OpenCV_mediapipe手势识别

# -*- coding:utf-8 -*-
import cv2
import mediapipe as mp

class HandDetector:
    def __init__(self, mode=False, maxHands=2,modelComplexity=1, detectionCon=0.5, minTrackCon=0.5):
        self.mode = mode
        self.maxHands = maxHands
        self.detectionCon = detectionCon
        self.minTrackCon = minTrackCon
        self.modelComplex=modelComplexity
        self.mpHands = mp.solutions.hands # mp的手部支持模块
        # Hands完成对Hands初始化配置
        self.hands = self.mpHands.Hands(self.mode, self.maxHands, self.modelComplex,
                                        self.detectionCon, self.minTrackCon)
        #mode为True为图片输入
        # maxhand 为最大手数目
        # modelcomplex为模型的复杂度
        # detectioncon和trackcon为置信度阈值，越大越准确
        self.mpDraw = mp.solutions.drawing_utils# 用于绘制
        self.tipIds = [4, 8, 12, 16, 20]#对应手指的指尖
        self.fingers = []
        self.lmList = []

    def findHands(self, img, draw=True):
        imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # 将传入的图转为RGB模式，
        # 返回一个列表，包含21个标志点的x、y、z的值
        self.results = self.hands.process(imgRGB)# 完成图像的处理，输入必须为RGB格式的图片

        if self.results.multi_hand_landmarks:# hands in list
            for handLms in self.results.multi_hand_landmarks:# 提取每个指头并绘制标点和连接线
                if draw:
                    self.mpDraw.draw_landmarks(img, handLms,
                                               self.mpHands.HAND_CONNECTIONS)
        return img

    def findPosition(self, img, handNo=0, draw=True):
        xList = []
        yList = []
        bbox = []
        bboxInfo =[]
        self.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
                px, py = int(lm.x * w), int(lm.y * h)
                xList.append(px)
                yList.append(py)
                self.lmList.append([px, py])
                if draw:
                    cv2.circle(img, (px, py), 5, (255, 0, 255), cv2.FILLED)
            xmin, xmax = min(xList), max(xList)
            ymin, ymax = min(yList), max(yList)
            boxW, boxH = xmax - xmin, ymax - ymin
            bbox = xmin, ymin, boxW, boxH
            cx, cy = bbox[0] + (bbox[2] // 2), \
                     bbox[1] + (bbox[3] // 2)
            bboxInfo = {"id": id, "bbox": bbox,"center": (cx, cy)}

            if draw:
                cv2.rectangle(img, (bbox[0] - 20, bbox[1] - 20),
                              (bbox[0] + bbox[2] + 20, bbox[1] + bbox[3] + 20),
                              (0, 255, 0), 2)

        return self.lmList, bboxInfo

    def fingersUp(self):
        if self.results.multi_hand_landmarks:
            myHandType = self.handType()
            fingers = []
            # Thumb
            if myHandType == "Right":
                if self.lmList[self.tipIds[0]][0] > self.lmList[self.tipIds[0] - 1][0]:
                    fingers.append(1)
                else:
                    fingers.append(0)
            else:
                if self.lmList[self.tipIds[0]][0] < self.lmList[self.tipIds[0] - 1][0]:
                    fingers.append(1)
                else:
                    fingers.append(0)

            # 4 Fingers
            for id in range(1, 5):
                if self.lmList[self.tipIds[id]][1] < self.lmList[self.tipIds[id] - 2][1]:
                    fingers.append(1)
                else:
                    fingers.append(0)
        return fingers
    # 判别左右手
    def handType(self):
        if self.results.multi_hand_landmarks:
            if self.lmList[17][0] < self.lmList[5][0]:
                return "Right"
            else:
                return "Left"

# -*- coding:utf-8 -*-
import cv2
from HandTrackingModule import HandDetector

class Main:
    def __init__(self):
        self.camera = cv2.VideoCapture(0,cv2.CAP_DSHOW)# 摄像头捕获
        self.camera.set(3, 1280)#分辨率
        self.camera.set(4, 720)
    # 手势识别的函数
    def Gesture_recognition(self):
        while True:
            self.detector = HandDetector()# 调用hand模块
            frame, img = self.camera.read()#捕获摄像头输入
            img = self.detector.findHands(img)# 调用findhand函数
            lmList, bbox = self.detector.findPosition(img)# 手势识别 lmlist关节位置方位bbox为方框

            if lmList:# 如果非空，返回TRUE
                x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
                x1, x2, x3, x4, x5 = self.detector.fingersUp()

                if (x2 == 1 and x3 == 1) and (x4 == 0 and x5 == 0 and x1 == 0):
                    cv2.putText(img, "2_TWO", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
                                (0, 0, 255), 3)
                elif (x2 == 1 and x3 == 1 and x4 == 1) and (x1 == 0 and x5 == 0):
                    cv2.putText(img, "3_THREE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
                                (0, 0, 255), 3)
                elif (x2 == 1 and x3 == 1 and x4 == 1 and x5 == 1) and (x1 == 0):
                    cv2.putText(img, "4_FOUR", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
                                (0, 0, 255), 3)
                elif x1 == 1 and x2 == 1 and x3 == 1 and x4 == 1 and x5 == 1:
                    cv2.putText(img, "5_FIVE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
                                (0, 0, 255), 3)
                elif x2 == 1 and (x1 == 0, x3 == 0, x4 == 0, x5 == 0):
                    cv2.putText(img, "1_ONE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
                                (0, 0, 255), 3)
                elif x1 and (x2 == 0, x3 == 0, x4 == 0, x5 == 0):
                    cv2.putText(img, "GOOD!", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
                                (0, 0, 255), 3)
            cv2.imshow("camera", img)# 显示图片
            # 点击窗口关闭按钮退出程序
            if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
                break
            # 点击小写字母q 退出程序
            if cv2.waitKey(1) & 0xFF == ord("q"):
                break
# main
if __name__ == '__main__':
    Solution = Main()
    Solution.Gesture_recognition()