import sys import time import numpy as np import cv2 import glob from math import * import pandas as pd from pathlib import Path 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) retval, rvec, tvec = cv2.solvePnP(objpoints[0], imgpoints[0], cam_mtx, cam_dist) # print(rvec.reshape((1,3))) RT = np.column_stack(((cv2.Rodrigues(rvec))[0], tvec)) RT = np.row_stack((RT, np.array([0, 0, 0, 1]))) # print(RT) 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 += "/*.jpg" 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) 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 = [0, 0, 0, 0, 5, 0] # 初始关节角度 move_step = 5 # 每次移动5度 move_range = [-15, 15] # 关节移动的阈值范围 # 保存 Excel 的路径和文件 excel_path = Path("robot_calibration_data.xlsx") # 创建 DataFrame 来存储机械臂坐标 data = { "x1": [], "x2": [], "x3": [],"x4": [],"x5": [],"x6": [],"image_path": [] } 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 # 遍历各轴，依次移动 for axis in range(6): if axis==4: continue for move in range(move_range[0], move_range[1] + 1, move_step): # 计算目标关节位置 joint_angles = initial_joint_angles.copy() joint_angles[axis] = move # 移动机械臂到指定位置 robot.send_position_command(*joint_angles,MoveType.AXIS) # 拍照 time.sleep(10) _,rgb_image = ca.get_img() # 保存图片 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() x1 = real_position.X x2 = real_position.Y x3 = real_position.Z x4 = real_position.U x5 = real_position.V x6 = real_position.W # 保存数据到DataFrame data["x1"].append(x1) data["x2"].append(x2) data["x3"].append(x3) data["x4"].append(x4) data["x5"].append(x5) data["x6"].append(x6) data["image_path"].append(str(img_path)) # 将数据写入Excel df = pd.DataFrame(data) 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 + '/robot_calibration_data.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]) 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") 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 = 12 - 1 # 标定板长度 chess_board_y_num = 9 - 1 chess_board_len = 30 / 1000 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) chess_base_mtx = np.zeros((4, 4), np.float32) # 结果验证,原则上来说,每次结果相差较小 for i in range(20): RT_end_to_base = np.column_stack((R_all_end_to_base_1[i], T_all_end_to_base_1[i])) RT_end_to_base = np.row_stack((RT_end_to_base, np.array([0, 0, 0, 1]))) RT_chess_to_cam = np.column_stack((R_all_chess_to_cam_1[i], T_all_chess_to_cam_1[i])) RT_chess_to_cam = np.row_stack((RT_chess_to_cam, np.array([0, 0, 0, 1]))) RT_cam_to_end = np.column_stack((R, T)) RT_cam_to_end = np.row_stack((RT_cam_to_end, np.array([0, 0, 0, 1]))) R_cam_to_end = RT_cam_to_end[:3, :3] T_cam_to_end = RT_cam_to_end[:3, 3] rotation_vector, _ = cv2.Rodrigues(R_cam_to_end) print("11111111", rotation_vector) print("222222222", R_cam_to_end) thetaX = rotation_vector[0][0] * 180 / pi thetaY = rotation_vector[1][0] * 180 / pi thetaZ = rotation_vector[2][0] * 180 / pi # print(RT_cam_to_end) RT_chess_to_base = RT_end_to_base @ RT_cam_to_end @ RT_chess_to_cam # 即为固定的棋盘格相对于机器人基坐标系位姿 print('') print(RT_chess_to_base) chess_base_mtx += RT_chess_to_base chess_pose = np.array([0, 0, 0, 1]) # 转换棋盘坐标到像素坐标 world_pose = RT_chess_to_base @ chess_pose.T cam_pose = np.linalg.inv(RT_end_to_base @ RT_cam_to_end) @ world_pose print(cam_pose) # cam_pose = RT_chess_to_cam@chess_pose.T zero3 = np.zeros((3, 1)) imgTocam_matrix = np.hstack((cam_mtx, zero3)) # 3*4 矩阵 imgTocam_matrix = np.row_stack((imgTocam_matrix, np.array([0, 0, 0, 1]))) img_pose = imgTocam_matrix @ (cam_pose / cam_pose[2]) # 像素坐标转换为世界坐标（机械臂基坐标） cam_pose = np.linalg.inv(imgTocam_matrix) @ img_pose cam_pose = cam_pose * (1 / cam_pose[3]) world_pose = RT_end_to_base @ RT_cam_to_end @ cam_pose print(chess_base_mtx / 20)