def create_reference_array(images_references_paths):
'''
New implementation of create reference image, performance was sacrificed in
favor of clarity. This method creates a reference image by calculating the
median of a set of images pixelwise.
'''
from madmex.mapper.bundle.rapideye import Bundle
import madmex.mapper.sensor.rapideye as sensor
identified = []
bands = None
rows = None
columns = None
for image_path in images_references_paths:
bundle = Bundle(image_path)
if bundle.can_identify():
if not bands:
bands = bundle.get_sensor().get_attribute(sensor.BANDS)
if not rows:
rows = bundle.get_sensor().get_attribute(sensor.ROWS)
if not columns:
columns = bundle.get_sensor().get_attribute(sensor.COLUMNS)
identified.append(bundle)
LOGGER.debug('Bands: %d, Rows: %d, Columns: %d.', bands, rows, columns)
number = len(identified)
LOGGER.debug('Number of identified images: %d', number)
my_array = numpy.empty((number, bands, rows, columns))
for index in range(number):
my_array[index] = identified[index].get_raster().read_data_file_as_array().astype(numpy.float)
medians = numpy.empty((bands, rows, columns))
for band in range(bands):
medians[band] = numpy.median(my_array[:, band, :, :], axis=0)
return medians
def create_reference_array(images_references_paths):
'''
New implementation of create reference image, performance was sacrificed in
favor of clarity. This method creates a reference image by calculating the
median of a set of images pixelwise.
'''
from madmex.mapper.bundle.rapideye import Bundle
import madmex.mapper.sensor.rapideye as sensor
identified = []
bands = None
rows = None
columns = None
for image_path in images_references_paths:
bundle = Bundle(image_path)
if bundle.can_identify():
if not bands:
bands = bundle.get_sensor().get_attribute(sensor.BANDS)
if not rows:
rows = bundle.get_sensor().get_attribute(sensor.ROWS)
if not columns:
columns = bundle.get_sensor().get_attribute(sensor.COLUMNS)
identified.append(bundle)
LOGGER.debug('Bands: %d, Rows: %d, Columns: %d.', bands, rows, columns)
number = len(identified)
LOGGER.debug('Number of identified images: %d', number)
my_array = numpy.empty((number, bands, rows, columns))
for index in range(number):
my_array[index] = identified[index].get_raster(
).read_data_file_as_array().astype(numpy.float)
medians = numpy.empty((bands, rows, columns))
for band in range(bands):
medians[band] = numpy.median(my_array[:, band, :, :], axis=0)
return medians
def handle(self, **options):
import time
start_time = time.time()
path = options['path'][0]
print path
features_array = []
bundle = Bundle(path)
raster_path = bundle.file_dictionary[_IMAGE]
data_array = open_handle(raster_path)
data_array = numpy.array(data_array)
print data_array, len(data_array)
print len(data_array[data_array != 0])
for i in range(data_array.shape[0]):
print 'band: %s' % (i + 1)
band = data_array[i, :, :].ravel()
band = band[band != 0]
features_array.append(numpy.nanpercentile(band, 10))
features_array.append(numpy.nanpercentile(band, 25))
features_array.append(numpy.nanpercentile(band, 50))
features_array.append(numpy.nanpercentile(band, 75))
features_array.append(numpy.nanpercentile(band, 90))
features_array.append(numpy.mean(band))
features_array.append(numpy.min(band) * 1.0)
features_array.append(numpy.max(band) * 1.0)
geotransform = get_geotransform(raster_path)
features_array.append(geotransform[0])
features_array.append(geotransform[3])
print features_array
features_array.append((bundle.get_aquisition_date() -
datetime.datetime(1970, 1, 1)).total_seconds())
tile_id = bundle.get_sensor().get_attribute(TILE_ID)
features = RapideyeFeatures(band_1_quant_10=features_array[0],
band_1_quant_25=features_array[1],
band_1_quant_50=features_array[2],
band_1_quant_75=features_array[3],
band_1_quant_90=features_array[4],
band_1_mean=features_array[5],
band_1_min=features_array[6],
band_1_max=features_array[7],
band_2_quant_10=features_array[8],
band_2_quant_25=features_array[9],
band_2_quant_50=features_array[10],
band_2_quant_75=features_array[11],
band_2_quant_90=features_array[12],
band_2_mean=features_array[13],
band_2_min=features_array[14],
band_2_max=features_array[15],
band_3_quant_10=features_array[16],
band_3_quant_25=features_array[17],
band_3_quant_50=features_array[18],
band_3_quant_75=features_array[19],
band_3_quant_90=features_array[20],
band_3_mean=features_array[21],
band_3_min=features_array[22],
band_3_max=features_array[23],
band_4_quant_10=features_array[24],
band_4_quant_25=features_array[25],
band_4_quant_50=features_array[26],
band_4_quant_75=features_array[27],
band_4_quant_90=features_array[28],
band_4_mean=features_array[29],
band_4_min=features_array[30],
band_4_max=features_array[31],
band_5_quant_10=features_array[32],
band_5_quant_25=features_array[33],
band_5_quant_50=features_array[34],
band_5_quant_75=features_array[35],
band_5_quant_90=features_array[36],
band_5_mean=features_array[37],
band_5_min=features_array[38],
band_5_max=features_array[39],
top=features_array[40],
left=features_array[41],
time=features_array[42],
footprint=tile_id,
path=raster_path)
klass = sessionmaker(bind=ENGINE, autoflush=False)
session = klass()
session.add(features)
session.commit()
print tile_id
print features_array
print len(features_array)
print("--- %s seconds ---" % (time.time() - start_time))
def handle(self, **options):
import time
start_time = time.time()
path = options['path'][0]
print path
features_array = []
bundle = Bundle(path)
raster_path = bundle.file_dictionary[_IMAGE]
data_array = open_handle(raster_path)
data_array = numpy.array(data_array)
print data_array, len(data_array)
print len(data_array[data_array!=0])
for i in range(data_array.shape[0]):
print 'band: %s' % (i + 1)
band = data_array[i,:,:].ravel()
band = band[band!=0]
features_array.append(numpy.nanpercentile(band,10))
features_array.append(numpy.nanpercentile(band,25))
features_array.append(numpy.nanpercentile(band,50))
features_array.append(numpy.nanpercentile(band,75))
features_array.append(numpy.nanpercentile(band,90))
features_array.append(numpy.mean(band))
features_array.append(numpy.min(band) * 1.0)
features_array.append(numpy.max(band) * 1.0)
geotransform = get_geotransform(raster_path)
features_array.append(geotransform[0])
features_array.append(geotransform[3])
print features_array
features_array.append((bundle.get_aquisition_date() - datetime.datetime(1970, 1, 1)).total_seconds())
tile_id = bundle.get_sensor().get_attribute(TILE_ID)
features = RapideyeFeatures(band_1_quant_10=features_array[0],
band_1_quant_25=features_array[1],
band_1_quant_50=features_array[2],
band_1_quant_75=features_array[3],
band_1_quant_90=features_array[4],
band_1_mean=features_array[5],
band_1_min=features_array[6],
band_1_max=features_array[7],
band_2_quant_10=features_array[8],
band_2_quant_25=features_array[9],
band_2_quant_50=features_array[10],
band_2_quant_75=features_array[11],
band_2_quant_90=features_array[12],
band_2_mean=features_array[13],
band_2_min=features_array[14],
band_2_max=features_array[15],
band_3_quant_10=features_array[16],
band_3_quant_25=features_array[17],
band_3_quant_50=features_array[18],
band_3_quant_75=features_array[19],
band_3_quant_90=features_array[20],
band_3_mean=features_array[21],
band_3_min=features_array[22],
band_3_max=features_array[23],
band_4_quant_10=features_array[24],
band_4_quant_25=features_array[25],
band_4_quant_50=features_array[26],
band_4_quant_75=features_array[27],
band_4_quant_90=features_array[28],
band_4_mean=features_array[29],
band_4_min=features_array[30],
band_4_max=features_array[31],
band_5_quant_10=features_array[32],
band_5_quant_25=features_array[33],
band_5_quant_50=features_array[34],
band_5_quant_75=features_array[35],
band_5_quant_90=features_array[36],
band_5_mean=features_array[37],
band_5_min=features_array[38],
band_5_max=features_array[39],
top=features_array[40],
left=features_array[41],
time=features_array[42],
footprint=tile_id,
path=raster_path)
klass = sessionmaker(bind=ENGINE, autoflush=False)
session = klass()
session.add(features)
session.commit()
print tile_id
print features_array
print len(features_array)
print("--- %s seconds ---" % (time.time() - start_time))
