更新 Trace/handeye_calibration.py

Trace/handeye_calibration.py

@@ -0,0 +1,80 @@
+import numpy as np
+from scipy.spatial.transform import Rotation as R
+
+def vec2rpy(normal):#首先是相机到法兰的转换，之后是法兰到新坐标系的转换（新坐标系就是与Z轴与法向量一致的坐标系)
+    # 将法向量作为机械臂末端执行器的新X轴
+    x_axis = normal / np.linalg.norm(normal)  # 归一化
+
+    # 选择一个合适的Y轴方向，尽量避免与X轴共线
+    if abs(x_axis[2]) != 1:
+        y_axis = np.array([0, 1, 0])
+    else:
+        y_axis = np.array([0, 0, 1])
+
+    y_axis = y_axis - np.dot(y_axis, x_axis) * x_axis  # 投影到垂直于x轴的平面
+    y_axis = y_axis / np.linalg.norm(y_axis)
+
+    # 计算Z轴方向，确保它与X轴和Y轴正交
+    z_axis = np.cross(x_axis, y_axis)
+
+    # 构造旋转矩阵
+    rotation_matrix = np.vstack([x_axis, y_axis, z_axis]).T
+
+    # 将旋转矩阵转换为RPY（roll, pitch, yaw）
+    rpy = R.from_matrix(rotation_matrix).as_euler('xyz', degrees=True)
+
+    return rpy
+
+#张啸给我的xyzuvw
+def R_matrix(x,y,z,u,v,w):
+    rx = np.radians(u)
+    ry = np.radians(v)
+    rz = np.radians(w)
+    # 定义绕 X, Y, Z 轴的旋转矩阵
+    R_x = np.array([
+        [1, 0, 0],
+        [0, np.cos(rx), -np.sin(rx)],
+        [0, np.sin(rx), np.cos(rx)]
+    ])
+
+    R_y = np.array([
+        [np.cos(ry), 0, np.sin(ry)],
+        [0, 1, 0],
+        [-np.sin(ry), 0, np.cos(ry)]
+    ])
+
+    R_z = np.array([
+        [np.cos(rz), -np.sin(rz), 0],
+        [np.sin(rz), np.cos(rz), 0],
+        [0, 0, 1]
+    ])
+    R = R_z @ R_y @ R_x
+    T = np.array([x, y, z])
+
+    # 构建齐次变换矩阵
+    transformation_matrix = np.eye(4)
+    transformation_matrix[:3, :3] = R
+    transformation_matrix[:3, 3] = T
+
+    return transformation_matrix
+
+rotation = R_matrix(559.365,0.704,731.196,179.996,66.369,179.996)#张啸给我的值填这里
+
+# 黄老师给我的xyz和法向量
+def getPosition(x,y,z,a,b,c):
+    target = np.asarray([x, y, z,1])
+    camera2robot = np.loadtxt('D:\BaiduNetdiskDownload\机械臂\GRCNN\\real\cam_pose.txt', delimiter=' ')
+    robot2base = rotation
+    camera2base = robot2base @ camera2robot
+    target_position = np.dot(camera2base, target)
+
+    angle = np.asarray([a,b,c])
+    noraml = camera2base[:3, :3]@angle
+    noraml_base = vec2rpy(noraml)
+
+    print(target_position, noraml_base)
+
+    return target_position,noraml_base
+
+if __name__ =="__main__":
+    getPosition(-96.09919,56.339145,444.640084,-0.09619,-0.45399, 0.88579)