google mediapipe 学习

参考资料：
http://www.woshicver.com/ThirdSection/2_3_OpenCV%E4%B8%AD%E7%9A%84%E7%BB%98%E5%9B%BE%E5%8A%9F%E8%83%BD/
https://google.github.io/mediapipe/solutions/hands#python-solution-api

小demo

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 static images:
IMAGE_FILES = ['2.jpg']
with mp_hands.Hands(
        static_image_mode=True,
        max_num_hands=2,
        min_detection_confidence=0.5) as hands:
    for idx, file in enumerate(IMAGE_FILES):
        # Read an image, flip it around y-axis for correct handedness output (see
        # above).
        image = cv2.flip(cv2.imread(file), 1)
        # Convert the BGR image to RGB before processing.
        results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

        # Print handedness and draw hand landmarks on the image.
        print('Handedness:', results.multi_handedness)
        if not results.multi_hand_landmarks:
            continue
        image_height, image_width, _ = image.shape
        annotated_image = image.copy()
        for hand_landmarks in results.multi_hand_landmarks:
            print('hand_landmarks:', hand_landmarks)
            print(
                f'Index finger tip coordinates: (',
                f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].x * image_width}, '
                f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].y * image_height})'
            )
            mp_drawing.draw_landmarks(
                annotated_image,
                hand_landmarks,
                mp_hands.HAND_CONNECTIONS,
                mp_drawing_styles.get_default_hand_landmarks_style(),
                mp_drawing_styles.get_default_hand_connections_style())
        cv2.imwrite(
            '/tmp/annotated_image' + str(idx) + '.png', cv2.flip(annotated_image, 1))

# 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
        cv2.circle(image, (60, 30), 30, (0, 0, 255), -1)
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        if results.multi_hand_landmarks:
            for hand_landmarks in results.multi_hand_landmarks:
                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())
                x = int(hand_landmarks.landmark[8].x * 1000)
                y = int(hand_landmarks.landmark[8].y * 1000)
                cv2.circle(image, (x, y), 30, (0, 0, 255), -1)
        cv2.imshow('MediaPipe Hands', image)
        if cv2.waitKey(5) & 0xFF == 27:
            break
cap.release()