API Reference

init_sdk

def init_sdk(key_path: str) -> int:

Name	init_sdk
Description	Initializes the SDK.
Input	key_path (str): Path to the license key file
Output	Status code indicating the result of the initialization. 0: Success Non-zero: Initialization failed

get_attribute

def get_attribute(image: np.ndarray, width: int, height: int, face_bbox: np.ndarray, attribute: np.ndarray, angles: np.ndarray, liveness: np.ndarray, age: np.ndarray, gender: np.ndarray, mask: np.ndarray, template: np.ndarray, template_len: np.ndarray, mode: int) -> int:

Name	get_attribute
Description	Detects and analyzes face.
Input	image (numpy.ndarray): Input image matrix width (int): Width of the input image height (int): Height of the input image face_bbox (numpy.ndarray): Face bounding box coordinates. Extracted face bbox will be stored attribute (numpy.ndarray): Wear_glass, Eye_open and Mouth_Close detections of detected face Wear_glass: 0 -> No, 1 -> Yes Eye_open: 0 -> Close, 1 -> Open Mouth_Close: 0 -> Close, 1 -> Open angles (numpy.ndarray): Pitch, Yaw, Roll degree of detected face liveness (numpy.ndarray): Liveness score of detected face 0 -> SPOOF 1 -> REAL -3 -> TOO SMALL FACE -4 -> TOO LARGE FACE -102 -> NO FACE -103 -> LIVENESS CHECK FAILED age (numpy.ndarray): Estimated age of the detected face gender (numpy.ndarray): Gender prediction of the detected face 0 -> Male, 1 -> Female mask (numpy.ndarray): Mask detection of the detected face 0 -> No, 1 -> Yes template (numpy.ndarray): Template buffer. Extracted template will be stored template_len (numpy.ndarray): Length of the extracted template mode (int): 0-> Enroll mode, 1-> Identify mode
Output	Status code indicating the result of getting attribute. 0: Success -1: SDK Initialization Error -2: No Face

calculate_similarity

def calculate_similarity(feature_1: np.ndarray, feature_1_len: int, feature_2: np.ndarray, feature_2_len: int) -> float:

Name	calculate_similarity
Description	Calculates the similarity between two features
Input	feature_1 (numpy.ndarray): 1st feature feature_1_len (int): Length of the 1st feature feature_2 (numpy.ndarray): 2nd feature feature_2_len (int): Length of the 2nd feature
Output	Similarity score between the two features The score ranges from 0.0 to 1.0 Default Threshold is 0.82