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 __getmetadata__(self,f=None):
'''Read Metadata for an ACRES ALOS AVNIR-2/PRISM/PALSAR format image as GDAL doesn't'''
#Local copies for brevity
vol=self._vol
led=self._led
if 'led-alpsr' in led.lower():driver='SAR_CEOS'
else:driver='CEOS'
extra_md={} #for holding any extra metadata which gets stuffed into self.metadata['metadata'] later on
self.metadata['satellite']='ALOS'
nodata = 0
#ACRES ortho product is in GTIFF format
tif=False
if driver=='SAR_CEOS': #PALSAR - assumes Level 1.5, Level 1.0 not implemented
#Format Description
#Level 1.0 - http://www.ga.gov.au/servlet/BigObjFileManager?bigobjid=GA10287
#Level 1.1/1.5 - http://www.eorc.jaxa.jp/ALOS/doc/fdata/PALSAR_x_Format_EK.pdf
self.metadata['sensor']='PALSAR'
self.metadata['filetype'] ='CEOS/ALOS PALSAR CEOS Format'
'''
volume file has 3-6 records, 360 bytes length:
Record Length
=========================
1 File descriptor 360
2 File pointer 360 (3-6 records = N+2 where N is number of polarization)
3 Text 360
'''
meta = open(vol,'rb').read()
#File descriptor record
offset = 0
#Number of file pointers
npointers=int(utilities.readbinary(meta,offset,161,164))
#Text record
offset = 360*(npointers+1)
#Product type specifier
start,stop = 17,56
prodspec = utilities.readbinary(meta,offset,start,stop)[8:] #Strip of the "'PRODUCT:" string
if prodspec[1:4] != '1.5':raise Exception, 'ALOS PALSAR Level %s not yet implemented' % level
if prodspec[0]=='H':self.metadata['mode']='Fine (high resolution) mode'
elif prodspec[0]=='W':self.metadata['mode']='ScanSAR (wide observation) mode'
elif prodspec[0]=='D':self.metadata['mode']='Direct Downlink mode'
elif prodspec[0]=='P':self.metadata['mode']='Polarimetry mode'
elif prodspec[0]=='C':self.metadata['mode']='Calibration mode'
level=prodspec[1:5]
orient=prodspec[4]
if orient=='G':self.metadata['orientation']='Map oriented'
else: self.metadata['orientation']='Path oriented'
orbit=prodspec[6]
'''
leader file has >9 records of variable length:
Record Length
============================================
1 File descriptor 720
2 Data set summary 4096
3 Map projection data 1620
4 Platform position data 4680
5 Attitude data 8192
6 Radiometric data 9860
7 Data quality summary 1620
8 Calibration data 13212
9 Facility related Variable
'''
meta = open(led,'rb').read()
#File descriptor
offset = 0
#Data set summary
offset = 720
#Scene ID
start,stop = 21,52
sceneid = utilities.readbinary(meta,offset,start,stop)
#Image date
start,stop = 69,100
#imgdate=utilities.readbinary(meta,offset,start,stop)[0:14]#Strip off time
#self.metadata['imgdate'] = time.strftime(utilities.dateformat,time.strptime(imgdate,'%Y%m%d')) #ISO 8601
imgdate=utilities.readbinary(meta,offset,start,stop)[0:14] #Keep time, strip off milliseconds
self.metadata['imgdate'] = time.strftime(utilities.datetimeformat,time.strptime(imgdate,'%Y%m%d%H%M%S')) #SAR Channels
start,stop = 389,392
nbands = int(utilities.readbinary(meta,offset,start,stop))
#Radar wavelength
start,stop = 501,516
wavelen = utilities.readbinary(meta,offset,start,stop)
extra_md['wavelength']=wavelen
#Nominal offnadir angle
start,stop = 1839,1854
self.metadata['viewangle'] = utilities.readbinary(meta,offset,start,stop)
#Map projection data
offset += 4096
#Cols & rows
start,stop = 61,76
ncols = int(utilities.readbinary(meta,offset,start,stop))
start,stop = 77,92
nrows = int(utilities.readbinary(meta,offset,start,stop))
#cell sizes (metres)
start,stop = 93,124
ypix,xpix = map(float,utilities.readbinary(meta,offset,start,stop).split())
#Orientation at output scene centre
start,stop = 125,140
rot = math.radians(float(utilities.readbinary(meta,offset,start,stop)))
#GeogCS
src_srs=osr.SpatialReference()
#src_srs.SetGeogCS('GRS 1980','GRS 1980','GRS 1980',6378137.00000,298.2572220972)
src_srs.SetWellKnownGeogCS( "WGS84" )
#Proj CS
start,stop = 413,444
projdesc = utilities.readbinary(meta,offset,start,stop)
epsg=0#default
if projdesc == 'UTM-PROJECTION':
nZone = int(utilities.readbinary(meta,offset,477,480))
dfFalseNorthing = float(utilities.readbinary(meta,offset,497,512))
if dfFalseNorthing > 0.0:
bNorth=False
epsg=32700+nZone
else:
bNorth=True
epsg=32600+nZone
src_srs.ImportFromEPSG(epsg)
#src_srs.SetUTM(nZone,bNorth) #generates WKT that osr.SpatialReference.AutoIdentifyEPSG() doesn't return an EPSG for
elif projdesc == 'UPS-PROJECTION':
dfCenterLon = float(utilities.readbinary(meta,offset,625,640))
dfCenterLat = float(utilities.readbinary(meta,offset,641,656))
dfScale = float(utilities.readbinary(meta,offset,657,672))
src_srs.SetPS(dfCenterLat,dfCenterLon,dfScale,0.0,0.0)
elif projdesc == 'MER-PROJECTION':
dfCenterLon = float(utilities.readbinary(meta,offset,737,752))
dfCenterLat = float(utilities.readbinary(meta,offset,753,768))
src_srs.SetMercator(dfCenterLat,dfCenterLon,0,0,0)
elif projdesc == 'LCC-PROJECTION':
dfCenterLon = float(utilities.readbinary(meta,offset,737,752))
dfCenterLat = float(utilities.readbinary(meta,offset,753,768))
dfStdP1 = float(utilities.readbinary(meta,offset,769,784))
dfStdP2 = float(utilities.readbinary(meta,offset,785,800))
src_srs.SetLCC(dfStdP1,dfStdP2,dfCenterLat,dfCenterLon,0,0)
srs=src_srs.ExportToWkt()
if not epsg:epsg = spatialreferences.IdentifyAusEPSG(srs)
units = spatialreferences.GetLinearUnitsName(srs)
#UL-LR coords
##ext=[float(coord)*1000 for coord in utilities.readbinary(meta,offset,945,1072).split()] #Get lat/lon instead of eastings/northings
ext=[float(coord) for coord in utilities.readbinary(meta,offset,1073,1200).split()]
uly,ulx,ury,urx,lry,lrx,lly,llx=ext
ext=[[ulx,uly],[urx,ury],[lrx,lry],[llx,lly],[ulx,uly]] #last xy pair closes the poly
self.metadata['nbits'] = 16
self.metadata['datatype']='UInt16'
#Generate a GDAL Dataset object
self._gdaldataset=geometry.OpenDataset(self._imgs[0])
else: #ALOS AVNIR2/PRISM
##Format - http://www.ga.gov.au/servlet/BigObjFileManager?bigobjid=GA10285
'''
leader file has 5 records, each is 4680 bytes long:
1. File descriptor record;
2. Scene header record;
3. Map projection (scene-related) ancillary record;
4. Radiometric transformation ancillary record;
5. Platform position ancillary record.
'''
#ACRES ortho product is in GTIFF format
if '.tif' in self._imgs[0].lower():
tif=True
level='ORTHOCORRECTED'
__default__.Dataset.__getmetadata__(self, self._imgs[0])
meta = open(led,'rb').read()
recordlength = 4680
#Record 2 - Scene header record
record=2
#Processing level
if not tif:
start,stop = 21,36
procinfo = utilities.readbinary(meta,(record-1)*recordlength,start,stop)
level=procinfo[1:4]
#if level != '1B2':raise Exception, 'Level %s PRISM is not supported' % level
self.metadata['level']==procinfo[1:4]
opt=procinfo[4:6].strip().strip('_')
if opt!='':level+='-'+opt
#SceneID
if level[0:3] == '1B2':start,stop = 197,212
else:start,stop = 37,52
sceneid = utilities.readbinary(meta,(record-1)*recordlength,start,stop)
#Lat/Lon of scene center
##if level[0:3] == '1B2':start,stop = 245,276
##else:start,stop = 85,116
##cenrow, cencol=map(float,utilities.readbinary(meta,(record-1)*recordlength,start,stop).split())
#Line & Pixel number for scene center
##if level[0:3] == '1B2':start,stop = 245,276
##else:start,stop = 85,116
##cenrow, cencol=map(float,utilities.readbinary(meta,(record-1)*recordlength,start,stop).split())
#Orientation Angle NNN.N = degrees
start,stop = 277,292
rot = float(utilities.readbinary(meta,(record-1)*recordlength,start,stop))
#Ascending/descendit orbit
start,stop = 357,372
orbit = utilities.readbinary(meta,(record-1)*recordlength,start,stop)
#view, sun elevation and azimuth angles
start,stop = 373,388
self.metadata['viewangle'] = utilities.readbinary(meta,(record-1)*recordlength,start,stop)
start,stop = 453,466
sunangles = utilities.readbinary(meta,(record-1)*recordlength,start,stop)
self.metadata['sunelevation'] = sunangles[6:9].strip()
self.metadata['sunazimuth'] = sunangles[11:].strip()
#Image aquisition date
start,stop = 401,408
imgdate = utilities.readbinary(meta,(record-1)*recordlength,start,stop)
imgdate = time.strptime(imgdate,'%d%b%y') #DDMmmYY
self.metadata['imgdate'] = time.strftime(utilities.dateformat,imgdate) #ISO 8601
#Sensor type and bands
start,stop = 443,452
sensor,bands=utilities.readbinary(meta,(record-1)*recordlength,start,stop).split()
if sensor=='PSM':
self.metadata['sensor']='PRISM'
if sceneid[5] == 'N': extra_md['SENSOR DIRECTION']='Nadir 35km'
elif sceneid[5] == 'W':extra_md['SENSOR DIRECTION']='Nadir 70km'
elif sceneid[5] == 'F':extra_md['SENSOR DIRECTION']='Forward 35km'
elif sceneid[5] == 'B':extra_md['SENSOR DIRECTION']='Backward 35km'
else:self.metadata['sensor']='AVNIR-2'
self.metadata['filetype'] ='CEOS/ALOS %s CEOS Format' % self.metadata['sensor']
self.metadata['bands']=','.join([band for band in bands])
#Processing info
if not tif:
start,stop = 467,478
procinfo = utilities.readbinary(meta,(record-1)*recordlength,start,stop)
orient=procinfo[-1]
if orient=='G':self.metadata['orientation']='Map oriented'
else:self.metadata['orientation']='Path oriented'
#No. bands
start,stop = 1413,1428
nbands = int(utilities.readbinary(meta,(record-1)*recordlength,start,stop))
#No. cols
start,stop = 1429,1444
ncols = float(utilities.readbinary(meta,(record-1)*recordlength,start,stop))
#No. rows
start,stop = 1445,1460
nrows = float(utilities.readbinary(meta,(record-1)*recordlength,start,stop))
#Resampling
start,stop = 1541,1556
res = utilities.readbinary(meta,(record-1)*recordlength,start,stop)
if res=='NNNNN':self.metadata['resampling']='' #Raw (L1A,L1B1)
elif res=='YNNNN':self.metadata['resampling']='NN' #Nearest neighbour
elif res=='NYNNN':self.metadata['resampling']='BL' #Bi-linear
elif res=='NNYNN':self.metadata['resampling']='CC' #Cubic convolution
#Lat/Lon extent
start,stop = 1733,1860
coords = utilities.readbinary(meta,(record-1)*recordlength,start,stop).split()
uly,ulx,ury,urx,lly,llx,lry,lrx = map(float, coords)
ext=[[ulx,uly],[urx,ury],[lrx,lry],[llx,lly],[ulx,uly]]
if not tif:
#Record 3
record=3
#Hemisphere
start,stop = 93,96
hemi = utilities.readbinary(meta,(record-1)*recordlength,start,stop)
#UTM Zone - revisit if we get polarstereographic projection products
start,stop = 97,108
utm = utilities.readbinary(meta,(record-1)*recordlength,start,stop)
if hemi=='1': #South
epsg=int('327%s' % utm) #Assume WGS84
else: #North
epsg=int('326%s' % utm)
src_srs = osr.SpatialReference()
src_srs.ImportFromEPSG(epsg)
units = 'm'
#Scene center position - revisit if we get polarstereographic projection products
##start,stop = 141,156
##ceny = float(utilities.readbinary(meta,(record-1)*recordlength,start,stop)) * 1000 #(Northing - km)
##start,stop = 157,172
##cenx = float(utilities.readbinary(meta,(record-1)*recordlength,start,stop)) * 1000 #(Easting - km)
#Orientation Angle NNN.N = radians
start,stop = 205,220
rot = float(utilities.readbinary(meta,(record-1)*recordlength,start,stop))
#Pixel size (x)
start,stop=541,572
xpix,ypix=map(float,utilities.readbinary(meta,(record-1)*recordlength,start,stop).split())
#Get extent of scene
##xmin=(cenx+xpix/2)-(cencol*xpix)
##ymin=(ceny+ypix/2)-(cenrow*ypix)
##xmax=xmin+(ncols*xpix)
##ymax=ymin+(nrows*ypix)
##if procinfo[-1]=='R': #Calculate rotated extent coordinates
## ##angc=math.cos(rot)
## ##angs=math.sin(rot)
## angc=math.cos(math.radians(rot))
## angs=math.sin(math.radians(rot))
## ulx = (xmin-cenx)*angc + (ymax-ceny)*angs+cenx
## uly = -(xmin-cenx)*angs + (ymax-ceny)*angc+ceny
## llx = (xmin-cenx)*angc + (ymin-ceny)*angs+cenx
## lly = -(xmin-cenx)*angs + (ymin-ceny)*angc+ceny
## urx = (xmax-cenx)*angc + (ymax-ceny)*angs+cenx
## ury = -(xmax-cenx)*angs + (ymax-ceny)*angc+ceny
## lrx = (xmax-cenx)*angc + (ymin-ceny)*angs+cenx
## lry = -(xmax-cenx)*angs + (ymin-ceny)*angc+ceny
##else: #Just use xmin etc...
## ulx,uly = xmin,ymax
## llx,lly = xmin,ymin
## urx,ury = xmax,ymax
## lrx,lry = xmax,ymin
##ext=[[ulx,uly],[urx,ury],[lrx,lry],[llx,lly],[ulx,uly]]
#Geotransform
##gcps=[];i=0
##lr=[[0,0],[ncols,0],[ncols,nrows],[0,nrows]]
##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
##geotransform = gdal.GCPsToGeoTransform(gcps)
self.metadata['nbits'] = 8
self.metadata['datatype'] = 'Byte'
#Generate a VRT GDAL Dataset object
self._imgs.sort()
img=self._imgs[0]
meta = open(img,'rb').read(1024)
start,stop = 187,192
record=1
recordlength=0
offset=utilities.readbinary(meta,(record-1)*recordlength,start,stop)
#don't use ALOS provided no. cols, as it doesn't include 'dummy' pixels
#vrt=geometry.CreateRawRasterVRT(self._imgs,self.metadata['cols'],self.metadata['rows'],self.metadata['datatype'],offset,byteorder='MSB')
vrt=geometry.CreateRawRasterVRT(self._imgs,offset,nrows,self.metadata['datatype'],offset,byteorder='MSB')
self._gdaldataset=geometry.OpenDataset(vrt)
#Reproject corners to lon,lat
##geom = geometry.GeomFromExtent(ext)
##tgt_srs=osr.SpatialReference()
##tgt_srs.ImportFromEPSG(4326)
##geom=geometry.ReprojectGeom(geom,src_srs,tgt_srs)
##points=geom.GetBoundary()
##ext=[[points.GetX(i),points.GetY(i)] for i in range(0,points.GetPointCount())]
#Fix for Issue 17
for i in range(1,self._gdaldataset.RasterCount+1):
self._gdaldataset.GetRasterBand(i).SetNoDataValue(nodata)
self.metadata['level'] = level
self.metadata['sceneid'] = sceneid
if orbit=='A':self.metadata['orbit']='Ascending'
else: self.metadata['orbit']='Descending'
self.metadata['metadata']='\n'.join(['%s: %s' %(m,extra_md[m]) for m in extra_md])
if not tif:
self.metadata['filesize']=sum([os.path.getsize(tmp) for tmp in self.filelist])
self.metadata['srs'] = src_srs.ExportToWkt()
self.metadata['epsg'] = epsg
self.metadata['units'] = units
self.metadata['cols'] = ncols
self.metadata['rows'] = nrows
self.metadata['nbands'] = nbands
if abs(math.degrees(rot)) < 1.0:self.metadata['rotation'] = 0.0
else: self.metadata['rotation'] = math.degrees(rot)
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['cellx'],self.metadata['celly']=xpix,ypix
self.metadata['nodata'] = nodata
self.metadata['compressionratio']=0
self.metadata['compressiontype']='None'
self.extent=ext
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 __getmetadata__(self, f=None):
'''
Generate metadata for generic imagery
@type f: string
@param f: a filepath to the dataset or a VRT XML string
@return: None
@todo: We force a NoData value. This is not ideal, but it makes for better overview images.
'''
if not f: f = self.fileinfo['filepath']
try:
cwd = os.path.abspath(os.curdir)
if os.path.exists(f) and os.path.dirname(f):
p = os.path.split(f)[0]
os.chdir(p)
if not self._gdaldataset:
self._gdaldataset = geometry.OpenDataset(
f
) #in case we're subclassed and there's already a dataset open
if self._gdaldataset:
driver = self._gdaldataset.GetDriver().ShortName
if driver[0:3] == 'HDF':
raise NotImplementedError, 'HDF files are not yet implemented except by custom formats'
self.metadata[
'filetype'] = driver + '/' + self._gdaldataset.GetDriver(
).LongName
self.metadata['cols'] = self._gdaldataset.RasterXSize
self.metadata['rows'] = self._gdaldataset.RasterYSize
self.metadata['nbands'] = self._gdaldataset.RasterCount
self.metadata['srs'] = self._gdaldataset.GetProjection()
if not self.metadata['srs'] and self._gdaldataset.GetGCPCount(
) > 0:
self.metadata['srs'] = self._gdaldataset.GetGCPProjection()
self.metadata['epsg'] = spatialreferences.IdentifyAusEPSG(
self.metadata['srs'])
self.metadata['units'] = spatialreferences.GetLinearUnitsName(
self.metadata['srs'])
geotransform = self._gdaldataset.GetGeoTransform()
if geotransform == (0, 1, 0, 0, 0, 1):
if self._gdaldataset.GetGCPCount() > 0:
gcps = self._gdaldataset.GetGCPs()
geotransform = gdal.GCPsToGeoTransform(gcps)
gcps = geometry.GeoTransformToGCPs(
geotransform, self.metadata['cols'], self.
metadata['rows']) #Just get the 4 corner GCP's
else:
raise NotImplementedError, 'Dataset is not georeferenced'
else:
gcps = geometry.GeoTransformToGCPs(geotransform,
self.metadata['cols'],
self.metadata['rows'])
ext = [[gcp.GCPX, gcp.GCPY] for gcp in gcps]
ext.append([gcps[0].GCPX, gcps[0].GCPY
]) #Add the 1st point to close the polygon)
#Reproject corners to lon,lat
geom = geometry.GeomFromExtent(ext)
src_srs = osr.SpatialReference()
src_srs.ImportFromWkt(self.metadata['srs'])
tgt_srs = osr.SpatialReference()
tgt_srs.ImportFromEPSG(4326)
geom = geometry.ReprojectGeom(geom, src_srs, tgt_srs)
points = geom.GetGeometryRef(0) #geom.GetBoundary()
ext = [[points.GetX(i), points.GetY(i)]
for i in range(0, points.GetPointCount())]
self.metadata['cellx'], self.metadata[
'celly'] = geometry.CellSize(geotransform)
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['UL'] = '%s,%s' % tuple(ext[0])
self.metadata['LL'] = '%s,%s' % tuple(ext[1])
self.metadata['LR'] = '%s,%s' % tuple(ext[2])
self.metadata['UR'] = '%s,%s' % tuple(ext[3])
rb = self._gdaldataset.GetRasterBand(1)
if rb:
self.metadata['datatype'] = gdal.GetDataTypeName(
rb.DataType)
self.metadata['nbits'] = gdal.GetDataTypeSize(rb.DataType)
nodata = rb.GetNoDataValue()
if nodata is not None:
self.metadata['nodata'] = str(nodata)
else:
ct = rb.GetColorTable() #Fix for Issue 31
if ct is None:
if self.metadata['datatype'][0:4] in [
'Byte', 'UInt'
]:
nodata = 0 #Unsigned, assume 0
else:
nodata = -2**(
self.metadata['nbits'] - 1
) #Signed, assume min value in data range
self.metadata['nodata'] = str(nodata)
#Fix for Issue 17
for i in range(1,
self._gdaldataset.RasterCount + 1):
self._gdaldataset.GetRasterBand(
i).SetNoDataValue(nodata)
else:
raise IOError, 'No valid rasterbands found.'
metadata = self._gdaldataset.GetMetadata()
self.metadata['metadata'] = '\n'.join(
['%s: %s' % (m, metadata[m]) for m in metadata])
self.metadata['filesize'] = sum(
[os.path.getsize(tmp) for tmp in self.filelist])
if self.metadata['filesize'] > 0:
self.metadata['compressionratio'] = int(
(self.metadata['nbands'] * self.metadata['cols'] *
self.metadata['rows'] *
(self.metadata['nbits'] / 8.0)) /
self.metadata['filesize'])
if self.metadata['compressionratio'] > 0:
try:
if driver[0:3] == 'JP2':
self.metadata['compressiontype'] = "JPEG2000"
elif driver[0:3] == 'ECW':
self.metadata['compressiontype'] = "ECW"
else:
mdis = self._gdaldataset.GetMetadata(
'IMAGE_STRUCTURE')
#self.metadata['compressiontype']=mdis['IMAGE_STRUCTURE']
self.metadata['compressiontype'] = mdis[
'COMPRESSION']
except:
self.metadata['compressiontype'] = 'Unknown'
else:
self.metadata['compressiontype'] = 'None'
self.extent = ext
else:
errmsg = gdal.GetLastErrorMsg()
raise IOError, 'Unable to open %s\n%s' % (f, errmsg.strip())
finally: #Cleanup
gdal.ErrorReset()
os.chdir(cwd)
