hypso.classification.WaterDetection.waterdetect.InputOutput
Module Contents
Classes
- class hypso.classification.WaterDetection.waterdetect.InputOutput.DWLoader(input_folder, shape_file=None, product='S2_THEIA', ref_band='Red', single_mode=False)
- property product_dict
- property area_name
Extracts the name of the area based on the shapefile name :return: name of the area
- property current_image_folder
Returns the full path folder of current (selected) image :return: Posixpath of current image
- property current_image_name
Returns the name of the current (selected) image :return: String name of the current (selected) image
- property bands_path
Return the directory of the bands depending on the product :return: PosixPath of the directory containing the bands
- property granule_metadata
Returns the full path folder of the image’s granule metadata :return: Posixpath of current image’s granule metadata
- property metadata
Returns the full path folder of the image’s metadata :return: Posixpath of current image’s metadata
- property glint_name
” Name of the glint image for the report
- property projection
- property geo_transform
- property x_size
- property y_size
- satellite_Dict
- band_ids
- __len__()
- __iter__()
- __next__()
- get_bands_files()
Retrieve the full path of bands saved for the current image, according to the product :return: Posix_path of bands files
- open_current_image(ref_band_name='Red')
Load a bands list, given a image_list and a dictionary of Keys(BandName) and identifiers to parse the filename ex. {‘Green’:’B3’, ‘Red’:’B4’…} The result, will be a dictionary with Keys(BandName) and GdalDatasets as values
- static open_gdal_image(bands_list, desired_band)
Get the image in the list corresponding to the informed Band. Return the image opened with GDAL as a RasterImage object If cant find the band return None If is more than 1 image, raise exception
- clip_bands(bands_to_clip, ref_band, temp_dir)
- get_offset(band)
Get the offset value to be applied to the band. This offset exists in the Sen2Cor images after 25 January 2022. https://sentinels.copernicus.eu/documents/247904/4830984/OMPC.CS.DQR.002.07-2022%20-%20i52r0%20-%20MSI%20L2A%20DQR%20August%202022.pdf/36edbb04-0c6c-fba3-5c34-0ba3be82e91c @param band: Reflectance band @return: Value to be added to the loaded band
- load_raster_bands(bands_list)
- Parameters:
bands_list (list) –
- update_mask(mask)
- load_masks(product_masks_list, external_mask, mask_name, mask_valid_value=None, mask_invalid_value=None)
- class hypso.classification.WaterDetection.waterdetect.InputOutput.DWSaver(output_folder, product_name, area_name=None)
- property area_name
- property temp_dir
- set_output_folder(image_name, geo_transform, projection)
For each image, the saver has to prepare the specific output directory, and saving parameters. The output directory is based on the base_output_folder, the area name and the image name :param image_name: name of the image being processed :param geo_transform: geo transformation to save rasters :param projection: projection to save rasters :return: Nothing
- update_geo_transform(geo_transform, projection)
- static create_base_name(product_name, image_name)
- static create_output_folder(output_folder, image_name, area_name)
- save_array(array, name, opt_relative_path=None, no_data_value=0, dtype=None)
- save_rgb_array(red, green, blue, name, opt_relative_path=None)
- save_multiband(array, name, opt_relative_path=None, no_data_value=0, dtype=None)
Save a multilayer array
- Parameters:
array – array with all bands wanted
name – name of the output
- Returns:
the complete filename
- class hypso.classification.WaterDetection.waterdetect.InputOutput.DWS2CORMaskProcessor(base_folder, x_size, y_size, shape_file=None, temp_dir=None)
- Sen2CorMaskList
- open_mask(shape_file, temp_dir)
- open_gdal_masks(shape_file, temp_dir)
- get_combined_masks(masks_list)