def __call__(self, src_ds):
dst_ds = create_mem(src_ds.RasterXSize, src_ds.RasterYSize,
1, gdal.GDT_Byte)
if not self.palette_file:
# create a color table as a median of the given dataset
ct = gdal.ColorTable()
gdal.ComputeMedianCutPCT(src_ds.GetRasterBand(1),
src_ds.GetRasterBand(2),
src_ds.GetRasterBand(3),
256, ct)
else:
# copy the color table from the given palette file
pct_ds = gdal.Open(self.palette_file)
pct_ct = pct_ds.GetRasterBand(1).GetRasterColorTable()
if not pct_ct:
raise ValueError("The palette file '%s' does not have a Color "
"Table." % self.palette_file)
ct = pct_ct.Clone()
pct_ds = None
dst_ds.GetRasterBand(1).SetRasterColorTable(ct)
gdal.DitherRGB2PCT(src_ds.GetRasterBand(1),
src_ds.GetRasterBand(2),
src_ds.GetRasterBand(3),
dst_ds.GetRasterBand(1), ct)
copy_projection(src_ds, dst_ds)
copy_metadata(src_ds, dst_ds)
return dst_ds
def __call__(self, src_ds):
dst_ds = create_mem(src_ds.RasterXSize, src_ds.RasterYSize, len(self.bands), self.datatype)
dst_range = get_limits(self.datatype)
multiple = 0
for dst_index, (src_index, dmin, dmax) in enumerate(self.bands, 1):
# check if next band is equal
if dst_index < len(self.bands) and (src_index, dmin, dmax) == self.bands[dst_index]:
multiple += 1
continue
# check that src band is available
if src_index > src_ds.RasterCount:
continue
# initialize with zeros if band is 0
if src_index == 0:
src_band = src_ds.GetRasterBand(1)
data = numpy.zeros((src_band.YSize, src_band.XSize), dtype=gdal_array.codes[self.datatype])
src_min, src_max = (0, 0)
# use src_ds band otherwise
else:
src_band = src_ds.GetRasterBand(src_index)
data = src_band.ReadAsArray()
src_min, src_max = src_band.ComputeRasterMinMax()
# get min/max values or calculate from band
if dmin is None:
dmin = get_limits(src_band.DataType)[0]
elif dmin == "min":
dmin = src_min
if dmax is None:
dmax = get_limits(src_band.DataType)[1]
elif dmax == "max":
dmax = src_max
src_range = (float(dmin), float(dmax))
# perform clipping and scaling
data = (dst_range[1] - dst_range[0]) * (
(numpy.clip(data, dmin, dmax) - src_range[0]) / (src_range[1] - src_range[0])
)
# set new datatype
data = data.astype(gdal_array.codes[self.datatype])
# write result
dst_band = dst_ds.GetRasterBand(dst_index)
dst_band.WriteArray(data)
# write equal bands at once
if multiple > 0:
for i in range(multiple):
dst_band = dst_ds.GetRasterBand(dst_index - 1 - i)
dst_band.WriteArray(data)
multiple = 0
copy_projection(src_ds, dst_ds)
copy_metadata(src_ds, dst_ds)
return dst_ds
def __call__(self, src_ds):
logger.info("Applying ColorIndexOptimization")
try:
dst_ds = create_temp(src_ds.RasterXSize,
src_ds.RasterYSize,
1,
gdal.GDT_Byte,
temp_root=self.temporary_directory)
if not self.palette_file:
# create a color table as a median of the given dataset
ct = gdal.ColorTable()
gdal.ComputeMedianCutPCT(src_ds.GetRasterBand(1),
src_ds.GetRasterBand(2),
src_ds.GetRasterBand(3), 256, ct)
else:
# copy the color table from the given palette file
pct_ds = gdal.Open(self.palette_file)
pct_ct = pct_ds.GetRasterBand(1).GetRasterColorTable()
if not pct_ct:
raise ValueError("The palette file '%s' does not have a "
"Color Table." % self.palette_file)
ct = pct_ct.Clone()
pct_ds = None
dst_ds.GetRasterBand(1).SetRasterColorTable(ct)
gdal.DitherRGB2PCT(src_ds.GetRasterBand(1),
src_ds.GetRasterBand(2),
src_ds.GetRasterBand(3),
dst_ds.GetRasterBand(1), ct)
copy_projection(src_ds, dst_ds)
copy_metadata(src_ds, dst_ds)
return dst_ds
except:
cleanup_temp(dst_ds)
raise
def __call__(self, src_ds):
logger.info("Applying ColorIndexOptimization")
try:
dst_ds = create_temp(src_ds.RasterXSize, src_ds.RasterYSize,
1, gdal.GDT_Byte,
temp_root=self.temporary_directory)
if not self.palette_file:
# create a color table as a median of the given dataset
ct = gdal.ColorTable()
gdal.ComputeMedianCutPCT(src_ds.GetRasterBand(1),
src_ds.GetRasterBand(2),
src_ds.GetRasterBand(3),
256, ct)
else:
# copy the color table from the given palette file
pct_ds = gdal.Open(self.palette_file)
pct_ct = pct_ds.GetRasterBand(1).GetRasterColorTable()
if not pct_ct:
raise ValueError("The palette file '%s' does not have a "
"Color Table." % self.palette_file)
ct = pct_ct.Clone()
pct_ds = None
dst_ds.GetRasterBand(1).SetRasterColorTable(ct)
gdal.DitherRGB2PCT(src_ds.GetRasterBand(1),
src_ds.GetRasterBand(2),
src_ds.GetRasterBand(3),
dst_ds.GetRasterBand(1), ct)
copy_projection(src_ds, dst_ds)
copy_metadata(src_ds, dst_ds)
return dst_ds
except:
cleanup_temp(dst_ds)
raise
def __call__(self, src_ds):
logger.info("Applying BandSelectionOptimization")
try:
dst_ds = create_temp(src_ds.RasterXSize,
src_ds.RasterYSize,
len(self.bands),
self.datatype,
temp_root=self.temporary_directory)
dst_range = get_limits(self.datatype)
multiple, multiple_written = 0, False
for dst_index, (src_index, dmin, dmax) in enumerate(self.bands, 1):
# check if next band is equal
if dst_index < len(self.bands) and \
(src_index, dmin, dmax) == self.bands[dst_index]:
multiple += 1
continue
# check that src band is available
if src_index > src_ds.RasterCount:
continue
# initialize with zeros if band is 0
if src_index == 0:
src_band = src_ds.GetRasterBand(1)
data = numpy.zeros((src_band.YSize, src_band.XSize),
dtype=gdal_array.codes[self.datatype])
src_min, src_max = (0, 0)
# use src_ds band otherwise
else:
src_band = src_ds.GetRasterBand(src_index)
src_min, src_max = src_band.ComputeRasterMinMax()
# get min/max values or calculate from band
if dmin is None:
dmin = get_limits(src_band.DataType)[0]
elif dmin == "min":
dmin = src_min
if dmax is None:
dmax = get_limits(src_band.DataType)[1]
elif dmax == "max":
dmax = src_max
src_range = (float(dmin), float(dmax))
block_x_size, block_y_size = src_band.GetBlockSize()
num_x = int(math.ceil(float(src_band.XSize) / block_x_size))
num_y = int(math.ceil(float(src_band.YSize) / block_y_size))
dst_band = dst_ds.GetRasterBand(dst_index)
if src_band.GetNoDataValue() is not None:
dst_band.SetNoDataValue(src_band.GetNoDataValue())
for block_x, block_y in product(range(num_x), range(num_y)):
offset_x = block_x * block_x_size
offset_y = block_y * block_y_size
size_x = min(src_band.XSize - offset_x, block_x_size)
size_y = min(src_band.YSize - offset_y, block_y_size)
data = src_band.ReadAsArray(offset_x, offset_y, size_x,
size_y)
# perform clipping and scaling
data = ((dst_range[1] - dst_range[0]) *
((numpy.clip(data, dmin, dmax) - src_range[0]) /
(src_range[1] - src_range[0])))
# set new datatype
data = data.astype(gdal_array.codes[self.datatype])
# write result
dst_band.WriteArray(data, offset_x, offset_y)
# write equal bands at once
if multiple > 0:
for i in range(multiple):
dst_band_multiple = dst_ds.GetRasterBand(
dst_index - 1 - i)
dst_band_multiple.WriteArray(
data, offset_x, offset_y)
multiple_written = True
if multiple_written:
multiple = 0
multiple_written = False
copy_projection(src_ds, dst_ds)
copy_metadata(src_ds, dst_ds)
return dst_ds
except:
cleanup_temp(dst_ds)
raise
def __call__(self, src_ds):
logger.info("Applying BandSelectionOptimization")
try:
dst_ds = create_temp(src_ds.RasterXSize, src_ds.RasterYSize,
len(self.bands), self.datatype,
temp_root=self.temporary_directory)
dst_range = get_limits(self.datatype)
multiple, multiple_written = 0, False
for dst_index, (src_index, dmin, dmax) in enumerate(self.bands, 1):
# check if next band is equal
if dst_index < len(self.bands) and \
(src_index, dmin, dmax) == self.bands[dst_index]:
multiple += 1
continue
# check that src band is available
if src_index > src_ds.RasterCount:
continue
# initialize with zeros if band is 0
if src_index == 0:
src_band = src_ds.GetRasterBand(1)
data = numpy.zeros(
(src_band.YSize, src_band.XSize),
dtype=gdal_array.codes[self.datatype]
)
src_min, src_max = (0, 0)
# use src_ds band otherwise
else:
src_band = src_ds.GetRasterBand(src_index)
src_min, src_max = src_band.ComputeRasterMinMax()
# get min/max values or calculate from band
if dmin is None:
dmin = get_limits(src_band.DataType)[0]
elif dmin == "min":
dmin = src_min
if dmax is None:
dmax = get_limits(src_band.DataType)[1]
elif dmax == "max":
dmax = src_max
src_range = (float(dmin), float(dmax))
block_x_size, block_y_size = src_band.GetBlockSize()
num_x = int(math.ceil(float(src_band.XSize) / block_x_size))
num_y = int(math.ceil(float(src_band.YSize) / block_y_size))
dst_band = dst_ds.GetRasterBand(dst_index)
if src_band.GetNoDataValue() is not None:
dst_band.SetNoDataValue(src_band.GetNoDataValue())
for block_x, block_y in product(range(num_x), range(num_y)):
offset_x = block_x * block_x_size
offset_y = block_y * block_y_size
size_x = min(src_band.XSize - offset_x, block_x_size)
size_y = min(src_band.YSize - offset_y, block_y_size)
data = src_band.ReadAsArray(
offset_x, offset_y, size_x, size_y
)
# perform clipping and scaling
data = ((dst_range[1] - dst_range[0]) *
((numpy.clip(data, dmin, dmax) - src_range[0]) /
(src_range[1] - src_range[0])))
# set new datatype
data = data.astype(gdal_array.codes[self.datatype])
# write result
dst_band.WriteArray(data, offset_x, offset_y)
# write equal bands at once
if multiple > 0:
for i in range(multiple):
dst_band_multiple = dst_ds.GetRasterBand(
dst_index-1-i
)
dst_band_multiple.WriteArray(
data, offset_x, offset_y
)
multiple_written = True
if multiple_written:
multiple = 0
multiple_written = False
copy_projection(src_ds, dst_ds)
copy_metadata(src_ds, dst_ds)
return dst_ds
except:
cleanup_temp(dst_ds)
raise
def _generate_footprint_wkt(self, ds):
""" Generate a fooptrint from a raster, using black/no-data as exclusion
"""
# create an empty boolean array initialized as 'False' to store where
# values exist as a mask array.
nodata_map = numpy.zeros((ds.RasterYSize, ds.RasterXSize),
dtype=numpy.bool)
for idx in range(1, ds.RasterCount + 1):
band = ds.GetRasterBand(idx)
raster_data = band.ReadAsArray()
nodata = band.GetNoDataValue()
if nodata is None:
nodata = 0
# apply the output to the map
nodata_map |= (raster_data != nodata)
# create a temporary in-memory dataset and write the nodata mask
# into its single band
tmp_ds = create_mem(ds.RasterXSize + 2, ds.RasterYSize + 2, 1,
gdal.GDT_Byte)
copy_projection(ds, tmp_ds)
tmp_band = tmp_ds.GetRasterBand(1)
tmp_band.WriteArray(nodata_map.astype(numpy.uint8))
# create an OGR in memory layer to hold the created polygon
sr = osr.SpatialReference(); sr.ImportFromWkt(ds.GetProjectionRef())
ogr_ds = ogr.GetDriverByName('Memory').CreateDataSource('out')
layer = ogr_ds.CreateLayer('poly', sr.sr, ogr.wkbPolygon)
fd = ogr.FieldDefn('DN', ogr.OFTInteger)
layer.CreateField(fd)
# polygonize the mask band and store the result in the OGR layer
gdal.Polygonize(tmp_band, tmp_band, layer, 0)
if layer.GetFeatureCount() != 1:
# if there is more than one polygon, compute the minimum bounding polygon
geometry = ogr.Geometry(ogr.wkbPolygon)
while True:
feature = layer.GetNextFeature()
if not feature: break
geometry = geometry.Union(feature.GetGeometryRef())
# TODO: improve this for a better minimum bounding polygon
geometry = geometry.ConvexHull()
else:
# obtain geometry from the first (and only) layer
feature = layer.GetNextFeature()
geometry = feature.GetGeometryRef()
if geometry.GetGeometryType() != ogr.wkbPolygon:
raise RuntimeError("Error during poligonization. Wrong geometry "
"type.")
# check if reprojection to latlon is necessary
if not sr.IsGeographic():
dst_sr = osr.SpatialReference(); dst_sr.ImportFromEPSG(4326)
try:
geometry.TransformTo(dst_sr.sr)
except RuntimeError:
geometry.Transform(osr.CoordinateTransformation(sr.sr, dst_sr.sr))
gt = ds.GetGeoTransform()
resolution = min(abs(gt[1]), abs(gt[5]))
simplification_value = self.simplification_factor * resolution
# simplify the polygon. the tolerance value is *really* vague
try:
# SimplifyPreserveTopology() available since OGR 1.9.0
geometry = geometry.SimplifyPreserveTopology(simplification_value)
except AttributeError:
# use GeoDjango bindings if OGR is too old
geometry = ogr.CreateGeometryFromWkt(GEOSGeometry(geometry.ExportToWkt()).simplify(simplification_value, True).wkt)
return geometry.ExportToWkt()
def generate_footprint_wkt(ds, simplification_factor=2):
""" Generate a fooptrint from a raster, using black/no-data as exclusion
"""
# create an empty boolean array initialized as 'False' to store where
# values exist as a mask array.
nodata_map = np.zeros((ds.RasterYSize, ds.RasterXSize), dtype=np.bool)
for idx in range(1, ds.RasterCount + 1):
band = ds.GetRasterBand(idx)
raster_data = band.ReadAsArray()
nodata = band.GetNoDataValue()
if nodata is None:
nodata = 0
# apply the output to the map
nodata_map |= (raster_data != nodata)
# create a temporary in-memory dataset and write the nodata mask
# into its single band
with temporary_dataset(ds.RasterXSize + 2, ds.RasterYSize + 2, 1,
gdal.GDT_Byte) as tmp_ds:
copy_projection(ds, tmp_ds)
tmp_band = tmp_ds.GetRasterBand(1)
tmp_band.WriteArray(nodata_map.astype(np.uint8))
# create an OGR in memory layer to hold the created polygon
sr = osr.SpatialReference()
sr.ImportFromWkt(ds.GetProjectionRef())
ogr_ds = ogr.GetDriverByName('Memory').CreateDataSource('out')
layer = ogr_ds.CreateLayer('poly', sr.sr, ogr.wkbPolygon)
fd = ogr.FieldDefn('DN', ogr.OFTInteger)
layer.CreateField(fd)
# polygonize the mask band and store the result in the OGR layer
gdal.Polygonize(tmp_band, tmp_band, layer, 0)
if layer.GetFeatureCount() != 1:
# if there is more than one polygon, compute the minimum bounding polygon
geometry = ogr.Geometry(ogr.wkbPolygon)
while True:
feature = layer.GetNextFeature()
if not feature:
break
geometry = geometry.Union(feature.GetGeometryRef())
# TODO: improve this for a better minimum bounding polygon
geometry = geometry.ConvexHull()
else:
# obtain geometry from the first (and only) layer
feature = layer.GetNextFeature()
geometry = feature.GetGeometryRef()
if geometry.GetGeometryType() not in (ogr.wkbPolygon, ogr.wkbMultiPolygon):
raise RuntimeError("Error during poligonization. Wrong geometry "
"type.")
# check if reprojection to latlon is necessary
if not sr.IsGeographic():
dst_sr = osr.SpatialReference()
dst_sr.ImportFromEPSG(4326)
try:
geometry.TransformTo(dst_sr.sr)
except RuntimeError:
geometry.Transform(osr.CoordinateTransformation(sr.sr, dst_sr.sr))
gt = ds.GetGeoTransform()
resolution = min(abs(gt[1]), abs(gt[5]))
simplification_value = simplification_factor * resolution
# simplify the polygon. the tolerance value is *really* vague
try:
# SimplifyPreserveTopology() available since OGR 1.9.0
geometry = geometry.SimplifyPreserveTopology(simplification_value)
except AttributeError:
# use GeoDjango bindings if OGR is too old
geometry = ogr.CreateGeometryFromWkt(
GEOSGeometry(geometry.ExportToWkt()).simplify(
simplification_value, True).wkt)
return geometry.ExportToWkt()
