261 lines
13 KiB
Python
261 lines
13 KiB
Python
import cv2
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import os
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from datetime import datetime
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import numpy as np
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import sys
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from pathlib import Path
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import time
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from scipy.spatial.transform import Rotation as R
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sys.path.append(str(Path(__file__).resolve().parent.parent))
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sys.path.append('/home/jsfb/jsfb_ws/MassageRobot_Dobot/Massage/MassageControl')
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from hardware.remote_cam import ToolCamera
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from hardware.aubo_C5 import AuboC5
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np.set_printoptions(precision=8, suppress=True)
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# 设置寻找亚像素角点的参数，采用的停止准则是最大循环次数30和最大误差容限0.001
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criteria = (cv2.TERM_CRITERIA_MAX_ITER | cv2.TERM_CRITERIA_EPS, 30, 0.001)
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# 标定计算类
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# -- 连接AuboC5
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# -- 连接 ToolCamera
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# -- 运动到初始位置 计算下一步运动位姿
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# -- 开始手眼标定
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# -- 输出结果
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class Calibration:
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def __init__(self, board_size, square_size, file_folder):
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"""
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初始化标定计算类。
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board_size: 标定板的格子数，格式为 (width, height)
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square_size: 格子的大小，单位为米
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"""
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self.board_size = board_size
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self.square_size = square_size
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self.file_address = file_folder
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self.objp = self._create_object_points()
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self.image_points = []
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self.robot_poses = []
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self.cam = ToolCamera(host='127.0.0.1')
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self.cam.start()
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self.size = None
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self.intrinsics_mtx = None
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self.disorder = None
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rgb_image, depth_image, camera_intrinsics = self.cam.get_latest_frame()
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if rgb_image is None :
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print(f'===============相机连接失败请检查并重试==============')
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else:
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print(f'===============相机连接成功==============')
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self.size = rgb_image.shape[:2]
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self.intrinsics = camera_intrinsics
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self.arm = AuboC5()
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self.arm.init()
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self.obj_points = []
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print(f'===============机械臂连接成功==============')
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def _create_object_points(self):
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"""创建标定板的3D点"""
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objp = np.zeros((self.board_size[0] * self.board_size[1], 3), np.float32)
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objp[:, :2] = np.mgrid[0:self.board_size[0], 0:self.board_size[1]].T.reshape(-1, 2) * self.square_size
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return objp
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def collect_data(self):
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#移动到初始位置
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pose = [(90) * (np.pi / 180), 4.23 * (np.pi / 180), 124.33 * (np.pi / 180), 61.56 * (np.pi / 180), -89.61 * (np.pi / 180), 0 * (np.pi / 180)]
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code = self.arm.move_joint(pose,4,wait=True)
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if code == -1:
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print("运动到拍照位置失败")
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#拍照
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self.check_corners()
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#运动到下一个位置
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# 目前只设计了44条轨迹
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for i in range (0,45):
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next_pose = self.get_next_pose_ugly(i)
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self.arm.move_joint(next_pose, 4,wait=True)
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if code == -1:
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print("运动到拍照位置失败")
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else:
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self.check_corners()
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def check_corners(self):
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rgb, depth, intrinsics = self.cam.get_latest_frame()
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gray = cv2.cvtColor(rgb, cv2.COLOR_BGR2GRAY)
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ret, corners = cv2.findChessboardCorners(gray, self.board_size, None)
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if ret:
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robot_pose = self.get_pose_homomat()
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self.add_sample(gray,corners,robot_pose)
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print(f"robot_pose = {robot_pose}")
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pic_name = self.file_address + "/"+str(len(self.image_points)) + ".jpg"
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print(f"save path as: {pic_name}")
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# 绘制检测到的角点 (用于可视化，可选)
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cv2.drawChessboardCorners(rgb, self.board_size, corners, ret)
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cv2.imwrite(pic_name, rgb)
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return True
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return False
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def add_sample(self, gray, corners,robot_pose):
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"""
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添加标定样本。
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image: 捕获的图像
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robot_pose: 机械臂的位姿，格式为 [x, y, z, roll, pitch, yaw]
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"""
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# 检测棋盘角点
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self.obj_points.append(self.objp)
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corners2 = cv2.cornerSubPix(gray, corners, (5, 5), (-1, -1), criteria) # 在原角点的基础上寻找亚像素角点
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if [corners2]:
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self.image_points.append(corners2)
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else:
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self.image_points.append(corners)
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self.robot_poses.append(robot_pose)
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def get_pose_homomat(self):
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pose = self.arm.robot_rpc_client.getRobotInterface(self.arm.robot_name).getRobotState().getTcpPose()
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x, y, z, rx, ry, rz = pose
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r = R.from_euler('xyz', [rx, ry, rz])
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rotation_matrix = r.as_matrix()
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translation_vector = np.array([x, y, z]).reshape(3, 1)
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homo_mat = np.eye(4) # 创建 4x4 单位矩阵
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homo_mat[:3, :3] = rotation_matrix
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homo_mat[:3, 3] = translation_vector.flatten()
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return homo_mat
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def camera_calib(self):
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# print(f"size is {self.size}")
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ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(self.obj_points, self.image_points, (self.size[1], self.size[0]), None, None)
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# print("rvecs,tvecs:",rvecs,tvecs)
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if ret:
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print("内参矩阵:\n", mtx) # 内参数矩阵
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print("畸变系数:\n", dist) # 畸变系数 distortion cofficients = (k_1,k_2,p_1,p_2,k_3)
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print("++++++++++相机标定完成++++++++++++++")
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self.intrinsics_mtx = mtx
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self.disorder = dist
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else:
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print("-----------相机标定失败--------------")
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return rvecs, tvecs
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def split_robot_pose(self):
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"""
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将包含 4x4 齐次矩阵的 robot_pose 列表拆分成 R_arm 和 t_arm。
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Args:
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robot_pose: 包含 4x4 齐次矩阵的 Python list。
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Returns:
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R_arm: 旋转矩阵的 Python list, 每个元素是 3x3 numpy 数组。
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t_arm: 平移向量的 Python list, 每个元素是 3x1 numpy 数组。
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"""
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R_arm = []
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t_arm = []
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for pose_matrix in self.robot_poses:
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# # 1. 提取旋转矩阵 (3x3)
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rotation_matrix = pose_matrix[:3, :3]
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R_arm.append(rotation_matrix)
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# R_arm.append(pose_matrix[:3])
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# 2. 提取平移向量 (3x1)
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translation_vector = pose_matrix[:3, 3] # 获取齐次矩阵的平移部分 (1x3 numpy 数组)
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translation_vector = translation_vector.reshape(3, 1) # 转换为 3x1 的列向量
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t_arm.append(translation_vector)
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return R_arm, t_arm
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def calibrate(self):
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rvecs, tvecs = self.camera_calib()
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r_arm, t_arm= self.split_robot_pose()
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rvecs_array = np.array(rvecs)
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tvecs_array = np.array(tvecs)
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t_arma = np.array(t_arm)
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r_arma = np.array(r_arm)
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print(rvecs_array.shape)
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print(tvecs_array.shape)
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print(t_arma.shape)
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print(r_arma.shape)
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R, t = cv2.calibrateHandEye(r_arma, t_arma, rvecs_array, tvecs_array, cv2.CALIB_HAND_EYE_TSAI)
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print("+++++++++++手眼标定完成+++++++++++++++")
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return R, t ,self.intrinsics_mtx
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#得到图片以后 根据pose判断方向：
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def get_next_pose_future(self):
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return None
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def get_next_pose_ugly(self,index):
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poselist =[
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.1267845650875392, -1.5116296651522887, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.1791444426473692, -1.4592697875924587, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0220648099678793, -1.5116296651522887, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 0.9697049324080494, -1.4592697875924587, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 0.9173450548482196, -1.4069099100326288, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.1267845650875392, -1.6163494202719486, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.1791444426473692, -1.6687092978317786, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.2315043202071991, -1.7210691753916085, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.109331272567596, -1.4941763726323454, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.1442378576074825, -1.424363202552572, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.1791444426473692, -1.3545500324727988, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.2140510276872558, -1.2847368623930255, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.109331272567596, -1.633802712791892, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.1442378576074825, -1.7036158828716652, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0395181024878226, -1.4941763726323454, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.004611517447936, -1.424363202552572, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 0.9697049324080493, -1.3545500324727988, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 0.9347983473681627, -1.2847368623930255, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0],
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[1.5707963267948966, 0.06510078109938851, 2.16996785900455, 1.0395181024878226, -1.4941763726323454, 0.0],
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[1.5707963267948966, 0.05637413483941686, 2.16996785900455, 1.004611517447936, -1.424363202552572, 0.0],
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[1.5707963267948966, 0.047647488579445216, 2.16996785900455, 0.9697049324080493, -1.3545500324727988, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0],
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[1.562069680534925, 0.07382742735936015, 2.16996785900455, 1.0395181024878226, -1.4941763726323454, 0.0],
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[1.5533430342749535, 0.07382742735936015, 2.16996785900455, 1.004611517447936, -1.424363202552572, 0.0],
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[1.544616388014982, 0.07382742735936015, 2.16996785900455, 0.9697049324080493, -1.3545500324727988, 0.0],
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[1.5358897417550104, 0.07382742735936015, 2.16996785900455, 0.9347983473681627, -1.2847368623930255, 0.0],
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[1.5271630954950388, 0.07382742735936015, 2.16996785900455, 0.8998917623282761, -1.2149236923132523, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0],
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[1.579522973054868, 0.07382742735936015, 2.16996785900455, 1.109331272567596, -1.5988961277520053, 0.0],
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[1.5882496193148397, 0.07382742735936015, 2.16996785900455, 1.1442378576074825, -1.633802712791892, 0.0],
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[1.5969762655748112, 0.07382742735936015, 2.16996785900455, 1.1791444426473692, -1.6687092978317786, 0.0],
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[1.6057029118347828, 0.07382742735936015, 2.16996785900455, 1.2140510276872558, -1.7036158828716652, 0.0],
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[1.6144295580947543, 0.07382742735936015, 2.16996785900455, 1.2489576127271425, -1.7385224679115518, 0.0],
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[1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0],
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[1.579522973054868, 0.07382742735936015, 2.16996785900455, 1.1267845650875392, -1.581442835232062, 0.0],
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[1.5882496193148397, 0.07382742735936015, 2.16996785900455, 1.1791444426473692, -1.5988961277520053, 0.0],
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[1.5969762655748112, 0.07382742735936015, 2.16996785900455, 1.2315043202071991, -1.6163494202719486, 0.0],
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[1.6057029118347828, 0.07382742735936015, 2.16996785900455, 1.283864197767029, -1.633802712791892, 0.0],
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[1.6144295580947543, 0.07382742735936015, 2.16996785900455, 1.336224075326859, -1.6512560053118353, 0.0]
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]
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return poselist[index]
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if __name__ == '__main__':
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# 标定板的width 对应的角点的个数 6
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# 标定板的height 对应的角点的个数 3
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calibration_board_size = [6,3]
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calibration_board_square_size = 0.027 #unit - meter
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systemPath = "/home/jsfb/Documents/"
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now = datetime.now()
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date_time_str = now.strftime("%d_%H_%M")
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folder_name = f"calibration_data_{date_time_str}"
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systemPath+=folder_name
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os.makedirs(systemPath, exist_ok=True)
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calib = Calibration(calibration_board_size,calibration_board_square_size,systemPath)
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calib.collect_data()
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R, t,intrin = calib.calibrate()
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print("旋转矩阵：")
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print(R)
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print("平移向量：")
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print(t)
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print(f"内参： {intrin}")
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