更新 Trace/handeye_calibration.py
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cdeyw
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import numpy as np
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rotation = R_matrix()#张啸给我的值填这里
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def flip_coefficient_if_positive(coefficient):
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# 检查 coefficient[2] 是否大于0
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if coefficient[2] > 0:
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# 取反所有分量
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coefficient = [-x for x in coefficient]
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print("翻转：\n")
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return coefficient
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def vec2ola(coefficient):#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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#如果不转换坐标轴，
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coefficient_raw = flip_coefficient_if_positive(coefficient)
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coefficient = coefficient_raw.reshape(-1)
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curZ = np.array(coefficient)# 定义 Z 方向的向量
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# print(curZ)
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curX = np.array([1, 1, 0],dtype=np.float64)# 定义初始 X 方向的向量
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curX /= np.linalg.norm(curX) # 归一化 X 向量
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curY = np.cross(curZ, curX)# 计算 Y 方向的向量
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curY /= np.linalg.norm(curY) # 归一化 Y 向量
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curX = np.cross(curY, curZ)# 重新计算 X 方向的向量
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curX /= np.linalg.norm(curX) # 归一化 X 向量
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# 创建旋转矩阵
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rotM = np.array([
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[curX[0], curY[0], curZ[0]],
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[curX[1], curY[1], curZ[1]],
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[curX[2], curY[2], curZ[2]]
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])
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# 打印旋转矩阵
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# print("Rotation Matrix:")
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# print(rotM)
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# 计算欧拉角（XYZ顺序）并转换为度
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r = R.from_matrix(rotM)
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euler_angles = r.as_euler('xyz', degrees=True)#或者是zxy
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# print("Euler Angles (degrees):")
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# print(euler_angles)
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return euler_angles
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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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# 黄老师给我的xyz和法向量
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def getPosition(x,y,z,a,b,c):
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target = np.asarray([x, y, z,1])
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camera2robot = np.loadtxt('D:\BaiduNetdiskDownload\机械臂\GRCNN\\real\cam_pose.txt', delimiter=' ')
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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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rpy = vec2ola(a, b, c)
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r, p, y = rpy[0], rpy[1], rpy[2] # r表示u,p表示v，y表示w
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target_angle_raw = np.asarray([r, p, y])
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target_angle = np.dot(camera2robot[0:3, 0:3], target_angle_raw)
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print(target_position, target_angle)
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return target_position,target_angle
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80
Trace/handeye_calibration.py
Normal file
80
Trace/handeye_calibration.py
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@ -0,0 +1,80 @@
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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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rotation = R_matrix(559.365,0.704,731.196,179.996,66.369,179.996)#张啸给我的值填这里
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# 黄老师给我的xyz和法向量
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def getPosition(x,y,z,a,b,c):
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target = np.asarray([x, y, z,1])
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camera2robot = np.loadtxt('D:\BaiduNetdiskDownload\机械臂\GRCNN\\real\cam_pose.txt', delimiter=' ')
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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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if __name__ =="__main__":
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getPosition(-96.09919,56.339145,444.640084,-0.09619,-0.45399, 0.88579)
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