import cv2 import mediapipe as mp import numpy as np

def count_open_fingers(hand_landmarks): # Define the landmarks for each finger finger_landmarks = { "thumb": [4, 3, 2, 1], "index": [8, 7, 6, 5], "middle": [12, 11, 10, 9], "ring": [16, 15, 14, 13], "pinky": [20, 19, 18, 17] }

open_fingers = 0

for finger in finger_landmarks.values():
    # Calculate the distance between the tip of the finger and the base
    length = np.linalg.norm(np.array(hand_landmarks[finger[0]]) - np.array(hand_landmarks[finger[3]]))
    # If the distance is greater than a threshold, consider the finger open
    if length > 30:  # You may need to adjust this threshold based on your setup
        open_fingers += 1

return open_fingers

Initialize MediaPipe hands module

mp_hands = mp.solutions.hands hands = mp_hands.Hands(static_image_mode=False, max_num_hands=2, min_detection_confidence=0.7, min_tracking_confidence=0.5)

cap = cv2.VideoCapture(0)

while True: ret, frame = cap.read() if not ret: break

# Convert the image to RGB
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Process the image with MediaPipe
results = hands.process(frame_rgb)

if results.multi_hand_landmarks:
    for hand_landmarks in results.multi_hand_landmarks:
        # Extract landmark positions
        hand_landmarks_dict = {}
        for idx, landmark in enumerate(hand_landmarks.landmark):
            hand_landmarks_dict[idx] = (int(landmark.x * frame.shape[1]), int(landmark.y * frame.shape[0]))

        # Count the open fingers
        open_fingers = count_open_fingers(hand_landmarks_dict)

        # Display the number of open fingers
        cv2.putText(frame, f"Open Fingers: {open_fingers}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

        # Determine which hand has more open fingers
        if open_fingers > 0:
            # Assuming the first hand detected is the left hand and the second one is the right hand
            if len(results.multi_hand_landmarks) == 1:
                print("left")
            elif len(results.multi_hand_landmarks) == 2:
                print("right")

        # Draw landmarks on the image
        for landmark in hand_landmarks.landmark:
            x, y = int(landmark.x * frame.shape[1]), int(landmark.y * frame.shape[0])
            cv2.circle(frame, (x, y), 5, (255, 0, 0), -1)

# Display the frame
cv2.imshow("Hand Tracking", frame)

if cv2.waitKey(1) & 0xFF == ord('q'):
    break

cap.release() cv2.destroyAllWindows()