def preprocess(self):
'''
Top of atmosphere is calculated and persisted into a file. Then a cloud
mask is created with the given algorithm.
'''
solar_zenith = self.get_sensor().parser.get_attribute(rapideye.SOLAR_ZENITH)
data_acquisition_date = self.get_sensor().parser.get_attribute(rapideye.ACQUISITION_DATE)
solar_azimuth = self.get_sensor().parser.get_attribute(rapideye.SOLAR_AZIMUTH)
geotransform = self.get_raster().get_attribute(raster.GEOTRANSFORM)
data = self.get_raster().read_data_file_as_array()
sun_earth_distance = calculate_distance_sun_earth(data_acquisition_date)
top_of_atmosphere_data = calculate_toa_rapideye(calculate_rad_rapideye(data), sun_earth_distance, solar_zenith)
top_of_atmosphere_directory = create_file_name(get_parent(self.path), 'TOA')
create_directory_path(top_of_atmosphere_directory)
output_file = create_file_name(top_of_atmosphere_directory, get_base_name(self.get_files()[2]) + '_toa.tif') # TODO: change [2] in self.get_files()[2]
create_raster_from_reference(output_file,
top_of_atmosphere_data,
self.file_dictionary[_IMAGE],
data_type=NumericTypeCodeToGDALTypeCode(numpy.float32)
)
LOGGER.debug('Top of atmosphere file was created.')
cloud_output_file = create_file_name(top_of_atmosphere_directory, get_base_name(self.get_files()[2]) + '_cloud.tif')
if self.algorithm == ANOMALY_DETECTION:
LOGGER.debug('Cloud mask by anomaly detection process.')
clouds = self.anomaly_detection_cloud_mask(top_of_atmosphere_data, cloud_output_file, solar_zenith, solar_azimuth, geotransform)
elif self.algorithm == TIME_SERIES:
LOGGER.debug('Cloud mask by reference with time series process.')
tile_id = self.get_sensor().get_attribute(TILE_ID)
clouds = self.masking_with_time_series(data, cloud_output_file, solar_zenith, solar_azimuth, geotransform, tile_id)
create_raster_from_reference(cloud_output_file,
clouds,
self.file_dictionary[_IMAGE],
data_type=NumericTypeCodeToGDALTypeCode(numpy.float32)
)
LOGGER.info('Cloud mask was created.')
def handle(self, **options):
output = options['output'][0]
for image_path in options['path']:
print image_path
basename = '%s.tif' % get_base_name(image_path)
print basename
target = create_file_name(output, basename)
print target
start_time = time.time()
#self.method_by_block(image_path, target)
self.mask_iterating_values(image_path, target)
print("--- %s seconds ---" % (time.time() - start_time))
print 'Dataset was written.'
def get_thumbnail_with_path(self, thumbnail_path):
'''
Creates a thumbnail for the scene in true color.
'''
from subprocess import call
thumnail_directory = create_file_name(thumbnail_path, 'thumbnail')
create_directory_path(thumnail_directory)
filename = self.file_dictionary[_BROWSE]
thumbnail = create_file_name(thumnail_directory, '%s.jpg' % get_base_name(filename))
resize_command = ['/Library/Frameworks/GDAL.framework/Programs/gdal_translate', filename, '-of', 'JPEG', thumbnail]
call(resize_command)
def handle(self, **options):
'''
This is the code that does the ingestion.
'''
interest_band = 1
for image_path in options['path']:
print image_path
ds = gdal.Open(image_path)
bands = ds.RasterCount
geotransform = ds.GetGeoTransform()
x_resolution = geotransform[1]
y_resolution = geotransform[5]
pixel_area = abs(x_resolution * y_resolution)
array = numpy.array(ds.GetRasterBand(interest_band).ReadAsArray())
print numpy.unique(array)
flat = numpy.ravel(array)
length = len(flat)
parent = get_parent(image_path)
basename = '%s.txt' % get_base_name(image_path)
target = create_file_name(parent, basename)
count = 1
values = {}
progress = 0
for value in flat:
count = count + 1
if not values.get(value):
values[value] = 1
else:
values[value] = values[value] + 1
if count % 1000000 == 0:
aux = progress
progress = math.floor(100 * count / float(length))
if not aux == progress:
print str(int(progress)) + '%\r'
added = self.add_up(INITIAL_ARRAY, values)
area = self.transform_to_area(added, pixel_area)
with open(target, "a") as f:
json.dump(area, f)
