Gazetimation provides an out of the box solution for gaze estimation.

Installation

pip install gazetimation

Usage

from gazetimation import Gazetimation
gz = Gazetimation(device=0) # or any other device id
gz.run()

To run a video file

gz.run(video_path='path/to/video')

To save as a video file

gz.run(video_output_path='path/to/video.avi')

The run method also accepts a handler function for further processing.

gz.run(handler=my_handler)

Attention

The handler function will be called by passing the frame and the gaze information

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

The solution can be customized by passing parameters to the Gazetimation constructor, and to the run method.

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.

Important

If you want to use a video file and not the direct video feed, you don’t need to pass any value for device argument.

If video_path is provided, then the system disregards the camera feed.

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])
  

Attention

If you’re not sure about how many people (faces) are present in the scene you can use the find_face_num method.

Issues

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

License

This project is licensed under the MIT license.