cars.applications.dense_matching.loaders.pandora_loader

CARS pandora loader file

Attributes

`input_configuration_schema_custom_cars`
`default_short_configuration_input_custom_cars`

Classes

PandoraLoader

PandoraLoader

Functions

`overide_pandora_get_metadata`(→ xarray.Dataset)	Read metadata from image, and return the corresponding xarray.DataSet
`get_config_input_custom_cars`(→ Dict[str, dict])	Get the input configuration
`check_input_section_custom_cars`(→ Dict[str, dict])	Complete and check if the dictionary is correct
`overload_pandora_conf_with_confidence`(conf, ...)	Overload pandora pipeline configuration with given confidence to add

Module Contents

class cars.applications.dense_matching.loaders.pandora_loader.PandoraLoader(conf=None, method_name=None, generate_performance_map_from_risk=False, generate_performance_map_from_intervals=False, generate_ambiguity=False, perf_eta_max_ambiguity=0.99, perf_eta_max_risk=0.25, perf_eta_step=0.04, use_cross_validation=True, denoise_disparity_map=False, used_band='b0', classification_3sgm=None)[source]

PandoraLoader

pandora_config = None

get_conf()[source]

Get pandora configuration used

Returns:: pandora configuration
Return type:: dict

get_classif_bands()[source]

Get the classification bands used in the pandora configuration

Returns:: list of classification bands

find_auto_conf(intersection_poly, land_cover_map, classif_to_config_mapping, epsg): Find the configuration that suits the most on the land cover map based on the roi

check_conf(user_cfg, nodata_left, nodata_right, bands_left, bands_right, bands_classif_left=None, bands_classif_right=None)[source]

Check configuration

Parameters:: user_cfg (dict) – configuration
Returns:: pandora configuration
Return type:: dict

cars.applications.dense_matching.loaders.pandora_loader.input_configuration_schema_custom_cars

cars.applications.dense_matching.loaders.pandora_loader.default_short_configuration_input_custom_cars

cars.applications.dense_matching.loaders.pandora_loader.overide_pandora_get_metadata(im_bands: list, classif_bands: list = None) → xarray.Dataset[source]

Read metadata from image, and return the corresponding xarray.DataSet

Parameters:

im_bands – list of band names
classif_bands – list of classification band names

Returns:

partial xarray.DataSet (attributes and coordinates)

Return type:

xarray.DataSet

cars.applications.dense_matching.loaders.pandora_loader.get_config_input_custom_cars(user_cfg: Dict[str, dict], nodata_left, nodata_right) → Dict[str, dict][source]

Get the input configuration

Parameters:: user_cfg (dict) – user configuration
Return cfg:: partial configuration
Rtype cfg:: dict

cars.applications.dense_matching.loaders.pandora_loader.check_input_section_custom_cars(user_cfg: Dict[str, dict]) → Dict[str, dict][source]

Complete and check if the dictionary is correct

Parameters:: user_cfg (dict) – user configuration
Returns:: cfg: global configuration
Return type:: cfg: dict

cars.applications.dense_matching.loaders.pandora_loader.overload_pandora_conf_with_confidence(conf, confidence_conf)[source]

Overload pandora pipeline configuration with given confidence to add just before disparity computation.

Parameters:

conf (OrderedDict) – current pandora configuration
confidence_conf (OrderedDict) – confidence applications config

Returns:

updated pandora pipeline conf

Return type:

OrderedDict