handDetectionModule.py

import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands

X = []
Y = []

def detectAndDraw(frame):
    with mp_hands.Hands(model_complexity=0,max_num_hands = 1,min_detection_confidence=0.5,min_tracking_confidence=0.5) as hands:
        image = frame

        # To improve performance, optionally mark the image as not writeable to
        # pass by reference.
        image.flags.writeable = False
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        image = cv2.flip(image, 1) 
        imgH, imgW, _ = image.shape
        results = hands.process(image)
        global X
        global Y
        X = []
        Y = []
        # Draw the hand annotations on the image.
        image.flags.writeable = True
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        if results.multi_hand_landmarks:
            for hand_landmarks in results.multi_hand_landmarks:
                for i in range(0,len(hand_landmarks.landmark)):
                    X.append((hand_landmarks.landmark[i].x)*imgW)
                    Y.append((hand_landmarks.landmark[i].y)*imgH)
                mp_drawing.draw_landmarks(
                    image,
                    hand_landmarks,
                    mp_hands.HAND_CONNECTIONS,
                    mp_drawing_styles.get_default_hand_landmarks_style(),
                    mp_drawing_styles.get_default_hand_connections_style())
            # Flip the image horizontally for a selfie-view display.
        return image
    
def getCoordinates():
    return X,Y