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the_code.py
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the_code.py
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# Import necessary libraries
import mediapipe as mp
import cv2
import numpy as np
from pycaw.pycaw import AudioUtilities, ISimpleAudioVolume
# Define global variables and initial values
state = True # On/Off state
valstate = True # Value On/Off state
global volume
volume = None
volmax = 1.0 # Maximum volume
volmin = 0.0 # Minimum volume
distmax = 150.0 # Maximum distance
distmin = 0 # Minimum distance
# Function to control audio volume
def volumecontrol(volume):
sessions = AudioUtilities.GetAllSessions()
for session in sessions:
volume_interface = session._ctl.QueryInterface(ISimpleAudioVolume)
volume_interface.SetMasterVolume(volume, None)
# Function to calculate distance between two points
def distCal(point1, point2):
return np.sqrt((point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2)
# Function to check if fingers are touching
def are_fingers_touching(thumb_tip, finger_tip, threshold=30):
distance = distCal(thumb_tip, finger_tip)
return distance < threshold
# Function to map volume based on distance
def mapvol(dist):
prod1 = volmax * dist
xvol = prod1 / distmax
if xvol >= 1.0:
xvol = 1.0
elif xvol <= 0.18:
xvol = 0.0
return xvol
# Function to map volume as a percentage value
def mapvoltxt(dist):
int(dist)
prod1 = 100 * dist
xvolc = prod1 / distmax
if xvolc >= 100:
xvolc = 100
elif xvolc <= 18:
xvolc = 0
return str(int(xvolc)) + str("%")
# Initialize Mediapipe libraries for hand detection and drawing
mp_drawing = mp.solutions.drawing_utils
mp_hands = mp.solutions.hands
# Initialize video capture from camera
cap = cv2.VideoCapture(0)
# Start hand detection
with mp_hands.Hands(min_detection_confidence=0.8, min_tracking_confidence=0.5) as hands:
while cap.isOpened():
ret, frame = cap.read()
# Convert frame color format and perform horizontal flip
image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image = cv2.flip(image, 1)
image.flags.writeable = False
results = hands.process(image)
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# Process hand detection results
if results.multi_hand_landmarks:
for num, hand in enumerate(results.multi_hand_landmarks):
# Draw hand landmarks on frame
mp_drawing.draw_landmarks(image, hand, mp_hands.HAND_CONNECTIONS,
mp_drawing.DrawingSpec(color=(121, 22, 76), thickness=2, circle_radius=4),
mp_drawing.DrawingSpec(color=(250, 44, 250), thickness=2, circle_radius=2),
)
# Get positions of interest points on fingers
thumb_tip = (int(hand.landmark[mp_hands.HandLandmark.THUMB_TIP].x * image.shape[1]),
int(hand.landmark[mp_hands.HandLandmark.THUMB_TIP].y * image.shape[0]))
index_finger_tip = (int(hand.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].x * image.shape[1]),
int(hand.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].y * image.shape[0]))
pinky_tip = (int(hand.landmark[mp_hands.HandLandmark.PINKY_TIP].x * image.shape[1]),
int(hand.landmark[mp_hands.HandLandmark.PINKY_TIP].y * image.shape[0]))
# Display distance, state, and volume control information on frame
cv2.putText(image, mapvoltxt(distCal(thumb_tip, index_finger_tip)) + " " + str(state), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
# Check if fingers are touching and update state
if are_fingers_touching(thumb_tip, pinky_tip) == True:
valstate = not valstate
elif are_fingers_touching(thumb_tip, pinky_tip) == False:
valstate = False
if valstate == True:
state = not state
# Control volume based on current state
if state == True:
print("On")
volumecontrol(mapvol(distCal(thumb_tip, index_finger_tip)))
elif state == False:
print("Off")
# Display frame with hand detection information
cv2.imshow('Hand Tracking', image)
# End video capture by pressing 'q' key
if cv2.waitKey(10) & 0xFF == ord('q'):
break
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release resources and close windows
cap.release()
cv2.destroyAllWindows()