Grid

class tools.manual.face_viewer.frame.Grid(canvas: FacesViewer, detected_faces: DetectedFaces)

Bases: object

Holds information on the current filtered grid layout.

The grid keeps information on frame indices, face indices, x and y positions and detected face objects laid out in a numpy array to reflect the current full layout of faces within the face viewer based on the currently selected filter and face thumbnail size.

Parameters:

canvas (FacesViewer) – The FacesViewer canvas
detected_faces (DetectedFaces) – The DetectedFace objects for this video

Attributes Summary

`columns_rows`	the (columns, rows) required to hold all display images.
`dimensions`	The (width, height) required to hold all display images.
`face_size`	The pixel size of each thumbnail within the face viewer.
`is_valid`	`True` if the current filter means that the grid holds faces.
`visible_area`	Tuple containing 2 arrays.

Methods Summary

`frame_has_faces`(frame_index)	Check whether the given frame index contains any faces.
`transport_index_from_frame`(frame_index)	Return the main frame's transport index for the given frame index based on the current filter criteria.
`update`()	Update the underlying grid.
`y_coord_from_frame`(frame_index)	Return the y coordinate for the first face that appears in the given frame.

Attributes Documentation

columns_rows

the (columns, rows) required to hold all display images.

Type:: tuple

dimensions

The (width, height) required to hold all display images.

Type:: tuple

face_size

The pixel size of each thumbnail within the face viewer.

Type:: int

is_valid

True if the current filter means that the grid holds faces. False if there are no faces displayed in the grid.

Type:: bool

visible_area

Tuple containing 2 arrays.

1st array contains an array of shape (4, rows, columns) corresponding to the viewable area of the display grid. 1st dimension contains frame indices, 2nd dimension face indices. The 3rd and 4th dimension contain the x and y position of the top left corner of the face respectively.

2nd array contains DetectedFace objects laid out in (rows, columns)

Any locations that are not populated by a face will have a frame and face index of -1

Type:: tuple[numpy.ndarray, numpy.ndarray]

Methods Documentation

frame_has_faces(frame_index: int) → bool | bool

Check whether the given frame index contains any faces.

Parameters:: frame_index (int) – The frame index to locate in the grid
Returns:: True if there are faces in the given frame otherwise False
Return type:: bool

transport_index_from_frame(frame_index: int) → int | None

Return the main frame’s transport index for the given frame index based on the current filter criteria.

Parameters:: frame_index (int) – The absolute index for the frame within the full frames list
Returns:: The index of the requested frame within the filtered frames view. None if no valid frames
Return type:: int | None

update() → None

Update the underlying grid.

Called on initialization, on a filter change or on add/remove faces. Recalculates the underlying grid for the current filter view and updates the attributes _grid, _display_faces, _raw_indices, _frames_list and is_valid

Return type:: None

y_coord_from_frame(frame_index: int) → int

Return the y coordinate for the first face that appears in the given frame.

Parameters:: frame_index (int) – The frame index to locate in the grid
Returns:: The y coordinate of the first face for the given frame
Return type:: int

property columns_rows: tuple[int, int]