From 1596810d10ce6d95361ab7cf954be3266bd1e386 Mon Sep 17 00:00:00 2001
From: cdeyw <827523911@qq.com>
Date: Wed, 18 Sep 2024 18:36:04 +0800
Subject: [PATCH] =?UTF-8?q?=E5=A2=9E=E5=8A=A0=E6=8A=93=E5=8F=96=E6=97=B6?=
 =?UTF-8?q?=E6=9C=BA=E6=A2=B0=E8=87=82=E7=9A=84=E5=A7=BF=E6=80=81?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 Trace/handeye_calibration.py | 29 ++++++++++++++---------------
 1 file changed, 14 insertions(+), 15 deletions(-)

diff --git a/Trace/handeye_calibration.py b/Trace/handeye_calibration.py
index 4c1a388..b2f5626 100644
--- a/Trace/handeye_calibration.py
+++ b/Trace/handeye_calibration.py
@@ -1,23 +1,15 @@
-import os
-
 import numpy as np
 from scipy.spatial.transform import Rotation as R
 
-def vec2rpy(normal):
+def vec2rpy(normal,long_edge_direction):
     # 将法向量的反方向作为机械臂末端执行器的新Z轴
     z_axis = -normal / np.linalg.norm(normal)  # 归一化并取反向作为Z轴
+    x_axis = long_edge_direction/np.linalg.norm(long_edge_direction)
 
-    # 尝试选择一个合适的X轴方向，尽量避免与Z轴共线
-    if abs(z_axis[2]) != 1:
-        x_axis = np.array([1, 0, 0])
-    else:
-        x_axis = np.array([0, 1, 0])
+    x_axis = x_axis-np.dot(x_axis,z_axis)*z_axis
+    x_axis = x_axis/np.linalg.norm(x_axis)
 
-    x_axis = x_axis - np.dot(x_axis, z_axis) * z_axis  # 投影到垂直于Z轴的平面
-    x_axis = x_axis / np.linalg.norm(x_axis)
-
-    # 计算Y轴方向，确保它与X轴和Z轴正交
-    y_axis = np.cross(z_axis, x_axis)
+    y_axis = np.cross(z_axis,x_axis)
 
     # 构造旋转矩阵
     rotation_matrix = np.vstack([x_axis, y_axis, z_axis]).T
@@ -62,16 +54,23 @@ def R_matrix(x,y,z,u,v,w):
 
 
 # 图像识别结果：xyz和法向量
-def getPosition(x,y,z,a,b,c,rotation):
+def getPosition(x,y,z,a,b,c,rotation,points):
     target = np.asarray([x, y, z,1])
     camera2robot = np.loadtxt('./Trace/com_pose.txt', delimiter=' ') #相对目录且分隔符采用os.sep
     robot2base = rotation
     camera2base = robot2base @ camera2robot
     target_position = np.dot(camera2base, target)
 
+    corner_points_camera = np.asarray(points)
+    corner_points_base = np.dot(camera2base[:3, :3], corner_points_camera.T).T + camera2base[:3, 3]
+    edges = np.array([corner_points_base[i] - corner_points_base[i - 1] for i in range(len(corner_points_base))])
+    edge_lengths = np.linalg.norm(edges, axis=1)
+    max_edge_idx = np.argmax(edge_lengths)
+    long_edge_direction = edges[max_edge_idx] / edge_lengths[max_edge_idx]  # 单位化方向向量
+
     angle = np.asarray([a,b,c])
     noraml = camera2base[:3, :3]@angle
-    noraml_base = vec2rpy(noraml)
+    noraml_base = vec2rpy(noraml,long_edge_direction)
 
     print(target_position, noraml_base)