Welcome to the documentation!#

Let’s take a look at the Gazetimation constructor.#

gazetimation.Gazetimation.__init__(self, face_model_points_3d: Optional[ndarray] = None, left_eye_ball_center: Optional[ndarray] = None, right_eye_ball_center: Optional[ndarray] = None, camera_matrix: Optional[ndarray] = None, device: int = 0, visualize: bool = True) → None

Initialize the Gazetimation object.

This holds the configurations of the Gazetimation class.

Parameters:

• face_model_points_3d (np.ndarray, optional) –

  Predefine 3D reference points for face model. Defaults to None.

  Note

  If not provided, it will be assigned the following values. And, the passed values should conform to the same facial points.

  self._face_model_points_3d = np.array(
      [
          (0.0, 0.0, 0.0),  # Nose tip
          (0, -63.6, -12.5),  # Chin
          (-43.3, 32.7, -26),  # Left eye, left corner
          (43.3, 32.7, -26),  # Right eye, right corner
          (-28.9, -28.9, -24.1),  # Left Mouth corner
          (28.9, -28.9, -24.1),  # Right mouth corner
      ]
  )
  

• left_eye_ball_center (np.ndarray, optional) –

  Predefine 3D reference points for left eye ball center. Defaults to None.

  Note

  If not provided, it will be assigned the following values. And, the passed values should conform to the same facial points.

  self._left_eye_ball_center = np.array([[29.05], [32.7], [-39.5]])
  

• right_eye_ball_center (np.ndarray, optional) –

  Predefine 3D reference points for right eye ball center. Defaults to None.

  Note

  If not provided, it will be assigned the following values. And, the passed values should conform to the same facial points.

  self._right_eye_ball_center = np.array([[-29.05], [32.7], [-39.5]])
  

• camera_matrix (np.ndarray, optional) –

  Camera matrix. Defaults to None.

  Important

  if not provided, the system tries to calculate the camera matrix using the find_camera_matrix method.

  This calculated camera matrix is estimated from the width and height of the frame, it’s not an exact solution.

• device (int, optional) –

  Device index for the video device. Defaults to 0.

  Attention

  if a negative device index is provided, the system tries to find the first available video device index using the find_device method. So, if not sure, pass device = -1.

  if device < 0:
      self._device = self.find_device()
  else:
      self._device = device
  

• visualize (bool, optional) – If visualize is true then it shows annotated images. Defaults to True.

Let’s go through the run method.#

gazetimation.Gazetimation.run(self, max_num_faces: int = 1, video_path: Optional[str] = None, smoothing: bool = True, smoothing_frame_range: int = 8, smoothing_weight='uniform', custom_smoothing_func=None, video_output_path: Optional[str] = None, handler=None)

Runs the solution

Parameters:

• max_num_faces (int, optional) – Maximum number of face(s)/people present in the scene. Defaults to 1.

• video_path (str, optional) – Path to the video. Defaults to None.

• smoothing (bool, optional) – If smoothing should be performed. Defaults to True.

• smoothing_frame_range (int, optional) – Number of frame to consider to perform smoothing.. Defaults to 8.

• smoothing_weight (str, optional) – Type of weighting scheme (“uniform”, “linear”, “logarithmic”). Defaults to “uniform”.

• custom_smoothing_func (function, optional) – Custom smoothing function. Defaults to None.

• video_output_path (str, optional) – Output path and format for output video.

• handler (function, optional) –

  If provided the output is passed to the handler function for further processing.

  Attention

  The handler will be called by passing the frame and the gaze information as shown below

  if handler is not None:
      handler([frame, left_pupil, right_pupil, gaze_left_eye, gaze_right_eye])
  

Issues#

If any issues are found, they can be reported here.

License#

This project is licensed under the MIT license.