python+mediapipe+opencv实现手部关键点检测功能(手势识别)

一、mediapipe是什么？

mediapipe官网

二、使用步骤

1.引入库

代码如下：

import cv2
from mediapipe import solutions
import time

2.主代码

代码如下：

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.识别结果

以上就是今天要讲的内容，本文仅仅简单介绍了mediapipe的使用，而mediapipe提供了大量关于图像识别等的方法。

补充：

下面看下基于mediapipe人脸网状识别。

1.下载mediapipe库：

pip install mediapipe

2.完整代码：

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()

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