python+mediapipe+opencv实现手部关键点检测功能(手势识别)
目录
- 一、mediapipe是什么?
- 二、使用步骤
- 1.引入库
- 2.主代码
- 3.识别结果
- 补充:
一、mediapipe是什么?
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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