243 lines
7.6 KiB
Python
243 lines
7.6 KiB
Python
# coding=utf-8
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"""
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眼在手上 用采集到的图片信息和机械臂位姿信息计算 相机坐标系相对于机械臂末端坐标系的 旋转矩阵和平移向量
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A2^{-1}*A1*X=X*B2*B1^{−1}
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"""
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import os
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import logging
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import yaml
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import cv2
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import numpy as np
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from scipy.spatial.transform import Rotation as R
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from libs.auxiliary import find_latest_data_folder
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from libs.log_setting import CommonLog
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from save_poses import poses_main
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np.set_printoptions(precision=8,suppress=True)
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logger_ = logging.getLogger(__name__)
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logger_ = CommonLog(logger_)
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current_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),"eye_hand_data")
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images_path = os.path.join("eye_hand_data",find_latest_data_folder(current_path))
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file_path = os.path.join(images_path,"RobotToolPose.csv") #采集标定板图片时对应的机械臂末端的齐次变换矩阵 从 第一行到最后一行 需要和采集的标定板的图片顺序进行对应
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with open("config.yaml", 'r', encoding='utf-8') as file:
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data = yaml.safe_load(file)
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XX = data.get("checkerboard_args").get("XX") #标定板的中长度对应的角点的个数
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YY = data.get("checkerboard_args").get("YY") #标定板的中宽度对应的角点的个数
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L = data.get("checkerboard_args").get("L") #标定板一格的长度 单位为米
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import numpy as np
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def eulerZYXToRotationMatrix(rx, ry, rz):
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# 绕 x 轴的旋转矩阵
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R_x = np.array([[1, 0, 0],
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[0, np.cos(rx), -np.sin(rx)],
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[0, np.sin(rx), np.cos(rx)]])
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# 绕 y 轴的旋转矩阵
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R_y = np.array([[np.cos(ry), 0, np.sin(ry)],
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[0, 1, 0],
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[-np.sin(ry), 0, np.cos(ry)]])
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# 绕 z 轴的旋转矩阵
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R_z = np.array([[np.cos(rz), -np.sin(rz), 0],
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[np.sin(rz), np.cos(rz), 0],
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[0, 0, 1]])
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# 使用 @ 运算符进行矩阵乘法：R = R_x @ R_y @ R_z
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return R_x @ R_y @ R_z
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def eulerXYZToRotationMatrix(rx, ry, rz):
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# 绕 x 轴的旋转矩阵
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R_x = np.array([[1, 0, 0],
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[0, np.cos(rx), -np.sin(rx)],
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[0, np.sin(rx), np.cos(rx)]])
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# 绕 y 轴的旋转矩阵
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R_y = np.array([[np.cos(ry), 0, np.sin(ry)],
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[0, 1, 0],
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[-np.sin(ry), 0, np.cos(ry)]])
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# 绕 z 轴的旋转矩阵
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R_z = np.array([[np.cos(rz), -np.sin(rz), 0],
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[np.sin(rz), np.cos(rz), 0],
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[0, 0, 1]])
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# 使用 @ 运算符进行矩阵乘法：R = R_x @ R_y @ R_z
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return R_z @ R_y @ R_x
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def rotationMatrixToEulerZYX(R):
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# 提取 ry（绕 Y 轴的旋转角度）
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ry = np.arcsin(R[0, 2]) # R(0, 2) = sin(ry)
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cy = np.cos(ry)
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if np.abs(cy) > 1e-6: # 正常情况
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rx = np.arctan2(-R[1, 2], R[2, 2])
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rz = np.arctan2(-R[0, 1], R[0, 0])
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else: # 万向节锁：cy ≈ 0
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rx = 0 # 任意选择
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if ry > 0:
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rz = np.arctan2(R[1, 0], R[1, 1])
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else:
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rz = np.arctan2(-R[1, 0], R[1, 1])
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return np.array([rx, ry, rz])
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def rotationMatrixToEulerXYZ(R):
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# 提取 ry（绕 Y 轴的旋转角度）
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ry = np.arcsin(-R[2, 0]) # R(2, 0) = -sin(ry)
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cy = np.cos(ry)
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if np.abs(cy) > 1e-6: # 正常情况
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rx = np.arctan2(R[2, 1], R[2, 2])
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rz = np.arctan2(R[1, 0], R[0, 0])
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else: # 万向节锁：cy ≈ 0
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rx = 0 # 任意选择
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if ry > 0:
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rz = np.arctan2(-R[1, 2], R[1, 1])
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else:
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rz = np.arctan2(R[1, 2], R[1, 1])
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return np.array([rx, ry, rz])
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def func():
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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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# 获取标定板角点的位置
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objp = np.zeros((XX * YY, 3), np.float32)
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objp[:, :2] = np.mgrid[0:XX, 0:YY].T.reshape(-1, 2) # 将世界坐标系建在标定板上，所有点的Z坐标全部为0，所以只需要赋值x和y
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objp = L*objp
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obj_points = [] # 存储3D点
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img_points = [] # 存储2D点
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images_num = [f for f in os.listdir(images_path) if f.endswith('.jpg')]
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for i in range(1, len(images_num) + 1): #标定好的图片在images_path路径下，从0.jpg到x.jpg
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image_file = os.path.join(images_path,f"{i}.jpg")
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if os.path.exists(image_file):
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logger_.info(f'读 {image_file}')
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img = cv2.imread(image_file)
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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size = gray.shape[::-1]
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ret, corners = cv2.findChessboardCorners(gray, (XX, YY), None)
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if ret:
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obj_points.append(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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img_points.append(corners2)
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else:
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img_points.append(corners)
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# 绘制角点并保存图片
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cv2.drawChessboardCorners(img, (XX, YY), corners2 if corners2 is not None else corners, ret)
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corner_folder = os.path.join(images_path, "corner")
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os.makedirs(corner_folder, exist_ok=True) # 如果不存在就创建
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save_path = os.path.join(corner_folder, f"corner_{i}.jpg")
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cv2.imwrite(save_path, img)
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logger_.info(f"保存带角点的图片到: {save_path}")
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N = len(img_points)
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# 标定,得到图案在相机坐标系下的位姿
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ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, size, None, None)
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# logger_.info(f"内参矩阵:\n:{mtx}" ) # 内参数矩阵
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# logger_.info(f"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("-----------------------------------------------------")
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tool_pose = np.loadtxt(file_path,delimiter=',')
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R_tool = []
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t_tool = []
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N = tool_pose.shape[0] // 4 # 矩阵个数，每个矩阵占4行
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for i in range(N):
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mat = tool_pose[4*i:4*i+4, :] # 取第i个4x4矩阵
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# # 提取旋转矩阵
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# R_zyx = mat[0:3, 0:3]
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#
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# # 将 ZYX 顺序的旋转矩阵转换为欧拉角（ZYX 顺序）
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# euler_zyx = rotationMatrixToEulerZYX(R_zyx)
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#
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# # 将欧拉角转换为 XYZ 顺序的旋转矩阵
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# R_xyz = eulerXYZToRotationMatrix(euler_zyx[0], euler_zyx[1], euler_zyx[2])
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#
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# # 将转换后的旋转矩阵添加到 R_tool
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# R_tool.append(R_xyz)
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#
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# # # 将 ZYX 顺序的旋转矩阵转换为欧拉角（返回值是 rx, ry, rz）
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# # euler_zyx = R.from_matrix(R_zyx).as_euler('zyx', degrees=False)
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# #
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# # # 将欧拉角重新转换为 XYZ 顺序的旋转矩阵
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# # R_xyz = R.from_euler('xyz', euler_zyx, degrees=False).as_matrix()
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# #
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# # # 将转换后的旋转矩阵添加到 R_tool
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# # R_tool.append(R_xyz)
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R_tool.append(mat[0:3, 0:3]) # 提取旋转矩阵
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t_tool.append(mat[0:3, 3]) # 提取平移向量
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R1, t = cv2.calibrateHandEye(R_tool, t_tool, rvecs, tvecs, cv2.CALIB_HAND_EYE_TSAI)
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return R1,t
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if __name__ == '__main__':
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# 旋转矩阵
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rotation_matrix, translation_vector = func()
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# 将旋转矩阵转换为四元数
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rotation = R.from_matrix(rotation_matrix)
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quaternion = rotation.as_quat()
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x, y, z = translation_vector.flatten()
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logger_.info(f"旋转矩阵是:\n { rotation_matrix}")
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logger_.info(f"平移向量是:\n { translation_vector}")
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logger_.info(f"四元数是：\n { quaternion}")
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