Python+OpenCV手势识别Mediapipe(新手入门)
前言
本篇文章适合刚入门OpenCV的同学们。文章将介绍如何使用 Python利用 OpenCV图像捕捉,配合强大的 Mediapipe库来实现 手势检测与识别;本系列后续还会 继续更新Mediapipe手势的各种衍生项目,还请多多关注!
项目效果图
视频捕捉帧数稳定在(25-30)
; 认识Mediapipe
项目的实现,核心是强大的 Mediapipe ,它是 google的一个 开源项目:
功能详细人脸检测 FaceMesh从图像/视频中重建出人脸的3D Mesh人像分离从图像/视频中把人分离出来手势跟踪21个关键点的3D坐标人体3D识别33个关键点的3D坐标物体颜色识别可以把头发检测出来,并图上颜色
以上是 Mediapipe的几个常用功能 , 这几个功能我们会在后续一一讲解实现
Python安装 Mediapipe
pip install mediapipe==0.8.9.1
也可以用 setup.py 安装
https://github.com/google/mediapipe
项目环境
Python 3.7
Mediapipe 0.8.9.1
Numpy 1.21.6
OpenCV-Python 4.5.5.64
OpenCV-contrib-Python 4.5.5.64
实测也支持Python3.8-3.9
; 代码
核心代码
OpenCV摄像头捕捉部分:
import cv2
cap = cv2.VideoCapture(0)
while True:
success, img = cap.read()
imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
cv2.imshow("HandsImage", img)
cv2.waitKey(1)
mediapipe 手势识别与绘制
mpHands = mp.solutions.hands
hands = mpHands.Hands()
mpDraw = mp.solutions.drawing_utils
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:
for id, lm in enumerate(handLms.landmark):
h, w, c = img.shape
cx, cy = int(lm.x * w), int(lm.y * h)
print(id, cx, cy)
cv2.circle(img, (cx, cy), 15, (255, 0, 255), cv2.FILLED)
mpDraw.draw_landmarks(img, handLms, mpHands.HAND_CONNECTIONS)
视频帧率计算
import time
pTime = 0
cTime = 0
while True
cTime = time.time()
fps = 1 / (cTime - pTime)
pTime = cTime
cv2.putText(img, str(int(fps)), (10, 70), cv2.FONT_HERSHEY_PLAIN, 3,
(255, 0, 255), 3)
完整代码
import cv2
import mediapipe as mp
import time
cap = cv2.VideoCapture(0)
mpHands = mp.solutions.hands
hands = mpHands.Hands()
mpDraw = mp.solutions.drawing_utils
pTime = 0
cTime = 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:
for id, lm in enumerate(handLms.landmark):
h, w, c = img.shape
cx, cy = int(lm.x * w), int(lm.y * h)
print(id, cx, cy)
cv2.circle(img, (cx, cy), 15, (255, 0, 255), cv2.FILLED)
mpDraw.draw_landmarks(img, handLms, mpHands.HAND_CONNECTIONS)
'''''
视频FPS计算
'''
cTime = time.time()
fps = 1 / (cTime - pTime)
pTime = cTime
cv2.putText(img, str(int(fps)), (10, 70), cv2.FONT_HERSHEY_PLAIN, 3,
(255, 0, 255), 3)
cv2.imshow("HandsImage", img)
cv2.waitKey(1)
项目输出
; 结语
以此篇文章技术为基础,后续会更新利用此篇基础技术实现的《 手势控制:音量,鼠标》
项目下载地址https://github.com/BIGBOSS-dedsec/HandsDetection_Python
Original: https://blog.csdn.net/weixin_50679163/article/details/124391674
Author: BIGBOSSyifi
Title: Python+OpenCV手势识别Mediapipe(基础篇)
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