def generateCompositeProducts(self, dataset, out_path):
"""
Placeholder
"""
# create product path
product_path = os.path.join(out_path, 'composite')
if not os.path.exists(product_path):
os.makedirs(product_path, 0o755)
# get channel images pertaining to product
print('Creating composite products: {}'.format(product_path))
for product in self._products['composite']:
rgb = []
for index in product['channels']:
rgb.append(self.getChannelData(dataset, index))
# save rgb image
rgb_pathname = os.path.join(product_path, product['name'] + '.jpg')
save_rgb(rgb_pathname, np.dstack(rgb), stretch=(0.02, 0.98))
# save decorrelation stretch version of rgb image
dcs_pathname = rgb_pathname.replace('.jpg', '-dcs.jpg')
execute(
os.path.join(os.path.dirname(sys.path[0]), '../bin/dstretch'),
[rgb_pathname, dcs_pathname])
# copy dcs image into geotiff
self.writeGeoImage(dcs_pathname, dataset['srs'])
return
def generatePrincipalComponentProducts(self, dataset, out_path):
"""
Placeholder
"""
# create product path
product_path = os.path.join(out_path, 'pca')
if not os.path.exists(product_path):
os.makedirs(product_path, 0o755)
# get channel images pertaining to product
print('Creating principal component products: {}'.format(product_path))
for product in self._products['pca']:
channels = []
for index in product['channels']:
channels.append(self.getChannelData(dataset, index))
img = np.dstack(channels)
# compute pca transformation
pc = principal_components(img)
img_pc = pc.transform(img)
# save rgb image
rgb_pathname = os.path.join(product_path, product['name'] + '.jpg')
save_rgb(rgb_pathname, img_pc[:, :, :3], stretch=(0.05, 0.95))
# save decorrelation stretch version of rgb image
dcs_pathname = rgb_pathname.replace('.jpg', '-dcs.jpg')
execute(
os.path.join(os.path.dirname(sys.path[0]), '../bin/dstretch'),
[rgb_pathname, dcs_pathname])
# copy dcs image into geotiff
self.writeGeoImage(dcs_pathname, dataset['srs'])
return
def getPansharpenImage( self, pathname ):
"""
Placeholder
"""
# get masked datasets
code = None
datasets = self.getMaskedDatasets ( os.path.dirname( pathname ) )
if len( datasets ) == 3:
# define pansharpen options
args = [ os.path.join( os.path.dirname( pathname ), 'B8_merge.tif' ) ] + datasets + [ pathname ]
options = [ '-co', 'PHOTOMETRIC=RGB', '-co', 'COMPRESS=DEFLATE', '-nodata', '0' ]
# manage execution of gdal pansharpen
out, err, code = execute( 'gdal_pansharpen.py', args + options )
return code
def checkScene(pathname):
sceneOK = False
# decompress product file
path = os.path.dirname(pathname)
out, err, code = ps.execute('unzip', ["-o", "-d", path, pathname])
if (code <= 1):
filelist = fio.getFileList('*.tiff', path)
if len(filelist) > 0:
# open scene and extract gcps
in_ds = gdal.Open(filelist[0])
gcps = in_ds.GetGCPs()
min_x = 180
max_x = -180
for gcp in gcps:
min_x = min(min_x, gcp.GCPX)
max_x = max(max_x, gcp.GCPX)
# large longitude difference when crossing antimeridian
if max_x - min_x < 10:
sceneOK = True
else:
print('... scene crosses antimeridan - skipping: {}'.format(
pathname))
# housekeeping of raw zip sub-folder
zip_path = os.path.splitext(pathname)[0]
if (os.path.exists(zip_path)):
shutil.rmtree(zip_path)
return sceneOK
# generate scene list from arguments
scene_list = getSceneList(args, product)
if len(scene_list) > 0:
# for each scene
for scene in scene_list:
print('processing scene: ' + scene)
# generate multi-band vrt
out_pathname = compileVrt(scene, product)
if out_pathname is not None:
# execute ingestion
out, err, code = ps.execute(
'/sac/bin/DBIngest-Raster.py', [
out_pathname, args.cfg,
product.attributes["name"].value
])
code = 0
if code != 0:
print(str(err))
else:
print('... ok!')
else:
# missing files
print('... unable to create vrt for scene: ' + scene)
else:
# configuration issue
print('... no files added to scene list for path: ' + args.path)