Neurotechnology 的python虹膜识别试用版接口文件

import argparse from pynsdk.biometrics import NBiometricType, NSubject, NBiometricCaptureOptions, NLivenessMode, NFace from pynsdk.biometric_client import NBiometricClient from pynsdk.core import NAsyncStatus from pynsdk.devices.ncamera import NCamera from pynsdk.devices.ndevice import NDeviceType from pynsdk.licensing import NLicense import cv2 def main(args): NLicense.obtain("/local", 5000, "FaceClient") client = NBiometricClient.create() client.set_biometric_types(NBiometricType.Face) client.use_device_manager(True) client.initialize() client.set_property("Faces.LivenessMode", NLivenessMode.nlmPassive) device_manager = client.get_device_manager() device_manager.set_device_types(NDeviceType.nCamera) device_manager.init_devices() device = device_manager.get_device(args.camera_idx) client.set_face_capture_device(device) subject = NSubject.create() face = NFace.create() face.set_capture_options(NBiometricCaptureOptions.Stream | NBiometricCaptureOptions.Manual) subject.faces.add(face) status = client.capture_async(subject) #wait 1s if not (NCamera(device).is_capturing()): cv2.waitKey(1000) while(status.get_status() != NAsyncStatus.naCompleted): nframe = face.get_image() frame = nframe.to_numpy() frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) face.objects if face.objects.count > 0: face_attribute = face.objects[0] rect = face_attribute.get_rect() left_eye = face_attribute.get_left_eye_center_point() right_eye = face_attribute.get_right_eye_center_point() mout_center = face_attribute.get_mouth_center_point() cv2.rectangle(frame, (rect.x, rect.y), (rect.x + rect.width, rect.y + rect.height), (0, 255, 0), 1) cv2.circle(frame, (int(left_eye.x), int(left_eye.y)), 1, (0, 255, 0), -1) cv2.circle(frame, (int(right_eye.x), int(right_eye.y)), 1, (0, 255, 0), -1) cv2.circle(frame, (int(mout_center.x), int(mout_center.y)), 1, (0, 255, 0), -1) cv2.putText(frame, f'Liveness: {face_attribute.get_liveness_score()}', (rect.x, rect.y), cv2.FONT_HERSHEY_DUPLEX, 0.67, (0, 0, 0), thickness=1) feature_points = face_attribute.feature_points for i in range(0, feature_points.count): cv2.circle(frame, (int(feature_points[i].x), int(feature_points[i].y)), 1, (0, 255, 0), -1) cv2.imshow('frame', frame) key = cv2.waitKey(1) if key == ord('q'): break if key == ord('s'): client.force_start() if __name__ == "__main__": parser = argparse.ArgumentParser(description='Face liveness sample') parser.add_argument('--camera_idx', type=int, default=0, help='camera index') args_ = parser.parse_args() main(args_)