def main():
usage = '''
Usage:
-----------------------------------------------------------------------
python %s [-d spatialDimensions] [-p bandPositions [-r resolution ratio]
[-b registration band] msfilename panfilename
-----------------------------------------------------------------------
bandPositions and spatialDimensions are lists,
e.g., -p [1,2,3] -d [0,0,400,400]
Outfile name is msfilename_pan_atwt with same format as msfilename
Note: PAN image must completely overlap MS image subset chosen
-----------------------------------------------------''' %sys.argv[0]
options, args = getopt.getopt(sys.argv[1:],'hd:p:r:b:')
ratio = 4
dims1 = None
pos1 = None
k1 = 1
for option, value in options:
if option == '-h':
print usage
return
elif option == '-r':
ratio = eval(value)
elif option == '-d':
dims1 = eval(value)
elif option == '-p':
pos1 = eval(value)
elif option == '-b':
k1 = eval(value)
if len(args) != 2:
print 'Incorrect number of arguments'
print usage
sys.exit(1)
gdal.AllRegister()
file1 = args[0]
file2 = args[1]
path = os.path.dirname(file1)
basename1 = os.path.basename(file1)
root1, ext1 = os.path.splitext(basename1)
outfile = '%s/%s_pan_atwt%s'%(path,root1,ext1)
# MS image
inDataset1 = gdal.Open(file1,GA_ReadOnly)
try:
cols = inDataset1.RasterXSize
rows = inDataset1.RasterYSize
bands = inDataset1.RasterCount
except Exception as e:
print 'Error: %e --Image could not be read'%e
sys.exit(1)
if pos1 is None:
pos1 = range(1,bands+1)
num_bands = len(pos1)
if dims1 is None:
dims1 = [0,0,cols,rows]
x10,y10,cols1,rows1 = dims1
# PAN image
inDataset2 = gdal.Open(file2,GA_ReadOnly)
try:
bands = inDataset2.RasterCount
except Exception as e:
print 'Error: %e --Image could not be read'%e
sys.exit(1)
if bands>1:
print 'PAN image must be a single band'
sys.exit(1)
geotransform1 = inDataset1.GetGeoTransform()
geotransform2 = inDataset2.GetGeoTransform()
if (geotransform1 is None) or (geotransform2 is None):
print 'Image not georeferenced, aborting'
sys.exit(1)
print '========================='
print ' ATWT Pansharpening'
print '========================='
print time.asctime()
print 'MS file: '+file1
print 'PAN file: '+file2
# read in MS image
band = inDataset1.GetRasterBand(1)
tmp = band.ReadAsArray(0,0,1,1)
dt = tmp.dtype
MS = np.asarray(np.zeros((num_bands,rows1,cols1)),dtype = dt)
k = 0
for b in pos1:
band = inDataset1.GetRasterBand(b)
MS[k,:,:] = band.ReadAsArray(x10,y10,cols1,rows1)
k += 1
# if integer assume 11-bit quantization, otherwise must be byte
if MS.dtype == np.int16:
fact = 8.0
MS = auxil.byteStretch(MS,(0,2**11))
else:
fact = 1.0
# read in corresponding spatial subset of PAN image
gt1 = list(geotransform1)
gt2 = list(geotransform2)
ulx1 = gt1[0] + x10*gt1[1]
uly1 = gt1[3] + y10*gt1[5]
x20 = int(round(((ulx1 - gt2[0])/gt2[1])))
y20 = int(round(((uly1 - gt2[3])/gt2[5])))
cols2 = cols1*ratio
rows2 = rows1*ratio
band = inDataset2.GetRasterBand(1)
PAN = band.ReadAsArray(x20,y20,cols2,rows2)
# if integer assume 11-bit quantization, otherwise must be byte
if PAN.dtype == np.int16:
PAN = auxil.byteStretch(PAN,(0,2**11))
# out array
sharpened = np.zeros((num_bands,rows2,cols2),dtype=np.float32)
# compress PAN to resolution of MS image using DWT
panDWT = auxil.DWTArray(PAN,cols2,rows2)
r = ratio
while r > 1:
panDWT.filter()
r /= 2
bn0 = panDWT.get_quadrant(0)
# register (and subset) MS image to compressed PAN image using selected MSband
lines0,samples0 = bn0.shape
bn1 = MS[k1-1,:,:]
# register (and subset) MS image to compressed PAN image
(scale,angle,shift) = auxil.similarity(bn0,bn1)
tmp = np.zeros((num_bands,lines0,samples0))
for k in range(num_bands):
bn1 = MS[k,:,:]
bn2 = ndii.zoom(bn1, 1.0/scale)
bn2 = ndii.rotate(bn2, angle)
bn2 = ndii.shift(bn2, shift)
tmp[k,:,:] = bn2[0:lines0,0:samples0]
MS = tmp
smpl = np.random.randint(cols2*rows2,size=100000)
print 'Wavelet correlations:'
# loop over MS bands
for k in range(num_bands):
msATWT = auxil.ATWTArray(PAN)
r = ratio
while r > 1:
msATWT.filter()
r /= 2
# sample PAN wavelet details
X = msATWT.get_band(msATWT.num_iter)
X = X.ravel()[smpl]
# resize the ms band to scale of the pan image
ms_band = ndii.zoom(MS[k,:,:],ratio)
# sample details of MS band
tmpATWT = auxil.ATWTArray(ms_band)
r = ratio
while r > 1:
tmpATWT.filter()
r /= 2
Y = tmpATWT.get_band(msATWT.num_iter)
Y = Y.ravel()[smpl]
# get band for injection
bnd = tmpATWT.get_band(0)
tmpATWT = None
aa,bb,R = auxil.orthoregress(X,Y)
print 'Band '+str(k+1)+': %8.3f'%R
# inject the filtered MS band
msATWT.inject(bnd)
# normalize wavelet components and expand
msATWT.normalize(aa,bb)
r = ratio
while r > 1:
msATWT.invert()
r /= 2
sharpened[k,:,:] = msATWT.get_band(0)
sharpened *= fact # rescale dynamic range
msATWT = None
# write to disk
driver = inDataset1.GetDriver()
outDataset = driver.Create(outfile,cols2,rows2,num_bands,GDT_Float32)
gt1[0] += x10*ratio
gt1[3] -= y10*ratio
gt1[1] = gt2[1]
gt1[2] = gt2[2]
gt1[4] = gt2[4]
gt1[5] = gt2[5]
outDataset.SetGeoTransform(tuple(gt1))
projection1 = inDataset1.GetProjection()
if projection1 is not None:
outDataset.SetProjection(projection1)
for k in range(num_bands):
outBand = outDataset.GetRasterBand(k+1)
outBand.WriteArray(sharpened[k,:,:],0,0)
outBand.FlushCache()
outDataset = None
print 'Result written to %s'%outfile
inDataset1 = None
inDataset2 = None
def register(fn1, fn2, warpband, dims1=None, outfile=None):
gdal.AllRegister()
print '--------------------------------'
print' Register'
print'---------------------------------'
print time.asctime()
print 'reference image: '+fn1
print 'warp image: '+fn2
print 'warp band: %i'%warpband
start = time.time()
try:
if outfile is None:
path2 = os.path.dirname(fn2)
basename2 = os.path.basename(fn2)
root2, ext2 = os.path.splitext(basename2)
outfile = path2 + '/' + root2 + '_warp' + ext2
inDataset1 = gdal.Open(fn1,GA_ReadOnly)
inDataset2 = gdal.Open(fn2,GA_ReadOnly)
try:
cols1 = inDataset1.RasterXSize
rows1 = inDataset1.RasterYSize
cols2 = inDataset2.RasterXSize
rows2 = inDataset2.RasterYSize
bands2 = inDataset2.RasterCount
except Exception as e:
print 'Error %s --Image could not be read in'%e
sys.exit(1)
if dims1 is None:
x0 = 0
y0 = 0
else:
x0,y0,cols1,rows1 = dims1
band = inDataset1.GetRasterBand(warpband)
refband = band.ReadAsArray(x0,y0,cols1,rows1).astype(np.float32)
band = inDataset2.GetRasterBand(warpband)
warpband = band.ReadAsArray(x0,y0,cols1,rows1).astype(np.float32)
# similarity transform parameters for reference band number
scale, angle, shift = similarity(refband, warpband)
driver = inDataset2.GetDriver()
outDataset = driver.Create(outfile,cols1,rows1,bands2,GDT_Float32)
projection = inDataset1.GetProjection()
geotransform = inDataset1.GetGeoTransform()
if geotransform is not None:
gt = list(geotransform)
gt[0] = gt[0] + x0*gt[1]
gt[3] = gt[3] + y0*gt[5]
outDataset.SetGeoTransform(tuple(gt))
if projection is not None:
outDataset.SetProjection(projection)
# warp
for k in range(bands2):
inband = inDataset2.GetRasterBand(k+1)
outBand = outDataset.GetRasterBand(k+1)
bn1 = inband.ReadAsArray(0,0,cols2,rows2).astype(np.float32)
bn2 = ndii.zoom(bn1, 1.0 / scale)
bn2 = ndii.rotate(bn2, angle)
bn2 = ndii.shift(bn2, shift)
outBand.WriteArray(bn2[y0:y0+rows1, x0:x0+cols1])
outBand.FlushCache()
inDataset1 = None
inDataset2 = None
outDataset = None
print 'Warped image written to: %s'%outfile
print 'elapsed time: %s'%str(time.time()-start)
return outfile
except Exception as e:
print 'registersms failed: %s'%e
return None
def main():
usage = '''
Usage:
-----------------------------------------------------------------------
python %s [-d spatialDimensions] [-p bandPositions [-r resolution ratio]
[-b registration band] msfilename panfilename
-----------------------------------------------------------------------
bandPositions and spatialDimensions are lists,
e.g., -p [1,2,3] -d [0,0,400,400]
Outfile name is msfilename_pan_dwt with same format as msfilename
Note: PAN image must completely overlap MS image subset chosen
-----------------------------------------------------''' %sys.argv[0]
options, args = getopt.getopt(sys.argv[1:],'hd:p:r:b:')
ratio = 4
dims1 = None
pos1 = None
k1 = 0
for option, value in options:
if option == '-h':
print usage
return
elif option == '-r':
ratio = eval(value)
elif option == '-d':
dims1 = eval(value)
elif option == '-p':
pos1 = eval(value)
elif option == '-b':
k1 = eval(value)-1
if len(args) != 2:
print 'Incorrect number of arguments'
print usage
sys.exit(1)
gdal.AllRegister()
file1 = args[0]
file2 = args[1]
path = os.path.dirname(file1)
basename1 = os.path.basename(file1)
root1, ext1 = os.path.splitext(basename1)
outfile = '%s/%s_pan_dwt%s'%(path,root1,ext1)
# MS image
inDataset1 = gdal.Open(file1,GA_ReadOnly)
try:
cols = inDataset1.RasterXSize
rows = inDataset1.RasterYSize
bands = inDataset1.RasterCount
except Exception as e:
print 'Error: %e --Image could not be read'%e
sys.exit(1)
if pos1 is None:
pos1 = range(1,bands+1)
num_bands = len(pos1)
if dims1 is None:
dims1 = [0,0,cols,rows]
x10,y10,cols1,rows1 = dims1
# PAN image
inDataset2 = gdal.Open(file2,GA_ReadOnly)
try:
bands = inDataset2.RasterCount
except Exception as e:
print 'Error: %e --Image could not be read'%e
sys.exit(1)
if bands>1:
print 'PAN image must be a single band'
sys.exit(1)
geotransform1 = inDataset1.GetGeoTransform()
geotransform2 = inDataset2.GetGeoTransform()
if (geotransform1 is None) or (geotransform2 is None):
print 'Image not georeferenced, aborting'
sys.exit(1)
print '========================='
print ' DWT Pansharpening'
print '========================='
print time.asctime()
print 'MS file: '+file1
print 'PAN file: '+file2
# image arrays
band = inDataset1.GetRasterBand(1)
tmp = band.ReadAsArray(0,0,1,1)
dt = tmp.dtype
MS = np.asarray(np.zeros((num_bands,rows1,cols1)),dtype=dt)
k = 0
for b in pos1:
band = inDataset1.GetRasterBand(b)
MS[k,:,:] = band.ReadAsArray(x10,y10,cols1,rows1)
k += 1
# if integer assume 11bit quantization otherwise must be byte
if MS.dtype == np.int16:
fact = 8.0
MS = auxil.byteStretch(MS,(0,2**11))
else:
fact = 1.0
# read in corresponding spatial subset of PAN image
if (geotransform1 is None) or (geotransform2 is None):
print 'Image not georeferenced, aborting'
return
# upper left corner pixel in PAN
gt1 = list(geotransform1)
gt2 = list(geotransform2)
ulx1 = gt1[0] + x10*gt1[1]
uly1 = gt1[3] + y10*gt1[5]
x20 = int(round(((ulx1 - gt2[0])/gt2[1])))
y20 = int(round(((uly1 - gt2[3])/gt2[5])))
cols2 = cols1*ratio
rows2 = rows1*ratio
band = inDataset2.GetRasterBand(1)
PAN = band.ReadAsArray(x20,y20,cols2,rows2)
# if integer assume 11-bit quantization, otherwise must be byte
if PAN.dtype == np.int16:
PAN = auxil.byteStretch(PAN,(0,2**11))
# compress PAN to resolution of MS image
panDWT = auxil.DWTArray(PAN,cols2,rows2)
r = ratio
while r > 1:
panDWT.filter()
r /= 2
bn0 = panDWT.get_quadrant(0)
lines0,samples0 = bn0.shape
bn1 = MS[k1,:,:]
# register (and subset) MS image to compressed PAN image
(scale,angle,shift) = auxil.similarity(bn0,bn1)
tmp = np.zeros((num_bands,lines0,samples0))
for k in range(num_bands):
bn1 = MS[k,:,:]
bn2 = ndii.zoom(bn1, 1.0/scale)
bn2 = ndii.rotate(bn2, angle)
bn2 = ndii.shift(bn2, shift)
tmp[k,:,:] = bn2[0:lines0,0:samples0]
MS = tmp
# compress pan once more, extract wavelet quadrants, and restore
panDWT.filter()
fgpan = panDWT.get_quadrant(1)
gfpan = panDWT.get_quadrant(2)
ggpan = panDWT.get_quadrant(3)
panDWT.invert()
# output array
sharpened = np.zeros((num_bands,rows2,cols2),dtype=np.float32)
aa = np.zeros(3)
bb = np.zeros(3)
print 'Wavelet correlations:'
for i in range(num_bands):
# make copy of panDWT and inject ith ms band
msDWT = copy.deepcopy(panDWT)
msDWT.put_quadrant(MS[i,:,:],0)
# compress once more
msDWT.filter()
# determine wavelet normalization coefficents
ms = msDWT.get_quadrant(1)
aa[0],bb[0],R = auxil.orthoregress(fgpan.ravel(), ms.ravel())
Rs = 'Band '+str(i+1)+': %8.3f'%R
ms = msDWT.get_quadrant(2)
aa[1],bb[1],R = auxil.orthoregress(gfpan.ravel(), ms.ravel())
Rs += '%8.3f'%R
ms = msDWT.get_quadrant(3)
aa[2],bb[2],R = auxil.orthoregress(ggpan.ravel(), ms.ravel())
Rs += '%8.3f'%R
print Rs
# restore once and normalize wavelet coefficients
msDWT.invert()
msDWT.normalize(aa,bb)
# restore completely and collect result
r = 1
while r < ratio:
msDWT.invert()
r *= 2
sharpened[i,:,:] = msDWT.get_quadrant(0)
sharpened *= fact
# write to disk
driver = inDataset1.GetDriver()
outDataset = driver.Create(outfile,cols2,rows2,num_bands,GDT_Float32)
projection1 = inDataset1.GetProjection()
if projection1 is not None:
outDataset.SetProjection(projection1)
gt1 = list(geotransform1)
gt1[0] += x10*ratio
gt1[3] -= y10*ratio
gt1[1] = gt2[1]
gt1[2] = gt2[2]
gt1[4] = gt2[4]
gt1[5] = gt2[5]
outDataset.SetGeoTransform(tuple(gt1))
for k in range(num_bands):
outBand = outDataset.GetRasterBand(k+1)
outBand.WriteArray(sharpened[k,:,:],0,0)
outBand.FlushCache()
outDataset = None
print 'Result written to %s'%outfile
inDataset1 = None
inDataset2 = None
def register(file0, file1, dims=None, outfile=None):
import auxil.auxil1 as auxil
import os, time
import numpy as np
from osgeo import gdal
import scipy.ndimage.interpolation as ndii
from osgeo.gdalconst import GA_ReadOnly, GDT_Float32
print('========================= ')
print(' Register SAR')
print('=========================')
print(time.asctime())
try:
if outfile is None:
path = os.path.abspath(file1)
dirn = os.path.dirname(path)
path = os.path.dirname(file1)
basename = os.path.basename(file1)
root, ext = os.path.splitext(basename)
outfile = dirn + '/' + root + '_warp' + ext
start = time.time()
gdal.AllRegister()
# reference
inDataset0 = gdal.Open(file0, GA_ReadOnly)
cols = inDataset0.RasterXSize
rows = inDataset0.RasterYSize
bands = inDataset0.RasterCount
print('Reference SAR image:\n %s' % file0)
if dims == None:
dims = [0, 0, cols, rows]
x0, y0, cols, rows = dims
# target
inDataset1 = gdal.Open(file1, GA_ReadOnly)
cols1 = inDataset1.RasterXSize
rows1 = inDataset1.RasterYSize
bands1 = inDataset1.RasterCount
print('Target SAR image:\n %s' % file1)
if bands != bands1:
print('Number of bands must be equal')
return 0
# create the output file
driver = inDataset1.GetDriver()
outDataset = driver.Create(outfile, cols, rows, bands, GDT_Float32)
projection0 = inDataset0.GetProjection()
geotransform0 = inDataset0.GetGeoTransform()
geotransform1 = inDataset1.GetGeoTransform()
gt0 = list(geotransform0)
gt1 = list(geotransform1)
if projection0 is not None:
outDataset.SetProjection(projection0)
# find the upper left corner (x0,y0) of reference subset in target (x1,y1)
ulx0 = gt0[0] + x0 * gt0[1] + y0 * gt0[2]
uly0 = gt0[3] + x0 * gt0[4] + y0 * gt0[5]
GT1 = np.mat([[gt1[1], gt1[2]], [gt1[4], gt1[5]]])
ul1 = np.mat([[ulx0 - gt1[0]], [uly0 - gt1[3]]])
tmp = GT1.I * ul1
x1 = int(round(tmp[0, 0]))
y1 = int(round(tmp[1, 0]))
# create output geotransform
gt1 = gt0
gt1[0] = ulx0
gt1[3] = uly0
outDataset.SetGeoTransform(tuple(gt1))
# get matching subsets from geotransform
rasterBand = inDataset0.GetRasterBand(1)
span0 = rasterBand.ReadAsArray(x0, y0, cols, rows)
rasterBand = inDataset1.GetRasterBand(1)
span1 = rasterBand.ReadAsArray(x1, y1, cols, rows)
if bands == 9:
# get warp parameters using span images
print('warping 9 bands (quad pol)...')
rasterBand = inDataset0.GetRasterBand(6)
span0 += rasterBand.ReadAsArray(x0, y0, cols, rows)
rasterBand = inDataset0.GetRasterBand(9)
span0 += rasterBand.ReadAsArray(x0, y0, cols, rows)
span0 = np.log(np.nan_to_num(span0) + 0.001)
rasterBand = inDataset1.GetRasterBand(6)
span1 += rasterBand.ReadAsArray(x1, y1, cols, rows)
rasterBand = inDataset1.GetRasterBand(9)
span1 += rasterBand.ReadAsArray(x1, y1, cols, rows)
span1 = np.log(np.nan_to_num(span1) + 0.001)
scale, angle, shift = auxil.similarity(span0, span1)
# warp the target to the reference and clip
for k in range(9):
rasterBand = inDataset1.GetRasterBand(k + 1)
band = rasterBand.ReadAsArray(0, 0, cols1,
rows1).astype(np.float32)
bn1 = np.nan_to_num(band)
bn2 = ndii.zoom(bn1, 1.0 / scale)
bn2 = ndii.rotate(bn2, angle)
bn2 = ndii.shift(bn2, shift)
bn = bn2[y1:y1 + rows, x1:x1 + cols]
outBand = outDataset.GetRasterBand(k + 1)
outBand.WriteArray(bn)
outBand.FlushCache()
elif bands == 4:
# get warp parameters using span images
print('warping 4 bands (dual pol)...')
rasterBand = inDataset0.GetRasterBand(4)
span0 += rasterBand.ReadAsArray(x0, y0, cols, rows)
span0 = np.log(np.nan_to_num(span0) + 0.001)
rasterBand = inDataset1.GetRasterBand(4)
span1 += rasterBand.ReadAsArray(x1, y1, cols, rows)
span1 = np.log(np.nan_to_num(span1) + 0.001)
scale, angle, shift = auxil.similarity(span0, span1)
# warp the target to the reference and clip
for k in range(4):
rasterBand = inDataset1.GetRasterBand(k + 1)
band = rasterBand.ReadAsArray(0, 0, cols1,
rows1).astype(np.float32)
bn1 = np.nan_to_num(band)
bn2 = ndii.zoom(bn1, 1.0 / scale)
bn2 = ndii.rotate(bn2, angle)
bn2 = ndii.shift(bn2, shift)
bn = bn2[y1:y1 + rows, x1:x1 + cols]
outBand = outDataset.GetRasterBand(k + 1)
outBand.WriteArray(bn)
outBand.FlushCache()
elif bands == 3:
# get warp parameters using span images
print('warping 3 bands (quad pol diagonal)...')
rasterBand = inDataset0.GetRasterBand(2)
span0 += rasterBand.ReadAsArray(x0, y0, cols, rows)
rasterBand = inDataset0.GetRasterBand(3)
span0 += rasterBand.ReadAsArray(x0, y0, cols, rows)
span0 = np.log(np.nan_to_num(span0) + 0.001)
rasterBand = inDataset1.GetRasterBand(2)
span1 += rasterBand.ReadAsArray(x1, y1, cols, rows)
rasterBand = inDataset1.GetRasterBand(3)
span1 += rasterBand.ReadAsArray(x1, y1, cols, rows)
span1 = np.log(np.nan_to_num(span1) + 0.001)
scale, angle, shift = auxil.similarity(span0, span1)
# warp the target to the reference and clip
for k in range(3):
rasterBand = inDataset1.GetRasterBand(k + 1)
band = rasterBand.ReadAsArray(0, 0, cols1,
rows1).astype(np.float32)
bn1 = np.nan_to_num(band)
bn2 = ndii.zoom(bn1, 1.0 / scale)
bn2 = ndii.rotate(bn2, angle)
bn2 = ndii.shift(bn2, shift)
bn = bn2[y1:y1 + rows, x1:x1 + cols]
outBand = outDataset.GetRasterBand(k + 1)
outBand.WriteArray(bn)
outBand.FlushCache()
elif bands == 2:
# get warp parameters using span images
print('warping 2 bands (dual pol diagonal)...')
rasterBand = inDataset0.GetRasterBand(2)
span0 += rasterBand.ReadAsArray(x0, y0, cols, rows)
span0 = np.log(np.nan_to_num(span0) + 0.001)
rasterBand = inDataset1.GetRasterBand(2)
span1 += rasterBand.ReadAsArray(x1, y1, cols, rows)
span1 = np.log(np.nan_to_num(span1) + 0.001)
scale, angle, shift = auxil.similarity(span0, span1)
# warp the target to the reference and clip
for k in range(2):
rasterBand = inDataset1.GetRasterBand(k + 1)
band = rasterBand.ReadAsArray(0, 0, cols1,
rows1).astype(np.float32)
bn1 = np.nan_to_num(band)
bn2 = ndii.zoom(bn1, 1.0 / scale)
bn2 = ndii.rotate(bn2, angle)
bn2 = ndii.shift(bn2, shift)
bn = bn2[y1:y1 + rows, x1:x1 + cols]
outBand = outDataset.GetRasterBand(k + 1)
outBand.WriteArray(bn)
outBand.FlushCache()
elif bands == 1:
# get warp parameters using span images
print('warping 1 band (single pol)...')
span0 = np.log(np.nan_to_num(span0) + 0.001)
span1 = np.log(np.nan_to_num(span1) + 0.001)
scale, angle, shift = auxil.similarity(span0, span1)
# warp the target to the reference and clip
for k in range(1):
rasterBand = inDataset1.GetRasterBand(k + 1)
band = rasterBand.ReadAsArray(0, 0, cols1,
rows1).astype(np.float32)
bn1 = np.nan_to_num(band)
bn2 = ndii.zoom(bn1, 1.0 / scale)
bn2 = ndii.rotate(bn2, angle)
bn2 = ndii.shift(bn2, shift)
bn = bn2[y1:y1 + rows, x1:x1 + cols]
outBand = outDataset.GetRasterBand(k + 1)
outBand.WriteArray(bn)
outBand.FlushCache()
inDataset0 = None
inDataset1 = None
outDataset = None
print('Warped image written to: %s' % outfile)
print('elapsed time: ' + str(time.time() - start))
return outfile
except Exception as e:
print('registersar failed: %s' % e)
return None