pandora2d.state_machine

This module contains class associated to the pandora state machine

Classes

`Machine`	A GraphMachine which defaults to graphviz engine.
`MarginsProperties`	Properties of Margins used in Margins transitions.
`BaseMachine`	Base model and state machine for pandora2d.
`CheckMachine`	State Machine that checks Pandora2d configuration.
`Pandora2DMachine`	Pandora2DMachine class to create and use a state machine

Module Contents

class pandora2d.state_machine.Machine(*args, **kwargs)[source]

Bases: transitions.extensions.GraphMachine

A GraphMachine which defaults to graphviz engine.

class pandora2d.state_machine.MarginsProperties[source]

Bases: TypedDict

Properties of Margins used in Margins transitions.

type: Literal['aggregate', 'maximum'][source]

margins: Annotated[list[int], ["left, "up", "right", "down"]][source]

class pandora2d.state_machine.BaseMachine[source]

Bases: transitions.Machine, abc.ABC

Base model and state machine for pandora2d.

step: list | None = None[source]

pipeline_cfg: dict[source]

window_size: int | None = None[source]

margins_img[source]

margins_disp[source]

abstract check_conf(cfg)[source]

Check configuration and transitions

Parameters:: cfg – pipeline configuration
Returns:

abstract estimation_run(cfg, input_step)[source]

Estimation’s computation step.

Parameters:

cfg – configuration
input_step – current step

Returns:

None

abstract matching_cost_run(cfg, input_step)[source]

Matching cost computation step.

Parameters:

cfg – configuration
input_step – current step

Returns:

None

abstract cost_volume_confidence_run(cfg, input_step)[source]

Cost volume confidence’s computation.

Parameters:

cfg – configuration
input_step – current step

Returns:

None

abstract disparity_run(cfg, input_step)[source]

Disparity’s computation.

Parameters:

cfg – configuration
input_step – current step

Returns:

None

abstract refinement_run(cfg, input_step)[source]

Refinement’s configuration.

Parameters:

cfg – configuration
input_step – current step

Returns:

None

class pandora2d.state_machine.CheckMachine[source]

Bases: BaseMachine

State Machine that checks Pandora2d configuration.

check_conf(cfg: dict[str, dict]) → None[source]

Check configuration and transitions

Parameters:: cfg – pipeline configuration
Returns:

estimation_run(cfg: dict[str, dict], input_step: str) → None[source]

Check the estimation computation configuration

Parameters:

cfg – configuration
input_step – current step

Returns:

None

matching_cost_run(cfg: dict[str, dict], input_step: str) → None[source]

Check the matching cost computation configuration

Parameters:

cfg – configuration
input_step – current step

Returns:

None

cost_volume_confidence_run(cfg: dict[str, dict], input_step: str) → None[source]

Check the cost volume confidence computation configuration

Parameters:

cfg – configuration
input_step – current step

Returns:

None

disparity_run(cfg: dict[str, dict], input_step: str) → None[source]

Check the disparity computation configuration

Parameters:

cfg – configuration
input_step – current step

Returns:

None

refinement_run(cfg: dict[str, dict], input_step: str) → None[source]

Check the refinement configuration

Parameters:

cfg – configuration
input_step – current step

Returns:

None

class pandora2d.state_machine.Pandora2DMachine[source]

Bases: BaseMachine

Pandora2DMachine class to create and use a state machine

left_img: xarray.Dataset | None = None[source]

right_img: xarray.Dataset | None = None[source]

completed_cfg: dict[source]

cost_volumes: xarray.Dataset[source]

dataset_disp_maps: xarray.Dataset[source]

matching_cost_: pandora2d.matching_cost.BaseMatchingCost | None = None[source]

run_prepare(img_left: xarray.Dataset, img_right: xarray.Dataset, cfg: dict) → None[source]

Prepare the machine before running

Parameters:

img_left –
left Dataset image containing :
- im : 2D (row, col) xarray.DataArray
- msk : 2D (row, col) xarray.DataArray
img_right –
right Dataset image containing :
- im : 2D (row, col) xarray.DataArray
- msk : 2D (row, col) xarray.DataArray
cfg – configuration

run(input_step: str, cfg: dict[str, dict]) → None[source]

Run pandora 2D step by triggering the corresponding machine transition

Parameters:

input_step – step to trigger
cfg – pipeline configuration

Returns:

None

run_exit() → None[source]

Clear transitions and return to state begin

Returns:: None

check_conf(cfg: dict[str, dict]) → None[source]

Check configuration and transitions

Parameters:: cfg – pipeline configuration
Returns:

remove_transitions(transition_list: dict[str, dict]) → None[source]

Delete all transitions defined in the input list

Parameters:: transition_list – list of transitions
Returns:: None

matching_cost_prepare(cfg: dict[str, dict], input_step: str) → None[source]

Matching cost prepare

Parameters:

cfg – pipeline configuration
input_step – step to trigger

Returns:

None

estimation_run(cfg: dict[str, dict], input_step: str) → None[source]

Shift’s estimation step

Parameters:

cfg – pipeline configuration
input_step – step to trigger

Returns:

None

matching_cost_run(_, __) → None[source]

Matching cost computation

Returns:: None

cost_volume_confidence_run(cfg: dict[str, dict], input_step: str) → None[source]

Cost volume confidence computation

Returns:: None

disparity_run(cfg: dict[str, dict], input_step: str) → None[source]

Disparity computation and validity mask

Parameters:

cfg – pipeline configuration
input_step – step to trigger

Returns:

None

refinement_run(cfg: dict[str, dict], input_step: str) → None[source]

Subpixel disparity refinement

Parameters:

cfg – pipeline configuration
input_step – step to trigger

Returns:

None