diff --git a/Trace/calibrate_test.py b/Trace/calibrate_test.py new file mode 100644 index 0000000..4776fe1 --- /dev/null +++ b/Trace/calibrate_test.py @@ -0,0 +1,218 @@ +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) \ No newline at end of file