def __init__(self, f):
if f[:4] == '/vsi': raise NotImplementedError
self.filelist = glob.glob(os.path.dirname(f) + '/*')
if os.path.splitext(f)[1].lower() != 'imd':
imd = glob.glob(os.path.splitext(f)[0] + '.[Ii][Mm][Dd]')
if imd:
self.__setfileinfo__(imd[0])
else:
raise NotImplementedError, 'No matching IMD file'
self.exts = {
'.tif': 'GTiff/GeoTIFF',
'.img': 'HFA/Erdas Imagine Images (.img)',
'.ntf': 'NITF/National Imagery Transmission Format (.ntf)',
'.pix': 'PCI Geomatics Database File (.pix)'
}
self.til = False
self.img = False
btil, self.til = utilities.exists(
os.path.splitext(f)[0] + '.til', True)
if not btil:
for ext in self.exts:
bext, ext = utilities.exists(
os.path.splitext(f)[0] + ext, True)
if bext:
self.img = ext
break
if not self.img:
raise NotImplementedError, 'Matching DigitalGlobe imagery file not found:\n'
def v1(self,f=None):
dom = self._dom
self.metadata['sceneid'] = dom.xpath('string(/Dimap_Document/Dataset_Id/DATASET_NAME)')
bands=dom.xpath('/Dimap_Document/Spectral_Band_Info/BAND_DESCRIPTION')
self.metadata['bands']=','.join([band.xpath('string(.)') for band in bands])
try:__default__.Dataset.__getmetadata__(self, f) #autopopulate basic metadata
except geometry.GDALError,err: #Work around reading images with lowercase filenames when
#the DATA_FILE_PATH is uppercase
# - eg samba filesystems which get converted to lowercase
dfp=dom.xpath('/Dimap_Document/Data_Access/Data_File/DATA_FILE_PATH')[0]
fn=utilities.encode(dfp.xpath('string(@href)')) #XML is unicode, gdal.Open doesn't like unicode
if not os.path.dirname(fn):fn=os.path.join(os.path.dirname(f),fn)
exists,img=utilities.exists(fn,True)
if exists and not os.path.exists(fn):
import tempfile
tmpfd,tmpfn=tempfile.mkstemp(suffix='.dim',prefix='metadata')
dfp.set('href',img)
tmpfo=os.fdopen(tmpfd,'w')
tmpfo.write(etree.tostring(dom))
tmpfo.flush()
tmpfo.close()
cwd=os.path.abspath(os.curdir)
tmp=os.path.split(tmpfn)
os.chdir(tmp[0]) #CD to the tmp dir so __default__.Dataset.__getmetadata__ doesn't
__default__.Dataset.__getmetadata__(self, tmp[1])
gdalmd=self._gdaldataset.GetMetadata()
self._gdaldataset=geometry.OpenDataset(img)
self._gdaldataset.SetMetadata(gdalmd)
os.unlink(tmpfn)
os.chdir(cwd)
else:raise
def xlsxcallback(xlsxarg,updatearg):
#xlsx=xlsxarg.value.get()
xlsx=xlsxarg.value
if utilities.exists(xlsx):
updatearg.enabled=True
#updatearg.value.set(True)
updatearg.value=True
else:
updatearg.enabled=False
#updatearg.value.set(False)
updatearg.value=False
def __getmetadata__(self, f=None):
'''Read Metadata for a DIMAP image as GDAL doesn't quite get it all...'''
if not f: f = self.fileinfo['filepath']
#dom=etree.parse(f) #Takes tooo long to parse the whole file, so just read as far as we need...
strxml = ''
for line in open(f, 'r'):
if line.upper().strip() == '<DATA_STRIP>': break
else: strxml += line
if not '</Dimap_Document>' in strxml: strxml += '</Dimap_Document>'
dom = etree.fromstring(strxml)
self.metadata['sceneid'] = dom.xpath(
'string(/Dimap_Document/Dataset_Id/DATASET_NAME)')
bands = dom.xpath(
'/Dimap_Document/Spectral_Band_Info/BAND_DESCRIPTION')
self.metadata['bands'] = ','.join(
[band.xpath('string(.)') for band in bands])
try:
__default__.Dataset.__getmetadata__(
self, f) #autopopulate basic metadata
except geometry.GDALError, err: #Work around reading images with lowercase filenames when
#the DATA_FILE_PATH is uppercase
# - eg samba filesystems which get converted to lowercase
dfp = dom.xpath(
'/Dimap_Document/Data_Access/Data_File/DATA_FILE_PATH')[0]
fn = utilities.encode(
dfp.xpath('string(@href)'
)) #XML is unicode, gdal.Open doesn't like unicode
if not os.path.dirname(fn):
fn = os.path.join(os.path.dirname(f), fn)
exists, img = utilities.exists(fn, True)
if exists and not os.path.exists(fn):
import tempfile
tmpfd, tmpfn = tempfile.mkstemp(suffix='.dim',
prefix='metadata')
dfp.set('href', img)
tmpfo = os.fdopen(tmpfd, 'w')
tmpfo.write(etree.tostring(dom))
tmpfo.flush()
tmpfo.close()
cwd = os.path.abspath(os.curdir)
tmp = os.path.split(tmpfn)
os.chdir(
tmp[0]
) #CD to the tmp dir so __default__.Dataset.__getmetadata__ doesn't
__default__.Dataset.__getmetadata__(self, tmp[1])
gdalmd = self._gdaldataset.GetMetadata()
self._gdaldataset = geometry.OpenDataset(img)
self._gdaldataset.SetMetadata(gdalmd)
os.unlink(tmpfn)
os.chdir(cwd)
else:
raise
def __init__(self,f):
if f[:4]=='/vsi':raise NotImplementedError
self.filelist=glob.glob(os.path.dirname(f)+'/*')
if os.path.splitext(f)[1].lower() !='imd':
imd=glob.glob(os.path.splitext(f)[0]+'.[Ii][Mm][Dd]')
if imd:
self.__setfileinfo__(imd[0])
else:raise NotImplementedError, 'No matching IMD file'
self.exts={'.tif':'GTiff/GeoTIFF','.img':'HFA/Erdas Imagine Images (.img)','.ntf':'NITF/National Imagery Transmission Format (.ntf)','.pix':'PCI Geomatics Database File (.pix)'}
self.til=False
self.img=False
btil,self.til = utilities.exists(os.path.splitext(f)[0]+'.til',True)
if not btil:
for ext in self.exts:
bext,ext = utilities.exists(os.path.splitext(f)[0]+ext,True)
if bext:
self.img=ext
break
if not self.img:raise NotImplementedError, 'Matching DigitalGlobe imagery file not found:\n'
def v1(self, f=None):
dom = self._dom
self.metadata['sceneid'] = dom.xpath(
'string(/Dimap_Document/Dataset_Id/DATASET_NAME)')
bands = dom.xpath(
'/Dimap_Document/Spectral_Band_Info/BAND_DESCRIPTION')
self.metadata['bands'] = ','.join(
[band.xpath('string(.)') for band in bands])
try:
__default__.Dataset.__getmetadata__(
self, f) #autopopulate basic metadata
except geometry.GDALError, err: #Work around reading images with lowercase filenames when
#the DATA_FILE_PATH is uppercase
# - eg samba filesystems which get converted to lowercase
dfp = dom.xpath(
'/Dimap_Document/Data_Access/Data_File/DATA_FILE_PATH')[0]
fn = utilities.encode(
dfp.xpath('string(@href)'
)) #XML is unicode, gdal.Open doesn't like unicode
if not os.path.dirname(fn):
fn = os.path.join(os.path.dirname(f), fn)
exists, img = utilities.exists(fn, True)
if exists and not os.path.exists(fn):
import tempfile
tmpfd, tmpfn = tempfile.mkstemp(suffix='.dim',
prefix='metadata')
dfp.set('href', img)
tmpfo = os.fdopen(tmpfd, 'w')
tmpfo.write(etree.tostring(dom))
tmpfo.flush()
tmpfo.close()
cwd = os.path.abspath(os.curdir)
tmp = os.path.split(tmpfn)
os.chdir(
tmp[0]
) #CD to the tmp dir so __default__.Dataset.__getmetadata__ doesn't
__default__.Dataset.__getmetadata__(self, tmp[1])
gdalmd = self._gdaldataset.GetMetadata()
self._gdaldataset = geometry.OpenDataset(img)
self._gdaldataset.SetMetadata(gdalmd)
os.unlink(tmpfn)
os.chdir(cwd)
else:
raise
def __getmetadata__(self,f=None):
'''Read Metadata for a DIMAP image as GDAL doesn't quite get it all...'''
if not f:f=self.fileinfo['filepath']
#dom=etree.parse(f) #Takes tooo long to parse the whole file, so just read as far as we need...
strxml=''
for line in open(f, 'r'):
if line.upper().strip()=='<DATA_STRIP>':break
else: strxml+=line
if not '</Dimap_Document>' in strxml:strxml+='</Dimap_Document>'
dom=etree.fromstring(strxml)
self.metadata['sceneid'] = dom.xpath('string(/Dimap_Document/Dataset_Id/DATASET_NAME)')
bands=dom.xpath('/Dimap_Document/Spectral_Band_Info/BAND_DESCRIPTION')
self.metadata['bands']=','.join([band.xpath('string(.)') for band in bands])
try:__default__.Dataset.__getmetadata__(self, f) #autopopulate basic metadata
except geometry.GDALError,err: #Work around reading images with lowercase filenames when
#the DATA_FILE_PATH is uppercase
# - eg samba filesystems which get converted to lowercase
dfp=dom.xpath('/Dimap_Document/Data_Access/Data_File/DATA_FILE_PATH')[0]
fn=utilities.encode(dfp.xpath('string(@href)')) #XML is unicode, gdal.Open doesn't like unicode
if not os.path.dirname(fn):fn=os.path.join(os.path.dirname(f),fn)
exists,img=utilities.exists(fn,True)
if exists and not os.path.exists(fn):
import tempfile
tmpfd,tmpfn=tempfile.mkstemp(suffix='.dim',prefix='metadata')
dfp.set('href',img)
tmpfo=os.fdopen(tmpfd,'w')
tmpfo.write(etree.tostring(dom))
tmpfo.flush()
tmpfo.close()
cwd=os.path.abspath(os.curdir)
tmp=os.path.split(tmpfn)
os.chdir(tmp[0]) #CD to the tmp dir so __default__.Dataset.__getmetadata__ doesn't
__default__.Dataset.__getmetadata__(self, tmp[1])
gdalmd=self._gdaldataset.GetMetadata()
self._gdaldataset=geometry.OpenDataset(img)
self._gdaldataset.SetMetadata(gdalmd)
os.unlink(tmpfn)
os.chdir(cwd)
else:raise
def __getmetadata__(self):
'''Read Metadata for an ACRES Landsat FastL7A format image as GDAL doesn't get it all.
Format description: http://www.ga.gov.au/image_cache/GA10348.pdf
Note:
hrf = ~30m VNIR/SWIR (bands 1-5 & 7)
htm = ~60m thermal (band 6)
hpn = ~15m pan (band 8)
'''
f = self.fileinfo['filepath']
d = os.path.dirname(f)
hdr = open(f).read()
err = 'Unable to open %s' % f
md = self.metadata
md['filesize'] = sum([os.path.getsize(file) for file in self.filelist])
md['filetype'] = 'FAST/EOSAT FAST Format'
rl = 1536 #recordlength
######################################
##Record 1 - administrative
######################################
rec = 1
req_id = utilities.readascii(hdr, (rec - 1) * rl, 9, 28)
loc = utilities.readascii(hdr, (rec - 1) * rl, 35, 51)
acquisition_date = utilities.readascii(hdr, (rec - 1) * rl, 71, 78)
md['imgdate'] = '%s-%s-%s' % (
acquisition_date[:4], acquisition_date[4:6], acquisition_date[6:])
md['satellite'] = utilities.readascii(hdr, (rec - 1) * rl, 92, 101)
md['sensor'] = utilities.readascii(hdr, (rec - 1) * rl, 111, 120)
md['mode'] = utilities.readascii(hdr, (rec - 1) * rl, 135, 140)
md['viewangle'] = float(
utilities.readascii(hdr, (rec - 1) * rl, 154, 159))
product_type = utilities.readascii(hdr, (rec - 1) * rl, 655, 672)
product_size = utilities.readascii(hdr, (rec - 1) * rl, 688, 697)
level = utilities.readascii(hdr, (rec - 1) * rl, 741, 751)
md['resampling'] = utilities.readascii(hdr, (rec - 1) * rl, 765, 766)
md['cols'] = int(utilities.readascii(hdr, (rec - 1) * rl, 843, 847))
md['rows'] = int(utilities.readascii(hdr, (rec - 1) * rl, 865, 869))
md['cellx'] = float(utilities.readascii(hdr, (rec - 1) * rl, 954, 959))
md['celly'] = md['cellx']
md['nbits'] = 8 #int(utilities.readascii(hdr,(rec-1)*rl,984,985)) always 8 bit
md['datatype'] = 'Byte'
md['nodata'] = '0'
bands_present = utilities.readascii(hdr, (rec - 1) * rl, 1056, 1087)
bandindices = [[1131, 1159], [1170, 1198], [1211, 1239], [1250, 1278],
[1291, 1319], [1330, 1358]]
bandfiles = {}
for i in bandindices:
band = utilities.readascii(hdr, (rec - 1) * rl, i[0], i[1])
if band:
exists, path = utilities.exists(os.path.join(d, band), True)
if exists: bandfiles[band] = path
else: #Assume ACRES format (band*.dat) instead Fast format (l7*.fst)...
bandid = band[23:25]
if bandid == '61': bandid = '6l'
elif bandid == '62': bandid = '6h'
else: bandid = bandid[0]
exists, path = utilities.exists(
os.path.join(d, 'band%s.dat' % bandid), True)
if not exists:
raise RuntimeError, 'Unable to open band data files.'
bandfiles[band] = path
else:
break
md['nbands'] = len(bandfiles)
md['sceneid'] = os.path.basename(
bandfiles.keys()[0]
)[3:
21] #Use path/row & aquisition date as sceneid - L7f[ppprrr_rrrYYYYMMDD]_AAA.FST
if self._filetype == 'HRF':
md['bands'] = '1 (BLUE),2 (GREEN),3 (RED),4 (NIR),5 (SWIR),7 (SWIR)'
elif self._filetype == 'HTM':
md['bands'] = '6L (THERMAL),6H (THERMAL)'
elif self._filetype == 'HPN':
md['bands'] = '8 (PAN)'
######################################
##Record 2 - radiometric
######################################
#Move along, nothing to see here...
######################################
##Record 3 - geometric
######################################
rec = 3
map_projection = utilities.readascii(hdr, (rec - 1) * rl, 32, 35)
prjcode = spatialreferences.GCTP_PROJECTIONS.get(map_projection, 0)
ellipsoid = utilities.readascii(hdr, (rec - 1) * rl, 48, 65)
ellcode = spatialreferences.GCTP_ELLIPSOIDS.get(ellipsoid, 0)
datum = utilities.readascii(hdr, (rec - 1) * rl, 74, 79)
zone = utilities.readascii(hdr, (rec - 1) * rl, 521, 526)
#Workaround for UTM zones as GDAL does not pick up southern hemisphere
#as some FST headers don't include a negative zone number to indicate southern hemisphere
#as per the FAST format definition
zone = int(zone) if zone else 0
usgs_indices = (
(110, 133), #Semi-major axis
(135, 158), #Semi-minor axis
(161, 184),
(186, 209),
(211, 234),
(241, 264),
(266, 289),
(291, 314),
(321, 344),
(346, 369),
(371, 394),
(401, 424),
(426, 449),
(451, 474),
(481, 504))
usgs_params = []
for i in usgs_indices:
p = utilities.readascii(hdr, (rec - 1) * rl, i[0], i[1])
if p: usgs_params.append(float(p))
else: usgs_params.append(0.0)
ulx = geometry.DMS2DD(
utilities.readascii(hdr, (rec - 1) * rl, 566, 578),
'DDDMMSSSSSSSH')
uly = geometry.DMS2DD(
utilities.readascii(hdr, (rec - 1) * rl, 580, 591), 'DDMMSSSSSSSH')
urx = geometry.DMS2DD(
utilities.readascii(hdr, (rec - 1) * rl, 646, 658),
'DDDMMSSSSSSSH')
ury = geometry.DMS2DD(
utilities.readascii(hdr, (rec - 1) * rl, 660, 671), 'DDMMSSSSSSSH')
lrx = geometry.DMS2DD(
utilities.readascii(hdr, (rec - 1) * rl, 726, 738),
'DDDMMSSSSSSSH')
lry = geometry.DMS2DD(
utilities.readascii(hdr, (rec - 1) * rl, 740, 751), 'DDMMSSSSSSSH')
llx = geometry.DMS2DD(
utilities.readascii(hdr, (rec - 1) * rl, 806, 818),
'DDDMMSSSSSSSH')
lly = geometry.DMS2DD(
utilities.readascii(hdr, (rec - 1) * rl, 820, 831), 'DDMMSSSSSSSH')
ext = [[ulx, uly], [urx, ury], [lrx, lry], [llx, lly], [ulx, uly]]
md['UL'] = '%s,%s' % tuple(ext[0])
md['UR'] = '%s,%s' % tuple(ext[1])
md['LR'] = '%s,%s' % tuple(ext[2])
md['LL'] = '%s,%s' % tuple(ext[3])
if zone > 0 and uly < 0: zone *= -1
srs = osr.SpatialReference()
srs.ImportFromUSGS(prjcode, zone, usgs_params, ellcode)
if datum == 'GDA': #Workaround for GDA94 datum as GDAL does not recognise it
#as per the FAST format definition
if map_projection == 'UTM':
epsg = 28300 + abs(zone)
srs.ImportFromEPSG(epsg)
md['srs'] = srs.ExportToWkt()
md['epsg'] = epsg
md['units'] = 'm'
else:
srs.SetGeogCS('GDA94', 'Geocentric_Datum_of_Australia_1994',
'GRS 1980', usgs_params[0], 298.257)
md['srs'] = srs.ExportToWkt()
md['epsg'] = spatialreferences.IdentifyAusEPSG(md['srs'])
md['units'] = spatialreferences.GetLinearUnitsName(md['srs'])
else:
md['srs'] = srs.ExportToWkt()
md['epsg'] = spatialreferences.IdentifyAusEPSG(md['srs'])
md['units'] = spatialreferences.GetLinearUnitsName(md['srs'])
md['rotation'] = float(
utilities.readascii(hdr, (rec - 1) * rl, 995, 1000))
if abs(md['rotation']) < 1:
md['orientation'] = 'Map oriented'
md['rotation'] = 0.0
else:
md['orientation'] = 'Path oriented'
md['sunelevation'] = utilities.readascii(hdr, (rec - 1) * rl, 1062,
1065)
md['sunazimuth'] = utilities.readascii(hdr, (rec - 1) * rl, 1086, 1090)
try: ##Open dataset
self._gdaldataset = geometry.OpenDataset(f)
metadata = self._gdaldataset.GetMetadata()
md['metadata'] = '\n'.join(
['%s: %s' % (m, metadata[m]) for m in metadata])
#Fix for Issue 17
for i in range(1, self._gdaldataset.RasterCount + 1):
self._gdaldataset.GetRasterBand(i).SetNoDataValue(
float(md['nodata']))
except: #build a VRT dataset - if we want to use this for anything other than overview generation, should probably fill out the geotransform, srs, metadata etc...
bands = bandfiles.values()
bands.sort()
#vrtxml=geometry.CreateRawRasterVRT(bands,md['cols'],md['rows'], md['datatype']) #Fix for Issue 17
vrtxml = geometry.CreateRawRasterVRT(bands,
md['cols'],
md['rows'],
md['datatype'],
nodata=md['nodata'])
self._gdaldataset = geometry.OpenDataset(vrtxml)
md['metadata'] = hdr
if self._filetype == 'HRF':
self._gdaldataset.GetRasterBand(4).SetRasterColorInterpretation(
gdal.GCI_BlueBand)
self._gdaldataset.GetRasterBand(3).SetRasterColorInterpretation(
gdal.GCI_GreenBand)
self._gdaldataset.GetRasterBand(2).SetRasterColorInterpretation(
gdal.GCI_RedBand)
if level == 'SYSTEMATIC': md['level'] = '1G '
elif level == 'SYSTERRAIN': md['level'] = '1Gt'
elif level == 'PRECISION': md['level'] = '1P'
elif level == 'TERRAIN': md['level'] = '1T'
md['compressionratio'] = 0
md['compressiontype'] = 'None'
self.extent = ext
for m in md:
self.metadata[m] = md[m]
def __gettilevrt__(self,f,imddata):
til=iter(open(f).readlines())
tileinfo={}
datasets={}
line=til.next()
while line: #Extract all keys and values from the header file into a dictionary
line=line.strip().strip(';').replace('"','')
if line == 'END':break
if 'BEGIN_GROUP' in line:
line=til.next()
while line:
line=line.strip().strip(';').replace('"','')
if 'END_GROUP' in line:break
else:
dat=map(string.strip, line.split('=',1))
if not dat[0] in datasets:datasets[dat[0]]=[]
datasets[dat[0]].append(dat[1])
line=til.next()
else:
var=map(string.strip, line.split('=',1))
tileinfo[var[0]]=var[1]
line=til.next()
curdir=os.path.dirname(f)
bimg,img=utilities.exists(os.path.join(curdir,datasets['filename'][0]),True)
ds=geometry.OpenDataset(img)
rb=ds.GetRasterBand(1)
DataType=gdal.GetDataTypeName(rb.DataType)
GeoTransform=ds.GetGeoTransform()
Projection=ds.GetProjection()
if GeoTransform==(0.0, 1.0, 0.0, 0.0, 0.0, 1.0):
GeoTransform=gdal.GCPsToGeoTransform(ds.GetGCPs())
if Projection=='':
Projection=ds.GetGCPProjection()
GeoTransform=','.join(map(str, GeoTransform))
numTiles=int(tileinfo['numTiles'])
BlockXSize,BlockYSize=rb.GetBlockSize()
vrtXML = []
vrtXML.append('<VRTDataset rasterXSize="%s" rasterYSize="%s">' % (imddata['numColumns'],imddata['numRows']))
vrtXML.append('<SRS>%s</SRS>' % Projection)
vrtXML.append('<GeoTransform>%s</GeoTransform>' % GeoTransform)
for b, band in enumerate(imddata['bands']):
b+=1
vrtXML.append(' <VRTRasterBand dataType="%s" band="%s">' % (DataType,b))
#vrtXML.append(' <ColorInterp>Gray</ColorInterp>')
for tile in range(0,numTiles):
tileSizeX=int(datasets['URColOffset'][tile])-int(datasets['ULColOffset'][tile])+1
tileSizeY=int(datasets['LLRowOffset'][tile])-int(datasets['ULRowOffset'][tile])+1
ULColOffset=datasets['ULColOffset'][tile]
ULRowOffset=datasets['ULRowOffset'][tile]
bimg,img=utilities.exists(os.path.join(curdir,datasets['filename'][tile]),True)
vrtXML.append(' <SimpleSource>')
vrtXML.append(' <SourceFilename relativeToVRT="0">%s</SourceFilename>' % img)
vrtXML.append(' <SourceBand>%s</SourceBand>' % (b))
vrtXML.append(' <SourceProperties RasterXSize="%s" RasterYSize="%s" DataType="%s"/>'%(tileSizeX,tileSizeY,DataType))# BlockXSize="%s" BlockYSize="%s"/>'(tileSizeX,tileSizeY,DataType,BlockXSize,BlockYSize))
vrtXML.append(' <SrcRect xOff="0" yOff="0" xSize="%s" ySize="%s"/>' %(tileSizeX,tileSizeY))
vrtXML.append(' <DstRect xOff="%s" yOff="%s" xSize="%s" ySize="%s"/>' % (ULColOffset,ULRowOffset,tileSizeX,tileSizeY))
vrtXML.append(' </SimpleSource>')
vrtXML.append(' </VRTRasterBand>')
vrtXML.append('</VRTDataset>')
vrtXML='\n'.join(vrtXML)
return vrtXML
def __getmetadata__(self):
'''Read Metadata for an ACRES Landsat FastL7A format image as GDAL doesn't get it all.
Format description: http://www.ga.gov.au/image_cache/GA10348.pdf
Note:
hrf = ~30m VNIR/SWIR (bands 1-5 & 7)
htm = ~60m thermal (band 6)
hpn = ~15m pan (band 8)
'''
f=self.fileinfo['filepath']
d=os.path.dirname(f)
hdr=open(f).read()
err='Unable to open %s' % f
md=self.metadata
md['filesize']=sum([os.path.getsize(file) for file in self.filelist])
md['filetype'] = 'FAST/EOSAT FAST Format'
rl=1536#recordlength
######################################
##Record 1 - administrative
######################################
rec=1
req_id=utilities.readascii(hdr,(rec-1)*rl,9,28)
loc=utilities.readascii(hdr,(rec-1)*rl,35,51)
acquisition_date=utilities.readascii(hdr,(rec-1)*rl,71,78)
md['imgdate']='%s-%s-%s'%(acquisition_date[:4],acquisition_date[4:6],acquisition_date[6:])
md['satellite']=utilities.readascii(hdr,(rec-1)*rl,92,101)
md['sensor']=utilities.readascii(hdr,(rec-1)*rl,111,120)
md['mode']=utilities.readascii(hdr,(rec-1)*rl,135,140)
md['viewangle']=float(utilities.readascii(hdr,(rec-1)*rl,154,159))
product_type=utilities.readascii(hdr,(rec-1)*rl,655,672)
product_size=utilities.readascii(hdr,(rec-1)*rl,688,697)
level=utilities.readascii(hdr,(rec-1)*rl,741,751)
md['resampling']=utilities.readascii(hdr,(rec-1)*rl,765,766)
md['cols']=int(utilities.readascii(hdr,(rec-1)*rl,843,847))
md['rows']=int(utilities.readascii(hdr,(rec-1)*rl,865,869))
md['cellx']=float(utilities.readascii(hdr,(rec-1)*rl,954,959))
md['celly']=md['cellx']
md['nbits']=8 #int(utilities.readascii(hdr,(rec-1)*rl,984,985)) always 8 bit
md['datatype']='Byte'
md['nodata']='0'
bands_present=utilities.readascii(hdr,(rec-1)*rl,1056,1087)
bandindices=[[1131,1159],[1170,1198],[1211,1239],[1250,1278],[1291,1319],[1330,1358]]
bandfiles={}
for i in bandindices:
band=utilities.readascii(hdr,(rec-1)*rl,i[0],i[1])
if band:
exists,path=utilities.exists(os.path.join(d,band), True)
if exists:bandfiles[band]=path
else:#Assume ACRES format (band*.dat) instead Fast format (l7*.fst)...
bandid=band[23:25]
if bandid == '61':bandid='6l'
elif bandid == '62':bandid='6h'
else:bandid=bandid[0]
exists,path=utilities.exists(os.path.join(d,'band%s.dat'%bandid), True)
if not exists:raise RuntimeError, 'Unable to open band data files.'
bandfiles[band]=path
else:break
md['nbands']=len(bandfiles)
md['sceneid']=os.path.basename(bandfiles.keys()[0])[3:21] #Use path/row & aquisition date as sceneid - L7f[ppprrr_rrrYYYYMMDD]_AAA.FST
if self._filetype=='HRF':
md['bands']='1 (BLUE),2 (GREEN),3 (RED),4 (NIR),5 (SWIR),7 (SWIR)'
elif self._filetype=='HTM':
md['bands']='6L (THERMAL),6H (THERMAL)'
elif self._filetype=='HPN':
md['bands']='8 (PAN)'
######################################
##Record 2 - radiometric
######################################
#Move along, nothing to see here...
######################################
##Record 3 - geometric
######################################
rec=3
map_projection=utilities.readascii(hdr,(rec-1)*rl,32,35)
prjcode=spatialreferences.GCTP_PROJECTIONS.get(map_projection,0)
ellipsoid=utilities.readascii(hdr,(rec-1)*rl,48,65)
ellcode=spatialreferences.GCTP_ELLIPSOIDS.get(ellipsoid,0)
datum=utilities.readascii(hdr,(rec-1)*rl,74,79)
zone=utilities.readascii(hdr,(rec-1)*rl,521,526)
#Workaround for UTM zones as GDAL does not pick up southern hemisphere
#as some FST headers don't include a negative zone number to indicate southern hemisphere
#as per the FAST format definition
zone=int(zone) if zone else 0
usgs_indices = ((110,133),#Semi-major axis
(135,158),#Semi-minor axis
(161,184),
(186,209),
(211,234),
(241,264),
(266,289),
(291,314),
(321,344),
(346,369),
(371,394),
(401,424),
(426,449),
(451,474),
(481,504))
usgs_params=[]
for i in usgs_indices:
p=utilities.readascii(hdr,(rec-1)*rl,i[0],i[1])
if p:usgs_params.append(float(p))
else:usgs_params.append(0.0)
ulx=geometry.DMS2DD(utilities.readascii(hdr,(rec-1)*rl,566,578), 'DDDMMSSSSSSSH')
uly=geometry.DMS2DD(utilities.readascii(hdr,(rec-1)*rl,580,591), 'DDMMSSSSSSSH')
urx=geometry.DMS2DD(utilities.readascii(hdr,(rec-1)*rl,646,658), 'DDDMMSSSSSSSH')
ury=geometry.DMS2DD(utilities.readascii(hdr,(rec-1)*rl,660,671), 'DDMMSSSSSSSH')
lrx=geometry.DMS2DD(utilities.readascii(hdr,(rec-1)*rl,726,738), 'DDDMMSSSSSSSH')
lry=geometry.DMS2DD(utilities.readascii(hdr,(rec-1)*rl,740,751), 'DDMMSSSSSSSH')
llx=geometry.DMS2DD(utilities.readascii(hdr,(rec-1)*rl,806,818), 'DDDMMSSSSSSSH')
lly=geometry.DMS2DD(utilities.readascii(hdr,(rec-1)*rl,820,831), 'DDMMSSSSSSSH')
ext=[[ulx,uly],[urx,ury],[lrx,lry],[llx,lly],[ulx,uly]]
md['UL']='%s,%s' % tuple(ext[0])
md['UR']='%s,%s' % tuple(ext[1])
md['LR']='%s,%s' % tuple(ext[2])
md['LL']='%s,%s' % tuple(ext[3])
if zone > 0 and uly < 0:zone*=-1
srs=osr.SpatialReference()
srs.ImportFromUSGS(prjcode,zone,usgs_params,ellcode)
if datum=='GDA':#Workaround for GDA94 datum as GDAL does not recognise it
#as per the FAST format definition
if map_projection=='UTM':
epsg=28300+abs(zone)
srs.ImportFromEPSG(epsg)
md['srs']=srs.ExportToWkt()
md['epsg']=epsg
md['units']='m'
else:
srs.SetGeogCS('GDA94','Geocentric_Datum_of_Australia_1994','GRS 1980', usgs_params[0], 298.257)
md['srs']=srs.ExportToWkt()
md['epsg'] = spatialreferences.IdentifyAusEPSG(md['srs'])
md['units'] = spatialreferences.GetLinearUnitsName(md['srs'])
else:
md['srs']=srs.ExportToWkt()
md['epsg'] = spatialreferences.IdentifyAusEPSG(md['srs'])
md['units'] = spatialreferences.GetLinearUnitsName(md['srs'])
md['rotation']=float(utilities.readascii(hdr,(rec-1)*rl,995,1000))
if abs(md['rotation']) < 1:
md['orientation']='Map oriented'
md['rotation']=0.0
else:md['orientation']='Path oriented'
md['sunelevation']=utilities.readascii(hdr,(rec-1)*rl,1062,1065)
md['sunazimuth']=utilities.readascii(hdr,(rec-1)*rl,1086,1090)
try:##Open dataset
self._gdaldataset = geometry.OpenDataset(f)
metadata=self._gdaldataset.GetMetadata()
md['metadata']='\n'.join(['%s: %s' %(m,metadata[m]) for m in metadata])
#Fix for Issue 17
for i in range(1,self._gdaldataset.RasterCount+1):
self._gdaldataset.GetRasterBand(i).SetNoDataValue(float(md['nodata']))
except:#build a VRT dataset - if we want to use this for anything other than overview generation, should probably fill out the geotransform, srs, metadata etc...
bands=bandfiles.values()
bands.sort()
#vrtxml=geometry.CreateRawRasterVRT(bands,md['cols'],md['rows'], md['datatype']) #Fix for Issue 17
vrtxml=geometry.CreateRawRasterVRT(bands,md['cols'],md['rows'], md['datatype'],nodata=md['nodata'])
self._gdaldataset = geometry.OpenDataset(vrtxml)
md['metadata']=hdr
if self._filetype=='HRF':
self._gdaldataset.GetRasterBand(4).SetRasterColorInterpretation(gdal.GCI_BlueBand)
self._gdaldataset.GetRasterBand(3).SetRasterColorInterpretation(gdal.GCI_GreenBand)
self._gdaldataset.GetRasterBand(2).SetRasterColorInterpretation(gdal.GCI_RedBand)
if level == 'SYSTEMATIC' :md['level'] = '1G '
elif level == 'SYSTERRAIN':md['level'] = '1Gt'
elif level == 'PRECISION' :md['level'] = '1P'
elif level == 'TERRAIN' :md['level'] = '1T'
md['compressionratio']=0
md['compressiontype']='None'
self.extent=ext
for m in md:self.metadata[m]=md[m]
def __gettilevrt__(self, f, imddata):
til = iter(open(f).readlines())
tileinfo = {}
datasets = {}
line = til.next()
while line: #Extract all keys and values from the header file into a dictionary
line = line.strip().strip(';').replace('"', '')
if line == 'END': break
if 'BEGIN_GROUP' in line:
line = til.next()
while line:
line = line.strip().strip(';').replace('"', '')
if 'END_GROUP' in line: break
else:
dat = map(string.strip, line.split('=', 1))
if not dat[0] in datasets: datasets[dat[0]] = []
datasets[dat[0]].append(dat[1])
line = til.next()
else:
var = map(string.strip, line.split('=', 1))
tileinfo[var[0]] = var[1]
line = til.next()
curdir = os.path.dirname(f)
bimg, img = utilities.exists(
os.path.join(curdir, datasets['filename'][0]), True)
ds = geometry.OpenDataset(img)
rb = ds.GetRasterBand(1)
DataType = gdal.GetDataTypeName(rb.DataType)
GeoTransform = ds.GetGeoTransform()
Projection = ds.GetProjection()
if GeoTransform == (0.0, 1.0, 0.0, 0.0, 0.0, 1.0):
GeoTransform = gdal.GCPsToGeoTransform(ds.GetGCPs())
if Projection == '':
Projection = ds.GetGCPProjection()
GeoTransform = ','.join(map(str, GeoTransform))
numTiles = int(tileinfo['numTiles'])
BlockXSize, BlockYSize = rb.GetBlockSize()
vrtXML = []
vrtXML.append('<VRTDataset rasterXSize="%s" rasterYSize="%s">' %
(imddata['numColumns'], imddata['numRows']))
vrtXML.append('<SRS>%s</SRS>' % Projection)
vrtXML.append('<GeoTransform>%s</GeoTransform>' % GeoTransform)
for b, band in enumerate(imddata['bands']):
b += 1
vrtXML.append(' <VRTRasterBand dataType="%s" band="%s">' %
(DataType, b))
#vrtXML.append(' <ColorInterp>Gray</ColorInterp>')
for tile in range(0, numTiles):
tileSizeX = int(datasets['URColOffset'][tile]) - int(
datasets['ULColOffset'][tile]) + 1
tileSizeY = int(datasets['LLRowOffset'][tile]) - int(
datasets['ULRowOffset'][tile]) + 1
ULColOffset = datasets['ULColOffset'][tile]
ULRowOffset = datasets['ULRowOffset'][tile]
bimg, img = utilities.exists(
os.path.join(curdir, datasets['filename'][tile]), True)
vrtXML.append(' <SimpleSource>')
vrtXML.append(
' <SourceFilename relativeToVRT="0">%s</SourceFilename>'
% img)
vrtXML.append(' <SourceBand>%s</SourceBand>' % (b))
vrtXML.append(
' <SourceProperties RasterXSize="%s" RasterYSize="%s" DataType="%s"/>'
% (tileSizeX, tileSizeY, DataType)
) # BlockXSize="%s" BlockYSize="%s"/>'(tileSizeX,tileSizeY,DataType,BlockXSize,BlockYSize))
vrtXML.append(
' <SrcRect xOff="0" yOff="0" xSize="%s" ySize="%s"/>' %
(tileSizeX, tileSizeY))
vrtXML.append(
' <DstRect xOff="%s" yOff="%s" xSize="%s" ySize="%s"/>' %
(ULColOffset, ULRowOffset, tileSizeX, tileSizeY))
vrtXML.append(' </SimpleSource>')
vrtXML.append(' </VRTRasterBand>')
vrtXML.append('</VRTDataset>')
vrtXML = '\n'.join(vrtXML)
return vrtXML
def v2(self, f=None):
if not f: f = self.fileinfo['filepath']
dom = self._dom
self.metadata['sceneid'] = dom.xpath(
'string(/Dimap_Document/Dataset_Identification/DATASET_NAME)')
try:
self._gdaldataset = geometry.OpenDataset(f)
__default__.Dataset.__getmetadata__(
self) #autopopulate basic metadata
except:
ncols = dom.xpath('number(//*/NCOLS)')
nrows = dom.xpath('number(//*/NROWS)')
nbands = dom.xpath('number(//*/NBANDS)')
nbits = dom.xpath('number(//*/NBITS)')
if nbits == 16: datatype = 'UInt16'
else: datatype = 'Byte'
if nbands == 1:
bands = [1]
else:
bands = [
int(b[1:]) for b in [
dom.xpath('string(//*/RED_CHANNEL)'),
dom.xpath('string(//*/GREEN_CHANNEL)'),
dom.xpath('string(//*/BLUE_CHANNEL)')
]
]
self.metadata['bands'] = ','.join(map(str, bands))
if dom.xpath('string(//*/DATA_FILE_TILES)') == 'true':
import math
ntiles = dom.xpath('number(//*/NTILES)')
if dom.xpath('boolean(//*/NTILES_COUNT/@ntiles_x)'):
ntiles_x = dom.xpath('number(//*/NTILES_COUNT/@ntiles_x)')
ntiles_y = dom.xpath('number(//*/NTILES_COUNT/@ntiles_y)')
elif dom.xpath('boolean(//*/NTILES_COUNT/@ntiles_C)'):
ntiles_x = dom.xpath('number(//*/NTILES_COUNT/@ntiles_C)')
ntiles_y = dom.xpath('number(//*/NTILES_COUNT/@ntiles_R)')
tile_cols = math.ceil(ncols / ntiles_x)
last_tile_cols = tile_cols - (ntiles_x * tile_cols - ncols)
tile_rows = math.ceil(nrows / ntiles_y)
last_tile_rows = tile_rows - (ntiles_y * tile_rows - nrows)
srcrects, dstrects = [], []
files = []
for df in dom.xpath('//*/Data_File'):
col = df.xpath('number(@tile_C)')
row = df.xpath('number(@tile_R)')
datafile = os.path.join(
os.path.dirname(f),
df.xpath('string(DATA_FILE_PATH/@href)'))
exists, datafile = utilities.exists(
datafile, True
) #Work around reading images with lowercase filenames when the DATA_FILE_PATH is uppercase
# - eg samba filesystems which get converted to lowercase
if (row, col) == (1, 1): r1c1 = datafile
srcrect = [0, 0, tile_cols, tile_rows]
dstrect = [(ntiles_x - 1) * tile_cols,
(ntiles_y - 1) * tile_rows, tile_cols,
tile_rows]
if col == ntiles_x: #last col
srcrect[2] = last_tile_cols
dstrect[2] = last_tile_cols
if row == ntiles_y: #last row
srcrect[3] = last_tile_rows
dstrect[3] = last_tile_rows
files.append(datafile)
srcrects.append(srcrect)
dstrects.append(dstrect)
self._gdaldataset = geometry.OpenDataset(
geometry.CreateMosaicedVRT(files, bands, srcrects,
dstrects, ncols, nrows,
datatype))
ds = geometry.OpenDataset(r1c1)
self._gdaldataset.SetGeoTransform(ds.GetGeoTransform())
self._gdaldataset.SetProjection(ds.GetProjection())
else:
datafile = os.path.join(
os.path.dirname(f),
dom.xpath('string(//*/DATA_FILE_PATH/@href)'))
exists, datafile = utilities.exists(datafile, True)
self._gdaldataset = geometry.OpenDataset(datafile)
__default__.Dataset.__getmetadata__(self)
dates = {}
for src in dom.xpath('//Source_Identification'):
datetime = '%sT%s' % (src.xpath('string(//*/IMAGING_DATE)'),
src.xpath('string(//*/IMAGING_TIME)')[:8])
dts = time.mktime(time.strptime(
datetime, utilities.datetimeformat)) #ISO 8601
dates[dts] = datetime
if len(dates) == 1:
self.metadata['imgdate'] = datetime
else:
self.metadata['imgdate'] = '%s/%s' % (dates[min(
dates.keys())], dates[max(dates.keys())])
self.metadata['satellite'] = '%s %s' % (src.xpath(
'string(//*/MISSION)'), src.xpath('string(//*/MISSION_INDEX)'))
try:
self.metadata['sensor'] = '%s %s' % (
src.xpath('string(//*/INSTRUMENT)'),
src.xpath('string(//*/INSTRUMENT_INDEX)'))
except:
self.metadata['sensor'] = '%s' % src.xpath(
'string(//*/INSTRUMENT)')
try:
sunangles = dom.xpath(
'//*/Located_Geometric_Values[LOCATION_TYPE="Center"]/Solar_Incidences'
)[0]
self.metadata['sunelevation'] = sunangles.xpath(
'number(SUN_ELEVATION)')
self.metadata['sunazimuth'] = sunangles.xpath(
'number(SUN_AZIMUTH)')
except:
pass
try:
self.metadata['viewangle'] = dom.xpath(
'number(//*/Located_Geometric_Values[LOCATION_TYPE="Center"]/Acquisition_Angles/VIEWING_ANGLE)'
)
self.metadata['satelevation'] = dom.xpath(
'number(//*/Located_Geometric_Values[LOCATION_TYPE="Center"]/Acquisition_Angles/INCIDENCE_ANGLE)'
)
self.metadata['satazimuth'] = dom.xpath(
'number(//*/Located_Geometric_Values[LOCATION_TYPE="Center"]/Acquisition_Angles/AZIMUTH_ANGLE)'
)
except:
pass
try:
self.metadata['level'] = dom.xpath(
'string(//*/Processing_Information/Product_Settings/PROCESSING_LEVEL)'
)
except:
pass
try:
self.metadata['resampling'] = dom.xpath(
'string(//*/Processing_Information/Product_Settings/Sampling_Settings/RESAMPLING_KERNEL)'
)
except:
pass
self.metadata['metadata'] = etree.tostring(dom, pretty_print=True)
def __getmetadata__(self):
'''Read Metadata for an Digital Globe format image as GDAL doesn't quite get it all...
@todo: Fix QB GDA94 Geographic CS "Unknown datum" problem
'''
f=self.fileinfo['filepath']
imddata=self.__getimddata__(f)
exts={'.tif':'GTiff/GeoTIFF','.img':'HFA/Erdas Imagine Images (.img)','.ntf':'NITF/National Imagery Transmission Format (.ntf)','.pix':'PCI Geomatics Database File (.pix)'}
btil,til = utilities.exists(os.path.splitext(f)[0]+'.til',True)
if btil:
vrt=self.__gettilevrt__(til,imddata)
__default__.Dataset.__getmetadata__(self, vrt)
for tmp in self.filelist:
for ext in exts:
if tmp[-4:].lower()==ext:
self.metadata['filetype']=exts[ext]
break
else:
img=False
for ext in exts:
bext,ext = utilities.exists(os.path.splitext(f)[0]+ext,True)
if bext:
__default__.Dataset.__getmetadata__(self, ext)
img=True
break
if not img:raise IOError, 'Matching DigitalGlobe imagery file not found:\n'
self.metadata['metadata']=open(f).read()
if imddata.has_key('IMAGE_1'):imgkey='IMAGE_1'
else:imgkey='SINGLE_IMAGE_PRODUCT'
if imddata.has_key('MAP_PROJECTED_PRODUCT'):
imgdate1=imddata['MAP_PROJECTED_PRODUCT']['earliestAcqTime'][0:19]#Already in ISO 8601 format, just strip off millisecs
imgdate2=imddata['MAP_PROJECTED_PRODUCT']['latestAcqTime'][0:19]
if imgdate1==imgdate2:self.metadata['imgdate']='%s'%(imgdate1)
else:self.metadata['imgdate']='%s/%s'%(imgdate1,imgdate2)
elif imddata[imgkey].has_key('firstLineTime'):
self.metadata['imgdate']=imddata[imgkey]['firstLineTime'][0:19] #Already in ISO 8601 format, just strip off millisecs
if imddata[imgkey]['satId']=='QB02':
self.metadata['satellite']='Quickbird (QB02)'
elif imddata[imgkey]['satId']=='WV01':
self.metadata['satellite']='Worldview-1 (WV01)'
elif imddata[imgkey]['satId']=='WV02':
self.metadata['satellite']='Worldview-2 (WV02)'
else:
self.metadata['satellite']=imddata[imgkey]['satId']
if imddata['bandId'] == 'P':self.metadata['sensor']='PANCHROMATIC'
else:
if imddata['panSharpenAlgorithm']== 'None':self.metadata['sensor']='MULTISPECTRAL'
else:self.metadata['sensor']='MULTI/PAN'
#if imddata['bandId']=='Multi':
# if imddata['nbands'] == 3:self.metadata['bands'] = 'B,G,R'
# elif imddata['nbands'] == 4:self.metadata['bands'] = 'B,G,R,N'
#else: #'BGRN','RGB','P'
# self.metadata['bands'] = ','.join([l for l in imddata['bandId']])
self.metadata['bands'] = ','.join([b.split('_')[1] for b in imddata.keys() if b[0:5]=='BAND_'])
if imddata[imgkey].has_key('meanSunEl'):
self.metadata['sunelevation'] = imddata[imgkey]['meanSunEl']
self.metadata['sunazimuth'] = imddata[imgkey]['meanSunAz']
elif imddata[imgkey].has_key('sunEl'):
self.metadata['sunelevation'] = imddata[imgkey]['sunEl']
self.metadata['sunazimuth'] = imddata[imgkey]['sunAz']
self.metadata['level'] = imddata['productLevel']
if imddata[imgkey].has_key('cloudCover'):
self.metadata['cloudcover'] = imddata[imgkey]['cloudCover']
elif imddata[imgkey].has_key('manualCloudCover'):
self.metadata['cloudcover'] = max([0, imddata[imgkey]['manualCloudCover']]) #hack for -999 cloud cover
elif imddata[imgkey].has_key('autoCloudCover'):
self.metadata['cloudcover'] = max([0, imddata[imgkey]['autoCloudCover']])
if imddata[imgkey].has_key('offNadirViewAngle'):
self.metadata['viewangle'] = imddata[imgkey]['offNadirViewAngle']
elif imddata[imgkey].has_key('meanOffNadirViewAngle'):
self.metadata['viewangle'] = imddata[imgkey]['meanOffNadirViewAngle']
if imddata[imgkey].has_key('CatId'):
self.metadata['sceneid'] = imddata[imgkey]['CatId']
if imddata[imgkey].has_key('resamplingKernel'):
self.metadata['resampling'] = imddata[imgkey]['resamplingKernel']
elif imddata.has_key('MAP_PROJECTED_PRODUCT') and imddata['MAP_PROJECTED_PRODUCT'].has_key('resamplingKernel'):
self.metadata['resampling'] = imddata['MAP_PROJECTED_PRODUCT']['resamplingKernel']
if imddata.has_key('MAP_PROJECTED_PRODUCT') and imddata['MAP_PROJECTED_PRODUCT'].has_key('DEMCorrection'):
self.metadata['demcorrection'] = imddata['MAP_PROJECTED_PRODUCT']['DEMCorrection']
def __getmetadata__(self):
'''Read Metadata for an Digital Globe format image as GDAL doesn't quite get it all...
@todo: Fix QB GDA94 Geographic CS "Unknown datum" problem
'''
f = self.fileinfo['filepath']
imddata = self.__getimddata__(f)
exts = {
'.tif': 'GTiff/GeoTIFF',
'.img': 'HFA/Erdas Imagine Images (.img)',
'.ntf': 'NITF/National Imagery Transmission Format (.ntf)',
'.pix': 'PCI Geomatics Database File (.pix)'
}
btil, til = utilities.exists(os.path.splitext(f)[0] + '.til', True)
if btil:
vrt = self.__gettilevrt__(til, imddata)
__default__.Dataset.__getmetadata__(self, vrt)
for tmp in self.filelist:
for ext in exts:
if tmp[-4:].lower() == ext:
self.metadata['filetype'] = exts[ext]
break
else:
img = False
for ext in exts:
bext, ext = utilities.exists(
os.path.splitext(f)[0] + ext, True)
if bext:
__default__.Dataset.__getmetadata__(self, ext)
img = True
break
if not img:
raise IOError, 'Matching DigitalGlobe imagery file not found:\n'
self.metadata['metadata'] = open(f).read()
if imddata.has_key('IMAGE_1'): imgkey = 'IMAGE_1'
else: imgkey = 'SINGLE_IMAGE_PRODUCT'
if imddata.has_key('MAP_PROJECTED_PRODUCT'):
imgdate1 = imddata['MAP_PROJECTED_PRODUCT']['earliestAcqTime'][
0:19] #Already in ISO 8601 format, just strip off millisecs
imgdate2 = imddata['MAP_PROJECTED_PRODUCT']['latestAcqTime'][0:19]
if imgdate1 == imgdate2:
self.metadata['imgdate'] = '%s' % (imgdate1)
else:
self.metadata['imgdate'] = '%s/%s' % (imgdate1, imgdate2)
elif imddata[imgkey].has_key('firstLineTime'):
self.metadata['imgdate'] = imddata[imgkey]['firstLineTime'][
0:19] #Already in ISO 8601 format, just strip off millisecs
if imddata[imgkey]['satId'] == 'QB02':
self.metadata['satellite'] = 'Quickbird (QB02)'
elif imddata[imgkey]['satId'] == 'WV01':
self.metadata['satellite'] = 'Worldview-1 (WV01)'
elif imddata[imgkey]['satId'] == 'WV02':
self.metadata['satellite'] = 'Worldview-2 (WV02)'
else:
self.metadata['satellite'] = imddata[imgkey]['satId']
if imddata['bandId'] == 'P': self.metadata['sensor'] = 'PANCHROMATIC'
else:
if imddata['panSharpenAlgorithm'] == 'None':
self.metadata['sensor'] = 'MULTISPECTRAL'
else:
self.metadata['sensor'] = 'MULTI/PAN'
#if imddata['bandId']=='Multi':
# if imddata['nbands'] == 3:self.metadata['bands'] = 'B,G,R'
# elif imddata['nbands'] == 4:self.metadata['bands'] = 'B,G,R,N'
#else: #'BGRN','RGB','P'
# self.metadata['bands'] = ','.join([l for l in imddata['bandId']])
self.metadata['bands'] = ','.join(
[b.split('_')[1] for b in imddata.keys() if b[0:5] == 'BAND_'])
if imddata[imgkey].has_key('meanSunEl'):
self.metadata['sunelevation'] = imddata[imgkey]['meanSunEl']
self.metadata['sunazimuth'] = imddata[imgkey]['meanSunAz']
elif imddata[imgkey].has_key('sunEl'):
self.metadata['sunelevation'] = imddata[imgkey]['sunEl']
self.metadata['sunazimuth'] = imddata[imgkey]['sunAz']
self.metadata['level'] = imddata['productLevel']
if imddata[imgkey].has_key('cloudCover'):
self.metadata['cloudcover'] = imddata[imgkey]['cloudCover']
elif imddata[imgkey].has_key('manualCloudCover'):
self.metadata['cloudcover'] = max([
0, imddata[imgkey]['manualCloudCover']
]) #hack for -999 cloud cover
elif imddata[imgkey].has_key('autoCloudCover'):
self.metadata['cloudcover'] = max(
[0, imddata[imgkey]['autoCloudCover']])
if imddata[imgkey].has_key('offNadirViewAngle'):
self.metadata['viewangle'] = imddata[imgkey]['offNadirViewAngle']
elif imddata[imgkey].has_key('meanOffNadirViewAngle'):
self.metadata['viewangle'] = imddata[imgkey][
'meanOffNadirViewAngle']
if imddata[imgkey].has_key('CatId'):
self.metadata['sceneid'] = imddata[imgkey]['CatId']
if imddata[imgkey].has_key('resamplingKernel'):
self.metadata['resampling'] = imddata[imgkey]['resamplingKernel']
elif imddata.has_key('MAP_PROJECTED_PRODUCT') and imddata[
'MAP_PROJECTED_PRODUCT'].has_key('resamplingKernel'):
self.metadata['resampling'] = imddata['MAP_PROJECTED_PRODUCT'][
'resamplingKernel']
if imddata.has_key('MAP_PROJECTED_PRODUCT') and imddata[
'MAP_PROJECTED_PRODUCT'].has_key('DEMCorrection'):
self.metadata['demcorrection'] = imddata['MAP_PROJECTED_PRODUCT'][
'DEMCorrection']
def v2(self,f=None):
dom = self._dom
self.metadata['sceneid'] = dom.xpath('string(/Dimap_Document/Dataset_Identification/DATASET_NAME)')
ncols=dom.xpath('number(//*/NCOLS)')
nrows=dom.xpath('number(//*/NROWS)')
nbands=dom.xpath('number(//*/NBANDS)')
nbits=dom.xpath('number(//*/NBITS)')
if nbits==16:datatype='UInt16'
else:datatype='Byte'
if nbands==1:
bands=[1]
else:
bands=[int(b[1:]) for b in [dom.xpath('string(//*/RED_CHANNEL)'),
dom.xpath('string(//*/GREEN_CHANNEL)'),
dom.xpath('string(//*/BLUE_CHANNEL)')]]
self.metadata['bands']=','.join(map(str,bands))
if dom.xpath('string(//*/DATA_FILE_TILES)')=='true':
import math
ntiles=dom.xpath('number(//*/NTILES)')
ntiles_x=dom.xpath('number(//*/NTILES_COUNT/@ntiles_x)')
ntiles_y=dom.xpath('number(//*/NTILES_COUNT/@ntiles_y)')
tile_cols=math.ceil(ncols/ntiles_x)
last_tile_cols=tile_cols-(ntiles_x*tile_cols-ncols)
tile_rows=math.ceil(ncols/ntiles_x)
last_tile_rows=tile_rows-(ntiles_y*tile_row-nrows)
srcrects,dstrects=[],[]
files=[]
for df in dom.xpath('//*/Data_File'):
col=df.xpath('number(@tile_C)')
row=df.xpath('number(@tile_R)')
datafile=os.path.join(os.path.dirname(f),df.xpath('string(DATA_FILE_PATH/@href)'))
exists,datafile=utilities.exists(datafile,True) #Work around reading images with lowercase filenames when the DATA_FILE_PATH is uppercase
# - eg samba filesystems which get converted to lowercase
srcrect=[0,0,tile_cols,tile_rows]
dstrect=[(ntiles_x-1)*tile_cols,(ntiles_y-1)*tile_rows,tile_cols,tile_rows]
if col==ntiles_x:#last col
srcrect[2]=last_tile_cols
dstrect[2]=last_tile_cols
if row==ntiles_y:#last row
srcrect[3]=last_tile_rows
dstrect[3]=last_tile_rows
files.append(datafile)
srcrects.append(srcrect)
dstrects.append(dstrect)
self._gdaldataset=geometry.OpenDataset(geometry.CreateMosaicedVRT(files,bands,srcrects,dstrects,ncols,nrows,datatype))
else:
datafile=os.path.join(os.path.dirname(f),dom.xpath('string(//*/DATA_FILE_PATH/@href)'))
exists,datafile=utilities.exists(datafile,True)
self._gdaldataset=geometry.OpenDataset(datafile)
__default__.Dataset.__getmetadata__(self)
dates={}
for src in dom.xpath('//Source_Identification'):
datetime='%sT%s'%(src.xpath('string(//*/IMAGING_DATE)'),src.xpath('string(//*/IMAGING_TIME)')[:8])
dts=time.mktime(time.strptime(datetime,utilities.datetimeformat))#ISO 8601
dates[dts]=datetime
if len(dates)==1:
self.metadata['imgdate']=datetime
else:
self.metadata['imgdate']='%s/%s'%(dates[min(dates.keys())],dates[max(dates.keys())])
self.metadata['satellite']='%s %s' % (src.xpath('string(//*/MISSION)'),src.xpath('string(//*/MISSION_INDEX)'))
try:self.metadata['sensor']='%s %s' % (src.xpath('string(//*/INSTRUMENT)'),src.xpath('string(//*/INSTRUMENT_INDEX)'))
except:self.metadata['sensor']='%s' % src.xpath('string(//*/INSTRUMENT)')
try:
sunangles=dom.xpath('//*/Located_Geometric_Values[LOCATION_TYPE="Center"]/Solar_Incidences')[0]
self.metadata['sunelevation'] = sunangles.xpath('number(SUN_ELEVATION)')
self.metadata['sunazimuth'] = sunangles.xpath('number(SUN_AZIMUTH)')
except:pass
try:
self.metadata['viewangle'] = dom.xpath('number(//*/Located_Geometric_Values[LOCATION_TYPE="Center"]/Acquisition_Angles/VIEWING_ANGLE)')
self.metadata['satelevation'] = dom.xpath('number(//*/Located_Geometric_Values[LOCATION_TYPE="Center"]/Acquisition_Angles/INCIDENCE_ANGLE)')
self.metadata['satazimuth'] = dom.xpath('number(//*/Located_Geometric_Values[LOCATION_TYPE="Center"]/Acquisition_Angles/AZIMUTH_ANGLE)')
except:pass
try:self.metadata['level'] = dom.xpath('string(//*/Processing_Information/Product_Settings/PROCESSING_LEVEL)')
except:pass
try:self.metadata['resampling'] = dom.xpath('string(//*/Processing_Information/Product_Settings/Sampling_Settings/RESAMPLING_KERNEL)')
except:pass
self.metadata['metadata']=etree.tostring(dom, pretty_print=True)