def ingest_reflectance_image(mrgeo, filtered_metadata, band, image_name):
cnt = 0
red_refl_paths = mrgeo.gateway.new_array(mrgeo.gateway.jvm.String,
len(filtered_metadata))
driver = gdal.GetDriverByName('GTiff')
for key in filtered_metadata:
md = filtered_metadata[key]
red_ds = gdal.Open(md[band], GA_ReadOnly)
bqa_ds = gdal.Open(md["BQA"], GA_ReadOnly)
ndv, xsize, ysize, GeoT, Projection, data_type = GetGeoInfo(md[band])
if ndv is None:
ndv = 0
red_band = red_ds.GetRasterBand(1)
red_data = red_band.ReadAsArray()
bqa_band = bqa_ds.GetRasterBand(1)
bqa_data = bqa_band.ReadAsArray()
data_type = gdal.GDT_Float32
red_data = numpy.float32(red_data)
# Change nodata value to NaN since we're changing the type to Float32
red_data[red_data == ndv] = RasterMapOp.nan()
ndv = RasterMapOp.nan()
toa_reflectance(red_data, bqa_data, md, band)
# Now turn the array into a GTiff.
ntf = tempfile.NamedTemporaryFile(suffix=".tif", delete=False)
red_temp_file = ntf.name
CreateGeoTiff(red_temp_file, red_data, driver, ndv, xsize, ysize, GeoT,
Projection, data_type)
red_refl_paths[cnt] = red_temp_file
cnt += 1
# close the datasets
red_ds = None
bqa_ds = None
red = mrgeo.ingest_image(red_refl_paths)
red.save(image_name)
for rf in red_refl_paths:
os.remove(rf)
return red
def ingest_reflectance_image(mrgeo, filtered_metadata, band, image_name):
cnt = 0
red_refl_paths = mrgeo.gateway.new_array(mrgeo.gateway.jvm.String, len(filtered_metadata))
driver = gdal.GetDriverByName('GTiff')
for key in filtered_metadata:
md = filtered_metadata[key]
red_ds = gdal.Open(md[band], GA_ReadOnly)
bqa_ds = gdal.Open(md["BQA"], GA_ReadOnly)
ndv, xsize, ysize, GeoT, Projection, data_type = GetGeoInfo(md[band])
if ndv is None:
ndv = 0
red_band = red_ds.GetRasterBand(1)
red_data = red_band.ReadAsArray()
bqa_band = bqa_ds.GetRasterBand(1)
bqa_data = bqa_band.ReadAsArray()
data_type = gdal.GDT_Float32
red_data = numpy.float32(red_data)
# Change nodata value to NaN since we're changing the type to Float32
red_data[red_data == ndv] = RasterMapOp.nan()
ndv = RasterMapOp.nan()
toa_reflectance(red_data, bqa_data, md, band)
# Now turn the array into a GTiff.
ntf = tempfile.NamedTemporaryFile(suffix=".tif", delete=False)
red_temp_file = ntf.name
CreateGeoTiff(red_temp_file, red_data, driver, ndv,
xsize, ysize, GeoT, Projection, data_type)
red_refl_paths[cnt] = red_temp_file
cnt += 1
# close the datasets
red_ds = None
bqa_ds = None
red = mrgeo.ingest_image(red_refl_paths)
red.save(image_name)
for rf in red_refl_paths:
os.remove(rf)
return red
def createRasterMapOp(self,
tileIds,
zoomLevel,
tileSize,
name='',
imageNoData=None,
imageInitialData=None):
if imageNoData is None:
imageNoData = self._defaultImageNoData
if imageInitialData is None:
imageInitialData = self._defaultImageInitialData
# Capture image initial and nodata to compare against
self._imageInitialData = imageInitialData
self._imageNoData = imageNoData
mapop = self._rasterMapOpTestSupport.createRasterMapOp(
tileIds, zoomLevel, tileSize, name, imageNoData, imageInitialData)
return RasterMapOp(mapop=mapop,
gateway=self._mrgeo.gateway,
context=self._sparkContext)
def createRasterMapOpWithBounds(self,
tileIds,
zoomLevel,
tileSize,
w,
s,
e,
n,
name='',
imageNoData=None,
imageInitialData=None):
if imageNoData is None:
imageNoData = self._defaultImageNoData
if imageInitialData is None:
imageInitialData = self._defaultImageInitialData
bounds = self._jvm.Bounds(w, s, e, n)
mapop = self._rasterMapOpTestSupport.createRasterMapOpWithBounds(
tileIds, zoomLevel, tileSize, name, bounds, imageNoData,
imageInitialData)
return RasterMapOp(mapop=mapop,
gateway=self._mrgeo.gateway,
context=self._sparkContext)
with open(landsat['json']) as metafile:
metadata = json.load(metafile)
mrgeo = MrGeo()
# mrgeo.usedebug()
mrgeo.start()
bqa = mrgeo.ingest_image(landsat['bqa'])
# bqa.export('/data/export/landsat-bqa.tif', singleFile=True)
cloud_mask = bqa < 32768 # 0 where clouds, 1 where no clouds
# cloud_mask.export('/data/export/landsat-clouds.tif', singleFile=True)
red = cloud_mask.con(positiveRaster=mrgeo.ingest_image(landsat[4]),
negativeConst=RasterMapOp.nan())
green = cloud_mask.con(positiveRaster=mrgeo.ingest_image(landsat[3]),
negativeConst=RasterMapOp.nan())
blue = cloud_mask.con(positiveRaster=mrgeo.ingest_image(landsat[2]),
negativeConst=RasterMapOp.nan())
# red = mrgeo.ingest_image(landsat[4])
# green = mrgeo.ingest_image(landsat[3])
# blue = mrgeo.ingest_image(landsat[2])
red.save('landsat-dn-r')
# red.export('/data/export/landsat-dn-r.tif', singleFile=True)
# green.export('/data/export/landsat-dn-g.tif', singleFile=True)
# blue.export('/data/export/landsat-dn-b.tif', singleFile=True)
rgb = red.bandcombine(green, blue)
rgb.export('/data/export/landsat-rgb-dn.tif', singleFile=True)
with open(landsat["json"]) as metafile:
metadata = json.load(metafile)
mrgeo = MrGeo()
# mrgeo.usedebug()
mrgeo.start()
bqa = mrgeo.ingest_image(landsat["bqa"])
# bqa.export('/data/export/landsat-bqa.tif', singleFile=True)
cloud_mask = bqa < 32768 # 0 where clouds, 1 where no clouds
# cloud_mask.export('/data/export/landsat-clouds.tif', singleFile=True)
red = cloud_mask.con(positiveRaster=mrgeo.ingest_image(landsat[4]), negativeConst=RasterMapOp.nan())
green = cloud_mask.con(positiveRaster=mrgeo.ingest_image(landsat[3]), negativeConst=RasterMapOp.nan())
blue = cloud_mask.con(positiveRaster=mrgeo.ingest_image(landsat[2]), negativeConst=RasterMapOp.nan())
# red = mrgeo.ingest_image(landsat[4])
# green = mrgeo.ingest_image(landsat[3])
# blue = mrgeo.ingest_image(landsat[2])
red.save("landsat-dn-r")
# red.export('/data/export/landsat-dn-r.tif', singleFile=True)
# green.export('/data/export/landsat-dn-g.tif', singleFile=True)
# blue.export('/data/export/landsat-dn-b.tif', singleFile=True)
rgb = red.bandcombine(green, blue)
rgb.export("/data/export/landsat-rgb-dn.tif", singleFile=True)
r_rad = toa_radiance(red, metadata, 4)
