inference.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

# For webcam input:
cap = cv2.VideoCapture(0)
with mp_hands.Hands(
    model_complexity=0,
    max_num_hands = 1,
    min_detection_confidence=0.5,
    min_tracking_confidence=0.5) as hands:
  while cap.isOpened():
    success, image = cap.read()
    if not success:
      print("Ignoring empty camera frame.")
      # If loading a video, use 'break' instead of 'continue'.
      continue

    # 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)

    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:
      #print(len(results.multi_hand_landmarks))
      for hand_landmarks in results.multi_hand_landmarks:
        #print(hand_landmarks.landmark)
        #print(len(hand_landmarks.landmark))
        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)
        print(X,Y)
        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.
    cv2.imshow('MediaPipe Hands', image )
    if cv2.waitKey(5) & 0xFF == 27:
      break
cap.release()