가위바위보를 인식하는 Mediapipe hand Python 예제

Python용 Mediapipe의 손가락 인식 코드를 수정하여 가위, 바위, 보를 인식하여 화면에 출력하는 예제 코드를 만들었습니다.

OpenPose의 손인식에 비해 주위 환경 영향이 거의 없으며 손가락 인식이 끊김없이 지속적으로 잘 됩니다.  

2021. 02. 19  최초작성

테스트 영상입니다.

실행 결과를 보여주고 있는 유튜브 영상입니다.

https://youtu.be/iFeu2xpIiiU?feature=shared

전체 소스코드입니다.

 

# 원본 코드 https://google.github.io/mediapipe/solutions/hands.html """The 21 hand landmarks.""" # 손가락 위치 정의 참고 https://google.github.io/mediapipe/images/mobile/hand_landmarks.png # # WRIST = 0 # THUMB_CMC = 1 # THUMB_MCP = 2 # THUMB_IP = 3 # THUMB_TIP = 4    엄지 # INDEX_FINGER_MCP = 5 # INDEX_FINGER_PIP = 6 # INDEX_FINGER_DIP = 7 # INDEX_FINGER_TIP = 8  검지 # MIDDLE_FINGER_MCP = 9 # MIDDLE_FINGER_PIP = 10 # MIDDLE_FINGER_DIP = 11 # MIDDLE_FINGER_TIP = 12  중지 # RING_FINGER_MCP = 13 # RING_FINGER_PIP = 14 # RING_FINGER_DIP = 15 # RING_FINGER_TIP = 16  약지 # PINKY_MCP = 17 # PINKY_PIP = 18 # PINKY_DIP = 19 # PINKY_TIP = 20  새끼 # 필요한 라이브러리 # pip install opencv-python mediapipe pillow numpy import cv2  import mediapipe as mp   from PIL import ImageFont, ImageDraw, Image import numpy as np       mp_drawing = mp.solutions.drawing_utils mp_hands = mp.solutions.hands mp_drawing_styles = mp.solutions.drawing_styles # For webcam input: cap = cv2.VideoCapture(0) with mp_hands.Hands(     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     # Flip the image horizontally for a later selfie-view display, and convert     # the BGR image to RGB.     image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)     # To improve performance, optionally mark the image as not writeable to     # pass by reference.     image.flags.writeable = False     results = hands.process(image)     # Draw the hand annotations on the image.     image.flags.writeable = True     image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)     image_height, image_width, _ = image.shape     if results.multi_hand_landmarks:       for hand_landmarks in results.multi_hand_landmarks:         # 엄지를 제외한 나머지 4개 손가락의 마디 위치 관계를 확인하여 플래그 변수를 설정합니다. 손가락을 일자로 편 상태인지 확인합니다.         thumb_finger_state = 0         if hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_CMC].y * image_height > hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_MCP].y * image_height:           if hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_MCP].y * image_height > hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_IP].y * image_height:             if hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_IP].y * image_height > hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_TIP].y * image_height:               thumb_finger_state = 1         index_finger_state = 0         if hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_MCP].y * image_height > hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_PIP].y * image_height:           if hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_PIP].y * image_height > hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_DIP].y * image_height:             if hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_DIP].y * image_height > hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].y * image_height:               index_finger_state = 1         middle_finger_state = 0         if hand_landmarks.landmark[mp_hands.HandLandmark.MIDDLE_FINGER_MCP].y * image_height > hand_landmarks.landmark[mp_hands.HandLandmark.MIDDLE_FINGER_PIP].y * image_height:           if hand_landmarks.landmark[mp_hands.HandLandmark.MIDDLE_FINGER_PIP].y * image_height > hand_landmarks.landmark[mp_hands.HandLandmark.MIDDLE_FINGER_DIP].y * image_height:             if hand_landmarks.landmark[mp_hands.HandLandmark.MIDDLE_FINGER_DIP].y * image_height > hand_landmarks.landmark[mp_hands.HandLandmark.MIDDLE_FINGER_TIP].y * image_height:               middle_finger_state = 1         ring_finger_state = 0         if hand_landmarks.landmark[mp_hands.HandLandmark.RING_FINGER_MCP].y * image_height > hand_landmarks.landmark[mp_hands.HandLandmark.RING_FINGER_PIP].y * image_height:           if hand_landmarks.landmark[mp_hands.HandLandmark.RING_FINGER_PIP].y * image_height > hand_landmarks.landmark[mp_hands.HandLandmark.RING_FINGER_DIP].y * image_height:             if hand_landmarks.landmark[mp_hands.HandLandmark.RING_FINGER_DIP].y * image_height > hand_landmarks.landmark[mp_hands.HandLandmark.RING_FINGER_TIP].y * image_height:               ring_finger_state = 1         pinky_finger_state = 0         if hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_MCP].y * image_height > hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_PIP].y * image_height:           if hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_PIP].y * image_height > hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_DIP].y * image_height:             if hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_DIP].y * image_height > hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_TIP].y * image_height:               pinky_finger_state = 1         # 손가락 위치 확인한 값을 사용하여 가위,바위,보 중 하나를 출력 해줍니다.         font = ImageFont.truetype("fonts/gulim.ttc", 80)         image = Image.fromarray(image)         draw = ImageDraw.Draw(image)                 text = ""         if thumb_finger_state == 1 and index_finger_state == 1 and middle_finger_state == 1 and ring_finger_state == 1 and pinky_finger_state == 1:           text = "보"         elif thumb_finger_state == 1 and index_finger_state == 1 and middle_finger_state == 0 and ring_finger_state == 0 and pinky_finger_state == 0:           text = "가위"         elif   index_finger_state == 0 and middle_finger_state == 0 and ring_finger_state == 0 and pinky_finger_state == 0:           text = "주먹"         w, h = font.getsize(text)         x = 50         y = 50         draw.rectangle((x, y, x + w, y + h), fill='black')         draw.text((x, y),  text, font=font, fill=(255, 255, 255))         image = np.array(image)         # 손가락 뼈대를 그려줍니다.         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())     cv2.imshow('MediaPipe Hands', image)     if cv2.waitKey(5) & 0xFF == 27:       break cap.release()