机器臂(二)--视觉控制关节点

在先前配置好的机器臂教程接着下做

先是视觉方面demo

手势识别的mediapipe 是谷歌开源项目这个的主要功能是识别手势然后封装成类给下面的控制模块调用

import cv2
import mediapipe as mpclass HandDetector:def __init__(self, mode=False, maxHands=2, detectionCon=0.5, minTrackCon=0.5):self.mode = modeself.maxHands = maxHandsself.detectionCon = detectionConself.minTrackCon = minTrackConself.mpHands = mp.solutions.handsself.hands = self.mpHands.Hands(static_image_mode=self.mode, max_num_hands=self.maxHands,min_detection_confidence=self.detectionCon,min_tracking_confidence=self.minTrackCon)self.mpDraw = mp.solutions.drawing_utilsself.tipIds = [4, 8, 12, 16, 20]self.fingers = []self.lmList = []def findHands(self, img, draw=True, flipType=True):imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)self.results = self.hands.process(imgRGB)allHands = []h, w, c = img.shapeif self.results.multi_hand_landmarks:for handType, handLms in zip(self.results.multi_handedness, self.results.multi_hand_landmarks):myHand = {}## lmListmylmList = []xList = []yList = []for id, lm in enumerate(handLms.landmark):px, py, pz = int(lm.x * w), int(lm.y * h), int(lm.z * w)mylmList.append([px, py, pz])xList.append(px)yList.append(py)## bboxxmin, xmax = min(xList), max(xList)ymin, ymax = min(yList), max(yList)boxW, boxH = xmax - xmin, ymax - yminbbox = xmin, ymin, boxW, boxHcx, cy = bbox[0] + (bbox[2] // 2), \bbox[1] + (bbox[3] // 2)myHand["lmList"] = mylmListmyHand["bbox"] = bboxmyHand["center"] = (cx, cy)if flipType:if handType.classification[0].label == "Right":myHand["type"] = "Left"else:myHand["type"] = "Right"else:myHand["type"] = handType.classification[0].labelallHands.append(myHand)## drawif draw:self.mpDraw.draw_landmarks(img, handLms,self.mpHands.HAND_CONNECTIONS)cv2.rectangle(img, (bbox[0] - 20, bbox[1] - 20),(bbox[0] + bbox[2] + 20, bbox[1] + bbox[3] + 20),(255, 0, 255), 2)cv2.putText(img, myHand["type"], (bbox[0] - 30, bbox[1] - 30), cv2.FONT_HERSHEY_PLAIN,2, (255, 0, 255), 2)if draw:return allHands, imgelse:return allHandsdef fingersUp(self, myHand):"""判断多少个手指起来"""myHandType = myHand["type"]myLmList = myHand["lmList"]if self.results.multi_hand_landmarks:fingers = []# Thumbif myHandType == "Right":fingers.append(0)#右手if myLmList[self.tipIds[0]][0] > myLmList[self.tipIds[0] - 1][0]:fingers.append(1)else:fingers.append(0)else:fingers.append(1)#左手if myLmList[self.tipIds[0]][0] < myLmList[self.tipIds[0] - 1][0]:fingers.append(1)else:fingers.append(0)# 4 Fingersfor id in range(1, 5):if myLmList[self.tipIds[id]][1] < myLmList[self.tipIds[id] - 2][1]:fingers.append(1)else:fingers.append(0)return fingersdef main():cap = cv2.VideoCapture(0)detector = HandDetector(detectionCon=0.8, maxHands=2)while True:# Get image framesuccess, img = cap.read()# Find the hand and its landmarkshands, img = detector.findHands(img)  # with draw# hands = detector.findHands(img, draw=False)  # without drawif hands:# Hand 1hand1 = hands[0]lmList1 = hand1["lmList"]  # List of 21 Landmark pointshandType1 = hand1["type"]  # Handtype Left or Rightfingers1 = detector.fingersUp(hand1)print(fingers1)if len(hands) == 2:# Hand 2hand2 = hands[1]lmList2 = hand2["lmList"]  # List of 21 Landmark pointsbbox2 = hand2["bbox"]  # Bounding box info x,y,w,hcenterPoint2 = hand2['center']  # center of the hand cx,cyhandType2 = hand2["type"]  # Hand Type "Left" or "Right"fingers2 = detector.fingersUp(hand2)print(fingers2)# Displaycv2.imshow("Image", img)cv2.waitKey(1)if __name__ == "__main__":main()

控制关节点的python demo 模块

#! /usr/bin/python3.8
# -*- coding: utf-8 -*-
from operator import ge
from time import sleep
import HandTrackingModule as Hd
import rospy
import cv2
from sensor_msgs.msg import JointState
import sys, termiosmsg = """
Control Your Turtlebot!
---------------------------
Moving around:q    w    e   r   t   ya    s    d   f   g   hj/l : increase/decrease precision by 0.05
space key, k : reset
anything else : stop smoothly
b : switch to arm_four/arm_six
precision is not less than or equal to zero
CTRL-C to quit
"""mode = 0 #六自由度模式precision = 0.05 #默认精度(rad)#键值对应转动方向
rotateBindings = {'q':(1,1),#第一个关节顺时针'a':(1,-1),'w':(2,1),'s':(2,-1),'e':(3,1),'d':(3,-1),'r':(4,1),'f':(4,-1),'t':(5,1),'g':(5,-1),'y':(6,1),'h':(6,-1)}#键值对应精度增量
precisionBindings={'j':0.01,'l':-0.01}#以字符串格式返回当前控制精度
def prec(precision):return "currently:\tprecision %s " %precision
# 检测手势
def detect_hands_gesture(result):
#0 1 2 3 4 5 6
#7 8 9 10 11 12if result[0]==0:if (result[1:] == [0,1,0,0,0]):gesture = "a" #return  gestureelif (result[1:] == [0,1,1,0,0]):gesture = "s"return  gestureelif (result[1:] == [0,0,1,1,1]):gesture = "d"return  gestureelif (result[1:] == [0,1,1,1,1]):gesture = "f"return  gestureelif (result[1:] == [1,1,1,1,1]):gesture = "g"return  gestureelif (result[1:] == [1,0,0,0,1]):gesture = "h"return  gestureelif (result[1:] == [1,1,0,0,1]):gesture = " " #R&K手臂直立return  gestureelif result[0]==1:if (result[1:] == [0,1,0,0,0]):gesture = "q"return  gestureelif (result[1:] == [0,1,1,0,0]):gesture = "w"return  gestureelif (result[1:] == [0,0,1,1,1]):gesture = "e"return  gestureelif (result[1:] == [0,1,1,1,1]):gesture = "r"return  gestureelif (result[1:] == [1,1,1,1,1]):gesture = "t"return  gestureelif (result[1:] == [1,0,0,0,1]):#小拇指gesture = "y"return  gesture#主函数
if __name__=="__main__":settings = termios.tcgetattr(sys.stdin) #获取键值初始化，读取终端相关属性rospy.init_node('arm_teleop') #创建ROS节点pub = rospy.Publisher('/joint_states', JointState, queue_size=5) #创建机械臂状态话题发布者#关节1-6对应弧度状态joints = [0,0,0,0,0,0]"""机械臂关节初始化"""jointState = JointState() #创建ROS机械臂装态话题消息变量jointState.header.stamp = rospy.Time.now()jointState.name=["joint1","joint2","joint3","joint4","joint5","joint6"]jointState.position=jointspub.publish(jointState) #ROS发布机械臂状态话题# 接入USB摄像头时，注意修改cap设备的编号cap = cv2.VideoCapture(0)detector = Hd.HandDetector(detectionCon=0.8, maxHands=2)if not cap.isOpened():print("Can not open camera.")exit()try:print(msg) #打印控制说明print(prec(precision)) #打印当前精度key = " "while(1):# Get image framesuccess, img = cap.read()# Find the hand and its landmarkshands, img = detector.findHands(img)  # with draw# hands = detector.findHands(img, draw=False)  # without drawcv2.imshow("Image", img)cv2.waitKey(1)if hands :key  = detect_hands_gesture(detector.fingersUp(hands[0])) #判断键值是在控制机械臂运动的键值内if key in rotateBindings.keys():joints[rotateBindings[key][0]-1] = joints[rotateBindings[key][0]-1] + precision*rotateBindings[key][1]if joints[rotateBindings[key][0]-1]>1.57:joints[rotateBindings[key][0]-1]=1.57elif joints[rotateBindings[key][0]-1]<-1.57:joints[rotateBindings[key][0]-1]=-1.57#判断键值是否在精度增量键值内elif key in precisionBindings.keys():if (precision+precisionBindings[key])<0.01 or (precision+precisionBindings[key])>0.1:passelse:precision+=precisionBindings[key]print(prec(precision)) #精度发生变化，打印出来#根据机械臂自由度给joint_states话题赋予消息if mode:jointState.header.stamp = rospy.Time.now()jointState.name=["joint1","joint2","joint3","joint4"]jointState.position=joints[0:4]else:jointState.header.stamp = rospy.Time.now()jointState.name=["joint1","joint2","joint3","joint4","joint5","joint6"]jointState.position=jointspub.publish(jointState) #ROS发布关节状态话题 elif key ==" ":joints = [0,0,0,0,0,0]    #运行出现问题则程序终止并打印相关错误信息except Exception as e:print(e)#程序结束前发布速度为0的速度话题finally:print("Keyboard control off")#程序结束前打印消息提示termios.tcsetattr(sys.stdin, termios.TCSADRAIN, settings)

感觉判断的地方有点小拉跨

所有的python 写完记得

sudo chmod 777 *.py

启动桌面机器人的基础服务

roslaunch table_arm base_serial.launch

启动

roslaunch table_arm arm_opencv_move.py

机器臂直立 R&K 直立

ros_opencv_机器爪控制-2_哔哩哔哩_bilibili

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机器臂(二)--视觉控制关节点

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