For every input image array, output a list of found faces as bounding boxes (left, upper, right, lower) edges.
- upsample: Nummber of times toupsample image. Increases accuracy at the cost of peroformance, set to 0 for fastest results.
- default: any list of signals with
$frameattribute.
- default: A signal containing the location of the found faces.
None
- face_recognition
Find a face encoding within a frame from an incoming signal, compare the encoding with encoding of known faces from an input signal, output a signal containing the name of the found face.
- accuracy: Degree of confidence for finding faces.
- capture: The frame (or image) to compare to the known faces.
- enrich:
- location: Include coordinate location of face on output signal.
- known: Signal to add the known face encodings and names to compare found faces against. Expects a 'faces' object which contains a list of objects with attributes 'name', 'user_id', 'id', and 'encoding'.
- unknown: Signal to with an image to search for faces.
- default: A signal containing the name of the identified face.
None
- face_recognition
- numpy
{
'faces': [
{
'name': 'Barack',
'user_id': 'bobama',
'id': '4999011a-8ded-49c4-a927-77a09dcdb578',
'encoding': 'gANjbnVtcHkuY29yZS5tdWx0aWFycmF5Cl9yZWNvbn...'
}
]
}
{
'found': 'Barack'
}
Load face image file and serialize the encoding.
- image_paths: Full path to the image file that will be added.
- sname: Name of the face being added to the database.
- uid: Id of the face being added to the database.
- default: Any list of signals with the path to the face image.
- default: A signal containing the facial encoding, user id, and name.
None
- face_recognition
{
'encoding': 'gANjbnVtcHkuY29yZS5tdWx0aWFycmF5Cl9yZWNvbn...',
'name': 'Barack',
'user_id': 'bobama'
}