ExtractRunner

class lib.infer.runner.ExtractRunner(handler: HandlerT)

Bases: Generic[HandlerT]

Runs an extract plugin

Parameters:: handler (HandlerT) – The plugin handler that this runner will execute

Attributes Summary

`handler`	The plugin handler that this runner is executing
`out_queue`	The output queue from this plugin runner
`uuid`	Unique identifier for plugin ordering and multi-plugin tracking

Methods Summary

`__call__`(input_runner, profile)	Build and start the plugin runner
`flush`()	Flush all data currently within the pipeline
`put`(-> None)	Put a frame into the pipeline.
`put_direct`(filename, image, detected_faces)	Put an item directly into this runner's plugin and return the result
`put_media`(media)	Put a frame into the pipeline that is within a FrameFaces object.
`register_external_error_state`(state)	Register an external error state object.
`start`()	Start the threads.
`stop`()	Indicate to the runner that there is no more data to be ingested

Attributes Documentation

handler: The plugin handler that this runner is executing

out_queue: The output queue from this plugin runner

uuid: Unique identifier for plugin ordering and multi-plugin tracking

Methods Documentation

__call__(input_runner: ExtractRunner | None, profile: bool) → None

Build and start the plugin runner

Parameters:

input_runner (ExtractRunner | None) – The input plugin that feeds this plugin or None if data is to be fed through the runner’s put method.
profile (bool) – True if the runner is to be profiled, indicating that threads will not be started

Raises:

ValueError – If the input runner has not been called and assigned a UUID or if this runner has already been called

Return type:

None

flush() → None

Flush all data currently within the pipeline

Return type:: None

put(filename: str, image: ndarray[tuple[Any, ...], dtype[uint8]], detected_faces: list[DetectedFace] | None = None, source: str | None = None, is_aligned: bool = False, frame_metadata: PNGSource | None = None, passthrough: Literal[False] = False) → None

put(filename: str, image: ndarray[tuple[Any, ...], dtype[uint8]], detected_faces: list[DetectedFace] | None = None, source: str | None = None, is_aligned: bool = False, frame_metadata: PNGSource | None = None, *, passthrough: Literal[True]) → FrameFaces

Put a frame into the pipeline.

Note

When a pipeline is built using the __call__ method, this method will always put items into the first plugin in the pipeline

Parameters:

filename (str) – The filename of the frame
image (npt.NDArray[np.uint8]) – The loaded frame as UINT8 BGR array
detected_faces (list[DetectedFace] | None) – The detected face objects for the frame. None if not any. Default: None
source (str | None) – The full path to the source folder or video file. Default: None (Not provided)
is_aligned (bool) – True if the image being passed into the pipeline is an aligned faceswap face. Default: False
frame_metadata (PNGSource | None) – If the image is aligned then the original frame metadata can be added here. Some plugins (eg: mask) require this to be populated for aligned inputs. Default: None
passthrough (bool) – True if this item is meant to be passed straight through the extraction pipeline with no caching, for immediate return. Default: False

Return type:

If passthrough is True returns the output FrameFaces object, otherwise None

put_direct(filename: str, image: npt.NDArray[np.uint8], detected_faces: list[DetectedFace], is_aligned: bool = False, frame_size: tuple[int, int] | None = None) → ExtractBatch

Put an item directly into this runner’s plugin and return the result

Parameters:

filename (str) – The filename of the frame
image (npt.NDArray[np.uint8]) – The loaded frame as UINT8 BGR array
detected_faces (list[DetectedFace]) – The detected face objects for the frame
is_aligned (bool) – True if the image being passed into the pipeline is an aligned faceswap face. Default: False
frame_size (tuple[int, int] | None) – The (height, width) size of the original frame if passing in an aligned image

Raises:

ValueError – If attempting to put an ExtractBatch object to the first runner in the pipeline or if providing an aligned image with insufficient data

Returns:

The output from this plugin for the given input

Return type:

ExtractBatch

put_media(media: FrameFaces) → None | FrameFaces

Put a frame into the pipeline that is within a FrameFaces object.

Note

When a pipeline is built using the __call__ method, this method will always put items into the first plugin in the pipeline

Parameters:

media (FrameFaces) – The FrameFaces object to put into the pipeline

Returns:

If the FrameFaces’s passthrough is True returns the output FrameFaces object,
otherwise None

Return type:

None | FrameFaces

register_external_error_state(state: ErrorState) → None

Register an external error state object.

If we are not running any threads (specifically, file handler), the pipeline can hang the calling thread. The error state from the calling thread can be populated here. This can only be called if no threads have been registered for the runner

Parameters:: state (ErrorState) – The ErrorState object to register
Return type:: None

start() → None

Start the threads. Callback for when the profiler has finished executing

Return type:: None

stop() → None

Indicate to the runner that there is no more data to be ingested

Return type:: None

__call__(input_runner: ExtractRunner | None, profile: bool) → None

Build and start the plugin runner

Parameters:

input_runner (ExtractRunner | None) – The input plugin that feeds this plugin or None if data is to be fed through the runner’s put method.
profile (bool) – True if the runner is to be profiled, indicating that threads will not be started

Raises:

ValueError – If the input runner has not been called and assigned a UUID or if this runner has already been called

Return type:

None

__next__() → FrameFaces

Obtain the next item from the plugin’s output

Return type:: The media object with populated detected faces for a frame

flush() → None

Flush all data currently within the pipeline

Return type:: None

property handler: HandlerT: The plugin handler that this runner is executing

property out_queue: Queue[ExtractBatch]: The output queue from this plugin runner

put(filename: str, image: ndarray[tuple[Any, ...], dtype[uint8]], detected_faces: list[DetectedFace] | None = None, source: str | None = None, is_aligned: bool = False, frame_metadata: PNGSource | None = None, passthrough: Literal[False] = False) → None

put(filename: str, image: ndarray[tuple[Any, ...], dtype[uint8]], detected_faces: list[DetectedFace] | None = None, source: str | None = None, is_aligned: bool = False, frame_metadata: PNGSource | None = None, *, passthrough: Literal[True]) → FrameFaces

Put a frame into the pipeline.

Note

When a pipeline is built using the __call__ method, this method will always put items into the first plugin in the pipeline

Parameters:

filename (str) – The filename of the frame
image (npt.NDArray[np.uint8]) – The loaded frame as UINT8 BGR array
detected_faces (list[DetectedFace] | None) – The detected face objects for the frame. None if not any. Default: None
source (str | None) – The full path to the source folder or video file. Default: None (Not provided)
is_aligned (bool) – True if the image being passed into the pipeline is an aligned faceswap face. Default: False
frame_metadata (PNGSource | None) – If the image is aligned then the original frame metadata can be added here. Some plugins (eg: mask) require this to be populated for aligned inputs. Default: None
passthrough (bool) – True if this item is meant to be passed straight through the extraction pipeline with no caching, for immediate return. Default: False

Return type:

If passthrough is True returns the output FrameFaces object, otherwise None

put_direct(filename: str, image: npt.NDArray[np.uint8], detected_faces: list[DetectedFace], is_aligned: bool = False, frame_size: tuple[int, int] | None = None) → ExtractBatch

Put an item directly into this runner’s plugin and return the result

Parameters:

filename (str) – The filename of the frame
image (npt.NDArray[np.uint8]) – The loaded frame as UINT8 BGR array
detected_faces (list[DetectedFace]) – The detected face objects for the frame
is_aligned (bool) – True if the image being passed into the pipeline is an aligned faceswap face. Default: False
frame_size (tuple[int, int] | None) – The (height, width) size of the original frame if passing in an aligned image

Raises:

ValueError – If attempting to put an ExtractBatch object to the first runner in the pipeline or if providing an aligned image with insufficient data

Returns:

The output from this plugin for the given input

Return type:

ExtractBatch

put_media(media: FrameFaces) → None | FrameFaces

Put a frame into the pipeline that is within a FrameFaces object.

Note

When a pipeline is built using the __call__ method, this method will always put items into the first plugin in the pipeline

Parameters:

media (FrameFaces) – The FrameFaces object to put into the pipeline

Returns:

If the FrameFaces’s passthrough is True returns the output FrameFaces object,
otherwise None

Return type:

None | FrameFaces

register_external_error_state(state: ErrorState) → None

Register an external error state object.

If we are not running any threads (specifically, file handler), the pipeline can hang the calling thread. The error state from the calling thread can be populated here. This can only be called if no threads have been registered for the runner

Parameters:: state (ErrorState) – The ErrorState object to register
Return type:: None

start() → None

Start the threads. Callback for when the profiler has finished executing

Return type:: None

stop() → None

Indicate to the runner that there is no more data to be ingested

Return type:: None

property uuid: str: Unique identifier for plugin ordering and multi-plugin tracking