import sys import time import numpy as np import cv2 import glob from math import * import pandas as pd from pathlib import Path import openpyxl from COM.COM_Robot import RobotClient from app import MainWindow from Vision.tool.CameraRVC import camera from Model.RobotModel import DataAddress,MoveType from PySide6.QtWidgets import QMainWindow, QApplication, QLineEdit # 用于根据欧拉角计算旋转矩阵 def myRPY2R_robot(x, y, z): Rx = np.array([[1, 0, 0], [0, cos(x), -sin(x)], [0, sin(x), cos(x)]]) # 这是将示教器上的角度转换成旋转矩阵 Ry = np.array([[cos(y), 0, sin(y)], [0, 1, 0], [-sin(y), 0, cos(y)]]) Rz = np.array([[cos(z), -sin(z), 0], [sin(z), cos(z), 0], [0, 0, 1]]) # R = Rx@Ry@Rz R = Rz @ Ry @ Rx return R # 用于根据位姿计算变换矩阵 def pose_robot(x, y, z, Tx, Ty, Tz): thetaX = Tx / 180 * pi thetaY = Ty / 180 * pi thetaZ = Tz / 180 * pi R = myRPY2R_robot(thetaX, thetaY, thetaZ) t = np.array([[x], [y], [z]]) RT1 = np.column_stack([R, t]) # 列合并 RT1 = np.row_stack((RT1, np.array([0, 0, 0, 1]))) return RT1 ''' #用来从棋盘格图片得到相机外参 :param img_path: 读取图片路径 :param chess_board_x_num: 棋盘格x方向格子数 :param chess_board_y_num: 棋盘格y方向格子数 :param chess_board_len: 单位棋盘格长度,mm :param cam_mtx: 相机内参 :param cam_dist: 相机畸变参数 :return: 相机外参 ''' def get_RT_from_chessboard(img_path, chess_board_x_num, chess_board_y_num, chess_board_len, cam_mtx, cam_dist): # termination criteria criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) # prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0) # 标定板世界坐标 objp = np.zeros((chess_board_y_num * chess_board_x_num, 3), np.float32) for m in range(chess_board_y_num): for n in range(chess_board_x_num): objp[m * chess_board_x_num + n] = [n * chess_board_len, m * chess_board_len, 0] # Arrays to store object points and image points from all the images. objpoints = [] # 3d point in real world space imgpoints = [] # 2d points in image plane. img = cv2.imread(img_path) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Find the chess board corners ret, corners = cv2.findChessboardCorners(gray, (chess_board_x_num, chess_board_y_num), None) # If found, add object points, image points (after refining them) if ret == True: objpoints.append(objp) corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria) imgpoints.append(corners2) # 可视化棋盘格角点检测结果 img = cv2.drawChessboardCorners(img, (chess_board_x_num, chess_board_y_num), corners2, ret) cv2.imshow('Corners', img) cv2.waitKey(500) # 显示500ms retval, rvec, tvec = cv2.solvePnP(objpoints[0], imgpoints[0], cam_mtx, cam_dist) RT = np.column_stack(((cv2.Rodrigues(rvec))[0], tvec)) RT = np.row_stack((RT, np.array([0, 0, 0, 1]))) return RT return 0 # 计算board to cam 变换矩阵 # img_path = "./hand_eye" 棋盘格存放文件夹 def get_chess_to_cam_mtx(img_path, chess_board_x_num, chess_board_y_num, chess_board_len, cam_mtx, cam_dist): img_path += "/image_*.png" R_all_chess_to_cam_1 = [] T_all_chess_to_cam_1 = [] images = glob.glob(img_path) for fname in images: RT = get_RT_from_chessboard(fname, chess_board_x_num, chess_board_y_num, chess_board_len, cam_mtx, cam_dist) print("chesstocam",RT) R_all_chess_to_cam_1.append(RT[:3, :3]) T_all_chess_to_cam_1.append(RT[:3, 3].reshape((3, 1))) return R_all_chess_to_cam_1, T_all_chess_to_cam_1 def save_images(image, path, img_num): """保存拍摄的图片""" img_path = path / f'image_{img_num}.png' cv2.imwrite(str(img_path), image) def save_pic_and_excel(): # 将机械臂的末端位姿保存在excel文件里,以及拍摄的图片保存在pic里# 动，拍照，保存 robot.send_switch_tool_command() # 定义初始关节位置 initial_joint_angles = [5, 0, 0, 0, -12, 0] # 初始关节角度 fixed_joint_positions = [ [0.000,0.000,-0.005,0.001,-68.752,0.000], [13.944,-2.732,-0.007,0.001,-71.402,0.000], [7.256,-2.732,-0.011,0.000,-71.402,-13.003], [1.061,3.196,-4.484,0.000,-71.402,-13.003], [1.061,-7.045,-4.486,8.935,-60.210,-13.003], [-12.811,-7.045,-4.488,15.131,-60.210,-5.156], [-12.811,3.929,-12.911,15.131,-62.695,-5.156], [14.269,3.929,-12.915,-7.996,-62.695,-5.156], [14.269,3.929,-9.102,-9.642,-66.652,6.868], [14.269,10.147,-13.732,-9.642,-66.652,6.868], [4.356,10.147,-13.736,-2.658,-66.652,6.868], [4.356,10.147,-13.736,-2.658,-66.652,21.705], [4.356,10.147,-3.741,-2.658,-74.334,21.705] ] # 保存 Excel 的路径和文件 excel_path = Path("calibration_images//robot_calibration_data.xlsx") # 创建 DataFrame 来存储机械臂坐标 data_list = [] img_num = 0 # 图片编号 image_save_path = Path("calibration_images") image_save_path.mkdir(exist_ok=True) # 创建目录保存图像 # 遍历固定点位，依次移动机械臂 for joint_angles in fixed_joint_positions: # 移动机械臂到指定位置 robot.send_position_command(*joint_angles, MoveType.AXIS) time.sleep(20) # 获取图像并保存 _, rgb_image = ca.get_img() save_images(rgb_image, image_save_path, img_num) img_path = image_save_path / f'image_{img_num}.png' img_num += 1 # 获取当前机械臂的世界坐标 real_position = robot.robotClient.status_model.getRealPosition() position = [real_position.X, real_position.Y, real_position.Z, real_position.U, real_position.V, real_position.W] # 将当前机械臂的坐标和图片路径存储为元组 data_list.append([position, str(img_path)]) # 将数据写入 Excel df = pd.DataFrame(data_list, columns=["Position", "Image Path"]) df.to_excel(excel_path, index=False) print(f"数据已保存至 {excel_path}") # 计算end to base变换矩阵 # path = "./hand_eye" 棋盘格和坐标文件存放文件夹 def get_end_to_base_mtx(path): img_path = path + "/image_*.png" coord_file_path = path + '/机械臂末端位姿.xlsx' # 从记录文件读取机器人六个位姿 sheet_1 = pd.read_excel(coord_file_path) R_all_end_to_base_1 = [] T_all_end_to_base_1 = [] # print(sheet_1.iloc[0]['ax']) images = glob.glob(img_path) # print(len(images)) for i in range(len(images)): pose_str = sheet_1.values[i][0] pose_array = [float(num) for num in pose_str[1:-1].split(',')] # print(pose_array) RT = pose_robot(pose_array[0], pose_array[1], pose_array[2], pose_array[3], pose_array[4], pose_array[5]) print("end_base",RT) R_all_end_to_base_1.append(RT[:3, :3]) T_all_end_to_base_1.append(RT[:3, 3].reshape((3, 1))) return R_all_end_to_base_1, T_all_end_to_base_1 if __name__ == "__main__": print("手眼标定测试\n") np.set_printoptions(suppress=True) # app = QApplication(sys.argv) # robot = MainWindow() # ca = camera() # dataaddress = DataAddress() # 相机内参 cam_mtx = np.array([[2402.1018066406, 0.0000000000, 739.7069091797], [0.0000000000, 2401.7873535156, 584.7304687500], [0.00000000e+00, 0.00000000e+00, 1.00000000e+00]], dtype=np.float64) # 畸变系数 cam_dist = np.array([-0.0424814112, 0.2438604534, -0.3833343089, -0.3833343089, 0.0007602088], dtype=np.float64) # save_pic_and_excel() chess_board_x_num = 11 # 标定板长度 chess_board_y_num = 8 chess_board_len = 30 path = "./calibration_images" R_all_chess_to_cam_1, T_all_chess_to_cam_1 = get_chess_to_cam_mtx(path, chess_board_x_num, chess_board_y_num, chess_board_len, cam_mtx, cam_dist) R_all_end_to_base_1, T_all_end_to_base_1 = get_end_to_base_mtx(path) R, T = cv2.calibrateHandEye(R_all_end_to_base_1, T_all_end_to_base_1, R_all_chess_to_cam_1, T_all_chess_to_cam_1, cv2.CALIB_HAND_EYE_TSAI) # 手眼标定 RT = np.column_stack((R, T)) RT = np.row_stack((RT, np.array([0, 0, 0, 1]))) # 即为cam to end变换矩阵00 print('相机相对于末端的变换矩阵为：') print(RT)