def get_thumbnail(self):
'''
Creates a thumbnail for the scene in true color.
'''
from subprocess import call
file_1 = self.file_dictionary[_BASE %
(self.get_mission(), 'B1[0-9].TIF')]
file_2 = self.file_dictionary[_BASE %
(self.get_mission(), 'B2[0-9].TIF')]
file_3 = self.file_dictionary[_BASE %
(self.get_mission(), 'B3[0-9].TIF')]
parent_directory = get_parent(self.path)
thumnail_directory = create_filename(parent_directory, 'thumbnail')
create_directory_path(thumnail_directory)
parent_directory
filename = create_filename(thumnail_directory, 'vrt.tif')
merge_command = [
'/Library/Frameworks/GDAL.framework/Programs/gdalbuildvrt',
'-separate', '-o', filename, file_3, file_2, file_1
]
call(merge_command)
thumbnail = create_filename(thumnail_directory, 'thumbnail.jpg')
resize_command = [
'/Library/Frameworks/GDAL.framework/Programs/gdal_translate',
filename, '-of', 'JPEG', '-outsize', '5%', '5%', thumbnail
]
call(resize_command)
def test_maf_image(self):
'''
Perform a maf transformation with the result of imad transformation
'''
from madmex.mapper.data import raster
from madmex.transformation import maf
gdal_format = "GTiff"
#image_imad = '/Users/erickpalacios/test_imad_pair_images/result_change_detection.tif'
#image_imad = '/Users/erickpalacios/Documents/CONABIO/Tareas/1_DeteccionCambiosSpot/2_AdapterParaDeteccionDeCambios/Tarea2/res12CambiosMadTransfJulian/593_318_031210_SP5_593_318_021114_SP5_mad.tif'
image_imad = '/LUSTRE/MADMEX/staging/antares_test/test_imad_pair_images/result_mad_prueba.tif'
image_imad_class = raster.Data(image_imad, gdal_format)
width, height, bands = image_imad_class.get_attribute(
raster.DATA_SHAPE)
print 'bands:'
print bands
geotransform = image_imad_class.get_attribute(raster.GEOTRANSFORM)
projection = image_imad_class.get_attribute(raster.PROJECTION)
maf_class = maf.Transformation(
image_imad_class.read_data_file_as_array())
maf_class.execute()
output = get_parent(image_imad)
output += '/result_maf.tif'
print output
image_maf = image_imad_class.create_from_reference(
output, width, height, bands - 1, geotransform, projection)
print 'write'
image_imad_class.write_raster(image_maf, maf_class.output)
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_filename(get_parent(self.path),
'TOA')
create_directory_path(top_of_atmosphere_directory)
output_file = create_filename(
top_of_atmosphere_directory,
get_basename(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_filename(
top_of_atmosphere_directory,
get_basename(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 test_calculate_ndvi(self):
from madmex.util import get_parent
from madmex.mapper.data import raster
from madmex.processing.raster import calculate_ndvi
import numpy
image = "/Users/erickpalacios/Documents/CONABIO/Tareas/4_RedisenioMadmex/5_Clasificacion/rapideyemapgrid/folder_test/rapideye/1649125/2014/2014-01-23/L3A/1649125_2014-01-23_RE4_3A_301519.tif"
gdal_format = "GTiff"
data_class = raster.Data(image, gdal_format)
array = data_class.read_data_file_as_array()
width, height, bands = data_class.get_attribute(raster.DATA_SHAPE)
feature_bands = numpy.zeros([2, width, height])
feature_bands[0, :, :] = calculate_ndvi(array[4, :, :], array[2, :, :])
feature_bands[1, :, :] = calculate_ndvi(array[3, :, :], array[2, :, :])
out = get_parent(image) + 'result_ndvi'
raster.create_raster_tiff_from_reference(data_class.metadata, out, feature_bands)
def test_calculate_ndvi(self):
from madmex.util import get_parent
from madmex.mapper.data import raster
from madmex.processing.raster import calculate_ndvi
import numpy
image = "/Users/erickpalacios/Documents/CONABIO/Tareas/4_RedisenioMadmex/5_Clasificacion/rapideyemapgrid/folder_test/rapideye/1649125/2014/2014-01-23/L3A/1649125_2014-01-23_RE4_3A_301519.tif"
gdal_format = "GTiff"
data_class = raster.Data(image, gdal_format)
array = data_class.read_data_file_as_array()
width, height, bands = data_class.get_attribute(raster.DATA_SHAPE)
feature_bands = numpy.zeros([2, width, height])
feature_bands[0, :, :] = calculate_ndvi(array[4, :, :], array[2, :, :])
feature_bands[1, :, :] = calculate_ndvi(array[3, :, :], array[2, :, :])
out = get_parent(image) + 'result_ndvi'
raster.create_raster_tiff_from_reference(data_class.metadata, out,
feature_bands)
def get_thumbnail(self):
'''
Creates a thumbnail for the scene in true color.
'''
from subprocess import call
file_1 = self.file_dictionary[_BASE % (self.get_mission(), 'B1[0-9].TIF')]
file_2 = self.file_dictionary[_BASE % (self.get_mission(), 'B2[0-9].TIF')]
file_3 = self.file_dictionary[_BASE % (self.get_mission(), 'B3[0-9].TIF')]
parent_directory = get_parent(self.path)
thumnail_directory = create_file_name(parent_directory, 'thumbnail')
create_directory_path(thumnail_directory)
parent_directory
filename = create_file_name(thumnail_directory, 'vrt.tif')
merge_command = ['/Library/Frameworks/GDAL.framework/Programs/gdalbuildvrt', '-separate', '-o', filename, file_3, file_2, file_1]
call(merge_command)
thumbnail = create_file_name(thumnail_directory, 'thumbnail.jpg')
resize_command = ['/Library/Frameworks/GDAL.framework/Programs/gdal_translate', filename, '-of', 'JPEG', '-outsize', '5%', '5%', thumbnail]
call(resize_command)
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):
'''
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)
def test_maf_image(self):
'''
Perform a maf transformation with the result of imad transformation
'''
from madmex.mapper.data import raster
from madmex.transformation import maf
gdal_format = "GTiff"
#image_imad = '/Users/erickpalacios/test_imad_pair_images/result_change_detection.tif'
#image_imad = '/Users/erickpalacios/Documents/CONABIO/Tareas/1_DeteccionCambiosSpot/2_AdapterParaDeteccionDeCambios/Tarea2/res12CambiosMadTransfJulian/593_318_031210_SP5_593_318_021114_SP5_mad.tif'
image_imad = '/LUSTRE/MADMEX/staging/antares_test/test_imad_pair_images/result_mad_prueba.tif'
image_imad_class = raster.Data(image_imad, gdal_format)
width, height , bands = image_imad_class.get_attribute(raster.DATA_SHAPE)
print 'bands:'
print bands
geotransform = image_imad_class.get_attribute(raster.GEOTRANSFORM)
projection = image_imad_class.get_attribute(raster.PROJECTION)
maf_class = maf.Transformation(image_imad_class.read_data_file_as_array())
maf_class.execute()
output = get_parent(image_imad)
output+= '/result_maf.tif'
print output
image_maf = image_imad_class.create_from_reference(output, width, height, bands-1, geotransform, projection)
print 'write'
image_imad_class.write_raster(image_maf, maf_class.output)
def get_thumbnail(self):
'''
Creates a thumbnail for the scene in true color.
'''
parent_directory = get_parent(self.path)
self.get_thumbnail_with_path(parent_directory)
def get_thumbnail(self):
'''
Creates a thumbnail for the scene in true color.
'''
parent_directory = get_parent(self.path)
self.get_thumbnail_with_path(parent_directory)
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.')
if __name__ == '__main__':
#path = '/Users/amaury/Documents/rapideye/df/1447813/2012/2012-03-14/l3a'
#bundle = Bundle(path)
#print bundle.get_files()
#print bundle.can_identify()
image = "/Users/agutierrez/Documents/rapideye/df/1447813/2012/2012-03-14/l3a/2012-03-14T182106_RE2_3A-NAC_11040070_149070.tif"
gdal_format = "GTiff"
data_class = raster.Data(image, gdal_format)
array = data_class.read_data_file_as_array()
width, height, bands = data_class.get_attribute(raster.DATA_SHAPE)
feature_bands = numpy.zeros([2, width, height])
from madmex.processing.raster import calculate_ndvi
feature_bands[0, :, :] = calculate_ndvi(array[4, :, :], array[2, :, :])
feature_bands[1, :, :] = calculate_ndvi(array[3, :, :], array[2, :, :])
out1 = get_parent(image) + 'result_ndvi_1.tif'
out2 = get_parent(image) + 'result_ndvi_2.tif'
create_raster_from_reference(out1, feature_bands[0, :, :], image)
create_raster_from_reference(out2, feature_bands[1, :, :], image)
print 'Done'