pandora2d.check_configuration

This module contains functions allowing to check the configuration given to Pandora2d pipeline.

Attributes

input_configuration_schema
disparity_schema
segment_mode_configuration_schema
roi_configuration_schema
expert_mode_profiling_schema
output_configuration_schema

Functions

check_datasets(→ None)	Check that left and right datasets are correct
check_conf(→ dict)	Validate and complete the user configuration.
get_section_config(→ dict[str, dict])	Get the section configuration from key
update_global_conf(→ None)	Update global_cfg with completed_cfg
check_segment_mode_section(→ None)	Complete and check if the segment mode dictionary is correct
check_pipeline_section(→ None)	Check if the pipeline is correct by
check_subpix_value_with_dichotomy(→ None)	Check if we have a subpix value of 1 with a dichotomy refinement method,
check_matching_cost_method_with_ambiguity(→ None)	Check the correlation method used in relation to ambiguity
check_window_size_limit(→ None)	Check that matching_cost window_size does not exceed left image dimensions.
check_output_section(→ None)	Validate the given output section.
check_expert_mode_section(→ None)	Check if expert mode section is correct
check_input_section(→ None)	Complete and check if the input is correct
check_disparity(→ None)	All checks on disparity
check_disparity_grids(→ None)	Check that disparity grids contains two bands and are the same size as the input image
load_attributes(→ dict)	Load attributes from json file in disparity directory.
check_step_from_attributes(→ None)	Validate that the initial disparity attributes match the pipeline configuration.
check_disparity_grids_from_directory_within_image(...)	Check that disparity grids lie within image boundaries.
update_roi_from_disparity_grid(→ dict)	Construct ROI from input disparity grids when there are smaller than image,
get_dictionary_from_init_grid(→ dict)	Get correct dictionaries to give as input of check_disparity_ranges_are_inside_image method
check_disparity_ranges_are_inside_image(→ None)	Raise an error if disparity ranges are out of image.
check_roi_section(→ None)	Complete and check if roi section is correct
check_roi_coherence(→ None)	Check that the first ROI coords are lower than the last.
check_right_nodata_condition(→ None)	Check that only int is accepted for nodata of right image when matching_cost_method is sad or ssd.
build_default_short_configuration_input(→ dict)	Default configuration input
build_default_segment_mode_configuration(→ dict)	Default segment mode

Module Contents

pandora2d.check_configuration.check_datasets(left: xarray.Dataset, right: xarray.Dataset) → None

Check that left and right datasets are correct

Parameters:

• left – dataset

• right – dataset

Raises:

ValueError – If required disparities are missing or dataset shapes differ.

pandora2d.check_configuration.check_conf(user_cfg: dict, pandora2d_machine: pandora2d.state_machine.Pandora2DMachine) → dict

Validate and complete the user configuration.

Parameters:

• user_cfg – user configuration dictionary

• pandora2d_machine – Pandora2DMachine instance

Returns:

global configuration

pandora2d.check_configuration.get_section_config(user_cfg: dict[str, dict], key: str) → dict[str, dict]

Get the section configuration from key

Parameters:

• user_cfg – user configuration dictionary

• key – section name

Return cfg:

configuration section dictionary or empty dict

pandora2d.check_configuration.update_global_conf(global_cfg: dict[str, dict], completed_cfg: dict[str, dict]) → None

Update global_cfg with completed_cfg

Parameters:

• global_cfg – configuration to be updated

• completed_cfg – configuration used for the update

pandora2d.check_configuration.check_segment_mode_section(user_cfg: dict[str, dict]) → None

Complete and check if the segment mode dictionary is correct

Parameters:

user_cfg – user configuration dictionary

pandora2d.check_configuration.check_pipeline_section(user_cfg: dict[str, dict], pandora2d_machine: pandora2d.state_machine.Pandora2DMachine) → None

Check if the pipeline is correct by - Checking the sequence of steps according to the machine transitions - Checking parameters, define in dictionary, of each Pandora step

Parameters:

• user_cfg – user configuration dictionary

• pandora2d_machine – Pandora2DMachine instance

Raises:

KeyError – If the pipeline section is missing

pandora2d.check_configuration.check_subpix_value_with_dichotomy(refinement_method: str, subpix: int) → None

Check if we have a subpix value of 1 with a dichotomy refinement method, in which case we return a warning to prevent aliasing.

Parameters:

• refinement_method – refinement method in user configuration

• subpix – subpix value in user configuration

pandora2d.check_configuration.check_matching_cost_method_with_ambiguity(matching_cost_method: str) → None

Check the correlation method used in relation to ambiguity

Parameters:

matching_cost_method – matching_cost method in user configuration

pandora2d.check_configuration.check_window_size_limit(user_cfg: dict) → None

Check that matching_cost window_size does not exceed left image dimensions.

Expected call order within check_conf: after check_pipeline_section (completed matching_cost including window_size) and check_input_section (left image path).

Parameters:

user_cfg – user configuration dictionary

Raises:

ValueError – if window_size is larger than image rows or columns

pandora2d.check_configuration.check_output_section(user_cfg: dict[str, dict]) → None

Validate the given output section.

Parameters:

user_cfg – user configuration dictionary

pandora2d.check_configuration.check_expert_mode_section(user_cfg: dict[str, dict]) → None

Check if expert mode section is correct

Parameters:

user_cfg – user configuration dictionary

pandora2d.check_configuration.check_input_section(user_cfg: dict[str, dict], estimation_config: dict = None) → None

Complete and check if the input is correct

Parameters:

• user_cfg – user configuration dictionary

• estimation_config – get estimation config if in user_config

Raises:

KeyError – If the input section is missing or incompatible with estimation mode

pandora2d.check_configuration.check_disparity(image_metadata: xarray.Dataset, input_cfg: dict, user_cfg: dict) → None

All checks on disparity

Parameters:

• image_metadata – left image metadata

• input_cfg – input configuration with default value

• user_cfg – user configuration dictionary

Raises:

• AttributeError – If disparity definitions or grids are invalid

• ValueError – If disparity ranges are inconsistent with the image

pandora2d.check_configuration.check_disparity_grids(image_metadata: xarray.Dataset, disparity_row_reader: rasterio.io.DatasetReader, disparity_col_reader: rasterio.io.DatasetReader, row_path: pathlib.Path, user_cfg: dict) → None

Check that disparity grids contains two bands and are the same size as the input image

Parameters:

• image_metadata – left image metadata

• disparity_row_reader – row disparity raster reader

• disparity_col_reaser – col disparity raster reader

• row_path – disparity file or directory path

• user_cfg – user configuration dictionary

Raises:

AttributeError – If grid dimensions, bands, or attributes are invalid

pandora2d.check_configuration.load_attributes(disparity_directory: pathlib.Path) → dict

Load attributes from json file in disparity directory.

Parameters:

disparity_directory – directory where to find attributes’ file.

Returns:

attributes dictionary

pandora2d.check_configuration.check_step_from_attributes(attributes: dict, expected_step_value: list[int]) → None

Validate that the initial disparity attributes match the pipeline configuration.

Parameters:

• attributes – dictionary grid attributes

• expected_step_value – expected step values.

Raises:

AttributeError – If the steps do not match.

pandora2d.check_configuration.check_disparity_grids_from_directory_within_image(attributes: dict, disparity_row_reader: rasterio.io.DatasetReader, image_metadata: xarray.Dataset) → dict | None

Check that disparity grids lie within image boundaries.

Parameters:

• attributes – dictionary grid attributes

• disparity_row_reader – row disparity raster reader

• image_metadata – left image metadata

Returns:

ROI dictionary if grids define a sub-area, otherwise None

Raises:

AttributeError – If disparity grids exceed image boundaries

pandora2d.check_configuration.update_roi_from_disparity_grid(row_min: int, row_max: int, col_min: int, col_max: int) → dict

Construct ROI from input disparity grids when there are smaller than image,

Parameters:

• row_min – minimum row index

• row_max – maximum row index (exclusive)

• col_min – minimum col index

• col_max – maximum col index (exclusive)

Returns:

ROI dictionary

pandora2d.check_configuration.get_dictionary_from_init_grid(disparity_reader: rasterio.io.DatasetReader, disp_range: int) → dict

Get correct dictionaries to give as input of check_disparity_ranges_are_inside_image method from initial disparity grids.

Parameters:

• disparity_reader – disparity grid reader

• disp_range – range of exploration

Returns:

a disparity dictionary to give to check_disparity_ranges_are_inside_image() method

Raises:

ValueError – If the disparity grid is full on invalid values

pandora2d.check_configuration.check_disparity_ranges_are_inside_image(image_metadata: xarray.Dataset, row_disparity: dict, col_disparity: dict) → None

Raise an error if disparity ranges are out of image.

Parameters:

• image_metadata – left image metadata

• row_disparity – row disparity configuration

• col_disparity – column disparity configuration

Raises:

ValueError – If ranges exceed image bounds

pandora2d.check_configuration.check_roi_section(user_cfg: dict[str, dict]) → None

Complete and check if roi section is correct

Parameters:

user_cfg – user configuration dictionary

pandora2d.check_configuration.check_roi_coherence(roi_cfg: dict) → None

Check that the first ROI coords are lower than the last.

Parameters:

roi_cfg – user configuration for ROI

Raises:

ValueError – If first coordinate is greater than last

pandora2d.check_configuration.check_right_nodata_condition(cfg_input: dict, cfg_pipeline: dict) → None

Check that only int is accepted for nodata of right image when matching_cost_method is sad or ssd. :param cfg_input: inputs section of configuration :param cfg_pipeline: pipeline section of configuration :raises ValueError: If nodata type is invalid

pandora2d.check_configuration.build_default_short_configuration_input() → dict

Default configuration input

pandora2d.check_configuration.build_default_segment_mode_configuration() → dict

Default segment mode

pandora2d.check_configuration.input_configuration_schema

pandora2d.check_configuration.disparity_schema

pandora2d.check_configuration.segment_mode_configuration_schema

pandora2d.check_configuration.roi_configuration_schema

pandora2d.check_configuration.expert_mode_profiling_schema

pandora2d.check_configuration.output_configuration_schema