#!/usr/bin/env python # Conversion of csv / space separated text file # into a vrt data set # Author: [email protected] import sys import os try: from osgeo import gdal except ImportError: import gdal gdal.AllRegister() argv = None nr = 0 header = 0 if argv is None: argv = sys.argv argv = gdal.GeneralCmdLineProcessor(argv) if argv is None: sys.exit(0) if argv < 14 or argv > 15: print "Usage: oft-csv2vrt <-l layer> <-x xcol> <-y ycol> <-v varcol> <-s separator> [-h header] <-i infile> <-o outfile>" sys.exit(0) # process arguments
def main(argv=None):
global verbose, quiet
verbose = 0
quiet = 0
names = []
format = None
out_file = 'out.tif'
ulx = None
psize_x = None
separate = 0
copy_pct = 0
nodata = None
a_nodata = None
create_options = []
pre_init = []
band_type = None
createonly = 0
bTargetAlignedPixels = False
start_time = time.time()
gdal.AllRegister()
if argv is None:
argv = sys.argv
argv = gdal.GeneralCmdLineProcessor(argv)
if argv is None:
sys.exit(0)
# Parse command line arguments.
i = 1
while i < len(argv):
arg = argv[i]
if arg == '-o':
i = i + 1
out_file = argv[i]
elif arg == '-v':
verbose = 1
elif arg == '-q' or arg == '-quiet':
quiet = 1
elif arg == '-createonly':
createonly = 1
elif arg == '-separate':
separate = 1
elif arg == '-seperate':
separate = 1
elif arg == '-pct':
copy_pct = 1
elif arg == '-ot':
i = i + 1
band_type = gdal.GetDataTypeByName(argv[i])
if band_type == gdal.GDT_Unknown:
print('Unknown GDAL data type: %s' % argv[i])
sys.exit(1)
elif arg == '-init':
i = i + 1
str_pre_init = argv[i].split()
for x in str_pre_init:
pre_init.append(float(x))
elif arg == '-n':
i = i + 1
nodata = float(argv[i])
elif arg == '-a_nodata':
i = i + 1
a_nodata = float(argv[i])
elif arg == '-f' or arg == '-of':
i = i + 1
format = argv[i]
elif arg == '-co':
i = i + 1
create_options.append(argv[i])
elif arg == '-ps':
psize_x = float(argv[i + 1])
psize_y = -1 * abs(float(argv[i + 2]))
i = i + 2
elif arg == '-tap':
bTargetAlignedPixels = True
elif arg == '-ul_lr':
ulx = float(argv[i + 1])
uly = float(argv[i + 2])
lrx = float(argv[i + 3])
lry = float(argv[i + 4])
i = i + 4
elif arg[:1] == '-':
print('Unrecognized command option: %s' % arg)
Usage()
sys.exit(1)
else:
names.append(arg)
i = i + 1
if len(names) == 0:
print('No input files selected.')
Usage()
sys.exit(1)
if format is None:
format = GetOutputDriverFor(out_file)
Driver = gdal.GetDriverByName(format)
if Driver is None:
print('Format driver %s not found, pick a supported driver.' % format)
sys.exit(1)
DriverMD = Driver.GetMetadata()
if 'DCAP_CREATE' not in DriverMD:
print(
'Format driver %s does not support creation and piecewise writing.\nPlease select a format that does, such as GTiff (the default) or HFA (Erdas Imagine).'
% format)
sys.exit(1)
# Collect information on all the source files.
file_infos = names_to_fileinfos(names)
if ulx is None:
ulx = file_infos[0].ulx
uly = file_infos[0].uly
lrx = file_infos[0].lrx
lry = file_infos[0].lry
for fi in file_infos:
ulx = min(ulx, fi.ulx)
uly = max(uly, fi.uly)
lrx = max(lrx, fi.lrx)
lry = min(lry, fi.lry)
if psize_x is None:
psize_x = file_infos[0].geotransform[1]
psize_y = file_infos[0].geotransform[5]
if band_type is None:
band_type = file_infos[0].band_type
# Try opening as an existing file.
gdal.PushErrorHandler('CPLQuietErrorHandler')
t_fh = gdal.Open(out_file, gdal.GA_Update)
gdal.PopErrorHandler()
# Create output file if it does not already exist.
if t_fh is None:
if bTargetAlignedPixels:
ulx = math.floor(ulx / psize_x) * psize_x
lrx = math.ceil(lrx / psize_x) * psize_x
lry = math.floor(lry / -psize_y) * -psize_y
uly = math.ceil(uly / -psize_y) * -psize_y
geotransform = [ulx, psize_x, 0, uly, 0, psize_y]
xsize = int((lrx - ulx) / geotransform[1] + 0.5)
ysize = int((lry - uly) / geotransform[5] + 0.5)
if separate != 0:
bands = 0
for fi in file_infos:
bands = bands + fi.bands
else:
bands = file_infos[0].bands
t_fh = Driver.Create(out_file, xsize, ysize, bands, band_type,
create_options)
if t_fh is None:
print('Creation failed, terminating gdal_merge.')
sys.exit(1)
t_fh.SetGeoTransform(geotransform)
t_fh.SetProjection(file_infos[0].projection)
if copy_pct:
t_fh.GetRasterBand(1).SetRasterColorTable(file_infos[0].ct)
else:
if separate != 0:
bands = 0
for fi in file_infos:
bands = bands + fi.bands
if t_fh.RasterCount < bands:
print(
'Existing output file has less bands than the input files. You should delete it before. Terminating gdal_merge.'
)
sys.exit(1)
else:
bands = min(file_infos[0].bands, t_fh.RasterCount)
# Do we need to set nodata value ?
if a_nodata is not None:
for i in range(t_fh.RasterCount):
t_fh.GetRasterBand(i + 1).SetNoDataValue(a_nodata)
# Do we need to pre-initialize the whole mosaic file to some value?
if pre_init is not None:
if t_fh.RasterCount <= len(pre_init):
for i in range(t_fh.RasterCount):
t_fh.GetRasterBand(i + 1).Fill(pre_init[i])
elif len(pre_init) == 1:
for i in range(t_fh.RasterCount):
t_fh.GetRasterBand(i + 1).Fill(pre_init[0])
# Copy data from source files into output file.
t_band = 1
if quiet == 0 and verbose == 0:
progress(0.0)
fi_processed = 0
for fi in file_infos:
if createonly != 0:
continue
if verbose != 0:
print("")
print(
"Processing file %5d of %5d, %6.3f%% completed in %d minutes."
%
(fi_processed + 1, len(file_infos), fi_processed * 100.0 /
len(file_infos), int(round(
(time.time() - start_time) / 60.0))))
fi.report()
if separate == 0:
for band in range(1, bands + 1):
fi.copy_into(t_fh, band, band, nodata)
else:
for band in range(1, fi.bands + 1):
fi.copy_into(t_fh, band, t_band, nodata)
t_band = t_band + 1
fi_processed = fi_processed + 1
if quiet == 0 and verbose == 0:
progress(fi_processed / float(len(file_infos)))
# Force file to be closed.
t_fh = None
def generate_data(self):
'''
Description:
Provides the main processing algorithm for building the Land
Surface Temperature product. It produces the final LST product.
'''
try:
self.retrieve_metadata_information()
except Exception:
self.logger.exception('Failed reading input XML metadata file')
raise
# Register all the gdal drivers and choose the ENVI for our output
gdal.AllRegister()
envi_driver = gdal.GetDriverByName('ENVI')
# Read the bands into memory
# Landsat Radiance at sensor for thermal band
self.logger.info('Loading intermediate thermal band data [{0}]'.format(
self.thermal_name))
dataset = gdal.Open(self.thermal_name)
x_dim = dataset.RasterXSize # They are all the same size
y_dim = dataset.RasterYSize
thermal_data = dataset.GetRasterBand(1).ReadAsArray(0, 0, x_dim, y_dim)
# Atmospheric transmittance
self.logger.info(
'Loading intermediate transmittance band data [{0}]'.format(
self.transmittance_name))
dataset = gdal.Open(self.transmittance_name)
trans_data = dataset.GetRasterBand(1).ReadAsArray(0, 0, x_dim, y_dim)
# Atmospheric path radiance - upwelled radiance
self.logger.info(
'Loading intermediate upwelled band data [{0}]'.format(
self.upwelled_name))
dataset = gdal.Open(self.upwelled_name)
upwelled_data = dataset.GetRasterBand(1).ReadAsArray(
0, 0, x_dim, y_dim)
self.logger.info('Calculating surface radiance')
# Surface radiance
with np.errstate(invalid='ignore'):
surface_radiance = (thermal_data - upwelled_data) / trans_data
# Fix the no data locations
no_data_locations = np.where(thermal_data == self.no_data_value)
surface_radiance[no_data_locations] = self.no_data_value
no_data_locations = np.where(trans_data == self.no_data_value)
surface_radiance[no_data_locations] = self.no_data_value
no_data_locations = np.where(upwelled_data == self.no_data_value)
surface_radiance[no_data_locations] = self.no_data_value
# Memory cleanup
del thermal_data
del trans_data
del upwelled_data
del no_data_locations
# Downwelling sky irradiance
self.logger.info(
'Loading intermediate downwelled band data [{0}]'.format(
self.downwelled_name))
dataset = gdal.Open(self.downwelled_name)
downwelled_data = dataset.GetRasterBand(1).ReadAsArray(
0, 0, x_dim, y_dim)
# Landsat emissivity estimated from ASTER GED data
self.logger.info(
'Loading intermediate emissivity band data [{0}]'.format(
self.emissivity_name))
dataset = gdal.Open(self.emissivity_name)
emissivity_data = dataset.GetRasterBand(1).ReadAsArray(
0, 0, x_dim, y_dim)
# Save for the output product
ds_srs = osr.SpatialReference()
ds_srs.ImportFromWkt(dataset.GetProjection())
ds_transform = dataset.GetGeoTransform()
# Memory cleanup
del dataset
# Estimate Earth-emitted radiance by subtracting off the reflected
# downwelling component
radiance = (surface_radiance -
(1.0 - emissivity_data) * downwelled_data)
# Account for surface emissivity to get Plank emitted radiance
self.logger.info('Calculating Plank emitted radiance')
with np.errstate(invalid='ignore'):
radiance_emitted = radiance / emissivity_data
# Fix the no data locations
no_data_locations = np.where(surface_radiance == self.no_data_value)
radiance_emitted[no_data_locations] = self.no_data_value
no_data_locations = np.where(downwelled_data == self.no_data_value)
radiance_emitted[no_data_locations] = self.no_data_value
no_data_locations = np.where(emissivity_data == self.no_data_value)
radiance_emitted[no_data_locations] = self.no_data_value
# Memory cleanup
del downwelled_data
del emissivity_data
del surface_radiance
del radiance
del no_data_locations
# Use Brightness Temperature LUT to get skin temperature
# Read the correct one for what we are processing
if self.satellite == 'LANDSAT_8':
self.logger.info('Using Landsat 8 Brightness Temperature LUT')
bt_name = 'L8_Brightness_Temperature_LUT.txt'
elif self.satellite == 'LANDSAT_7':
self.logger.info('Using Landsat 7 Brightness Temperature LUT')
bt_name = 'L7_Brightness_Temperature_LUT.txt'
elif self.satellite == 'LANDSAT_5':
self.logger.info('Using Landsat 5 Brightness Temperature LUT')
bt_name = 'L5_Brightness_Temperature_LUT.txt'
elif self.satellite == 'LANDSAT_4':
self.logger.info('Using Landsat 4 Brightness Temperature LUT')
bt_name = 'L4_Brightness_Temperature_LUT.txt'
bt_data = np.loadtxt(os.path.join(self.lst_data_dir, bt_name),
dtype=float,
delimiter=' ')
bt_radiance_lut = bt_data[:, 1]
bt_temp_lut = bt_data[:, 0]
self.logger.info('Generating LST results')
lst_data = np.interp(radiance_emitted, bt_radiance_lut, bt_temp_lut)
# Scale the result
lst_data = lst_data * MULT_FACTOR
# Add the fill and scan gaps back into the results, since they may
# have been lost
self.logger.info('Adding fill and data gaps back into the Land'
' Surface Temperature results')
# Fix the no data locations
no_data_locations = np.where(radiance_emitted == self.no_data_value)
lst_data[no_data_locations] = self.no_data_value
# Memory cleanup
del radiance_emitted
del no_data_locations
product_id = self.xml_filename.split('.xml')[0]
lst_img_filename = ''.join([product_id, '_lst', '.img'])
lst_hdr_filename = ''.join([product_id, '_lst', '.hdr'])
lst_aux_filename = ''.join([lst_img_filename, '.aux', '.xml'])
self.logger.info('Creating {0}'.format(lst_img_filename))
util.Geo.generate_raster_file(envi_driver, lst_img_filename, lst_data,
x_dim, y_dim, ds_transform,
ds_srs.ExportToWkt(), self.no_data_value,
gdal.GDT_Int16)
self.logger.info('Updating {0}'.format(lst_hdr_filename))
util.Geo.update_envi_header(lst_hdr_filename, self.no_data_value)
# Memory cleanup
del ds_srs
del ds_transform
# Remove the *.aux.xml file generated by GDAL
if os.path.exists(lst_aux_filename):
os.unlink(lst_aux_filename)
self.logger.info('Adding {0} to {1}'.format(lst_img_filename,
self.xml_filename))
# Add the estimated Land Surface Temperature product to the metadata
espa_xml = metadata_api.parse(self.xml_filename, silence=True)
bands = espa_xml.get_bands()
sensor_code = product_id[0:4]
# Find the TOA Band 1 to use for the specific band details
base_band = None
for band in bands.band:
if band.product == 'toa_refl' and band.name == 'toa_band1':
base_band = band
if base_band is None:
raise Exception('Failed to find the TOA BLUE band'
' in the input data')
lst_band = metadata_api.band(product='lst',
source='toa_refl',
name='land_surface_temperature',
category='image',
data_type='INT16',
scale_factor=SCALE_FACTOR,
add_offset=0,
nlines=base_band.get_nlines(),
nsamps=base_band.get_nsamps(),
fill_value=str(self.no_data_value))
lst_band.set_short_name('{0}LST'.format(sensor_code))
lst_band.set_long_name('Land Surface Temperature')
lst_band.set_file_name(lst_img_filename)
lst_band.set_data_units('temperature (kelvin)')
pixel_size = metadata_api.pixel_size(base_band.pixel_size.x,
base_band.pixel_size.x,
base_band.pixel_size.units)
lst_band.set_pixel_size(pixel_size)
lst_band.set_resample_method('none')
valid_range = metadata_api.valid_range(min=1500, max=3730)
lst_band.set_valid_range(valid_range)
# Set the date, but first clean the microseconds off of it
production_date = (datetime.datetime.strptime(
datetime.datetime.now().strftime('%Y-%m-%dT%H:%M:%S'),
'%Y-%m-%dT%H:%M:%S'))
lst_band.set_production_date(production_date)
lst_band.set_app_version(util.Version.app_version())
bands.add_band(lst_band)
# Write the XML metadata file out
with open(self.xml_filename, 'w') as metadata_fd:
metadata_api.export(metadata_fd, espa_xml)
# Memory cleanup
del lst_band
del bands
del espa_xml
del lst_data
def mmse_filter(infile, m, dims=None):
gdal.AllRegister()
inDataset = gdal.Open(infile,GA_ReadOnly)
cols = inDataset.RasterXSize
rows = inDataset.RasterYSize
bands = inDataset.RasterCount
if dims == None:
dims = [0,0,cols,rows]
x0,y0,cols,rows = dims
path = os.path.dirname(infile)
basename = os.path.basename(infile)
root, ext = os.path.splitext(basename)
outfile = path + '/' + root + '_mmse' + ext
# get filter weights from span image
b = np.ones((rows,cols))
band = inDataset.GetRasterBand(1)
span = band.ReadAsArray(x0,y0,cols,rows).ravel()
if bands==9:
band = inDataset.GetRasterBand(6)
span += band.ReadAsArray(x0,y0,cols,rows).ravel()
band = inDataset.GetRasterBand(9)
span += band.ReadAsArray(x0,y0,cols,rows).ravel()
elif bands==4:
band = inDataset.GetRasterBand(4)
span += band.ReadAsArray(x0,y0,cols,rows).ravel()
edge_idx = np.zeros((rows,cols),dtype=int)
print '========================='
print ' MMSE_FILTER'
print '========================='
print time.asctime()
print 'infile: %s'%infile
print 'number of looks: %i'%m
print 'Determining filter weights from span image'
start = time.time()
print 'row: ',
sys.stdout.flush()
for j in range(3,rows-3):
if j%50 == 0:
print '%i '%j,
sys.stdout.flush()
windex = get_windex(j,cols)
for i in range(3,cols-3):
wind = np.reshape(span[windex],(7,7))
# 3x3 compression
w = congrid.congrid(wind,(3,3),method='spline',centre=True)
# get appropriate edge mask
es = [np.sum(edges[p]*w) for p in range(4)]
idx = np.argmax(es)
if idx == 0:
if np.abs(w[1,1]-w[1,0]) < np.abs(w[1,1]-w[1,2]):
edge_idx[j,i] = 0
else:
edge_idx[j,i] = 4
elif idx == 1:
if np.abs(w[1,1]-w[2,0]) < np.abs(w[1,1]-w[0,2]):
edge_idx[j,i] = 1
else:
edge_idx[j,i] = 5
elif idx == 2:
if np.abs(w[1,1]-w[0,1]) < np.abs(w[1,1]-w[2,1]):
edge_idx[j,i] = 6
else:
edge_idx[j,i] = 2
elif idx == 3:
if np.abs(w[1,1]-w[0,0]) < np.abs(w[1,1]-w[2,2]):
edge_idx[j,i] = 7
else:
edge_idx[j,i] = 3
edge = templates[edge_idx[j,i]]
wind = wind.ravel()[edge]
gbar = np.mean(wind)
varg = np.var(wind)
if varg > 0:
b[j,i] = np.max( ((1.0 - gbar**2/(varg*m))/(1.0+1.0/m), 0.0) )
windex += 1
print ' done'
# filter the image
outim = np.zeros((rows,cols),dtype=np.float32)
driver = inDataset.GetDriver()
outDataset = driver.Create(outfile,cols,rows,bands,GDT_Float32)
geotransform = inDataset.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))
projection = inDataset.GetProjection()
if projection is not None:
outDataset.SetProjection(projection)
print 'Filtering covariance matrix elememnts'
for k in range(1,bands+1):
print 'band: %i'%(k)
band = inDataset.GetRasterBand(k)
band = band.ReadAsArray(0,0,cols,rows)
gbar = band*0.0
# get window means
for j in range(3,rows-3):
windex = get_windex(j,cols)
for i in range(3,cols-3):
wind = band.ravel()[windex]
edge = templates[edge_idx[j,i]]
wind = wind[edge]
gbar[j,i] = np.mean(wind)
windex += 1
# apply adaptive filter and write to disk
outim = np.reshape(gbar + b*(band-gbar),(rows,cols))
outBand = outDataset.GetRasterBand(k)
outBand.WriteArray(outim,0,0)
outBand.FlushCache()
outDataset = None
print 'result written to: '+outfile
print 'elapsed time: '+str(time.time()-start)
def main():
usage = '''
Usage:
------------------------------------------------
Calculate the ratio of two polSAR images
python %s [OPTIONS] filename1 filename2
Options:
-h this help
-p <list> band positions e.g. -p [1,2,3,4,5,7]
-d <list> spatial subset
'''
options, args = getopt.getopt(sys.argv[1:], 'hd:p:')
dims = None
pos = None
for option, value in options:
if option == '-h':
print(usage)
return
elif option == '-d':
dims = eval(value)
elif option == '-p':
pos = eval(value)
if len(args) == 2:
fn1 = args[0]
fn2 = args[1]
else:
print('Incorrect number of arguments')
print(usage)
sys.exit(1)
path = os.path.dirname(fn1)
basename1 = os.path.basename(fn1)
basename2 = os.path.basename(fn2)
root1, ext = os.path.splitext(basename1)
root2, ext = os.path.splitext(basename2)
outfile = path + '/(' + root1 + '-over-' + root2 + ')' + ext
gdal.AllRegister()
inDataset = gdal.Open(fn1, GA_ReadOnly)
cols = inDataset.RasterXSize
rows = inDataset.RasterYSize
bands = inDataset.RasterCount
if dims:
x0, y0, cols, rows = dims
else:
x0 = 0
y0 = 0
if pos is not None:
bands = len(pos)
else:
pos = range(1, bands + 1)
g1 = np.zeros((cols * rows, bands))
k = 0
for b in pos:
g1[:, k] = np.nan_to_num(
inDataset.GetRasterBand(b).ReadAsArray(x0, y0, cols, rows).ravel())
k += 1
inDataset = gdal.Open(fn2, GA_ReadOnly)
g2 = np.zeros((cols * rows, bands))
k = 0
for b in pos:
g2[:, k] = np.nan_to_num(
inDataset.GetRasterBand(b).ReadAsArray(x0, y0, cols, rows).ravel())
# g2[:,k] = np.nan_to_num(inDataset.GetRasterBand(b).ReadAsArray(0,0,cols,rows).ravel())
k += 1
g2 = np.where(g2 == 0, 0.0001, g2)
ratio = g1 / g2
driver = inDataset.GetDriver()
outDataset = driver.Create(outfile, cols, rows, bands, GDT_Float32)
projection = inDataset.GetProjection()
geotransform = inDataset.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)
for k in range(bands):
outBand = outDataset.GetRasterBand(k + 1)
outBand.WriteArray(np.reshape(ratio[:, k], (rows, cols)), 0, 0)
outBand.FlushCache()
outDataset = None
print('Ratio image written to: %s' % outfile)
def main():
usage = '''
Usage:
------------------------------------------------
python %s [-h] [-d dims] filename enl
Run a gamma Map filter in the diagonal elements of a C or T matrix
------------------------------------------------''' % sys.argv[0]
options, args = getopt.getopt(sys.argv[1:], 'hd:')
dims = None
for option, value in options:
if option == '-h':
print usage
return
elif option == '-d':
dims = eval(value)
if len(args) != 2:
print 'Incorrect number of arguments'
print usage
sys.exit(1)
infile = args[0]
m = int(args[1])
gdal.AllRegister()
inDataset = gdal.Open(infile, GA_ReadOnly)
cols = inDataset.RasterXSize
rows = inDataset.RasterYSize
bands = inDataset.RasterCount
if dims == None:
dims = [0, 0, cols, rows]
x0, y0, cols, rows = dims
path = os.path.abspath(infile)
dirn = os.path.dirname(path)
basename = os.path.basename(infile)
root, ext = os.path.splitext(basename)
outfile = dirn + '/' + root + '_gamma' + ext
# process diagonal bands only
driver = inDataset.GetDriver()
if bands == 9:
outDataset = driver.Create(outfile, cols, rows, 3, GDT_Float32)
inimage = np.zeros((3, rows, cols))
band = inDataset.GetRasterBand(1)
inimage[0] = band.ReadAsArray(x0, y0, cols, rows)
band = inDataset.GetRasterBand(6)
inimage[1] = band.ReadAsArray(x0, y0, cols, rows)
band = inDataset.GetRasterBand(9)
inimage[2] = band.ReadAsArray(x0, y0, cols, rows)
elif bands == 4:
outDataset = driver.Create(outfile, cols, rows, 2, GDT_Float32)
inimage = np.zeros((2, rows, cols))
band = inDataset.GetRasterBand(1)
inimage[0] = band.ReadAsArray(x0, y0, cols, rows)
band = inDataset.GetRasterBand(4)
inimage[1] = band.ReadAsArray(x0, y0, cols, rows)
else:
outDataset = driver.Create(outfile, cols, rows, 1, GDT_Float32)
inimage = inDataset.GetRasterBand(1)
outimage = np.copy(inimage)
print '========================='
print ' GAMMA MAP FILTER'
print '========================='
print time.asctime()
print 'infile: %s' % infile
print 'equivalent number of looks: %i' % m
start = time.time()
if bands == 9:
for k in range(3):
outimage[k] = gamma_filter(k, inimage, outimage, rows, cols, m)
elif bands == 4:
for k in range(2):
outimage[k] = gamma_filter(k, inimage, outimage, rows, cols, m)
else:
outimage = gamma_filter(0, inimage, outimage, rows, cols, m)
geotransform = inDataset.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))
projection = inDataset.GetProjection()
if projection is not None:
outDataset.SetProjection(projection)
if bands == 9:
for k in range(3):
outBand = outDataset.GetRasterBand(k + 1)
outBand.WriteArray(outimage[k], 0, 0)
outBand.FlushCache()
elif bands == 4:
for k in range(2):
outBand = outDataset.GetRasterBand(k + 1)
outBand.WriteArray(outimage[k], 0, 0)
outBand.FlushCache()
else:
outBand = outDataset.GetRasterBand(1)
outBand.WriteArray(outimage, 0, 0)
outBand.FlushCache()
outDataset = None
print 'result written to: ' + outfile
print 'elapsed time: ' + str(time.time() - start)
