def read(self, obj):
ds = open_gdal(obj)
if ds is not None:
driver = ds.GetDriver()
size = (ds.RasterXSize, ds.RasterYSize)
gt = ds.GetGeoTransform()
extent = None
if gt != (0.0, 1.0, 0.0, 0.0, 0.0, 1.0):
x_extent = (gt[0], gt[0] + size[0] * gt[1])
y_extent = (gt[3] + size[1] * gt[5], gt[3])
extent = (
min(x_extent),
min(y_extent),
max(x_extent),
max(y_extent)
)
projection = ds.GetProjection()
values = {
"size": (ds.RasterXSize, ds.RasterYSize),
}
if projection:
values["projection"] = (projection, "WKT")
if extent:
values["extent"] = extent
reader = self._find_additional_reader(ds)
if reader:
additional_values = reader.read_ds(ds)
for key, value in additional_values.items():
values.setdefault(key, value)
if ds.GetGCPCount() > 0:
rt_prm = rt.suggest_transformer(raw_metadata)
fp_wkt = rt.get_footprint_wkt(raw_metadata,**rt_prm)
values["footprint"] = GEOSGeometry(
fp_wkt
)
driver_metadata = driver.GetMetadata()
frmt = driver_metadata.get("DMD_MIMETYPE")
if frmt:
values["format"] = frmt
return values
raise Exception("Could not parse from obj '%s'." % repr(obj))
def getExtentFromReferenceableDS( ds ):
""" Calculates the extent tuple of the given gdal.Dataset. The dataset
must be encoded using the tie-points.
"""
filelist = ds.GetFileList()
if 1 != len( filelist ) :
raise RuntimeError( "Cannot get a single dataset filename!" )
rt_prm = rt.suggest_transformer(filelist[0])
fp_wkt = rt.get_footprint_wkt(filelist[0],**rt_prm)
return GEOSGeometry( fp_wkt ).extent
path = argv[1]
except IndexError:
print("Requires filename")
exit(1)
reader = GDALDatasetEnvisatMetadataFormatReader(env)
ds = gdal.Open(path)
if not ds:
print("Cannot open '%s' as GDAL Dataset." % path)
exit(1)
elif not reader.test_ds(ds):
print("Dataset '%s' does not contain required ENVISAT metadata." %
path)
exit(1)
md = reader.read_ds(ds)
del ds
footprint = GEOSGeometry(get_footprint_wkt(ds))
footprint.srid = 4326
encoder = EOP20Encoder()
xml = encoder.serialize(
encoder.encode_earth_observation(EOMetadata(footprint=footprint,
**md)))
with open(os.path.join(os.path.dirname(path))) as f:
f.write(xml)
second = int(m.group(6))
return datetime(year, month, day, hour, minute, second)
if __name__=="__main__":
path = argv[1]
ds = gdal.Open(path)
eo_id = os.path.splitext(os.path.basename(path))[0]
begin_time = parse_timestamp(ds.GetMetadataItem("MPH_SENSING_START"))
end_time = parse_timestamp(ds.GetMetadataItem("MPH_SENSING_STOP"))
del ds
footprint = GEOSGeometry(get_footprint_wkt(path))
footprint.srid = 4326
encoder = EOPEncoder()
xml = DOMElementToXML(
encoder.encodeEarthObservation(
eo_id,
begin_time,
end_time,
footprint
)
)
xml_file = open(os.path.join(os.path.dirname(path), "%s.xml" % eo_id), "w")
xml_file.write(xml)
def read(self, obj):
ds = open_gdal(obj)
if ds is None:
raise Exception("Could not parse from obj '%s'." % repr(obj))
driver = ds.GetDriver()
size = (ds.RasterXSize, ds.RasterYSize)
values = {"size": size}
# --= rectified datasets =--
# NOTE: If the projection is a non-zero string then
# the geocoding is given by the Geo-Trasnformation
# matrix - not matter what are the values.
if ds.GetProjection():
values["coverage_type"] = "RectifiedDataset"
values["projection"] = (ds.GetProjection(), "WKT")
# get coordinates of all four image corners
gt = ds.GetGeoTransform()
def gtrans(x, y):
return gt[0] + x*gt[1] + y*gt[2], gt[3] + x*gt[4] + y*gt[5]
vpix = [(0, 0), (0, size[1]), (size[0], 0), (size[0], size[1])]
vx, vy = zip(*(gtrans(x, y) for x, y in vpix))
# find the extent
values["extent"] = (min(vx), min(vy), max(vx), max(vy))
# --= tie-point encoded referenceable datasets =--
# NOTE: If the GCP projection is a non-zero string and
# there are GCPs we are dealing with a tie-point geocoded
# referenceable dataset. The extent is given by the image
# footprint. The fooprint must not be wrapped arround
# the date-line!
elif ds.GetGCPProjection() and ds.GetGCPCount() > 0:
values["coverage_type"] = "ReferenceableDataset"
projection = ds.GetGCPProjection()
values["projection"] = (projection, "WKT")
# parse the spatial reference to get the EPSG code
sr = osr.SpatialReference(projection, "WKT")
# NOTE: GeosGeometry can't handle non-EPSG geometry projections.
if sr.GetAuthorityName(None) == "EPSG":
srid = int(sr.GetAuthorityCode(None))
# get the footprint
rt_prm = rt.suggest_transformer(ds)
fp_wkt = rt.get_footprint_wkt(ds, **rt_prm)
footprint = GEOSGeometry(fp_wkt, srid)
if isinstance(footprint, Polygon):
footprint = MultiPolygon(footprint)
elif not isinstance(footprint, MultiPolygon):
raise TypeError(
"Got invalid geometry %s" % type(footprint).__name__
)
values["footprint"] = footprint
values["extent"] = footprint.extent
# --= dataset with no geocoding =--
# TODO: Handling of other types of GDAL geocoding (e.g, RPC).
else:
pass
reader = self._find_additional_reader(ds)
if reader:
additional_values = reader.read_ds(ds)
for key, value in additional_values.items():
values.setdefault(key, value)
driver_metadata = driver.GetMetadata()
frmt = driver_metadata.get("DMD_MIMETYPE")
if frmt is None:
_driver_name = "GDAL/" + driver.ShortName
_frmt = getFormatRegistry().getFormatsByDriver(_driver_name)
if _frmt:
frmt = _frmt[0].mimeType
if frmt:
values["format"] = frmt
return values
def apply(self, src_ds):
# setup
dst_sr = osr.SpatialReference()
gcp_sr = osr.SpatialReference()
dst_sr.ImportFromEPSG(self.srid if self.srid is not None else self.gcp_srid)
gcp_sr.ImportFromEPSG(self.gcp_srid)
logger.debug("Using GCP Projection '%s'" % gcp_sr.ExportToWkt())
logger.debug(
"Applying GCPs: MULTIPOINT(%s) -> MULTIPOINT(%s)"
% (
", ".join([("(%f %f)") % (gcp.GCPX, gcp.GCPY) for gcp in self.gcps]),
", ".join([("(%f %f)") % (gcp.GCPPixel, gcp.GCPLine) for gcp in self.gcps]),
)
)
# set the GCPs
src_ds.SetGCPs(self.gcps, gcp_sr.ExportToWkt())
# Try to find and use the best transform method/order.
# Orders are: -1 (TPS), 3, 2, and 1 (all GCP)
# Loop over the min and max GCP number to order map.
for min_gcpnum, max_gcpnum, order in [(3, None, -1), (10, None, 3), (6, None, 2), (3, None, 1)]:
# if the number of GCP matches
if len(self.gcps) >= min_gcpnum and (max_gcpnum is None or len(self.gcps) <= max_gcpnum):
try:
if order < 0:
# let the reftools suggest the right interpolator
rt_prm = rt.suggest_transformer(src_ds)
else:
# use the polynomial GCP interpolation as requested
rt_prm = {"method": rt.METHOD_GCP, "order": order}
logger.debug(
"Trying order '%i' {method:%s,order:%s}"
% (order, rt.METHOD2STR[rt_prm["method"]], rt_prm["order"])
)
# get the suggested pixel size/geotransform
size_x, size_y, geotransform = rt.suggested_warp_output(
src_ds, None, dst_sr.ExportToWkt(), **rt_prm
)
if size_x > 100000 or size_y > 100000:
raise RuntimeError("Calculated size exceeds limit.")
logger.debug("New size is '%i x %i'" % (size_x, size_y))
# create the output dataset
dst_ds = create_mem(size_x, size_y, src_ds.RasterCount, src_ds.GetRasterBand(1).DataType)
# reproject the image
dst_ds.SetProjection(dst_sr.ExportToWkt())
dst_ds.SetGeoTransform(geotransform)
rt.reproject_image(src_ds, "", dst_ds, "", **rt_prm)
copy_metadata(src_ds, dst_ds)
# retrieve the footprint from the given GCPs
footprint_wkt = rt.get_footprint_wkt(src_ds, **rt_prm)
except RuntimeError, e:
logger.debug("Failed using order '%i'. Error was '%s'." % (order, str(e)))
# the given method was not applicable, use the next one
continue
else:
logger.debug("Successfully used order '%i'" % order)
# the transform method was successful, exit the loop
break
def render(self, params):
# get the requested coverage, data items and range type.
coverage = params.coverage
data_items = coverage.data_items.filter(semantic__startswith="bands")
range_type = coverage.range_type
subsets = params.subsets
# GDAL source dataset. Either a single file dataset or a composed VRT
# dataset.
src_ds = self.get_source_dataset(
coverage, data_items, range_type
)
# retrieve area of interest of the source image according to given
# subsets
src_rect, dst_rect = self.get_source_and_dest_rect(src_ds, subsets)
# deduct "native" format of the source image
native_format = data_items[0].format if len(data_items) == 1 else None
# get the requested image format, which defaults to the native format
# if available
frmt = params.format or native_format
if not frmt:
raise RenderException("No format specified.", "format")
if params.scalefactor is not None or params.scales:
raise RenderException(
"ReferenceableDataset cannot be scaled.",
"scalefactor" if params.scalefactor is not None else "scale"
)
maxsize = WCSConfigReader(get_eoxserver_config()).maxsize
if maxsize is not None:
if maxsize < dst_rect.size_x or maxsize < dst_rect.size_y:
raise RenderException(
"Requested image size %dpx x %dpx exceeds the allowed "
"limit maxsize=%dpx." % (
dst_rect.size_x, dst_rect.size_y, maxsize
), "size"
)
# perform subsetting either with or without rangesubsetting
subsetted_ds = self.perform_subset(
src_ds, range_type, src_rect, dst_rect, params.rangesubset
)
# encode the processed dataset and save it to the filesystem
out_ds, out_driver = self.encode(
subsetted_ds, frmt, getattr(params, "encoding_params", {})
)
driver_metadata = out_driver.GetMetadata_Dict()
mime_type = driver_metadata.get("DMD_MIMETYPE")
extension = driver_metadata.get("DMD_EXTENSION")
time_stamp = datetime.now().strftime("%Y%m%d%H%M%S")
filename_base = "%s_%s" % (coverage.identifier, time_stamp)
result_set = [
ResultFile(
path, mime_type, "%s.%s" % (filename_base, extension),
("cid:coverage/%s" % coverage.identifier) if i == 0 else None
) for i, path in enumerate(out_ds.GetFileList())
]
if params.mediatype and params.mediatype.startswith("multipart"):
reference = "cid:coverage/%s" % result_set[0].filename
if subsets.has_x and subsets.has_y:
footprint = GEOSGeometry(reftools.get_footprint_wkt(out_ds))
if not subsets.srid:
extent = footprint.extent
else:
extent = subsets.xy_bbox
encoder_subset = (
subsets.srid, src_rect.size, extent, footprint
)
else:
encoder_subset = None
encoder = WCS20EOXMLEncoder()
content = encoder.serialize(
encoder.encode_referenceable_dataset(
coverage, range_type, reference, mime_type, encoder_subset
)
)
result_set.insert(0, ResultBuffer(content, encoder.content_type))
return result_set
def apply(self, src_ds):
# setup
dst_sr = osr.SpatialReference()
gcp_sr = osr.SpatialReference()
dst_sr.ImportFromEPSG(self.srid if self.srid is not None
else self.gcp_srid)
gcp_sr.ImportFromEPSG(self.gcp_srid)
logger.debug("Using GCP Projection '%s'" % gcp_sr.ExportToWkt())
logger.debug("Applying GCPs: MULTIPOINT(%s) -> MULTIPOINT(%s)"
% (", ".join([("(%f %f)") % (gcp.GCPX, gcp.GCPY) for gcp in self.gcps]) ,
", ".join([("(%f %f)") % (gcp.GCPPixel, gcp.GCPLine) for gcp in self.gcps])))
# set the GCPs
src_ds.SetGCPs(self.gcps, gcp_sr.ExportToWkt())
# Try to find and use the best transform method/order.
# Orders are: -1 (TPS), 3, 2, and 1 (all GCP)
# Loop over the min and max GCP number to order map.
for min_gcpnum, max_gcpnum, order in [(3, None, -1), (10, None, 3), (6, None, 2), (3, None, 1)]:
# if the number of GCP matches
if len(self.gcps) >= min_gcpnum and (max_gcpnum is None or len(self.gcps) <= max_gcpnum):
try:
if ( order < 0 ) :
# let the reftools suggest the right interpolator
rt_prm = rt.suggest_transformer( src_ds )
else:
# use the polynomial GCP interpolation as requested
rt_prm = { "method":rt.METHOD_GCP, "order":order }
logger.debug("Trying order '%i' {method:%s,order:%s}" % \
(order, rt.METHOD2STR[rt_prm["method"]] , rt_prm["order"] ) )
# get the suggested pixel size/geotransform
size_x, size_y, geotransform = rt.suggested_warp_output(
src_ds,
None,
dst_sr.ExportToWkt(),
**rt_prm
)
if size_x > 100000 or size_y > 100000:
raise RuntimeError("Calculated size exceeds limit.")
logger.debug("New size is '%i x %i'" % (size_x, size_y))
# create the output dataset
dst_ds = create_mem(size_x, size_y,
src_ds.RasterCount,
src_ds.GetRasterBand(1).DataType)
# reproject the image
dst_ds.SetProjection(dst_sr.ExportToWkt())
dst_ds.SetGeoTransform(geotransform)
rt.reproject_image(src_ds, "", dst_ds, "", **rt_prm )
copy_metadata(src_ds, dst_ds)
# retrieve the footprint from the given GCPs
footprint_wkt = rt.get_footprint_wkt(src_ds, **rt_prm )
except RuntimeError as e:
logger.debug("Failed using order '%i'. Error was '%s'."
% (order, str(e)))
# the given method was not applicable, use the next one
continue
else:
logger.debug("Successfully used order '%i'" % order)
# the transform method was successful, exit the loop
break
else:
# no method worked, so raise an error
raise GCPTransformException("Could not find a valid transform method.")
# reproject the footprint to a lon/lat projection if necessary
if not gcp_sr.IsGeographic():
out_sr = osr.SpatialReference()
out_sr.ImportFromEPSG(4326)
geom = ogr.CreateGeometryFromWkt(footprint_wkt, gcp_sr)
geom.TransformTo(out_sr)
footprint_wkt = geom.ExportToWkt()
logger.debug("Calculated footprint: '%s'." % footprint_wkt)
return dst_ds, footprint_wkt
def apply(self, src_ds):
# setup
dst_sr = osr.SpatialReference()
dst_sr.ImportFromEPSG(self.srid)
logger.debug("Using internal GCP Projection.")
num_gcps = src_ds.GetGCPCount()
# Try to find and use the best transform method/order.
# Orders are: -1 (TPS), 3, 2, and 1 (all GCP)
# Loop over the min and max GCP number to order map.
for min_gcpnum, max_gcpnum, order in [(3, None, -1), (10, None, 3),
(6, None, 2), (3, None, 1)]:
# if the number of GCP matches
if num_gcps >= min_gcpnum and (max_gcpnum is None
or num_gcps <= max_gcpnum):
try:
if (order < 0):
# let the reftools suggest the right interpolator
rt_prm = reftools.suggest_transformer(src_ds)
else:
# use the polynomial GCP interpolation as requested
rt_prm = {
"method": reftools.METHOD_GCP,
"order": order
}
logger.debug("Trying order '%i' {method:%s,order:%s}" %
(order, reftools.METHOD2STR[rt_prm["method"]],
rt_prm["order"]))
# get the suggested pixel size/geotransform
size_x, size_y, gt = reftools.suggested_warp_output(
src_ds, None, dst_sr.ExportToWkt(), **rt_prm)
if size_x > 100000 or size_y > 100000:
raise RuntimeError("Calculated size exceeds limit.")
logger.debug("New size is '%i x %i'" % (size_x, size_y))
# create the output dataset
dst_ds = create_mem(size_x, size_y, src_ds.RasterCount,
src_ds.GetRasterBand(1).DataType)
# reproject the image
dst_ds.SetProjection(dst_sr.ExportToWkt())
dst_ds.SetGeoTransform(gt)
reftools.reproject_image(src_ds, "", dst_ds, "", **rt_prm)
copy_metadata(src_ds, dst_ds)
# retrieve the footprint from the given GCPs
footprint_wkt = reftools.get_footprint_wkt(
src_ds, **rt_prm)
except RuntimeError, e:
logger.debug("Failed using order '%i'. Error was '%s'." %
(order, str(e)))
# the given method was not applicable, use the next one
continue
else:
logger.debug("Successfully used order '%i'" % order)
# the transform method was successful, exit the loop
break
def render(self, params):
# get the requested coverage, data items and range type.
coverage = params.coverage
data_items = coverage.data_items.filter(semantic__startswith="bands")
range_type = coverage.range_type
subsets = params.subsets
# GDAL source dataset. Either a single file dataset or a composed VRT
# dataset.
src_ds = self.get_source_dataset(
coverage, data_items, range_type
)
# retrieve area of interest of the source image according to given
# subsets
src_rect, dst_rect = self.get_src_and_dst_rect(src_ds, subsets)
# deduct "native" format of the source image
def _src2nat(src_format):
if src_format is None:
return None
frmreg = getFormatRegistry()
f_src = frmreg.getFormatByMIME(src_format)
f_dst = frmreg.mapSourceToNativeWCS20(f_src)
f_nat = frmreg._mapSourceToNativeWCS20(f_src)
if src_format == 'application/x-esa-envisat' and f_src == f_nat:
return src_format
elif f_dst is not None:
return f_dst.mimeType
source_format = data_items[0].format if len(data_items) == 1 else None
native_format = _src2nat(source_format)
# get the requested image format, which defaults to the native format
# if available
output_format = params.format or native_format
if not output_format:
raise RenderException("Failed to deduce the native format of "
"the coverage. Output format must be provided!", "format")
if params.scalefactor is not None or params.scales:
raise RenderException(
"ReferenceableDataset cannot be scaled.",
"scalefactor" if params.scalefactor is not None else "scale"
)
# check it the requested image fits the max. allowed coverage size
maxsize = WCSConfigReader(get_eoxserver_config()).maxsize
if maxsize < dst_rect.size_x or maxsize < dst_rect.size_y:
raise RenderException(
"Requested image size %dpx x %dpx exceeds the allowed "
"limit maxsize=%dpx!" % (dst_rect.size_x,
dst_rect.size_y, maxsize), "size"
)
# get the output backend and driver for the requested format
def _get_driver(mime_src, mime_out):
"""Select backend for the given source and output formats."""
# TODO: make this configurable
if mime_src == 'application/x-esa-envisat' and \
mime_out == 'application/x-netcdf':
return "BEAM", "NetCDF4-BEAM"
elif mime_src == 'application/x-esa-envisat' and \
mime_out == 'application/x-esa-envisat':
return "EOXS", "envisat"
frmreg = getFormatRegistry()
fobj = frmreg.getFormatByMIME(mime_out)
if fobj is None:
raise RenderException("Invallid output format '%s'!"%mime_out, "format")
backend, _, driver = fobj.driver.partition("/")
return backend, driver
driver_backend, driver_name = _get_driver(source_format, output_format)
if driver_backend not in ("GDAL", "BEAM", "EOXS"):
raise RenderException("Invallid output format backend name %s!"
"" % driver_backend, "format")
# prepare output
# ---------------------------------------------------------------------
if driver_backend == "BEAM":
path_out, extension = self.encode_beam(
driver_name,
src_ds.GetFileList()[0],
src_rect,
getattr(params, "encoding_params", {})
)
mime_type = output_format
path_list = [path_out]
time_stamp = datetime.now().strftime("%Y%m%d%H%M%S")
filename_base = "%s_%s" % (coverage.identifier, time_stamp)
result_set = [
ResultFile(
path_item, mime_type, "%s.%s" % (filename_base, extension),
("cid:coverage/%s" % coverage.identifier) if i == 0 else None
) for i, path_item in enumerate(path_list)
]
# ---------------------------------------------------------------------
elif driver_backend == "EOXS": #EOxServer native backend
result_set = [ResultAlt(
file(src_ds.GetFileList()[0]),
content_type=output_format,
filename=basename(src_ds.GetFileList()[0]),
identifier="cid:coverage/%s" % coverage.identifier,
close=True,
)]
mime_type = output_format
subsets = Subsets(()) # reset all subsets
# ---------------------------------------------------------------------
elif driver_backend == "GDAL":
# get the output driver
driver = gdal.GetDriverByName(driver_name)
if driver is None:
raise RenderException("Unsupported GDAL driver %s!" % driver_name)
# perform subsetting either with or without rangesubsetting
subsetted_ds = self.get_subset(
src_ds, range_type, src_rect, dst_rect, params.rangesubset
)
# encode the processed dataset and save it to the filesystem
out_ds = self.encode(driver, subsetted_ds, output_format,
getattr(params, "encoding_params", {}))
driver_metadata = driver.GetMetadata_Dict()
mime_type = driver_metadata.get("DMD_MIMETYPE")
extension = driver_metadata.get("DMD_EXTENSION")
path_list = out_ds.GetFileList()
time_stamp = datetime.now().strftime("%Y%m%d%H%M%S")
filename_base = "%s_%s" % (coverage.identifier, time_stamp)
result_set = [
ResultFile(
path_item, mime_type, "%s.%s" % (filename_base, extension),
("cid:coverage/%s" % coverage.identifier) if i == 0 else None
) for i, path_item in enumerate(path_list)
]
# ---------------------------------------------------------------------
if params.mediatype and params.mediatype.startswith("multipart"):
reference = "cid:coverage/%s" % result_set[0].filename
if subsets.has_x and subsets.has_y:
footprint = GEOSGeometry(reftools.get_footprint_wkt(out_ds))
if not subsets.srid:
extent = footprint.extent
else:
extent = subsets.xy_bbox
encoder_subset = (
subsets.srid, src_rect.size, extent, footprint
)
else:
encoder_subset = None
encoder = WCS20EOXMLEncoder()
content = encoder.serialize(
encoder.encode_referenceable_dataset(
coverage, range_type, reference, mime_type, encoder_subset
)
)
result_set.insert(0, ResultBuffer(content, encoder.content_type))
return result_set
try:
path = argv[1]
except IndexError:
print "Requires filename"
exit(1)
reader = GDALDatasetEnvisatMetadataFormatReader(env)
ds = gdal.Open(path)
if not ds:
print "Cannot open '%s' as GDAL Dataset." % path
exit(1)
elif not reader.test_ds(ds):
print "Dataset '%s' does not contain required ENVISAT metadata." % path
exit(1)
md = reader.read_ds(ds)
del ds
footprint = GEOSGeometry(get_footprint_wkt(ds))
footprint.srid = 4326
encoder = EOP20Encoder()
xml = encoder.serialize(
encoder.encode_earth_observation(EOMetadata(footprint=footprint, **md))
)
with open(os.path.join(os.path.dirname(path))) as f:
f.write(xml)
def render(self, params):
# get the requested coverage, data items and range type.
coverage = params.coverage
data_items = coverage.data_items.filter(semantic__startswith="bands")
range_type = coverage.range_type
subsets = Subsets(params.subsets)
# GDAL source dataset. Either a single file dataset or a composed VRT
# dataset.
src_ds = self.get_source_dataset(
coverage, data_items, range_type
)
# retrieve area of interest of the source image according to given
# subsets
src_rect = self.get_source_image_rect(src_ds, subsets)
# deduct "native" format of the source image
native_format = data_items[0].format if len(data_items) == 1 else None
# get the requested image format, which defaults to the native format
# if available
format = params.format or native_format
if not format:
raise Exception("No format specified.")
# perform subsetting either with or without rangesubsetting
if params.rangesubset:
subsetted_ds = self.perform_range_subset(
src_ds, range_type, subset_bands, src_rect,
)
else:
subsetted_ds = self.perform_subset(
src_ds, src_rect
)
# encode the processed dataset and save it to the filesystem
out_ds, out_driver = self.encode(subsetted_ds, format)
driver_metadata = out_driver.GetMetadata_Dict()
mime_type = driver_metadata.get("DMD_MIMETYPE")
time_stamp = datetime.now().strftime("%Y%m%d%H%M%S")
filename_base = "%s_%s" % (coverage.identifier, time_stamp)
result_set = [
ResultFile(
path, mime_type, filename_base + splitext(path)[1],
("cid:coverage/%s" % coverage.identifier) if i == 0 else None
) for i, path in enumerate(out_ds.GetFileList())
]
if params.mediatype.startswith("multipart"):
reference = result_set[0].identifier
if subsets.has_x and subsets.has_y:
footprint = GEOSGeometry(reftools.get_footprint_wkt(out_ds))
encoder_subset = (
subsets.xy_srid, src_rect.size, coverage.extent, footprint
)
else:
encoder_subset = None
encoder = WCS20EOXMLEncoder()
content = encoder.serialize(
encoder.encode_referenceable_dataset(
coverage, range_type, reference, mime_type, encoder_subset
)
)
result_set.insert(0, ResultBuffer(content, encoder.content_type))
return result_set
def read(self, obj):
ds = open_gdal(obj)
if ds is None:
raise Exception("Could not parse from obj '%s'." % repr(obj))
driver = ds.GetDriver()
size = (ds.RasterXSize, ds.RasterYSize)
values = {"size": size}
# --= rectified datasets =--
# NOTE: If the projection is a non-zero string then
# the geocoding is given by the Geo-Trasnformation
# matrix - not matter what are the values.
if ds.GetProjection():
values["coverage_type"] = "RectifiedDataset"
values["projection"] = (ds.GetProjection(), "WKT")
# get coordinates of all four image corners
gt = ds.GetGeoTransform()
def gtrans(x, y):
return gt[0] + x * gt[1] + y * gt[2], gt[
3] + x * gt[4] + y * gt[5]
vpix = [(0, 0), (0, size[1]), (size[0], 0), (size[0], size[1])]
vx, vy = zip(*(gtrans(x, y) for x, y in vpix))
# find the extent
values["extent"] = (min(vx), min(vy), max(vx), max(vy))
# --= tie-point encoded referenceable datasets =--
# NOTE: If the GCP projection is a non-zero string and
# there are GCPs we are dealing with a tie-point geocoded
# referenceable dataset. The extent is given by the image
# footprint. The fooprint must not be wrapped arround
# the date-line!
elif ds.GetGCPProjection() and ds.GetGCPCount() > 0:
values["coverage_type"] = "ReferenceableDataset"
projection = ds.GetGCPProjection()
values["projection"] = (projection, "WKT")
# parse the spatial reference to get the EPSG code
sr = osr.SpatialReference(projection, "WKT")
# NOTE: GeosGeometry can't handle non-EPSG geometry projections.
if sr.GetAuthorityName(None) == "EPSG":
srid = int(sr.GetAuthorityCode(None))
# get the footprint
rt_prm = rt.suggest_transformer(ds)
fp_wkt = rt.get_footprint_wkt(ds, **rt_prm)
footprint = GEOSGeometry(fp_wkt, srid)
if isinstance(footprint, Polygon):
footprint = MultiPolygon(footprint)
elif not isinstance(footprint, MultiPolygon):
raise TypeError("Got invalid geometry %s" %
type(footprint).__name__)
values["footprint"] = footprint
values["extent"] = footprint.extent
# --= dataset with no geocoding =--
# TODO: Handling of other types of GDAL geocoding (e.g, RPC).
else:
pass
reader = self._find_additional_reader(ds)
if reader:
additional_values = reader.read_ds(ds)
for key, value in additional_values.items():
values.setdefault(key, value)
driver_metadata = driver.GetMetadata()
frmt = driver_metadata.get("DMD_MIMETYPE")
if frmt:
values["format"] = frmt
return values
""" Print simple usage help. """
exename = basename(sys.argv[0])
print >>sys.stderr, (
"USAGE: %s <input image> [%s]" % (exename, "|".join(OUTPUT_FORMATS))
)
if __name__ == "__main__":
DEBUG = False
FORMAT = "WKB"
try:
INPUT = sys.argv[1]
for arg in sys.argv[2:]:
if arg in OUTPUT_FORMATS:
FORMAT = arg # output format
elif arg == "DEBUG":
DEBUG = True # dump debugging output
except IndexError:
error("Not enough input arguments!\n")
usage()
sys.exit(1)
# get the referenceable dataset outline
#NOTE: It is assumed, that the outline is not wrapped around the date-line.
ds = gdal.Open(INPUT)
prm = rt.suggest_transformer(ds)
geom = setSR(parseGeom(rt.get_footprint_wkt(ds, **prm)), OSR_WGS84)
# print geometry
sys.stdout.write(dumpGeom(geom, FORMAT))
def render(self, params):
# get the requested coverage, data items and range type.
coverage = params.coverage
data_items = coverage.data_items.filter(semantic__startswith="bands")
range_type = coverage.range_type
subsets = params.subsets
# GDAL source dataset. Either a single file dataset or a composed VRT
# dataset.
src_ds = self.get_source_dataset(coverage, data_items, range_type)
# retrieve area of interest of the source image according to given
# subsets
src_rect, dst_rect = self.get_source_and_dest_rect(src_ds, subsets)
# deduct "native" format of the source image
native_format = data_items[0].format if len(data_items) == 1 else None
# get the requested image format, which defaults to the native format
# if available
frmt = params.format or native_format
if not frmt:
raise RenderException("No format specified.", "format")
if params.scalefactor is not None or params.scales:
raise RenderException(
"ReferenceableDataset cannot be scaled.",
"scalefactor" if params.scalefactor is not None else "scale")
maxsize = WCSConfigReader(get_eoxserver_config()).maxsize
if maxsize is not None:
if maxsize < dst_rect.size_x or maxsize < dst_rect.size_y:
raise RenderException(
"Requested image size %dpx x %dpx exceeds the allowed "
"limit maxsize=%dpx." %
(dst_rect.size_x, dst_rect.size_y, maxsize), "size")
# perform subsetting either with or without rangesubsetting
subsetted_ds = self.perform_subset(src_ds, range_type, src_rect,
dst_rect, params.rangesubset)
# encode the processed dataset and save it to the filesystem
out_ds, out_driver = self.encode(
subsetted_ds, frmt, getattr(params, "encoding_params", {}))
driver_metadata = out_driver.GetMetadata_Dict()
mime_type = driver_metadata.get("DMD_MIMETYPE")
extension = driver_metadata.get("DMD_EXTENSION")
time_stamp = datetime.now().strftime("%Y%m%d%H%M%S")
filename_base = "%s_%s" % (coverage.identifier, time_stamp)
result_set = [
ResultFile(path, mime_type, "%s.%s" % (filename_base, extension),
("cid:coverage/%s" %
coverage.identifier) if i == 0 else None)
for i, path in enumerate(out_ds.GetFileList())
]
if params.mediatype and params.mediatype.startswith("multipart"):
reference = "cid:coverage/%s" % result_set[0].filename
if subsets.has_x and subsets.has_y:
footprint = GEOSGeometry(reftools.get_footprint_wkt(out_ds))
if not subsets.srid:
extent = footprint.extent
else:
extent = subsets.xy_bbox
encoder_subset = (subsets.srid, src_rect.size, extent,
footprint)
else:
encoder_subset = None
encoder = WCS20EOXMLEncoder()
content = encoder.serialize(
encoder.encode_referenceable_dataset(coverage, range_type,
reference, mime_type,
encoder_subset))
result_set.insert(0, ResultBuffer(content, encoder.content_type))
return result_set
def read(self, obj):
ds = open_gdal(obj)
if ds is None:
raise Exception("Could not parse from obj '%s'." % repr(obj))
driver = ds.GetDriver()
size = (ds.RasterXSize, ds.RasterYSize)
values = {"size": size}
projection = ds.GetProjection()
# --= rectified datasets =--
# NOTE: If the projection is a non-zero string then
# the geocoding is given by the Geo-Trasnformation
# matrix - not matter what are the values.
if projection and not (ds.GetGCPProjection() and ds.GetGCPCount() > 0):
sr = osr.SpatialReference(projection)
if sr.srid is not None:
projection = 'EPSG:%d' % sr.srid
gt = ds.GetGeoTransform()
values['origin'] = [gt[0], gt[3]]
values['grid'] = {
'coordinate_reference_system': projection,
'axis_offsets': [gt[1], gt[5]],
'axis_types': ['spatial', 'spatial'],
'axis_names':
['x', 'y'] if sr.IsProjected() else ['long', 'lat'],
}
if sr.GetLinearUnitsName() in ('metre', 'meter', 'm') \
and abs(gt[1]) == abs(gt[5]):
values['grid']['resolution'] = abs(gt[1])
# --= tie-point encoded referenceable datasets =--
# NOTE: If the GCP projection is a non-zero string and
# there are GCPs we are dealing with a tie-point geocoded
# referenceable dataset. The extent is given by the image
# footprint. The fooprint must not be wrapped arround
# the date-line!
elif ds.GetGCPProjection() and ds.GetGCPCount() > 0:
projection = ds.GetGCPProjection()
sr = osr.SpatialReference(projection)
if sr.srid is not None:
projection = 'EPSG:%d' % sr.srid
values['grid'] = {
'coordinate_reference_system': projection,
'axis_offsets': [None, None],
'axis_types': ['spatial', 'spatial'],
'axis_names':
['x', 'y'] if sr.IsProjected() else ['long', 'lat']
}
values['origin'] = [None, None]
# # parse the spatial reference to get the EPSG code
sr = osr.SpatialReference(ds.GetGCPProjection(), "WKT")
# NOTE: GeosGeometry can't handle non-EPSG geometry projections.
if sr.GetAuthorityName(None) == "EPSG":
srid = int(sr.GetAuthorityCode(None))
# get the footprint
rt_prm = rt.suggest_transformer(ds)
fp_wkt = rt.get_footprint_wkt(ds, **rt_prm)
footprint = GEOSGeometry(fp_wkt, srid)
if isinstance(footprint, Polygon):
footprint = MultiPolygon(footprint)
elif not isinstance(footprint, MultiPolygon):
raise TypeError("Got invalid geometry %s" %
type(footprint).__name__)
values['footprint'] = footprint
pass
# --= dataset with no geocoding =--
# TODO: Handling of other types of GDAL geocoding (e.g, RPC).
else:
pass
reader = self._find_additional_reader(ds)
if reader:
additional_values = reader.read_ds(ds)
for key, value in additional_values.items():
values.setdefault(key, value)
driver_metadata = driver.GetMetadata()
frmt = driver_metadata.get("DMD_MIMETYPE")
if frmt:
values["format"] = frmt
return values