def create_shadow_corrected_image(casi_normalized, casi, shadow_map):
ratio = calculate_shadow_ratio(casi,
shadow_map,
numpy.logical_not(shadow_map).astype(int))
add_coef = numpy.repeat(numpy.expand_dims(shadow_map, axis=2), casi_normalized.shape[2], axis=2) * (ratio - 1)
final_casi = casi + (casi * add_coef)
imwrite("muulf_hsi_shadow_corrected.tif", final_casi.astype(numpy.float32), planarconfig='contig')
def load_shadow_map(self, neighborhood, data_set):
shadow_map = imread(self.get_model_base_dir() + 'muulf_shadow_map.tif')
shadow_map = numpy.pad(shadow_map, neighborhood, mode='symmetric')
shadow_ratio = None
if data_set is not None:
shadow_ratio = calculate_shadow_ratio(
data_set.casi, shadow_map,
numpy.logical_not(shadow_map).astype(int))
return shadow_map, shadow_ratio
def load_shadow_map(self, neighborhood, data_set):
shadow_map = imread(self.get_model_base_dir() + 'shadow_map.tif')
shadow_map = numpy.pad(shadow_map, neighborhood, mode='symmetric')
shadow_ratio = None
if data_set is not None:
shadow_ratio = calculate_shadow_ratio(
data_set.concrete_data[:, :,
0:data_set.concrete_data.shape[2] - 1],
shadow_map,
numpy.logical_not(shadow_map).astype(int))
shadow_ratio = numpy.append(shadow_ratio,
[1]).astype(numpy.float32)
return shadow_map, shadow_ratio