def test_create_image(self):
import numpy
array = numpy.array(
[[
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
],
[
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
],
[
1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0,
1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1
],
[
1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0,
1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1
],
[
1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1
],
[
1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0,
1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1
],
[
1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0,
1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1
],
[
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
],
[
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
],
[
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
]])
red = (1 - array) * 1000
green = (1 - array) * 1000
blue = array * 1000
create_raster(
create_filename(getattr(SETTINGS, 'TEST_FOLDER'), 'single.tif'),
array)
create_raster(
create_filename(getattr(SETTINGS, 'TEST_FOLDER'), 'multi.tif'),
numpy.array([red, green, blue]))
def test_create_image(self):
import numpy
array = numpy.array(
[
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1],
[1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1],
[1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1],
[1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1],
[1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
]
)
red = (1 - array) * 1000
green = (1 - array) * 1000
blue = array * 1000
create_raster(create_file_name(getattr(SETTINGS, "TEST_FOLDER"), "single.tif"), array)
create_raster(create_file_name(getattr(SETTINGS, "TEST_FOLDER"), "multi.tif"), numpy.array([red, green, blue]))
def handle(self, **options):
"""
This process will call the change detection process from a set of two
individual images. It will perform the harmonization and the multivariate
alteration detection on the images. It will then perform a maximum
correlation factor on them and work with the resulting bands.
"""
image_a = options["ima"][0]
image_b = options["imb"][0]
output_image = options["output"][0]
LOGGER.info(
"Image %s will be compared against image %s. Output will be available" " at %s.",
image_a,
image_b,
output_image,
)
gdal_format = "GTiff"
image_a_data_class = raster.Data(image_a, gdal_format)
image_b_data_class = raster.Data(image_b, gdal_format)
# TODO : remove references to class harmonized
harmonized_class = harmonized.Data(image_a_data_class, image_b_data_class)
if harmonized_class:
data_shape_harmonized = harmonized_class.get_attribute(harmonized.DATA_SHAPE)
width, height, bands = data_shape_harmonized
geotransform_harmonized = harmonized_class.get_attribute(harmonized.GEOTRANSFORM)
projection_harmonized = harmonized_class.get_attribute(harmonized.PROJECTION)
image_a_data_array, image_b_data_array = harmonized_class.harmonized_arrays(
image_a_data_class, image_b_data_class
)
imad_class = imad.Transformation([image_a_data_array, image_b_data_array])
imad_class.execute()
maf_class = maf.Transformation(imad_class.output)
maf_class.execute()
mad_result = imad_class.output
maf_result = maf_class.output
pdf_file = create_file_name(getattr(SETTINGS, "TEST_FOLDER"), "maf_pdf.png")
thresholds = calc_threshold_grid(maf_result, pdf_file)
class_result = recode_classes_grid(maf_result, thresholds)
LOGGER.debug("mad_result.shape: %s", mad_result.shape)
LOGGER.debug("maf_result.shape: %s", maf_result.shape)
LOGGER.debug("class_result.shape: %s", class_result.shape)
mad_output_file = create_file_name(getattr(SETTINGS, "TEST_FOLDER"), "mad.tif")
maf_outputfile = create_file_name(getattr(SETTINGS, "TEST_FOLDER"), "maf.tif")
class_outputfile = create_file_name(getattr(SETTINGS, "TEST_FOLDER"), "class.tif")
create_raster(mad_output_file, mad_result, geotransform_harmonized, projection_harmonized)
create_raster(maf_outputfile, maf_result, geotransform_harmonized, projection_harmonized)
create_raster(class_outputfile, class_result, geotransform_harmonized, projection_harmonized)
print "Output written in: %s" % mad_output_file
print "Shape is ", imad_class.output.shape