Python Challenge Response Example

Initialize Challenge Response module

from oneML import faceAPI as api
challenge = api.ChallengeResponse()

Initialize Challenge Response module from existing FaceDetector and FaceLandmarkDetector

from oneML import faceAPI as api
face_detector = api.FaceDetector()
face_landmark = api.FaceLandmarkDetector()
challenge = api.ChallengeResponse(face_detector, face_landmark)

Initialize Challenge Response module with licensing

from oneML import faceAPI as api
license_manager = api.LicenseManager()
license_manager.set_key("LICENSE_KEY_VALUE_HERE")
license_manager.activate_key()
challenge = api.ChallengeResponse(license_manager)

Initialize Challenge Response module with licensing from existing FaceDetector and FaceLandmarkDetector

from oneML import faceAPI as api
license_manager = api.LicenseManager()
license_manager.set_key("LICENSE_KEY_VALUE_HERE")
license_manager.activate_key()

face_detector = api.FaceDetector(license_manager)
face_landmark = api.FaceLandmarkDetector(license_manager)
challenge = api.ChallengeResponse(face_detector, face_landmark, license_manager)

Run the module from videos

video_path = "path/to/video.mp4"
ops = api.ChallengeResponseOps(True)
result = challenge.run_video(video_path, ops)

Run the module from stream of images (can be used with opencv VideoCapture)

# if we can still read frame from image stream
ops = api.ChallengeResponseOps(True)
while can_read_frame:
    # use any function to get each frame from image stream
    frame = get_one_frame()
    result = challenge.run_frame(frame, ops)
challenge.reset()

Run the module with numpy array of images

# create numpy array of images
utils = api.Utils()

video_path = "path/to/video.mp4"
images = utils.video_to_images(video_path)
ops = api.ChallengeResponseOps(True)

# run ChallengeResponse with numpy array of images
result = challenge.run(images, ops)

Get result

print("status:", int(result.get_return_status()))
print("is_blink:", int(result.is_blink()))
print("number of blinks:", result.get_num_blinks())
print("liveness score:", result.get_liveness_score())
best_frame = result.get_best_frame()