ovl.visions.multi_vision module

class ovl.visions.multi_vision.MultiVision(visions: Union[List[Union[Vision, AmbientVision]], Dict[Any, Union[Vision, AmbientVision]]], update_connection: ovl.connections.connection.Connection, update_location: Optional[ovl.connections.network_location.NetworkLocation] = None, default_vision=0)

Bases: object

An object used to switch between multiple vision objects. It does this by having a list of visions and a connection and network_location to update from So it can be automatically updated to swap to the desired Vision object use the start method to start an infinite loop that returns images and detections

connection = ovl.NetworkTablesConnection(1937)

vision1 = ovl.Vision(....)
vision2 = ovl.Vision(....)
vision3 = ovl.Vision(....)

update_location = ovl.NetworkLocation(table_key="current_vision")

multi_vision = ovl.MultiVision([vision1, vision2, vision3], connection, update_location)

for directions, contours, image  in multi_vision.start():

    # do something with the generated data

    # like sending the data or displaying the contours

    multi_vision.send(directions)

    ovl.display_contours(image, contours, delay=1)

is_ambient() → bool

Returns True if the current_vision vision is an Ambient vision object

Returns:if the current vision is an AmbientVision

send(data, *args, **kwargs) → Any

Sends the value to the target destination of the current_vision vision object

Parameters:data – the data to be sent Returns:depends on the connection object

set_vision(index)

Sets the current to the given index

Parameters:index – the index to set Returns:the index

start(yield_ratios=False) → Generator[Tuple[List[numpy.ndarray], numpy.ndarray, Any], None, None]

A function that starts an infinite generator that takes an image detects with the current vision and returns the list of contours the image and directions and should be used as follows:

connection = ovl.NetworkTablesConnection(1937)

vision1 = ovl.Vision(....)
vision2 = ovl.Vision(....)
vision3 = ovl.Vision(....)

update_location = ovl.NetworkLocation(table_key="current_vision")

multi_vision = ovl.MultiVision([vision1, vision2, vision3], connection, update_location)

for directions, contours, image  in multi_vision.start():

    # do something with the generated data

    # like sending the data or displaying the contours

    multi_vision.send(directions)

    ovl.display_contours(image, contours, delay=1)

Note: automatically updates AmbientVision’s vision swapping (AmbientVision.update_vision())!

Parameters:yield_ratios – if True also yields the list of ratios returned from Yields:contours image directions and ratios if yield_ratios if True

switch_vision(index: Any)

Switches the current vision used to the vision at the given index (can be a key for dictionaries)

Parameters:index – the index of the new vision, can be an int if the container of the visions is a list or any immutable object if it is a dictionary Returns:the index set (the index given if it is valid and

update_current() → int

Reads the updated current vision from the update network location and then updates the current vision

Returns:the index received