def __getmetadata__(self):
'''Read Metadata for a "scene01/*.tif" (possibly) multiband image'''
f = self.fileinfo['filepath']
d = os.path.dirname(f)
bands = sorted([t for t in self.filelist if t[-4:].lower() == '.tif'])
ds = geometry.OpenDataset(f)
vrt = geometry.CreateSimpleVRT(bands,
ds.RasterXSize,
ds.RasterYSize,
gdal.GetDataTypeName(
ds.GetRasterBand(1).DataType),
relativeToVRT=0)
vrtds = geometry.OpenDataset(vrt, gdalconst.GA_Update)
vrtds.SetGeoTransform(ds.GetGeoTransform())
vrtds.SetProjection(ds.GetProjection())
vrtds.SetGCPs(ds.GetGCPs(), ds.GetProjection())
vrtds.SetMetadata(ds.GetMetadata())
#Kludge... the drivers are designed to "open" strings, i.e. filenames and vrt xml
#(maybe look at allowing GdalDataset objects in future)
vrtds = geometry.CreateVRTCopy(
vrtds) #This updates the Description property to include all the
vrtxml = vrtds.GetDescription() #SRS/GCP/GT info we just added.
#GetDescription() is the only way I know of to get at the
#underlying XML string.
#Autopopulate metadata
__default__.Dataset.__getmetadata__(self, vrtxml)
self.metadata['filetype'] = 'GTiff/GeoTIFF'
def __opendataset__(self):
bandlookup = {
'pan': ['pan'],
'blu': ['blu'],
'grn': ['grn'],
'red': ['red'],
'nir': ['nir'],
'bgrn': ['blu', 'grn', 'red', 'nir'],
'bgr': ['blu', 'grn', 'red'],
'rgb': ['red', 'grn', 'blu']
}
bandfiles = {}
bandnames = []
for d in self._datafiles:
band = d.split('_')[2]
bands = bandlookup.get(band, band)
for band in bands:
bandfiles[band] = os.path.join(
os.path.dirname(self.fileinfo['filepath']), d)
bandnames += [band]
try:
f = bandfiles['red']
rgb = True
except:
f = bandfiles['pan']
rgb = False
ds = geometry.OpenDataset(f)
rb = ds.GetRasterBand(1)
cols = ds.RasterXSize
rows = ds.RasterYSize
datatype = gdal.GetDataTypeName(rb.DataType)
nbits = gdal.GetDataTypeSize(rb.DataType)
nbands = len(bandnames)
bandnames = ','.join(bandnames)
if rgb and bandfiles['red'] != bandfiles['blu']:
ds = geometry.OpenDataset(
geometry.CreateSimpleVRT(
[bandfiles['red'], bandfiles['grn'], bandfiles['blu']],
cols, rows, datatype))
return ds, nbands, bandnames, f
def __getmetadata__(self):
'''Read Metadata for a Landsat Geotiff with Level 1 Metadata format image as GDAL doesn't get it all.'''
f = self.fileinfo['filepath']
d = os.path.dirname(f)
hdr = parseheader(f)
bands = sorted(
[i for i in hdr['PRODUCT_METADATA']['BAND_COMBINATION']])
if hdr['PRODUCT_METADATA'][
'SENSOR_ID'] == 'ETM+': #Landsat 7 has 2 data files for thermal band 6
#Format=123456678
bands[5] = bands[5].replace('6', '61')
bands[6] = bands[6].replace('6', '62')
bandfiles = [
os.path.join(d, hdr['PRODUCT_METADATA']['BAND%s_FILE_NAME' % b])
for b in bands
]
__default__.Dataset.__getmetadata__(self, bandfiles[0])
md = self.metadata
md['metadata'] = open(f).read().replace('\x00', '')
md['sceneid'] = os.path.basename(d)
md['filetype'] = 'GTIFF/Landsat MTL Geotiff'
md['bands'] = ','.join(bands)
md['nbands'] = len(bands)
md['level'] = hdr['PRODUCT_METADATA']['PRODUCT_TYPE']
md['imgdate'] = '%sT%s' % (
hdr['PRODUCT_METADATA']['ACQUISITION_DATE'],
hdr['PRODUCT_METADATA']['SCENE_CENTER_SCAN_TIME'][0:8]
) #ISO 8601 format, strip off the milliseconds
md['satellite'] = hdr['PRODUCT_METADATA']['SPACECRAFT_ID']
md['sensor'] = hdr['PRODUCT_METADATA']['SENSOR_ID']
md['demcorrection'] = hdr['PRODUCT_METADATA'].get(
'ELEVATION_SOURCE', '') #Level 1G isn't terrain corrected
md['resampling'] = hdr['PROJECTION_PARAMETERS']['RESAMPLING_OPTION']
md['sunazimuth'] = hdr['PRODUCT_PARAMETERS']['SUN_AZIMUTH']
md['sunelevation'] = hdr['PRODUCT_PARAMETERS']['SUN_ELEVATION']
vrtxml = geometry.CreateSimpleVRT(bandfiles, md['cols'], md['rows'],
md['datatype'])
self._gdaldataset = geometry.OpenDataset(vrtxml)
self._stretch = ['PERCENT', [3, 2, 1], [2, 98]]
def __getmetadata__(self):
'''Read Metadata for a Landsat Geotiff with Level 1 Metadata format image as GDAL doesn't get it all.'''
f=self.fileinfo['filepath']
d=os.path.dirname(f)
hdr=parseheader(f)
if hdr['L1_METADATA_FILE'].get('LANDSAT_SCENE_ID'):self.__getnewmetadata__(f,d,hdr)
else:self.__getoldmetadata__(f,d,hdr)
md=self.metadata
vrtxml=geometry.CreateSimpleVRT(self.bandfiles,md['cols'],md['rows'], md['datatype'])
self._gdaldataset = geometry.OpenDataset(vrtxml)
for i in range(self._gdaldataset.RasterCount):
self._gdaldataset.GetRasterBand(i+1).SetNoDataValue(0)
#Fix quicklook stretch for Landsat 8 data
if md['satellite']=='LANDSAT_8':
self._stretch=['PERCENT',[4,3,2],[1,99]]
else:
self._stretch=['PERCENT',[3,2,1], [2,98]]
def __getmetadata__(self, f=None):
'''Read Metadata for ASTER HDF images as GDAL doesn't.'''
if not f: f = self.fileinfo['filepath']
hdf_sd = self._gdaldataset.GetSubDatasets()
hdf_sd = [sd for sd, sz in hdf_sd if 'ImageData' in sd]
hdf_md = self._hdf_md
#sd,sz = hdf_sd[0]
sd = hdf_sd[0]
sd = geometry.OpenDataset(sd)
nbands = len(hdf_sd)
ncols = []
nrows = []
nbits = []
bands = []
datatypes = []
cellxy = []
for i in range(0, len(hdf_md['PROCESSEDBANDS']), 2):
band = hdf_md['PROCESSEDBANDS'][i:i + 2]
if i / 2 + 1 <= 4:
bands.append('VNIR' + band)
cellxy.append('15')
elif i / 2 + 1 <= 10:
bands.append('SWIR' + band)
cellxy.append('30')
else:
bands.append('TIR' + band)
cellxy.append('90')
if band.isdigit(): band = str(int(band)) #Get rid of leading zero
cols, rows, bytes = map(
int, hdf_md['IMAGEDATAINFORMATION%s' % band].split(','))
if bytes == 1: datatypes.append('Byte')
elif bytes == 2: datatypes.append('UInt16')
ncols.append(str(cols))
nrows.append(str(rows))
nbits.append(str(bytes * 8))
ncols = ','.join(ncols)
nrows = ','.join(nrows)
nbits = ','.join(nbits)
bands = ','.join(bands)
datatypes = ','.join(datatypes)
cellxy = ','.join(cellxy)
uly, ulx = [float(xy) for xy in hdf_md['UPPERLEFT'].split(',')]
ury, urx = [float(xy) for xy in hdf_md['UPPERRIGHT'].split(',')]
lry, lrx = [float(xy) for xy in hdf_md['LOWERRIGHT'].split(',')]
lly, llx = [float(xy) for xy in hdf_md['LOWERLEFT'].split(',')]
ext = [[ulx, uly], [urx, ury], [lrx, lry], [llx, lly], [ulx, uly]]
#SRS reported by GDAL is slightly dodgy, GDA94 is not recognised and doesn't set the North/South properly
#Get it anyway so we can work out if it's GDA94 based on the spheroid
srs = sd.GetGCPProjection()
src_srs = osr.SpatialReference(srs)
tgt_srs = osr.SpatialReference()
geogcs = osr.SpatialReference()
if src_srs.GetAttrValue('SPHEROID') == 'GRS 1980':
geogcs.ImportFromEPSG(4283) #Assume 'GDA94'
else:
geogcs.ImportFromEPSG(4326) #Assume 'WGS84'
tgt_srs.CopyGeogCSFrom(geogcs)
if hdf_md['PROCESSINGLEVELID'].upper() == '1A':
units = 'deg'
else:
#projparams=map(float, hdf_md['PROJECTIONPARAMETERS1'].split(','))
if hdf_md['MPMETHOD1'] == 'UTM': #Universal Transverse Mercator
if uly < 0:
bNorth = False #GDAL doesn't set the North/South properly
else:
bNorth = True
nZone = int(hdf_md['UTMZONECODE1'])
tgt_srs.SetUTM(nZone, bNorth)
units = 'm'
#Other projections not (yet?) implemented...
#elif hdf_md['MPMETHOD1'] == 'PS':#Polar Stereographic
# #dfCenterLon = ? GTCP projection params don't list cenlon/lat for PS
# dfCenterLat = ?
# dfScale = ?
# tgt_srs.SetPS(dfCenterLat,dfCenterLon,dfScale,0.0,0.0)
#elif hdf_md['MPMETHOD1'] == 'LAMCC':#Lambert Conformal Conic
# dfCenterLon = ?
# dfCenterLat = ?
# dfStdP1 = ?
# dfStdP2 = ?
# tgt_srs.SetLCC(dfStdP1,dfStdP2,dfCenterLat,dfCenterLon,0,0)
#elif hdf_md['MPMETHOD1'] == 'SOM':#Space Oblique Mercator
# dfCenterLon = ?
# dfCenterLat = ?
# srs.SetMercator(dfCenterLat,dfCenterLon,0,0,0)
#elif hdf_md['MPMETHOD1'] == 'EQRECT':#Equi-Rectangular
# dfCenterLon = ?
# dfCenterLat = ?
# tgt_srs.SetMercator(dfCenterLat,dfCenterLon,0,0,0)
else: #Assume Geog
units = 'deg'
srs = tgt_srs.ExportToWkt()
self.metadata['UL'] = '%s,%s' % tuple(ext[0])
self.metadata['UR'] = '%s,%s' % tuple(ext[1])
self.metadata['LR'] = '%s,%s' % tuple(ext[2])
self.metadata['LL'] = '%s,%s' % tuple(ext[3])
self.metadata['metadata'] = '\n'.join(
['%s: %s' % (m, hdf_md[m]) for m in hdf_md])
self.metadata['satellite'] = 'Terra'
self.metadata['sensor'] = 'ASTER'
self.metadata['filetype'] = self._gdaldataset.GetDriver(
).ShortName + '/' + self._gdaldataset.GetDriver().LongName + ' (ASTER)'
self.metadata['sceneid'] = hdf_md['ASTERSCENEID']
self.metadata['level'] = hdf_md['PROCESSINGLEVELID']
if '-' in hdf_md['CALENDARDATE']: imgdate = hdf_md['CALENDARDATE']
else:
imgdate = time.strftime(utilities.dateformat,
time.strptime(hdf_md['CALENDARDATE'],
'%Y%m%d')) #ISO 8601
imgtime = hdf_md.get('TIMEOFDAY')
if imgtime:
self.metadata['imgdate'] = time.strftime(
utilities.datetimeformat,
time.strptime(imgdate + imgtime[0:6],
'%Y-%m-%d%H%M%S')) #ISO 8601
else:
self.metadata['imgdate'] = imgdate
#self.metadata['imgdate'] = hdf_md['CALENDARDATE']
self.metadata['cloudcover'] = float(hdf_md['SCENECLOUDCOVERAGE'])
if hdf_md['FLYINGDIRECTION'] == 'DE':
self.metadata['orbit'] = 'Descending'
else:
self.metadata['orbit'] = 'Ascending'
self.metadata['rotation'] = float(
hdf_md.get('MAPORIENTATIONANGLE',
hdf_md.get('SCENEORIENTATIONANGLE')))
if abs(self.metadata['rotation']) < 1.0:
self.metadata['orientation'] = 'Map oriented'
else:
self.metadata['orientation'] = 'Path oriented'
self.metadata['sunazimuth'], self.metadata['sunelevation'] = map(
float, hdf_md['SOLARDIRECTION'].split(','))
self.metadata['viewangle'] = float(hdf_md['POINTINGANGLE'])
self.metadata['cols'] = ncols
self.metadata['rows'] = nrows
self.metadata['nbands'] = nbands
self.metadata['datatype'] = datatypes
self.metadata['nbits'] = nbits
self.metadata['nodata'] = ','.join(['0' for i in range(0, nbands)])
self.metadata['bands'] = bands
self.metadata['resampling'] = hdf_md.get(
'RESMETHOD1') #Assume same for all...
self.metadata['srs'] = srs
self.metadata['epsg'] = spatialreferences.IdentifyAusEPSG(srs)
self.metadata['units'] = units
self.metadata['cellx'], self.metadata['celly'] = cellxy, cellxy
#Geotransform
ext = [[ulx, uly], [urx, ury], [lrx, lry], [llx, lly], [ulx, uly]]
ncols = map(int, str(ncols).split(','))
nrows = map(int, str(nrows).split(','))
cellx, celly = [], []
j = 0
while j < len(ncols):
gcps = []
i = 0
lr = [[0, 0], [ncols[j], 0], [ncols[j], nrows[j]], [0, nrows[j]]]
while i < len(ext) - 1: #don't need the last xy pair
gcp = gdal.GCP()
gcp.GCPPixel, gcp.GCPLine = lr[i]
gcp.GCPX, gcp.GCPY = ext[i]
gcp.Id = str(i)
gcps.append(gcp)
i += 1
j += 1
geotransform = gdal.GCPsToGeoTransform(gcps)
x, y = geometry.CellSize(geotransform)
cellx.append(str(x))
celly.append(str(abs(y)))
self.metadata['cellx'] = ','.join(cellx)
self.metadata['celly'] = ','.join(celly)
srs = osr.SpatialReference()
srs.ImportFromEPSG(4326)
self.metadata['srs'] = srs.ExportToWkt()
self.metadata['UL'] = '%s,%s' % tuple(ext[0])
self.metadata['UR'] = '%s,%s' % tuple(ext[1])
self.metadata['LR'] = '%s,%s' % tuple(ext[2])
self.metadata['LL'] = '%s,%s' % tuple(ext[3])
self.metadata['metadata'] = '\n'.join(
['%s: %s' % (m, hdf_md[m]) for m in hdf_md])
self.metadata['filesize'] = sum(
[os.path.getsize(file) for file in self.filelist])
self.metadata['compressionratio'] = 0
self.metadata['compressiontype'] = 'None'
self.extent = ext
#Build gdaldataset object for overviews
vrtcols = ncols[0]
vrtrows = nrows[0]
#vrtbands=[sd for sd,sn in hdf_sd[0:4]]#The 4 VNIR bands
vrtbands = hdf_sd[0:4] #The 4 VNIR bands
vrt = geometry.CreateSimpleVRT(vrtbands, vrtcols, vrtrows,
datatypes.split(',')[0])
self._gdaldataset = geometry.OpenDataset(vrt)
for i in range(1, 5):
self._gdaldataset.GetRasterBand(i).SetNoDataValue(0)
def hyp_l1t(self, f):
md = parseheader(f)
self.metadata['level'] = md['PRODUCT_METADATA']['PRODUCT_TYPE']
self.metadata['sceneid'] = self.metadata['filename'].split(
'_')[0].upper()
self.metadata['filetype'] = 'GTiff/GeoTIFF'
self.metadata['satellite'] = 'EO1'
self.metadata['sensor'] = md['PRODUCT_METADATA']['SENSOR_ID']
bands = glob.glob(os.path.join(os.path.dirname(f), 'eo1*_b*.tif'))
band = geometry.OpenDataset(bands[0])
self.ncols = band.RasterXSize
self.nrows = band.RasterYSize
self.nbands = len(bands)
uly = float(md['PRODUCT_METADATA']['PRODUCT_UL_CORNER_LAT'])
ulx = float(md['PRODUCT_METADATA']['PRODUCT_UL_CORNER_LON'])
ury = float(md['PRODUCT_METADATA']['PRODUCT_UR_CORNER_LAT'])
urx = float(md['PRODUCT_METADATA']['PRODUCT_UR_CORNER_LON'])
lry = float(md['PRODUCT_METADATA']['PRODUCT_LR_CORNER_LAT'])
lrx = float(md['PRODUCT_METADATA']['PRODUCT_LR_CORNER_LON'])
lly = float(md['PRODUCT_METADATA']['PRODUCT_LL_CORNER_LAT'])
llx = float(md['PRODUCT_METADATA']['PRODUCT_LL_CORNER_LON'])
self.geoext = [[ulx, uly], [urx, ury], [lrx, lry], [llx, lly],
[ulx, uly]]
uly = float(md['PRODUCT_METADATA']['PRODUCT_UL_CORNER_MAPY'])
ulx = float(md['PRODUCT_METADATA']['PRODUCT_UL_CORNER_MAPX'])
ury = float(md['PRODUCT_METADATA']['PRODUCT_UR_CORNER_MAPY'])
urx = float(md['PRODUCT_METADATA']['PRODUCT_UR_CORNER_MAPX'])
lry = float(md['PRODUCT_METADATA']['PRODUCT_LR_CORNER_MAPY'])
lrx = float(md['PRODUCT_METADATA']['PRODUCT_LR_CORNER_MAPX'])
lly = float(md['PRODUCT_METADATA']['PRODUCT_LL_CORNER_MAPY'])
llx = float(md['PRODUCT_METADATA']['PRODUCT_LL_CORNER_MAPX'])
self.prjext = [[ulx, uly], [urx, ury], [lrx, lry], [llx, lly],
[ulx, uly]]
self.metadata['imgdate'] = md['PRODUCT_METADATA']['ACQUISITION_DATE']
self.metadata['resampling'] = md['PROJECTION_PARAMETERS'][
'RESAMPLING_OPTION']
try:
self.metadata['viewangle'] = float(
md['PRODUCT_PARAMETERS']['SENSOR_LOOK_ANGLE'])
except:
pass #Exception raised if value == 'UNAVAILABLE'
try:
self.metadata['sunazimuth'] = float(
md['PRODUCT_PARAMETERS']['SUN_AZIMUTH'])
except:
pass #Exception raised if value == 'UNAVAILABLE'
try:
self.metadata['sunelevation'] = float(
md['PRODUCT_PARAMETERS']['SUN_ELEVATION'])
except:
pass #Exception raised if value == 'UNAVAILABLE'
#EPSG:32601: WGS 84 / UTM zone 1N
#EPSG:32701: WGS 84 / UTM zone 1S
srs = osr.SpatialReference()
zone = int(md['UTM_PARAMETERS']['ZONE_NUMBER'])
if zone > 0: epsg = 32600 + zone #North
else: epsg = 32700 - zone #South
srs.ImportFromEPSG(epsg)
self.metadata['units'] = 'm'
self.metadata['srs'] = srs.ExportToWkt()
self.metadata['epsg'] = str(epsg)
#########################################################################################################
##set self._gdaldataset for use in overview generation
##we'll use bands 21, 30 & 43 for RGB (http://edcsns17.cr.usgs.gov/eo1/Hyperion_Spectral_Coverage.htm)
##as they're near the center of the equivalent ALI bands
self._gdaldataset = geometry.OpenDataset(
geometry.CreateSimpleVRT([bands[43], bands[30], bands[21]],
self.ncols, self.nrows, 'Int16'))
self._gdaldataset.GetRasterBand(1).SetNoDataValue(0)
self._gdaldataset.GetRasterBand(2).SetNoDataValue(0)
self._gdaldataset.GetRasterBand(3).SetNoDataValue(0)
#self._stretch=('STDDEV',(1,2,3),[2])
self._stretch = ('PERCENT', (1, 2, 3), [2, 98])
def ali_l1g_tiff(self, f):
self.metadata['level'] = 'L1G'
self.metadata['sensor'] = 'ALI'
self.metadata['sceneid'] = self.metadata['filename'].split('_')[0]
self.metadata['filetype'] = 'GTiff/GeoTIFF'
ncols = []
nrows = []
nbands = 0
bands = glob.glob(os.path.join(os.path.dirname(f), 'eo1*_b*.tif'))
for band in bands:
band = geometry.OpenDataset(band)
ncols.append(str(band.RasterXSize))
nrows.append(str(band.RasterYSize))
nbands += 1
#rb=sd.GetRasterBand(1)
#get all multi bands for use in overview generation
pancols = max(ncols)
panindex = ncols.index(pancols)
multibands = bands
multincols = ncols
multinrows = nrows
if pancols > min(ncols): #there is a pan band
multibands.pop(panindex)
multincols.pop(panindex)
multinrows.pop(panindex)
multibands.sort()
self._gdaldataset = geometry.OpenDataset(
geometry.CreateSimpleVRT(multibands, multincols[0], multinrows[0],
'Int16'))
self._gdaldataset.GetRasterBand(2).SetNoDataValue(0)
self._gdaldataset.GetRasterBand(3).SetNoDataValue(0)
self._gdaldataset.GetRasterBand(4).SetNoDataValue(0)
self._stretch = ('STDDEV', (4, 3, 2), [2])
self.ncols = ','.join(ncols)
self.nrows = ','.join(nrows)
met = f
md = {}
for line in open(met, 'r'):
line = line.replace('"', '')
line = [l.strip() for l in line.split('=')]
if line[0] == 'END': #end of the metadata file
break
elif line[0] == 'GROUP': #start of a metadata group
if line[1] != 'L1_METADATA_FILE':
group = line[1]
md[group] = {}
elif line[0] == 'END_GROUP': #end of a metadata group
pass
else: #metadata value
md[group][line[0]] = line[1]
uly = float(md['PRODUCT_METADATA']['PRODUCT_UL_CORNER_LAT'])
ulx = float(md['PRODUCT_METADATA']['PRODUCT_UL_CORNER_LON'])
ury = float(md['PRODUCT_METADATA']['PRODUCT_UR_CORNER_LAT'])
urx = float(md['PRODUCT_METADATA']['PRODUCT_UR_CORNER_LON'])
lry = float(md['PRODUCT_METADATA']['PRODUCT_LR_CORNER_LAT'])
lrx = float(md['PRODUCT_METADATA']['PRODUCT_LR_CORNER_LON'])
lly = float(md['PRODUCT_METADATA']['PRODUCT_LL_CORNER_LAT'])
llx = float(md['PRODUCT_METADATA']['PRODUCT_LL_CORNER_LON'])
self.geoext = [[ulx, uly], [urx, ury], [lrx, lry], [llx, lly],
[ulx, uly]]
uly = float(md['PRODUCT_METADATA']['PRODUCT_UL_CORNER_MAPY'])
ulx = float(md['PRODUCT_METADATA']['PRODUCT_UL_CORNER_MAPX'])
ury = float(md['PRODUCT_METADATA']['PRODUCT_UR_CORNER_MAPY'])
urx = float(md['PRODUCT_METADATA']['PRODUCT_UR_CORNER_MAPX'])
lry = float(md['PRODUCT_METADATA']['PRODUCT_LR_CORNER_MAPY'])
lrx = float(md['PRODUCT_METADATA']['PRODUCT_LR_CORNER_MAPX'])
lly = float(md['PRODUCT_METADATA']['PRODUCT_LL_CORNER_MAPY'])
llx = float(md['PRODUCT_METADATA']['PRODUCT_LL_CORNER_MAPX'])
self.prjext = [[ulx, uly], [urx, ury], [lrx, lry], [llx, lly],
[ulx, uly]]
self.metadata['imgdate'] = md['PRODUCT_METADATA']['ACQUISITION_DATE']
self.metadata['resampling'] = md['PROJECTION_PARAMETERS'][
'RESAMPLING_OPTION']
try:
self.metadata['viewangle'] = float(
md['PRODUCT_PARAMETERS']['SENSOR_LOOK_ANGLE'])
except:
pass #Exception raised if value == 'UNAVAILABLE'
try:
self.metadata['sunazimuth'] = float(
md['PRODUCT_PARAMETERS']['SUN_AZIMUTH'])
except:
pass #Exception raised if value == 'UNAVAILABLE'
try:
self.metadata['sunelevation'] = float(
md['PRODUCT_PARAMETERS']['SUN_ELEVATION'])
except:
pass #Exception raised if value == 'UNAVAILABLE'
#EPSG:32601: WGS 84 / UTM zone 1N
#EPSG:32701: WGS 84 / UTM zone 1S
srs = osr.SpatialReference()
zone = int(md['UTM_PARAMETERS']['ZONE_NUMBER'])
if zone > 0: epsg = 32600 + zone #North
else: epsg = 32700 - zone #South
srs.ImportFromEPSG(epsg)
self.metadata['units'] = 'm'
self.metadata['srs'] = srs.ExportToWkt()
self.metadata['epsg'] = str(epsg)
def ali_l1g_hdf(self, f):
self.metadata['level'] = 'L1G'
self.metadata['sensor'] = 'ALI'
self.metadata['sceneid'] = self.metadata['filename'].split('_')[0]
gdalDataset = geometry.OpenDataset(f)
#if not gdalDataset: #Error now raised in geometry.OpenDataset
# errmsg=gdal.GetLastErrorMsg()
# raise IOError, 'Unable to open %s\n%s' % (f,errmsg.strip())
self.metadata['filetype'] = '%s/%s (%s %s)' % (gdalDataset.GetDriver(
).ShortName, gdalDataset.GetDriver().LongName, self.metadata['sensor'],
self.metadata['level'])
hdf_sd = gdalDataset.GetSubDatasets()
hdf_md = gdalDataset.GetMetadata()
sd, sz = hdf_sd[0]
sd = geometry.OpenDataset(sd)
sd_md = sd.GetMetadata()
self.ncols = []
self.nrows = []
self.nbands = 0
bands = []
for sd, sz in hdf_sd:
bands.append(sd)
ds = geometry.OpenDataset(sd)
self.ncols.append(str(ds.RasterXSize))
self.nrows.append(str(ds.RasterYSize))
self.nbands += 1
#get all multi bands for use in overview generation
pancols = max(self.ncols)
panindex = ncols.index(pancols)
multibands = bands
multincols = self.ncols
multinrows = self.nrows
if pancols > min(self.ncols): #there is a pan band
multibands.pop(panindex)
multincols.pop(panindex)
multinrows.pop(panindex)
multibands.sort()
self._gdaldataset = geometry.OpenDataset(
geometry.CreateSimpleVRT(multibands, multincols[0], multinrows[0],
'Int16'))
self._gdaldataset.GetRasterBand(2).SetNoDataValue(0)
self._gdaldataset.GetRasterBand(3).SetNoDataValue(0)
self._gdaldataset.GetRasterBand(4).SetNoDataValue(0)
self._stretch = ('STDDEV', (4, 3, 2), [2])
self.ncols = ','.join(self.ncols)
self.nrows = ','.join(self.nrows)
met = f.lower().replace('_hdf.l1g', '_mtl.l1g')
md = {}
for line in open(met, 'r'):
line = line.replace('"', '')
line = [l.strip() for l in line.split('=')]
if line[0] == 'END': #end of the metadata file
break
elif line[0] == 'GROUP': #start of a metadata group
if line[1] != 'L1_METADATA_FILE':
group = line[1]
md[group] = {}
elif line[0] == 'END_GROUP': #end of a metadata group
pass
else: #metadata value
md[group][line[0]] = line[1]
uly = float(md['PRODUCT_METADATA']['PRODUCT_UL_CORNER_LAT'])
ulx = float(md['PRODUCT_METADATA']['PRODUCT_UL_CORNER_LON'])
ury = float(md['PRODUCT_METADATA']['PRODUCT_UR_CORNER_LAT'])
urx = float(md['PRODUCT_METADATA']['PRODUCT_UR_CORNER_LON'])
lry = float(md['PRODUCT_METADATA']['PRODUCT_LR_CORNER_LAT'])
lrx = float(md['PRODUCT_METADATA']['PRODUCT_LR_CORNER_LON'])
lly = float(md['PRODUCT_METADATA']['PRODUCT_LL_CORNER_LAT'])
llx = float(md['PRODUCT_METADATA']['PRODUCT_LL_CORNER_LON'])
self.geoext = [[ulx, uly], [urx, ury], [lrx, lry], [llx, lly],
[ulx, uly]]
self.prjext = self.geoext
self.metadata['imgdate'] = md['PRODUCT_METADATA']['ACQUISITION_DATE']
self.metadata['resampling'] = md['PROJECTION_PARAMETERS'][
'RESAMPLING_OPTION']
try:
self.metadata['viewangle'] = float(
md['PRODUCT_PARAMETERS']['SENSOR_LOOK_ANGLE'])
except:
pass #Exception raised if value == 'UNAVAILABLE'
try:
self.metadata['sunazimuth'] = float(
md['PRODUCT_PARAMETERS']['SUN_AZIMUTH'])
except:
pass #Exception raised if value == 'UNAVAILABLE'
try:
self.metadata['sunelevation'] = float(
md['PRODUCT_PARAMETERS']['SUN_ELEVATION'])
except:
pass #Exception raised if value == 'UNAVAILABLE'
#EPSG:32601: WGS 84 / UTM zone 1N
#EPSG:32701: WGS 84 / UTM zone 1S
srs = osr.SpatialReference()
zone = int(md['UTM_PARAMETERS']['ZONE_NUMBER'])
if zone > 0: epsg = 32600 + zone #North
else: epsg = 32700 - zone #South
srs.ImportFromEPSG(epsg)
self.metadata['units'] = 'm'
self.metadata['srs'] = srs.ExportToWkt()
self.metadata['epsg'] = str(epsg)
def __getmetadata__(self):
'''Read Metadata for recognised EO1 ALI (L1G & L1R) & Hyperion (L1R) images as GDAL doesn't'''
f = self.fileinfo['filepath']
self.metadata['satellite'] = 'E01'
self.metadata['nbits'] = 16
self.metadata['datatype'] = 'Int16'
self.metadata['nodata'] = 0
if re.search(r'\.m[1-4]r$', f, re.I):
self.metadata['level'] = 'L1R'
self.metadata['sensor'] = 'ALI'
gdalDataset = geometry.OpenDataset(f)
#if not gdalDataset: #Error now raised in geometry.OpenDataset
# errmsg=gdal.GetLastErrorMsg()
# raise IOError, 'Unable to open %s\n%s' % (f,errmsg.strip())
self.metadata['filetype'] = '%s/%s (%s %s)' % (
gdalDataset.GetDriver().ShortName,
gdalDataset.GetDriver().LongName, self.metadata['sensor'],
self.metadata['level'])
srs = osr.SpatialReference()
srs.ImportFromEPSG(4326)
self.metadata['srs'] = srs.ExportToWkt()
self.metadata['epsg'] = 4326
self.metadata['units'] = 'deg'
hdf_sd = gdalDataset.GetSubDatasets()
hdf_md = gdalDataset.GetMetadata()
sd, sz = hdf_sd[0]
sd = geometry.OpenDataset(sd)
sd_md = sd.GetMetadata()
nbands = sd.RasterCount
ncols = str(
sd.RasterXSize * 4 - 30
) #Account for the four SCA strips and the overlap between SCA strips
nrows = sd_md[
'Number of along track pixels'] #sd.RasterYSize is incorrect
if len(hdf_sd) == 6: #Includes pan band (3 sds each)
sd, sz = hdf_sd[3]
sd = geometry.OpenDataset(sd)
sd_md = sd.GetMetadata()
cols = str(
int(sd_md['Number of cross track pixels']) * 4 - 30
) #Account for the four SCA strips and the overlap between SCA strips
rows = sd_md['Number of along track pixels']
#set up to create multispectral only self._gdaldatset for overview generation
if nbands == 1:
multi = 3
multibands = sd_md['Number of bands']
multicols = cols
multirows = rows
else:
multi = 0
multibands = nbands
multicols = ncols
multirows = nrows
multibands = range(1, int(multibands) + 1)
#Make a csv list of cols, bands
ncols = [ncols for i in range(0, nbands)]
nrows = [nrows for i in range(0, nbands)]
ncols.extend([cols for i in range(0, sd.RasterCount)])
nrows.extend([rows for i in range(0, sd.RasterCount)])
#nbands='%s,%s' % (nbands, sd_md['Number of bands'])
nbands = nbands + int(sd_md['Number of bands'])
ncols = ','.join(ncols)
nrows = ','.join(nrows)
else:
#set up to create multispectral only _gdaldatset for overview generation
multi = 0
multibands = range(1, nbands + 1)
multicols = ncols
multirows = nrows
#create multispectral only _gdaldatset for overview generation
#Get all the data files and mosaic the strips
#strips=[s for s in utilities.rglob(os.path.dirname(f),r'\.m[1-4]r$',True,re.I,False)]
strips = glob.glob(os.path.join(os.path.dirname(f), '*.m[1-4]r'))
strips.sort()
strips.reverse() #west->east = *.m4r-m1
scols = (int(multicols) +
30) / 4 # +30 handles the 10 pixel overlap
xoff = 10
yoff = 400
srows = int(multirows) - yoff
srcrects = [[0, yoff, scols, srows]] * 4
dstrects = []
srcrect = [0, yoff, scols, srows]
#dstrect=[None,0,scols,srows]
files = []
sd, sz = hdf_sd[multi]
vrt_opts = []
indatasetlist = []
for i in range(0, 4):
files.append(sd.replace(f, strips[i]))
dstrects.append([i * (scols - xoff), 0, scols, srows])
self._gdaldataset = geometry.OpenDataset(
geometry.CreateMosaicedVRT(files, multibands, srcrects,
dstrects, multicols, srows,
'Int16'))
self._gdaldataset.GetRasterBand(2).SetNoDataValue(0)
self._gdaldataset.GetRasterBand(3).SetNoDataValue(0)
self._gdaldataset.GetRasterBand(4).SetNoDataValue(0)
self._stretch = ('STDDEV', (4, 3, 2), [2])
#Extract other metadata
met = os.path.splitext(f)[0] + '.met'
for line in open(met, 'r').readlines():
if line[0:16] == 'Scene Request ID':
line = line.strip().split()
self.metadata['sceneid'] = line[3]
if line[0:14] == 'ALI Start Time':
line = line.strip().split()
hdf_md['ImageStartTime'] = line[3] + line[4]
if line[0:8] == 'PRODUCT_':
line = line.strip()
line = map(string.strip, line.split('='))
if line[0] == 'PRODUCT_UL_CORNER_LAT': uly = float(line[1])
if line[0] == 'PRODUCT_UL_CORNER_LON': ulx = float(line[1])
if line[0] == 'PRODUCT_UR_CORNER_LAT': ury = float(line[1])
if line[0] == 'PRODUCT_UR_CORNER_LON': urx = float(line[1])
if line[0] == 'PRODUCT_LR_CORNER_LAT': lry = float(line[1])
if line[0] == 'PRODUCT_LR_CORNER_LON': lrx = float(line[1])
if line[0] == 'PRODUCT_LL_CORNER_LAT': lly = float(line[1])
if line[0] == 'PRODUCT_LL_CORNER_LON': llx = float(line[1])
geoext = [[ulx, uly], [urx, ury], [lrx, lry], [llx, lly],
[ulx, uly]]
prjext = geoext
elif re.search(r'eo1.*_mtl\.tif$', f):
self.metadata['level'] = 'L1G'
self.metadata['sensor'] = 'ALI'
self.metadata['sceneid'] = self.metadata['filename'].split('_')[0]
self.metadata['filetype'] = 'GTiff/GeoTIFF'
ncols = []
nrows = []
nbands = 0
bands = glob.glob(os.path.join(os.path.dirname(f), 'eo1*_b*.tif'))
for band in bands:
band = geometry.OpenDataset(band)
ncols.append(str(band.RasterXSize))
nrows.append(str(band.RasterYSize))
nbands += 1
#rb=sd.GetRasterBand(1)
#get all multi bands for use in overview generation
pancols = max(ncols)
panindex = ncols.index(pancols)
multibands = bands
multincols = ncols
multinrows = nrows
if pancols > min(ncols): #there is a pan band
multibands.pop(panindex)
multincols.pop(panindex)
multinrows.pop(panindex)
multibands.sort()
self._gdaldataset = geometry.OpenDataset(
geometry.CreateSimpleVRT(multibands, multincols[0],
multinrows[0], 'Int16'))
self._gdaldataset.GetRasterBand(2).SetNoDataValue(0)
self._gdaldataset.GetRasterBand(3).SetNoDataValue(0)
self._gdaldataset.GetRasterBand(4).SetNoDataValue(0)
self._stretch = ('STDDEV', (4, 3, 2), [2])
ncols = ','.join(ncols)
nrows = ','.join(nrows)
met = f
md = {}
for line in open(met, 'r'):
line = line.replace('"', '')
line = [l.strip() for l in line.split('=')]
if line[0] == 'END': #end of the metadata file
break
elif line[0] == 'GROUP': #start of a metadata group
if line[1] != 'L1_METADATA_FILE':
group = line[1]
md[group] = {}
elif line[0] == 'END_GROUP': #end of a metadata group
pass
else: #metadata value
md[group][line[0]] = line[1]
uly = float(md['PRODUCT_METADATA']['PRODUCT_UL_CORNER_LAT'])
ulx = float(md['PRODUCT_METADATA']['PRODUCT_UL_CORNER_LON'])
ury = float(md['PRODUCT_METADATA']['PRODUCT_UR_CORNER_LAT'])
urx = float(md['PRODUCT_METADATA']['PRODUCT_UR_CORNER_LON'])
lry = float(md['PRODUCT_METADATA']['PRODUCT_LR_CORNER_LAT'])
lrx = float(md['PRODUCT_METADATA']['PRODUCT_LR_CORNER_LON'])
lly = float(md['PRODUCT_METADATA']['PRODUCT_LL_CORNER_LAT'])
llx = float(md['PRODUCT_METADATA']['PRODUCT_LL_CORNER_LON'])
geoext = [[ulx, uly], [urx, ury], [lrx, lry], [llx, lly],
[ulx, uly]]
uly = float(md['PRODUCT_METADATA']['PRODUCT_UL_CORNER_MAPY'])
ulx = float(md['PRODUCT_METADATA']['PRODUCT_UL_CORNER_MAPX'])
ury = float(md['PRODUCT_METADATA']['PRODUCT_UR_CORNER_MAPY'])
urx = float(md['PRODUCT_METADATA']['PRODUCT_UR_CORNER_MAPX'])
lry = float(md['PRODUCT_METADATA']['PRODUCT_LR_CORNER_MAPY'])
lrx = float(md['PRODUCT_METADATA']['PRODUCT_LR_CORNER_MAPX'])
lly = float(md['PRODUCT_METADATA']['PRODUCT_LL_CORNER_MAPY'])
llx = float(md['PRODUCT_METADATA']['PRODUCT_LL_CORNER_MAPX'])
prjext = [[ulx, uly], [urx, ury], [lrx, lry], [llx, lly],
[ulx, uly]]
self.metadata['imgdate'] = md['PRODUCT_METADATA'][
'ACQUISITION_DATE']
self.metadata['resampling'] = md['PROJECTION_PARAMETERS'][
'RESAMPLING_OPTION']
try:
self.metadata['viewangle'] = float(
md['PRODUCT_PARAMETERS']['SENSOR_LOOK_ANGLE'])
except:
pass #Exception raised if value == 'UNAVAILABLE'
try:
self.metadata['sunazimuth'] = float(
md['PRODUCT_PARAMETERS']['SUN_AZIMUTH'])
except:
pass #Exception raised if value == 'UNAVAILABLE'
try:
self.metadata['sunelevation'] = float(
md['PRODUCT_PARAMETERS']['SUN_ELEVATION'])
except:
pass #Exception raised if value == 'UNAVAILABLE'
#EPSG:32601: WGS 84 / UTM zone 1N
#EPSG:32701: WGS 84 / UTM zone 1S
srs = osr.SpatialReference()
zone = int(md['UTM_PARAMETERS']['ZONE_NUMBER'])
if zone > 0: epsg = 32600 + zone #North
else: epsg = 32700 - zone #South
srs.ImportFromEPSG(epsg)
self.metadata['units'] = 'm'
self.metadata['srs'] = srs.ExportToWkt()
self.metadata['epsg'] = str(epsg)
elif re.search(r'eo1.*_hdf\.l1g$', f):
self.metadata['level'] = 'L1G'
self.metadata['sensor'] = 'ALI'
self.metadata['sceneid'] = self.metadata['filename'].split('_')[0]
gdalDataset = geometry.OpenDataset(f)
#if not gdalDataset: #Error now raised in geometry.OpenDataset
# errmsg=gdal.GetLastErrorMsg()
# raise IOError, 'Unable to open %s\n%s' % (f,errmsg.strip())
self.metadata['filetype'] = '%s/%s (%s %s)' % (
gdalDataset.GetDriver().ShortName,
gdalDataset.GetDriver().LongName, self.metadata['sensor'],
self.metadata['level'])
hdf_sd = gdalDataset.GetSubDatasets()
hdf_md = gdalDataset.GetMetadata()
sd, sz = hdf_sd[0]
sd = geometry.OpenDataset(sd)
sd_md = sd.GetMetadata()
ncols = []
nrows = []
nbands = 0
bands = []
for sd, sz in hdf_sd:
bands.append(sd)
ds = geometry.OpenDataset(sd)
ncols.append(str(ds.RasterXSize))
nrows.append(str(ds.RasterYSize))
nbands += 1
#get all multi bands for use in overview generation
pancols = max(ncols)
panindex = ncols.index(pancols)
multibands = bands
multincols = ncols
multinrows = nrows
if pancols > min(ncols): #there is a pan band
multibands.pop(panindex)
multincols.pop(panindex)
multinrows.pop(panindex)
multibands.sort()
self._gdaldataset = geometry.OpenDataset(
geometry.CreateSimpleVRT(multibands, multincols[0],
multinrows[0], 'Int16'))
self._gdaldataset.GetRasterBand(2).SetNoDataValue(0)
self._gdaldataset.GetRasterBand(3).SetNoDataValue(0)
self._gdaldataset.GetRasterBand(4).SetNoDataValue(0)
self._stretch = ('STDDEV', (4, 3, 2), [2])
ncols = ','.join(ncols)
nrows = ','.join(nrows)
met = f.lower().replace('_hdf.l1g', '_mtl.l1g')
md = {}
for line in open(met, 'r'):
line = line.replace('"', '')
line = [l.strip() for l in line.split('=')]
if line[0] == 'END': #end of the metadata file
break
elif line[0] == 'GROUP': #start of a metadata group
if line[1] != 'L1_METADATA_FILE':
group = line[1]
md[group] = {}
elif line[0] == 'END_GROUP': #end of a metadata group
pass
else: #metadata value
md[group][line[0]] = line[1]
uly = float(md['PRODUCT_METADATA']['PRODUCT_UL_CORNER_LAT'])
ulx = float(md['PRODUCT_METADATA']['PRODUCT_UL_CORNER_LON'])
ury = float(md['PRODUCT_METADATA']['PRODUCT_UR_CORNER_LAT'])
urx = float(md['PRODUCT_METADATA']['PRODUCT_UR_CORNER_LON'])
lry = float(md['PRODUCT_METADATA']['PRODUCT_LR_CORNER_LAT'])
lrx = float(md['PRODUCT_METADATA']['PRODUCT_LR_CORNER_LON'])
lly = float(md['PRODUCT_METADATA']['PRODUCT_LL_CORNER_LAT'])
llx = float(md['PRODUCT_METADATA']['PRODUCT_LL_CORNER_LON'])
geoext = [[ulx, uly], [urx, ury], [lrx, lry], [llx, lly],
[ulx, uly]]
prjext = geoext
self.metadata['imgdate'] = md['PRODUCT_METADATA'][
'ACQUISITION_DATE']
self.metadata['resampling'] = md['PROJECTION_PARAMETERS'][
'RESAMPLING_OPTION']
try:
self.metadata['viewangle'] = float(
md['PRODUCT_PARAMETERS']['SENSOR_LOOK_ANGLE'])
except:
pass #Exception raised if value == 'UNAVAILABLE'
try:
self.metadata['sunazimuth'] = float(
md['PRODUCT_PARAMETERS']['SUN_AZIMUTH'])
except:
pass #Exception raised if value == 'UNAVAILABLE'
try:
self.metadata['sunelevation'] = float(
md['PRODUCT_PARAMETERS']['SUN_ELEVATION'])
except:
pass #Exception raised if value == 'UNAVAILABLE'
#EPSG:32601: WGS 84 / UTM zone 1N
#EPSG:32701: WGS 84 / UTM zone 1S
srs = osr.SpatialReference()
zone = int(md['UTM_PARAMETERS']['ZONE_NUMBER'])
if zone > 0: epsg = 32600 + zone #North
else: epsg = 32700 - zone #South
srs.ImportFromEPSG(epsg)
self.metadata['units'] = 'm'
self.metadata['srs'] = srs.ExportToWkt()
self.metadata['epsg'] = str(epsg)
else:
self.metadata['level'] = 'L1R'
self.metadata['sensor'] = 'HYPERION'
gdalDataset = geometry.OpenDataset(f)
#if not gdalDataset: #Error now raised in geometry.OpenDataset
# errmsg=gdal.GetLastErrorMsg()
# raise IOError, 'Unable to open %s\n%s' % (f,errmsg.strip())
self.metadata['filetype'] = '%s/%s (%s %s)' % (
gdalDataset.GetDriver().ShortName,
gdalDataset.GetDriver().LongName, self.metadata['sensor'],
self.metadata['level'])
srs = osr.SpatialReference()
srs.ImportFromEPSG(4326)
self.metadata['srs'] = srs.ExportToWkt()
self.metadata['epsg'] = 4326
self.metadata['units'] = 'deg'
hdf_sd = gdalDataset.GetSubDatasets()
hdf_md = gdalDataset.GetMetadata()
sd, sz = hdf_sd[0]
sd = geometry.OpenDataset(sd)
sd_md = sd.GetMetadata()
nbands = sd.RasterCount
ncols = sd.RasterXSize
nrows = sd.RasterYSize
met = os.path.splitext(f)[0] + '.met'
for line in open(met, 'r').readlines():
if line[0:16] == 'Scene Request ID':
line = line.strip().split()
self.metadata['sceneid'] = line[3]
if line[0:14] == 'HYP Start Time':
line = line.strip().split()
imgdate = time.strptime(line[3] + line[4], '%Y%j')
self.metadata['imgdate'] = time.strftime(
'%Y-%m-%d', imgdate) #ISO 8601
if line[0:8] == 'PRODUCT_':
line = line.strip()
line = map(string.strip, line.split('='))
if line[0] == 'PRODUCT_UL_CORNER_LAT': uly = float(line[1])
if line[0] == 'PRODUCT_UL_CORNER_LON': ulx = float(line[1])
if line[0] == 'PRODUCT_UR_CORNER_LAT': ury = float(line[1])
if line[0] == 'PRODUCT_UR_CORNER_LON': urx = float(line[1])
if line[0] == 'PRODUCT_LR_CORNER_LAT': lry = float(line[1])
if line[0] == 'PRODUCT_LR_CORNER_LON': lrx = float(line[1])
if line[0] == 'PRODUCT_LL_CORNER_LAT': lly = float(line[1])
if line[0] == 'PRODUCT_LL_CORNER_LON': llx = float(line[1])
geoext = [[ulx, uly], [urx, ury], [lrx, lry], [llx, lly],
[ulx, uly]]
prjext = geoext
#########################################################################################################
##set self._gdaldataset for use in overview generation
##we'll use bands 21, 30 & 43 for RGB (http://edcsns17.cr.usgs.gov/eo1/Hyperion_Spectral_Coverage.htm)
##as they're near the center of the equivalent ALI bands
##This generates verrrryyy looong overviews cos hyperion data is one long thin strip eg. 256*3000 etc...
#self._gdaldataset = geometry.CreateVRTCopy(sd)
##Instead we can clip out some of the centre rows
#vrtcols=ncols
#vrtrows=int(ncols*1.5)
#srcrect=[0, int(nrows/2-vrtrows/2),ncols,vrtrows]
#dstrect=[0, 0,ncols,vrtrows]
##Or we can fill out the ncols with nodata
vrtcols = int(nrows / 1.5)
vrtrows = nrows
srcrect = [0, 0, ncols, nrows]
dstrect = [int(nrows / 2.5 - ncols), 0, ncols, nrows]
vrt = geometry.CreateMosaicedVRT([sd.GetDescription()],
[43, 30, 21], [srcrect],
[dstrect], vrtcols, vrtrows,
self.metadata['datatype'])
self._gdaldataset = geometry.OpenDataset(vrt)
self._gdaldataset.GetRasterBand(3).SetNoDataValue(0)
self._gdaldataset.GetRasterBand(2).SetNoDataValue(0)
self._gdaldataset.GetRasterBand(1).SetNoDataValue(0)
self._stretch = ('STDDEV', (1, 2, 3), [2])
#########################################################################################################
self.metadata['cols'] = ncols
self.metadata['rows'] = nrows
self.metadata['nbands'] = nbands
#Geotransform
ncols = map(int, str(ncols).split(','))
nrows = map(int, str(nrows).split(','))
cellx, celly = [], []
j = 0
while j < len(ncols):
gcps = []
i = 0
lr = [[0, 0], [ncols[j], 0], [ncols[j], nrows[j]], [0, nrows[j]]]
while i < len(prjext) - 1: #don't need the last xy pair
gcp = gdal.GCP()
gcp.GCPPixel, gcp.GCPLine = lr[i]
gcp.GCPX, gcp.GCPY = prjext[i]
gcp.Id = str(i)
gcps.append(gcp)
i += 1
j += 1
geotransform = gdal.GCPsToGeoTransform(gcps)
x, y = geometry.CellSize(geotransform)
cellx.append(str(x))
celly.append(str(abs(y)))
self.metadata['cellx'] = ','.join(cellx)
self.metadata['celly'] = ','.join(celly)
self.metadata['UL'] = '%s,%s' % tuple(geoext[0])
self.metadata['UR'] = '%s,%s' % tuple(geoext[1])
self.metadata['LR'] = '%s,%s' % tuple(geoext[2])
self.metadata['LL'] = '%s,%s' % tuple(geoext[3])
self.metadata['rotation'] = geometry.Rotation(geotransform)
if abs(self.metadata['rotation']) < 1.0:
self.metadata['orientation'] = 'Map oriented'
self.metadata['rotation'] = 0.0
else:
self.metadata['orientation'] = 'Path oriented'
self.metadata['filesize'] = sum(
[os.path.getsize(tmp) for tmp in self.filelist])
self.metadata['compressionratio'] = 0
self.metadata['compressiontype'] = 'None'
self.extent = geoext