python+mediapipe+opencv實現手部關鍵點檢測功能(手勢識別)

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一、mediapipe是什麼？

mediapipe官網

二、使用步驟

1.引入庫

代碼如下：

import cv2
from mediapipe import solutions
import time

2.主代碼

代碼如下：

cap = cv2.VideoCapture(0)
mpHands = solutions.hands
hands = mpHands.Hands()
mpDraw = solutions.drawing_utils
pTime = 0
count = 0
while True:
    success, img = cap.read()
    imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    results = hands.process(imgRGB)
    if results.multi_hand_landmarks:
        for handLms in results.multi_hand_landmarks:
            mpDraw.draw_landmarks(img, handLms, mpHands.HAND_CONNECTIONS)
    cTime = time.time()
    fps = 1 / (cTime - pTime)
    pTime = cTime
    cv2.putText(img, str(int(fps)), (25, 50), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 3)
    cv2.imshow("Image", img)
    cv2.waitKey(1)

3.識別結果

以上就是今天要講的內容，本文僅僅簡單介紹瞭mediapipe的使用，而mediapipe提供瞭大量關於圖像識別等的方法。

補充：

下面看下基於mediapipe人臉網狀識別。

1.下載mediapipe庫：

pip install mediapipe

2.完整代碼：

import cv2
import mediapipe as mp
import time
mp_drawing = mp.solutions.drawing_utils
mp_face_mesh = mp.solutions.face_mesh
drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
cap = cv2.VideoCapture("3.mp4")
with mp_face_mesh.FaceMesh(
    min_detection_confidence=0.5,
    min_tracking_confidence=0.5) as face_mesh:
  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 = face_mesh.process(image)
    time.sleep(0.02)
    # Draw the face mesh annotations on the image.
    image.flags.writeable = True
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    if results.multi_face_landmarks:
      for face_landmarks in results.multi_face_landmarks:
        mp_drawing.draw_landmarks(
            image=image,
            landmark_list=face_landmarks,
            connections=mp_face_mesh.FACE_CONNECTIONS,
            landmark_drawing_spec=drawing_spec,
            connection_drawing_spec=drawing_spec)
    cv2.imshow('MediaPipe FaceMesh', image)
    if cv2.waitKey(5) & 0xFF == 27:
      break
cap.release()

到此這篇關於python+mediapipe+opencv實現手部關鍵點檢測功能(手勢識別)的文章就介紹到這瞭,更多相關python mediapipe opencv手勢識別內容請搜索WalkonNet以前的文章或繼續瀏覽下面的相關文章希望大傢以後多多支持WalkonNet！

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