Real-Time AI Virtual Mouse System Using Computer Vision
AI Virtual Mouse is a software that allows users to give inputs of a mouse to the system without using the actual mouse. To the extreme, it can also be called hardware as it uses an ordinary camera. A virtual muse can usually be operated with multiple input devices, which may include an actual mouse or computer keyboard. The virtual mouse uses a web camera with the help of different image processing techniques. Using figures detection methods for instant Camera access and a user-friendly interface makes it more easily accessible. The system is used to implement a motion-tracking mouse, a physical mouse that saves time and also reduces effort. The hand movements of a user are mapped into mouse inputs. A web camera is set to take images continuously. Most laptops today are equipped with webcams, which have recently been used in security applications utilizing face recognition. To harness the full potential of a webcam, it can be used for vision-based CC which would effectively eliminate the need for a computer mouse or mouse pad. The usefulness of a webcam can also be greatly extended to other HCI applications such as a sign language database or motion controller.
Software Specification:
- Python Libraries: Various Python libraries like OpenCV, NumPy, PyAutoGUI, and TensorFlow can be used for building the Al virtual mouse system.
- Open CV: This library is used for image and video processing, which can be used for hand detection and tracking.
- NumPy: NumPy is used for numerical computations, and it is used to process the captured images.
- PyAutoGUI: PyAuto GUI is used to control the mouse movements and clicks.
- Mediapipe: A cross-platform framework for building multi-modal applied machine learning pipelines.
- Comtypes: A Python module that provides access to Windows COM and .NET components.
- Screen-Brightness-Control: A Python module for controlling the brightness of the screen on Windows, Linux, and macOS.
Detecting which Fingure is Up and Performing the particular Mouse Function
Over here we are detecting which finger is Up using the tip ID of the respective finger that we found using the MideaPipe and the respective figure that we found using the Mediapipe and the respective coordinates of the fingers that are up, according to that we found using the MediaPipe and the respective co-coordinates of the figures that are up and according to that the particular mouse function is performed.
Mouse Functions depending on the Hand Gestures and Hand Tip Detection. Using Computer Vision for the mouse cursor moving around the system window. If the index fingure is up with tip Id = 1 or both the index finger with tip Id = 1 and the middle finger with tip Id =2 are up, the mouse cursor is made to move around the window of the computer using the AutoPy package of Python.
Flow Chart of AI Virtual Mouse
How to set up and run AI Virtual Mouse
Pre requisiretes: Check Python Version – (3.6 or 3.8.5) must be installed in your system. You need to install Anaconda dIstribution in your system. Refer to this article:
Set the project file using Pycham and follow the steps mentioned below:
Step 1: Add a file at path folder_name/requirements.txt
pyautogui==0.9.53
opencv-python==4.5.3.56
mediapipe==0.8.6.2
comtypes==1.1.10
pycaw==20181226
screen-brightness-control==0.9.0
Step 2:
conda create --name gest python=3.8.5
Step 3:
conda activate gest
Step 4:
pip install -r requirements.txt
Step 5: cd src
python Virtual_Mouse.py(run the saved file )
Error correction if any error similar
pip install opencv-python==4.5.5.64
python.exe -m pip install --upgrade pip
pip install opencv-python==4.5.5.64
DescriptorsError: It cannot not be created directly. If this call came from a _pb2.py file, your generated code is outdated and must be regenerated with protoc >= 3.19.0.
pip install protobuf==3.20.0
pip install --upgrade comtypes
Importing Required Modules
Firstly we need to sets up the necessary dependencies for a Python program that may involve computer vision, audio control, and screen brightness control.
Python3
import cv2 import mediapipe as mp import pyautogui import math from enum import IntEnum from ctypes import cast, POINTER from comtypes import CLSCTX_ALL from pycaw.pycaw import AudioUtilities, IAudioEndpointVolume from google.protobuf.json_format import MessageToDict import screen_brightness_control as sbcontrol |
The imported libraries and modules in the code snippet serve various purposes for the Python program. he code snippet also includes two custom classes, Gest and HLabel, which define enumerations for gesture encodings and multi-handedness labels, respectively. These enumerations assign integer values to different gestures and hand labels for easier identification and processing in the program.
Python3
# To create GUI import tkinter as tk from PIL import ImageTk, Image pyautogui.FAILSAFE = False mp_drawing = mp.solutions.drawing_utils mp_hands = mp.solutions.hands # Gesture Encodings class Gest(IntEnum): # Binary Encoded FIST = 0 PINKY = 1 RING = 2 MID = 4 LAST3 = 7 INDEX = 8 FIRST2 = 12 LAST4 = 15 THUMB = 16 PALM = 31 # Extra Mappings V_GEST = 33 TWO_FINGER_CLOSED = 34 PINCH_MAJOR = 35 PINCH_MINOR = 36 # Multi-handedness Labels class HLabel(IntEnum): MINOR = 0 MAJOR = 1 |
Convert Mediapipe Landmarks to recognizable Gestures
In this code provided is a class called “HandRecog” which is used for gesture recognition using hand tracking. It initializes various variables such as finger count, gesture type, frame count, and hand result. The class has methods to update the hand result, calculate the signed distance, distance, and change in z-coordinate of hand landmarks. It also has a method to set the finger state based on the hand result and handle fluctuations due to noise.
Python3
class HandRecog: def __init__( self , hand_label): self .finger = 0 self .ori_gesture = Gest.PALM self .prev_gesture = Gest.PALM self .frame_count = 0 self .hand_result = None self .hand_label = hand_label def update_hand_result( self , hand_result): self .hand_result = hand_result def get_signed_dist( self , point): sign = - 1 if self .hand_result.landmark[point[ 0 ]].y < self .hand_result.landmark[point[ 1 ]].y: sign = 1 dist = ( self .hand_result.landmark[point[ 0 ]].x - self .hand_result.landmark[point[ 1 ]].x) * * 2 dist + = ( self .hand_result.landmark[point[ 0 ]].y - self .hand_result.landmark[point[ 1 ]].y) * * 2 dist = math.sqrt(dist) return dist * sign def get_dist( self , point): dist = ( self .hand_result.landmark[point[ 0 ]].x - self .hand_result.landmark[point[ 1 ]].x) * * 2 dist + = ( self .hand_result.landmark[point[ 0 ]].y - self .hand_result.landmark[point[ 1 ]].y) * * 2 dist = math.sqrt(dist) return dist def get_dz( self ,point): return abs ( self .hand_result.landmark[point[ 0 ]].z - self .hand_result.landmark[point[ 1 ]].z) # Function to find Gesture Encoding using current finger_state. # Finger_state: 1 if finger is open, else 0 def set_finger_state( self ): if self .hand_result = = None : return points = [[ 8 , 5 , 0 ],[ 12 , 9 , 0 ],[ 16 , 13 , 0 ],[ 20 , 17 , 0 ]] self .finger = 0 self .finger = self .finger | 0 #thumb for idx,point in enumerate (points): dist = self .get_signed_dist(point[: 2 ]) dist2 = self .get_signed_dist(point[ 1 :]) try : ratio = round (dist / dist2, 1 ) except : ratio = round (dist / 0.01 , 1 ) self .finger = self .finger << 1 if ratio > 0.5 : self .finger = self .finger | 1 # Handling Fluctations due to noise def get_gesture( self ): if self .hand_result = = None : return Gest.PALM current_gesture = Gest.PALM if self .finger in [Gest.LAST3,Gest.LAST4] and self .get_dist([ 8 , 4 ]) < 0.05 : if self .hand_label = = HLabel.MINOR : current_gesture = Gest.PINCH_MINOR else : current_gesture = Gest.PINCH_MAJOR elif Gest.FIRST2 = = self .finger : point = [[ 8 , 12 ],[ 5 , 9 ]] dist1 = self .get_dist(point[ 0 ]) dist2 = self .get_dist(point[ 1 ]) ratio = dist1 / dist2 if ratio > 1.7 : current_gesture = Gest.V_GEST else : if self .get_dz([ 8 , 12 ]) < 0.1 : current_gesture = Gest.TWO_FINGER_CLOSED else : current_gesture = Gest.MID else : current_gesture = self .finger if current_gesture = = self .prev_gesture: self .frame_count + = 1 else : self .frame_count = 0 self .prev_gesture = current_gesture if self .frame_count > 4 : self .ori_gesture = current_gesture return self .ori_gesture |
Executes commands according to detected gestures
Now we defines a Python class called “Controller” that provides methods for gesture recognition and control of various system functions such as screen brightness, system volume, and scrolling. The class has variables to keep track of hand positions, gesture flags, and pinch thresholds. The class methods include “getpinchylv”, “getpinchxlv”, “changesystembrightness”, “changesystemvolume”, “scrollVertical”, “scrollHorizontal”, “get_position”, “pinch_control_init”, “pinch_control”, and “handle_controls”. These methods are used to calculate distances and changes in hand landmarks, set finger states, determine the current gesture, and control system functions based on the recognized gestures.
Python3
class Controller: tx_old = 0 ty_old = 0 trial = True flag = False grabflag = False pinchmajorflag = False pinchminorflag = False pinchstartxcoord = None pinchstartycoord = None pinchdirectionflag = None prevpinchlv = 0 pinchlv = 0 framecount = 0 prev_hand = None pinch_threshold = 0.3 def getpinchylv(hand_result): dist = round ((Controller.pinchstartycoord - hand_result.landmark[ 8 ].y) * 10 , 1 ) return dist def getpinchxlv(hand_result): dist = round ((hand_result.landmark[ 8 ].x - Controller.pinchstartxcoord) * 10 , 1 ) return dist def changesystembrightness(): currentBrightnessLv = sbcontrol.get_brightness() / 100.0 currentBrightnessLv + = Controller.pinchlv / 50.0 if currentBrightnessLv > 1.0 : currentBrightnessLv = 1.0 elif currentBrightnessLv < 0.0 : currentBrightnessLv = 0.0 sbcontrol.fade_brightness( int ( 100 * currentBrightnessLv) , start = sbcontrol.get_brightness()) def changesystemvolume(): devices = AudioUtilities.GetSpeakers() interface = devices.Activate(IAudioEndpointVolume._iid_, CLSCTX_ALL, None ) volume = cast(interface, POINTER(IAudioEndpointVolume)) currentVolumeLv = volume.GetMasterVolumeLevelScalar() currentVolumeLv + = Controller.pinchlv / 50.0 if currentVolumeLv > 1.0 : currentVolumeLv = 1.0 elif currentVolumeLv < 0.0 : currentVolumeLv = 0.0 volume.SetMasterVolumeLevelScalar(currentVolumeLv, None ) def scrollVertical(): pyautogui.scroll( 120 if Controller.pinchlv> 0.0 else - 120 ) def scrollHorizontal(): pyautogui.keyDown( 'shift' ) pyautogui.keyDown( 'ctrl' ) pyautogui.scroll( - 120 if Controller.pinchlv> 0.0 else 120 ) pyautogui.keyUp( 'ctrl' ) pyautogui.keyUp( 'shift' ) # Locate Hand to get Cursor Position # Stabilize cursor by Dampening def get_position(hand_result): point = 9 position = [hand_result.landmark[point].x ,hand_result.landmark[point].y] sx,sy = pyautogui.size() x_old,y_old = pyautogui.position() x = int (position[ 0 ] * sx) y = int (position[ 1 ] * sy) if Controller.prev_hand is None : Controller.prev_hand = x,y delta_x = x - Controller.prev_hand[ 0 ] delta_y = y - Controller.prev_hand[ 1 ] distsq = delta_x * * 2 + delta_y * * 2 ratio = 1 Controller.prev_hand = [x,y] if distsq < = 25 : ratio = 0 elif distsq < = 900 : ratio = 0.07 * (distsq * * ( 1 / 2 )) else : ratio = 2.1 x , y = x_old + delta_x * ratio , y_old + delta_y * ratio return (x,y) def pinch_control_init(hand_result): Controller.pinchstartxcoord = hand_result.landmark[ 8 ].x Controller.pinchstartycoord = hand_result.landmark[ 8 ].y Controller.pinchlv = 0 Controller.prevpinchlv = 0 Controller.framecount = 0 # Hold final position for 5 frames to change status def pinch_control(hand_result, controlHorizontal, controlVertical): if Controller.framecount = = 5 : Controller.framecount = 0 Controller.pinchlv = Controller.prevpinchlv if Controller.pinchdirectionflag = = True : controlHorizontal() #x elif Controller.pinchdirectionflag = = False : controlVertical() #y lvx = Controller.getpinchxlv(hand_result) lvy = Controller.getpinchylv(hand_result) if abs (lvy) > abs (lvx) and abs (lvy) > Controller.pinch_threshold: Controller.pinchdirectionflag = False if abs (Controller.prevpinchlv - lvy) < Controller.pinch_threshold: Controller.framecount + = 1 else : Controller.prevpinchlv = lvy Controller.framecount = 0 elif abs (lvx) > Controller.pinch_threshold: Controller.pinchdirectionflag = True if abs (Controller.prevpinchlv - lvx) < Controller.pinch_threshold: Controller.framecount + = 1 else : Controller.prevpinchlv = lvx Controller.framecount = 0 def handle_controls(gesture, hand_result): x,y = None , None if gesture ! = Gest.PALM : x,y = Controller.get_position(hand_result) # flag reset if gesture ! = Gest.FIST and Controller.grabflag: Controller.grabflag = False pyautogui.mouseUp(button = "left" ) if gesture ! = Gest.PINCH_MAJOR and Controller.pinchmajorflag: Controller.pinchmajorflag = False if gesture ! = Gest.PINCH_MINOR and Controller.pinchminorflag: Controller.pinchminorflag = False # implementation if gesture = = Gest.V_GEST: Controller.flag = True pyautogui.moveTo(x, y, duration = 0.1 ) elif gesture = = Gest.FIST: if not Controller.grabflag : Controller.grabflag = True pyautogui.mouseDown(button = "left" ) pyautogui.moveTo(x, y, duration = 0.1 ) elif gesture = = Gest.MID and Controller.flag: pyautogui.click() Controller.flag = False elif gesture = = Gest.INDEX and Controller.flag: pyautogui.click(button = 'right' ) Controller.flag = False elif gesture = = Gest.TWO_FINGER_CLOSED and Controller.flag: pyautogui.doubleClick() Controller.flag = False elif gesture = = Gest.PINCH_MINOR: if Controller.pinchminorflag = = False : Controller.pinch_control_init(hand_result) Controller.pinchminorflag = True Controller.pinch_control(hand_result,Controller.scrollHorizontal, Controller.scrollVertical) elif gesture = = Gest.PINCH_MAJOR: if Controller.pinchmajorflag = = False : Controller.pinch_control_init(hand_result) Controller.pinchmajorflag = True Controller.pinch_control(hand_result,Controller.changesystembrightness, Controller.changesystemvolume) |
Entry point of Gesture Controller
Python3
class GestureController: gc_mode = 0 cap = None CAM_HEIGHT = None CAM_WIDTH = None hr_major = None # Right Hand by default hr_minor = None # Left hand by default dom_hand = True def __init__( self ): GestureController.gc_mode = 1 GestureController.cap = cv2.VideoCapture( 0 ) GestureController.CAM_HEIGHT = GestureController.cap.get(cv2.CAP_PROP_FRAME_HEIGHT) GestureController.CAM_WIDTH = GestureController.cap.get(cv2.CAP_PROP_FRAME_WIDTH) def classify_hands(results): left , right = None , None try : handedness_dict = MessageToDict(results.multi_handedness[ 0 ]) if handedness_dict[ 'classification' ][ 0 ][ 'label' ] = = 'Right' : right = results.multi_hand_landmarks[ 0 ] else : left = results.multi_hand_landmarks[ 0 ] except : pass try : handedness_dict = MessageToDict(results.multi_handedness[ 1 ]) if handedness_dict[ 'classification' ][ 0 ][ 'label' ] = = 'Right' : right = results.multi_hand_landmarks[ 1 ] else : left = results.multi_hand_landmarks[ 1 ] except : pass if GestureController.dom_hand = = True : GestureController.hr_major = right GestureController.hr_minor = left else : GestureController.hr_major = left GestureController.hr_minor = right def start( self ): handmajor = HandRecog(HLabel.MAJOR) handminor = HandRecog(HLabel.MINOR) with mp_hands.Hands(max_num_hands = 2 ,min_detection_confidence = 0.5 , min_tracking_confidence = 0.5 ) as hands: while GestureController.cap.isOpened() and GestureController.gc_mode: success, image = GestureController.cap.read() if not success: print ( "Ignoring empty camera frame." ) continue image = cv2.cvtColor(cv2.flip(image, 1 ), cv2.COLOR_BGR2RGB) image.flags.writeable = False results = hands.process(image) image.flags.writeable = True image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) if results.multi_hand_landmarks: GestureController.classify_hands(results) handmajor.update_hand_result(GestureController.hr_major) handminor.update_hand_result(GestureController.hr_minor) handmajor.set_finger_state() handminor.set_finger_state() gest_name = handminor.get_gesture() if gest_name = = Gest.PINCH_MINOR: Controller.handle_controls(gest_name, handminor.hand_result) else : gest_name = handmajor.get_gesture() Controller.handle_controls(gest_name, handmajor.hand_result) for hand_landmarks in results.multi_hand_landmarks: mp_drawing.draw_landmarks(image, hand_landmarks, mp_hands.HAND_CONNECTIONS) else : Controller.prev_hand = None cv2.imshow( 'Virtual Mouse Gesture Controller' , image) if cv2.waitKey( 5 ) & 0xFF = = 13 : break GestureController.cap.release() cv2.destroyAllWindows() |
GUI Code
Over here Python script that creates a graphical user interface (GUI) using the Tkinter library. The GUI displays a label, an image, and a button. When the button is clicked, it calls the runvirtualmouse() function. runvirtualmouse() function: This function creates an instance of the GestureController class and starts it. The exact implementation of the GestureController class and its functionality is not provided in the code snippet.
Python3
def runvirtualmouse(): gc1 = GestureController() gc1.start() root = tk.Tk() root.geometry( "300x300" ) label = tk.Label(root, text = "Welcome to Virtual Mouse" , fg = "brown" ,font = 'TkDefaultFont 16 bold' ) label.grid(row = 0 , columnspan = 5 , pady = 10 , padx = 10 ) image = ImageTk.PhotoImage(Image. open ( "tap.png" )) img_label = tk.Label(image = image , width = 100 , height = 100 , borderwidth = 3 , relief = "solid" ) img_label.grid(row = 1 , columnspan = 5 , pady = 10 , padx = 10 ) start_button = tk.Button(root,text = " Track Mouse" ,fg = "white" , bg = 'green' , font = 'Helvetica 12 bold italic ' ,command = runvirtualmouse , height = "4" , width = "16" ,activebackground = 'lightblue' ) start_button.grid(row = 3 ,column = 2 , pady = 10 , padx = 20 ) root.mainloop() label.geometery( "400X300" ) root.geometery(row = 0 ,columnspan = 5 , pady = 10 ,padx = 10 ) root.main loop() |
Output:
1. When run the .py file: GUI will be open
2. Click on Track Mouse Button: Webcam will be open to capture hand gestures.
3. No action performed : When all the five figures up then the cursor will stop moving.
4) Cursor Moving: When both index and multiple fingers up.
5)Left Button Click: Lower the index finger and raise the middle finger.
6)Right Button Click: Lower the middle finger and raise the index finger.
7)Brightness Controll: Make pinch of index finger and thumb and raise all the rest of fingers and move hand horizontally.
8)Volume Control: Make pinch of index finger and thumb and raise all the rest of fingers. Move hand vertically.
9)Scrolling Vertically: In left hand, make pinch of index finger and thumb and raise all the rest of fingers and move hand vertically.
10)Drag & Drop: Lower the all the fingers after selecting element then drag the the element and drop it wherever we want.
11)Double Click: Join/closed both index finger and middle finger then double click action perform.
Conclusion
The main objective of the proposed virtual AI mouse is to furnish an alternative to the conventional physical mouse that provides mouse functions with the help of computer vision enabled computer that houses a web camera that recognizes fingers and hand uses a machine learning algorithm to execute the defined mouse functions.
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