79 lines
2.2 KiB
Python
79 lines
2.2 KiB
Python
import os
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import numpy as np
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from scipy.spatial.transform import Rotation as R
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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)
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# 将法向量作为机械臂末端执行器的新X轴
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x_axis = normal / np.linalg.norm(normal) # 归一化
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# 选择一个合适的Y轴方向，尽量避免与X轴共线
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if abs(x_axis[2]) != 1:
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y_axis = np.array([0, 1, 0])
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else:
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y_axis = np.array([0, 0, 1])
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y_axis = y_axis - np.dot(y_axis, x_axis) * x_axis # 投影到垂直于x轴的平面
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y_axis = y_axis / np.linalg.norm(y_axis)
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# 计算Z轴方向，确保它与X轴和Y轴正交
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z_axis = np.cross(x_axis, y_axis)
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# 构造旋转矩阵
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rotation_matrix = np.vstack([x_axis, y_axis, z_axis]).T
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# 将旋转矩阵转换为RPY（roll, pitch, yaw）
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rpy = R.from_matrix(rotation_matrix).as_euler('xyz', degrees=True)
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return rpy
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#张啸给我的xyzuvw
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def R_matrix(x,y,z,u,v,w):
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rx = np.radians(u)
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ry = np.radians(v)
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rz = np.radians(w)
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# 定义绕 X, Y, Z 轴的旋转矩阵
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R_x = np.array([
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[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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])
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R_y = np.array([
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[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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])
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R_z = np.array([
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[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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])
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R = R_z @ R_y @ R_x
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T = np.array([x, y, z])
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# 构建齐次变换矩阵
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transformation_matrix = np.eye(4)
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transformation_matrix[:3, :3] = R
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transformation_matrix[:3, 3] = T
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return transformation_matrix
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# 图像识别结果：xyz和法向量
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def getPosition(x,y,z,a,b,c,rotation):
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target = np.asarray([x, y, z,1])
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camera2robot = np.loadtxt('./cam_pose.txt', delimiter='') #相对目录且分隔符采用os.sep
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robot2base = rotation
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camera2base = robot2base @ camera2robot
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target_position = np.dot(camera2base, target)
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angle = np.asarray([a,b,c])
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noraml = camera2base[:3, :3]@angle
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noraml_base = vec2rpy(noraml)
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print(target_position, noraml_base)
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return target_position,noraml_base
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