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Python module/script allowing a user to control any media player using hand gestures and face tracking.

This project is being made and maintained by the Embedded Systems and Robotics Club, NIT Kurukshetra (EmR) for its Image processing workshop. However, members outside the club are welcome to contribute (refer to contribution guidelines)

Features

  • Hand gesture recognition and processing to allow user to play/pause, seek, change volume.
  • Face tracking for automatic play/pause based on whether user is viewing media/is present.
  • Functions to allow tuning of the image processing algorithms.
  • Use of auto keystroke and clicker modules to allow application to any media player.

Installation and Usage

Dependecies

Command to run the python script

 python Gesture_control.py

Project Explanation

The project working can be summarized into two parts:

  • Hand Motion Tracking:

    • Mask of hand is created to detect the hand.

         lower = np.array([hue_min, sat_min, val_min])
   upper = np.array([hue_max, sat_max, val_max])
   mask = cv2.inRange(imgHSV, lower, upper)

    • Contours of mask is found out using findContours(). The contour with maximum area is selected as the contour of hand as other contours are due to noise.

         contours, heirarchy = cv2.findContours(
      thresh, cv2.RETR_EXTERNAL,
      cv2.CHAIN_APPROX_SIMPLE) 
   max_cntr = max(contours, key=lambda x: cv2.contourArea(x))
   epsilon = 0.005 * cv2.arcLength(
        max_cntr, True
   )  # maximum distance from contour to approximated contour.
   max_cntr = cv2.approxPolyDP(max_cntr, epsilon, True)

    • Centroid of contour of hand is found out which will be used as a center of mass for tracking hand motion.

         def centroid(contour):
      if len(contour) == 0:  # if the array is empty return (-1,-1)
         return (-1, -1)
      M = cv2.moments(
         contour)  # gives a dictionary of all moment values calculated
      try:
          x = int(
           M['m10'] / M['m00']
          )  # Centroid is given by the relations, 𝐶𝑥 =𝑀10/𝑀00 and 𝐶𝑦 =𝑀01/𝑀00
          y = int(M['m01'] / M['m00'])
      except ZeroDivisionError:
          return (-1, -1)
      return (x, y)

    • Hand motion is tracked by using the coordinates of centroid in different frames.

      Let say there are two frames: F1 and F2. The position of centroid in F1 is (x1,y1) and in F2 is (x2,y2). The time difference b/w F1 and F2 is t. Then hand motion can be tracked as shown below:

         def velocity(x1, x2, t):
       return (x2 - x1) / t

   def detect_motion(x1, y1, x2, y2, t):
       vel_x = velocity(x1, x2, t)
       vel_y = velocity(y1, y2, t)

       if vel_x > VEL_THRESHOLD:
           return MOTION_RIGHT
       elif vel_x < -VEL_THRESHOLD:
           return MOTION_LEFT
       elif vel_y > VEL_THRESHOLD:
           return MOTION_DOWN
       elif vel_y < -VEL_THRESHOLD:
           return MOTION_UP
       else:
           return NO_MOTION

    • Hand motion is mapped to keyboard's keys using PyAutoGUI for controlling the media player.

         def control_media_player(hand_motion):
       if hand_motion == MOTION_RIGHT:
           pyautogui.press(KEY_FORWARD)
       elif hand_motion == MOTION_LEFT:
           pyautogui.press(KEY_BACKWARD)
       elif hand_motion == MOTION_UP:
           pyautogui.press(KEY_VOLUME_UP)
       elif hand_motion == MOTION_DOWN:
           pyautogui.press(KEY_VOLUME_DOWN)

  • Eye Detection

    Eye detection is done using mediapipe for tracking the user's eyes. If the user is not looking at the screen, then the media player pauses and when user looked back at the screen, it resumes.

       eyes = EyeDetection.detectEyes(frame) > 0
   if eyes and not IS_VIDEO_PLAYING:
      play_video()
   elif not eyes and IS_VIDEO_PLAYING:
      pause_video()

Demonstrations

Coming soon...

Contributions

Members of EmR are requested to refer to the M_CONTRIBUTING.md file before raising PRs or issues. Individuals outside of EmR must refer to the CONTRIBUTING.md file before raising PRs or issues.