python|基于MediaPipe的手势识别视频处理|图像识别|人工智能

1.HandTrackingMin.py

import cv2 import mediapipe as mp import timecap = cv2.VideoCapture(0)mpHands = mp.solutions.hands hands = mpHands.Hands() mpDraw = mp.solutions.drawing_utilspTime = 0 cTime = 0while 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,cx), 15, (255, 0, 255), cv2.FILLED)mpDraw.draw_landmarks(img, handLms, mpHands.HAND_CONNECTIONS)cTime = time.time() fps = 1 / (cTime - pTime) pTime = cTimecv2.putText(img, str(int(fps)), (10,70), cv2.FONT_HERSHEY_PLAIN, 3,(255, 0, 255), 3)cv2.imshow("Image", img) cv2.waitKey(1)

2.HandTrackingModule.py

import cv2 import mediapipe as mp import timeclass handDetector(): def __init__(self, mode=False,maxHands=2,detectionCon=0.5,trackCon=0.5): self.mode = mode self.maxHands = maxHands self.detectionCon = detectionCon self.trackCon = trackConself.mpHands = mp.solutions.hands self.hands = self.mpHands.Hands(self.mode, self.maxHands, self.detectionCon, self.trackCon)self.mpDraw = mp.solutions.drawing_utilsdef findHands(self, img, draw=True): imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) self.results = self.hands.process(imgRGB)if self.results.multi_hand_landmarks: for handLms in self.results.multi_hand_landmarks: if draw: self.mpDraw.draw_landmarks(img, handLms, self.mpHands.HAND_CONNECTIONS) return imgdef findPosition(self, img, handNo=0, draw=True):lmList = [] if self.results.multi_hand_landmarks: myHand = self.results.multi_hand_landmarks[handNo] for id, lm in enumerate(myHand.landmark): h, w, c = img.shape cx, cy = int(lm.x * w), int(lm.y * h) lmList.append([id, cx, cy]) if draw: cv2.circle(img, (cx, cy), 15, (255, 0, 255), cv2.FILLED)return lmListdef main(): pTime = 0 cTime = 0 cap = cv2.VideoCapture(0) detector = handDetector() while True: success, img = cap.read() img = detector.findHands(img) lmList = detector.findPosition(img) if len(lmList) != 0: print(lmList[4])cTime = time.time() fps = 1 / (cTime - pTime) pTime = cTimecv2.putText(img, str(int(fps)), (10,70), cv2.FONT_HERSHEY_PLAIN, 3, (255, 0, 255), 3)cv2.imshow("Image", img) cv2.waitKey(1)if __name__ == "__main__": main()

3.VolumeHandControl_demo01.py

import cv2 import time import numpy as np import HandTrackingModule as htm############################################ wCam, hCam = 1, 720 ############################################ cap = cv2.VideoCapture(0) cap.set(3, wCam) cap.set(4, hCam) pTime = 0detector = htm.handDetector()while True: success, img = cap.read() img = detector.findHands(img)cTime = time.time() fps = 1/(cTime-pTime) pTime = cTimecv2.putText(img, f'FPS:{int(fps)})', (40,50), cv2.FONT_HERSHEY_COMPLEX, 1, (255,0,0),3)frame = cv2.flip(img,1) cv2.imshow("Img", img) c = cv2.waitKey(50) if c == 27: break

4.VolumeHandControl_demo02.py

import cv2 import time import numpy as np import HandTrackingModule as htm import math from ctypes import cast, POINTER from comtypes import CLSCTX_ALL from pycaw.pycaw import AudioUtilities, IAudioEndpointVolume############################################ wCam, hCam = 720, 480 ############################################ cap = cv2.VideoCapture(0) cap.set(3, wCam) cap.set(4, hCam) pTime = 0detector = htm.handDetector(detectionCon=0.7)devices = AudioUtilities.GetSpeakers() interface = devices.Activate( IAudioEndpointVolume._iid_, CLSCTX_ALL, None) volume = cast(interface, POINTER(IAudioEndpointVolume)) # volume.GetMute() # volume.GetMasterVolumeLevel() volRange = volume.GetVolumeRange() # volume.SetMasterVolumeLevel(0, None) minVol = volRange[0] maxVol = volRange[1] vol = 0 volBar = 400 volPer = 0while True: success, img = cap.read() img = detector.findHands(img) lmList = detector.findPosition(img, draw=False) if len(lmList) != 0: # print(lmList[4], lmList[8])x1, y1 = lmList[4][1], lmList[4][2] x2, y2 = lmList[8][1], lmList[8][2] cx, cy = (x1 + x2)//2 , (y1 + y2)//2cv2.circle(img, (x1,y1), 15, (255, 0, 255), cv2.FILLED) cv2.circle(img, (x2,y2), 15, (255, 0, 255), cv2.FILLED) cv2.line(img, (x1,y1), (x2,y2), (255, 0, 255), 3) cv2.circle(img, (cx, cy), 15, (255, 0, 255), cv2.FILLED)length = math.hypot(x2 - x1, y2 - y1) # print(length)# Hand range 50 - 300 # Volume Range -65 -0 vol = np.interp(length,[50,300],[minVol,maxVol]) volBar = np.interp(length,[50,300],[400,150]) volPer = np.interp(length,[50,300],[0,100]) print(int(length), vol) volume.SetMasterVolumeLevel(vol, None)if length <= 50: cv2.circle(img, (cx, cy), 15, (0, 255, 0), cv2.FILLED)cv2.rectangle(img, (50, 150), (85, 400), (0, 255, 0), 3) cv2.rectangle(img, (50, int(volBar)), (85, 400), (0, 255, 0), cv2.FILLED) cv2.putText(img, f'{int(volPer)} %', (40, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (250, 0, 0), 3)cTime = time.time() fps = 1/(cTime-pTime) pTime = cTime cv2.putText(img, f'FPS:{int(fps)})', (40,50), cv2.FONT_HERSHEY_COMPLEX, 1, (255,0,0),3)frame = cv2.flip(img,1) cv2.imshow("Img", img) c = cv2.waitKey(50) if c == 27: break