參考鏈接：

1）github代碼鏈接：https://github.com/google/mediapipe

2）說明文檔：https://google.github.io/mediapipe

3）python環境配置文檔：https://google.github.io/mediapipe/getting_started/python

4）API簡單調用的使用文檔：https://google.github.io/mediapipe/solutions/hands#python-solution-api

0.環境準備
python環境配置文檔：https://google.github.io/mediapipe/getting_started/python

ubuntu20.04
cuda11.2
python3.8
opencv-python==4.1.2.30
mediapipe==0.8.2
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sudo apt install -y protobuf-compiler
sudo apt install -y cmake
1.簡介
稍微說明下，文檔基本在第一個鏈接中，python中是通過安裝mediapipe的pypi庫，調用API來用的。

說明文檔：https://google.github.io/mediapipe

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import cv2 import mediapipe as mp mp_drawing = mp.solutions.drawing_utils mp_hands = mp.solutions.hands file_list=[] # For static images: hands = mp_hands.Hands(static_image_mode=True,max_num_hands=2,min_detection_confidence=0.5) for idx, file in enumerate(file_list):# 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:continueimage_hight, image_width, _ = image.shapeannotated_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_hight})')mp_drawing.draw_landmarks(annotated_image, hand_landmarks, mp_hands.HAND_CONNECTIONS)cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', cv2.flip(annotated_image, 1)) hands.close()# For webcam input: hands = mp_hands.Hands(min_detection_confidence=0.5, min_tracking_confidence=0.5) cap = cv2.VideoCapture(0) 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 = Falseresults = hands.process(image)# Draw the hand annotations on the image.image.flags.writeable = Trueimage = 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)cv2.imshow('Hands', image)if cv2.waitKey(5) & 0xFF == 27:break hands.close() cap.release()

基于Openpose改進的Python純CPU深度學習實時識別手勢和檢測手指關節點

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總結

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