Dy
INTERPMASK[0:InterpMask.shape[0],0:InterpMask.shape[1]] = InterpMask
CHIPSIZEX[0:ChipSizeX.shape[0],0:ChipSizeX.shape[1]] = ChipSizeX
SEARCHLIMITX[0:SearchLimitX.shape[0],0:SearchLimitX.shape[1]] = SearchLimitX
SEARCHLIMITY[0:SearchLimitY.shape[0],0:SearchLimitY.shape[1]] = SearchLimitY
DX[noDataMask] = np.nan
DY[noDataMask] = np.nan
INTERPMASK[noDataMask] = 0
CHIPSIZEX[noDataMask] = 0
SEARCHLIMITX[noDataMask] = 0
SEARCHLIMITY[noDataMask] = 0
if SSM is not None:
SSM[noDataMask] = False
DX[SEARCHLIMITX == 0] = np.nan
DY[SEARCHLIMITX == 0] = np.nan
INTERPMASK[SEARCHLIMITX == 0] = 0
CHIPSIZEX[SEARCHLIMITX == 0] = 0
if SSM is not None:
SSM[SEARCHLIMITX == 0] = False
import scipy.io as sio
sio.savemat('offset.mat',{'Dx':DX,'Dy':DY,'InterpMask':INTERPMASK,'ChipSizeX':CHIPSIZEX})
# ##################### Uncomment for debug mode
# sio.savemat('debug.mat',{'Dx':DX,'Dy':DY,'InterpMask':INTERPMASK,'ChipSizeX':CHIPSIZEX,'ScaleChipSizeY':ScaleChipSizeY,'SearchLimitX':SEARCHLIMITX,'SearchLimitY':SEARCHLIMITY})
# conts = sio.loadmat('debug.mat')
# DX = conts['Dx']
# DY = conts['Dy']
# INTERPMASK = conts['InterpMask']
# CHIPSIZEX = conts['ChipSizeX']
# GridSpacingX = conts['GridSpacingX']
# ScaleChipSizeY = conts['ScaleChipSizeY']
# SEARCHLIMITX = conts['SearchLimitX']
# SEARCHLIMITY = conts['SearchLimitY']
# origSize = (conts['origSize'][0][0],conts['origSize'][0][1])
# noDataMask = conts['noDataMask']
# #####################
netcdf_file = None
if grid_location is not None:
t1 = time.time()
print("Write Outputs Start!!!")
# Create the GeoTiff
driver = gdal.GetDriverByName('GTiff')
outRaster = driver.Create("offset.tif", int(xGrid.shape[1]), int(xGrid.shape[0]), 4, gdal.GDT_Float32)
outRaster.SetGeoTransform(tran)
outRaster.SetProjection(proj)
outband = outRaster.GetRasterBand(1)
outband.WriteArray(DX)
outband.FlushCache()
outband = outRaster.GetRasterBand(2)
outband.WriteArray(DY)
outband.FlushCache()
outband = outRaster.GetRasterBand(3)
outband.WriteArray(INTERPMASK)
outband.FlushCache()
outband = outRaster.GetRasterBand(4)
outband.WriteArray(CHIPSIZEX)
outband.FlushCache()
del outRaster
if offset2vx is not None:
ds = gdal.Open(scale_factor)
band = ds.GetRasterBand(1)
scale_factor_1 = band.ReadAsArray()
band = ds.GetRasterBand(2)
scale_factor_2 = band.ReadAsArray()
band=None
ds=None
scale_factor_1[scale_factor_1 == nodata] = np.nan
scale_factor_2[scale_factor_2 == nodata] = np.nan
ds = gdal.Open(offset2vx)
band = ds.GetRasterBand(1)
offset2vx_1 = band.ReadAsArray()
band = ds.GetRasterBand(2)
offset2vx_2 = band.ReadAsArray()
if ds.RasterCount > 2:
band = ds.GetRasterBand(3)
offset2vr = band.ReadAsArray()
else:
offset2vr = None
band=None
ds=None
offset2vx_1[offset2vx_1 == nodata] = np.nan
offset2vx_2[offset2vx_2 == nodata] = np.nan
if offset2vr is not None:
offset2vr[offset2vr == nodata] = np.nan
ds = gdal.Open(offset2vy)
band = ds.GetRasterBand(1)
offset2vy_1 = band.ReadAsArray()
band = ds.GetRasterBand(2)
offset2vy_2 = band.ReadAsArray()
if ds.RasterCount > 2:
band = ds.GetRasterBand(3)
offset2va = band.ReadAsArray()
else:
offset2va = None
band=None
ds=None
offset2vy_1[offset2vy_1 == nodata] = np.nan
offset2vy_2[offset2vy_2 == nodata] = np.nan
if offset2va is not None:
offset2va[offset2va == nodata] = np.nan
VX = offset2vx_1 * (DX * scale_factor_1) + offset2vx_2 * (DY * scale_factor_2)
VY = offset2vy_1 * (DX * scale_factor_1) + offset2vy_2 * (DY * scale_factor_2)
VX = VX.astype(np.float32)
VY = VY.astype(np.float32)
############ write velocity output in Geotiff format
outRaster = driver.Create("velocity.tif", int(xGrid.shape[1]), int(xGrid.shape[0]), 2, gdal.GDT_Float32)
outRaster.SetGeoTransform(tran)
outRaster.SetProjection(proj)
outband = outRaster.GetRasterBand(1)
outband.WriteArray(VX)
outband.FlushCache()
outband = outRaster.GetRasterBand(2)
outband.WriteArray(VY)
outband.FlushCache()
del outRaster
############ prepare for netCDF packaging
if nc_sensor is not None:
if nc_sensor == "S1":
swath_offset_bias_ref = [-0.01, 0.019, -0.0068, 0.006]
import netcdf_output as no
DX, DY, flight_direction_m, flight_direction_s = no.cal_swath_offset_bias(indir_m, xGrid, yGrid, VX, VY, DX, DY, nodata, tran, proj, GridSpacingX, ScaleChipSizeY, swath_offset_bias_ref)
if geogrid_run_info is None:
vxrefname = str.split(runCmd('fgrep "Velocities:" testGeogrid.txt'))[1]
vyrefname = str.split(runCmd('fgrep "Velocities:" testGeogrid.txt'))[2]
sxname = str.split(runCmd('fgrep "Slopes:" testGeogrid.txt'))[1][:-4]+"s.tif"
syname = str.split(runCmd('fgrep "Slopes:" testGeogrid.txt'))[2][:-4]+"s.tif"
maskname = str.split(runCmd('fgrep "Slopes:" testGeogrid.txt'))[2][:-8]+"sp.tif"
xoff = int(str.split(runCmd('fgrep "Origin index (in DEM) of geogrid:" testGeogrid.txt'))[6])
yoff = int(str.split(runCmd('fgrep "Origin index (in DEM) of geogrid:" testGeogrid.txt'))[7])
xcount = int(str.split(runCmd('fgrep "Dimensions of geogrid:" testGeogrid.txt'))[3])
ycount = int(str.split(runCmd('fgrep "Dimensions of geogrid:" testGeogrid.txt'))[5])
cen_lat = int(100*float(str.split(runCmd('fgrep "Scene-center lat/lon:" testGeogrid.txt'))[2]))/100
cen_lon = int(100*float(str.split(runCmd('fgrep "Scene-center lat/lon:" testGeogrid.txt'))[3]))/100
else:
vxrefname = geogrid_run_info['vxname']
vyrefname = geogrid_run_info['vyname']
sxname = geogrid_run_info['sxname']
syname = geogrid_run_info['syname']
maskname = geogrid_run_info['maskname']
xoff = geogrid_run_info['xoff']
yoff = geogrid_run_info['yoff']
xcount = geogrid_run_info['xcount']
ycount = geogrid_run_info['ycount']
cen_lat = int(100*geogrid_run_info['cen_lat'])/100
cen_lon = int(100*geogrid_run_info['cen_lon'])/100
ds = gdal.Open(vxrefname)
band = ds.GetRasterBand(1)
VXref = band.ReadAsArray(xoff, yoff, xcount, ycount)
ds = None
band = None
ds = gdal.Open(vyrefname)
band = ds.GetRasterBand(1)
VYref = band.ReadAsArray(xoff, yoff, xcount, ycount)
ds = None
band = None
ds = gdal.Open(sxname)
band = ds.GetRasterBand(1)
SX = band.ReadAsArray(xoff, yoff, xcount, ycount)
ds = None
band = None
ds = gdal.Open(syname)
band = ds.GetRasterBand(1)
SY = band.ReadAsArray(xoff, yoff, xcount, ycount)
ds = None
band = None
ds = gdal.Open(maskname)
band = ds.GetRasterBand(1)
MM = band.ReadAsArray(xoff, yoff, xcount, ycount)
ds = None
band = None
DXref = offset2vy_2 / (offset2vx_1 * offset2vy_2 - offset2vx_2 * offset2vy_1) * VXref - offset2vx_2 / (offset2vx_1 * offset2vy_2 - offset2vx_2 * offset2vy_1) * VYref
DYref = offset2vx_1 / (offset2vx_1 * offset2vy_2 - offset2vx_2 * offset2vy_1) * VYref - offset2vy_1 / (offset2vx_1 * offset2vy_2 - offset2vx_2 * offset2vy_1) * VXref
DXref = DXref / scale_factor_1
DYref = DYref / scale_factor_2
# stable_count = np.sum(SSM & np.logical_not(np.isnan(DX)) & (DX-DXref > -5) & (DX-DXref < 5) & (DY-DYref > -5) & (DY-DYref < 5))
stable_count = np.sum(SSM & np.logical_not(np.isnan(DX)))
V_temp = np.sqrt(VXref**2 + VYref**2)
try:
V_temp_threshold = np.percentile(V_temp[np.logical_not(np.isnan(V_temp))],25)
SSM1 = (V_temp <= V_temp_threshold)
except IndexError:
SSM1 = np.zeros(V_temp.shape).astype('bool')
# stable_count1 = np.sum(SSM1 & np.logical_not(np.isnan(DX)) & (DX-DXref > -5) & (DX-DXref < 5) & (DY-DYref > -5) & (DY-DYref < 5))
stable_count1 = np.sum(SSM1 & np.logical_not(np.isnan(DX)))
dx_mean_shift = 0.0
dy_mean_shift = 0.0
dx_mean_shift1 = 0.0
dy_mean_shift1 = 0.0
if stable_count != 0:
temp = DX.copy() - DXref.copy()
temp[np.logical_not(SSM)] = np.nan
# dx_mean_shift = np.median(temp[(temp > -5)&(temp < 5)])
dx_mean_shift = np.median(temp[np.logical_not(np.isnan(temp))])
temp = DY.copy() - DYref.copy()
temp[np.logical_not(SSM)] = np.nan
# dy_mean_shift = np.median(temp[(temp > -5)&(temp < 5)])
dy_mean_shift = np.median(temp[np.logical_not(np.isnan(temp))])
if stable_count1 != 0:
temp = DX.copy() - DXref.copy()
temp[np.logical_not(SSM1)] = np.nan
# dx_mean_shift1 = np.median(temp[(temp > -5)&(temp < 5)])
dx_mean_shift1 = np.median(temp[np.logical_not(np.isnan(temp))])
temp = DY.copy() - DYref.copy()
temp[np.logical_not(SSM1)] = np.nan
# dy_mean_shift1 = np.median(temp[(temp > -5)&(temp < 5)])
dy_mean_shift1 = np.median(temp[np.logical_not(np.isnan(temp))])
if stable_count == 0:
if stable_count1 == 0:
stable_shift_applied = 0
else:
stable_shift_applied = 2
DX = DX - dx_mean_shift1
DY = DY - dy_mean_shift1
else:
stable_shift_applied = 1
DX = DX - dx_mean_shift
DY = DY - dy_mean_shift
VX = offset2vx_1 * (DX * scale_factor_1) + offset2vx_2 * (DY * scale_factor_2)
VY = offset2vy_1 * (DX * scale_factor_1) + offset2vy_2 * (DY * scale_factor_2)
VX = VX.astype(np.float32)
VY = VY.astype(np.float32)
########################################################################################
############ netCDF packaging for Sentinel and Landsat dataset; can add other sensor format as well
if nc_sensor == "S1":
if geogrid_run_info is None:
chipsizex0 = float(str.split(runCmd('fgrep "Smallest Allowable Chip Size in m:" testGeogrid.txt'))[-1])
gridspacingx = float(str.split(runCmd('fgrep "Grid spacing in m:" testGeogrid.txt'))[-1])
rangePixelSize = float(str.split(runCmd('fgrep "Ground range pixel size:" testGeogrid.txt'))[4])
azimuthPixelSize = float(str.split(runCmd('fgrep "Azimuth pixel size:" testGeogrid.txt'))[3])
dt = float(str.split(runCmd('fgrep "Repeat Time:" testGeogrid.txt'))[2])
epsg = float(str.split(runCmd('fgrep "EPSG:" testGeogrid.txt'))[1])
# print (str(rangePixelSize)+" "+str(azimuthPixelSize))
else:
chipsizex0 = geogrid_run_info['chipsizex0']
gridspacingx = geogrid_run_info['gridspacingx']
rangePixelSize = geogrid_run_info['XPixelSize']
azimuthPixelSize = geogrid_run_info['YPixelSize']
dt = geogrid_run_info['dt']
epsg = geogrid_run_info['epsg']
runCmd('topsinsar_filename.py')
# import scipy.io as sio
conts = sio.loadmat('topsinsar_filename.mat')
master_filename = conts['master_filename'][0]
slave_filename = conts['slave_filename'][0]
master_dt = conts['master_dt'][0]
slave_dt = conts['slave_dt'][0]
master_split = str.split(master_filename,'_')
slave_split = str.split(slave_filename,'_')
import netcdf_output as no
pair_type = 'radar'
detection_method = 'feature'
coordinates = 'radar, map'
if np.sum(SEARCHLIMITX!=0)!=0:
roi_valid_percentage = int(round(np.sum(CHIPSIZEX!=0)/np.sum(SEARCHLIMITX!=0)*1000.0))/1000
else:
raise Exception('Input search range is all zero everywhere, thus no search conducted')
# out_nc_filename = 'Jakobshavn.nc'
PPP = roi_valid_percentage * 100
if ncname is None:
out_nc_filename = f"./{master_filename[0:-4]}_X_{slave_filename[0:-4]}" \
f"_G{gridspacingx:04.0f}V02_P{np.floor(PPP):03.0f}.nc"
else:
out_nc_filename = f"{ncname}_G{gridspacingx:04.0f}V02_P{np.floor(PPP):03.0f}.nc"
CHIPSIZEY = np.round(CHIPSIZEX * ScaleChipSizeY / 2) * 2
# d0 = datetime(np.int(master_split[5][0:4]),np.int(master_split[5][4:6]),np.int(master_split[5][6:8]))
# d1 = datetime(np.int(slave_split[5][0:4]),np.int(slave_split[5][4:6]),np.int(slave_split[5][6:8]))
d0 = datetime.strptime(master_dt,"%Y%m%dT%H:%M:%S.%f")
d1 = datetime.strptime(slave_dt,"%Y%m%dT%H:%M:%S.%f")
date_dt_base = (d1 - d0).total_seconds() / timedelta(days=1).total_seconds()
date_dt = np.float64(date_dt_base)
if date_dt < 0:
raise Exception('Input image 1 must be older than input image 2')
date_ct = d0 + (d1 - d0)/2
date_center = date_ct.strftime("%Y%m%dT%H:%M:%S.%f").rstrip('0')
IMG_INFO_DICT = {
'id_img1': master_filename[0:-4],
'id_img2': slave_filename[0:-4],
'absolute_orbit_number_img1': master_split[7],
'absolute_orbit_number_img2': slave_split[7],
'acquisition_date_img1': master_dt,
'acquisition_date_img2': slave_dt,
'flight_direction_img1': flight_direction_m,
'flight_direction_img2': flight_direction_s,
'mission_data_take_ID_img1': master_split[8],
'mission_data_take_ID_img2': slave_split[8],
'mission_img1': master_split[0][0],
'mission_img2': slave_split[0][0],
'product_unique_ID_img1': master_split[9][0:4],
'product_unique_ID_img2': slave_split[9][0:4],
'satellite_img1': master_split[0][1:3],
'satellite_img2': slave_split[0][1:3],
'sensor_img1': 'C',
'sensor_img2': 'C',
'time_standard_img1': 'UTC',
'time_standard_img2': 'UTC',
'date_center': date_center,
'date_dt': date_dt,
'latitude': cen_lat,
'longitude': cen_lon,
'roi_valid_percentage': PPP,
'autoRIFT_software_version': version
}
error_vector = np.array([[0.0356, 0.0501, 0.0266, 0.0622, 0.0357, 0.0501],
[0.5194, 1.1638, 0.3319, 1.3701, 0.5191, 1.1628]])
netcdf_file = no.netCDF_packaging(
VX, VY, DX, DY, INTERPMASK, CHIPSIZEX, CHIPSIZEY, SSM, SSM1, SX, SY,
offset2vx_1, offset2vx_2, offset2vy_1, offset2vy_2, offset2vr, offset2va, scale_factor_1, scale_factor_2, MM, VXref, VYref,
DXref, DYref, rangePixelSize, azimuthPixelSize, dt, epsg, srs, tran, out_nc_filename, pair_type,
detection_method, coordinates, IMG_INFO_DICT, stable_count, stable_count1, stable_shift_applied,
dx_mean_shift, dy_mean_shift, dx_mean_shift1, dy_mean_shift1, error_vector
)
elif nc_sensor in ("L4", "L5", "L7", "L8", "L9"):
if geogrid_run_info is None:
chipsizex0 = float(str.split(runCmd('fgrep "Smallest Allowable Chip Size in m:" testGeogrid.txt'))[-1])
gridspacingx = float(str.split(runCmd('fgrep "Grid spacing in m:" testGeogrid.txt'))[-1])
XPixelSize = float(str.split(runCmd('fgrep "X-direction pixel size:" testGeogrid.txt'))[3])
YPixelSize = float(str.split(runCmd('fgrep "Y-direction pixel size:" testGeogrid.txt'))[3])
epsg = float(str.split(runCmd('fgrep "EPSG:" testGeogrid.txt'))[1])
else:
chipsizex0 = geogrid_run_info['chipsizex0']
gridspacingx = geogrid_run_info['gridspacingx']
XPixelSize = geogrid_run_info['XPixelSize']
YPixelSize = geogrid_run_info['YPixelSize']
epsg = geogrid_run_info['epsg']
master_path = indir_m
slave_path = indir_s
master_filename = os.path.basename(master_path)
slave_filename = os.path.basename(slave_path)
master_split = str.split(master_filename,'_')
slave_split = str.split(slave_filename,'_')
# master_MTL_path = master_path[:-6]+'MTL.txt'
# slave_MTL_path = slave_path[:-6]+'MTL.txt'
#
# master_time = str.split(str.split(runCmd('fgrep "SCENE_CENTER_TIME" '+master_MTL_path))[2][1:-2],':')
# slave_time = str.split(str.split(runCmd('fgrep "SCENE_CENTER_TIME" '+slave_MTL_path))[2][1:-2],':')
import netcdf_output as no
pair_type = 'optical'
detection_method = 'feature'
coordinates = 'map'
if np.sum(SEARCHLIMITX!=0)!=0:
roi_valid_percentage = int(round(np.sum(CHIPSIZEX!=0)/np.sum(SEARCHLIMITX!=0)*1000.0))/1000
else:
raise Exception('Input search range is all zero everywhere, thus no search conducted')
# out_nc_filename = 'Jakobshavn_opt.nc'
PPP = roi_valid_percentage * 100
if ncname is None:
out_nc_filename = f"./{master_filename[0:-7]}_X_{slave_filename[0:-7]}" \
f"_G{gridspacingx:04.0f}V02_P{np.floor(PPP):03.0f}.nc"
else:
out_nc_filename = f"{ncname}_G{gridspacingx:04.0f}V02_P{np.floor(PPP):03.0f}.nc"
CHIPSIZEY = np.round(CHIPSIZEX * ScaleChipSizeY / 2) * 2
d0 = datetime(np.int(master_split[3][0:4]),np.int(master_split[3][4:6]),np.int(master_split[3][6:8]))
d1 = datetime(np.int(slave_split[3][0:4]),np.int(slave_split[3][4:6]),np.int(slave_split[3][6:8]))
date_dt_base = (d1 - d0).total_seconds() / timedelta(days=1).total_seconds()
date_dt = np.float64(date_dt_base)
if date_dt < 0:
raise Exception('Input image 1 must be older than input image 2')
date_ct = d0 + (d1 - d0)/2
date_center = date_ct.strftime("%Y%m%dT%H:%M:%S.%f").rstrip('0')
master_dt = d0.strftime("%Y%m%dT%H:%M:%S.%f").rstrip('0')
slave_dt = d1.strftime("%Y%m%dT%H:%M:%S.%f").rstrip('0')
IMG_INFO_DICT = {
'id_img1': master_filename[0:-7],
'id_img2': slave_filename[0:-7],
'acquisition_date_img1': master_dt,
'acquisition_date_img2': slave_dt,
'collection_category_img1': master_split[6],
'collection_category_img2': slave_split[6],
'collection_number_img1': np.float64(master_split[5]),
'collection_number_img2': np.float64(slave_split[5]),
'correction_level_img1': master_split[1],
'correction_level_img2': slave_split[1],
'mission_img1': master_split[0][0],
'mission_img2': slave_split[0][0],
'path_img1': np.float64(master_split[2][0:3]),
'path_img2': np.float64(slave_split[2][0:3]),
'processing_date_img1': master_split[4][0:8],
'processing_date_img2': slave_split[4][0:8],
'row_img1': np.float64(master_split[2][3:6]),